From 868327c2bbe58fc502fb563b37c06e20827eff81 Mon Sep 17 00:00:00 2001 From: AlexanderKroll <74175710+AlexanderKroll@users.noreply.github.com> Date: Thu, 20 Apr 2023 16:18:47 +0200 Subject: [PATCH] Updating after manuscript revision --- ...rison of kcat predictions-checkpoint.ipynb | 585 ---- .../01 - Comparison of kcat predictions.ipynb | 2 +- ... and reaction information-checkpoint.ipynb | 818 +++++- ... and plotting the results-checkpoint.ipynb | 2018 ------------- ...ting additional ML models-checkpoint.ipynb | 690 +++++ ... and plotting the results-checkpoint.ipynb | 2603 +++++++++++++++++ .../05 - Comparison to ENKIE-checkpoint.ipynb | 720 +++++ ...with enzyme and reaction information.ipynb | 816 +++++- ... - Testing additional input features.ipynb | 84 +- ...- Analyzing and plotting the results.ipynb | 2589 ---------------- .../03 - Testing additional ML models.ipynb | 690 +++++ ...- Analyzing and plotting the results.ipynb | 2603 +++++++++++++++++ .../01 - Data preprocessing-checkpoint.ipynb | 67 +- ...nd enzyme representations-checkpoint.ipynb | 118 +- .../01 - Data preprocessing.ipynb | 52 +- ...gerprints and enzyme representations.ipynb | 114 +- 16 files changed, 8971 insertions(+), 5598 deletions(-) delete mode 100644 code/DLKcat_comparison/.ipynb_checkpoints/01 - Comparison of kcat predictions-checkpoint.ipynb delete mode 100644 code/model_fitting/.ipynb_checkpoints/03 - Analyzing and plotting the results-checkpoint.ipynb create mode 100644 code/model_fitting/.ipynb_checkpoints/03 - Testing additional ML models-checkpoint.ipynb create mode 100644 code/model_fitting/.ipynb_checkpoints/04 - Analyzing and plotting the results-checkpoint.ipynb create mode 100644 code/model_fitting/.ipynb_checkpoints/05 - Comparison to ENKIE-checkpoint.ipynb delete mode 100644 code/model_fitting/03 - Analyzing and plotting the results.ipynb create mode 100644 code/model_fitting/03 - Testing additional ML models.ipynb create mode 100644 code/model_fitting/04 - Analyzing and plotting the results.ipynb diff --git a/code/DLKcat_comparison/.ipynb_checkpoints/01 - Comparison of kcat predictions-checkpoint.ipynb b/code/DLKcat_comparison/.ipynb_checkpoints/01 - Comparison of kcat predictions-checkpoint.ipynb deleted file mode 100644 index 5461f97..0000000 --- a/code/DLKcat_comparison/.ipynb_checkpoints/01 - Comparison of kcat predictions-checkpoint.ipynb +++ /dev/null @@ -1,585 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### We used the python code from the GitHub repository \"https://github.com/SysBioChalmers/DLKcat\" to reproduce the DLKcat model and to make predictions for their test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import os\n", - "from os.path import join\n", - "from sklearn.metrics import mean_squared_error, r2_score\n", - "from scipy import stats\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.pyplot import figure\n", - "import matplotlib as mpl\n", - "import json\n", - "\n", - "from Bio import pairwise2\n", - "from Bio.Emboss.Applications import NeedleCommandline\n", - "\n", - "import pickle\n", - "import torch" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def load_tensor(file_name, dtype):\n", - " return [dtype(d).to(device) for d in np.load(file_name + '.npy', allow_pickle=True)]\n", - "\n", - "\n", - "def load_pickle(file_name):\n", - " with open(file_name, 'rb') as f:\n", - " return pickle.load(f)\n", - "\n", - "def shuffle_dataset(dataset, seed):\n", - " np.random.seed(seed)\n", - " np.random.shuffle(dataset)\n", - " return dataset\n", - "\n", - "def split_dataset(dataset, ratio):\n", - " n = int(ratio * len(dataset))\n", - " dataset_1, dataset_2 = dataset[:n], dataset[n:]\n", - " return dataset_1, dataset_2\n", - "\n", - "def calculate_identity(fasta_file_1, fasta_file_2):\n", - " needle_cline = NeedleCommandline(asequence = fasta_file_1, bsequence = fasta_file_2,\n", - " gapopen=10, gapextend=0.5, filter = True)\n", - "\n", - " out = needle_cline()[0]\n", - " out = out[out.find(\"Identity\"):]\n", - " out = out[:out.find(\"\\n\")]\n", - " percent = float(out[out.find(\"(\")+1 :out.find(\")\")-1].replace(\" \", \"\"))\n", - " return(percent)\n", - "\n", - "device = \"cpu\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Loading results from DLKcat prediction" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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y_truey_predSequence
0-2.207608-0.071899MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...
1-3.657577-2.707640MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...
20.9493900.831021MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...
31.6720981.513026MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...
4-1.790485-2.830310MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...
............
1679-1.920819-0.281649MNYPAEPFRIKSVETVSMIPRDERLKKMQEAGYNTFLLNSKDIYID...
16802.7403630.945056MIEADYLVIGAGIAGASTGYWLSAHGRVVVLEREAQPGYHSTGRSA...
16811.1986571.115256MNLREKYGEWGLILGATEGVGKAFCEKIAAGGMNVVMVGRREEKLN...
16820.7403630.917627MALLSQAGGSYTVVPSGVCSKAGTKAVVSGGVRNLDVLRMKEAFGS...
16831.5010591.663697MDFYYLPGSAPCRAVQMTAAAVGVELNLKLTNLMAGEHMKPEFLKI...
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1684 rows × 3 columns

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" - ], - "text/plain": [ - " y_true y_pred Sequence\n", - "0 -2.207608 -0.071899 MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...\n", - "1 -3.657577 -2.707640 MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...\n", - "2 0.949390 0.831021 MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...\n", - "3 1.672098 1.513026 MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...\n", - "4 -1.790485 -2.830310 MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...\n", - "... ... ... ...\n", - "1679 -1.920819 -0.281649 MNYPAEPFRIKSVETVSMIPRDERLKKMQEAGYNTFLLNSKDIYID...\n", - "1680 2.740363 0.945056 MIEADYLVIGAGIAGASTGYWLSAHGRVVVLEREAQPGYHSTGRSA...\n", - "1681 1.198657 1.115256 MNLREKYGEWGLILGATEGVGKAFCEKIAAGGMNVVMVGRREEKLN...\n", - "1682 0.740363 0.917627 MALLSQAGGSYTVVPSGVCSKAGTKAVVSGGVRNLDVLRMKEAFGS...\n", - "1683 1.501059 1.663697 MDFYYLPGSAPCRAVQMTAAAVGVELNLKLTNLMAGEHMKPEFLKI...\n", - "\n", - "[1684 rows x 3 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with open(join(\"..\", \"..\", \"data\", \"DLKcat\", \"sequences_test.pkl\"), 'rb') as f:\n", - " sequences_test = pickle.load(f)\n", - " \n", - "with open(join(\"..\", \"..\", \"data\", \"DLKcat\", 'sequences_train.pkl'), 'rb') as f:\n", - " sequences_train = pickle.load(f)\n", - " \n", - "with open(join(\"..\", \"..\", \"data\", \"DLKcat\", 'y_pred_test.pkl'), 'rb') as f:\n", - " y_test_pred = pickle.load(f)\n", - "\n", - "interactions = load_tensor(join(\"..\", \"..\", \"data\", \"DLKcat\", 'regression'), torch.FloatTensor)\n", - "interactions = shuffle_dataset(interactions, 1234)\n", - "interactions_train, interactions_ = split_dataset(interactions, 0.8)\n", - "interactions_dev, interactions_test = split_dataset(interactions_, 0.5)\n", - "\n", - "#converting kcat values from log2 to log10:\n", - "interactions_test = [np.log10(2**float(kcat)) for kcat in interactions_test]\n", - "\n", - "df_pred = pd.DataFrame({\"y_true\" : interactions_test, \"y_pred\" : y_test_pred, \"Sequence\" : sequences_test})\n", - "df_pred" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculating RMSE and coefficient of determination R²" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1.1195636742162083, 0.44447253110852536)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.sqrt(mean_squared_error(interactions_test,y_test_pred)), r2_score(interactions_test,y_test_pred)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Calculating the maximal sequence identity compared to all sequences in the training set:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (a) Creating Fasta files for all training and test sequences:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "for ind in df_pred.index:\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"DLKcat\", \"Fasta_files\", \"test_seq_\" +str(ind) + \".fasta\", \"w\")\n", - " ofile.write(\"> seq_\" + str(ind) + \"\\n\" + df_pred[\"Sequence\"][ind] + \"\\n\")\n", - " ofile.close()\n", - " \n", - "for ind in range(len(sequences_train)):\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"DLKcat\", \"Fasta_files\", \"Fasta_files\", \"train_seq_\" +str(ind) + \".fasta\", \"w\")\n", - " ofile.write(\"> seq_train_\" + str(ind) + \"\\n\" + sequences_train[ind] + \"\\n\")\n", - " ofile.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (b) Calculating the maximal pairwise sequence identity:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "'''from Bio.Emboss.Applications import NeedleCommandline\n", - "import os\n", - "from os.path import join\n", - "import pandas as pd\n", - "import sys\n", - "import time\n", - "import numpy as np\n", - "\n", - "\n", - "arg = int(sys.argv[1])\n", - "\n", - " \n", - "def calculate_identity(fasta_file_1, fasta_file_2):\n", - " needle_cline = NeedleCommandline(asequence = fasta_file_1, bsequence = fasta_file_2,\n", - " gapopen=10, gapextend=0.5, filter = True)\n", - "\n", - " out = needle_cline()[0]\n", - " out = out[out.find(\"Identity\"):]\n", - " out = out[:out.find(\"\\n\")]\n", - " percent = float(out[out.find(\"(\")+1 :out.find(\")\")-1].replace(\" \", \"\"))\n", - " return(percent)\n", - "\n", - "\n", - "identities = []\n", - "for i in range(13470):\n", - " ident = calculate_identity(\n", - " fasta_file_1 = join(\"..\", \"..\", \"data\", \"DLKcat\", \"Fasta_files\", \"test_seq_\" + str(arg) + \".fasta\"),\n", - " fasta_file_2 = join(\"..\", \"..\", \"data\", \"DLKcat\", \"Fasta_files\", \"train_seq_\" + str(i) + \".fasta\"))\n", - " identities.append(ident)\n", - "\n", - "\n", - "ofile = open(join(\"..\", \"..\", \"data\", \"DLKcat\", \"DLkcat_ident\", \"test_seq\" + str(arg) + \".txt\"), \"w\")\n", - "ofile.write(str(max(identities)))\n", - "ofile.close()''';" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading the results:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:8: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " \n" - ] - }, - { - "data": { - "text/html": [ - "
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y_truey_predSequencemax_ident
0-2.207608-0.071899MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...22.8
1-3.657577-2.707640MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...100.0
20.9493900.831021MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...100.0
31.6720981.513026MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...100.0
4-1.790485-2.830310MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...99.4
...............
1679-1.920819-0.281649MNYPAEPFRIKSVETVSMIPRDERLKKMQEAGYNTFLLNSKDIYID...100.0
16802.7403630.945056MIEADYLVIGAGIAGASTGYWLSAHGRVVVLEREAQPGYHSTGRSA...100.0
16811.1986571.115256MNLREKYGEWGLILGATEGVGKAFCEKIAAGGMNVVMVGRREEKLN...100.0
16820.7403630.917627MALLSQAGGSYTVVPSGVCSKAGTKAVVSGGVRNLDVLRMKEAFGS...99.8
16831.5010591.663697MDFYYLPGSAPCRAVQMTAAAVGVELNLKLTNLMAGEHMKPEFLKI...100.0
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1684 rows × 4 columns

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" - ], - "text/plain": [ - " y_true y_pred Sequence \\\n", - "0 -2.207608 -0.071899 MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI... \n", - "1 -3.657577 -2.707640 MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA... \n", - "2 0.949390 0.831021 MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG... \n", - "3 1.672098 1.513026 MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS... \n", - "4 -1.790485 -2.830310 MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW... \n", - "... ... ... ... \n", - "1679 -1.920819 -0.281649 MNYPAEPFRIKSVETVSMIPRDERLKKMQEAGYNTFLLNSKDIYID... \n", - "1680 2.740363 0.945056 MIEADYLVIGAGIAGASTGYWLSAHGRVVVLEREAQPGYHSTGRSA... \n", - "1681 1.198657 1.115256 MNLREKYGEWGLILGATEGVGKAFCEKIAAGGMNVVMVGRREEKLN... \n", - "1682 0.740363 0.917627 MALLSQAGGSYTVVPSGVCSKAGTKAVVSGGVRNLDVLRMKEAFGS... \n", - "1683 1.501059 1.663697 MDFYYLPGSAPCRAVQMTAAAVGVELNLKLTNLMAGEHMKPEFLKI... \n", - "\n", - " max_ident \n", - "0 22.8 \n", - "1 100.0 \n", - "2 100.0 \n", - "3 100.0 \n", - "4 99.4 \n", - "... ... \n", - "1679 100.0 \n", - "1680 100.0 \n", - "1681 100.0 \n", - "1682 99.8 \n", - "1683 100.0 \n", - "\n", - "[1684 rows x 4 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_pred[\"max_ident\"] = np.nan\n", - "\n", - "for ind in df_pred.index:\n", - " try:\n", - " with open(join(\"..\", \"..\", \"data\", \"DLKcat\", \"DLkcat_ident\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", - " ident = f.readlines()\n", - " ident = float(ident[0])\n", - " df_pred[\"max_ident\"][ind] = ident\n", - " except FileNotFoundError:\n", - " pass\n", - "df_pred" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "df_pred.to_pickle(join(\"..\", \"..\", \"data\", \"DLKcat\", \"df_pred.pkl\"))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate model performance for different sequence identities:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lower bound: 0, upper bound: 40, no. of data points: 82, R2: -0.6072304105234347, RMSE: 2.0954145982947594\n", - "Lower bound: 40, upper bound: 80, no. of data points: 42, R2: 0.34280134977895493, RMSE: 1.2434758603533023\n", - "Lower bound: 80, upper bound: 99, no. of data points: 27, R2: 0.48622435213243465, RMSE: 1.1191845445308464\n", - "Lower bound: 99, upper bound: 100, no. of data points: 1536, R2: 0.5128517542754034, RMSE: 1.0371892758113466\n" - ] - } - ], - "source": [ - "lower_bounds = [0, 40, 80, 99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "\n", - "for i in range(len(lower_bounds)):\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " help_df = df_pred.loc[df_pred[\"max_ident\"] >= lb].loc[df_pred[\"max_ident\"] <= ub]\n", - " if len(help_df) > 0:\n", - " y_pred, y_true = np.array(help_df[\"y_pred\"]), np.array(help_df[\"y_true\"])\n", - " R2 = r2_score(y_true, y_pred)\n", - " RMSE = np.sqrt(mean_squared_error(y_true, y_pred))\n", - " \n", - " print(\"Lower bound: %s, upper bound: %s, no. of data points: %s, R2: %s, RMSE: %s\" % (lb, ub, len(help_df), R2, RMSE))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/code/DLKcat_comparison/01 - Comparison of kcat predictions.ipynb b/code/DLKcat_comparison/01 - Comparison of kcat predictions.ipynb index f9e5d52..21c3be7 100644 --- a/code/DLKcat_comparison/01 - Comparison of kcat predictions.ipynb +++ b/code/DLKcat_comparison/01 - Comparison of kcat predictions.ipynb @@ -577,7 +577,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/model_fitting/.ipynb_checkpoints/01 Training xgboost models with enzyme and reaction information-checkpoint.ipynb b/code/model_fitting/.ipynb_checkpoints/01 Training xgboost models with enzyme and reaction information-checkpoint.ipynb index 96ff16a..445bd30 100644 --- a/code/model_fitting/.ipynb_checkpoints/01 Training xgboost models with enzyme and reaction information-checkpoint.ipynb +++ b/code/model_fitting/.ipynb_checkpoints/01 Training xgboost models with enzyme and reaction information-checkpoint.ipynb @@ -12,12 +12,12 @@ "\n", "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", "or from the matplotlib source distribution\n", "\n", "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", "or from the matplotlib source distribution\n" ] } @@ -205,7 +205,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.6156521084065655, 0.5927689215737512, 0.5450373027741603, 0.6635468722921142, 0.5465250301214235]\n", + "[0.6156521084065656, 0.592768921573751, 0.5450373027741603, 0.6635468722921141, 0.5465250301214234]\n", "[0.8251322874089426, 0.8206497271502332, 0.9401031382230501, 0.9269849644397952, 1.061793264104686]\n", "[0.3720653400563064, 0.34881598167570627, 0.2894002515317964, 0.4219034383575203, 0.2977699878192147]\n" ] @@ -269,6 +269,15 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b.npy\"), test_Y)" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b = y_test_pred" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -285,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -358,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -378,14 +387,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.6089419355661412, 0.5895091873624034, 0.5917286045189503, 0.642803324015263, 0.5233641744058668]\n", + "[0.6089419355661412, 0.5895091873624032, 0.5917286045189503, 0.6428033240152629, 0.5233641744058668]\n", "[0.830309790220053, 0.8254878887860261, 0.8642504226487573, 0.9497311132104741, 1.1100478926967097]\n", "[0.3681252040118652, 0.3449769094978131, 0.34673536553812156, 0.4077182348220084, 0.26585619671739524]\n" ] @@ -395,7 +404,7 @@ "R2 = []\n", "MSE = []\n", "Pearson = []\n", - "\n", + "y_valid_pred_esm1b_ts = []\n", "\n", "for i in range(5):\n", " train_index, test_index = train_indices[i], test_indices[i]\n", @@ -405,6 +414,7 @@ " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", " \n", " y_valid_pred = bst.predict(dvalid)\n", + " y_valid_pred_esm1b_ts.append(y_valid_pred)\n", " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", @@ -420,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -448,6 +458,15 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts.npy\"), test_Y)" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ts = y_test_pred" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -457,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -473,31 +492,456 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.9839122286376514, 0.0) 0.05006636413883383 0.9650757482138885\n" + ] + } + ], + "source": [ + "param = {'learning_rate': 0.2831145406836757,\n", + " 'max_delta_step': 0.07686715986169101, \n", + " 'max_depth': 4.96836783761305,\n", + " 'min_child_weight': 6.905400087083855,\n", + " 'num_rounds': 313.1498988074061,\n", + " 'reg_alpha': 1.717314107718892,\n", + " 'reg_lambda': 2.470354543039016}\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]\n", + "\n", + "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", + "dtest = xgb.DMatrix(test_X)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + "\n", + "y_test_pred = bst.predict(dtest)\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)\n", + "\n", + "pickle.dump(bst, open(join(\"..\", \"..\", \"data\", \"training_results\", \"saved_models\",\n", + " \"xgboost_sequence_only_train_and_test.pkl\"), \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training a model with only reaction information (DRFP):" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Hyperparameter optimization:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "'''def cross_validation_mse_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " del param[\"num_rounds\"]\n", + " param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " \n", + " MSE = []\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " y_valid_pred = bst.predict(dvalid)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "\n", + "from hyperopt import fmin, tpe, rand, hp, Trials\n", + "\n", + "space_gradient_boosting = {\n", + " \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 1),\n", + " \"max_depth\": hp.uniform(\"max_depth\", 4,12),\n", + " #\"subsample\": hp.uniform(\"subsample\", 0.7, 1),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 20, 200)}\n", + "\n", + "\n", + "trials = Trials()\n", + "best = fmin(fn = cross_validation_mse_gradient_boosting, space = space_gradient_boosting,\n", + " algo=rand.suggest, max_evals = 200, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (c) Training and validating model:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.08987247189322463,\n", + " 'max_delta_step': 1.1939737318908727,\n", + " 'max_depth': 11.268531225242574,\n", + " 'min_child_weight': 2.8172720953826302,\n", + " 'num_rounds': 109.03643430746544,\n", + " 'reg_alpha': 1.9412226989868904,\n", + " 'reg_lambda': 4.950543905603358}\n", + "\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5703243058726097, 0.5534056710833766, 0.583291365495366, 0.5706660736746292, 0.5608173001699261]\n", + "[0.8877274220430679, 0.8796146121877297, 0.8815364701552285, 1.082140247084615, 1.0382516796274228]\n", + "[0.3244297606705625, 0.3020274560618814, 0.3336692874551086, 0.32514379407155003, 0.313339503762757]\n" + ] + } + ], + "source": [ + "R2 = []\n", + "MSE = []\n", + "Pearson = []\n", + "y_valid_pred_DRFP = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " \n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " \n", + " y_valid_pred = bst.predict(dvalid)\n", + " y_valid_pred_DRFP.append(y_valid_pred)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", + "\n", + "print(Pearson)\n", + "print(MSE)\n", + "print(R2)\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_DRFP.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_DRFP.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_DRFP.npy\"), np.array(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.619 0.886 0.382\n" + ] + } + ], + "source": [ + "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", + "dtest = xgb.DMatrix(test_X)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + "\n", + "y_test_pred = bst.predict(dtest)\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_DRFP.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_DRFP.npy\"), test_Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_drfp = y_test_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (d) Training model with test and train data for production mode:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "train_DRFP = np.array(list(data_train[\"DRFP\"]))\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_DRFP = np.array(list(data_test[\"DRFP\"]))\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))\n", + "\n", + "train_X = np.concatenate([train_DRFP, test_DRFP])\n", + "train_Y = np.concatenate([train_Y, test_Y])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.8681560564738647, 3.0446983463533428e-260) 0.4027527000636768 0.7190561578317212\n" + ] + } + ], + "source": [ + "param = {'learning_rate': 0.08987247189322463,\n", + " 'max_delta_step': 1.1939737318908727,\n", + " 'max_depth': 11.268531225242574,\n", + " 'min_child_weight': 2.8172720953826302,\n", + " 'num_rounds': 109.03643430746544,\n", + " 'reg_alpha': 1.9412226989868904,\n", + " 'reg_lambda': 4.950543905603358}\n", + "\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]\n", + "\n", + "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", + "dtest = xgb.DMatrix(test_X)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + "\n", + "y_test_pred = bst.predict(dtest)\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)\n", + "\n", + "pickle.dump(bst, open(join(\"..\", \"..\", \"data\", \"training_results\", \"saved_models\",\n", + " \"xgboost_reaction_only_train_and_test.pkl\"), \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Training a model with only reaction information (difference fingerprint):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a) Creating input matrices:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"difference_fp\"]))\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"difference_fp\"]))\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Hyperparameter optimization:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "'''def cross_validation_mse_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " del param[\"num_rounds\"]\n", + " param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " \n", + " MSE = []\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " y_valid_pred = bst.predict(dvalid)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "\n", + "from hyperopt import fmin, tpe, rand, hp, Trials\n", + "\n", + "space_gradient_boosting = {\n", + " \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 1),\n", + " \"max_depth\": hp.uniform(\"max_depth\", 4,12),\n", + " #\"subsample\": hp.uniform(\"subsample\", 0.7, 1),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 20, 200)}\n", + "\n", + "\n", + "trials = Trials()\n", + "best = fmin(fn = cross_validation_mse_gradient_boosting, space = space_gradient_boosting,\n", + " algo=rand.suggest, max_evals = 200, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (c) Training and validating model:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.14154883958006167,\n", + " 'max_delta_step': 0.02234358170535966,\n", + " 'max_depth': 10.869653004093198,\n", + " 'min_child_weight': 1.7936882442746056,\n", + " 'num_rounds': 361.6168542774665,\n", + " 'reg_alpha': 4.825525325323308, \n", + " 'reg_lambda': 2.74944090578774}\n", + "\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5933818458131599, 0.5290768248822283, 0.5426074027598811, 0.5889341652634298, 0.5248561000592911]\n", + "[0.8733503195891379, 0.9112072460648096, 0.9525515259896388, 1.0924558329257095, 1.1054594918609029]\n", + "[0.3353708922662406, 0.27695876037247324, 0.27999083584524465, 0.31871067494359473, 0.2688907919476974]\n" + ] + } + ], + "source": [ + "R2 = []\n", + "MSE = []\n", + "Pearson = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " \n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " \n", + " y_valid_pred = bst.predict(dvalid)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", + "\n", + "print(Pearson)\n", + "print(MSE)\n", + "print(R2)\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_diff_fp.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_diff_fp.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_diff_fp.npy\"), np.array(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(0.9839122286376513, 0.0) 0.05006636413883383 0.9650757482138885\n" + "0.6 0.948 0.339\n" ] } ], "source": [ - "param = {'learning_rate': 0.2831145406836757,\n", - " 'max_delta_step': 0.07686715986169101, \n", - " 'max_depth': 4.96836783761305,\n", - " 'min_child_weight': 6.905400087083855,\n", - " 'num_rounds': 313.1498988074061,\n", - " 'reg_alpha': 1.717314107718892,\n", - " 'reg_lambda': 2.470354543039016}\n", - "\n", - "num_round = param[\"num_rounds\"]\n", - "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", - "\n", - "del param[\"num_rounds\"]\n", - "\n", "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", "dtest = xgb.DMatrix(test_X)\n", "\n", @@ -508,17 +952,27 @@ "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", "\n", - "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", "\n", - "pickle.dump(bst, open(join(\"..\", \"..\", \"data\", \"training_results\", \"saved_models\",\n", - " \"xgboost_sequence_only_train_and_test.pkl\"), \"wb\"))" + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_diff_fp.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_diff_fp.npy\"), test_Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_diff_fp = y_test_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Training a model with only reaction information (difference fingerprint):" + "## 5. Training a model with only reaction information (structural fingerprint):" ] }, { @@ -530,14 +984,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "train_X = np.array(list(data_train[\"difference_fp\"]))\n", + "train_X = ();\n", + "for ind in data_train.index:\n", + " train_X = train_X + (np.array(list(data_train[\"structural_fp\"][ind])).astype(int), )\n", + "train_X = np.array(train_X)\n", "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", "\n", - "test_X = np.array(list(data_test[\"difference_fp\"]))\n", + "\n", + "test_X = ();\n", + "for ind in data_test.index:\n", + " test_X = test_X + (np.array(list(data_test[\"structural_fp\"][ind])).astype(int), )\n", + "test_X = np.array(test_X)\n", "test_Y = np.array(list(data_test[\"log10_kcat\"]))" ] }, @@ -550,7 +1011,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -601,18 +1062,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.14154883958006167,\n", - " 'max_delta_step': 0.02234358170535966,\n", - " 'max_depth': 10.869653004093198,\n", - " 'min_child_weight': 1.7936882442746056,\n", - " 'num_rounds': 361.6168542774665,\n", - " 'reg_alpha': 4.825525325323308, \n", - " 'reg_lambda': 2.74944090578774}\n", - "\n", + "param = {'learning_rate': 0.01126910440903659,\n", + " 'max_delta_step': 0.5777120839605732,\n", + " 'max_depth': 5.486901609313889,\n", + " 'min_child_weight': 6.14467742389769,\n", + " 'num_rounds': 488.943459090126,\n", + " 'reg_alpha': 4.629840853377147,\n", + " 'reg_lambda': 2.1047561335691745}\n", "\n", "num_round = param[\"num_rounds\"]\n", "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", @@ -622,16 +1082,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.5933818458131599, 0.5290768248822284, 0.542607402759881, 0.5889341652634299, 0.5248561000592911]\n", - "[0.8733503195891379, 0.9112072460648096, 0.9525515259896388, 1.0924558329257095, 1.1054594918609029]\n", - "[0.3353708922662406, 0.27695876037247324, 0.27999083584524465, 0.31871067494359473, 0.2688907919476974]\n" + "[0.5536292775258076, 0.5323143816237441, 0.4889091394899996, 0.6104056516948199, 0.5039256159780712]\n", + "[0.917613544024189, 0.9056644419444148, 1.015607786275439, 1.064522657133946, 1.1289411204418558]\n", + "[0.3016860962550283, 0.2813569650394473, 0.23232823280029657, 0.3361306693344771, 0.2533609285723336]\n" ] } ], @@ -656,21 +1116,21 @@ "print(MSE)\n", "print(R2)\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_diff_fp.npy\"), np.array(Pearson))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_diff_fp.npy\"), np.array(MSE))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_diff_fp.npy\"), np.array(R2))" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_str_fp.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_str_fp.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_str_fp.npy\"), np.array(R2))" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.6 0.948 0.339\n" + "0.561 0.994 0.307\n" ] } ], @@ -688,41 +1148,29 @@ "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", "\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_diff_fp.npy\"), bst.predict(dtest))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_diff_fp.npy\"), test_Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Training a model with only reaction information (structural fingerprint):" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_str_fp.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_str_fp.npy\"), test_Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### (a) Creating input matrices:" + "## 6. Training a model with enzyme and reaction information (ESM1b_ts/DRFP):" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "train_X = ();\n", - "for ind in data_train.index:\n", - " train_X = train_X + (np.array(list(data_train[\"structural_fp\"][ind])).astype(int), )\n", - "train_X = np.array(train_X)\n", + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", "\n", - "\n", - "test_X = ();\n", - "for ind in data_test.index:\n", - " test_X = test_X + (np.array(list(data_test[\"structural_fp\"][ind])).astype(int), )\n", - "test_X = np.array(test_X)\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", "test_Y = np.array(list(data_test[\"log10_kcat\"]))" ] }, @@ -735,7 +1183,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -763,7 +1211,7 @@ "\n", "space_gradient_boosting = {\n", " \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 1),\n", - " \"max_depth\": hp.uniform(\"max_depth\", 4,12),\n", + " \"max_depth\": hp.uniform(\"max_depth\", 6,14),\n", " #\"subsample\": hp.uniform(\"subsample\", 0.7, 1),\n", " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", @@ -786,17 +1234,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.01126910440903659,\n", - " 'max_delta_step': 0.5777120839605732,\n", - " 'max_depth': 5.486901609313889,\n", - " 'min_child_weight': 6.14467742389769,\n", - " 'num_rounds': 488.943459090126,\n", - " 'reg_alpha': 4.629840853377147,\n", - " 'reg_lambda': 2.1047561335691745}\n", + "param = {'learning_rate': 0.05221672412884108,\n", + " 'max_delta_step': 1.0767235463496743,\n", + " 'max_depth': 11.329014411591299,\n", + " 'min_child_weight': 14.724796449973605,\n", + " 'num_rounds': 298.9598325756988,\n", + " 'reg_alpha': 2.8295816318634452,\n", + " 'reg_lambda': 0.6528469146574993}\n", "\n", "num_round = param[\"num_rounds\"]\n", "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", @@ -806,16 +1254,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.5536292775258077, 0.532314381623744, 0.4889091394899996, 0.61040565169482, 0.5039256159780713]\n", - "[0.917613544024189, 0.9056644419444148, 1.015607786275439, 1.064522657133946, 1.1289411204418558]\n", - "[0.3016860962550283, 0.2813569650394473, 0.23232823280029657, 0.3361306693344771, 0.2533609285723336]\n" + "[0.6353444924580318, 0.5880301926572151, 0.5241899073773033, 0.6588493325838651, 0.5473715375037416]\n", + "[0.7895750755455662, 0.8261521353734187, 0.9646511784030429, 0.9310396416328481, 1.0594944425073949]\n", + "[0.3991247656547007, 0.34444983107735405, 0.270845020229982, 0.4193748159593236, 0.2992903417080939]\n" ] } ], @@ -840,27 +1288,28 @@ "print(MSE)\n", "print(R2)\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_str_fp.npy\"), np.array(Pearson))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_str_fp.npy\"), np.array(MSE))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_str_fp.npy\"), np.array(R2))" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_ESM1b_ts_DRFP.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_ESM1b_ts_DRFP.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_ESM1b_ts_DRFP.npy\"), np.array(R2))" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.561 0.994 0.307\n" + "0.636 0.856 0.403\n" ] } ], "source": [ "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", - "dtest = xgb.DMatrix(test_X)\n", + "dtest = xgb.DMatrix(test_X, label = test_Y)\n", + "\n", "\n", "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", "\n", @@ -872,15 +1321,24 @@ "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", "\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_str_fp.npy\"), bst.predict(dtest))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_str_fp.npy\"), test_Y)" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_DRFP.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_DRFP.npy\"), test_Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ts_drfp = y_test_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Training a model with enzyme and reaction information (ESM1b_ts/diff_fp):" + "## 7. Training a model with enzyme and reaction information (ESM1b_ts/diff_fp):" ] }, { @@ -892,7 +1350,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -914,7 +1372,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -965,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -977,6 +1435,7 @@ " 'reg_alpha': 7.333074414515098,\n", " 'reg_lambda': 0.8545111451043885}\n", "\n", + "\n", "num_round = param[\"num_rounds\"]\n", "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", "\n", @@ -985,14 +1444,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.6445379171956692, 0.5663538267118359, 0.5813292687854973, 0.6603671022176677, 0.5437710214323066]\n", + "[0.6445379171956691, 0.5663538267118355, 0.5813292687854972, 0.660367102217668, 0.5437710214323066]\n", "[0.7778549566615786, 0.8621287188783706, 0.8811802952099859, 0.9212592601438562, 1.0674061317478523]\n", "[0.40804390380771904, 0.31590247958588724, 0.33393851092241733, 0.4254741650612315, 0.2940578488872041]\n" ] @@ -1026,7 +1485,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 47, "metadata": { "scrolled": true }, @@ -1058,6 +1517,15 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_diff_fp.npy\"), test_Y)" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ts_drfp = y_test_pred" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1067,7 +1535,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1085,14 +1553,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(0.9910659621749647, 0.0) 0.027904669605058757 0.9805348416235519\n" + "(0.9906766451895581, 0.0) 0.02970620593719598 0.9792781634215774\n" ] } ], @@ -1130,7 +1598,96 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Training a model with enzyme rep., reaction information, and additional features (ESM1b_ts/diff_fp/flux/KM):" + "## 8. Model with enzyme and reaction information (ESM1b_ts/DRFP [mean]):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cross-Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.6427432566631278, 0.6226108619000825, 0.6371590251884234, 0.6630733248247811, 0.5920048794950177]\n", + "[0.7840422477361155, 0.7768955691409662, 0.8045088804939567, 0.9293225562214953, 0.9845290313461734]\n", + "[0.40333530789389016, 0.38353482393964156, 0.39189245852320176, 0.42044564365386916, 0.3488696368237689]\n" + ] + } + ], + "source": [ + "R2 = []\n", + "MSE = []\n", + "Pearson = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " y_valid_pred = np.mean([y_valid_pred_DRFP[i], y_valid_pred_esm1b_ts[i]], axis =0)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", + "\n", + "print(Pearson)\n", + "print(MSE)\n", + "print(R2)\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_ESM1b_ts_DRFP_mean.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_ESM1b_ts_DRFP_mean.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_ESM1b_ts_DRFP_mean.npy\"), np.array(R2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Validation on test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.671 0.808 0.436\n" + ] + } + ], + "source": [ + "y_test_pred = np.mean([y_test_pred_drfp, y_test_pred_esm1b_ts], axis =0)\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_DRFP_mean.npy\"), y_test_pred)\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_DRFP_mean.npy\"), test_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Training a model with enzyme rep., reaction information, and additional features (ESM1b_ts/diff_fp/flux/KM):" ] }, { @@ -1142,7 +1699,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1151,7 +1708,7 @@ "(3421, 850)" ] }, - "execution_count": 36, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1167,19 +1724,19 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_X = ();\n", - "train_X = np.array(list(data_train[\"difference_fp\"]))\n", + "train_X = np.array(list(data_train[\"DRFP\"]))\n", "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"])),\n", " np.reshape(np.array(list(data_train[\"KM\"])), (-1,1)),\n", " np.reshape(np.array(list(data_train[\"flux\"])), (-1,1))], axis = 1)\n", "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", "\n", "test_X = ();\n", - "test_X = np.array(list(data_test[\"difference_fp\"]))\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"])),\n", " np.reshape(np.array(list(data_test[\"KM\"])), (-1,1)),\n", " np.reshape(np.array(list(data_test[\"flux\"])), (-1,1))], axis = 1)\n", @@ -1188,7 +1745,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1232,7 +1789,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1252,19 +1809,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.6381726941853013, 0.5782540928851373, 0.5816409524748034, 0.6501114403040922, 0.5359555917016532]\n", - "[0.7820629557858503, 0.843033028077367, 0.8823309866269835, 0.9415727118823687, 1.085306625916712]\n", - "[0.40484157063101067, 0.33105487439829584, 0.33306873291802874, 0.412806066811956, 0.28221913728185244]\n" - ] - } - ], + "outputs": [], "source": [ "R2 = []\n", "MSE = []\n", @@ -1293,17 +1840,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6203728110199708 0.8836397180206959 0.3836090051439036\n" - ] - } - ], + "outputs": [], "source": [ "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", "dtest = xgb.DMatrix(test_X)\n", @@ -1321,11 +1860,18 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_diff_fp_flux_KM.npy\"), bst.predict(dtest))\n", "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_diff_fp_flux_KM.npy\"), test_Y)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1339,7 +1885,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/model_fitting/.ipynb_checkpoints/03 - Analyzing and plotting the results-checkpoint.ipynb b/code/model_fitting/.ipynb_checkpoints/03 - Analyzing and plotting the results-checkpoint.ipynb deleted file mode 100644 index 6a8c40b..0000000 --- a/code/model_fitting/.ipynb_checkpoints/03 - Analyzing and plotting the results-checkpoint.ipynb +++ /dev/null @@ -1,2018 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", - "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", - "or from the matplotlib source distribution\n", - "\n", - "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", - "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", - "or from the matplotlib source distribution\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\alexk\\projects\\kcat_prediction\\code\\model_fitting\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "from sklearn import metrics\n", - "from scipy import stats\n", - "from scipy.stats import wilcoxon\n", - "from sklearn.metrics import roc_auc_score, r2_score\n", - "import os\n", - "from os.path import join\n", - "import pandas as pd\n", - "\n", - "CURRENT_DIR = os.getcwd()\n", - "print(CURRENT_DIR)\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "plt.style.use('CCB_plot_style_0v4.mplstyle');\n", - "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", - "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Plotting performance of different models:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (a) Pearson r" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "models = [\"str_fp\", \"diff_fp\", \"ESM1b\", \"ESM1b_ts\", \"ESM1b_ts_diff_fp\"]\n", - "model_names = {\"str_fp\" : \"str. FP\",\n", - " \"diff_fp\" : \"diff. FP\",\n", - " \"ESM1b\" : \"ESM-1b\",\n", - " \"ESM1b_ts\" : \"ESM-$1b_{ts}$\",\n", - " \"ESM1b_ts_diff_fp\" : \"ESM-$1b_{ts}$/ \\ndiff. FP\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "\n", - "\n", - "plt.rcParams.update({'font.size': 28})\n", - "plt.ylim(-0.01, 1)\n", - "plt.xlim(0.5, len(models) + 0.5)\n", - "\n", - "labs = [model_names[model] for model in models]\n", - "Boxplots = []\n", - "ticks = []\n", - "for i, model in enumerate(models):\n", - " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", - " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", - " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", - " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - " Pearson_test = stats.pearsonr(test_y, pred_y)[0]\n", - " \n", - " if i == 0:\n", - " plt.scatter(i+1, Pearson_test, c='darkblue', marker='^', linewidths= 8, label = \"test set\")\n", - " else:\n", - " plt.scatter(i+1, Pearson_test, c='darkblue', marker='^', linewidths= 8)\n", - " \n", - " Boxplots.append(Pearson_CV)\n", - " ticks.append(i+1)\n", - "\n", - " \n", - "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", - " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", - " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", - "\n", - "\n", - "\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=8)\n", - "\n", - "ticks1 = ticks\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "#ax.locator_params(axis=\"y\", nbins=4)\n", - "\n", - "\n", - "ticks2 = list(np.array(ticks)-0.01)\n", - "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", - "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", - "#ax.yaxis.set_major_locator(loc)\n", - "\n", - "plt.ylabel(\"Pearson r\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "#plt.legend(loc = \"upper right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (b) MSE" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "plt.ylim(0.5, 1.4)\n", - "plt.xlim(0.5, len(models) + 0.5)\n", - "\n", - "labs = [model_names[model] for model in models]\n", - "Boxplots = []\n", - "ticks = []\n", - "for i, model in enumerate(models):\n", - " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", - " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", - " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", - " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - " MSE_test = np.mean(abs(test_y - pred_y)**2)\n", - "\n", - " \n", - " if i == 0:\n", - " plt.scatter(i+1, MSE_test, c='darkblue', marker='^', linewidths= 8, label = \"test set\")\n", - " else:\n", - " plt.scatter(i+1, MSE_test, c='darkblue', marker='^', linewidths= 8)\n", - " \n", - " Boxplots.append(MSE_CV)\n", - " ticks.append(i+1)\n", - "\n", - " \n", - "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", - " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", - " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", - "\n", - "\n", - "\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=8)\n", - "\n", - "ticks1 = ticks\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "#ax.locator_params(axis=\"y\", nbins=4)\n", - "\n", - "\n", - "ticks2 = list(np.array(ticks)-0.01)\n", - "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", - "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", - "#ax.yaxis.set_major_locator(loc)\n", - "\n", - "plt.ylabel(\"Mean squared error\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "#plt.legend()\n", - "leg = plt.legend(loc = \"upper left\", frameon=True)\n", - "leg.get_frame().set_linewidth(3.0)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (c) Coefficients of determination" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qK4gl5sPRVeW4KDG91JvJPZwjIiIy4syefT8zZtzVZ5sZM+5i9uz729OhQVJlRGcmsT/TpsyfkHwG8D7g0ymlU4GziGKCO5W2GfjdQDo7iH4LfLPcngL0NjG5aznOBc5v5gLuvnR/bcxsJjCptO+rwrSIiMiA9DZl1VO7kTyqU2VE59xy3CyltELd4w78jUg63p4IBKYDm5Xn7wGOqNjPQeXu1xMBHMDOZjaxexsz+wKwSbl7mrv/t9vzq5lZLj8zu58vIiIyXDQymlMz0kd1mh7RyTnfmVJ6M7Gx5VN1j7+SUvow8GPgs0S+DsRIzkXAHjnnJwbe5UGzNzCbGIW6xMwOBy4j3qMty/MADwHfGpIeioiItMCxx/69/0bd2m+wwfhB6s3gqrQFRM75rpzzv3POz3d7/Imc8w7ECqb3EjV3Xp9z3iLn3Mj2CkPG3W8EtgGeJHJ1DgWuBmYB+xKB3f3A5u7+wBB1U0REZMAuv7y5HZmabT+cVN3rqk8556dzzn/LOf815/x4/2cMD+5+CbA2cCRwK7Fn11PADcDBwNplmktERGTEGjt2TFPtx41rrv1wMpA6Oh3J3e8D9i8/zZx3F12bmFa99uSBnC8iItKIiRPHM31649tPTpzYSJm54WlQRnRERERk+DriiI1YdtnGStstu+zCHHHEyC06W3lEJ6U0Bvg4sRz6zcQu4P2NbeWc8yb9tBEREZFBtMoqS3LjjTux336zmDXrPh544LW7Gq200uJMmrQyRx45ifHjlxiCXrZGpUAnpfQ+om7Om+sf7uOUXJ7vjB3CRERERrjx45fgzDM3H+puDLqmA52U0urADGJzywS8TGzw+Tgwr6W9ExERERmAKiM6U4lpqnnESqQf5Zyf7vMMERERkSFQJdCpbefwk5zzd1rcHxEREZGWqbLqavlyPKeVHRERERFptSqBzmPl+GwrOyIiIiLSalUCnWvL8e2t7IiIiIhIq1UJdI4lVlt9scV9EREREWmppgOdnPNlwOHAxJTSCSmlsa3vloiIiMjAVamjsyPwT2A2sCvw8ZTSOcQmmM/1d37O+bRmrykiIqPTvHmZ00//B0cffT0PP/wcK6ywKPvssy477PBOFlhgQNsLyihRZXn5Kcxf4XhF4CsNnpsBBToiItKQQw+dzSGHXPPq/Yceepadd76Eu+56ioMO2mAIeyYjRdW9rhRGi8iwdvzxx3PmmWe25Vpz5swBYPLkyW253nbbbcfuu+/elmsNpWuueWC+IKfewQfP5iMfWY0PfGClNvdKRpoqgc6bWt4LEZEWO/PMM5kzZw4TJkwY9Gu14xo1taBqNAQ6Bx88u9/nL710mzb1RkaqpgOdnPPdg9EREZFWmzBhAjNnzhzqbrRUu0aNhtrs2fczY8ZdfbaZMeMuZs++nw02GN+eTsmIVGV5uYiIyKDqbcqqajsZvarm6IiIyCg3kDyo2hRcT+bOncczz7zc0OvMmAFLLLErY8b0/L296rTiaMmDGg00oiMiIpXU8qBa7cUX5w5q+/7MmTOnbYnsMvh6HdFJKV1ebuac8yY9PF7FfK8lIiIj22DkQa2wwk/573/7Lcv2qmWWWZSHH/5Sy64/WvKgRou+pq4ml2Pu4fFMc0vMa+27v5aIiMh8xo4d01T7ceOaay+jS1+BzpX0HJj09riIiMiATZw4nunT/9lE+5UHsTcy0vUa6OScJzfzuIiISCscccRGXHrpXTz++Av9tl122YU54oiN2tArGamUjCwiIsPKKqssyY037sS2276DlVZavMc2K620ONtu+w5uvHEnVl55iTb3UEYSLS8XEZFhZ/z4JTjzzM2HuhvSATSiIyIiIh1rwCM6KaUliP2vlgD6TX3POV850GuKiIiINKJSoJNSSsBuwB7AOk2cmqteU0RERKRZTQcdKaWFgPOAzWoPtbJDIiIiIq1SZXTla8DHyu27gVOBG4AngXmt6ZaIiIjIwFUJdHYox2uATXPOz7ewPyIiIiItU2XV1ZuIXJvvKcgRkdFs3rzMqafezIQJp/KGN/yMCRNO5dRTb2bePBWPFxkuqozoPAGsCNzX4r6IiIwohx46m0MOuebV+w899Cw773wJd931FAcdtMEQ9kxEaqoEOjcQgc5qwJxWdkZEpJU2nzWLo9LgrZdYHDiypycOhqMOHpxrbg5cOGnS4Ly4SAeqMnV1HLHSakqL+yIiIiLSUk2P6OScL04pHQvsmVI6CvhGznlu67smIjIwF06axMyZM1v+urNn388HPzi933ZXX70tG2wwvqXXnjx5cktfb6AGe9RsKGjUrLNUKt6Xc947pXQ3cBiwTUrpt8BtwHMNnHtalWuKiAwX9Xk5/bW79NJtBrk3ItKXqpWRFwGWAV4GVgH2bvDUDCjQERlk8+ZlTj/9Hxx99PU8/PBzrLDCouyzz7rssMM7WWCBzvr23W6zZ9/PjBl3NdR2xoy7mD37/paP6gwn+wGTKox+zJkzp+V96cmECROaPmfOnDk0f5YMV1UqIy8KzAA+UHuopT0SkQHTaqDBc+yxf2+6facGOjNnzhx2U2mtMGHCBLbbbruh7oa0SJURnX2B2l/KPwPHo8rIIsPGNdc80OvUysEHz+YjH1mND3xgpTb3qnNcfvk9g9p+pBmMHCiRVqoS6GxHTEH9HvhkzlnBjcgwcvDBs/t9Xnkj1Y0dO6ap9uPGNddeRFqryvLy1crxGAU5IsNLI/kjtbwRqWbixOamoSZOXHmQeiIijagS6DxRjo+2siMiMnDNrAaSao44YiOWXXbhhtouu+zCHHHERoPcIxHpS5VA59pyfFsrOyIiA1NlNZA0b5VVluTGG3di223fwUorLd5jm5VWWpxtt30HN964EyuvvESbeygi9ark6BwDbAF8JaV0Vs5Zu9cJ0N5CZkqAfC2tBmqf8eOX4MwzNx/qbohIA6pURr4ipTQV+C7wq5SS5ZyfbHnPpOPV6mhUqXMxWvUVTM6e/QDQeJHy3/xmDJMnH9Xr8womRaQTVKmj8+1y86/AZ4CPp5T+QOOVkQ9t9poyMjT7wVj70NYHamv0XIX/gXJ87XLyBapMXIvIkOi0rTbauc1Glamrg4nl5ZTjYsCWTZyvQEekor6Cwu22u5Dp0//Z7dGfleMer2m/9dZr8MtffqJlfRMRGY4qbQHBa6shd06YKTJCHXHERlx66V08/vgL/bbVaiCRkWWwNqgdKu3M6Wx68DrnvMBAfgbjHyEiWg0kItKTqiM6IjIMdV8NNHnyhQDMnPn1oeqSiMiQanqEJaX0RErp8ZTSPoPRIREREZFWqTKiswiwELHqSkRERGTYqpIz81A5vtjKjoiIiIi0WpVA58/luFYrOyIiIiLSalUCnZ+X4z4ppbGt7IyIiIhIK1VZXj4TmAasDVycUlq11Z0SERERaYWqW0C8AtwIfAj4d0ppNnAD8AT9bLajLSBERESkXVqxBcQYYMPy0wgFOiIiItIW2gJCZAgcf/zxnHnmmYN+ndoO8e0qt77ddtux++67t+VaIiKNaDrQ0TYOIgN35plnMmfOHCZMmDCo1xns169XC6oU6IjIcKItIESGyIQJE7RJn4g0ZNasWR31/1g7vujVaHRGRERkGJs5cyaTJk0a9OvMmTPn1ZHZwTZhwgS22267tlxLIzoiIiLDXDtGf2sjRp000gwDDHRSShsDuwIfAFYEFgbWyTnfUtdmI6KK8lM55zMGcr12MLOVgT2BLYBViaX0dwLnAse6+xMDeO3xwEeB9YB3E+/ZcsTeYY8RS/bPA05z9+eq/yuGt3nzMqef/g+uu+4hXnppHhMmnMo++6zLDju8kwUWUF67iIi0TqWpq5TSIimlXwF/BLYD3gwsSs+rr+YBxwGnppTeWrWj7WBmmwE3AfsDawCLAUsBE4BDgJvMbN0BXGJb4ETgi8D7gDeWa4wF3kAEQT8DbhngdYa1Qw+dzc47X8Kzz77Myy/P5YYbHmHnnS/hO9+5Zqi7JiIiHaZqjs504DNEYHMd8IPeGuacrwJqIzxbVbzeoDOzdYBzgKWB54CDiNpAk4GjiUKI44ELzWylipfJwK2AA3sAnwTWByYBOwKXlnZvBP4wgOsMW9dc8wCHHNJzQHPwwbO55poH2twjERHpZFUqI29JfEBn4Es555+Xx7/Wx2nnAmsSH+hHVOhnO/yIGF2ZC3zM3a+se26Wmf0NOJ2YbjqMmLJr1jHu3mtQCJxuZl8lAqtlgK8D+1a4zrB18MGz+33+0ku3aVNvRESk01UZ0dm5HH9VC3IacG05rlHheoOuTBNtXO6e0i3IAcDdzwAuL3d3NLPlm72Ou7/SQLPjgGfK7YnNXmM4mz37fmbMuKvPNjNm3MXs2fe3p0MiItLxqgQ66xOjOc2UdX2wHJsODtqkfkrtxD7anVSOY4hRrZYrwdAL5e7Cg3GNodLblFXVdiIiIv2pEugsV47NfO2ubfQ5XOv21Pbpeo6u0aeeXNHDOS1lZpvQ9R7/czCuMRQaGc2p0aiOiIi0SpXl5U8T+SMrNnHOquX4WIXrtcOa5Xh7X9NL7v6AmT0NLFF3zoCZ2ZLAKkSCd31OzjGtusZQO/bYvzfdfoMNxg9Sb0REZLSoEujcQdSBeSdwSYPnbF6ON1e43qAys3F0jaDc18Ap9xJBzioDvO5RQG8J3K8Ae7v7VQO5xnBy+eX3DGp7ERGRnlQJdC4h8nT2TCkdk3PuM8E2pfRuYHsir+eiCtcbbEvU3X6m11avbbP4IPQF4v3d291vG6TX79Ng7ar95JMP0DWDWVNbSv6z17T/3//GMHnyWS3tg3bWFhEZfarkzBxHTF+tApycUhrXW8OyFP0SoiDeo/Sd6DtUFqm7/VID7V/s4bwqjgLWLj/vB3YBZgKbAb82s/UG+PqV1HbVbrWll+7p12Sl8vNaSy3V669VJXPmzBmUAE5ERIa3pkd0cs6PppR2I4oGbgd8OKV0cV2T/VNKCwAfBFYjigrOBXbIOQ/HbQ2er7s9toH2tU/g5/ts1Q93fwh4qO6hvwCnmNlUok7PlWa2pbvPGMh1qhiMXbXvvfcpJkw4jccff6HftssuuzDXX78TK6+8RL9tG9VJu/6KiEjjKu11lXM+K6U0F/gFsWR8J2JqCmCHcqxtB/EUsGPOue0f2A16uu52I9NRtTaNTHM1zd2nmVmtYvIJZrZ6bwnSZrY7MN9czNixY5k2bdp87SZOnMhGG200GN1t2CqrLMmNN+7EfvvNYtas+3jggde+fSuttDiTJq3MkUdOYvz41gU5IiIyelXe1DPn/JuU0hXAl4iaMhPqXi8Tice/A47JOT86wH4OGnd/0cweJRKSV27glFqbewevV/yOCHRWLcceywm7+/HA8fWPTZs2LU+dOnUQu1bd+PFLcOaZm/ffUKRFZs2a1XGjeXPmzGHChAlD3Q2REWNAdW1yzo/nnA/LOa9PFLdbnticclzOeZ2c84HDOcipU9uL661m1mvwV/aeWrLbOYPhkbrbbxzE64h0rJkzZzJp0qS2XGvOnDmDktvWkwkTJrDddtu15VoinaDyiE53Oed5RMLxSHQVsBGxA/t6QG+leSd3O2ew1BeQGZQpMpHRoNW5Zr2pjRq163oi0rgqm3peTkxN7ZpzvrvBc1YCzgByznmTZq/ZBr8FvlluT6H3QKe2kedc4PzB6IiZLQBsXffQTYNxHRER6WzNTtvWRiWrTPcO5yC/yojOZCLQWayJcxapO2/YcffrzWwm0cedzexUd/9TfRsz+wJQC9JOc/f/dnt+NeDOcneWu0/u9vyiRADzS3ef11M/zGwMsex8rfLQn9z9rmr/KhERkcZ1au5Xy6auOsDeRNLvYsAlZnY4cBnxHm1ZnodYEv6tCq8/FjgNOMzMzgH+TFRifp7YUuPdxM7wa5f2TwFfrvIPERERGc6jLO3UrkCnNvrTfxGVIeLuN5rZNkR9oKWBQ8tPvfuBLd39Aapblfn3s+rJLcAO7q5pKxERkQFoV6DzsXJsZC+pIePul5jZ2sBexP5cqxL5OHcC5wI/dvcnKr72k2b2fuCjwPuIYoorEEHVM8R+CH8r1znf3V8e0D9GRCqpkp9QNbdB37hFBl+/gU5K6aRenjospfRkP6ePA1YnVjJlYFZTvRsC7n4fsH/5aea8u+gqkthbm78QFZBFpIN0am6DSCdoZERnZ16bRJyIvJVG1D78Hwe+1+A5IiJDQqMsIp2lkUDnHuYPdN5Y7j8I9DW9komcnAeJJN+f5ZwHktsiIiIi0pR+A52c82r191NKtaXRH8k5D2Z1YBEREZEBqZKMfCUxWvNsi/siIiIi0lJNBzo558mD0A8RERGRllPBQHmNzWfN4qjU5wKyEWdz4MI2bfAoIiLDx4ACnZTSxsT+Tx8AViR2MF+nPncnpbQRsaXBUznnMwZyPZFO0mkBpYJJERmOKgU6KaVFgJOBz9QeKsee9rKaBxwH5JTSX3LOt1e5prTPhZMmddwS2ypF4EREZOSrOqIzHdiCCHCuJRKUv9ZTw5zzVSmlW4A1gK2AIypeU6SjdFpAqWBSRIajpgOdlNKWwCeJ0Zsv5Zx/Xh7vMdApzgXWBCahQEcEgFmzZnVUcDBnzhxVCBaRYWeBCufsXI6/qgU5Dbi2HNeocD2RjjNz5kwmtSGfZc6cOa/uwzTYJkyYwHbbbdeWa4mINKrK1NX6xGjOmU2c82A5Ll/heiIdqR3TVrURo06aIhMRaUaVEZ3lyvH+Js6ZO4DriYiIiFRSJfB4uhxXbOKcVcvxsQrXExEREamkSqBzRzm+s4lzNi/HmytcT0RERKSSKjk6lxB5OnumlI7JOb/SV+OU0ruB7Ym8nosqXE9EimZXadUSkaus7lJej4h0giojOscR01erACenlMb11rAsRb8EGAs8CpxYpZMiUs2ECRO05FtGvOnTp7PWWmsxZswY1lprLaZPnz7UXZIRpMqmno+mlHYjigZuB3w4pXRxXZP9U0oLAB8EViOKCs4Fdsg5PzfwLouMXhplkdFm+vTpTJ06lRNPPJENN9yQq666iilTpgCw7bbbDnHvZCSotAoq53wW8FngKWLJ+E50bf+wA/AF4E1EkPMUsHXOecaAeysiIqPKtGnTOPHEE9l4441ZaKGF2HjjjTnxxBOZNm3aUHdNRojKy71zzr8BVge+DVxPjNokuva9uhmYBrwl53z+APspIiKj0K233sqGG24432Mbbrght9566xD1SEaaAdW1yTk/nnM+LOe8PrFz+fLAG4BxOed1cs4H5pwfbUVHRURk9FljjTW46qqr5nvsqquuYo01VGhfGtOyAn4553k550dzzg/3txJLRESkEVOnTmXKlClcccUVvPzyy1xxxRVMmTKFqVOnDnXXZISounu5iIjIoKslHO+5557ceuutrLHGGkybNk2JyNIwBToiIjKsbbvttgpspLJeA52U0n8G4Xo557z6ILyuiIiIyGv0NaKzWoOvUVtWnhp4PCMiIiLSJn0FOqf2c+4E4F1EIPMk8Hfg4fLcCuX5ZYjg5obyIyIiItI2vQY6OeddensupbQrURX5PuBrwLndV1qllMYAWwFHAmsCP8k5awsIERERaZuml5enlN4L/Bx4BHhfzvnsnpaT55zn5pzPBt4PPAb8tJwrIiIi0hZVVl3tA4wBvptzfrC/xjnnh1JK3wWOBfYlRoJkGJs1a1al3a6Hszlz5mhzSxGRUahKwcCJ5fjXJs75Szlu2GcrGXIzZ85k0qRJbbnWnDlzmDNnTluuNWHCBLbbTjG2iMhoU2VE5/XluHAT59Tavr7PVjIstGuH7NqokXbkFhGRwVJlROeRcvxEE+fU2mrfKxEREWmbKoHO5cSS8q+mlDbur3FKaRLwVWKZ+WUVriciIiJSSZVA53DgRWAscGlK6ecppfenlBaqNUgpLVQe+xnwB2Ac8FI5V0RERKQtms7RyTn/M6W0E3BGOX+38jMvpfQUMXKzFF1BVAJeAXbJOf+zJb0WERERaUCVER1yzmcBHwSuJwKZRCw5XwZYttyuPf43YMOc869a0WERERGRRlXevTznfC2wXikCuCmwNhHkADwB3AT8sbQTERERabvKgU5Nzvk64LoW9EVERESkpSpNXYmIiIiMBAp0REREpGMp0BEREZGOpUBHREREOpYCHREREelYCnRERESkYynQERERkY6lQEdEREQ6Vq8FA1NKO5ab5+Wcn2pTf2QEmzx5clPt58yZU+k8gJkzZzZ9joiIjD59VUY+hdig8zrgltqDKaXLy+O75pzvHtTeSUebMGHCUHdBREQ6XJUtICYTgc5ire2KjHQaZRERkeGmrxydF8txiXZ0RERERKTV+gp07ivHjdrREREREZFW62vq6o+AAdNSSusB/wJernv+Syml/zZ7wZzzoc2eIyIiIlJFX4HOd4FtgNcBW3d7LgF7VLymAh0RERFpi16nrnLO9wLvAX4B3EWM5iQiEZlyu8qPiIiISFv0ueqqBDu71z+WUppHBDtr55xv6fFEERERkWFAlZFFRESkY1Wpo7NLOd7XZysRERGRIdZ0oJNzPnUwOiIiIiLSalVGdOaTUhoHvA9YG1i2PPw4cBPwl5zzi72dKyIiIjKYKgc6KaXFgEOAKcCSvTR7KqX0C+DgnPOzVa8lIiIiUkWlZOSU0irAHGAfYCl6X0q+FLAv8PeU0sot6K+IiIhIw5oe0UkpLQj8Hli9PPQv4GTgz8BD5bEViemsXYB3AG8BLk4pvTvnPHegnRYRERFpRJWpqynAmkQtnSOBqT0EL7cBV6aUfggcBnwDeGc59/jq3R18ZrYysCewBbAq8ApwJ3AucKy7PzGA114I+BDwYeD9wNuBpYHngLuBWYC7+80D+CeIiIhIUWXq6jPleGHO+YC+RmhyznNzzv8PuJCYyvpMb22HAzPbjEii3h9YA1iMmH6bQOQj3WRm61Z87dcDDwKXAF8DPggsRwSbSxLJ3F8BbjCzaQP6h4iIiAhQLdBZmxjN+UUT55xQjutUuF5bmNk6wDl0jbAcBGwITAaOBuYC44ELzWylCpcYR+wbBnAzMA3YHHgvMIkIpP5H/Df5ppl9p+I/RURERIoqU1dLl2MzBQNrbZeqcL12+RExgjMX+Ji7X1n33Cwz+xtwOpF/dBiwa5Ovn4kd4Q9y99k9PH+lmf0SmE2M9BxgZie5+51NXkdERESKKiM6tRyVVZo4p7bi6skK1xt0ZTpq43L3lG5BDgDufgZwebm7o5kt38w13P1+d/9wL0FOrc3tdO3uviDwqWauISIiIvOrEujcVI67NXHO/5XjjRWu1w5b1d0+sY92J5XjGOCTg9SXK+puv2WQriEiIjIqVAl0ziYSiz+eUjoypTSmt4YppQVSStOIFUy5nDscbViOzwHX9tGuPgjZsNdWAzO27raW4ouIiAxAlUDnJOAfRLCzL3BrSmlqSmmTlNLaKaW1yu1vlnYHlPNupmtEZLhZsxxvd/dXemvk7g8AT3c7p9Um1d2+dZCuISIiMipU2dTzlZTSx4HLiKmV1enKK+lJAm4HPj4ciwWa2Tgi+RcaS7C+lwhymslRarQviwFfLXdfBH7X6muIiIiMJpW2gMg530vUljmSSDDubQuIJ4HvA+/JOd8/4N4OjiXqbj/TQPtam8UHoS9HEUUKAY4rI0giIiJSUeVNPXPOzwHfSCl9i6gFsxbz715+M3BdzvnlAfdycC1Sd/ulBtrXdmNfpM9WTTKzXYEvlrv/AA5s5euLiIiMRpUDnZoSyFxTfkai5+tuj+21VZdxPZw3IGb2MeDn5e6jwFbu3rLXFxERGa0GHOh0gKfrbjcyHVVr08g0V7/MbCPgN8BCRGXkj7r7bQ2euzuwe/1jY8eOZdq0+XeQmDhxIhtttFEruisiIjKijPpAx91fNLNHiYTklftrX9fm3oFe28zWJ/YBWwR4FviEu/+t0fPd/Xi6bZI6bdq0PHXq1IF2TUREpCNUSkbuQLeU41vNrNfgr+xxtWS3cyoxs3cRG3wuQeT9fMrdrx7Ia4qIiMj8FOiEq8pxUWC9PtpN7uGcppnZGsAfgGWAl4HPuPsfq76eiIiI9EyBTvht3e0pfbSrbeQ5Fzi/yoXMbHVic8/Xl9fZ3t0vqPJaIiIi0jcFOoC7Xw/MLHd3NrOJ3duY2ReATcrd09z9v92eX83McvmZ2f380mYVotDiSsSWGFPc/azW/CtERESku1GfjFxnb2A2sBhwiZkdTgQlCwJblucBHgK+1eyLm9nriJGcN5aHfgJcb2Zr9XHas+5+Z7PXEhERkaARncLdbwS2Iao5L0psa3E1MIvY02sMcD+wecWKxWsDb6u7/xViJ/i+fk6ucB0REREpFOjUcfdLiIDkSGJDzWeBp4AbgIOBtcs0l4iIiIwAKefc3Akpfbvc/GnO+dEGz1kG2BMg59zXBqAyQKqjIyIio0zq68kqOToHE4m05xDbFTRi2brzFOiIiIhIW2jqSkRERDpWuwKdhcpxuO9kLiIiIh2kXYHOhHJ8pE3XExEREek/RyeltGMvT22ZUnpvP6ePA1YnKgpn4NrmuiciIiJSXSPJyKcQQUq9BBzWxHUSMA84polzRERERAak0amrVPfT02N9/bxMFN77ZM55Vmu6LSIiItK/RkZ03lR3OwH/IUZ4Pgrc3sd5GXgBeCznPLdyD0VEREQq6jfQyTnfXX8/pVcHdR7o/pyIiIjIcNJ0wcCcs2rviIiIyIigoEVEREQ6lgIdERER6VhV9roCXt2oc3tgEvBmYAlgTD+n5Zzz6lWvKSIiItKMSoFOSmlL4CRg6dpDDZ7a3FbpIiIiIgPQdKCTUnoPcFY5NwEPAn8DHieKAoqIiIgMC1VGdA4gNul8EdgdOCPnrJEaERERGXaqBDobElNQ3885n97i/oiIiIi0TJVVV8uW4+9b2RERERGRVqsS6Dxcjq+0siMiIiIirVYl0LmqHNduZUdEREREWq1KoPNDYC7w1ZTSQi3uj4iIiEjLNB3o5JyvB74KvBM4L6X0+lZ3SkRERKQVqtTR+Xa5+VfgY8BdKaU/ArcCz/V3fs750GavKSIiIlJFleXlB9NV4TgDiwCbl59GKNARERGRtqi611X3LR8a3QJCREREpG2aDnRyztrxXEREREYEBS0iIiLSsRToiIiISMdSoCMiIiIda0CBTkpp45TS6SmlO1JKz6SUXkkprdmtzUYppS+llLYfWFdFREREmlNp1VVKaRHgZOAztYfKMffQfB5wHJBTSn/JOd9e5ZoiIiIizao6ojOdCHIScB3wg94a5pyvAm4pd7eqeD0RERGRpjUd6KSUtgQ+We5+Kef8vpzzfv2cdi4RFE1q9noiIiIiVVUZ0dm5HH+Vc/55g+dcW45rVLieiIiISCVVAp31iVycM5s458FyXL7C9UREREQqqRLoLFeO9zdxztwBXE9ERESkkiqBx9PluGIT56xajo9VuJ6IiIhIJVUCnTvK8Z1NnFPb2fzmCtcTERERqaRKoHMJsYJqz5RSv3V4UkrvBrYn8nouqnA9ERERkUqqBDrHEdNXqwAnp5TG9dawLEW/BBgLPAqcWKWTIiIiIlU0XRk55/xoSmk3omjgdsCHU0oX1zXZP6W0APBBYDVi9GcusEPO+bmBd1lERESkMZW2gMg5n5VSmgv8glgyvhNd2z/sUI61bSGeAnbMOc8YSEdFREREmlV5uXfO+TfA6sC3geuJUZtEV4BzMzANeEvO+fwB9lNERESkaZVGdGpyzo8DhwGHlemqZYExwGM551da0D8RERGRygYU6NTLOc8jEo5FREREhgVVKhYREZGOpUBHREREOlavU1cppcvLzZxz3qSHx6uY77VEREREBlNfOTqTyzH38Hima3VVI2rtu7+WiIiIyKDpK9C5kp4Dk94eFxERERlWeg10cs6Tm3lcREREZLhRMrKIiIh0LAU6IiIi0rEU6IiIiEjHajrQSSmtlVL6T0rp9pTS+Abar5xSuiOl9O+U0tuqdVNERESkeVVGdL4ArAbckXO+v7/GOef7gNvLOTv03VpERESkdaoEOpOI5eUXNnHO74g6OhtXuJ6IiIhIJVUCnbeX4w1NnHNzt3NFREREBl2VQGeJcny6iXNqbZescD0RERGRSqoEOv8rx+WbOOf15fhsheuJiIiIVFIl0Pl3OX6oiXM+XI53VbieiIiISCVVAp0/EonFe6SUVu2vcUrpjcAXiQTmP1S4noiIiEglVQKdnwEvErk6l6WU1uutYUppfSIwWgJ4GfhplU6KiIiIVNHX7uU9yjnfn1LaHzgGeDPw55TSNcCfgAdLszcAGwHvr50GfDPnfPfAuzy4zGxlYE9gC2BV4BXgTuBc4Fh3f2KAr78KsD6wXjmuS1eS9iHufvBAXl9ERES6NB3oAOScj00pLQJMA8YAHyg/3SVgLnBgzvmHlXvZJma2GTAdWLrbUxPKz+5mtqW7X1/x9d+I8pRERETapvJeVznn7wPvAc4EniSCmvqfJ4EzgPfknA8faEcHm5mtA5xDBDnPAQcBGwKTgaOJgG08cKGZrVTxMqnudgbuAK6s+FoiIiLSj0ojOjU555uA7VNKidjiobaM/FHgzpxzHlj32upHwGJEQPMxd68PQGaZ2d+A04EVgcOAXStc42ngW8Bfgevc/QkzmwxcMYB+i4iISC8GFOjUlIDmzvIz4pjZunRtT3FKtyAHAHc/w8x2IZbV72hmB7j7f5u5jrs/Rkz3iYiISBtUnrrqMFvV3T6xj3YnleMY4JOD1x0RERFpBQU6YcNyfA64to929VNMG/baSkRERIaFXqeuUkq10Yucc57Sw+NVzPdaw8ia5Xi7u7/SWyN3f8DMnibqAq3ZWzsREREZHvrK0dmZWBkEMKWXx5uRynnDKtAxs3HAcuXufQ2cci8R5KwyaJ0SERGRlugr0LmHngOa3h4fqZaou/1MA+1rbRYfhL6IiIhIC/Ua6OScV2vm8RFskbrbLzXQ/sUezhMREZFhSMnI8Hzd7bENtB/Xw3kiIiIyDPWVjDwPmAesk3O+pe7xHcvN83LOTw1y/9rh6brbjUxH1do0Ms01qMxsd2D3+sfGjh3LtGnzl+qZOHEiG220UTu7JiIiMiz0VzAw9fDYKUSOznXALT08P6K4+4tm9iiRkLxyA6fU2tw7eL1qjLsfDxxf/9i0adPy1KlTh6hHIiIiw0tfU1dzy7El1ZOHuVrA9lYz6/XfW/a4qu00PuKDPBERkU7XV6DzRDmu3o6ODLGrynFRYL0+2k3u4RwREREZpvoarbkO2Az4bkrpBeA24OW659+QUmo6TyXnfE+z57TBb4FvlttTgGt6aVfbyHMucP5gd0pEREQGpq9A5ydEoPM24MJuzyVgRoXr5X6uOSTc/Xozm0mM2OxsZqe6+5/q25jZF4BNyt3Tum/oaWar0bWp6Sx3nzyYfRYREZH+9VVH56KU0leI3baX6qFJT4nKI9newGxgMeASMzscuIx4j7YszwM8BHyr6kXMbDNgxbqH3lF3e4KZ7Vzf3t1PqXotERGR0a7P0ZWc809TSicD7wXGEzVkTiZGZg4E7h/0HraJu99oZtsA04GlgUPLT737gS3d/YEBXOoAYFIvz21ZfuqdMoBriYiIjGr9TiPlnJ8HXp3GKYEPRB2djlp55O6XmNnawF7A5sCqRD7OncC5wI/d/Yk+XkJERESGkZRzz9tWpZRWLTfvzznPrXv8CmJEZ5ec892D30VphuroiIjIKNNnKk1fIzp3USojM3/NmJlEoPPsADsmIiIiMqiqVEY+iAh0zgEebXmPRERERFqkr4KBr5TjuD7aiIiIiAxbfQU6tdGad7ajIyIiIiKt1tfU1WxgK+CIlNKSwL+YvzLyeiml5Zq9YM75ymbPEREREamir0Dnh0RNlxWBY7s9l4CTKlxvWFZGFhERkc7U69RVzrk2ovNvIrCp/dSkij8iIiIibdFfZeQLgAtSSqsQlZEXBi4nRmam0LW3k4iIiMiw09A0Us75XuBegJReHZT5a6dVRhYREZHOUiVf5jRiREdbIYiIiMiw1nSgk3PeeRD6ISIiItJyLVkBlVJamVidtShwbdkIVERERGRIVQ50UkpLAF8HdgVWqntqber2xkopbQt8Gvhfznm3qtcTERERaValQCeltDpwMfAW5l8y3tNW6H8GTo/T0ik556urXFNERESkWX1tAdGjlNI44ELgrcDzwJHAFr21zznfCdSqIX+yQh9FREREKqkyorM78HbgOWBSzvl6mG/ZeU8uBiYDH6hwPREREZFKmh7RAbYmpqiOrQU5DbihHN9a4XoiIiIilVQJdGq7mV/axDm1ndCXqXA9ERERkUqqBDpLluNTTZyzUDm+UuF6IiIiIpVUCXQeK8dVmzjnHeX4cIXriYiIiFRSJdC5sRybSSzejsjr+WuF64mIiIhUUiXQOY+onbNHSun1/TVOKe0IfKTcPafC9UREREQqqRLonAj8B1gM+GNK6T3dns8AKaVVU0pHAyeVx27IOf9mIJ0VERERaUaVTT1fTil9iigCuDZwbUrp7rom55TtIcaX+wl4BNhmgH0VERERaUqVER1yzjcD7wWuJgKZ1eqeXgNYuTyeiC0g3pdz/s+AeioiIiLSpMqbepbAZWJK6YPAlkTgszwwhqib83fgdznny1rRUREREZFmVQ50asomndqoU0RERIadSlNXIiIiIiPBgEd0alJKiwHLlruP55yfbdVri4iIiFQxoBGdlNI6KSVPKf2H2BLirvLzVErpPymln6eU1hl4N0VERESaVznQSSkdDlwP/B+x6ip1+1kN2A34W0rpewPtqIiIiEizKk1dpZSOAb5CBDQA/ySWkT9U7q8IvI9Yap6A/VNKi+Scvzqg3oqIiIg0oelAJ6X0AWBPotrxP4Hdc85X9dL2g4ADawJ7ppR+lXP+8wD6KyIiItKwKlNXVo73Ah/sLciBV5eeTwRqlZO/WOF6IiIiIpVUCXQ2IkZzDs85P9Ff49Lm+8QU1kYVriciIiJSSZVAZ8VyvK6Jc67tdq6IiIjIoKsS6LxUjuOaOKfW9qU+W4mIiIi0UJVA555y3KyJcz5ajnf32UpERESkhaoEOjOIfJt9Ukrr9dc4pfRuYB8ir+fSCtcTERERqaRKoPMj4AVgEeCKlNK3U0rjuzdKKa2UUvoWMAtYrJxzzAD6KiIiItKUpuvo5JzvSyn9H3A6EewcBByUUnoA+C8xcrMCsFI5JZXHpuSc729Jr0VEREQaUKkycs75zJTS48DxwMrl4fHlp7v7gN1yzpq2EhERkbaqvHt5zvmSlNKbgU8BHwbWom73cuBm4A/AeTnnVwbYTxEREZGmVQ50AEoAc075ERERERlWKu9eLiIiIjLcNTSik1Jastx8Oef8fKMvnlJaBFgIIOf8VPPdExEREamu3xGdlNI+wBPl52NNvv7HgCeBJ1JKezTdOxEREZEB6DPQSSktDEwtdz3n/NtmXry0/xmxxPzglNJClXopIiIiUkF/IzqfIlZSPUfUy6niIOAZYLnyeiIiIiJt0V+g8/FyPD/n/EiVC+ScHwXOJ0Z1PlHlNURERESq6C/QeS9R1fiSAV6ndv57B/g6IiIiIg3rL9BZsRzvGuB1aruWr9RnKxEREZEW6i/QWawcnx7gdZ7p9noiIiIig66/QOeJclxugNd5XbfXExERERl0/QU6D5XjewZ4nXeX44MDfB0RERGRhvUX6FxFrJbaeoDX2YZIar56gK8jIiIi0rD+Ap0Ly/G9KaXPVrlAOW+9bq8nIiIiMuj6DHRyzpcAc4hRnRNTSpOaefGU0mTgJGI05+/l9URERETaopHdy78CvAQsCvwxpXRMSunNfZ2QUlo9pfRj4A/lvJeAPQfaWREREZFm9Lt7ec55dkppN2JkZgEi8PlySuk24HrgYeBZYun4CsC6wNuIUaAEzAV2yzlfMyj/AhEREZFe9BvoAOScT08p3Qv8EngDEcC8vfz0JJXjg8AXcs4zB9hPERERkaY1MnUFQAlWViemoK4H5tE1alP/M688/xVgdQU5IiIiMlQaGtGpyTm/APwE+ElKaQlgbaIY4BJE9eTHgJtyzgOtpCwiIiIyYE0FOvVKMDO7hX0RERERaanKgU6nMrOViem5LYBVgVeAO4FzgWPdvSXbWJjZ+sCXgElE3tNTwD+IPKiT3X1uK64jIiIymjWcozMamNlmwE3A/sAaxEqypYAJwCHATWa2bguu803gGmAnYDVgHPB6YDJwAnClmS0z0OuIiIiMdgp0CjNbBzgHWBp4DjgI2JAIPo4mlsmPBy40s5UGcJ1dgWnEe383YMD6wObABaXZBsC5Zqb/PiIiIgOgqasuPyJGcOYCH3P3K+uem2VmfwNOB1YEDgN2bfYCZrY0cFS5ez/wPnd/uK7JRWZ2AvB/xJTW9sBpzV5HREREgkYMgDIdtXG5e0q3IAcAdz8DuLzc3dHMlq9wqSlAbUrqgG5BTs0+wP/K7f0qXENEREQKBTphq7rbJ/bR7qRyHAN8cgDXeRo4u6cG7v5M3XNrmdlbKlxHREREUKBTs2E5Pgdc20e7K3o4pyFmthCRiwPwZ3d/cTCuIyIiIl0U6IQ1y/F2d3+lt0bu/gAxGlN/TqPeRldO1C39tP1nD30TERGRJo36QMfMxgHLlbv3NXDKveW4SpOXWrnudn/XubfudrPXERERkWLUBzrE9hU1zzTQvtZm8UG8Tv3zzV5HRERECgU6sEjd7ZcaaF/LrVmkz1YDu059/k6z1xEREZFCgQ48X3d7bAPtx/VwXquvM67udrPXERERkUIFA7uSi6GxaaJam0amuapep/75Xq9jZrsDu9c/NnbsWKZNmzZfu4kTJ7LRRhs12E0REZHOMeoDHXd/0cweJRKSV+6vfV2be/ts9Vr1Ccj9Xac+AbnX67j78cDx9Y9NmzYtT506tcmuiYiIdCZNXYXacu+3mlmvwV/Z42rJbuc06jZiJ3Tof8n4O3rom4iIiDRJgU64qhwXBdbro93kHs5piLu/DPy13H2/mfWVp1P5OiPNlVe+ZrcNaTG9x+2h93nw6T0efJ34HivQCb+tuz2lj3a1jTznAucP4DpLAJ/tqYGZLV733M3ufkeF64wYf/rTn4a6Cx1P73F76H0efHqPB18nvscKdAB3vx6YWe7ubGYTu7cxsy8Am5S7p7n7f7s9v5qZ5fIzs/v5xYnAE+X293rZGPSHwFLl9pGN/ytERESku1GfjFxnb2A2sBhwiZkdDlxGvEdblucBHgK+VeUC7v6kme0H/IJISP6LmX0XmAO8HjC6NgudBZxR6V8iIiIigEZ0XuXuNwLbAE8SuTqHAlcTAce+xI7l9wOblz2vql7nRCJQmgesRqya+itwEV1BzmxgK3efV/U6IiIiAinnPNR9GFbMbGVgL2BzYFUiH+dO4Fzgx+7+RC/nrVbaAcxy98n9XGd94MvAJGBFos7OP4hRnJPdfW7F/j8C3F3l3CGyBnDrUHeiw+k9bg+9z4NP7/HgG4nv8aPuvlmvz+ac9aOfIfvZfffdrxvqPnT6j95jvc+d8qP3WO9xlR9NXYmIiEjHUqAjIiIiHUuBjoiIiHQsBToy1I7vv4kMkN7j9tD7PPj0Hg++jnuPtepKREREOpZGdERERKRjKdARERGRjqVAR0RERDqWAh0RERHpWAp0REREpGMp0BEREZGOpUBHREREOpYCHREREelYCnRERESkYynQERERkY6lQEdEREQ6lgIdERER6VgKdETkVWaWhroPIjI8jdS/DwsOdQdEemJmKwNvBZYCbgAed/f/DW2vOpOZrQm8HfhX+Zk7tD0SkeHEzDYh/gb/faj7UkXKOQ91H0ReZWZjAAP+HzC+PPwv4DfAme5+i5kt4O7zhqqPncLMNgR2AqbUPfxrwN19ZmmT3F1/JERGKTNbALgbyMAn3P2mIe5S0zR1JcPNV4FjgGWBfwAPE6MN3wSONrM3KsgZODNbHjiWCHKeB2rf1D4HnG9mUwAU5IiMeosCZwMrA0eY2Zvg1S+lI4ICHRk2zOwtwPeAh4CPAJOBVYH9gXnAh4EfD1X/OsyPgXcRwc47iPf7A8AsYHHgSDN799B1b3QZqbkP0vnc/Rl33xc4DPgocLiZLezuI2aKW4GODCc/AV4A9nf3q4En3f1ldz8K+ATwJLC2ma2qD4bqzGwy8FngDOBAd78XeMLd/wJ8HpgBLE0EmvoQHgTd39PuI2d6z2W4qBu5OQY4GfgMcJKZrVKeH/ZxxLDvoIwOZvYJYsTmDOC35eFc9z/ZzUSgswqwgKZUBuRY4H7gFHd/yszGuPu8ko/zMPDX0u51oOmrVqr7UFjAzFY0sylmdpiZ7WVm65vZaqD3vB1qf1vMbCUze8NQ92e4qo3cuPtjRO7kBcQXov8rjw/7VAIFOjJcHAE8CJzs7i/WPnzdfW75dvsIEehcCzw9hP0c0UruzTuJhOPLy8O10YNaUPlfIvHwb+UcjS60SN2HwgHAZcAJRP7Zj4CLgB+Y2XZm9jrQez9YSlBfm3r5MTEd86bubdrfs+GrvGePEAsYzgMONLMjzWyJoe1Z/xToyJAzs32ANYml5B8zs9fX/giZ2ULl2+37gAnEaMNzQ9XXkczMFiI+WAG2MLM1ANz9FTNbkMiDAtiOWGXxQHleowsDUBvFKe8xZvZJ4DvAisQI5gHEB8dTwKeJoF/J4IOr9t/kCGAN4DR3v7M8Nq7u786ImJppB3fPJdh5kvgdvRXYF9i+1ma4BodaXi5DzsxWB34GbFoeuoNIfDvb3Z8vbf5CJCZ/wd0v17Ln5pnZOOAbwG50Ld0/BfhG+aaGmX2GWGL+IyJX6pX297Rz9FQKwcxuJlay/F/dqBpmtgHxobFjef4Qdz9E5RRaq/a3w8xWJD6s9wB+4+4vm9lWwLbAJOBn7n7QUPZ1OCsjYGcRI8Tm7qcPcZd6pUhVhpy7/9vdP0Jk9N8CvIX4AL7AzNYysx2B9YDTgSuGrKMjnLu/6O6HAhvRtXptZ+ABM9u33D8CuA04q4z06G9ERWa2EvBbM/tI3WPvI0Zyfl0LcmojPe4+myivUPtw/T8zG68gp7XqviBtCjwG/L0EORsQI2zLEnW7DjSz3Yeom8NaCb7vBA4G/gf83My2rz033EZ2NKIjQ8bMFgee6+Eb71eAw4lvtRA5OQ8CW5WCgWNG0tLG4cDMlnX3x7s99gEiuXDz8tBjRALyV939x+WP1RiN6lRjZkcCXyNyns4hksDvAe4CdnD3GfW/y/UjN2Z2CjGys5+7/2AIut/xzOw9wJXAF4HlgD2BvwC7AAsBFwOXufshQ9bJEaAsJDmdyKPc1t3/NsRdeg19W5OhdDSwXwl46r/ZHgesRCw3B1iCKFb1sfKBrSCnCWb2ISLZ8h31j7v7NcDWxAqKmymrrIBtzOxN7p5r+TvD7RvaCHEK8F0i2ftLRLLxocQWG+8v7+mr72tZ+bZQuXsBkRA+rp0d7mRmlmq/x2a2bvlAPhc4jciTuooIQF8ExpYf/d73ojba6+4XEdXsVyJG4T88pB3rgQIdGRJm9jki4XJpSnJx+VBN5VvuU+6+J1HU7gpidOdIYKaZfbbuA0H6dzoxLfia96zUKToL2Bj4NjEMvSHwbzP7sZkt4e6vlJwG/b1ogrv/A5gGbEVMiaxGBDzLAB8HXlcbLasro1AL4lcjPmQ15N4CZYFDLr/HexEVfse6+w7AWsT/H3uUVZ5LAzsQSco/LOcr4OmmVpKi3D2fyKt8AzHlt+rQ9ey1NHUlQ8LM7iGmpHZy9+u6Jxeb2Vhgbt2w/pbECNBqpcm5wDHufmV7ez6ymNnXiADx2+5+WLfnakmZC9Z94L6dWEmxW2n2VDlXFakHwMyWATYBvkLkSEHko33V3f9Y2ixQPjzeBPycSIhdzd0fGoo+d4ryfv4/4EUicLmVqLb+M+CVHoo1HkcEPse5+zGaKu9Sey9K/ll29we7Pb8HMRJ/MbCduz81FP3sTt/QpO3M7NvEVNQp7n4dvLp08dW9U9z9pfI/1Jhy/3fu/mZiiPklYhnuRRrZ6Z2ZLUksY54NTC+PvZooWPsDX5+D4+7/cncDPgT8EVgS+JGZ7dLm7ncUd3+CKIS5I5EL8g+ipMI5ZnaKma0PrGlm6wK/ILbjOFhBTnV1ow1ziYrrHwOuI3KmppfRzFybMi/nvI4oq3Caux9THlYyOH3XHqp7r88FjidG6YdNfR2N6EhbWWwmeT/wJ2Bnd7+nbq53XlkCfTFwkLtfVXde/ajD6wEH/uTuR7f9HzFCmNkviMTK3d39xLrHa9/K9iVWmHzX3V9Tm8jMFiaG8D/n7pt2f16qKaOVbyfe2/8jpm8hNrBdjAhMf+XupwxF/zqVRb2uHwCPE6M6P3D38+qeT8AKxJewv5f/R7S0v6j7u/F9Ykuevdz9svLcOGBeWb22ALCmu988lP2tt2D/TURa6mjiG9bxJchJJcCpjeZ8n8gXOYtIDgS68neI7R8eIfIepBdm9i5gV+BXlC01yh+gVP5YrQIcRcytL0oPRRjd/QXgBDM7tW0d7wD91Xhy95eAm8zsEOD3RK7adsSH7J+BcxTkVGexOfDbgZeB54G7PPZz+xdwHPFF6xPAcWa2LXC4u/+9jO4cSawENRgZ2xu0Q200p9Qe2o2oPXRlee7TxO/vZDM73t2nEosbhg1NXUnblBoi2wIziWmR2pTV2PI/0duIYf3ziSHQ+ZIASzLh3LqgSHr3Y2KK74IybdL9j/aPieH86e7+aF8vVD6YpR9mtik0Xs3Y3Z919yuAfYgPiquB9wNfGLROdiDr2rNqNTM7mghoLgAuAX4JnG5mXwb+4O57ufsRxN+ZXxIJx78xsxPN7GDi79OPyuvp87Hop/bQL4mR4XOA/2fDsPaQ/kNKOx1QjqsBO5VRh/oP0uOIBOVT3P2/vX0zVmJg3ywK1E0kvtFuYGYfKPk6lEDxQ8CWRN7OBeUcrSoZgFJLZIaZ/cnMJtY93u/7WkYozybyd75NJCxLg+r+HhwH7E0peEns1fYEXQUyTys5ULj7De7+DeDrxJ5jGxMB5gHufmuZptFozmvdQhS8XM/Mvkqs6DyPWEW4HzEKP+w2SFWgI21Rkob/SAzVr0IkyR5pYVz5cN4UOJP4JibVPUr88XkR+DKRHPhlM3tnef5HwH3A6e7+XH9TLdKQb5XjB4FZZnaClVpE0H/A4+5zPSrNHl6WpUs/SmL92HL7s8SH7W+B97v754ENiByobxLBzzZ0rSYEwN1nENWoP17a14ozKsihc2oPKRlZ2qpk6X8O+CxRI+cxopDaR4gP5q3d/e/1ycdSjZltTcylf6g89Hvi/d4emObuB5Z2SrgcADPbBPgD8G/gd8TyfIi8p+8Qy5SfLW1TffCjALN5ZWrkuvKhWys0ejqx6e8X3P1vZrZwyTHDYnftDwM/BZYHvu7uP+y++lDmZ1F7qLYH3l7AJ4GPu/tLZrYmUZfr9vJlaWliV/NDgVXc/anh9PutQEfaovuHaVlO+wViCmUV4lvAbGIo+S/6MKjO5t9WYEkiKXkKsfkexHD+t4CTyjcxqajkcfw/IqA5wN2/b2ZvJZLqtyzN/kXUIjq7nJOIpPDadg87A7e4+1/b3f+RxsxWA/5T7v6EWJ35uJmdDrzT3d/T298MM9uJGN28Bviofvd7Zx1We0hTV9IWdX/Ua0vJ/0rsA7QnkZ/wIJGI+QNiW4h3lHYNDf1Ll5KHk0pw+ZS7/4ioO3QMcDdRmfcbwHdK/s6ifbyc9G0xIoB8BrijfMje7u6fpmuT2rcDvzazi81svZJUX/v/YXPiA3vaEPV/xCh/A+4l9gyDmJa918x2I97n++C1IzQ2f42Xu4kcEq047oF1aO0hjejIkLD5NzBcjq49lz4AvEJs+3A2MENF06orgWWuCxgnE9sQbA4sDPyd2J7gEuC24fQtbKQws88QOR7fcff/mNlC7v5y3fPdN6n9CVEM8DEzu5SYVvmUu5/f7r6PVGXE4SgigIdYxLAEMMXdT66flrKu+i+LE6NrzwMfdPeHh6LvI4l1SO0hBToyZLrPkZfl5dsSSYPvJEZ5fk/kPVyoKazqugWWiXif9yCSZ+cRRRp/B5zr3XY5l/71lFNm8xe5XJIYtflyefpxIq/nc8Sy54+2s78jldXVgir3P0SsqFqzNLke2Mfrio3WnTsFOAH4ibvvqWnxLtZL7SEz+ziwGV21h95MlEE43N3/Xs49nbraQ8ORAh0Zct3/4JjZhkTC7EeBNxLfIvYbqv51km75O68jVqXsBLwDeJbYW+mxIeziiNPXB2YJKsfUBTzrEKM7m9U1e6e73zr4Pe0c3XNAyqjZd4HFy0PHEvkkzxMJ+B8GjgAWAd5V8nqG3chDu9SNcq1GLMnfi67VUvcReVBnE4VdXy7nvIuo9/Qx4n2+gphK/BawdlmWPyzfUwU6Mmx0G3VYmEjm/BzwRXf/75B2roP0kAy7JjAV+Je7HzqknetQPYxEnE1M1x7n7nsNaedGsG6jZosTo2Z7lqefBv4JrAo8QuSR/NDdZwy3ZNmhYmYXEtOu/wJuAN5CrKZaG8hEPaKj3P36unM+AnyG2KR2LuDuflQj7+lQjaIp0JFhp1vAs1htaa601nD99tWp6r5Fr09MEy4KvMF72GdMGtfDqNkaxEqh2nTgM8Bh7v79IerisFEC7gXLEvHP0rVFzBR3/1+pS/QuIojZiQh8TnT3L3Z7ncWI1bKPAY+WXKjeVrutSUyL/Yv4MtX2AFOBjgxLmj9vH327HTw9/R6b2VlEHtpX3P2nQ9OzkcHMliVq39zRX12tuhWdtS9JnyT21ntTafIud79pELs7bA1F7aGSgrATUdqi5tdxus8sbdryd16BjohIi9T+cPc2WmZmKxOrhca7+8TXvoLUM7PLiamUY4CrGlmB2UP+zkHA+u7+icHr6fA1FLWHzGx54FJidOh5Ygrx3eXpZ4iE8RMH8M9qiuroiIxApg0Hh6W6D4stzGz7+noj5fn7iP2stm1750YYM/sYMJlYGXgScJiZbVxWsPWqLg9qwXL/EGCL+sdGiyGsPfRjIsg5lljo8BGidMgsIpH5SDN7d++nt5b+WIqMTIeXVRDSJv0VrawFn2b2HspS8p6mW9z9thLwSN92KccZRDXvXYGTga+b2bvMbFxfJ7v7Kxb76L26QWd/01+dphSnnOvuewOrE4HLIoATe1Vtbma7wPz7WtEVG8wjimKOoWtFW59Kra7PEvW5DnT3e4En3P0vRK20GcDSRBDblmKwCnSk5TTaMLjMbE9iq4zNh7ovo0kt4bKnP8xl6H9e+d3fjqjrclx5bkybuzrilSB+HWLK5fPECNhZRL7It4h6OLuY2Zvq/95YbB5MeXw5d39xtOefWWx+Osbd73T3rYnNk28hCiwCfMnMNixBUYb5doT/HDGa83t3f7jBoORYou7OKR57Xo0p/2+kUqSxttXJ68q1Bj1/ZlQN40l71CUDHkBU3L1Rq3tao3yL/X/EH4sLy2PvAv7n7ncNYdc6WvnWe3Gtmm4fK9Y2BXYArnH3X8J8HxrSuDcQNbQuBnD32WZ2DfHBuwcwEXgP8f/AKWZ2tbs/4l0VqX8CbGZm760l4I5WdX+Px5TRncuBtepqD60LXGlmPdUe+gYRtBxUXi4Ry857VIoyvpPY2+3yunMgRoVeIbaTyEAtMXrQE5L1zVtaqvbt1cyM+J9ot16SMlO3+wtoJKghnwFWBK509xvK+3g2sK/FDsL90ghDc8zsMCIh80dm9mmLLR66792Wy/v/VeD1lArIeq8re4xYgn+Juz9ZAsvs7r8itn34BnAHsaP2ScDBZjYJwMy2JJZH3zHag5x6PeQuHQesRFf+zp7AX4iRs9uBQ4gRtSkleXlMX19Yy2jaCeXuFmWZf20KcUG69r/ajsj7eaA8rxEdGVlKnZAFgR2J/2lOgB6/AScgW5QYv0WjEQ17qRyXL8cfELUu7nD3J7s3rr3vZvZ6YuXJRRphaJyZrQR8s9zdmhhJ2MTMznD3P3cLeDLwMHCmu88p773e6wrc/Voz+yoxugDxt6JW6PJxIpn1QmLfts8TozybmtmviK0KFgIMet6eYzQrgUet9tAzwN5m9nO6ag+tR6yMOrq+9lADv8sLAAcDu5XX+IeZnQJ8w90fgVf3hXs/8CNis9C20PJyaTkzWwa4klgOukcf7VYnqnE+Q3xjPki1c/pmsSfNBUSxrj8Q1aP/CGzv7v/trSaOmf2EyHO4GjjA3f/Rxm6PWOUP86+JPYBmEe/724GbiG++0939P3XtE7BACfhVn2gQdP/SZGabAl8htiZYgJgiOc/dtxqiLg4pG+LaQxYbrta2lYConvyNUofnP8SXtZ3d/c99TAG3lKYKpCVqw6Fl+H5R4EmidkJfw/erEFH98sBkBTm9M7PXm9nS7n4HcCCx6/gniVGE54HlLapI9xTkvI7YqRzi267e5wa5+9nEh+jLwDLEMP+viBUsBwLHm9muJbifbxheQc7g6GHa8I/u/ikiSblm39JmNE4dnkOMpH/KzFbsq6G7zysjvmPK/fPdfXVi2ur3jQY5Jbiqvead7v5VoizAhUTgeZSZPQKsBvy0BDmJNsUgCnSkJeq+OfwBuBlYDtjBzN7Qxx/8a4D9gO8RU13Su1OBHc1sEXc/hxhlSMR+PlsApxGpUW/vtgoleWzS+V3gSGA/d7+l/d0feerex7OIUbR1id2bpwH7E8HmZOJ9Pc7MPl4/XaWcs8FVW8lT9z4vTXyoHuHud43GETUbgtpDFjvIH25m7+j2mtcQ072fJz4TXlee2sbM3lRyrl4xswUbXM1VmaaupGVKVD+DWA0BMSU1DfhJmQvu7bxF3P353p4f7cxsP2Ln5QPdfVp57ARiqP44oiDXlsRy0T8QQc8sd3+g2+vMt5mnNM5iw8iziSTX3dz9VIuCZ1sT2zm8DbiLqFPySyXBtod1VaLeBLiIqLezcpk6HHV7uVnX9iIzgDWIUfN7iL8JvwH+6f1UNy4rO19pNEg0s/uJ1VSb9zYCVEaV9wC+BixVHj4OmOruT5c2g/bfS4GOtFxZYvgNIkkW4pvF4WXapb6d9rPqR/mAvY1Y4rmTu99iZtsSVUZ/5+6XlTafJVYzbAQ8R/xRmw5c6+7/G5redwbr2ozzvcR7ujrwOXc/u0xZvR/YiphKXI5YNjsdONfd7xyqfney7n87zGwPYkn5Hu7uozEB2aLMxK+JRUbvJWo57UX8Xi5MpAmcRGzNcHfdFOBC7v5yya152t0fbeKaXyNGNL/t7od1e64WhNbvMP92Ylpxt9LsqXLuj6v+uxuhQEcq65Z4+SHgBXefXZ5biihqtxcx0vA4MSpxQk+rg6RnZans2cCp7r5bGUZ+HLgW2MXd76lr+2aizsjngbWJJZynEd9057j7S91fX3rW27fLMjXwSyLw3Mrdby+PrwR8iHj/JxFVZM9wd03JDlDdysHxwNvc/Yoe2iwDrOXuf2p/D4cHM9uMGFG8mFgS/mT5G11fe2gepfYQcHVtNVQ5/2JgM6Ch2kNlOuwhIrDfyd3/XVt92N8XWIvqyd8k6k5R+ntyE//cpijQkab0NApTpqxuJ75NTAWerLUp87bfIkYbAG4EDiVGI0bV/HkVZXrkeqKexReA/yN2A97D3b206b4K5X3A9kS9kZWI/zYfrg+KpGd9BDiv5ntY7BPkxB/4Xd39xrp27yCmFL8E/L+STyUtYGbfIv52fLav93W0jhSb2XrE1NBl7n5C/e9y+Rs9pfy8jVgsMh04y91nlS9UZxEjPW9r8Hq/ILbp2N3rNuisGwHdF1gW+K67P9fD+QsTxTU/5+6bdn++lZQsJw2rG4pc2sw+X5fgdhSxIuV6d3+itFmgtP+nu28PfJxIPl6HWBVwqZkt0fOVpM4txB+kNxPf1qYQS/d/Da+uKqkFlbVVKH8hhof3AC4j6hQpyGnMnmZ2uJl9xszWqEuwrE+WPB04nMhF273+ZHf/J/BT4NMKchrT18qo2u90Cd53BO7q730djUEORO0homDlWeWh2pYlC7j74+5+JPHl5yfEku89iFWDhxArCOerPdTXtco02a7E36HflscWqAtyViE+F9YiVuH21N8X3P0E4rNhUGlER5pmZmcQIzTHEdn0Pyf++H/V3Z+o/0bVfeWDmX2RKEz1Z3f/UPt7P3LUBZZvIL75fJv4o3E3sTvw+e7+71pbeLVCb/03udcDLylPp38WxSsvJD4ExgDPEkHkLcRI5JNE4PhfIun1EOK/y3HESOYzMHo/aJtlZm+pz9vrYWSy9vs/lvib8SVgS3e/YDTm4AxED+/tgGoPmdks4H3E9Pn0usdrgc65RPHBXdz91y38p1SiQEeaYmaLEInGnyVW+9RWS+1bN5Xy6oduL/dXAF5Urk7/6vITdiJ2br6FWEmxCDGycwaR9Pq/bu3ne8+lf2b2GDEy+TBRhPFO4tvmUsB4umoRzSOCmpeJJbP/BDb0qNgrDSgre24CbgWOquXW9LQy0Mw+TSTRXufuHx6K/naKHgKe/YjSE2OAN3sDy/LN7CPEHobPErk+ZwL/cPenyvMfIv7/ORnY092fG+rpRG0BIU1x9+fN7Bjil/vHwEeIb707l2Hm89z9Qegqi18X4EwAHnP3e22Q6yaMZBaFAZ+EruJoxBDwbcQQcyaGnTchpk82NbPTgRl17bWMvEF1v4tnEUP3KxB7iu1HvMcLAosRK9qWJlazvIv4Jvw6orDa44O5PLYDHUmsylwZeLeZ/ZooJHcnMeVS21JjASKnZClKpd3+Poild3Vfgmp/H5am+dpDjwLnEf8/fJmo23OmmZ3vUXH9R8B9wOnDIcgBjejIAJjZo8SIzm3ABsQ33RnENNZFXlevwczWIvZletDdd25/b0cGi0qmNxEVeL/nZTdmM1sOWNbdbyv31yBWSOxE5D3dTeQ+/dLd5wxB1zuCma1GTJN8qjz0L+Dg2vB73XTKksA4YCzwsEfhMwU6DSiJsdcRVXLvJoLFxYlp8BOAk71b3S0zW8fdb1SQM3DWotpDZrY18YWrloLwe2Iz1u2Bae5+YGk35P9fKBlZmmZRyXIB4BiiIOCOxLetG4jCdT8hdnresO607YEPE/sFSe+2JP7wb+5R22LB8ofpUWL1FADufiuRG2VEADmWWHHxczObWqYHpUFmtlDJ+7ir5ClsQuTlvB2YbmaXmdkGtW+mZZj+MXe/n8jpYaj/mI8UZYrvmHL3WWAfYjRtrfL4b83sU91Ou6mcqyCnoh6ms99G/N04uAQ5CzbyO2xd20X8hqgftS/wDyLfZ3sicHqgTE8Oi/8vNKIjA1KXE7II8aGwJZGg+WZi/v0yooDd14jidRsMWWdHCIt6GI+6+3VlSupNwD5lVUVPxdKWJYrWfYF4/xelwmZ8o1W35Pn5klzNzIDvE7WgIILL77n7vd3PleaY2QFEfsgM4j1emyif8E4iB+q3wHHufl1pr7yzBtkg1x7qnktlsdnwl4mR0DcS1ZjPIlaK3uA9LC9vJwU60rC6Ic/3ELkh57v7f7u1WZLYE+jzxFLG5cpT9wCf0rRK78xsrNcV9bMom/5PYoTnFWJ1z9F1H7LdEwtXATYHVnT3g9ra+RHKzNYlvon+yd1n1T1eX811MeA7xNJdiGquhxI5JS+0t8cjX93fkfHAz4jf2W+4+5FmtjYxQvwFYEXgXmKfNy+jZ8rRaYINcu2hHvIwJxOr4zYnkvf/TiyYuAS4baj+uynQkYbULRtck8imfxvxB+ninuZ2y9TJ24kqsQsQBQLnDEHXR5S6YeFacboViKH9vYmckAeJTVBPqeUxdB+RKOdr6W0/SpBzDJFYfBjwY++255q9tnz9D4nACGI65WB3P7d9ve4sFrW0ziYWNexJ1CBaGlifGBnemvi9/xsxmnaGgsu+g7260Zz3EfmSC7r7mwe5P/UlLRKwLZG/80Eid/Ni4HfECtG2r05UoCNNMbMZRCnxA939qKHuTycowcmvgO+4+w11j6W6ZOQJxIqrT5fT/goc5u4Xluc1rN+k8ru8KXAAMUXyXLfnX92rB5hbF0x+HDgaeGtpupvXVYaVnvUw5Vr7QP4IUXjuJWBrd7+q/D6PBzYmqu9OLs//DdjO3e9qd/+HAxvmtYds/griryOmInciSpE8C6zm7o8NZh96okBH+lU3mrMLcCLwc3f/Urc2Hyay7+8jPoSvJ6rJ5uGQjDac1Q0vQ3yj/aZ31aRYmCj4V/u29CliS43aDvG/JnJGbkQaZmZTgYOJ6ae96x6fr9glpZhaT6MIZrY/sZ/bakOdgzBS1H0Qd6+t9WEi2H8E+Hxt9NfMFiKWoX+CGNl8xd3fOBR9H2o2QmoP9ZC/syZRUPNf7n5onycPEgU60pAyF3srUXPhM+7+9/L4ckTU/t265n8n9j+5vu0dHYHKt69Dgf3LQ88DB7j7sXVtFq592JYRhr2IOi8rEMneJxJ5DqN+WL8/ZrY8cBcRkO/spX4IMK/uQ3gtYuj9fcAdRK7Ub8oS5/rprPnyquS1LCp7TwauJvKb5rr7093ajCMCmVpy8k7u/nDd84sRwf2j7n5rO0Ynhhsz+zFRzfgFopZNfe2hV/NliOD868QU9zvL+9X2vKbuo01DSYGONMTMPgqcD/za63ZkNrMfEkmafyWqYW5A/FH7PTFkOqr+GDWr29z2m4kd3rcuT99ObKvx+/L8gsTowovl/ngiONqT2ANoUOfhO4WZHUxsp7GPux9T93httGFvIsip39zwOeAKYD93/2cJjDRa2QAzm0kUl3uFGJF4mli6fzOxFPkq4AWP7WMOJLbW+Cmwt5KOg43g2kNDfX1QoCMNstgZ9y/Aj9x937KcfCfiD9Ic4EPu/mRJLvw3MWT6cS2/7V/5JrZA3SjBjkSS7FKlyQXA12pz8+Xb79y69hsCj7j7v9re+RGm/N6eRhRbfIe7329mC3nULFqIyNm5qDT/F5Eo+zZi+f6qxAaru2gUpzFmtg2xzPgFInB/gVhGPpauOm4vECNsTxJT35sSCck7uvsZbe3wMFYC8KOJbWCOJuqSfbY8/UdidOe8uvb621toCwhp1P+IqpefNrM7iJGbzxDfxvb3rn2rliBWSbxiZot3/5YhPUoelXXfTOwI/CFipcI9wPLAFsAWZnYU8O26KayFiT3Drhqifo84HluYLFnuLloee7nc35wYTYDIbzislvRqZp8lvjlvQdQ1UlDZmMnluDAxCvEdIqG4Vll6U6IExRpEhe+3EkHOs0QNLn1gF+5+TAnUv0v87f0+cA2ROrAp8H4ze7X2UE+5UI0aTtNOraDKyNIQj60HjiX+KB1H7F5+JbCXu/+5rumniT9gf1GQ05i6Yd2fEpWO/wy8m6g6Ohk4vjz/daLi6G7lPOXjNKlM/91P7F31VYstHzCzTxLJyWsBM4nf61ruDu5+FjGiuRilErL0z91rO2TPBlYi6uZMBRZ294vdfV9giruvR4yafRT4HJGj82CZ9hj1QU4tYCGWi19ILMdft0y9bkdUR3+GKPnxGzM71MzGu3suAc+YJi95uJm9q1X9H2qaupKmmNkWxLeux4mCga9uZmhm7wR+QXw7W6V7wqG8Vt2Ktu2Iwlo/cPf9emg3kVguum556HoiYfmy9vW2M1gUNfsNMaI9h8i/mUiM8PwOONzd/9I9t8DMZhM7x29eKwMgveueMGxmBnyDyDOBmEI8Fript6lAjea8lg1y7SEz25OYOj/Q3ae1tPNDRIGOtIyZ/Q74OJFE+NOh7s9IUb6tXULMua/vsfXDgmU669Wh5xJk/pIYiV2UWHa+8JB1fAQpQfiDJTBfkCjAuBuRd7MwMVV4CfG7+++682pB/AeAPxAJoRvrw7cxJf9sjHfVg3odEex8GViEWD10LDFScbfe1/l1D/RskGsPlfy/O4lpc3P3G8rIzv8aOX+4UqAjA1bqJBxAbOh2qrvvMsRdGlHKH6gLiCqi67v77b2tVDCzE4mpk3uJb8LntbWzI5CZrU7k1OxLJGy+Uj6ANyUKma1E5INcUz/d2q3my1HE8ucvuPv0tv8jRpCSR/IeYlf3WgJ9IhLua8Xk3kXUg6qtMLyRSLC9wIegcu5w1s7aQ2a2PTHSdpS771+u+S/iS8C363Ix+3qNIV9l1Z2SkaUVFiU2cvtR+ZEmlD9iTxCrrD5BrGyb7w9F3TRAJjbs3H0IujpS/ZAYsXmkFuSURMsZ5ac3iXi/tyX2XrpGQU5DdiCCmHvM7EvufmP5cJ5rXXsj3QB8puRGfQt4L7G1zEVm9iPivR61RRi71x4ys9fUHiJyJI8kkpMPN7Od3P3hMnp2q5ndQ+SVPVpes5HaQ7UpxOXL8QdE0HRHT0FO3QjT64kvaRcNtyAHNKIjLVKmAxZz9/8NdV9Gom7LcKcQ32xre1mNc/cXy7D/GcCywEcb+XY12pVvvZcSCd1fdfcXyrfUMd3yR+rrGdX+eCdiJdClRMDzcS+FMqVnZvYJYhPOV4gA8eqe8m+srtBiSZTdkyiA+YbS5GfENOKorMM1VLWHLHYhv4DIRfsDsCWxdH17d/9vHyPNP6H89yZyB/9RtQ+DQYGOyDBhZkcTe9PcQSxv/gPwz7oPhG8Sy3OP87ptC6R3ZnYrkQvyBXe/uvuy2ZIge4qXIozdzv0ysb/YgsBUd/d29XskKgHLzUQhuynuPqM83lBCsZmtRBTA3IvYYPWrozEZeShqD5URmZc9aqFtQ+SwbVCevpBYKXenuz/bw7mvI5a6f7r0YS13v6XZPgwmTV2JDLG6P+YnEMv3P09USN4KeNzM/kXkknyc2L38wKHq60hiZnsBbwcOcferAcpITS3Rezti5OD1xO7l9eeuQvyhf4AYvj+7rZ0fmQ4j3u/9a0FOkYDaRpPvIX6PXyRWbp7n7g8CuPsDxJL/04htZF49t039Hy4ml2M7aw+dClxiZie4+zlmtiKRM/gUUTtqFeAMM7sIuL1u9DO5+2Nm9l3iC9pLwy3IAY3oiAwrZrY0MXX1OWB1YJm6py8j8ncu6uFUqWNmiwIPEyMMO7n7bXX5IbVkzvuJIpi7uPv1PaxweR0xHXvPEPwTRpTywXgzsWLn0+5+n82/f9hiRCC0PfHhDVH35e/ANHefYaVC9VD0f7ix2HLnQLpGVc4HTqj9v29d1bzXIkZ6Vieqpf+22WRgM9uP+GL16nJyMzuBqH90HPEla0uiGOwfiGTlWSUwrX+d12wuOlxoREdkGCl5Nz+wqHD6LiJBeQXiG91VzdTDGOWOJIr7nexR7BJiZGAMUbX7UCIf5IdeNp8tH8i1/JxViG/QdwxB30eijxC5Y8eXICfVrbAaQ4yK7U6sGLyZGKlZjBg12NvMrnX3J4am68NHbbTR3S8FLq2rPfRJ4JNltOtYIm8Hd7+5nPq3updpONAws8WJlVnXA+eWx7YlNhbeyd0vK22uJAoTbgJ8gChKOB24tpaXWb4kDMuREwU6IsOQx47Edw51P0YiM3sHsSnn/cAjZSSH8k3zlbKi5ZvA5UQuxKs7P5cgZwyxFH0yMbJ222suIt3VRmL+U46LAM+Z2QrEtia1VYJfJ2pBPU9MYR1OjBxsTRQbHdXqSh+McfeX3d3N7By6ag/tSLxvx5pZj7WHmpyy2oSYBrvI3W8pi0ocuJYoqUBZFHFSSZD+HDG1vguxVc1pZTprTk9J58OFtoAQGcasq/S7NG6FchxPfHgeAaxV6rtAlEB4BTjJ3e+FV4Ogmo8AOxPTLgpyGlObKvm4mS1WtzT8y8BXy+1vemxZ8Ji7P+vuZxNJ9xB5UqP2993MFjGzD5rZW9x9XpmWSmUa6jF335+YxvoNEZgcApwH7Gixs3lV9xADHhub2fuInLXFgbNqU7Z1XxT+4+7fI4LWnwALEdNrpwMrDqAPg06BjsgwNtpWnLSCu88ipkWmETlOXyOqyH7OzLYmNkScTmz3gJktUFeUbRni2+pSxEiENGYOsQT6U0TS6j5mdixRI2ccsaXAkaVtsqjAC3B3OT4Do/r3fQfid/IUM1sH4r3w2B6m9vt5g7t/hniPryMSkU8GTjWzTUpeWrNuKdd9MzF1NYWYpvo1vDrtWMtpqwU8fyFGPPcg8gZvGe55bAp0RKTjuPvz7n4gsVrkImI10EnAiUQdkt/Vlsp2G835NDGNcrJrP6s+lRGHsfDqpr+7ALcSias/IEolQCQhH1n70C4jFrXl/JuV47D+oBxMpfbQd4mk4gOBf9Y/X96vXPden0/kyexLrML8BJEkfGSZemr0uqn8d/g6Udl+yfLUG4FdzGx1d5/rXVWZXw14yrTaBUTezs4V/+lto1VXItLxyrD8GcTqlEwMtzuR1Plcyc1ZnVhm+x7gDa7il30qCd1PAL9w96fLyqpNib2WJhE1YC5291O6nVdL+F6fyJF62t3Xbm/vh4ehrj1U999iJ2J06Bbiy8EixMjOGcC5tf8XbP5imiNmBE7JyCLS8Tx2I38HsBORX7AjMZpwAjHVchuRZLkBUQdGQU4fLPa3+xZwMSXPpoyQ/c7MZrj78/UfuHUfkLXjgnRtqvqZ0qaRLQo6TdtrD5nZ0mV1Z/1o5lpE0v2B5dw9iETl9wCblsTnGXXth+Uy8t5oREdERhUzW4IowrZXeejvxJ5XXwBedPe3DlXfRgozu5yo1ruru19Q/w2/LpjptS6OmR1EVNs9z90/276eDx9DUXuoXPMmYon697xrV/nlgGVryfdmtgbxRWAnIhfobuAc4JdeNg8dSZSjIyKjirs/7e5fJTYrnAm8m1i+uzJR+l76YGafJpbeTyd2ta4ZU45LA9R/ANcCITN7U6miexBRu+Ub5fExjD612kN/qK89VIKcWu2hvYn382bgH8QGnbXaQ8tUKLC4JRE0bV5Wdi1YrvsoMdUIgLvfCvwcsNKPsURS/8/NbGopGzBiKNARkVGpLJf9EDFE/xIxNH/xEHdrJDia+FA8vXxYLlBWCNWmna42s9PqVwHV5XJ8kpgWmQXs5+531hcXHGV6qj1ECSL2Z/7aQx8C3k8EhnfTVXuoKe7uxDRYLVH8ZOBPZrZe3TRjbXTu+bLC6nvENON0YorrO3Ttbj4iKNARkVHN3a8gytt/bqj7MtyZ2f8j8mpOcfdroWv/sPL8FCLn5NG6Wjr1fk8s29/Z3a9qU7eHq7bWHqpbtXWJu19nscXJZkRe2tVm9kMzW6U+r6q0f7x8ATiA2GH+O+5+U/V/dvspR0dERPpVCtM9APyZ2CX7nlqdFe+qKP1fYsRhB3f/h712t/hx3sNO8aORmb2NqEC8BFHT6Uqins2Xic00Twa+XluWDyzk7i+a2fbEflN7u/uxTV5zDIB3bc+xArEFxN5EvaMHiRGcUzwqIs+3AqwW0I60pHEFOiIi0i+LjR6nAAe4+/frHq/tBv8D4kNzL3c/ru75WnLyekTC9yXufkn31x8NyujLQl62SzCzrYipoDVKk0ysmtofONPdH+ghWDyDqF/zaXf/XQPXXBD4FTESc0PdY6kuGXkCseLq0+W0vwKHufuFdf0eMcvJu1OgIyIifTKzt9C159ffiDydmcB/S57OakSuycXA/7n7Q1a3W3yZNjkO+D9gkzJdOOoMRe0hM/sWcGi5+1NiOuyp8tzCwEu1QMrMPkWUDXhPaf9rYnXWjRX/ycOCcnRERKRP7n4HsQLnRuJD8HQicNm0NPkJ8CxRUfqhMt1RX2fl40S9nBmjOMip1R7ahPLZW/Jufgf8P3d/N/C5WpBTy5HppfbQweW5Rmrhfb/8QCQhP2hme5brv1Bee+Fy/zwi6fnrwMNE3tpsMzum1mYkUqAjIiL9cvcTiMTVbxOF67YEfm1m5xGrgH4NXFia1zakzGa2PF37h+0Fo3Y5+XHAY8Tip/9ZbKFRSySu5S29GrjUAsW6gHEqUdfmHHf/TXmuz1yZEiS95O4HEOUUfkOs7jrGzP5lZh8rr/NCWWo+zt1fcfcfAu8l6u0sCmzh7i8M+B0YIqqMLCIifSofyAuUlUGHmdmv6Prg/SSxVPpFYEkz+18tB6XYGtgCONbdbysB0KhaTl5Xe+g4Xlt76BWiVs7j3WsPlUDxTcRIzgHAX6irPdTf+1hGaxYg/tv9B/iMme1IbLL6VuAiM7sA+FoZtXvFYsPVue5+P1Gv52zgkQG/CUNIgY6IiPSpJKHWVv/UprJ2KYmxBwEbEvVx1iGKyl1WprDeRozmPEUk2EIf2xN0sJ5qD80jghyI5d3XAl+sLTPvpfbQgRVqD6WSLP5mYmn/h4B5xEaqyxNB6BZmdhTw7drITZmqerETygBo6kpERBrisZN2bSk57n6Zu29EjDj8m6jaezrwQzPbmFil9V4iB+XFskJrxOyR1ApDXXuoLiD6KZFn9WeiGvhWxCjT8eX5rwMPmNlu5bwRO1XVnVZdiYhIJfXTJ2a2NFFQbk9iN+4ngCWBW9z9XUPWySE01LWHav99zGw7YifyH7j7fj20mwj8EFi3PHQ9UUbgsirXHW40oiMiIpWUD9Fa4vGT7j4V+ACRmLwMkYPyFWh4hVCnOYLYJ+pid78HXh1hqX32fp94n05y93+U52t5NZTaQ0eY2WZVLl7770PkUkH8d6FuNCmVabA/EUvQnwGeIwKei6pcczgajb94IiLSIt3yd3L5wN7WzH4LTHD3P5UP0xFVTXegSu2hKeXuZ83sfl5be2gfovbQOeWc2ns4r9Qe2o2oPdRvYcB+vAz8r/xAyZOqLwDosQv92cTWFPcSu5x3BI3oiIjIgJX8ndrO27j72WWEB6La76gyXGoPlWDmCWJ5/yfKY/MlMteNtmXgXe7+nVJTpyMo0BERkZapy9lJdY+NqgTkmmFUe6g2IvRdM9vWzBavPVGrnWOxyef48tjSA7jWsKNkZBERkRarqz1UC/zeQlftIYjppF8QVY7nqz1kZnsQIz7Huvverag9ZGZHE5WR7yB2QP8D8E/v2nfrm8S+W8e5+94DudZwoxwdERGRFhsutYesa/fxE4DlgM8TSdJbAY+b2b+AdxBTZQ8Sm3t2FI3oiIiIDLLuozKlfs4BwOrloV8RwchmxDL9L7v7z0rtoZYkcpcpqSnEHlarEyu+ai4DfuTuHbPaqkaBjoiISJsMh9pDZVuJdxE5QCsQO9Jf1UlFAusp0BEREWmjHvJ33knsbP650mRSWZbfstGc0UyBjoiIyBCoq5uTy/3PELWHptbl1gzm9Qf9GsOBAh0REZEh1NOqqu5bQUh1CnRERESGgdEywtJuCnRERESkY6kysoiIiHQsBToiIiLSsRToiIiISMdSoCMiIiIdS4GOiIiIdCwFOiIiItKxFOiIiIhIx1KgIyIiIh1LgY6IiIh0LAU6IiIi0rEU6IiIiEjHUqAjIiIiHUuBjoiIiHQsBToiIiLSsRToiIiISMdSoCMiIiIdS4GOiIiIdCwFOiIiItKxFOiIiIhIx1KgIyIiIh3r/wOQo9hBYddHkgAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "plt.ylim(-0.01, 0.5)\n", - "plt.xlim(0.5, len(models) + 0.5)\n", - "\n", - "labs = [model_names[model] for model in models]\n", - "Boxplots = []\n", - "ticks = []\n", - "for i, model in enumerate(models):\n", - " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", - " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", - " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", - " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - " R2_test = r2_score(test_y, pred_y)\n", - "\n", - " \n", - " if i == 0:\n", - " plt.scatter(i+1, R2_test, c='darkblue', marker='^', linewidths= 8, label = \"test set\")\n", - " else:\n", - " plt.scatter(i+1, R2_test, c='darkblue', marker='^', linewidths= 8)\n", - " \n", - " Boxplots.append(R2_CV)\n", - " ticks.append(i+1)\n", - "\n", - " \n", - "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", - " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", - " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", - "\n", - "\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=8)\n", - "\n", - "ticks1 = ticks\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "#ax.locator_params(axis=\"y\", nbins=4)\n", - "\n", - "\n", - "ticks2 = list(np.array(ticks)-0.01)\n", - "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", - "\n", - "plt.ylabel(\"Coefficient of determination\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (d) Statistical tests" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['str_fp', 'diff_fp', 'ESM1b', 'ESM1b_ts', 'ESM1b_ts_diff_fp']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[0] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[0] + \".npy\"))\n", - "errors_str_fp = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[1] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[1] + \".npy\"))\n", - "errors_diff_fp = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[2] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[2] + \".npy\"))\n", - "errors_esm1b = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[3] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[3] + \".npy\"))\n", - "errors_esm1b_ts = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[4] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[4] + \".npy\"))\n", - "errors_esm1b_diff_fp = abs(pred_y-test_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Difference between predictions with ESM1b/diff.fp and ESM1b_ts 0.004583666723206348\n", - "Difference between predictions with ESM1b/diff.fp and diff.fp 0.0426644405268635\n", - "Difference between predictions with ESM1b/diff.fp and str.fp 0.0003262368022994496\n", - "Difference between predictions with ESM1b_ts and ESM1b 0.40887357002408387\n", - "Difference between predictions with diff.fp and str.fp 0.004446304512309338\n" - ] - } - ], - "source": [ - "d = errors_esm1b_diff_fp - errors_esm1b_ts\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and ESM1b_ts\", p)\n", - "\n", - "d = errors_esm1b_diff_fp - errors_diff_fp\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and diff.fp\", p)\n", - "\n", - "d = errors_esm1b_diff_fp - errors_str_fp\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and str.fp\", p)\n", - "\n", - "d = errors_esm1b_ts - errors_esm1b\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b_ts and ESM1b\", p)\n", - "\n", - "d = errors_diff_fp- errors_str_fp\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with diff.fp and str.fp\", p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Plotting predictions versus experimental values:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading predictions for the best model (ESM1b/diff. fp)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.7050695993004074, 5.070719642604094)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model = \"ESM1b_ts_diff_fp\"\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - "\n", - "np.mean(abs(pred_y-test_y)), 10**np.mean(abs(pred_y-test_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 5.1, ymin = -3.5)\n", - "plt.xlim(xmax = 5.1, xmin = -3.5)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "ax.text(1.9, -2, r'$R^2=0.40$', fontsize=22, c = \"grey\")\n", - "ax.text(1.9, -2.4, r'$MSE=0.87$', fontsize=22, c = \"grey\")\n", - "ax.text(1.9, -2.8, r'Pearson $r=0.63$', fontsize=22, c = \"grey\")\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "\n", - "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.scatter(test_y, pred_y, alpha = 0.6, s=30, c=\"darkblue\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Comparison to the results of the DLkcat model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "model = \"ESM1b_ts_diff_fp\"\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", - "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", - "data_test[\"y_true\"] = test_y\n", - "data_test[\"y_pred\"] = pred_y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (a) First, we need to calculate the maximal sequence identity for all proteins in the test set compared to all proteins in the training set:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (a)(i) Creating a fasta file for every sequence in the training set and for every sequence in the test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "for ind in data_test.index:\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", - " \"test_seq_\" + str(ind) + \".fasta\"), \"w\")\n", - " ofile.write(\"> seq_test_\" + str(ind) + \"\\n\" + data_test[\"Sequence\"][ind] + \"\\n\")\n", - " ofile.close()\n", - " \n", - " \n", - "train_sequences = list(set(data_train[\"Sequence\"]))\n", - "for ind, seq in enumerate(train_sequences):\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", - " \"train_seq_\" + str(ind) + \".fasta\"), \"w\")\n", - " ofile.write(\"> seq_train_\" + str(ind) + \"\\n\" + seq + \"\\n\")\n", - " ofile.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (a)(ii) Calculating the maximal pairwise sequence identities (Calculations were done on a HPC):" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "'''from Bio.Emboss.Applications import NeedleCommandline\n", - "import os\n", - "from os.path import join\n", - "import pandas as pd\n", - "import sys\n", - "import time\n", - "import numpy as np\n", - "\n", - "\n", - "arg = int(sys.argv[1])\n", - "\n", - "CURRENT_DIR = join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\")\n", - " \n", - "def calculate_identity(fasta_file_1, fasta_file_2):\n", - " needle_cline = NeedleCommandline(asequence = fasta_file_1, bsequence = fasta_file_2,\n", - " gapopen=10, gapextend=0.5, filter = True)\n", - "\n", - " out = needle_cline()[0]\n", - " out = out[out.find(\"Identity\"):]\n", - " out = out[:out.find(\"\\n\")]\n", - " percent = float(out[out.find(\"(\")+1 :out.find(\")\")-1].replace(\" \", \"\"))\n", - " return(percent)\n", - "\n", - "\n", - "identities = []\n", - "for i in range(len(data_test)):\n", - " ident = calculate_identity(fasta_file_1 = join(CURRENT_DIR, \"test_seq_\" + str(arg) + \".fasta\"),\n", - " fasta_file_2 = join(CURRENT_DIR, \"train_seq_\" + str(i) + \".fasta\"))\n", - " identities.append(ident)\n", - "\n", - "\n", - "ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(arg) + \".txt\"), \"w\")\n", - "ofile.write(str(max(identities)))\n", - "ofile.close()''';" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (a)(iii) Mapping the results to the test DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...difference_fpESM1bESM1b_tsgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECy_truey_predmax_ident
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.020693962, 0.16804111, 0.0377352, 0.1768811...[0.83155197, 0.08632717, -0.42143562, 0.419359...2.3404440.6656531.0000000.1146602.3404441.08239320.8
1Reaction_3629Sequence_3212[0.92][Q0PC20][1][0][0][False]MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL...{InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.07429815, 0.14984865, -0.08539086, 0.098546...[0.13206507, -0.10826899, -0.31126085, 0.95038...-0.0362120.3407411.0000000.090196-0.0362120.37071535.3
2Reaction_375Sequence_26[21.0][Q0GYU4][0][1][0][False]MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL...{InChI=1S/p+1, InChI=1S/C4H8O2/c1-3(5)4(2)6/h3......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[-0.0272103, 0.2500836, 0.08181338, 0.03990136...[0.3617253, 0.8765441, -1.0668296, 1.5401511, ...1.3222190.1750000.1478871.0000001.322219-0.11979540.1
3Reaction_4312Sequence_3788[4.4][Q8ZNC4][0][0][1][False]MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT...{InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.079942256, 0.23130149, -0.012637342, 0.0787...[0.7798445, -0.7589981, -0.2779501, 0.2643281,...0.6434531.0000001.0000001.0000000.6434531.03006625.9
4Reaction_2115Sequence_712[4.5][P53602][1][0][0][False]MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD...{InChI=1S/C6H14O10P2/c1-6(9,4-5(7)8)2-3-15-18(......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.086191244, 0.21010432, 0.1960825, -0.041225...[-0.6100984, -0.054886594, -0.09893316, 0.2822...0.6532131.0000000.8490570.1125000.6532130.75596149.3
\n", - "

5 rows × 27 columns

\n", - "
" - ], - "text/plain": [ - " Reaction ID Sequence ID kcat_values Uniprot IDs from_BRENDA \\\n", - "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] [1] \n", - "1 Reaction_3629 Sequence_3212 [0.92] [Q0PC20] [1] \n", - "2 Reaction_375 Sequence_26 [21.0] [Q0GYU4] [0] \n", - "3 Reaction_4312 Sequence_3788 [4.4] [Q8ZNC4] [0] \n", - "4 Reaction_2115 Sequence_712 [4.5] [P53602] [1] \n", - "\n", - " from_Sabio from_Uniprot checked \\\n", - "0 [0] [0] [False] \n", - "1 [0] [0] [False] \n", - "2 [1] [0] [False] \n", - "3 [0] [1] [False] \n", - "4 [0] [0] [False] \n", - "\n", - " Sequence \\\n", - "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", - "1 MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL... \n", - "2 MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL... \n", - "3 MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT... \n", - "4 MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD... \n", - "\n", - " substrates ... \\\n", - "0 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9... ... \n", - "1 {InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15... ... \n", - "2 {InChI=1S/p+1, InChI=1S/C4H8O2/c1-3(5)4(2)6/h3... ... \n", - "3 {InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,... ... \n", - "4 {InChI=1S/C6H14O10P2/c1-6(9,4-5(7)8)2-3-15-18(... ... \n", - "\n", - " difference_fp \\\n", - "0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "3 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "\n", - " ESM1b \\\n", - "0 [0.020693962, 0.16804111, 0.0377352, 0.1768811... \n", - "1 [0.07429815, 0.14984865, -0.08539086, 0.098546... \n", - "2 [-0.0272103, 0.2500836, 0.08181338, 0.03990136... \n", - "3 [0.079942256, 0.23130149, -0.012637342, 0.0787... \n", - "4 [0.086191244, 0.21010432, 0.1960825, -0.041225... \n", - "\n", - " ESM1b_ts geomean_kcat \\\n", - "0 [0.83155197, 0.08632717, -0.42143562, 0.419359... 2.340444 \n", - "1 [0.13206507, -0.10826899, -0.31126085, 0.95038... -0.036212 \n", - "2 [0.3617253, 0.8765441, -1.0668296, 1.5401511, ... 1.322219 \n", - "3 [0.7798445, -0.7589981, -0.2779501, 0.2643281,... 0.643453 \n", - "4 [-0.6100984, -0.054886594, -0.09893316, 0.2822... 0.653213 \n", - "\n", - " frac_of_max_UID frac_of_max_RID frac_of_max_EC y_true y_pred \\\n", - "0 0.665653 1.000000 0.114660 2.340444 1.082393 \n", - "1 0.340741 1.000000 0.090196 -0.036212 0.370715 \n", - "2 0.175000 0.147887 1.000000 1.322219 -0.119795 \n", - "3 1.000000 1.000000 1.000000 0.643453 1.030066 \n", - "4 1.000000 0.849057 0.112500 0.653213 0.755961 \n", - "\n", - " max_ident \n", - "0 20.8 \n", - "1 35.3 \n", - "2 40.1 \n", - "3 25.9 \n", - "4 49.3 \n", - "\n", - "[5 rows x 27 columns]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_test[\"max_ident\"] = np.nan\n", - "\n", - "for ind in data_test.index:\n", - " try:\n", - " with open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", - " ident = f.readlines()\n", - " ident = float(ident[0])\n", - " data_test[\"max_ident\"][ind] = ident\n", - " except FileNotFoundError:\n", - " pass\n", - "data_test.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (b) Comparing the results with predictions from the DLkcat paper:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (b)(i) Loading results from DLkcat paper" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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y_truey_predSequencemax_ident
0-2.207608-0.071899MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...22.8
1-3.657577-2.707640MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...100.0
20.9493900.831021MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...100.0
31.6720981.513026MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...100.0
4-1.790485-2.830310MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...99.4
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" - ], - "text/plain": [ - " y_true y_pred Sequence \\\n", - "0 -2.207608 -0.071899 MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI... \n", - "1 -3.657577 -2.707640 MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA... \n", - "2 0.949390 0.831021 MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG... \n", - "3 1.672098 1.513026 MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS... \n", - "4 -1.790485 -2.830310 MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW... \n", - "\n", - " max_ident \n", - "0 22.8 \n", - "1 100.0 \n", - "2 100.0 \n", - "3 100.0 \n", - "4 99.4 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_test_DLkcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"DLkcat\", \"df_pred.pkl\"))\n", - "data_test_DLkcat.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (b)(ii) Plotting performances for different sequence identities:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% 0.28063149276564336 -0.6072304105234347\n", - "40-80% 0.5020172360035243 0.34280134977895493\n", - "80-99% 0.6215297829261641 0.48622435213243465\n", - "99-100% 0.6652709664287431 0.5128517542754034\n" - ] - }, - { - "data": { - "image/png": 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0+LpaNDXFV/jb1cAp1tr1rTX0z50GrPIPdVUt7Fv+NgM4oo12M1q4JmqO4+yDN4UawFuu667t6D1D7p1Nw1RzW2N1XxEREel7vv76a2bPnl3//o477iAzM7PFtu+//379g3UJCQn83//9HwkJvW8Cs2g+0dF4I5Z/CadG2G/zZ7zR4qOj6C8aT4Tst5WIX+lvA8DTMej3OyH7sV7W2aGhdjtWdcoiIiLSx6xYsYLjjz+ezZu95Rzmzp3L9773vRbbvvLKK5x00kns2bOHhIQE7r77bk47rau++O9a0STFw/zt6giu+bTJtZ3Kdd2P8GbBAJjjOE6zQhrHcS4F6opmHmg6xZnjOOMcx7H+642m17dwPwNc7r+tAB4NJ1bHcQ52HGdiO21mAjeH3PuecO4tIiIiEurNN99k+vTp5OfnY4zhzjvv5Oabb26x7eOPP86ZZ55JaWkpKSkp5OXlMWfOnK4NuAtF86BdBd5UZwMjuKaubjeW05615yfAO0A/4EXHcf4IvIr3mWf658F7OPBXMehvGjDO31/kuu6eMK87HLjbT7xfwJvOrgBIBPbBm+HjXBpGiX/quq7KJ0RERCQizz33HBdddBEVFRUkJydz7733cumll7bY9q677uIHP/gBwWCQzMxMFi1a1OIDeL1JNEnxWuAw4Dzg9TCvOdfffh1Ff1FxXXel4zgXAguAQcBc/xVqCzAzRklmtMs6gzdif6L/ak0Z8BPXde+ONDARERHp2x5//HFyc3Opra0F4IYbbmDs2LG89VbDI1X77rsv2dnZ/PWvf+W6664DIDExkd/97nekpqY2anvwwQczcGAk46PdXzTlE8/hjVr+wBhzdnuNjTGnA9fg1SE/F0V/UXNd90XgYOB24HO8xHIPsAK4BTjYL7XoEMdxMoAL/bdb8KaoC9fzeHXPdwEf4k0NV4G30t42/16/AMYpIRYR6Tk+/vhjcnNzGTNmDCkpKaSlpTFx4kSuueYaNm1qPAvo+vXr+dnPfsaxxx5LWloaxhiMMb36q2rpWs8880x9QgzeqnVTp05t9Hr++ecBWLRoUX27QCDAdddd16ztJ5980uWfobNFM1L8V+BavNHXRcaYB4C7gQ/qHrzzF/f4Ft6DbN/FKwUoBP4Wg5gj4rruZuDn/iuS69bT8qIkLbUtp6FEJCJ+LfM9qE5YRKTXWLZsGSeccAJVVVWNjn/99dd8/fXXPPHEE6xcuZLs7GwAli9fzh133BGPUEXEF/FIsbW2CG9UtNq//rvAUqDMGLPLGFOANyL7Jt70bYl4o54X+teKiIj0an//+9/rE+JvfetbPPvsszz00ENkZWUBkJ+fz8KFC+vb9+vXj5NPPpmbb76ZWbNmxSVm6d3uu+8+rLVtvuq+mXjjjTfabTtjxoy4fp7OENUkc9ba1/FWrHsfbzTV4CW/g4Esf7/u+PvAkdbaN2IQr4iISLdXXFxcv/+DH/yAM888k0svvZQzzjij/nhNTU39/imnnMLLL7/M3LlzmTx5cleGKiK+aJd5xlq7EjjaGPMtvOWcJ+ElxOCVSqwGXrbWftjhKEVERHqQGTNm1Ndn/vOf/2T48OEUFxfzwgsvANC/f3/OPffcOEYoIk1FnRTX8ZNeJb4iIiK+6667jo0bN+K6Lh9++CFnnXVW/bmTTjqJO++8k7Fjx8YxQhFpqvet0SciIhJnycnJTJw4kaFDhzY79+677/Lcc106GZOIhEFJsYiISIz97ne/4yc/+Qnbtm3j0ksvZdeuXaxbt45DDz2UsrIy/ud//odHHnkk3mGKSIhWk2JjzF51r9aOR/Pq/I8kIiISX67r1u/ffPPNZGVlMW7cOH7wgx/UH3/iiSfiEZpIvWAwyP3338+UKVMYMWIEU6ZM4f777ycYDMY7tLhoq6Z4nb+1Tdqta6FtuJreS0REpNcpKCio3y8pKWl3XyQe5s6dy29/+9v699u3b2fOnDmsX7+e3/zmN3GMLD7aSlBbW7girAUtRERE+qqDDjqIjz/+GIBrrrmGm2++md27d3P77bfXtzn00EPr93fu3MmSJUsAWL16df3xDRs21M9nfMQRR+jhPImZZcuWNUqIQ91yyy2ceuqpHHPMMV0cVXwZa23LJ4z5bt2+tfb+lo5HI/Re0rnmzZtnb7rppniHISLS57zwwgucc845jZbVDTVmzBg+/PDD+hXt3njjDU444YQ273nvvfdq2WeJmdNOO42XXnqp1fOnnnoqixcv7sKIgDgPvLY6Utxa8qqkVkREpG1nnHEGb7/9Nn/5y194++23yc/PJzExkbFjx3L66afzy1/+sj4hFulq77zzTpsJMcBLL73EO++8w7HHHttFUcWf6ntFREQ6wZFHHhn2DBMzZsygtW9uRcIVDAbZvXs3RUVFFBYWUlRU1Gi/bvvMM8+Edb/f/va38RgtjpuIk2JjzHf83SettXvCvKY/cD6AtfaBSPsUERER6QustZSXl7eYzDbdNj1WXFwc01+u+tpocTQjxffhzSLxIfBZmNfk+NcFASXFIiIi0qtVV1c3S1zDTXSrq6uj7nfgwIEMHjyYrKysRtu6/YULF/LBBx+Efb/58+crKe4kmrlCREREeoSWyhHCHbUtKyuLut/09PQWE9vWEt267cCBA0lKaju1+8tf/hJRLK+99lrUn6On6aqkONHftvwYroiIiEgnqCtHaC+JbWnbkXKExMTEVpPX9hLdtLS0GP8pNEhJSYmofWpqaidF0v10VVK8n78t7KL+REREpBdpqRwh3FHbmpqaqPttrxyhtW3//v0xpvt9QT516lQWLFgQUfu+ot2k2BgzrZVTRxhjhrZzeSowAfhvvDrk5RFFJyIi0osEg0EefPBB7rzzTvLz88nJyeH666/n8ssvJyEhId7hdbq6coRoRm27azlCT3PbbbexePFiCgvbH6fMysritttu64Kouodw/ku/gZfQhjLAPRH0Y/x7uO01FBER6a16w7K6rZUjhDNq2xvLEXqaMWPGsHLlSm644QaWLFnC1q1bm7UZOXIk06dP5/bbb2fUqFFxiDI+Wl3Rrr6BMcEY9LMZuNVa+88Y3EvCpBXtRES6j2XLlrX5FP8777zTpcvq1pUjRDNqq3IE6STdc0W7EKHrThrgNbxR36uAdW1cZ4FKYJu1dlPUEYqIiPQCt9xyS7vnI10oIRAI1M+OEOmorcoRRBpr9/9Ma+2S0Pchv6W9b60Nd55iERGRPivcZXXvv/9+xo4dG/bo7e7duztUjhBpGULdvsoRpDeK5te18f52SywDERER6a3CrReeM2dOxPdWOYJIbEScFFtrN3RGICIiIr3B7t27WbFiBcuXL2fFihW8/fbbfPnll2Fff8ghhzBu3DiVI4h0Mf1NEhERiYK1lg0bNtQnwHWv9evXd+i+BxxwQETzyIpIbHQoKTbGZAInA4cAQ4F02n5y0Fprr+pInyIiIl2tqqqKzz77rD7xrUuEd+/e3axtamoqBx98MFOmTOGQQw7h5ptvpri4OOy++tKyuiLdSVRJsTEmAbgZ+BnQL9zLaJi1QkREpFsqKChoVP6wfPlyPv/8c2pra5u1HTZsGIceeiiHHHIIU6ZMYcqUKey7776NShpuu+22iJLivrSsrkh3Eu1I8X3ApXiJbgDYBWTjJb2bgcFAf7+tBQqA8o4EKiIiEkvBYJC1a9c2K3/YsqX5c+TGGPbbb7/6xLcuCR4+fHi7D6tpWV2RniHipNgYcxpwGV6yex/eaPEoYCWAtXas324/4IfAfwFFwLnW2i9iErWIiEgEysvLWb16daPyhxUrVrQ4V2+/fv2YPHlyo+R30qRJ9OsX7hejjWlZXZGeIZqR4iv87afW2isBjDEjmzay1n4JXGeMeRV4AnjeGHOotbZ5AZaISAd98sknPPbYY7z55pts2LCB/Px80tPTOeSQQ3Ach9mzZ9e3DWcaqnvvvbd+eqynn36ae++9lxUrVrBz504qKyvJysri0EMP5fvf/z4XXHBBZ30sicL27dub1f6uWbOGYLD5Aq2jRo1qVPowZcoUJkyYQEJCQszi0bK6Ij1DNEnx0XijxP8bTmNr7TPGmPvxkukfA7+Lok8RkTa5rovruo2OVVdXs3TpUpYuXconn3zC7bffHvb9kpOT6/eff/55nnzyyUbnd+zYweLFi1m8eDG33347//3f/92h+CVytbW1rFmzplH5w4oVK8jPz2/WNjExkUmTJjUqfzjkkEMYNmxYl8Q6atQoHn744S7pS0SiE01SnO1v14QcC9TtGGNSrbVVTa5ZCFwJnIeSYhHpJNnZ2Vx55ZVMmzaN8vJybrvtNj744AMA/vKXv3DNNdcwfvx43nzzzWbXrl+/nssvvxzwlrA9/fTT689NmDCBX/ziFxx66KEMGzaMLVu2cOutt/LFF15F2J133qmkuJOVlJSwcuXKRsnvqlWrqKysbNZ2wIABjUofpkyZwoEHHqhV2ESkTR2Zki20OKokZD8b2NSk7Q5/O64D/YmItOrSSy/ljjvuICMjo/7Y9OnTGT58OIFAAGstH3zwAePHj+f4449vdv3TTz9dvz979myGDBlS//6GG25o1j4zM5PzzjsPgD179sTyo/Rp1lo2b97crPxh7dq1LbYfN25co+S3buELrdQmIpGKJinOB/YCspocqwaSgck0T4rH+lv9mi4inaKlJ/aHDh1KVlYWO3fuBGj1Qany8nLuvvvu+vc//vGPW+2ntraWDRs2cO+999YfO+mkk6INu0+rrq7m888/b1b+0NIDaSkpKRx00EGNRoAnT57M4MGD4xC5iPRG0STFq/CS4gOB1wGstbXGmE+AI/Fqh59rcs0P/a2WiBaRLvPmm2/WJ8SZmZlMmzatxXb/+c9/6hOx6dOnM3ny5BbbJSUlEQjUV4uRkJDA2WefzV133RXjyHufoqKiZsnvp59+Sk1NTbO2Q4YMaVb+sP/++zeq8xYRibVokuI3gLPwVrILfdjuIeAo4Dz/wbpH8Rb2+K7f1gJPdSRYEZFwff31141mnLjjjjvIzMxsse38+fPr99saJW4qISGB5ORkrLXRB9rLBINB1q9f36z8YePGjS22nzhxYrPyh1GjRqn8QUS6XDRJ8SLgz8Dpxpgca23dY74u3ijxYXjzGF/W5LqNgCZfFJFOt2LFCk477bT6WQjmzp3L9773vRbbLlmyhFWrVgEwduxYZs6c2ep9lyxZQlVVFWvXruXOO+/k888/Z+HChWzcuJH33nsv9h+km6usrGT16tWNRoBXrlzZYo11enp6/dLHdcnvwQcf3OovKiIiXS3ipNhau84YszeQCOwJOV5rjDkF+BtwMV59MXgjxM8BP7TWFnU8ZBGR1r355pucffbZ7N69G2MMd9xxB9ddd12r7f/2t7/V719zzTUkJia22va4444D4MQTT+Skk05iwoQJALz//vusWbOGfffdNzYfohvasWNHs/KHL774olE5SZ3hw4c3K3+YOHFim3+2IiLxFtXsE9ba9a0cLwIuN8ZcA0z07/+1tbb9ZXxERDroueee46KLLqKiooLk5GTuvfdeLr300lbbb9y4kaee8qq6MjIyWhxNrq2tBbx64lBNv94vLi7uYPTdQyAQ4Ouvv25U+rB8+XK2bdvWrG1CQgIHHHBAo4UvDjnkEHJycuIQuYhIx3RkSrZWWWtLgI87494iIi15/PHHyc3NrU9ib7jhBsaOHctbb71V32bfffclOzu7/v0//vGP+pHOSy+9lKysLJravHkzxxxzDJdffjlTpkwhJyeHDRs2cMcdd9S3ycjI4MADD+ysj9ZpSktLWbVqVaPkd9WqVZSXlzdr279///qR39Clj9PT0+MQuYhI7HVKUiwi0tWeeeaZ+oQY4NZbb+XWW29t1CZ06ebKykr+/e9/159r6wG77du3t7oaXkJCAn/961/p379/B6LvXNZatm7d2qz84auvvmrxIcExY8Y0W/p4/PjxMV36WESku1FSLCJ90oIFCygoKADghBNOYNKkSS22Gzp0KDfeeCNLly7lm2++YdeuXSQlJTFq1CiOPfZY/uu//osjjzyyK0NvU01NDV9++WWz8oe6zxoqKSmJAw88sNnSx6ELl4iI9BUm2qmEjDGJwLeB6cDeQCbew3dtsdbaLp3l3nGc0cC1wNl48yvXAuvwZtGY77pu1A//OY4zA3+u5jDc77runDDueQpwNXA03uqAu4DlwL2u6z4WSXzz5s2zN910UySXiEgPsnv3blasWNEo+f3000+pqqpq1nbQoEGNkt8pU6ZwwAEHkJqaGofIRURaFNe5GKMaKTbGHIU3L/HeoYfbuMT657t0Mk/HcU4HFgCDmpya4r+udhxnpuu6H3VlXC1xHMfgzfv8wyanRvivMxzHeQq4xHXd5j/xRKTXstaycePGZnP/rlu3rsX2e++9d7PZH8aMGaO5f0VE2hBxUmyMmQC8BPTHS3RrgDVAIRCMaXQd4DjOZGAh3gIi5XhzJL+K95lnAj8GRgHPOo5zuOu6WzvY5ZXAB22cb29Eei4NCfEqvHi/xFsi+zrgeLy47wK+05FARaT7qqqq4rPPPmuU/K5YsaLF2S1SU1OZNGlSoxHgyZMnM3DgwK4PXESkh4tmpPgmvFKJIHAL8P/82Sa6m/+HlxAHgDNc110acm6J4zgfAw8Cw4Hf4yW1HbHOdd3V0VzoOM4E4Bf+2+XA8a7rlvnvP/RHiJ8BTgcudxznLtd13+xgvCISZ7t27WpW+/v55583emCwzrBhw5qVP+y3337NpooTEQmXDVpKPtxN0ZJCAiW1JGYmMXh6FpnfGohJ6HvfLEXzr2ndks3/a639XYzjiQnHcQ4HTvDf3tckIQbAdd2HHMe5AjgR+I7jODe6rrujK+MMcR0Ni51cG5IQA+C6bq3jOD8AvgESgJ8DSopFeohgMMg333xTn/jWJcKbN29u1tYYw3777dds6eMRI0ao/EFEYqrwpQIKFzc8hBsoCZC/YBs1hTUMOX1YHCOLj2iS4rpJPhfGMpAYOz9k/+422t2DlxQnAucA/26jbafwa4nP9d+ucV33rZbaua67wXGc1/B+KTnZcZz+ruuWdlGYIhKm8vJyVq9e3WgEeOXKlZSWNv/rmpGRweTJk5stfdyvX784RC4ifUnF+vJGCXGowsUFZOzfj/RxGV0cVXxFkxTvwis5KGuvYRwd72/LabvON3TmiOOJQ1IMjANG+/tL2mn7Ol5SnAZ8C3ij06IS6SWCwSAPPvggd955J/n5+eTk5HD99ddz+eWXd3je3e3btzcqfVi+fDlr1qwhGGz+eMXIkSObPfw2YcIELX0sInFR+GLLCXHo+VE/2KuLoukeokmKP8Cb3mw/uu+qdXVLS33lum7z4jyf67pbHccpwauR7uhyVPMcxxmFN1NEObAJWOp1464KI1aAz9rp44sm170RRZwifcrcuXP57W9/W/9++/btzJkzh/Xr1/Ob3/wmrHsEAgHWrFnTbPaH/Pz8Zm0TExOZNGlSs/KHYcP63leRItI9Vawrp/zLtsc2y78so2JdOenj+85ocTRJ8Xy8UoMf4E131q04jpMKDPXfNi/Ya24TXoI5poNdHxuyn4I3DdzBwH85jvM34L9d161p4brRIfvtxbspZL+j8Yr0esuWLWuUEIe65ZZbOPXUUznmmGMaHS8pKWHlypWNkt9Vq1ZRWVnZ7B4DBgxolvwedNBBpKWldcrnEZG+ywYsweogtjpIsNr626B/rOVzjY7XBLFVlmBNkKqt4c3sWri4b40WR5wUW2tfNcb8EbjRGHMX8F/W2urYhxa1zJD9cGpu69pEu0brduAJ4C28B+FqgJHAaXgzWmTgTf82EJjTwvWRxBt6vvuuKSvSTdxyyy1tnr/xxhu54YYbGpU/rF27tsW2Y8eObVb+MG7cOD38JiLecukBQhLTpslpkGCNxVb529DjrSa0IddUB+My6W1fGy2OZp7i7+B9jf8OXtL3bWPMQuBzvLKBNllrH4i0zwilh+yHk6zX/bqU3marln0A7NXCCPDHePMf/x14BW80+LuO4zzmuu5zHYg39Fe7aOIV6TPeeecdXnrppTbbLF26lKVLG09Ok5yc3Kz8YfLkyQwePLgzw5U+QlNgxYe1Fhuw3khpdRBbEyRYFcTWhDHCWneu7ppqb7S16TWdnrQmQEJKAiYlgYQU428TMMmGhFT/eLIJaZOASTHNril6bRcVX7WbrtUrfrNISXEb7qPxynTDgR+Fea0FOjsprgjZTwmjfd0apxVttmpB06nTWjj/peM4l9FQ+/tjoGlSHEm8oeuxRhyvSF/SWtlEU1lZWcyZM6c+Cd5///1JSQnnnw6RyGkKrJZZa7G1ttXktK1SgBZHWFu4R6evqVuXtKZ6yWlDYto4iW2U0PqJrEkNSXBDrmmU4CbF5pem/P9EtlZZxVfdeV6F2Ip21vfu/Ots6EIi4ZQY1LXplOnNXNdd4jjO58ABwDTHcRJc1w39fTKSeEPPtxqv4zhXA1enpKQwb948AKZOncq0adMiC16kB9qyZQu33XZbu6PEdQoLC7ngggs49thj228s0gE9eQosay22JiRp9UdNvVFX64+6tj3a2ijBbeGaTk9aE/GT0iaJanICJtV42zZGWEOPt3gusTunRg0iTa5jlYz3BNEkxeNjHkUMua5b5ThOAd7DdqPbax/SZlObrTrmU7ykOA0YAuwMORf6cF178YY+XNdqvK7r/gv417x58+xNN90UYagiPU9BQQELFy4kLy+PpUuXevV9EZg/f76SYul0nTkFlg16I63hlAI0fSjLG3VtWuva5Joa2+lJq0k0XnKakoBJbmFEtYXk1CQnkNDomqYJbs9LWjtb2t4ZlH68J+z26Xt3z1/UOkM0D9pt6IxAYuwzYBow0XGcpNamZXMcZyQwIOSaztLWPyWh/bY3Ldz+rVwn0ufs3r2bJ598kry8PF5++WUCgQAAqalelVFVVXhPVwO89tprnRKjSJ1wp8Da+WQ+iZlJ/ghqewmubSglqO7sYVZvxLC9elaTkkBCO+UDjUdp/dKBZCWtXWXoWdmUf1FGsDzQbtuEjESGnJ3dbrveItryie7uLbykOAM4AljWSrsZTa7pLAf52yq8xU9CrQe2AKOA6e3cZ0bIfT6MUWwiPUZ5eTnPPvsseXl5PP/88/WJb1JSEmeccQa5ubmce+65HHTQQS0uodyaukRapDPYWsvOJ5vPad2S4iWFUfdjkk3j5LS9r/tT/BHV1CYJbnLTelbvGj0I2DskD05m7A3j2fnMDirWlhPY3XzcMHFgEukTMhh2djZJg5LjEGV89Nak+Angl/7+VbSeFF/pbwPA050RiOM4U2kYAX6rST0xrutax3EW4T2suK/jOMe3tNSz4zhj8ZakBnhZSzxLX1FdXc3ixYvJy8vjqaeeoqzMG20zxjBjxgxyc3O54IILGDp0aP01U6dOZcGC8KdRnzp1aszjlr6rdncNlRsqqFhXQeX6Cio3VXg/ZcLU/9ABpAxLaaeetclDWMlGSauELWlQMiMuHxXvMLqdXpkUu677keM4b+CNrM5xHOd+13XfDG3jOM6lwEn+2wdc193R5Pw4YJ3/donrujOanB8MTHFdN3SpaJq02Q/4T8ih/22l6V/xFkNJAv7mOM7U0JktHMdJAv4J1K0He3trfYr0BoFAgNdff528vDwef/xxiouL688dddRR5ObmcvHFFzNy5MgWr7/ttttYvHgxhYXtj7plZWVx2223xSp06WNswFK1pZLK9RVUrPeS4NqiltZpisyQM/ruTBQi8dJqUmyMqSuys9bak1o4Ho1G9+pkP8GbS7kf8KLjOH8EXsX7zDP98+AtvvGrKO4/EHjNcZzVwJPAR8BWvMU7RtF48Q6APNd1F7V0I9d1v/bj+xVwKLDMf78G7+G6nwLH+80fdF13aUv3EenJgsEgy5YtIy8vj8cee6zREsqTJ08mNzeXSy65hL333rvde40ZM4aVK1dyww03sGTJErZubT4F0ciRI5k+fTq33347o0ZpxETCU1tS643+rq+gYn05VZsqvYfQQpjUBNLGppM+Lp20cels/88WgmXhT2Lbl6bAEulO2hopnuFvm1bvz/CPRfI9TV37zn8SwOe67krHcS7EW4p6EDDXf4XaAsx0XTeySfsam+S/WmOBvwP/3c59fo03Y8YP8JaH/k8LbZ4Cvh9FjCLdkrWWTz75hLy8PB555BE2btxYf26fffZh1qxZ5ObmcuCB7T2D2tyoUaN4+OGHYxmu9DE2YKnaVkXl+vL6RLhmV/NR4ORhKaSNa0iCU4anNiplSEhOIBjByg59aQoske6kraR4KS0nsa0d73Zc133RcZyD8RbNOAvYC6+yax2wCPib67pFUd5+K3ARcDTew3yj8ZLadGAP8DXew3t3u677eRixWuCHjuM8ATj+fYcBhcBy4B7XdR+LMlaRbuXzzz8nLy+PvLw81qxZU398zJgxXHLJJcyaNYtDDz1USyhLlwqU1lKxoaI+Aa7cWNFsVgeTYkjby0t+08alkz42ncT+bVciagoskZ7BRDqfp/QcmqdYupN169bxyCOPkJeXx4oVK+qPZ2dnc9FFFzFr1iyOOeYYEhIS4hil9BU2aKneXuWVQayroHJDBTU7q5u1Sx6SXJ8Ap43LIHVEasRTh9UU1bDxz+vCngJrrxvGk9yHnvgXCRHXkZBe+aCdiHQPW7du5bHHHiMvL4933323/vigQYM4//zzmTVrFjNmzCApSf8USecKlAeo9EeBK9aVU7WxkmBV45IGk2xIHZNG+riM+kQ4KbPj/29qCizpbmytpfCVAu/vxIYKghXe34WkwcmM//U+jdruenFnqysxAgw5cxhZJzfM/lP02i7KviilZkc1gfIAWEjMTCJ9fDqDThhC2ui05vEELXveLWbHY9vfxpvGNg3YgTf97G0T7zzgvY5/6vbpJ5GIxNSuXbt4/PHHycvL44033qhfXS4jI4OZM2cya9YsTj31VM0NLJ3GBi3V+dVUbiincp03K0TNjuajwEmDk0kb75VApI1LJ3VUWqctIKEpsKQ7CdYE20x0O2L3e8XN/r7VFtVQUlRD6YoSRl2zV6MSoWB1kG33bK5b3CZ0adEx/mspoKRYRHqGPXv28NRTT5GXl8dLL71Eba03EpaSksK3v/1tZs2axZlnnkm/fv3iHKn0RoGKhlHgyvX+yFdlk1HgJEPq6DQvCR6XTtrYdJIGakRW+igDqXulkT4unYS0RApfCi9BHn3t2GbHkrIa/z1KH5fOgCMHkjIshYS0RKq2VVH4wk6CVUFswLL77aJGSXHBsztCV3tcjjc5wXpgMHC4v98lOpwUG2MygfFAJg3z6LbKWqvpxER6gYqKCp577jny8vJ47rnnqKysBCAxMZHTTjuN3NxczjvvPAYOHBjnSKU3sUFLzc7q+jmBK9dXUJ1f1ezx76RBSfV1wOnj0kkZlUpCkurVRQAS0xLZ6/rxAFSsrwg7KQ7nIdCcWY3nj8/Ytx+1hdUUL/XmNQj9hbV2Ty273/GOp+SkUJ1ffezEOw+oCLl8YViBxUhUSbHxHgn/PvBDYHIEl9po+xSR+Kuurubll18mLy+PJ598ktJSb2FFYwzTpk0jNzeXCy+8kGHDtPCAxEawMkDlxsr6eYErN1QQLG8yvVkipI1OIy2kFlgPqonE3rq5X1G7p5bE9ETSxqUz+MQhpI9vPVEO1gSp3lZF2WcNi/Cm79vwjWH5F6X1qz2mjEyjOr/60a+u//wYvJriD4C5E+88oNVF0mIt4gTVGJOMt1jF6XWHYhmQiHQvgUCAJUuW1K8uF7pK3BFHHFG/utzo0aPjGKX0BtZaagpq6ucFrlhfQfW25qPAiQOS6ucEThuXTuroNBKSNQos0tlqi7zSuEBpgLLVpZR9VsrwS0eSeVjjbwQrviln8/wNjY6ZFMOg4wYzaOrg+mNV26rq90s/2QPe9Ll1ZgBTv7r+8wsn3nnAkzH9IK2IZtT2Z8AZ/v4G4H5gBVAMEcxOLiLdlrWWd999l7y8PB599FG2b99ef27SpEnMmjWLSy65hAkTJsQxSunpglVBKjf5ZRD+tGiBsibTliVA6ui0kCQ4g6TBSZrDWqSLmOQE+k/JJGP//iQNSqK2uJaiVwqoKaiBIOx4PJ9+kzJJSGn7F1OTYMCYRr/kNq39B/4PeAa4HJiNV5Y7/6vrP3964p0HdHqOGU1SfLm/XQacbK2taKuxiPQM1lpWrFhRv6jGhg0Nv+VPmDChPhGeNKmtBRxFWmatpXZXTcPiGOvKvVGippUQ/RMb1QKnjklr94dtT1C5uZLSFXuoWFtObVENgZKANwXcqDQGHDOIAYc3jLR9dX276z2RM2sEA44c1IkRi3iyThrS7Fj6hAw2zFsLQLA8QOW6CjL2ayiLSBmZyuhrxxKsDlK1uZKi13YRrAhS9NousJah5+QAjVdvTBqYRO3u2msn3nlA8KvrP18CnAtk4C2OdgDwaad9yLoYorhmPF6e/wclxCI935dfflmfCH/xxRf1x0eNGkVubi65ubkcfvjhGpmTiASrg1RtqvQeiPOnRguUtjAKPCrVqwX2p0ZLGpLcK/9f2/1OEXuWFTc6ZgOWirXlVKwtp2pLJcP8RCEsnTR1nEg4UoamkNgvsf6bndrSxnNvJ6Yl1j+U12///iRmJrEjbxsAez7aU58Uh87JnTQ4mfG3TAwCTLzzgIqvrv98BzDOPz2gEz9OQwxRXFMEDAc2xzgWEekiGzZs4JFHHmHBggUsX768/viwYcO46KKLyM3N5bjjjtPqchIWay21RbWNaoGrtlQ2GwVO6JfozQk83i+FGJNOQmrf+X8ssX8iA44aRPqEDILV3qhZ1UZv1pbiNwoZdNxgkoektDjtVU1hDfn/2Qp4i4z021/TG0rXqNxQQdrY9EbHqndWNyp1ShrgpZPB6mC73+wEKxquS9+74b61RTV8df3nZuKdB9ivrv88DcgOuWxj9J8gfNEkxSvwkuJxePPJiUgPsH37dh577DEWLFjAsmXL6o8PHDiQ888/n9zcXE488UStLiftCtZ4X4nWJcCV6ysI7GmySpvxvkINrQVOHto7R4HDMeDwgQw7N6dRwpAxIYNvfvOV98uDhcqNlSQPSWlx2quy1fn1+5mHDSCxn/6eSseULN8DQE1Bw0IbtiZYfzw5K5m0vdLZ9Nf1ZOzXj/6TM0kekkJNUQ1FrzRM4ZaY6ZU8AZQu30PRG4VkHjaAlOwUTGoC1VuqKHy1oX3qmIYV7dLHZ5AyMpXqrVXUeis9/u2r6z9/FrgMr3QCYNnEOw/Y0hl/Bk1F87fq73gzT1yFNwuFiHRThYWFPPHEEyxYsIA33niDYNAbuktPT+ecc85h1qxZnH766VpdTtpUU1zTsDDG+nKqNldhA42nhEjISCBtbHrDEsl7pZGQ1u7U9X1G+oTmiW5i/yQSMxLry0oSUlr+hSFYHWT3e7vr3w+altU5QUqfsv3+5nlmoDRQfzzziIEMn50OFsq/KKP8i7Jm7U2SIWfWyEazv1Rvq2LXcztb7DMhPYFh5zYuE8qZNZIt/7uh7qG7H/mvOkXA1RF+tKhFnBRba583xswHrjXG/Bn4hbU20N51ItI1SkpKePrpp1mwYAGLFy+uX10uOTmZs846i1mzZnHWWWfRv3//OEcq3ZGttVRt8WuB/XKI2uIWRoGHp9ZPiZY+Lp3kYSne0+UStoq15Q0JcWpCi4kzQMlHuwmWe+3SJ2SQOjKtxXYinWHElaMpXVXizQ6zp5ZgTZCkAcmk75PB4BOHkDq8YVAlbWw6A48dRMX6Cmp31xKsCJCQnEDSkGQy9uvHoGlZzeYQTxudxpifjqdw8U5KPtqzHRgK7AAW481TvL6rPmtU379Ya39ijNkA/B640BjzBLAGKA/j2gei6VNEWldRUcELL7zAggULePbZZ+tXl0tISOCUU05h1qxZnHvuuQwePLidO0lfU7u7hsoNfhnEugqqNldia5uMAqd5o8B1SXDa2HQS0zUK3BHVO6vZ/lDDSN3Qc7NbHVkvfrOofn/QNP0dltiYeOcBYbXrf3Am/Q/ODKttSk4q2ReNiDiWlGEpDL9sFMMvGxX5xTEU7Yp26XhrUtcAY4CfhHmpBZQUi8RATU0Nr7zyCnl5eSxatIiSkpL6c8cffzyzZs3iwgsvJDs7u427SF9iA94ocOX6Cm9qtHUV1BbVNGuXnJ3i1QKPTydtbAYpORoFjqWqLZVscTcSKPFGf7POGMrAo1tOdsu/LvMWMMF7Or/fpPCSExGJXDQr2mUALwHH1B2KaUQi0qpAIMCbb75JXl4eCxcuZNeuXfXnDj/8cGbNmsXFF1/MmDFj4hildBe1JbX1tcAV68up2lSJrWk8CmxSE0jby18cY3wGaXulk9hPo8CdpWJtOVv/vcmrnzQwdGYOg6e3XiMcOko88LjB+uVEpBNFM1L8U+BYf/9d4F9oRTuRTmOt5f333ycvL49HHnmEbdu21Z878MAD6xfVmDhxYhyjlHizAUvVtqr6OuDK9RXU7GphFHhYSn0pRPr4dFKGpyrR6iJln5aw7f4t3i8mid4DRqGLdjRVU1RD2WrvGyCTYhh49KAuilSkb4omKZ6NVwbxAnCOtVaJsEiMWWtZtWoVCxYsIC8vj/Xr19ef23vvvesX1Zg0aVKfneKqrwuU1jasDre+gsqNFdjqJqPAKYa0vRpqgdPHppPYX1N5xUPJij1sf2BL/dDR4BOGkDw4mYpvGh7FSR6WQlJmw3+f3W8X1bfPPGygRvBFOlk0/zqO87d/VUIsEltr1qypX13u888blnodOXIkl1xyCbm5uRxxxBFKhPsYG7RUb69qNC9wzc7qZu2ShyQ3PAw3LoPUEakYrXzWLZR9Wtrou9SiV3ZR9MquRm1Cl24O1gTZ825x/Tk9YCfS+Tqyol1Bew1FpH0bN27kkUceIS8vj48//rj++JAhQ+pXlzv++ONJTNQoUV8RKA9QuaGiYXW4DRUEqxqPQZhkQ+qYtIZ5gcelNxpllJ6t5OM99SuGpe+TQeoITcMm0tmi+Rf0A+BsYF/gk9iGI9I35Ofn89hjj5GXl8fbb79df3zAgAGcd9555ObmctJJJ5GcnNzGXaQ3sEFLdX41lRv8JZLXVVCzo/kocNLg5Po5gdPGpZM6Kk2jwD3I8NkjGT57ZNjtBx41iIFHDeq8gESkmWiS4r/iJcU/MsY8aq217V0gfcf3vvc97r777vr3CxYsIDc3FyCsr/zvvfde5syZ0+x4VVUVhx12GJ999ln9sW3btjF8+PCOB91FioqKeOKJJ8jLy+O1115rtLrc2WefTW5uLmeccQZpaRoR6s0CFQ2jwHVbfyWneibJkDo6rdHiGEkD9QuSiEhnimZFu9eNMTcBtwJ5xhjHWlsc88ikx3n55ZcbJcTRaG1kdO7cuY0S4p6itLSUp59+mry8PF588UVqarzZAJKTkznzzDPJzc3l7LPPJjNTc4/2RtZaanZU19cBV26ooHp7lfeocoikQUkhi2NkkDo6lYSkhJZvKiIinSKaeYp/7e++D1wEfNsY8zLhr2g3N9I+pfsrLS3l+9//PgBpaWn1K6qFevPNN5sdW79+PZdffjngjZiefvrpzdp88skn/OlPf2r1vt1NZWUlL774IgsWLOCZZ56hoqIC8FaXO/nkk8nNzeW8884jK6v1uUmlZwpWBqjcWFk/L3DlhgqC5U2eR070ljVNG9tQC5w8WKPAIiLxFk35xC00jHNYoB8wM4LrlRT3Qr/4xS/YsGEDZ5xxBuXl5SxZsqRZm+OPP77Zsaeffrp+f/bs2QwZMqTR+draWq688kpqa2uZN28ev/jFL2IffAzU1NTw2muvsWDBAhYtWsSePXvqzx133HHk5uZy4YUX9qhyD2mbtZaagpr6eYEr1ld4K481GQVOHJDUuBZ4dBoJyRoFFhHpbqJ9VLlpcaie9ujDli5dyj/+8Q8GDBjAv/71Ly677LKwrisvL29UbvHjH/+4WZs//OEPLF++nCOOOIKf/exn3SopDgaDvPXWWyxYsICFCxdSUNAwIcthhx1Gbm4uF198MWPHjo1jlGKDlpIPd1O0pJBASS2JmUkMnp5F5rcGRrRoRbAqSOWmkHmB11fUzw5QL4H6WuB0vxQiaXCSptATEekBoqkp1hCH1KuoqOCqq67CWsuf//xnRo8eHfa1//nPfygsLARg+vTpTJ48udH5Tz/9lN///vekpKRw7733dospyay1fPjhhyxYsIBHH32ULVu21J/bf//961eX22+//eIYpYQqfKmAwsUNv7AESgLkL9hGTWENQ04f1uI11lpqd9U0WhyjamtlszU7E/snhjwMl0HqmDQSUvRPpIhIT6RJLaVDbrrpJr7++mtOPvnk+pricM2fP79+v+kocSAQ4IorrqC6uprf/e53HHTQQTGJN1qrV6+uX13um2++qT8+bty4+tXlJk+erBHBbqZifXmjhDhU4eICMvbvR/q4DILVQao2VXoPxPlTowVKWhgFHpVKWsi8wMlDkvXfXESkl4jmQbsivKq531lr74x9SNJTfPHFF/z1r3+lf//+3HXXXRFdu2TJElatWgXA2LFjmTmzcVn6PffcwwcffMCUKVO48cYbYxZzJL7++uv61eU+/fTT+uPDhw+vX13uqKOOUlLUjRW+2PYaQ9vv30LigCSqNjcfBU7ol0j62IYlktP2SichVaPAIiK9VTQjxelAMt7sE9KHbd++nWAwSGlpKePHj2+xzaxZs5g1axZFRUUMGjSo/vjf/va3+v1rrrmmWWlEXVnC8uXLW52mbcSIERxyyCEsX768Yx8kxKZNm3j00UfJy8vjww8/rD+elZXFhRdeSG5uLtOmTesWpRzStop15ZR/WdZmm9riWmqLa8FAyshU0sY2PBCXPCxFv/CIiPQh0STF24ExQFWMY5E+YuPGjTz11FMAZGRk8L3vfS+u8ezYsYOFCxeSl5fXaNq4/v37168ud/LJJ5OSkhLHKCVSrZVNNJU6Oo1R/7UXiWn6RUdEpC+LJil+Fy8pngR82E5b6cX22Wcf7ryzeQXN3//+d9auXQvAd77zHQ499FDS09Prz//jH/8gEPDqNS+99NIW5+s9/fTTG40s17n++uvr9+fOncvee+8dVezFxcUsWrSIvLw8Xn311fp40tLSOOuss8jNzeXb3/52o7il5whnlLhO1eZKqrdVkT4+o5OjEhGR7sxEukqzMWYG8CqwGjjCWlsd+7AkFubNm2dvuummLu93xowZ9fMUhy7zDN7CFmPGjKmfvmzVqlVMmjQp7HuHfp0d6TLPZWVlPPPMM+Tl5fHCCy9QXe39r5uUlMRpp51Gbm4uM2fO1OpyvcC2B7ZQ+sme9hv6+h86gBHfGdWJEYmISBjiWrMWzZRsbxhj5gG/Ap43xlxprd0Y+9CkN1qwYEF9QnzCCSdElBBHo6qqihdffJG8vDyefvppysu9RReNMZx44onk5uZy/vnnN1s0RHq2iq/CGyWOtr2IiPQ+0S7zXAusBE4E1hpj3gFWAEVAoI3LtcxzH/DGG2+0eu6KK67giiuuiPre4XyzUVtby2uvvUZeXh5PPPEEu3fvrj93zDHHkJuby0UXXcSIESOijkO6N5MU2WBDpO1FRKT3icUyz4nA8f4rHEqKJeaCwSBvv/02eXl5PPbYY+zcubP+3JQpU8jNzeWSSy5h3Lhx8QtSukzq+HRqPykJu3363qonFhHp67TMs/RY1lo++ugj8vLyeOSRR9i8eXP9uX333ZdZs2aRm5vL/vvvH8coJR5Sh6dSRnhJcUJGIkPOzu7kiEREpLvTMs/S43z66af1i2p8/fXX9cf32muv+tXlpkyZojlm+6jqndUUvboLgLTx6dQU1hDYXdusXeLAJNInZDDs7GySBrU8F7aIiPQdWuZZeoS1a9fyyCOPkJeXV78SHkBOTg4XX3wxubm5HH300SQk6He2vswGLfkLtmKrrWaUEBGRiCgplm5ry5Yt9avLvf9+wwKKgwcP5oILLiA3N5cZM2ZodTmpV7y0kMp1FSRmJpJ9QU68wxERkR5ESbHEXDAY5MEHH+TOO+8kPz+fnJwcrr/+ei6//PJ2R3J37tzJ448/Tl5eHkuXLq2fbaJfv36ce+655Obmcuqpp2p1OWmmOr+KXc95D1hmXzyCxH76501ERMLXoZ8axpgTgCuBY4DhQBow2Vr7WUibaXir3+2x1j7Ukf6kZ5g7dy6//e1v699v376dOXPmsH79en7zm980a797926efPJJ8vLyePnll+tXl0tNTeXMM88kNzeXM888k4wMzRAgLbMBy/aHt2JrLZlHDKT/JC3AIiIikYkqKTbGpAP3AhfVHfK3LU0iGwT+DlhjzHvW2q+i6TNajuOMBq4Fzgb2wptjeR2wCJjvum5RB+6djDdX8ynA0cB+wCCgHNgALAFc13VXt3OfW4Dm2WLLrnBd977oIu58y5Yta5QQh7rllls49dRTOeaYYygvL+fZZ58lLy+P559/nqqqKsBbXe6MM84gNzeXc889lwEDBnRl+NJDFb2+i6qNlSQNTGLYeSqbEBGRyEU7UrwAL8k0wAfAUuBnLTW01r5ljPkMOAA4H7gtyj4j5jjO6XixDmpyaor/utpxnJmu634Uxb2HAZ8DLS2FNgA42H9d4zjOH13X7fr1luPglltuafP8Nddcw4EHHshTTz1FWZm3ipgxhhkzZjBr1izOP/98hg4d2gWRSm9Rta2Swhe9VRKzc0eQmK4acxERiVw0K9rNBM7BGxW+xlr7T/94i0mxbxFwIDCdLkqKHceZDCwE+uGN3N4GvIr3mWcCPwZGAc86jnO467pbI+wilYaEeDXwFLAM2O73eSJwHTAQ+KXjOEHXdW8O474Ht3N+czvn4+add97hpZdearPN8uXLWb58OQBHHXUUubm5XHzxxYwcObILIpTexgYs+f/Zig1YBhwziH7792+1bX7eVva817C64fDLR5J52EAAKtaWU7qqhIp15dQW1xIoqyUhLZG0MWkMmpZFvwNav6+IiPQO0YwUz/G3eXUJcRg+8LcHRNFftP4fXnIaAM5wXXdpyLkljuN8DDyIVwv9e7za6EhY4BXgN67rvtPC+aWO4/wHeAcYCtzoOM49ruuua+um7ZVadGetlU00tc8++7B48WL23nvvTo5IervClwuo2lJF0uBkhp3T+gIcZV+WNkqIm93nlQLKvyhrdCxYFqD8izLKvyhj2Pk5DJqaFbO4RUSk+4lmUtcj8RLChyO4Zpu/7ZJloxzHORw4wX97X5OEGADXdR8CXvPffsdxnIhic113i+u6p7SSENe1+YqGZa2TgHMj6aMnCWeUuM7XX3/N9u3bOzki6e0qN1VQ+LJXNpEzawQJaS2XTQSrgux4xPv/zSS3vqBLUlYyWWcMZaQzhpzLRpI8rGGGk4JndxCsDsYwehER6W6iSYrrCj63RHBNoAP9ReP8kP2722h3j79NxCsJ6Qyvh+zv00l9xN38+fM7tb1IqGBtkPwF2yAIA48fTMbEfq22LXhmB7VFNWQc0I+0vdJbbDP4hCGM++UEhpw6jH7792fA4QMZ8d2GhT9staV6W1XMP4eIiHQf0SSpJf52eATX7OVvd0XRXzSO97flNJRutCQ0YT2+1VYdEzqhbqDVVj3ca6+91n6jDrQXCVW4uIDqbVUkD01m6Fmtf8lTsbac3e8UkZCWQPbFI1ptl7FvP0xi41Hk0JFiAJOi1RJFRHqzaGqKvwaOAA4CXgzzmrP8bVfVyx7ob79yXbe2tUau6251HKcEyAy5Jtamh+x/3l5jx3EW482MMRgoBtYALwP/dF03vxPii4lIF9NITU3tpEikt6vcUEHRq7vAQM6skSSktpysBquD5OdtBQtDz8kmeVByRP2Uriip308ekkxKjhaMERHpzaIZ+ngRbyq2a40x7SbVxphDgcvw6pCfi6K/iDiOk0pDiUc4MzVs8rdjOiGWfngzUABU4c1Q0Z5T8Wqvk4FhwHHALcA3juPMiXWMsTJ16tRObS8CXqK7/WEv0R00PYv0vVtf0GXX8zupKaghfd9+DDxmcET9VG6oYOcTft27gWEXDscktF6PLCIiPV80SfHf8UooxgD3GmNaHfLzp297Ea+EoIC263tjJXQpq9Iw2te16Yw5l/5MQ+nI39uZ9m01MA9vurhvAUcBs4En8H6hyADu7a6J8W233UZWVnhP52dlZXHbbV02XbX0Irte2EnNjmqSs1MYcsawVttV51dRvLQQk5pAziWRVHpB+ZdlbP7HRoKVQW80OndEm1O9iYhI7xBx+YS1tsAY8328RTFmA6cYY54PafJzY0wC3gjnOLxR5QBwubW2vOMhtyv0SZrqMNrXPT3T8hM4UXIc50rgB/7bT4G25ij+f67r3tLC8feBBY7jzAQewxs9nu84zvOu6+6IZbwdNWbMGFauXMkNN9zAkiVL2Lq1ef4/cuRIpk+fzu23386oUaNauItI6yq+Kad4SSEYGD57JAlt1PjWltSCBVsVZP3v1rbYZvuDW9n+4Fb2vnXf+gU/SlbsIf9Bb95jk2jIuWwkmVO0qqKISF8Q1Yp21tpHjTEB4N94X/V/l4Ylni/3t3XfNe4BvmOtDW++ro6rCNkPpwiwbqS7os1WEXAc5wygbg7nAuB813Vbvb/rusVt3c913accx5mHV0bRH7gK+ENMgo2hUaNG8fDDkczUJxKeYFWQ/AVe2cTgk4eQNjamv8MCsHtZETse2w4WElITGHHlaDL2bX1WCxER6V2iXeYZa+3jxpjXgWvwpjObEnI/S8Mqb3+11hZ0MM5IlITsh/OdZ12bcEot2uU4zjTgcbxR3d3Aaa7rronBrf8J/Abvl40ZtJEUO45zNXB1SkoK8+bNA7wa3mnTpsUgDJGuV/DsDmoKakgZkUrWae0vA548NIWh5+Y0O777rUJqCmoAyPzWQFJHp2GSDUVLCil40n+ONQGyzhiGSTJUfNPw5VbKiFQtIS0i0otFnRQDWGsL8VaD+71fMpGFN+fvLmttq7M+dCbXdascxynAe9hudBiX1LXZ1GarMDiOcyTwLF4pRhlwpuu6H3f0vgCu6+Y7jrOLMD6X67r/Av41b948e9NNN8Wie5G4Kf+qjN1vFUEC5MweSUJS+49CJA9KZvD05jXuZatK6pPifgf0q1/muWxVyO/SQRoS5BCj/msvMvbRyLGISG8Vs4k3rbVBa22BtTY/XglxiM/87UTHcVpN/B3HGQnUFQx+1lq7cDiOcwjeQ4WZeHXK57qu+3ZH7tkC234Tkd4jUBnwFukAsk4ZStrotDhHJCIivVXEI8XGmNfwkrMrrbUbwrxmJPAQYK21J0XaZxTeAqbhzdhwBLCslXYzmlwTFcdxDsCbS3gwUANc5LruK9Her5U+smmYaq6tWSxEeo2Cp73V6FJHp5F1SvtlE+0Z/aOxER0XEZG+I5qR4hn+K5LvEdNDrusKT4TsX9VGuyv9bQB4OpqOHMeZALyCN6dwALjMdd1norlXe13R8PDikk64v0i3UvZFKXuWFXuzQMwe0WzFORERkVjqleuWuq77EfCG/3aO4zjNVopwHOdSoG7U+oGmU5w5jjPOcRzrv95oer3fZgzwKjASb/T8Ktd1H40kVsdxDnYcZ2I7bWbSMKVbBXBPJH2I9DSBigA78vyyidOHkjpCZRMiItK5OvSgXQTqRpUru6g/gJ8A7/h9v+g4zh/xEtgkvAUyfuK32w78KtKbO44zBG+EuO571/8FPnIcZ1Ibl5W5rruuybHDgbv9xPsFYCXeNG6JwD7ARcC5NIwS/7SdRUBEerydi/Kp3V1L2tg0Bp8wJN7hiIhIH9BVSfEZ/jacZZdjwnXdlY7jXIi3yMggYK7/CrUFmBllknkwsG/I+x/5r7YsoeUSkgTgRP/VmjLgJ67rdsWqgCJxU7q6hJIPdmOSDTmzR6psQkREukS7SbExprWv6n9vjClu5/JUYALew26WLq6FdV33RcdxDgZ+DJyFt+RyAFgHLAL+5rpuUVfG1ILn8eqejwYOBXLwHqhLAArxVsN7GbjHdd2unO9ZpMsFymrZ8ahXNjHk28NIyW51FXkREZGYMta2PcuXMSZI46nA6oZtwp0erK59IXCEtbZp+YB0Es1TLD3Ntge2UPrJHtLGpzP6R2MxCRolFhHpQ+L6j3445RMbaZwAj/Xfb8Obfqw1Fq+GeBtebe8/rLWqhRWRFpUs30PpJ3swKX7ZhBJiERHpQu0mxdbacaHv/ZFjgFOttR1a8EJEBKC2pJadC7cDMPTsbFKGpsQ5IhER6WuiedBuKd4ocFmMYxGRPshay46F2wmUBUifmMHAYwfHOyQREemDIk6KrbUzOiEOEemjSj7eQ9nKEhJSE8jJVdmEiIjER69cvENEeoba3TXsfMIvmzg3m+Ss5DhHJCIifVWHkmJjzAnGmAeNMV8bY0qNMbXGmAObtJlmjLnGGHNZx0IVkd7EWkv+o9sJlgfJOKAfA44aFO+QRESkD4tq8Q5jTDpwL95qa9D2NG1B4O+ANca8Z639Kpo+RaR32fP+bso/KyUhLYGci0dgjMomREQkfqIdKV6AlxAb4EPgL601tNa+BdTNUnF+lP2JSC9SU1RDwZP5AAw7P4ekQSqbEBGR+Io4KTbGzATO8d9eY609ylp7QzuXLcJLoKdH2p+I9C7WWnbkbSNYGaTfpP5kfmtgvEMSERGJaqR4jr/Ns9b+M8xrPvC3B0TRn4j0InuWFVO+poyEjESyL1LZhIiIdA/RJMVH4tUOPxzBNdv8bXYU/YlIL1Gzq5qdT3llE9kXDidpQFSPNYiIiMRcNEnxUH+7JYJrAh3oT0R6ARu05C/Yhq229D8kk8xDB8Q7JBERkXrRJKkl/nZ4BNfs5W93RdGfiPQCu98qomJtOYn9E8m+MJJ/PkRERDpfNEnx1/72oAiuOcvfro6iPxHp4ap3VFHw7A4Asi8eQWJ/lU2IiEj3Ek1S/CLeTBLXGmPa/clmjDkUuAyvDvm5KPoTkR6svmyixpJ5+AD6H5wZ75BERESaiSYp/jteCcUY4F5jTGprDf3p214EUoAC4O5oghSRnqv4jUIq11eQOCCJYeerbEJERLqniL/DtNYWGGO+j7eAx2zgFGPM8yFNfm6MSQCOA8bhjSoHgMutteUdD1lEeoqq7VXsemEnADmXjCAxIzHOEYmIiLQsqsI+a+2jxpgA8G+8ada+S8MSz5f727rJR/cA37HWvtSRQEWkZ7EBS/7DW7G1lgFHDaTfgf3jHZKIiEirop4izVr7ODAB+DXwEd5osKEhGV4NzAP2sdY+3cE4RaSHKXptF1WbKkkalMTQmTnxDkdERKRNHXoE3FpbCPwe+L1fMpEFJAK7rLW1MYhPRHqgqi2V7Frsl03kjiQxXWUTIiLSvcVsXiRrbRDvYToR6cNsrWX7w1shAAOPG0TGfv3iHZKIiEi7tMKciMRU4csFVG+tInlIMkPPVtmEiIj0DEqKRSRmKjdWUPhKARjImTWShFT9EyMiIj1Dq+UTxphvOqE/a62d0An3FZE4C9YEyX94KwRh0LQs0idkxDskERGRsLVVUzwuzHvUTcVmwjhuEZFeqfDFAqrzq0kelsKQM4fFOxwREZGItJUU39/OtVOAQ/CS3mLgEyDfP5fjnx+Mlwiv8F8i0gtVrC+n6PVdXtnE7BEkpKhsQkREepZWk2Jr7RWtnTPGXIm3mt1m4GfAoqZTsBljEoHzgduBA4H/tdZqmWeRXiZYHST/4W1gYfCJQ0gfp7IJERHpeSIezjHGfAv4J7ATOMpa+1hLcxJbawPW2seAo4FdwP/514pIL7LruZ3U7KwmJSeFrNOHxjscERGRqETzHef1eAt03Gqt3dZeY2vtduBWIBn4aRT9iUg3VbG2nOI3CyEBci4dSUKyyiZERKRniuYn2FR/+34E17znb4+Poj8R6YaCVUFvkQ4LWScPJW1MerxDEhERiVo0SXHdY+VpEVxT11aPpIv0EgXP5FNbWEPqqFSyTlHZhIiI9GzRJMU7/e2ZEVxT11bLQIv0AuVflrH77WJI9BbpMElNZ2QUERHpWaJJil/Dm4btOmPMCe01NsZMB67Dm5rt1Sj6E5FuJFARID9vKwBDTh1G6qhIvjQSERHpnqJJiv8IVAEpwGJjzD+NMUcbY5LrGhhjkv1j/wBeBlKBav9aEenBCp7Kp7a4ltQxaQw+aUi8wxEREYmJthbvaJG19gtjzHeBh/zrv++/gsaYPXgjwgNpSLgNUAtcYa39IiZRi0hclH1Wyp73dmOSDDmzR2ISVTYhIiK9Q1TzJ1lrHwWOAz7CS3oN3jRtg4Esf7/u+MfA8dbavFgELCLxESgPkP+oNwvjkDOGkTo8Nc4RiYiIxE7EI8V1rLUfAEf4C3KcDByMlxADFAGrgFf8diLSw+18YjuB3bWkjUtn0Iys9i8QERHpQaJOiutYaz8EPoxBLCLSTZWuKqHkoz2YZL9sIkFlEyIi0rto+SkRaVOgtJYdftnE0LOySRmWEueIREREYk9JsYi0acfC7QRKA6Tvk8HA4wfHOxwREZFOoaRYRFpV8skeSleUYFIMObkjVDYhIiK9lpJiEWlR7Z5adizcDsDQc3JIHqKyCRER6b06/KBdd+c4zmjgWuBsYC+8OZPXAYuA+a7rFsWonyOBa4DpwAhgD/Ap8B/gXtd1A2He5xTgauBoIBvYBSz37/FYLGIVaY+1lh2PbSNYHiBj334MPHZQvEMSERHpVMZaG+8YOo3jOKcDC4BBrTTZAsx0XfejDvbzS+B3tD7y/g5wVlsJuOM4Bvhf4IdtdPUUcInrulXhxDVv3jx70003hdNUpJE9H+wm/+GtJKQlsNfP9yZ5cHL7F4mIiHRMXGv0em35hOM4k4GFeAlxOfAb4HhgBnAnEABGAc86jjOyA/1cCczD+7PcADjAkcBZwDN+s2OBRY7jtPXnPZeGhHgVcBlwBHAh8JZ/fCZwV7SxioSjtriGnU/4ZRPn5ighFhGRPqE3l0/8P6AfXvJ7huu6S0POLXEc52PgQWA48Hvgykg7cBxnEPBn/+0W4CjXdfNDmjznOM5dwPfwyiouAx5o4T4TgF/4b5cDx7uuW+a//9BxnKfwEuzTgcsdx7nLdd03I41XpD3WWvIf2UawMki/A/sz4MiB8Q5JRESkS7Q6cmmM+Y7/GtCVAcWC4ziHAyf4b+9rkhAD4LruQ8Br/tvvOI6THUVXV+EtbQ1wY5OEuM71wG5//4ZW7nMdUDccd21IQlwXay3wAyDoH/p5FLGKtGvPe8WUf1FGQkYC2RcPxxjNNiEiIn1DW1/n3wfcC4wOPWiMec0Y86oxZmxnBtZB54fs391Gu3v8bSJwTgf6KQFafAjOdd3SkHOTHMfZJ/S8X0t8rv92jeu6b9EC13U30JDEn+w4Tv8o4hVpVU1hDQVP7gBg2PnDSRqosgkREek7oqkpnuG/+sU0ktg63t+WAx+00e71Fq4Ji+M4yXi1wwDvtvPwW1v9jKPhF48l7XRbd5804FthhCkSFhu05OdtJVgVpN/kTDIP63FfEImIiHRIW0lxXZKX2RWBxNiB/vYrv/SgRa7rbsUb5Q29Jlz70lCT/Vk7bb9oIbaW3nfkPiJR2/1OERVflZPYL5HsC1U2ISIifU9bSfFmfzutKwKJFcdxUoGh/tvNbbX1bfK3YyLsKrSspL1+NoXsN+0nVvcRiUp1QTUFz/hlExcOJymzNz9/KyIi0rK2fvq9gje92DxjzBHAl0BNyPlrjDE7Iu3QWjs30msiFDqyXRpG+7o2kdboRtJP6Pmm/cTqPiIRs0FL/oKt2GpL/0MHkDlFZRMiItI3tZUU34o3R+4Q4IIm5wxtLzLRls5OitND9qvDaF9XJpLeZquO9RNab9y0n1jdRyRixUsLqfymgsTMRLIvyIl3OCIiInHTavmEtXYTcBjwb2A93iixAeqWwDNRvjpbRch+ShjtU1u4Ltb9pIbsN+0nVvcRiUj1jip2Pb8TgOyLR5DYT2UTIiLSd7X5U9BPjK8OPWaMCeIlxgdba9t7MCweSkL2wykxqGsTTqlFtP2Enm/aT6zuU89xnKuBq1NSUpg3bx4AU6dOZdq0HlUeLp3IBiz5D2/F1lgyjxhI/0k98XlaERGR2Ol1Q0Ou61Y5jlOA97Dd6Pbah7TZ1Gar5kIfimuvn9CH4pr2E6v71HNd91/Av+bNm2dvuummdm4pfVHR67uo3FBJ0sAkhp2nsgkREZFo5im+Am9J5HBmdoiXuhHsiY7jtJr4O44zEqh7sijSUe81QN10b+1Nj7Z/C7G19L4j9xEJS9W2SgpfLAAgO3cEiemJcY5IREQk/iJOiq219/uvPZ0RUIzUrQqXARzRRrsZLVwTFtd1a4D3/bdHO47TVj1wW/2sB7b4+9Pb6bbuPlXAh+0GKdKEVzaxDRuwDDh6EP321yQmIiIiEN1IcSPGmFRjzDRjzH8ZY272X//lH0tt/w6d4omQ/avaaHelvw0AT3egn0zg4pYa+Msx151b7bru16HnXde1wCL/7b6O47S4sp7jOGOBE/23L/vLR4tEpPCVAqo2V5I0OJlhM7PjHY6IiEi3EXVSbIzpZ4z5M7Adb/nhvwG3+K+/+ce2G2NuN8Z06ZLQrut+BLzhv53jOM7Upm0cx7kUOMl/+4DrujuanB/nOI71X280vd53N1Dk7//BcZyWsow7gIH+/u2t3OevNJRi/M1xnEZ/Xn4JyD+Buu+5W7uPSKsqN1dS+JJXNpEzawQJaSqbEBERqRPVg3bGmDHAa8DetD3N2kDgp8BMY8yJ1tqurEP+CfAO0A940XGcPwKv4n3mmf558JL6X0XTgeu6xY7j3IA3bd1o4D3HcW4FlgPD8BY/OcdvvgR4qJX7fO3H9yvgUGCZ/34N3sN1PwXqRpAfdF13aTTxSt8VrA2S//BWCMLA4weTMbFLf08VERHp9iIeKTbGJAEvABPwEuIvgRvx6l33918zgF8AX/ht9gGeN8Z02dCU67or8RYfKcarLZ4LvI2XnP4Ub9R1C3CW67pbO9DP3XjJbBAYB/wLr9b4ORoS4neA813XDbZxq1/jjQYDHAz8B/gAr0SjLiF+Cvh+tLFK31W4uIDqbVUkD01m6FkqmxAREWkqmvKJq/BmSbDAn4BJ1to/WWuXWmvX+K+l1trb8ZK72/zrDqLt+t6Yc133RT+G24HPgTJgD7ACr8zjYL/UoqP9zAOOAR4ANuA9CFeAl4B/H5jmum5hO/ewruv+EDgVeBwvYa/GG8l+EbjYdd1zXdetauM2Is1Ubqig6NVdYCBn1kgSUjv8KIGIiEivY6y17bcKvcCYV/Ae+HrGWjszzGueBs4CXrXWnhJxlBIVzVMsweogG/+yjpod1QyakcWwmZqTWEREuq2uWPm4VdEMGR2MN0r87wiuucvfTo6iPxGJ0q4XdlKzo5rk7BSGnDEs3uGIiIh0W9EkxYP8bSQPzdW1HdhmKxGJmYpvyileUggGhs8eSUKKyiZERERaE81PybopyMa02aqxuuWLi6PoT0QiFKwKkr9gK1gYfNIQ0samxzskERGRbi2apHiVv41kFoTv+duVUfQnIhEqeG4HNQU1pIxIJeu0ofEOR0REpNuLJil+DK8Q+tv+whytTrNmjEkwxswDzsarQ34sujBFJFzlX5Wx+80iSICc2SNJSFLZhIiISHuiWbzjHuBavCnW6hbmuB94F9iBl/zmAEcBlwP7+tet9q8VkU4SrAyQn7cNgKxThpI2Oi3OEYmIiPQMESfF1tpaY8y38VaH2wdvEY+5bVxigK+Ab1trA1FFKSJh2fn0DmoLa0gdnUbWKSqbEBERCVdU36taazcBU/AWxSjGS3xbehXjLfBxmLV2S4ejFZFWlX1Ryp5lxZhEQ87sEZjEuE73KCIi0qNEUz4BgLW2HPiFMeZXwLeASUCWf7oQr1ziQ2ttTYejFJE2BSoC7Kgrmzh9KKkjVDYhIiISiaiT4jp+0rvMf4lIHOx8Mp/a3bWkjU1j8AlD4h2OiIhIj6PH0kV6uNJPSyh5fzcm2ZAza6TKJkRERKKgpFikBwuU1bLjEa9sYsi3h5GSkxrniERERHomJcUiPdiOJ/IJlARIG5/OoGlZ7V8gIiIiLVJSLNJDlazYQ+nHezAphpzZIzEJKpsQERGJlpJikR6otqSWnY9tB2Do2dmkDE2Jc0QiIiI9m5JikR7GWsuOhdsJlAVIn5jBwGMHxzskERGRHk9JsUgPU/rJHspWlpCQmkBOrsomREREYkFJsUgPUru7hh2P+2UT52aTnJUc54hERER6ByXFIj2EtZYdj24nWB4kY/9+DDhqULxDEhER6TUiXtHOGPNrf/f/rLUFYV4zGLgWwFo7N9I+RQRKPthN2WelJKQlkH3JCIxR2YSIiEisRLPM8y2ABRYCYSXFQFbIdUqKRSJUU1TDzkX5AAw7P4fkQSqbEBERiSWVT4h0c9ZadjyyjWBlkH6T+pP5rYHxDklERKTX6aqkuG5Yq6aL+hPpNfYsK6b8yzISMhLJvkhlEyIiIp2hq5LiKf52Zxf1J9Ir1OyqZudTXtlE9oXDSRoQTcWTiIiItKfdn7DGmO+0cmqmMeZb7VyeCkwArsSrJ/4gsvBE+i4btOTnbcNWW/ofkknmoQPiHZKIiEivFc6w0314CW0oA/w+gn4MEAT+GsE1In3a7reKqPi6nMT+iWRfODze4YiIiPRq4ZZPmJBXS8faetUAbwPnWGuXxCZskd6temc1Bc/uACD7ouEk9lfZhIiISGcK5yft+JB9A3yDN3J8GvBVG9dZoBLYZa0NRB2hSB9jg5b8h7diayyZhw+g/2SVTYiIiHS2dpNia+2G0PchT75vbXpORDqueEkhlesrSByQxLDzVTYhIiLSFSL+TtZaq7mNRTpJ1fYqdj3vTdKSc8kIEjMS4xyRiIhI36AEV6SbsAFL/oKt2FrLgKMG0u/A/vEOSUREpM9QUizSTRS9touqjZUkDUpi6MyceIcjIiLSp0T9SLsxZjBwGTAd2BvIBNr7rtdaaydE26dIb1W1tZJdi/2yidyRJKarbEJERKQrRZUUG2NmAvcAg+oOhXlp0/mORfo8W+vNNkEABh43iIz9+sU7JBERkT4n4qTYGHMY8Kh/rQG2AR8DhXgLdIhIBApfLqBqSxVJWckMPVtlEyIiIvEQzUjxjUAyUAVcDTxkrdUIsEgUKjdVUPhKAQA5s0aQkKoyfxERkXiIJik+Hq8M4k/W2gdjHI9InxGsDZL/8DYIwqBpg8nYR2UTIiIi8RLNsFSWv30hloGI9DWFLxRQvb2K5GEpDDkzO97hiIiI9GnRJMX5/rY2loGI9CUV6ysoen0XGMiZPYKEFJVNiIiIxFM0P4nf8rcHxzIQkb4iWB30ZpuwMPiEIaSPy4h3SCIiIn1eNEnxHUAAuM4YkxzjeER6vV3P76RmZzUpOSlknT403uGIiIgIUSTF1tqPgOuAg4AnjTHDYh2USG9Vsbac4qWFkAA5l44kIVllEyIiIt1BNPMU/9rffR84A1hvjHkF+Bwob+96a+3cSPuMluM4g4FrgfOA8XifdyPwDDDfdd3NHbx/AnAccJq/3R8Ygjdd3RbgbeDfrusua+c+c4B7w+z2t67r3hJlyBJHwaog+Qu8somsU4aSNiY93iGJiIiIL5op2W6hYWU6C6QDZ/mvcHRJUuw4zuHAU8CoJqcO8F9XO44zy3XdFzvQzTpgrxaOJwP7+a8rHce5F/iB67rVHehLeriCZ3ZQs6uGlJGpZJ2isgkREZHuJKplnmm+rHO4yzx3CcdxRgLPATl49c9/w0uQa4GT8BYgGQQsdBznWNd1V0bZVV3CvQ54HG9keDOQgjdyfD0wArgCL1G+PIx7ngZsbeP8jihjlTgqX1PG7reLIBGGzx6JSepWf2VERET6vIiTYmttTyiCnIeXEAN813Xd/4Sce9txnCXAa0A/4E68RDkaH+CNfL/oum7TVf3ecRznfrzZOiYClzmO47qu+1bTmzSxxnXd9VHGI91QoDJAfp73e86QU4eROiotzhGJiIhIUz0hwY2I4zjZNIzIvtokIQbAdd0lwH3+2xP9UouIua57jOu6L7SQENed3wH8LOTQRdH0Iz1bwZP51BbVkjomjcEnDYl3OCIiItKCXpcUAzOBRH//7jba3ROyf17nhcPrIfv7dGI/0g2VfVbKnvd2Y5IMObNHYhJVNiEiItId9cak+PiQ/SVttHufhtkyjm+jXUelhOwHOrEf6WYC5QHyH90GwJAzhpE6PDXOEYmIiEhron3QDgBjzAnAlcAxwHAgDZhsrf0spM00YBKwx1r7UEf6C9OB/na367qtPrDmum6N4zhr8VbmO7C1djEwPWT/8zDa3+s4zr7AMKAE+AZvtPkfruuu64T4pJPsXJRPYHctaePSGTQjK97hiIiISBuiGik2xqQbY/KAV4DZwN5ABi3PQhEE/g7cb4yZGG2gERjtb8OZg3iTvx3mOE7Mh/H8eYz/J+TQo2FcNgMYiTdbRRbwLeAGYI3jOP/TxnXSjZSuKqHkw92YZL9sIkFlEyIiIt1ZtOUTC/AeGjPAh8BfWmtorX0LqBs5Pj/K/iKR6W9Lw2gb2qZ/J8RyA3CEv/+467oftdF2Hd6f44XAkXjJ8AXA/UAN3qj+rY7j3NIJcUoMBUpr2eGXTQw9K5uUYSntXCEiIiLxFs2KdjOBc/AW7rjGWvtP//jP2rhsEV6JwnTgtijijETdMmHhLJRR1cJ1MeE4zil4U8MBbAd+2EbzRcD9Lcxi8RHwhOM4LvAiMAC42XGcx13XXRXLeCV2djy+nUBpgPQJGQw8fnC8wxEREZEwRFNTPMff5tUlxGH4wN8eEEV/ADiOMwpoLcMoC6m3rcCbfzic4bnQkomKaGNrynGcw4CFeLNgVAAXuq67s7X2ruvubut+rusucxznx3jTyCUAPwKcWMUrsVPyyR5Kl5dgUgw5uSNUNiEiItJDRJMUH4k3SvxwBNds87fZUfRXZx7w3VbOLcGrxQXv4bR+hFcOEdomnHKLdjmOcwCwGG9Utwa4wHXdt2Nw6/8A8/HKQ2a0E8PVwNUpKSnMm+cNVk+dOpVp06bFIAxpTW1JLTse3w7A0HNySB6qsgkREZGeIpqkeKi/3RLBNXVTkXXFFHCb8WbCGN1eQ2CMv93pum5Vmy3D4DjOBLyHD4fifebZruu+0NH7AriuW+s4zpd4tcZtfjbXdf8F/GvevHn2pptuikX30g5rLTse3UawLEDGvv0YeOygeIckIiIiEYgmKS7BK2MYHsE1e/nbXVH0B4DrunNoKN1oy2d4ieNAx3FGtjYtm+M4ycCEkGs6xHGcMcCreDNHWOBK13UXdvS+TbS4cp7EX8lHeyhbXUpCWgLZuSMwRmUTIiIiPUk0I7df+9uDIrjmLH+7Oor+IvVWyP70Vlt5s0JktHBNxBzHGY6XEI/1D13juu4DHblnC30kAfv5b1udf1m6Xm1xDTuf8Msmzs0heXBynCMSERGRSEWTFL+INxXbtcaYdkeajTGHApfhjXI+F0V/kXqahnKNq9pod2XI/qJoO3McZyheyUTdHMw/c1033AcQIzELr04Z2l6pT7qQtZb8R7cRrAjS78D+DDhyYLxDEhERkShEkxT/Ha+EYgxwrzGm1UUv/OnbXsSbCaIAuDuaICPhum4+8KD/9iTHcWY3beM4zjQaSjFeb23+YMdxrP9a38r5gXgP1dWNmv/add07IonXcZxx/mwVbbU5Bu8hO/B+ufi/SPqQzrPnvd2Uf15GQkYC2RcPV9mEiIhIDxVxTbG1tsAY8328BTxmA6cYY54PafJzY0wCcBwwDm9UOQBcbq0t73jIYfkVcAaQAzzgOM7hwFNALXAy8Au86dLKgOui6cBfAe85oC6hXQQschxnUhuXVbuuu6bJsXHA647jvAc8AywH8vGS37F4c0JfSsN/qz+5rvtxNDFLbNUU1lDwZD4Aw84fTtJAlU2IiIj0VNE8aIe19lFjTAD4N940a9+l4SGwy/1t3ZDZHuA71tqXOhJoJFzX3eI4zpl4ifAo4Kf+K1QxMMt13ZVRdjMCL/Gvc57/assGvCS4JUf5r9bUAL8Fbg0zPulENmjJz9tKsCpIv4MzyTxsQPsXiYiISLcVVVIMYK193BjzOnAN3mjmlJD7WbyH6p4C/mqtLehgnBFzXfcjx3EOBn6Ml6yOxxsd3gg8C/zNdd3NXR1XCz7Cq7k+Gjgcb/aKoUAyXuL+JfA68G/XdTfFKUZpYvc7RVR8VU5iv0SyL1LZhIiISE9nrI3NLF9+yUQWXuK5y1pbG5MbS9Q0T3HnqC6oZuPt32CrLcO/O4rMKRolFhERiYG4jjBFPVLclLU2iPcwnUivZYOW/AVbsdWW/ocOUEIsIiLSS3TFCnMivUbxm0VUflNBYmYi2RfkxDscERERiRElxSJhqt5Rxa7ndgCQffEIEvvF7IsWERERibNWf6obY17zd6219qQWjkej0b1EegobtOQ/vA1bY8k8YiD9J2XGOyQRERGJobaGumb426ZP4s3wj0VSDF3XPjZP9Yl0saLXd1G5oYKkgUkMO09lEyIiIr1NW0nxUlpOYls7LtIrVW2rpPAF7xnS7EtGkJieGOeIREREJNZaTYqttTMiOS7SG9mAXzYRsAw4ehD9Dugf75BERESkE+hBO5E2FL5SQNXmSpIGJzNsZna8wxEREZFOoqRYpBVVWyopfMkrm8iZNYKENJVNiIiI9FZKikVaYGst2x/eCkEYePxgMib2i3dIIiIi0okiToqNMZOMMd8YY74yxowKo/1oY8zXxpi1xph9owtTpGvtemkn1VurSB6azNCzVDYhIiLS20UzUnwpMA742lq7pb3G1trNwFf+NZdH0Z9Il6rcWEHRq7vAQM6skSSk6gsVERGR3i6an/bT8aZkezaCa57Cm6f4hCj6E+kywZog+X7ZxKDpWaTvnRHvkERERKQLRJMU7+dvV0Rwzeom14p0S7te2El1fjXJ2SkMOWNYvMMRERGRLhJNUly3vm1JBNfUtR0QRX8iXaLim3KK3ygEA8NnjyQhRWUTIiIifUU0P/V3+9tInj6qG3Iri6I/kU4XrAqSv2ArWBh80hDSxqbHOyQRERHpQtEkxWv97YkRXHOKv10fRX8inW7XczuoKaghZUQqWacNjXc4IiIi0sWiSYpfwXto7ofGmL3aa2yMGQv8AO/hvJej6E+kU5V/XUbxm0WQADmzR5KQpLIJERGRviaan/7/AKrwaotfNcYc0VpDY8yReEl0JlAD/F80QYp0lmBlgPwF2wDIOmUoaaPT4hyRiIiIxENSpBdYa7cYY34O/BXYG3jXGLMMeBPY5jcbAUwDjq67DPiltXZDx0MWiZ2dT++gtrCG1NFpZJ2isgkREZG+KuKkGMBaO98Ykw7MAxKBY/xXUwYIADdba++IOkqRTlD2RSl7lhVDIuTMHoFJNPEOSUREROIk6uJJa+2fgMOAh4FivAQ49FUMPAQcZq39Y0cDFYmlQEWAHY94X2wMOX0YqSNUNiEiItKXRTVSXMdauwq4zBhj8JZxrpt6rQBYZ621HQtPpHPsfDKf2uJaUvdKY/AJQ+IdjoiIiMRZh5LiOn7yu85/iXRrpZ+WUPL+bkyyYfjskSqbEBERkejLJ0R6okBZSNnEt4eRkpMa54hERESkO1BSLH3Kzie2EygJkDY+nUHTsuIdjoiIiHQTrZZPGGPu8XettfaqFo5Ho9G9RLpS6co9lHy8B5NiyJk9EpOgsgkRERHxtFVTPAdvfmGAq1o5HgnjX6ekWLpcbWktOx7bDsDQs7NJGZoS54hERESkO2krKd5Iy8lva8dFuiVrLTsf206gNED6xAwGHjs43iGJiIhIN9NqUmytHRfJcZHuqvSTPZSuLMGkJpCTO0JlEyIiItKMHrSTXq12dw07HvfKJobNzCY5S2UTIiIi0lyrSbExJmiMqTXGHNjk+Hf814DOD08ketZadjy6nWB5kIz9+zHg6EHxDklERLrQ+vXrMcZ0+LV+/XreeOONRsfOOeecdvu/5ZZb6tt/8cUXzc7fd999rfbZr18/xo8fzwUXXMAjjzxCIBDojD8iCdHeSHFL3zPfB9wLjI55NCIxVPLBbso+KyUhLYHsS0bgLbwoIiLScc888wzvv/9+p92/vLyc9evX88QTT5Cbm8txxx1Hfn5+p/UnbT9oF8BLmmOy6p1IV6oprmHnIu8fj2Hn55A8KDnOEYmISFcbNWoUq1atavX8aaedxtatWxk5ciSLFy9u8z7r169vdvxXv/oVL730UixC5fe//z0zZ86sf19SUsJ7773HHXfcwaZNm3jvvfc499xzeeeddzTI00naSniLgCHABGBl14Qj0nHWWnbkbSNYGaTfpP5kfmtgvEMSEZE4SE5OZtKkSW2eD6ddU0OHDqWgoICXX36ZpUuXMm3atA7HOmrUqGYxHHPMMVx22WUceeSRrFu3jnfffZfnnnuOs846q8P9SXNtlU98iFc+casx5gxjzARjzF4h50cYY/aK9NW5H0cE9iwrpvzLMhIyEsm+SGUTIiISWz/60Y/IyMgA4Oabb+7UvoYOHcovf/nL+vcvvPBCp/bXl7WVFP+vv90XeBZYA6zzjxngJf99JK9vYhu+SGM1hdXsfHoHANkXDidpgKp/REQktnJycrj22msBWLp0acxKKFpz9NFH1+9v2LChU/vqy1pNiq21zwE/AvbgJcF1rzomypdIp7BBS/6CbdiqIP0PySTzUE2QIiIinePnP/85AwZ4P2c6e7S4rswD0CwUnajNYTRr7f8ZY+4FvgWMAlLxZp6wwM3Alk6PUCRMu98qouLrchL7JzLswuHxDkdERHxr165l/vz55Ofn14+yTpgwId5hdUhWVhbXX389v/3tb3n//fd5+umnw5qmLRorVzY82jVy5MhO6UPCmFnCWlsBvFn33k+SAZ601n7WWYGJRKJ6ZzUFz/plExcNJ6m/yiZERLqDp59+mvPOO49gMFh/bP78+Tz55JOcffbZcYys4376058yf/58CgsL+fWvf83ZZ58d8+dYqquruf322+vfn3DCCTG9vzRoa/GOuofjEpucWuK/yjo1MpEweWUTW7E1lszDB9B/ssomRES6gw0bNnDppZc2SogBgsEgs2fP7vH1sQMGDOCGG24AYMWKFTz22GMxu3dpaSmvvvoqJ554Ih988AEA48aN46KLLopZH9JYWw/arcd7MG6/Jsff8F9KiqVbKF5SSOW6ChIHJDHsfJVNiIh0F7feeiulpaUtnistLeXWW2/t4ohi78c//jE5OTkA/OY3v4m65veKK65otKJdZmYmJ598Mm+//TYAw4cP56mnniI1NTVmsUtj7X3H3NJ3AL/BqyleCBTEPKIYchxnMHAtcB4wHu/zbgSeAea7rru5g/cfR8OMHO1Z4rrujDDueSRwDTAdGIH3oOOnwH+Ae13XVYV9iOr8KnY9vxOAnEtGkJjR9IsNERGJhw0bNnDPPfe02eaee+7hl7/8JWPHju2iqGIvIyOD//mf/+G6667jiy++4KGHHuK73/1uzO4/ceJELrzwQn72s58xZMiQmN1XmmtrpLjW3/bIX0kcxzkcWAX8FpgCDAT6AQcAPwdWOY5zetwCbIHjOL8ElgHfBcbh/dkPA2YAdwFL/URfABuwbH94K7bWMuCogfQ7sH+8QxIREd+tt95KbW1tm21qa2t7xWjxD37wA0aPHg3A3Llzqampifgev//971m1ahWrVq1i9erVrF27lj179rBmzRpuvfVWJcRdoK2kuG4U+KCuCCSWHMcZCTyHN2NGALgTL7E8Hm+kuwIYBCx0HGdyjLr9FXBwG68r2on5SmAe3n+TDYADHAmchTeyDXAssMhxnLb+u/UZRa/tompjJUmDkhg6Myfe4YiIiC+cUeI699xzT4+vLU5NTeVXv/oVAN98803Ynz1U3Yp2kyZN4qCDDmLvvfcmMzMz1qFKG9oqn3gHOB+4zRgzAPgSCP3V5whjzNBIO7TWLo30mijMA+qypO+6rvufkHNvO46zBHgNb+T4TuCkGPS5xXXd1dFc6DjOIODPdfcBjnJdNz+kyXOO49wFfA+vrOIy4IEOxNrjVW2tZNdiv2widySJ6SqbEBHpLh5++OF2R4nr1NbWsmDBAm688cZOjqpzXXnllfzpT3/im2++4fe//z1z5syJd0gSobZGHO/AG2UdDszHW8Hudf+cAe7x30fyei3mn6AJx3Gygcv9t682SYgBcF13CXCf//ZEv9Qinq4C6soibmySENe5Htjt79/QJVF1U7bWkv/wVgjAwGMHkbFfv3iHJCIiId59992I2i9btqyTIuk6ycnJ/PrXvwZg8+bN/POf/4xzRBKptla0qxspXkvPWtFuJlA3bHh3G+1Cv9s4r/PCCcv5/rYEaHE+F9d1S0POTXIcZ5+uCKw7Kny5gKotVSRlJTP0HJVNiIh0N3WzMYRr+PDeMXPQZZddxv777w/AH/7wB8rLy+MckUSivRXtngGeMcaMwavPTcMb7bV4o5vhzrzQlY4P2V/SRrv3gXIgo8k1XcpxnGS82mGAd13XrWqj+et4JRTgxfx1Z8bWHVVuqqDwFa/cPWfWCBJSVV4tItLdXHjhhdx1110Rte8NEhMTueWWW8jNzSU/P5/77rsv3iFJBMJa9stauwnYBISu1PJ+N13R7kB/u9t13a2tNXJdt8ZxnLV4D8Ed2Fq7CFzrOM5NwBigGtgKvI03jdpbbVy3Lw3/Hdr78/wiZD8WMfcowdog+Q9vgyAMmjaYjH1UNiEi0h2deuqpXH/99dx5553ttr3++us55ZRTuiCqrnHxxRdz6623snLlSnbu3BnvcCQC0ayF+wDeSHFRjGOJldH+Npw5iDfhJcXDHMdJbWeUtj2Hheyn4i16sh9wpeM4jwLfc123pI14of2YN4Xsj4kqyh6s8MUCqrdXkTwshSFnZsc7HBERacMdd9zBqaeeypNPPsmXX36Jtbb+nDGG/fbbj3PPPZfTT+9Ws6N2mDGGuXPncu6558Y7FIlQxEmxtXZOJ8QRS3Xzl7S8hE5joW36A9EkxcXAk3ir/H2FV5KRgzcF3NVAFnAxkOU4zhmu6zZ9HDd0vpX2Ym4ab59Rsb6Cotd2gYGc2SNISFHZhIhId3f66af3uqQ3HDNnzuSII46oX55ZeoZoRoqbMcaMxpulIgP4wFpbEYv7Rind31aH0TY0CU5vtVXrtgKjXNdtqZJ+seM4fwNeBCYDJ+PNPfy/TdqF9ttezB2Nt0cKVgfJX7AVLAw+cQjp4zLiHZKIiPQC69evD7vtjBkzGo12t+f9999vt82cOXM0dVs3EnVSbIzJBP4buBIYGXLqYEJqY40xs/Bmd9htrf1+tP05jjOKhmnLmipzXbfuob8KvPmHU8K4behqfREn8q7rVtNGIuu67jbHcS7A+/NIBn5M86Q4tN/2Yu5QvD3Vrud3UrOjmpScFLJOj3hqbBEREZF2RfUdtDFmAvAh3ipuo2h7urV38aYcu9IYc1w0/fnm4S3b3NLr3pB2dXW74ZQXhLYJp9wiYq7rfg284r/d13GcEU2ahNYZtxdzWPE6jnO14zgfbt++nXnz5jFv3jyWLu2KNVNir2JtOcVLCyEBcmaPJCFZZRMiIiISexGPFBtjUoFngYl49bP/CyylYSniRqy164wxS/FWYjsHb0aGzrQZr5RjdHsNaXhYbWcHH7Jrz6fAGf7+aGBbyLnQh+vaizn04bpNrTVyXfdfwL/mzZtnb7rppkji7FaCVQ1lE1knDyFtrz5TMSIiIiJdLJryiavxZlUoB6Zbaz+CRlO1teR5vAfPjomiPwBc150DzAmj6WfAt4CBjuOMbG1aNn9+4Akh13SmtoqQ1gC1eP8t2ptmbf+Q/e44HV5MFTy7g5pdNaSMTCXr1GHxDkdERER6sWi+i74AL8mbX5cQh2GFv50YRX+RCp0TeHob7Y7AezCw6TWd4aCQ/UZJuuu6NXgLiQAc7ThOW3XFM0L2OzvmuCpfU8but4ogEYbPHolJ6orFEEVERKSviiYprkvwFkdwTYG/be1BuVh6Ggj4+1e10e7KkP1FnRWM4zgTgLpZyde6rrulhWZP+NtMvOnbWrpP/5Bzq/1a5V4pUBkgP8/73WHIqcNIHZUW54hERESkt4smKR7gb/dEcE2yv206R2/Mua6bDzzovz3JcZzZTds4jjONhlKM113XbXHE23Ec67/Wt3L+PMdxWh3C9B+qe5yGz9905ok6d9OwGMofHMdpaWWKO4CB/v7trfXZGxQ8tYPaolpSx6Qx+KQh8Q5HRERE+oBoaop34S1OsRfwcZjX1NXC5kfRXzR+hfdgWw7wgOM4hwNP4SXlJwO/ABKBMuC6DvTzBPCN4zhP4JVAbAIqgWzgBBoW7wDvYcQWk2LXdYsdx7kB+Dfew3bvOY5zK7AcGIY3v/E5fvMlwEMdiLlbK/u8lD3vFmOSDDmzR2ISVTYhIiIinS+akeKV/jaSh+Zm49Uhtz+TdQz4JQpnAlvwkt+f4iWTbwO/xaslLgYudF13ZSu3CdfeePM1PwosAz7BKy25kYaE+BHgbH9e49ZivhsvmQ8C44B/4f15PUdDQvwOcL7rusEOxtwtBcoD5D/iTcwx5IxhpA5PbecKERERkdiIJil+Em9O4h8aY9qdEsAY8x3gVP/twij6i4pfEnEwcAveg3578EaGP8crPzjYdd0XO9jN2cCtwKvA13iJdi1eKcRy4P+AI13XzXVdt91yE9d15+H9svEAsAFvBbsCvIT++8A013ULOxhzt7VzUT6B3bWkjUtn0Iys9i8QERERiRETyZKFAMaYZLzEcjywGrjCWvuxMSaINxo8yVr7uTFmL+B64Fq8JHqFtfawmEYvbepJ8xSXriph2z2bMcmGvf57PCnZGiUWERHpY+JaMxlxTbG1tsYYcy5ejezBwAfGmA0hTRb6S0CP8t8bYCdwYQdjlV4qUFrLjkf9sokzs5UQi4iISJeLas1ca+1qvAUy3sZLeseFnD4A72GxuqWf3wWOstZ+06FIpdfa8UQ+gdIA6RMyGDS1K2btExEREWksmtknAPCT3KnGmOOAmXhJcjbeg20FeA+cPWWtfTUWgUrvVLJ8D6Wf7MGkGHJyR2ASNNuEiIiIdL2ok+I61tq38UaMRSJSW1LLjoXbARh6Tg7JQ9tazE9ERKT3uO+++7jiiisAWLduHePGjYtvQBJd+YRIR1lr2fHYNoJlATL27cfAYwfFOyQREenF3njjDYwxzV6JiYkMHjyYcePGcdxxx/HjH/+YBQsWUFFR0e49Z8yYUX8f6flilhQbY/oZY8b4r36xuq/0TiUf7aFsVSkJaQlk547QPygiIhIXwWCQ4uJiNmzYwDvvvMP8+fOZPXs2I0eO5MYbbwwrOe6r6n4pmDFjRrxDiYkOlU8YYyYD/wWcAoxtcm4D8BLwf9baji6QIb1IbXENO5/wyybOzSF5cHI7V4iIiMTOD3/4Q6655pr692VlZRQXF7N69WqWLFnC888/T3FxMbfddhvPPPMMzz77LOPHj49jxNIVok6KjTF/BH6GN9rc0jDfOLwFJ75njLndWvs/0fYlvYe1lvxHtxGsCJJxQD8GHDkw3iGJiEgfk52dzaRJk5odP+200/jZz37GunXruOqqq3j99df57LPPOOuss1i2bBkDBgyIQ7TSVaJKio0xfwV+REMy/AXe1Gvb/ffDgaPwpmczwM+NMenW2us6FK30eHve303552UkZCSQc0nzsonKTRUUvV5I5bpyaktqMcaQNCiJjP36MfikoY1GlUs/LaH881Iq11dQu7uWQHmAxH6JpI3LYPCJQ0gfl97VH09ERHqB8ePH8/LLL3PWWWfx4osv8tlnn/Hb3/6Wv/zlL/EOTTpRxDXFxphj8FapAy8ZnmatPdBae6W19pf+60pr7UHAVOAzvMT4WmPM0TGLXHqcmqIaChblAzDs/OEkDWxcNlGxvpzNf9tA6Sd7qC2uhQDYWktNQQ273y5m053rqC2prW9f8PQOdr9dTNWWKgKlAQhCoCRA2aoSNs9fT+nqki79fCIi0nskJiZy//33k5GRAYDruuzatatLY9i2bRuTJ0/GGENqaiqPP/54i+02b97MTTfdxFFHHcXQoUNJTk4mKyuLY445hhtvvJFVq1Y1u6a6uppnnnmGH/3oRxxxxBEMHjyY5ORkhgwZwlFHHcUtt9xCQUFBi/3NmTMHYwxLliwBYMmSJc0eYOyJs2lEM1Ls+NtNwHHW2qLWGlpr3zbGTAU+BvYCfoA3oix9jLWW/LxtBKuC9Ds4k8zDmn8FtfvNImytt+x46pg0hpw2lEBlkJ1P5BMsDxAoCVC6Yg+Djs+qvyZleCoDjhpIyohUagtr2PXCTgIlXoK8c1E+/SdldtlnFBGR3iU7O5vZs2fz73//m7KyMl566SVmzZrVJX2vXbuWU045hXXr1tG/f3+efPJJTjrppGbt5s+fzw033EBVVVWj40VFRbz77ru8++675OXlsX79+kbnr776au6///5m9yssLOT999/n/fff5+9//ztPPfUUxx13XEw/W3cVTVI8DbDAH9tKiOtYa4uMMX+C/9/eecdbUVwP/DtUadIRxILYuyIYxR5F/WlMVNQo9hLHXhJNwdijJmqigCUTRbD3SBBRERVFUSNqVCwRIyBK772++f1xZt/dt+/evfe++x4P3jvfz2c/t+y03Tm7e/bMmTPcG/Iq9ZCF4xaw/JulNGzRkE4nds4abWLt8rXl31v3bkuLnUWhXfbVEhZ/tAgAn0lCx+M2ofn2LSqU1bBlI6Y/9AMAa+atZs3iNTRqVXI4bkVRFKUEVs1ZxcKx81izeC2NWjWk9QHtaLKBxKY//PDDefDBBwEYO3bsOlGKP/30U4444ghmzpxJhw4dGDlyJL169aqU7vbbb+d3v/sdAK1ateKCCy7g0EMPpVOnTixevJhPP/2UESNGMHHixEp516xZQ/fu3TnuuOPYe++92WKLLWjUqBFTpkxh9OjRPPTQQ8ydO5fjjjuOCRMm0KlTp/K8t9xyC1dddRVnn30248ePp2fPngwZMqRC+U2abBj9G6cq2kLn8Dm+iDwfJvIqdRhf5lk8fiHz35rH2sVraNCsIWvmrQag4wmdcyqpzbdpwbKvlgKwcNx8GrVqyNoVZSwN/5mmDWi5a8by22KHlpXKaNyx4kXYoImG4lYURalNlkxYLMYKn/lvwdj5dDlnsw1iNK9Hjx7l37/55psar2/s2LEcc8wxLFy4kM0335xRo0axww47VEr32Wef0b9/fwC6d+/O6NGjK0XIOPDAA7n00kuZOnVqpfw33ngj3bt3r2Sk6tmzJ3379uWiiy6id+/ezJ49m0GDBnHzzTeXp+natStdu3alRQuJwNuiRYusExc3NKqiMawKn02LyBOlXZWaSqkTzBs1h5lPTmfVtJWsXbyW1bNW4dd4mnRuQqs9cs/cbXNQO1rv3xYawsqpK5j24A/MfGwaZcvW0mzb5mx+2ZY0bpcevm3JZ4vKvzfbpjkNmqpSrCiKUlusnreaGY9Nq6AQA+BhxmPTWB0MJusz7du3L/8+b968Gq1rxIgRHHHEESxcuJAddtiBd999N6tCDGIlXrtWhk8ff/zx1JBxm2++eaX/tt5669Q1AnbddVfOO+88AIYNG1bEUWy4VMVS/D2wM3AkhS/vfET4nFKF+pQNiOWTlzHv1eyO+atmrGL55GU069Y8637T0NCkYxMatmjE2kVrKuxbMWU5S79cQtNNN8pZ95IJi5k3Suo2TQwdj92kikehKIqiVAfzRs/BryzLus+vLGPe6DlsclKXddyq4mjZMjMquXhxzU3gfvTRRznnnHNYs2YNvXr1YuTIkXTo0CFr2rKyMkaOHAnAvvvuyz77lB7HYP78+cybN48VK1bgvbzFtGnTBoAvv/yS1atX07hx3V5XoCpK8ShgF+BKY8xw7/2HaYmNMXsCVyLvia9WoT5lA2LeK9kV4vj+rhdskXXf3FGzmfey5G+118Z0PK4zZSvXMv2hH1j540rmvjSbxu2b0GrPytbmReMXMvPJaVAGprGhy9mb0bRrbgVaURRFqVlWz1vNog8WpKZZ9MEC2h3WIe8oYG0SV4RrKk7xgAEDGDBgAN57Dj30UIYNG1ZBGU8yefJk5s+XaV0HHHBAlev9/PPPueuuu3j55ZeZMWNGznRlZWXMnz+/gl9xXaQqY8t3AyuAZsCbxpjrjDFdk4mMMZsaY/4IvAW0CHkGlNBWZT1n+aRlLPvv0tQ0y/67lOWTlmXdt2jcgvLv7Q7vQMMWDWncrgmte7ct/z/uHhGx4O15zHxCFOIGGzWgq90iq7+xoiiKsu6YN3oOZDcSZygL6dZj4mHJ2rVrl5Ky6tx9991472nfvj0vvPBCqkIMMHv27PLvXbpUzdI+ePBgevTowZAhQ1IV4oj6sNx10Uqx9/4H4LzwsxlwPfC9MWaqMeYjY8x4Y8xUJGTbjUBLxEp8rvf+x2pqt7IeksttotB0a5dmQkuUrcjcSctWZv8OMPfl2cx+YSZ4aNiqIZtdsiXNts7unqEoiqKsGwqxEkcs+mDBeu1b/PHHH5d/33777WukjhNOOAGAuXPn0q9fP1atqtkpWF9//TUXXHABa9asoVOnTtxxxx189NFHzJ07l1WrVuG9x3vP4MGDy/NELhV1mSrFqvLeP2GMmQf8A9gs/N01bEl+AH7lvVfXiTpMIVbiiMha3Gyrisprk85NWfnDCgBmPT+Ddn06ULaijPlvZoKlx10iZr8wkwVvy6QH08TQ4ZhOlK0sY/l3GUt00y02okEjnWynKIqyLln88cL8VuKIMknf7rDs/rO1zahRo8q/l+KqkMYdd9zBpptuysCBAxkxYgQnnHACzz33XM6wZnFf4+nTpxdd39ChQ1mzZg0NGzbkrbfeyjmZr6YnFq5vVDmAq/f+FWNMd+BYoA/iZxyNK8wDJgCvAcO892uyFqLUGRaMzRuyulL6pFLc/qiOTHtwKpTByu9XMH3wDxX2N2rTiDYHZoaulnye8fPyqzwzn6h8Y+h27dY0aLfhxUpUFEXZkFkxpbih9mLTrytmzZrFk08+CciEuz59+tRYXZFP8aBBg3jxxRc58cQTee6557JObttqq61o27Yt8+fP5+233y66ri+++AKA3XffPadCDDB+fHr03bToFRsiJZnQvPdrvPfPee+t934/7/2OYdsv/PecKsT1g+UTC7MSp6VvsWNLNr+sGy33aEWj1o2gAZhGhsYdm9DmgLZs/uutdCEORVGUDYCGLYu7VzdcD+/ta9eu5cwzzyz3pbXW1phPccTAgQO55JJLABg+fDgnnngiq1dXdi1p0KABRx99NED5qnXFsGaNqGZLl+Z+dk+fPp3hw4enlrPRRjJ6m1xNb0Nl/ZNCZYPENCrubTFX+o22bEaXMzfLui/JVtdtU1SdiqIoyrqh5e6tWPT+gqLSr09MmjSJc889lzfffBOAnXbaieuuu26d1D1o0CC899x7773861//4qSTTuKZZ56pZDG++uqrefLJJ1m7di2nnnoqr7/+Ot26dcta5tSpUyvEKt5222155ZVXmDhxIuPGjaN3794V0i9btox+/frlnVwXTfL77rvv8N5v8JbjgpRiY0wUg2S1977gMQ5jTDOgMYD3vnLYAKXOsFH35iz5uPAubtZdJ8MpiqLUVVrs0JI2B7VjwVv5fVLbHNSOFtuv24hBs2bNYsKECeW/ly1bxvz58/niiy8YM2YMI0eOLF8YY6eddmLEiBF5w7ENHTo0b71NmjShX79+edPdc889eO+57777GDZsGCeffDJPP/00jRpl1LbddtuNm2++mf79+/Pdd9+x2267ccEFF9CnTx86duzI4sWLmTBhAsOHD+ebb77hf//7X3ne008/nUGDBlFWVsbRRx/N1Vdfzf77789GG23ERx99xF133cXEiRPZb7/9ePfd3EtS9O7dmyFDhjBr1ix+/etfc9ppp9G6dWsAGjduzJZbbpn3WNcnTL7ZhMaYK4E7w88Tvff/LLhwY44HnkOiT1zivb+/qg1ViueWW27x11xzzTqpa/X81Xx/5yTKlq3Nm7ZB84ZscfVWNG6z/salVBRFUUpn6VdLWPL5YlbPXlVxVTsDjTs2oeWurWix47pRiMeMGcMhhxxScPo2bdpw/vnnc8MNN9CsWbOsaQ4++GDeeuutgsts3bo1CxYsAESJPvvsswGxTGez8l588cXcd999APTt25ennnqqgmIMcOedd9K/f/+sbhYRW265JZMnT67w30033cT111+fM89vfvMbdtlll9Q2LlmyhN13353vvvuuoDoLoFZNzamWYmPMRkCkVbliFGIA7/0/jTH3AxcCNxhjHvTer79xV5Qq07htY7a8eitmvziL5f9bxtqFlV3JG7ZuRLOtm9PxmE40UoVYURSlztNix5brTOmtKg0aNKBVq1ZsvPHGbLbZZuy111707t2bY489NqcyvK6ILMb3338/zz//PKeccgpPPvlkBcX4qquuom/fvtx333289tprTJ48maVLl7Lxxhuz/fbbc8ghh3D66adXKvu6666jZ8+eDBgwgA8//JClS5fSqVMn9t5773KLcz7rd8uWLRk3bhy33XYbo0aNYsqUKSxbln0tgg2BVEuxMeZk4AlgKdDdez87Z+LcZXQAvkMW8DjZe/9sFduqFMm6tBQriqIoiqKUSK1aivNFnzgqfA6vikIM4L2fAwxHDvToqpShKIqiKIqiKDVJPqW4J+IF9EqJ9UT5e5ZYjqIoiqIoiqJUO/mU4s7hc3KJ9UwJn5uWWI6iKIqiKIqiVDv5lOIW4XNxaqr8LEmUpyiKoiiKoijrDfmU4mjt3lIXJG+fKE9RFEVRFEVR1hvyKcUzwmePEuvZM3xOL7EcRVEURVEURal28inF7yBRI/qWWM8JyIS93MuiKIqiKIqiKEotkU8pHhE+expjTqpKBSFfr0R5iqIoiqIoirLekKoUe+9fAf6DWIsHG2MOKqZwY8zBwEOIlfiTUJ6iKIqiKIqirFfksxQDXAKsApoDo40xA4wx3dMyGGO2NsYMBF4L+VYBl5baWEVRFEVRFEWpCRrlS+C9H2eM+RVi8W2AKMkXG2O+AT4CZiLLQLcANgH2ArZDrMsGWAv8ynv/Xo0cgaIoiqIoiqKUSF6lGMB7/6gxZirwONAFUXa3D1s2orWrpwOneu/HlNhORVEURVEURakxCnGfACAotlsjbhAfAWVkrMHxrSzsvwTYWhViRVEURVEUZX2nIEtxhPd+BXAvcK8xphWwK7IwRytk1bu5wOfe+1JXwFMURVEURVGUdUZRSnGcoPiOq8a2KIqiKIqiKEqtYLz3td0GpYaw1s4GptR2OxRFURRFUQpgjnPuyNqqXJViRVEURVEUpd5T8EQ7RVEURVEURamrqFKsKIqiKIqi1HtUKVYURVEURVHqPaoUK4qiKIqiKPUeVYoVRVEURVGUeo8qxYqiKIqiKEq9R5ViRVEURVEUpd6jSrGiKIqiKIpS71GlWFEURVEURan3qFKsKIqiKIqi1HtUKVYURVEURVHqPaoUK4qiKIqiKPUeVYoVRVEURVGUeo8qxYqiKIqiKEq9R5ViRVEURVEUpd6jSrGiKIqiKIpS72lU2w1Qqhdr7bHABcCeQGtgGvAK8Ffn3P9qoL5tgM+BjcJfZzvnhubJ0wq4EjgB2ApYC3wDPAHc65xbnZJ3L+BWoDfggXeAPzjnPk3J0wD4EOgBHOCce6egg1MKplg5KEUGimzX/wHnAXsDnUI9PyByc59zbnwBZRwMXArsC7QDZgFjgLucc5+k5GsC/AE4E+gKTAUeAm53zq1JyXcs8AIw2jnXJ1/7FMFa2w24DDgMkammwDzgU+Ap4BHn3NqU/JsAvwF+DmwBLAO+AIYCQ51zvhra2BA4LWx7Aq2AmcBbwEDn3IcFlNE9tLMPsBmwEpgIPA3c45xbmZJ3E+A24BigJXJ8f3bOPZenzruBy4E/OuduydfGuk6JfVCyDBTQvo7IPS/aegHtw+5TnHNPFViOAc4K285Ac+B7YDiiU8wsoIwqX1fW2j7ADch5WgG8BvzeOTcpJU9L4GugA7BzsXqPWorrCNZaY60djDxMj0AUgKbIw+FC4D/W2qNqoOr7yShChbRzK+A/wI3ArsiNuTVy0d4FvGetbZsj797AWODwkK8V8H/AO9baPdKqRRTix1QhrjEKloNSZKBQrLUNrbWPACOB45EHVxOgGbAtcDbwgbX2mjzlXA+8EcroglxTmwOnh/znpmR/HLmhbxXq3hq4BRiSUl8z5BysRhRxpQDCi8QE5EUrkqnGwCbI/eIh4N2Ue0tP4DPgamB7RE7aAweGvCOttU1LbGMbYDSiDBwWym+CyNNpiNxfkaeMXyIKxUWIHDcD2iDXzp3AeGttl5T630FkvwNyve4FPGutPS+lzl2Bi4FvQx31mmrog5JkoECeBUYA1wFHklGICybI+0hE/g8MZTRDro+rgc/CdZNWRpWvK2vtL4CXEQNYM6AtcBIwzlq7WUq11yJGiDuqYghUpbju8AfgnPD9GWB3RDH+BTAZeUg8ba3drroqtNaehlzYOd/aEumbAC8C3YHlyEN/M0RpuAEoQ27ST+co4h7k4ngNeWvdGXgdObZ7ctTZHvgTsBi5MJVqphg5qAYZKJTfIooriFW3D3Kj7A70Qx7wDYA/5XpZtNaeGtpkEMV4H+SaOhRR6hsDzlp7YJa8RyFW8BWIlWUz5PpcCZxmrT0sR7t/D3QDBjjnvi74aOsx4SXrSaAFYsW/ENgBUYj3QxQEgJ8ALkv+DojlqxMwF1FOuoQy7g3JjgQGldjUx4GDw/chwB6IgtALGAY0BP4WlIFKWGt7A48hyuyPwKlAZ0S2zkes4rsA/wrWyCT9gW2AKaEd3YC/hX13BYUtG4OQUeUr0iyg9YFq6IOSZKAKLEUMSQ9VIe8gRO5BroPtkeviNOQ4OwHDw/VTiVKuK2ttI+A+5Hw8ishtL0TB7gz8OUed2wFXINbsW4s41nLUfaIOEIYn+oefLwEnx4YkhltrP0eGtlsilqoTq6HOtsBfkeHoK5ELOh/nI4oswJnOuWdj+2601i4H/gL0sdYe7Zx7KVZfd+SiWAb80jk3P/x/MjIsvZ+1tptzbnKizluRIe+rnHMzijtKJR9VkIMqy0ARbTLAJeHnx8DhCXeMSdbad5BrojUykjAyUcZGZG68/wGOjJXxhrX2EMRatCliHdo70YyTw+fdzrmHw/ch1tqdgKuQB8ToRJ1bIcr8dOCmYo65nvMrREkpA36WGH6ehViWnkes/X2ttR2cc3NiaX6PPKyj/O+H/2cAlwT3qwuBc621g5xznxfbQGvt4UD08vWIc+6c2O551trjEavYEYhSNDKLC9GdyDN7NXCoc+6/sX0PWGu/AN5F7pNnAYMT+SOZtM65t8L331hrDwh5jkUsmPF2nwIcBIyoyrVYB6lyH1STDBTKLYjB4Uvn3NrgWnROepYMYXQgGj24zzl3SWz349ba75BRhy7A78hucCrlujoQubdOBc6J3M2stf2QEaETrLXnZnlJG4hY3n/tnFte6PHGUUtx3eAMxEoCcE3SRyf43zwQfh5vre1UDXXegbwBDkJ89grhwvD5SUIZirgLmJ1IG7Fr+HwvUogBwsPtvUQaoNz/+DzgK+RiUaqfYuWgFBkolA7IDRXg2WwPFufcVMSHDyDb6MkxiPUH4PpkGc65BcDt4WevLMOIkSwmFYkXE/vj3I0od1c75xZn2a9kZ/fwOTHFH/Ox8NkAcWMBwFrbmMzD/1+xB3ec6xElqAHyAlUVfhk+y4A/JneGe3bkytOdjPIUtXNzxKcd4OGEMhaVMQ4ZMofMS2GUvzUyRL8aGWmLE+VJ3j9bItf3SsT6Vq8ptQ8oUQaKwTn3mnPu8zQf+jxcgIyQrUJGy5Llv4dYgQHOC5bdcqrhuopkcVR8/oVz7gtkRLIpifu2tfY45IXiNefc83mOLyeqFNcNjgmfE1MmnEUTKRoAPyulMmvt/shb53REsAvJ0x3YKdGWCgTF41/h56HW2uax3a3C56wsWSNn/41j9RnEpaIBcGl1TdxSMhQrB9UgA4WyIva9LCVdtG92ln3RNbWMhBU5RvwYfp7Yl0teK8kqlE8I/Dkw1jn3eK4GK1mJ+ruQvoaK/X0AMloAuWVyNpkXqGQ/F0qkuP8YXsiy8QmihIC4vWXLDxkjQDYi5WMPa+2Wsf8jeZzjnEuep6wyScY38/aamKS9AVJqH5QqA+uS6P73VpD/bETXSxvEshun1Ouq2Od9M8QVaDUy2bbKqFJcN9gzfH6QkmY8MsQNMumsSoQ3QIe8Rf7aObeowKzxOtPaGe3biIwCBeITDGIFTNIhkQZkMsk+iKXw9QLbqBRIFeWgVBkoiGBl/Tb8PC6bb19wOYpu5KOyFBO19eNckSKccz8ifoXx9BG55LWSrAY/6wHI9XlxtrqUVD4On9tZa3fLkeak8DkRmWMRUaxMbp7LhzIPkYIwLVeCoKxGLl575cifWkZiX/zYInlrFwwGcbLJ5PaIO9QUJFqFUnoflCoD64Qg35uHn4VcE1D5/lfqdVXs8/4PiI/83aXOxVCleAPHWtsV8RUG+C5XuuB7Mz383L6EKn+LKCqjCw3rkqXOnO2k4mSteJ4J4XMfK+G8gPLZvPuEn5+H/1ojN/JlSCgYpfqpihyUKgPFcGP43Ad4wVr7E2ttK2ttB2vt0cgQcjvEMnNXPGPwddu2gHbG25psZySvhyf+/7/wGfefuyrUd19V/FUV7gPmIJNyXrTW9rPWdrHWNrPW7mKtfQCZXLkcuDBhKY36rQxRAHNRqkxGL42b5koQ5G6TqI6E8hp/6cxZRmLfDtEX59xCJBRhU8RHOE42mRyITCStsm9mHaSkPqB0GVhXFHqfnkJmBCZ5TZR6XUX3z8PCOQHAWrst4v60CgnhGY1AXo28bJQ8F0OV4g2f+JtUrmGOiGgooujwLADW2q0RX6hVVPaXykeh7YwPl5S3MwzfjUeGVR621m4efLyGIi8F/47FLrwZ8XO9JWWYSqkiJchBSTJQDM65x5CJJsuQocD3kYfSbMTnL5rBfIBzbmkieytkska+dsbbmmxn9KLwG2vtcdbajcNEmivi+4MM9w/lXFfQwSkVCHMMDkJik26BzPCfhvT954iLzwvA/llGjSKZnJ8WO5rSZfKr8Nk1JZzUHojSSvhsGdv3Vez7T1Lqie9LWtkimfy7tXZPa207a+11SISO5QQf0SCnhyO+mf9Mqau+UWoflCoD64qC7tPB1W1B+Jm8Jkq9rt5GjHhbAYOstZ1CZIlHkNHJYc65yG3qbjJzMZak1FUQGn1iw6dF7PuKnKmE6I2/qhdaFIv21myTDPJQaDvjVolkOy8F3gSOC1vEMoJyFmbNXoQMn/81/NcA6Av8FLnRfIIEDdfJTFWjqnJQHTJQDPcjoYD+TmV/yY2RiXSdqBxKruRryjn3krV2GDKjP6lYPOucezl8/2uo77IweS9ahOIMxCIyB3guTGxRcuCc+9JKrOKhZEaOIhogfd2NjKtFRNTXhfYzVE0mhyHhuxogL+1nx3cGi+CfEnlaEoaInXPfhsgGOwNnWWvvdM5NTJSxDxlf0GztvBW5D25P5fNwtXNubsI3szxOtrV235C3I2I9fDhLpJ86TTX0wTBKkIF1SHXoFCVdV8651dbaixF/5IvCFjEbiXgRzcU4BnjbOfdE+K8JEt0nmhT5HrJGwSoKQC3FSkGEUCh9EH+85IW7TggzWA9CQlktBZYArwIHxmad34MMo17mnFsZQmu9jMRuvgC5EQ1EAo93W7dHsOGzPshBIQSf4XeQFfI+RGILd0AmDp2C+AKfhoTr2qWGmnEy8vCbgigZk5AJiaeGNv4UCY/4PmFBj+Da8SXi/nEG8OvQxoImtNZXwvn5Cpm1fz6wJWJ52heJU9wLeN5aW1uxyl8APgrfz7LWPmit3dVa2zZEyXkWcWOIKxHJlb6iiAVNgNettSdbazex1m5qZRGZEWn5g0V9f8TaNheJKvEJssJZFDf2D8i5uyt64bXW3gSMQ1zRzkCiEXxprT2S+kcpfVAdMlBvcM69AByNKLUrEKv080Bv59xkK4t+DETmYkRGsXbI/XQwEv3ivPD9PVvgglBqKd7wiQ/95ltRrFn4XALlb6YtcqRdG/mSBWGKgrxfVkUfs2Q7cw1zNIt9r5TGOfdvRCmrhJXFFg4EhscscX9EhgLHIcOoCxC/o/OBh6nsX6fkoBrkoCQZCBaAJpWTA7AiFsuyMeIzvGv4PDLhR/qUtfYNJIRcZ0QO4pNaqnxNxQl+/NeRxS0ihDAahPjcXeKc82GiyWOIL6dFXuR6IMPeN1hrxzrn3sjTnnqHtfb3iKK2DHlBjo9evA+cZK19CHkh/rO19jXn3H/C/qivC+1nqCiTTZH+ysbyKCRWiBV7PPISvwNwbtjivIvExI4mWy6I73TODQvHehsyEerJRP5VSMzmKC72gsR+nHPTkGXHKxF8M3+LvDDeHP47DIlCMQPxy/4Eedm7B3jCWrutc25ujuOvc5TSB6XKQLDiZ1sQBGBpMhRrCVTH/a+k6yrCOfcK8EqOvFchi3oMjM3FGIgEHhiGLEvukYVCjkHcLLLKfhy1FG/4xIPQd8yTNtof3cS2RIZmsm0vx/Jdhzj/D3fOvUjVKLSd8X0F32zD5LvbkTfKK8J/UXDwMuB059x/nazVfhGy4s2BKbPVlcqUKgelykB/csvrabF0x5GJc3ljlhBUOOdmkVlVqUdCDhaTCYtU7DVVKJcjExUfdM5F1qPTkPBGjzvn/uGcWxCU4Ch2abF+/HWe8KL0u/DziRR3nujFpAFi7YyIZLKNTcRaTZBLJh25ZfKAeAHOue+Bnoji+WFIsxxZpes3yEpnkRIxJ+YzGS/jL4j1+0lEeV2FhKh6GllAJv7SVOx8igGIe1ncNzOSuT84594MMvn3UF9bwqhHfaKUPihRBl4mt6zFQ7+VSkH36XC9tAk/k/e/Uq+rVLLNxQhGhV8i5+MM59z3YU7RGYiS3s/KCrepqKV4w2ca8obVEnFKz0qwaESzXov1B47K/bm1Nt/b6BBr7ZAoX8zvLF7nVuReEjh+DMW08zrk+G6KTbjbFokwMNE5Vz6LNryxv4a8pe+L3JCU/JQqBzUtAxG9Y9+TvpNxPop935EgB865MmvtRMR3MOc1FYj2F9xOa21nxI1iHpmVKCEzQScZIi56Qd0XJclOZB7MOfvaOfeDtXYW4kO+Y2xX1G8NkUl6uWbblyqTUTuWIgti3JFtv7U2attH2faHMj5ArLbZ8sdH0XKWkSXfUUj8+recc3HrZ5pM9kNkst4tjFRKH1SHDNQwyft0LrYkY1hNXhM1fV39DWiOjLItDP/thei078fnCznnFlhr30dc6HoilvqcqFK8gROGXT9BrBJpM2L3IjP08nHIOxmZybkuiD+wfkLFt2kS+0Asvl8WUrC1dgfE8jaZimuiRzNg5yTzkJlVW6XoBkqVKEkGnHM3kGV1pSw0y58EqCj7SWvyx4hS3MNa2yjbDGpr7aZkVr1LU76T3IFEuLgoMfScS15VVnNTaF9Dpr/jfZ2UyVwP70gmp7rYEtHOubOQ5XxLxkp4yV7hZ+qDO4UoBOAcClSqgsEkipN9aWK3ymTxFN0HEWky4Jw7uNSGFYJzbo61diriHlJolI3k/a+k6yoNa+2hwAmIa9TQ2K5qed6r+0TdIBrK3i5EX8jGCeGzjMwylIVyJeKnk2s7Opb2+tj/5UHKg6U2UnBOIAthmCVaxed159yyAts3CPHruzLh5xp9z7asdadEGiU/JclBDctAnBmx72kL1cT9iL9P7IuuqRZk4rgmiR/D8BxpKmBlFcDTkIeGS+zOJa8qq7kpqK/DcGs0VBvv67FAZGnKJZMdyMw9KKifq4hF7mMrgUeLzmztxmRcQx5xha/iGflm3pMlTrbKZBGU0AflRVCCDFQj0f3v4BSXg+h6WYCEUItTI9dVmC9SYS5GbHe1PO/VUlw3eASxoDUHbiGxZGKIsnB++PnP4E9ZMDF3hKxYaxfEfn4fm8SS5H5EoHtYa/u6yuuTX0FGeO8vpG3W2hOAw4BXnXPDEru/Qawf3a2128dmUzdC1kiHAq3RSrXJQbXLQBZeJ+NDeq219qikX7G1thOZMD9zqWzReRHxF+wK3GitfSX+gLOyQMxvw8/xzrnx+RplZWW9e5DJH5dk8XX+CpkQchQy4S4iWpZdZTWBc26StXYSMgzbz1p7ezJMVuDG2PdyC1wI/fQg4s/5C2vt3mEyb5wbkAmensovMtVCCOV1bfj5l0KtZrH8DYEHkGtnLgWuQmet3QJx4ZlJ9qXav0L8ZI8C/hH7X2UyQVX7IJa/JBmoZhwyH6cJIv8VRhCstT8hY7x4MDmSVoPX1eWI+5OLzcWIiOJA72utbeMyIS7bkXE9yyuvqhTXAZxzM621tyAK8THW2qfC9xnI8MRAxOK1hMykndrgH0hYtJ2BR0PYrGHIm/GZZBSZ15xzL+UrzFrbHInzuoos650755ZYa19GbuBPWGvPB+YjF2NXxEn/zdIOSSmSapWBbDjn3rbWvo1EIjkCeMVaeyuykEOT8P8tSOQJgD9luamvsNb+DlFO9wRettb2R4YBd0Xkrivy0lXoqokXArsjMV6zxR1+BlG0f2mt/TD83pNM6LunC6ynvvEnJOxSc+Bta+0fER/YJcB2SP+cGNJ+QmWr1J8R/9AuwEvW2suRF6uNEWUgigTwYBZLasFYawch7h5PIyt2rUT8Mk9CXgY3QsII3poj/5ZIzOuHkPvWdGQuyU8QuekFrAHOKUKhinwzL475ZsZ5BlGKb7XWTkPO3y/JWP+eKbCeOkGpfVCqDBTRzo5InPOILrHv2wQFPOIH59wP8fzOuc+CUvsr4JIwh2QQsgDSoYhO0QA5/r/kaEa1XlfW2i7I82EeWfQY59xX1toJwC5IhKErw667kXP+acpE3HLUfaLucBtyoYLctD5DlL4XESvKEuCXzrlvaqd54CR49jGIYtEMmf3/I+ILfCPi8/wR0v5C6I848d+VclxXIcM7PZAV8f4HnI4Mv1zkCgzorVQPNSADuTiRjPW3D/IAm4O4cjxF5oEx0Dl3d462Ph7a5JEHwQeIb9obiLK6GrDOueTQYSXCUOFNyJDi77KlCZaP+5H78t+QZXlfRCaLjkeWM1YSOOceQl5yPPKi8yDiIjEP8TuMFOIvgGOTFvqgvPwcuV92QFbEm4GMNEUWsleo7G9bLK2Qyb2jEDmci7jR/B5Rhl4CjnISyi8bBrmP3ROOZV44zigO8wLgZOdcoUPRhyELcrxHJoRYknsQRbg9Ios/IC+EDZBr55NC6qpDlNoHpcpAoUTxfaMtvoDQzYl95+Uo41Iy4dAuRa6HGcj10R65Xn6e6wWsBq6raC7GNS53GMCLkBeNIxCr8JeIj/cKKi4AkhNViusIzjnvnDsXiOIgzkaEYxKyotcezrmRtdhEoHwIfg9kqO5zJFTKIuSh/2tgXydB5lOxstTwVcRiauao77/I0MkwRCFZgdwIjs4ydK+sA6pLBvLUMQvp97ORmfLTkRGF5YhC/igS0/byPOXcgKyE+AJyQ1+FKAaPAT9xzg0usEl/RkJY3eAkLGAuLkHOwX9DXdMRC81h+gKXG+fcHxGL5mDk3C1FLHazEevUhUDPEBIrW/7xwG7AnchDezmi8IxF4ptXh6LyD+Sl5/NQdiRLzwO/cM79zKWvsjkTuee9ioT6WoHc0/6DvHDtWOg9LfhmDiS7b2Y54Zh/isjgtNDm/yLzC64opK46Rql9UKoMrDNC3x+FyP9YpL3LkevjTmC3fG5j1XVdhbkYpyIvEP/Ilc45NxYJazcaMQQuDd8Pcs6Ny1cPgPG+3i6YoiiKoiiKoiiAWooVRVEURVEURZViRVEURVEURVGlWFEURVEURan3qFKsKIqiKIqi1HtUKVYURVEURVHqPaoUK4qiKIqiKPUeVYoVRVEURVGUeo8qxYqiKIqiKEq9R5ViRVEURVEUpd6jSrGiKIqiKIpS71GlWFEURVEURan3qFKsKIqiKIqi1HtUKVYURVEURVHqPaoUK4qiKIqiKPUeVYoVRVEURVGUeo8qxYqiKIqiKEq9R5ViRVEURVEUpd6jSrGiKIqiKIpS71GlWFEURVEURan3qFKsKIqiKOsYY4wP2w0llHFwrJyDq61xtYwxZnI4pqG13RalfqFKsaLUEIkHVqHbsNput6IoiqLUR1QpVhRFURSlRjHGjAkv/mNKLKdbzIhwVvW0TlGERrXdAEWpJ9wP3FdAukU13RBFUWof772p7Tasr3jvu9V2G5T6iSrFirJumOW9n1DbjVAURVEUJTvqPqEoiqIoiqLUe1QpVpT1nORMbGPMdsaYvxtjJhljVhhjZhtjXjLGHJpSRlET/mL5/hn+m2+M2aiAtk4I6T9L/D80/D85/O5sjLnTGPONMWaZMeZHY8wzxpidE/m6GWMGhnTLjTEzjTGPG2O2LvDc7W+MecgY860xZqkxZokx5itjzKBCy8hTfmdjzC3GmPHGmAXGmNXGmFnGmC/CubPGmI4p+ZsYYy4wxrxqjJlujFlljJljjHnLGHNZged8x3B+pwZ5mGqMecIY0yvsr3DuE3kLjl5QaEQAY8yuxph7jTFfGmMWhf791hgz2BizR0q+Sm0xxvQ1xrwWzukKY8z/gjx0znNaojJ7hWvlS2PMwnB+pxtjRhljrk4rxxjT0RhzgzHmA2PM3JB3mjFmmDHm2ELqz9O2vNEnjDHNjDH9jTGfBvmda4x51xjzK2NMwc9vY0wDY0y/0PYfjDErwzX9gTHmGmNM65S8yWu3tTHm+nCtLwnn9T1jzPnGmIa58gMHhb8OMpXvOZMTebLKWihnUuyvIVnKuiGk/Tj8/rqA89M0nFtvjBmeL71Sh/He66abbjWwAQcDPmw3lFDO5FDGUOBYYEms3OR2SY4ycqXPusXy/V/s/1PztPMnsbSXJ/YNDf9PBnYHpueoewmwf8jzU2BBjnTzgJ1T2tIUeDjPca4CzimhX/YD5hdwPi/IkX8X4Ns8eb8Gtklpw0nAihx5VwPnxs99Hhk9uFA5zLHfAH8G1qYcTxlwbQHXy0+BR1LKmQZsm6f/hxTQN7mO5STEvz8t77+AFiXIT+q9AegMfJlS/yvA4fn6D9gMGJ/nWKYDvXLkL5cfYHvgu5RyngFMjvxp2+REnqyyVkA55ecTuDD23355+uKXsbS/qGqf6rbhb+pTrCgbDrsiN+9ZwDXAvxEF5KDwuzXwN2PMaO990jqya56ydwEeAxoC38f+fzX83gI4B3g8pYxzwueqUFY2mgMvAE2A/sBb4RiODL9bAI8aY/oAw4CFwHXAB8gciL7AFUBbYDCwT456ngF+Hr6PAp5AHuYrgB6hjB2AB40xs7z3I1KOqxLGmCbA00AbRJF3wOtI3zQCtkReEo7Nkb87MDbkXwr8HRiHnOuNgSOASxEl5GVjTE/v/cJEGb2Q/miEnPMBwIhwjHsj5/N+RLFaFwwIbQbpr8GI0r8I2Am4GDknNxlj5nnv700p62agN/ASotxOAtoDZwH9gC7AQ8AByYzGGAM8Dxwd/poM3ItcL4uBTsj5OSFbxcaYvsBTiJI/FRgEfAHMALqG+k9G5GsIokBXK8aYRkhf7hj+ej0cw/fA5sBFiIy0y1NOO0TOuiEvSUOB0cg5aYq8iFyBKOAvG2N6eO+/r1wSINfui8j5+zNyXS1E+vZaYDvgROSeMTiW7xrgTuRc9UQU9LMTZa9KO44YuwKbhjoA/oi8nMSZFT6fCPU2R+5N76aUG927ZiIyp9RXalsr1023urpR0fJ1H6J45tsqWZ7IWE088AnQNkuag2Jp7iqynR0RpcMjSsPuif3Xk7HydctRRjPkAemBZ7LsHxpr32xg6yxpLo6lmQV8A3TMku72WLo9s+w/N+xbAxyb0t4xId0koFGR5+ynsTYck5LO5OivsSHvBKBzjrw9EYXZAzdn2f9h7DgPy7J/U0Spy2qNyyKjB+c55kgOh2bZd1isnEtz5G+IKCoeUZTbpLQlzYL6UCzN7ln2XxTb/xLQLOWYNk/8bk9mdOIJoEmOfHEr5KHFyE6sjJzHmbgWKp3vkObBxPmq1H/Ao2Hfj8D2OcrZClH4PfBolv1DY3UsBHbLkqYDcs164JMc9UTX25gCzk2arHWLteesPOUMIXNfy2rVR14yotGN26vSl7rVna3WG6CbbnV1y/KQL2Q7OEs5k2P790ip7/2Q5uMi2tgUeCfkW0uWocPEQ+PGHOWcHmvjEVn2xx+sudwJmgHLY+mOzJFuq1iayxL7DDAx7Ls3z7HvFCunT5F92y+Wd+Mi8+4fy5t1yDqWNnoB+DHxf69YGQ+k5D8plm5yHhmtJHs55HBoln2jw76X8pTRloy7x69S2vIxiWH4WLodUvq/ATAl7JsBtC6yb/5I5qWseZ60/w5pHy+mjlj+NKX4i7BvLtAqR/6WZBTRSv2HjFasCftOzNOW6EViVfK4E9fu5Sll/JnMy3Ol807tKMX7xdKenSPNtbE0O1SlL3WrO5tOtFOUDYcJ3vv/pOwfHz67F1HmA8iDA+AP3vvkUCTe+6lkhivPyjHBJxp+nAq8llKfR1wbKu/wfjmi0IL46r6aI90kxPIDlY91J2Cb8P3ZlHbgvf8SUToA9k1Lm4Vpse/JoeB8/CJ8TvHef5gn7dvhc1NjzBax/w+LfR+Skv8FxPJZYxhjNkYUWsh/zucj1nFIP+ePe+99jjK+RlxWoHL/74a4+gA85BMuJwUQ9c1I7/2yPGmjvilWdlIxxnRB5Bjgee/94mzpvPdLyHEtBX6GWOdXU9nFIEl0LI2REYqsVZLuPhXdfwzy4lrreO/fBb4KP89N7g+uNmeFn+/6ym5nSj1DlWJFWTfc6L03BWxjUsr4KmUfyOQzgFaFNMgY0x+x8AI87L2/PSX5A+FzCyoqZJF/7EHh5xDvfVlKOXO89/NS9i8In9/mUooS6ZLHGn+gv5llZnoyykb7kLagaAYx3kX8ZQHuNsZ8GGbxH2DyR4yI2rhlAe17MZYv3sbIR7wM+ChXRd771YjLTU2yJ6J8QfZoAMlj2iukTTvn+WR9fvhM9n+P2PexBbU+ECIn7BF+nlnAcfwmpC1WdvIR9//P99L075R9kZw1BlbmOZbPY/lyHc8c7/2clPri13VB96B1RHTv2s8Ys11i38FkXqwGo9R7VClWlA2HfJarSBnNe10bY44H/hR+vgOcnyfLi8hwNGSswhFnI9YhT7rVEgo/hkLTJUNAdcqTLxfNi0kclM1jyFg9eyLn821ggTHmDWPMeWFCXpLqaGM0wWqR935lnnwzq1hfodTEOa9q/8fD300vsj3tqNqCVs2qkCdfOyJm5UwlpPVtdfdLoX0ClfulNnkEiK6R5L0r+p3P6q7UEzT6hKLUM4wxPZAJOAaZZHa89z519rf3fo0x5mHgd8Cxxph23vt5wZXizJDsDe/95BpseiHEH8Z9kcl6hTA/f5KKeO+/NsbsjoSt+wVwIBItoilwSNiuNsYc7b3/NpY1auPXyGz9QpmUrRnFtrsGiJ/zy4A3C8y3tAbaUgrx43gU8eeubUrp3+h4FpFxkSqEH0qoc73Dez/XGDMMidxzhjHmGu/92uD20zcke9p7v77Jo1ILqFKsKPUIY8ymwHDEGrQYiZwwu8DsDwK/RZS+U5FQVX2QiXiwfgw/xod3F/kaXlo7uIq8FDaMMZ2Qc2KRcGHbIaHb9oplm4Moz61KaF+kxLc2xjTNYy3eJGVf3LqXb4ShRY7/4+d8eU2f8zzE29IF+E8ReeciSqgBGtTiccRf0NL6Lt/+6Fy0BCYWMKJQl3kAUYq7AEchI1+nkLHyrw/3LmU9QN0nFKWeYIxphky46YooQ6d4778oNH+wdo4JP89JfM5HJnXVNnH/2f3XdeXe+1ne+8cRH+toomAPY8w2sWRRG7saY7pVsarIB7QBFRXuCoR4t3uklBOfxNU2pZwOSNitbHxKxqK5zs95grh/9YHFZAwuMdH1sF+YhFUbxP17e+VJm7Y/krMGVPNkwCpSXaMaVSnnDSROOVS+d33lvX+v5FYpdQJVihWlHhAe8A+TmXxztfe+KkHqo0krexhZVjqarf+E935Fic2sDj5BImAAnGeMaVkbjQiTBN+I/RVXKOORAK6sYhWjY9/PzJkKjiNF2aWiS0aagtUv144w0jAu/DzRGNM1pZya5jMyi8+cbVKWL85B1DfdyLHwSk3jvZ9GZqLh8blk2BjTgvSFQ14ko0BWVc6qk+j+0LSayim4rHA9Phh+Hm2MOQRZwAUk7rWiAKoUK0p94SYy/quDvfd/q2I5/yQTxuwxMg+l9WL4Mbgz3BJ+dgWeNMbknNBljNnIGHNJAREjkvkOMMZsm7K/AXBo1Cwk7mrUxtFITGmAS40xZ+Wpq7sxpoJS6r3/NxLLF+Dc8JBP5uuMrOiVE+/9AsTSC6JEVrIGG2N2QeQnjZvDZ3Pgn9nKiZXX0BhzmjFmszxlFk3o/8gXeBPg8TBCkqstyTYMQHxwAf4R/O9zEuTgoLQ0VeT+8Nk+tCkbfyNlMp33/hvEdQfg58aYa9MqNMZ0NsacV2xDiyCa+Ni9RCv8XDIr4G1dRL4hSNzmxsjCLCDh6h4poS1KHUN9ihVl3dApKBf5WBUeZtWGMeanyKIEIBPP7s3Xllz+lN77lcaYx4DLyYRu+sR7X9Nhv4rhH0jYuBOQWK1fGWMc8B7i5tES2Bbx+T0OWWp5aJF1HApca4x5BxiJKJazkJeE7khM1EhRfcF7PyORvx8STqsDEsbsFORB/TXyoO4ARJP4DkJcU55IlHEREjmkEbJE792Ib3N8meeOoW27pxzLIMSK1gl4xxhzM7I0dGvgcGTp5umIItIxWwHe+1eNMX9FwpTtTeacv4WsYNgciV3bGzgekZ1dqZlJXfcj/X4kstTzF8aY+DLPHZERk5OQc3NW7DhmG2POQF7+OgDvBXkfgVigGyJ+qb0QS/LOyPl5qwaO4Wwk3N05xpjNkVUxo2WeL0SWeR5P7rjCIDLSE4ndfZMx5mhEOfwcWSinLbKSZp9Q3mdkLKrVzTjkmDohy9E/hqyQB7Daez+lkELCpN8PkcmD5xhjPkF8x1eHJPOyhX303s8wxryEjG5F964R3vt8ET6U+kRtrx6im251daNqK9pNzlLOZHKs7pRId0NUTuL/s4ptR556dk6kv6SAczE01/El0o2hgFWv8p0TRFEcQGYlvrRtCSlLAec713m2MWRZ5jmUsQ3yMC+knIdylHEKEm4qW57VwK/ynXtkYtmzKXVPQiYG5pVD4A8p7YlvK4FtUq6Xg0vs/2ZklpRO23LlPxJR5gvpmzOqeH+I8t+QY/+myEtSrnpfRV5aUs8ZooS+XuCxvFHCtZvaf8jL6P9y1Ds5kTZf/x6NzIvIVlbW8xnLF0/7s6r0nW51d1P3CUVRisLL5LxPws8VpK9yVSt479d47y9HrJF3I8rnfERJXoRMqHoMOAPo4mU1vWK4A7F43odYoKcg52IlYs17AbFEHuJlFbdsbfwWWWziJCRG6mTEercasTq/i7g/HOi9T8ZXjcp4ErEmPoqssrcK+DGUt7/3/oFs+RJleOBkJGLG+4g1dRni1/onYE/v/X/zlRPKug2xwt+KWGbnIkPWS5BRiueAC4CuvmKYumrFe7/ce98PGQ0Ygihjy5BzOx1RKK8ErsqR/xXEsn0FskJjZClfgfTvq4glfgfvfY0Mv3vxLd4TGeWZgMjGAqSPLkJGEVJDKYZyZnnvD0UU/UeQRWeWIP0yF1kg5B4kKkOf6j6OWDuWICMFAxDZyhf3OK2sl5DRmn8hcr86PUc5r5CJyjENeLmqbVDqJkbuh4qiKIURfDRnABsDTwblQ1lPMcYMRSbjTfHed6vd1ihK7RHcUCYj86lu8973r90WKesbailWFKVYTkQUYlhPJtgpiqIUwNmI3uPRqBNKFlQpVhSlYEJUhWjI+Rsqhh1TFEVZLwmh7S4KP0fXpPuOsuGi0ScURUnFGNMOiLbITxdk+FH9rxRFWS8JK0xujEQMuZbMCoC31lqjlPUaVYoVRcnHZcD1if/GIIuBKIqirK/cTuXFbR7x3o+phbYoGwCqFCuKUihrkJn3zwK3qJVYUZQNhFXIMs9DgbtqtynK+oxGn1AURVEURVHqPTrRTlEURVEURan3qFKsKIqiKIqi1HtUKVYURVEURVHqPaoUK4qiKIqiKPUeVYoVRVEURVGUes//A29YrYraEtECAAAAAElFTkSuQmCC\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "points1 = []\n", - "points2 = []\n", - "n_points1, n_points2 = [], []\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " R2 = r2_score(y_true, y_pred)\n", - " abs_error = abs(y_true - y_pred)\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " R2_DLkcat = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat = abs(y_true - y_pred)\n", - " \n", - "\n", - " print(split, R2, R2_DLkcat)\n", - " points1.append(R2)\n", - " points2.append(R2_DLkcat)\n", - " \n", - " n_points1.append(n_kcat)\n", - " n_points2.append(n_DLkcat)\n", - "\n", - "\n", - "ticks2 = np.array(range(len(splits)))\n", - "labs = splits\n", - "ax.set_xticks(ticks2)\n", - "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", - "ax.tick_params(axis='x', length=0, rotation = 0)\n", - "\n", - "plt.ylim((-0.7,1))\n", - "plt.xlim((-0.2, 3.2))\n", - "plt.legend(loc = \"lower right\", fontsize=20)\n", - "plt.ylabel('Coefficient of determination R²')\n", - "plt.xlabel('Enzyme sequence identity')\n", - "ax.yaxis.set_label_coords(-0.15, 0.5)\n", - "ax.xaxis.set_label_coords(0.5,-0.13)\n", - "\n", - "\n", - "\n", - "plt.plot([0,1,2,3], points1, c= \"black\", linewidth=2)\n", - "plt.plot([0,1,2,3], points2, c= \"orchid\", linewidth=2)\n", - "\n", - "for i, split in enumerate(splits):\n", - " points1.append(R2)\n", - " points2.append(R2_DLkcat)\n", - " \n", - " if i ==0:\n", - " plt.scatter(i, points1[i], c='black', marker='^', linewidths= 8, label =\"TNP\")\n", - " plt.scatter(i, points2[i], c='orchid', marker='^', linewidths= 8, label =\"DLkcat\")\n", - " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_points2[i], (i+0.06, points2[i]-0.01), fontsize=17, c='orchid', weight = \"bold\")\n", - "\n", - " else:\n", - " plt.scatter(i, points1[i], c='black', marker='^', linewidths= 8)\n", - " plt.scatter(i, points2[i], c='orchid', marker='^', linewidths= 8)\n", - " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_points2[i], (i-0.04, points2[i]-0.10), fontsize=17, c='orchid', weight = \"bold\")\n", - " \n", - "\n", - "plt.legend(loc = \"lower right\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Testing if differences in model performance is statistically significant using a one-sided Mann-Whitney U test" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% MannwhitneyuResult(statistic=10987.0, pvalue=1.6032343879692165e-10)\n", - "40-80% MannwhitneyuResult(statistic=4124.0, pvalue=0.00170977094803271)\n", - "80-99% MannwhitneyuResult(statistic=702.0, pvalue=0.0027672622913853277)\n", - "99-100% MannwhitneyuResult(statistic=13930.0, pvalue=0.0784854835728501)\n" - ] - } - ], - "source": [ - "from scipy.stats import mannwhitneyu\n", - "\n", - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " R2 = r2_score(y_true, y_pred)\n", - " abs_error = abs(y_true - y_pred)\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " R2_DLkcat = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat = abs(y_true - y_pred)\n", - " \n", - " res = mannwhitneyu(abs_error, abs_error_DLkcat, alternative=\"less\")\n", - " print(split, res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Comparing different sources of kcat values in test dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source: from_BRENDA, no. of data points: 608, MSE: 0.8574824713302306, R²: 0.39698031604949713, pearson r: 0.6331327633575967\n", - "Source: from_Sabio, no. of data points: 51, MSE: 0.8127901985861423, R²: 0.16465494057842, pearson r: 0.5939783455489556\n", - "Source: from_Uniprot, no. of data points: 193, MSE: 0.9016225712161992, R²: 0.40665476596565797, pearson r: 0.6402935796342673\n", - "Source: checked, no. of data points: 284, MSE: 0.9139695986758268, R²: 0.36941692142423044, pearson r: 0.6148609077564716\n" - ] - } - ], - "source": [ - "model = \"ESM1b_ts_diff_fp\"\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", - "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", - "data_test[\"y_true\"] = test_y\n", - "data_test[\"y_pred\"] = pred_y\n", - "\n", - "\n", - "for ind in data_test.index:\n", - " data_test[\"from_BRENDA\"][ind] = np.mean(data_test[\"from_BRENDA\"][ind])\n", - " data_test[\"from_Sabio\"][ind] = np.mean(data_test[\"from_Sabio\"][ind])\n", - " data_test[\"from_Uniprot\"][ind] = np.mean(data_test[\"from_Uniprot\"][ind])\n", - " data_test[\"checked\"][ind] = np.mean(data_test[\"checked\"][ind])\n", - " \n", - " \n", - "columns = [\"from_BRENDA\", \"from_Sabio\", \"from_Uniprot\", \"checked\"]\n", - "for column in columns:\n", - " help_df = data_test.loc[data_test[column] == 1]\n", - " y_pred, y_true = np.array(help_df[\"y_pred\"]), np.array(help_df[\"y_true\"])\n", - " \n", - " MSE = np.mean(abs(np.reshape(y_true, (-1)) - y_pred)**2)\n", - " R2 = r2_score(np.reshape(y_true, (-1)), y_pred)\n", - " pearson_r = stats.pearsonr(y_true, y_pred)[0]\n", - " \n", - " print(\"Source: %s, no. of data points: %s, MSE: %s, R²: %s, pearson r: %s\" %\n", - " (column, len(help_df), MSE, R2, pearson_r))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Predicting Proteom allocation" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (14,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "conditions = [\"$Petri_1$\", \"$Johan_1$\", \"$Johan_2$\", \"$Johan_3$\", \"$Johan_4$\",\n", - " \"$Joao_1$\", \"$Joao_2$\", \"$Joao_3$\", \"$Joao_4$\", \"$Joao_5$\", \"$Joao_6$\",\n", - " \"$Joao_7$\", \"$Joao_8$\", \"$Joao_9$\", \"$Joao_{10}$\", \"$Joao_{11}$\", \"$Tyler_1$\",\n", - " \"$Keiji_1$\",\"$Keiji_3$\", \"$Tyler_2$\", \"$Tyler_3$\"]\n", - "\n", - "\n", - "data = [[1.3958, 1.0951, 1.1511, 1.1255, 1.1380, 1.1731, 1.2599, 1.1834, 1.1232,\n", - " 1.2105, 1.1553, 1.2801, 1.1873, 1.2479, 1.1843, 1.1711, 1.3075, 1.3824, 1.1935,1.0210, 1.1693],\n", - " [ 1.3348, 1.0608, 0.9795, 1.1813, 1.1117, 1.0074, 1.0357, 0.9859, 0.9882,\n", - " 0.9457, 0.9748, 1.1171, 1.0519, 1.0512, 1.0897, 0.9635, 1.2411, 1.2818, 1.0986 ,1.0474, 1.0883]]\n", - "\n", - "X = np.arange(len(conditions))\n", - "ax = fig.add_axes([0,0,1,1])\n", - "\n", - "barWidth = 0.35\n", - "eps = 0.04\n", - "\n", - "plt.xticks([r + barWidth for r in range(len(data[0]))], conditions, rotation = 90, fontsize=26)\n", - "plt.yticks( fontsize=30)\n", - "\n", - "ax.bar(X + 0.00, np.array(data[0])**2, color = 'orchid', width = barWidth, label = \"DLKcat\")\n", - "ax.bar(X + barWidth + eps, np.array(data[1])**2, color = 'black', width = barWidth, label = \"TNP\")\n", - "\n", - "plt.ylabel('Mean squared error', fontsize=30)\n", - "plt.legend(loc = \"upper center\", ncol = 2, fontsize=26)\n", - "plt.ylim((0,2.2))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 0.08549516, 0.06166165, 0.2759263 , -0.10161391, 0.04568734,\n", - " 0.26254789, 0.32423483, 0.30593107, 0.22593842, 0.38965247,\n", - " 0.28806311, 0.23845371, 0.21507534, 0.29040404, 0.15337626,\n", - " 0.32311409, 0.09898887, 0.14024822, 0.15270559, -0.05238259,\n", - " 0.13374579]),\n", - " 0.1836787453514725)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(np.array(data[0])**2 -np.array(data[1])**2)/np.array(data[0])**2, np.mean((np.array(data[0])**2 -np.array(data[1])**2)/np.array(data[0])**2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. Plotting orginal kcat values and log10-transformed kcat values" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"final_kcat_dataset.pkl\"))\n", - "kcat_values = 10**np.array(df_kcat[\"geomean_kcat\"])\n", - "log10_kcat_values = np.array(df_kcat[\"geomean_kcat\"])\n", - "\n", - "\n", - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 699, ymin = 0)\n", - "plt.xlim(xmax = 5.1, xmin = -3)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "#plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "\n", - "plt.ylabel(\"Count\", fontsize = 22)\n", - "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.hist(log10_kcat_values, alpha = 0.9, color=\"darkblue\",rwidth = 0.95, bins = 20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 2500, ymin = 0)\n", - "#plt.xlim(xmax = 5, xmin = -0.1)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.yticks([0,1000,2000,3000], [\"0\",\"1000\",\"2000\",\"3000\"])\n", - "plt.xticks([0,250,500, 750, 1000], [\"0\",\"250\",\"500\", \"750\", \"1000\"])\n", - "\n", - "plt.ylabel(\"Count\", fontsize = 22)\n", - "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.hist(kcat_values[kcat_values<1000], alpha = 0.9, color=\"darkblue\", rwidth = 0.95, bins = 40)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6.Calculating how much mean deviation we have between two measurements for the same enzyme-reaction pair:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "df_kcat_old = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"kcat_data_merged.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstratesproductsMW_fracmax_kcat_for_UIDmax_kcat_for_RIDECsmax_kcat_for_ECstructural_fpdifference_fpESM1bESM1b_ts
0Reaction_0Sequence_309[2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0][P20932, P20932, P20932, P20932, P20932, P2093...[0, 0, 0, 0, 0, 0, 0][1, 1, 1, 1, 1, 1, 1][0, 0, 0, 0, 0, 0, 0][False, False, False, False, False, False, False]MSQNLFNVEDYRKLRQKRLPKMVYDYLEGGAEDEYGVKHNRDVFQQ...{InChI=1S/C8H8O3/c9-7(8(10)11)6-4-2-1-3-5-6/h1...{InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1...1.0360.0360.0[1.1.99.31]550.01100110100001000000000110111010001000001111010...[0.0, 0.0, 0.0, 0.0, -10.0, 0.0, 0.0, 0.0, 0.0...[0.13688426, 0.20014146, -0.20241867, 0.083636...[0.08041849, -0.05214988, -0.7103536, 0.786840...
1Reaction_1Sequence_309[1.2, 3.4, 0.61, 0.07][P20932, P20932, P20932, P20932][0, 0, 0, 0][1, 1, 1, 1][0, 0, 0, 0][False, False, False, False]MSQNLFNVEDYRKLRQKRLPKMVYDYLEGGAEDEYGVKHNRDVFQQ...{InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1...{InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C17H21N4O9...1.0360.03.4[1.1.99.31]550.01100010100000001010000110110000001000001111000...[0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0,...[0.13688426, 0.20014146, -0.20241867, 0.083636...[0.08041849, -0.05214988, -0.7103536, 0.786840...
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" - ], - "text/plain": [ - " Reaction ID Sequence ID kcat_values \\\n", - "0 Reaction_0 Sequence_309 [2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0] \n", - "1 Reaction_1 Sequence_309 [1.2, 3.4, 0.61, 0.07] \n", - "\n", - " Uniprot IDs from_BRENDA \\\n", - "0 [P20932, P20932, P20932, P20932, P20932, P2093... [0, 0, 0, 0, 0, 0, 0] \n", - "1 [P20932, P20932, P20932, P20932] [0, 0, 0, 0] \n", - "\n", - " from_Sabio from_Uniprot \\\n", - "0 [1, 1, 1, 1, 1, 1, 1] [0, 0, 0, 0, 0, 0, 0] \n", - "1 [1, 1, 1, 1] [0, 0, 0, 0] \n", - "\n", - " checked \\\n", - "0 [False, False, False, False, False, False, False] \n", - "1 [False, False, False, False] \n", - "\n", - " Sequence \\\n", - "0 MSQNLFNVEDYRKLRQKRLPKMVYDYLEGGAEDEYGVKHNRDVFQQ... \n", - "1 MSQNLFNVEDYRKLRQKRLPKMVYDYLEGGAEDEYGVKHNRDVFQQ... \n", - "\n", - " substrates \\\n", - "0 {InChI=1S/C8H8O3/c9-7(8(10)11)6-4-2-1-3-5-6/h1... \n", - "1 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... \n", - "\n", - " products MW_frac max_kcat_for_UID \\\n", - "0 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... 1.0 360.0 \n", - "1 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C17H21N4O9... 1.0 360.0 \n", - "\n", - " max_kcat_for_RID ECs max_kcat_for_EC \\\n", - "0 360.0 [1.1.99.31] 550.0 \n", - "1 3.4 [1.1.99.31] 550.0 \n", - "\n", - " structural_fp \\\n", - "0 1100110100001000000000110111010001000001111010... \n", - "1 1100010100000001010000110110000001000001111000... \n", - "\n", - " difference_fp \\\n", - "0 [0.0, 0.0, 0.0, 0.0, -10.0, 0.0, 0.0, 0.0, 0.0... \n", - "1 [0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0,... \n", - "\n", - " ESM1b \\\n", - "0 [0.13688426, 0.20014146, -0.20241867, 0.083636... \n", - "1 [0.13688426, 0.20014146, -0.20241867, 0.083636... \n", - "\n", - " ESM1b_ts \n", - "0 [0.08041849, -0.05214988, -0.7103536, 0.786840... \n", - "1 [0.08041849, -0.05214988, -0.7103536, 0.786840... " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_kcat.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ReactionSequencekcats
0Reaction_0Sequence_309[2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0]
1Reaction_1Sequence_309[1.2, 3.4, 0.61, 0.07]
2Reaction_2Sequence_3142[6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,...
3Reaction_4Sequence_3263[57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11....
4Reaction_5Sequence_2101[2.98, 0.87]
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" - ], - "text/plain": [ - " Reaction Sequence \\\n", - "0 Reaction_0 Sequence_309 \n", - "1 Reaction_1 Sequence_309 \n", - "2 Reaction_2 Sequence_3142 \n", - "3 Reaction_4 Sequence_3263 \n", - "4 Reaction_5 Sequence_2101 \n", - "\n", - " kcats \n", - "0 [2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0] \n", - "1 [1.2, 3.4, 0.61, 0.07] \n", - "2 [6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,... \n", - "3 [57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11.... \n", - "4 [2.98, 0.87] " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"merged_and_grouped_kcat_dataset_with_FPs_and_ESM1bs_ts.pkl\"))\n", - "df = pd.DataFrame({\"Reaction\": df_kcat[\"Reaction ID\"], \"Sequence\" : df_kcat[\"Sequence ID\"],\n", - " \"kcats\" :df_kcat[\"kcat_values\"]})\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Uniprot IDkcatSubstratesProductsPMIDsubstrate_IDsproduct_IDschecked#UIDscompletefrom BRENDAfrom Uniprotfrom SabioECORGANISMBRENDA reaction ID
0P209322.8(S)-Mandelate;Riboflavin-5-phosphateReduced FMN;alpha-Oxo-benzeneacetic acid15311930[C01984, C00061][C02137, C01847]False1True001NaNNaNNaN
1P209320.05(S)-Mandelate;Riboflavin-5-phosphateReduced FMN;alpha-Oxo-benzeneacetic acid15311930[C01984, C00061][C02137, C01847]False1True001NaNNaNNaN
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" - ], - "text/plain": [ - " Uniprot ID kcat Substrates \\\n", - "0 P20932 2.8 (S)-Mandelate;Riboflavin-5-phosphate \n", - "1 P20932 0.05 (S)-Mandelate;Riboflavin-5-phosphate \n", - "\n", - " Products PMID substrate_IDs \\\n", - "0 Reduced FMN;alpha-Oxo-benzeneacetic acid 15311930 [C01984, C00061] \n", - "1 Reduced FMN;alpha-Oxo-benzeneacetic acid 15311930 [C01984, C00061] \n", - "\n", - " product_IDs checked #UIDs complete from BRENDA from Uniprot \\\n", - "0 [C02137, C01847] False 1 True 0 0 \n", - "1 [C02137, C01847] False 1 True 0 0 \n", - "\n", - " from Sabio EC ORGANISM BRENDA reaction ID \n", - "0 1 NaN NaN NaN \n", - "1 1 NaN NaN NaN " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_kcat_old.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "df_kcat[\"PMIDs\"] = \"\"\n", - "for ind in df_kcat.index:\n", - " UIDs = df_kcat[\"Uniprot IDs\"][ind]\n", - " kcats = df_kcat[\"kcat_values\"][ind]\n", - " PMIDs = []\n", - " for i, UID in enumerate(UIDs):\n", - " help_df = df_kcat_old.loc[df_kcat_old[\"Uniprot ID\"] == UID].loc[df_kcat_old[\"kcat\"] == kcats[i]]\n", - " if len(help_df) > 0:\n", - " PMIDs.append(list(help_df[\"PMID\"])[0])\n", - " else:\n", - " PMIDs.append(np.nan)\n", - " \n", - " df_kcat[\"PMIDs\"][ind] = PMIDs\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.61, 4.11, 709)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.DataFrame({\"Reaction\": df_kcat[\"Reaction ID\"], \"Sequence\" : df_kcat[\"Sequence ID\"],\n", - " \"kcats\" : df_kcat[\"kcat_values\"], \"PMIDs\" : df_kcat[\"PMIDs\"]})\n", - "\n", - "deviations = []\n", - "measurements = []\n", - "means = []\n", - "\n", - "\n", - "for ind in df.index:\n", - " kcats = df[\"kcats\"][ind]\n", - " pmids = df[\"PMIDs\"][ind]\n", - " if len(kcats) > 2:\n", - " kcats = [np.log10(float(kcat)) for kcat in kcats]\n", - " for i in range(len(kcats)):\n", - " kcats_i = [kcats[j] for j in range(len(kcats)) if j != i]\n", - " pmids_i = [pmids[j] for j in range(len(pmids)) if j != i]\n", - " pmid = pmids[i]\n", - " select = np.array(pmids_i) != pmid\n", - " kcats_i = np.array(kcats_i)[select]\n", - " if len(kcats_i) > 1:\n", - " \n", - " mean_kcat = np.mean(kcats_i)\n", - " deviations.append(abs(kcats[i] - mean_kcat))\n", - " measurements.append(kcats[i])\n", - " means.append(mean_kcat)\n", - "\n", - " \n", - "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2), len(deviations)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.75, 5.67)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deviations = []\n", - "\n", - "for ind in df.index:\n", - " kcats = df[\"kcats\"][ind]\n", - " if len(kcats) > 1 :\n", - " for i in range(len(kcats)):\n", - " for j in range(i+1, len(kcats)):\n", - " deviations.append(abs(np.log10(float(kcats[i])) - np.log10(float(kcats[j]))))\n", - " \n", - "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.52, 3.31, 3214)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "deviations = []\n", - "measurements = []\n", - "means = []\n", - "\n", - "\n", - "for ind in df.index:\n", - " kcats = df[\"kcats\"][ind]\n", - " \n", - " if len(kcats) > 2:\n", - " kcats = [np.log10(float(kcat)) for kcat in kcats]\n", - " \n", - " for i in range(len(kcats)):\n", - " kcats_i = [kcats[j] for j in range(len(kcats)) if j != i]\n", - " mean_kcat = np.mean(kcats_i)\n", - " deviations.append(abs(kcats[i] - mean_kcat))\n", - " measurements.append(kcats[i])\n", - " means.append(mean_kcat)\n", - " \n", - " \n", - "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2), len(deviations)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 5.1, ymin = -4.5)\n", - "plt.xlim(xmax = 5.1, xmin = -4.5)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "ax.text(1.9, -3, r'$R^2=0.52$', fontsize=22, c = \"grey\")\n", - "ax.text(1.9, -3.4, r'$MSE=0.82$', fontsize=22, c = \"grey\")\n", - "ax.text(1.9, -3.8, r'Pearson $r=0.78$', fontsize=22, c = \"grey\")\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "\n", - "plt.ylabel(\"Measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n for same enzyme-reaction paris \", fontsize = 22)\n", - "plt.scatter(means, measurements, alpha = 0.6, s=30, c=\"darkblue\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.781 0.522 0.8196947601569\n" - ] - } - ], - "source": [ - "R2 = r2_score(means, measurements)\n", - "Pearson = stats.pearsonr(means, measurements)\n", - "\n", - "print(np.round(Pearson[0],3), np.round(R2,3), np.mean((np.array(means)-np.array(measurements))**2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/code/model_fitting/.ipynb_checkpoints/03 - Testing additional ML models-checkpoint.ipynb b/code/model_fitting/.ipynb_checkpoints/03 - Testing additional ML models-checkpoint.ipynb new file mode 100644 index 0000000..9b39c5d --- /dev/null +++ b/code/model_fitting/.ipynb_checkpoints/03 - Testing additional ML models-checkpoint.ipynb @@ -0,0 +1,690 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n", + "\n", + "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import pandas as pd\n", + "import os\n", + "from os.path import join\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.linear_model import ElasticNet, LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn import preprocessing\n", + "from scipy import stats\n", + "import xgboost as xgb\n", + "from hyperopt import fmin, tpe, rand, hp, Trials\n", + "\n", + "from tensorflow.keras import regularizers, initializers, optimizers, models, layers\n", + "from tensorflow.keras.losses import MSE\n", + "from tensorflow.keras.activations import relu\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import figure\n", + "import matplotlib as mpl\n", + "plt.style.use('CCB_plot_style_0v4.mplstyle')\n", + "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", + "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading training and test data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3421, 850)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "\n", + "\n", + "data_train.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "data_test.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "len(data_train), len(data_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_indices = list(np.load(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_train_indices.npy\"), allow_pickle = True))\n", + "test_indices = list(np.load(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_test_indices.npy\"), allow_pickle = True))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "mean_y, std_y = np.mean(train_Y), np.std(train_Y)\n", + "train_Y = (train_Y-mean_y)/std_y\n", + "test_Y = (test_Y-mean_y)/std_y\n", + "\n", + "scaler = preprocessing.StandardScaler().fit(train_X[:, 2048:])\n", + "train_X[:, 2048:] = scaler.transform(train_X[:, 2048:])\n", + "test_X[:, 2048:] = scaler.transform(test_X[:, 2048:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Training and validation machine learning models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (i) Performing hyperparameter optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_r2_linear_regression(param):\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + "\n", + " reg = ElasticNet(alpha = param[\"alpha\"], l1_ratio = param[\"l1_ratio\"]).fit(train_X[train_index], train_Y[train_index])\n", + " y_valid_pred = reg.predict(train_X[test_index])\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "\n", + "#Defining search space for hyperparameter optimizationhp.uniform(\"reg_alpha\", 0, 5)\n", + "space_linear_regression = {'alpha': hp.uniform('alpha', 0,5),\n", + " 'l1_ratio': hp.uniform('l1_ratio', 0,1)}\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "best = fmin(fn = cross_validation_neg_r2_linear_regression, space = space_linear_regression,\n", + " algo=rand.suggest, max_evals = 2000, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#param = trials.argmin" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'alpha': 0.3960857176137572, 'l1_ratio': 0.003735725013911728}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "test_Y = (test_Y+mean_y)*std_y" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.542 1.014 0.293\n" + ] + } + ], + "source": [ + "reg = ElasticNet(alpha = param[\"alpha\"], l1_ratio = param[\"l1_ratio\"]).fit(train_X, train_Y)\n", + "y_test_pred = reg.predict(test_X)\n", + "y_test_pred = (y_test_pred+mean_y)*std_y\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(np.round(Pearson[0],3) , np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#create input matrices:\n", + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))\n", + "\n", + "\n", + "scaler = preprocessing.StandardScaler().fit(train_X)\n", + "train_X = scaler.transform(train_X)\n", + "test_X = scaler.transform(test_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_r2_random_forest(param):\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + "\n", + " reg = RandomForestRegressor(max_depth = param[\"max_depth\"],\n", + " min_samples_leaf = param[\"min_samples_leaf\"],\n", + " n_estimators = param[\"n_estimators\"]).fit(train_X[train_index], train_Y[train_index])\n", + " y_valid_pred = reg.predict(train_X[test_index])\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_random_forest = {'n_estimators': hp.choice('n_estimators', [50, 100, 200]),\n", + " 'max_depth': hp.choice('max_depth', [5,6,7,8,9,10,11,12,13,14,15,16]),\n", + " 'min_samples_leaf': hp.choice('min_samples_leaf', [1,2,5,10,20])}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "best = fmin(fn = cross_validation_neg_r2_random_forest, space = space_random_forest,\n", + " algo=rand.suggest, max_evals = 2000, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#trials.argmin" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'max_depth': 15, 'min_samples_leaf': 1, 'n_estimators': 100}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.622 0.911 0.364\n" + ] + } + ], + "source": [ + "reg = RandomForestRegressor(max_depth = param[\"max_depth\"],\n", + " min_samples_leaf = param[\"min_samples_leaf\"],\n", + " n_estimators = param[\"n_estimators\"]).fit(train_X, train_Y)\n", + "y_test_pred = reg.predict(test_X)\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))\n", + "\n", + "mean_y, std_y = np.mean(train_Y), np.std(train_Y)\n", + "train_Y = (train_Y-mean_y)/std_y\n", + "test_Y = (test_Y-mean_y)/std_y\n", + "\n", + "scaler = preprocessing.StandardScaler().fit(train_X[:, 2048:])\n", + "train_X[:, 2048:] = scaler.transform(train_X[:, 2048:])\n", + "test_X[:, 2048:] = scaler.transform(test_X[:, 2048:])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(learning_rate=0.001, decay =10e-6, momentum=0.9, l2_parameter= 0.1, hidden_layer_size1 = 256,\n", + " hidden_layer_size2 = 64, input_dim = 1024, third_layer = True): \n", + " model = models.Sequential()\n", + " model.add(layers.Dense(units = hidden_layer_size1,\n", + " kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev= np.sqrt(2./ input_dim), seed=None),\n", + " activation='relu', input_shape=(input_dim,)))\n", + " model.add(layers.BatchNormalization())\n", + " model.add(layers.Dense(units= hidden_layer_size2,\n", + " kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev = np.sqrt(2./ hidden_layer_size1), seed=None),\n", + " activation='relu'))\n", + " model.add(layers.BatchNormalization())\n", + " if third_layer == True:\n", + " model.add(layers.Dense(units= 16,\n", + " kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev = np.sqrt(2./ hidden_layer_size2), seed=None),\n", + " activation='relu'))\n", + " model.add(layers.BatchNormalization())\n", + " \n", + " model.add(layers.Dense(1, kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev = np.sqrt(2./ 16), seed=None)))\n", + " model.compile(optimizer=optimizers.SGD(learning_rate=learning_rate, momentum=momentum, nesterov=True),\n", + " loss='mse', metrics=['mse'])\n", + " return model\n", + "\n", + "\n", + "\n", + "def cross_validation_neg_r2_fcnn(param):\n", + " \n", + " param[\"num_epochs\"] = int(np.round(param[\"num_epochs\"]))\n", + "\n", + " \n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " model = build_model(input_dim = 1280+2048, \n", + " learning_rate= param[\"learning_rate\"],\n", + " decay = param[\"decay\"],\n", + " momentum = param[\"momentum\"], \n", + " l2_parameter = param[\"l2_parameter\"],\n", + " hidden_layer_size1 = param[\"hidden_layer_size1\"],\n", + " hidden_layer_size2 = param[\"hidden_layer_size2\"]) \n", + "\n", + " model.fit(np.array(train_X[train_index]), np.array(train_Y[train_index]),\n", + " epochs = param[\"num_epochs\"],\n", + " batch_size = param[\"batch_size\"],\n", + " verbose=0)\n", + "\n", + " R2.append(r2_score( np.reshape(train_Y[test_index], (-1)),\n", + " model.predict(np.array(train_X[test_index])).reshape(-1) ))\n", + " return(-np.mean(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "'''space = {\"learning_rate\": hp.uniform(\"learning_rate\", 1e-6, 1e-2),\n", + " \"hidden_layer_size1\": hp.choice(\"hidden_layer_size1\", [256,128,64]),\n", + " \"hidden_layer_size2\": hp.choice(\"hidden_layer_size2\", [128,64,32]),\n", + " \"batch_size\": hp.choice(\"batch_size\", [8,16,32,64,96]),\n", + " \"decay\": hp.uniform(\"decay\", 1e-9, 1e-5),\n", + " \"l2_parameter\": hp.uniform(\"l2_parameter\", 0, 0.01),\n", + " \"momentum\": hp.uniform(\"momentum\", 0.1, 1),\n", + " \"num_epochs\": hp.uniform(\"num_epochs\", 20, 100)}\n", + " \n", + "trials = Trials()\n", + "best = fmin(fn = cross_validation_neg_r2_fcnn, space = space, algo=rand.suggest, max_evals= 500, trials=trials)''';" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'batch_size': 96,\n", + " 'decay': 8.925865617547346e-06,\n", + " 'hidden_layer_size1': 128,\n", + " 'hidden_layer_size2': 64,\n", + " 'l2_parameter': 0.0033008915899278156,\n", + " 'learning_rate': 0.006808549614442447,\n", + " 'momentum': 0.9054104435951468,\n", + " 'num_epochs': 62.68663708309369}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "36/36 [==============================] - 1s 6ms/step - loss: 2.0003 - mse: 0.9252\n", + "Epoch 2/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.6849 - mse: 0.6270\n", + "Epoch 3/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.5191 - mse: 0.4923\n", + "Epoch 4/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.3920 - mse: 0.3958\n", + "Epoch 5/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.3207 - mse: 0.3543\n", + "Epoch 6/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.2364 - mse: 0.2991\n", + "Epoch 7/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.1669 - mse: 0.2581\n", + "Epoch 8/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.1248 - mse: 0.2438\n", + "Epoch 9/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.0808 - mse: 0.2267\n", + "Epoch 10/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.0345 - mse: 0.2062\n", + "Epoch 11/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.9915 - mse: 0.1887\n", + "Epoch 12/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.9617 - mse: 0.1836\n", + "Epoch 13/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.9230 - mse: 0.1688\n", + "Epoch 14/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8986 - mse: 0.1674\n", + "Epoch 15/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8757 - mse: 0.1667\n", + "Epoch 16/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8451 - mse: 0.1576\n", + "Epoch 17/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8030 - mse: 0.1366\n", + "Epoch 18/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7789 - mse: 0.1331\n", + "Epoch 19/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7582 - mse: 0.1323\n", + "Epoch 20/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7438 - mse: 0.1367\n", + "Epoch 21/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7180 - mse: 0.1293\n", + "Epoch 22/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6865 - mse: 0.1157\n", + "Epoch 23/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6692 - mse: 0.1160\n", + "Epoch 24/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6430 - mse: 0.1068\n", + "Epoch 25/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6295 - mse: 0.1097\n", + "Epoch 26/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6212 - mse: 0.1172\n", + "Epoch 27/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6002 - mse: 0.1114\n", + "Epoch 28/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5760 - mse: 0.1018\n", + "Epoch 29/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5613 - mse: 0.1014\n", + "Epoch 30/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5434 - mse: 0.0974\n", + "Epoch 31/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5324 - mse: 0.0997\n", + "Epoch 32/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5300 - mse: 0.1103\n", + "Epoch 33/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4996 - mse: 0.0924\n", + "Epoch 34/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4970 - mse: 0.1019\n", + "Epoch 35/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4783 - mse: 0.0948\n", + "Epoch 36/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4584 - mse: 0.0863\n", + "Epoch 37/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4553 - mse: 0.0943\n", + "Epoch 38/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4456 - mse: 0.0950\n", + "Epoch 39/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4338 - mse: 0.0934\n", + "Epoch 40/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4173 - mse: 0.0868\n", + "Epoch 41/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4133 - mse: 0.0924\n", + "Epoch 42/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3981 - mse: 0.0865\n", + "Epoch 43/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3945 - mse: 0.0917\n", + "Epoch 44/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3867 - mse: 0.0925\n", + "Epoch 45/50\n", + "36/36 [==============================] - 0s 5ms/step - loss: 0.3825 - mse: 0.0965\n", + "Epoch 46/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3695 - mse: 0.0916\n", + "Epoch 47/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3576 - mse: 0.0874\n", + "Epoch 48/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3501 - mse: 0.0874\n", + "Epoch 49/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3387 - mse: 0.0833\n", + "Epoch 50/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3314 - mse: 0.0830\n", + "27/27 [==============================] - 0s 1ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "0.3237192981547061" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = build_model(input_dim = 1280+2048, \n", + " learning_rate = param[\"learning_rate\"],\n", + " decay = param[\"decay\"],\n", + " momentum = param[\"momentum\"], \n", + " l2_parameter = param[\"l2_parameter\"], \n", + " hidden_layer_size1 = param[\"hidden_layer_size1\"],\n", + " hidden_layer_size2 = param[\"hidden_layer_size2\"]) \n", + "\n", + "model.fit(np.array(train_X), np.array(train_Y),\n", + " epochs = 50,# int(np.round(param[\"num_epochs\"])),\n", + " batch_size = param[\"batch_size\"],\n", + " verbose=1)\n", + "\n", + "y_test_pred = model.predict(np.array(test_X))\n", + "r2_score(test_Y, y_test_pred.reshape(-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.593 0.969 0.324\n" + ] + } + ], + "source": [ + "y_test_pred = (y_test_pred.reshape(-1) + mean_y)*std_y\n", + "test_Y = (test_Y + mean_y)*std_y\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred.reshape(-1))**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred.reshape(-1))\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred.reshape(-1))\n", + "\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/model_fitting/.ipynb_checkpoints/04 - Analyzing and plotting the results-checkpoint.ipynb b/code/model_fitting/.ipynb_checkpoints/04 - Analyzing and plotting the results-checkpoint.ipynb new file mode 100644 index 0000000..ca96337 --- /dev/null +++ b/code/model_fitting/.ipynb_checkpoints/04 - Analyzing and plotting the results-checkpoint.ipynb @@ -0,0 +1,2603 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n", + "\n", + "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\projects\\GitHub\\kcat_prediction\\code\\model_fitting\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from sklearn import metrics\n", + "from scipy import stats\n", + "from scipy.stats import wilcoxon\n", + "from sklearn.metrics import roc_auc_score, r2_score\n", + "from sklearn.linear_model import LinearRegression\n", + "import scipy\n", + "import os\n", + "from os.path import join\n", + "import pandas as pd\n", + "\n", + "CURRENT_DIR = os.getcwd()\n", + "print(CURRENT_DIR)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "plt.style.use('CCB_plot_style_0v4.mplstyle');\n", + "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", + "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Plotting performance of different models:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Pearson r" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "models = [\"str_fp\", \"diff_fp\", \"DRFP\", \"ESM1b\", \"ESM1b_ts\", \"ESM1b_ts_DRFP\", \"ESM1b_ts_DRFP_mean\"]\n", + "model_names = {\"str_fp\" : \"str. FP\",\n", + " \"diff_fp\" : \"diff. FP\",\n", + " \"ESM1b\" : \"ESM-1b\",\n", + " \"DRFP\" : \"DRFP\",\n", + " \"ESM1b_ts\" : \"ESM-$1b_{ESP}$\",\n", + " \"ESM1b_ts_diff_fp\" : \"ESM-$1b_{ESP}$\\n + diff. FP\",\n", + " \"ESM1b_ts_DRFP\": \"ESM-$1b_{ESP}$\\n + DRFP\",\n", + " \"ESM1b_ts_DRFP_mean\": \"ESM-$1b_{ESP}$\\n + DRFP (mean)\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0NC6PtQNw6Gh2TDgs1//5ngScusce6/9AU4ATNTUVLFy4kOOPP5699toLgL322ovjjz+e+fPnGwg3DIT7KDNPA07r2fb2rv8PA69p/qllttlmM664ojcQvqT5uvUt9t96680mfEzSIBvPkwgYxYlEwkfH4QQCBuskQuNn2bJlzJkzZ5Vtc+bMYdmyZX0a0dRjICyNo3Vpyr86m2++EVtttRmXXvqfte671VabsfnmG41421jbKtmUf3o7dY89xuUqwNZbHz2m0pytttqUSy55+Xo9py3AprfxPpGYTIN6IjF79myWLFmyIiMMsGTJEmbPnt3HUU0tBsLSOOo05R/rilCrc8973hagKxi+ZSZ4q602W7Hf+uosKGAgrLZN1ly8ePGYA/GJXhxoXVaWk7otWLCAefPm3aJGuHcyfZsZCEvjbLya8ndbuvRijjrqFyxefCGXX34dW255K/bcczsOOmh821dNx4zcRI+5rTXWbZys2dbf9XgZr6sR/TAd3/tGo1MHPH/+/BVdIxYuXGh9cBcDYWkasA/u+Ohk8MYrYz/I7IUrDYa5c+ca+K6BgbCkaW0sGSiX+B49e+FKagOXWJYk3UKnF+5o2AtX0nRlICxJGtERRzxqrcGwvXAlTWcGwpoUS5dezNy5p/KjH13CWWddyNZbH83cuaeydGnvqtKSpooNNpjJMcfszZIlc5k79z5stdWmzJgxxFZbbcrcufdhyZK5HHPM3i6vLGnaskZYE2r58ps4+ODvdi3TehMAl156LSeddAEnnXTBioySH6Y69thj+XfEhD3+k5qvE9nr9DaZA9d+zsmakgaVGWFNqPnzz+wKgkeWeR7z5585SSPSVDZei5H00yD8DJLUFmaENWGWLr2YzPNGtW/meTz/+TuadZK9SCVJk8aMsCbMkUf+Ykz7H3XU2PaXJElaH2aENWHOOuvCMe2/ePHY9tdgOuuss6ZtZnU8l9eWJE08M8KaMJddNvrlWQEuv/y6CRqJpovFixezxx57TNjjn3vuuStWl5sIu+yyC/vvv/+EPb4kaXyZEdaEufOdN+XSS0cfDG+55a0mcDSaLiayPtiV5SRJ3QyEdQvHHnvsuMx8v/HGK4HeLO8lzdejb7H/TTfdij33/Px6P+/+++8/cO2rtHpjKaPoZIPHch+DZkkaXJZG6BZOPPHEcbl8vM02m42wdevm3wi3bD3S/mNz7rnn2r5Kq7XLLrtYwytJWsGMsEa0yy67jEsm7GUv+/aoWqhF7Mwxx+y93s83XSdZad2ZsZUkrSsDYU2oI454FMAag+HOynKS1p9dN9QvvvY0Ha0xEB4aGpoJfGd4eHivSRqPBswGG8zkmGP25vnP35GjjvoFixdfyOWXX8eWW96KPffcjoMOeoCLaEjjZPHixWMORMZSBvWf//wHgM02G30Z01iCC7tuTF/r8tobi87rdKKCVV977bXGQHh4ePimoaGhm4eGhjYfHh6+erIGpcGz667btCbgfdJZZ3HY0FC/h7FOnkSt7Kbpa6ylIhOdwbN0pT3s+KLpaDSlEf8BfjU0NPRtYEUvrOHh4VdO2KgkSZPCwEJSm40mED65+SdpFE7dY49pG1xM1/o+SZLWxVoD4eHh4U9PxkAkSZKkyWTXCI1outa5WuMqSZJGywU1JEmS1EpmhDWi6VrnOhVqXO2lKanbokWLWLhwIcuWLWP27NksWLCAuXPn9ntYkhhFIDw0NHQv4PXAXbv3Hx4edgUEqYd9XCV1W7RoEQsWLOD4449nzpw5LFmyhHnz5gEYDEtTwGgywl8EjgGOA26a2OFI0599XCV1LFy4kOOPP5699qp1qfbaay+OP/545s+f3/pAeCzvfZ2EwVju43ufRmM0gfCNw8PDR0/4SKSW8s1aGlzLli1jzpw5q2ybM2cOy5Yt69OIpidLtjRRRhMIf31oaOgVwFeA6zsbh4eH/zlho5IkaQDMnj2bJUuWrMgIAyxZsoTZs2f3cVRTg0kATQWj6RrxAqpG+IfAz5p/50zkoCRJGgQLFixg3rx5fO9732P58uV873vfY968eSxYsKDfQ5PE6BbU2H4yBiJJ0qDp1AHPnz9/RdeIhQsXtr4+WJoqRtM1YgPg5cDuzabFQA4PDy+fwHFJkjQQ5s6da+ArTVGjqRE+GtgA+Hjz/fObbS+ZqEFJkiRJE200gfBDhoeHd+76/syhoaHzJmpAkiRJ0mQYTSB809DQ0D2Gh4f/CDA0NHR37Cc88Kbr6miujCZJkkZrNIHw64HvDQ0N/QkYolaYe9GEjkp9tS6ro41Wpyn6RAWrrowmSZJGazRdI747NDR0T+DezabfDg8PX7+m+2j6m6j+jp0A2/6RkiSp39baR3hoaOhZwIbDw8O/BPYFFg0NDT1wwkcmSZIkTaDRLKjxtuHh4WuGhobmAI8Gjqe6RkiSJEnT1mgC4c7EuCcCxw0PD38D2HDihiRJkiRNvNEEwhcPDQ0lsB9w2tDQ0EajvJ8kSZI0ZY2ma8SzgX2Aw4aHh68aGhraiuokIa0w2i4Tna4RY+lK4cQ6SZI0EdYYCA8NDc0Efj48PHyfzrbh4eFLgUsnemAaTPb4lSRJU8UaA+Hh4eGbhoaGfjs0NHSX4eHhv03WoDT9mLWVJEnTzWhKI24HnD80NPRT4NrOxuHh4X0nbFSSJEnSBBtNIPy2CR+FJEmSNMlGs7LcWZMxEEmSJGkyjWZluYcPDQ2dPTQ09J+hoaEbhoaGbhoaGvr3ZAxOkiRJmiij6Qd8JDAX+D2wCfAS4KiJHJQkSZI00Ua1MMbw8PAfgJnDw8M3DQ8Pf4rqKyxJkiRNW6OZLHfd0NDQhsC5Q0NDH6R6CLuynCRJkqa10QS0z2/2O5hqn7Yd8IyJHJQkSZI00UbTNeKvQ0NDmwBbDQ8Pv2sSxiRJkiRNuNF0jXgycC7wreb7XYaGhk6Z4HFJkiRJE2o0pRHvBB4KXAUwPDx8LrD9hI1IkiRJmgSjCYSXDw8PX92zbXgiBiNJkiRNltF0jTh/aGhof2Dm0NDQPYFXAj+c2GFJkiRJE2s0GeH5wH2B64ETgauBV03gmCRJkqQJt9qM8NDQ0MbAy4AdgF8BjxgeHr5xsgYmSZIkTaQ1ZYQ/DTyYCoIfDxw2KSOSJEmSJsGaaoR3HB4evh/A0NDQ8cBPJ2dIkiRJ0sRbU0Z4eec/lkRIkiRp0KwpI7zz0NDQv5v/DwGbNN8PAcPDw8O3mfDRSZIkSRNktYHw8PDwzMkciCRJkjSZRtM+TZIkSRo4BsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJaaVa/B9BmEbEP8FFgJvCJzHz/avZ7BvAl4CGZec4kDlGSJGlgmRHuk4iYCRwFPB7YEZgbETuOsN+tgUOAn0zuCCVJkgabgXD/PBT4Q2b+KTNvAE4CnjLCfv8HfAD432QOTpIkadAZCPfPNsCFXd9f1GxbISIeCGyXmd+YzIFJkiS1gTXCU1REzAA+BLxwFPseCBzYvW3DDTdk4cKFq+y32267sfvuu4/jKCVJkqYvA+H+uRjYruv7bZttHbcGdgIWRwTAnYFTImLf3glzmXkscGz3toULFw4vWLBgIsYtSZI0EAyE++ds4J4RsT0VAD8H2L9zY2ZeDdyh831ELAZeZ9cISZKk8WGNcJ9k5o3AwcDpwDLgC5l5fkS8OyL27e/oJEmSBt/Q8PBwv8egCWBphCRJmkaG+vGkZoQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrTSr3wNos4jYB/goMBP4RGa+v+f21wAvAW4ErgBenJl/nfSBSpIkDSAzwn0SETOBo4DHAzsCcyNix57dfgE8ODPvD3wJ+ODkjlKSJGlwmRHun4cCf8jMPwFExEnAU4DfdHbIzO917f9j4HmTOkJJkqQBZka4f7YBLuz6/qJm2+rMA745oSOSJElqETPC00BEPA94MLDHam4/EDiwe9uGG27IwoULV9lvt912Y/fdd5+oYUqSJE0rBsL9czGwXdf32zbbVhERjwEWAHtk5vUjPVBmHgsc271t4cKFwwsWLBi/0UqSJA0YA+H+ORu4Z0RsTwXAzwH2794hIh4AJLBPZv598ocoSZI0uKwR7pPMvBE4GDgdWAZ8ITPPj4h3R8S+zW6HApsBX4yIcyPilD4NV5IkaeAMDQ8P93sMmgCWRkiSpGlkqB9PakZYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQliRJUisZCEuSJKmVDIQlSZLUSgbCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1Eqz+j2ANouIfYCPAjOBT2Tm+3tu3wj4f8CDgCuB/TLzL5M9TkmSpEFkRrhPImImcBTweGBHYG5E7Niz2zzgX5m5A/Bh4AOTO0pJkqTBZSDcPw8F/pCZf8rMG4CTgKf07PMU4NPN/78EPDoihiZxjJIkSQPLQLh/tgEu7Pr+ombbiPtk5o3A1cAWkzI6SZKkAWeN8ACIiAOBA7u3bbjhhixcuHCV/XbbbTd23333yRzaiL7//e9PiXFMVx6/9ePxWz8ev3XnsVs/Hr/14/EbmYFw/1wMbNf1/bbNtpH2uSgiZgGbU5PmVpGZxwLHdm9buHDh8IIFC8Z1wOPlBz/4gX+M68Hjt348fuvH47fuPHbrx+O3fjx+IzMQ7p+zgXtGxPZUwPscYP+efU4BXgD8CHgmcGZmDk/qKCVJkgaUNcJ90tT8HgycDiwDvpCZ50fEuyNi32a344EtIuIPwGuAN/VntJIkSYPHjHAfZeZpwGk9297e9f//Ac+a7HFJkiS1wdDwsFfaB1FEXAH8td/jWI3ZVBZc68bjt348fuvH47fuPHbrx+O3fqb68ftHZu4z6c86PDzsP/9N6r8DDzzwnH6PYTr/8/h5/Dx+0/Ofx87j5/Gbev+sEZYkSVIrGQhLkiSplQyEJUmS1EoGwuqHY9e+i9bA47d+PH7rx+O37jx268fjt348fiOwa4QkSZJayYywJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRKBsKSJElqJQNhSZIktZKBsCRJklrJQFiSJEmtZCAsSZKkVjIQlgZYRAz1ewySJE1Vs/o9AKkjIrYF7glsDpwH/DMzr+7vqKafiNgRuDfw2+bfTf0dkSRJU5MZYfVdRMyMiFcAPwK+C5wMnAa8vgnqiAhfq2sREXMi4jjg18CXm6+fjYg9u/YxQzyCznGJiJn9HoskafIMDQ8P93sMarmIeC3wfuAG4E/AHYAtm5u/DRyYmX/t0/CmhYi4E3A6sDPwX+AC4AHNzf8BXp2Zx/dpeNNCRMzIzJv7PQ5J0uQxy6a+iogdgPcBlwGPBfYE7gK8AbgZ2Bv4WL/GN418jAqCjwDuQx3LRwBnAZsBh0bEA1Z/93aKiO0i4v0R8XXgvIh4U0Tct3MFwisR48srEpPH1+7IPC7qZUZYfRURp1MBW2TmooiYmZk3Nbc9DlgEXEUFyBdmpi/YHk3pw5nAZ4GDM/PfnexmRGwJfJoKjF+bmR+OiCGPI0TEI4DDgYf33HQB8O7MPGnyRzVY1vZa87U4PiJie+CJVHLrSuBrmfmf/o6q/zwuGg3PjNQ3EfFEKuP7WaouGGC4q07z11QQvB0www/M1ToCuBg4oQmCZzZB8FBmXg78tNlvCwCP4wpHUkHwZ4E3A68E/kZl1E9s6tbNYq6DrqzbjIi4c0TMi4j3RMQrI+KhEXE38LW4PiJiVvP1McDXqatCHwGOBY6NiLv3b3T943HRWBkIq58+AFwKfCozr+8EcJl5UxN8XEEFwmcD1/RxnFNWRMwD7gtkZp7ZbO4Ebp0Tir8Dw8DPm/u0PrCLiLdTNdQfy8wDMvMDmXkkcD+gU0u9DxisrYuuWus3URNgjwPeQgUk3wAOj4j9I2IL8DU5Vs1J7o3Nt0md7M4B7gZ8HngOcGB/Rtc/HhetCwNh9UVEvBrYkWqV9viIuGNXScQGTfDxMGAX6s3sun6NdaqKiA2oAAPgyRExGyAzb2yyIp1gZH/gr8Alze2tDuyaiYVvo8pJPtxsmxkRszLzGuBT1Ottr4i4V/9GOr101VV3MnL7Av8H3JnKur8J+Crwb+Bp1InwPPA1uQ5mAkTEe5vv3wH8KDP/lpkvprrGPDkiturXAPvE46Ixs4+w+uUU4PHAY4B3As+LiPcAX8zM/zb7HAZcDpySmf+1nvAWZlDH7qXAQ4DzI+IE4I2ZeQVARDyLuvz/EeCcvoxy6vkQcCNwTGb+tamn7u61/FuqRGIIuLAfA5xuujtudGXk3gv8BXhJ19UKIuKRwPOAA4D3R8Qmmfkuu3aMXnOye1vq5OLgzLwQICJu3ZzM/ZQqO/t3/0Y5+TwuWhdmhNUXmfnHzHws8DjgN8AOwAnA1yNip4g4gAruPgN8r28DncIy8/rMfDewOys7a7wQuCQiXtN8/wHgd8AXmg+JVv/NN0HY/lSAdg7UZfyImNVVm74J1bnkHGCDfoxzOomIrYGTI+KxXdseRmWCP98JgjuZ4sz8IfAqKlsH8JKI2MYgeMweTZWOvS0iDgFogj2ApwInZ+a1LeyN7XHRmNg1QpMqIjYDruv90IuIg6lewrdqNl1D1Q8/PTN/091NQhARt8/Mf/ZsewQ16etJzaYrqQlyr8rMjzV1mDO7MnatExFvoF5nAMuBBZl5WHPbzKY+/WPAQcC8zDyhPyOdPiLiUOC1VC36l6jJm3+jTjaen5ln9HSDWZH5ba5gHAC8PjMP78Pwp62I2BzYjap7fSpV/vRi6j30G8DDM/OXfRtgn3hcNFatzg6pLz5MrRi3GaySJToS2Bo4qtnv1sC2VP3w7Q2CV4qIR1GXlO/TvT0zfwQ8g/oA+DVNlwjgmRGxfWYOd+qH2zo5KTM/SH1IfoPK9n4wIn4fEU9tguA7AQcDp1KrGzqRa+1OoMoghoBXUMf23dTS3g9vjt+KY9hk4DuZ9q9TEzk3mswBD4LMvDozTwVeT524/R34ATU58aROsNd5j20Lj4vGyoywJk1E7Ef1Bf4AlYnrZIWGqPZonYzR/aia1r2au/4aeA/wlcxcPtnjnmoi4mKqxvVJmfmr1eyzBfByKlO3ebP5SOq4X9Ps09qazIjYkDppWEBN2oSqW789cH9gbmZ+0ysRoxMRmwAPBIIqPbmeSrScR71O/9Hs18m6d/pcvxY4lHpdvq9Pw58Wuo7dnYFNgUu65lPQTJZ9SvNvZ+Bo4E2d98xBnWPhcdH6MiOsyXQosAz4cqfPLdSM8eaNbMPmTe1Xmfloamb5X4CdgJOAkyJi934NfipoAoetgON6g+DO8Wy6H1yZme+hJsp1OkscDFwYEa+EVVpcDbyIeFREPKjzfWbekJmLqIVaFlCTZ/alWi39iab3cvO6NHO0Fpn538xcChxCXZE4m8ryPhRY3PR0pScI3p5a6OUGqlOHVqMJ1jonZB+mMumzO7cBZOYyaoGYN1F/888H/hQRBzW3D1yw53HReDAjrEnR9G19J9XR4NCu7bfIuPVua+o63w1sCFwL3L6NmeGIuA21FPXPgRdk5h+byW/Da3szj1p97i1Ulw6o+tdWBB9Rizf8nroSsbA7W9S1z32oD8oDmk3XAG/OzI937TOrzfXVo9W8JrcFngy8jOpz/W+qddrHqdZ0GwEfpCbEvjcz3z/igwlYJev5Huq4HpOZR69h/y2APaiTkmcCT8jMb03OaCePx2VwdTL1XSfOE3YF00BYE66pu7yYqtN6YWb+rdO9oHmBb0TVY74jM5d03W9F4BERd6QapP8gMz886T/EFBARnwBeBByYmcd3be98GLyGurT/3sy8Rd/liNiYyobsl5mP6b19UEXEV6lWfS/IEZZN7r402mQu38zKspxfAYdk5uLJGe3gaMpP7k295l4C3La56XLqEvYPqZrNE/oxvumiKxDYhlr++5XA5zLzhq597k6dcNwRODUz/95svxtw/8w8ZfJHPrE8LoOtyejPouYQ0JuEGM/A2EBYEy4iPkfVY74wM0/qOtPrBHAfBeYDL8/M7LnvKvXDbRUROwO/oEpEDsrMfzUnE0PNMdyOmh19CtW39R9reKwNuz8sBllEPBr4NnVJ9FXZ9KOGVS+J9nQy2ITKGC0AOsuxng48pS3HbTRGW1sZEZtSJRLzqPphgB8Dn8zMT0zgEAdK1CIRu1GZzL93nby9GngjcCcqaPgddTXjqz33H8haWI/L4Inq3PM4apLtpdScg/OAXwJ/y8yzuvZd79+fNcKaUFH9ROcCi4HvQAUgTTB2U9TKXfOpAO4rzX26Z5h36ofb3vPxY1Qt5dcz819wixrfjwH/AxatKQhu7temYO5j1KIYn22C4BnNa6rzYfmE5oRsxbFs6l0/RX24vqvZvGnLjttqddX7jurDJzOvzczvAa+mAuGlVO36cydskIPpRiow6A72DmHlUvXPYuVKfq+OWlhihQEO9jwuAyQibkeVVv2RasN4e+BR1MTv46m1BpZGxBNhRTwxI9aju4+BsCbam5qvdwNe0GQ2u4OxI6l6zBMy8++rO7trc0Y4aqGC3ai+t4+MiEc09cKdyUePomZEL6Imi9jyC2gmw8ymXls/gJWLZzS3P4tacvWAke6fmZdm5ruAB7CyN3OrNR8+Z0TEDyJit67ta329Za12+EXqeL+dmryp0buM6nDyxIi4R0TsT00C+xHwvMz8cma+kzq2D2Vl+8RB53EZIE2iZ25mPgF4OvX+uzM13+Ao6vf9CCog/k5EzM7Mm5uAeJ0+95wNrQkT1Sv0O9Qktz2ps/LHRcSXqd6je1CTtxJwwsLq/YOaaLQ71RdzT+DEiDglM8+nWs1dBHwmM6/zUt+Keuj3UBMLP91smwXc3FVr9hGqnOS8NT1WZq7x9pZ5a/N1V+CsiDieqkn/M6z9MmVzQvvniHi/Ew/H7LtUS8RPA1dTyYVvA2/JzPO7Jhn/HbgEuF2/BjrJPC6D5waAzOwshX0+cH7zvv5e6mrS26lM8fkR8TrgyHW9ameNsCZc0yZpP+DZ1JndlVTT/cdStT/PyMxfOCt/zSLiGdQb/qOaTd+kjuXzqG4Ib2v2a21/4I6IOJy6FH8yVSf412yWmG6ywu+k3kjfkM3KclqzrnrrPwJfAzrLeF9HneQemZnXNvt2T0Bs/YnZWHXPlKcmdZ3bbJ9FvW53oCZyfqxzzJvbZ1LdeZ6UmQ+Y9IFPMI9LO430HtKUuLyXyhT/j1qy/SOZuXys7zmWRmjCdHWG+HNWe6SXUaUQ11GXR7eiztA3al64nQ4Rrb+s361TH52ZX6YuFb2GOkN+PBUE/wu4pOm+0ar+wCOJWrXwYVQpyVOoy2nPj4i7Nx+it6Mmwi0GvtDcx/fCNWiOz8Obb4/LzNdRHSG+Ri1d+z7gnKbcpFO3N9SpyW4e44UR8dA+DH866rwHHgG8PCI2ajKbN2bm24EDMvN9mXltrNrnendqdb9DYeV7xwCZsOPi587UNUIQPDMzr8rMV1CdlP5OtQd95kj7r40ZYU24nhn5s6gA7rlU3euWwE+oD9RTMvOCrvuZSWo0b9JDXcdxB6pM4qnAXalJBV+gJhyelyO0T2uTiLg98AKqbddsqg79FOpE7I3AE2naqfk6W7uIuDVVwvQk4IXUKo+dAHdvajGDzgp936JaIZ7ddf8nAZ8HfpiZe0/i0KedWNlN597UAkRP7+50EKtZ7TAiXkZ157k6M585aQOeJB6XdomePsJr2G9TKiH0IWri5DMy8ztjuTJqIKxJ0xMQ34F6c3oOVfh+I/A9ajLNGZl5Wd8GOoVFzwIaUQtlvIIKUDamWqx9lgpGftfGSYY9r7N7UeUkc6k2Sn9vvn4LeFFmXt63gU4zTbb3CcD/ZeafImKD7FrYJiIOBt5PZYihMvHvzMwrI+J0YG/gqWnv1lGJiB8AV1ATh65vtg1RJ7+nd5/sRsS2VF/mb1MnIRetLjCc7ibiuETE5lQ28e7AP6nEwtfWteZUE2d1iYuIeAlwLOvQ6tJAWJMqenq4NoHKXOpN6L5Uu5tvUhniU83Ujawn2BuijuHLqUlMN1MLlHyNytz9s28D7ZPVnDAcRNVX35aqc/0Q8H3ggraXk4zWSHX8serCN7cBFlLHGiqo+DY1R+Dbmfm4yRzvdNM5lhHxNKoLzJzMPKerNvZ44EHAI3uv+jQTiW6dmVcM2lWOiTwuEbEH8DrqKhHU++dVwJnAhzPzRxP706mj6/e5M/BIajL9L6kJzedm5l+69r1Fxjgi3kqtQvvKzDxytM9rIKy+6H1Diog51OWNx1GX+g/PzNf3a3zTRXd2I2r50JdQJQH3oZajvltmXtnHIfZF1xtqd5A2k5WrnD0SuInKHnwOOCszL+nbgKeBNQVXzcnYzK5jfX8qO7xP1273zcxlEz/S6S8iLqGWC353J/MeNen4t1Qm9Ms9+2/Rhr/z8T4uEXEr4GyqfOosatXD7YB7Uv1rT6FKqK6ZmJ9IHV2lL/ekSvx27Lr5n9Scji8B38mmV/4IccSdgFOBuwD36J4wuSYGwuqrnrO5janJTfsBL8tmOUyt2Qj1wztSk8F+m5nv7uvgJlmMsGpe9KxOGLVc90upWtcdqMmGJ1Or9v08mwVLNHbRtdph8/0XqRKoIzPzlX0d3DQR1av568AZ1GqbndZ036W67Dw1V11GeCtq4ZgPZOY5fRjypJiI4xIRRwNB9R1+Q5Nh3J56zb4d2Ax4W2YunLifTN0i4tvAo6kWq2fSZPqpfsJXUlc6vwgszcz/NffpjiOeCeycTRel0TAQ1pTQ80LedLRnclppLJMDBk2TDX8WVfpwGyqw/TFwfldQ1nvCsBN1Cf9ZVPbnH1Qbuo9O/k8wOLoyOw9lZVeJrdo+gXNtui713g94GpUQuBvV6eAc6sP/kdRk2Ju77nc48JzM3GbyRz3xJuq4NH//v6Syjy9rSie6r7C9kSrzOZ0KspeP9DgaP00J27eoIHh+55hHxAOo0pXnUJniC6jJ4V/NpoVez+OM6bPQQFhTxqDVtfXLoE6SWZ0mg/MxVtb4dXwbeHdmLu3Zf0bPB+Y+rJxw+OTM/MYED3ngrKb28gtU7f/Bmfnx/oxseopqhfhw6vjNpU7UfpCZezS3D1GtxO7HyhXUTh6phnuQjOdxiYgzqb72z8nMb3edwHXKqjp9s0/PzMdP2g/ZYk029xiqI8j3u6/wNb/7R1K/+6dSv/tfAx/PzE81+6xTDGEgLGlai4iTqTfGs6gPv1sD86guGv+iJtYs6/qA63ztfpPdGNgrM7/Zn59iehlpokrP7dsChwHbZOZut3wEdUS1Qrw31ff6f8CfM/PC5rYtgL2oTOhTqQ/+12bmkub2rwJ3yMw5kz/yiTWRxyUinkpdNfom8JLMvLTZvqLWPSIeCfyAmnz1njYlFyZT1wnIFlRr1aOALTPzf13v1d0L9GxBlU48iypheX5mfm59xmAgLE0BbS5rWB8RsR81i/wrmfmMru13p3oG70PVE2afhjjQIuIp1InHSSNk3O4FXJeZF/VlcFNU1wf/3YBDgFeycqGIi4A/UZfrT8jMq5v73I2aSPwi4IFUOcC3geOA+2XmBdP9StBkHpeI+AYVdF1KXWI/Dfh+Nu3Ymn2+CuxBZZW9SjQBuk6o70CVoHyXmif0gsz8cc8+vRPj7gk8IDO/sL7jMBCWpoCI+CDwucw8r99jmU4i4mIq6/vCrHZKGwA3d80+/iW1/O8SamLcDsBfgC2opVhvA/wb+HFnJnLbre3yYleW5oHA/wOuycxHTN4IB0NEnEr1Zf4t1R5qB2AD6rL+jcCXgUMz8+dd99mZyoDOBe5FrfIX0z0I7jYZxyUinkwFXE8C7khllL8MnNa8jzyUmmNwAlWreq2le2UikjYREcDRwA3AhtSaAq/OzF+OsO9IZVjr9buZtfZdpLUzo7nuImI+1cfyauqNX6MQEe+iluk+vDMrvGdCyz+AnwJvAN6zhoe6jqo3EyuXR+78v/u25gPn5qY7xP7UxJXnN7cNTDA2EZpjNiszb4iIZ1PB3snAvMy8OiI2pGpWH021QHwm9Z7wss5jZOZ5EfFbKkh7NLWsLMC0DdD6cVwy8+sR8T2qJdf+1BLMbwF2i4jPUu8ZFwOfaILgVr62m/kXTwRm0HRsyMz/TMBTfZ4Kgp8JPITm9xHVH/rszLyqs+NIAe/6nqCYEda4iog3UbM+f2lgvHbNBIA/UysZRfOGvjO1HOhf+jq4KSyqNdJfqWP3jMz8dW89WdTSm+dQdYbLqA+2y6mWSDdRmaCbgS86matExIuorNjlzfe9Ews7x/axwGeAP2Tmrn0a7rQQEQcC53QymFHLzH8G2AV4bmb+PCI2zpWtoG5NrcL3cWoVxNdl5od6T04695muSYh+HJeI2La3VCci7kJ1ong2VV5xPTW/4MvASztBWFsywrFy8ZLHAB9hZT/f/1JdYN6amX8ax+frrv+9B/W7eBZ18vM3aqXUrwC/7i5dGU8GwlpvXbVdncsbR2fmQSPs11vjMwNgOr6Jj5eIeB51efmwzHxD86b+W+pk4u3dZ8JreIzWZSsi4u3AO5tvf0Yt3vDV5nXYabR/MNVN4ivAc6mSiRu6brdNX5eIeA/wRqpp/ReolR077Yu62xveFjiRqr9+YGae28bX4Gg0NaydoOEoapnff0bEZ6gFRh64ugArIl5ALRn7I+Bx2bWc8HQPyPpxXCLiEcBS4IDM/OwIJ3kPot4nHkeVV1zOyuXql3b9LUz74786PUHpH6kJyJ+garTfSfVe/2Bmvmmcn3eV94+mNOV51GTIbaiExqep38Wfxvv4zxjPB1M7NcHHLOAA4CfURIUVgW6XoWb7EyLibpl5c5uD4EanAfydmq+HUzVxfxgpCO4c04i4Y1SDedoYgGQtFLI/cCHVcP2LwOcj4mFNkLsh1QP0Z9QJxf+Am5o3+uXNV4PgRkRsTV0anknNxD4C+HBEPBxWnqw2r79hKkg4sQmCZ7TxNbg2zUnthdSxhOpZfWFEvBT4DRVcjFh+0vz3K9RVj63oKmOc7kFYH4/Lh4ArqEv8va9pMvNnVInaG6kTwY2b7w8FXhu1UNG0P/5rMRMgIt7bfP8O4EeZ+bfMfDGVJX9yc0Vu3PS+f2TmT4HXUG0tv0StlHookFTpzLgyI6xxERG3A74PLMnMl69hv3tQdbD/oc7q3zHgbyxrFNUi6OvUsp7fpiZwfIeaqfz3NUz2OIqaGLIUeFNmnj+Jw+6rEbIHnfXloUod3k2dTDwPO0aMSkQ8i6rTW05lgbajSkp+RQUFi7ovh0bXan1mg9euqbU8jMpwAVxD0+YvMz/VfXm/6wrbZtTVof8Cu3bKVQbJZB2XiDiAmvh2KPV+eYsuBD37364Z0/7ArtQkvR9SfwufGuQETnPF55909f+OiFtn5jUR8Xpq1dJt1jWR0JN1vhtwV+r4/gq4vrf8IaqjxNNZWcv99Mz86ro89+qYEdY6a7LAnT+cWwFXUSu+EBEzV3O37ajLHHcC9mxrENxkdG+bmX8A3gb8AtiXyrb9F7hTc+l+pCB4CypbATWRoVXHsPkwHGqyvmTme4BtqTZqM6hLeHOpY3oGjHh1Ql0y84vAwVQgfDsqW3cScA/q9XlsRLy4CRBWyYoZBK9eRMxoArg/Z7X3ewyV9bx1s8srImJOZg53jmnX8dyPynp+MzMv78qITnt9OC6HUoHW55ogeJX3g+b9uHPFcigz/5WZnwSCmmj7W6o+edtBDoIbj6Y+y98WEYcAZOY1zW1PBU7OZgLhWB+4+Z0PR8RtI+ItVFef71H9ms8E5kfELlHdf2ie+x+ZeSyVHX7xeAfBYEZY4yAizqYycJcB11Krc126mn03Au5P/UEdly2dEBYRp1H1Tsdl5n+76ln/TbX0OpeqT/sG8Puuy3idyUr3oCZ33JCZh/fjZ+iHnmzCBtmz7GlEzAE+Sq1LD9U27QPUBDDf7EYQKycZ3oEKgPejLiN/iuqj+nxqJvfV1Gv2c8C3ui8ttyA4WC8jXMU4GHgvNXET6rgfTZ0EX0kFXR8ANgF2bupnB+44T8ZxiYj3UeUOr8nMj3Rt70wKex0ViB+UmX9sbuudz7IrdbVuQe97zqCJiM2B3ajljJ9KlaG8mEp2fQN4eI7Q1myUj935/DqCKolZRl0J3ZH6HUC9Z/8/4MxczaS88a7TNhDWeomI21NZtwc2m/5D1WYelWtosxIRm2TmfydhiFNOc3npA8DbMnNhs+04qsH7kVQ9VGehgm9TbwpnZeYlPY8zBAwN2ofjSLreQLcGHkxN1Loj1SLtu1Rj/J/lyhnmLwHeR/ULhgrePpiZv5r0wU8jzWXnL1JZoZdm5qcj4gFU3fAzqUlEf6HqND+XXb1ctXbRtdRvc6wXAvObm6+hrqjdhaplvQT4UGaeMejlJxN1XCJiG6oe+VTgFZl5UVdN8M3Nc11BTdzbtxMId92/e5LowE6SG0lTB/xYaoLcrlQ99glNrfAqv7NRPl6ntGUPKgu8hFre+pKmNOtTVCvL21ClEidTNclLc4J7vBsIa1xExDzqrHuHZtMngfc3l/6792vVm0mv5o33d1Qrrxdk5m8iYi7wCKpH43ebfZ7Nypqo66g3hEVUT8Wr+zP6/mkuw92Xmjl8b1aWhkBNOPw1VaN+cq5cZnVD4F3U6xLqasVxwFs6AbNW6vqgejD1WrsHsF9mfrEpiXg4Vau3L3AH4OesXNXvz/0a93QTXcv4Nt/PpjLwj2t2+Q+1pO8H+zTEvpiI4xIRX6GSCm/JzPc322ZS9e3LI+JYKtt5YFMKsdqxDernVtff/Z2BTYFLupNUze/hKc2/nakM/ZtyHbtoRMSZ1Hv4vMz8VlMnfgQ1Ce4RVNa/M+fjQmrVv/+XmT9azx91tQyENSY9k2QeBfwvM3/Y3LY5Ncv2lVQ2859U5vO4HEUbsDaIWpL2i8CnM/OlTZ31P4GzgRdl5t+69r07dZn6OdSqSn+lssPfAM7NzBt6H39QRcRzgbcD96RqyX5G9RDeHZgDbNns+lPqJOwrubL/5/bU6/CZwOWZOa4znqez1V1WjojHU1n0i6nJKb9vtm8NPIp6Xe5BXcL+bGYeMHmjnrqaK2R3orq+rDFbFj3tIyNiX+DDwPbNLjsPyhWMfhyXqP7Av6NWKruBatN2eOfKWkTsQp3MfRl4WWZeOcgB70h6Ss0WUYHu87J6OK9ShgY8kjoRnkv1Wn5/Zh41ludpSte+Bnyj857RVRZ4eGa+vtm2gAqGl1FlEy/OzBPG6+fuZSCstRrpzaF5Y/s9NdN8AXBV1x/NfYC3UhlNqIL4d1MZz4G9vDcazWXmn1GX4p4LvASYR1d3g97gJCIexsqeiltTx33v7qB5kEXEQ6iJW3eiTgrO7MlY7EQFZs+gykp+S9XyndzzOPsAFw9KcLE+1hAAr7jUHNXOKqlg4cXddYHN3/jjqQksb87ML03OyKe2Jtu1AVWnviQzLxvFfXrrZN8BPDQznzhxI51c/TguEbExdRXjedSKdDOp1myHZuYREfFtqvXiCzPzlLYFwbBKNvg9wJOBYzLz6DXsvwV1AvwcKrHwhMz81hier9O948mZ+Y1mrstx1CIq22VNwpsF7ESVT3wSuDEz37iahxwXzqTWGnWdyd02Ip4TEbdpbjqMml3+s6wZtsNRM4GHMvOCzHwedanjR9TkuC8Bp0etDtRmv6EuJ9+dqrOcR13S/zysuGzXOaHoZEZ+QvVUfDlVD/ubtgTBjY8Am1MnC9+g6b3cHCsy89eZ+Taq68H3qctuX4qIZzT7bdDs9y2D4BXmR8T7I+JZETG7CWyh6fXd+Ay1UMkDgQO775yZF1ArfD3NILg0WfQ9qXrKTwLviYi9ut4zR9R14jGr+f5dVFCyYtt01q/jkpn/y8zF1HLJ+1HvndsCH41ajvnRwCcz85TmLmPugjCdNSfDNzV11IdQ77PH9+xz94h4clTHmDtl5pVNguENwFPHEgQ3bqCy9Oc039+ROhnJJgjesLlicAH1XvSdThAcE9j5x4ywRiVq/fX9qclcvwaOoT4oX5WZ/+q5jNJ7Jv8yqtbrx5n5qMkf/dTQdVKxFTUT/+3UTNy/UpeGTsmuWcuwon9m94SNO1KdIlpRJxwRr6TeoA/LzDc0226xQmHX8dmYyjA8l1rw4XltzPSsSUQ8gZo8dBP14X8tdfL1G+rqzVVU0PB34F9UnfXzqb/9BVSt5qAvLDBmEfEFKkt2BjCbahX5N6qc6cvABbmWJWKjuurcOEhXzqbCcWmCqG2p+vZXUFeOoHq2H5hN96Lm5PrmNr22oxbP2I36Hf2963P81dT8ijtR7w+/o67+fLXn/mOtEb4LdQX537FyZdVXZ+ZHu/aZS8UYb83MI1bzUOPGQFhrFRGbUH8Qz6beQDqXpV/TdTm/d7333u+3pJplXzWpg59iYmWrqhdQs2R/Q30wbEJlMz9L1bde3bP/KsezDSLiVtQKZr+i6qd/u7pL+s3+nct8jwQWU0HeDulErlVExJXU1ZzLqUDgz9TVm82p5Uw7ExFvpoLe5VT3jQuAOZn5z8ke81QXETtTV3VmUV1NdqTmSuxLHc9zqGzo6cBfu07cOst9bw9ckxM8O36y9eu4dCUdesvMNqSuGD2fuhp3O+pE8CjgA5n5r2a/MXVEmM4i4t1U3f9uXZ/Xh1C9l8+n+ijfj8oa/xJ4yrp8jjcnI8O5ahKj8zn4FapO+4qm/OLz1NyP+2bmHyc6mWEgrFGJmjV+Rypz+VjqDPGn1NncV7PpG9z7Yo+akHBlZl7Y1sxc1MIZV/VsO5S6zPdm6li+nLpU929qluxngDO6juPA9RBdm6jV815OvUEe22wb1WsoIr5Efdjuuw6X7wZS52SKKmmI5v83AK+n/o5nUbPGdwduSwUtO1MldA8DPpyZr23ja3Ftmvrzr1B/u/My86rmeO9HvYZ3o04sTqVqJJdm5hVd9z+Nagn44ByglnRT5bj0BrZRnXkeAryUqneFmrfx/sz8xLo+z3QUEa+ggt0DqMlpD6PeD5ZSLefOb/abD3wQ2Cl72sytx3NvDZxClU+eTF2NegD1uzk0M984Ge83BsIak4j4B5UR/h01i/Rm6pLXZ6iZoNd37bsTcDhwaWa+cPJH239RLWl+RbWHeV+ubDlzB+D2mfm75vvZ1Bv+C6g3hb9SddWfy8xz+zD0vmpqVn9DdS14JTXTmLW9IXbVkX2C6n/50Mw8Z/X3aKeopU0/RDXMh5pg+M7M7NSqdzJqtwE2ombeX561AIGBcI+oCZ2vBb6bmcf1lOvcnso+zqP6MF9FzRP4QmaeFdVJ5gtURvReffkBJshkH5emtOGZVBeZTYAf5MquRre4qtaUmj2GCsrnNJt/SQXtP1ufn326iIh7U581W1OL5tyN6l//lsz8WdeVtv2oxU72G+t7apPl3ZL6HQ9l5sXN9lnUSdFbqY5AM6iOFKcAB2Tm9QbCmjKaF+zNVI3gFcDXqcupL6Jm5l4OfJUK3Dp9XN9PFdXPy8xP9WHYfRcRQfVdPCczH9ocx5tyhLXumxKU+1Nv5PtTy4j+lDrWn8jMyyf/J+iPWNl0HapO9ZPUydYFuYa2cV1v2p+jMpuPyqb1l1ZMHBzOlb1a96JaU92/2eV71EIvP+y6z4rynDZe0Rmt5qT3v5l5dVf2fagr8JtN1ac+hyo1+T3VDeWJ1ITER2fm9wbtsvxkHZfmcd5MdYmAqoG/nspu/l+ufrXTmdRiHU9vxrE9A5aZ7+j6W54B3L+TZGk+l95OrQPwK+BjmXlt1/1mUkvXPykzH3CLB77l83Teh2dT7dbmUZ9nFwF/oErXsvOZFhH3pa4039g8/08z87qYpIVkDIQ1Zl1/TJtQ9VZPoWqu7k5dWvkutQjEa6kFIB7Zt8FOAc3lwX9k5jkR8RnqjfbVmXl2c3tvQHx76uTiudSxvRUD1FN0tJrX11uoky+oGtWjqezwRb1Zgq7X5VbAedSkzmdl5pWTOOwpK1ad0Np7qTioy56dri7HUFcwLuy9r8ZmhDrVx1AdTh5PZcBmUuVlT+/TEPtivI9LRHyLCqZ+TdW230wF0hsDH83MV/fs3/u+uzHVxmv7zFy0Hj/alNUVoB5FlUK9kq5JiLHqCn/d/9+LyhrPz8wT1xSgdr0P35qag/AQKri9girF2odaLvvDVO/gNU6UnAwGwlqjrkukD6TeVE7JzL/37HMbqgXKc6het3dobvob1WLl3Ekc8pQR1Qrmhq7vOxOOtqDOfI+k6i47wUbvB8N2wJOAO2fmOyZ18FNIVHufY6gMEdSCGkcD38uuiVtdr9VDqKDu3dksYd12EfEgKsD4QWae1bW9+8NuU+D/gFc1N/+b6v/98XQlvvU2wt/366lLzTOBu2fmXyYrAzaVjMdxiepwcDg1yWoeFXDNplalewNV3vOotpQ7jKQrCL43lbB6enZ1gFjdMY7q+vQM4OrMfOYonqcTCB9H/S4+TLMSXfMZ2OlMc0hmfieqI8hGmfnvcfgx14mBsFar6w9nR2pm572ogvrTmu29b2BbUhniPagz+q+1NQjuiJW9bjtn3FsCr6Zm4G4EXAq8j1rD/T/NPqtk7pr7D8yl0nUVEQ+nyiPuQWV7PkvVAv+cWuHw5qgG7Z+hLvHdIzOv6dd4p4omCP4oNfHtPdRlz//27NMdEN+bqh9+fHPzr6j64a9M3qgHU1MaMNS8VhdSl/I/kJlvbmMQ3LE+x6VJxFwGnEstjvG7rts2oa4ovYXKRP6cem/YkCqd6ARfFwHbZOavx/2Hm2Ii4gdUdnZuJxvbHP+nAqdn5nVd+24L/JCqGX5HZl60lmxwJxlxbyrgPQN4QSdhETUx70iqVOUdzbb7UZn8z+UoFlqZCNO+WbcmTteL/SNUDeHbMvPrXbff3LP/5VSt8Pcna4xTURO8nkT9sZ/XtW2oOUZvioiTgLdRGfSPAc+LiPdk5qmd+mEwAO6WmT+OmkT3fCpDfAD14XYc8NmI+B11VeLhwOsNgld4HzWx9U3AkSMEwUNZk+A69eu/BZ4Y1W/4w1TrpC9HxEsz8/jeB9fYNMHeo6nSscupiULQLKTTVutxXA6nAtvMlZOPO++f/42IL1PvFW+hEjVbUqUTmzXPc0dqktjvqfeOgdM50Y2Ip1GlCnNy1Ylon6Cu6p7efb8m8L0XcOus1mZDazop6So1eSy1kuA3u4LgTaj3orOpSZEdO1Kt2n5CndBMOgNhjagrG/wialbtMZl5WM8+e1P9By+iJnX9jFoNZrg3SG6ZN1ETL54eER+nZt/+G1bUod3QZMqfERFPpd7wHwqcEhGfp2ozfzniI7dc8yZ8QvPh9n9UjdsCauLmGVRd9R8z8/D+jXLqiIgFwF7AEZn5wa7t3fWRM5rJMzO7T7wy8zTgtIh4A/A6Vv3w0hh0jnfXMb8XFby9s3mfHagJcqO1vsclqk/xPOAHVIu2jiFWBtDXUuVo21KX5C8F/kF1Sfg3VRe/IVUGNJC6juFRwHuz5qt092p+PpUhvq77fhGxRdYci/81j7PGk5Ku95XOfj/uuvlQ6lh/Kmtlyo4nU4v3rHKCPplcYlkj6pQ+UEHdn6isG1CtvyLiTdTZ4xup1mDHAA/IzJtaHgRD1ad2go5XAJdG9WDsLPt5cxMQ09RoPZwKNC6nWsn8MCI+2tlHt5SZ12Tmq6jLnIup3pNvpD7sDunfyKaOiLgTdZKwlMrsEhEzuy5fDjWXJY+gPrA+HRHvioj7N/t2ynI+CGzb+yGpkTXvm0TENs0ko5ECiJOAPTJrQaI2BMETdFxe1XzdAXhdRDy0ud/NrEz0vZhqpXYKVTN8byqBswOVubwrtZhEdyA9cCLiicCdgUdGxPbZtPKkssHfoboTde+/FXBMRDx4tM/R9fvsLFf9qOaxdqQ+C79GrSbYeY4HUhnq31BlK31hjbBWKyIeR715fD4zD+ja/iHqDein1B/QI6m15L9JrToz8G/qqxOr9sm8O/ABaqIB1KW3V2XmN5vbZ1FZuE6d1jbUxI75wF8y8+6TPf7pqvlg/SawODP36fd4poKIeCfVEql3+dLuSYUvp7JwHddRLdRen5kXNDXubb/Cs04i4q1UlvHZmfmlNezXqo4c43VcmvfPZ1HlUU+gVok7j5ow9+WsFcnuSCUYvkfVD3cmJremHrvr7/1+VCneflSv4EOplf2+SH2Gn5erzvk5HHhOZm6zDs95XypBcQW1UNTxzXO8ODNP7trvo9Tn3dzM/Hy/fi8GwlqtqGboPwE+kpmvaWp8XkCtTHUuNQv3qqg2KX+keuU+oW1v7L2azMeMXDn56ABqstLmzS5fB16bmX9obt+Iqs3s7D8HuKKp1dQoRfXIvVU2y1O3WfO3+v+oIOE+mXlx16XQDahyp280u/+W+jC8F3V14i5UGcSLcg09m9tqLZOFOjPmH0ZN2pzVlhPafh2XiLgL9Tp/FrUoxs1UqcTHgZdQgdgLMvNLfjbFRtTf+DOp/r63pzrJ7NHcPkSVldwP+BHwvMw8eaylO837z9FUbfY/m+f5RGYe2LXPK6hJuedk5pwRH2iSGAhrtZoi+aXUxIJDqTO6Z1HB8Rsy88fNfltT7VjOAvbPpvtBW3XVV9+duiz3KOpy3DXAnai+lgCHAW/PpjVVUwpxfZvfqDV+IuJ06m/2gdm1qEgzYeZdwE7UQiXvycy/NLc9myqDGgIe4snYShGxQ+fktfm+t2tOJ/O2IfUB/wrqCtnXB7kGuF/HpTeobeqFn0xdgbs/9bl1a+BbVD/xa6OrO8W6POd0EhE7UJ87y6ka3z93ZcS3oOYOPI3qFvFrKjnTWQzrq8Ad1idAbX7fx1ArfEJlnz9MXXXal1qM6y/A0zLzvH7+jRgIa40i4u3A64FNm03fpS6bntu1z0HUzN3/S/u2rhDV4P1BVAbkI1R/5VlUcNw5M74KeGNmHtfcp9UZC42P5rLxsdSH0NHAoVn9WPelJhnej7p0+aTsWcEpIs6gsmj37g5w2qzJpP2KOuE/LDN/0Gy/RWDVnGh8ksp07d2P8U6WqXBcel67G1Enf0+nssT3oCbHHQ98JTN/0XW/gXqv7UrA3I2aJ/FK6oQWakL7n4CvUK06r27uczeqbvpF1DoBX6RapR0H3K9THrWmcoU1Hcem3O/Z1Gp/vSvSfY1q5fi9fv8uDIS1VhHxZGod8H9SC2r8s+tS132pYvvZwHbZ8pZVXW9G+1N9bg/PzNePsN9uVHbkQc2mn1FNx787eaPVIIuIPamJKbOoUqbrgN2olQq/Brw/M3/S+0EXET8EtqOC5PMme9xTUUR8jFr17H9Ux4HPUwuN/Lm5fQY1U34GNfH1fcB9M3PZINejTtXjEhF3oMp/nkYFeptSGcmTgC/kapZbHgQRcSpVM/1bqmZ6B6qV2f2ohZy+TJ0Y/7zrPjtTmeG5VInUcZkZo/0dNeVWD6DeYy6mrmxe19x2a+r9ZEdq2fu/UpngU7vmxxgIa/qKiK9Rf3SHZObH+z2eqaDJhnwL2Bt4aFarmk4fx05/y+HmBONz1IfErai2anaK0DprTkwvbU5WZ1GZoZdSdb8bU/WT36L+Xv/Ydb/Oie0jqIzQOcBeg5QxW1dRS56fQ00w+ivVimsz6nLycVQ7qP/03Of+mfnLAQ+C+3Jcmtf1PaiTuntRE7KuBU7MzKt69r079fn0TOARVMC+mFou+LOD8PpuTjZmZeYNTWnTScDJwLzMvLopUdiZplaaCoyPz8yX9TzOxtRiWI+mWn7e2Fvm0rN/5zNtT6pjz67U7//H1CT604Gf5sruFCM9xpTIyhsIa51EtUN5E3XJ49OZ+aI+D2nKaILdr1NvDA/NzN+v7o0/Io6nVji6EPhVdi15KY1F1Kp6vwVeQ2Xlbmw+JB8D3Ifqm/pd4EfdAUpXDecQVbf+auC5mWnf4EZUh40PU22ePkyd5D67ufk71PH+atf+U+IDfqJN9nFpsovvpQK6zZrNN1PJhP9SbSuPysx/9NzvQVTG88lU/fAJmfnidR3HVBARB1JlJj9vvp9FleHtQv39/jwiNu6ag3Jr6vfzcWquyusy80PdyZlmv40z839rCYI77xkzgD9QJ0PnUp07bgPclspGnwR8IzPP77rvlKuXNxDWOonqLXg4dUn/I5n5tz4PaUqJiM9Qizu8JjM/MsLtnbPpT1C1WA+b7DFqsDRXZx5PzZBftKYPsp77dbLB+1PlOn/MzF0nerzTTVTv9PdSC7d8kLrU/BLgvtTErJOplfvOafZfJcAYVJN1XJqrHW+jAu2LqYzjcmpluO2ofrRQl91fmz1LgkfErahL888E3tWZODYdNbW9f2q+PYpa/vifzefOfTPzgas76YiIF1DzB34EPG5dyhO6AuG3UMu2H5qZb4xqV7cX8MTm36ZUBv4k4IypWpJiIKx11pyBbpq2q7qFiHgm8AXqUtw84OudLFxEbJS1vOUWVB3x7ak3pKv6NV5Nb1GrPJ5OfcC9qsnoDNGzWlys2ue6EwAPUVmy06l6zid0Typqu64P/W2oiYdPoia4HhrVm/UA6qT3ztSVnU9Ty/1e3Nx/IMsjJvO4NK/Rr1DdBj5E1bD+trntdlRryr2oid33ae52GLCQWj1uqOt1v1lvycZ00hyLGdRxmN9s/i/V2/8OwCMyc9+R7tf8vm5DlbQMUx1lrl3HcWxItWi8G/DMzLyo67atqMVKnkP9Xv4DfBU4FfhWJ0s9VRgISxMkIj5MtQr6AzVb+tvABdn0Zm3Opv+Pypa4GprWWUQso1bPem5mLu3NBkdEUJeDrx/hvgdRmbZZwILMWtVLt9RcXv4i9SE/n7rMfFtqifTnU627NqJWyTqGqkOdUh/6E2Gij0vUIhzvaO73oq7tva/zu1IB4cuoADg6pRmDWK4StTzyYdSkQKgWnbem6oM/1TMnpTORezOqhOq/wK6Zefl6PP/nm8ffb6RMf0TMpspRnkVNDL+MCr4vW9fnnAgusSyNs84bAjVh5AvUhI4PUFmTL0fEYc3M3vdQbwxv68tANRAi4pVUv9BPZeZSqCVmmys2NCUPR1PZst77bke1m7qEmlj3qcka91TX9Xfc+X5GVlecDwFXU6v27ZqZ/6LKAt5EtUVcTLWiOhI4s7mMPTAm+7hELZjxdqpP/buabTNhxVLKK2TmX6mSvROpkomPNq/xgSpRiYgZTWD758x8BjUP4DdUEAzwioiYk5nDnZ+7K/u+H7AV8M3MvLz39zlG3wW2jWpbN4OmXVtXULyMel0cQiWDTsjMy9bzOcedGWFpAkXEbanSiP2omc6367r5u1R99TdGuKu0Vk3d4+XUTP0XZObvmgksKz4AI+Ji4Epqpbif9WbGmhKdTa3zv6WeiYTdE4r2puoer6CWoT232b4BNSv/idSkwxsz8679GPtEmszjEhGnUJ0MXpyZnx/lfbakSikeDjw+M08fw483bfSWl0TEwVS9dmci4RHUSfB/qfeAvamkzCbAztnVCnWMz7s1VQqzHdWV5oWZ+a3mthXvP93vNU1JxnW5lm4U/WAgLE2C5hLWzlQt25bUJcIlbbhsqokTEUcBLwdelpnHNttW1AZHxLuBt1KL4Bzedb9OffB21CzvP4xUNtFGTX3jntSqmv+mlj+/pmefjaiArjNJ7AXdl5gjYlMq+/mPrJ65U26m/Fj147hEteY6kyq1OKS5tL/GICpWLiX+Dqqc4rWZ+eF1+qGnie7j2JQ+LGRl/fA1wAVUC8UrqKs/H8rMM8ZYp92Z4P106mrmfbpuHqaO9ccz85/N/tOmNt5AWJKmoYi4D3U59GJqFamvwcrLxU3gciF1SfpFmXlhT7ZmJlVfuCewX2b+btJ/iCkoIhZT3QVupFZNuwb4JZV1/xewBPhfZv4rIt5GXa5fEaj1ZdCToB/HJSL+ANydWgb87V3b15pRjIgXU4s9vTkzP7Auzz+d9E6ObepzP0QtKAI1Ye09mfnBdXns5j3jVlS3ijtRkx+XU5O9n0qVRvwAODozT+q675QPiK0RlibRVKuN0rS2ZfN1G+oD/wPAThGxSbP9I1TQ8slsWkX1BA+PpZZgvtkguER1e9md6vayjDp+D6UmvR5DrZz2R2Bp1Ap896OWSX8FtSrXQOrHcYmInVi5RPDrI+KLEfEoWOVk7xYxTKd+mOrPDvD7dXn+qSQibh8R9+nU/Y+kqQe+sakfnpGZyzLz8VSQ+meqXOL9UR09xqSrlOoQqg75jc2kxYOpKwD7U0HwbsAJEbEoInZt7julg2AwEJYm1SBN2FB/ZeZZVJ/OhVTt+WupgGS/iHgGNVN7EU2muPmA7GR2bge8iCrVmdYLC4yzPZuvG1MrpX2SWm3rcVRN5EeoVcmupVrO7UV1R7iWqvkf1JPdPZuvk3lc/kh1nHgfVQf/DODEiPhIRNwbVkwKndH92F2B1zOputgp2bt2jL5ETb5+akTceU07ZubNzXHpTCg8JTPvQWXov5mZvxrrk3cd3zsCF1FtP8nMG5qT7C9Rv6vXUFeh9gMWRcTRaxvvVGBphCRNc1G9XI+hJiNBU8NJtVH6atd+nUD4xdQH6wmZOW+yxzuVRcTjqE4uj2w2nUL1rf1Gc3unBnUnYENqEuxNmXnydLgMvK76dVyilnJ+GNWT9qlURvIC4HhqqeCrmv06gd9NUUuFf5FqV/mYdXneqSIiHg90JlT/h+pE9DngZ5n571Hcv7t+uDM3YJ1q1iPiAGD/zNxnNbdvQNUOv4BqYXcr4G5TfSKugbAkDYiIeBiVrbkHNYHlM0BSNZ3XNR+C96Dq+x4IbJUuiAOsGjA03wfwRmrBAKjFA46glkK/YTWPMYi9aqfEcWkmdu5FLdLxGKps4iyqJvULXfttSJUJHQI8Paf5svUR8QUqu30GMJvq1PA36rh/mQr21zjRtZnEeONYT0a6Tpyf1zzfacADgCdm5rmrO8FpJkXuAdwxMz891U8QDYQlaYA0mbEXUEuvbgT8ncr+fhb4HdBZyOUNmXlYv8Y5FTU1pzMzc3nz/RZU0HcQ1XLqH1TQ9xngr4MW9K5OP49LTwuuWVTP7CdSAfH9qPKLU6iOBUsjYncqQPxtZs4Zr3H0Q0TsTJU7zQIeDOxITYzdlypTOYcqUzmdOu6d2ulOdn574JrM/McYn7e3xeJHmucFuAE4lFrW+eZOnfZaOnlM6RNEA2FJGkBRq339Hys/wH5BZZWeC1yfmffs19imkmZy4QOByzPzD822IWBGJ4vVBCRvpepUobolfJhaOv2fkz/qiTeVj0uTcdyled7OAhEXASdTXSaeBDwkM382UWOYDBGxD9UP+TSqzOmq5newH9U2cTfgZmrp4hOApZl5Rdf9TwP2AR6cmT8fxfNtuIas/u7Ax6gacKiVUl+VtWhG5yTlpqkc8K6OgbAkDbCIuDvVVWLPrs1PyszT+jOiqSUiDqSCub8Br8jMX3bd1rs4yb7Nvg9udvkGNVHsR5l53WSOe6JNh+MSEXcEHkF1ptiXyk5DdUp5yUQ972SJiIdQk2C/m5nHdbeNa2qn5zX/7kV16VgEfCEzz4qIp1D1xH/NzHuN8vm+BPwmM98eK5dknkl1lun8rucBH2Tl4lAfA96amf9pbp92PbMNhCWpBSJiL+CbwOLVTXZpm4h4IlUvfSMVTC0dKSPWnSlrAoP51JLVWzW7HE31y51WAcDqTLfj0pQAPIrqgPJgYMvOJLrprum68N/MvLqre8NQV0A8m2pT9xyqo8fvqdX9nkhl9B+dmd9bW4AaEZtTLdAAHtkJbLtu7550tzHwbuB1zc3XUKVWuf4/8eQzEJaklmhmdd/KCXIrArdfU/1V52XmGc32UdUzRi0z+waq9ORjmfmqqV4LORrT6bj01A9vSJVLkJk/He/nmmqiZ1GRiHgM1df38VRr3JnAVzPz6WN4zMcC36Jqrg/KzIt7jnHvoh07AIcDT24e4jLgsZn56/X+ASfRapszS5IGSzPZqfVBcOM91MSrN3SCvcYQMNwEVg8EngBcD/yTCiwuBcjMS4BXRcT/o+qvV9x3ksY/UabNcekOrpvM9MAHwB1dGeEZWb2DvwN8JyJeTy1xDdXXd1SruzXlLj+k6o33bf7/waZrROc5hoEbuybI/QF4ShNAH0V1q7lm5GeYuswIS5Japbnc/Gtqxa2nZeZF3bWQzWSs9wDPoy43Q/Vw/QWwMDPP6MzM78f4J4rHZXpqMrVDTReHhcCbgQ9k5pvH2rosIu5ELcLzMOCd1O+1Mzmyt5vEKo8dEQ/PzB9PtzphV5aTJLXNY4HbA99ugr2hzLypCfZmUpd7D6FWR/s1cD7VImxX4JCIuN2ABnsel2mqCYIfTU2uu5yavAhjyMQ3md+/Ax9tNr0T+HjUIikrMvCdWuWuAHlW8/2Pm6/TJggGA2FJUvt0grU/NV83AYiILan61gOb7a+jJmE9nOqb+1eqBvMZDCaPyzTSFZB2gt17Uav6vbPp+DBrTf19e3X2zcyTqN/v76muFIdGxDMi4g6d54uupa2nW+Dby0BYktQ2ncu5T4iITbtafB0EvKr5/1sy86PAlZl5bWZ+kVq8AOCOsDIQGSAelymqU5cbEds0HWBWqZFunATs0enesC4BakQMNVcCFlMnPN8H9gY+BHwwIp4VEbftqhlecb/ur9OJNcKSpFaJiHsBZwO3puohv08txHAQ8G/gU8DrmqzaDGCDzLw+Vi41e0hmHtGf0U+cqXJcOpOzml65GzcT8ARExFup1mXPzswvrWG/cenU0fQyfjHwOOCu1GS4C4EvUa+PG6iJdZtSvaWvXd/nnGxmhCVJA6/JdG0IkJm/A14ELAOeQtW+vqLZ9T3AoZ1gr8l8Xd/c1um//LdJHPqEmorHpety/nzgdxHxyPF43KmsqcFe3W2dbPDDgAOAv6wpCIYRs8VjHU+n7OFsqu745dRCGucD9wXeQS3tvJiqST4H+HVEbLY+z9sPBsKSpDZ4F3BQ1NLTUB/ib6FWxjoP+DLw4sw8rJOBbLKSnSDkocAc4PzM/Nqkj37i9OW4rC7w63rcB1I1x9dn5g/X6SebBppevN0Tz2b03N7pBrEh8HxgB2rC4opJahOhe2JcZl6XmadTmehHAQ+iyibeRvUc/ibwO+Aj2bMQx3RgaYQkaaBFxI5Ul4PTgOd2LygSEZtk5n97Fg7oXJrvfJ1FrZI2D3hWZn55urWIGslUOC4R8UHglMxc0rVtJnAYFfA9OzO/NAjHu1dEbAT8isrAH5aZP2i2r2iH1rXv06ha7HMyc+9JHue0XyhmTcwIS5IG3ZHAlUBms1Rt16SezuX9Fdm1rtnznUBkAfAC4EuZ+eXmtkEIyvp6XCLiwVRm8cyIOCoi7trc9Dgq+7m0UwIwIMe716FUhndv4HMRcWhEbJ+Zw50TjuZ3MpPqCLE5tWLfGkspxqo3C92rNwjumhg3s/v+03GiHBgIS5IGWJNJ2xNYRC0f29EJJG4LK1bd69yn80G/fUS8l6qH/BnVKmxcg5B+mSLHZRlVk/wdqgb17GYy2Euofsbzm8cduFVwm4mAT2q+vZw63q8FvhYR8yNis05nhqaX8weAXTJzWYxxkYwRnvtuEbFtRGwLK0pdRh3EdgLjzhi6TpCmZdbY0ghJ0sCKiL9Q2c3nZebZncv6XbcvozolvKyrXVjntkOoRQXOBd6WmUsG5TLxVDouEbEdsB+1Yt39qUUgzpzsEoDJ1hzHDwO/ab7uDTy7ufk7wMcz86td+6/Xay8iHk6VsTyNWmr9b83zHJ+Zl63r4053ZoQlSQMpIt4M3AU4oZn93sl+zWpunwfcG/hHb7DX+CbVOuqF3TWs091UOS5dnQkuzMzDqAzzpc3Nj46IL0bEg9b18ae6ph/zW4AdgWcBxwKvpjozPAb4fxFxQlNC0lnIYmgs2duOiNiNanE3jyqx2J6a5Ph2avW42ePwI01LA3e5QZKk5tLzO6hep59rts2kep3e2Pz/g1RW8/jm9lWyosBfm5ZiK0z3bPBUOi4j3OeewFbN884AngvsGxFHUK3bLh/rc0xVXdndzwCPpMokvpuZh0bEmVSbtOc2X/eKiE9TtdwXN/cfa3nEUVQ98nuBrwPbUaUxzwCeAPwFeM04/GjTjhlhSdIg+gC13Oxpmfk3WFHT2Pnc+yBwO+CTmXl+c3t3W7CHAB+IiH1u8cjT25Q6Ll2P+wiqTviPmflSqmPEK4AfUQHaXyLi6ePxnFNBV53txVTAewZ1XA+iOnm8l6qfPhG4E/BWqn74JRGx8ViC4KbueifgfZn51sz8STMJ8Z1AUq+HF0fE1uP2A04jZoQlSQOl6c06r/n22RFxMdX4/++ZuTwi7kZdgj6NWiGrE5B1ZutvCLyUmrQ1MD2Dp9pxiVV75D6Xuly/L0DTj/aTEbEYmEsFxZeu7rGmi9463ybbfk1EfAh4GFWqcF5Td30GVSZxOhUU70l1+nhxROyfmX8ZxfPdoXnMxTRLYTfHe3lm/gN4Z0TsC+xCZYlbt4qfGWFJ0kDJzD8AAfwSeCB1+flIqu4S6jLxtcCnMvOyTkDW9RBPoGo2z8jM703eyCfWVDsuXQHh46ng+juZeSqszBRn5p8ycyGwe2b+aH2fs986db6woka6kxk+g5ooNwv4RETs0nSMuAg4iVrm+g1Uu7ttRhMENw5vHvOEzPxjs215M44Nmrrwq4C/Uksnt46BsCRp4GTmcVTt5duBf1JLBn8+Ir5KBV6fB05tdh9qai6HI+JOVPZtQnq29ttUOy5NdvLeVBeDgzuP2wnAuwLiP672QaaBiNgqIuZGxF2AzSPi1k2g210n/X2qt/C9gPdHxJZQLewycxm1eMlzaJa0XltbuYi4P9WP+V/AnSNim+bxhptjvBzYjDop+iVw82ofbIDZPk2SNFCaTNuMXLls7Q6sXPwBYDnwCapG8urMvKHrvi+nMqNHZOYh69uzdSqZqselCfjunZnfH2Fi3kBoSjx2B26kVpO7hgo+f00FqkuA/2XmvyLibdTS1x8HDlnX4xwR+1G/3+2b5/0u8EVqUt4/mn0+Qp3YRHOS1DoGwpKkgdSVTexkFx9NdUyY0+yyFDiGCgwui4h7AZ+lMnJbZub1gxiYeVwmV0Q8E/gC8D/g983X+1GT1DpX5v9HdW64CriIKle5LXBAZn52PZ77fsD+wNOprhwXAd+g6oWvo4LyU6lA+NJB6ZM9FgbCkqSB1pu9bPrkvgm4R7PpJOA46pLz64GDMvPoiJiVg7m0L+BxmSwRcSQ12Q9qMtr/AT8HbgNsRAW9dwBmN//+C2xB1WvfazwC1OZk53lU+csdgV80z78VFWx/pauvc6sCQwNhSVIrdAd+EXFbKribT9VJ/osKDH6TmTv3bZB94HGZeBHxOOBtVH02wCnAcZn5jeb2DZrOHTtRmeJ7ADdl5sljLUOJiFcBf8nMr3Zn7iPi1lRN+HObcdwauIxq1fal7Fpdrk2ZYQNhSVJrjFAne1+qR+t+zS57ZOYP2pb19LhMjN7jFREBvBG4W7Pp/wFHAL/qrsnueYxRB6URsS21dPJi4JmZ+c/ex2gm7M2lguIHUxMVv0OVb3wvM68a2085vRkIS5Jap6s/bic4eBawS2YuaFM2rJfHZfw1x7TTpYGI2IIKhg8CNgH+QQXDn6FW7VufEoiTgb2BeZn5hd5xdNd1Ry3dvD8VEG9P1SifTPWRPmtQJomujYGwJKm1Rrrs7EQwj8v6iohNqLZklzf9m0fKuu9MZd2f0dztl8CHga93MrljfM49gTOBTwGvzMxru+t+RzqRaYL0fahyicdSqwr+AXh0Nss5DzoDYUlS65ntHJnHZd1ExIFUkPs34BWZ+cuu23qz7vs2+z642eUbwEeAH2XmdWN4zl9SgezzMvOsEVaxewGwtCsw764fvh2VGT6EKtM4YN1+8unHQFiSJGmcRMQTgU9TvXvnUsHnLep/I2LDzvZmcZL51ETFrZpdjqb6CK+1Jrurz/P7MnNB1/ZZmXlj01N4EVWScVhXEL5Kp4iIuAeVxf7PIPXQXhMDYUmSpHHQBLS/pjpuzGuWTh51Zj0itqaWUn4l8LHMfNXa7tuszncxtUzyEzLz7822G7syvn+mehW/KDN/PEK2uLWZ/zUuzydJkqRRew+1ZPQbOkFwYwgYbgLUBwJPAK6nlrn+amZeCpCZlwCvioj/R/X6XXHfNTznB6i+w9cADwdOycwbImID4OaIeCtwV+DNmfnj5nlWeby2BsFgRliSJGm9RcSdqWzwn4GnZeZFTYb45may2qZUoPw8KnAF+A8V8C7MzDM6/YTH8JwzgZdSHSju22w+tXm8n0TERlSAvJTKBv/FSY+rMhCWJElaTxFxAHAC8P7MfEtP796ZVA3vgcBNwDIq07spcBfgdGqS27/W4Xk3oBbgmNs8/pbADcDhVHZ6X+AFmbmozSUQqzNj7btIkiRpLTqZ3D81XzcBiIgtqbrfA5vtrwMeRZUxvJGq7X08K9uojUlmLs/MC6gSiWdSE/VmAm8Gnk5lnL/V2b8JytUwEJYkSVp/nQ4LT4iITbtanx0EvKr5/1sy86PAlZl5bWZ+Efhkc9sdYWUnh9Hq6vxwXWYuBV5LrQh4ZrPLQ4CvR8STM3M4M2+KiKGmjVvreRAkSZLW37lUPe5Tgc9GxKsj4giqR/BGwEeBQ5t9h5r6XaiMMFS98KgmrnUHsU398czOtmYxjq8CL6b6Al8APBL4WkR8PiJ2agLim80OWyMsSZK0Tpps7AZd/YCfDvwfMLvZZZiqBX4DcGJmXjLCUsefpZY6flpmfm0Mz70ZcAfgisy8ttm2Su/fpkvFvakJei+hFty4FvgY8KHMvHLdfvLBYSAsSZK0DiLi3cC/gE9k5jVNZ4jHAHsBewC/B07LzBN67jejycg+FPgCcE1m3m+Uz7kL8GzgRVT/4P8AJ/Q+R899NqNKJF4KPIuqIV6cmY8a/U87mCyNkCRJGqOI2JEqe3g0TTzV1P1+jerZ+wBgv06A2ild6AqCZ1GB6V2Adza3rXF9h4i4O7Xi3Juo7hD3B3YHPhkRH4+IO4x0v8z8T2Z+jyqVeD5wPnBE85itLo8wEJYkSRq7I4ErgczMq5sJaJ2Jbtc3X1cEtp1yiK6yiAXAC4AvZeaXm9vWtpzyEcDDgE8BTwYOBo5rxjGX6kQBrBJ4z2y+DmXmFcDJwGMz8yvNcw78Mspr4spykiRJYxARTwP2pILhb3XdNBO4Ebgt8M/uxTE6PXwjYnsqE/wm4CdUC7Vb1PeO8JzPotqsfRqITtAcEXcFrgDeQpVLnAqrBN43NV+HmzHcAFy6nodgYFgjLEmSNAYR8Rcq6/u8zDx7hAlwy4CzgZd1tVHr3HYIVQpxLvC2zFwymoUuIuIi4N/ACzPzpxGxYWeSXnP7L4C/UB0jHktN1NuaKoO4bfN1B+DozPzZOv/wA8bSCEmSpFGKiDdTdb0nZObZUNnXTn1vRMyjOjX8ozcIbnyTam32wsxcMsrnfAsV1GZm/rTZ3MkIb9B8/xvgiVTZxFyqE8WeVB/j5wLvbbZdMJafd9BZGiFJkjQKEXF74B3A94HPNdtmAsOZeWPz/w9S2d7jm9tXyRYDf83M33U/7pqywRGxBfAe4H/ARp1McCf47iq/uB8V150B/JgKlC8F7glcBtwH+GpmXtvcb231yK1gICxJkjQ6HwA2pFqi/Q2qBrfJBt9MBcG3A96Rmec3t9/c1SniIcBzI+Jbmfmt1TxHr4dTbdi2A14PPKDpPfytrjrhg4CdgM9l5vPX9oAGwStZGiFJkrQWEbEDMK/59tkR8dyI2CYiNmiywXcDXg2cBnypuc+Mpv735mZxi5cCr2RlV4nROKO538epGuH9qO4Rh0fETs0+7wF+RrNyXWfVuu6Wbev4Yw88D4wkSdJaZOYfgAB+CTwQ+AzVNeIxzS5HUau2fSozL+sEwF0P8QRqMYszmp6+a9U8xvLM/D61Yt1rgROB2wDzgWMj4gfA5sDHM/OXTdB7QzPm3pZt6mHXCEmSpFGKiFsBrwFeBdyeWtntTGBf4JPAQZl5fROQDjWlE3ei+v0+GbhPZv5ube3Sup5vCFbWEUfEXYB9qKB6DrAR1Uf4ncAxncccoTZZIzAQliRJWosmIJ3RFWjuwMpFMQCWA5+gAtKre1qbvZzKGB+RmYeMNghey3h2poLvJwEPBv5OlVF8NjO/vT6P3SYGwpIkSaPUqbftZFsj4tFUJ4k5zS5LgWOA7zYlEvcCPgvcC9iyky1eXba2u6dwU+u7NXAVcPvM/GPPvhsDjwSeRgXF2wF/oGqUv5iZvxi3H3xAGQhLkiSNUW9Wt+kf/CbgHs2mk6hyiH2obg8HZebRo2ld1mSfX0EFtw8HrgH+BSwDDs/Mn/TsvwXwaKpc4jHAJsDfgKdk5rL1/VkHmYGwJEnSOuoOiCPitlTQOx/YjApebwP8JjN3HuXjPYhadvmZzaYrqclwy6kAF2ARtSrdn3rue3cq8D4QuDYzd133n6wdDIQlSZLWwwj1w/cF3kq1OgPYIzN/sLZscETMpmqJ96TKGz4H/JxaFGNLYG/ghc3uNwIHZ+axIzzOw4BLMvNCF89YMwNhSZKkcdDUDw931fg+C9glMxd01/6u4f6nUMskvy0z39tz2xC1ENoc4M1UCcQNwJsz88PN7b0t27QWBsKSJEnjaKSuEGtrZxYRB1KT7D6ZmS9pto0Y3EbEXYHXUSUQFwJP6F22WaPjghqSJEnjqKtEYqhr25qC4I2p5ZnPAT7cbJuZmcMj3S8z/0qtNHcecHfgueP6A7SIGWFJkqQ+ioijgJcDL8/MHMP9Hgt8C/gO8MTMXD5BQxxYZoQlSZL6JCLuTQXBZwInNNs26M4mj3Cfzm0XAFcAOwK3mtiRDiYDYUmSpP55V/P13sD8iLhzZi7PzOGImDnSHbpuuwr4N3BBZl69puBZI7M0QpIkqQ+alePmAk8FnkB1hfgxNWnuc121xqtMtOt8HxH3AH4PJPCazPzvJP8I054ZYUmSpD7IzOuBz1ALaLwe+Bm1klwCn4+IPZv9Oss5r7K8M3AAcDPwM4PgdWMgLEmS1AdNb+GbMvO3wLHAwVT3iMuBpwMnRcRHmjpimizwBs19d6BWkbuQWs4ZSyPGztIISZKkKSIibg88DHgOVTJxa2pS3PHApzLzn81+bwXeDby2WVDjFr2LtXYGwpIkSVNMRGwH7EX1CH4MMAR8HziM6hRxArBhZt6j2X+tK9fpliyNkCRJmiI65Q2ZeSFwIvAaaknlXwG7A4uoIPjezW1ExCyD4HVjRliSJGkKi4hNgV2AZwBPA+4KfDcz9+7nuAaBGWFJkqQpLDOvzcylwPuAVwNfAl4BtRRzP8c23ZkRliRJmkYiYovMvLK3v7DGzkBYkiRpGnBC3PgzEJYkSVIrWSMsSZKkVjIQliRJUisZCEuSJPVBp2ewSyP3j4GwJEnSJOtMfIuILYC3RsSe/R5TGxkIS5IkTbKu7g9PAd4FHNDH4bSWgbAkSdIE6Cx2ERG3iojNR9i+A/Ai4BrgVc02Y7NJNKvfA5AkSRpEmXlT899PAY+JiJcDX+7a/hxgV+D1mfnviJiVmTf2Y6xtZSAsSZI0QSJiE+AS4FrgJODbEfFaYGPghcCfMvPwZvebRnwQTRgX1JAkSZpgEbEXFfg+A9gE+A1wX+BxmfntiNggM5f3cYitZB2KJEnSBOnU/Gbm94D5wPOBX1FB8A3A3SJiM4Pg/jAQliRJmiCZeTNAU//7b+B04O/NzX8BElgaEU/pzwjbzdIISZKkSRIRrwCOBD4KHAa8jpo0tyVwMvCBzDy7fyNsFzPCkiRJE6hTHhER96Hapf0beENmXgy8odl2IvD05qsmiYGwJEnSBOqUR1D1wQ8C3pSZyyNio8xcnpnfAg4GDgLmwcpew5pYlkZIkiRNsIh4APAF4ObMvHfX9iFYZaU5TSIzwpIkSRPvGuAsKitMRMyCCoAzc9gV5frDjLAkSdIkcOW4qcezD0mSpAkWETMMgqceM8KSJElqJTPCkiRJaiUDYUmSJLWSgbAkSZJayUBYkiRJrWQgLEmSpFYyEJYkSVIrGQhLkiSplQyEJUmS1EoGwpIkSWolA2FJkiS1koGwJEmSWslAWJIkSa1kICxJkqRWMhCWJElSKxkIS5IkqZUMhCVJktRK/x/Zp/kktQRJ9AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "\n", + "\n", + "plt.rcParams.update({'font.size': 28})\n", + "plt.ylim(-0.01, 1)\n", + "plt.xlim(0.5, len(models) + 0.5)\n", + "\n", + "labs = [model_names[model] for model in models]\n", + "Boxplots = []\n", + "ticks = []\n", + "for i, model in enumerate(models):\n", + " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", + " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", + " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", + " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + " Pearson_test = stats.pearsonr(test_y, pred_y)[0]\n", + " \n", + " if i == 0:\n", + " plt.scatter(i+1, Pearson_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", + " else:\n", + " plt.scatter(i+1, Pearson_test, c='darkblue', marker=\"o\", linewidths= 8)\n", + " \n", + " Boxplots.append(Pearson_CV)\n", + " ticks.append(i+1)\n", + "\n", + " \n", + "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", + " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", + " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", + "\n", + "\n", + "\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=8)\n", + "\n", + "ticks1 = ticks\n", + "ax.set_xticks(ticks1)\n", + "ax.set_xticklabels([])\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "#ax.locator_params(axis=\"y\", nbins=4)\n", + "\n", + "\n", + "ticks2 = list(np.array(ticks)-0.01)\n", + "\n", + "ax.set_xticks(ticks2, minor=True)\n", + "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", + "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", + "#ax.yaxis.set_major_locator(loc)\n", + "\n", + "plt.ylabel(\"Pearson r\")\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "#plt.legend(loc = \"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) MSE" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "plt.ylim(0.5, 1.4)\n", + "plt.xlim(0.5, len(models) + 0.5)\n", + "\n", + "labs = [model_names[model] for model in models]\n", + "Boxplots = []\n", + "ticks = []\n", + "for i, model in enumerate(models):\n", + " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", + " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", + " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", + " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + " MSE_test = np.mean(abs(test_y - pred_y)**2)\n", + "\n", + " \n", + " if i == 0:\n", + " plt.scatter(i+1, MSE_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", + " else:\n", + " plt.scatter(i+1, MSE_test, c='darkblue', marker=\"o\", linewidths= 8)\n", + " \n", + " Boxplots.append(MSE_CV)\n", + " ticks.append(i+1)\n", + "\n", + " \n", + "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", + " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", + " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", + "\n", + "\n", + "\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=8)\n", + "\n", + "ticks1 = ticks\n", + "ax.set_xticks(ticks1)\n", + "ax.set_xticklabels([])\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "#ax.locator_params(axis=\"y\", nbins=4)\n", + "\n", + "\n", + "ticks2 = list(np.array(ticks)-0.01)\n", + "\n", + "ax.set_xticks(ticks2, minor=True)\n", + "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", + "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", + "#ax.yaxis.set_major_locator(loc)\n", + "\n", + "plt.ylabel(\"Mean squared error\")\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", + "#plt.legend()\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_MSE.svg\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_MSE.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Coefficients of determination" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "plt.ylim(-0.01, 0.5)\n", + "plt.xlim(0.5, len(models) + 0.5)\n", + "\n", + "labs = [model_names[model] for model in models]\n", + "Boxplots = []\n", + "ticks = []\n", + "for i, model in enumerate(models):\n", + " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", + " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", + " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", + " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + " R2_test = r2_score(test_y, pred_y)\n", + "\n", + " \n", + " if i == 0:\n", + " plt.scatter(i+1, R2_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", + " else:\n", + " plt.scatter(i+1, R2_test, c='darkblue', marker=\"o\", linewidths= 8)\n", + " \n", + " Boxplots.append(R2_CV)\n", + " ticks.append(i+1)\n", + "\n", + " \n", + "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", + " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", + " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", + "\n", + "\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=8)\n", + "\n", + "ticks1 = ticks\n", + "ax.set_xticks(ticks1)\n", + "ax.set_xticklabels([])\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "#ax.locator_params(axis=\"y\", nbins=4)\n", + "\n", + "\n", + "ticks2 = list(np.array(ticks)-0.01)\n", + "\n", + "ax.set_xticks(ticks2, minor=True)\n", + "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", + "\n", + "plt.ylabel(\"Coefficient of determination\")\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", + "\n", + "leg = plt.legend(loc = \"upper left\", frameon=True)\n", + "leg.get_frame().set_linewidth(3.0)\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_R2.svg\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_R2.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (d) Statistical tests" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['str_fp',\n", + " 'diff_fp',\n", + " 'DRFP',\n", + " 'ESM1b',\n", + " 'ESM1b_ts',\n", + " 'ESM1b_ts_DRFP',\n", + " 'ESM1b_ts_DRFP_mean']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[0] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[0] + \".npy\"))\n", + "errors_str_fp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[1] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[1] + \".npy\"))\n", + "errors_diff_fp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[2] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[2] + \".npy\"))\n", + "errors_drfp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[3] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[3] + \".npy\"))\n", + "errors_esm1b = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[4] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[4] + \".npy\"))\n", + "errors_esm1b_ts = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[5] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[5] + \".npy\"))\n", + "errors_esm1b_drfp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[6] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[6] + \".npy\"))\n", + "errors_esm1b_drfp_mean = abs(pred_y-test_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference between predictions with ESM1b/DRFP(mean) and ESM1b_ts 1.177364363105206e-07\n", + "Difference between predictions with ESM1b/DRFP(mean) and ESM1b 0.0015088024807966155\n", + "Difference between predictions with ESM1b/DRFP(mean) and DRFP 0.004861912227347217\n", + "Difference between predictions with ESM1b_ts and ESM1b 0.40887357002408387\n", + "Difference between predictions with DRFP and str.fp (two-sided) 0.0002605996952975724\n", + "Difference between predictions with DRFP and diff.fp (two-sided) 0.06363682244584662\n" + ] + } + ], + "source": [ + "d = errors_esm1b_drfp_mean - errors_esm1b_ts\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b/DRFP(mean) and ESM1b_ts\", p)\n", + "\n", + "d = errors_esm1b_drfp_mean - errors_esm1b\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b/DRFP(mean) and ESM1b\", p)\n", + "\n", + "d = errors_esm1b_drfp_mean - errors_drfp\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b/DRFP(mean) and DRFP\", p)\n", + "\n", + "d = errors_esm1b_ts - errors_esm1b\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b_ts and ESM1b\", p)\n", + "\n", + "d = errors_drfp- errors_str_fp\n", + "w, p = wilcoxon(d, alternative='two-sided')\n", + "print(\"Difference between predictions with DRFP and str.fp (two-sided)\", p)\n", + "\n", + "d = errors_drfp- errors_diff_fp\n", + "w, p = wilcoxon(d, alternative='two-sided')\n", + "print(\"Difference between predictions with DRFP and diff.fp (two-sided)\", p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Plotting predictions versus experimental values:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading predictions for the best model (ESM1b/diff. fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "model = \"ESM1b_ts_DRFP_mean\"\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_test[\"n_values\"] = [len(data_test[\"kcat_values\"][ind]) for ind in data_test.index]\n", + "n_values = np.array([len(data_test[\"kcat_values\"][ind]) for ind in data_test.index])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6850700979619714, 4.842505224693218)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(abs(pred_y-test_y)), 10**np.mean(abs(pred_y-test_y))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(0.06823529411764706, 0.027777777777777776)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "\n", + "\n", + "plt.ylim(ymax = 5.1, ymin = -3.5)\n", + "plt.xlim(xmax = 5.1, xmin = -3.5)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "\n", + "\n", + "reg = LinearRegression().fit(test_y.reshape(-1,1), pred_y.reshape(-1,1),)\n", + "reg.score(test_y.reshape(-1,1), pred_y.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([-3.5,5.1], [beta0 + -3.5*beta1, beta0 + 5.1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "\n", + "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "\n", + "for i in range(len(test_y)):\n", + " if n_values[i] <= 2:\n", + " plt.scatter(test_y[i], pred_y[i], alpha = 0.6, s=30, c=\"navy\")\n", + " else:\n", + " plt.scatter(test_y[i], pred_y[i], alpha = 0.9, s=30, c=\"darkorange\")\n", + " \n", + "\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"scatter_plot.eps\"))\n", + "plt.show()\n", + "np.mean(n_values > 2 ), np.mean(n_values[test_y < 1e-1] > 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "\n", + "\n", + "plt.ylim(ymax = 5.1, ymin = -3.5)\n", + "plt.xlim(xmax = 5.1, xmin = -3.5)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "ax.text(1.9, -2, r'$R^2=0.44$', fontsize=22, c = \"grey\")\n", + "ax.text(1.9, -2.4, r'$MSE=0.81$', fontsize=22, c = \"grey\")\n", + "ax.text(1.9, -2.8, r'Pearson $r=0.67$', fontsize=22, c = \"grey\")\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "\n", + "\n", + "reg = LinearRegression().fit(test_y.reshape(-1,1), pred_y.reshape(-1,1),)\n", + "reg.score(test_y.reshape(-1,1), pred_y.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([-3.5,5.1], [beta0 + -3.5*beta1, beta0 + 5.1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "\n", + "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "plt.scatter(test_y, pred_y, alpha = 0.6, s=30, c=\"darkblue\")\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"scatter_plot.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Comparison to the results of the DLkcat model" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "model = \"ESM1b_ts_DRFP_mean\"\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_test[\"y_true\"] = test_y\n", + "data_test[\"y_pred\"] = pred_y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a) First, we need to calculate the maximal sequence identity for all proteins in the test set compared to all proteins in the training set:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (a)(i) Creating a fasta file for every sequence in the training set and for every sequence in the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "'''for ind in data_test.index:\n", + " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", + " \"test_seq_\" + str(ind) + \".fasta\"), \"w\")\n", + " ofile.write(\"> seq_test_\" + str(ind) + \"\\n\" + data_test[\"Sequence\"][ind] + \"\\n\")\n", + " ofile.close()\n", + " \n", + " \n", + "train_sequences = list(set(data_train[\"Sequence\"]))\n", + "for ind, seq in enumerate(train_sequences):\n", + " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", + " \"train_seq_\" + str(ind) + \".fasta\"), \"w\")\n", + " ofile.write(\"> seq_train_\" + str(ind) + \"\\n\" + seq + \"\\n\")\n", + " ofile.close()''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (a)(ii) Calculating the maximal pairwise sequence identities (Calculations were done on a HPC):" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "'''from Bio.Emboss.Applications import NeedleCommandline\n", + "import os\n", + "from os.path import join\n", + "import pandas as pd\n", + "import sys\n", + "import time\n", + "import numpy as np\n", + "\n", + "\n", + "arg = int(sys.argv[1])\n", + "\n", + "CURRENT_DIR = join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\")\n", + " \n", + "def calculate_identity(fasta_file_1, fasta_file_2):\n", + " needle_cline = NeedleCommandline(asequence = fasta_file_1, bsequence = fasta_file_2,\n", + " gapopen=10, gapextend=0.5, filter = True)\n", + "\n", + " out = needle_cline()[0]\n", + " out = out[out.find(\"Identity\"):]\n", + " out = out[:out.find(\"\\n\")]\n", + " percent = float(out[out.find(\"(\")+1 :out.find(\")\")-1].replace(\" \", \"\"))\n", + " return(percent)\n", + "\n", + "\n", + "identities = []\n", + "for i in range(len(data_test)):\n", + " ident = calculate_identity(fasta_file_1 = join(CURRENT_DIR, \"test_seq_\" + str(arg) + \".fasta\"),\n", + " fasta_file_2 = join(CURRENT_DIR, \"train_seq_\" + str(i) + \".fasta\"))\n", + " identities.append(ident)\n", + "\n", + "\n", + "ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(arg) + \".txt\"), \"w\")\n", + "ofile.write(str(max(identities)))\n", + "ofile.close()''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (a)(iii) Mapping the results to the test DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...ESM1bESM1b_tsgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECDRFPy_truey_predmax_ident
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9......[0.020693962, 0.16804111, 0.0377352, 0.1768811...[0.83155197, 0.08632717, -0.42143562, 0.419359...2.3404440.6656531.0000000.114660[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...2.3404440.78154420.8
1Reaction_3629Sequence_3212[0.92][Q0PC20][1][0][0][False]MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL...{InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15......[0.07429815, 0.14984865, -0.08539086, 0.098546...[0.13206507, -0.10826899, -0.31126085, 0.95038...-0.0362120.3407411.0000000.090196[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...-0.0362120.53721435.3
2Reaction_375Sequence_26[21.0][Q0GYU4][0][1][0][False]MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL...{InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1......[-0.0272103, 0.2500836, 0.08181338, 0.03990136...[0.3617253, 0.8765441, -1.0668296, 1.5401511, ...1.3222190.1750000.1478871.000000[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...1.3222190.92722540.1
3Reaction_4312Sequence_3788[4.4][Q8ZNC4][0][0][1][False]MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT...{InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,......[0.079942256, 0.23130149, -0.012637342, 0.0787...[0.7798445, -0.7589981, -0.2779501, 0.2643281,...0.6434531.0000001.0000001.000000[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.6434530.95992925.9
4Reaction_2115Sequence_712[4.5][P53602][1][0][0][False]MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD...{InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15......[0.086191244, 0.21010432, 0.1960825, -0.041225...[-0.6100984, -0.054886594, -0.09893316, 0.2822...0.6532131.0000000.8490570.112500[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.6532130.93309849.3
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5 rows × 28 columns

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" + ], + "text/plain": [ + " Reaction ID Sequence ID kcat_values Uniprot IDs from_BRENDA \\\n", + "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] [1] \n", + "1 Reaction_3629 Sequence_3212 [0.92] [Q0PC20] [1] \n", + "2 Reaction_375 Sequence_26 [21.0] [Q0GYU4] [0] \n", + "3 Reaction_4312 Sequence_3788 [4.4] [Q8ZNC4] [0] \n", + "4 Reaction_2115 Sequence_712 [4.5] [P53602] [1] \n", + "\n", + " from_Sabio from_Uniprot checked \\\n", + "0 [0] [0] [False] \n", + "1 [0] [0] [False] \n", + "2 [1] [0] [False] \n", + "3 [0] [1] [False] \n", + "4 [0] [0] [False] \n", + "\n", + " Sequence \\\n", + "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", + "1 MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL... \n", + "2 MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL... \n", + "3 MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT... \n", + "4 MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD... \n", + "\n", + " substrates ... \\\n", + "0 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9... ... \n", + "1 {InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15... ... \n", + "2 {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1... ... \n", + "3 {InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,... ... \n", + "4 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... ... \n", + "\n", + " ESM1b \\\n", + "0 [0.020693962, 0.16804111, 0.0377352, 0.1768811... \n", + "1 [0.07429815, 0.14984865, -0.08539086, 0.098546... \n", + "2 [-0.0272103, 0.2500836, 0.08181338, 0.03990136... \n", + "3 [0.079942256, 0.23130149, -0.012637342, 0.0787... \n", + "4 [0.086191244, 0.21010432, 0.1960825, -0.041225... \n", + "\n", + " ESM1b_ts geomean_kcat \\\n", + "0 [0.83155197, 0.08632717, -0.42143562, 0.419359... 2.340444 \n", + "1 [0.13206507, -0.10826899, -0.31126085, 0.95038... -0.036212 \n", + "2 [0.3617253, 0.8765441, -1.0668296, 1.5401511, ... 1.322219 \n", + "3 [0.7798445, -0.7589981, -0.2779501, 0.2643281,... 0.643453 \n", + "4 [-0.6100984, -0.054886594, -0.09893316, 0.2822... 0.653213 \n", + "\n", + " frac_of_max_UID frac_of_max_RID frac_of_max_EC \\\n", + "0 0.665653 1.000000 0.114660 \n", + "1 0.340741 1.000000 0.090196 \n", + "2 0.175000 0.147887 1.000000 \n", + "3 1.000000 1.000000 1.000000 \n", + "4 1.000000 0.849057 0.112500 \n", + "\n", + " DRFP y_true y_pred \\\n", + "0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 2.340444 0.781544 \n", + "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... -0.036212 0.537214 \n", + "2 [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ... 1.322219 0.927225 \n", + "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.643453 0.959929 \n", + "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.653213 0.933098 \n", + "\n", + " max_ident \n", + "0 20.8 \n", + "1 35.3 \n", + "2 40.1 \n", + "3 25.9 \n", + "4 49.3 \n", + "\n", + "[5 rows x 28 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_test[\"max_ident\"] = np.nan\n", + "\n", + "for ind in data_test.index:\n", + " try:\n", + " with open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", + " ident = f.readlines()\n", + " ident = float(ident[0])\n", + " \n", + " \n", + " data_test[\"max_ident\"][ind] = ident\n", + " except FileNotFoundError:\n", + " pass\n", + "data_test.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Using kcat values from the most similar enzymes from the training set as predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...ESM1b_tsgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECDRFPy_truey_predmax_identsim_pred
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9......[0.83155197, 0.08632717, -0.42143562, 0.419359...2.3404440.6656531.00.11466[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...2.3404440.78154420.82.024332
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1 rows × 29 columns

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" + ], + "text/plain": [ + " Reaction ID Sequence ID kcat_values Uniprot IDs from_BRENDA \\\n", + "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] [1] \n", + "\n", + " from_Sabio from_Uniprot checked \\\n", + "0 [0] [0] [False] \n", + "\n", + " Sequence \\\n", + "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", + "\n", + " substrates ... \\\n", + "0 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9... ... \n", + "\n", + " ESM1b_ts geomean_kcat \\\n", + "0 [0.83155197, 0.08632717, -0.42143562, 0.419359... 2.340444 \n", + "\n", + " frac_of_max_UID frac_of_max_RID frac_of_max_EC \\\n", + "0 0.665653 1.0 0.11466 \n", + "\n", + " DRFP y_true y_pred \\\n", + "0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 2.340444 0.781544 \n", + "\n", + " max_ident sim_pred \n", + "0 20.8 2.024332 \n", + "\n", + "[1 rows x 29 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_train_seq(ind):\n", + " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", + " \"train_seq_\" + str(ind) + \".fasta\"), \"r\")\n", + " return(ofile.readlines()[1].replace(\"\\n\", \"\"))\n", + "\n", + "data_test[\"sim_pred\"] = np.nan\n", + "\n", + "for ind in data_test.index:\n", + " try:\n", + " with open(join(\"..\", \"..\", \"data\",\"enzyme_data\", \"kcat_similar\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", + " ident = f.readlines()\n", + " indices = ident[0].split(\" \")\n", + " indices = [int(float(k)) for k in indices[1:]]\n", + " \n", + " kcats = []\n", + " Sequences = [get_train_seq(k) for k in indices]\n", + " for seq in Sequences:\n", + " kcats = kcats + list(data_train[\"geomean_kcat\"].loc[data_train[\"Sequence\"] == seq])\n", + " \n", + " data_test[\"sim_pred\"][ind] = np.mean(kcats[:3])\n", + " except:\n", + " pass\n", + "data_test.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "data_test = data_test.loc[~pd.isnull(data_test[\"sim_pred\"])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Comparing the results with predictions from the DLkcat paper:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (b)(i) Loading results from DLkcat paper" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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y_truey_predSequencemax_identsim_predsim_pred_1sim_pred_3
0-2.207608-0.071899MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...22.8-1.486273-2.275724-1.486273
1-3.657577-2.707640MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...100.0-2.369079-2.221849-2.369079
20.9493900.831021MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...100.00.9466181.2304490.455934
31.6720981.513026MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...100.01.0455791.6720981.045579
4-1.790485-2.830310MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...99.4-1.7331130.995635-1.733113
\n", + "
" + ], + "text/plain": [ + " y_true y_pred Sequence \\\n", + "0 -2.207608 -0.071899 MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI... \n", + "1 -3.657577 -2.707640 MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA... \n", + "2 0.949390 0.831021 MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG... \n", + "3 1.672098 1.513026 MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS... \n", + "4 -1.790485 -2.830310 MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW... \n", + "\n", + " max_ident sim_pred sim_pred_1 sim_pred_3 \n", + "0 22.8 -1.486273 -2.275724 -1.486273 \n", + "1 100.0 -2.369079 -2.221849 -2.369079 \n", + "2 100.0 0.946618 1.230449 0.455934 \n", + "3 100.0 1.045579 1.672098 1.045579 \n", + "4 99.4 -1.733113 0.995635 -1.733113 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_test_DLkcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"DLkcat\", \"df_pred.pkl\"))\n", + "data_test_DLkcat.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.45244626585841485\n", + "0.44447253110852536\n", + "0.003277284826373785\n", + "0.00655456965274757\n" + ] + } + ], + "source": [ + "help_df =data_test_DLkcat\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"sim_pred\"])\n", + "abs_error_sim = abs(y_true - y_pred)\n", + "R2_sim = r2_score(y_true, y_pred)\n", + "print(R2_sim)\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"y_pred\"])\n", + "abs_error = abs(y_true - y_pred)\n", + "R2 = r2_score(y_true, y_pred)\n", + "print(R2)\n", + "\n", + "d = abs_error- abs_error_sim\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(p)\n", + "\n", + "d = abs_error- abs_error_sim\n", + "w, p = wilcoxon(d, alternative='two-sided')\n", + "print(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (b)(ii) Plotting performances for different sequence identities:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.239011719049261\n", + "0.43631116846784246\n", + "1.1460586039980551e-05\n" + ] + } + ], + "source": [ + "help_df =data_test\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"sim_pred\"])\n", + "abs_error_sim = abs(y_true - y_pred)\n", + "R2_sim = r2_score(y_true, y_pred)\n", + "print(R2_sim)\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"y_pred\"])\n", + "abs_error = abs(y_true - y_pred)\n", + "R2 = r2_score(y_true, y_pred)\n", + "print(R2)\n", + "\n", + "d = abs_error- abs_error_sim\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0-40% TurNuP: R2:0.3282062960029778, Similarity method: 0.016279639392235312, p = 3.3778532715996525e-09 \n", + "0-40% DLKcat: R2:-0.6072304105234347, Similarity method: 0.10837547424294935, p = 0.9957653015502506, p(two-sided) = 0.008469396899498782\n", + "40-80% TurNuP: R2:0.5293866492629986, Similarity method: 0.4960913599652834, p = 0.5531581331990386 \n", + "40-80% DLKcat: R2:0.34280134977895493, Similarity method: 0.1800891304717046, p = 0.5174532691949906, p(two-sided) = 0.9650934616100189\n", + "80-99% TurNuP: R2:0.688850177486657, Similarity method: 0.7281503207725486, p = 0.9851054666995749 \n", + "80-99% DLKcat: R2:0.48622435213243465, Similarity method: 0.3392639181110536, p = 0.8129782958108247, p(two-sided) = 0.3740434083783506\n", + "99-100% TurNuP: R2:0.6749766930471568, Similarity method: 0.20645953288274277, p = 0.0023424625396728516 \n", + "99-100% DLKcat: R2:0.5128517542754034, Similarity method: 0.48197442980722505, p = 6.379793252512187e-05, p(two-sided) = 0.00012759586505024374\n" + ] + } + ], + "source": [ + "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", + "lower_bounds = [0,40,80,99]\n", + "upper_bounds = [40,80,99,100]\n", + "\n", + "points1 ,points1_sim = [], []\n", + "points2, points2_sim = [], []\n", + "n_points1, n_points2 = [], []\n", + "n_points1_sim, n_points2_sim = [], []\n", + "\n", + "for i, split in enumerate(splits):\n", + "\n", + " lb, ub = lower_bounds[i], upper_bounds[i]\n", + " \n", + " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_kcat = len(y_pred)\n", + " R2 = r2_score(y_true, y_pred)\n", + " abs_error = abs(y_true - y_pred)\n", + " \n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_sim = len(y_pred)\n", + " R2_sim = r2_score(y_true, y_pred)\n", + " abs_error_sim = abs(y_true - y_pred)\n", + " \n", + " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_DLkcat = len(y_pred)\n", + " R2_DLkcat = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat = abs(y_true - y_pred)\n", + " \n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_DLkcat_sim = len(y_pred)\n", + " R2_DLkcat_sim = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat_sim = abs(y_true - y_pred)\n", + " \n", + " \n", + " \n", + " points1.append(R2)\n", + " points1_sim.append(R2_sim)\n", + " points2.append(R2_DLkcat)\n", + " points2_sim.append(R2_DLkcat_sim)\n", + " \n", + " n_points1.append(n_kcat)\n", + " n_points1_sim.append(n_kcat_sim)\n", + " n_points2.append(n_DLkcat)\n", + " n_points2_sim.append(n_kcat_DLkcat_sim)\n", + " \n", + " d = abs_error- abs_error_sim\n", + " w, p = wilcoxon(d, alternative='less')\n", + " \n", + " d_DLkcat = abs_error_DLkcat- abs_error_DLkcat_sim\n", + " w, p_DLkcat = wilcoxon(d_DLkcat, alternative='less')\n", + " w, p_DLkcat_two_sided = wilcoxon(d_DLkcat, alternative='two-sided')\n", + " \n", + " print(\"%s TurNuP: R2:%s, Similarity method: %s, p = %s \" % (split, R2, R2_sim, p))\n", + " print(\"%s DLKcat: R2:%s, Similarity method: %s, p = %s, p(two-sided) = %s\" % (split, R2_DLkcat, R2_DLkcat_sim, p_DLkcat, p_DLkcat_two_sided))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MannwhitneyuResult(statistic=338596.0, pvalue=0.012594610167587074)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs_error_turnup = abs(np.array(data_test[\"y_pred\"])- np.array(data_test[\"y_true\"]))\n", + "abs_error_sim = abs(np.array(data_test[\"sim_pred\"])- np.array(data_test[\"y_true\"]))\n", + "mannwhitneyu(abs_error_turnup, abs_error_sim, alternative=\"less\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0-40% 0.3282062960029778 0.016279639392235312 -0.6072304105234347 0.10837547424294935\n", + "40-80% 0.5293866492629986 0.4960913599652834 0.34280134977895493 0.1800891304717046\n", + "80-99% 0.688850177486657 0.7281503207725486 0.48622435213243465 0.3392639181110536\n", + "99-100% 0.6749766930471568 0.20645953288274277 0.5128517542754034 0.48197442980722505\n" + ] + }, + { + "data": { + "image/png": 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+KDL4I9pfSzoyERHpdtFfU//kJz9hxIgRTJo0iW9+85uR4w899FA6QpMBavr06a0SYoBRo0YxYsSIyPtBgwYBfolPdEIMMGXKlFbvm9v2J0mNFBtjcoC/AV9oPtROszzgNOA0Y8x9wFecc/GM3HZVVdR+PCURzW3iKbXolLV2KH4JBMB6YGkqrgvcBvwX/p/1LOCXMWK4ErgyJyeH+fP9XHr69OnMmDEjRaGIiEgsO3fujOxXVVV1ui+SDsuXL2fHjh0AFBYWxswTHnywpRJ033335aCDDur2+HpasuUTd+HPBWyAJuAZ4EVgW3C+BL8m9vTgHhfhj0p3+/dEnufVW2t34j9sNz6OLs1tNqUohIvwfyEA+JvneSmZvs7zvFJr7S7i+Fye590O3D5//nx3/fXXp+L2IiKSgEMOOYTXXnsNgKuuuoqf/OQn7N69O/IgExD5Khpgx44dLF3qj6G8/XbLwqYbNmzggQceAOC4447Tw3mSMmvXrm0148RNN91EYWFhu21ffvnlyIN1GRkZ/N///R8ZGf3vsbRkVrQ7G/g8/sNfy/FHgNd30HYScCcwE7jQGHOmc+6p9tqm2LvADOAAa21WR9OyWWvHAkOi+qRCdOnE31J0zWa9fX5oEREBfvGLX3D++efT1NTE6tWrOf/881udnzBhAv/2b/8Wef/OO+/whS98oe1lWLJkCUuWLAHgzjvv5PLLL+/OsGWAeOONNzjzzDMpLfVner3hhhv4+te/3m7bZ555hrlz51JdXU1GRgZ33HEHZ555Zk+G22OSSfO/GmzfBk7vKCEGCM6dCbwVHOqpWtgVwbYAOC5Gu1nt9EmatXZ//OnTAFZ4nvdxV68Zde0iWqaa25Kq64qISOqdffbZrFy5kosuuohx48aRlZVFbm4uU6ZM4dvf/jarV6+OLPEs0pOWL1/OzJkzKS0txRjDzTffzE9+8pN22z744IOcc845VFdXk5OTw7333tuvfzFLpnziRPwRy/+Jp0bYOddgjPkt/gNnJyZxv2Q8BPxnsP81YFUH7a4ItiHg4RTc98tR+6le1tnSUrudqjplERHpJscffzz//Oc/42o7a9YsnNOXgdK9HnvsMb7whS9QW1tLdnY2d955J5dcckm7bf/4xz/yzW9+k3A4TGFhIQsXLuTUU0/t4Yh7VjJJ8ehg+3bMVq2906Zvt/I871Vr7RL8keDLrbV/9TxveXQba+0lQPN/3b+1neLMWjsJaJ6wb6nnebNi3dNaa4DLgre1wH3xxBqsfFfned5HMdrMBpp/jasF/hzPtUVERETAH/X90pe+RFOTX1H6/e9/n4kTJ7JiRcsX5VOmTKGoqIjf/e53XHfddQBkZmby85//nNzc3FZtDzvsMIYOHdqjn6G7JZMU1+JPdZbIn0Rz3W4qpz3rzL8BLwCDgCettb8CnsX/zLOD8+A/HPjjFNxvBjAp2F/oeV5lnP2OAe4Ikvgn8Kez2wlkAvvjz/Axh5ZR4u96nqfyCREREYnbI488EkmIwV+17sYbb2zVprlufeHChZFjoVAokiBHe/7555k1a1Z3hZsWydQUN9fJzk2gz5xguzaJ+yXF87w3gQuBCvza4huAlfilB9/FTzo3A+emKMlMdlln8P87fBb4DfAU8Cr+wh134/85G2AP8HXP827reqgiIiIiEi2ZpPgx/CTtm8aY8zprbIw5C7gKvw65R9e19DzvSeAw/GTzPfzEshJ4A/gZcJjnea929T7W2gL8BBz8RPuZBLo/jl/3/EfgFfyp4WrxV9rbGlzrh8Akz/Pu6GqsIiIiMvD85S9/wTkX89X8EN2SJUs6bdvfRokBTKKF/caY4fgjvsPwE92/AXcAq5sfvAsW9zgW/0G2r+CPypYBBzjnylMVvMSmeYpFRESkD2lvMbgek/BIcZDUXgg0BP2/AiwD9hhjdhljduKPyC7Hn74tE3/U80IlxCIiMpCtXLmSiy++mLFjx5KRkcHYsWO5+OKLWblyZbpDExnwklqOxDn3PP6KdS/jZ/UGP/kdDowI9puPvwwc75xbkoJ4RURE+pzGxkastUybNo17772XrVu34pxj69at3HvvvUybNo1vfvObNDY2pjtUkQEr2WWecc69CZxojDkWfznnQ/ETYvBLJd4GnnbOvdLlKEVERPqwa6+9lttvvz1mG8/zALjtNj1PLZIOSSfFzYKkV4mviIhIO1auXBlJeDvjeR6XXXYZU6dO7byxiKRUUuUTIiIiEp9bbrklofa33nprN0Ui0ppq3FtLePYJ6Ts0+4SISPqNHTuWrVu3xt0+IyODyZMnk52dTU5ODjk5OZH9RLfd1SczMxNj0jpRgHRBY2Mj11xzTcySHmstCxYsIDs7uwcjS+/sEx2WTxhj9mned85tbO94MqKvJSIi0h+FQiHeeOMNlixZklBCDBAOh/n44487b5hmqUrCuzuBj95mZ2crmUc17h2JVVO8Lti6Nu3WtdM2Xm2vJSIi0uc1NTXx+uuvs2TJEpYuXcry5cvZvXt3UtcaPXo0K1eupLGxkYaGhsg2ej/Rbar7hsPhyLk9e/ak+E+ze2VlZfVYEp6qpD87O5uMjNRUvKrGvWOxEtSOfpXSr1giIjKgNTU1sWbNGpYsWcKSJUtYsWIFlZWVrdrsu+++zJw5k3fffZeXXnop7mufdtppHHDAAakOOaVCoRCNjY09nox3tU9TU1PkVVNTk+4/xoRkZmamJAlftmxZQve99dZblRTjL7yRyHEREZF+qbGxkddee42lS5dGkuCqqqpWbfbbbz9mzZrFzJkzmTlzJvvs41cbrly5kmnTpsV9r6uvvjqlsXeHzMxMMjMzycvLS3coCQmHwzQ1NfWKBD7RvqFQiNraWmpra3v0z2zJkiU9er906jApds79NZHjIiIi/UVjYyOvvvpqZCR45cqVVFdXt2pzwAEHMHPmzEgiPH78+HavNXXqVKy1cX1lba0dMKNy6ZCRkREZRe1LnHM0NTWlJBn/+te/ntC9S0tLu+lT9T6q7xURkQGvoaGBV155JTISvHLlyr1qZadMmdJqJHjcuHFxX3/BggUAMRPj5qf9RdoyxkRqiwsKCrp0rZ/85CcJPfxZXFzcpfv1JQknxcaYLwe7i5xzlTEbt/QZDFwA4Jz7W6L3FBERSaWGhgZWr14dGQl+4YUX9qox/cxnPtNqJHjMmDFJ3y87O5vbbruNyy67jFtvvZUlS5ZQWlpKcXExs2bN4uqrr9YIsfSImTNncu+998bdftasWd0XTC+T8DzFxpgw/iwShznn3o2zz37AR0DYOafR6R6ieYpFRHz19fW8/PLLkZHgF154Ya/azIMOOqjVSHBJSUmaohXpPonWuK9YsaInf2HrnfMUdxPNXCEiIt2urq6Ol19+OTISvGrVKurq6lq1OeSQQyIjwTNmzBhQXxPLwKUa9471VFKcGWybeuh+IiIygNTV1fHiiy9GRoJXrVpFfX19qzaHHnpoZCR4xowZFBUVpSlakfRSjXv7eiopPjDYlvXQ/UREpB+rra3lxRdfjIwEv/TSS3slwYcffnirkeBRo0alKVqR3kU17u3rNCk2xszo4NRxxpjO/oXJBfYD/h2/Dvn1hKITEREBampqWLVqVWQk+KWXXqKhoSFy3hjDEUcc0WokeOTIkWmMWKT3mzp16oBMfjsSz0jxEvyENpoB/pzAfUxwjfjWFRQRkQFtz549rFq1KrJs8ksvvURjY2PkvDGGo446KjISPH36dEaMGJHGiEWkr4u3fKK9B+QSeWjuU+BG59yiBPqIiMgAUV1dzQsvvBAZCV69evVeSfDRRx8dGQmePn06w4cPT2PEItLfxJMUnxK1b4Dn8Ed9vwasi9HPAXXAVufcpqQjFBGRfqe6upqVK1dGRoJXr15NU1PLs9gZGRkcc8wxzJo1i1mzZjFt2jSGDRuWvoBFpN/rNCl2zi2Nfm9MZID45XjnKRYRkYGtqqqKFStWREaCX3nlFUKhUOR8RkYGxx13XKQcYtq0aQwdOjSNEYvIQJPM7BOTg+3mVAYiItIVa9as4f7772f58uVs2LCB0tJS8vPzOeKII7DWMm/evEjbqF/uO3TnnXdy+eWXA/Dwww9z55138sYbb7Bjxw7q6uoYMWIERx11FN/4xjf4/Oc/310fq8+qrKxkxYoVkZHgV199tVUSnJmZyfHHHx8ph5g2bRpDhgxJY8QiMtAlnBQ75zZ0RyAiIl3hed5ec242NDSwbNkyli1bxpo1a/jNb34T9/Wys7Mj+48//jiLFi1qdX779u089dRTPPXUU/zmN7/h3//937sUf1+3e/fuSBK8ZMkSXnvtNcLhcOR8ZmYmJ554YmQkeOrUqRQWFqYxYhGR1rTksoj0G0VFRVxxxRXMmDGDmpoa/vu//5vVq1cD8D//8z9cddVVTJ48meXLl+/Vd/369Vx22WUA5Ofnc9ZZZ0XO7bfffvzwhz/kqKOOYvTo0WzevJkbb7yR999/H4Cbb755wCXFFRUVLF++PDISvGbNmlZJcFZWFieccEJkJHjq1KkMHjw4jRGLiMTWpaTYGFMInAYcAYwC8ok9K4Vzzn2tK/cUEWnPJZdcwk033URBQUHk2MyZMykpKSEUCuGcY/Xq1UyePJlp06bt1f/hhx+O7M+bN6/VHLff//7392pfWFjI3LlzAb9UoL8rLy+PJMFLlizh9ddfx7mW2Tqzs7M56aSTIiPBJ598MoMGDUpjxCIiiUkqKTbGZAA/Ab4HxPuvXvNcxUqKRSTlpk+fvtexUaNGMWLECHbs2AHQYZJWU1PDHXfcEXn/7W9/u8P7NDU1sWHDBu68887IsVNPPTXZsHutsrIyli1bFhkJfuONN/ZKgqNHgk866SQlwSLSpyU7UvwX4BL8RDcE7AKK8JPeT4HhQPP3ZA7YCdR0JVARkUQtX748khAXFhYyY0b7C3TeddddlJX5q9DPnDmTww8/vN12WVlZe82YcN555/HHP/4xxZH3vF27dkWS4CVLlvDWW2+1SoJzcnJa1QSfeOKJrUblRUT6uoSTYmPMmcCl+MnuX/BHi8cBbwI45yYG7Q4EvgVcDZQDc5xz76ckahGRTqxdu7bVjBM33XRThw92LViwILIfa5S4rYyMDLKzs1slj33Fjh07Wo0Ev/XWW63O5+bmcuKJJ0ZGgk888UTy8/PTFK2ISPdLZqT4q8H2HefcFQDGmLFtGznnPgCuM8Y8CzwEPG6MOco5tzvpaEVE4vDGG29w5plnUlpaCsANN9zA17/+9XbbRieEEydOZPbs2R1ed+nSpdTX1/Pxxx9z880389577/HAAw+wceNGXnrppdR/kBTavn17q5Hgd955p9X53NxcTj755MhI8AknnEBeXl6aohUR6XnJJMUn4o8S3xpPY+fcI8aYv+In098Gfp7EPUVE4rJ8+XLOO+88du/ejTGGm266ieuuu67D9r///e8j+1dddRWZmZkdtp06dSoAn/3sZzn11FPZb7/9AHj55Zf58MMPmTJlSmo+RAqUlpZGFspYunQp777beq2lvLw8Tj755MhI8PHHH68kWEQGtGSS4qJg+2HUsUiRnTEm1zlX36bPA8AVwFyUFItIN3nsscf4whe+QG1tLdnZ2dx5551ccsklHbbfuHEjixcvBqCgoKDd0eTmpYezslr/c9l2AZCKioouRt8127ZtiyTBS5YsiUwX1yw/P5+pU6dGRoKPO+44cnNz0xStiEjv05Up2cqi9qui9ouATW3abg+2k7pwPxGRDj344IN86UtfiiSx3//+95k4cSIrVqyItJkyZQpFRUWR93/4wx8iD85dcskljBgxYq/rfvrpp5x00klcdtllHHnkkRQXF7NhwwZuuummSJuCggIOPvjg7vpo7dqyZUurkeAPPvig1fmCggKmTp0aGQk+7rjjyMnJ6dEYRUT6kmSS4lJgH2BEm2MNQDZwOHsnxRODrb6bE5Fu8cgjj0QSYoAbb7yRG2+8sVWb6KWb6+rq+NOf/hQ5F+sBu23btnW4Gl5GRga/+93vun1his2bN7caCf7oo49anR80aFAkCZ41axbHHHOMkmARkQQkkxS/hZ8UHww8D+CcazLGrAGOx68dfqxNn28FWy0RLSK9wj333MPOnTsBOOWUUzj00EPbbTdq1Ch+9KMfsWzZMj755BN27dpFVlYW48aN4+STT+bqq6/m+OOPT3l8n376aWQUeMmSJaxdu7bV+cGDBzNt2rTISPAxxxzTamlqERFJjEl0KiFjzHeB3wKLnXNzo45fDSzAfwjvH8B9+At7fAU4Ozj+G+fcj1ITunRm/vz57vrrr093GCISh40bN7Yqh/j4449bnS8sLIwkwbNmzeLoo4/eq85ZRKSPi7UqcrdL5l/UhfhJ8VnGmGLnXGlw3MMfJT4afx7jS9v02wj8d7KBioj0Jxs2bGg1Erxu3bpW54cMGcL06dMjD8YdddRRSoJFRLpRwv/COufWGWP2BTKByqjjTcaY04HfAxfh1xeDP0L8GPAt51x510MWEel71q9fH6kHXrp0KevXr291fujQoUyfPj0yEnzkkUfGnB5ORERSK6lhB+fc+g6OlwOXGWOuAg4Irr/WOVfWXnsRkf7IOce6detajQRv3LixVZthw4YxY8aMyEjwEUccoSRYRCSNuuW7OOdcFfBad1xbRKS3cc7xySeftBoJ3rSp9SQ8w4cPZ8aMGZGR4MMOO0xJsIhIL6ICNRGRBDnnWLt2bauR4M2bN7dqM2LECGbOnBkZCT7ssMPIyMhIU8QiItIZJcUi0q+tXLmSW265haVLl7Jt2zZKSkqYOXMm11xzTWTZ5s445/joo49ajQRv2bKlVZuRI0dGEuBZs2ZxyCGHKAkWEelDkk6KjTGZwOeAmcC+QCH+w3exOOfcqcneMxnW2vHAtcB5+PMrNwHr8GfRWOB5XtIP/1lrZxHM1RyHv3qed3kc1zwduBI4EX91wF3A68Cdnufdn1SgIgNQY2Mj11xzDbfffnur41u3buXee+/l3nvvxVrLggUL9prf1znHBx980GokeNu2ba3ajBo1KjJH8KxZszj44IOVBIuI9GFJJcXGmBPw5yLeN/pwjC4uOJ/YpMhdZK09C7gHGNbm1JHB60pr7WzP817tybjaY601wK20LHTSbEzwOttauxj4oud59T0dn0hfc+211+6VELfleR7gL/f8/vvvtxoJLi0tbdV29OjRkVHgmTNncvDBB2NMWqfUFBGRFEo4KTbG7Af8CxiMn+g2Ah8CZUA4pdF1gbX2cOAB/AVEavDnSH4W/zPPBr4NjAMetdYe43nelo6uFacrgNUxznc2In0DLQnxW/jxfoC/RPZ1wDT8uP8IfLkrgYr0dytXrowkvJ3xPI/77ruP8vLW/4sWFxe3Ggn+zGc+oyRYRKQfS2ak+Hr8Uokw8DPgf4PZJnqb/8VPiEPA2Z7nLYs6t9Ra+xrwd6AE+AV+UtsV6zzPezuZjtba/YAfBm9fB6Z5nrcneP9KMEL8CHAWcJm19o+e5y3vYrwi/dYtt9ySUPvy8nJKSkpajQQfeOCBSoJFpF+r/aSGihXl1H5cQ6iqiczCLPL3K2DYtOHk71uQ7vB6XDIFcKfhl0Hc6pz7eW9MiK21xwCnBG//0iYhBsDzvH8AzwVvv2ytLeqp+NpxHS2LnVwblRAD4HleE/BNWkbif9BzoYn0PUuXLk2o/ejRo9myZQv33HMP1lqNCotIv+ZCjtL7tvLpgg1Ur6kkVNkEDkKVTVSvqeTTBRsovW8rLtSjVa9pl0xS3Jw8PpDKQFLsgqj9O2K0+3OwzQTO775wOhbUEs8J3n7oed6K9tp5nreBliT+NGvt4B4IT6RPqa+v54knnmDr1q0J9du1a5eSYBEZMLY/uI3KVRUx21SuqmD7g9titulvkkmKdwXbPTFbpde0YFtD7Drf6JkjpnXYqntNAsYH+50NbzXHmwcc210BifQlu3fv5p577uGLX/wio0aN4nOf+1zC1yguLu6GyEREep/aT2o6TYibVa6qoPaTmu4NqBdJpqZ4Nf70ZgfSe1etOzjYfhSUHrTL87wt1toq/BrpgztqF6f51tpx+DNF1ACbgGX+bby34ogV4N1O7vF+m35LkohTpM/bsmULDz/8MIsWLeK5556jsbExcu7II4+kqamJt9+Ov8R/1qxZ3RCliEjvU7EisZloK1aWD5j64mSS4gX4pQbfxJ/urFex1uYCo4K3n8bRZRN+gjmhi7c+OWo/B38auMOAq621vwf+3fO8xnb6jY/a7yze6HVjuxqvSJ/ywQcfsGjRIhYtWsSLL74YOZ6RkcHMmTOZM2cOs2fPZvLkyaxcuZJp0+L/8ufqq6/ujpBFRFLGhRzhhjCuIUy4wQXbcHCs/XOtjjeGcfWOmo8T+6K/dq1GijvknHvWGPMr4EfGmD8CVzvnGlIfWtIKo/ar42jf3CbZGt1twEPACuAT/CnqxgJn4s9oUYA//dtQ4PJ2+icSb/R51RRLvxYOh3nllVdYtGgRCxcu5P33W74oycvL44wzzmDOnDmce+65jB49ulXfqVOnYq2Na1o2a23cK9uJiLTHOQchohLTtslpmHCjw9UH2+jjHSa0UX0awmmb9DZU1eEX7v1OMvMUfxn/a/wX8JO+zxljHgDewy8biMk597dE75mg/Kj9eJL15oUw8mO2at9qYJ92RoBfw5//+BbgGfzR4K9Ya+/3PO+xLsQbvWhHMvGK9GoNDQ0sXbqURYsWsXjxYjZv3hw5N3z4cM4991zmzJnDmWeeyaBBg2Jea8GCBQAxE+PmFe1EeoqmwEoP5xwu5HD1QQLaGCZcH8Y1xjHC2nyuuU+D80dd2/Tp9qQ1AzJyMjA5GWTkmGCbgck2ZOQGx7NNVJsMTI7Zq8+2v28hXBOK+7aZhUkvftznJPNJ/0LrlelKgGvi7OuA7k6Ka6P2c+Jon9tOv7i0nTqtnfMfWGsvpaX299tA26Q4kXhzo/YTjlekN6qurubJJ59k4cKFPPbYY+zevTtybvz48cyZM4e5c+cyffr0vZZjjiU7O5vbbruNyy67jFtvvZUlS5ZQWloaWZTj6quv1gix9BgXcu0+8d88BVb1mkqGnDSMos+XYDIH3kwozjlck+swOe2oFKC95LSja3T7mrrNSWuun5y2JKatk9hWCW2QyJrcqAQ3qk+rBDcrNX8vCg4cRPWayrjb5+8/cH5ZSzb9783/x0bPmxxPiUFzm3hKLRLmed5Sa+17wEHADGtthud50b9PJhJv9PkO47XWXglcmZOTw/z58wGYPn06M2bMSCx4kW6yfft2HnnkERYuXMgzzzxDfX3LlyCHHHJIJBE++uijuzxV2tSpU5X8StrFOwUWQPFFY7o/oAQ553CNUUlrMGrqj7q6YNQ19mhrqwS3nT7dnrRmEiSlbRLV7AxMrvG3MUZYo4+3e66P/DIzbNrwhJLiYVOHd2M0vUsySfHklEeRQp7n1Vtrd+I/bDe+s/ZRbTbFbNU17+AnxXnASGBH1Lnoh+s6izf64boO4/U873bg9vnz57vrr78+wVBFusfHH38ceVBu5cqVfg0eYIzh5JNPZu7cucyePZsDDjggzZGKpFaiU2ANOXZowqUULuyPtMZTCtD2oSx/1LVtrWubPo2u25NWk2n85DQnA5PdzohqO8mpyc4go1Wftglu30tau1v+vgUMOWlYXH8nh5w0bECV9STzoN2G7ggkxd4FZgAHWGuzOpqWzVo7FhgS1ae7xPqnJPq+nU0L95kO+on0Os451qxZE0mE33qrZWbCnJwcTjvtNObMmcN5551HSUlJGiMV6V6JToG1/cFtDD6sMI4E17WUEjR0/8pjJst0Ws9qcjLI6KR8oPUobVA6kK2ktScVfd7/NzdWYtxczjOQ9Nfq6RX4SXEBcBywqoN2s9r06S6HBNt6WhY/abYe2AyMA2Z2cp1ZUdd5JUWxiaRMU1MTy5cvjyTCGzdujJwbMmQI55xzDnPmzOGss85iyJAhMa4k0n/UrE1sCqyGLfWUbanvvGEbJtu0Tk47+7o/JxhRzW2T4Ga3rWf1+5gMJa39hck0FF80hiHHDqViZTm1a6Me/Ny/gGFTB+aDn/01KX4I+M9g/2t0nBRfEWxDwMPdEYi1djotI8Ar2tQT43mes9YuxH9YcYq1dlp7Sz1baycCnw3ePu15XrfUQIskqqamhn/9618sWrSIRx55hLKyssi5MWPGMHv2bObMmcMpp5xCTk48z76K9G1Nuxup21BL7bpa6tbXEq6K/0n/ZiPOGNVJPWubh7CyjZJWSVj+vgUDMvntSL9Mij3Pe9VauwR/ZPVya+1fPc9bHt3GWnsJcGrw9m+e521vc34SsC54u9TzvFltzg8HjvQ8L3qpaNq0ORC4K+rQrR00/R3+YihZwO+ttdOjZ7aw1mYBtwGZwaHfdHRPkZ6wa9cuHn30URYtWsRTTz1FbW3LZChTpkxh7ty5zJkzh+OPP56MjGRWkxfpG1zIUb+5jrr1tdSu95PgpvL21mmKX+aQLEaePbrzhiKSUh0mxcaY54Jd55w7tZ3jyWh1rW72b/hzKQ8CnrTW/gp4Fv8zzw7Og7/4xo+TuP5Q4Dlr7dvAIuBVYAv+4h3jaL14B8C9nuctbO9CnuetDeL7MXAUsCp4/yH+w3XfBZqX5/q753nLkohXpEs2bNjA4sWLWbhwIcuXLycUahn9Ov744yMzRnzmM5+JcRWRvq2pqom6IPmtXV9D/aY6/yG0KCY3g7yJ+eRPyidvUj67V1Ww562qDq64t4E0BZZIbxJrpHhWsG1bvT8rOJbI9zTN7bv/SYCA53lvWmsvxF+KehhwQ/CKthmY7Xneli7c6tDg1REH3AL8eyfX+Sn+jBnfxF8e+q522iwGvpFEjCIJc87x9ttvs3DhQhYtWsSaNWsi57Kysjj99NMjSyuPGzcujZGKdA8XctRvradufU0kEW7ctfcocPboHPImtSTBOSW5rUoZMnIzEkqKB9IUWCK9SaykeBntJ7EdHe91PM970lp7GP6iGecC++DXD68DFgK/9zwvsceCW2wBvgCciP8w33j8pDYfqATW4j+8d4fnee/FEasDvmWtfQiwwXVHA2XA68CfPc+7P8lYReISCoVYtWpVJBH+5JNPIucGDRrE2Wefzdy5c/nc5z7HsGHD0heoSDcIVTdRu6E2kgDXbazda1YHk2PI28dPfvMm5ZM/MZ/MwbErETUFlkjfYJrnCpX+R/MUSzzq6up45plnWLRoEQ8//DA7drRMoz169GjOP/985s6dy6mnnkpeXl4aIxVJHRd2NGyr98sg1tVSt6GWxh0Ne7XLHpkdSYDzJhWQOyY3qanDOlrRLtpAXtFOJJDWv/z98kE7EYmtoqKCxx57jEWLFvHEE0+wZ0/LlFH77rtv5EG5k046iczMzBhXEukbQjUh6oJR4Np1NdRvrCNc32oyIEy2IXdCHvmTCiKJcFZhan5Magos6U1ck6PsmZ3+/xMbagnX+v8vZA3PZvJP92/VdteTOyh7ameH1xp5zmhGnDYq8r78uV3seb+axu0NhGpC4PD/nk/OZ9gpI8kbv/fgigs7Kl+sYPv921biT2ObB2zHn372vw+4+aCXuv6pO6ekWGSA2Lx5M4sXL2bRokU8//zzNDW1rGlz9NFHM2fOHObMmcOhhx7a5aWVRdLJhR0NpQ3Ubaihbp0/K0Tj9r1HgbOGZ5M32S+ByJuUT+64vG4fpdUUWNIbhBvDMRPdrtj9UsVe/781lTdSVd5I9RtVjLtqn1b/D4Qbwmz986fUfLAH4OSobhOC1zJASbGIJM85x/vvvx9ZSOPll1+OnMvMzOSUU06JPCg3ceLENEYq0jWh2pZR4Lr1wchXXZtR4CxD7vg8PwmelE/exHyyhmanKWKRNDOQu08e+ZPyycjLpOxf8SXI46/d+2dF1ojW/x/lT8pnyPFDyRmdQ0ZeJvVb6yl7Ygfh+jAu5Ni9srxVUrzz0e3NCTH4z1Ddgr+w2XDgmGC/R3Q5KTbGFAKTgUJa5tHtkHNO04mJdJNwOMzLL7/MokWLWLhwIR9++GHkXH5+PmeeeSZz5szh3HPPZeTIkWmMVCQ5Luxo3NEQmRO4bn0tDaX1ez3+nTUsK1IHnD8pn5xxuWRkac5sEYDMvEz2+c5kAGrX18adFMfzLUfxxWNbvS+YMoimsgYqlvnzGkT/wtpU2cTuF/zjOcU5NJQ2nHzAzQfVRnV/IK7AUiSppNj4361+A/gWcHgCXV2y9xSR9jU0NPD888+zcOFCFi9ezLZt2yLnRowYwXnnncecOXM444wzKCjQ17bSt4TrQtRtrIvMC1y3oZZwTetRYDIhb3weeVG1wNnDNAoskmrrbviIpsomMvMzyZuUz/DPjiR/csc/V8KNYRq21rPn3ZZFePOnDIrs17xf7c8JBuSMzaOhtOG+j77z3kn4NcWrgRsOuPmgDhdJS7WEE1RjTDb+YhVnNR9KZUAi0rnKykqefPJJFi5cyOOPP05lZWXk3D777BNZSGPatGlkZen3UOkbnHM07myMzAtcu76Whq17jwJnDsmKzAmcNymf3PF5ZGRrFFikuzWV+8+ihKpD7Hm7mj3vVlNyyVgKjx7aql3tJzV8umBDq2MmxzBs6nCGTW+Zh7t+a31kv3pNJfjT5zabBUz/6DvvXXjAzQctSukH6UAyPy2/B5wd7G8A/gq8AVQA4Q76iEgXbdu2jYcffphFixbx7LPP0tDQ8iDDYYcdFkmEjzzySD0oJ31CuD5M3aagDCKYFi20J9S6UQbkjs+LSoILyBqepb/jIj3EZGcw+MhCCj4zmKxhWTRVNFH+zE4adzZCGLY/WMqgQwvJyIn9i6nJMGBMq19y29b+A/8HPAJcBszDL8td8NF33nv4gJsP6vYcM5mk+LJguwo4zTlXG6uxiCTvo48+ijwot2rVKprnFTfGMG3aNObOncvs2bPZb7/90hypSGzOOZp2NbYsjrGuxh8lalsJMTizVS1w7oS8Tn/Y9gV1n9ZR/UYltR/X0FTeSKgq5E8BNy6PIScNY8gxLSNtH32n0/WeKL54DEOOH9aNEYv4Rpy69/Mn+fsVsGH+xwCEa0LUraul4MCWsoicsbmMv3Yi4YYw9Z/WUf7cLsK1Ycqf2wXOMer8YsB/ALZZ1tAsmnY3XXvAzQeFP/rOe0uBOUAB/uJoBwHvdNuHbI4hiT6T8fP8XyohFkkt5xyvvvpqJBF+552WfwNyc3MjSyufd955FBUVpTFSkdjCDWHqN9X5D8QFU6OFqtsZBR6X69cCB1OjZY3M7pejwLtfKN9r4Q4XctR+XEPtxzXUb65jdJAoxEULfEga5YzKIXNQZuSbnabqplbnM/MyIw/lDfrMYDILs9h+71YAKl+tjCTFWVG1/1nDs5n8swPCAAfcfFDtR995bzswKTg9pBs/TksMSfQpB0qAT1Mci/QDX//617njjjsi7++55x6+9KUvAcT1g+7OO+/k8ssv3+t4fX09Rx99NO+++27k2NatWykpKel60GnW2NjIsmXLIonwp5+2/K81dOhQzj33XObMmcOZZ55JYWFhGiMVaZ9zjqbypla1wPWb6/YaBc4YlOnPCTw5KIWYkE9Gbt8fBY5X5uBMhpwwjPz9Cgg3+KNm9RvrAKhYUsawqcPJHpnT7rRXjWWNlN61BfAXGRn0mUF7tRHpDnUbasmbmN/qWMOOhlalTllD/HQy3BDu9JudcG1Lv/x9W67bVN7IR995zxxw80Huo++8lwdEj/xsTP4TxC+ZpPgN/KR4Ev58ciIAPP30060S4mRkZ7f/xPgNN9zQKiHu6/bs2cNTTz3FwoULefTRR6moqIicGzt2bGQhjZkzZ5KTk5O+QEXaEW70vxJtToDr1tcSqmw9UoTxv0KNrgXOHtU/R4HjMeSYoYyeU9wqYSjYr4BP/usj/5cHB3Ub68gemdPutFd73i6N7BcePYTMQXqAVrqm6nX/Ae3GnS3Pp7jGcOR49ohs8vbJZ9Pv1lNw4CAGH15I9sgcGssbKX+mZQq3zEK/5Amg+vVKypeUUXj0EHKKcjC5GTRsrqfs2Zb2uRNaVrTLn1xAzthcGrbU07S7CeD3H33nvUeBS/FLJwBWHXDzQZu748+grWT+r7oFf+aJr+HPQiFCdXU13/jGNwDIy8ujrq5urzbLly/f69j69eu57DK/TD0/P5+zzjprrzZr1qzh17/+dYfX7St27NjBo48+ysKFC3n66adbfZaDDjookggfe+yxZGQMnNEz6f0aKxpbFsZYX0P9p/W4UOspITIKMsibmN+yRPI+eWTkaYnwZvn77Z3oZg7OIrMgM1JWkpHT/i8M4YYwu1/aHXk/bMaI7glSBpRtf907zwxVhyLHC48bSsm8fHBQ8/4eat7fs1d7k2Uovnhsq9lfGrbWs+uxHe3eMyM/g9FzWpcJFV88ls23bmh+6O6a4NWsHLgywY+WtISTYufc48aYBcC1xpjfAj90zoU66yf92w9/+EM2bNjA2WefTU1NDUuXLt2rzbRp0/Y69vDDD0f2582bt9eCEk1NTVxxxRU0NTUxf/58fvjDH6Y++G60bt06Fi9ezMKFC1mxYgXhcMv3ySeeeGIkET7wwAPTGKVIC9fkqN8c1AIH5RBNFe2MApfkRqZEy5+UT/boHP/pcolb7cc1LQlxbka7iTNA1au7Cdf47fL3KyB3bF677US6w5grxlP9VpU/O0xlE+HGMFlDssnfv4Dhnx1JbklupG3exHyGnjyM2vW1NO1uIlwbIiM7g6yR2RQcOIhhM0bsNYd43vg8Jnx3MmVP7aDq1cptwChgO/AU/jzF63vqsyb1/Ytz7t+MMRuAXwAXGmMeAj4EauLo+7dk7im917Jly/jDH/7AkCFDuP3227n00kvj6ldTU9Oq3OLb3/72Xm1++ctf8vrrr3Pcccfxve99r9cnxc453nzzTRYuXMiiRYt44403Iueys7MjD8qdf/75jB07NsaVRHpG0+5G6jYEZRDraqn/tA7X1GYUOM8fBW5OgvMm5pOZr1HgrmjY0cC2f7SM1I2aU9ThyHrF8vLI/rAZw9ttI5KoA24+KK52gw8rZPBh8T3PklOcS9EXxiQcS87oHEouHUfJpeMS75xCya5ol4+/JnUjMAH4tzi7OkBJcT9SW1vL1772NZxz/Pa3v2X8+PFx973rrrsoKysDYObMmRx+eOvFEd955x1+8YtfkJOTw5133klmZu/8IdzU1MTKlSsjD8qtX78+cm7w4MF87nOfY+7cuZx99tkMHTq04wuJdDMX8keB69bX+lOjraulqbxxr3bZRTl+LfDkfPImFpBTrFHgVKrfXMdmbyOhKn/0d8TZoxh6YvvJbs3aPf4CJvhP5w86VA/binSXZFa0KwD+BZzUfCilEUmfcv3117N27VpOO+20SE1xvBYsWBDZbztKHAqF+OpXv0pDQwM///nPOeSQQ1ISb6rU1tby9NNPs2jRIh555BF27mx5iKCoqIjZs2czd+5cPvvZz5KbmxvjSiLdp6mqKVILXLu+hvpNdbjG1qPAJjeDvH2CxTEmF5C3Tz6Zg3rnL6D9Qe3HNWz50ya/ftLAqNnFDJ/ZcY1w9Cjx0KnD9cuJSDdKZqT4u8DJwf6LwO1oRbsB6f333+d3v/sdgwcP5o9//GNCfZcuXcpbb70FwMSJE5k9e3ar83/+859ZvXo1Rx55JD/60Y9SFnNXlJWV8dhjj7Fo0SKefPJJampaqoX2339/5s6dy5w5czjhhBN67ai29F8u5KjfWh+pA65bX0vjrnZGgUfnREoh8ifnk1OSq0Srh+x5p4qtf93s/2KS6T9gFL1oR1uN5Y3sebsK8JfIHXrisB6KVGRgSiYpnodfBvEEcL5zTonwALVt2zbC4TDV1dVMnjy53TYXX3wxF198MeXl5QwbNixy/Pe//31k/6qrrtoridy82a+1e/311zucpm3MmDEcccQRvP766137IDFs2rSJxYsXs2jRIpYsWUIo1PJM6THHHBNJhA8++OABO9WUpEeouqlldbj1tdRtrMU1tBkFzjHk7dNSC5w/MZ/MwZrKKx2q3qhk2982R4aOhp8ykuzh2dR+0vLLdfboHLIKW/777F5ZHmlfePRQjeCLdLNk/nWcFGx/p4RYkrFx40YWL14MQEFBAV//+tfTHFEL5xzvvvsuixYtYuHChbz66quRc5mZmZx66qnMmTOH2bNnM2HChDRGKgOJCzsattW3mhe4cUfDXu2yR2a3PAw3qYDcMbkYrXzWK+x5p7rVd6nlz+yi/JldrdpEL90cbgxT+WJF5JwesBPpfl1Z0W5nZw2lf9t///25+eab9zp+yy238PHH/proX/7ylznqqKPIz29ZteYPf/hDZMT1kksuYcSIvevpzjrrrFYjy82+853vRPZvuOEG9t13365+DMLhMC+++GIkEV67dm3kXEFBAWeddRZz5szhnHPOaTdWkVQL1YSo21DbsjrchlrC9a3HIEy2IXdCXsu8wJPyW40ySt9W9VplZMWw/P0LyB2jadhEuptxznXeKrqDMYuA84B5zrl/dkdQkhrz5893119/fY/fd9asWZF5iqOXeQaoq6tjwoQJkQfT3nrrLQ499NC4rx1dotCVZZ7r6+t57rnnWLhwIQ8//DClpS2rRY0cOZLzzz+fOXPmcPrpp7dK6EVSzYUdDaUN1G0IlkheV0vj9r1HgbOGZ0fmBM6blE/uuDyNAotIf5PWf9SSGVb4HX5SfI0x5j6XaFYtA9o999wTSYhPOeWUhBLirtq9ezePP/44ixYt4vHHH6e6ujpybtKkSZGFNKZOnUpWlkbcpHuEaltGgZu3wUpOESbLkDs+r9XiGFlD26+tFxGR1Eh4pBjAGPMj4EbgfsA65ypSHJekQLpGinuTLVu28PDDD7No0SKee+45GhtbnsY/4ogjmDNnDnPnzuXwww/Xg3KScs45Grc3ROqA6zbU0rCt3n9UOUrWsKyoxTEKyB2fS0aWlvoWkQGnb40UG2N+Guy+DHwB+Jwx5mniX9HuhkTvKZKIDz74ILKQxosvvhg5npGRwYwZMyIjwh3NmCGSrHBdiLqNdZF5ges21BKuafM8cqa/rGnexJZa4OzhGgUWEUm3ZL4j/hkt4xwOGATM7rD13pQUS0qFw2FeeeWVSCL83nvvRc7l5uZyxhlnMHfuXM4991xGjx6dxkilP3HO0bizMTIvcO36Wn/lsTajwJlDslrXAo/PIyNbo8AiIr1NsoWTbYe39b2z9KiGhgaWLl3KokWLWLx4cWReY4Bhw4Zx3nnnMWfOHM444wwGDx6cxkgl3Wo/qaFiRTm1H9cQqmoiszCL/P0KGDZtOPn7FsR9nXB9mLpNUfMCr6+NzA4QkUGkFjg/KIXIGp6l0hwRkT4g4aTYOachDkmL6upqnnzySRYtWsSjjz7K7t27I+fGjx8fKYuYMWNGhwt+yMDhQo7tD26jclVFq+Ohyiaq11RSvaaSIScNo+jzJXvN4uCco2lXY6vFMeq31O21Zmfm4Myoh+EKyJ2QR0aO/okUEemL9Ii99Grbt2/nkUceYeHChTzzzDPU19dHzh1yyCGRRPiYY47RaJy00l5C3Fbz+dFziqnfVOc/EBdMjRaqamcUeFwueVHzAmePzNbfOxGRfiKZB+3K8avmfu6c23vlBhnwVq5cyS233MLSpUvZtm0bJSUlzJw5k2uuuYapU6d22v/jjz+O1AevXLmS5hlSjDGcfPLJkUT4gAMO6O6PIn1U7Sc1nSbEzSpXVVD5UsVeo8AZgzLJn9iyRHLePvlk5GoUWESkv0pmpDgfyMaffUIkorGxkWuuuYbbb7+91fGtW7dy7733cu+992KtZcGCBa3KG5xzrFmzJpIIv/XWW5FzOTk5kaWVzz///KQX65CBpWJFeWIdwpAzNpe8iS0PxGWPztEosIjIAJJMUrwNmADUd9ZQBpZrr712r4S4Lc/zAH8p6OXLl0cS4Y0bN0baFBYWcs455zBnzhzOPvtshgwZ0q1xS/9T+3Gns0O2klGYycTvd33JcBER6buSSYpfxE+KDwVeSW040letXLkykvB2xvM87rnnHiorKyPHSkpKmD17NnPnzmXWrFnk5uZ2V6gyAISqmhJqH64Odd5IRET6tWSS4tvwF+34jjHmbudcQ4pjkj7olltuSah9ZWUlU6ZMYe7cucyZM4fjjz+ejAzVa0pqZBZmEaqMPzHOLNQzxyIiA10yU7ItMcbMB34MPG6MucI5t7GzftK/LV26NKH2o0aN4v3331fNpnSL/P0KqF5T2XnD5vb7xz9fsYiI9E/JLvPcBLwJfBb42BjzAvAGUA7E/B5Syzz3T9u2bUuofVlZmRJi6TbDpg1PKCkeNnV4N0YjIiJ9QSqWec4EpgWveCgp7odKSkrYunVr3O2Li4u7MRoZ6PIm5ZM5JL4SiiEnDUtoZTsREemfki3iNFGvtu87e0k/NHPmzITaz5o1q3sCEQF2v1BOqLIJkxX7n5zmFe1ERES0zLOkxDXXXMO9994bd/urr766G6ORgaxhRwM7H9kOQPGlY8kqzKJiZTm1a2sIVTWRWZhF/v4FDJs6XCPEIiISoUeuJSWmTp2KtTauadmstXGtbCeSKBd2lN6zBdfgGHzUEAqP8Oe4VvIrIiKd0aivpMyCBQuw1sZs07yinUh3qFhWRt26WjILMyn6vOrWRUQkfkqKJWWys7O57bbbWLFiBRdffDFjxowhIyODMWPGcPHFF7NixQpuu+22Vks8i6RKQ2k9ux7bAUDRRWPIHKQvwkREJH5d+qlhjDkFuAI4CSgB8oDDnXPvRrWZgb/6XaVz7h9duZ/0DVOnTlV5hPQoF3Jsu3sLrslReNxQBh9amO6QRESkj0kqKTbG5AN34q9sBy2zSrh2moeBWwBnjHnJOfdRMvdMlrV2PHAtcB6wD/4cy+uAhcACz/PKu3DtbPy5mk8HTgQOBIYBNcAGYCngeZ73difX+RnwX3He9que5/0luYhF+qfy53dRv7GOrKFZjJ6rsgkREUlcsuUT9+AnxAZ4Bfifjho651YAzSPHFyR5v6RYa88C3gJ+ABwEDAKGAkcC/w94y1p7TJLXHg1sBZ4EvgdMBUbh/6IxBDgMuAZ4w1o7v0sfREQ6VL+1jrIndwJQ9KUxZOZnpjkiERHpi5JZ0W42cD7+qPBVzrnbguPfi9FtIXAwMBP47yTiTJi19nDgAfxEuCa477P4n3k28G1gHPCotfYYz/O2JHiLXGBksP82sBhYBWwL7vlZ4Dr8JPw/rbVhz/N+Esd1D+vk/KcJxinSb7mQo/SuLbiQY8hJwxj0mcEdti29dwuVL+2OvC+5bCyFRw8FoPbjGqrfqqJ2XQ1NFU2E9jSRkZdJ3oQ8hs0YwaCDOr6uiIj0D8mUT1webO9tTojjsDrYHpTE/ZL1v/jJaQg42/O8ZVHnllprXwP+jl8L/Qv82uhEOOAZ4L88z3uhnfPLrLV3AS/gjyD/yFr7Z8/z1sW6aGelFiLSouzpndRvridreDajzy/qsN2eD6pbJcR7XeeZndS8v6fVsfCeEDXv76Hm/T2MvqCYYdNHpCxuERHpfZIpnzgePyG8O4E+zev/dvxTK4WCkohTgrd/aZMQA+B53j+A54K3X7bWJhSb53mbPc87vYOEuLnNR7Qsa50FzEnkHiLSsbpNtZQ97ZdNFF88hoy89ssmwvVhtv9zGwAmu+MV7rJGZDPi7FGMtRMovnQs2aNzIud2PrqdcEM4hdGLiEhvk8xI8ahguzmBPqFg21NTwEXXLt8Ro92f8cscMvFLQv7UDbE8H7W/fzdcX2TACTeFKb1nK4Rh6LThFBwwqMO2Ox/ZTlN5IwUHDcI1OGo/rtmrzfBTRpK/XwEmsyVpzi3JZeNv/S92XIOjYWs9eRPzU/9hRESkV0gmSa0KtiUJ9Nkn2O5K4n7JmBZsa2gp3WhPdMI6rcNWXZMTtR/qsJWIxK3sqZ00bK0ne1Q2o87t+Eue2o9r2P1CORl5GRRdNKbDdgVTBrVKiIFWI8UAJkfTuouI9GfJjBSvBY4DDsGfeSEe5wbbnqqXPTjYfuR5XlNHjTzP22KtrQIKo/qk2syo/fc6a2ytfQp/dozhQAXwIfA0cJvneaXdEJ9In1K3oZbyZ3eBgeKLx5KR236yGm4IU3rvFnAw6vwisocltmhM9RtVkf3skdnkFOfEaC0iIn1dMkMfT+JPxXatMabTpNoYcxRwKX4d8mNJ3C8h1tpcWko84pmpYVOwndANsQzCn4ECoB5/horOnIFfe50NjMaf6u1nwCfW2stTHaNIXxJuCLPtbj/RHTZzBPn7FnTYdtfjO2jc2Uj+lEEMPWl4Qvep21DLjof8OmQMjL6wBJPRcT2yiIj0fckkxbfgl1BMAO40xuR21DCYvu1J/BKCncSu702V6KWsquNo39ymO+Zc+i0tpSO3dDLt29vAfPzp4o4FTgDmAQ/h/0JRANypxFgGsl1P7KBxewPZRTmMPHt0h+0aSuupWFaGyc2g+IuJVHpBzQd7+PQPGwnXhf3R6C+NiTnVm4iI9A8Jl08453YaY76Bv4DHPOB0Y8zjUU1+YIzJwB/hnIQ/qhwCLnPO7f2ES+pFPwnTEEf7+nb6dZm19grgm8Hbd4BYcxT/r+d5P2vn+MvAPdba2cD9+KPHC6y1j3uetz2V8Yr0drWf1FCxtAwMlMwbS0aMGt+mqiZw4OrDrP/5x+222fb3LWz7+xb2vXFKZMGPqjcqKf27P++xyTQUXzqWwiOHdMvnERGR3iWpJ0ecc/cBFwGV+F/1f4WWJZ4vAy4BJuMnxJXA551z/+pytPGpjdqPpwiweaS7NmarBFhrzwaa53DeCVzgeV6H1/c8ryLW9TzPW4w/igz+iPbXUhCmSJ8Rrg9Teo9fNjH81JHdMgvE7lXlbPvrZlzIkZGbwdgrJyghFhEZQJJ50A4A59yDxpjngavwpzM7Mup6jpZV3n7nnNvZxTgTURW1H893ns1t4im16JS1dgbwIP6o7m7gTM/zPkzBpW8D/gv/F41ZwC9jxHAlcGVOTg7z5/u59PTp05kxY0YKwhDpeTsf3U7jzkZyxuQy4sxRnbbPHpXDqDnFex3fvaKMxp2NABQeO5Tc8XmYbEP50jJ2LgqeY82AEWePxmQZaj9p+XIrZ0yulpAWEenHkk6KAZxzZfirwf0iKJkYgT/n7y7nXIezPnQnz/PqrbU78R+2Gx9Hl+Y2m2K2ioO19njgUfxSjD3AOZ7nvdbV6wJ4nldqrd1FHJ/L87zbgdvnz5/vrr/++lTcXiRtaj7aw+4V5ZABxfPGkpHV+Rdc2cOyGT5z7xXo9rxVFUmKBx00KLLM8563on6XDtOSIEcZd/U+FOzf8XzIIiLSt6Vs4k3nXNg5t9M5V5quhDjKu8H2AGtth4m/tXYs0Pz96LsdtYuHtfYI/IcKC/HrlOd4nreyK9dsh+u8iUj/EaoL+Yt0ACNOH0Xe+Lw0RyQiIv1VwiPFxpjn8JOzK5xzG+LsMxb4B+Ccc6cmes8krABm4M/YcBywqoN2s9r0SYq19iD8uYSHA43AFzzPeybZ63VwjyJappqLNYuFSL+x82F/Nbrc8XmMOL3zsonOjL9mYkLHRURk4EhmpHhW8Erke8T8qH494aGo/VgPpV0RbEPAw8ncyFq7H/AM/pzCIeBSz/MeSeZand0Kv54YYGk3XF+kV9nzfjWVqyr8WSDmjdlrxTkREZFU6pfrlnqe9yqwJHh7ubV2ets21tpLgOZR67+1neLMWjvJWuuC15K2/YM2E4BngbH4o+df8zzvvkRitdYeZq09oJM2s2mZ0q0W+HMi9xDpa0K1IbbfG5RNnDWK3DEqmxARke7VpQftEtA8qlzXQ/cD+DfgheDeT1prf4WfwGbhL5Dxb0G7bcCPE724tXYk/ghx8/eutwKvWmsPjdFtj+d569ocOwa4I0i8nwDexJ/GLRPYH/gCMIeWUeLvdrIIiEift2NhKU27m8ibmMfwU0amOxwRERkAeiopPjvYxrPsckp4nvemtfZC/EVGhgE3BK9om4HZSSaZhwFTot5fE7xiWUr7JSQZwGeDV0f2AP/meV5PrAookjbVb1dRtXo3JttQPG+syiZERKRHdJoUG2M6+qr+F8aYik665wL74T/s5ujhWljP85601h4GfBs4F3/J5RCwDlgI/N7zvPKejKkdj+PXPZ8IHAUU4z9QlwGU4a+G9zTwZ8/zenK+Z5EeF9rTxPb7/LKJkZ8bTU5Rh6vIi4iIpJRxLvYsX8aYMK2nAmsetol3erDm9mXAcc65tuUD0k00T7H0NVv/tpnqNZXkTc5n/DUTMRkaJRYRGUDS+o9+POUTG2mdAE8M3m/Fn36sIw6/hngrfm3vH5xzqoUVkXZVvV5J9ZpKTE5QNqGEWEREelCnSbFzblL0+2DkGOAM51yXFrwQEQFoqmpixwPbABh1XhE5o3LSHJGIiAw0yTxotwx/FHhPimMRkQHIOcf2B7YR2hMi/4AChp48PN0hiYjIAJRwUuycm9UNcYjIAFX1WiV73qwiIzeD4i+pbEJERNKjXy7eISJ9Q9PuRnY8FJRNzCkie0R2miMSEZGBqktJsTHmFGPM340xa40x1caYJmPMwW3azDDGXGWMubRroYpIf+Kco/S+bYRrwhQcNIghJwxLd0giIjKAJbV4hzEmH7gTf7U1iD1NWxi4BXDGmJeccx8lc08R6V8qX95NzbvVZORlUHzRGIxR2YSIiKRPsiPF9+AnxAZ4Bfifjho651YAzbNUXJDk/USkH2ksb2TnolIARl9QTNYwlU2IiEh6JZwUG2NmA+cHb69yzp3gnPt+J90W4ifQMxO9n4j0L845tt+7lXBdmEGHDqbw2KHpDklERCSpkeLLg+29zrnb4uyzOtgelMT9RKQfqVxVQc2He8goyKToCyqbEBGR3iGZpPh4/NrhuxPoszXYFiVxPxHpJxp3NbBjsV82UXRhCVlDknqsQUREJOWSSYpHBdvNCfQJdeF+ItIPuLCj9J6tuAbH4CMKKTxqSLpDEhERiUgmSa0KtiUJ9Nkn2O5K4n4i0g/sXlFO7cc1ZA7OpOjCRP75EBER6X7JJMVrg+0hCfQ5N9i+ncT9RKSPa9hez85HtwNQdNEYMgerbEJERHqXZJLiJ/FnkrjWGNPpTzZjzFHApfh1yI8lcT8R6cMiZRONjsJjhjD4sMJ0hyQiIrKXZJLiW/BLKCYAdxpjcjtqGEzf9iSQA+wE7kgmSBHpuyqWlFG3vpbMIVmMvkBlEyIi0jsl/B2mc26nMeYb+At4zANON8Y8HtXkB8aYDGAqMAl/VDkEXOacq+l6yCLSV9Rvq2fXEzsAKP7iGDILMtMckYiISPuSKuxzzt1njAkBf8KfZu0rtCzxfFmwbZ58tBL4snPuX10JVET6FhdylN69BdfkGHLCUAYdPDjdIYmIiHQo6SnSnHMPAvsBPwVexR8NNrQkw28D84H9nXMPdzFOEeljyp/bRf2mOrKGZTFqdnG6wxEREYmpS4+AO+fKgF8AvwhKJkYAmcAu51xTCuITkT6ofnMdu54Kyia+NJbMfJVNiIhI75ayeZGcc2H8h+lEZABzTY5td2+BEAydOoyCAwelOyQREZFOaYU5EUmpsqd30rClnuyR2Yw6T2UTIiLSNygpFpGUqdtYS9kzO8FA8cVjycjVPzEiItI3dFg+YYz5pBvu55xz+3XDdUUkzcKNYUrv3gJhGDZjBPn7FaQ7JBERkbjFqimeFOc1mqdiM3Ecd4hIv1T25E4aShvIHp3DyHNGpzscERGRhMRKiv/aSd8jgSPwk94KYA1QGpwrDs4Px0+E3wheItIP1a6vofz5XX7ZxLwxZOSobEJERPqWDpNi59xXOzpnjLkCfzW7T4HvAQvbTsFmjMkELgB+AxwM3Oqc0zLPIv1MuCFM6d1bwcHwz44kf5LKJkREpO9JeDjHGHMscBuwAzjBOXd/e3MSO+dCzrn7gROBXcD/BX1FpB/Z9dgOGnc0kFOcw4izRqU7HBERkaQk8x3nd/AX6LjRObe1s8bOuW3AjUA28N0k7icivVTtxzVULC+DDCi+ZCwZ2SqbEBGRvimZn2DTg+3LCfR5KdhOS+J+ItILhevD/iIdDkacNoq8CfnpDklERCRpySTFzY+V5yXQp7mtHkkX6Sd2PlJKU1kjueNyGXG6yiZERKRvSyYp3hFsz0mgT3NbLQMt0g/UfLCH3SsrINNfpMNktZ2RUUREpG9JJil+Dn8atuuMMad01tgYMxO4Dn9qtmeTuJ+I9CKh2hCl924BYOQZo8kdl8iXRiIiIr1TMknxr4B6IAd4yhhzmzHmRGNMdnMDY0x2cOwPwNNALtAQ9BWRPmzn4lKaKprInZDH8FNHpjscERGRlIi1eEe7nHPvG2O+Avwj6P+N4BU2xlTijwgPpSXhNkAT8FXn3PspiVpE0mLPu9VUvrQbk2UonjcWk6myCRER6R+Smj/JOXcfMBV4FT/pNfjTtA0HRgT7zcdfA6Y55+5NRcAikh6hmhCl9/mzMI48ezS5JblpjkhERCR1Eh4pbuacWw0cFyzIcRpwGH5CDFAOvAU8E7QTkT5ux0PbCO1uIm9SPsNmjei8g4iISB+SdFLczDn3CvBKCmIRkV6q+q0qql6txGQHZRMZKpsQEZH+RctPiUhMoeomtgdlE6POLSJndE6aIxIREUk9JcUiEtP2B7YRqg6Rv38BQ6cNT3c4IiIi3UJJsYh0qGpNJdVvVGFyDMVfGqOyCRER6beUFItIu5oqm9j+wDYARp1fTPZIlU2IiEj/1eUH7Xo7a+144FrgPGAf/DmT1wELgQWe55Wn6D7HA1cBM4ExQCXwDnAXcKfneaE4r3M6cCVwIlAE7AJeD65xfypiFemMc47t928lXBOiYMoghp48LN0hiYiIdCvjnEt3DN3GWnsWcA8wrIMmm4HZnue92sX7/CfwczoeeX8BODdWAm6tNcCtwLdi3Gox8EXP8+rjiWv+/Pnu+uuvj6epSCuVq3dTevcWMvIy2OcH+5I9PLvzTiIiIl2T1hq9fls+Ya09HHgAPyGuAf4LmAbMAm4GQsA44FFr7dgu3OcKYD7+n+UGwALHA+cCjwTNTgYWWmtj/XnfQEtC/BZwKXAccCGwIjg+G/hjsrGKxKOpopEdDwVlE3OKlRCLiMiA0J/LJ/4XGISf/J7ted6yqHNLrbWvAX8HSoBfAFckegNr7TDgt8HbzcAJnueVRjV5zFr7R+Dr+GUVlwJ/a+c6+wE/DN6+DkzzPG9P8P4Va+1i/AT7LOAya+0fPc9bnmi8Ip1xzlH6z62E68IMOngwQ44fmu6QREREekSHI5fGmC8HryE9GVAqWGuPAU4J3v6lTUIMgOd5/wCeC95+2VpblMStvoa/tDXAj9okxM2+A+wO9r/fwXWuA5qH466NSoibY20CvgmEg0M/SCJWkU5VvlRBzft7yCjIoOiiEozRbBMiIjIwxPo6/y/AncD46IPGmOeMMc8aYyZ2Z2BddEHU/h0x2v052GYC53fhPlVAuw/BeZ5XHXXuUGvt/tHng1riOcHbDz3PW0E7PM/bQEsSf5q1dnAS8Yp0qLGskZ2LtgMw+oISsoaqbEJERAaOZGqKZwWvQSmNJLWmBdsaYHWMds+30ycu1tps/NphgBc7efgt1n0m0fKLx9JObtt8nTzg2DjCFImLCztK791CuD7MoMMLKTy6z31BJCIi0iWxkuLmJK+wJwJJsYOD7UdB6UG7PM/bgj/KG90nXlNoqcl+t5O277cTW3vvu3IdkaTtfqGc2o9qyByUSdGFKpsQEZGBJ1ZS/GmwndETgaSKtTYXGBW8/TRW28CmYDshwVtFl5V0dp9NUftt75Oq64gkpWFnAzsfCcomLiwhq7A/P38rIiLSvlg//Z7Bn15svjHmOOADoDHq/FXGmO2J3tA5d0OifRIUPbJdHUf75jaJ1ugmcp/o823vk6rriCTMhR2l92zBNTgGHzWEwiNVNiEiIgNTrKT4Rvw5ckcCn29zzhB7kYlYujspzo/ab4ijfXOZSH7MVl27T3S9cdv7pOo6IgmrWFZG3Se1ZBZmUvT54nSHIyIikjYdJsXOuU3GmKOBnwCn4i90kQM4/KS4txYd1kbt58TRPredfqm+T27Uftv7pOo6CfvLX/6y17FDDjmE4447jsbGRu666669zh955JEceeSR1NTUcN999+11/thjj+XQQw9l9+7dLFy4cK/zJ510EgceeCA7d+7k0Ucf3ev8jBkz2Hfffdm2bRtPPvnkXudPPfVUJkyYwKZNm3j22Wf3On/WWWdRUlLCJ598wrJle83Ex7nnnsuoUaP44IMPWLVq1V7n586dy9ChQ3n77bd55ZVX9jp/0UUXUVBQwOuvv87rr7++1/lLLrmE7OxsVq9ezTvvvLPX+csvvxyAF154gQ8//LDVuezsbC655BIAli5dyrp161qdLygo4KKLLgLgmWee4dNPW1fbDBkyhAsu8CdEefLJJ9m2bVur8yNHjuS8884D4JFHHqFm8x6O+PAwMsngrZHv8uHyTzjrrLMAeOihh6isrGzVf/z48Zx22mkA3HfffdTU1LQ6P3nyZGbOnAnAXXfdRWNjY6vzU6ZM4eSTTwb0d2+g/93btWtXq/MlJSX6u6e/e/q7p797kb8r6RKzeNA5twm4MvqYMSaMnxgf5pzr7MGwdKiK2o+nxKC5TTylFsneJ/p82/uk6joR1torgStzcnKYP38+ANOnT2fGjD5VHi7dKQwHbNqPTJdB6fAdlA+toISSdEclIiKSNsY5l1iH3p8UY63dgf+w3Rue5x3ZSdvdwBBgted5x8dq26bfIcDbwdvfeZ53XYy2RwOvBm9/43neD6LOnQM0//r2Hc/z/jfGdS4AHgzeXu153v/FinH+/Pnu+uuvj9VEBqiyZ3ay67EdZA3NYp8f7ktmfma6QxIREUlrFUIy8xR/FX9J5HhmdkiX5mT9AGtth6Ph1tqx+AlxdJ94fQg0T/fW2fRon2kntvbed+U6InGp31pH2ZM7ASj60hglxCIiIiSRFDvn/hq8KjtvnTbNq8IVAMfFaDernT5x8TyvEXg5eHuitTZWPXCs+6wHNgf7Mzu5bfN16oG9C79EOuFCjtK7t+JCjiEnDmPQZzSJiYiICCQ3UtyKMSbXGDPDGHO1MeYnwevq4Fhu51foFg9F7X8tRrsrgm0IeLgL9ykELmqvQbAcc/O5tz3PWxt93vM8BzRXx0+x1ra7sp61diLw2eDt08Hy0SIJKXtmJ/Wf1pE1PJvRs4vSHY6IiEivkXRSbIwZZIz5LbANf/nh3wM/C16/D45tM8b8xhjTo0tCe573KrAkeHu5tXZ62zbW2kvwZ9UA+JvnedvbnJ9krXXBa0nb/oE7gPJg/5fW2vayjJuAocH+bzq4zu9oKcX4vbW21Z9XUAJyG9D8PXdH1xHpUN2ndZT9yy+bKL54DBl5KpsQERFpltTSVcaYCcBzwL7ELooeCnwXmG2M+axzrifrkP8NeAEYBDxprf0V8Cz+Z54dnAc/qf9xMjfwPK/CWvt94E/4K9O9ZK29EXgdGI2/+Mn5QfOlwD86uM7aIL4fA0cBq4L3H+KvXPddoHkE+e+e5+0974lIDOGmMKV3b4EwDJ02nIIDevT3VBERkV4v4ZFiY0wW8ASwH35C/AHwI/x6188Er1nAD4H3gzb7A48bY3psaMrzvDfxFx+pwK8tvgFYiZ+cfhd/1HUzcK7neVu6cJ878JPZMDAJuB2/1vgxWhLiF4ALPM8Lx7jUT/FHgwEOA+4CVuOXaDQnxIuBbyQbqwxcZU/tpGFrPdmjshl1rsomRERE2kqmfOJr+LMkOODXwKHOuV8755Y55z4MXsucc7/BT+7+O+h3CLHre1PO87wngxh+A7wH7AEqgTfwyzwOC0otunqf+cBJwN+ADfgPwu3ET8C/AczwPK+sk2s4z/O+BZyBP+3aZvwV7rYBTwIXeZ43x/O8+hiXEdlL3YZayp/dBQaKLx5LRm6XHyUQERHpd5KZp/gZ/Ae+HnHOzY6zz8PAucCzzrnTE45SkqJ5iiXcEGbj/6yjcXsDw2aNYPRsLeUsIiK9Vp+bp/gw/FHiPyXQ54/B9vAk7iciSdr1xA4atzeQXZTDyLNHpzscERGRXiuZpHhYsE3kobnmtkNjthKRlKn9pIaKpWVgoGTeWDJyVDYhIiLSkWR+SjZPQTYhgT7jg21FEvcTkQSF68OU3rMFHAw/dSR5E/PTHZKIiEivlkxS/FawTWQWhK8H2zeTuJ+IJGjnY9tp3NlIzphcRpw5Kt3hiIiI9HrJJMX34xdCfy5YmKPDadaMMRnGmPnAefh1yPcnF6aIxKvmoz3sXl4OGVA8bywZWSqbEBER6Uwyi3f8GbgWf4q15oU5/gq8CGzHT36LgROAy4ApQb+3g74i0k3CdSFK790KwIjTR5E3Pi/NEYmIiPQNCSfFzrkmY8zn8FeH2x9/EY8bYnQxwEfA55xzoaSiFJG47Hh4O01ljeSOz2PE6SqbEBERiVdS36s65zYBR+IvilGBn/i296rAX+DjaOfc5i5HKyId2vN+NZWrKjCZhuJ5YzCZaZ3uUUREpE9JpnwCAOdcDfBDY8yPgWOBQ4ERweky/HKJV5xzjV2OUkRiCtWG2N5cNnHWKHLHqGxCREQkEUknxc2CpHdV8BKRNNixqJSm3U3kTcxj+Ckj0x2OiIhIn6PH0kX6uOp3qqh6eTcm21B88ViVTYiIiCRBSbFIHxba08T2f/plEyM/N5qc4tw0RyQiItI3KSkW6cO2P1RKqCpE3uR8hs0Y0XkHERERaZeSYpE+quqNSqpfq8TkGIrnjcVkqGxCREQkWUqKRfqgpqomdty/DYBR5xWRMyonzRGJiIj0bUqKRfoY5xzbH9hGaE+I/AMKGHry8HSHJCIi0ucpKRbpY6rXVLLnzSoycjMo/pLKJkRERFJBSbFIH9K0u5HtDwZlE3OKyB6RneaIRERE+gclxSJ9hHOO7fdtI1wTpuAzgxhywrB0hyQiItJvJLyinTHmp8Hu/znndsbZZzhwLYBz7oZE7ykiULV6N3verSYjL4OiL47BGJVNiIiIpEoyyzz/DHDAA0BcSTEwIqqfkmKRBDWWN7JjYSkAoy8oJnuYyiZERERSSeUTIr2cc47t/9xKuC7MoEMHU3js0HSHJCIi0u/0VFLcPKzV2EP3E+k3KldVUPPBHjIKMin6gsomREREukNPJcVHBtsdPXQ/kX6hcVcDOxb7ZRNFF5aQNSSZiicRERHpTKc/YY0xX+7g1GxjzLGddM8F9gOuwK8nXp1YeCIDlws7Su/dimtwDD6ikMKjhqQ7JBERkX4rnmGnv+AntNEM8IsE7mOAMPC7BPqIDGi7V5RTu7aGzMGZFF1Yku5wRERE+rV4yydM1Ku9Y7FejcBK4Hzn3NLUhC3SvzXsaGDno9sBKPpCCZmDVTYhIiLSneL5STs5at8An+CPHJ8JfBSjnwPqgF3OuVDSEYoMMC7sKL17C67RUXjMEAYfrrIJERGR7tZpUuyc2xD9PurJ9y1tz4lI11UsLaNufS2ZQ7IYfYHKJkRERHpCwt/JOuc0t7FIN6nfVs+ux/1JWoq/OIbMgsw0RyQiIjIwKMEV6SVcyFF6zxZck2PICUMZdPDgdIckIiIyYCgpFuklyp/bRf3GOrKGZTFqdnG6wxERERlQkn6k3RgzHLgUmAnsCxQCnX3X65xz+yV7T5H+qn5LHbueCsomvjSWzHyVTYiIiPSkpJJiY8xs4M/AsOZDcXZtO9+xyIDnmvzZJgjB0KnDKDhwULpDEhERGXASToqNMUcD9wV9DbAVeA0ow1+gQ0QSUPb0Tuo315M1IptR56lsQkREJB2SGSn+EZAN1ANXAv9wzmkEWCQJdZtqKXtmJwDFF48hI1dl/iIiIumQTFI8Db8M4tfOub+nOB6RASPcFKb07q0QhmEzhlOwv8omRERE0iWZYakRwfaJVAYiMtCUPbGThm31ZI/OYeQ5RekOR0REZEBLJikuDbZNqQxEZCCpXV9L+fO7wEDxvDFk5KhsQkREJJ2S+Um8ItgelspARAaKcEPYn23CwfBTRpI/qSDdIYmIiAx4ySTFNwEh4DpjTHaK4xHp93Y9voPGHQ3kFOcw4qxR6Q5HRERESCIpds69ClwHHAIsMsaMTnVQIv1V7cc1VCwrgwwovmQsGdkqmxAREekNkpmn+KfB7svA2cB6Y8wzwHtATWf9nXM3JHrPZFlrhwPXAnOByfifdyPwCLDA87xPu3j9DGAqcGaw/QwwEn+6us3ASuBPnuet6uQ6lwN3xnnb/+d53s+SDFnSKFwfpvQev2xixOmjyJuQn+6QREREJJDMlGw/o2VlOgfkA+cGr3j0SFJsrT0GWAyMa3PqoOB1pbX2Ys/znuzCbdYB+7RzPBs4MHhdYa29E/im53kNXbiX9HE7H9lO465GcsbmMuJ0lU2IiIj0Jkkt88zeyzrHu8xzj7DWjgUeA4rx659/j58gNwGn4i9AMgx4wFp7sud5byZ5q+aEex3wIP7I8KdADv7I8XeAMcBX8RPly+K45pnAlhjntycZq6RRzYd72L2yHDKhZN5YTFav+l9GRERkwEs4KXbO9YUiyPn4CTHAVzzPuyvq3Epr7VLgOWAQcDN+opyM1fgj3096ntd2Vb8XrLV/xZ+t4wDgUmut53neirYXaeNDz/PWJxmP9EKhuhCl9/q/54w8YzS54/LSHJGIiIi01RcS3IRYa4toGZF9tk1CDIDneUuBvwRvPxuUWiTM87yTPM97op2EuPn8duB7UYe+kMx9pG/buaiUpvImcifkMfzUkekOR0RERNrR75JiYDaQGezfEaPdn6P253ZfODwftb9/N95HeqE971ZT+dJuTJaheN5YTKbKJkRERHqj/pgUT4vaXxqj3cu0zJYxLUa7rsqJ2g91432klwnVhCi9bysAI88eTW5JbpojEhERkY4k+6AdAMaYU4ArgJOAEiAPONw5925UmxnAoUClc+4fXblfnA4Otrs9z+vwgTXP8xqttR/jr8x3cEftUmBm1P57cbS/01o7BRgNVAGf4I82/8HzvHXdEJ90kx0LSwntbiJvUj7DZo1IdzgiIiISQ1IjxcaYfGPMvcAzwDxgX6CA9mehCAO3AH81xhyQbKAJGB9s45mDeFOwHW2tTfkwXjCP8X9EHbovjm6zgLH4s1WMAI4Fvg98aK39jxj9pBepfquKqld2Y7KDsokMlU2IiIj0ZsmWT9yD/9CYAV4B/qejhs65FUDzyPEFSd4vEYXBtjqOttFtBndDLN8Hjgv2H/Q879UYbdfh/zleCByPnwx/Hvgr0Ig/qn+jtfZn3RCnpFCouontQdnEqHOLyBmd00kPERERSbdkVrSbDZyPv3DHVc6524Lj34vRbSF+icJM4L+TiDMRzcuExbNQRn07/VLCWns6/tRwANuAb8VovhD4azuzWLwKPGSt9YAngSHAT6y1D3qe91Yq45XU2f7gNkLVIfL3K2DotOHpDkdERETikExN8eXB9t7mhDgOq4PtQUncDwBr7TigowxjT1S9bS3+/MPxDM9Fl0zUJhtbW9bao4EH8GfBqAUu9DxvR0ftPc/bHet6nuetstZ+G38auQzgGsCmKl5Jnao1lVS/XoXJMRR/aYzKJkRERPqIZJLi4/FHie9OoM/WYFuUxP2azQe+0sG5pfi1uOA/nDaI+MohotvEU27RKWvtQcBT+KO6jcDnPc9bmYJL3wUswC8PmdVJDFcCV+bk5DB/vj9YPX36dGbMmJGCMKQjTVVNbH9wGwCjzi8me5TKJkRERPqKZJLiUcF2cwJ9mqci64kp4D7FnwljfGcNgQnBdofnefUxW8bBWrsf/sOHo/A/8zzP857o6nUBPM9rstZ+gF9rHPOzeZ53O3D7/Pnz3fXXX5+K20snnHNsv28r4T0hCqYMYujJw9IdkoiIiCQgmaS4Cr+MoSSBPvsE211J3A8Az/Mup6V0I5Z38RPHodbasR1Ny2atzQb2i+rTJdbaCcCz+DNHOOAKz/Me6Op122h35TxJv6pXK9nzdjUZeRkUfWkMxqhsQkREpC9JZuR2bbA9JIE+5wbbt5O4X6JWRO3P7LCVPytEQTt9EmatLcFPiCcGh67yPO9vXblmO/fIAg4M3nY4/7L0vKaKRnY8FJRNzCkme3h2miMSERGRRCWTFD+JPxXbtcaYTkeajTFHAZfij3I+lsT9EvUwLeUaX4vR7oqo/YXJ3sxaOwq/ZKJ5DubveZ4X7wOIibgYv04ZYq/UJz3IOUfpfVsJ14YZdPBghhw/NN0hiYiISBKSSYpvwS+hmADcaYzpcNGLYPq2J/FngtgJ3JFMkInwPK8U+Hvw9lRr7by2bay1M2gpxXi+o/mDrbUueK3v4PxQ/IfqmkfNf+p53k2JxGutnRTMVhGrzUn4D9mB/8vF/yVyD+k+lS/tpua9PWQUZFB0UYnKJkRERPqohGuKnXM7jTHfwF/AYx5wujHm8agmPzDGZABTgUn4o8oh4DLnXE3XQ47Lj4GzgWLgb9baY4DFQBNwGvBD/OnS9gDXJXODYAW8x4DmhHYhsNBae2iMbg2e533Y5tgk4Hlr7UvAI8DrQCl+8jsRf07oS2j5b/Vrz/NeSyZmSa3GskZ2LioFYPQFJWQNVdmEiIhIX5XMg3Y45+4zxoSAP+FPs/YVWh4CuyzYNg+ZVQJfds79qyuBJsLzvM3W2nPwE+FxwHeDV7QK4GLP895M8jZj8BP/ZnODVywb8JPg9pwQvDrSCPw/4MY445Nu5MKO0nu3EK4PM+iwQgqPHtJ5JxEREem1kkqKAZxzDxpjngeuwh/NPDLqeg7/obrFwO+cczu7GGfCPM971Vp7GPBt/GR1Mv7o8EbgUeD3nud92tNxteNV/JrrE4Fj8GevGAVk4yfuHwDPA3/yPG9TmmKUNna/UE7tRzVkDsqk6AsqmxAREenrjHOpmeUrKJkYgZ947nLONaXkwpI0zVPcPRp2NrDxN5/gGhwlXxlH4ZEaJRYREUmBtI4wJT1S3JZzLoz/MJ1Iv+XCjtJ7tuAaHIOPGqKEWEREpJ/oiRXmRPqNiuXl1H1SS2ZhJkWfL053OCIiIpIiSopF4tSwvZ5dj20HoOiiMWQOStkXLSIiIpJmHf5UN8Y8F+w659yp7RxPRqtrifQVLuwovXsrrtFReNxQBh9amO6QREREJIViDXXNCrZtn8SbFRxLpBi6uX1qnuoT6WHlz++ibkMtWUOzGD1XZRMiIiL9TaykeBntJ7EdHRfpl+q31lH2hP8MadEXx5CZn5nmiERERCTVOkyKnXOzEjku0h+5UFA2EXIMOXEYgw4anO6QREREpBvoQTuRGMqe2Un9p3VkDc9m9OyidIcjIiIi3URJsUgH6jfXUfYvv2yi+OIxZOSpbEJERKS/UlIs0g7X5Nh29xYIw9Bpwyk4YFC6QxIREZFulHBSbIw51BjziTHmI2PMuDjajzfGrDXGfGyMmZJcmCI9a9e/dtCwpZ7sUdmMOldlEyIiIv1dMiPFlwCTgLXOuc2dNXbOfQp8FPS5LIn7ifSouo21lD+7CwwUXzyWjFx9oSIiItLfJfPTfib+lGyPJtBnMf48xackcT+RHhNuDFMalE0MmzmC/H0L0h2SiIiI9IBkkuIDg+0bCfR5u01fkV5p1xM7aChtILsoh5Fnj053OCIiItJDkkmKm9e3rUqgT3PbIUncT6RH1H5SQ8WSMjBQMm8sGTkqmxARERkokvmpvzvYJvL0UfOQ254k7ifS7cL1YUrv2QIOhp86kryJ+ekOSURERHpQMknxx8H2swn0OT3Yrk/ifiLdbtdj22nc2UjOmFxGnDkq3eGIiIhID0smKX4G/6G5bxlj9umssTFmIvBN/Ifznk7ifiLdqmbtHiqWl0MGFM8bS0aWyiZEREQGmmR++v8BqMevLX7WGHNcRw2NMcfjJ9GFQCPwf8kEKdJdwnUhSu/ZCsCI00eRNz4vzRGJiIhIOmQl2sE5t9kY8wPgd8C+wIvGmFXAcmBr0GwMMAM4sbkb8J/OuQ1dD1kkdXY8vJ2mskZyx+cx4nSVTYiIiAxUCSfFAM65BcaYfGA+kAmcFLzaMkAI+Ilz7qakoxTpBnver6ZyVQVkQvG8MZhMk+6QREREJE2SLp50zv0aOBq4G6jAT4CjXxXAP4CjnXO/6mqgIqkUqg2x/Z/+FxsjzxpN7hiVTYiIiAxkSY0UN3POvQVcaowx+Ms4N0+9thNY55xzXQtPpHvsWFRKU0UTufvkMfyUkekOR0RERNKsS0lxsyD5XRe8RHq16neqqHp5NybbUDJvrMomREREJPnyCZG+KLQnqmzic6PJKc5Nc0QiIiLSGygplgFlx0PbCFWFyJucz7AZI9IdjoiIiPQSHZZPGGP+HOw659zX2jmejFbXEulJ1W9WUvVaJSbHUDxvLCZDZRMiIiLii1VTfDn+/MIAX+vgeCJM0E9JsfS4puomtt+/DYBR5xWRMyonzRGJiIhIbxIrKd5I+8lvR8dFeiXnHDvu30aoOkT+AQUMPXl4ukMSERGRXqbDpNg5NymR4yK9VfWaSqrfrMLkZlD8pTEqmxAREZG96EE76deadjey/UG/bGL07CKyR6hsQkRERPbWYVJsjAkbY5qMMQe3Of7l4DWk+8MTSZ5zju33bSNcE6bgM4MYcuKwdIckIiIivVRni3e09z3zX/Bril8B3k11QCKpUrV6N3verSYjL4OiL47BX3hRREREZG+xyidCwTYlq96J9KTGikZ2LCwFYPQFxWQPy05zRCIiItKbxUqKy4Ptfj0RiEiqOOfYfu9WwnVhBh06mMJjh6Y7JBEREenlYo0CvwKcBdxojKkDPgQao86PMcZUJ3pD59zGRPuIJKJyVQU1H+whoyCToi+obEJEREQ6FyspvhU/KZ4CPNrmnAH+lcT9XCf3FOmSxrIGdjy8HYCiC0vIGqK/biIiItK5DssnnHOPAdcAlfhJcPOrmUnyJdItXNhRes9WXH2YwUcUUniUJkgRERGR+MQcRnPO/Z8x5k7gWGAckAvciT/i+xNgc7dHKBKn3SvKqV1bQ+bgTEZfWJLucERERKQP6fS7ZedcLbC8+X2QJAMscs5pSjbpFRp2NLDz0aBs4gslZA1W2YSIiIjEr8PMwRizT7C72TkXijq1FH+keE93BiYSL79sYguu0VF4zBAGH66yCREREUlMrOG09UAYOJzWi3QsQUmx9CIVS8uoW1dL5pAsRl+gsgkRERFJXDIr2v0XflL8ALAz5RGlkLV2OHAtMBeYjP95NwKPAAs8z/u0i9efBKyLs/lSz/NmxXHN44GrgJnAGPwHHd8B7gLu9DwvFKP7gNNQWs+ux3cAUPzFMWQWZKY5IhEREemLYi3e0RRsc3sikFSz1h4DvAX8P+BIYCgwCDgI+AHwlrX2rLQF2A5r7X8Cq4CvAJPw/+xHA7OAPwLLgkRfABdybLt7C67JMeSEoQw6eHC6QxIREZE+KlZS3DwKfEhPBJJK1tqxwGP4M2aEgJvxE8tp+CPdtcAw4AFr7eEpuu2PgcNivL7aScxXAPPx/5tsACxwPHAu/sg2wMnAQmttrP9uA0b5c7uo31hH1rAsRs0uTnc4IiIi0ofFKp94AbgA+G9jzBDgA1qvaHecMWZUojd0zi1LtE8S5gPNWdJXPM+7K+rcSmvtUuA5/JHjm4FTU3DPzZ7nvZ1MR2vtMOC3zdcBTvA8rzSqyWPW2j8CX8cvq7gU+FsXYu3z6rfUseupoGziS2PJzFfZhIiIiCQv1ojjTfijrCXAAvwV7J4Pzhngz8H7RF7PpfwTtGGtLQIuC94+2yYhBsDzvKXAX4K3nw1KLdLpa0BzWcSP2iTEzb4D7A72v98jUfVSrslRevcWCMHQk4dRcOCgdIckIiIifVysFe2aR4o/pm+taDcbaB42vCNGuz9H7c/tvnDickGwrQLub6+B53nVUecOtdbu3xOB9UZlT++kfnM9WSOyGXW+yiZERESk6zpb0e4R4BFjzAT8+tw8/NFehz+6Ge/MCz1pWtT+0hjtXgZqgII2fXqUtTYbv3YY4EXP8+pjNH8ev4QC/JjXdmdsvVHdplrKnvHL3YsvHkNGrsqrRUREpOviWvbLObcJ2ARgTGSw9+VeuqLdwcF2t+d5Wzpq5Hleo7X2Y/yH4A7uqF0CrrXWXg9MABqALcBK/GnUVsToN4WW/w6d/Xm+H7Wfipj7lHBTmNK7t0IYhs0YTsH+KpsQERGR1EhmmO1vwas8xbGkyvhgG88cxJuC7WhrbVennjsa2B9/GrVC4EDgCmC5tfaf1trCDvqNj9rvLOZNUfsTkg20ryp7cicN2+rJHp3DyHOK0h2OiIiI9CMJJ8XOucudc191zm3tjoBSoDn5rI6jbXSbZCe5rcB/aO9yYCpwFHAW8CugLGhzEfCQtba9kfnoZLmzmFMRb59Uu76W8ud2gYHieWPIyFHZhIiIiKROXOUTnTHGjMefpaIAWO2cq03FdZOUH2wb4mgbXb+b32Grjm0BxnmeV9POuaestb8HnsRfKvs0/LmHb23TLvq+ncXc1Xj7pHBDmNJ7toCD4Z8dSf6kgnSHJCIiIv1M0kmxMaYQ+Hf8EoGxUacOI6o21hhzMf7sDrudc99I9n7W2nG0TFvW1h7P85of+qvFn384J47LRpdMJJzIe57XQIxE1vO8rdbaz+P/eWQD32bvpDj6vp3F3KV4+6pdj++gcXsDOcU5jDgr4amxRURERDqV1HfQxpj9gFfwV3EbR+zp1l7En3LsCmPM1GTuF5iPv2xze687o9pVBdt4ygui28RTbpEwz/PWAs8Eb6dYa8e0aVIVtd9ZzHHFa6290lr7yrZt25g/fz7z589n2bKeWDMl9Wo/rqFiWRlkQPG8sWRkq2xCREREUi/hkWJjTC7wKHAA/pRmtwLLaFmKuBXn3DpjzDL8ldjOx5+RoTt9il/KMb6zhrQ8rLajk6nQuuod4OxgfzwQXY8d/XBdZzFHP1y3qaNGnufdDtw+f/58d/311ycSZ68Srm8pmxhx2kjy9hkwFSMiIiLSw5Ipn7gSf2aFGmCmc+5VaDVVW3seB2YBJyVxPwA8z7sc/2G2zrwLHAsMtdaO7WhatmB+4P2i+nQnF+Pch0AT/n+LzqZZ+0zUfm+cDi+ldj66ncZdjeSMzWXEGaPTHY6IiIj0Y8l8F/15/CRvQXNCHIc3gu0BSdwvUdFzAs+M0e44/AcD2/bpDodE7bdK0j3Pa8RfSATgRGttrLriWVH73R1zWtV8uIfdK8ohE0rmjcVk9cRiiCIiIjJQJZMUNyd4TyXQZ2ew7ehBuVR6GAgF+1+L0e6KqP2F3RWMtXY/4PTg7cee521up9lDwbYQf/q29q4zOOrc20Gtcr8UqgtReq//u8PIM0aTOy4vzRGJiIhIf5dMUjwk2FYm0Cc72DYlcb+EeJ5XCvw9eHuqtXZe2zbW2hm0lGI873leuyPe1loXvNZ3cH6utbbDIczgoboHafn8bWeeaHYHLYuh/NJa297KFDcBQ4P933R0z/5g5+LtNJU3kTshj+Gnjkx3OCIiIjIAJFNTvAsoBvYBXouzT3MtbGkS90vGj/EfbCsG/matPQZYjJ+Unwb8EMgE9gDXdeE+DwGfWGsfwi+B2ATUAUXAKfj11yOCtsvoICn2PK/CWvt94E/4D9u9ZK29EXgdGI0/v/H5QfOlwD+6EHOvtue9aipfrMBkGYrnjcVkqmxCREREul8yI8VvBttEHpqbh1+H/HJnDVMhKFE4B9iMn/x+Fz+ZXAn8P/xa4grgQs/z3uzgMvHaF3++5vuAVcAa/NKSH9GSEP8TOC+Y17ijmO/AT+bDwCTgdvw/r8doSYhfAC7wPC/cxZh7pVBNiNJ/+hNzjDx7NLklXV15W0RERCQ+ySTFi/DnJP6WMabTKQGMMV8GzgjePpDE/ZISlEQcBvwM/0G/SvyR4ffwyw8O8zzvyS7e5jzgRuBZYC1+ot2EXwrxOvB/wPGe533J87xOy008z5uP/8vG34AN+CvY7cRP6L8BzPA8r6zjK/RtOxaWEtrdRN6kfIbNGtF5BxEREZEUMc7Fmi2snQ7GZOMnlpOBt4GvOudeM8aE8UeDD3XOvWeM2Qf4DnAtfhL9hnPu6JRGLzH1pXmKq9+qYuufP8VkG/b598nkFGmUWEREZIBJa81kwjXFzrlGY8wc/BrZw4DVxpgNUU0eCJaAHhe8N8AO4MIuxir9VKi6ie33BWUT5xQpIRYREZEel9Sauc65t/EXyFiJn/ROijp9EP7DYs1LP78InOCc+6RLkUq/tf2hUkLVIfL3K2DY9J6YtU9ERESktWRmnwAgSHKnG2OmArPxk+Qi/AfbduI/cLbYOfdsKgKV/qnq9Uqq11RicgzFXxqDydBsEyIiItLzkk6KmznnVuKPGIskpKmqie0PbANg1PnFZI+KtZifiIiISPdJqnxCpKucc2y/fyvhPSEKpgxi6MnD0h2SiIiIDGBdHiluZowZRMu8vGXOuT2purb0P1WvVrLnrWoy8jIo+tIYjFHZhIiIiKRPl0aKjTGHG2M8Y8wn+PMArw9elcaYT4wxtxljDu96mNKfNFU0suOhoGxiTjHZw7M76SEiIiLSvZJOio0xvwJeBb6OP/uEafOahL/gxGvGmF92NVDpH5xzlN63lXBtmIKDBjHk+KHpDklEREQkufIJY8zvgGtomWT5ffyp17YF70uAE/CnZzPAD4wx+c6567oUrfR5lS/vpua9PWQUZFD8xb3LJuo21VL+fBl162poqmrCGEPWsCwKDhzE8FNHtRpVrn6nipr3qqlbX0vT7iZCNSEyB2WSN6mA4Z8dSf6k/J7+eCIiItJHJZwUG2NOwl+lzuEnw1c651Z00HYq4AEHA9caY+51zr3YhXilD2ssb2TnwlIARl9QQtbQ1mUTtetr2HzrRlxTyyqLDkfjzkZ276yg+s0q9vn+vmQV+n9tdz68ncbtDa2uEaoKseetKva8U8WYr45n8KGF3fypREREpD9IpnzCBttNwNSOEmKITNc2HWhe8e6bSdxP+gHnHKX3biVcH2bQYYUUHj1krza7l5dHEuLcCXmM/fp4ii8dS0ZBJuAnvNVvVLbqk1OSy6jZRYz95gSKLiohs9BvSxh2BAm4iIiISGeSKZ+YgT9K/CvnXHlnjZ1z5caYXwO3Bn1lANr9QgW1H+4hc1AmRV8oaXe2iVBtKLI/9OThDDrEH+Wtea+aqlf9ZNi1NGH03GIKDhzU6lqZg7PY+udPAWgqa6SpqikysiwiIiLSkWSyhZJg+0oCfVa36Sv9WO0nNVSsKKf24xpCVU1kDMokXONns6MvLOkwSS3YfxA17/kz+e1+oZyswkxCdWH2BMdMbgaDD2sphxj0mcF7XSN7dOsFQDJyNBW3iIiIdC6ZpLgByA1e8Wpu2xCzlfRpLuTY/uA2KldVtDoerm4Z3q35cA+DDyvEZO49Ujxs5ggayxvZvaqc+k11bPnTp5Fz+QcUMHpOMdkjYk/fVv1mS3lF/v4FZOQqKRYREZHOJZMxbAy2ZyXQ58xguyFmK+nT2kuI26pcVcH2B7e1e85kGnJG55A5aO/f1eo21LLn3eqY165+u4qyf+30r5VjGD2nOL7ARUREZMBLJin+F/40a98xxhzXWWNjzFHAd/DrkJ9K4n7SB9R+UtNpQtysclUFtZ/U7HV81792sGNhKaHKJgqPGcK+v5jCpJ/sR+64XFyDY9djO6haU9nOFaHyld1svfNTCIHJNoz56nhyx+V15SOJiIjIAJJMUvy/QB2QDzxvjPmpMWZc20bGmLHGmB8DS4FBQZ/fdSFW6cUqVnT6zGXr9iv3bl/5QkVkf8QZo8gclEn2iByGnjw8cjy6PCJyrWVllN69BcKQkZfBOLtPu/XGIiIiIh1JuKbYOfepMebrwN/xE+P/Av7LGLMF2I4/IlwMjA26mODY15xzm1MStfQ6tR/vPfIbs/3avduH9rTUHofrwi379e3vA+x6YkekZCKzMJNxdh+NEIuIiEjCkpqryjl3tzGmDLgdGB8cHhe82voU+IZzTqUT/VioqqnL7XNKcqn/tA7w65NHnD6KcF2Y8ud3RdpEJ7w7FpZSsawM8GuIR51XRLg+3Ko0I3efPDKy9LCdiIiIxJb0BK7OuSeNMfsCc4DTgUOBEcHpMuBt4GlgkXMusYxJ+pzMwixClfH/Z85sZ1q2kZ8bzZY/bYIw1G+sY+sdn7Y6nzUsi2EzRkTeV79VFdl3DY7Su7fudc1JP9mPjBE5ex0X+f/tnXe8HUX1wL8n1AQIBEIgFAmhKiAQiIBUaSJI70U6jHRQUOmhioj04lBDVUAk0pEivUgJ0gX8EWogJCRAICEB5vfHmc3d7Nvdu/fd9/KSd8/389nPLTszO7tzZvfsmTNnDMMwDCNNU6saRGX3b3EzWpieS/RifMEkuNz0S/Zq898c35+TRQ8dwNiHxjDx7Ql888U3SA9h5j6zMMeyc9Bno762EIdhGIZhGJ2CaRhGhzDPWn0aUornWbNP7v+zL9aT/nsskrsvy+InLFn5eIZhGIZhGGVUcrYUkd5x69lI4SLSM8nbvuoZMwo9B/ai9xrzVErbe4156DmwraXYMAzDMAyjq6irFIvIEcDYuP2swfJ/BowDxorIAQ3Xzpih6LftgnUV495rzEO/bW21b8MwDMMwpi9KlWIRmR04Nv70IYS/N1J4TH8JGpZtiIiUr9FrzNDITMICO/RnkUMWY85BvZmp98wgMFPvmZlzUG8WOWQxFtihf+4Sz4ZhGIZhGF1JPZ/irdCIEl+i8Yjbw4nAL4C+sbyb21mOMYPQc2Avc48wDMMwDGOGop77xKbx87YQwiftOUAIYTRwG2ot3qw9ZRiGYRiGYRhGZ1JPKV4VXY3uniaPk+RftclyDMMwDMMwDKPDqacUJzOiRjR5nHfi50KlqQzDMAzDMAyjC6inFM8RP78oTVWf8ZnyDMMwDMMwDGO6oZ5SPDZ+9m3yOPNlyjMMwzAMwzCM6YZ6SvFH8XNQk8dZOX6ObLIcwzAMwzAMw+hw6inFj6FRI7Zt8jjboRP2Hm+yHMMwDMMwDMPocOopxXfEz1VFZIf2HCDmG5wpzzAMwzAMwzCmG0qV4hDCPcALqLX4ChFZt5HCRWQ94ErUSjw8lmcYhmEYhmEY0xX1LMUABwOTgF7A/SJynogMLMsgIkuIyPnAfTHfJOCQZitrGIZhGIZhGJ1BvWWeCSE8ISL7oRbfHqiSfJCIvAE8B3yMLgM9B7AAsAqwNGpdFuBbYL8QwpOdcgaGYRiGYRiG0SR1lWKAEMK1IvIecD3QH1V2l4lbHhI/RwK7hhAearKehmEYhmEYhtFpVHGfACAqtkugbhDPAd9Rswant+/i/oOBJUwhNgzDMAzDMKZ3KlmKE0IIE4GLgItEZC5gBXRhjrnQVe/GAC+FEJpdAc8wDMMwDMMwphkNKcVpouL7RAfWxTAMwzAMwzC6BAkhdHUdjE7COfcJ8E5X18MwDMMwDKMCo733m3TVwU0pNgzDMAzDMFqeyhPtDMMwDMMwDKO7YkqxYRiGYRiG0fKYUmwYhmEYhmG0PKYUG4ZhGIZhGC2PKcWGYRiGYRhGy2NKsWEYhmEYhtHymFJsGIZhGIZhtDymFBuGYRiGYRgtjynFhmEYhmEYRstjSrFhGIZhGIbR8phSbBiGYRiGYbQ8phQbhmEYhmEYLY8pxYZhGIZhGEbLY0qxYRiGYRiG0fKYUmwYhmEYhmG0PKYUG4ZhGIZhGC3PzF1dAaNjcc5tBfwSWBmYG/gQuAf4k/f+f51wvCWBl4DZ4197ee+H1skzF3AEsB2wOPAt8AZwA3CR935ySd5VgNOBHwMBeAw42nv/n5I8PYBngEHA2t77xyqdnFGZRuWgGRlosF4/A/YFfgT0i8d5H5Wbi733z1YoYz3gEGANYF5gFPAQcI73fnhJvlmBo4E9gIWB94ArgTO999+U5NsKuBW433u/Ub36GYpzbgBwKLAhKlOzAZ8C/wH+Clzjvf+2JP8CwK+BLYDvAV8BrwBDgaHe+9ABdZwJ2C1uKwNzAR8DDwPne++fqVDGwFjPjYBFgK+BN4EbgQu991+X5F0A+D2wOTAnen5neO//VueY5wKHAcd570+rV8fuTpNt0LQMVKjf/Og9L9kGA/PF3Tt77/9asRwB9ozbckAv4F3gNlSn+LhCGe3uV865jYAh6HWaCNwH/M57/3ZJnjmB14G+wHKN6j1mKe4mOOfEOXcF+jD9KaoAzIY+HA4AXnDObdoJh76EmiJUpZ6LAy8AJwEroDfmudFOew7wpHOuT0HeHwGPAhvHfHMBPwMec86tVHZYVCG+zhTiTqOyHDQjA1Vxzs3knLsGuAvYBn1wzQr0BJYC9gKeds4dW6ecE4EHYxn90T61KPCLmH+fkuzXozf0xeOxlwBOA64qOV5P9BpMRhVxowLxReJl9EUrkalZgAXQ+8WVwOMl95ZVgReBo4BlUDmZD1gn5r3LOTdbk3WcB7gfVQY2jOXPisrTbqjcH16njB1RheJAVI57AvOgfecs4FnnXP+S4z+Gyn5ftL+uAtzsnNu35JgrAAcBb8VjtDQd0AZNyUBFbgbuAE4ANqGmEFcmyvtdqPyvE8voifaPo4AXY78pK6Pd/co5tyVwN2oA6wn0AXYAnnDOLVJy2ONRI8Qf22MINKW4+3A0sHf8fhOwIqoYbwmMQB8SNzrnlu6oAzrndkM7duFbWyb9rMDtwEBgAvrQXwRVGoYA36E36RsLirgQ7Rz3oW+tywEPoOd2YcEx5wNOBb5AO6bRwTQiBx0gA1X5Daq4glp1N0JvlAOBXdAHfA/g1KKXRefcrrFOgirGq6N9agNUqZ8F8M65dXLybopawSeiVpZF0P75NbCbc27Dgnr/DhgAnOe9f73y2bYw8SXrL8AcqBX/AGBZVCFeE1UQAFYDfE7+vqjlqx8wBlVO+scyLorJNgEuaLKq1wPrxe9XASuhCsJgYBgwE3B2VAba4Jz7MXAdqsx+AOwKLIjK1v6oVXx54B/RGpnlGGBJ4J1YjwHA2XHfOVFhy+MCdFT58DILaCvQAW3QlAy0gy9RQ9KV7ch7ASr3oP1gGbRf7IaeZz/gtth/2tBMv3LOzQxcjF6Pa1G5HYwq2AsCZxQcc2ngcNSafXoD5zoFc5/oBsThiWPizzuBnVJDErc5515Ch7bnRC1V23fAMfsAf0KHo49AO3Q99kcVWYA9vPc3p/ad5JybAPwB2Mg5t5n3/s7U8QaineIrYEfv/dj4/07osPSazrkB3vsRmWOejg55H+m9/6ixszTq0Q45aLcMNFAnAQ6OP58HNs64Y7ztnHsM7RNzoyMJd2XKmJ3ajfcFYJNUGQ86536CWosWQq1DP8pUY6f4ea73/ur4/Srn3A+AI9EHxP2ZYy6OKvMjgZMbOecWZz9USfkO+Hlm+HkUalm6BbX2b+uc6+u9H51K8zv0YZ3kfyr+/xFwcHS/OgDYxzl3gff+pUYr6JzbGEhevq7x3u+d2v2pc24b1Cr2U1QpuivHhegs9Jk9GdjAe//f1L7LnHOvAI+j98k9gSsy+ROZdN77h+P3Xzvn1o55tkItmOl67wysC9zRnr7YDWl3G3SQDFTlNNTg8Kr3/tvoWrR3eZYacXQgGT242Ht/cGr39c65/0NHHfoDvyXf4NRMv1oHvbe+B+yduJs553ZBR4S2c87tk/OSdj5qef+V935C1fNNY5bi7sHuqJUE4Nisj070v7ks/tzGOdevA475R/QN8ALUZ68KB8TP4RllKOEc4JNM2oQV4ueTiUIMEB9uT2bSAFP8j/cFXkM7i9HxNCoHzchAVfqiN1SAm/MeLN7791AfPoC80ZPNUesPwInZMrz344Az48/BOcOIiSxmFYnbM/vTnIsqd0d577/I2W/ks2L8fLPEH/O6+NkDdWMBwDk3C7WH/z9SD+40J6JKUA/0Bao97Bg/vwOOy+6M9+zElWcgNeUpqeeiqE87wNUZZSwp4wl0yBxqL4VJ/rnRIfrJ6EhbmiRP9v45J9q/v0atby1Ns21AkzLQCN77+7z3L5X50Nfhl+gI2SR0tCxb/pOoFRhg32jZnUIH9KtEFv+Znn/hvX8FHZGcjcx92zm3NfpCcZ/3/pY651eIKcXdg83j55slE86SiRQ9gJ83czDn3FroW+dIVLCr5BkI/CBTl6mIisc/4s8NnHO9Urvnip+jcrImzv69U8cT1KWiB3BIR03cMmo0KgcdIANVmZj6/l1JumTfJzn7kj71FRkrcor0OWyR2Vckr21kFaZMCNwCeNR7f31RhY1ckvau0tYwdXuvjY4WQLFMfkLtBSrbzlVJFPcP4gtZHsNRJQTU7S0vP9SMAHkkysdKzrnFUv8n8jjae5+9TrkySc0388zOmKQ9A9JsGzQrA9OS5P73cJT/PJL+Mg9q2U3TbL9q9HnfE3UFmoxOtm03phR3D1aOn0+XpHkWHeIGnXTWLuIboEffIn/lvf+8Ytb0McvqmeybnZoCBeoTDGoFzNI3kwZ0MsnqqKXwgYp1NCrSTjloVgYqEa2sb8WfW+f59kWXo+RG/s+cYpK6Pl8UKcJ7/wHqV5hOn1Akr21kNfpZn4f2z4PyjmWU8nz8XNo598OCNDvEzzfRORYJjcrkokU+lHVIFIQPixJEZTVx8VqlIH9pGZl96XNL5G3eaDBIkyeTy6DuUO+g0SqM5tugWRmYJkT5XjT+rNInoO39r9l+1ejz/mjUR/7cZudimFI8g+OcWxj1FQb4v6J00fdmZPy5TBOH/A2qqNxfNaxLzjEL68nUk7XSeV6On6s7DecFTJnNu3r8+VL8b270Rv4VGgrG6HjaIwfNykAjnBQ/Vwdudc6t5pybyznX1zm3GTqEPC9qmTknnTH6ui1VoZ7pumbrmcjrxpn/fxY/0/5zR8bjXdwef1WDi4HR6KSc251zuzjn+jvnejrnlnfOXYZOrpwAHJCxlCbt9h2qABbRrEwmL40LFSWIcrdAcoyM8pp+6SwsI7Nv2eSL9/4zNBThbKiPcJo8mTwfnUjabt/MbkhTbUDzMjCtqHqffofaCEy2TzTbr5L754bxmgDgnFsKdX+ahIbwTEYgj0JfNpqei2FK8YxP+k2qaJgjIRmKaDg8C4BzbgnUF2oSbf2l6lG1nunhkin1jMN3z6LDKlc75xaNPl5D0ZeCf6diF56C+rmeVjJMZbSTJuSgKRloBO/9dehEk6/QocCn0IfSJ6jPXzKDeW3v/ZeZ7HOhkzXq1TNd12w9kxeFXzvntnbO9Y4TaQ5P748yfEws54RKJ2dMRZxjsC4am/R76Az/D9G2fwl18bkVWCtn1CiRybFlsaNpXiZfi58Ll4STWglVWomfc6b2vZb6vlrJcdL7sla2RCb/7Jxb2Tk3r3PuBDRCxwSij2iU041R38y/lxyr1Wi2DZqVgWlFpft0dHUbF39m+0Sz/eoR1Ii3OHCBc65fjCxxDTo6Ocx7n7hNnUttLsb4kmNVwqJPzPjMkfo+sTCVkrzxt7ejJbFoT8+bZFCHqvVMWyWy9TwE+BewddwSviIqZ3HW7IHo8Pmf4n89gG2B9dEbzXA0aLhNZmof7ZWDjpCBRrgEDQX0Z9r6S/ZGJ9L1o20ouab7lPf+TufcMHRGf1axuNl7f3f8/qd4vEPj5L1kEYrdUYvIaOBvcWKLUYD3/lWnsYqHUhs5SuiBtvUAaq4WCUlbV21naJ9MDkPDd/VAX9r3Su+MFsFTM3nmJA4Re+/fipENlgP2dM6d5b1/M1PG6tR8QfPqeTp6H1yGttfhKO/9mIxv5pQ42c65NWLe+VHr4dU5kX66NR3QBsNoQgamIR2hUzTVr7z3k51zB6H+yAfGLeETNOJFMhdjc+AR7/0N8b9Z0eg+yaTIJ9E1CiZRAbMUG5WIoVA2Qv3xsh13mhBnsK6LhrL6EhgP3Ausk5p1fiE6jHqo9/7rGFrrbjR28y/RG9H5aODxAdP2DGZ8pgc5qEL0GX4MXSHvGTS2cF904tDOqC/wbmi4ruU7qRo7oQ+/d1Al4210QuKusY7ro+ERnyIu6BFdO15F3T92B34V61hpQmurEq/Pa+is/f2BxVDL0xponOLBwC3Oua6KVX4r8Fz8vqdz7nLn3ArOuT4xSs7NqBtDWonIrvSVRCyYFXjAObeTc24B59xCTheRuaMsf7Sor4Va28agUSWGoyucJXFjj0av3TnJC69z7mTgCdQVbXc0GsGrzrlNaD2aaYOOkIGWwXt/K7AZqtRORK3StwA/9t6PcLrox/noXIzEKDYvej+9Ao1+sW/8/qSruCCUWYpnfNJDv/VWFOsZP8fDlDfTOQrSfpv4kkVhSoK8H9pOH7NsPYuGOXqmvrdJ473/N6qUtcHpYgvrALelLHHHoUOBT6DDqONQv6P9gatp619nFNABctCUDEQLwKxtkwMwMRXLchbUZ3iF+LlJxo/0r865B9EQcguicpCe1NLuPpUm+vGfQI5bRAxhdAHqc3ew9z7EiSbXob6cDn2RG4QOew9xzj3qvX+wTn1aDufc71BF7Sv0BTk9evEUsINz7kr0hfgM59x93vsX4v6krau2M0wtk7Oh7ZXHhCQkVowVuw36Er8ssE/c0jyOxsROJluOS+/03g+L5/p7dCLUXzL5J6Exm5O42OMy+/Hef4guO96G6Jv5G/SF8ZT434ZoFIqPUL/s4ejL3oXADc65pbz3YwrOv9vRTBs0KwPRip+3IAjAl9lQrE3QEfe/pvpVgvf+HuCegrxHoot6nJ+ai3E+GnhgGLoseUAXCtkcdbPIlf00Zime8UkHoZ+/Ttpkf3ITWwwdmsnb7k7lOwF1/r/Ne3877aNqPdP7Kt9s4+S7M9E3ysPjf0lw8O+AX3jv/+t1rfYD0RVv1imZrW60pVk5aFYGjqFYXndLpduaWpzLk3JCUOG9H0VtVaVBGTn4glpYpEb7VFUOQycqXu69T6xHu6Hhja733l/qvR8XleAkdmmjfvzdnvii9Nv484YSd57kxaQHau1MSGRyHpeJtZqhSCY9xTK5droA7/27wKqo4vlMTDMBXaXr1+hKZ4kSMTrlM5ku4w+o9fsvqPI6CQ1RdSO6gEz6panR+RTnoe5lad/MROaO9t7/K8rkn+Px+hBHPVqJZtqgSRm4m2JZS4d+a5ZK9+nYX+aJP7P3v2b7VSl5czGiUWFH9Hrs7r1/N84p2h1V0ndxusJtKWYpnvH5EH3DmhN1Ss8lWjSSWa+N+gMn5W7hnKv3NnqVc+6qJF/K7yx9zMUpXhI4fQ6N1PME9PxOTk24WwqNMPCm937KLNr4xn4f+pa+BnpDMurTrBx0tgwk/Dj1Pes7mea51PfvE+XAe/+dc+5N1HewsE9Fkv2V6+mcWxB1o/iU2kqUUJugkw0Rl7ygroGR5QfUHsyFbe29f985Nwr1If9+alfSbjOhk/SKZts3K5NJPb5EF8T4Y95+51xSt+fy9scynkattnn506NohWXk5NsUjV//sPc+bf0sk8ldUJlsuYWRmmmDjpCBTiZ7ny5iMWqG1Wyf6Ox+dTbQCx1l+yz+twqq0z6Vni/kvR/nnHsKdaFbFbXUF2JK8QxOHHYdjlolymbErkJt6OX5mHcEOpNzWpB+YK3G1G/TZPaBWnxfrVKwc25Z1PI2gqnXRE9mwI7O5qE2q7Zd0Q2MdtGUDHjvh5CzulIOPesnAaaW/aw1+XlUKR7knJs5bwa1c24haqvelSnfWf6IRrg4MDP0XCSvJqvFVG1rqLV3uq2zMln08E5k8j2fWiLae78nupxv0zgNLzk4/ix9cJeQhAAcTUWlKhpMkjjZh2R2m0w2TsNtkFAmA9779ZqtWBW896Odc++h7iFVo2xk739N9asynHMbANuhrlFDU7s65Hlv7hPdg2Qoe+kYfSGP7eLnd9SWoazKEaifTtG2WSrtian/pwQpj5baRMHZjhziMEuyis8D3vuvKtbvAtSv74iMn2vyPW9Z636ZNEZ9mpKDTpaBNB+lvpctVJP2I343sy/pU3NQi+OaJX0OtxWkmQqnqwDuhj40fGZ3kbyarBZTqa3jcGsyVJtu60eBxNJUJJN9qc09qNTO7cSh97GvgWsbzuxcb2quIdf46qt4Jr6ZF+bEyTaZbIAm2mBKETQhAx1Icv9br8TlIOkv49AQamk6pV/F+SJTzcVI7e6Q571ZirsH16AWtF7AaWSWTIxRFvaPP/8e/Skrk3JHyMU5Ny71893UJJYsl6ACPcg5t61vuz754dSE95IqdXPObQdsCNzrvR+W2f0Gav0Y6JxbJjWbemZ0jXSoaI02OkwOOlwGcniAmg/p8c65TbN+xc65ftTC/IyhrUXndtRfcGHgJOfcPekHnNMFYn4Tfz7rvX+2XqWcrqx3ITr54+AcX+fX0Akhm6IT7hKSZdlNVjN47992zr2NDsPu4pw7MxsmK3JS6vsUC1wM/XQ56s+5pXPuR3Eyb5oh6ATPQNsXmQ4hhvI6Pv78Q1WrWSr/TMBlaN8ZQ8VV6Jxz30NdeD4mf6n211A/2U2BS1P/m0xmaG8bpPI3JQMdjEfn48yKyv9UIwjOudWoGS8uz46kdWK/Ogx1f/KpuRgJSRzoNZxz8/haiMt5qbme1ZVXU4q7Ad77j51zp6EK8ebOub/G7x+hwxPnoxav8dQm7XQFl6Jh0ZYDro1hs4ahb8Z7UFNk7vPe31mvMOdcLzTO6yRy1jv33o93zt2N3sBvcM7tD4xFO+PCqJP+v5o7JaNBOlQG8vDeP+KcewSNRPJT4B7n3OnoQg6zxv9PQyNPAJyac1Of6Jz7Laqcrgzc7Zw7Bh0GXAGVu4XRl66qqyYeAKyIxnjNizt8E6po7+iceyb+Xpla6LsbKx6n1TgVDbvUC3jEOXcc6gM7HlgabZ/tY9rhtLVKnYH6h/YH7nTOHYa+WPVGlYEkEsDlOZbUyjjnLkDdPW5EV+z6GvXL3AF9GZwdDSN4ekH+xdCY11ei962R6FyS1VC5GQx8A+zdgEKV+GYelPLNTHMTqhSf7pz7EL1+O1Kz/t1U8TjdgmbboFkZaKCe86NxzhP6p74vGRXwhPe99++n83vvX4xK7X7AwXEOyQXoAkgboDpFD/T8/1BQjQ7tV865/ujz4VNy9Bjv/WvOuZeB5dEIQ0fEXeei1/w/JRNxp2DuE92H36MdFfSm9SKq9N2OWlHGAzt679/omuqB1+DZm6OKRU909v8HqC/wSajP83No/atwDOrEf07JeR2JDu8MQlfE+x/wC3T45UBfMaC30TF0ggwUsT016+9G6ANsNOrK8VdqD4zzvffnFtT1+lingD4InkZ90x5EldXJgPPeZ4cO2xCHCk9GhxR/m5cmWj4uQe/LZ6PL8t6OThZ9Fl3O2Mjgvb8SfckJ6IvO5aiLxKeo32GiEL8CbJW10EflZQv0ftkXXRHvI3SkKbGQ3UNbf9tGmQud3PtPVA7HoG40v0OVoTuBTb2G8stD0PvYhfFcPo3nmcRhHgfs5L2vOhS9Ibogx5PUQohluRBVhOdDZfF99IWwB9p3hlc5Vjei2TZoVgaqksT3Tbb0AkKnZPbtW1DGIdTCoR2C9oeP0P4xH9pftih6AeuEfpXMxTjWF4cBPBB90fgpahV+FfXxnsjUC4AUYkpxN8F7H7z3+wBJHMRPUOF4G13RayXv/V1dWEVgyhD8SuhQ3UtoqJTP0Yf+r4A1vAaZL8XpUsNHkoqpWXC8/6JDJ8NQhWQieiPYLGfo3pgGdJQM1DnGKLTd90Jnyo9ERxQmoAr5tWhM28PqlDMEXQnxVvSGPglVDK4DVvPeX1GxSmegIayGeA0LWMTB6DX4bzzWSNRCs6G9wBXjvT8OtWhegV67L1GL3SeodeoAYNUYEisv/7PAD4Gz0If2BFTheRSNb94Risql6EvPS7HsRJZuAbb03v/cl6+y+TF6z7sXDfU1Eb2nvYC+cH2/6j0t+maeT75v5hTiOa+PyuCHsc7/RecXHF7lWN2MZtugWRmYZsS23xSV/0fR+k5A+8dZwA/ruY11VL+KczF2RV8gLi1K571/FA1rdz9qCPwyfl/Xe/9EveMASAgtu2CKYRiGYRiGYQBmKTYMwzAMwzAMU4oNwzAMwzAMw5RiwzAMwzAMo+UxpdgwDMMwDMNoeUwpNgzDMAzDMFoeU4oNwzAMwzCMlseUYsMwDMMwDKPlMaXYMAzDMAzDaHlMKTYMwzAMwzBaHlOKDcMwDMMwjJbHlGLDMAzDMAyj5TGl2DAMwzAMw2h5TCk2DMMwDMMwWh5Tig3DMAzDMIyWx5RiwzAMwzAMo+UxpdgwDMMwDMNoeUwpNgzDMAzDMFoeU4oNwzAMwzCMlseUYsMwDMMwDKPlMaXYMAzDMKYxIhLiNqSJMtZLlbNeh1WuixGREfGchnZ1XYzWwpRiw+gkMg+sqtuwrq63YRiGYbQiphQbhmEYhtGpiMhD8cX/oSbLGZAyIuzZMbUzDGXmrq6AYbQIlwAXV0j3eWdXxDCMrieEIF1dh+mVEMKArq6D0ZqYUmwY04ZRIYSXu7oShmEYhmHkY+4ThmEYhmEYRstjSrFhTOdkZ2KLyNIi8mcReVtEJorIJyJyp4hsUFJGQxP+Uvn+Hv8bKyKzV6jryzH9i5n/h8b/R8TfC4rIWSLyhoh8JSIfiMhNIrJcJt8AETk/ppsgIh+LyPUiskTFa7eWiFwpIm+JyJciMl5EXhORC6qWUaf8BUXkNBF5VkTGichkERklIq/Ea+dEZP6S/LOKyC9F5F4RGSkik0RktIg8LCKHVrzm34/X970oD++JyA0iMjjun+raZ/JWjl5QNSKAiKwgIheJyKsi8nls37dE5AoRWakkX5u6iMi2InJfvKYTReR/UR4WrHNZkjIHx77yqoh8Fq/vSBH5p4gcVVaOiMwvIkNE5GkRGRPzfigiw0RkqyrHr1O3utEnRKSniBwjIv+J8jtGRB4Xkf1EpPLzW0R6iMguse7vi8jXsU8/LSLHisjcJXmzfXduETkx9vXx8bo+KSL7i8hMRfmBdeNf60rbe86ITJ5cWYvlvJ3666qcsobEtM/H369XuD6zxWsbROS2eumNbkwIwTbbbOuEDVgPCHEb0kQ5I2IZQ4GtgPGpcrPbwQVlFKXP3VL5fpb6f9c69VwtlfawzL6h8f8RwIrAyIJjjwfWinnWB8YVpPsUWK6kLrMBV9c5z0nA3k20y5rA2ArX85cF+ZcH3qqT93VgyZI67ABMLMg7Gdgnfe3ryOh6VeWwYL8AZwDflpzPd8DxFfrL+sA1JeV8CCxVp/2vqtA2ReeyA+rfX5b3H8AcTchP6b0BWBB4teT49wAb12s/YBHg2TrnMhIYXJB/ivwAywD/V1LOTYAU5C/bRmTy5MpahXKmXE/ggNR/a9Zpix1Tabdsb5vaNuNv5lNsGDMOK6A371HAscC/UQVk3fh7buBsEbk/hJC1jqxQp+zlgeuAmYB3U//fG39/D9gbuL6kjL3j56RYVh69gFuBWYFjgIfjOWwSf88BXCsiGwHDgM+AE4Cn0TkQ2wKHA32AK4DVC45zE7BF/P5P4Ab0YT4RGBTLWBa4XERGhRDuKDmvNojIrMCNwDyoIu+BB9C2mRlYDH1J2Kog/0Dg0Zj/S+DPwBPote4N/BQ4BFVC7haRVUMIn2XKGIy2x8zoNT8PuCOe44/Q63kJqlhNC86LdQZtrytQpf9z4AfAQeg1OVlEPg0hXFRS1inAj4E7UeX2bWA+YE9gF6A/cCWwdjajiAhwC7BZ/GsEcBHaX74A+qHXZ7u8A4vItsBfUSX/PeAC4BXgI2DhePydUPm6ClWgOxQRmRlty+/Hvx6I5/AusChwICoj89YpZ15UzgagL0lDgfvRazIb+iJyOKqA3y0ig0II77YtCdC+ezt6/c5A+9VnaNseDywNbI/eM65I5TsWOAu9VquiCvpembInlZ1HihWAheIxAI5DX07SjIqfN8Tj9kLvTY+XlJvcuz5GZc5oVbpaK7fNtu66MbXl62JU8ay3tbE8UbOaBGA40CcnzbqpNOc0WM/5UaUjoErDipn9J1Kz8g0oKKMn+oAMwE05+4em6vcJsEROmoNSaUYBbwDz56Q7M5Vu5Zz9+8R93wBbldT3oZjubWDmBq/Z+qk6bF6STgra69GY92VgwYK8q6IKcwBOydn/TOo8N8zZvxCq1OVa43JkdL0655zI4dCcfRumyjmkIP9MqKISUEV5npK6lFlQr0ylWTFn/4Gp/XcCPUvOadHM7/mojU7cAMxakC9thdygEdlJlVF4npm+0OZ6xzSXZ65Xm/YDro37PgCWKShncVThD8C1OfuHpo7xGfDDnDR90T4bgOEFx0n620MVrk2ZrA1I1WfPOuVcRe2+lmvVR18yktGNM9vTlrZ1n63LK2Cbbd11y3nIV9nWyylnRGr/SiXHeyqmeb6BOs4GPBbzfUvO0GHmoXFSQTm/SNXxpzn70w/WIneCnsCEVLpNCtItnkpzaGafAG/GfRfVOfcfpMrZqMG23SWVt3eDeddK5c0dsk6lTV4APsj8PzhVxmUl+XdIpRtRR0bbyF6BHA7N2Xd/3HdnnTL6UHP32K+kLs+TGYZPpVu2pP17AO/EfR8BczfYNsdReynrVSftv2Pa6xs5Rip/mVL8Stw3BpirIP+c1BTRNu2HjlZ8E/dtX6cuyYvEpOx5Z/ruYSVlnEHt5bnNdadrlOI1U2n3KkhzfCrNsu1pS9u6z2YT7QxjxuHlEMILJfufjZ8DGyjzMvTBAXB0CCE7FEkI4T1qw5V7FkzwSYYf3wPuKzleQF0b2u4IYQKq0IL66t5bkO5t1PIDbc/1B8CS8fvNJfUghPAqqnQArFGWNocPU9+zQ8H12DJ+vhNCeKZO2kfi50Ii8r3U/xumvl9Vkv9W1PLZaYhIb1ShhfrXfCxqHYfya359CCEUlPE66rICbdv/h6irD8CVIeNyUoGkbe4KIXxVJ23SNo3KTiki0h+VY4BbQghf5KULIYynoC9Ffo5a5yfT1sUgS3Ius6AjFLmHpNx9Krn/CPri2uWEEB4HXos/98nuj642e8afj4e2bmdGi2FKsWFMG04KIUiF7aGSMl4r2Qc6+QxgrioVEpFjUAsvwNUhhDNLkl8WP7/H1ApZ4h+7bvx5VQjhu5JyRocQPi3ZPy5+vlWkFGXSZc81/UD/V87M9GyUjfli2krRDFI8jvrLApwrIs/EWfxrS/2IEUkdF6tQv9tT+dJ1THzEvwOeKzpQCGEy6nLTmayMKl+QHw0ge06rxLRl17yerI+Nn9n2H5T6/mil2kdi5ISV4s89KpzHr2PaRmWnHmn//3ovTf8u2ZfI2SzA13XO5aVUvqLzGR1CGF1yvHS/rnQPmkYk9641RWTpzL71qL1YXYHR8phSbBgzDvUsV4kyWrdfi8g2wKnx52PA/nWy3I4OR0PNKpywF2odCpRbLaH6OVRNlw0B1a9OviJ6NZI4KpubU7N6ropez0eAcSLyoIjsGyfkZemIOiYTrD4PIXxdJ9/H7TxeVTrjmre3/dPh70Y2WJ95ad+CVj3bkadePRJGFaZSytq2o9ulaptA23bpSq4Bkj6SvXclv+tZ3Y0WwaJPGEaLISKD0Ak4gk4y2yaEUDr7O4TwjYhcDfwW2EpE5g0hfBpdKfaIyR4MIYzoxKpXIf0w3hadrFeFsfWTTE0I4XURWRENW7clsA4aLWI24CdxO0pENgshvJXKmtTxdXS2flXezqtGo/XuBNLX/FDgXxXzfdkJdWmG9Hlci/pzdzXNtG9yPp9Tc5GqwvtNHHO6I4QwRkSGoZF7dheRY0MI30a3n21jshtDCNObPBpdgCnFhtFCiMhCwG2oNegLNHLCJxWzXw78BlX6dkVDVW2ETsSD6WP4MT28+3no5KW1o6vInXFDRPqh18Sh4cKWRkO3rZLKNhpVnudqon6JEj+3iMxWx1q8QMm+tHWv3gjDHAX/p6/5hM6+5nVI16U/8EIDecegSqgAPbrwPNIvaGVtV29/ci3mBN6sMKLQnbkMVYr7A5uiI187U7PyTw/3LmM6wNwnDKNFEJGe6ISbhVFlaOcQwitV80dr50Px596Zz7HopK6uJu0/u9a0PngIYVQI4XrUxzqZKDhIRJZMJUvquLCIDGjnoRIf0B5MrXBPRYx3u1JJOelJXH1KyumLht3K4z/ULJrT/JpnSPtXr9NIxugSk/SHNeMkrK4g7d87uE7asv2JnPWggycDtpOOGtVoTzkPonHKoe2967UQwpNN18roFphSbBgtQHzAX01t8s1RIYT2BKlPJq2sJLqsdDJb/4YQwsQmq9kRDEcjYADsKyJzdkUl4iTBB1N/pRXKdCSAI9p5iPtT3/coTAVbU6LsMrVLRpmCtUvRjjjS8ET8ub2ILFxSTmfzIrXFZ/aSkuWLC0jaZgAFC690NiGED6lNNNymSIZFZA7KFw65nZoC2V4560iS+8NsHVRO5bJif7w8/txMRH6CLuACGvfaMABTig2jVTiZmv/qFSGEs9tZzt+phTG7jtpDaboYfozuDKfFnwsDfxGRwgldIjK7iBxcIWJENt/aIrJUyf4ewAZJtdC4q0kd70djSgMcIiJ71jnWQBGZSikNIfwbjeULsE98yGfzLYiu6FVICGEcaukFVSLbWINFZHlUfso4JX72Av6eV06qvJlEZDcRWaROmQ0T2z/xBV4AuD6OkBTVJVuH81AfXIBLo/99IVEO1i1L004uiZ/zxTrlcTYlk+lCCG+grjsAW4jI8WUHFJEFRWTfRivaAMnEx4FNWuHHUFsBb4kG8l2Fxm2eBV2YBTRc3TVN1MXoZphPsWFMG/pF5aIek+LDrMMQkfXRRQlAJ55dVK8uRf6UIYSvReQ64DBqoZuGhxA6O+xXI1yKho3bDo3V+pqIeOBJ1M1jTmAp1Od3a3Sp5aENHmMD4HgReQy4C1UsR6EvCQPRmKiJonprCOGjTP5d0HBafdEwZjujD+rX0Qd1XyCZxLcu6ppyQ6aMA9HIITOjS/Sei/o2p5d5nj/WbcWSc7kAtaL1Ax4TkVPQpaHnBjZGl24eiSoi8+cVEEK4V0T+hIYp+xG1a/4wuoJhLzR27Y+BbVDZWYHOmdR1Cdrum6BLPb8iIullnudHR0x2QK/Nnqnz+EREdkdf/voCT0Z5vwO1QM+E+qUORi3Jy6HX5+FOOIe90HB3e4vIouiqmMkyzwegyzw/S3FcYVAZWRWN3X2yiGyGKocvoQvl9EFX0twolvciNYtqR/MEek790OXor0NXyAOYHEJ4p0ohcdLvM+jkwb1FZDjqOz45Jvk0L+xjCOEjEbkTHd1K7l13hBDqRfgwWomuXj3ENtu660b7VrQbkVPOCApWd8qkG5KUk/l/z0brUec4y2XSH1zhWgwtOr9MuoeosOpVvWuCKornUVuJr2wbT8lSwPWudZ3tIXKWeY5lLIk+zKuUc2VBGTuj4aby8kwG9qt37dGJZTeXHPttdGJgXTkEji6pT3r7GliypL+s12T796S2pHTZVpR/E1SZr9I2u7fz/pDkH1KwfyH0JanouPeiLy2l1wxVQh+oeC4PNtF3S9sPfRn9X8FxR2TS1mvfzdB5EXll5V7PVL502p+3p+1s676buU8YhtEQQSfnDY8/J1K+ylWXEEL4JoRwGGqNPBdVPseiSvLn6ISq64Ddgf5BV9NrhD+iFs+LUQv0O+i1+Bq15t2KWiJ/EnQVt7w6voUuNrEDGiN1BGq9m4xanR9H3R/WCSFk46smZfwFtSZei66yNwn4IJa3Vgjhsrx8mTICsBMaMeMp1Jr6FerXeiqwcgjhv/XKiWX9HrXCn45aZsegQ9bj0VGKvwG/BBYOU4ep61BCCBNCCLugowFXocrYV+i1HYkqlEcARxbkvwe1bB+OrtCYWMonou17L2qJXzaE0CnD70F9i1dGR3leRmVjHNpGB6KjCKWhFGM5o0IIG6CK/jXoojPj0XYZgy4QciEalWGjjj6PVD3GoyMF56GyVS/ucVlZd6KjNf9A5X5yeY4p3EMtKseHwN3trYPRPRG9HxqGYVQj+mh+BPQG/hKVD2M6RUSGopPx3gkhDOja2hhG1xHdUEag86l+H0I4pmtrZExvmKXYMIxG2R5ViGE6mWBnGIZRgb1QvSdgUSeMHEwpNgyjMjGqQjLk/AZThx0zDMOYLomh7Q6MP+/vTPcdY8bFok8YhlGKiMwLJFvipws6/Gj+V4ZhTJfEFSZ7oxFDjqe2AuDpXVYpY7rGlGLDMOpxKHBi5r+H0MVADMMwplfOpO3iNteEEB7qgroYMwCmFBuGUZVv0Jn3NwOnmZXYMIwZhEnoMs9DgXO6tirG9IxFnzAMwzAMwzBaHptoZxiGYRiGYbQ8phQbhmEYhmEYLY8pxYZhGIZhGEbLY0qxYRiGYRiG0fKYUmwYhmEYhmG0PP8PTd0dXdw0rswAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", + "lower_bounds = [0,40,80,99]\n", + "upper_bounds = [40,80,99,100]\n", + "\n", + "points1 ,points1_sim = [], []\n", + "points2, points2_sim = [], []\n", + "n_points1, n_points2 = [], []\n", + "n_points1_sim, n_points2_sim = [], []\n", + "\n", + "for i, split in enumerate(splits):\n", + "\n", + " lb, ub = lower_bounds[i], upper_bounds[i]\n", + " \n", + " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_kcat = len(y_pred)\n", + " R2 = r2_score(y_true, y_pred)\n", + " abs_error = abs(y_true - y_pred)\n", + " \n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_sim = len(y_pred)\n", + " R2_sim = r2_score(y_true, y_pred)\n", + " abs_error_sim = abs(y_true - y_pred)\n", + " \n", + " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_DLkcat = len(y_pred)\n", + " R2_DLkcat = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat = abs(y_true - y_pred)\n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_DLkcat_sim = len(y_pred)\n", + " R2_DLkcat_sim = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat_sim = abs(y_true - y_pred)\n", + " \n", + "\n", + " print(split, R2, R2_sim, R2_DLkcat, R2_DLkcat_sim)\n", + " points1.append(R2)\n", + " points1_sim.append(R2_sim)\n", + " points2.append(R2_DLkcat)\n", + " points2_sim.append(R2_DLkcat_sim)\n", + " \n", + " n_points1.append(n_kcat)\n", + " n_points1_sim.append(n_kcat_sim)\n", + " n_points2.append(n_DLkcat)\n", + " n_points2_sim.append(n_kcat_DLkcat_sim)\n", + "\n", + "\n", + "ticks2 = np.array(range(len(splits)))\n", + "labs = splits\n", + "ax.set_xticks(ticks2)\n", + "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", + "ax.tick_params(axis='x', length=0, rotation = 0)\n", + "\n", + "plt.ylim((-0.7,1))\n", + "plt.xlim((-0.2, 3.2))\n", + "plt.legend(loc = \"lower right\", fontsize=20)\n", + "plt.ylabel('Coefficient of determination R²')\n", + "plt.xlabel('Enzyme sequence identity')\n", + "ax.yaxis.set_label_coords(-0.15, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.13)\n", + "\n", + "plt.plot([-0.15,4], [0,0], color='grey', linestyle='dashed')\n", + "\n", + "\n", + "plt.plot([0,1,2,3], points1, c= \"black\", linewidth=2)\n", + "plt.plot([0,1,2,3], points2, c= \"orchid\", linewidth=2)\n", + "\n", + "for i, split in enumerate(splits):\n", + " points1.append(R2)\n", + " points2.append(R2_DLkcat)\n", + " \n", + " if i ==0:\n", + " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8, label =\"KCATpred\")\n", + " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8, label =\"DLkcat\")\n", + " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", + " ax.annotate(n_points2[i], (i+0.06, points2[i]-0.01), fontsize=17, c='orchid', weight = \"bold\")\n", + "\n", + " else:\n", + " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8)\n", + " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8)\n", + " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", + " ax.annotate(n_points2[i], (i-0.04, points2[i]-0.10), fontsize=17, c='orchid', weight = \"bold\")\n", + " \n", + "\n", + "\n", + " \n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"comparison_DLKcat.svg\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"comparison_DLKcat.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Testing if differences in model performance is statistically significant using a one-sided Mann-Whitney U test" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0-40% MannwhitneyuResult(statistic=10717.0, pvalue=4.3070137916732266e-11)\n", + "40-80% MannwhitneyuResult(statistic=4127.0, pvalue=0.0017400394232513574)\n", + "80-99% MannwhitneyuResult(statistic=617.0, pvalue=0.0003661062666722406)\n", + "99-100% MannwhitneyuResult(statistic=13408.0, pvalue=0.048009706357389804)\n" + ] + } + ], + "source": [ + "from scipy.stats import mannwhitneyu\n", + "\n", + "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", + "lower_bounds = [0,40,80,99,0]\n", + "upper_bounds = [40,80,99,100,100]\n", + "\n", + "for i, split in enumerate(splits):\n", + "\n", + " lb, ub = lower_bounds[i], upper_bounds[i]\n", + " \n", + " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_kcat = len(y_pred)\n", + " R2 = r2_score(y_true, y_pred)\n", + " abs_error = abs(y_true - y_pred)\n", + " \n", + " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_DLkcat = len(y_pred)\n", + " R2_DLkcat = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat = abs(y_true - y_pred)\n", + " \n", + " res = mannwhitneyu(abs_error, abs_error_DLkcat, alternative=\"less\")\n", + " print(split, res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predicting Proteom allocation" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (14,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "conditions = [\"$Petri_1$\", \"$Johan_1$\", \"$Johan_2$\", \"$Johan_3$\", \"$Johan_4$\",\n", + " \"$Joao_1$\", \"$Joao_2$\", \"$Joao_3$\", \"$Joao_4$\", \"$Joao_5$\", \"$Joao_6$\",\n", + " \"$Joao_7$\", \"$Joao_8$\", \"$Joao_9$\", \"$Joao_{10}$\", \"$Joao_{11}$\", \"$Tyler_1$\",\n", + " \"$Keiji_1$\",\"$Keiji_3$\", \"$Tyler_2$\", \"$Tyler_3$\"]\n", + "\n", + "\n", + "data = [[1.3958, 1.0951, 1.1511, 1.1255, 1.1380, 1.1731, 1.2599, 1.1834, 1.1232,\n", + " 1.2105, 1.1553, 1.2801, 1.1873, 1.2479, 1.1843, 1.1711, 1.3075, 1.3824, 1.1935,1.0210, 1.1693],\n", + " [ 1.3348, 1.0608, 0.9795, 1.1813, 1.1117, 1.0074, 1.0357, 0.9859, 0.9882,\n", + " 0.9457, 0.9748, 1.1171, 1.0519, 1.0512, 1.0897, 0.9635, 1.2411, 1.2818, 1.0986 ,1.0474, 1.0883]]\n", + "\n", + "X = np.arange(len(conditions))\n", + "ax = fig.add_axes([0,0,1,1])\n", + "\n", + "barWidth = 0.35\n", + "eps = 0.04\n", + "\n", + "plt.xticks([r + barWidth for r in range(len(data[0]))], conditions, rotation = 90, fontsize=26)\n", + "plt.yticks( fontsize=30)\n", + "\n", + "ax.bar(X + 0.00, np.array(data[0])**2, color = 'orchid', width = barWidth, label = \"DLKcat\")\n", + "ax.bar(X + barWidth + eps, np.array(data[1])**2, color = 'black', width = barWidth, label = \"KCATpred\")\n", + "\n", + "plt.ylabel('Mean squared error', fontsize=30)\n", + "plt.legend(loc = \"upper center\", ncol = 2, fontsize=26)\n", + "plt.ylim((0,2.2))\n", + "\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"comparison_DLKCcat_proteome.svg\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting orginal kcat values and log10-transformed kcat values" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"final_kcat_dataset.pkl\"))\n", + "kcat_values = 10**np.array(df_kcat[\"geomean_kcat\"])\n", + "log10_kcat_values = np.array(df_kcat[\"geomean_kcat\"])\n", + "\n", + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "plt.ylim(ymax = 699, ymin = 0)\n", + "plt.xlim(xmax = 5.1, xmin = -3)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "#plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "plt.ylabel(\"Count\", fontsize = 22)\n", + "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.hist(log10_kcat_values, alpha = 0.9, color=\"darkblue\",rwidth = 0.95, bins = 20)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9SWXLwsaNUK5cHIqUsCkEioiISJ72jPicXVf2ppotzQWMZxLnBWx3yCGwfLkCYHGgy8EiIiISlG/3XhZf9gClr+jIYnsiDVkQNADWrAkrVyoAFhfqCRQREZGAfOs28PMp3Wi4dQpv43AHr5BBmYBty5aFf/7R/X/FiXoCRURE5AC+H34k9cSGnLx1Jr35gJt5O2gArFkTtmxRD2BxoxAoIiIi+1lL5utvktmyFZt3pNCcWXxI76DNjzwSVq1SACyOFAJFRETEs3Mn9OpFidtvZZI5j0bM52fqB21ety789ZcuARdXCoEiIiICf/8NzZvD0KF81+ZxLsr8mq1UC9i0ZEkYORIWLlQALM40MERERCTZff01XH01lCjB7s/HcnmvC7BBmtas6Q0A0eXf4k89gSIiIsnK54P+/eHSS7HHHsvXjy+g3OUXsG1b4OaHHKL7/xKJegJFRESS0aZNcNVV8O23+K65jiazX2dBn7JBm5coAa6ry7+JRCFQREQk2cydC507Y//9l/nOu7Qacj3p6aEPqVkTLr44NuVJbOhysIiISLKwFt55B1q0wBpD19o/0NjNOwACvPGGNyBEEod6AkVERJJBejrceit88AG+c9txysKP+WvzQWEd2qYNdOhQuOVJ7KknUEREJNEtWwZnnQUffMCvnR6hzKRvwgqAJUrAZ5/BpEnqBUxE6gkUERFJZGPHQo8eWGt5qO4Ynhl1UViHVa4M69drJHAiU0+giIhIIvL54NFHvdEcdeowYcB8nlkUXgCsUEEBMBkoBIqIiCSaLVu88PfEE9CzJ6nfzuKi24/J87DSpeGBB7zDFQATny4Hi4iIJJIFC6BTJ1i7Ft5+m21X3kj1gwyZmaEPq1MHlizRPIDJRD2BIiIiiWLQIDjzTO9S8IwZbOvqULVa3gHwtNMUAJORQqCIiEhxt2sX3HADXHcdtGwJCxaQekoTqlfP+9D774efflIATEYKgSIiIsXZihXQogW89x489BCMH09q2RpUqUKePYBt2sCAAZr+JVkpBIqIiBRXEyZAw4betdwvvyTj0QHcc39JKlXKOwDee6/m/0t2CoEiIiLFTWYmPPkktG8Phx9O2vT5NH/6UsqUgRdeyPvwVq3g6acVAJOdQqCIiEhx8t9/cOml8Mgj+Lp1596WP1Kx/nH8+GN4h99zD0yZogAomiJGRESk+PjpJ2/6l1WryHjxdao8eAu7dpuwDz/1VHjmGQVA8agnUEREpDgYMgSaN4fdu/F9N53jX741ogBYp443haACoGRRCBQRESnKdu+Gm2+GXr2geXN8cxdw/+fNWLky/FOceqrmAZQD6XKwiIhIUbVqFXTuDHPmwH33kfHoAE5vVIrFi8M7vHx5GDwYOnZUD6AcSCFQRESkKJo0Cbp183oCR40ivX1HjjgCNm/O+9CTT/YGD192mcKfBKcQKCIiUpRkZsKzz0L//l6aGzWK1MNPpErFvOf+M8YbPFylSmxKleJNIVBERKSo2LrVu/fvq6+ga1d4911SqRjW6h8A27dDxYqFXqUkCA0MERERKQoWLYLGjWHsWHjlFfjkk4gC4ObNCoASGYVAERGRePv4Y2jaFNLSYOpU6NOHbdsNlSuHFwA3bIDq1Qu9SkkwEYdAY0yKMeaAy8jGc7MxZpgx5nNjjGOMUcgUEREJJiMDbr8devTwegEXLMDX7Cw++giqVgVr8z7F5s1w8MGFXqkkoIjuCTTG3Ai8BXwK9Mi1+2ugfVZT4FLgIv+ziIiIZLd6NVxxBcyaBXffDU8/zbadpTmsMuzcmffhGgQiBRVpT11WyBuSfaMx5hLgQv/b4cAHwB7gImPMVQWqUEREJNF89x00auTdB/jZZ6Q/OZBLO5WmatXwAmCJEt4gEAVAKYhIQ+Cp/uc5ubZfDVjgaWvtVdba64Db8XoEexasRBERkQRhLTz3HJx7LlSvzq7pc+gwtAvly8PXX4d3ihIlYNs2DQKRgot0ipiaQJq1dmuu7ef4n9/Ntm0o8DbQIH+liYiIJJDt2+Gaa2D0aHyduvBAjfcZ2LBSRKdQAJRoijQElgMysm8wxpwIVAf+ttauyNpurU03xmwFqhawRhERkeLtt9+gY0fs33/zyhEv0HdUX7yLZeFLSdE0MBJdkV4O3gCUN8Ycnm1b1n2C3wdoXxbYlp/CREREEsKwYdgmTdi2ahutfVPou+ouIg2AffvCjh0KgBJdkYbA2f7nR/1TwtQAbsO7H3Bi9obGmDp4PYdrC1yliIhIcbNnD3tvuxO6deOHnQ04OX0BM2gV0SkqVfIWEXnxRa8nUCSaIg2Br+H99+U6vB6+VcAxwBpgdK625/ufFxSkQBERkWJn3ToyzjqbUm+8wsvcwdl8xzoOi+gUQ4dqChgpXBGFQGvtNOAmIA2oCJQBlgCXW2t352p+rf95UkGLFBERKS5Sx05nY+0G7Jm7kK58Sl9eZi+lwz7+7rth927o3h1KlizEQiXpRTowBGvtO8aYj4DTgO3AEmttjkVtjDGlgWf9bycXuEoREZGizlq2PfYSFZ64jzUcSxsm8/u+mdXydued8OyzuuwrsRNxCARv5C8wN8T+PcCX+S1KRESkWNmxg7Ru11HlmxGM5nJ6M5gdVA7r0GbN4NtvNehDYq9Aa/tmDQ7xDwIRERFJPn/8QUaDppT9ZhT38hydGBVWAOzTx7vsO2uWAqDER756Ao0xzYEHgbOB8nijg0tl218VeMG/vY+1NoxFcERERIqXPcNG4ut5Ddv3lONKJjGVs/M8pkkTmDxZwU/iL+IQaIy5FXgZCHq7qrV2q3/6mIuBacBH+S1QRESkqMnYuZfpZz7AuT+/wDya0YURrKF2yGNKlIAtWzTaV4qOiC4HG2OaAK8AmcADQB3g3yDNP8CbTubCghQoIiJSlGz4ZT0zK5zLuT+/wOvcSmum5RkA+/aF9HQFQClaIu0JzJrm/DFr7XMAxgSd9Xya/7lh/koTEREpOtLTof/ZP3D37C40YSs9+IiP6ZHncVu3KvxJ0RRpCGzpf34zr4bW2v+MMTsgj/8eiYiIFHGpOywPVX6NF7ibfziKCxjPIuqFPKZCBVi/Xvf+SdEV6ejgGsB2a2246wH78vEZIiIiRYZvWyrja3TnVe5gLBfSmLl5BsATTvDu/1MAlKIs0oC2DahkjMlzKkv/wJAqwMb8FCYiIhJ3f/3F1pOacXnGcB7kf1zO52yjashDXBd+/12TPkvRF+nl4J+Bc/AuC+e1EkhvvPsHZ0deloiISHylf/I5vqt7kZlZhnZMYDLnhmx/7LEwf77u/5PiI9KewCF4we5pY0zQTm5jzPnAE3jzBA7Kf3kiIiIxtncv6Xc8QLnuHfk98yQaMT/PALhhAyxdqgAoxUukIXAoXg/gGcBsY0xfoAyAMeYSY8xtxphvgLFAWeALa+24aBYsIiJSaDZsIPO88yn36rO8xU20ZAarCL4o1i23eKt+HHxwDGsUiZKILgdba60x5nK8yZ87AAOz7f7C/5w1Z8xooGdBCxQREYmJH3+Ezp3Zu34zNzCYIfQK2XzxYjjxxBjVJlIIIh65a61NtdZeDpwHfAIsB3YBGcAqYDjQ3lrbWcvFiYhIkWct6QPfIKN5K5atSaGJb1aeAXDhQgVAKf7ytXYwgLV2MnkPDhERESmyUjfsZOqJDhdvHcoYLuJqPmIr1UIeM28e1K8fm/pECpPm8BMRkaS0dvpSlh3SjAu3fkx/nuRSvsozAP72GzRqFKMCRQpZvnsCRUREiqt/3/2K8jf25HBK0p5xTKRdnscsWQLHHReD4kRiJGgINMZEbVCHtXZItM4lIiKSbz4f651HOPT9/zGPRnRmJCs4Ks/DFi5UAJTEE6oncDDePH/RoBAoIiJxlbF2E4sbdKPehkm8y/Xczmvspmyex82erXsAJTGFCoHTiV4IFBERiZsVI+ZgrujMCWzgOt5jENfleUyZMvD333D44TEoUCQOgoZAa22bGNYhIiISdak7LM8f/w4P/duHtRzGWfzAAkKP7DjmGFiwQKt/SOLTwBAREUlI//yRznen3MLjDGYcF9CDoWzhoJDHrFgBdYIvECKSUDRFjIiIJJTUVGh3/DL+O+VMevEhj/EoFzMmzwC4erUCoCQX9QSKiEhC2LYNTjkFTl87lmF0B+BixjCOC0Med9RR8NNPuvwrySfUFDFT/C9XWGuvybUtEtZa2zY/xYmIiOQlIwN694bhn/p4hCd4lCdYSH06MYrlHBPy2JkzoXnz2NQpUtSE6gls439eHGBbJDTCWERECkVqKlSqBNXZzDd05wIm8AG9uYU32UW5oMelpMCyZRr5K8ktVAi8xv+8LcA2ERGRuFq0COrVg4bMZxSdqMU6bsTlXW4ATNDj1Psn4gk1RcyH4WwTERGJpY0boWZN7/W1vM8b3MoGatKC75lH45DHauk3kf00OlhERIqNpUu9AFiGXbzL9bzP9UynFQ1ZEDIA1qsHO3YoAIpkF1EINMZMMcaMiKD9p8aYyZGXJSIiktP8+XD88XAk//A9Lbie93mKfrRnHJupEfCYChVg3Tr4+WeoWDHGBYsUcZFOEdMGWB9B+2ZAzGddchynEdAeaAGcCtQE9uLVPhsY4rru+FjXJSIikVu/HmrV8l63Yzwf052S+LiErxjDJQGPqVzZ6zU8+OAYFipSzBT25eCSxHh0sOM404B5wJNAO6A2kAKUB44BugHjHMcZ4ziOZoUSESmifD4YMMALgIZM+vMkY7mQ1dTmDOYFDYBTp3pzBioAioRWaJNFG2PK4PXAbS+szwgia8D/v8BIYDqwAi+MngHcCRwPXAR85TjO2a7rZsa4RhERCSE1FapVg717oSr/8RFXczHf8BE9cHBJp/wBx5Qo4fX+HX10HAoWKYZChkBjTB3gqFybU4wxLQk+/t4AVfF63FKAmQUrMWKLgf7ASNd19+baN8dxnA+BicCZQCvgKmBobEsUEZFgtmyBg/wrvNVnIaPoRG1Wcwtv8BY3E+ifnxkzoEWL2NYpUtzl1RN4DfBIrm3VgKlhnDvrT+nLkZVUMK7rXpzH/jTHcW4CfvFv6oJCoIhIkTB/Ppxxhve6F4N5i5vZzEG0YjqzaRbwmF9+gbp1Y1ikSILI657ArcDKbA+AzFzbcj/+wQtYnwBtrbVfRbvognJddxGw2f9WEwaIiMSZzwf33usFwBR28xY3MZhrmMmZNGRB0AC4YoUCoEh+hewJtNa+AryS9d4YkwlstNYmwh0Xpf3PvrhWISKSxHw+ePVVuOsu7/0RrGQknWnCXJ7mAR7mSXwB/qnq1Qveecdb/k1E8ifSgSGPA6mFUUgsOY7TAKjsf/tHPGsREUlWa9ZA7dr737dlEsPoSmn2cBmf8yWXHXDMQQfBX39B9eqxq1MkUUU0RYy19nFr7QuFVUwM9c/2enjcqhARSVJz5uwPgIZMHuR/TKAd6zmUxswNGAC//BI2bVIAFImWQpsipqhyHKcr0NH/dh7weRzLERFJOtOmQZs23usqbOVDetGBr/iEbtzAu+ykwgHHTJwI550X2zpFEl2+QqAx5gKgM3Aa3mjh0iGaW2vtsfn5nGhzHKce8J7/7U7gatd1YzqZtYhIskpNhdNPh2XLvPd1+YXRdORIVnA7r/I6txFo+pclS7Tmr0hhiCgEGmNK410+7ZC1KYzDikTIchznKGAsUAFvhHMv13UXx7UoEZEkkX3pN4DuDOUdbuQ/qtGGqczkrAOOueceeOYZKFkyhoWKJBFjbfgZzRjTH3gCL9h9A3wBrAF2hTrOWjst/yUWnOM4tfBWDsn6v+QNruu+F+KQrONuBG7Mvi0lJaXRoYcemqNdy5YtadWqVZSqFRFJLIsWQb163uvSZPAid3EbbzCV1nRlGP9y6AHH/PYbnHJKjAsVSUxBO+wiDYF/ACcAD1prn4tCYYXOcZwaeJNbn+rf1Nd13Zfze74BAwbYfv36RaEyEZHElp4O554LM/3rRh3OakbQheb8yPPcw4M8fcD0L5MnwznnxKFYkcQVNARGek/gUXiXUl8rSDWx4jhOFbwl4rIC4MMFCYAiIhKelSvhyCP3v2/DdwznSsqRTmdGMIrOBxyjlT9EYiuiKWLwVhDZYa1NL4RaospxnIrAeKCBf9Ozrus+FceSRESSwpQp2QOg5V6eYxLnsokaNGZuwAC4ZIkCoEisRRoCpwFVjDFHFEYx0eI4Tjnga9i3ztBrrus+EMeSREQSXkYGtG8Pbdt67yuxnVF04jnuZxSdaMps/uSkHMfUrg1bt2r0r0g8RBoCn8IbBPJsIdQSFY7jpACjgDb+Te8Dd8StIBGRJPD771CmDIwf770/hd+YS2Mu5Sv68iJXMpxUKuU45ssvYdUqqFIlDgWLSGT3BFprfzXGXAYMN8aMwwuDc621aYVRXD59ArT3v54FvAqc6jhO0ANc1/01BnWJiCQcnw8eeAAGDty/rSuf8h7Xs53KnMMUZnDg7AkzZ0Lz5jEsVEQOEOk8gb5sb8/3PzAm5HSB1loby5VJOmV73Rz4OYxjwpnvUEREsvH54IQT9k/+XJoMnude7uBVZtCCK/iM9dTKcUz16t79f1r6TST+Ir0cbPLxiPQzRESkiPvzTyhVan8ArMVapnAOd/AqL3En5zDlgAA4Zgxs3qwAKFJURNpDd3ShVBFFruuqV09EpJBs2QIHHZRzWyumMZwrqUgqVzKMz7jygOMWL4YTT4xRkSISlkjvCVxRWIWIiEjRNmsWnHlm9i2Wu3iRZ7mfpRxHWybz+75pWT1NmsDUqVCuXCwrFZFw6FKtiIjk6dtvcwbAiuzgM67gBe7hSzrQhDkHBMDJk2H2bAVAkaIqlgM2RESkGJo2Dc4/f//7k/iD0XTkBP7iXp5jIPeQfXxdtWqwdKnu/RMp6vIdAo0xLYGzgMOACgQfYWuttdfl93NERCR+pk2DNm32v+/MCAZxLTspz7lMYipn52j/9tsQYkYuESlCIg6BxpjT8ObiOzX3Lv+zzbXNAgqBIiLFSEYGdOwI33zjvS/FHp7hAe7mRWbSnC6MYC2H5zhm3Di44II4FCsi+RLpPIG1gMnAwcDvwLd4q3GkAi8DhwDnAMcCmwAX2Bu9ckVEpLCtXw+1ss3ucgjrGc6VtGY6r3I79zCQPaTs21+rFvzxh1b+ECluIh0Ycg9eABwPNLDW9vVvT7XWPmKtday1xwM3AVWBhsAT0SpWREQK15QpOQPgWXzPAhpyBvPozlDu4NUcAXDGDFi7VgFQpDiKNARegHd5t5+1dk+wRtbad4B+/va35r88ERGJlfffh7Zts95Z+vAK33E2aVSgGT/yCd1ztP/lF2jRIuZlikiURBoCjwR8wE/ZtlmgTIC2b/v39cxXZSIiEhPp6VC/Plx/vfe+Aql8wlW8wp18w0WcwTx+pW6OY2bPhrp1DzyXiBQfkQ4MyQS2WWuzD/5IBSobY0paa/etLWyt3WGM2Q6cEIU6RUSkEKxZA7Vr739/An8ymo6cxGIe4Gme4z5srv6CmTO9SaBFpHiLtCdwDV7gy37cP/7z1Mve0BhTBe++wBRERKTImTMnZwC8nNHMpTE12cD5TORZHsgRAEuUgBUroHnzOBQrIlEXaQj8E6/38ORs22bgTQVzT662T/qff89faSIiUhi2bAFjoGlT731J9vIM9zOaTvzByTRkAVNom+OYIUPA54M6deJQsIgUikhD4ES8wHdxtm2vAXuArsaYRcaYj40xP+MNCLHAW1GpVERECmzWLDjooP3va/IvEzmf+3mON7mZVkxnNUfkOGb2bLj66hgXKiKFLtJ7AocDRwNpWRustX8aY3oB7+BNIJ01ibQFXrLWvh+NQkVEJP9SU+Gkk7x7ALM0YxYj6MJBbKYnH/JRgHF8mzdr+TeRRBVRCLTWbgbuDbB9mDFmEtAeqA1sAyZZa/+KSpUiIpJvuZd+A8utvMGL3MUqjqA5s/iZ+jmOad8evvgCUnRXt0jCyvfawblZazcBH0XrfCIiUjBbtuS89AtQnjRcHHrwMV9zMT0Zwlaq5WgzeDD06hW7OkUkPiK9J1BERIqBzz8/MAAexxJm0Zyr+IT+PEkHvjwgAI4bpwAokiyi1hMoIiLxt3IlHHnkgdsv5UuG0JO9lKI945hIuxz7S5WCf/6Bww+PTZ0iEn8RhUBjzLJ8fIa11h6bj+NERCRM69fnXPM3Swl8PMnDPMTTzOUMOjOSleRMiRMnwnnnxahQESkyIu0JPCofn2HzbiIiIvn11VfQocOB22uwkU+4ivOYxDvcQB9eZTdl9+0vU8br/Tv00NjVKiJFR6Qh8Ow89lcBGgPXA6WBPnirjIiISCF47TXo0+fA7Y2Zw0g6U5MNXMv7fMC1OfZPngznnBOjIkWkSIp0iphpYTT7yhjzMjAJeApolI+6REQkhKVL4fjjA+2xOLi8wh2s5TDOZCYLabhv77HHwk8/QcWKsapURIqqQhkd7J9P8Ga8y8ePFMZniIgko/R0OOOMwAGwLOl8wDW8zc1M4RwaMT9HAJw3zwuPCoAiAoU4RYy19ke8lUUuK6zPEBFJJitXQvnyMH/+gfuOZhkzOZOeDOExHuUivuE/vKU++vSBvXuhka7LiEg2hTZFjDGmpP/8AcariYhIJP7801v2LZAL+Yah9MBiuIhvGE/7ffuWLIHjjotRkSJSrBTmZNEXAmWAjYX4GSIiCe/bbwMHwBL4eJxH+IaLWc7RNGL+vgA4ezZYqwAoIsFFtSfQGJOCt3ZwB6A/3vQwY6L5GSIiyWLjRqhZM/C+6mzmY7pzARMYxDXcyhvsohylS3vHVakS21pFpPiJqCfQGOML9QDSgSXAQKAasBwNDBERidiUKcEDYCPmMZ9GnM133MA7XMf77KIcfft6A0cUAEUkHJH2BJow2/0DfAY8Y63dGuFniIgktW+/hfPPD7zvOt7jDW5lPYfSgu+ZR2NKlIDly6FOndjWKSLFW6Qh8Og89u8Ftlpr0/JZj4hIUvv0U7jqqgO3lyWd17mN6xjEBM6nOx+zmRq89x5cd13s6xSR4i/SyaJXFFYhIiLJLNT9f0fyD6PoRCMW8CT9eYzHyKQkgwdDr14xLVNEEkihTREjIiLhWb4cjjkm8L52jOdjulMSH5fwFWO4BNCybyJScIU5RYyIiORhypTAAdCQycM8wVguZBVH0Ij5jOESDj0Utm5VABSRgouoJ9AYMyVKn2uttW2jdC4RkWLH54Nrr4UhQw7cV40tfMTVXMRYPqQnN/MW6ZRn4kQ477zY1yoiiSnSy8Ftsr22BB8tHGpf1n4RkaQU6v6/+ixkFJ2ozWpu4i1cHMDo8q+IRF2kIfAaoDre3H9VgOnANGCNf/9hQGugFbAVeAL4LxqFiogkgqVL4fjjA+/rzQe8yS1sogYtmcEcmgKwcCHUrx+7GkUkOUQaAr8A5gK7gVbW2u8DNTLGnAmMAm4CmlhrtxekSBGRRDB+PLRvf+D2FHbzKn1weIfJnENXhrGJg3EceOMNKFky9rWKSOKLdGDII8CxwHXBAiCAtXYmcD1wAvBw/ssTESn+tm2DEiUCB8AjWMkMWuLwDk/zAO2YwCYOZtAgePttBUARKTyRhsDLgHRr7TdhtB2Lt4zc5ZEWJSKSKH76CapWBRvgTuhz+ZYFNORE/uQyPuchnsZHKSZOhGuuiXWlIpJsIg2BhwGZ4TS01lrA5z9GRCTpDB8ODRocuN2QyUMMYALtWEctzmAeX3IZNWt6079oBLCIxEKkIXAzUMEYc1ZeDf1tKgJb8lOYiEhx9uGH0LXrgdursJUvuIwB9OdTutGMH1nK8bz+Ovz7L1SpEvtaRSQ5RRoCx+JN/fKBMea4YI2MMccCH+BNBRPOpWMRkYTx8cfQu/eB2+vyC/M4g/aM4zZeowdD2UkFBg2CW2+NeZkikuQiHR38KN59gccCi4wxo/GmiFnr338Y3vQwHYGywAb/MSIiCS8jwxv8MSXAtPo9+AgXh/+oRmumMYszAZg6FVq3jm2dIiIQYQi01q4zxrQGRgInA139j9wM8DvQxVq7vsBViogUcStXwpFHHri9NBm8RF9u5U2m0porGc4GDuGYY+Dnn6FixdjXKiIC+Vg72Fr7B3A60BP4Gm+i6Az/Y41/29VAfX9bEZGENnJk4AB4OKuZTitu5U2e417OZRIbOIRPPoG//1YAFJH4ivRyMADW2r3AUP9DRCQp/f47nHpq4H1nM4VhdKUc6XRiJKPpBGj1DxEpOiLuCRQREfj002AB0HIfz/It57GJGjRm7r4AuGSJAqCIFB0KgSIiEfrwQ7jqqgO3V2Ybo+jEszzACLrQhDn8yUkA/PYbHBd0TgURkdhTCBQRicAbbwSe/uVUfmUujbmEr7mTl+jGp6RRkapVYfNmOOWUWFcqIhKaQqCISBjS073Lv7fdduC+rnzKbJpSiR2cwxRe4U7A8N578N9/UL16rKsVEclbvgaGiIgkk0WLoF69A7eXJoOB3EMfXmM6LbmS4aynFgDz5kGjRjEuVEQkAuoJFBEJ4YMPAgfAWqzlO86mD6/xIn1py2TWU4vLLoPduxUARaToU0+giEgA69dDrVqB97ViGsO5koqkcgXDGcEVgDf4Q/f+iUhxoZ5AEZFsfD64//5gAdByFy8wmbZspSpNmLMvAC5bpgAoIsWLegJFRPw2boSaNQPvq8gOBnEtXRjJSDpxLYPYQWUOOQT+/BOqVIltrSIiBZWvnkDj6WiMecsYM8YYMznX/grGmFbGmJbRKVNEpHAtXRo8AJ7EH8yhCZfzOXczkC6MYAeVefll77KxAqCIFEcR9wQaY44HRgOnAMa/2eZqtgt4HzjGGNPaWvt9gaoUESlEU6ZA27aB93XhMwZxLWlUoC2TmU5rAIYNgyuvjGGRIiJRFlFPoDGmGjAJOBVYBDwCbM/dzlrrA97CC4mdCl6miEj0rV8PxgQOgKXYwwvcxWdcyc+cTkMWMJ3WlCgBK1YoAIpI8Rfp5eC7gSOACcAZ1tqngPQgbb/yP5+Zz9pERApFVvgLNvr3UNYxmbbcxUu8yu2czXes5XDee88bOFKnTmzrFREpDJFeDu6Ad+n3bmvt3lANrbVLjTEZgFbLFJEiI9jEz1nO4ntG0IXKbOcqPuZTvEWCNf2LiCSaSHsCjwZ2WWt/D7P9DqBShJ8hIhJ1Ph/06xcqAFr68ArfcTY7qERTZvMpV/HOO2CtAqCIJJ5IewItUDKchsaYUkBlAtwzKCISSz4fHHcc/PNP4P0VSOU9rqcrw/mcy+jNYLZThaFDoXv3mJYqIhIzkYbA5cCpxphjrLXL8mjbFigN/JGvykREomDLFjjooOD7T+BPRtORk1jM/TzDc9wHGGbMgBYtYlamiEjMRXo5+Bu8Eb99QzUyxlQAnsfrOfwyf6WJiBTMlCmhA2BHRjGXxhzMRs5nIs9xP7VqGbZuVQAUkcQXaQh8AfgPuMUY85QxJsdfr8aYSsaYLsA84DRgLd5UMSIiMfX++8Hn/ivJXp7lPkbRmd85hUbMZwptWbwY1q7V5M8ikhwiCoHW2k14I4S3Aw8C64GDAYwxW/AC4jDgRGALcJm1Ni2aBYuIhJKRAeeeC9dfH3h/Tf7lW87jPp7nDW6hNdN448sjsBZOPDG2tYqIxFPEy8b5V/84HfgU8PnPYYCq/tc+YDjQyFo7P2qViojk4fvvoUwZmDw58P5mzGIBDWnKbK5mCLfxBgt/K8Oll8a2ThGRoiDiZeMArLUrgR7GmBuARkAtvAD4LzDPWpsavRJFRPL20UfQs2ewvZZbeYMXuYuV1OFCxrLj6NPZuwRKhjXfgYhI4slXCMxirU0HtC6wiMTVhx9C796B95UnDReHHnzMV1xCT4bQq09VXnxRAVBEkluBQqCISDytXx986TeA41jCaDpyKr/Rj6d4mgeZO68EjRrFrkYRkaJKIVBEip1t2+Coo2Dr1uBtLuVLhtCTPZTmAsbzQ/nz2f4vVKwYqypFRIq2oANDjDG+KD1CrjEsIhKJWbOgatXgAbAke/kfD/Ill/EXJ9CI+Zx42/ls364AKCKSXaieQBOlz4jWeUQkyc2ZA2eeGXx/DTbyKd04l8m43MgdvML02WVp0iR2NYqIFBehQuDRMatCRCQPU6YEn/wZoAmzGUlnarCJaxjEYK5h82aoXj12NYqIFCdBQ6C1dkUsCxERCWTbNjjkENi9O1gLi4PLq/RhNbU5k5msP7QBWxdr5Q8RkVAinixaRCRWli/37v8LFgDLsZPB9OZtbuZbzuMM5vHAsAasW6cAKCKSlwKPDjbGHIV/6Thgo7X2n4KeM1ocx6kN3A5cAtQB9gLLgc+B11zX/S+O5YlICIsWQb16wfcfw9+MpiN1WcQjPM7TJfrz9/IS1KkTuxpFRIqzfPUEGmOONsa8Y4zZDPwN/Oh//G2M2WyMedsYE9d7Ch3HuQBYBNwHnAxUAKoA9YHHgUWO42i2MJEiaOXK0AHwIsYwn0YcwSouZCwd5j3CHp8CoIhIJCIOgcaYLnjh6jqgGt7o3+yPasANwK/+tjHnOE49YCTeesY7gUeBFkAb4CW89Y0PB8Y4jnNYPGoUkcB++gmOPDLwvhL4eIKHGcMl/M2xNGI+A+ZdoMmfRUTyIaLLwcaYxsAnQEngT+AFYBqwxt/kMLyg1Rev9+1jY8wya+38aBUcppfxev58QHvXdadn2zfNcZwFwEfAocBTwLUxrk9EAvj4Y+jRI/C+6mzmE66iHRN5n2u5lTdY8FtZTjkltjWKiCSKSHsC++MFwIlAfWvte9baJdbanf7HUmvte0ADf5tSwMPRLTk0/yXes/1vB+cKgAC4rjsUmOJ/29NxnJqxqk9EAvv88+ABsBHzWEBDWjON63mX8p+8zy6rACgiUhCRhsCzAAvcbK0NPmGDtRnALf63LfJZW351zPb6/RDtBvmfSwKXFl45IhJKejq0aAEdOwbefx3v8QNnAdCC7+k87nq6dYthgSIiCSrSEFgW2GatXZ5XQ2vtMmArUCYfdRVEVujcCcwN0e67AMeISAytWQPly8MPPxy4ryzpvMd1vMcNTKUNjZjPazPP4IILYl+niEgiijQE/g1UMMbkGeyMMWXx7stbmp/CCiDrAtES13WDrlvsuu5aYEeuY0QkRpYuhdq1A+87iuX8wFlcxyCe4GEuZCyfTqxB8+axrVFEJJFFGgIHA6UBJ4y2N/rbDo7wM/LNcZwyQA3/29VhHLLK/3xE4VQkIoFs2QLHHx943wWMYz6NOJrlXMzXPMoTzJlXkvPOi22NIiKJLtIQ+DLeRMsDjTEPGGPK5W5gjClrjLkfGAiMBl4tcJXhq5TtdWoY7bPaVCyEWkQkgG3boEaNA7cbMnmEx/mGi1hJHc5gHpPKXMzu3WgKGBGRQhDpiiHvA9uBNGAA0M8YM4+cU8ScgXcZeBve5db3jTG5z2Ottdflt+gQsofSjDDaZw1uOSDMikj0bdwINQOMxa/GFobSgwsZx2B6cQtvkk55tv4LKSmxr1NEJBlEGgJ7440Ozkp1FYDWQdpWBXoF2WfxJpuOtvRsr8P5pyPr3sb0kK1EpMBSUwMHwPosZDQdOZw1OLzNO9xI7dqGX3/V+r8iIoUp0hD4eKFUET07sr0O5xJvVpuAl44dx7kR797GfVJSUhgwYECOdi1btqRVq1YRlCmSXDIyoFKlA7f35gPe4mY2cjAtmcEcmjJxIrr/T0QkBiIKgdbaIh0CXdfd7TjOJrzBIUHGHeaQ1WZVoJ2u674DvJN924ABA2y/fv0KVKdIMtm2DQ45JOe2MuziVfpwI+8yibZ041M2cTADByoAiojESsRrBxcDv/ufj3ccJ2jI9a8ZXDnXMSISRXPmQNWqsDvb1PJ1WMEMWnIj7/I/HqQdE9jEwdSrB3feGa9KRUSSTyKGwO/9z+WBxiHatQlwjIhEgc8H/fpB06Y5t5/HRBbQkBP4iw58QT/+RyYl6d8fFiyAkiXjU6+ISDKK9J7AfYwxtYHTgGp48wEGZa0dkt/PyYfRwEP+19cBs4K0u9b/7AO+KuyiRJKFzwcNGsCiRfu3GTJ5iP/xBI/wK6fRiVEsxZsoUPcAiojER8Qh0BjTHHiJ0L1sucUsBLquO99xnKl4PX29Hcf50HXdGdnbOI7THWibVZvruhtiVZ9IonvmmZwBsApb+YiruYQxDKU7Di47qQB46wUrAIqIxEdEIdAY0wL4lv3TrywF/sXrTStK7gBm4k1hM95xnGeAyXhfbwf/foD1QP+4VCiSYHw+eOwxeOqp/dvq8TOj6cgRrOJWXudNbiFrhqlSpeDTT+NSqoiIEHlP4AC8ufVmAldZa1dGv6SCc133F8dxOgOf4s1X+IT/kd0aoIN/DWERKYBAl4CvZgguDluoTmum8SP7F/7t1g0GD9ZE0CIi8RTpwJBGeBM9dyuqATCL67rjgbrA88AfeKucbAd+Bh4D6rquOz9uBYokiG3bvBHAWQEwhd28wS0MoRc/0oyGLMgRAJctg08+UQAUEYm3SHsC04E91tqA8+oVNa7rrgbu8z9EJMq2bIGDDtr/vjarGElnmjKHZ7mPfgzA5/9rpk4dWLJE4U9EpKiINAQuAM4xxlS21m4vjIJEpHjw+eCww/a/P4fJDKMrZdhNR0bxOR1ztF+8WAFQRKQoifRy8HP+Y+4thFpEpJhIT4f69bMmgbbczzNM5Hw2UJPGzM0RACtXhh07oFy5eFUrIiKBRBQCrbWTgduB+4wx7xpjji2cskSkqFq6FMqXh19/hcpsYzQdeYYHGUEXmjKbvzhxX9tmzbxLxhXDWclbRERiKuJ5Aq21bxpjquONtr3WGLMLb5qYEIdYhUWRBDBtGrRp470+jUWMohNHs5w7eJlX6UPW9C/grf4xbZpWARERKaoinSewDDAcuCRrE1AOOCrEYTZflYlIkTJlCrT1T7HejU94lxvYTmXO5jt+oEWOtj17wrvv6h5AEZGiLNKewIeAS4G9eKuATAI2UPQmixaRKFq0yAuApclgIPfQh9eYTkuuZDjrqZWjbffu8OGHcSpURETCFmkI7IHXs3eTtXZQIdQjIkXM0qVQrx4cxhpG0IUzmcUL3MUDPMPeXMuGly4Ng/Q3g4hIsRDp6OBawB5iuBawiMTPn3/C8cdDa6aygIbU4xeuYDj38MIBAbBHD0hN1SVgEZHiItKewLVATWvt3sIoRkSKjp9+ggYNLHfzAs/wAEs4njZMZTEnH9B2yRI47rjY1ygiIvkXaU/gaKCCMaZ5ni1FpFjy+eDpp6FVg+2MoAsDuZfPuZwmzDkgANauDTt3KgCKiBRHkfYEPok3MOR9Y8xF1trlhVCTiMSJz+fN7Zc273fm0JHjWMpdvMBL9CX79C9Z/v5bl39FRIqrSEPg5cDbwKPAYmPMCGARsC7UQdZa3UMoUgwMGQLHzBvO+1xHKhVpy2Sm0zpg29WrFQBFRIqzSEPgYLzRwVldAt38j7woBIoUcVv+3cPWa+9jOC/zA2fShRGs47CAbZctg8MPj3GBIiISVZGGwOlo8meRhLNuwTqWNrqCvnzPK/ThXp5nD4G7+VavVgAUEUkEEYVAa22bQqpDROLk17dmcNAtV9CQ7XTjE4YF6dwvUwbWroXq1WNcoIiIFIqI1w4WkcSQsdvyadOX6f7zvfzNsZzHt/zGaQHblisHO3ZoHWARkUSiECiShDK2pPJljevoZT9jNJfTm8HsoHLQ9qtXKwCKiCSaSOcJFJFizvfbYtbVaUJHO5L7eJZOjAoZAJct0yVgEZFElK+eQGNMY+Am4CzgMKBCiObWWqseR5EiwDdiFLu69qZcZjnO41u+45yQ7X/5BY4+OkbFiYhITEUczowx9wMDCL8X8cAZZkUktvbuhQcfpOTAgfxCM7owgjXUDnnIsmUKgCIiiSyiy8HGmLOBp/GmiXkEaOjftRE4Dq9n8FFgk//RAdA/IyLx9O+/cN55MHAgr3MrrZkWMgA+84yXGRUARUQSW6Q9gbfjBcBHrbX/AzDGAPistcuAZcAsY8x7wFTgfaBB1KoVkcjMnAldupC55T968hEf0yNk89mzoUmTGNUmIiJxFenAkKb+53dCncdauw64BagBPJS/0kQk36yF116D1q3ZU7oc9Xf9mGcAnDdPAVBEJJlEGgJrAGnW2k3Ztu0FygdoOwVIB9rnszYRyY+0NOjRA/r0wXd+ew5eMY9F1At5yMsvQ6NGsSlPRESKhkhD4H+AL8C2CsaYKtk3WmstkAnUyn95IhKRv/6CZs1g2DAYMIBLM79gG1VDHlKpEtx2W2zKExGRoiPSELgaqGyMqZht2+/+5zbZGxpjTsebOiYt39WJSPi++AIaN4Z160gbNZ7D33iIsePz/iO+dq0mghYRSUaRhsD5/uem2bZ9hTcNzEBjTGNjTGljTEPgQ7xBJNMKXqaIBLV3LzzwAFx+OZx4Imu+XkDFy89j7dq8D92wASpWzLudiIgknkhD4Bd4ga9rtm1vAUuAY4EfgV3AXKAe3j2BjxW0SBEJYsMGaNcOnn0WHIdtY2ZQ+8w6YR26dSscfHDhliciIkVXpFPETADqAhlZG6y1u4wxrYFXgEuBMng9gLOAvtbaRVGqVUSymz0bOneGTZvggw9Iv7I3h4cZ6jZsgCpV8m4nIiKJK6IQaK3NBH4LsH09cKUxpjTeCOId1trU6JQoIjlYC2+/DXfcAbVrw6xZbKlTn4MCjdEP4PXX1QMoIiL5XDs4GGvtHmBdNM8pItns3Ak33QQffQQXXghDh5JetlrYAfCMM7zDRUREIr0nUETi5e+/oXlzGDoUnngCvv6a9LLVOPbY8A5/91348UeNBBYREU9UQqAx5nZjzEJjTJox5j9jzHfGmA7ROLeIAF9/7c3mvHo1jB0LDz/Mth0lKF8e1oXR937vvXD99QqAIiKyX8gQ6J/yZYsx5m9jTEqQNsOAl/FGA5cDqgCtgdHGmPujXK9IcvH5oH9/uPRSOPZYmD8fLriAjAw46KDwTlG5Mjz1VOGWKSIixU9ePYHnAFWBsdbajNw7jTFXAVfgTRuzAW9N4ZeA5f5tTxpjTo5mwSJJY9MmaN8eBgyA666DH36Ao45i2zZvYIcv99o9AdxyC2zcCCkB/wsnIiLJLK+BIS3xpnv5PMj+O/zPK4FG1trNAMaY/sD3QH3gOuCeAlcqkkzmzvWmf/n3X+9mvuuvB2DbNqhaNbxTDB4MvXoVWoUiIlLM5dUTeAxeCJyde4cxpgbQ2L//iawACGCtzZok2uBdGhaRcFgL77wDLVqAMV7vnz8ApqbCIYeEd5rmzaFHj0KsU0REir28QuChwHZrbaD1f8/0P1vg6wD7J/ufj8lnbSLJJT3du+zrOHD22d79f40aAV4ArFQJdu/O+zSuCzNmaBCIiIiEltfl4ArA3iD7Gvufl1prN+beaa3daYzZBlQqQH0iyWH5cujUCRYuhEce8R7ZUtx554V3mtWr4fDDC6lGERFJKHmFwM3AIcaYmtbaDbn2NcPrBZwX4vgUsi0xJyIBjB3rXbu1FsaMgYsuyrF72zZvfr+8LFmiACgiIuHL63Lwz/7nHHcX+e8HbOl/Oy3QgcaYQ/GmjFlTkAJFElZmJjz2GFx8MRx5pHf5N1cABGjSJO9TDR4Mxx0X9QpFRCSB5RUCh+MN7njEGHO5MSbFGHM0MIT9vXzBRg5nhcRfo1KpSCLZssULfI8/Dj17wsyZcEzO22e3bfOy4V9/hT5VixYaBCIiIpHLKwR+BMwHKgMjgXRgKdAO71Lw69baTUGO7epv8310ShVJEAsWeAM+pkyBt9+GDz6AcuVyNFmzxpsKZuXK0Ke6806YOlWDQEREJHIhQ6C11ge0B77F6xHM/vgIeDDQccaYY4BL/W8DjRwWSU6DBsGZZ3ozPX//vTcS2JgcTTZuhNq18z7VEUfAwIEKgCIikj95DQzB39PXzhhzIlDXv3m+tXZ5iMMygcuAPdbapQWuUqS427UL+vTxJn4+91z49FOoUeOAZhs3Qs2a4Z3yzz8VAEVEJP/yDIFZrLV/An+G2fYf4J/8lSSSYFas8KZ/mT8fHnoInngiYHpbudK7BzAc69YdcAVZREQkInndEygiBTFhAjRs6M3f8uWX3jrAuQKgz+d1EIYbAJcsgUMPLYRaRUQkqSgEihSGzEx48klo396bvG/+fLj00gOa+XzQqhXceGPepzQGNmzQVDAiIhIdYV8OFpEw/fcfXH01fPONN3eL60L58gGbfvKJNztMONLSdAlYRESiRyFQJJp++sm7/2/VKnj9dbjllgNG/2bZssWbIjAcW7cqAIqISHTpcrBItAwZAs2bw+7dMH063HprwADo88GHH8JBB+V9yoMOgh07oEqVQqhXRESSmkKgSEHt3g033wy9enkhcMECaNYsYFOfD1q3ht698z7tu+/Cv/9CxYrRLVdERAR0OVikYFatgs6dYc4cuP9+eOopKBX8j9WwYfDDD3mfdsUKqFMninWKiIjkohAokl+TJkG3bl5P4OjRcPnlIZuvXx/eGr+3364AKCIihU+Xg0UilZkJTz8N7drBIYfAvHl5BsAtW6BWrbxPXaqUtxSciIhIYVNPoEgktm717v376iuvF/Cdd/K8aS89PeAKcQdISYHNm71nERGRwqaeQJFwLVoEjRvD2LHwyivw8cchA2BGBtxzjzdFoLWhT33ppd4oYA0CERGRWFFPoEg4Pv4YbrgBqlaFqVPhrLNCNt+yJbwpYABatPBuKQywnLCIiEihUU+gSCgZGd5IjR49vF7ABQuiGgDvvNPLlAqAIiISawqBIsGsWQNt2ngrf9x9tzca+NBDgzb3+bwOw3AD4OGHe4NAFABFRCQedDlYJJDvvoOuXWHnThgxwpsLMAifD0aO9JaAy8gI/yOWLFEAFBGR+FFPoEh21sLzz8O553pdenPm5BkA27b18mIkAXDzZq0FLCIi8aUQKJJl+3Yv8N13H3TqBLNnw8knB22eng4tW8K0aeF/RLNm3ijg6tWjUK+IiEgB6HKwCMBvv0HHjvD33/DCC9C3LxgTtHkkgz8AmjaF6dM1B6CIiBQd6gkUGT7cS2nbtsGUKXDXXQEDYHq611GYkhJZAKxcWQFQRESKHoVASV579ng9fl27Qv363vQvrVod0Cwjw8uF5cvDqFHeYeG6+27YuFEBUEREih5dDpbktG4dXHEFfP893HGHNxikdOkcTTIy4KGHvKvDkTrpJC9TavCHiIgUVQqBknymT/cC4I4d8OmnXk9gLqmp3uCNSHr9slSpAj//rN4/EREp2nQ5WJKHtfDii3DOOV5SmzMnaACsUiXyAFi6NNx/P2zYoAAoIiJFn0KgJIcdO+DKK72b9C69FObOhVNPzdEkI8ObHaZSJcjMjOz0n3ziDRx55hkFQBERKR50OVgS3x9/ePP+/fknPPcc3HPPvtG/GRnwyCPw7rvetC+RKF8eLrwQhgzRvX8iIlL8KARKYhs5Eq65xktpkybB2Wfv25We7k31kp4e2SkPPhiWLYOKFaNcq4iISAzpcrAkpr17vR6/Ll3gtNO8obrZAqDPB6ecElkALF8ePvvMG1isACgiIsWdQqAknvXrvbV/X3gBbrvNW9etdu0cTcaMgX/+Cf+U997rrSrXpQuULBndckVEROJBIVASyw8/QMOG3sjfoUPhtddyjNTImvvvqqvCP2Xr1vD00wp/IiKSWBQCJTFYC6++Cm3aQIUK8OOP0L37vt0+n7c6XNmyXqDbuTPvU1ap4t1SOHmyAqCIiCSehBsY4jjOycCFQGugLnCof9dGYB7wKTDadV1ffCqUqEtLgxtu8CZ+7tABBg+GqlX37fb54Lzz4Lvv8j5VuXJwxhneanKXXqrwJyIiiSuhQqDjOB8CPYPsPsL/uBz40XGcLq7rro5ZcVI4/voLOnb0poH53/+82ZpL7O/gzsjwOgTDCYBVq8KmTQp+IiKSHBLtcvDh/uetwCDgauAs4AzgGmCBf38zYJLjOBViXaBE0eefe912//4LEybAgw9CiRL4fDB6NLRsCWXKeJd0w3HrrQqAIiKSPBKqJxBYDdwMfOi6bu7JP+Y7jjMUGAZ0Ak4E+gJPxbZEKbC9e6F/f3j2WWjSxEt5RxyBzwdffgk33QQbN0Z2ymrVvEmjRUREkkVChUDXdXvnsX+v4zgOcAmQAnRBIbB42bDBW+/3u+/gppvIeO5lnni2DF9+6U3gHM6Aj+yOPNK7XPzoo1ruTUREkktChcBwuK672XGcRUAj4Lh41yMR+PFH6NwZNm+GwYNJv6IXBx/sjQuJlDFeB2LHjtEvU0REpDhItHsCw5XV56MRwsWBtfDmm9CqFbZMGSY/NYtW7/eiUqX8BUDwZpLp0CGqVYqIiBQrSdcT6DhOTeBk/9s/4lmLhGHnTjJvdCjx8VAW1LqIyzd+xMp7quXrVKVKQfPmmv5FREQEkjAEAvez/+seHs9CJA9Ll2I7dYJfFvGweZIB6x7C5qPzukoVeP99uOwyBT8REZEsSRUCHcc5C+jjf7sKeCuO5UguPp+3pu/w4XDkL1/x0OKe+ChJV8YxwbaL+HyHHAJvvaVePxERkUCSJgQ6jnM4MBLva84EegaYRkbiIGtql5tvhk0bfDzOo/RnAPNoRGdGsoKjwjpPSgo0bQpHHAFXXAEXX6zwJyIiEkyxCIH+ABfsRrA013WX53F8NWA8+5eQu8d13anRq1Dyy+eD88+HqVOhWuYmxnEV5/Mt73I9t/Mauykb1nnq1oV58zTNi4iISLiKRQgEBgC9guybBrQJdqDjOBWBccBp/k1Pua77Ujgf6jjOjcCN2belpKQwYMCAHO1atmxJq1atwjmlZOPzefPzTZ0KjTLnMJLO1GQD1/Eeg7guz+PLlPF6/u68U5d8RUREIlVcQmC+OI5TDvgaaOrf9Irrug+He7zruu8A72TfNmDAANuvX7/oFZkkfD4YNw7mz4c9e2DpUm++5w0bLDfyDq/Sh7Ucxln8wAIaBT2PMdCqlTfCV5d7RURE8q9YhED/SiC9IznGcZwUYDT7ewnfw1smTmIsI8Nb3e33370AmKUs6QziFq5hMOO4gB4MZQsHBT1PyZLQujVMnKjwJyIiUlDFIgRGynGcUnjTv1zg3/QJ4Liua+NXVXLIGuE7YgRkZsJRR8GgQfDvvznbHc0yRtGJ0/mZx3iUJ3mYTPYnuypVoF49r+fPGDj8cA32EBERiaaEC4GO45QAhgCX+Td9DvRyXTczbkUliaxBHtOmea+Dac9YPqY7ABczhnFcuG9fiRLw4IPw+OMKeyIiIoUpoUKg4zgG7x6+bv5N44GuruvujV9ViSvrPr+FC71eu7lzYfr04AGwBD4e4Qke5QkWUp9OjGI5x+zbn3W5VwFQRESk8CVUCASeh33DSpcAjwAnOI4T6pg/XdfdE6qBHMjng3btYPZsSE31QltmprfMbyDV2cxQetCe8XxAb27hTXZRDmOgZk1o21aXe0VERGIp0UJg52yvjwfmhHHM0cA/hVJNAhs3bn8AhNCXfxsyn1F0ohbruMm4TKhzA5efaShZEjp3VvATERGJh0QLgRIjCxdCWlre7a7lfd7gVjZSkysP+55r3mzMGwp9IiIicZdQIdB13aPiXUOyaNAAKlTY3xOYW4WSuxh28O1cvP49/j72PJY+/gmjutZQ+BMRESkiEioESmz4fN6jZk3Yuxd27drfs5eZCSeX+4fRJTtz4vr50K8fxz7+OMcq/YmIiBQpCoESluwrfnz+Ofz9t9cLWLYsHHMMPP+8FwTTRk+g8+dXUbqED776Ci65JN6li4iISAAKgZKn3COBs9u1CzZsgJRSmVz88wD46FGoWxdGjYLjjotPwSIiIpInhUDJIfvcfw0aeJM/P/UUzJjhLf8WSOnU/zjx3qvhr2/g6qvh7behfPnYFi4iIiIRUQhMYLkDXfv23vZg27Jf6k1L83JcSor3OlgAPJ2fGG06cdSyVfDmm3DTTd46byIiIlKkKQQmqOyXcNPSvJG8TZp4++bMCbwt96XetLTQ08D04kPe4iZ2pNTATp4OZzUrnC9GREREok4hMEHlnsw5NRVmzvRe79oVfFs4KpTazQdV7qDLZpdN9c7hoPGfUrJWzegVLyIiIoVOIbCYC3TJt2TJwJM5Bwp6kYQ/gGNLr2TqwZ2pvXYuPPAANZ58Ekrp10hERKS40b/exVigS75Nm8KECYEncy5b1nvOHvwCbQPvtr6sewIzMmDnTriozCSG+rpSOXWPd/PgZZcV6tcnIiIihUchsBgLdMl39mxve/v2XiCM5J7ArMEgxx0HHTtCw4be6OCJ4zOp8NoztJnyMJx8Mmb0aDjhhPh80SIiIhIVCoHFWKBLvmlp8NNPcPHFXo/guHHe+/r1c44EzmvbvgU+tm7l4vd7waSvoFs3ePddLz2KiIhIsaYQWIwFuuRboYIX5MALchdf7D2yC3cbv/zidQmuWAGvvgq33abpX0RERBKEQmAhCDZYI9rnCHTJt2nT/b17BTJ0KNx4I1SrBlOnwllnReGkIiIiUlQoBEZZqMEa4QbBcM9RsmTgS76RBs4cMjLgrrvgjTegdWsYNgwOPbQAJxQREZGiqES8C0g02QdrWJtzsEZhnCPrkm///t5zgQLg6tVe8HvjDbjnHpg0SQFQREQkQSkERlmowRqxPEfEvvvOGw78668wYgQ8/7zm/xMREUlgCoFRljVYI7vsgzVidY6wWQvPPQfnngs1asDcudC5cyF8kIiIiBQlCoFRljVYo2JFbyBtxYqRD9aIxjnCsn27F/juv997njMHTjopyh8iIiIiRZGu90VZNAZrFMqAj9x++82b/uXvv+HFF+HOOzX9i4iISBJRCCwEwebni/U5gho2DK67DipX9u4FbNmyED5EREREijJdDk4me/Z4PX7dunmDQBYsUAAUERFJUgqByWLtWjj7bHjlFejbF6ZMgVq14l2ViIiIxIkuByeD6dPhiiu8CQeHDYMrr4x3RSIiIhJn6glMZNbCCy/AOedA1are6F8FQBEREUE9gYlrxw649loYOdIbBfzBB95AEBERERHUE5iY/vgDmjSB0aO9lT9GjlQAFBERkRzUE5hoRozwegDLl4fJk6FNm3hXJCIiIkWQegITxZ49cPfd3gCQunW96V8UAEVERCQI9QQmgvXrvQEf06fD7bfDwIGQkhLvqkRERKQIUwgs7r7/3uv927YNPv4Yrroq3hWJiIhIMaDLwcWVtd7Ez2efDRUqwI8/KgCKiIhI2BQCi6PUVC/w3XknXHQRzJvn3QcoIiIiEiaFwOLmzz+haVP47DN4+mlvGpgqVeJdlYiIiBQzuiewOPn8c+jVC8qUgYkToW3beFckIiIixZR6AouDvXvh/vu9lT9OPtmb/kUBUERERApAPYFF3YYN0LUrfPcd3HwzvPSS1xMoIiIiUgAKgUXZrFnQpQts3gwffgg9e8a7IhEREUkQuhxcFFkLb7wBrVt7vX4//qgAKCIiIlGlEFjUpKV5ge+226BdO2/6l9NPj3dVIiIikmAUAouSpUuheXNv5Y8nn4Qvv4Rq1eJdlYiIiCQg3RNYVHz1ldcDWLIkjB8P558f74pEREQkgaknMN58PujXDzp0gOOP96Z/UQAUERGRQqaewHjauNFb/m3SJLjxRm8t4LJl412ViIiIJAGFwHiZMwc6d/bmARw0CK65Jt4ViYiISBLR5eBYsxbefhtatvTu/5s5UwFQREREYk4hMJbS073Ad/PN3rJv8+dDw4bxrkpERESSkEJgrCxbBmeeCUOGwGOPwZgxUL16vKsSERGRJKV7AmPhm2+gRw8wxnvdvn28KxIREZEkp57AwuTzwSOPwMUXw9FHe5d/FQBFRESkCFBPYGHZvBm6d4cJE+Daa+H116FcuXhXJSIiIgIoBBaO336Diy6Cdevg3Xfh+uvjXZGIiIhIDgqBheGQQ+DII2HECGjcON7ViIiIiBxAIbAw1KgB06bFuwoRERGRoDQwRERERCQJKQSK5MP06dPjXYIkGP1OSbTpd0ryohAokg8zZsyIdwmSYPQ7JdGm3ynJi0KgiIiISBJSCBQRERFJQgqBIiIiIknIWGvjXUOx4jjORmBFvOuQuDsZ+CPeRUhC0e+URJt+pwRgk+u6FwTaoRAokg+O48xzXfeMeNchiUO/UxJt+p2SvOhysIiIiEgSUggUERERSUIKgSIiIiJJSCFQJH/eiXcBknD0OyXRpt8pCUkDQ0RERESSkHoCRURERJKQQqCIiIhIElIIFBEREUlCCoEiIiIiSahUvAsQKU4cxzkZuBBoDdQFDvXv2gjMAz4FRruu64tPhVKcOI5TG7gduASoA+wFlgOfA6+5rvtfHMuTYsJxnEZAe6AFcCpQE+93aT0wGxjiuu74+FUoRZVGB4uEyXGcD4GeYTT9Eejiuu7qQi5JijHHcS7A+09D1SBN1gAdXNedH7OipNhxHGca0CqMpt8A3V3X3VbIJUkxosvBIuE73P+8FRgEXA2cBZwBXAMs8O9vBkxyHKdCrAuU4sFxnHrASLwAuBN4FK8Xpw3wEuDD+30b4zjOYfGpUoqJrL+X/gXeAK7E+zuoKXArsMS//yLgK8dx9O++7KPLwSLhWw3cDHzoum56rn3zHccZCgwDOgEnAn2Bp2JbohQTLwMV8MJee9d1p2fbN81xnAXAR3i3GzwFXBvzCqW4WAz0B0a6rrs31745/isYE4Ez8XoMrwKGxrZEKap0OVgkihzHOQhYC6QAv7iue3qcS5Iixn//1jz/2/dd170+SLvJwDl4QfEw13U3xKhESTCO49QFfvG//cp13Q7xrEeKDnULi0SR67qbgUX+t8fFsxYpsjpme/1+iHaD/M8lgUsLrxxJdK7rLgI2+9/q7yXZRyFQJPpS/M8aISyBtPA/7wTmhmj3XYBjRPKrtP9Zfy/JPgqBIlHkOE5N4GT/2z/iWYsUWaf4n5cEuIdrH9d11wI7ch0jEjHHcRoAlf1v9feS7KMQKBJd97N/wNXweBYiRY/jOGWAGv634UwhtMr/fEThVCRJon+21/p7SfZRCBSJEsdxzgL6+N+uAt6KYzlSNFXK9jo1jPZZbSoWQi2SBBzH6cr++1Dn4U1ELgIoBIpEheM4h+PN+1YKyAR6BphGRqRcttcZYbTfHeA4kbD456N8z/92J3C167qaEkT20TyBkvT8Aa5akN1prusuz+P4asB49i8hd4/rulOjV6EkkOz/MUgJ2mq/MgGOE8mT4zhHAWPx5qPMBHq5rrs4rkVJkaMQKAIDgF5B9k3DW8UhIMdxKgLjgNP8m55yXfelqFYniWRHttfhXOLNahPOpWMRABzHqQV8y/7VRBzXdUfGsSQponQ5WCSfHMcpB3yNtzwTwCuu6z4cx5KkiHNddzewyf+2dhiHZLVZFbKViJ/jODXwAmDWfIB9Xdd9L8QhksTUEyhJz3Xd3kDvSI5xHCcFGM3+XsL38JaJE8nL73jLdx3vOE6pYNPE+NcMrpztGJGQHMepgrdE3Kn+TQ+7rvty/CqSok49gSIRchynFN40Cxf4N32Cd7lFN1xLOL73P5cHGodo1ybAMSIB+W9NGQ808G961nVdrV0uISkEikTAcZwSwBDgMv+mz/FuuM6MW1FS3IzO9vq6EO2u9T/7gK8Krxwp7rLdmtLMv+k113UfiGNJUkwoBIqEyXEcA7wDdPNvGg90DbXqg0huruvOB6b63/Z2HKdl7jaO43QH2vrfDnFdd0OMypNixn9ryij29xy/D9wRt4KkWNE9gSLhe579PTdLgEeAExzHCXXMn67r7inswqTYuQOYiTd9x3jHcZ4BJuP9ndyB/f+Iryfnag8iuX0CtPe/ngW8Cpwa6u8l13V/jUFdUgwoBIqEr3O218cDc8I45mjgn0KpRoot13V/cRynM/ApUBV4wv/Ibg3Qwb+GsEgwnbK9bg78HMYxppBqkWJGl4NFROLAdd3xQF28HuY/gDRgO94/4o8Bdf2XjkVECoWxVgMaRURERJKNegJFREREkpBCoIiIiEgSUggUERERSUIKgSIiIiJJSCFQREREJAkpBIqIiIgkIYVAERERkSSkECgiIiKShBQCRURERJKQQqCIiIhIElIIFBEREUlCCoEiIiIiSUghUCTBGGOOMsZYY4yNdy3FjTFmqv971zvOdehnGEWhvp/GmMf8+wbHoTSRuCoV7wJEJKcC/MM/zVrbJpq1JApjzFFAb2CrtfbluBYjUWWMuQyoD0y11k6NazEixYxCoEjR82+Q7dWB0sAuYFuA/VsKraLi7yjgUWAF8HKIdiuBPwn8/ZWi6TKgl//11CBt9uD9XEUkG4VAkSLGWntooO3GmKlAa2C4tbZ3LGtKFtbanvGuQaLPWrsGOCnedYgUNbonUERERCQJKQSKJDhjzGnGmGHGmPXGmF3GmMXGmIeNMSl5HNfCf9xqY8xuY8xmY8wkY0w3Y4wJcVxl/832PxtjUv2PX4wxjxtjqgQ5Zt/N+caYEsaY24wxc4wxW/3b6+dqf4kx5kv/15RhjNlgjPnaGNMuwLn/Ab7zvz0ya4BAtkfvbG1DDgwxniuNMd/4P3u3MWaNMWa6MaavMeagXO1rG2PuMcaMN8YsMcbsNMZsN8Ys9H8/qgb7PhaEMaas/2e82P8zX+f/WZ6SxyCJf/z72oQ4d9b37ahc28sYY7oYY4b4f/ab/J+9whjzsTGmUYhz7vtcY0x1Y8yLxpjl2b6/7xpjauU6po3/a8i6FPxo7p9ttrYFGmiTnz8LxpijjTFvGWP+Msak+3/2K/y/Yw8aY2rkpxaRqLLW6qGHHsXggXe/kwUG59HuKH87C5wP7PS/3gr4su37IsQ5ns3WzuLdI5eZ7f2nQIkAxx0H/JOtXZr/kfV+BXB8gOMe8+//EPjC/3ov8J//dX1/u9LA0AC1ZX//bK5zz8W7X9L6v/71uR5XBvge9w5QYxXg22yfk+k/b3q2bb1zHTMy277dwOZcP4OlQO1QP8N8/J5UBH7M9blZ36NUoFuwc2f72bUJcf6s8x6Va/vFeXxv9gBXBzln1uf2yPY6De/+16zjlwPVsh1zpv/nl/UZqbl/tuF8P7P97gX8c0U+/iwADYHt2dpksP93OetxQbz/TtFDD/UEiiS24cDXwNHW2qpAZeBBvH+EOhhjLsx9gDHmDuA+vAEqNwJVrbVVgApAV7x/YLsC9+c6LgUYBRwJrMILoBX9j3PxBl3UAT43xpQJUm9H4ALgFqCytbYacAiwzL//OaA7Xni6Aqjor62y/5gdwH3GmG5ZJ7TWNvafF2CVtfbQXI/hIb+D+33s/zrSgTuA6tba6kB54BTgCbx/6LP7A+gDnACUs9YeBJQF2uCF02MBN8zPD9dLQFN/ndew/3t0ur+et6L8eVlSgVeBVv7PrG6tLYf3+/Ay3j3o7xhj6oQ4x2t438MzrbUV8H53OuD9B+YovN9dAKy1M613/2zWz29g7p9tQb+g/P5ZAAYClYDZQENrbYr/d7kC0Bjv+6HBRxJ/8U6heuihR3gP8tcTOBEwAdp87d8/KNf2qnhBKh04Pcj5m7O/pycl2/ar2d/rcVqA407177PAtbn2PZat5huDfO7x/s/dABwRpE1X/zl+zbW9jX/7P2F+j3vn2n4h+3u4otKDgzfae4P/nEcF+xlGeM4j2d/T2DvEZ0a9JzCM2t73H/doiM9dDxwUYP/d/v3LAuwb7N/3WDh/JgLsy/rdG5xre0H+LGT1vjeNxu+KHnoU1kM9gSKJ7RlrrQ2w/Qv/82m5tnfC632ZZK39OdAJrbWz8F+aA7Lf59XZ//yltfbXAMf9hnd5FLxevEA2A4OC7OsJGLzR0auCtBmJd/nz1Nz3kBVQ1qjhCdba8dE4obV2CzAT72s6MxrnxOvxLAGsBYYE+czC6gnMy9f+57NCtHnHWrs5wPYv/M9HG2MqRLWq4AryZ2G7/zmav4MiUacpYkQS29wg29f4n6vl2p4VRs4xxqwPcd7q/ucjgFn+1w39z9+FOG4K3j1pDYPsn2et3RtkX1ZtvYwxXUJ8Rulsta0L0S4SzfzPYyM90BjTBLgJr/7aeJcEczss/6XlkPV9nWGtzQzSZlqUPusAxpjqwK1Ae+BEvPsoS+ZqFuprzev3FbweurR8lhiJgvxZGIt3KX6IMeZNvBA731q7pzAKFckvhUCRBGat3RFk1y7/c+lc27N6Lsr7H3nJ3uZg//OaQA39VvufDzLGmAC9lBtDHJtVWyX/I5LaCuoQ//PKSA4yxtyDdx9j1ghSH949bxn+91Xw7hGMVu9W1s9gbYg2oX4++WaMOQUv5B+SbXPW5VQLpOD9pyPU1xrw99VauyvbINzcv7OFpSB/Fu7FC8Fn4t0veD+wyxgzCxiBd+k5PYq1iuSLLgeLSHZZfye8Yq01YTwGBzhH2QJ8vi+M2vqGWdvUAtRRYMaYU/FGlhrgdbx7IstYb8BE1sCFrMvjQafcKUY+wAuAC/AG91Sy1la21h7i/1qzem+Ly9ea7z8L/kvaLYDz8AbLLMQLwWcDbwK/GmNqx/bLETmQQqCIZJe1ZF2oEZzBZPXihTo26x++zUHuVQylILUVVNZnHxnBMZ3w/o6dYK293Vr7u7U2d8g9JMBxBZH1Mwh1yTXUvqxL8QGDvAk+z2MdoAleiL/UWjvBWpuaq1m0v9bCVqDfN+uZZK29w1rbEKgBOHiDSI7BG8UtElcKgSKSXdY9TW2MMeUiPHaB//nsEG3OydU2Elm1XZCPY7Puj8tvL9SP/ucDptQJISvwLgy00z/AoVmgfQWQ9X1tEWwSY7ylB4PZ6n8O1kvVOMj2rPYbrbdEWyDnhvjcgijozzaYgvxZOIC19j9r7TvAQ/5NoX4OIjGhECgi2Y3Au+m+GvBIqIbGmNyDSrIubbY3xjQI0P5U9o8g/iwftQ3Bu7fsZGOME2FtWaM1A/ZkhfnZAOcbY8INoVnzwNUNsr8f4d3bGInReKHocLyJl3Pwf19uCnH8Iv9zhwDHGg6cDy9L1td6iDGmZoBj6wJXhfjcgsj62VaN8nnz9WfBeCvehLrfPutewGBzZYrEjEKgiOzjv5cpa0LeB/zLdZ2Qtd8YU84Y09IY8xbe9CbZDQd+8b/+whhzblZvlDGmLd6IydLAb3gTL0da2+/sv4T2pjHm6ez3VRljKhljzjfGDMX7Bzy7JXgrVlQxxnSK9LOBcf6HAUYZY27PWvLNeE4xxrxgjLks2zHf+p8v8i8TVt7f/mBjzPN43+dA06Hkm7V2Bfun2HnbGNPTGFPa/7l1gfGEvmczK5xfZIy5P2s6FuMtEfcpOadBye4PvEE/BhhujDnOf1xpY0xHvO9F7svD0fKb//mCaE4LVIA/C5WBpcaYfsaYusaYkv72Jfx/Dgb4202IVq0i+RbviQr10EOP8B7kY7LoEG3aEGLyZKA/OZfGSsW7lyn7kmfLAxwXzrJxJwQ47rEwv7aSeDfW517Ga2uuer8LcOyH2fZv9df5D9A5wPe4d4Djq2bbb/3fi82EXjZuVLZ9WZMKZ9X5HkEmOg7nZxjie5R72bhd/q836+cYdNm4ADVnjWa2eBMgn59t31G5jrs81+/Hdrw5G7N+7j2C/c5RsOXqavh/Dln1rsv62Ybz/czrd48I/yz4f0+y/35m+Ovbm23b3wRYLlAPPWL9UE+giBzAWvsU3jJj7+D1opXAm9pjHV4Pxn1AywDHLfUf9wSQfcLoX4EngXrW2r8KUJfPWnsL3sjLoXjhogxe79ZK4CvgNvZfds7uJuBpYLH/mCP9j4phfvZWvHsaewGT8IJAJbx/4KcBd/o/P7srgQfwesr24PWU/QD0stZeH87nRsp6AzLa4F3CzPpe78LrqW3C/nvdgumGd6n6T7zgsgcvGDaz1k4M8bmf431/vsWb6qU03s9nINCA/dMDRZW1dhPefaij8QbGHMz+n200zh/pn4XteOsovwzM8ddUCe8/Q3Pxvrf1rbWF8v0QiYSxNtIBeiIiUlz5L+0uB7DWFpfpWkSkEKgnUERERCQJKQSKiIiIJCGFQBEREZEkpBAoIiIikoQ0MEREREQkCaknUERERCQJKQSKiIiIJCGFQBEREZEkpBAoIiIikoQUAkVERESS0P8B5JOBwRqHfyIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.08, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "\n", + "\n", + "stats.probplot(log10_kcat_values, dist=\"norm\", plot=ax)\n", + "ax.set_title(\" \")\n", + "plt.ylabel(\"Sample quantiles\", fontsize = 24)\n", + "plt.xlabel(\"Theoretical quantiles\", fontsize = 24)\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"S1b.svg\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "plt.ylim(ymax = 2500, ymin = 0)\n", + "#plt.xlim(xmax = 5, xmin = -0.1)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.yticks([0,1000,2000,3000], [\"0\",\"1000\",\"2000\",\"3000\"])\n", + "plt.xticks([0,250,500, 750, 1000], [\"0\",\"250\",\"500\", \"750\", \"1000\"])\n", + "\n", + "plt.ylabel(\"Count\", fontsize = 22)\n", + "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.hist(kcat_values[kcat_values<1000], alpha = 0.9, color=\"darkblue\", rwidth = 0.95, bins = 40)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "#plt.ylim(ymax = 2500, ymin = -100)\n", + "#plt.xlim(xmax = 2500, xmin = -100)\n", + "#plt.xlim(xmax = 5, xmin = -0.1)\n", + "\n", + "#ax.yaxis.set_label_coords(-0.08, 0.5)\n", + "#ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "stats.probplot(kcat_values[kcat_values<2000], dist=\"norm\", plot=ax)\n", + "ax.set_title(\" \")\n", + "plt.yticks([0,1000,2000], [\"0\",\"1000\",\"2000\"])\n", + "plt.ylabel(\"Sample quantiles\", fontsize = 24)\n", + "plt.xlabel(\"Theoretical quantiles\", fontsize = 24)\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"S1a.svg\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating how much mean deviation we have between two measurements for the same enzyme-reaction pair:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ReactionSequencekcats
0Reaction_0Sequence_309[2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0]
1Reaction_1Sequence_309[1.2, 3.4, 0.61, 0.07]
2Reaction_2Sequence_3142[6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,...
3Reaction_4Sequence_3263[57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11....
4Reaction_5Sequence_2101[2.98, 0.87]
\n", + "
" + ], + "text/plain": [ + " Reaction Sequence \\\n", + "0 Reaction_0 Sequence_309 \n", + "1 Reaction_1 Sequence_309 \n", + "2 Reaction_2 Sequence_3142 \n", + "3 Reaction_4 Sequence_3263 \n", + "4 Reaction_5 Sequence_2101 \n", + "\n", + " kcats \n", + "0 [2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0] \n", + "1 [1.2, 3.4, 0.61, 0.07] \n", + "2 [6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,... \n", + "3 [57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11.... \n", + "4 [2.98, 0.87] " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"merged_and_grouped_kcat_dataset_with_FPs_and_ESM1bs_ts.pkl\"))\n", + "df = pd.DataFrame({\"Reaction\": df_kcat[\"Reaction ID\"], \"Sequence\" : df_kcat[\"Sequence ID\"],\n", + " \"kcats\" :df_kcat[\"kcat_values\"]})\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.75, 5.67)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deviations = []\n", + "x_value = []\n", + "y_value = []\n", + "\n", + "for ind in df.index:\n", + " kcats = df[\"kcats\"][ind]\n", + " if len(kcats) > 1 :\n", + " for i in range(len(kcats)):\n", + " for j in range(i+1, len(kcats)):\n", + " \n", + " deviations.append(abs(np.log10(float(kcats[i])) - np.log10(float(kcats[j]))))\n", + " x_value.append(np.log10(float(kcats[i])))\n", + " y_value.append(np.log10(float(kcats[j])))\n", + "\n", + " \n", + "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "deviations = []\n", + "x_value = []\n", + "y_value = []\n", + "\n", + "for ind in df.index:\n", + " kcats = df[\"kcats\"][ind]\n", + " if len(kcats) > 1 :\n", + " for i in range(len(kcats)):\n", + " for j in range(i+1, len(kcats)):\n", + " if np.log10(float(kcats[i])) > -2.5 and np.log10(float(kcats[j])) > -2.5:\n", + " deviations.append(abs(np.log10(float(kcats[i])) - np.log10(float(kcats[j]))))\n", + " x_value.append(np.log10(float(kcats[i])))\n", + " y_value.append(np.log10(float(kcats[j])))\n", + " \n", + " \n", + "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2)\n", + "\n", + "x_value = np.array(x_value)\n", + "y_value = np.array(y_value)\n", + "\n", + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "\n", + "x0, x1, y0, y1 = -3, 7, -3,7\n", + "plt.ylim(ymax = y1, ymin = y0)\n", + "plt.xlim(xmax = x1, xmin = x0)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4,6], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\", \"$10^{6}$\"])\n", + "plt.yticks([-2,0,2,4,6], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\", \"$10^{6}$\"])\n", + "\n", + "\n", + "\n", + "'''reg = LinearRegression().fit(x_value.reshape(-1,1), y_value.reshape(-1,1),)\n", + "reg.score(x_value.reshape(-1,1), y_value.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([x0,x1], [y0,y1], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([x0,x1], [beta0 + x0*beta1, beta0 + x1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "''';\n", + "plt.xlabel(\"Measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.ylabel(\"Additional measurment for $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "\n", + "plt.scatter(x_value, y_value, alpha = 0.4, s=30, c=\"navy\")\n", + "\n", + " \n", + "\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"measured_vs_measured.eps\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"measured_vs_measured.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating kcat measurements and predictions for different EC classes:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_train.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "data_test.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "\n", + "model = \"ESM1b_ts_DRFP_mean\"\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_test[\"y_pred\"] = pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "EC_kcat =[[] for _ in range(6)]\n", + "\n", + "for ind in data_train.index:\n", + " try:\n", + " EC = int(data_train[\"ECs\"][ind][0][0])\n", + " EC_kcat[EC-1].append(data_train[\"log10_kcat\"][ind])\n", + " except IndexError:\n", + " pass\n", + " \n", + "EC_kcat_pred =[[] for _ in range(6)]\n", + "\n", + "for ind in data_test.index:\n", + " try:\n", + " EC = int(data_test[\"ECs\"][ind][0][0])\n", + " EC_kcat[EC-1].append(data_test[\"log10_kcat\"][ind])\n", + " EC_kcat_pred[EC-1].append(data_test[\"y_pred\"][ind])\n", + " except IndexError:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "classes = [str(i) for i in range(1,7)]\n", + "#100 -8\n", + "for i in range(len(EC_kcat)):\n", + " \n", + " \n", + " \n", + " circle = plt.Circle((np.mean(EC_kcat[i]), np.mean(EC_kcat_pred[i]) ),\n", + " np.sqrt(len(EC_kcat_pred[i]))/500, color='navy', fill = True)\n", + " ax.add_artist(circle)\n", + " if i ==5:\n", + " ax.annotate(\"EC\"+ str(i+1), (np.mean(EC_kcat[i])+0.01, np.mean(EC_kcat_pred[i])-0.05), fontsize=17, c='black', weight = \"bold\")\n", + " else:\n", + " ax.annotate(\"EC\"+ str(i+1), (np.mean(EC_kcat[i])+0.03, np.mean(EC_kcat_pred[i])-0.01), fontsize=17, c='black', weight = \"bold\")\n", + " \n", + "\n", + "ticks2 = [0.6,1,1.4,1.8]\n", + "labs = ticks2\n", + "ax.set_xticks(ticks2)\n", + "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", + "ax.tick_params(axis='x', length=0, rotation = 0)\n", + "\n", + "ax.set_yticks(ticks2)\n", + "ax.set_yticklabels(labs, y= -0.03, fontsize=26)\n", + "ax.tick_params(axis='y', length=0, rotation = 0)\n", + "\n", + "plt.ylim((0.5,1.9))\n", + "plt.xlim((0.5, 1.9))\n", + "plt.legend(loc = \"upper left\", fontsize=20)\n", + "plt.xlabel('mean measured \\n $k_{cat}$ value on $\\log_{10}$-scale')\n", + "plt.ylabel('mean predicted \\n $k_{cat}$ value on $\\log_{10}$-scale')\n", + "ax.yaxis.set_label_coords(-0.15, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.13)\n", + "\n", + "plt.plot([0.5,1.9], [0.5,1.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"EC_classes_mean_kcat.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing different reaction similarities:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "model = 'ESM1b_ts_DRFP_mean'\n", + "\n", + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_train.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "data_test.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_test[\"y_pred\"] = pred_y\n", + "data_test[\"y_true\"] = test_y\n", + "\n", + "\n", + "train_fps = [np.array(list(data_train[\"structural_fp\"][ind][:3276])).reshape(1,-1).astype(int) for ind in data_train.index]\n", + "test_fps = [np.array(list(data_test[\"structural_fp\"][ind][:3276])).reshape(1,-1).astype(int) for ind in data_test.index]\n", + "\n", + "\n", + "max_sim = []\n", + "\n", + "for fp in test_fps:\n", + " jaccard_sim = np.array([1- scipy.spatial.distance.cdist(fp,train_fp, metric='jaccard')[0][0] for train_fp in train_fps])\n", + " max_sim.append(np.max(jaccard_sim))\n", + " \n", + "data_test[\"reaction_sim\"] = max_sim\n", + "\n", + "data_test[\"reaction_sim\"]= (data_test[\"reaction_sim\"] - np.min(data_test[\"reaction_sim\"]))\n", + "data_test[\"reaction_sim\"] = data_test[\"reaction_sim\"]/np.max(data_test[\"reaction_sim\"])\n", + "\n", + "data_test[\"y_true\"] = test_y" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "'''all_fps = list(set(data_train[\"structural_fp\"] + data_train[\"structural_fp\"]))\n", + "\n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", 'all_structural_fps.pkl'), 'wb') as f:\n", + " pickle.dump(all_fps, f)\n", + " \n", + " \n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", 'all_structural_fps.pkl'), 'rb') as f:\n", + " all_fps = pickle.load(f)''';" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 - 0.4 0.06430806812248102 0.28800920705781385 1.255855379466648 17\n", + "0.4 - 0.8 0.2007273222088296 0.47469369882327617 1.3067111634862465 129\n", + "0.8 - 1 0.4141624233212502 0.65263923852372 0.9083716908949246 350\n", + "identical: 0.571383822236111 0.7787732209816836 0.5057315733031261 354\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_reactions = list(set(data_train[\"Reaction ID\"]))\n", + "data_test[\"new reaction\"] = [r_ID not in train_reactions for r_ID in list(data_test[\"Reaction ID\"])]\n", + "\n", + "help_df = data_test.loc[data_test[\"new reaction\"]]\n", + "\n", + "\n", + "sim_bins_lb = [0.0, 0.4, 0.8]\n", + "sim_bins_ub = [0.4, 0.8, 1]\n", + "r2_scores, n_points, pearson_r = [], [], []\n", + "for i in range(len(sim_bins_lb)):\n", + " help_df2 = help_df.loc[help_df[\"reaction_sim\"] <= sim_bins_ub[i]].loc[help_df[\"reaction_sim\"] >= sim_bins_lb[i]]\n", + " pred = np.array(help_df2[\"y_pred\"])\n", + " true = np.array(help_df2[\"y_true\"])\n", + " r2_scores.append(r2_score(true, pred))\n", + " pearson_r.append(stats.pearsonr(true, pred)[0])\n", + " mse = np.mean(abs(true - pred)**2)\n", + " n_points.append(len(pred))\n", + " print(\"%s - %s\" % (sim_bins_lb[i], sim_bins_ub[i]), r2_scores[-1], pearson_r[-1], mse, len(pred))\n", + " \n", + "help_df = data_test.loc[~data_test[\"new reaction\"]] \n", + "\n", + "pred = np.array(help_df[\"y_pred\"])\n", + "true = np.array(help_df[\"y_true\"])\n", + "r2_scores.append(r2_score(true, pred))\n", + "pearson_r.append(stats.pearsonr(true, pred)[0])\n", + "mse = np.mean(abs(true - pred)**2)\n", + "n_points.append(len(pred))\n", + "print(\"identical:\", r2_scores[-1], pearson_r[-1], mse, len(pred))\n", + "\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "#plt.rc('font', **font)\n", + "\n", + "fig, ax = plt.subplots(figsize= (8,6))\n", + "\n", + "for i in range(len(sim_bins_lb) + 1): \n", + " plt.scatter(i, r2_scores[i], c='navy', marker=\"o\", linewidths= 8)\n", + " ax.annotate(n_points[i], (i-0.06, r2_scores[i]+0.03), fontsize=17, c= \"black\", weight = \"bold\")\n", + "\n", + "\n", + "\n", + "plt.xlabel('Reaction similarity score')\n", + "plt.ylabel('Coefficient of \\n determination R²')\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.23)\n", + "\n", + "ticks2 = np.array(range(len(sim_bins_lb)+1))\n", + "labs = [\"%s - %s\" % (sim_bins_lb[i], sim_bins_ub[i]) for i in range(len(sim_bins_lb))] +[\"same \\nreaction\"]\n", + "ax.set_xticks(ticks2)\n", + "ax.set_xticklabels(labs, y= -0.03, fontsize=24)\n", + "ax.tick_params(axis='x', length=0, rotation = 0)\n", + "\n", + "plt.ylim((-0.1,0.8))\n", + "plt.xlim((-0.5, 3.2))\n", + "\n", + "plt.plot([-0.49, 4], [0,0], color='grey', linestyle='dashed')\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"Reaction_Similarity_Score.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "496 1.023881801116698 0.34994372195140233 0.6011238358501336\n", + "354 0.5057315733031263 0.571383822236111 0.7787732209816836\n" + ] + } + ], + "source": [ + "data_test[\"y_pred\"] = pred_y\n", + "data_test[\"y_true\"] = test_y\n", + "\n", + "pred = data_test[\"y_pred\"].loc[data_test[\"new reaction\"]]\n", + "true = data_test[\"y_true\"].loc[data_test[\"new reaction\"]]\n", + "\n", + "MSE = np.mean(abs(np.reshape(true, (-1)) - pred)**2)\n", + "R2 = r2_score(np.reshape(true, (-1)), pred)\n", + "pearson_r = stats.pearsonr(true, pred)[0]\n", + "print(len(true), MSE, R2, pearson_r)\n", + "\n", + "pred = data_test[\"y_pred\"].loc[~data_test[\"new reaction\"]]\n", + "true = data_test[\"y_true\"].loc[~data_test[\"new reaction\"]]\n", + "\n", + "MSE = np.mean(abs(np.reshape(true, (-1)) - pred)**2)\n", + "R2 = r2_score(np.reshape(true, (-1)), pred)\n", + "pearson_r = stats.pearsonr(true, pred)[0]\n", + "print(len(true), MSE, R2, pearson_r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance for membrane proteins vs. non-membrane proteins" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "\n", + "data_test[\"y_true\"] = np.round(test_y,5)\n", + "data_test[\"y_pred\"] = np.round(pred_y,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating input file for UniProt mapping service" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "IDs = []\n", + "for ind in data_test.index:\n", + " IDs = IDs+ data_test[\"Uniprot IDs\"][ind]\n", + "\n", + "IDs = list(set(IDs)) \n", + "f = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"UNIPROT_IDs_test_set.txt\"), \"w\") \n", + "for ID in IDs:\n", + " f.write(str(ID) + \"\\n\")\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading results from UniProt" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "63" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_location = pd.read_csv(join(\"..\", \"..\", \"data\", \"enzyme_data\",\n", + " \"uniprot-download_true_fields_accession_2Ccc_subcellular_location_for-2023.04.13-18.49.21.41.tsv\"), sep = \"\\t\")\n", + "\n", + "data_test[\"membrane\"] = False\n", + "\n", + "for ind in data_test.index:\n", + " IDs = list(set(data_test[\"Uniprot IDs\"][ind]))\n", + " for ID in IDs:\n", + " try:\n", + " location = list(df_location[\"Subcellular location [CC]\"].loc[df_location[\"From\"] == ID])[0]\n", + " if not pd.isnull(location):\n", + " if \"membrane\" in location.lower():\n", + " data_test[\"membrane\"][ind] = True\n", + " #print(location)\n", + " except IndexError:\n", + " pass\n", + " \n", + "is_membrane = np.array(data_test[\"membrane\"])\n", + "np.sum(is_membrane)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3605761664082414 0.43880739645243383\n", + "0.6478576822890754 0.6724504396913856\n", + "0.683574459043685 0.8180548403855614\n" + ] + } + ], + "source": [ + "print(r2_score(test_y[is_membrane], pred_y[is_membrane]), r2_score(test_y[~is_membrane], pred_y[~is_membrane]))\n", + "print(stats.pearsonr(test_y[is_membrane], pred_y[is_membrane])[0], stats.pearsonr(test_y[~is_membrane], pred_y[~is_membrane])[0])\n", + "print(np.mean(abs(test_y[is_membrane] - pred_y[is_membrane])**2), np.mean(abs(test_y[~is_membrane] - pred_y[~is_membrane])**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.0690475129835626, 1.4577078087170903)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.var(test_y[is_membrane]), np.var(test_y[~is_membrane])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/model_fitting/.ipynb_checkpoints/05 - Comparison to ENKIE-checkpoint.ipynb b/code/model_fitting/.ipynb_checkpoints/05 - Comparison to ENKIE-checkpoint.ipynb new file mode 100644 index 0000000..50e2aba --- /dev/null +++ b/code/model_fitting/.ipynb_checkpoints/05 - Comparison to ENKIE-checkpoint.ipynb @@ -0,0 +1,720 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n", + "\n", + "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\projects\\GitHub\\kcat_prediction\\code\\model_fitting\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from sklearn import metrics\n", + "from scipy import stats\n", + "from scipy.stats import wilcoxon\n", + "from sklearn.metrics import roc_auc_score, r2_score\n", + "from sklearn.linear_model import LinearRegression\n", + "import scipy\n", + "import os\n", + "from os.path import join\n", + "import pandas as pd\n", + "\n", + "CURRENT_DIR = os.getcwd()\n", + "print(CURRENT_DIR)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "plt.style.use('CCB_plot_style_0v4.mplstyle');\n", + "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", + "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data From ENKIE:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#TurNuP data\n", + "model = 'ESM1b_ts_DRFP_mean'\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_test[\"y_true\"] = np.round(test_y,5)\n", + "data_test[\"y_pred\"] = np.round(pred_y,5)\n", + "\n", + "#ENKIE data\n", + "df =pd.read_csv(\"C:\\\\Users\\\\alexk\\\\Downloads\\\\kcat_4.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "test_y, pred_y = np.array(df[\"error\"]), np.array(df[\"pred_error\"])\n", + "\n", + "\n", + "plt.ylim(ymax = 4, ymin = -0.2)\n", + "plt.xlim(xmax = 4, xmin = -0.2)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "ax.text(0.3, 3, r'$R^2=-0.05$', fontsize=26, c = \"black\")\n", + "\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([0,1,2,3], [str(i) for i in range(4)])\n", + "plt.yticks([0,1,2,3], [str(i) for i in range(4)])\n", + "\n", + "\n", + "\n", + "plt.ylabel(\"Predicted absolute error for \\n $k_{cat}$ prediction on $\\log_{10}$-scale\", fontsize = 22)\n", + "plt.xlabel(\"Actual absolute error for \\n$k_{cat}$ prediction on $\\log_{10}$-scale\", fontsize = 22)\n", + "plt.scatter(test_y, pred_y, alpha = 0.6, s=30, c=\"darkblue\")\n", + "plt.savefig(join(\"C:\\\\Users\\\\alexk\\\\Downloads\", \"scatter_plot_errors_ENKIE.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "test_y, pred_y = np.array(df[\"value\"]), np.array(df[\"pred_value\"])\n", + "\n", + "\n", + "plt.ylim(ymax = 5.1, ymin = -3.5)\n", + "plt.xlim(xmax = 5.1, xmin = -3.5)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "\n", + "\n", + "reg = LinearRegression().fit(test_y.reshape(-1,1), pred_y.reshape(-1,1),)\n", + "reg.score(test_y.reshape(-1,1), pred_y.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([-3.5,5.1], [beta0 + -3.5*beta1, beta0 + 5.1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "\n", + "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "plt.scatter(test_y, pred_y, alpha = 0.6, s=30, c=\"darkblue\")\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"scatter_plot.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.04563359885698626, 0.3254941054701782, 0.9444722012777762)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(df[\"error\"], df[\"pred_error\"]), r2_score(df[\"value\"], df[\"pred_value\"]), np.mean(abs(df[\"value\"]- df[\"pred_value\"])**2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...ESM1bESM1b_tsgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECDRFPy_truey_predy_pred_ENKIE
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9......[0.020693962, 0.16804111, 0.0377352, 0.1768811...[0.83155197, 0.08632717, -0.42143562, 0.419359...2.3404440.6656531.0000000.114660[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...2.340440.781540.057496
1Reaction_3629Sequence_3212[0.92][Q0PC20][1][0][0][False]MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL...{InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15......[0.07429815, 0.14984865, -0.08539086, 0.098546...[0.13206507, -0.10826899, -0.31126085, 0.95038...-0.0362120.3407411.0000000.090196[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...-0.036210.537210.740980
2Reaction_375Sequence_26[21.0][Q0GYU4][0][1][0][False]MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL...{InChI=1S/p+1, InChI=1S/C4H8O2/c1-3(5)4(2)6/h3......[-0.0272103, 0.2500836, 0.08181338, 0.03990136...[0.3617253, 0.8765441, -1.0668296, 1.5401511, ...1.3222190.1750000.1478871.000000[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...1.322220.927221.153964
3Reaction_4312Sequence_3788[4.4][Q8ZNC4][0][0][1][False]MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT...{InChI=1S/p+1, InChI=1S/C6H14N2O2/c7-4-2-1-3-5......[0.079942256, 0.23130149, -0.012637342, 0.0787...[0.7798445, -0.7589981, -0.2779501, 0.2643281,...0.6434531.0000001.0000001.000000[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.643450.959930.984732
4Reaction_2115Sequence_712[4.5][P53602][1][0][0][False]MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD...{InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15......[0.086191244, 0.21010432, 0.1960825, -0.041225...[-0.6100984, -0.054886594, -0.09893316, 0.2822...0.6532131.0000000.8490570.112500[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.653210.933100.971932
..................................................................
845Reaction_3029Sequence_1106[1.14][Q8PDQ6][1][0][0][False]MSLAQLEHALQHDLQRLAHGGEPWVRPRVHPAGHVYDVVIVGAGQS...{InChI=1S/p+1, InChI=1S/C5H4N4O3/c10-3-1-2(7-4......[0.07993014, 0.11095398, -0.0057218825, -0.049...[0.7720898, 0.107216544, -0.4964384, 0.1257281...0.0569051.0000001.0000000.027143[0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...0.056900.908360.207524
846Reaction_3310Sequence_455[5.8, 5.9, 4.8][C7P8V7, C7P8V7, C7P8V7][1, 1, 1][0, 0, 0][0, 0, 0][False, False, False]MILFFEYAIASGFEDEGILEEGKMMFNTLLNQFLEIDNVTSLIHKD...{InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15......[0.15469086, 0.08214222, 0.006613599, 0.003951...[-1.1673366, -0.3592899, 0.034033816, -0.01010...0.7385071.0000001.0000001.000000[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.738510.985340.587646
847Reaction_1253Sequence_1211[3.3333][O33289][1][0][0][True]MTERPRDCRPVVRRARTSDVPAIKQLVDTYAGKILLEKNLVTLYEA...{InChI=1S/C5H10N2O3/c6-3(5(9)10)1-2-4(7)8/h3H,......[0.095282555, 0.077073924, 0.1310218, -0.01710...[-0.6074202, 0.69103533, -0.38513482, 0.311095...0.5228741.0000001.0000004.273462[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.522870.826240.315951
848Reaction_1626Sequence_783[18.9][P0AEP7][1][0][0][True]MAKMRAVDAAMYVLEKEGITTAFGVPGAAINPFYSAMRKHGGIRHI...{InChI=1S/C2H2O3/c3-1-2(4)5/h1H,(H,4,5)}...[0.07920394, 0.22367033, 0.120473295, 0.001293...[0.80772597, -0.8157556, -0.43358412, 0.318514...1.2764621.0000001.0000000.959391[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...1.276461.263340.946862
849Reaction_898Sequence_3700[800.0][P00387][1][0][0][True]MGAQLSTLGHMVLFPVWFLYSLLMKLFQRSTPAITLESPDIKYPLR...{InChI=1S/6CN.Fe/c6*1-2;/q;;;;;;-3, InChI=1S/C......[-0.055920795, 0.26620504, 0.008486553, -0.058...[-0.21385467, -0.02001476, -0.59286845, 1.5044...2.9030901.0000000.7272730.644641[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...2.903092.171992.896796
\n", + "

825 rows × 28 columns

\n", + "
" + ], + "text/plain": [ + " Reaction ID Sequence ID kcat_values Uniprot IDs \\\n", + "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] \n", + "1 Reaction_3629 Sequence_3212 [0.92] [Q0PC20] \n", + "2 Reaction_375 Sequence_26 [21.0] [Q0GYU4] \n", + "3 Reaction_4312 Sequence_3788 [4.4] [Q8ZNC4] \n", + "4 Reaction_2115 Sequence_712 [4.5] [P53602] \n", + ".. ... ... ... ... \n", + "845 Reaction_3029 Sequence_1106 [1.14] [Q8PDQ6] \n", + "846 Reaction_3310 Sequence_455 [5.8, 5.9, 4.8] [C7P8V7, C7P8V7, C7P8V7] \n", + "847 Reaction_1253 Sequence_1211 [3.3333] [O33289] \n", + "848 Reaction_1626 Sequence_783 [18.9] [P0AEP7] \n", + "849 Reaction_898 Sequence_3700 [800.0] [P00387] \n", + "\n", + " from_BRENDA from_Sabio from_Uniprot checked \\\n", + "0 [1] [0] [0] [False] \n", + "1 [1] [0] [0] [False] \n", + "2 [0] [1] [0] [False] \n", + "3 [0] [0] [1] [False] \n", + "4 [1] [0] [0] [False] \n", + ".. ... ... ... ... \n", + "845 [1] [0] [0] [False] \n", + "846 [1, 1, 1] [0, 0, 0] [0, 0, 0] [False, False, False] \n", + "847 [1] [0] [0] [True] \n", + "848 [1] [0] [0] [True] \n", + "849 [1] [0] [0] [True] \n", + "\n", + " Sequence \\\n", + "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", + "1 MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL... \n", + "2 MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL... \n", + "3 MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT... \n", + "4 MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD... \n", + ".. ... \n", + "845 MSLAQLEHALQHDLQRLAHGGEPWVRPRVHPAGHVYDVVIVGAGQS... \n", + "846 MILFFEYAIASGFEDEGILEEGKMMFNTLLNQFLEIDNVTSLIHKD... \n", + "847 MTERPRDCRPVVRRARTSDVPAIKQLVDTYAGKILLEKNLVTLYEA... \n", + "848 MAKMRAVDAAMYVLEKEGITTAFGVPGAAINPFYSAMRKHGGIRHI... \n", + "849 MGAQLSTLGHMVLFPVWFLYSLLMKLFQRSTPAITLESPDIKYPLR... \n", + "\n", + " substrates ... \\\n", + "0 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C7H5NO4/c9... ... \n", + "1 {InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15... ... \n", + "2 {InChI=1S/p+1, InChI=1S/C4H8O2/c1-3(5)4(2)6/h3... ... \n", + "3 {InChI=1S/p+1, InChI=1S/C6H14N2O2/c7-4-2-1-3-5... ... \n", + "4 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... ... \n", + ".. ... ... \n", + "845 {InChI=1S/p+1, InChI=1S/C5H4N4O3/c10-3-1-2(7-4... ... \n", + "846 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... ... \n", + "847 {InChI=1S/C5H10N2O3/c6-3(5(9)10)1-2-4(7)8/h3H,... ... \n", + "848 {InChI=1S/C2H2O3/c3-1-2(4)5/h1H,(H,4,5)} ... \n", + "849 {InChI=1S/6CN.Fe/c6*1-2;/q;;;;;;-3, InChI=1S/C... ... \n", + "\n", + " ESM1b \\\n", + "0 [0.020693962, 0.16804111, 0.0377352, 0.1768811... \n", + "1 [0.07429815, 0.14984865, -0.08539086, 0.098546... \n", + "2 [-0.0272103, 0.2500836, 0.08181338, 0.03990136... \n", + "3 [0.079942256, 0.23130149, -0.012637342, 0.0787... \n", + "4 [0.086191244, 0.21010432, 0.1960825, -0.041225... \n", + ".. ... \n", + "845 [0.07993014, 0.11095398, -0.0057218825, -0.049... \n", + "846 [0.15469086, 0.08214222, 0.006613599, 0.003951... \n", + "847 [0.095282555, 0.077073924, 0.1310218, -0.01710... \n", + "848 [0.07920394, 0.22367033, 0.120473295, 0.001293... \n", + "849 [-0.055920795, 0.26620504, 0.008486553, -0.058... \n", + "\n", + " ESM1b_ts geomean_kcat \\\n", + "0 [0.83155197, 0.08632717, -0.42143562, 0.419359... 2.340444 \n", + "1 [0.13206507, -0.10826899, -0.31126085, 0.95038... -0.036212 \n", + "2 [0.3617253, 0.8765441, -1.0668296, 1.5401511, ... 1.322219 \n", + "3 [0.7798445, -0.7589981, -0.2779501, 0.2643281,... 0.643453 \n", + "4 [-0.6100984, -0.054886594, -0.09893316, 0.2822... 0.653213 \n", + ".. ... ... \n", + "845 [0.7720898, 0.107216544, -0.4964384, 0.1257281... 0.056905 \n", + "846 [-1.1673366, -0.3592899, 0.034033816, -0.01010... 0.738507 \n", + "847 [-0.6074202, 0.69103533, -0.38513482, 0.311095... 0.522874 \n", + "848 [0.80772597, -0.8157556, -0.43358412, 0.318514... 1.276462 \n", + "849 [-0.21385467, -0.02001476, -0.59286845, 1.5044... 2.903090 \n", + "\n", + " frac_of_max_UID frac_of_max_RID frac_of_max_EC \\\n", + "0 0.665653 1.000000 0.114660 \n", + "1 0.340741 1.000000 0.090196 \n", + "2 0.175000 0.147887 1.000000 \n", + "3 1.000000 1.000000 1.000000 \n", + "4 1.000000 0.849057 0.112500 \n", + ".. ... ... ... \n", + "845 1.000000 1.000000 0.027143 \n", + "846 1.000000 1.000000 1.000000 \n", + "847 1.000000 1.000000 4.273462 \n", + "848 1.000000 1.000000 0.959391 \n", + "849 1.000000 0.727273 0.644641 \n", + "\n", + " DRFP y_true y_pred \\\n", + "0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 2.34044 0.78154 \n", + "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... -0.03621 0.53721 \n", + "2 [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ... 1.32222 0.92722 \n", + "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.64345 0.95993 \n", + "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.65321 0.93310 \n", + ".. ... ... ... \n", + "845 [0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ... 0.05690 0.90836 \n", + "846 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.73851 0.98534 \n", + "847 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.52287 0.82624 \n", + "848 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 1.27646 1.26334 \n", + "849 [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ... 2.90309 2.17199 \n", + "\n", + " y_pred_ENKIE \n", + "0 0.057496 \n", + "1 0.740980 \n", + "2 1.153964 \n", + "3 0.984732 \n", + "4 0.971932 \n", + ".. ... \n", + "845 0.207524 \n", + "846 0.587646 \n", + "847 0.315951 \n", + "848 0.946862 \n", + "849 2.896796 \n", + "\n", + "[825 rows x 28 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_test[\"y_pred_ENKIE\"]= np.nan\n", + "for ind in df.index:\n", + " r_ID, UID, kcat, kcat_pred = df[\"reaction_id\"][ind], df[\"uniprot_ac\"][ind], df[\"value\"][ind], df[\"pred_value\"][ind]\n", + " help_df = data_test.loc[data_test[\"Reaction ID\"] == r_ID].loc[data_test[\"y_true\"] == np.round(kcat,5)]\n", + " for ind2 in help_df.index:\n", + " data_test[\"y_pred_ENKIE\"][ind2] = kcat_pred\n", + "data_test.loc[~pd.isnull(data_test[\"y_pred_ENKIE\"])]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "y_true = np.array(data_test[\"y_true\"].loc[~pd.isnull(data_test[\"y_pred_ENKIE\"])])\n", + "y_pred = np.array(data_test[\"y_pred\"].loc[~pd.isnull(data_test[\"y_pred_ENKIE\"])])\n", + "y_pred_enkie =np.array(data_test[\"y_pred_ENKIE\"].loc[~pd.isnull(data_test[\"y_pred_ENKIE\"])])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.4435835116939041, 0.34139940194420637)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r2_score(y_true, y_pred), r2_score(y_true, y_pred_enkie)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/model_fitting/01 Training xgboost models with enzyme and reaction information.ipynb b/code/model_fitting/01 Training xgboost models with enzyme and reaction information.ipynb index 2e23775..445bd30 100644 --- a/code/model_fitting/01 Training xgboost models with enzyme and reaction information.ipynb +++ b/code/model_fitting/01 Training xgboost models with enzyme and reaction information.ipynb @@ -12,12 +12,12 @@ "\n", "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", "or from the matplotlib source distribution\n", "\n", "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", "or from the matplotlib source distribution\n" ] } @@ -205,7 +205,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.6156521084065655, 0.5927689215737512, 0.5450373027741603, 0.6635468722921142, 0.5465250301214235]\n", + "[0.6156521084065656, 0.592768921573751, 0.5450373027741603, 0.6635468722921141, 0.5465250301214234]\n", "[0.8251322874089426, 0.8206497271502332, 0.9401031382230501, 0.9269849644397952, 1.061793264104686]\n", "[0.3720653400563064, 0.34881598167570627, 0.2894002515317964, 0.4219034383575203, 0.2977699878192147]\n" ] @@ -269,6 +269,15 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b.npy\"), test_Y)" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b = y_test_pred" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -285,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -307,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -358,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -378,14 +387,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.6089419355661412, 0.5895091873624034, 0.5917286045189503, 0.642803324015263, 0.5233641744058668]\n", + "[0.6089419355661412, 0.5895091873624032, 0.5917286045189503, 0.6428033240152629, 0.5233641744058668]\n", "[0.830309790220053, 0.8254878887860261, 0.8642504226487573, 0.9497311132104741, 1.1100478926967097]\n", "[0.3681252040118652, 0.3449769094978131, 0.34673536553812156, 0.4077182348220084, 0.26585619671739524]\n" ] @@ -395,7 +404,7 @@ "R2 = []\n", "MSE = []\n", "Pearson = []\n", - "\n", + "y_valid_pred_esm1b_ts = []\n", "\n", "for i in range(5):\n", " train_index, test_index = train_indices[i], test_indices[i]\n", @@ -405,6 +414,7 @@ " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", " \n", " y_valid_pred = bst.predict(dvalid)\n", + " y_valid_pred_esm1b_ts.append(y_valid_pred)\n", " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", @@ -420,7 +430,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -448,6 +458,15 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts.npy\"), test_Y)" ] }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ts = y_test_pred" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -457,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -473,31 +492,456 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.9839122286376514, 0.0) 0.05006636413883383 0.9650757482138885\n" + ] + } + ], + "source": [ + "param = {'learning_rate': 0.2831145406836757,\n", + " 'max_delta_step': 0.07686715986169101, \n", + " 'max_depth': 4.96836783761305,\n", + " 'min_child_weight': 6.905400087083855,\n", + " 'num_rounds': 313.1498988074061,\n", + " 'reg_alpha': 1.717314107718892,\n", + " 'reg_lambda': 2.470354543039016}\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]\n", + "\n", + "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", + "dtest = xgb.DMatrix(test_X)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + "\n", + "y_test_pred = bst.predict(dtest)\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)\n", + "\n", + "pickle.dump(bst, open(join(\"..\", \"..\", \"data\", \"training_results\", \"saved_models\",\n", + " \"xgboost_sequence_only_train_and_test.pkl\"), \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Training a model with only reaction information (DRFP):" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Hyperparameter optimization:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "'''def cross_validation_mse_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " del param[\"num_rounds\"]\n", + " param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " \n", + " MSE = []\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " y_valid_pred = bst.predict(dvalid)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "\n", + "from hyperopt import fmin, tpe, rand, hp, Trials\n", + "\n", + "space_gradient_boosting = {\n", + " \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 1),\n", + " \"max_depth\": hp.uniform(\"max_depth\", 4,12),\n", + " #\"subsample\": hp.uniform(\"subsample\", 0.7, 1),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 20, 200)}\n", + "\n", + "\n", + "trials = Trials()\n", + "best = fmin(fn = cross_validation_mse_gradient_boosting, space = space_gradient_boosting,\n", + " algo=rand.suggest, max_evals = 200, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (c) Training and validating model:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.08987247189322463,\n", + " 'max_delta_step': 1.1939737318908727,\n", + " 'max_depth': 11.268531225242574,\n", + " 'min_child_weight': 2.8172720953826302,\n", + " 'num_rounds': 109.03643430746544,\n", + " 'reg_alpha': 1.9412226989868904,\n", + " 'reg_lambda': 4.950543905603358}\n", + "\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5703243058726097, 0.5534056710833766, 0.583291365495366, 0.5706660736746292, 0.5608173001699261]\n", + "[0.8877274220430679, 0.8796146121877297, 0.8815364701552285, 1.082140247084615, 1.0382516796274228]\n", + "[0.3244297606705625, 0.3020274560618814, 0.3336692874551086, 0.32514379407155003, 0.313339503762757]\n" + ] + } + ], + "source": [ + "R2 = []\n", + "MSE = []\n", + "Pearson = []\n", + "y_valid_pred_DRFP = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " \n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " \n", + " y_valid_pred = bst.predict(dvalid)\n", + " y_valid_pred_DRFP.append(y_valid_pred)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", + "\n", + "print(Pearson)\n", + "print(MSE)\n", + "print(R2)\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_DRFP.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_DRFP.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_DRFP.npy\"), np.array(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.619 0.886 0.382\n" + ] + } + ], + "source": [ + "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", + "dtest = xgb.DMatrix(test_X)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + "\n", + "y_test_pred = bst.predict(dtest)\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_DRFP.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_DRFP.npy\"), test_Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_drfp = y_test_pred" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (d) Training model with test and train data for production mode:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "train_DRFP = np.array(list(data_train[\"DRFP\"]))\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_DRFP = np.array(list(data_test[\"DRFP\"]))\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))\n", + "\n", + "train_X = np.concatenate([train_DRFP, test_DRFP])\n", + "train_Y = np.concatenate([train_Y, test_Y])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(0.8681560564738647, 3.0446983463533428e-260) 0.4027527000636768 0.7190561578317212\n" + ] + } + ], + "source": [ + "param = {'learning_rate': 0.08987247189322463,\n", + " 'max_delta_step': 1.1939737318908727,\n", + " 'max_depth': 11.268531225242574,\n", + " 'min_child_weight': 2.8172720953826302,\n", + " 'num_rounds': 109.03643430746544,\n", + " 'reg_alpha': 1.9412226989868904,\n", + " 'reg_lambda': 4.950543905603358}\n", + "\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]\n", + "\n", + "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", + "dtest = xgb.DMatrix(test_X)\n", + "\n", + "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + "\n", + "y_test_pred = bst.predict(dtest)\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)\n", + "\n", + "pickle.dump(bst, open(join(\"..\", \"..\", \"data\", \"training_results\", \"saved_models\",\n", + " \"xgboost_reaction_only_train_and_test.pkl\"), \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Training a model with only reaction information (difference fingerprint):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a) Creating input matrices:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"difference_fp\"]))\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"difference_fp\"]))\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Hyperparameter optimization:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "'''def cross_validation_mse_gradient_boosting(param):\n", + " num_round = param[\"num_rounds\"]\n", + " del param[\"num_rounds\"]\n", + " param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + " param[\"tree_method\"] = \"gpu_hist\"\n", + " param[\"sampling_method\"] = \"gradient_based\"\n", + " \n", + " MSE = []\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " y_valid_pred = bst.predict(dvalid)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "\n", + "from hyperopt import fmin, tpe, rand, hp, Trials\n", + "\n", + "space_gradient_boosting = {\n", + " \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 1),\n", + " \"max_depth\": hp.uniform(\"max_depth\", 4,12),\n", + " #\"subsample\": hp.uniform(\"subsample\", 0.7, 1),\n", + " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", + " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", + " \"max_delta_step\": hp.uniform(\"max_delta_step\", 0, 5),\n", + " \"min_child_weight\": hp.uniform(\"min_child_weight\", 0.1, 15),\n", + " \"num_rounds\": hp.uniform(\"num_rounds\", 20, 200)}\n", + "\n", + "\n", + "trials = Trials()\n", + "best = fmin(fn = cross_validation_mse_gradient_boosting, space = space_gradient_boosting,\n", + " algo=rand.suggest, max_evals = 200, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (c) Training and validating model:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'learning_rate': 0.14154883958006167,\n", + " 'max_delta_step': 0.02234358170535966,\n", + " 'max_depth': 10.869653004093198,\n", + " 'min_child_weight': 1.7936882442746056,\n", + " 'num_rounds': 361.6168542774665,\n", + " 'reg_alpha': 4.825525325323308, \n", + " 'reg_lambda': 2.74944090578774}\n", + "\n", + "\n", + "num_round = param[\"num_rounds\"]\n", + "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", + "\n", + "del param[\"num_rounds\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.5933818458131599, 0.5290768248822283, 0.5426074027598811, 0.5889341652634298, 0.5248561000592911]\n", + "[0.8733503195891379, 0.9112072460648096, 0.9525515259896388, 1.0924558329257095, 1.1054594918609029]\n", + "[0.3353708922662406, 0.27695876037247324, 0.27999083584524465, 0.31871067494359473, 0.2688907919476974]\n" + ] + } + ], + "source": [ + "R2 = []\n", + "MSE = []\n", + "Pearson = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " dtrain = xgb.DMatrix(train_X[train_index], label = train_Y[train_index])\n", + " dvalid = xgb.DMatrix(train_X[test_index])\n", + " \n", + " bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", + " \n", + " y_valid_pred = bst.predict(dvalid)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", + "\n", + "print(Pearson)\n", + "print(MSE)\n", + "print(R2)\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_diff_fp.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_diff_fp.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_diff_fp.npy\"), np.array(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(0.9839122286376513, 0.0) 0.05006636413883383 0.9650757482138885\n" + "0.6 0.948 0.339\n" ] } ], "source": [ - "param = {'learning_rate': 0.2831145406836757,\n", - " 'max_delta_step': 0.07686715986169101, \n", - " 'max_depth': 4.96836783761305,\n", - " 'min_child_weight': 6.905400087083855,\n", - " 'num_rounds': 313.1498988074061,\n", - " 'reg_alpha': 1.717314107718892,\n", - " 'reg_lambda': 2.470354543039016}\n", - "\n", - "num_round = param[\"num_rounds\"]\n", - "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", - "\n", - "del param[\"num_rounds\"]\n", - "\n", "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", "dtest = xgb.DMatrix(test_X)\n", "\n", @@ -508,17 +952,27 @@ "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", "\n", - "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", "\n", - "pickle.dump(bst, open(join(\"..\", \"..\", \"data\", \"training_results\", \"saved_models\",\n", - " \"xgboost_sequence_only_train_and_test.pkl\"), \"wb\"))" + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_diff_fp.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_diff_fp.npy\"), test_Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_diff_fp = y_test_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Training a model with only reaction information (difference fingerprint):" + "## 5. Training a model with only reaction information (structural fingerprint):" ] }, { @@ -530,14 +984,21 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "train_X = np.array(list(data_train[\"difference_fp\"]))\n", + "train_X = ();\n", + "for ind in data_train.index:\n", + " train_X = train_X + (np.array(list(data_train[\"structural_fp\"][ind])).astype(int), )\n", + "train_X = np.array(train_X)\n", "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", "\n", - "test_X = np.array(list(data_test[\"difference_fp\"]))\n", + "\n", + "test_X = ();\n", + "for ind in data_test.index:\n", + " test_X = test_X + (np.array(list(data_test[\"structural_fp\"][ind])).astype(int), )\n", + "test_X = np.array(test_X)\n", "test_Y = np.array(list(data_test[\"log10_kcat\"]))" ] }, @@ -550,7 +1011,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -601,18 +1062,17 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.14154883958006167,\n", - " 'max_delta_step': 0.02234358170535966,\n", - " 'max_depth': 10.869653004093198,\n", - " 'min_child_weight': 1.7936882442746056,\n", - " 'num_rounds': 361.6168542774665,\n", - " 'reg_alpha': 4.825525325323308, \n", - " 'reg_lambda': 2.74944090578774}\n", - "\n", + "param = {'learning_rate': 0.01126910440903659,\n", + " 'max_delta_step': 0.5777120839605732,\n", + " 'max_depth': 5.486901609313889,\n", + " 'min_child_weight': 6.14467742389769,\n", + " 'num_rounds': 488.943459090126,\n", + " 'reg_alpha': 4.629840853377147,\n", + " 'reg_lambda': 2.1047561335691745}\n", "\n", "num_round = param[\"num_rounds\"]\n", "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", @@ -622,16 +1082,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.5933818458131599, 0.5290768248822284, 0.542607402759881, 0.5889341652634299, 0.5248561000592911]\n", - "[0.8733503195891379, 0.9112072460648096, 0.9525515259896388, 1.0924558329257095, 1.1054594918609029]\n", - "[0.3353708922662406, 0.27695876037247324, 0.27999083584524465, 0.31871067494359473, 0.2688907919476974]\n" + "[0.5536292775258076, 0.5323143816237441, 0.4889091394899996, 0.6104056516948199, 0.5039256159780712]\n", + "[0.917613544024189, 0.9056644419444148, 1.015607786275439, 1.064522657133946, 1.1289411204418558]\n", + "[0.3016860962550283, 0.2813569650394473, 0.23232823280029657, 0.3361306693344771, 0.2533609285723336]\n" ] } ], @@ -656,21 +1116,21 @@ "print(MSE)\n", "print(R2)\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_diff_fp.npy\"), np.array(Pearson))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_diff_fp.npy\"), np.array(MSE))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_diff_fp.npy\"), np.array(R2))" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_str_fp.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_str_fp.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_str_fp.npy\"), np.array(R2))" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.6 0.948 0.339\n" + "0.561 0.994 0.307\n" ] } ], @@ -688,41 +1148,29 @@ "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", "\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_diff_fp.npy\"), bst.predict(dtest))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_diff_fp.npy\"), test_Y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Training a model with only reaction information (structural fingerprint):" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_str_fp.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_str_fp.npy\"), test_Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### (a) Creating input matrices:" + "## 6. Training a model with enzyme and reaction information (ESM1b_ts/DRFP):" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ - "train_X = ();\n", - "for ind in data_train.index:\n", - " train_X = train_X + (np.array(list(data_train[\"structural_fp\"][ind])).astype(int), )\n", - "train_X = np.array(train_X)\n", + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", "\n", - "\n", - "test_X = ();\n", - "for ind in data_test.index:\n", - " test_X = test_X + (np.array(list(data_test[\"structural_fp\"][ind])).astype(int), )\n", - "test_X = np.array(test_X)\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", "test_Y = np.array(list(data_test[\"log10_kcat\"]))" ] }, @@ -735,7 +1183,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -763,7 +1211,7 @@ "\n", "space_gradient_boosting = {\n", " \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 1),\n", - " \"max_depth\": hp.uniform(\"max_depth\", 4,12),\n", + " \"max_depth\": hp.uniform(\"max_depth\", 6,14),\n", " #\"subsample\": hp.uniform(\"subsample\", 0.7, 1),\n", " \"reg_lambda\": hp.uniform(\"reg_lambda\", 0, 5),\n", " \"reg_alpha\": hp.uniform(\"reg_alpha\", 0, 5),\n", @@ -786,17 +1234,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ - "param = {'learning_rate': 0.01126910440903659,\n", - " 'max_delta_step': 0.5777120839605732,\n", - " 'max_depth': 5.486901609313889,\n", - " 'min_child_weight': 6.14467742389769,\n", - " 'num_rounds': 488.943459090126,\n", - " 'reg_alpha': 4.629840853377147,\n", - " 'reg_lambda': 2.1047561335691745}\n", + "param = {'learning_rate': 0.05221672412884108,\n", + " 'max_delta_step': 1.0767235463496743,\n", + " 'max_depth': 11.329014411591299,\n", + " 'min_child_weight': 14.724796449973605,\n", + " 'num_rounds': 298.9598325756988,\n", + " 'reg_alpha': 2.8295816318634452,\n", + " 'reg_lambda': 0.6528469146574993}\n", "\n", "num_round = param[\"num_rounds\"]\n", "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", @@ -806,16 +1254,16 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.5536292775258077, 0.532314381623744, 0.4889091394899996, 0.61040565169482, 0.5039256159780713]\n", - "[0.917613544024189, 0.9056644419444148, 1.015607786275439, 1.064522657133946, 1.1289411204418558]\n", - "[0.3016860962550283, 0.2813569650394473, 0.23232823280029657, 0.3361306693344771, 0.2533609285723336]\n" + "[0.6353444924580318, 0.5880301926572151, 0.5241899073773033, 0.6588493325838651, 0.5473715375037416]\n", + "[0.7895750755455662, 0.8261521353734187, 0.9646511784030429, 0.9310396416328481, 1.0594944425073949]\n", + "[0.3991247656547007, 0.34444983107735405, 0.270845020229982, 0.4193748159593236, 0.2992903417080939]\n" ] } ], @@ -840,27 +1288,28 @@ "print(MSE)\n", "print(R2)\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_str_fp.npy\"), np.array(Pearson))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_str_fp.npy\"), np.array(MSE))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_str_fp.npy\"), np.array(R2))" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_ESM1b_ts_DRFP.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_ESM1b_ts_DRFP.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_ESM1b_ts_DRFP.npy\"), np.array(R2))" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.561 0.994 0.307\n" + "0.636 0.856 0.403\n" ] } ], "source": [ "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", - "dtest = xgb.DMatrix(test_X)\n", + "dtest = xgb.DMatrix(test_X, label = test_Y)\n", + "\n", "\n", "bst = xgb.train(param, dtrain, int(num_round), verbose_eval=False)\n", "\n", @@ -872,15 +1321,24 @@ "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))\n", "\n", "\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_str_fp.npy\"), bst.predict(dtest))\n", - "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_str_fp.npy\"), test_Y)" + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_DRFP.npy\"), bst.predict(dtest))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_DRFP.npy\"), test_Y)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ts_drfp = y_test_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 5. Training a model with enzyme and reaction information (ESM1b_ts/diff_fp):" + "## 7. Training a model with enzyme and reaction information (ESM1b_ts/diff_fp):" ] }, { @@ -892,7 +1350,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -914,7 +1372,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -965,7 +1423,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -977,6 +1435,7 @@ " 'reg_alpha': 7.333074414515098,\n", " 'reg_lambda': 0.8545111451043885}\n", "\n", + "\n", "num_round = param[\"num_rounds\"]\n", "param[\"max_depth\"] = int(np.round(param[\"max_depth\"]))\n", "\n", @@ -985,14 +1444,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[0.6445379171956692, 0.5663538267118359, 0.5813292687854973, 0.6603671022176677, 0.5437710214323066]\n", + "[0.6445379171956691, 0.5663538267118355, 0.5813292687854972, 0.660367102217668, 0.5437710214323066]\n", "[0.7778549566615786, 0.8621287188783706, 0.8811802952099859, 0.9212592601438562, 1.0674061317478523]\n", "[0.40804390380771904, 0.31590247958588724, 0.33393851092241733, 0.4254741650612315, 0.2940578488872041]\n" ] @@ -1026,7 +1485,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 47, "metadata": { "scrolled": true }, @@ -1058,6 +1517,15 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_diff_fp.npy\"), test_Y)" ] }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "y_test_pred_esm1b_ts_drfp = y_test_pred" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1067,7 +1535,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1085,14 +1553,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(0.9910659621749647, 0.0) 0.027904669605058757 0.9805348416235519\n" + "(0.9906766451895581, 0.0) 0.02970620593719598 0.9792781634215774\n" ] } ], @@ -1130,7 +1598,96 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 6. Training a model with enzyme rep., reaction information, and additional features (ESM1b_ts/diff_fp/flux/KM):" + "## 8. Model with enzyme and reaction information (ESM1b_ts/DRFP [mean]):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cross-Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.6427432566631278, 0.6226108619000825, 0.6371590251884234, 0.6630733248247811, 0.5920048794950177]\n", + "[0.7840422477361155, 0.7768955691409662, 0.8045088804939567, 0.9293225562214953, 0.9845290313461734]\n", + "[0.40333530789389016, 0.38353482393964156, 0.39189245852320176, 0.42044564365386916, 0.3488696368237689]\n" + ] + } + ], + "source": [ + "R2 = []\n", + "MSE = []\n", + "Pearson = []\n", + "\n", + "for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " y_valid_pred = np.mean([y_valid_pred_DRFP[i], y_valid_pred_esm1b_ts[i]], axis =0)\n", + " MSE.append(np.mean(abs(np.reshape(train_Y[test_index], (-1)) - y_valid_pred)**2))\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " Pearson.append(stats.pearsonr(np.reshape(train_Y[test_index], (-1)), y_valid_pred)[0])\n", + "\n", + "print(Pearson)\n", + "print(MSE)\n", + "print(R2)\n", + "\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_ESM1b_ts_DRFP_mean.npy\"), np.array(Pearson))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_ESM1b_ts_DRFP_mean.npy\"), np.array(MSE))\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_ESM1b_ts_DRFP_mean.npy\"), np.array(R2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Validation on test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.671 0.808 0.436\n" + ] + } + ], + "source": [ + "y_test_pred = np.mean([y_test_pred_drfp, y_test_pred_esm1b_ts], axis =0)\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_ts_DRFP_mean.npy\"), y_test_pred)\n", + "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_ts_DRFP_mean.npy\"), test_Y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Training a model with enzyme rep., reaction information, and additional features (ESM1b_ts/diff_fp/flux/KM):" ] }, { @@ -1142,7 +1699,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1151,7 +1708,7 @@ "(3421, 850)" ] }, - "execution_count": 36, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1167,19 +1724,19 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train_X = ();\n", - "train_X = np.array(list(data_train[\"difference_fp\"]))\n", + "train_X = np.array(list(data_train[\"DRFP\"]))\n", "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"])),\n", " np.reshape(np.array(list(data_train[\"KM\"])), (-1,1)),\n", " np.reshape(np.array(list(data_train[\"flux\"])), (-1,1))], axis = 1)\n", "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", "\n", "test_X = ();\n", - "test_X = np.array(list(data_test[\"difference_fp\"]))\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"])),\n", " np.reshape(np.array(list(data_test[\"KM\"])), (-1,1)),\n", " np.reshape(np.array(list(data_test[\"flux\"])), (-1,1))], axis = 1)\n", @@ -1188,7 +1745,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1232,7 +1789,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1252,19 +1809,9 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.6381726941853013, 0.5782540928851373, 0.5816409524748034, 0.6501114403040922, 0.5359555917016532]\n", - "[0.7820629557858503, 0.843033028077367, 0.8823309866269835, 0.9415727118823687, 1.085306625916712]\n", - "[0.40484157063101067, 0.33105487439829584, 0.33306873291802874, 0.412806066811956, 0.28221913728185244]\n" - ] - } - ], + "outputs": [], "source": [ "R2 = []\n", "MSE = []\n", @@ -1293,17 +1840,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.6203728110199708 0.8836397180206959 0.3836090051439036\n" - ] - } - ], + "outputs": [], "source": [ "dtrain = xgb.DMatrix(train_X, label = train_Y)\n", "dtest = xgb.DMatrix(test_X)\n", @@ -1321,6 +1860,13 @@ "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_ESM1b_diff_fp_flux_KM.npy\"), bst.predict(dtest))\n", "np.save(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_ESM1b_diff_fp_flux_KM.npy\"), test_Y)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1339,7 +1885,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/model_fitting/02 - Testing additional input features.ipynb b/code/model_fitting/02 - Testing additional input features.ipynb index 0a22bc5..acad832 100644 --- a/code/model_fitting/02 - Testing additional input features.ipynb +++ b/code/model_fitting/02 - Testing additional input features.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -37,7 +37,8 @@ "\n", "from sklearn import linear_model\n", "from sklearn.metrics import r2_score\n", - "from scipy import stats" + "from scipy import stats\n", + "from sklearn.linear_model import LinearRegression" ] }, { @@ -56,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -186,7 +187,7 @@ "[4286 rows x 4 columns]" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -221,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -276,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -288,7 +289,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -315,6 +316,13 @@ "ax.xaxis.set_label_coords(0.5, -0.1)\n", "\n", "\n", + "x0,x1,y0,y1 = 0.2, 0.9, -2, 5\n", + "reg = LinearRegression().fit(kcat.reshape(-1,1), cai.reshape(-1,1),)\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "#plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([x0, x1], [beta0 + x0*beta1, beta0 + x1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "\n", + "\n", "plt.xlabel(\"Codon Adaptation Index (CAI)\")\n", "plt.ylabel(\"$\\log_{10}$($k_{cat}$)\")\n", "plt.scatter(cai, kcat, alpha = 0.7, s=60, c= \"darkblue\")\n", @@ -330,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -384,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -396,7 +404,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -423,10 +431,16 @@ "ax.yaxis.set_label_coords(-0.13, 0.5)\n", "ax.xaxis.set_label_coords(0.5, -0.1)\n", "\n", + "x0,x1,y0,y1 = 0.2, 0.9, -2, 5\n", + "reg = LinearRegression().fit(cai.reshape(-1,1), kcat.reshape(-1,1),)\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "#plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([x0, x1], [beta0 + x0*beta1, beta0 + x1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", "\n", "plt.xlabel(\"Codon Adaptation Index (CAI)\")\n", "plt.ylabel(\"$\\log_{10}$($k_{cat}$)\")\n", "plt.scatter(cai, kcat, alpha = 0.8, s=60, c= \"darkblue\")\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"CAI.png\"))\n", "plt.show()" ] }, @@ -439,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -488,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -505,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -529,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -567,9 +581,11 @@ "ax.xaxis.set_label_coords(0.5, -0.1)\n", "\n", "\n", + "\n", "plt.xlabel(\"$\\log_{10}$($K_{M}$)\")\n", "plt.ylabel(\"$\\log_{10}$($k_{cat}$)\")\n", "plt.scatter(km, kcat, alpha = 0.5, s=60, c= \"darkblue\")\n", + "\n", "plt.show()" ] }, @@ -582,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -635,7 +651,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -647,7 +663,7 @@ }, { "data": { - "image/png": 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bm6CnJ8rly2neemt1rcONfQuHdWZmSkxOFgHo6grz8MPtBIMaTzyxh2984yxNTYuDEtd1qdVsDh8WXieW5dDdHSGdriw7FVlV2baiV2kIJ9lKZGAikUg2lY1onW10MbguvPVWkgceaPcCn3B49a3DExNFRkbyjI+LMk13d5hazaFYNCkUhND1yJFWjh5t4+TJKZqa/Ivam3t74wSDOhMTBXbsiBAOx3j55fnhg42pyFeupOnsjGyJ6HU1mRBpCCfZStSt3gGJRPLeoqtLiEiXo1g06ewMed+7rsvAQJJnnjnJ5z//c5555iQDA0ncFYbMrKZ1+Pq4XL6cJh4XZRrD0AiHDeJxP5cvp3FdF0VR+N3ffYQDB5pJJPwoikJLS4DHHtvB4cOtFAomzc1BwmGD9vYQvb0xMhmhKzFNG8uymZgobolZViMT8tWvvsGlS2lc1+HSpTRf/eobPPvsOe+43vpxlEhuHpkxWQeOHTv274B/t8W7IZHcFqxWrLq0nBAK6bzyygTPPXeejo4QTzyxmyeeWHynf6vZmNUO1evra+NTn7r7GnfOkZFc3R22xOXLGUIhY9HwwWLRJBz28dhj3Xz60/cCMDCQ3DQdx2ozIdIQTrKVyMBEIpFsKqu13F64iCqKWMAHB7MYhsrQUJZXX528Rjtyq63Dqqpw110Jr5SzsExz110JL1i4kTvnmTMp3nrrDS/46ugI09ERxnFchodzPP30IYBN13Gs1oxODpKTbCUyMJFIJJvKai23Fy6i09NFBgezxON+T6uRzVZ45JGuRXf6t9o63NUVJp+v0dPTxNBQpl6W8ROLBbh6tcDJk1M888xJL6uxkjvn9YKvj3ykB8dx+fznf8YPfjDEjh1hwuEEwaCx4TqO1WZCbrdBcrKD6M5CBiYSiWTTWY3l9sJFdHAwi2W5XL1aoFq10XUFy3JQlMV3+rc6AK2xIO/Z00RHR8jTuFy4MEe1avHYYztWldVYKfh6/PEeTp+e4WtfO1k3W1NIp6u8/PJVenvj9PW1behgv9VmQm6nQXKyg+jOQwYmEolkw7mZO9rGIhoMGgwNZb22W01TKBYtqtUSAwNJ7ror7t3p3+oAtKULcrFocu7cLH6/xr33ttLTI8pKq8lqLBd8DQwkefHFUfbsaWJwUGhQlrYRd3SENkzHsdpMyFqO41ZnK2QH0Z2HDEwkEsmGstwd7cWLaY4fHyUW89HdHaW7O3zNYtZYRC2rSKFQwzDUutkZKIoouzQs3t///i7v9W5lANrSBfn73x+krS3IkSPt9aF74nHrMSAwHDaYmxPBVqONeGgoQ0dHaMN0HGvJhKzmOG6HbMV6D3HcDmx1sLfVyMBEIpFsKEvvaF0XLl3KMDKSpVq1eewxlWKxds1i1lhEv/71NwmFDHK5Gq5r4zguiUTAs60fHEzzB3/w6Lrt78IFuTHHZ7nSx60OCOztjTE1Ne9x4vOpFArmuug4rrewLTSjO3Nmlrm5Ms3NAVKpMmfOpNa0+G2HbMWd1kG0HYK9rUYGJhKJZENZekc7M1NicDBDIhGgVLKYmCjy/vd3X7OYNbIXP/nJGENDaa5eFZmT1tYgsZiPUsmiXLbYuze+rOZh8eJcxHHcesZDWTZDsxzr3Z2ycHsNj5PBwSw+n4ZlOYTDOsPDuVvScaxmYTt8uJXTp2ewrGTdCM5Yk0Nug+2QrbjTOoi2Q7C31cjARCKRbChL72iHhkT5pZElaJitLbeYKYpCX18rwaDGhz60qx7UCD+Q5maDfftivO99Xdcsoo7j8Ed/9Brf/e5lLMuhWrUplUx8PpX9+5spFKqrugNd7+6UpdtreJxcuZJlYqLAY4/t4OmnD61KD7MSq1nYXJd1Wfy2KluxMOgcGEhy5kyKBx5op7Mz7B237dpBdCO2Q7C31cjARCKRbChL72jzeREgANRqDs3NAe+xyy1m84t50PMDATxPkI98ZM+ix7uuyx/90et84xtnaW7247owPV1E11UCAT9jYzl6eqLs2dN0w0V4vbtTltueqip0doZ5+ul71yVNv1rX1vVY/LYiW7E0I9TSEqSpyc8LL4zS29vE/fe3UypZ27KDaDXcaaWpm0EGJhKJZENZmiWIRg1mZyvoulqfLxPzHrvcYrbW4ODMmRTf/e5Fmpv9RCI+xsfz+P06uq6QTJYxDJUf/3iYAwcSxON+Xnxx+LqdNbfS5bPR21uO1Sxsrsu6LH5b4XeyXEbogx/cwV13xTl1aoq5uSpHjrSu6zHdTO600tTNIAMTiUSyoSwNLLq6IgwNCeHrvn0x2tvFbJyVFrO1LuYnToxgWS6hkLi8Vas2mgalkkWpZKHrKpqmMjdXYXw8T7Fo8dnPPrTiAnYrXT6bsb2lrHZhW4/Fbyv8TpbLCDW6tB55pIv9+xNrmk693bjdzO02AhmYSCSSDWVpYDE5WeShhzrJ5Wp0dYUpl60bLmZrWcwnJ0skEn7yeRPD0PD7NbLZKuWyha6LrqBAQCccNnBdl+npAmfPpu6Yuv1qFjbXZV0Wv83IAC3lehmhUMhgYCDFM8+cvG3bbG8nc7uNQgYmEolkw1kaWLiuy9mzqQ1ZzLq6QrS1hZmdnSUU0onHA0xPF1FVcF2x7UTCj+u6mKbDwYMt6yIo3C7eE6td2NZr8dvoDNBSVsoINYKtXK5KMKjdtm22WxHsbTdkYCKRSDad9V7MlnZpXLmSJpEIMDdXrutLNEqlGiAG6ikKZDJV9u6NEQxqfP/7g7cUTMwLMoepVh2SySLpdJXnnjvHxz9+gN/7vUdQVXVd3uuN3v/kZInOzhD/5J/sY3Q0x/R0edmF7XZd/FbKCE1NFRkayvLkk7tpaxPlwdu1zXazg73thgxMJBLJbc1yXRqxWIChoQxtbSFCIY1gUMO2dfx+Ddt2yOdrHDnShmna/PSnYyiKQj5fw+dTOH58lH/6T/dz330d/OQno6sKWIQgc5hstsbwsPAlCYV0qlWbb3zjDIoCv/d779uQBX8535KFniQr6Wdu18VvpYzQqVNT9PY20dkZWvT491Kb7Z2CDEwkEsm2ZTXlkRt1aezd20Iw6GNgIElHR4hAQKNWc3j77SSpVBnbdti1K4phKFSrDsPDGf7jf3yNgweb6emJrqokcOLESP258xOQAQxDA+A737nExz62b8WF8VbKQO81Q66VSh3lsk1Li3/Z4/VeabO9U5CBiUQi2XY0pvp+5Suv8/bbSeJxP3fdFSeXq1wzY+fq1QKRiOHZ3c/MlBgaypDPm/j9Go7j0tIS4MEHOxgezlKrCWO3bLZKPl9jx44ogYDO9HSJatXGdV3S6SqtreI5cOOFfnKyRDJZ9IzjFuL3a5TL1op37LdqQf5eNORaLtvzzDMnuXQpTTjsu+bx75U22zsFGZhIJJItZ6l9/Ph4nvHxPJOTBVpbg1SrNqdOzWAYKrWaRa3meDN2fvazcXbvbqKtLcjAQMob7CdcZS1+/ONhDh1q5YMf7Ka7O8LQUIZCwcQ0hQV8qWQyNmaiaSqappDL1TBNm8uXM3z0o6xqcF9XV4h0uuq1KC+kVnOIx/0r3rHfasZDGnIJZJvtnYMMTCQSyZayNGNQKlmcPDlFLlcjGNQxDA3DgELB5NKlNLGYH8NQeffdNE8+uZvu7giXL6cJhw0GBzOLSim67hAMakxNFZiZKdPREaKjQ2gQrl4tkM1WKRZrtLbOTw5WFDAMlVLJ5Pz52fpjTMJhg66uMJOTxWvew+OP7+a5585Rrdpe+abx3mo1m/b22DXahwa3mvGQhlyCW2mz3S4dVRKBDEwkEsmWsjBjoCgKL7wwTLlskc/XyOdrBAIajiMCCdeFSsUCdC5fztDREWLv3igXL87xk5+Momli9k487icU0qnVbLq6IuRyNQYHMwBemcd1XQoFk0BAY+Hao6oKpukQDBr84hfj7NgRxecThmzDw1kefrgT13UXLVhHjrTy8Y8f4BvfOAOI8k2t5lCr2ezdG8MwtLp/yLUL4Ntvp2ht9S97bFaT8ZCZAsHNttnKab7bDxmYSCSSLaWRMVAUhYGBJJcvZ1BVBV1XqVZthofz6LoKuBiGiuuK4CEU0rlyJUs2W8WyxCLc1GRQKJik0xXCYcMb7Pb3f3+VwcEMk5MFbNulVDLJ52tYlkOtplCtWui66NhRVQVNU1FV0DTFK5OI/bHIZmvXGLIpisLv/d4jKIoQupbLFvG4n/Z2EZR89KN7OHSoZdkF8OzZGWIxPx/4wI5rFsDVZDykIdc8N9Np9F4TD98OyMBEIpFsKQ2NhJgcnCEa9VEuW17Gw7YdXFcEJZblousqtu3Q3CxcYy9cmKO7O4xhKNi2C0AopKPrKp2dETo6QkQiPi5dmiMW81MsWl5wEwzqVKsWpZJNOCwCkuZmg2DQolCooSgKpml72Y99+xJ0dYWXLa+oqsrv/d77+NjH9nl37J2dIe+OfaUF8IEHOnnhhRFv2w1Wm/GQhly3xntRPLzdkYGJRCLZUhoaiaEhIVpNJFQKBVHC8fk0SiULRRELtchoaCQSAcJhg1SqjONAreayf38zZ88m69kOBcPQGBrK0t4ewrZt7rmnhaGhLH6/is+ne+6vly9ncF2Xjo4w7e0hentjnD6dZGamSGdnGEVRaGkJsHdvnPb2EOWyuWJ55Xp37CstgJ2dYfbujXHq1DSPPNJ5UxmP5Zx1BwaS/O7v/oxTp6ZxXYWHHmrnqafupa+v7bYKVjZa/yHFw9sPGZhIJJJ142YWkYZGIper4fOp6LrqubZqmkIs5qNUMnEcl/b2IMGgQTRqYFkOhYLopvH5VKamCgQCOvl8jWrVxrYrFAo12ttDdHREKZVEa/DCRch1Xdrbw8zOlgmHDY4ebaNYNLEshz174nzwgzsA18vmnDmTBOCxx7qv0ZnciJUWQEWBBx5oZ3a2wv79iVvOeLiuyze/+Q5/9mcDJJOluijW5Xvfu8xLL03Q39/H008fui2Ck83Qf0jx8PZDBiYSiWRduNlFpKGROH8+RblsE4v5iEQMHMfF79fQNIWJiSJ33RXjySf3MDNTrgtYRddOT0+UfN4kkQiQSAQoFk3S6SqFQo1KRUwTrlRMLl5M09YWxHV1bz8URSEe95FI+PH7NVRVZf/+BI8/3sN3v3sZx3E4ezbF4KBwczUMhVSqwmuvTfL00z/wvFRWcwd/vQWwVDLp62tdl6m4Z86k+Pa3L5LLVb2MD0BTk/Bn+da33iUc9jE2ltv2HSibof+Q4uHthwxMJBLJunCzi0hDIxEKGXzpS68BCi0tQR5+uJP29hCO43LixBjNzQFSqTLRqMG+fXHyeZN/9I/28Z3vvIvPp3qLaiTiIxTSGR62UVWFS5fm2L+/mYGBFGNjeSoVm46OEIqi4Lou1aro3AkGdVwhUeGuuxI8+eRuvv3ti1y8OEdLSwDTdCkULJqafKTTFaamip6Xymru4NdjAVxNRurEiRHSaTEjaOG+KIqCz6dx8WKaL33pNa9stJ07UDZD/yHFw9sPGZhIJJJ14VYWEUVR+MQn9lMqmYsWiFSqTD5v0t/fx333tXPixOiiUsehQy28/PIEw8OZ+sKrUqs5ZDIVajWbQEBneDhHOOxj794m3n13jmSyRCCgYRia141z6VKahx/uwnUdb6H+yEd6OHiwmXS6jAiWDJqa/Lz77hyJRIBSyWJiosj739+9qjv45RbAQqHG2FiBpiYfzz57jq6ulbMvSzNSoZDOK69M8Nxz5+noECLbJ57YzcREkVrNxee7dmigZTnMzZVpawvdFoPuNkP/IcXD2w8ZmEgkm8x2N3O62f27lUWk8ZqpVBldV7h6tUBzc5AjR1r4yEf2eAtEX1/7Nc/9yEd28dprOum0MEJLJPyk0xVM08G2LSIRnbm5CtWqzY4dUWZmiqRSZQ4cSNDcHODSpTRHjrRx8GAzijK/UL/44ii6rvDYYzu88surr054tvPCWdYEVh98LVwAJyeL5HI1AGIxIcS9XvZisd8LDAwkGRzMYhgqQ0NZXn11krfemsFxHAxDOM4uNHsDmJsro+sakcjiz2m7dqBslv7jdh1oeKciAxOJZBPZSjOn1QQct7J/N7uILH3N1tYgwaBOPm/S2hq64V3rE0/s4a23krzvfc2oqsL0dJHTp5MEgxqm6dLSEiIcFhmGdLrKBz+4k3y+xsMPd3HhwiwPP9zlBSUNVFUhEjE4f36WixfncF2xcE9NlTzb+VrNobk54D1nYfB1vWN95Egbhw+38ld/dZETJ0YwDA3TtL2un5WyFwszUtPTRQYH5wcGKopCNlvhkUe6eOutGXRdJ58vEwrpiz7bctmmqcmgtzd2zXHcjh0ot4P+Y7vfaNyOyMBEItlEtsrMabUBR2P/du9uIpUqMzCQJJ83CYd1vvWtdzl6tG3ZrAXc/CJyq8dkaYnk3Xfn0DSFctmmrS3kZXEURcHv10ilyvzTf3qAz372IT7/+Z/jug5L1w/XhZGRHGNjeTRNtAvPzlaYmSkSCOjs3BmhVrMXLfCN4OtGx/qpp+7huefO8/Wvn6JctvH5NGZnK0xOXqW3N05fX9ui7EVj4fubvxmkWKwSjwfI52sYhpgFlMlUKJctUqkye/fG2bUrQiYjzOOmpopeV06pZBGN6uzZ00R7+7X2+NuxA2W76z+ka+zGIAMTieQmuNm7pK0yc1pu8Q8GdSzL4etfP8VPfjJKX19bfVKvXu9EmR+Gl05XyeVyfPnLr/Pss7963e6atS4iqzkmDYOylY53o0Ty4ovD/Pzn4/XsgMXUVJFi0aSjQwQohqGQyVS9IGmlLM/0dJGzZ1P4/SqOA8PDORKJIG1tIYaGsui6yqFDLd4CvzD4ulGgFQoZvPDCCD6fis/nq88C0giFdAYHM3R2hr3szMKFr1YzqdWcujV+DrUuIdE0FcdxsG2Fl1++ys6dUQ4fbuELX3iEZ589x5tvTuO6cPRoFNd1OXlymldemWTfvhjt7UIEvNYMxEZlCZbb7oc/3MPRo23X6Iu2g/5DusZuDDIwkawJmba8tbukrTJzWrr4Nwy4Bgez2LbL8HCWYFDnZz8bJ5Hwk83WSCTmh+GJxVPh7beT19ixN1gaIJw5M8vcXNnrpjlzJrXseXKjYzI5WVzV8T58uJW33pqmUDCpVCwMQ6NSsahULLLZKi0tQQxD5aGHOnEcl2eeOcnbb6c4e3aGBx7orLfWimzJL34xTjpdYdeuKLGYrx6clYlEDDo7QxiGSleXcJ5dGnz9yZ+8ed1A65vffIcdOyJEIj7m5iqeDqTRNTM0lGHfvjj79ycWLXzhsMHLL18lFNLx+1VmZsq0tgYwDJVKxaG9XQRfly+nOXSohb6+dr7ylfZF52sk4mPPnhiXL6cZGcly110Jdu+OUihYq85AbFSW4Ebb/cxnHtx21xjpGrsxyMBEsmpk2lJwK3dJW2XmtHTxF4ZhQqMg5sU4tLWF6O6O8NZb01477UJM0yUe999Q4Hn4cCunT89gWUl27Ih4i+Vbby1/ntzomDQyDDc63g3/Dl1X8ft1/H4Ny9Ipl22qVYt8vsqhQ60cOtTC1752sq5n8ROL+XnhhRH27o3xwAPtjI7mGR8v0N4epLU1iKIotLXptLYGyWSqPPBAO4qiEA776tkI4ap65EgrAwNJvv/9K+TzJs3Nfk830ni7jeDzwIEEvb2xeqllXgfi86n14YUie3H8+PzCJ1xp4wwOZrAsUX7K52uEQobnhLscS8/XtrYQPT1NXLmSZmQkx6FDLfzrf33/qjMQG5UluB2zD9I1dmO4tp9MIlmBhReOtrYQoZBBW1uIPXuaeOGFEc6eTW31Lm4Kq7lLWonHH99NPi9cTBey0WK+rq6Q10ECeIZhiqJQqzlel8a+fTFs26VQqC16vuu61Go2+/fHb3ixXet5cqNjoihc93i/+OIwAwNJ/u2//QWnTk1TqYhgplp1AIhEDMJhH6qqEg77uHBhztu3cNjHBz6wgyef3E0uV2V2tkK1atHREaJSsRkayjI2lveOR8MHpFq1mZ4ucfBgMw891E6hUOPf/Jvj/Jt/8yKVio1pWszOVnj55asMDCQ9f5Ri0aSzU3wWDfv7TEZ0E5mmTTZbo1azvezFwoVPUaCvr43HHttBIKATCAjfldbWIM3Nfkoli0ymyl13JRYFGEvPV0WBjo4Qjz22g//hf9hJd3eEI0dWb1N/K+f/Vmx3I1n6d7WQxmctWTsyYyJZNTJtKbiVu6StEvMtFaYWiyY+n+oFHHv3xgFobw/R2RliaqrkPaYxwK63N0YwqN/wYrvW8+RGx+TMmdSKxzsU0nnxxVFOnBjj1KlpMpkq4OI4bt3eXscwIBzWiUZ9VComTU3ijtx1ReZIuMia+P0qLS0BpqeLWJZDuWwRCGiUyyII8flUmpp82LZLIhHggQc6vPcoshw1wOXgwRbefXeOUEhfpBtpawuSz5s8/fQhvve9y7S0BOnra6OzM8zgYJZCwSQY1PnCF97HJz6xH0VRrskmNYKKgwebGRvLE4v5iUZ9FIsmzc2i20ZRWDQMcL3v6jcqS7DR2YeNKEPfDl1DtyMyMJGsGpm2FNxKOWarzJyWLv4+n9AoaJpCb2/cE3GKUkwbipKqm4iZNDcH6O2N0doaZGQkf8OL7VrPkxsdkz/5kzcXHW/Xdb1S1PBwlmSyRGurKJfYtoNpOqiqgm3bNDXpuK6LaYq5Og3fENt2vZbbhsC3WLT4zncuYdsuzc1+dF1lcrKEZdkoikKpZGKaDoahUi5bizp5Bgez+P1CK5LNVujtjXnbVlWFM2dm6O0VbrK//ut3LTKSi0Z93HVX3AvEGkEJrLzw7d4d4+LFDB/8YNs1E4mHh3OLPqP1Lh9uVDlyvba7koD27bdnePHF0XUtQ2/3rqHbFRmYSFaNHHYluNW7pK0wc1q6+JfLNvl8bZHoE8SCXSpZ7NnTxPBwnkTCR3d3BEVRGBnJr+piu9bzZKU72UagtvB4LzUWS6UqmKZLJlPBMFQcR3wmYhoxlMsWrutSqdhEIgY9PVGmp0uMjxcoly327o15n6GuO2iagmk65PMm0ajo4tE0DccBTRPtxolEgHfemWVuruKVYwqFmue0WixaPPpot5cJyWQq+P0Gn/vcw957Wmq0Fg6LRe3s2RR/8idvLvA8WX7hK5VMPvrRHopFk2SydM2CeOhQCwMDSU6cGFlW4As3f1e/UVmC9bLsX04Hd/z4KHNzFR5/fBeaJj6n9dCvSNfYjUEGJpJVI9OWgtv1LmlhQLTwAp5Kler26CZvvy2m5/b1iVbYS5cyvPHGFEePtvH5zz+yKi3CWs6T1QiqFx7vatXi4sU0waBez4yILIzfrzE3V8bv16jVHGxblHNKJZHZ0DQFXVepVm2qVRtNE4PzSiWTSMTnlbRCIWHEZtsuk5NFVFUlFNKwbRfTtNE0lUJB6EAymQq6rjI1VfQGDiqK8DxRFMXLQp05YwIux4+P4Lp4pYOG0dqN3v9KC9+hQy28887ssj9/7rnzCwzrrhX4lko3f77eqrX+Wra71r+rlQS0ly6lSaVKpFJlOjrmM0zrUYaWrrHrjwxMJKvmdl2Q15s74S5pufcQDhs0Nwe47742765y9+6YVx5oOIzeiLWcJ6vtxGjs6xe/+Av8fo3W1iB798b58Y+HvLKK64KuqzQ3+8jlal5QEgwaRKO61+I7NJQjmzVRVUilynUBsNDQNDIQkYiPWs1hZqZINmvh86kEgwaWZROPBxkdzTE7W0FVVeJxH/m8STZbwzQt8vkaQ0NZKhWLWs3B79f45V/edV1Duxu9/5UWvuV+PjCQvGabH/jADvbtS3Dq1DSzsxX6+lpv+ny9VWv91W73Zv6uVtI3FYtWXe+TXRSYwHurDH27IAMTyaq5Exbk9eJOuEta+h6eeeYkpZLpBSUNFna/uC43FA+u5TxZrVC2sa93393CgQMJr0zU1RXh3Xdn8fs1dF3FskQg4PNpWJaDz6cRj/uxbRe/X0dVVTo6QszOlqlWbcpli1yuSihkUCpZxON+hoYymKZDLldF0zTCYQ3bdkilygQCGrOzIpixbZd8vkYuV/VarkVWpoTjOJRKwkuluTlANltl9+4mWlqCiwKOjRCUL7dNIaQN88gjnezfn+Czn31oTdtcysJzZ2AgyVe/+gb3399+y22+t/p3tZK+KRo1KJXMZTto3ktl6NsFGZhI1sSdsCBvFLe7+dz1RKuhkM7x42O89VZyxZIDsOb3f6PXHBhI8cwzJ73tOY7jeZsA9PW1MjaWo1w2vUWxVrOpVEx0XUVVFdLpCqAQj4u7eb9fIxbzMzVVQlUVmpr8+Hwqc3MVJicLVKsOpZKwfk+lyliW43X5lEqiUycUMmhtDVCt2hQKNfJ5i87OsPeY2VnxmuASiRhcuSI6czo6wosCjo0QlG+2SH07deutpG/auzfO0FDWm6jc4L1Whr5dkIGJRLIO3Anmc9cTrY6O5pmeLvDAA8vfFR892sbbbyfX/P5Xek3XdXnrrRmy2SrBoOZtb3Q0Xxcx9qBpCh0dIR56qIN33pklna4QjweoVi3Uul+7ooiOnGBQZ3a2guOIQCEYNHAcl1jMd80+aZrKPfckABfLcigUarS2hpiYyOO6LnNzVSIRg0rFIpEI4DgutZpDsVhDVf2YptDW6LqC48D4eIG2tiBXrmTo6AgvCg4Wvv+F3UaNO/vHHuvGdV0URVl14LvZIvXVBkKbEbivpG9qbQ3S2hrC79eWFQu/V8rQtwsyMJGsids9K7BRrNW1smEJ/+yz5zh1at499Kmn7qWvb/VmV8txs5/R9USrg4NZensTK94VP/vsOaamSmt27VzpNaemigwO5njyyd3eXW4oJDQwJ06Mcfr0DD09UcJhg66uMJbl0tTkY+fOCDMzJX7+83Gy2SpNTX5cVwhdVRVmZ8tUKhbhsMGRI60cOJBgaEh4iLS0BNi7N8ZPfjLGlSsZIhEfigJNTX4sy/ZEtX6/SjCoUy5bFAoFTNOqdwEpdS8SDdPUME0HTRMtzNPTwhdGUUTm5tFHuxa9/+Zmpz6fSLQYG4bC7GyF8+fnePbZc97gv9UEfpstUl9NILRZgfv19E39/X3cd1/7tpy5I1mMDEwkq+ZOyApsFGtJZ7uuyze/+Q5/9mcDJJMlb/rr9753mZdemqC/v4+nnz60IfNGrvcZXe+i3tERoqcnuuzzwmGDN9+c4eDBlQOXpe9/PnAqYtsOb701w65dYn5MsWhy6tQMe/fG6OycFyq6rksqVQZcZmaKaJpCIhGgtTXAwYPNpFJl3n03zcREkY6OELt3x0ilRKeOafqZmxO6kpaWAEePttPSEiQcNjwxpOsK4WgmU/Gm9joOVKu2J5DVNKU+9E50+tRqFvm8iWGo9Vk3LsGgcGWtVqvYtlNvYVYxTZvZ2TKXLmU4elTMsGkc8299610uXpyjpSWAaboUixb33tvK4cOtvPDCCMGgmO5crdoMDpqEw8JMbffu6DWB30aK1JcLenfujPLmm9O0tARXDIQ2y25+NfqmlaZjS7YPMjCRrJrbcZbFZrGWun5jpksuV637Sohj2dTkkk5X+fa3L3L//R2bPm/kehf1F18c4fLl9LLvUZQd3BsO4hsYSHL8+AjHj48wPV2itzdGT0+UWMxPLlcjl6sRjfrZvz9BuWzR0hLwnFlzuRqFQo1q1SYY1PH7dbq7I7z9dpKLF4WwdXq6iKIoTE0VCIcNmpr8tLeH0HXR4hsORzFNB0VRmJurUCyaJBIBhoez5POmF/iEQgaZTBWfT7QXNyYVgwheIhGVQED17OODQb2+XxqlkoWqKkQivvpwPQu/X7QbV6s2IyM5YjEfzz13jsuX0/T1tfHhD/cwMDBDOl0GFFpajEUzdiIRnf/0n16jWKwt0sNMTRXp7Y3R1RVeFPith0h9LSZlb745TTisMzSUpanJt2wgtNJgQ0VRqFZtvvjFX3D33S3rkoGVOrjbHxmYSFbNdhK5bTfWUtc/cWKEdLqM368vuvgqijDwSqcrN30sb+Uzup7RmevCW2+tXB548MGORaLUhXbv09MlVBXOnZslFBILWDCoc+HCHLWaTV9fG62tQYaHc3Xfkjb++I9P8rd/O0gqVcLn0zBNm6tXC/UshY8DB8K4LuRy1XprsEkgIAb3BQI6+XyNtrYQqVSZ97+/23N5tW2X9vYgfr/KT34yht+v0tkZwedTGR8XM3EcB5qafORyJrYtyjeNWTeKAtlsDV3X6sfUh2k6hEKG5zybz9col616KUfFslxc18EwDIJBnXS6im2XeeUVl0BA59SpGWZnK7z//d2Ew9dqXkoli9HRPAcOxPD5xCXbMDSv/TUe9y/rpnuzi/PaTcqCDA1l+fjH9zM2lls2EFoucG9kqC5enMPv1zhwICEzsBJABiaSNSAt6VdmLXX9yckStZrrOYUuxOcTKf/NnjdyoxLQU0/dw5NP7ub554ep1WySyRLpdBVdV/n4x+/iV3+1l//yX96kudklmSzx0ktXmZ4u4feruK4Qgpqmg+uKxSwcFmLPK1eyGIZGLldlerrEF7/4C/7Df/ggO3dGGRrKsnNnGFVVGR+v4PdrGIZKJlMlFvMzOJihWDRJpUqASiCgUyqZVKtCDzI3V6GlJciZM0kKBZNYzEc2W+Puu1twXdFWXCjU6gP01HrAIAIRw/ChqlCruYus51VVBHnZbIVo1E8goHmma5WKXc+wCNGtKO9QF98adHdHmJ4uYdsutg1jY3lSqTJ3393M+fMpolGdgwdbrvlsLl/OEI0amKaLb0HcIkpHmlcaWvp53qwW7GZMypqafIyN5VZsQ14ucBdC3wzBoE5LS7BubiczsBIZmEjWgLSkX5m11PW7ukL4fArVqoNhaIu20zDkutmppKv5jBzH4a//+hLPPnvOW7T27Yvz05+O4PcbNDX5vFLCwq6bI0da+cu/PM9rr01hWTbBoEFnZ4hXXplg584IH/lID8eODTA+nieXq+L3i9KGbbvE437i8QAXLszR3R2p75FCPl/jF78YZ8eOCKZpc/LkFL/xG3+L36/S1hYgk6ni9wuhqeNApWKTSPjJZkUgk8/XPAt6TRP28ZqmUC7bTE0VKRZNajWblpYgrgv79on39eqrE3R0hIhGDdLpCrbtommgqiqWJdp9XZf6zxQcx8VxxF2+ZYmgIxTSKJVsUqmSZ4HfyKyYpkM06qdctr3swuRkkUpF6FUsy8F1XX784yFSqTK9vXGuXMly4EDzNYFtJlOlr6+N8+eFDb5p2hiGRiLh9wK1tbrpXi842QiTsuUC96GhDIahYpoOvb0x77EyAyuRgYlk1Tz++G7efHMay3IZGcl680R2745RKr23vQDWUtd//PHdHD8+yshIllBovpzjukKH0NkZ3rB5Ix/+8C5+67de4PnnRwmFdIJBnTfemOJHPxoiFNI5eDDB7GyFycmr9PbG6etrIxIx+PKXXyebrXLq1AyqCj6fjm07ZLM1HCfPn/3ZGT772Qdobg6QSpWoVnUiEYN4PMDUVMGb7xIICOv4eNxPsWhSKNTqpRfTEwI3NRmcP5+mudnPnj0xNE3x/EQ6OkL4fBrFokmlYqEoijcpuHEMy2UbxxFBn66L4XyFQo3du5vo62tFUfAmJ+fzwiQtHvdjWSrFojCYM00bANsW84NAZEscR7xOreYwMVFcUOIRniUNXBcyGTFtOBjUUVUoFERnkGmKx4kAyuLll6/S1RWmuTnID384WJ9e7KetTbzXvr42pqaKVCoWxaKJYWieJb6iKPT0RBfZwLuue0tasI0wKVsucJ+eLlGr2Rw4kPDs+xu81zOw73VkYCJZNYcPtxAO6zz//Ig30n18vMrFixk++tEeDh26Ng39XmK1df0jR1r55CcPcOzYAFNTRa8rp1SyaG0N8clPHrjpzokbZW4uXkzz/POjXomkUDDrDqkq5bJNLmfS1haq3xkLU7BKxeLtt5N1PYUYhgeiSyWVKlMqmUxNFfnKV97g4Yc7mZur0Noa9LJBokPFIZ2u0twcYHQ0z9iYsHS3LKHDmJsro+sqHR1hfD6daNTwSkYHDzbT1RXm8uUMc3MVgkGdnTujFIsmjiPcVkEEA6bpeBb14bBBa2uQ3btj1GoWqVSZmZkyHR3i/V2+nPEEs6bpUCqZdTO15Y/tQp0JiKBl/rN3r3mebbsEAiq1mujMsSx30fMtS5SJVBXGx/Ok01XuvjuBosDFi2nOnZvjwQc7+OVf3sWf/ulpdu9uolwWAUm1amPbDsWiRVtbaJENvOM4NDX5bloLtlzWzXVF27QQuhq8+uqEl1Vz3Ru3IS8XuO/e3UQgoHH33c3XZHDe6xnY9zoyMJGsmrNnZykWLZ58cjfDw1mvq2HPHjFj5J13ZmXqdRUoisLTTx/ivvvaefbZc7z55jSg8OST7Z7481Y6Eq6Xufn1X/8uoZDuGZBlMhU0TUXTVKpVi+HhHPl8DZ9Pw+/XGBxMUyyaxON+rl4t1IMNhWLR9DpVqlUbXVc5d26W9vYQkYjB3FzFC0zicdF1U61aOI6L64ryRK1m4ziQzYp92LkzSjgsLknNzUGuXs2Ty9V44YVRHMemUnG8/W1u9hMK6V5WRNPEPjVKN64Lc3MVolEfBw4kePfdND6fyuBgBsCbMCws7Of1JY2yTWP2zkIWBiaN7EkjGFkYpMx/FqDrGj6fSjZbvea54veKl0EplWq89dYMPp9GZ2eYtrYgly+nGR4WmbVsVpS1OjpCZDI1CgWTjo4grusu0mf81V9d4pFHOpY9P1aTiViadWuIVC9fTnvHeny8wNhYns7OCN3dYT760T1eMH09fcvCwL1hZS80OfOvL91YJTIwkayaEydGaGry0dYWoqtrcY05mSzJmvAaaPgpfOUr6++pcL3MzeRkiVBo/s++WrWpxyjYtgO4niFZLifaYQ1D433v6+Tq1QIApilmzOi6iuuKQEPXVXw+jcuX0xw92l7PBIkyVThsEIkY5PNim3v3xrAsh7GxvFcCUVXxuEZAZhgqsZif0dEcluXWRaZgWWI/33lnjn/8j/eRSATo7o4wMpLjwoU50umq5zESCunMzZV55ZUJOjrClMsWFy7MMT6eZ2wsj1OPKqpVZ1EpSFXFMEAhhL32+DrOvO7k+p+DCEKiUR/pdNX7eWObmoZ3vBs/VxSoVi3GxwuUSiY7dkRIJsuEwwYHDzaTzQqxrm3bdHeHicV8lEqWt21VVUgkfFy6lGH37nndRoPVZCIWZt0iEYOrVwu8/PIEigIdHSEOHkzU90PMGfqt37qPT3xiPwBvvz3Df/7Pr/PWW0kSCR/79zeTz1eX1bfIoaCSlZCBiWTVyK6c24+ld6/FYo1KxSIYFO2tfr9Wn4rr1EWkSl0IqmKaFnNzFR59tItgUKerK8y7787VDcMUT/CpaQqmadPTE6VSsUinRTfMhQuzOA4EAmq9FGCgaT4iER+RiMGhQy1cuDBXX+BqZLNVolEfrut6ZmaO43rdOGKSr+ZlQ15/fZK9e+P84hfjlEoWqVQJ13XRNKXuY+JDURQqFbGAtreHyOVqGIaG47i0tARxHJe5OSF0VVXqX5V6gKJ4+pKFKApeWeZ6CDGs2H4jM9LIDjT+WfMxhRfIKIqK368yN1clGvV7U52z2Rq9vTEGB7NculStd2+ptLb6mZ4ueb4n+/c388YbU97nNL8/12YiVspuPPXUPRw92saXv/w6r702iaYpdHYKT5iLFzP09sbo7Y1z9myS//1/P83oaJZSyebVV69y8WKalpYA1arDqVPT9PbGOXy4leefHyYUMhgbyy3yRjl6tE26sUoWIQMTyaqRXTm3F8t1Z+zYEeHllydQVYWurkhdnFqqd7Wo7NgRpqEfMQyNvr5W7r23lenpEocPtzI2lieZLNVLGGKhDQZ1IhEfjz7axdhYgYGBJKGQTlOTrz59V5SCuroi7NsXq3fTCFO2trYQ5XIey7LJZqtUq6LltqMjzORkEVWFRCKAzyeCm/kSksvISA7TtEmna3WtTCO94WKaNqWSRTiso2nC6Gx0NM8HPtBNJlNldraMbTsEAhqFgugMaZRlLEu0couMkLUoeGhkl5aWeVaiVLIJhYQTrNiWOGauyzKaFPGZNVxkNU1herrIvfe2kMlUGRrKMDlZJJ8XWRPLcgCFQEDjpZfG2bcvQV9fG8GgztGjbQwP566bibhR905fXxuaptLbG0NR8EpzjuNy8uQ0waBOOGxgmg6vvjrJG29MEwrpRKN+fD4dn48FWqUQExNFvvSl13jkkc5rXuszn3lwywIROWZj+yEDE8mq2ewZHJJbYzk/ive9r5NkssylS2kcxyWRCACipBOP++noCGGaImPR2xvnrrviqKrCk0/u5u/+boho1GBmBq8M0miFbWsL0dERJp2uEov52LEjSqlksnt3jN7eGC0tQZ577jxjY3na20N199Iq1arFjh3huvOr8EVpCCpLJQvDUNF1se8LS0iOI4bzNTUFyOVML/ujqoo306ZUMuvPFT+Lxfzs2hVlbKxAc3OA8fF8vQ15vuumETQ0hvRpmorjzActKwljlzKvJRH76feLNuZAQLRQr7QdxxGlqkqFuo+KzdGjbZw6NcPlyxk6OoT3SjAoOpnCYa0uXtYYHMzQ3h6iXLb4/OcfQVGU63aI3cgl+MwZMUm6qcnH7Oy8ZqhUsiiVTHw+oe+Jx0Vbd3Ozn9HR/IJ28HmvFWH1LyY2L5x9tNWeJXLMxvZEBiaSVSNrwrcXy/lRqKrKr/3aXl5/Pcj4eAFNU9mzp4lYTJRXikVrkSV6KiUWtU9/+l5CIZ0LF2bZs6eJycmiZ5SWSPip1Wymp4XT65EjrdcYhU1OFsjna+TzNebmKvh8QkPiusJozHXhgx/cgaoKvwwhoC2RSgnfDuFlYtffy/zE4Gy2iqYpnn28qqoYhgiWLEt0AgmXVA1VdXnllQlcF7LZKqbpeF1BpmnXgwkFXYdKxap7lqzcpbMQXW9kPKhnF9S6iNbFcVwqFZto1Ee1uoxoZQmiTORgmi7RqOgssm2Hhx7qZGQki66rhEJCt9MIcmZny4TDPk6dmuJ/+V8e9ATU11vsT5wQGpJksszQUMZr/9+7N04kYvDmm9M8+GAHe/fGmZy86mmGMhkRpJimXQ9gY5w5k8LvF4LpRjt4A59PZWJCaI5cV3wGxeL8vJ9IRN8yfZocs7E9kYGJZNWsxwwOyeaxkiZIVVX6+tq4774OvvSlDzEwkOQ//+fXCYUMz59maCiD47ieP42iKIyN5Xnf+7ppbQ0yMJD0JuH6fCqVis2pU1O0tYUJBLRFi8+ePU384AeD5HJCAGpZNpWKRSZTrQcRwpp9errEvn0xHn20G0URdu/f//4g+bxVNyWzvU4YRYGurrCX1QgGDa8t17YdLEuIWIXLqoNtO/XJw2K68OxsheZmP5blUiiYgO1pQjo6QpTLNul0dVHJZrlOncbPQUFRRJnGMFQiEYNstuplYJqaDPx+jVrNWbQNn0+0Ey/dviitiUXy9OkkHR0R7r03QaFQo6UlgGFouK5bd74tY5oO7e1B9u6Nr/ouf3KyyMhIjvHxvPc5Njxsdu6MeqWz9vYQvb1xBgcz+Hwa5bKFZQnRdG9vjPb2EOGwUXfaDdT9XVxvH8Tn4pDJVLEsB9N0Fs372bkzSjTqv8HebgxyzMb2RAYmkjUhB2TdPqxWE3T4cAuhkMYPfjCIZTneAnn27Cy/+qt7PH+aRqAjOora6OwMMziYrS9eQfbsiVGtWrzyyqQ3bG52tsLAQNKbl2MYGoGARrFooWkutu0SCumEwz4SCT+Dg1k6O8N0dIS5994WLl6cY3AwVw9GFFzXqfudhGhq8nnBU2NuTbFoUq06i5xYdV0ECzMzZQ4caOb8+Vlc163rUlyqVQtVFSJeUJieLqMoEAxqlMv2go6d5Y+zEKwuLvOIxVenWrVwXVFaaohsG+i6Unerne/UacQTjiOCEuG+GyCdrvDii6OUyxaaptDaGkRRxLBARVFobg5w111x9u9PrPoGQXQ3peoZowo+n0Y87icW83Hp0hwHD7bw+uuTGIZGNOrj7rubyWarJJMlTFPlgx/cwT33tKAoCr29Maamiui6EMmKIYgahqF4JbpazaKjI+S1qjfm/Vy+nN4yDyQp6N+eyMBEIrlFNkI8tx7bXK0m6MyZFGfPigF7Dc2FqioEgzpnz85y9myKvr72RYGOoih0dIQ9a/JkUlzgz58XA9lES7LC7GyF2dmyJ/a0LIdczq7bvYuul3zexHGEM6phqJ7luaIofOADO3jggU7S6QpXrmTJZMo8+mg3zc0BXn11kljMTz5fw3UdenqaGBsTuhGRLRGvEQwKM8BSyaRSMensDHsOrLYtpv+CaOu1bVH+EaZr81001xO7iizOvHma3y86h+x6tKFpQhciDNd0NE1ZJLAVQmPFE8b6fKK8FonoTE2VefHFUT7wgW5qNVHKmpgoYtsis1Mo1JicLGKadn3AXs+ibMX1zq/z5+eYm6sQiRhomkqpZJLP1wgGNVKpMrOzZVpagtg25HI1xseFb8nhw22k05VFxmjt7SH27o0xMJDioYc6CId1Ll3KkMlUeeihTgxDrbccr29W9Vb/TqSgf31Zr2uhDEwkkltgI8Rz67XN1WqCnnvuHKlUma6u8KLtuq7L1FSRZ589x1e+0n7DQCccNti1K4quCyMzy3JJpUpeJkEEJ/MOqQtbbstlk0uX0rS3h7xAwXFcCgWLz33uYY4caVt0XDRNYefOKJcuzaEoCoahUanY5PPVBV0tYvF3HLGwOo7LO++k6OyMEI36qFRsdu9uYnq66AVKc3Nmff+cRft9/c9LfG0EMLWaja6L4X6KIgzcSiXHy8iEQo1Myrwnim2LYxoK6Rw4kCAS8dUHA1r4fBqJRIADBxJcuZIlFvMzOVkgnRYOsOGwUZ8HFOK7371MqWSteI40Fo5vfvMdXnttEp9Po1q18flEBqdUMuvtzSp+v05TkzDHUxSFWMxf9y05SrFo8uKLo4vOq2jUx2/+5hGCQVGWO3q03SvzfuELP2f//sSislGt5lCr2dx11+qzPEvfy63+nUhB//qxntdCGZhIJLfARojn1mubK2mCxF01/MmfvMnkZInvfveSl14vFGqk01VvUJxhKJw8OQ1ca7xVLltcujRHOl3j/vvbSCbLtLYGvTLP8ePDVCr2Ii+Q5YSkwtNDIZ83KZdz1Go258/P4fOpixxFG+/n6NE2vvnNd0gmS/VFVakb/wWZnhaD+2IxP5WKVRevOp4brK6LLMXYWN4T0jaCFrHAC5O5hQP51oKuC02Fpmk0NQkLf9dV6gGSGBS4Y0cU27ZJpRqW/CJbEgyqdHeLDNT4eJ5UqoRpOsTjAYaGsjz6aJdXPrt0CebmyuzZE6OjI0Rvb8LrZlp6jjSCkePHh3nxxTFmZgr1UpNW3z+hxRH6HBefT/POuUjERzhseIMEVVWIlT/zmQe5//6OVWvNurrC5PM1enqaGBrKUCiYtLQE2Ls37umF1sp6/J1IQf/6sZ7XQhmYSCS3wEaI59Zzm0s1Qcvd1ZRKYjhcNlutL56qN2Bubk4MtWuUBxqBwZe//Dpvv50kHvfzyCMd9WGA0zQ1+fngB3fQ3h6iUnHqXhs32sfG+xP7V6nYDA5m+PVf389TT91zzUJ3+vQML744yvh4vp4tUSgUTGIxm9bWILWaMB6x7cZAP9cza9N1tS7QFIMFXdfF59PJ52tUqxXvOC9nrHY9dB3PnC4Y1AgEhBOuEN+6RKO+eveQzuRkkXhctC7PzpbrgxAVdF1jZqbM9HSpXuIRpndTU0Xm5sS+9fbGefTRbkxTtHf/43981zWf98JzZOHnLUYOZAgGdSYmigQCGrYtSl3VqhCkappKPl+tT4QOeNv0+TSGhjL1gYKlNWvNGpmJPXua6OiYH9jnOC7Dw7mbykysx9+JFPSvH+t53ZKBiURyC6xFPLfa+utGCvKWu6vp6WninXdSpNMVz8wMxAWlWhWPOXs2tWCGj4KmqXziE/sXXYQeeKCdF14Y9bxPMpnKqvZp3g1VBBliYejhwoW5a+YvDQwk+eM/fpOhoSzBoIbjQD5vYllVT3DpOJBOVxdZvje6dDQNpqZKRCI+mpuDJJMlAgEVVfVhWQ75fG3NQQkIIWcjmKvVHKJR4ccyNJQlHveh6yIL0dkZprk5wORkAUXxE4/7cV2FlhY/2axJMlmsb0/F7xflKUURHi7j4wWmpor09sZJp6skEst3siw8Rxqf9+7dUY4fHyWXq9Xfo0M2a9PcHKBUMuvlrirBoIHjCC1MOGx4nT+zs2UmJwsUCiaPPda9Kh3LQjYiM7FefydS0L8+rOd1SwYmEsktsFrx3FrqrxspyFvurqavr5Vz52brpmZmfU6Mi2XZhEIGhw+3LrrbWenOqKMjRFtbgB/+cLA+TVe0ht5IPNpwndU0UWqJRIxFd1mHD7d6Ad1f/uV5Ll5MEwiolEo25bKJaNUVx0Z06KiEQiJbMf/6YiH1+zWKReFGq6p4Nvy6ruC6ar3zZz6IMc0bH1NdF9tQVaF1qdVsKhUxebkxbycUUsnlqpw+PYPPJ4KOUsmkVnN46KEOOjvD/M3fXCYW82MYYuqz0J/g6U8mJvI0Nwc5ezaJz6fR3h5adn8WniPCq0Tn7NkUly9n6vuooqoKlYpJsSjKK7OzZebmhAi3tVXofBzHZmysQCpVwXGESdzYWJ5XX53k6ad/QHd3lO7u8KrEjRuRmZDC1e3Fen4eMjCRSG6BtXS+rLb+upGCvOXuajo6xCTbZHJea6DrCpFIgEOHWujpiS6621luG41sUD5fQ9dVMpmKZ062sFSznMZEiCE1qlWLQECnt1cMnwuHDSYni15AF4nojI7mqFYtymXqgtXFjq2uK8zJOjtj1GoiOGhM3xXZAgvTbOg6RBuvEJiqiEyQgmmKgKWRZbmewVpjQnAwKDqaGuZqPp/oxAmFDHw+tW6lL+b/VCoiq5LL1Th0qIW+vlYURSUc9tWzTA33WgfXdeuBiQiyGlOa9+6NeQ64y50jjz/ew8BAku9//wrT0yXm5soLdC4KoZCBbYvtlct2vU04QC5X48iRFsDl1VenyWTEnJ/GVOhCocbgYIapqQIf+IBKsVjj1KkZPvKRHo4ebecnPxldMRu43pkJKVzdXqzn5yEDE4nkFlhtinot9df1SnsvVzpyXcdbMBsoisLevTHP6bQx6r6tLUhHR5hi0WL//igDA0lOnBjh5MlJqlWbI0da64PjFGZmSly5kqVctqhWbSIRH6oqsgSW5XpZk+WyJ2LGjdC2NLYJeAZtjVLE2bMp8nnTC0Jgfr4MLAwgXMbH8+zaFWX37iaGh3Nks1VqtUb7rkKt1njvws+jWqU+9E+UTRodQze6kW+UoIJBHUURLc/NzQEeeaSLeNzPK69MEIn4GB8veM6pritExn6/Rj5vkkyW6egI09sb4/Jll2y2QqVi1b1UdGIxnXzewrYdTNOuT2qucs89Lbz7bvqac+QjH+nxdDjVqk06XcY0XRzHxjRd4nGRMQkEdHw+DcuyURSNX/mVPSQSAc6fn6VWs7Bt27P0b4iLhYeJg2FoTEwUef/7u2ludjh2bIDm5gA9PdFNs3WXwtXtxXp+HjIwkUhugdWmqNdSf11tN831PAJWKh2NjubrfhdBb84NwJ49MU6fThKL+YjHAxiGSiZT5Xvfu4ymqfT1tXLixCi7dkXo6Wni5Zev8tJLE+zbF6Ovr43BwSyW5ZDJ1Lyhb6OjeS9T4bpiyrAoEbmeKZmuq5imSyTi48iRVj74QeH6urAFORoVtulnz6YwTfuaDMbS7xuZk3S6UreFp95xIn7f0JCo828fRXGpVm1qNYfW1hD5fJVSya7/bnEw5fMpdVGrCHLEe7AxDJVHH+1ix44on//8I/zFX7wDQCYj9C5LPyO/X0NRFAYHs7S3hzFNh7m5Sl08q3sDDRcKaOdLUTWee+48v/qre2lMH26cd47j8rWvnWTPnibCYZ2LF9P1OUIamUyVYtHE79cwTYeWliC6rvL5zz/Cpz99LyD0RF/84i/QNI32dh8tLcLZ9erVfF0rI0p+xaKoc6VSZVKpEuGwvqlzcKRwdXuxnp+HDEwkkltkNSnqtdZfV9NNc7270pVKR83NQU6cGOX06aR3d1ssmkxMFIjF/ASDwuo8mSx5nSCa5nD2bIp4PICui/26995WrlxJc+7cLLGYj5GRHFevFuqi1yrxuJ9Ewl+fVSPMyizLxe/XaWsz6hN3fei6gqpCd3eUXbsiVCo2xWLFu8s6ezZFKGTwox8NcfVqHtO8vjBVZDDwBJyTk0WamnyUSvNikUbpZ2FAoyiiGygU0uu+IUF0vVov64jBdQ13WNsW3T2xmEGhYHrOq4cPt+H3azz5pAgO3nxzGstymZ4ueQMGG0P6mpuDRCLCf6RQMJmZKZFKlenoCDE3V0bTFC94K5fF7KBQSKdYFKWXcFhnZqbE2Fgen09fdA4888xJLzvX3h6ioyPExEQBv1/3Mjsgpja3tAT5wAe6F50/R460cffdLeRyVS84BDHoseHFIvZB/HxwMFs3sFswhpnNsXWXwtXtxXp9HjIwkUg2gVutv67VI+D48RGqVZvXX59cNJytvT3E0aNtRCIG3d0R765GmKNFAIW3306SzdaIxwMkEgGmpwsAiyzjG14lAwPTvP76NKWS6WkXymWTQsEkkfCxa1fE0zJomkIs5mP37ih9fe20tYUYGcnxO7/zEADPPnuON9+cAVwefLCDvr42Uqkyr746ycREAdN0Pfv2lcS0wpMDymWbkZEcmqZQKNQWlWaE0LWhp2k8Uwhjo1Ffvbwj2mV9PrXeciyEvK5LPUARfig9PVFMU7jFhsM6n/3sg7z9dpKvfe0ktZpFU5Ovns6uAW49YIugKOJ4ZrM1RkdzjIxkcV2IxfzEYn78fo2pqSK5nNDsKIowPysULMJhHb9fR9cV0ukq73tf86JzYGl27uDBBFNTRUol0R4difj42Mf2ecf/6acPXXM329UVoq0tzOzsrFeCEoJdC3C9OTlAPXOiEIlcG3RLW3fJzSADE4lkE7jV+utaNCqu63L8+AhDQ1lPfNkYztbbG+euu+IoisLjj+/29CcnT87Q0xNh9+4molEfu3c3eYvb5CSA6/lZNCzjOzpC7N/fzMmT0/zKr/Ty/e9fBoRBlyilVKlWbSzLqXtlWMzNVbyW1b6+Nv7hP+zl8OFWnnvuPFNTJQ4eTHjH5mtfO8mBA3HOnEl6c23gxrqPBg2nWdN00PV5i3nTdL05Na47P3gvHm+YsrnUamIasGjnhUjE5w3lEzNgxDC71tYg7e0hstkaZ8/O8rd/O8SFC7Ps3RsjHDZ4+eWr7NgRYWgoQ61me+UPVVXqk31LHDrUwrlzs1QqNplMhXvuaeEDH+hmZqbEt751sT68UIhPo1EfoZBOpWLR3h6hWDSvOQca2blgUGdgIMmVK1nPSbZRFrp8OU2xaHoGdkv1SK7rUCya7NkjNDo+n0Yk4mNurozrKtx/f5unBQqFxPvYuzd+zWdwo26MjRjnILn9kYGJRLIJ3Gr9dS0alTNnUkxPlwgGde85jYFpYkKsuNv/6lff8IKkWs3klVcmyGarFAo1z8sEGsPtBI0OkwaXL2eIxQwURezHyEgeRRELr2naJJNgGKL84ffrWJZDuWxx/vwckYiff/7P7+bs2dkVyk4OP/zhICMjOUxzvu6yGkfWxj4YhuqJXhsdNo3gRLQLiwxLICB8RjIZYS7WEMGWSrW6URpEIgY+n45p2uTzJoGA8BkxDI1YzAco/Lf/dg7Lcnnppat1p1mlLma1qdVsymUx00ZRxMybe+9t9hxXazUhQj1/fpaWlgAHDzbT3R32Mj+VigM41GrCpn5qqkgs5mN6ukgkMn8ONLJzluUwOJglkRBltXy+xthYAU0TAxp37Wqir0+YsD333PlFQbNwAK4ALgcPNpNMlkinq8RiAWIxP3ffnaBctjy9SmtrENt2ePXVCS9Dt3t3zJtOvRwbMc5BcmcgAxOJZJO4lfrrWjQqJ06M0Nsb48KFuUVGWMLATOXs2RS9vXHuv7/dCwSOHGnnpZfG63fXhtd54bpCqwFiIanVHJqbhSOo4wiBaXNzgJdfnsCyRKlFLKDz1GoiCLAssU2/X/z/1Klp/tf/9Wfs3du0bDYomSxx8WKacnmxdmE1iBKN61nRC78SoSVp6EvmyzJCP5HPF7Dt+U4b14V83kJRLHp6ImiaRrlsYtsuhqFQq9lks1WvG8e2HTIZMUE4GNQ9g7SF2ov5DiKRyXnnnVnCYaMeBFn1cgt873tXePXVSS/TEwppOI7oeDLNxv4Jrcvx46MA7NwZ4ZlnTvLhD/fwxBO7+MpX3mBurkIqVUZVxecQDGq0t4exbYdLl+b42tdOcvfdzV6WZ2Fg+PjjQU6fTrJ7dxN798bp7Azx+OM9gCgVnjkzy9ycGEMwO1vmhz8cIhYTFvbj41UuXszw0Y/2rDg5eCPGOUjWh63OZMnARCK5DVitRsV1Xd5+O8XMTJFy2SKZLJFIBIjFfNRqQgshpviKmTS9vTHa20O0t4fYty/BuXOp+uRd4aFhmo63sFy5kqFatTl8uIVkskQ+b7J7dxNXrmSIRAxmZ8s0zM6WZjUch3qwo6Kqat3EzeIv/uIddu1qoq0tgOO4XvsxwMBAilyudtPHrHGcbNutW8+LDiBFof5/4bPS0RGu27CLIEWUeBrGbyKjUirZ9PSEKBRMrzOoVrPrnTOi/GOaNrY9L67N501Pl9K4ljc6kSxrPktSq9mEwz7EROOGjsVlcrJYn/KseUGlKN0IQWx3dxjbdslkREbm3ntbuHQpzZtvTnvlnsYxME2HUskiGPSTTle84YCHDrXxne9c9Nx6F6JpQkPT3R3hs599yPu5OMeSWFaSHTsilEoWqVS5rkURx8U0bZqafJw8Oc13vnOJT3ziwDUL2kaMc5DcOtshkyUDE4nkNmA1GpXGBeXs2RnKZZvWVtH2m05XME0bv1/Hth00Tegk5uYqdYtz0fLb19dGPO5ndDRHMGgwPV3g4EFhsFYsCo1HLOajvT1MV1eYJ57YzV/8xTtcuZJhZqZUX+jnW3GXMyYrly10vTGLRqFatalUTGZmXFKpCdragmiaQqlk8c47KS+QuJHR2XIYhlo3THPqk2znxatiWJ8IULq6wszNleuiV1F+8fs1zzcEIJksY1k2gYDuba9h8Gaarve4RlAiHF8dL0BrfBWBjgvMe68oCpRKJn6/im0rXpZLdOUoxOMB5uYqXoakoTcRtvu1+vyfIOWyRVtbCMsSGYeOjjCW5RAOG4yN5alULKamyt7cnkbpJZOpEo36OHjw2szGcuLVpZmOV1+d8Dq6hodzBINiKrHPp5LN1vjSl15bduLxRo5ekNw82yGTJQMTiWQFtjqduZDVaFQGBpK88MIIDzzQyauvTmAYGm1tIVpbg0xMFMjlatRqtjcYrr09TCw232nT3h6mVLKIRn0cONDMPfckSCbLnDo1jesqPPRQO7/xG2Ko3okTo/zxH5/k7/9+nFLJJpks1d1SnQX7vDhz0jBGa3iAKIoo8YTDPhzHpVq1OHVqhu7ucF2IanvPFQuyc92OnKVUq9d6njQyIAv3aXg4uyizYVkuPp/r/axBuWzXu2vm359haPh8KpZle74mqupeU85ajHuN2Zyw8G8ENorncWJZDqmU8DYpFEx8Ps2z2C+VhJnbzp1Ni7Q/IyPZRdkL0WYs7O91XcGybFRVlHTCYZ3p6RKTk8sHAcuJV5dmOvJ5E59P9YZB6rrqBRxCe8OyC5q0lN+ebIdMlgxMJJJl2A7pzKXcSKPSuKC0tobo7Y3Xha4ahiGcWSsVm+7uCLGYj7GxPGNjORKJAJGIj8HBLOfPz3HmTJK2thDZbJVSyWRmpkxvb5wHH2ynUKjxb/7NcUDoTqamiuTzJtWqBYhFtLHgNizdl6Oh4xAZAZVQSIhiL11KAzA5KdqTGxfGhmGaqqrY9urTJstlWBYGJQ0qFct7bKOTp1ZzFu1/Ixiw6nKRhjmbouC5tIrTwUXXtbqb7fKvvzD7szg4aWRhRNlGtDM79SDE9TIl4liL4xEI6EQiInvW0P7k86bX4tvbG2NwMEulMq9zqdUcOjrChMNCtCyeX13R3n6peHVppiMaNZidrZDJVDEMbVFwWqs5tLQEll3QpKX89mQ7ZLJkYCKRLMON0plHj7YByrLZlMbzNzvT0rigKAqez8jQUIbp6ZJXhunpaQKEmVg6XSGZLKHrCmfO5MnnTVpbAzQ3+5mZKTExUaS9PUgyWaRQEHfj+XyNcnn+7lss1k5dj6F45mbuDdIa8wJUh/HxPKZpEwyKAKVadUinK7S2BpicLGLb1I3V1j7190bUas41g/pcF6pV57o/awQWDXEtzAccjeBp4QyfhSwMmDQNL9hZOFdI01SqVdN735rm1jMgDoah1McGuBQKZl2UbHu+ItGowdhYhVhMrRvDCSddxxGvl0gE6OgIe8GW36/R3R1leDi3qlb2pZmOvXvjTE5epVoVwZhhaPVjJvZr7974sguatJTfnmyHTJYMTCSSZbheOjMSMfjyl19H09Rrsikf+YjoWnjxxdFNz7QsvKAoipj229ER4pVXJpicLOLzqfWFT6GjI0QkYjAzU2JmpkytZtHTE6W1NYiiKFQqZQIBjUymRmtrgKGhTN3DQ+Xq1QrlsuVZ12uaiuva9Tvua11VV6IxOffq1UJ98J3YVsNptVQyl81wrDerLQ2tpHMxDM3TmIjhgPOzgRqaFlj8XPFeFa9cNT/NWGRGhI296+1bo+NJCJIbOhiVctkina6yb1/M8xXZtSvKa69NMTNTQtNUgkGNYFDHccTE4qYmH5Zl13U3Nl1dYf7RP9rLk0/uXVUr+9JMR3u7yNAlkxNUKiadnaF66cimt1eY+qVSpWXdjaWl/PZjO2SyZGAikSzD9dKZ5bLF228n+cQn9l+TTfn2ty8CLGrF3Szh2EoXlEKhRjis1wMI15utEon48PlUBgdzJBJBSiWToaEahqFRLJqe/4hwHBVphYYPiaapntdJLCZmuDRS+ZrmUK3a13SjqGpD9Dq/aFerTl3X4XjTiDVtvnNlu9DICDXEvQ2E5sX2hLPBoIqqOp6Zm6qKDIZpOlQqtpcVaWry10tZ4jiDUn/vwutEuKsqLMwSCWEu9TKPyCwlEn56eqJ0dYUply0KBZM33piqZ7GEiVy53Ji543jOsg0b/aYmH0NDWc6enWVysuQFrrBywNbIdDz//DC1mkMyWSSdrtbt7kVprqnJ7zkNu+7KC9py5cntpO16L7IdMlkyMJFIluF66cxLl+aIx/3LZlPSadEyuxXCsYUXlEhEzC65fDnDlStZolGDnTujpFJl/H4h2KzVHLLZar0LpuaJLoVxVq0uYvRRLouJtq4LIyM5VFVZMgBPLIDt7UFaW4P09DTx+utTzM1VFukWGjqMRnDSyCA0TM4aiMV+jS04G0zDlO16v28EWJqmoGnz04qrVaduMKdi29St7w0cx6W1NUgsJlqF+/ra+NGPhpicLHrb9fnEcWpkYRRFxTCEMDYY1Nm1K8r/9D8dYWwsx9RUqe6J4nDggHD3TaermKZNKBSgVLK8mT3vf38Xb701w5tvzrB3bxPT00VeeukqAHfdlaBQqK6Y5VMUhaeeuoexsTzf+c4lbFsESAcOxBkfLwAKvb1xIhGDVKq0pgVtO2q73mtsh0yWDEwkkmW4Xjozna7xyCMdyz6vVnNRlOUX1bUKx9Z659i4oBw92sZXvvI6p08nicd9vO99XZw/P0syWaKtLehlRBKJQN1OvAgozM6WUVURZEQiPtLpKrpuYRhq3W7c5a23Zup27qq3GIuOGhu/X2VsrEBXV4R4PEA6XUVRnEXliKWai0ZLbMP4rMFmlHDWE+HL4tS1PMIrRBw/UXIR712Uunw+g1LJJJutEg6LrpWdO6N13YdCIiFKZNlslVDIYG6ujK4rdYdYB10XA/0aPix/+qdv8bGP7eM3fuMejh8fxucT04kNQ9jINygWa97Mm9nZKtlslSef3I2iwCuvTNDZGQZgfDxPT0+UPXuaVszynT07y4ULc/zKr+xd9Pdx4EAzp08niUQMVFVd84K2HVpVJVs/HPGWAxNFUSLA/UAv0AmEARPIAKPAO67rXr7V15FINpPrpTPvv7+NYHDxn47ruszMlEgmi5imwyuvTHjmZY0L8lqEY8vdOV68mOb48VFiMR/d3VG6u8PXBCriq4Kqql6pqeF8euVKmqtXCzz2WDehkEE+L9xKOztDnD8vskBC2yAM0AxDIZercd997fXOjRqRiEGpJO7WC4UaoFCpWJ4ItqUlwJtvTlOpWBiGQrWqeuLYlXQnS4OV1Wo+tgvCvn4+AJudrXgaEzFV2V70ntLpqvd9pVIhmxVTnF9+eYK5uYp3vCzL8WbbLMwu6bpSN8lTKZVqzM6W+fM/f4fnnjtHIGAQj/soFCxPhNrA5xPZsI99bB8AwaBoJ3/11Yl6G7LiPa4xD2mlLF9Dg6UoMD1dZHAwS7EoJhHH4366usL89m8/vOZjuR1aVSVbz00FJoqi3A38BvCPEEHJdUNhRVHmgBeB7wDfc123ejOvK5FsFtdLZzqOy9e+dpLWVtfrjmgMS3Mc6nek5UXmZQ0n0Cee2L2qTMjSO0fXhUuXMoyMZKlWbR57TKVYrC2b4l56cV/YpXPmzAyjowU+9rFennhiN88+ew7btkmlypw/P4ttu5imiaqqhEIGBw5E+R//x7uZni4Rifj40Id28POfj+M4QqtSrVrYtkMkIkoThYJJsWgRjRrYtkN7e4jp6TLlsrnscW5Yx9/OLM3uNAKtRoJgYVCyXHBmWXDu3BzJZAlFcSmXLU/TIlqxFz+/ETy2tBikUhXCYZFBqVZtzp1LsWNHGFC9luEG1aqNpin1z/28p6Fq+JA0WOiJsjDLt/C8/cu/fJdQSLj4JpPz5cG5uQrj43mKRYvPfvahNaX9G39Hw8NZTp9OLpqI3ZjFtJ6tqlLLsn1ZU2CiKMo/BT4DPNb40Sqf2gJ8qv4vpyjKN4Cvu647upbXl0g2k5XSma7rLsqmFIsm587N4vdrPPBAOwCDg1kMQ+XixXT9oq3z5JO7OXSoZVU19KXBxcxMicHBDImE0ApMTBR5//u7l01xLyfcbXTpRKM7UFXVsxjv7Azxgx8Metb1pZJJuWyjqtDTE+VTn7qb3/7th7x9DocNolE/Q0PZeieJyIhkMjViMR/lsuWJHRuL3QMPtPHOO3PMzVVWedy3Z9akoZFZrQPtWt9DKlWpW/Y3LPyv3YBhNAS4bj1rIozSDENMPO7oCDM+XuTo0Xbm5sr4fCJgqFZt5uaq/OZvHuHQoRYcx+GVV67iuqKEp2kqra0BikWLmZkimqbWHV0DPPpo1zIZPI3R0TyzsxXa24MkEv66bb6YrzQ9XeDs2dSqsxuN7Z85k6JctojFfIsmYvf1ta1rq6rUsmxvVhWYKIryT4D/CNzLfDBSA04DrwFvAjPAXP1fEGgGEsDdwPuAR4BuIAb8NvA/K4ryX4F/77pucn3ejkSy8SzNpnz/+4O0tQU5cqTda9ns7BSj7hvGZr//++/n8OHWVdfQlwYXQ0MZL92+8I52uRT3an0IXFeUCN56K0kopOH367S0BAmHRfZjaCjHmTMp/sW/+DvOnp3hgQc6vWzA/v0JMpkKV68WURRoafEvKDcInUqt5lAoCJO2fH4+SXoje/ntGJSACAoiER+lknkDZ9ebQ7RZOyQSAdLpipeJWRioCQO2RleOXXdvNeq/czEM0Xl17lyK7u4IIFqvdV3lN3/zCJ///MM899x5LlyYI5ks09ISQNMUJibyzM2V61b6Lrt2CaH0u++m6egI8bu/+zN+9KMhursjhEIxWlqCnDw5jW27TEwU0HWV1tYgIMpaBw+2rKns0vi7eOCB9vrwQnXRROz29hDlsrVurapSy7K9uWFgoijKC8DjiIDEBH4EPAd833Xd1d0CzW/rIKIE9BsITcq/Bn5DUZRPu677wzXuu0SyZSzMpkxOlnBdZ1Eg0PAQKZVEWaRxkTtxYoRIxCCZLDM0lPFGxO/dGycS0fmLv3iHHTsivPHGFLWa6QU7CzMQCyf8wrUp7hv5EDz+eA9vvz3DV77yOsePj2JZNoWCQ7lsk8sJHUm1alGpOPz0p2OeE2o2O4rfr+H3a4hhfYoX3IjOHGGnXq1aXomrON9gAsybjq3EzczE2QxEtkShWDSvMV9bT3Rdw3FcfD4xmVjY3gvNj+sKfYl4fYVw2LfIKG16ukQyWSIY1AmHDTKZKvm8SU9PlM9//hE+8Yn9nD07W19429B1hcHBLMGgTiTiY2qqSChk0N4eRNdVajWbRCLA3/7tFWIxH4YhSjXnz896ZSrHcXAchZGRHOWyRSTiY9++OD090TWVXeZdi4OeW20j22PbDr/4xTj339/ON7/5zoIymbKszmotrye1LNuT1WRMnkAIWf8EUX5J3+yLua57Afi3wL9VFOXDwBeBXwYeAmRgIrktWYtT4uRkkZGRHOPjee/COztbYWLiqjfV95d/eRc9PRFeeWWCl14aZ9++BJGIwdxcBV0XaflYzMcrr0x4mZPHHuv2PEquJ9z9yEd6OH16hm9/+yIXL87Vp9camKZdt0IXgteGB0elYnsaF2FTb5FI+Ot6BbWeGbHJZkVGxLLc+twXt/69hWnaizxJrheYbLegZN46Xyxa1aq9oaWmWs3GtoWZmqqK1wwEdGxbZEMiEYNyWZTb/H7VCxCKRZN0ulJ331UoFi127IjQ1RVidrbCn//5O5TLYgpwY0Hu6AjV5+QUKRZrhMOG5747O1shFvNRKNRobg4wO1uhuztMtepQrVooikIsJuYZianKLpWKxUMPdXLPPS3LGqpdj3nXYsXTQw0OZikUTG+SczTq4513Zrl8WSxBN2prXs3rLYccILj1rCYw+beIgCS3ni/suu5PgJ8oivJBRMlHIrktWYtTouO4XL6crmdTLKanS9Rqdr0NuUpfX2t98J5LNlvlypUs586lOHiwhVyuSrUqui0uXJjD79cxDIXZ2Qrnz8/x7LPnvIvzjYS71apFU5O/bqBmEYkYVCoOnZ1Rxsay5HKi68M0HXRdLIDBoE4uV2NmpuTdqTfu5G3b9czSFq4NritaqMU0W3fbBR7L0XgPwqtFtDKrqpjq6/dr9W4be0Namh0HDEN8bZixCTt5l2BQTDw2DI3W1hCFgkmhUMPv1+pjBxyiUT+ZTIXe3lg9i1Ihk6kwMzPOyy9P4PMp7NwZJRr1e5423d1hz7umUDDZsSOC3y80JKWS6QVhtZpDJlNB1zWEYNmtB7Y6oZBBc3OAXK56XUO1lVjsWqzQ0RGmoyPM9HSREydG63b7CuPj+TW1Na/m9ZYiBwhuPTcMTFzX/Y8buQOu6/5iI7cvkWw0a3FKbKTdZ2ZKpNM1NE1oBnK5GqZpk0yWvczHfCdNikKhxkMPdTI+XmByMk9raxDTdCkWLe69t5XDh1sXXZxXEu4+88xJolGDK1csfD6VeDxAPp/DMPS6riRDJlP1htklEiJ4yeVqFIsmpml7d7DC4l7xTMRABCKGITIsYtDc/OA6uHV/kuWyFetd/lnYvjw/y8bBMFSamgwsy2VubmOMVhY65AaDKpWKcNoNhUSXU7ksbN5/+7cfYHy8wHe/e3lRF8/4eA7HgbNnU/j9GpomsieO46LrQlibzwszvZ07IyQSAa9cJD5THVDQddXLhIyN5WhpCZDNVqlULDRNtKQrCiQSBtPTZZqbxXZmZkoMD+fW7BC6UnB/5UoWgH37Eot0VrC6tua1vp4cILg9kAZrEsktsjanRIWOjghnz6YIBLS6kFF4VYRCSzMSSv2i60NVVf7oj36Jz3/+Z7z8sg0otLTMt1OCaAf94hd/wd13tyw7VPD48RH+r//rDJWKVR/uBh0dYRKJABMTBWo1u64bmV+YTbMRcAidSeMuvtFh05i/szBYaGxD06DRCmyabr3M4N50GaSxaIO7KMBpaA7Wu7wihgyK4Er4tljMzFQ2VJwrPFHsercT9VlEjffs8sEP7uSP/uiD9PWJ7q+PfWwff/7nZ/nTP3170dwd03QxTcvL/BiGWs+AuZ7IVpR1/EQiBuJzUggENNLpMoWCVg9oxOdv23iW9435SGK+kcb997dhGBrJZImeniY+97mH1+wQulJwPzFR4K67ErS3hxgYSK6qrflWXk8OENweyMBEIlkHVuuU2N0dxnEcurvDXn0+GNTrA92EZXjjLrBBI7UsshMqjz22Y1EK2nVhYCDJxYtz+P0aBw4klhkqOMLVq0WSybI3Edc0xQyVcFgIG0H8TFjOi0WqVBI+GsGgcBoVDq8a0aiPTEY4k1qWW5/FIoIRMV1YlHcWu7ne2ooeDuuoquItRgvfP2xMcKIoEI36qNXmM0IbiThuDVGpmI3j9xvs3RvDNEWp7MSJMRRF5fDhFt5+O8nPfjZeD2auHaDY+F58vgo+n4JlOfU5RTazsyUUJUSl4hCP++sDBGtomnCsLRSEqFu4/8YYGclRLJo4jujcOXq0rT4PB4aHc3zucw/flGh0peA+HDYoFMz652AwO1vxjOMWisDXWn7ZDrbrkpXZ0MBEUZQPAcPX8ytRFGUXsNd13Z9v5L7cKv39/Q8CvwJ8EDgEtAMWMIVomf6LY8eO/Xjr9lByO/D447v5r//1DLGYj7a2+T+/fL7G4GCW5ubAooV3aWp5udp4w+MkGBTtvqGQcc1Qwe7uCKlUiR07IoyPF/D7FTIZ0Ro6PV0kGNS87o+2NtHRIZxHnfp+qIBLIKCzY0cE2xaLWzhsUCpZnoZEVdW6C6zLel/bTdMhGNSXLQctDUo07dbLRpomOjSq1c3zx1/cFizeQ6ViUypZNDX5GB7O8uqrk7z11gwHDiR47bVJzp+fBRqZqmsjJyFkVusaFB+VioWuK5imGDbY0hLwvE7KZas+lVghGvUTjfrJ5aqIGUdOPXun0N0d4ejRViIRH6lUeV0yDcsF9wMDSb761TdobXXZuzfO5ORVQiHxdyOmF8duuvyy1bbrkpXZ6IzJT4B/D/yH6zzm/1n/vXadx2wp/f39PwM+tMyvfIi2517gn/f39/8AeOrYsWPZzdw/ye3DkSOtHD3axsmTUzQ1+b1heqbpsG9fjFyuRjQqvDKWSy0vVxsfGsp4QlUhEhSIoYIVwMU0bXw+4QuRSPhJp6v4/cKUq1KxqVTEc0V2wK7P0ClhmvMdOpomRJd+v8qVKwXPpdRxnAULqePdsa93dsGyHHK55R1kFwYmN2pJXun5YrGef2KjTdqyhAB4s2kEVpblUCzWaGrykc/XeP11Mdfmr//6EqZpU6s59ZLNymWySsWqBxV451dTk49Dh1p49NFupqdLvPTSOJqmsnNnlFBIzNpxHJepKZW2tiCKIgY17tnTyWc/+yAnTowyNVXirrvi9PQ0MTqa5wtf+Pt1dVBdPJhSDKJc2JWjKNyUpkWyvdnowGQ1Z6UooG5vdtS/TgP/Hfg5MILY74eAzwL7gV8F/qa/v//Dx44duw36DyRr5VZtrBVF4Xd/9xH+4A9+QbUqtB4tLQH27o3T2hrg9Okk99zTvOIAtOVq443OngMHEp7epIFpCj1KwwdF6FZCRCI+ZmdLmKYoK+m6mK0zM1Pi5ZcnaG8PEokYXL6c8e6Us1kxlyUcNohEdLLZGrYt7OcDAd3rxhDOpPPoekNAunYWHtJGaWM5oavrgt8vgjPDWHsHkKYp12hVXFeYk4l92Lo/Z8cR7a2Fgonfr2GaDtPTJcple5Ht/fUyRI7jUqmYlMsK8bgPXdeo1Rymp4v88IdDGIYYzNjIRpRKJrouSnc7d0YxDJVCwaRWs2lpCXLkSBt9fe04jsMf/dFrfOlLr2NZYspwW1uIN9+c5qMf3XPLDqpLSy7RqAimGr/r6grL8ssdyHbQmOwG8lu9EzfgAvAHwH8/duzY0kvs6/39/X8OPI+w6v8QwkDu2c3dRclGs1421n19bXzqU3dfI7wbGcnzqU/dfd3tLFcb3727iUBA4+67m695nqjHu0Qi8/V5RVGIRAwUJURzc4B4PMDgYKauDRGtqRcupPH7G74ZVt3bxCUU8tWn3IrHJhI+FEWYfZVKFoZhY9vzC2YwqKPrCtns8pmO67Gw9bghel3Oph0aC7ODYYj25JUyB8vpMIBFPivLbXul190sXFfoKGo1EWjatrMmTY1ludi2TSikUyiY2LZLIhEgGDTq21LZsSPC+9/fTSZT4Uc/Gqa1VWhOUilhbW8YCqlUhQsXRGv6U0/dwx/90et84xtnaW72Ewrp5PMms7Nz7N0b4/nnh9fFQXW7lVzkjJ2NZ90DE0VR/u2SH/3yCh+WBvQA/wzY1i3Dx44d+7Ub/L7Y39//r4CB+o8+iQxM7jjWy8Z6LcK7610EG6/VqMMvHBwHjXZfIQ7s7o4wOVn0BruJLhubPXtilEomH//4Xfzwh0OkUsI5tLnZz+xs1euEMQwFVdXqrawiw6CqCpbVsEF3aG0NUC6bhEKq180iAqCbC0wWdgctzcLAvPHZfMnjxpmZha3Ltxu23Qi+Vr/4NbqmhBeKEJD6/TrJZInZ2TK6rvDww10cPNgMwLvvpvmd33mIvr52vvWtd7l4cY6WlsCC1vQWrzU9FDL47ncv0tzsJxLxAXg28kNDWe65p/mOc1CVM3Y2h43ImPy7Bf93Ec6uv3ydx18FvrAB+7GpHDt27Ex/f/8sYmDhXVu9P5L1Zz1trFdzF+i6Lt/85jt8+9sXSacrmKaNYWgcPz7KJz95gKefPnRDp9dPfvIAAM8/P0IgoPPuu2lPM7F7d5RiscY/+Ad7OXKklVdfnSQc1imVLDo7w3z4w3EuX04zMpJDVUXXUKOTyDC0eruzQiikY1miS+PQoVYuXJgjl6uSzVaZnd249trNMGvTdaXewrt9qs2i9frG2ZJgUKOrK0y5PN9GDAozMyVPNzQzU+aFF0a4ciXDr/5qL9GowYkTo3zmMw9y5kyyrlGC5maD3t5YfdKvON+/+c13sCzXK/8sxLIc3nxzmuFhkQy/UzIKcsbO5rARgcmH618V4ATw/wX+fJnH2cAs8K7runeKHqPRKrF5Mn7JprHZNtYDA0mOHRsgn695Y+WrVZuRkRzHjg1w333t9PW13zAD47ou4+MF3n13lng8gG3b6LqYQXLvva089dQ9fP3rp9i1K4rr4s3wGRrKkEyWCYV0yuX5+Td+vxBFVqs2+bzp2dTncrW6EVuNVKqMYaj1ss+6HpZbZi0BTcMHRNdFxmIrBwwuLN3c6JhqmmgHHhsroKrg9+vE436mp0sYhuIFCI0gY3Awy4ULc+zZ08TUVGlBa3r3su6ojfM9kfCTz5teC29jZs/sbBnXhV27ondURkHO2Nkc1j0wcV33Z43/K4ry74GfbPdW4PWgv7//fqCp/u35rdwXycaw2TbWzz13jlSqRGdn2LuYN1Llk5NF/st/Ocl997UvKvF85jMPXnPhP3MmxYULc/zKr/Re43J54cIc77wzu+IMn5mZIoahEgwapNNVolHRHlwqmViWWOHLZZtqtUQyWfaG/GmaUncYFVmH62k4tjONTIkw9nJuWsR7KxiGUg+mVh/kNR6nqi61motl1SiVzHr2p2GAp6DrQhhdrVr89KdjfPjDPTz6aBdw4/O9szOE368zOzvrlQgbM3savjh3391CW1vojskoyBk7m8OGil9d1/33G7n9bcYfLPj//2/L9kKyIbiuy86dUZ577hyGodHU5PNcV9cyG2QtwrmTJ2e8C/5ixDC5H/94mHD4xnXu1dzlNWb4LA2CuroiXLqURlEU4nHhaSFM1xYv0o2F07Icb77Owvd8u9MIwlZrf7+eZm+WJVxz1/q6jVNAjD1QqFZddF3YzVcqFqYpgkafj7pxXY033pji6NE2XNe9oW37008f4rvfvcSePTGGh8U04NnZstdmff/97V6X2EZmFDZTjCpn7GwOm9aVoyiKBrQC/uV+fz0Ttu1Of3//PwM+Uf/2JPCdLdwdyTrTELw9//wwsZifyckiuVyNsbE8nZ0RurvDfPSje27oo7BW4ZyiCJvwpRSLJrlclVgsQFubuPBfr869mru8la7fjcAnnzfZtStCtWrXtSZi9WtoHRrCW9N0sG3xtVH62W6lnJth7a3HN2+9v5TG3KHVrLELX1PXVfx+lVLJWjDt2cV1Le8za7wvy3Lx+VSOHGnh5Zev8vnP/wxQcByHt96aYdeuKJHIYtv2X//1uyiVTJ5/fpiDB1tIJouMjgo7/SNHmtm/P86rr4oJ2OGwQVdXmMnJ4vocFO/9bq4YVc7Y2Rw2PDBRFOVB4D8h2mh9KzzM3Yx92Qj6+/v7gP9a/7YEPH3s2LHb/xZR4tEQvO3dG6O3N1Z3WhUj2XO5Kr/1W/fxiU/sv+EFcK3CuQce6OBv/uYyTU2+RdtOp8Wgv+7u8KLtr3RXupq7vImJInfdlVhUyqnVHGo1m/vv7yCVKuLz6aRSZU+roOtiAGFjns7CAERMx31v/BkszY50dIRIpUrUauv7/lcKdJZmcfx+tR4UCp3J0qBq8Zwh15vN8773dZLP1zhzJsXp08JZtrU1iOuKSdfRqO+a7rGl2qamJj9+vzh3Xn110juX5uYqDA9nefjhTm9I5Xqw2WJUOWNnc9hoS/r7gL9HWLc/D3wMeBth4/4A0Ab8FGFWdtvR39+/B/ghEEY4MP2/jh07dmFLd0qy7iwthTRGsgMkkyXGxnKrutCuVTj36U/fy8svT5BOV/D7dS9YyGZrRCJ++vquveAuV+deepfnusLG/sqVNOPjhbqHSo50urIgs6LQ3BygtzeGosChQy1MT5cIhQzOn5/1rOcbbajvZRYGDMJ11tlUkWzj+BuGgmFo3sLv8ynk89dPVzmOCFQOHWrGslxOn06iqpDL1Th3bg5VFTqReNzPpz997zWL/NLusoGBJL//+3/PyEiORMLv/V3oukq1apHN1jh7NrVuwcJmi1HljJ3NYaOzFF+sf32f67rnFUVxgO+4rvsfFEUJAl8D/inwLzZ4P9ad/v7+LuAF5l1h+48dO/bfV/G8fwn8y4U/8/l8/OEf/uGix/3SL/0SH/rQci74ks1mvQRva91OX18b/f199XbhMqbp4verdHSE6OwM09ERumY7y9W5l9p6j4zkuHw5jeu6BIMGr7wyUW8DdujqEjNVentj9PW1ecPZ/tW/uo+3307yrW+9S7EoJsyWy9aWdqlsR4RLa3lLXtuyXDo6AkxPl4hGNbLZ2ip1KUIbdPbsLI7jUKk4+HxierBlOZw+PUOplODFF2+8yB850kpTk6/uarw487ZvX4KurvCywYLjOPz1X1/i2WfPeTqRT3/6Xj7xif2o1zGe2Qox6nYzfLsT2ejA5IPA37iuu7BLRQFwXbesKMr/jHBL/U8It9Tbgv7+/lZEUNLwK/ntY8eO/dfrPMXj2LFjfwb82cKf/eEf/qH7+7//++u7k5J1Y70Ebyttx3VdRkZyVKs2n//8zxeJ955++hD339/h3Z11dobYtauJ73730iJDNdd1mZoqcurUDOWyxTPPnFwkAGzc5f3FX7zD8HCWlpYgxaLJ5GSRSMSgtTXAzEyJVKqM4ziMj+c5f34WTVPZsyfG8eOjPP54D319rfyLf/FjLlyYA+bn0txMgNJYb1abcdmI6cGbyUbuf8Ml9+DBZpqafIyO5lal7dE00HWN0dE8pZJwhFUUBcNQsW0Xn09DUWBkJMeZM6lFz11JdLpjRwSfbweTkwUKBdMbudDeHqJcNq8JFhzH4bd+6wWef36UUEgnFNK5fDnD7/7uz/i//+8LPPZYF9PTlWVFrVKMemey0YFJDBhc8L0JRBrfuK7rKIryU+Cfb/B+rBv9/f0xRFnqUP1HXzx27NgzW7dHko1mvQRvy23HdV3efjvJwECqXn93rhHvLb07c12XUsn06tyhkM5bb80wOJhj794YLS3BZQWAR4600d0dpqsrTDJZZm6uUjdOsxkfL9QXJZtAQMc0HUZH8/j9GpWKRSZT5cQJYez2jW/8Q37jN/6Wycki5bKNZc3rGNa68Das5lcTnGxkULJw5sxGsV7bbgR0jYBQVSEU0mlrC9LVFWH//jg///lV3nlndlX7pGnUu3QardGKpytpjBTI503+/u/HvYD38OEWnnvu/LKiU9t2iMX8PPpo9zWvt1yw8Nd/fYnnnx9l586wlx0JBg1qNZu/+7thCoUafX1ty57TUox6Z7LRgckMsPAsnEIMu1tIALg2J70N6e/vjwA/Bu6v/+jLx44d+49buEuSTWC9BG/LbWdkJMfAQIojR1o5eLAZRbmxeG9pnXtgIEU2W+XJJ3fX231FGnu5bQwMpBgaylGtWiSTZcCt61c08vkaiUSAjo4QmUzFm4kzOVmkWLRIJn0cO/Y2f/qnH2Hv3oS3L5lMlenpIooiWlFNUwQqN1qIGy3GW6VRWZrBEC25218zI4YZKqiqKN80xK89PU38k39yF6OjOU6dShKL+eozjlbelpiqrFKr2d6AREURomZVhXy+BrhomkogoHrBwd13N3Phwix798auEZ2+9dYMuVyN1tbgqoKFZ589RyikLyrZFIsm2WyVQEBjaCjLo492L/t3IcWodyYbHZicA+5e8P1LwMcVRXnUdd1XFUW5B/gUYkjetqa/vz8IfB94tP6j/+3YsWO3vZW+5Masl+BNURSeeuoeQiFh5z01VaJQqHHgQJz77mtb1A56I/Hewjr3M8+cJBjUvNbhlbYhsjMzTE4W6nfbYhBfsWhSKIhZNrlclbExl3JZmJSIllKXTKZCrSaG+T377Hk+8pFdvPaaTjpdJR4PEAoZTE8X8flUslmnLsCcbyNeuDgu/H6rgwDDUDBNtz4EcP1afDca255vw/b7dZqafLS3h/ne9y7z5JO7+dSnDvDVr75BLle74bZM02XXrggzMyWqVbu+bac+vNBB0xTa2gJ0dUU8s7Tvfvci+/bFlxWd7toVIZerMTycW1WwMDlZusbWPpOpoGkqmiaClIXbX3hOSzHqnclGByY/AP5YUZQu13UngS8Dvw68pCjKHCKboiI0JtuW/v5+H/BXzM/8+QbwmS3bIcmm0wgEDh9u9erqzz57Dsdp+EsodHeHr2vs5Lqul/7esSPCgQMJXnhhhLGxPMGgQV/f4uBkteK9hgDQdV2vlXk574gzZ1Ikk2Vs2yUQ0LBthXLZWhQc2LZb15mIFL/IIIh/hYKJqlr86EeD/Mqv9HL27Czd3RH6+lppbQ3wd383woULc55nRmORX2j4pevKlgYASw3IhG2+fY3d/HbVszSyI40STDTqo7U1yKFDrdxzTwuu6/L888MkEgHy+RsPTnQcqNUswKW1NcjERAHD0NA0lVLJ9GYhJRJ+9u2L47ouyWSJiYkiMzMl0unqohk6AJGIj2hUdPGsJljo6gpx9myKubkK1aqN369RKAiBdaViEw4bvPrqBPm8STRq0NUVWeSHIsWodx4bHZj8H8C3gDSA67pvK4ryBPD7QC/wJvC/ua77gw3ej1vlvwG/Uv//K8DXgUP9/f0rPuHYsWNnN2G/JJvIQjOnhd0tAHfdlaBQqF7X2Gk5z4X29hCzs2UGBzPXdNoUiyZ33ZVgYCB5XVfLrq4QFy/OcelSmsHB7IreESdOjKDrCn6/hmWJ6cJLMxam6SzJZohvGiLXatXh0qUMH/pQjd27m7h8Oc3wcJZgUKdUqpFI+LFtF9N0PH8TmA8CGuWCxnY3m8bsm0bJolq1l83qLHVP3Q4zcnQdVFWta4HEkL6DBxPs2hUjm61y/PiI16Fy7lyK3bujXLqUoVZbOS2laULjMzlZIh73oeti5o2uKwQCOrbtUK3aDA7mGB09j8+nEQzqOI5NpaIwN1dhaqrodXE1bOn370+sKlhwXTH88fjxUSIRHV3XKJUsikXTK1WFQjqzsxVvTMLQUJaHHlpfPxTJ9mKjLelNYHrJz14GfnUjX3cD+H8s+P/7EV4sN0L+xdxhLAwsksky4+N5OjuFn8n4eJ6enih79jStqA1ZznOhtzfG1JSYRzM0lPECE8dxyeVqlMsWX/3qG9d1tXz88d0cPz56Q++IiYliPWPhYFnusvNrdF3xRJANYaX4qtIIJhzHwXUVHnqok56eJk6fnub8+TnuuaeFXbsi/OIXV3FdqFSES6ymKfUgyAU0DMPBtjd/pVcUMfPGccQMmcaU3obdu+PMZ3IaAdRWl5pgPiiyLFAUZ1EgNTNTYna2SqUi3F11XaFYtIhEDP7hP9xDLmcyM1OiVrOvCbaElkRB00Qw4Dg1du4M4/NppNMVymUxTVpRlPrMHYd8XmRRwmGDQEDzumgGB7N0doZpawutSXR65kyKdLrC/v0Jhoay6HojgBWC21BIp6cngqY1AiYxyDKXE+f0wgzmRtvRSzaPjTZYewJ4Cvj9eiln6e+7gf8I/IXruj/dyH2RSG6VhYHF0FCm3kopLn4+n8bgYJaOjvCK2pDlPBfa20P09sa4eDHNzEyJYtFkdDTP4GCaQMDg/PkUDz7YRWtraEVh7Gq8I158cZiJiTyZTA3bdpe9i1ZVobmw7XkBqAhKxF11wxbdMHQGBzOAmEQ8NVUiGNRRFMhkRCfH9LRwp7UsB1XV0HXhRmqaDs4WrfYNHYmiiK/BoChZlMuWl0lp6DYsa777ZTuxsDxWLFoMDeUJhzUCAQPTdLAsh0rFolazUBSFBx5o5803p8lkKhQK87ohw1Dw+TQMQwSvPp8Qu8bjAfx+DVCYna0CEAppqKooq+i6EAgXi2JEQSZTxefTUFURZPT2xtckOj1xYoRYzM+v/dpeLlyY4+23kxSLJu3tIQKBKqbZ8FVhwTkd887p06dnNs2OXrJ5bHQp538BDiwXlAC4rjuhKMqjQBzhALstOXbsmDy7JXWRnsH0dIl3301Tq9kEgzrxeACfT/VEeitpQ5bzXFAUhb6+tnpbrs3583NMTxfZty/O9HSRSsXh1Vcn6O2NexqU5QSAO3dG8fm0Fb0jzpyZJZutEQzq5PM1dJ1lJ+UmEgGy2RqFguVpQhr7KbInLori8vOfj/PSS1eJRHz1TIjLpUtpVFUsXLqu1IMftz70b3tgmi6GoaAoilemaMRJS9ewzSjfNAKiRuvvWl6z8dhKxaFWq9V/JoLKYtHiv//3ixw4EKe52Y9tO/WsiHjNQEBn585Ife6RgeOI7h6/X2NiokguV0V04yh1/Y27YMSAgqqKz/fxx3cxNJQhna7i92t87nMPc+hQy6qzGI1gXVVV7r23lXvvnQ9ofvCDQSoVk+bmAMWi6TkRi3Pa4syZWSwruWl29JLNY6MDkwcQnh/X4yXgH2zwfkgkt0xnZ4i//dtBUqkStu14F/t8PkcoZHDggGihXcnYaSXPBdcFn0/nk588yPe+d5kHHmhHVRXGxwvEYj50Xb1Gg7I0+OnqClMo1Fb0jpibK7NrV5Th4Syzs5VF+o+F+5FMVmhu9lGpWPj9+qL9LJdNbBsv26JpGtlsFdcVRlxNTT7m5ioEg0KHUK3aywY/W02jVLXUgGxpUNAQmG7kEEKRxbn252sR34pMllvXi8xnfmZnK5w7N0elYuM4jmfCJjIcCqlUua7hEF41iUSAZLKMZTlUq46XIQuF1PqARnBdhUBApJKKRZOOjhAdHSGSSSFwPXy4dU1D9a5nkOb3q/j9Ad7//pXP6R07IptmRy/ZPDY6MGkDls2WLGCm/jiJZFuza1eUoaEsO3eGMQyVsbG8Vw/PZKrEYv7rGjvdyHNhdDS7SIMSDhvMzVUwDA2fT1ukQVka/Dz++G7efHMay3IZGcl6HQy7d8colcTdZiRi0NTkZ+fOCMPDuWXLOabpkEpV6eoKk07XUFURNCmKUg9mHC8Q0XXRzlmtmpTLVr3DxSWTqaEopqcjWSg43Y6dLtdjKyYjLx3Kt1o0TWQyqtX5nS4Uavh8qtcSraoNvYjofqlWTRxHjDoQpR4R4DQyZY4D5bJFc3MAXVfJZqve9otFYfIXDuv4/Tr9/UfXPFTvegZpiUTQ+/9yfijNzYFNt6OXbA4bHZikgb03eMweIL/B+yGR3DJjYzl6e4Xw1TBUL0MA0NRkcPVqjkBAX7HGfiPPhS984e8XXWgbwthQSAzwa3iNLBf8HD7cQjis8/zzI54gcXy8ysWLGT760R4OHGjmypUM0ajB2JhV1xEI3chS11aRwdE4erSNd9+dw7IcDEOtZ0Gs+p204olFGwP9MplqvcUYlnbdbMUCf7tyM0GJqoouGiFWnf+58KGhHkSK8pVh6J4WJRgU2pRo1Ec6XSUSMVAUpa5hEUGLZTlkMlVUVfECVEWBWs1mfFy4A7e1hTh9eobZ2fKahupdL1j/5CcPAPDii6PLBvKpVJnLl6Ud/Z3IRgcmLwP/RFGUva7rDi39paIovcDHEXNnJJJtzdRUmfvvb6dQMBkczOL3656pmWXZBAI+Pve5h69r7HQ9z4Wlae2GMHZwMIttu7S3B0km///s/XmcHed534l+31pOnbXPOb03djTABcRCUgJFarEUkZRkx7G12HIWirm2kwki+05M51py5sq+TjzxRJLlEe2ZcQLHTsYS5Ywta7NjWRFBypK4ijs2EgTQ3VgavffZt9re+8dbVd0NNIBudANogOf3+eDTjT7nVNXZ6n3qeX5LfVGjqsOHZ6jVXD7wgc2MjCgfk3w+zpYtWWo1h82bO3j11Sk2b87y8suTgfxUx/O888YAAtMUlMsODz7Yza5d3bz22iQHD84AkmxWyYHrdSd4PgACx1HEyMWUPucTS5eDteonshYwnyckROg340c8kpAjZFl69BpqmqCjI4auKw8bRZr2mJhoREVlOKbs6lJ8o1bLo9n0SCZVcnFogZ/NxnAcj3vvHeC22/IcOHAaw9Do7o4veryLdTEuV6wDC7Ki5t926NA0r7zStqO/GXG1C5P/HfgwylDt/4fim4yiEnk/BPw2YKFShttoY00jLBz6+lL09aUW3BbO2Fcy0z6/rR0SY3t7U7z00jgdHXFGR6t0dsaZnm5w6NB0RCh88slTdHTE6OlJ0t+fjIzWjhxRwWvPPXeOBx/cxOOPnyKbjVGptIIiQhKLaUFhIoKFR4uswJNJg3rdpaNDXa06jh+pI0L1TrjozTdOO38ccSXFhVIAqe1cK67KjVIIqaJDx/e9SNbcbLoRQdU0QxKyUh45jgeoyAD1eIGuCyzLYMOGDEeOzFCptNB1h1wuTj6fJpk0qNUcXn99JrCsVyPFfD7Ohg1pNE0RvsvlFrqukcmYjI5WSST0ZXUxLmeQdrHb2nb0Ny+uto/J00KIXwEeBfYvchcf+BUp5Q+v5nG00cZqYKmBYRdLXb2ct8LFTrT1usPevX3U6y6ZTIxUyuTEiQKvvDJHKJzv/nrw4NQCo7V63eFv/3aE3bt7+NSn7uHLXz7Kn//5McbHlYV8aJpVKrUigzXFVSkHC5HyRjl1qhRcPatxzvkL+PxC5PxxwlIR5tUApFJGJIu9Vgg7DeHva5UXo4i5gkzGoFxW/J6wuFRuvpJYzCAeV58BxQvxicdj+L7PzEyLjo4Yk5N1OjpirFuX4swZH8PQyOcVHwkgnTaxLINdu7r5+39/kAMHTkXFK7BgxJhKmXR2JqhUnGvSxWjb0d+8uNodE6SU/5cQ4vvAJ4F7UNLgIvAj4D9JKdsOqW3cEFjKFdp8d9jleitc7ES7cWOGb33rBFu2XBiYFhIKw25OpWIzNFQil5tvtObT02Nx4MBp7r67j89//n3s3t3N//K//JCpqTqO41Mu21FhoWk+xWKT6ekG6bRJLKYIvl1diSiF+HLwvCsjvKr8HvX7fFOwa4VQiQNcYFO/VqBpRCTXRkOSShnRKC4W09F1qNc9OjrU529qqonjeEgpcZxmlJPkuh7d3UlaLZfZ2WZUcLZaBbZsyUYeJ/m8xdatHcBCQjYohVZXlxrd1GoOu3d3092duGZdjLYd/c2Jq16YAATFxy9fi3210cbVwlKu0A4enFqWKmGxfZx/on300RfJZGKXJBSG3RzVBZkzfpNSRkZrmgZPPHEqKqDSaZOpKS7ofti2RNM8dF1QrzvBSMdk48YM1apNteosqYuhnEXV70vll8zvuoRKkmuJeFyj2VTBdXItViWo933+2KxadUkmdXRdEVN7ehLUah6Vih2Zx2UyJo2Gh+sqGbBlCVIpxS85d66G46j3Uyl4fM6erZDPW9x+exf33TfAG2/M4vtyASEbVPG4dWsu6og8+KD6LlztLsaVdiVvFNzsz+9yuCaFSRtt3Cy43BXaYrbzsDJvhcUcY0OEhMKwm/O5z/0I01ScgpALMjg4Z7Q2Pl7n0KFpvvrVN/F9SSplUirZ0Qjj/EC/0Brf9yWzsyrxNZ02l1SYzHVgREDUXN5Cfz3qgkZDHfRyj/VaYn5XKUS97hGPK7n2+HiD9euTZDLqvZVSkstZkfPu9HQDx1FKKymhUGiRz1uYpiLJZrMW3d1JbNvj539+Fx/96PYofDKdVpyU+RlRQsDISDnqiCy3i3G5Rfj82/v7E9TrHseOzUSjzZvJ8VVKyZe/fISvfvVNCoUGti2JxQRPPHGaj3/8Vh5+eOcN/fyWgisuTIQQ75ZSPn2Fj/09KeX/50r33UYbaxVLKSKWezV0KROqkFAYdnMOHZrimWdGARG5v/b0JJicrHHo0DSWpfP669McOjRNtWrjeX6kmJm/2M0vUBIJDdPUOHeuimFokSLnUjAMEeWaxGLKoMt1r45m+EYhrK4WLtZ9arV8dF2RYstlh2w2Rk9PgmbTY2ysRjodAySJhIHj2DQaHiCjKAHDUIXKe9+7gR07upiebnDmTBlN0xZ0CjMZi507uwBVqA8MpHjggc3LcnwNcbnR50MP7YiKovD2554b44UXJti9u5utW3MXjWq4UXHw4BR/9EcHKZdbWJYRcIR8Tp0qsX//Qe66q5c9e3qv92FeVaykY/LXQoj3LpcjIoT4Isqqvl2YtHHT4XJFxPbtuctyUIAFJ3gpfU6frtDZmYi4BHAhoVAVJzsZG1Ot9pGRMgcPTlIu24G/heTd797As8+em9eOF5GN/Pkhb4ah3D6VyZaH5/mBt8XlXwdl5EXUPWq1vKtWQLyVipJLQZGWAXxmZhoIIXBdG9NUXYcwMkHXBfG4TrOpTPDC7pDjeKTTJm+8MYvj+GzfnovkvZfrgpxfYCSTJs8+O8ZXvnKU3t40Dz64kQce2HJBkXI5Q7Zk0rjg9mKxRWenxchIiXXr0pHp4M3i+PrYY0eZmqrT35+KXivTVIGJ4+M1HnvsKJ//fLswuRhywHeEEO+RUo4s5QFCiEdRRUn7VNLGTYnLKXc2bergW986edET8Z139vDaa1MLCpdq1WZ2tsmTT57mzjt7glHK4oTCOaO100HhITl7toIQgsHBHBs3ZnjmGYlhqFC2sJ0/H2Gq8Hw/DPX70tvHoUeKrkM6HcMw3EBivKKX9y2J5RZ0vq98aUqlFqapkUzGMU0d1/XJZEwKBZtMxsQ0lfNrSPhVJmyqy3XyZAnL0heNOFgM8wuMkGs1NFTENDVGRoo8/7zBK69MXTBqudzo88tfPoplGfzoR2ORm/HkZBgaKRa4IcPN4fj68ssTJJPmBd83IQTJpMlLL01cpyO7dlhJYXIW2AB8VwjxY1LKS75aQojfB/7n4L9/tYL9ttHGmsXlbecrlzwRP/bYUcbH6xcULvffn+DVV6dIp1Xg2cUIhYcPz1CvK6O1U6dKHDtWoKPDoqcnieP4TE2pIMJQQqr4CjLqkszvmoSSVMNQhloqlVflqFwOvq98TqpVZR2ez1vUak4wPmhjOVhuR8gwNBIJHdPUqVQcbFsVoJ7nAwIhFF9I+cQoUzzT1EmlTFIpg2KxRVeXxsmTJT7zmXcuaZ/zC4zx8RpHjkzjecrzRgg4fbrKAw9sumDUcqnRZzJpcOjQNPG4Hn1mZ2aaTEzUsCwjyIdaOFa8GRxfpVRp3he5Fbi5+SWwssLkg8APgW2ozsn7pJTlxe4ohPg/mFPlfAv4uRXst4021iyWazs/H6mUyUsvTXL77fkLChdd19i0KcO6dWkeeWTvRfevFghltDYwkKJScQC18NRqyrG2tzdJodBkZqaB46juiW37FxQlqlMisW1Jd7eSiWqaIBbTFs3ZmY9Way407syZCrGYFm2zPXpZXcwvKoVQCcHxuIlhaLiuT6ViB542ehChINF1LSgclJNryDUJR3qzs00GB3NLlvfO+ejA00+PMjPTwLKUjNm2fU6cKNDXpz6T80ctlxp9nj5dwbaVXDn8zpimzsBAmqGhEomEwaZNmej+N4vj6969vXzrWyfo6JALLjqklMFFx5WPcW4Utc8VFyZSyjeEEH8feALYg+KcfFBK2Zp/PyHE/wn8UvDfbwI/J6Vcg5mjbbSxOliO7fx8KA6AvMQVpMnBg9M8+uiLF5xUQLXT//qvTwYBZxZbt+ZIp+d8J0xTY3KyHjh6uiQSOpmMjhBKmaFpKsRPcULmChXL0qnVbBxHuYx2dMSYnm5e5jVQ6bC27eN5c4WKri9uW9/G5XG+9NowREAqltE4JpuNkUwqkqvjeAv4Q62WF/nblEotQMUPaJpygG021bgtNGozDLHAXfhSmPPRcZiYUJ8xXdei402lYgwNFcnlrAWjlkuNPoeGStxxRzfj47Wgq6duT6dNcrkYExM1du/uoV53birH14ceuoOnnz5HodDCsvToQqDVUr4zIQ9tuViJx9K1xorkwlLKF4QQHwP+O/Ae4KtCiI9IKX0AIcT/hTJWA/gG8A/bRUkbNwOu9MrjchyUt7+9j1rNuaBwkRJefnmScrlFIqEvOKk8+OAmQIWdtVoejuMyMyMZGxulqytBq+WRSBiMjdVotVzWrUuTzZpMTjbI52Ps3NnJyEiF06fLxGJqgQr3CcqrIvTO0HVBImFEHJSLJeEqi3R/QXdESoJtzN2n3T1ZGjSNSDqby8XwPNXp0jRBtWojpVqwb701z4YNac6cqXD0qApg7OiIEY+raAHl2ipIpRRHo153EUK9CbquXFx1XSVKNxouv/u7P+KDH9xy2UVrzkenSjyu47rqfZZS4nmQz8cRAo4fn+XOO5cW4tfXl+T22ztJJo0FTsa27ZNIGGzcmOW++waYmLi5HF/37Olh3749gVxYmeNZlk5/f4qPf/zWKyb2Ljf5+XpixT4mUsoDQohPAP8P8JPAfxVC/Dzwh8C+4G5fB/5Ruyhp42bASq48LsdB2bOnh9/7vRcvKFzGx2sMD5f4wAc2R8GB4Unlq199Eykl69en8X3J6GiNdNokn49HnJKjR6cDq/A4jYZLImGwd28fhqHx5ptFYjGNd75zHU89NYrvS0xTQ111SxzHB1x6e5XdeLPpRt4kIC461rkYqRauLEH3rYxwTJPJWHR0xMhkYvT2JkkkTIaHi9RqDgMDSVxX8uabRTo7LbLZGNWqQ1eXGt1pmkoNbjRcOjvjVKs28bgqQGo1G8+TAY9IdcXGxqokEgbf/e7IZRet8HP92c/+iGTSYGZGjYxUUWKRShk4jkexaC8YtVxq9HngwClOnCiwZ08P/f0phoZUOGVnZ5xt27Lce+8Av/qrFx9r3qgQQvDwwzsXhBf29ydXXHhdDY+lq4VVMViTUv6lEOKXgP8EfALYC9we3Pw14B+3i5I2bhas5MpjKWmqixUuL788zuBgB/39yQXb0zTB7GydqakmZ85UME2NbDbG7GwzaNcTtPMhkdBptTzOnKmQTpsBMVEwM9OIOCiep0ZJahGTCOFFnQ3HmUsXjsXc6GpbXc1f2t01LFLaBcmVI5lUn4dWy2PDBou3va2PiYk6d999K4cPTzM0VCQW00mlDA4fniGRMLj77j5MU6PRUAGMfX0pzpwp4/uSzs4Es7MNDANaLTUq0TRIJAy2bcsGqpcSO3Z0XnbRmu+j8/TTqritVh26u+NkszHqdZdyucU99/RfMGq52OhTSoL04MSC4Ezfl4yMlHnwwS2r/hqvFVwNq/2leCytFaya86uU8o+EEN3Avwd2BH8OOyVtKn4bNw2WcuURxrJfbNRzqZPO/MJFeZKofZVKNs8/P8bWrcrJNbxwKpUcZmcb7Nyptp1KmWSzccbHq0xNNejujtPdnaRWs6lUHDzPZ2ZGFS6eJ4PxikulQkCIBMtSrf5QTiyEoFZzuO++Aep1h+eeO4dhiGg04DjtmczVhOKTCFxXVXbhaCSTMdF1EXUVhoeLVKtO1Pr/sR/bgBAwOJjj6afPBnk5HtWqQ39/iq6uJGfPqrBGy9IxDMU3GR4uU6moLkqh0CCRMBfwPBZD6KMzPl7n/vs3MT3diLoc+bxJf3+KT3/6HYtuY7HR6Pvfv4kHH9zEgQOn2+nBq4ClGDWuFayqJb2U8n8TQnQBvwp8FdUpaV8jtXFT4XJXHmNjtSWPei7FVdm1qzvajupo2MzMNBkbG2VwMMeePT0IAbWajWnO5eOExUmoiikUWlSrDq2WTzpt4DgyylUJW/nh/zVNjWZ83wlM0gSWpRYtELznPet59NGX6epKUCq1AumwWuzaWF0IMTf68jxJrWZTrwu6uxPce+8ApVKTet3luefORR4fqmhN8O1vDzEyUuZLXzqCENDfn8JxJKdPF4MMIEm53MJ1JclkjHhcve/VqkOz6eH7YREimZho8P3vn+HLXz5yWTv080eVd97Zs6CYWKwYv9Ro9MEHN/Frv7aXJ5883U4PXiGWmo6+FnDZwkQIcSVnHAn8LPCzF/nwSCllO6enjRsSl7vySCQM/uIvjtFqeQwNOaRSJoODWTZvziwY9VyOq7JnT080MkqlDJ555hzJpBGQAYv096fo6Umg6xrJpIiuaKWEiYkaExMNXNfHsvSI1d9qKdMzIZRaI/QyCRODw3A4x5ERKRKU8uPHfmw9f/Znr1OptIjF9OC+sq2yuUqQUo1VWi0veo3Xr0+zfXuOb3zjTcbG6ti2S19fmmw2xsxMk3PnRtF1OH68SL3uMjvbQAg4e7aMlIJ0WsmIc7k49bpDLKbIs63Wwq6XaWoIIfA8H9MU6Lrgq199k7vv7lvSSGc5IX6XGo2GidiXksi3sTQsJR19rWApxUG7LG1jzWAt6PAvdeVRLtuMjJQ5cUIZm5mm4OzZCocOTZFOq+ySL3/5CJ/73Psuy1U5dGgqGhn19iYZHMxG6gRNExw6NMngYJ53vGOAiYkaU1N1PE9SLLaYnlZFiVLAqOIh9BBRXJCwEFHKmfk+Juej1fLo7bX4+Z/fySc/+cQ8NYfiJLSboqsP01QqKNtWVv5dXXHe//6N2LbH0aOzlEotCgUl2a7XC8TjBvl8AtMUnDhRxPMIVDHzuT+SUsnGMJQc3DBEoHQROI4qKmzbDcZ3At/3cRzJwECKXC7O7GyDL33pCOvXpy/53VsuP+JGImXeyLiSovF6YSmFyb+76kfRRhtLwFrR4V/qyuP22zv5sz97nXhcZ3a2QbHYwraV3E8pI+Db3x5m9+4epqcblzwhv/jiJHv3KjMlIcQCdUKx2MSyTD71qXvwfcnv/u6P8H04dmwmMlUzjNAzxEfTwiC9hcm0FzM90zSIx3V0XYs8SR577HVqNTvyJAkf38bqI+xe2LYkmdTJ5SzeeGOW06crgWmZF8mvVefLptl0abX8KEpgMe6PlOpfreYEbrDQ25sIgvycoBASwSgHBgZSbNqUwXF8xsdr/O3fDvO+921Y1e/eUkajBw9OrXlTsBsBV4NUezVw2cJEStkuTNpYE1grOvxLXXk8/vgIjuMxNtaIFgBQXQfL0qlWbe64o5vHHz+FYSi+AKjFYnKyzvBwkUrFwbI0ajWHatWORkZCiEidMDWl9rlrVzcHD05RKDR55ZUJMhmTRMIITLUkUvpRdsr5QX1SzuXhzC8whFALQkdHDIB63eH06SqnTlWj+7QLkmsH31dOrJ4nqdedKPdGyvNl3TJSPYXjuMUQ+tGEic/T003uuKOLnh44fHgaKaGvL0kuF4+4SqWSTbnssG1b/gK5+kq/e5cajVarNuWyzRe+8MKywgHbuLHR5nm0ccNgLbV8L3bl8cUvvojr+riupNn0ItVK6B/h+0ohoWmC11+f4c03Z5FSUKm0aDY9slmLWExjcrKBlJLXXpvm/vsTkYsmzJHV7r9/U9RBKhZb9Pcnqddd6nUHKWUgLXWDLonqoIRckoXPRf0Miw1NU8VIo+FGnJM2rg9C8msspjMz08AwNAxD0GzOjc98X6l2QpfXy0myw/cz7Ja1Wi7nzlXJ5+ORCmvduhRCqOK4UGgwPq7yaXK52Dwuk2Rqqs7QUJFHHnmSn/qpbVfUybjUaPTMGVUM331377LCAdu4sdEuTNq4YXAj6PBVnowGqBm9OtGqk6VaNNSqcOpUmTNnKlEE/ehoNXLy7O1NouuC++5bz+HD07z66hSbNmUuIKtJSdRBGhoq0tWVoL9fp6fH5vjxQnBlrOP7Hrqu/E2mp1t4nocQGq6ryAfhflUhtZCT0Mb1haaB6/o0m+pNcV2fWEwnTHxWGTciUNrMFZmXgvKckUHHRI1Im02PREInHtcpl22OHStiWRqVih103sD3fb73vTO8/vos73rXOiYmagwPl4MoA+OKRzuXGo12dMTIZi00TTAxUY9s7YVQ8ulCocWWLXF+//df4mtfOw5IOjvj7N7dwwMPtMc9NyrahUkbNwxuBB1+Pp+gWnVIpw1AqV5ALTDK/Erl3czM1Nm6NUs6bfL882NomoZp6kxO1mg0XDo6Yhw+PAUIurribN+eY2KisYCs9vu//1LUQUql5jJx0mmTrq4ExWIT3yfguKh8nEwmhmkq0uO5c1VyOYt43EBKyfh4/bLhfG1cW8RiyjE17IK5rhpveN7CAkNKia6rEd5iXbEQ8/OKTFPH8/zoc2maOl1dSd75zvW88sokxWKLZNLEtr0g6sDHsgzOnavyne+MIKVky5YOGg2PfF4lWF/JaOdSo9HHHjsaFV3Dw8WgKFOFhmlqDA+XGBurMTvb4OzZKl1dcZpNl6eeGuWP//ggd93Vy6c//Y5AWt8uUG4ULEUu/IvAn14tkzQhxC3Aeinl312N7bdx8+BG0OHv2dPFk0+eol5XJldKGaOcV2MxjXTa4Ny5Cpals327MkobGSlTLDYDoqqg0XDp6VHck3rd5sUXJ3jggc088sjeBSfX+R2kwcEs4+M1kklljKaUFBbxuM7rr88G6cRp7ryzhzNnqhw7VsA0NRzHp15vBuocedHsmzauPdSYxSNM/g19ReY77KpOhuKadHZa+L7J9HQTx1lcZaVpauyjeEhEY5lazeH112fp60vS3Z2gv1917aSU1GoOuq46ao6jguUqlVakznIcn61bc8H2r2yserHR6MBAKroYqVQcYrG5kWapZFOt2mQyMVotj3hcSdirVQfX9dmwIcOLL47zG7/xFD/3c7e1Rz03ELTL34U/Bt4QQvyCEEJfrR0LIW4RQnwJOAK8d7W228bNi7DlOzJSZmqqTr3uMDVVZ2SkvGZ0+A88sIWengQ9PUk6OiwSCVX7J5NGwA/QaTY9tm/PB+6tanTT3Z2kry+JYWhYlkY6HcM0dQxDZ926FI8/forDh6cX7GtgIBmRa0M5cbHYolZT/JBUymD79jyf+tQ9/OIv7iSfT3LmTJW3va2PL37xfWzdmo3cPVOp2AJybBtrA6oDQlSYWJYWRQCE3Q9NE/T1qc9PKqUk6Zs2pdE0JRk2zYXJxK7r09lpBbcr/pNpCvL5OMVii6997TiHD88wOVmjWGzRanmBC7DqsFSrDqWSUgGdOlVmcDBLb+9cVMJqjlXvv38zlYqD70syGTPq6EmpHGnDYw7HpYVCi3hcDzqEDh0dFq2Wu+j3p421i6WMco4AO1EFymeFEH8OfEVK+fxydyaEyAEfBx5CpRFrgAO8sdxttfHWw1rV4S/0VqnR15fk1VcnicV0fF95iJTLNvm8ClZ7z3vWB7buY9Rq6qRbKrUCpYWygw+3a9se27bl0TQuuAo9v4MUyolPniwyNFQil7MYGSnx0kvjkeW4aeq88cYs2azFnXf20tmZ4MiR6eA42mqbtQRNm+P7hIZ3YYFiGOrzkc1aJJMGzaZHPG6wa1dX0BGbJZOJBdJiH9v2iMVExHNSOTZJPM9j8+Ysk5N1CoUmuq4Rj2sUi82Ar+IFxZCGEF7A7VAEW9f1qdUcDh+epl53GRxUHcDVHKvO559ks3HOnKkEtvo+qVSMbDZGuVwJjlWg66oDpOsEz1mjXnfbfig3GJZSmNwFfBL4DaAX+GXgl4UQ54DngR8BLwMTwCxQBOJAJ5AHbgXuAd4B3A2YzJm2/RXwaSnlm6vybNq46bEWdPjzC5Fz52qcO1ehVLLZuDFDOm2ycWOG554bZ2KigWGovBlN06IruMHBLP/3/32Uzk4rcGX1aDZdCoUWhiFIp2PUaspTIjzZNxrOgqtQRUiUeJ7P1772JqapRZHwpZJNLmfR3R3nmWfGaDZdPM/HMFSD1LZ9vve906xfn8LzoF53sW0Pw2hn3qwFqE4IaJqG583N1TRN/VMSYcUTArj//k0IobFnTzfj43XuvLOXTZs6ePPN2SDCoBok/MbI5+MAdHbGGR2tUKv5gfKmSTxuRHwVpfBRcQO+r0ZIjuNHMmNQnjiWpVEstjh7tsr4eI0tW7J0dMRWbaw6/2LkwIFT1GoOExM1duzoDEwFlYw6nTZxHC9Sr3meJJk0sG2frq74miHHt7E0LMXHxAP+TyHEfwH+JfD/BrYA64GPBv+WgrAYcVHhfr8rpXxpuQfcRhvXE+ebvNVqDi+8MB4EoGns2dPDyEgZz/Pp6DBJpWIIAaapk83GOH68iO9Ldu/uZmSkFLTndXp6EpTLNh0dVsATISDD1nnuuXPkchb33TeAlJKDB6f4/Od/xGuvTZHNxrAsjaGhEq2Wh2FouK5yfz1zpoLj+BiGuuKu1200TQTcBZ8TJ0rR/8P5fDvz5vpDGdrpQQ7R/L+LYByoFt9Wy8WyYhw/XuBnf/a2Bbbtv/7rP6CnJ8HBg1NkMrEFaraQTGuaGsmkwdhYFd8Px0Y+ngfptEG5bOP70Gh4Qbck9L1RRYthKMI2COp1m66uBIcOTfPP//nuJY9Vw8/zV75ylBdfnEQIydve1scnPnFHRFidfzHyyCNv5/DhaZ544hQHD05TqUyyc2cXhw9P02iojJ9UysTzfLLZFLbtsXVrbs2Q49tYGpasypFS1oH/XQjxKPAg8A+B96OKlMvBBZ5DFST/TUo5sewjbaONNYDzTd5OnDgXFRNhfs3Bg5OYph50MXQ2bsxEjx8bq3H6dIW/9/c2sW5dOkqD7elRPJPp6Tq9vUmGh0uBXbjGzEyD48eL7NnTw5e/fISvfvVN3nxzlq4uNV8fGSlHqp9azQmkv060sKkrcA3PU1fChqHRbKqxkSK7yshB9HqhTbqdg+9DreZFIX6+H451lItvyCtR75+gWLSjDkXYzTt2bIZTp8oUiy2yWSvyLAHVMevsVJ4l587VAit6B8fxSKdNcjmLs2fLuC4RnyV0jDVNIhl8NmuxcWOG2dkGjuPT1ZVg+/YcicSccuZSkFLy5S8fYf/+g0xP10kmDUDwV391gmeeOce+fXsuCA2cX6T4vs8v/dLjfPe7p9B1DSndwP3Wo6Mjhm0rLld3d4JTp8prghzfxtKwbLlwkBb83eAfQoj1wLuADUAP0IUyQCgCp1AclZellNXFttdGGzcSzjd5q9WUUkAI1VoPnVtNUwsSey/sQLRaLkIQERZD1Go2zz8/zsGD03R2WtFoxnF8du/u4n/8jxFmZhoRL8W2FXfF8/yo3R6SImGhkRaoQsR11eofLnih3FSF+2kXtae/mkVDWDhJefG8nrcqwtc9/Om6kkbDjYrWZtPlrrt62LWre0E3z7I0ymWb2VlljpbJxNi0KRPJf7duzfLKK5NMTzdIJnUSCaXmKpdtCoUWjuOTTKrkaSGUokzxpZSqJ5lUI8tMJoamKUn7ffeto153mJhoLOm5HTo0zVe/+iaVik1/fyoqQDo6YhQKzcuGBh4+PEO97vKBD2xhZKTE5GSd8fEazabKcdq2LcfAQIpTp9YOOf5mwLXIK1uxj4mUchT46iocSxttrHmcb/I23z8kFtOoVlX8/MxMC9AiVc58hOTW81Gvu3R2xhkYSFMqNalWHbq64mzdqqTASvarkl49T3LmTJlKxcYwtCjjRHVG/AUZOHNmXIpM6fv+gqJlzvH1wupj/mOvFpR9ertdcj4uVqR5nrpNdTfifPrT70AIwWuvTQap1i7Dw0VmZppRNEG53OTECZdMxmLv3j58X7m23nlnN2fPVqlUapFbccjPyOdj+L4fjPh8hBCkUuqzm8/HSafNiKAdyoWXMzJ58slTFApNLEu/oCtiWQaFQiMirC62GI6OVkmnY/T2JhkYSEWv2eRknUOHJqlUHG699fqT428mXKu8srbBWhttLAPnm7zN9w8JiXbr1vXy+OMjCCHJ51PRY33fR9c1uroSPPPMaGB4ZrJ1a47u7gSVikNnZ5zu7gTJZGf0OOV4WQqi6CEeV1eypqlTKLQiKaiUqvOgSIqKQKmcPRc+h/PzckK47oXPt93BuH6Y//7MP9er5GEf14WHHrqDXbu6ee21SX7hF77D8HAJXVcFglog5opVUJ0P09R55ZUJBgezvPOdAzz99DkmJ+s0m+oxmgbNpsvMjI9h6KTTeuBh4mHbSv1TrdocPjyN4/jk83FOnizg+yrLZ6kjk7GxOo7jLfAmCRGLaTiOMv272GL4/e+fZfPmDnp6ktHrE3YiM5n1aJq2gHfTxspxrfLK2oVJG20sA+dLdEP/kBMnilQqNkLA+HgNw1An1kbDRdNElGFzyy25yP8hmTSo1x2Gh0t0dyfZt28PMzNNTpwokEiYUajfsWOFYFwTJs56lEotYjE9cPH0oqsUIZTFfNg1mUsYJro97KKE/660I9JOGL66OD8FOoTvSzRNGar9+q/fw1e+8jp/8RfHGB4uIaVPqeTi+zL4fKjxSzYbQ9c1Wi2PV16ZoLMzwYYNaaamGszMNNmzp5s33yzSbLpBppOSBKfTBpZlRO7Avg+lUgtQxUsYTHnkyAzHjxf50Ic2s3Nn15Ke38BAEtPUabW8gEQ7B9v2sSyN/v7kRRfDdevSnDhRYNOmjgUjUVg7TtBXgmsxKrlSXKu8smtSmAghDCAb/LckpVzk2qyNNtY+Fsv16O9PMjxcRgiB4yhi4a235pmYaFCtttA0wfbtOd773o289NIYqVSMw4enGRurAdDfnyKft7jrrl5A8PLLE5w9W2VkRBFgbdvDdT1KJQdQqgMpJZWKjZJzgqbJiAug64JWa65IEcJF07RoZKKkqHOBb/MLjOUUKyF5NpSQtqXGVx+xmOIyaZqg1fI4fHiGxx8/FaVXl0puICvWcF0fwzDQdT8gwcZZvz5JLGZgmoJnnx2L/G00TSOdNiM5cqnUIkwVNk0XKSGRUF3B/v4kjYbLtm05isUWtq3CHvfu7aNWczlyZGZJi9P992/miSdOR0V6uOhKKWm1XPr7szzwwGaeeGLxxXDbtiynTpU4ebKwoDBZS07Qy8W1GpVcKa5VXtlSnF+vCEKI+4QQ/0UIMQQ0gcngX1MIMSSE+BMhxL1Xa/9ttHE1EPoqfOpT93DLLXk0TSOTUeqET3ziDj784e28970bueOObt73vg3s3NnDf/7PH+Kb3/woQkgmJuo8//wYjuOzbl2K7u4E9bpDs+kGVxvd3H57F4cOTUfeJKrQULJfUAWJIrHKyBRN5Z0YAUdA8QHicWXw1tERZ9u2HOvXpzFNDcsyME19wVW4IjQaxGIaiYQepdpe/vVQrrbqKv4qvOBtRIjHdfr6UnR1JYjFdBzH5ytfOUomY1KvO9HCLYQWWM2r7pr6pxZ72/bJ5Sx27+7FsnQmJuoRITqXswLljww6fop4rYjWHsViK0qolhIymRgbN2bYti3P+vWZQO4e44knTi3p+eze3c3HP34rmUyM8fEapVKTUqkVkHUtPv7xW9m1q/uii2Fvb5Lt2/OcO1dbs07Qy8X87lBPT5Jk0qSnJ8mWLR1rwr12vtv0+ajVHPr7k4vetlxclY5JICn+n1HeJTXgKFAObu4AtgK/APy8EOIPpJS/ejWOo402rgbON3l79NEXozyR+Ti/vXnw4AxjYzW6uxPzgsj0wEuixuHDMwghSCZ13vGOPgoFZS+/YUMHx48XggLFDgLbtGB/frTfLVuyeJ5kfLxKpeLieUTdFcWDMenpSVAstiI1Thjo5jgSx1GNTMdZ+njH9wlCC82LnrBWC4spht5K0DQ1PvQ8n1bLo6srzp//+TG2bcsG3S+fRMKIRjFAFDMgBDQaLidPFnEcj61bOxgczDE2Vmd8fG7hV4Z/AiHcoNum3uNs1iJMzA4J3vMRiykDweVcNQshePjhndx1Vy+PPXaUl16aBGTUGdi9uyfIfVo8vFMIwebNGXbu7GLduvSacYJeCa7VqORKca3yyla9MBFC/E/AvwJ+CPwm8FQgMZ5/Hw34MeB/Bf6VEOKIlPKPV/tY2mjjWmCp7c1CoRGdLKtVO2iDe5GD5/S0ut/4eINNmzq4/Xa1TSnVbWfOVPB9dTUb2t2r9rqH6/qUSi1yuTimaXDHHRmqVZdEQhU96bQZqStc18c0xaLEWFge5yRU9ZTLTjAimhsFrXYR8VYuSkCZ4lUqdmA/rxOPG9TrLpOTDVotj1rNJSxUXXchtygM+0ulFHfp618/QTZr0dsbZ2JCEUy7uxPs3dvPwYNTnDxZpFSy6e6O09+fIpUyGR2tUiqpz2wqtfDKOPRGWS63QwjBnj29fP7zvRe9z6UWw2rV5ZOfvPumsZq/VqOSK8Vio+xazaFScVa1S3U1OiafBA4DD1yMSxIUKt8XQjyAsrP/JVQWTxtt3HC42BUdLCTh5fNx6nWbycl61GFJJo0gc8Tl7NkqX/ziC7zwwji27bB7d28Q9Adbt2YZHa1EC79paoE6x4kydmZmmoDyKhkdVR2SLVsUOXdoqBTt1zQ1mk0PWLh4hbhS35Iw1yUe13EcL3AqFTSbbSnwasDzVEcjnY6xeXOGRsNjw4YM1aqDEBLX9XBdeQHhOUSo2FJmeqq71dERI5OxEEJjcDBLOh2j0XDp6oqzYUMmkgm7ro9lqfFRLmcFnCWVSxNKhrdsyV4Vbse1WgzXApZ6LrleuFZ5ZVejMLkd+P2lEFyllI4Q4m9QY5822rghcakrunLZZuPGDr74xRd57rlzjI3VA88R1Z4NOyaxmMbISInnnx9j06Y0zz57jqefPsu2bXn27OlhcDDHU0+NousanZ0JTFPDcdRVsuPIQBosKRTUmMYw1LYnJ+t8+MPb6e9P8e1vD1EuhwoNFyl9yuULv6bzi5IrGZ8ovolyJ00kjGD8dKWvbhshFLlZj0aBtu2xd28fx44VeOWVCqmUQb3u02pd+J7quogK0tA3xDAE9brDT/3Uds6dU54gmqaxeXMHlmVw2215pqaUVL1Wc1i/Pk0mY+K6kmw2zvh4NSpMBgZS1OsOH/zgllUvFNZqeOfVwLUalawE1yKv7GoUJg1gYBn3H0CRY9to44bExa7oymWbVMrgm988jm17zMw0sW0vshIP2+uhudiGDRkKhRb33ddJqdTi5MkSR49Ok8tZJBIG+bzF9HQT3/epVJSsMyQu+j7YtouUoYRYKXDC1m9fX4qNGzvQtCquq1w91Qhm8euH+Tbky4VtK+WP70sKBfvKXtR5xwELk3bfqpASJiZqQYdCEZp7e1MMDZVYty5NodCkVmtEPJPw/QtHbK2WegFV2J0ReJBY9PcnMQzBunVpHnlkLwcPTvGFL7wAqM9NX5/y4vF9yfBwiY985BZOny5z6NA0s7MNuroS7NrVxYMPbrlqhcJaCO+8FngrdYcuhatRmDwN/EMhxJ9JKb97qTsKIX4clbnzP67CcbRxE2ItavwvdkW3cWMH3/zmcbZuzfLEE6fwPD8wjlLFhLpyJbAY18jlVKqwlKrjUS63mJ5u8td/fZJ/8A8G+b3fex//4T/8iNHRWmRLHkbAS6m2F9rMx2LKKCvsmvT0JMnn4+i64OjRGSzLYGbm4tbh8wuSK+maqKLrSl7NC7djGCIifb6VYZoi4pXce28nO3Z0IYQiH+dyMcrlJsmkSbOpunCuq8Y2nqdew7CT5fuSbNaioyPGhg2ZwNF1jr+wa1cXt93WyTe/+SauK8nnLXp6UsRiGh/84BY+9rFbrvp3bS1+z68F3krdoUvhahQmvwV8APhbIcSTwOPAcaAU3J4FbgE+iAoBbAWPaaONS2Ita/wXu6J79NEX6ehQWSLnztUiXoiUqtPRaCiDKpUTo5Qx+bzB3/zNMENDJQxDI5MxcRyfAwdO8+abswwMJBkfr5FKmViWBshowQ6zcDxPBknDyr7+4MFptm3L8fGP3wrAb/3WM0xNNaIr6MvhSromq2ljrzJa2rMgz5PB50Hy2muTjI5WA3WMkgIr8itR8atkwx6+rz4XoatrZ2ecrVs7KJVsBgeVvVTIX5BS8pWvvM4bb8ywbVuOyck6xWKLSsXhox+9hYce2nFNipK1+j2/FnirdIcuhVUvTKSUrwghPgj8CfAAcP9F7ipQBcs/l1K+utrH0cbNh2tlh7xaWIxhr4qHUKobBv0p+WerpbgnQ0OlSDJo2x66LrBtl8OHZ3j/+zeSz8cpFFqkUjE2b+7g6NFZfN+nXvcimWgspgWkWodCocmv/dpedu7s4utfP053d5yzZytLKjh0XY1QrqdUt506rEYxqZTqcnkelMs2iYQRhObVKBZbUWBkLGZE3Tnf1wLzOz8iTXd1JSgWbXp6Epw8WeTgwSls2+f++zdx8OAUjz9+iq1bs2ia4PbblYur70veeGN2yeZpK8GN9j1vY/VxVXxMpJQ/FELsQHVE/h5wK/OcX4E3gb8DvielfItPjttYKta6xv98zGfYDwykOHp0Btv25kl1JSCiRb+rK8HwsOqUhByUsKuRTseQUvDGG7Ns3ZollVJR83v29DA11eDUqTLKGEtEYX5hUaS2L/nlXz7Ad797mkRCJ5k0qVbtSy76yaTq5jQazlue33GtMN+kbr4Dr8q4UWnT4X2mphrYtkej4UXKLPWZcaPuWTodI5UyqFRsTNNg48YMuq4KlLGxaqSu6e9P881vnkBKGXX5QH1uJifrkarrN3/zKX77t99zVUcqN9r3vI3Vx1WzpA8KjgPBvzbaWDHWusb/fLz//Zt44onTHD9eoF53abVUR0PXCYoPDd/3gwj5NBMTVcbG6vi+j20rgytd1zAMgeN4tFou5865dHUlKZdtMhmT4eEyPT1JzpypAmpxisd10mkT35cUizanTpX50Ie+RrPp0N+fotFwARktdOd3QkJbel1XXZetW7NMTTWoVOx2gXIVoesLxzDKUl5gmjpCKFVGaJiWzRpBxIAXkJnVyE952oTxBDqWpTKb+vtTfPjDt/C5z72Xr3/9OL/1W09Tr7sIIVi3LsUtt+To6Uny9a8f5x3v6ANUUXLw4BRDQyoawTQ1Tp8u84UvvHBVRyo32ve8jdVHO8SvjRsGa13jPx9SKh7A7GyT6ek6iYSBYWg0mw6gbN8zGZNEwuSOO7pwHI9jxwqYpqAxj5Pquj6OI4N0V7VQnTlTxra9oOOhCgwlH5bE4wbpdIxqtRVxDoQQFAoNPE9y8mSJZNIMHD79RdUuQkAioQeSY8nkpLoyVwTUNtfjasH3FYE5FlOqmnhcGe9pmuqC+b4XeJlopFIxKhU7IAcTuL36aJqGaRLxjDzP5KMf3Uaz6fFP/+lOhBB84xvHcV2fnp4ksZgqTp999hyDgzlyuRjHjxfZvDkbdUpyOQshBLWaQ2/vnD36+SOV1SKsXsn3/K1Klr1Z0S5M2rhhcCNo/EMcOjTNgQOnuf/+jUxPNxgaKlEq2dTrJr7vs359mr6+FNmsxdmzFV5/fZbe3gR79w7wwgvjZDImQggmJ2tBXgmB8kaj2XSDHBQZmF3pdHXF0XXV46/VnKCI0dA0ia7rNJuqSAkD0nxfXZHPH+Vo2lzuTjJp4rp+MFJSFvjKxl62PUmuEjQNBgbSzMw0aTTcwEnVpdVycd25wMVYTI8KRPX+iUACLIK/zeUWVSo2L7wwwb/+129n165uDh2a5rXXpujqihOLqdN/GIswNFRky5YsJ08W8X0ZdUrmm6ht3ZpbdKSymoTV5X7P1yJZtl0orQzXvTARQvwz4N1Syl+83sfSxtrGUjT+a+WEEM7JdV2LvCAGB3M888xoREBMJk2OHZtldrYRJQGPjVXIZmOUSnZEjA07Goahuh/1urqaDt02f/VX7+Ezn/khp06VyOfjnD1bpV53kNIPcm/m+AegfEaUemfhMYfBf8rzwgVEEAioFq+3ulx3Pq4GGVgIGB4u02qpImNyso7vSyxLD9xc1T7rdeXem8nEKJdbNJtedDyWpSOlRqvlRRk5Z88qz5FDh6Z54okRcjmLZtPFthfGIliWztRUjTvv7GFkpMzkZB3TVBk4tu0xOJijt1dZ0Z8/UllNwupyvTzWGll2LRZKNxque2ECvAf4p0C7MGnjkricxh9YMyeExebkvb1JBgdzvPnmLKdOVdB1AmdUn66uON3diei+t93WGRQtynswJD+GPhS1mks+b3HqVCVKad2//yDj47UoTyXkKCQSKn12vuR2ca6IDEZHqgDRdXWl7PveggKpjatTmLguuK4X/D638XrdQ9cJ3H3Ve9douMHna85+3vdlUJCqx2ma8j1pNj3+7M9e5+/+7jTxuMngYIa/+7vRqADSdY163aFUalEstviP//EDaJrGb/7mU5w+Xaa3N8nWrbkoHgEuHKmsJmF1uV4eTz55inTaWOBSm0qZgcW+cc3JsmutULoRsRYKkzbaWDIupfF/7bVJ/uIvjtFquZw86ZLJmGzdmmPz5sVn4lcT4Zw8kTCiWX14wtywIcPZsxUsS48i7JVNvTqJxePKm2LdujTnzlUBgWXp0bZjMZ1EQgu4BxemtP75nx+jWGySSBh0dMQCIzaw7dYlF1NVePiRGkQl1hJF3V8KF1uob9ZE4GstYfZ9VZgoUrJ6b6amGhEPJTye+eGJUqpCJfzsSAlHj05TKDSwbZ9azQYEhqFFHTHLikXfsd/+7ffwhS+8sGCBVfu4cKSy2oTV5Xh5nDtX49SpCmfPVqJ4h9nZJuPjNTZsyJDJWMva90rRVhWtHFcjXfifLPMhg6t9DG289SCl5POf/xFvvjlLR4dFLKYxM9NkbGyUwcEcAwOpa3pCuP/+zbz00gSjo1WGh0vRCXNmpsHsbIsNG9Lce28/qVSMiYkazzxzLgpFM03B8HCZQqE1zxpemYypk52P5/m0WvC2tykFxfkprX/xF8ciR9mQzFoqXeKAA8zvioSL3aXMzS5lXW8Y6sQci2nU6+12y0ogpXpvlDeJjIjQrqthGIrACnOKqvA9UYRo1TlJpUyyWYvXX59F10XAHyHohvmkUjHuuaefJ588zZ49vcsaqVxfYrrkxIkC/f2pqLgPeTMnThTYubPrKu77QrRVRSvH1eiYPAYs5xpJLPP+bbRxAS5H6svlrFU7ISyFx7J7dze3397Jn/zJYTo7rUj94Dg+u3d3Mzpa4cyZCrff3hWMeLIR2bBSUbN/0xR0dyepVOxAYeGjrnBFREw9enSa116bZM+ennktbsEdd3Rx6NA0pZKN56nk2fnFQ2ictvLXYu73cPcht0Htp63kWS2E/B/HUQWKpoV8E4FpqjFPqLKa/77U6y6+L5mYqFMoNKNCd45MqzompqmRTBrR92Q5I5XrSUxfax25G0k9uFZxNQoTGxgH/usS7/8RYM9VOI423kJ48slT5HIWrZZHLDb3dyHUleHx47PceefKuyVLJbYJIUgmTe65p59SqUm16tDVFY9m9fG4wcmTRW69tRNNE+zZ00N/f4qTJ0ucOVNh3boUvb0pDh+eAghIqCKQgSqfinTa4PDhaT7zmR/yD//h7Tz00A4OH57hzTdnGRurYVk61apaHDxPLrsYWe4YZv59Q4dbz5tTiLSxMqjOiIemKZJryBFRYx4Nz/Nx3bmiMISuC4rFZiT3Nk3lOGxZevAYn3Xr0iSTBsePF7jzzt7osUsdqVzP8DlNE2zfnl8wyrFt5emyfXv+mhNNbyT14FrF1ShMDgMbpJT/bil3FkJsoV2YtLFCjI3V2b49xyuvTJJMGgtORqYpKBbtVTkhLIfYNj5eZ/PmDMlk5wXb2bQpQ63mMDJSjk7kmibo60tyyy05pqcbvPzyRDDCUd0R15VRNyKTiZFI6FSrahv/438Mc+ZMhTfemMWyjCAAULnDuq6P60p0XURy30sVKKuZ6Hu5fbWxdDSbquuVSCj5dxg/oD4X8qK8F8/zaTaVYZ/vq86L70vicY1WSwU9Kj5Gmnrdu6LvyfUMnxsYSFGp2Gza1MHwcHHBRYAQ6vZriXZC8MpxNQqTl4C7hRAbpJRnr8L222jjAgwMJKlUmgtGIuGVU7nc4p57+lflhLAcYtulWrr1usMDD2ziwQc3Ryfy7dtzNBoeL788wdhYlUwmhusqBY6maQjhRZJQ1cZX3iTnzlVZvz7FN77xJj/xE4MIAc88c5ZazVlAhpxfnLjuxZ+jlKqYUz8VT+FaIeyutPNxLoTv+6TTJo2GGuvpuhZJwee/n+d3uTxPFSea5pNIGHieRzxuUijYAaFW0Gi4DA+X2b2764o5GdcrfC7sUGzZ0kFfXzL6u+9LRkbK17xD0U4IXjmuRmHyA1Ry8K3AUgqTp67CMbTxFkN4ctq1q5v+/lSkgsnn4/T3p/j0p9+xKieE5RDbLtfSffDBzQtO5AcPTvGFL7xAR4fF+HgNTRNYlhZ1RkB1M1QHRI2LbNvH912eeuocuZzF1FQDKSXVqrpCDk3UwgX/YlfWSu0hIjvz+cXItVTWtAuSxaFp0NERwzA0qlWXWExH0wTNpiQWExH59VKQkiAQUtBqeeRyMVotD8eRJBIGXV0W+XwiCupbK55Al8OVdiiu5vNrJwSvDFcjXfgrwFeWcf8/QSURt9HGFeP8k9Odd/YsODmt1gliOcS25Z4w57oxKpq+XHbQdYLRjB0VB0IQJA+rMY1pCppNl0ZD41vfOoHjeDQaDiDQdRV1H7p3Ltb9UPtQzq5KJrzw9rVGLnwrIp026O1NUKkoGXyj4eI4ftABU4XL5Yo6TYPt2/OcPl2mVLKJxyXptEm97tLVFWfnzu5IvbZrV/eSuFRroXi5kg5F2wRtbaPtY9LGTYHVbJ9e6mS7XGLbnj09HDo0xYsvTiKE5G1v62PfvjvZvbvngmMKuzHptLqSzWbjFItNbNsPjLJUaqymCRxHSUZDIiP4NBoe8bhBteoE3I7QG0VGXY/FFjDPg2bTXxPdClVEtXkp56NedzFNnUQC8vk4ExO1KHog7HIthjl1FMRiBq6rRkDptBmEBQrWr0/z7nevp7c3RaPhMD5eXxKXaqnFy7XAcjsUbRO0tY12YdLGTYPVaJ9e7krqoYd2LKkLcv529u7tpVZzmJio89prU4seY9iN2bo1y/BwCc/zsW0fy9Lp708yNlbDdf0g0M2PJKOqQNEjW/vQyl4VJ2G748K2x/wRzcXGO9e6W7IWiqO1CM+DoaESP/dzt5FIGDz//BiHDk0H+UUXf59UyJ/qbKii1+TMGQ/b9shmLX76p7fR25u6wNF1KVwqKblhF/e2CdraRlvI10Yb8zD/SqqnJ0kyadLTM5eoeuTIDA89tIMPf3gbo6NVfvCDUUZHq3z4w9t46KEd0RXi5bZz+PD0Bfu+//7NlMs2585VaTZdisUWjuNTrdpMTtYjp9jBwSyWpQeyZI1UyiSVMkinTYpFO+CdXFylEaI9olkZwtC81YBpXnpbKiPH40c/Gufw4RlKpRaJhBonLqUpEXqX2LbPpk2ZQKEjF3Ci5nf9lsKlWsrivlbRNkFb27jijokQYtMVPEwCTaAkpbSvdN9ttHG1cLmT7YEDp3j11Ukef/wU69enufXWPLWaw7e+dZJ63Y3a1/O3I6VcYEsP8OUvH+Fzn3vfglb3rl1d5HJxvv3tYQxDBeqpVFnJ5s1Z8nkLTdOwLJ163cV1ffr6UoyPV2m1PLq6EkxONgCdel1e0thsKSOTduFyaayWcZwas+iAd1kF1IkTRSYn6zSbLvG4QTyu/Ewud5whuXl2tkk2awZyc4OjR6fJ5SySSWNB128pXKobeXFvm6CtbaxklDO8kh0LIUaB54A/lVL+zUq21UYbq4XLnWwPHZrGdf3Ltq/D7UgpOXhwaoGEuV53+Pa3h9m9u2cBifArX3mdZ58dJZ+3cF0/MtDataubd797HdPTDcplG03TyOetoOuiCpd0Oka1auM4XjDGuXSLX2XgzP1fuclefqG9WbNvrifmxi0ajuMtcNA9H74vaTTcyLFV160gnVouKDJDa3r1GEVuFkJQLtvMzjbZurWDe+7p58iRGU6fLvNTP7VtAR9rKVyqJ544dcMu7m0TtLWNlRQmK+1jbgB+BvgZIcSTwD+UUs6ucJtt3IS4lsz/y11Jzc42WL8+fdnZdLidSsVmaKhELmdFx2oYPj091oJCJhz9xGI6fX1JTDMMXpNMTzeYmmqQycTIZCw+8Yk7OHBghFrNZWKiyq5dPRw5MsP0dP2SnJHzcb73xVKKjnZRsvrwfSiVnCBtWqfZXLwDojKPDDxPBkRmje3bcxw6NEWz6eJ5MuIWhXwjKSWWpdHTk0BKQSqlkctZgYOs4F3vWoemaTzyyN4F+1qKokxKbtjFvW2CtraxksLkF4Kf/xK4FzWi+R/Ai8BUcFsPsBf4EGABPwL+COgAdgEfBrqB+4G/At6zguNp4ybEtZb1Xe5KqrMzsaT2dbid8fFaEJYmouejPCTiDA0V+JVf+R4//dODjI5WSadNOjpizMw0o8IktNQfGiqxfXuOW27JRwTfRx7Zy+HD03zpS4c5fHgqsBxX+1gO1NW3wDSVoqdNQL0+8Dz1OVvMcTccxaRSJrbtUas5wQhHks/HKZdbgUpnbjSkRjgqGqG7O0EmYyGlKrCLxRbf+c4I69enuO++dbz22iTf+97pBYX/Qw/tuKTK7UZe3NsmaGsbYrknsQUPFuI/Af8TqqjYJ6WcvMj9elEFyU8B/1VK+c+Dv8eB/wT8UxT/5CEp5f9zxQd0g+J3fud35Gc+85nrfRhXBSvtdoSmY4tFr4+MlPnUp+5ZVfb8YoXQ/JPt9HSdEyeK9PQkL3js1JQ6uT3yyN5oO5/73I+icDTb9oMsH+VIqxJeNfbu7ef73z/L5s0dbNyY4dlnzy3osDiOBwgGB7N85CPbOXOmsuC1fOKJUzz33CjPPTfOzEwd15VRTs3lECYPg8pR8Tzvkq6wbVw9CAGmGXKInAXFia4rg7VMRuVBNZsOqVQsihyo1ewoMycWU4ToatXBtpVrsGEIslkL21ZJwqHMPJUySSZjpNMme/b0kE4v/LxfrvCXUnL48HS0uPf3J9uLexvLwaIfkpWQXz8C/AvgaeBj8hIVjpRyUgjxUZTL6y8IIb4rpfwLKWVTCPGLqO7J3cA/At5yhcnNitXodjz55CnSaYOpqTnyaCplMjiYJZ02Vl3Wd7krqUOHpnnllRcu274Ot3Po0BTPPDMKCLq64nR0WLzxxiz5vEW97pLLxenpSbJuXZoTJwps2pRhcDDH0FAx4qSUSi2khJMnJb/1W0+Ty8XZvj1HpdLk5ZcnmZ1tUKs59PQkSCTUa6Xs6r1LklvnG6/ZtuKlGIZGR4dOpeJEYxsVFCfbY5yrDCmV7bym6RFpOnRrVUZ5oUmeRyplsm1bjkqlxcxMK8jO8dE0yGRMpFT+NbquRe9vuWxHIxzb1shkTDZsyATcJfUZSybNS0p+L3ah8Su/8vZ2IdLGqmElo5xfQnU5/uBSRUkIKaUUQjwK/DmqoPmL4O++EOI/A/8RePsKjqeNNYbVMDE6d67GqVOVBcmhs7PNIHQsQyZjrfpxX8oPZWmz97mT99RUk1bL421v66e/P8Xzz5/DsnSklJRKLUxT48CBU/i+GvGcOFHgXe/aQH9/iuHhIuWyTb3u0tkZZ2amQVdXnFbL45VXJhkczLJrVzevvz5DpWLT1RWPvCqGhkq4rs9i/iUh1MhG0mjMqTY8L8zmCRfK1VOftLEUqNwa09TI52M0Gh6OoxKFazU3MFMT9PQked/7NqJpgsnJOt/5zjBTU7WgK+eTzVokEgajoxVMU4uM+ZT6RwVD7t7dDQgsSy0Dw8PFKGtmMT+PtltqG9cKKylMwkTgE8t4THjf3ef9/bXg55WlR7WxJrE6JkaSEycK9PenopOeaeokkwYnThSuOHDsSnG5jgqw4OTd3W2RzSqi69atWSoVG8dxmZioASrZ1bJ0bNvDdSVHj85yyy15MpkY27blOHWqQr3uUi63KJcdPE+Sy8XJZi2Ghkr096cYHMzyzDOjpNMmhqFydUxTkRxnZ5tL4oyElxbzs3RWusakUjqWZTA721rZht5CEEJE/iiZTIxsFmZmWtRqNp4nMQzBnXeqolnTRKD4KuK6PlIKDEOj1XIxzQSO45FIGDSbXpCbpCzopYRUygi8URxiMWVnFeYxhThf8tt2S23jWmElhUlH8LN3GY8J75s57++N4KdDGzcNVsPnYC2ODy7VUTl4cOqCk/e7372ebdvyvPzyRDA+0UgkDPr6kgghqNUcCoUWjYaDbfv84Aej9PYmePvb+/F9n1qtxeysjet6VKvKMry7O04yafC9750mk7EwTZ3x8TqxWJPJyXqQOLs8m3k1NlCJteH/VwIVMtcmrCwHYWGo64KpqQZ9fQk0DXI5i1hMJ5mMUSi0OH26gpQwNFTEMFRh4TgeUko0zaBQaCKE4pBomsA0tSjCoK8vRSwmqNddUimT2dkmAF1d8QXHcr7kt+2W2sa1wkoKk9PALcAnUGqcpeCfznvsfPQHP6do46bBapgYaZpg+/b8glGObfvYtsf27fk11zpe7OQthGBgIMU73tFPKmXy5JOn0XWN0dEq09MNmk0vWJAklmUwPd2gvz/J66/P8OqrkwFx1sP3fYQA25acPl2JCJHxuEFfX4LR0Rqjo9VA9rt4YN+loHgkq1cJNpsevn9pP5W3KkxTLPr+hKRlXVdRA6OjPvG4TldXnHQ6RldXAinhxIkChUIzGPU5aJogkVAmaa2Wi6YpToquC3p6EsRiOrff3sWxY7OkUkYwHlRcrbGxGiDZujUXHcd8zpTv+3z968f5wz98hUrFIZuNsWdPL7ff3hl9zlfLUG0thAK2cf2xksLkr4BfA/6JEOI1KeUXLnVnIcSngH+MGnr/1Xk33xv8HFnB8bSxxrAaJkYDAykqFZtNmzoYHi5SrTp0dcXZujWHEOr2tYTLdYlAdTJGRyu4row6CsrwLCSiekxONkilDBoNl3rdJZ1WIxHfV4uZysGBRsNjZqbJvff2Mz3djAitzabqeui6ut9SPUpWq4BQC6vENOdInG91hAGKQhC9j/MRvkfhexsa4DUaLufO1ejvh717+5idbfLKK01GR6tIqYrTWEzDMGKBSZ76DGUyMVIpE9PU2bYtx+23d+I4PidPFmi1PHbt6gLUd7NUsnnuuXPk8xY9PSliMY0PfnALd9zRyS/90uN897unaTQcPM9nZqbF44+f4uTJIj/5k4NomlgVQ7U2h6WNECspTD6L6oD0AJ8TQjwEfAV4iYU+Jm8HHmKOkzIVPHY+woLl8RUcTxtrDBcjipbLNrff3sWBAyM89tjrl7wqCoubLVs6ImIezMmF15qJ0+W6RGqh0OjuTnL2bCUq2LQgtUp1RJSkc3y8jmlq1GoOicTcV3V+FyJ8uV59dYpCoYmua0EHJry3QNMun5tzNRCLaeRyavSw2EJ8s2N+MRg6sWqaMj5zHH/B/UKy8RzXh4DoqmTlKlnaZ2KizsmTJZJJk1KpFbm5mqZGLKazcWOaVCrG6GiVjg6Te+4ZoFy2GRhI0Wy6DAykcF2fbDZGT0+Sc+eqbNiQ5rbb8kxN1SkWW1QqDh/96C089NAOvv7143z3u6fZsCFFve5y5kwlCImUHDkyg+tK7ruvn3rdXfF3sc1haSPEFRcmUspZIcQHgO8AA6jCY88lHiKAceDH5zu8CiEGUTLip4CvXenxtLH2sBhRdPv2PI2Gy+uvz9DREbvsVdFqmThdqxbx5bpESuqcY3T0LIYh0PW5rBPfVxH2HR0GtZoTPF4pKapVG11XXIFGQ3VZDEMRJGMxjdOnK1iWkoZ6nowKHd9XYwEhRKSuEUKRH3Vdo1S6OpFV6opf8RjeikVJWGioroeIQhUNQ0TFSaiY8jyJaSoC9PnbCGXasZhOreYyNFQin7ewLC3opjmBIZukpydBd3cCUHyR++5bxx//8YcW+IzcemueT37yrkj6/oUvvMDb3taHpgl27FBEct+XvPHGLEeOzPDYY0dJJg00TYv4KrOzzWj0+MYbM8zMNPjJn9y6YiL6pTgs6bTJl750hPXr0+0Rz1sAK+mYIKU8JIS4A/i3wP8LyF3kriXgS8C/lVIWztvGEMqkrY2bEOcTRUPDtK1bs0u6KloNh8Zr2SK+XCF1+PA0mzZlSKVilMs2nucHYXoS1d0QWJZGq+WxdWuO48dnSaVMGg2PalX5UISdklTKxPcJFD5q4bPtC4mr4VggFlN250KAZek4jn9V+R/xuEa16lzSS+Vmgq6rUU0os1ZdDC3qeChXXvWZ7u6O02q5JBIGrZbi4iireR9dV3bzoczXMAS27VOrOaxfrxyBbdtn+/Yc4+M1ajXl1luvO9Tryhl2YCDFrl1dlyRqL4XMOjZWJ5lUy0St5kSFkO8rnxQlc3f57/99iI6OH/DwwzuvuFi42BhUSjh1qsypU2Xe974N7RHPWwArKkwApJQl4FeFEL+Osp/fBYTDxgJwBHhRStnWDLZxRcz+S51cl4Jr0SJe2JGp0d+fjI59fiH1+7//EsePFxgc7MD3fSYmGui6xPeVpNOyQitxVeTU6zanTlUiuaimCYRQRUi16mBZqqCxLD1YFCXxuEar5UfW5kJAJmNhGGo80NOTACT1ukssJrDtq8MBqVSurr39WiPVzh+tgSK4CgGtlpKCm6ZGIqGzeXMWx/EZHm7ieT7ptOKGaJoT8Ut0ncCdVYU+hp0T1/UpFlsMDmYxDJ2RkRJCCFzXpV6HfN5i69Yc9brDgw9uueTxLkU1NzCQ5MSJIsmkycREHdv2icUM5vxvVBevXnc4cGCEiYn6FRcLFxuDTk7WOXGiwObN2chxeS2MeNpE3asHbbU2JKW0pZTPSCn/SEr5ueDfH0kpn24XJW2EuB5R6UsphlaCsCPzhS+8wPHjBaSU1GpK1rtrVze/8itvZ/funii1tVJx2LIlRyplsmFDinQ6RjJpRKOWRsNl06YM9brDz/7srfT2JhBCRFyEeFypk6SEVsuNHFt1XWAYerBYiMg0TQgolZoUCi16exO8+93ryGSsoMgRV21xv9q8lrVUlJim+mwlEgaGMUf+bTQ8bNuPulnJZIxs1qK7O0Fvb5J63aNQaFGttqJEaAh5RpJYTEnL43GdVMoklTJ417vWAfDmm7NYlhF03dR7WSq1eOmlcQqFJgcOnOLgwakFSqsw7frRR1/kxRfHeOaZc0xM1C5QY9VqDv39ST7xiTuCcZwfjROFUAWS50kyGZNYTPnVFIs2W7Z08Pjjp4Lk6+Uh/G6cP/o7eVI12bdtyy74+2p9f68EF37nfY4fL/CFL7zAY48dXVV121sRq1aYtNHGUjAwkKRWW9yuJjwZrjaudjE0vyPT05MkmTTp6UkuepIORz31ukNPTzIwVlNunYr46pHPx7n11jz9/UmefPI0rZbL+vVpOjvjGIaOrivzNMNQi5Fl6cTjOtu35xgczEZjgJDbYBgqyC0W05iebvD1r5/gxIkC9bobjX7auHKojBsdXReBOytIKYJRTOiqq+5XLDaZmKgDgk2bOshmYzSbLoaho2laoKpShU7oQ9PVpTxtPvGJOxgczOP7RInVmzZ1sGlTB/G4jmlqHDtWoFq1mZxs8LWvHeMzn/khX/7ykUAZNbeYvvnmLOl0jGPHZvnLvzzO179+nPHxasQdCVVzH/vYLXzwg5s4e7YWFFgqwbjV8rEsfV53Qy3EKykWwu/GyEg5iFVwmJqqMzpaY/v2PL29F54brtbFzOWwnO98G8vHikc5IYQQFkr2uxvoDP48CxwCnm93TdqA1ZEQLxer4adyKag8H5PJyTqHDk1x7lwNIaC/P83GjWkOHBiJWs0hZ+bOO3v47Gd/xKlTZTSNqLhIpWJYls4Pf3iObduyVCoO9boHqIJl06YYxWIruPo2EQI2buzA9+Hs2SqWpdHZaTE93cDzJNlsjETCwHUljYYTEW3PV4HciFgroxxdV+MalWcjo5Ga5/n09iYDt181zrFtj/HxGvG4juPIoCjR8Dy10GuaKnI8z48K0Pe9byP1usPDD9/Ba69N8Qd/8BKep2zlbVuF+N1+exfHj89GxVFXl6DV8jl1qsT+/Qe5665eQPD446fYvDnD4cPTDA+XSKdjVKs2Z86Uefxxl61bOxgYSPPBD26JOFx/+Icf4BvfOMGv//r3OXu2QiplEI+rLo7K71GeK9u2Ken+lRYLF+OTqUBCe9HxyGp8f68EbbO5q4sVFyZCiBTw74B/xpwb7PkoCyH+GEV+ra10n23cuLgeUelXuxgaG6sxMlLi8OFpajUH01R5JEePTnHkyDQvvjgOwAMPbJk3f1b24Q88sJnnnlNpwiCYmlLbymRiHD06Q29vEstSduLFok0mY7FhgzJOnpysceZMhcnJOuvXp8nnLSYmajSbXpBePOfmGo8LZmcbGMYcF+J6SIhXE9eqKFEJzAaO413EFA1gYZqzlGocMzPTCLxFwm2JwEukRCZjRqZ26qdHMmkGxmiKFCuEIrXefntXREbVNBEQUgWdnXEGB7O8+uok9bpLImEGozw9im4YH6/x2GNH6e9PMTFR44c/PMPERD1wH07R2WkxOdmg0XA4dqxAOh1jerrBoUPT7N7djaZp/MzP3Mr27Tk++cnHmZpqBNJiN1KKJRIGe/aohXglxcJifLKQMN/dfe0uZi6H6zGSfithRYWJEGIj8CQwyEXiiwNkgX8NfFgIcb+U8uxK9tvGjYvVUNksF1e7GPJ9ydGjM7RaHum0CSjDKceRuK5LqSR4/vkxXnllKiIGhldcJ06onJOzZ6sUi80or0TlnRiMjlbRNA0pfQxDo1hsBnknqqVuGDrptLIVr1TsQPkxtxB6nk+h0KLVci8wWgsNv9q4NFR3aWHhcTmEr/H5hYwiufo4DnR2Wti2H/BKVMHSaKj3t6MjFoyANHbs6FogrzdNncnJGhs2qOvAQ4emOXasgBDQbDpIKRgaKmKaOvm8RTJp8MILYxQKLUZGypEaq9n0IvlxKmUwO+tgWfDyy5NMT9d58snTfPzjt/LwwzuDgqGb3bu7+Zu/GY4yeRQbwGfLlhQ9PcmoWLj//k0cPDi1KsTQ87+/yaTB6dMVhoZK9PUlOXDgFFJyTUmnV7sL+1bHFRcmQggD+FtgW/CnY8B/BZ5D+ZWAspq/F/gF4HZgO/BtIcTdUsq3iIiwjfOxUpXNlezvahZDQhB0SjSEENi2R6PhYhgC39cwDJ1CocW993ZGKgIlw1QpwMVikzBVNryCbjYFliVptXy8QG8rhIvr+pRKJvW6Spq9++5eursTPPXU2SD8TdLZmWB2thGMb7yArzDnGBvi/KJEdQaUKshx/DUx6lkL45rQYXc11jxFWFYv/NiYGuk0Gi6Nhhttv1Zz0HVBPG6yfXuON96YXSCv37Wri7/5mzKvvDLBunUZcrkYjuNHLsLZrIWUikRdrdrE4waO4zM2ViOR0AO+SWhZL5mdbVIoEJm0eZ7ikLzxxgz/9t8+w5NPnqa3N8nkZJ0XXhhn48Y09brLuXNVGg2XREKnWGxx7Jgi4z744CZefXWSAwdOr4o8f/7398CBEZ544gwTE1UGB/Ns2pThxIkCr7xybaXD12Mk/VbCSjom/wy4A8V6+l3gM4sUG28CPxBC/O/Avwd+HdgZPPaPVrDvNtpYFq5uMSTo6LAol1uAMhULnTsTCSOy7J4/fx4YSPLss2NUq4p6VS63goTY0AfDo1iUWJYetYxt20MIQT6f4AMf6GN6ukFXV4JDh6ZYvz7D7GwTTVPeGfG4QbnsIISk2Vzaym4Y6qo9lYqRzSqi7PUuCmIxLSBcXt8DWa3u0oWFloi8T0JICbWai2nqlEot1q9PX8BlCJ1kK5UWuVwMy9KoVpWKxzRVRo6uC3xfUCgoQ7R8Pk6lYqNpGq7rEfrmeJ4i2XZ2qhC/eNygUrEpl21aLZcf/GAU01TGasmkytkpFtV+169PB4VNkxMnCvzX//oTSAm/93svrqo8P/z+SgmvvDLF297WuyCn51pLh6/HSPqthJUUJh8Pfv53KeW/udQdg4LlfxFC7AT+QfDYdmHSxkVxI3kErFuXor8/STyuDMyqVZtYTCOZVFeq9brD6GiV5547x8BAmrGxGg89dAePPXYEIQTFYjOwHz/fktwPlDhKPNfbm2Dz5iz/2//2Y+ze3cMXv/gizz03xrFjBWzbo153sCwj4jeE21kKdB1iMQPbdgPDr+UlE18tOM7aOI7VOobQkdfz5tKXNU39HpJmQS3E8bhBtepQqzmcOHEuijSoVGx6ehJ0dsYplRQpNJ2OUau5gKRWUyofz1OS3mRSma6F8QbK3p4gX0cLjoVgPKNCA6enG8TjBq7rMzlZ4+1v76NedymXbTRN0NGhunZdXQkGB3PYthsUwuKqEkPXCun0eoyk30pYSWGyG9Ut+eNlPOY/owqTS1nXt/EWx40W5nX//Zt54onTVCoOGzYoS/BGw8G2fSoVm0TCIJuNMTPTZHi4xN69/eza1UUiEaNSKS7wr5hfnPg+VCp2IPnVuOWWPBs3pnniiVPs2tVNve7wwgvjuK4XLUKlUitKYE4m9UC1cfnqxLJ0hJCkUjFqNRt7FZzqQ5lsaBp2JVgLRUmI1RgrJRImrqsKP/WeEVnKz/8MuK5HpdKiUmlx4kQxMNBTqdrNpk86bdDbm+LWW/O8853rePzxU5imxvh4nVZLeaMYhiCbtdixI8czz4wzO9vEMNS4ptVS2zEMFV+g60rFk89bNJsuuq5FuU2hR4pl6RQKfuBlItB1jUKhRTodw3EkuZwVEXSvFjF0LZFOr/VI+q2ElfiY5IKfyyGyhvfNXvJebbylcaN5BOze3c3HP34rmUyM8fEahiEol1sUCk1AWcHruiCRUEZU5bLNkSMzdHXFsSyNZNIkHtcRYi5LJUToGqppgjfemOHQoWnGxqocOjTNG2/MsmdPN4mEGRBhNVzXp1JRI5xwEQ3N1i5WywmhOhOKVyKJx43IT+NKIIQidpqmiNJ02yBIWlYeJ0ppNXdbWLyFozwhRCAVd6lWW8zMNCKZuOf5lEpK4huO/9JpE9dVuUhhIdzRoaTno6PKQE3TRFSYpNPKrM331d/jcRUA2NeXDBRdKhXadX06OlQhkMtZkXU+EIT5eVEi9i235CK32PO9iqSUTEzUeOaZc7zwwhiPPvriBeZvS8H18EFq49pjJYVJmHmzcRmP2RD8LK5gv23c5LjaTq2rDSEEDz+8k//4Hx/kp396G7qu5L2GoZFOK3XFyEiZkZEyg4MdUdejszOOlCJKes3n45imHj3v8Ao9lTLIZNTi8NprUxw+PMMTT5yioyPGnXf28P73bySRmAv+C6XAKmVYRq6x4TaBgCirEPInDEONn/L5OLquLbs7MJeeq8zBdF2LclauF8KuzXIfs1ght5JuieqCSOp1B5CRiZoiSM9JuKUkyEvSg/dRFYqhJb2mKXmu56nP19RUg8nJOtmsRaHQxLIMtm/Pc8steQYHc/T3JxkaKpPLWeTzcWzbD8ZjMpCSC7q7E+za1Y1t+9TrirTdbHo0mx6WpZPNWoDqSCQS4ajQiwjSoUV+ImHQ35+8wME1dJt9+ulzTE3V2bSp44pdUi/mDtsmnd5cWMlZ4xBwPyqA76+W+Jh/Hvw8uIL9Xnfs27dvA/A/Az8FbAJcYBj4BvB/7N+/v3CJh7dxGayVdu1yeC5CCPbs6eXhhwUTEw1yOYt6XZHhHMcjlTIxDI3+/jTpdIzx8Tq7d/fw1FNnmZlRC0oioUfur2r/kEwadHRYwaKv8lZGRkocOjRFd7eyqtc05WfR359idrYZtPPVAqOublW7PrxCV4vufEmxCvmzLD24UvYvKAovBrXAasE+5vxZGg2Xvr4U09N1pFTqj+sR5nclYyTVuVLdp9Xye5kv0Q5VOdmsgeeB48y1TsICJtQRqA6FGxUptj3ndRIWGMeOKal6ImFEbrPFYovZ2QbNphrzOY7k1ltzzMw0GR1VahrFFbHo6oqzb9+d/OVfHqdQaAamaw7ZbJwNG9IBCVo9gXzeolZTPKRGw6W3N8ltt+W59dY8p09XI47FfGJoreZw9OgMlqVzxx3dbNrUgRBcEWG1TTp9a2AlhclXgQeAvy+E+F3g31xMAiyE0ID/FbWQy+CxNyT27dv348B/48Ik5buCf/9i3759H96/f/9L1/bIbh6sBY+AK+W5hN2ejg4l4dy4Mb7g2IeHS2zfnuOWW/IRN+XYsRl8HxzHo6PDwvN8ymUb31fcg1bLiwL5OjvjxGIas7NNEgkjkhxblvq9WnWC0Y1BLhejUGhFicJhZ0QRaxcet+IYeAFfRUlNLwbDENFCFY8bQTGjRghAVNR4nuK3LMf/43pD09ToTQiwLDPIuHEvUAUtl28SflTCIkflF7nzuiZygTxbSXrnj3bm9hmGPRqGRm9vgnpdFSsf+tAWLEvnmWfGmJioYVk669alaLVcCoUmk5MNgMhKXrnEOpRKLYQQ/M7vvIcnnzzN2FiNs2crlMs2GzakSCRMTpxQGVCKg+TiOD49PQlyOYvXXptidLTGvn17IuLnfGLoX//1ED09CXbv7qW3Nxm9FldCWG2TTt8aWElh8l9QXYOdzJmn/SnKx2QSVYD0oXxMHgZuDR53OHjsDYd9+/btAf4SSAF14HPAE6jX8cPAvwLWA/993759b9+/f/+563WsNzLWgkfAlSYSh92erVtzjI2Nkkwa0ckyFtOoVp3oOeza1c3P/dyt7N9/kOnpOh0dCWZnm9TrXtAtUeMU5diq0dWVwDR1cjmLzs4ElYpDV5cKDIzFlOJCtfN18nmDQqGFbXsBb0UErqNeIAueW1XD2yxLyVN9X40ZYjE9CHBb+BxDBZEitvpomsrhAfUelUp2wCm48nThkGdzrcmvvq/epw0bMnR1xRkfr3P2bAXHCb1krmy784uL8P/qp5KUh12U0PwsFtOiv4X3V6Mc5QQbi+ls25Zjx46uqFA/fryA76v77NjRGX3uurttzp2rMTFRQ9NENF4Mi6H77lPk2VQqFuxNsHdvP5s2ZTh9ukxHR5ydO7sCH5MJdu3qJp+PUyq1qNWcwJ3Y4K67eqN9zieGjo3VkdJf9ELjSjqgbdLpzY8rLkyklK4Q4u+jFubtKKO1377EQwRwHPj7N7C52qOoosQDfmL//v0/mHfb9/ft2/cy8GWUsdy/B37xmh/hTYC10K69Ulli2O3p7U0yOJhjaKhILKbSgEslpdAJn0PITbnrrl4ee+wo3//+GVotj9tuy3P2bBnXlbiuJBYz8H1loFWtOnR0mOza1UlPT4rHH1d8m3rdYWamiev6dHcrZZDrKuO20EEUoK8vRbPp0Gh4xGJ6kNOj+CmeJ4PsExn4oSj1RtgdubBDILBtie87VKt+IFs1yeVizM428P05VU6YzbPUscq1tMyfnxsUEjpHRyuUSja33ZZnZqZBPK5TLNoXdC+Wg8Xu73mQSsXwPJXcG3aXLmZup4pCn1hMZ9eu7qjIlVI5to6PV4OOjwi2I0kmDbZuzTI0VAz4K0rG7ro+g4NZduzo5Omnx/jsZ5/nHe/oJ5UyF5iWPfLIXoQQPProi6RSiogupWRyss7QUIlaTZF0H3vsKJ/7XM8FXYu10AFt48bCitKFpZRnUOOL30URWsVF/hWBzwNvk1KOrmSf1wv79u17O/D+4L//93lFCQD79+9/DGXRD/BP9+3b13utju9mQtiu/dSn7uGWW/JompLKfupT91wzqfCV8lxCcp6Ukj17enjXu9bT1aXGOYmEzr/5N/cueA4hN+Xzn/97/ON/vIOf+Zlb+MhHtrN9ez5wG5VB90B5XvT2JpicbLB5cy56jd71rnU4jioqNm7MkE7HKBbtKBywqytJZ2ccTVNX5MqqPE4mE6O3N4XvS0xTn5fZoha0UqmF5yleizLsCvkXIpCMzvmtuK5aNNXow0PTNFIpIyLjLrcoAXHNzN3CwglCHocMpNotyuUWyaSJ43iBs29ogLd6+/Y8H9NUEm8l3Z3zoQlf8xBq/5J4XOPcuSqe5/PYY0d54olTQUFbxXU9HMejVnMoFlts357nZ35mOxs3ZqJitKvL4gMf2MxP/uQgU1PNqKC5lAou/E6EZNZnnjnH7Kwyb6vVbL797eFFyaxtwmoby8WKKfNSyjrw60KI3wD2ArtYmC58GHhRSrm4xuvGwcfm/f4nl7jff0GRgnXgp1mez0sbAa53u/ZKr/LO7/ZkMibbtuWibs/HPnbLRQur8MQvhPKf6OmJB1wTiWmqYDbLMtiwIcnp0+XoNfrc597H7t09/MEfvESj4VEqNaJFQDmBajiOSq9VSbAS3/fp6IjR15ek1fKYnKxHEuEwLRaISJYLxxBK6eN5csECHVrzh7+H4W61mhMRes/vNJzvqBp6aizFe2W1EBZN85+fbfv4vsfhw9NkMhaNhnfVOjjF4hyXY3S0iudJpFTkVDUiU691+BoqTo+IPidSSk6cKFAu22zdmqVYbCLEXMCf4nUI+vpS9PUlefDBLQv2PzxcjLpd83F+dzD8TlQqNkNDJXI5K/osG4ZPT4+16JhzLXRA27ixsGpavqDweDb4dzPiPcHPOvDCJe73vfMe0y5MbkBcKc9lJeS8+cVQve6yeXMHtq1C+Op1h2zW4t3vXk86bTIxUb9gn8mkyWc/+zyFQpNUyqC3NxEpKlQooBY5d4bdlVrNpVazSadNuroSDA+X8DxvwVhh/lhlvksphNJgiWGAEFqkaAElV+7o0EkkjID7oHxSwiIgdMa1bT8akfg+F1xZXyvM74a0WqEqxqdUUgv9cj03lrI/pZzy0TSNbDZGrWYFvjVK1WUYGpXKXGGnSNVeoPiyOXmySLXqkMmYbN6cpVBo0dmZ4O67ey/43Obz8ej3+beVy4oTNDh4ob3U/O5g+J0YH69dMC6ybY9t2/JoGheMOduE1TaWi+trMnBj4Y7g5/H9+/dfVLKwf//+c/v27asAmXmPaeMGw0qu8q602zO/GEqlVGJwOq0SZYvFFu9613r6+pJMTdUv6NgIIfjYx26hVrP53Od+xOxsM+IRhMWIaWpRByQe1xkbq+H7iseSy8UoFpvE43OOn0Bw9b7wOMPiwTDUpFbTJEIopYga+2jUaso6tlp1IgMv21Zjn9DHI+zEzOd4zD2fax/etxivw/PUc70aa6d6rVQHZHa2STxu8O53r+eOO7r4y798g2KxRbHYikZghiGo1dwgPkDntdemWLcuTS6nXIXHxmp0dyfo60sxMlK+4HP78Y8r/cH8cD2Vgu0xMJCit/dCc7L53cHwO/G5z/0o6MJ52LZyox0czNHbm6TRcBYdc17vDmgbNxYuW5gIITZdjR1LKU9fje1eDezbt88CwpVoKU63Z1BFyXLM59pYQ7geV3nzi6FczuLs2QpSqqTf8MR/qY6NlJIzZyo0my6lkrIkDw3bUikDw9DIZGLs2dON78PwcAnH8QKSL1SrqpuRTptIKahUWtFIZ86HQ0QW+o4jEUK5jfq+4plkMiatljLfCosalWirTLt0XUQeIaHqJJQfnx9kdy0wXyVzsX1e6rYrhVI9acFrojo0qZTB2FiNgYEUfX0pjhyZAea6VEo5pQUeJBqZjBF0wpRaK5k0GB+vsXlzB7/6q3sX/dwC3H1334Lb7r9/M9/85vGoaxXi/M9a+J04dGiKZ54ZBQRdXXG2bs1FMuBrKee/UbK01iLW+uu3lI7J8FXYr1zivtcKMvN+ry7h/uF90lfhWNq4RrjWV3nnx7vXai4TE1Vuv72LTZsyTE/XL9qxkVLyH/7D8/zJnxwmn7fQNHUVHuaehGFuO3d2sWePUk7cckue558/F4wClKlWsehGYYCZTCwaMxSLzWAcI4LwubmCZT4/o1Bo0mr5QYdGjXkaDbX45nIxhBBR4RLC9+VV429cDvND9a4l1LjGD2TBHq7rMjZWo7PT4y//8k2mpxsBt8WNXnM1kvODUZpE17VoxAPq8yOEet8v9bkNbwsXpyeeOEWh0OSNN2YZHMyyaVOGet1d9LOmPqM7GR+vL5DSw7Ujs95oWVprDTfC67eU4qD9DkNi3u9LiTdrLfK4Ntq4LOYXQ488spfDh6cv2rGZf9Vz8OBUcKKJkU6rf9lsnBMnCpFbaHd3IipKpJSMjlY5daqCEHMFhTohKU6K66qRTzKp0dERo9Xyo5C/ZFIEUmJVnJimFpjE+UEukBGRX8tlm1bLo78/TTJpcPTozILnfD2D+mIxPXgOV4/cGmK+yZ2uQyZjBSGNyjiv1fIwDD0i/5qmjMjDoZQ5mTSwbS8YwfkLlGOq6yTp6rr8aef8xWnHji5SqRhDQwVqNZcHHtjIgw9uWbQ7eL3JrFfqMdSGwo3w+i2lMPmFq34Uax+Neb/HLnqvOViLPK6NGxS+7/P1rx/nsceORm3PT3ziDj72sVvQtBUp7hfFYm3Wf/JPdixos85fWNJpk0OHppmdbTIz02BmpsGGDUo23N2doFy2icf1aBwQyj2fffZcEOqmUmnDnBvfF0EWi1KnlEoOuZzF1q0JxsZqJJMmlqUxPd3AcdRVf9gJAVWkWJZGV1eSVMpgerqJ5/kMD5fo7k5EKqPz3VSvNWIxwa235jl7thId+2pg/joejoCyWXXaqFSUjDuRMGg0XBzHIx7XSaVUEGOx2MS2lXw4zK0RQo19pIRm08X3fZLJGM2mR2dnYgHXY2Agxa5dXZc9xsUWpx07OrnttjwjI2UefHDLJRenPXt6OHRoihdfnEQIydve1se+fXeye/eFPiarjSv1GGpD4UZ4/S5bmEgp//RaHMgaR2Xe70sZz4T3uWDss2/fvn8B/Iv5f4vFYvzO7/zOgvv92I/9GO9973uXeZhtrDZ83+eXfulxvvvd0ySTBsmkwYkTRT796R9w4MAIf/iHH1jV4mRhwWFQrdp861vH+c3ffJpYTGPPnh5+6Zfu5pZbcjz++Ck2b+7g8OFpTp0qRy3+SsVmZKRET0+SXM6iVGpRrTpMTdU5cOAUvi+ZmmrgeT7r1mWoVByy2VigkPHo6orj+2psYFnK9+InfmIrAwMp/uqvTlIsNmk0VH7L5s0dCCECSXA96AoIentTdHXFmZioR/wR151beOFCqfC1RjKp8ovUWORCm/7zofxboNVaWkEVcjZSKTNKbNZ1jVbLiRKhYzGLVMoITPAEtu0Fr7kKQrQsk0RCD0Y6oXJJ8XR27uwiFtOp1Rw6O+Ns2ZKlXncukAMvhitdnM7vtOzd20ut5jAxUee116auyYK2VrK0blTcCK/fjcTzuG7Yv39/a9++fdMoAuyGy91/3n3OLLKtPwL+aP7ffud3fkd+5jOfWfFxtrH6+PrXj/Pd755mw4ZUVIAkkya+7/Pd757mG984wc/8zK2X2crFcX53xPd93nhjljvv7ObQoSmeekpxQHRdUK26vPDCBL/8ywfYti3LXXf1Mj3dYGioSCYTQ0pJvS6DBV8GgWzKEKtScUgkDE6fLkcOsbmcha4TXLUbJJNQr7tRMvLAQJz77lvH1JQKg5uebpDPWziOIq7296fZuDFDtepQq9mR1Na2faanG1iWRrGopMhKXSKp1dx5duyr8Q5dOWo1h0bDDYLyLl0oha+prmsYhjrwxQqZ+Woi0xTk8wk2bkxhWQatlsfwcJldu3r40Ie28txz5zh6dAbPU4WR+qk6T7WajRDKfC0WMzGMGPW6cuft70+wa1cPXV0JOjpiF4xSfF/y6KMvXpLUeKWL01oYA7SdZFeGG+H1axcmS8dR4L3ALfv27TMuJhnet2/fOqBj3mPauIHx2GNHSSaNC7oimqaRTBp8+ctHrrgwWYyE9uyzo0xNNajXHY4fV/N+y9LQNA3P0wKJruTw4ZnAXt4lFtPJ5zUqFRvTVJJS2/ZwXTU+sW2P9evTNBoOoFxb43GTRsPl7Nk6qVQ4VvCjTkgiYaDrOs8+e469e/v5tV/by5NPnqbR8KhUbHp7EySTJuPjNU6fXjgKcRw/eA4uQsggBff6d0jOh+dJxsdrgRV/GJC3uLlbmNrbavl0dBh4ngDUa+95fuTWGoYkGgZksxZ79nSjaVCruXR1JbAsndOnKzz++AigukiVSgtN04JuiUEyqc9TP4mAt6ORSpls3hynXnd55ZVJduzoRNc7qNdVSF82a/FHf/QaJ06UkFLS0RFjcDDHiy+O86EPbV1AahwYSPLmm7ORWVqt5pBKmQwOZhECbr2184LXANbGGGAtZGndyLgRXr92YbJ0PIUqTJLAPVzcSO7vnfeYNm5gjI3VAMnJk0UqFcV7zmSUY2oiYayo7bnY1aeUSoL5+uszVCoOuk5UFIXBa4ahAx6vvTbJunUZHMejXLap1RxarbDlryS5jYbHwECSZNJg48YMQgjOnq1Qr7vEYkoxE4/r1OsOrqsWVsMQdHUl6OqKUyw2KZdthBA88sjeqJj6gz94icnJOhMTdWx7vgzYw3XV4lytOtGIwjBA13Vs27vunZIQvg/FYivi3yh10OIH12qp4sMw1HuUSBik00bgdKsFkl5VbJqmItWC4MiRae69d4B7713HoUPTnD5dwnV9Tp4sUa3atFpuwPtRqc6OY9NsKk+YTCaGZenUai5bt3ZQKtmcPVuNXFonJxscPjxDNmtx//3r+d73RhkeLqFp6viKRZsf/WicrVuVcdr8bsb737+Jr371TSoVG8vSo8TqsbEamUyMf/kv71r0dVgLY4DrTb690XEjvH7twmTp+Drw/w1+/2dcvDAJg/s84K+u9kG1cfUgpcR1PYaGSpEqAmB6ukG53CKdNrnnnoEr3v5iV5+ZjMnMTBMpiZKBWy0vWjAtS6k2LEunUnEol5ucO1eLrrCV8ZUkldLp6IgFVuZ+ZGEOkMvFqVTKxGIa8bjO+vVpHMdjdraJpqmipFZzeP31GXK5OGfOlHnkkSf5qZ/axv33b+ahh3aQTJo88sgTUVFiWXNOoF6gvZ1LxFVkWu9aa3KXANXBEZFhnO8L/Iu0dUJZtHK0jdFouFiWEWQF+fi+JJGYex3icR2QDA2VME2dkycLNJseIEmnTSqVFoYxlyIc2vGHvBTlzttBo+FSr3tB0WDSavmBmZly7S2Xbb7zndNMTNSJxTQMQ8dxVJpvIqEzPFyitzexiCMrKOeG+bh01bgWxgBtJ9mV4UZ4/dqFyRKxf//+l/bt2/d3qI7Iz+/bt+9P9+/f/8P599m3b99DwAPBf7+0f//+yWt7lG2sJg4dmgZEJJsNOxe6rsYThYLNe9975R56i119btmSZWioRLPpBmRUAmUGkRy3UlEqm3w+QaGguji2HebZaGSzBroOiYRJd3eSY8dmgu5GjVZLpQrH44o0GY8bVCothBDBQqosyhMJHV2fC4pLpZTc+KWXJvjgB7fw0EM7+C//5TBPPjmCpilVjuv6USaPUpU4UUid7y/NzTVcnM/P4bncYxQfY9lvAaDGKYqoKojFBM4lUr0Uj8anXneCQEQN09Qj6bTyEgmfgyoOlEvrJI7jMTFRJ5OJUSg0g8ybOefbsLgzTS2w61ejplTK5PTpUqB88snn41SrDqOjFULn3XPnqoFDrR68Jir0saMjhmFonDxZXNDN+N73TrNnTw9SqqycatWJzNKEgCefPM2ePRdmkK6VMUDbSXZlWOuvX7swWR5+BXgGSAHf2bdv32eBJ1Cv44eD2wHGgd+4LkfYxqrhySdPYVka+hIAlwAAm6BJREFU2awVjFX8iCfheZJczuJyV5iXwvlXn1JKJiZqNJvugpGH5ymn1VhMSXsbDWWAdu+9/TQaLkePznLyZCHgIRgIMafu2L27k1dfnaDV8iJlSLPpUqs5wdW1YHpa2aGbZpxKxY6u4hVnRSULN5tewGvo4rvfHeGuu3p58MGNvPrqOMWiHRUdKjRQDxbJOUfXpbqnqgJGRoqWpY59VsJdcRyffN7Ctv2IgBrmBC0G09QxDC0qHn3fifJnwmLF92Fyso5p6kxPN6KuVzg2qVTsQJ0kozGS56nUYMfxqdfdKA1YFV0qNiC0jT9zpoIQSiUkpVIK6brav2FoEXFWHa/KSOrvn7OcHxurk06bJJMmfX0Lrejr9cVt5eHGGAO0ceNj9U0YbmLs37//IPCzQBHFNflt4Gng+8C/RiUKjwL/YP/+/eeu02G2ESD063j00Rf59V//AY8++iIHD04tOYxtbKyO68L27Tm2bcuSSKhFP5Ew2LIly+bNaSYnr9yq5vw4+MnJOkNDJTZv7iCZVMnE4cITotn00DSNbds66OlJsnlzBx/72C3cdVcf+bxFLKY8MTZu7KCjw2J2trUgjC+U7bquR6PhUKu5/PRPb+PHf3wrtu1HZM7Z2Sa+70che74vmZlp8PrrM9i2xxNPnOKBB7awfXueWEwnm7WCQkfD83yaTSfqeijC7tLhecqAzDSX9rjzrdSvBIosapJOK4+Wix1zOP7o6UmSSJjoukZnZ5xYTCcW06OQvXLZxjTnOkfNphflBHle+J6q38P3Z64YkpEKamSkTKvlcdttnXR0WKTTMYrFVqAO0oIOjipKwg6G4/jB6FEL/q+6ZPO7GQMDySgJ+nzUas6CImbh81djgF/7tb2kUiYvvTTJG2/M0t+fZM+etXn13caNh3bHZJnYv3//d/bt27cb+FfAPwA2ofgkw8A3gD/Yv39/4ToeYhusju3ywEAS0xQUi8q5NJEwyOUscjmV0hqL6Rc9gYfHEFp+Hzo0xexsk3w+wZ49XTzwwBZ27epacPV57Ngsnicpl21yuTjJpM7MTJNCoUWr5eJ5sG5dijvv7OG22zpZty4ddVzuvLObWs2OouhrNZU4e/DgFPG4TjodD2TCSoESi6nxQ7VqMzlZZ8+eHlIpReZtNt2ggAFQV92+7+F5qqNw8OAUW7Zk2b27m1/8xV38+3//PBMTNTxP+aCE0ls10ljeiCW8v+exqDrmfITF1koJtY2GR7PpkU6bCKGhaT6GMScJVmOysGul02x6mKZOb2+Sn/u523n99RmeeupsYC8vicf1yI+k0QDTBMchKFCaQZaQ2nY4zgF1e/g6l0oiKvps2+fUqTL5vEWr5QXKKoNms4HvC3p7k0xPN9F1ief5EU+l1XJpNj3+8T/esaCbsdKRzGuvTTE+Xuf22/NR1+T3fu/FNWNp3saNjXZhcgXYv3//WeDTwb821iBWw2/h/e/fxJ/+6RFGRyskkwa6rtw4y+USlmVw5509Fz2BzxVGI4yO1hgfrwajjQJnzpR4+eXJiKsRktBeemmS3t4Et93WyVNPjXL2bBXLMujsjAcjA8m2bTmmpxtUqxNICadPV+jsjNPbm2RwMBsQLVXQ2+BgjpmZCbq6EuTzFs2mFyUDh0RekDz//BjDw+VI1bPYGCN8XKWiyJZf+9qbHDs2Sz4f5yMf2caxYwWefnqURsMLeBciUvksFSGPRr1+S3/cSqFpqqvgeQS28Bq2rQ4g7JyEUmIhVKeiUrFJJAz6+1MIATt2dOE4Pk88cQrD0CI/kjBfKHhWQfChf4H6J+SWhInL8bjiAb3nPRvYsaMTz/MZG6sxPFyOChfVKdJIpXR6euIB76kZyb0Vd0by4z++hS9+8e8tKBYuN5LZubOLgwenFg15WwteJm3c3GgXJm3clFgtv4VkUoXZ1esOZsBT9TzVan/nO9dddKYenrwTCZPp6Trd3Sq/pFq1OXp0hunpBm+8MUsyafCxj90aHcvx4wV8f26BNM2QL6D2+/LLk2SzMd7//k3Uag6zs02efPIMd97ZzfbtOSxL5+TJElu35rjvvgGmpmpMTtYpFm0syyAWU5fmUhJksKjMldHRCpWKfVmb+LBwOHWqHJArLQYGUhHRMh5XnZhy2cZ15QUFhmGERc6F276SYmSlBUw8rvg04XbCAiyE4n3ogfrGCxRGigzt+4roG46S9uzp4emnRxHCjXhBpqnRarnBaCUkMMuoSzJH9JVBh0iiaRq9vUl27uxhx44uhADD0HnwwY00mx4jI2VeeWUS09TYsCFDZ6cKbdy6VSedNunqSqBpgv7+FA8/vJOPfnT7BT48l1Jm7NzZxVe+8vpFu40qYHBtW5q3cWOjXZi0cVNiNfwWvve909x5p2KuHzw4FXiawOBglo0bVSDdxVrWYWF08mQx8LSAiYk6hYJK6S2VWqRSMT772eep110+8Yk7ovb6+HiVbFYtNoVCMzLoCpNkwzFBT0+C++/fxKuvTpJOx9A0jfvuW8dnPvPOSPa3cWOGT3/6B9i2O69LAq2WcjxNJs0grl6lEGva0rocUiplUiJhcOZMBSkl1arLunUpHMenVLKjxT5cE31fjUYMY+7/IVbDfG3+di+3rXAso2TWF94Wjoh0Xd1PyYCNINxQEo8bdHcnePPNApZlcNddvQihwvhmZhpoWugj40Rjs3Cfuq4Fqc/q/UgkFK/Ftn0aDXXgGzZkArfWuePKZCw6OgTvf/8mHOcgU1N1kkmDZtOlXnfp7k7yW7/1Lh5+eOeSRikXU2aEgZAX64gYhogK7fOxVizN27ix0S5M2rgpsRp+C0q5ECOZNOnvTy24rV53mJi4OPE1LIwqFaV+qdUcCoVm1FEoFFrYtk+1qvEXf3GMPXu6UX4aPi+/PIVlCXp6knR3Jzh3rhp0UASpVAwp4dlnzzE4mGXPnh42bcqwbl06MkA7dGia3//9lxgbq9PXl+Dtb+/hiSdOA4qX4DjKvVUtzFAoKDLlUknBIXxfMjJSwnF8NE1te3a2gZKwKlO1UD6rti2j4uT8eKEr4aOcj1CuqwIC5yTKquOgHF3ny4pVcbVwG8ooTSOVEoF7roxyg8IxSSYTY+vWDoSAmZlmwC1x2b27hy1bOhgdrbJuXZzp6UagnNHQdR/HkaTTRsDfUeOejo4YyWQM1/XJ5zWqVRvDEMzMNJmaqtPXN/e5C91ZDxw4zfvfv4np6UYk9e3pSc4rkFbG77hct3F0tEoiYaxpS/M2bmy0C5M2bkqsht/CSoqb8LGhYZpa/JVdfK3mYJpaIE2VHDs2wyc/+Tjbt+fp6IixdWuGoaEyJ06UME3lqJrLWYEMVXU5cjmLoaES/f0pMpkY4+P1RQm/J08WsSyTH//xrfzgB2ciEzbL0onHDQxD8WbUa7P0roWUKik3VIWEktawEAglsK7rRyMNIYi4G+fvJ7Ryn4/lFyoiMCgTC5Q6obz7fA6LGqks3KlhKEXO/NFOpeIihBtts1Kx+eEPRxkYSNHREaNQEDz99DnOnKnwEz+xlUwmxosvTuB5EsfxcBwlFVZ5RDquq0iuoXoq7OxJKaOunGlqDA2VosIk/NymUqFaS9DXl1wg9Z2aql/Uf+R8LJZgHXJILtdt7OxMUKk4y/puXWp/baJsG+ejLRdu46ZESO4bGSkzNVWnXlfpuiMj5SX7LZwv5w2xlOImfOzmzVls28O21eJUrztomiCVUuF6vb0pEgmDo0dnSSRMenqSdHRY+L6SrypSpvK1qFQcXFeSy8UDMzA9yjnp708uICX29CRJJtX2Bgez6LrGJz95N9u353EctTrXam7EDYnFNHzfjwqLy0F1PuaqC8+TgSxWjRXCsVO4LddVxE/TVN4bliUi5Q4szhVZbvfEMBT/I5TLhhyOWExEAYOmKS7o1oRQHBBJo+HSaLgLVEHhWCcc/di2ZGKiwalTlcBzRjI52eCFF8b5yZ/cyu/+7nuDAEX1+FQqRipl0Gz6xGIag4MZpJScO1fhzTcLDA0VGRursXNnF3fc0UWj4S76uRVCrHhEGRawX/jCCxw/XkBKn+PHC3zhCy/w2GNH6e9PXFJKvHt317K+W5fb33I7dW3c/Gh3TNq4KbEatssrMZMKH/vd747Q3Z1kYqJOuWzj+4pw6Xk+nZ0JUimToaEaiYTBqVMlNA2mphr09iYoFlvR1b5l6ZTLNp2dFqmU+trGYhrVqhMVSU88MdeCVwtlPSpcpFQ+JFNT9cCAS1AutyL79UbDixxIl/EqR/k9QLTf0Lo9dEDNZi2qVRvbVqMcFVRnUCi00LT5vh4rg237QXEio9A9TROR94gqhATxuEYyaQZGc3Npx6oQWvxA5kZAc7eH0l7D0ANSseTIkWn+3b97jjvv7CGbtUgkDDo6LIrFFo7jBYodZUKXzVoBt2fu+UsJd97ZE6QRu2iatuBz+/u//9KKR5TzC1ghBJOTdYaHi5TLNq+/Ps0/+kc7KJftiEQbIizIH3xwC7t2dS/5u9VW8bSxXLQLkzZuWqzUdnklxc38xx44cIqODpMnnjiNZSkTrnw+EVz5Slotj97eBNWqw9BQCcvSyectMpkYQ0MlWi2P7m4Ly1L8FBXAp1EqKclqWCQ99tjrUSfm4MEpTp4sRp2MUsmm0XDp7k6weXOGYtGm2XRpNJyoM7Fc8qnKv1FdkLBACX0/wrFHOm1gWQbFYivimgihbO8Xmo2tHMr+Hvr6khiGFhiRicAzxAdU0RLyhlxXYhhuFDp4uW1fDKE8t9XyAh+TBi+/PEFHh5JoN5t1+vuTxGI6tu0zOVnHdX0+8IHNPP/8WOQ9I6VkeFiNbyxL5zd+474LPrvLGVFebHwSFrBCCA4enGJoqBgYxGk0Gh7/7b+9zt69fQwPl+joiC1akC/nu7UWEonbuLHQLkzaeMvgSubcKylu5j/2kUfezsMP/w0vvDBOR4dySa3XHWzbo68viZQq2K1aVWRZlSAbY2AgTaHQZMOGNI7j02i4dHXFAx8NnX/zb+7lYx+7BSFExGupVGxOnixi2x6Fgo2uE/iTKC+MQqFJLKaRSpnBou1FY4/QSn7+GENxRuY6ImHxARcqbuY/RnEsJFL6gTeIF3FS5ncIVgvZbCwYxTjcd9+6KFX3z//8GI4TKpoMUimlRAq9S+YTb5d7PFJKEgl1Gm003EhaXC7bbNyYobMzzvh4LbCWV8nRat8+AwOpyHsmLAyUJHycf/Wv3r5oR26pXbxLGQzOzjbYsaMr6KgVo8IofA1BcaE+8pFbOHOmvOKQt7WQSNzGjYV2YdLGWwKr4QS7Eggh+NSn3sFv/MZTQdfDobMzzuBgFs+THDhwms2bs4yMlJidbWKaOlIqI7QdOzqZnm7geZLe3gTbtuWihSgsSoB5cuManicpFGwsS6NWc6PwPikl9bqL46hAOGVrrpQnpqls1Wu11gLXU2UupgULqnIxVbtUqcKO49FsKqdUZSevbPtDf45MRlmpnzxZJJk0AtMxuSIFzvkwDAIJb5zx8QbveMdAdIV+++1dHDkyRavlB86u6jFzJmWq6DJNPXK9XSp8X6VAh+OyRCJ8fgLb9kml1Mgmk4lx333rAPjWt06Qz6tiYM+eHvr7U9HIrbc3wdatuYt+HoUQQbqzwZe/fJTxcdWNefjhO/joR+c+C5can7z++gypVIxSqRkVTCFs26ezM05HRyxIld57JW/HAqyFROI2biy0C5M23hJYC3PuPXt6+Lmfu+2Cq91q1eGDH1SGabmcxdmzlcgnZNu2LH19KRoNh+HhcjAOmWHz5g4OHpzk05+emmfLLhkeLvLqq1N4no+ua9i26sxYlhF0N1RBosiobuBsqkYcsRg0Gvai6bqtlh91VVRnhWhMosLoVCETdlc8z8OyTAxDtVQ2bepgYqKO5/mBSdnKfUvOhxpjwaZNGUZGytFrvHFjmhMnCmiaF4XpqefsR8cbi6k0ZccRUcGyFG8VZZjmRZLpVsvD8yTJpMGpU2XSaQPTNAK3Xodk0sS2PXp6lA+IEIK+vlSkvpmaUt2JsFg4v8vX35+kXnd4441Z1q9Pc+uteWo1h29962TkhyOEuOT4RHVpCkEhOscEllJi2x6Dg9lV7WSslUTiNm4ctAuTNt4SWAtz7su5bR45MsOBAyPUai4TE1Vuv72TatXme987jZTQ25uk0VBx97OzzeDqXi2ys7MNGo2QXKnIrEL4EQFU2a77wXEsVMvounK4bbXmlDTzuwZzxQbouiSZjAVJwoLZ2dYFi3eowPF9B8PQGRurUig06eqKk06blEo2tZqN41z42CuF60K12uLcOYdt27LougjM4wT33TfAnj09PPvsOc6erTAxUafRcIKUYD/K5hFiTu68VHVS+HxVp0TlHGmaIBZTo6vRURWimEoZWJbOzMwsmib40Y/GyWbjDAykoiLk/IV6sS7fc8+N8cIL4+zZ083gYBYhxKIF9qXGJ5s2ZaLPWKPhkc3GsG0/KkpU7k5j1ToZ7UTiNpaLdmHSxk2PkAw6MlLi1VenyGRMtm7N0dubJAw7u1Zz7ktxVub4KHs5fHiaL33pCH/7t8Ns3pwll7N4440ZBgbSEUl2cLCDmZkm587VSKUMpJS0Wi6WpRxpQ38RRQqVQcYKkU9HmM3S0WEGYweTSsVe1PxszhdEsHVrDpX74tNsKpfXEPMD9VSKsY8QInCyhfHxOpWKjRAi8jq5GFQnQ6PZXFr1MjvboqMjhmFoPPPMKN3dSfbt28PDD+8E4NChKT73uR/hulPkcjk6Oy2+973TeJ4fcHCUWkYILzq2+XyY8JhgYSclJAHbth+QgYmUT42G6tA0mx4zM016e1N0dJgMD5f51rdOks3GyOVixGIG+Xycj3/81mihXqzLVyo16ey0GB4uMTAw12k5v8C+1PikXnd54IGNbNqU5bOffR4Q0VixtzcZeNSsXidjNRRybby10C5M2ripEV51Hjo0TaPhks3GmJlpMjY2yuBgjj17etbcnDssXtavT/O+922gpyfJc8+diwqOUqmFYWhMTjYoFpsYhggSZzV0XZmGzS8QhAgTZwWaJqMMHsfxSaUMNE3Dcdwg3E+5waZSMZpNNwri03Vlna5yX3xmZ5VpXDxuYNsejYY37/iZl04MPT1W4JRrB0ZqclFDtfOhnG5NMhmYmmpd9nW75ZYcPT3JoBCSFApNvvrVN7n77r6gEFQurB/72C2A5Ic/HMVxZGT6pp6rDCTP6jVMJHTq9blOkqYtJPmGyOViQfcolE5rVKstNE3Q0WHSaHhYlkZPTyLYrkGl0qJYbGFZehRbMB+LdfkqFQfLUryQoaESvb2pSO47O9vijTdmuf/+zbz//ZsuOT4JJb/1urOgkzE93bgqnYyVKuTaeGuhXZi0cVMjvOp829t6ee65sWBR1kkmDYaGisF4xF2Tc+757fjQ2h4U2dI0tch5VXFJvCArRlmpx2IariuDroUim5qmyoZJp2Pcfnue8XEVLthouFHejaYpRU4qZQZuqWpRM01loS6lxtBQOZIHVyqtaEQU4vyCw3F8arU5o7qlXiAbhh7kyOg4jqRYnOvMzLeZlxI6Oy16e1PzbhdYlkGh0Ii6COFCLwT88Iej/OhH4/i+jPxb5icvZ7MqTsC2/YhrMt/Ofj50Xe1LyiZA0CFRRnrZrEWrpXJxqlVF3lG8IptEQkUddHbGeec71+H7igQdFlKLjWNCJ+HQw2a+3Nd1PVotjS984QUefHATDz64iQMHTkdFR7Vqc/p0BSnhkUeeRErB29/ew0//9DbOnFEjrqvZyWi7v7axVLQLkzauK672ySpcjLq7E8uWZl5vzG/HhwtSqJxpNOxoPDOXASPnjRokvu+TTBrRSCVU3mgalEo2O3d2s2dPD0LA66/PMvT/b+/Pw+M6z/tu/HPOmX0GswAYLFwAAlwkigQpSqJsK15pSU3iOrGUKE0sK3Xe+Bckv15JnLex49Zx27RxI9lOHKf9NS/SqH0TUXHrNN4Sx45EUbZli9ooiQTFnViJdWYw+3pmzvn98Zw5AEhsJEECpJ7PdfHiYNYHZwbz3Oe+v/f3HkjR2dnAc8+NWh0mGum02IlFe3MVv9+BpikkkzoOB5bLqrlgiaNusFYsVq0OHezgaSm3z3r7rs/nYMuWBk6fTto6mfrz17MAdUfWTZsaLnsel0tF1027TDc+nief13njjWnefHMaXa/hdjustmbDDkrq1vVNTV6qVYNyud51c3nQpViD+cbH83aWxTQhk9GtbNSsZX+dVKpkO/fW5yjVn6tcrvK5z/2I225r4syZBG63g9tvb7Qf39UVZmJiDNM0cbkcdruveN4aPT3NRKM+Dh0a4Xd/9x727WvlueeGmZjIk05XGBvL2TN3wOTb377Aiy9O0Nu7h09+8u5l/+au9u91rbviJDcXMjCRrBk34suqftZ5Na2Za83cbob6huTzOQiF3ExM5DEMw+7G0TTVcgpVrMBFTAtuaHBhGNDc7CGdLhOLFfF4NO67b6OtsTEME5dL5aGHtnPyZJwNG/yMjeVsy3hhpS+eO5+v4HQ62LDBj2EYDA1lrHbi+nyZWU2GyESIUslcd1VFmd3dL+18qZdLFEUhl6vw5psxu9PF4RCOuaJjRmzMimLgdGrMzBRxOkV0lEyWbd+SpiaP7RMzPp7l1VcnKRREico0sYYjqnNed/Z33rhR6HlGRzPAwqUnp1OxzNsuDZpM8vkqum7YmZ3GRhcgXtM0TSIRt92eW9dBiWnFGjt2RHC7NV59dZJSqWpPL25p8bFlS4j+/jihkOgEqo8AqGtEFEXoTQ4fHuGTn7zHno792c++QK1mzBPcBoMmyWT5kpLXwlzL3+t66IqT3DzIwESyZtyIL6u5WYflWjNvBFdyxrl7dxO33Rbhm988T7UqZsAMD2cwDMOabaNSLArNgciWiOBADI0Dn0/DMEwiEQ9NTR7cbuEWm8vpHDkyhsul2RvnRz6yg9/7vf2cPDnDs88O8sIL45w9O2ObogGMjmZwuzUcDjFXp1yu4fU6KBSqdrZm7kTfevbg0sM7d4OfG5TUrdmFpkV09tQDCFUVbcpOpwO3W7V8U1S6ukLkchXS6QonTyas0syscDWZLPP1r5/j2LFp3nhj2ionmZbeBstMTryGx+OwWplFhsPtFroPl0uzDOIMq2wlHu90zmp1qlUDt1u1RggYqKrojhKZGHEAqlWD06cTpNMVTNPk9OkEqqryrndtYHIyz8BAGq/XQXOzF5/Pye23N1Iu1zh+PI7Ho+HzOTl3boZkssLOnY3EYnkURSEc9swLSuByQffhw8Mkk0Vbp1RHlLw0ksnSsp1py1nZ+3zOeb46c1kPXXGSmwcZmEjWjBvxZbWePBSu5IzTNE2efvoUZ87M0N0dJhbLk0zO2rpHIm58PheJRNGaXKyiqqY1mK9GJOLC7dYssywno6M5Uqky4bAL0zQZG8tRq5m0tfnZsaOJ06cT/M3fnOZjH7uDnp4ov/M7s2sNBJw8//wIiqJQqZg4HGKoYCZTwelU8Hg0SqUaiqKgaQvbzK/EtKyu3xCHQAWEYNfjEQFCR0eQUqlKMlnG5VLZuVMMkzt2bJrDh0cplWqXvU6tpvPSS2OcPBknEnFTLFYtbY5puduatsGa6GQS1vXhsIdKxSCZLON0ijECpincXUGUfOoBmTApA5dLlG5yOd3O3pimYetESqXanIGOYBgKbrfCyy9P8NZbCVpavOi6YXU9iaBBzM3ROH8+SblsEA67uffeVrxeB9PTBRoa3LzznRsuC/4uFXRPTBSoVMx5viV1RMmrtmxn2qxGZ2Er+8cff5lCQV8wcyLdXyVXggxMJGvGjfiyWk8eCotliBobDb72tTP098dQFJX2dh+bNjXw7LNDbNkSQlUVbr+9kenpAhcuJHn99Rjd3UH27o1aGZgEExN5ADZuDKIocPfdLYDo3JieLlAuV9m6NUShUCWVKnPHHU0AJJMlTBOmpgo88cQr9PfH+NjHdmGapr3W6ekCY2M5TBN7Y3W7NQzDoFIR83AAu5xUq82PDi71RVkO8Tr1VIrQpvj9wmdl8+Ygup6yM0SKotDWFrCmFs9a6ddfU7iwQjot/EQ8HsdlAtZ6GUfXq4BKKORi//5Wzp5NUS7r6LowZtN1g1yujMul0drqp1Sqkc1W7JZiMW1ZZFLqnTM+nwOXS+MXf/F2Xn11kh/+8KIlLDbQdTGc0ePRiMUKKIrJPfe009Lim7M24VNSLhs8/PD2ecH1XXe18OyzI2zbFqa9fVb4u1DQ3d7uw+US5npO5/wOoErFwO3WaGvzsRT1v9elrOwXy3RK91fJlSADE8macSO+rNaTh8JCGSLTNDlxIk5/f4wTJ+JEo2IDSSbLtLX56eoKY5rMO0N1uxVGRrJMTopgpKXFx4YNfioVg0ymzLZtYXK5Kl1dwjX2xRfHrLk5Zaam8gQCLvJ5HZ/PQS6n86MfjbFxYwNOp/D/mJwUDq2hkBtFge98R7iK1gfwmaY4+6+XbrJZnaYmL8Ggk0KhSjxeolYzrUDBmFfOWWmAMuu/glVWEW3JgC1WrQe1Q0NpQLU6dcTj57b01i8XizqlUo1AwIHf7yCf1y39itjM3W4Nt1tj164InZ1BZmbKDAykrOnOooRjmpDPiy4mt1vD6VTtduJKxURRhOi4XkqqVGo0NXk5ezbJ669PWWtUCQSctuBWVVVcLlF2q4uR53Lu3AzhsPuyzGJbm5/u7iCvvz7Jvfe2Lxl0i+F9IwwPp/H5Zss5wvumRlubf9nsYf3vdXAwtaiV/WKZzvWUuZSsf2RgIlkz6l9WjY0G8XjRFqX6fKJFtLd376q8znrxUFgoQzQ1VeDo0SkKBeGSWqlUmZwsEY8XGR3N0tTkpbHRM+8MNRr1MzKSsfxADDsLUi7XKJWqnDqVYOPGBk6fTtDVFebChZRdBqpbxI+O5vB6NfJ5HY9Hswb61QCFzs4gX/3qKRobPaTTFSYmCjidCrrOvOCkLnqtT0jWNDeapuL3OwiFPExNFezMx1z32PrPczMp9U4YqItmxeX6YMFSqWadlc+anHV3iyF92ayOadaDgcWDn0rFRNNq6LqKpomsxmyQpeBwqPzUT3Vxzz0txGJl7rqrhXS6zNhYDqdTBA+NjV4mJ/OUy0LY6vM5Lc8S3V6bYQjnXWEOVyWXq/Dss8MUCrp1HIU+x+0Wx727O0w8XmBysmAd3/kbdzJZ4d57Wy/7fRRFYd++FhKJEj6fk6NHpzBNhXvuaWHPnvmf9Z6eZh55ZAd9fceZnMxbJwNC1Nzc7Jtn7LYY9b/XTKZyxVb26ylzKVn/yMBEsmb09DRz//0d9PUdJx4v4PM5AIVYrEBzs48335ympye6LjtmroaFMkTHj8fJ53VcLiFgnZjIo2mq3Z77/PMjtLX58Xhmz3IdDrFJFgoVymWDVCppD+hTFLHxud0aPT3NHD06ydhYHodDIRLxAEKc6XSqxOMlnE4FcDA6mrVn9fz4x2Ok02U7o6AoWFOCZ3d8zaoGKAp0dooSUbEoTN46OkI4HAqTk3k8HpVy2bCzH3XqgU29E2a+u6piP7emicuFQsXqkqlgmgobNgRobhbzZupZqIXaeS+lVhPOpy6X8LMxDAgGXfh8TgzD5OWXJ+npifJHf/QevvKVo+zf30YwmOT06YRl217ENBUru+KgsdHD8HAGp1OUkeYGVyIL46BQ0Glu9lIoVKhWweMRrrfFYnVeoNrZOX/GT33j3rcvak8wvpR8XkyonpoqcPvtjfbj/viPX5unW1IUhcce28Wdd7Zw8OBJjh6dAhQeeKDF1hUt93dWDy5OnYpfsZX9espcStY/MjCRrBlC3NdCY6MHv190dwQCwi6+udk7z2zqVuDAgU6OHp2iWhVttvm8zpkzCUD4VyiKMifFX09zC7+Sjo4GwGmfnYrApG7pLrIY1apJIODA69VIJMrE4yWKxSpOp2q32ta1EiJImW0DFnb1orvkjTemaWhwUirV0HXxWnXBZn3/mNvVcv/9nZw5M0MqVaalxUciUSKZLNleMapq2CUZmJ8lmTvd1zRNO6OjadhdQ5omnFRjsQK1msndd7fwrndt4MyZJMGgi/b2gLX5Ln3865kYYRsvAsFIxG2/jt/vJBJx8/TTJ0mlSnz/+xfJ5cpUq6KzqVDQLTt9aGz0UKsZFAoiUyJ0GzXbU6Y+7FBVTXsoos/nJJfTL/M1EVmvGn/wBz/Bjh2Nl23chmHyx3/8Gs3Nl5dBRkdzANx5ZwvxeJHjx2Nks8JD5WtfO8PevVH27Gmx37M9e1r4whdarubjawcXPp/ziqzsF+pE++hHd0pjNcmiyMBEsig3wqnx+edH6OhoIBq9XHh3q7UR7t7dhN/v4JlnRvD5HPh8Dnvzd7sd+P0a+XyVYrGKoghvklrNpFDQGR7O4HBo6LqYTBuLCX2J0JzMihnrZQlNUzl9esYurcxuyKKDYmqqgK7XrMBAtMg2NYmuEK9XI5er0NjotbIm1XnZCLdbtSbxigCnpcXPzEyZbFanszPExETeaiV2ksmUbRO0emtquTy7gUNdoGri8Wi2E219OnJnZ5BUqkyhILJKgYCTVKrMSy9N8q53tePzOZicLLB/fxvPPTcMmAu6s9ZfB2b9VWbLScKoLhJxk05XSCSKvPjiOGAwPp7H5VJpbPTS1OQlm61QLFZJJEqEwy6KRWHlP3dAorCtN+3rAwEXpZIo7bhcqj0s0ePRKBZ1Ll7M8+CDHTz00HZUVb3s826a5qJlkGDQRSjk4sSJ+LwumWSyTCaT4YknXuHgwQ9d9vd6tX/biqLw8MPbV2xlL43VJFeDDEwkC3KjvlDeTm2EJ04kKBSqPPBAJ8PDaXI5nWjUSzJZQtcNayhezdJtKNRqNSIRD6qq2Dbk+/e3cuFCisZGL5lM2vbfyOUqdtahVKrS0ODGMEx8PpENcLlUWlq8jI7mLAdY0V5cqRiUSjrRqJ/WVh8DA2m726W+lkJBbLZ1/YTIbCioqsZ73rPRciEVBm2nTiVobvYxNZW3NmDF7o5xOEzbXdXtFlkYISxViUQ8lEo1WzTa3Oxj164mmpu9fOtbF4hEPITDHmtqr8HISJpkssSf//n97NnTgmEYbN/+JBMTOcrl2mWmbXM1K6ZpksvpaJpCpSKOTb2rZWamSDAotCwOh4amKXg8DsbHc7alvwjyapZRW32ujmlnbert0vVSlXDRdVAsVnG5NPx+EWCqqkJbm58/+IN389BD21AvnZ5or3/xMsjBgycX7JJxOjWcToVjx4Soem6wc61/21dSlpHGapKrQQYmkgW5UV8ob6c2QtGV4yIa9dkb4eRknu98Z4BEokg2W7GM0xTbPn7jxgYUBavEIzazdLpsayTqZ+qqqtj28F6vA8MQlvLhsJtksoTf77C1Bs3NHsTk2yrNzaINN5Mpk067UBShwQBhQBeJuKhWRbAjxK4iyNB1k23bwjQ1eRkayvDgg1t49NGdnDgR56mnTjI1lef8eWEl7/VqtumbCJI0OjoayGZ1dN2guzvIjh2NvPjiONPTBVpavNx33wZ8Pgc/+MFFDENs/qOjWXS9Zs86SqcrPPXUSb74xRZUVQzn+9rXzlAqVYnHi/OmF9dLRsJBVrEnAptmDcNQaGhwkUyKWTd+/+xsmcZGDzMzJSoVcezrnTiiTKNY7cR1S/75GhMQfzMul0oiUURRFDZvbiAcdlMu15iZKfPRj96xqCnZXBYTcLe3+/nxjy9e1iUD4j0Kh92XZR1X4297pYJyaawmuRpkYCJZkBv1hXKrtRFemiJvbRXTZH/4w1FefXUKv9/Bvn2t7NzZaDvR3nNPK6++OmW1/4qz7npZw+93kk6Xue++jWiayvbtEU6fTlIu6zz4YCdvvhmjVjOszVvH43HQ0CAmA/t8Tnw+B36/E01TmZoqIHQlNcupVHiRNDd7rcCkwqZNQcbGssRiectjw4nLJc72M5kyiqIQDLrYsiXE+9+/mQ0bAvaZMsCxYzGmpgrs2tVklWAqVqlGtYy4dKpVg3S6QmdnEKdT5d5729A0jd/4jTvp6AgyMpJmaqpIa6uXf/qnYWo1ww4OhPamQjIpNC5f//o5nnjivaiqysc+dgcvvjhOOl2iUKja+o96tqQenFxaTqpWDS5ezFnBB0xPFwmH3ZimGMInjNEMK9hz0t4eYHIyRypVmSfgrXum1LMqbrcoPYFCKlWhocFJKOS2HWR7epo5dSpxWUbjSjhwoJP//t/7CYXmB/Z1LdLu3U2XZR2X+9s+dGgI02RVSrhvp4yoZPWQgYlkQW7UF8qt1EZ4aYrc53Pw5JP9jIxkCQZdaBokEiWefXaYgYEUH/pQty1IdLk0fvzjMVsA3NjoxelUSafLdHeH8fmc7NgR4ZOfvIcDBzr50pdepbOzAV03GBhIE4l4SaVKTE8X8PtNurtDjIxkmZ4uWBoKMQnYNIXQdcOGAIGA08pCiPJFS4uP++/v5OtfP8PEBHi9qp1ZUFWFrq4wDQ1OGhpc7N/fzqOP3sHhw8McPHjKNoV75pkhurpCvPzyBG1tfsDPzEyRWKxgiXYdVqkKEokizc0+enqiPPbYrss2vePHYzzxxKuoqmK7utZqhhUEmIDC+HiOxx9/hX/zb97Bnj1Renv38OST/UxNFXA4xOPEEMNZUWrd+Ezoewz72IgJvRCNuqwsktCTmKaJ3+/A73exeXMD2WyZVKqCx6PZwmG3WyOTEYGKyD54rInPmqUtaQDEcWxq8tDVFba6WArXFOT39DSzb1+UV1+dJBh026WueqeM1+u4zDhtqb9tn8/Bc8+N8sYbsSXLPCvVqLydMqKS1UMGJpIFuVFfKLdSG+GlKfKTJxOMj+cJh10UCqI7plyu4XQqnD2bpLt7hp07m6w5KSnuuaeNc+dEp4k4e3XZHUrDwxk7ezQ3mGtv9xMOuzl7NoVpiim7gYCYRJzNViiXa/h8Dss7Q3TdRKNeNm4MAGLjFoFDkUjEQzxewDAUNm0KoOuiVdjrdRCJuPH7nVSrBslkibGxLF/60qvzNq+nnz5JKOSmuztEPq/jdotyUyDgxOt1MD4uBg9mMlVUVScaFTqSZ58dXrD76vDhYdrbfZw9m8IwxNwcoX/BmlYsumq+8Y1zfPjDW+0A58SJuO0+6/c76e+P2YF03fTM43Hg8WjzbOh37mwiHi/S1uZDVVV8PgdDQxlKJdEKXauZHD1aoFQStvRut0qtVrMzIPXSUbVqkM1WcLsd/LN/toUTJ+LW0Egf3d1hBgdTHD8uNv729oDt2ns1KIrCpz51L7//+z+iXK5RKOh2p4z43GQvyzou9bc9MpJlairHXXe1LFrm2b27ecUalVstIyq5McjARLIgN/ILZb0YoF0rl6bIjx+P2V0w9cDE7dYolcR02x/8YJREosjgYIbu7iDbtoXJZnUGB9N0dwfp6WmmUKgyPJyZlz26NJibmMjbzpubNwcoFmscPjyCaQpXWFUVDqyVikahIASv+bzIzLS2+vH7nYyMZFAUhe3bI/j9ToaH05w+nZwnqAQxGbdSEaWYffvmb15Op8bERJ6pKeEcOzCQtjtQRJtyzRLNilLH6dMJhobS3HZbhGefHbrs/Z+YKLB3bwvnz6ftacGX6jhaW33UaoaddRA6GpX77tuA1+vk+PEYqVR5jnAXqwVZaFUcDoVyGbZvD7FnTzM//vE4p0+LCb9NTaKNvVisWoP6KjidmpWBglSqMq+zpy7yrVRq1kRrsV6fT9i4V6sGL744ZnfOJBIlBgfT3HNP22XGalfCnj1RfuEXbrss6zg8nF0w67jU3/bAQJru7siSJVzTZMUalVspIyq5ccjARLIg8gvlyrk0RZ7NCo1DsVjF4RCW6Q0NTjwejVxOJ5UqMzmZ54EHOmlrE5Nh3/3ujWzbFub11yeZmSnT09O8YPZobjB37Ng0n/3sC8TjBV5/fYpksmRnFyYnxWsLHxPsdtXp6Txud4N1tl/j9tubcDpVJiYKtu36li1BhoYy9kZaF2zu3NnI5s0Nl21ewaCLTKbMj350kcnJAqlUGUUx55mrCZM205olA/l8hTfemCYeL/LBD4pSQH9/nIMHT/LNb56zzdBKJdMqIQinWdMUVu4zMyXa2/3zsg71jEA2qzMwkMLrdVpC11l7/EqlZmdeVFUhEHBx5MgEbrfGhg1+EokS4+N5AgEHbrdGS4uPcrmKw6HZ84Dqz1X3idF1w3a6bW724nQqnDiRIBLx4Pc7LTGxZ55RXr1kdC06kyvNOi71t93a6rM8cy6nXsK9Ev3ZrZQRldw4ZGAiWRD5hXLlXJoib2hwcvFiyf4Cr7fhCu8RYSYWibjnDWBTFNFpce+97WzfLjQlS2GaJk888TKvvjpJPq9TrZpUqzUrEBDljLoTrJhbI7p2NE1FURQaG91Uqy4uXswSjXoxTYN8XieZLAMmt98eIRYrkkqJCcaf+EQPqVRpwbV0dYU5dy7J5GSeatVAUcx5M2tg1kukPkMHRPljaqrAZz/7Ahs3BjhxIm531RQKQixb/33qgY6qiqGB2WwFTVMZG8tiGAYnTiQYG8vxgx9cRNeFE20wKATEsy28ii16VVWVLVsaOHMmiccjgpBw2G3b3Z86NYPTKQYFAqRSJSsrI9qeRUkJa7qzimnqNDf72Ly5gWSyjNut8fnPv5vHH3+ZF18cJ5UShm0Oh2hD3rWrifZ2/zWLya8k67jU3/ahQ8OcP59cUINSL+Feqf7sVsmISm4cMjCRLIr8QrkyLk2R79kTZWgojdOpYBgKXq/4Mq/rEJqbPej6wh7qKxUY9/fHeeWVSSqVmtUJotqGYXXqk2/rJYhSqUZ7u5/77+9kairPj388blnYt1idPE4OHOjgzTen6ewM0dUVoa3NZwelX/nK0QU1Ci0tPqu1WJRs6tmNS6m71NZFsCDm1Jw4EecHP7iI263S2RkiEHCSyVSYmSnZc3bmWs6LwEIEXKlUmccff4XTp2cIBJx0dgat4YWGFcRgBRCzDrQAPT1N5HJV2+ROuLvqRCJuWlq8VKsGmqbN08vkcjozMyXbor/ezmwYJi6Xg5YWH+985wZiMbHh9/RESafLdiAz11fFNMHvd93w7pTF/rZNE954Y+kS7nPPDUtBq+S6IgMTiWSVuDRF3tkpPCsSiZJlKGaQTuvUajWam734fA57Gi6ITWF6usDgYIqpqQKdnUGOH4/ZnQ4LdUKMj+cs/YhpZ2Qupe76Wjdgq1ZNxsZy5PM6/f1xyuUqd9zRTEvLbPeGpil0dDSwYUPgsqzNUhqFfL6CpinWbJ2FpwrXL9fXBKDrNWZmShiGQakEFy9m7fk1TU1imGDdX6XuIuv1OnA6VXs+0De+cZaf/Mlu4vEi1aqYB5TLVdA0jWjUh9utkUqV7SBu//5WNm0KWlogjWSyhKYJ35VYrIBhGIRCLmZmSgwMpPH5nJYI2EGp5LBEsPWBhMK5Nhh0E416mZjI8frr0xSLVT796R8wNJQhHHbT0OCecxxMBgfTuN0O3vWu9iv/wF0HVlLCNU2koFVyXZGBiUSySiyUIn/ssV0cPjzCxYtZ20Bt06YggYCTqakiiiJm2KiqyvHjMQYGUjidqtU5ovGlL73KAw908uijO3n66VOXdUJ8//sXrdk6NVRVW3RtQpg52zI7M1Pi/PkUbrfGffdtpKMjeNmsmSuZFJvLVThyZIJ8XhiOzYpCFz9eYvaOaZd06hkIVRVutvF4EY9HzP4ROo56GUy4xorgKYSiwMBAikBgvjW706lZJSuoVAy2bg2zeXMQ0zRtz5jx8TyxWAFFEV1QxaJOLle1Mk+zpZ9SqUY+X2FyMo/brdrHsi5odjhUe6hgOl3h2WdH6OoK0dTk5ciRMWtzr1iiWIVcrsLUlCiRiaDHwbFj0+zZc/VDK1djhMRKSrhSfya53sjARCJZRS5NkZumyX/+zy/x5JMn2Ly5wc6cVCo17r5bDF57880YPp+Ts2dn8HpFa++OHRFuu63R7oDw+RwLdkJs3OgnFitYg/YMQLXntcy1ZQesGTqKXZK4eDHLjh0RvF7HZUEJLJ6WX6graHg4QyxWpKHBRaVSuuy1F0NY1dcdWsWEXtMU7baie0m0V9fbcTVNGLzVSyJ1R9xMpkIk4plnza6qCj6fk1KpahnIlXE6RVCzbVuEkZEMhmHS3h7g4sWsZSinYZqQyZRJJEps3BjA63UwMpK1MjyQyVSsspBCe7vPnrFTLtfYuLGBYlG3BM1+KzhTaG/3c/FijslJ4eeSSBStMp6w8//xj8c4ezZJb++eBT1dlmM1R0gsV8KV+jPJ9UYGJhLJdUSYeTnZv7+NdLpELqfPM9iKxQoEAk7Onq23qHrtaa3iC96kXK7y7//9izidKtPTefuxigJbt0Y4cyZpT+YV3SKqrXmA2Tk3LpcKKLhcKq2tflwuYQqWyYgJxsPDaauTSDw2k9Hx+0XL7e7dTZw4kbjsbPy3f/tu+vvj/Oqvfo+2Nh+maZJOl6lUlkiVzEFVxUyaelkrFitRq2FbvlerJqlU2Z7v43RqGIawyBftrSm71bY+dbm+MbpcojW7XgYbHc1y220RuroijI3lmZzM43CobNwYIBLxkEyWLNv8mj17yOsVJZvbbpsVASuKQTDowOt1oqqQSJTx+x20tQnRcqGgzxtK2dAgfGVaW322D4xhgNcrMi1iQrKfZLLE3/7t2auaqH2jZ9JI/ZnkeiIDE4nkOjM5WaCzswGfr/Gy2wIBJ6qqsmNHI+Gwm4mJPP39cfx+J11dQSYn85w7lyIeL9LZGbTaWMesdlSVXE7H5xOiTYdDwzRFCahSmT9i1zCwJ9w2NwdwOFQCASebNgU4dmyat95K4PNpFApVZmaKGAZ0dYXIZit88Yuv4Pc7yOerBIOuy87G65oOEVyotLcHGB3Nrjhr4vM57HZkh0MFTCtzMjvPRgR4Kk1NbiYnC5RKNRoanFQqwiOmvT1APF5i82bRPVMvXWUyIsAIhdyoKly8mGN0NIfX67D1PyMjGRobheYnHhc6l7qGRWRSHHR0BAkG3YyOZsnnK5ZhXRXT1PB6xdyemZkSo6NZfu7nts/7/bq6wkxMjOH3O0gkSng8DjvIKpWqRCJexORlB8lk8ao6dORMGsmthAxMJJLrzHIuutu2hTl6dGqerXgiUeT06Rl7YB8o5PNVmps9ZDLC+2PjRj+hkBtNU2lp8dHZ2UA8XmJqKo9pQlOTh1isSD6vWxufMBSrlz+6u0OUSjXS6QoPPNBJf3+M6ekiTU2iPCGCG5HxeeaZYR54oNPOBMw9G3c4FCIRN9msjtMpBvQlk2VyucqSwUk9418sVvF6Pfh8quX3olCpVMlkDJxOBU0T+pJq1SSRKFGrmQSDThwODVWFe+5p413vauerXz3NxESe5mYv2WyF6WnhbVJ3e/V6NfJ5nVrNZPv2CMWi6K7J53UyGR2nU6WhwUmthjUsUKVQqJJMlqlWDVpb/VQqwuK+VKrhdqtkszqqquB0ioBqYiLPCy+M8c/+WZf9+9UdX0+ejFvzfoSuRgQlHrv11uVS0XXzqjp05Eya9cFq6HwkMjCRSK47y7nodnQEOXx4FLdblDRAzNRJJArWjBcHjY1exsdz5PMVayPXKBZrNDWJSbfvfOdGisUqTzzxfruds7nZy/HjMV5+eQJdF10jxWKVkZEsbW1+jh+PMT6eIxz20NbmY3DQSTTqtQzYChiGyZEjY/YAv+9/f4QDBzrsMlP9bHxsLEc06iORmMHnc6CqKs3NHlRVuKMClugWq4NmtszjdArfkFxOxzCEhqNaNdH1Go2NbjZvFqLceLxoz7WJRr00NXlty/56Weuee1p56604pVKNsbEcoBIOO6jVDAqFqq1JCQbdxGIFyuUapmnS0CA6b0olA6/XwaZNQnNSn5OjKEJzUg8qisWaXSZyuzVcLiE6rtVEWebcuSR797bY/jSKArt3N1OtGuTzFc6eFVOXW1r8cxxihUDX7VYvm22zEuRMmrVnNXU+b3dkYCKRrBILnS194AMdtq/I179+jlDITXOzl3g8Tyqls29flJdemmDzZlFeuXAhRT6vMzWVp1w2EKZiChs3+nE6FUZGRGnB6xXlm1SqTHd3iNZWH2fOJPnc537ExEQBn08lGvWRz+sEg27Gx3OoqjBfq1QMYrEi2awoPygKvPDCRY4fj9nW+R6PRqlUIx4v4XardrniH/9x0HZNDQZd3HZbE5GIm1rNpKsrxOBgGpdLo6HBbRuM1e9fN1MDsVnXszcej9BnvPWWmHHj8WgEAi4cDo2LF7NEIh6am300NXmYni4Qibh573s3X3b8OzuDTE4WuHgxh9/vxOFQ7a6gzZsbmJkp09QkOmfGx/PcfnsjqqqQTJasEpJwoj1/PoWqQmOjh0ymTLUqAhJNUygWRYuwYQiDuLo3jfCKqREOu4nFinzrW+dpa/PjcilUKqJ1+SMf2cGHPtTF5z73Y4aHM/j9s6Jj0xRaora20FW128qZNGvPjdb53MrIwEQiWQUWOls6ezbJ3/7tWQD27Glm//5WfvSjMd56K05Li4/77mvH53Py3e8O0tkZ5O67W8lkygwNpTEM0+pWUSiXq0xPF2lp8ROLlTBNA8MwCYVc3HffBqJRH/39cc6encHt1giFXJw9m+LNN+OEw27a24Wd+sREAV0XJQ2AbFboLzIZnVdfnaRaNa1uHiF8rdWEbqRWMymXxRC7ZLJkt9YmEmWef36EXbsa+Y3f2Mezzw6zc2cj09MFkskyoZCbjo6AbU9ft3F3OERZSVVVNE2lVKpSKgk3WrdbpaHBgaKITJBpqpbmxWT//lZ0vUYqVV7wPSgUqjQ2emhvF7qZdLo8x3vESaFQJZEQgVh9MnBrq59AwMnJkwl03bCzH06nyC6FQm6gTKVioGkq7e1+EokimYyOx6OhaSLwqFaFy2wiUcLrFVmjsbEctZpBW5uP226LcPp0gs2bA/z8z2/nL/6in8nJvJUhEw63zc0+Hnlkx1W12+7e3cRttzXyzW+epVoVww2jUT8ul8qDD26RLbw3AKnzWT1kYCKRrAILnS1lszrZbBkQP3s8wlV1zx4/6XQFr9dJS4ufDRsCtg14PF60NkOo1QySybJt/hUIuAgGnRSLVVpbfdx33wZaW/1MTRWsmTAOmpq8hEJu3nwzTkODyKqk0xXbql6052r4/U4ikQby+QoXLqRxOtU51vkqpinmwTidGk6n8OYQJmIODENkB4JBN4piMjqax+dz8ulP32u3j7a1+ThwoAOAf/fvfsyZMzOYJszMlPD5xDoVRWFqKm91E9XYtClgmbTp6LoQ8dZqhp1FaWnxMTNTJputl30uzww0NnpobvbS2OjhxRfH5g0hbGryMD6exzRNNm4U82DqA/gcDpEl8vudpFIVPB4HqiqCneZmL3fe2UI6XSGVKhEKCdFsPRPk9Tpwu90kEkU0TWhyCoUqd9whxM6pVJnGRi/RqI9Dh0b43d+9hz//81YOHjzJ0aPTgGmn+uuDCK8E0zR5+ulTnD6dYOvWMNPTIhDMZnUeemg7jz66U5YQbgBS57N6yMBEIlkFFjpbGhxM4XaLP7GBgTSKIlpYVVXF5dIYGEjT0uIjHHbx5ptlnn12iIYGFyCCEodDtU3CDAMSiQKaJszXGhs9DAyk6e+PE48XrGDCpLs7xMBAmnDYTaGg29OJ6zidQkRqGCL7kUgUURQol4Vewu0W03SrVVG6KJdr9sA9qHujmOi6EG82NnpxOBRr6N5DC54R/sf/+G6+9KVX6ewM8txzw1y8mGV6uoDLJUpEBw500N8fJ58v4/WKbNP58yl0vUZDg4tNm7wEAi5MUwhEH3poO6dPzyxo7hWPFzl/PmkLTutma0JYauD3O6hUDCYnhbGaqirkcrptj59IlNE0IX5VVZGtymYVxsZyBAIutmwJ8Y53tFEs1njyyRM0NrpxuzVGRrLWmAEf2WyZxsbZYX319xoUBgaSfPKT3+dnfqabj31sF0888b7LgoblBJSX3m4YBqdPz3DnnVE0TeX225us98rk9OkZ3norIc/UbwBS57N6yMBEIlkFFjpbymZ1q6NGfDHVN1YQ/+dyFY4fj3HhQgqvV7MG0omuEbGJOtmwwYff72JmpkilYtDW5qG7O8z580lrro1oca1WhbNpNOqlvz9Oe7ufdLrM8HAGRVFwOBQUxYlpGlSrBhMTeYJBMXVXdLyINfv9YrPPZiu2qVlLi+hGcbs1dN205spobN7cgN8vMjhLnQ329DRz//0d9PUdZ2wsRzZbwe3WKBR060wyj9ercf58EY+ngmEYtvutKDcJ0erQUIYHH9zCo4/u5K23Eguae/X3x+1ZL3v2RGlr8zM4mCKbreD1ahw40MHx43FOnoyjaSr5fJV8vmLbyRuGbk091i3jOcXWqSQSRc6dS7F3bwuf+cy9KIrCN75xlmKxiqpCc7OwvTcMF8HgrPW806kyOJhmcjKPqgqzuIVEkaZpcvx4jC984RWOHYsRDrvZti1MNluy77uQA/CRI2PEYkUcDmWec6wsIdxYpM5n9ZCBiUSyCix0tlQ31gKsM2jRbSPcR8XmOzCQJhLx2JmUalUIXrNZHY/HQSDgwu3W8PtdNDWpvOtdGzlyZJxg0EWpVEPXDZqaPDgcYsOLxYr4/U6ry6SGz+ckGHTZbqaZjOhEqQcZDodKuWxYegnRdtzQ4CKXqzA4mCYUcuFwKDQ2eiwfEwelkrBfTybLTE8X0HWDnTsbbaOzS1EUhb17W2hs9OD3OxgaSpPL6bS1+QkGnZw6lbCG41XI5UwrkFItN9sasViRD3ygg3/9r/fPsUVf2NzrUrv0hgYnW7eGyWQqhMMejhwZw+XSrMCtgqLU8PkcFIs1isUqbW1+Wlp8nD2bpFDQcbs1vF6Nclm4y/p8Dp5++iSpVIlHH72Df/7Puzl8eIS///sBymWdnp4WBgZSzMyUbL1KOl0hl6vQ3u6nUKgSDnuIRn3zRJG7dzdz8OBJvva1M5w9O0NTk4dyucYbb0zT3R1i9+7mRR2ATVOhqUlk0Nra/LS2zk6rliWEG4e06l89ZGAikawCC50tdXWFGR+/CCh0d4cAhYmJMbxeseE6naq9eem6wf79bZw5M0M47Caf163uFhe5nBBa3nffRo4cGaNUqhEKuWx79Y0bG4jHi7hcItDp7g4xOZm3DMBMwmEPItgp290x9YnDbrdKtWqyYYOwT5+YyKGqDTidivX8Js3NPjo7g3z/+6OAKDUMD4vShKaJ0lCtBgcPnly0JfL550fo6GggGvXxrndtYHq6wMBAmnxep7nZy8RE3urYEemJSqWKYYhylt/vJBr1reisfyG79G3bIhSLVb7+9bOW/4hmZ2wA3G4Hum7i9TpobRWt0E6nitcrgrVQyG3rSTwe0X784otjTE4WeOAB4X574EAnX/rSq0SjoutncnLMbv1OJotEIh7r96pZn4X5GY366IFyuWp52ThwuYT5XD3gaGhw8tRTJ9m4MTDvjLweANdLRnMDE1lCuHFIq/7VQwYmEskqsNDZkqJgT5NVFHH22tzsY3AwTXd3kGy2QrVqWC2/YXbubETXDQYGUqiqQq1mWKl83erqmEHXxQTfdLpsdeC4iceLRKNeWzexZ08z0aiXixezeDzCVVWUYhw4nTW7hRWE/mHbNh+aVg8yRJA0OponEnHzjne0E436bC+REyfiFIu6ZSiGXXIKhZw888zQoi2Rc0tdiiK6Yeob6NBQmjffjBEMuiiXa9YUYRNNm32N73znAh/72B08//zIssZVl2ZUjh+P8aUvvWoHJE6nsIKvt/86HKrVfl0lnxdZknJZCIXvu6+d1lYfR45MWLNvFHRdGM9t2RKc0wY6+/4LR90Gzp9PAlhOr4rd2j13inM9o1HXKF24ULXLffXfpR5w7N0bZXKywI4d8wONuc6y+bxuXy9LCDceadW/OsjARCK5ApYSJl462M7vd3Lffe3E4yXGx/M0Nhp86ENdgMIPfjDCuXMiANm/v42dOxtRFMXWRfT3T+N2O+0zrmefHWJ8PM/0dN42SysUqmSzFXw+J+3tfnbvjlIq6dbsFid790Y5ezZplZNMGhs91mZcQ9NMtm4NsXlzA6lU2Z4Do6oQiXh4//s3sXlzA4VClakpkRn4pV+6nZ//+W+TSAgRrMul0drqo7HRw/Bwlttvdy2qZ6iXurxeB1NTeY4fjzMxIZxZK5UqYoCfga4beL2are3Q9RrFos758ykefvibNDS4cbuFKPi550Z45JEdyw69q2/6gYAwUnM4VKamCrYLbN0Kf1bs68DvdxIOu9izR/jMzJ3BU6mI8tmlGo5HH92Jz+fkqafeIh4vsnFjgI6OICMjGSoVg56e5jkzkAT1jEY9cKtnP5zO2UnRLpdKPq+Tz+u0tQlvmrklw7nOstGoj0JBlyUEyU2NDEwkkhWyEmfHnp6orReo3y8aFXNYMpkKZ8/OkM9X2bSpgUjEw5Ej45w+LTw09uxpZnq6yIULSZLJCh/60EYOHBAby5e//BqTkzlrEm4OVQVNUzFNlVSqRH9/jHDYQ1dXiFisYAlTHUxNFRgZyRIMuggGXTgcKoVCiW3bwrS2+jlzJonLJXQUMzMmum6yaVMDt90mAiWhbRFaiP7+GMGgm7Y2/7yNE0SQEovlF9UzHDjQydGjU1y8mOWNN6bJ54V9vWkazMwIHU6pVB/eJ6zmdd2w3FZB00wmJ/NW662HUMjD8HCGvr7j3HlnC3v2tCz6vtU3/XqJyzAMu/06nS5Tqxm43Q42bw6g6wa7dzfT2Oix3nOssppqfwYqlRpdXWFgNuNhGAZ/9Ecv881vnqdaNSwfER+1mskHP9jJ6dMzVuZpYVFk3a23nv3w+RzzAqFIxEM2q/PYY3fwrW9dmFcynOssu3NnI6qqyhKC5KZGBiYSyQowTZO/+7uz/NmfHcXp1AgGZ+3QL3V2XMwBslo1582caW4Wk3gvXEjz1ltxEokCU1NiY9+2LUIuV+FLX3qVBx7otNp6hVNqJCJcVTVNDOYTvh8GW7dGOH8+ac3EUWlt9aFpKtu3h5mczFOtmmzdGqajo4EzZ5IkkyU2bPBb5aEqoZALl0t0kLS3z5Za6pmBo0en5s3EmYvLpZJKlRe1U+/paeb22xv58z8/RrEoOl5qNZNCoYamadRqNbsVuVisomkKtZppHXss7xTDnogcCnmIRNxMTuZ56qm3eOwxZdH22vZ2IWY1DJNCQWdkJGsLgBVFaGT8flHyKpVqvP76FL/5m/sAOHRoBMAakmhYGpGwXY4Rs44i/NEfvWK3D3u9GlNTBU6fnsHvd3LyZJyf+IlNDA6m7SGIl2Y0TBNef32azs7gvDZnp1MhkynT1ubngQc6eeih7RQK1QUFlr/wC7dJ23PJLYEMTCSSZahnSv7sz16356QkEiUmJsbo7g6zZ090Xkp/MQfI4eE0Pp/oSmlv988r3Rw5Ms7Zsyn27ImydWvITvk3N4ugR1GETwlAa6uPQMDF1FSOUqmK06mxaVMDPp+DYNBtCSZTTE/nrY4eJ5qm0tjo4V3v2oBpCg+TVKqCqqo0NTnp6gpz/HgMYYFvXCaiFPoQhWjUw8WLORKJIrpewzQVFEW0NzudKps3NyzYnSOmAztpbvYyMzN7ndOp4vc7SKUqmKaYiCxaas05jxWBia6bTE8XLC2MQVdXGK/XwT/+4yBTU8VFs1gf+EAHX/vaGUZHM+TzuqXfETNvVBV27IiwYYOfQqFGS4uXLVvCPPbYLgD27Wvlqafe4h//cZBo1M3WrRF7Ns/cWUePP/4SjY3CYXZqqkAyWUJVFdLpCqYJL744xsMP77CCluJlGY25GpX2dj/hsJtz52ZIpSrs39/Gpz99r22+JgWWklsdGZhIJMtQz4C4XCoul8tyQ9XsAKDeMVEvYyzmAJnN6vh8DnK5ClNTebsrxe934nKptLcLN1fTxOpaSdkunrWa0D4I/xHRQgwKXq+DUMhNZ2cDuVwFXa9x8aKYmDs6WqOjowHTdNg6BZgVVLa0+PjgBzuZmspz7Ni0Ja41aGhwoevGvAAjn9e5664oP/7xOMWi8P6o1bAH4WmaEP1985vnKRSqC565T04WcLtF8OJ0ijk4tZqB06nh9Rr2HB2PRyOXE74vHo9mvQb2P8MwSaXKnDmTwO124HCoS84nAeHgmstVbefaSkVBUUBVRalk69YIra1+YjGx0dfX3tMT5Ykn3kdPT5Rnnx1GVaFYnK/hGBlJU62a+HxCfJpMlvB4xBycSqVmlYo0Tp+e4VOf2r+gBmehgGPv3uiCAYcUWEpudWRgIpEsw6XiyXoZo77BDw6m2Lo1bLdlLuYA2dDgZHRUnEm/+OK47Ug6M1PiwoU0bW0+DEOIawcGUmQywv9itrXXsDtX6hvypk0BTFN0Zvz4x2OMj+dwux1ommJ112QplWoEAk57gB1g6TtMjh+f5ujRKfJ53e44SSSKFAo6x45Ns3dvC6YpgqoPfKCDI0cmCIdFyWdqqoCmCQt7v9/Fvn0ttLT4LhtYVhcMnzmTYGwsh6IotLT4yGYrVveLCJgCAQcu1/zZPKYJCyUBDANqNZOZmRKtrT6mpwsMDaXJZnUaGkQGKBAQWSwAv9/Bhg1+SiUR5NRqFQIBF4oiHGwHBtJEo74Fu1iWy1J85jMv2CWuZLJszRoSRnGgUKnUCARci5qdLSSo/uhHdy7YcSSRvB2QgYlEsgyXiifnChNdLpVstjJvQ1vMAbKzM8SJEwl8PoddygFwOFTLV6PK6dMzDAykLJGq6L7QdTGXRtdr9ln4jh1hFAVisQJ33NGM8CmpUKnUqFSEnbrfL7IDs0Pw2gCRcYhExOTcEyfilMti4xSYlkut2CxFu7HDygxk2LgxgGHA2FgCVVUIhTy0tPhQVYWhoVm/jUOHhjBNeO65YZ57bpipqQKNjR5cLpV0usK5c0nKZVFKUVWVSqVKqaTS2ell794ozz03QiZToVYz7dLLXBRFiGM1TSGTqXD48AihkBuXS7XLbJs2NVgW/6L0Ew677YCsXm6pP08sVmBwMM3ttzdx6NAQBw+eukyrsliWor3dRzTqJx5PkEyWKJWqaJqKokCtVqNSEYGlz3e52dlKBNUyOJG83ZCBieRtxXJzSBaingERbZliFk0925FOCzvz225rtDe0tjYvt93WyOnTiXlix0JBp6NDmKEVClXLX0QIKnfubOLixSzHjk0TDLpJJkuWj4cwL6t3iYi2VZOTJ2fsyb+5XIUTJ2JkMmU0TUXXa1Qqwk5eOLsK7UYg4CQWK5DN6jzyyA7+6Z+GOHVqxi6PiPk8Gu3tbqrVGuVyjVKpxmc/+y527Wriwx/+JkNDKXw+py1erVQM8nmdxkY3uZzIfPh8Dp57bpQ33ohRLtcYHEzj9YoyVCTiJZ8Xbc4AlYoJiADD73eQSBR54YVxdF2UP0zTnKc3mX0f6y3LIqipVg27fFYvs50/n2TXriY2bBBdROVyDadTs3xUfAQCTqanC9RqJh0dQXbubOLUqdn3bKUBQr3jqLHRw7lzSRSlrosR711bm59YrMjISJZ3vat93mMXE0pfKqiWSN5OyMBE8rbhas9OZzMgXlusOjCQnufIOjcIOX9elGF27myyfDtmU/9PPfUWsViRiYkcuZxOU5PH6u7x8tprU7z88gQ+nzD7MgyTUskkEnHZbbWVimE7kd51Vwc+n5O33oozPi4yOV6vwzJLE1mYWs2wNAqgaRrbtzfYJYgTJxK0tPhsvYjP5yQcFrbx1aqYqXP77U309EQ5fjzG9HQOr1d4fHg8Ytif06lYHUIKmzeLib0jI1mmpnLcdVcLr7wygc8njrUQ3ZZtozJR7hAOuIGAE49HI5/XSaVKuFwqpokdXNVqwhBOVbGuB69Xo1o1ME1IpUr26+fzOjMzJVKpMq+9Nslv/MY+VFXh4sUsDoeCy+UgEnFbv6+bzs4Qjz22i2996zxdXaErDhB6epp58MEtfOUrRy2r/qKd6Wlv91l+MDoDA0l+//ffOe+xiwml5ZwbydsZGZhI3jZc7dnp5fNXXHMcWUVm5PINzbug2HHDhgD5vM4737nhstfp7AxSLOpUKgYejxii19oqptUODWUolWrWvJ0iDQ1uJicL3H13i9VhU6JcFtN4N24MWM6zitVKXOPnf/42PvnJe+yM0Ve+cpSjRyfJ5yt2t8zcoKxSERmLevvv4cPDdHdHOH06gWmaRCJucrkKTqcDTYNkssR737sZwzAtW/wIqqrMG2SoKAput0Y8XmTr1hDj4zmKxRqbNgVIJstUKjXKZRGAeDwiOBIusCIQARGUuN0qqqraPiRut0apVCMeL1IuV4nFilSrQlR75swMn//8EXK5CoZhks+LmTgzM0Xcbgdbt4Z45JEdjIykrzpAqGtQnn9+lMHBFGNjQrwbiXgIhVwUi1WKxSpdXeHLzM4WE0qDnHMjefsiA5NbhKspUbzduNqz06XEj4cODREMulBVxe6mEdNsdRTF5K//+i2+8IXZ0fZLTSDN5XT+1b+6i2996zy7djXz0kvjmCZMTBSpVqsYhnifq1WTXK7MkSMTtr5jy5YQo6NZXC51jl4Eywq9wgc/2HlZxqijI8j58ynGx3PUagatrX57ym25XKOtzW/rZiYmCnR0NFCp1CyPDZVg0G3rVyIRDzMzRV57bZKZmRLFYoyXXx4nmRTzeTZuDNDc7MXlUi2djEGxWMPjmfVDqRuqeTwaHo/4agqHheV+rWZaWYjZNuO6lqZaNQgEnCSTJbLZejnJhaZBrQbDwxkCARfvec9GJibyjI/nMU0Tv9/J//1/38PDD+/gM5954ZoCBNH63YzXq/He926aNwuosdHJ1q0h3vGO9sv+FhcTSoOccyN5+yIDk1sAKaBbGddydrqY+PHgwVNWmULMZKkbY4n23Crf/e4ge/ZE7fdguQmkDz20jUJB55lnhmhu9vHmm1OUSrPzT2o1E6dTxeFQmZkp8r3vDeLxOEinK6iqKKtEIhXcbjHBOJMps39/G7t3N1+WMTJNuOuuFl59dZLx8Tyi/ViIcJubfTzyyA77DL++gdZLWYODKVwuBy0tXvL5CpWKycBAiu7uMGNjWUZHM6iq6Foql2tcuJCyDN0CtLUFyOXKlrtrjdHRLJqmUipVLWdVg2y2QlOTl02bAjQ1eZmaEuZzimLam7iuiyF/iiJKV8JSX7EmOSuUSlUrEHJQKOiMj+d58MEt9rGMxQqMjmZtE7ZrDRDmlvzmzgIyDJOhoQz3379l0cc0NprE40U7qPX7HbjdDnp79y77uhLJrYYMTG4BpIBuZVyPs9P6c2azOgMDKcJhtz3npVKpUK3WePzxV+jvj/Gxj+26bKbOQgZZ9dsPHRrm1Kk45bJKrSY8PUQL7Wy2ZWwsz5YtQVuDURe7ut0akYiHtjY/n/70vSiKclnGSFFgz54W2tr8vPTSuHV238ADD7TY9voLZXpaW320tHiZni5w4UKaqakCbrfGXXe1MjNTIpcTzrCahtVJJLIak5MFajV4//s3cvEilMsGExM5gkGnZaAmOleE+VkVj0ez3G5Fe7KuG9x5Z5RMpsL58ymam712SSkWm82q1I3TIhEP+byOw6Famaf8vPdubjC6VCZrpYPwrmbsfU9PM/ff30Ff33Hi8YL12TSJxwtWYDo9732QSN4OyMDkFuB6C+hu5jLR3LUfOxbnxIlp7rqrzRJgivtcyxTW+oY2OZmzB72ZJkxO5pieLrJhgx+XS/iWTE4W5s3UWeg9mbveyckCgYCwsnc4VLuLJ50uY5omhmHamY96VqG9XXihbN7cYLf51l9noYyRokBrq58HHtiCoig8+ugdHD48zNNPn6a9fcR+n+dPz3UwPJy1p+c6nSJr8dJLE0xPFyzhqkIuJ6Ynq2rdN6VGJiO6jf7bf/sgTz31Ft/+9gVSqQoej2qLekVHEWQyZcJht535ufvuFhobvdRqJtGol1KpxvR0AYdDlK+qVVEKMk2TzZtD+P1Oxsaytk7lUuYGo1cTVFzK1biyKorC3r0tNDZ68PudFAq63Zre3Ozl0KER9u1rlScWkrcVMjC5BbieArqbuUx06dqbm92EQm6efXaYrq4Qd93VQqFwbVNY6xva44+/Qq1mEI+LdtxCQbfm6HhsD44tW4JLZrAWOtZ+v4uhoQwej2bNlBGZEVH2EO3DhYJONOrjrrtacDhUYrGC3eY7d0NcKmOUy1XIZMRsnsXe5/qm+9RTbzE8nKGzM8jWrRHbyt7hUDl3LkUg4LSM10RHDWBZ47us2S86X/7yUZ5/fhTDMNmwwY/P56RQqDI9naezs4Fq1SCTEaZv9czPH/7hT3DsWIx/+29/RD5fwe0WX19199xo1EsiUcLrdRAIiN8xHPaQzWaoVg26u0P273tpMLpaVu9X48r6/PMjdHQ0EI1ePmdIduZI3o7IwOQW4HoK6G7mMlF97Z2dwjvk+PE4xWKVtjYfk5M54vGGRW2/V4rIMuzkf/2vU7zyyiSapmIYBh6P0DVMTxdt19XlMlgLHet7723lzJkZcrkqLpeJ06kAs46obrfINPzET2y0Z7gUCjqqql72GkuVK0ZHcwDceWeLdaxittbha187w969UfbsEe/1hg0B3ve+TfZG2tDgJJEQjrgul0qhULV1MAAOh0Io5LaM2xSOHp20B/KVy1VGR0VgsW9fC11dQQYGhMV7NCq8W8bHc+zdGwXEbKFQyIWimCiKyCKFww34fBoTE3lrjk7NngisKCKjY5qweXOAQkFfNBOyVlbvsjNHIpmPutYLkFw7Bw50ks3q9qyROtdSoqizkjLReuXwYVF2OHEizosvjjMzUwKElqFaNdD1Gr/923dfcw3/xIkEqqrS3h7gttsi+P3Cz8TrdTIzUySTqdhn60ttNAsda0VRaGgQHSb12TV1jxKXS6OpyccHPtBBa6vPLk3l8/qCU37r2Z2hoQyxWIFCQScWKzA0lCEYdLF5c8A6VmMkEiVA+I6cPTvDE0+8Yg8RvHQj7eoKU6nUMAyTcNhNqVS1BLFVajXDmk9j2kZv1aqB1yvcb1VVdNjMzJR54YUxpqYKbNsWplDQmZkpA8KxNhRy88d//Bpf+tKr7NrVTGOjl66uIJs2NRAIOFFVlWDQRUODix07mnC7VapVE7dbZd++Vn77t+/iHe9oJx4vMTaWw+FQiMeL9PfH7d9rrWhv99lzjC5lsfdSIrmVkRmTW4DVqI8vxno4m7tajcvERIFCocqFC2mcTpVksmQLQ/1+J2+8EePEifg1nyEfPjzM5s0BHA6FgYG03Q6rqqKLxu0WA/Ng6QzWQsd6cDBj6UWE1boQeBooikKtZlCriYm4dZYKRhcrVxw40MGXv3yU116bYnAwQ0ODcHadmRH29poGL788QX9/jD17Wi7L0LW0+OjqCnLkyASZTNmyeRebvcNRI5er4HCo1nDAGoVCze6oMQxIp8vW+2gyOJhmZCSLz+fgX/yL2yz3W0Fzs8nf/d059u9vpbs7PK8DqlIxKJVq3HFHM3/0R+/h8OERJicLtLWJQYW7djXx9NOnqFbjts/L+fNJ3nhj7UuSqyG8lUhuJWRgcgtwPUehr7XPwrVoXNrbffzoRxfJZisUCjqapqJpCoVClWSyRGOjh0OHrr1+PzFRIBBwsWePj7Y2P8eOxTl/Ponf76KlxWebnS230Sx0rOslCYdDtctEmqZYoleViYkCL7wwxt13t5LPVxgdzREMujh48CTt7f7LArhLyxX143viRJyLF7NoGpYPSdXWiJimaFF+/PFXePrpD9kW7NWqyfBwmkymwvR0gVyurglxU6mIab71jXb//lZOnZohmSxbXUUOkskSxaJwfhU2+mIO0KZNPopFnTNnkmQy5XmD+cJhF2++GaO11WcZuFXweh3WuIAw73xnO3v2tLBnT8u8Y3v8eGzdliSv54mFRHIzIgOTW4TrVR9f67O5a9G4HDjQyX/9r2+Qywl303r8IobCqfYk32tlbkAhOlz8tLb6GBhIWeUEzZ5Rs9RGs9Cx9vudjI5mLQ2HQkPDbMmmXK5SrcLUVJ54vEilUiOTqTAzU+TcuRQul8Jzz43wyCM7eOyxXQsGcPXje9ddLQwPZ1AU0aor5ryYtgU8wA9+MEp/f4zdu5vx+x0888wwPp8DMBkbEyZtTU1eduyIAIrdmdTU5OH8+TSxWAnDMO323Xy+imEYVKuK3XXk9zsZH89jGAY/+tFFNm6czRaNj49RKgndjsOh4POJCcvClM2By6Vy//0Lfx7Xs/X79TyxkEhuRmRgIlmStT6bu5YNpaenmVDIRTxeQNc1NE2xSyGRiAeHQ2FmpnjNa7w0oBD+IFFaWny8/vokXV3hFW00Cx3rcNjNa69N4XAoeDyzwVV9iF1Li5dIxENzs5cjR8bJZMq43WKTLpcNhofT9PUd5847L88iwOzxbWryEAy6GB7OUK3Oai5KJWFz7/FoVCo1/uRPXqOlxccPfzhGa6sXRVGZmMjhcAiNh7B91wkEXLS1BXA4NDKZMjMzJcJhN8ViFV2voWkqplmjVgMh5q3ZJa/JyTzT0yWcTu+8wXyGYTI4WKC11Yeui3KWaE0WAeYnPtGz6OdxJSXJtWyLXyvhrUSyHpGBiWRJ1vps7trdWltIpSqWlbsQXYoBbg4SiRJNTd55j7mazWmx4K1YrPJbv3X3ivULCx3rd76zncnJPCdOJDAMg1ptfnAVCgndxg9/eJFUqmQPyIPZKbuTk3kOHjzJF75weWAyMVHA53PQ3x9HUbBbm2ePB5RKOoYBfr/G9743xLZtYVtcWqlUcDpVmpo8VCqilTmZLBMIuFAUCIddpNMlIhE3HR0NzMyUGR/PUSjo8wIgwxAZoESiaB8LXZ/vPyImLospxIGAyKxUKjVcLhW328HLL4/T3x9f8L1ariS5bVv4pm2Ll0huNWRgIlmWtTybuxaNi2mKllNNE/Ni3G6NhgbxPOl0hfZ2P7t3N827/1Kb06OP7uTEicSCQctqBW8LH2uFTKZsdcpgB1fCkKuK260xNZW3ggHlsufz+ZwcPTq14Ou1t/s4cmScgYE0waDLzsgoiggWxGWVWq1GOl3D7XYwNJTG4VBpbvYSCrmJxQp4vU7K5Qq1mkkuVwEgEnFjmlCtmnR0NKDrpqUNwW4Jnl2nyIJNTORxOhWrxVexNTaVimE5yqqMj+fZskWUcTKZMqUShEIKIyNZvvjFV7j99kZ8PhG01t+jD3ygY8mSZEdHkG9968K61KBIJG83ZGAiWddcrcalHmScPp0ETNrbfdbZep7WVh/33beBQqE6b37JUnqWZ54ZYnQ0y+nTM4ueUV9p8LbS7MwHP9jJ4cMj1GpiWF79trnD9sQAvIUdTsEEFg6ODhzo5OmnT+F0ilkzHo+DUqmKoohgweGoD8yrousmwaAbp1Mll6tSLGaJRDyEw25bF1KpiKm++XyFZLKIw6Gxf38rgYCT06eTeL0a8XhpkcxMDYcDSiUhoN22LUxDg9uyyhcZmaGhJM3NfkxTOMPWBxam02Xa2vxksxWefPIE+/e30dnZYL9H99/fwf33d3Do0MiCJcmRkey61aBIJG83ZGAiWdfUZ4n87d+eJZksUqmYuFwKkYh33pC5S6kHGXfeKdxQBwZStLX5cToVS0iZ5xd+4bZ5j19Kz1Kp1PjGN87yUz/VfUVn1IsFH7t3i/bVlZQOenqaeeSRHfT1HWdyMm/PU5k7bK+/P8a3v32BYNCcF9SYprjfAw9cXsYB2L27Ca/XwdmzM/ZkXlUVHUQul4rTqVqeLyIw3LQpAEChkMPtFi3YoZCLWs3E63WiqlV8PhemaeJ2O2ht9fPxj+/m29++QFdXiJMnE6RS5XnZktm1iiyN16tRrZr09LTQ3u63b/+nfxrEMMDlUhkaSlMq1expxKYptC2Dg2kaG92k0yV8vkb7PTp0aITf/d172LevdcGs1tzpwpdOiXa7VYrFKseOTfP88yM33ViGW5mbeVyGZHFkYCK5iVBQFIOV+ALWgwxNU+ZNxM3ldKJRLzt3Nl6mG1hKzxKLFawulZWfUS9VGrrttkZOn07Q1RVaNtBRFIXHHtvFnXe2cPDgSasso8wbtnf8eIwXX5wgmSxb5mui/FEu12hu9vGxj91x2e9kmiZPP32KQkGIVSsVg2KxiqYptnOrGBoohgMKi3xxfCIRN8lkGcOAqakCfr+DYNCN2+0nGHQRCIh5L4oCFy/mePDBLTzzzJDVOn15UDJX1GuaJh0dQdsArp7dSKXKRCIeJicLlMtVK2A0KBZrhEIuUqkSLS1+XC6VXG7WsKz+Hh0+PMInP3nPggFkvWTo9TovmxI9PV1kcDDDmTNJ9u5tJhBwSf3JOuBmHpchWRoZmEjWNf39cWuQWctlpZylBpzNDTLEoDofra3C5Kxu2X4lAslkskwk4l5wjT6fk+PH4/zpn74276zNNM1FS0Pf/OZZtm4NrzjQURSFPXtaFhSwgugC6u3dY2WWSui6MDGr1QxCIbfl0KvMO5OsZ5XuvruVl16aIBRycfFiDrdbI5/X8XodVCrCCE1VFas1WOD3O8jnddLpMsVilc2bG7jjjmbS6TK5nE4uV+HYsRjVqsmZM0n+5E8+wN69UX7nd57H63Wi6xVUFas7R7yfhiGCoFDIwy//8h088MCWedmNfF7nyJFxolEvw8MZSiWheXG7RcdQtTo76LCpyTPv+CwnlK6XDKvV/CVTok0qlRpOp/BMESXECgMDaXK5CqdOJfD5HDz88A65Cd5gbuZxGZKlkYGJZF1zte3CVyOaXUrP4nCoRKP+yx5jmvD669NkMmW8Xm3eWVs9KFho7dWqyfR0gdtvb7rsOa/WUXfv3hZOnIjz2mtTjI/nyecrVpkmwcBAiqefPslDD93Gv/k396Kqqn1sm5u9dHeHuHAhjc/nJJer2KLVPXuaSSZLOBxiWODJkwk7iBCmbwrhsJt4vMSRI+O0tvpIpyt2G3Yg4KRS8fCrv/o9fD4HMzMldN3E4VCsbIwoPdWPkaqKfw88sOUyzc6nPvV96zldbNkSZnQ0g8cjvsJE8KBSLouArKsrPO/YLCeUrndW/dmfHaVWM61JxYZdKnK5NAB+/ONxVFWxsynFYpXHH3+ZQqF6U5+h34wlkfXsTSO5NmRgIlnXXG278NWIZpfybPnIR7Zx+vQMhjH/+SYn8wwOpnnggU57qF39rO3rXz/H/v1tC64vEnGTSpUXvO1KHXXnprQDASe6XiMeF6WnQkHH5dIs3YTJF77wCi++eJGPf7yHb33rAoVChXDYQ1dXkJYWH/39cYaHaxQKOooCe/dGqVQMvvOdARobPTQ2ehkdzaAootwTjfro7Azy2muTGIbYqOuiVNM0icdLiBIcpFJly5a+ah9H8T/WDCDhS9LdHb5MOySeq4iqKpw6NYPbreF2O+wOoFDIjculMTNTpqen2R4BsNx7Xn/u/v448XiRUsmgXK5SKKhEo162bg1z7FgMRRHvy9RUgZ07G+3NOhQS4ttnnx22Bw3eTJs73LwlkfUwLkNyfZCByTrmZjyLWW2utl34aozhlvJsqc9aufT5Xn99ku7u4LxBa6ZpEosVyOd1nnlmiJ6eZrq6wvb0X4Bo1G8PXrxWR925Ke1YrMiFCym7HFOrCW8SXTcwDBOfz8ELL4xx7Fgcn0/D53MyM1Oy2nSFNfyGDQGqVQO/38HZsylmZor09EQZGkpTLOoEg25UFYrFGps3N6AooltoZqbEwEAKVVWszh7F8kYx0HUDn08Yv4VCbpLJkvV7i6GEtZqBqioEAi5UVeHTn/4BqqrQ3u7nAx/o4NixaU6ciKNpKhs2BJiZKVIsVmls9BKNeikWq3R2Brn33nZOnUoQjxdW9J5fuilHox6mp0VGyO930tLiIxh0kUiUSCRKuN3avL+9etkoEHDyxBOvoGnqTbW5w81bElnrcRmS64cMTNYpN+tZzGpzte3CV2sMt5Rny0LPVyzWaGpyz2vhFeLJNG63SjZbIR4vMjGRp7s7zJ49UUxTdLw89ND2ee3HV+uoOzelPTiYwjDEBGWx4YusidfrwDCwtBiiswnE3KD2djeGYTAwkGbr1hA+n4NUqkxPTwsXLqSIx0VgsmGDn+99bwhNMy0vFeERk89XbcfbWs1E00SGRgzqg1xOx+cTQmRdF8JWXTcwTRPDEO9lMOiiudlLJiM6a8bHc2zbFiGbrfDccyPMzJTYty/Kyy9PEgq5CIfdmKZprTNKsVjlU5/az+7dzZw4EV/xe17flDs7g1bGpMb0dIFAwMlbbyVobfXT1RVmfPwipVKNjRtny3l1/UlXV9jq2onx8MPbb6rNHW7ekshaj8uQXD9kYLJOuVnPYlaba7HEX21juIWe70//9DXOnRMD+0C0mQ4MpAmH3eTzKi6Xhq4bOJ0qZ8/O4HJpuN0aDz64hUcf3clbbyWu2ZRtbko7m9XxejVyuYo1E8iw164oWIJR4dra2OimXK6RSpUtV1XRYdPY6KG7O0w06uWll8bJ5UTmZ/v2CBs2+CmXawQC9UnBVUzTJJks4fU60DQhQBVllZItHBXjAAx8PieBgJNo1Es6XaGxUehTOjuDZDIVNA06OhpQFIWLF7N0dAQplaqMjWUxDNE1FIsVbNfbWs3k9dcn+a3futs+blfynh8+LMpfJ07E7U6cUMjNzEyRWs3ghz+8yPvet4mGBjfZrG61Ttds/Ul3t8iEPffcEOHwwnqi9by5w81bElnrcRmS64cMTNYpN+tZzGpzJZmPtSh9XXrWNjCQtoWSum5w330bURThdBqLiTbX3//9d17VJroYc1PaDQ1OfD4XilLENEWXy2wrrgmYtuZEzBAy6OwMEY8XcTiEb8l9920kGvXS3x9jakrYvoOYOpxOlymVqvh8omOnsdFjiWXF0L+WFh+JRAnTNNE08cKaplCtGhgGhMPCIC4UchON+piYyOPxaDQ1eXG5RHeNqop2cJdLY2AgxdBQhnS6TK1msnlzAKdTtTuPNm5soLs7fNUZxImJAsVidV4njs/nJBRyMTaWY3g4w+uvT/He926mrc3PV796EoCmJo9dnhOBWYV7721d8DXW8+YON29JZK3HZUiuHzIwWafcrGcx14OVbOBrVfq69KwtlSpRrRqkUmW6u0MADA6myeV0wmE3jY3eVf/SnBscibJDnkjEw8REjqplBlutViwNh5NSSQQaqqoSCDjQ9ZoVSKhs2xamtdXH1FSegYE07e1+hoYyeDyi48jrdTA4mGZiIoeumygKDA9nMAwRAHk8KsGg0K2Y5mznTqlUo7XVi9/vwDSF5uXuu9vIZCrs37+Zzs4ghw4N43Zr9u8lPEQK5PMVW9vhcjmIRh00N3tJpcq0tvquKfBsb/fx4x+P4nLN1Y6Y5HI6lYoYYCis/3XefHOae+5pI5+vEgyK6+NxMTV6374oXu/CX6dzN/f1qBu7mUsicvjhrYkMTNYpN+tZzPViuS/0tSp9KYrCo4/uxOdz8NRTJ5mcLFCt1mhq8vLaa5Ok0xVcLpVAwGXNdqlw8ODJFQVKK93E5gZHgYCTTZv8DA9nrMm9AjH3xrS1HSLjYdLU5MPvd9LW5ufcuRShkPBqqWd+vF6HpQ9R7bk1waCLVEoYuem6QTDowjRFNk/XTaJRr92hNDaWwzAMNmxoIJst29mU9nY/hYLO3r2zG7rf7ySRKNrlpVxOiIMbGlwkk0W7XFY/7g6HeN87OoL83u/98Ko2+QMHOvnv/72fUGj27yyf15mZERmktjYflYpBNOqjqcnL4GCaj3xkO6OjmXln6IZh8sd//BrNzYtv7utVNyZLIpL1hgxM1ik381nMarOSL/S1Kn3V3VOffXaYDRsCJBJF3nwzRjJZRtdrtqV7tWqgKCqbNwd55pmhZQOlK9nELk1pj4/nUBQR3FarBvl8lWrVtGfe+P0ucrkqkYgbl0sMyqsP2hscTNPS4rMzP+l0mXvuabWcc0Xmx+93UC7X+KVf2ommKUxNFXjxxTFCIRfpdIW9e1tobfVjGKa9kY+MZOjvjzMzU6Spycvu3U3cf/+WeRt6V1eQU6cSlMs1NE2hXK5aw/sqKAroes0OjsrlGpOTBRRFIZfTCQSubpPv6Wlm374or746STDoxuVSmZoSrdbRqBeHQyUQEEGLqioEgy5GRzN88pP3XPZ+Lbe5r1fdmCyJSNYbMjBZp8izmFlW8oW+VqWvS1t10+kKTU0eLl7MWvoOFVWFUsmgrc1DIlGktdW7bKB0pZvY3JT2978/yqZNDTQ3ewGsOTJVUqkSY2M5VFXhwIEO0ukS+XyVpiYnXV1hAgEHiUSZ7dsjnD6dpFzW6elpmeMJIrp++vtFNiUeL9LS4qOlxUd3d5iBgRS1msmZMzOoqkI2q/Pgg1t4+OHti25uczf0clm3Oopq5PPGnHZjEVQBlEpVXC43breG06myfXvEXt/VbPKKovCpT93L7//+jyiXhX+Lw6GyeXMDwaAItOaatS32WVrJ5r6edWOyJCJZT8jAZJ0iz2JmWckX+lqVvi5t1XW7NaszRaNaNajVxDA7j0d0prjdGtPThWUDpWvZxCYmCvPs4xVFaEuEC2uNcrnGHXdc7jgbixXYs6eZT37yHg4c6ORLX3qVaFQEN/X5McJdtUpDg5MXXxyzW6Dr84jOnElQKNRW/Fmd+zn/3Od+RDTqxTQ9jI3lbFfXQkG3AhENVRWzj+peLdu2zX9fr2aT37Mnyi/8wm32SUB9Pk46XbG7buos9VlabnOXujGJZGXIwGQd83Y8i1lIV3HsWJzm5oXn1NS/0D/60Z1rUvoSQYCTqakCZ84kqVREuUHTVHsWTDDosk3OXC6VVKo8z5Btsee92k2svd3H+fOpBYM00ZFjLmvsNjdjVy7XOHt2Bq/Xga4btLcHcLuFGLY+tbk+i0hRsNd98OCpFek+6p/z225rJBLxcOTIOJqm4nZrhMNucjmdRKKIYegEgy76+6dJpSps2xaZFzSs9Pgs9PpzTwKKxRrZbIW77mqjrc1vdzVd62dpoeC5Psm4v38al8vBpz71fev1FDZs8K+5MFYiWQtkYCJZNyymqzhxYppQyM1P/MTGy76g62ewq1X6utKuibY2H//wDwOWBbzIkBiGia7XLPdQ0WVSqwlTMqGfUJfd3K4lA/Sxj93Bpz/9QwzDsFtvAQxDuL8+9NAOhoYySx6nSzMZbrdo6e3uDmGaJkeOTAAi0BkcTNHa6qNWMzl2LEZjo4dCQZ+ni7n//g727m3h+edHFjyupmly8WLWFgxrmjB/y2YrRCIeWlqEXsblUnG7nfz0T2+0JwhPTRUYHEyRzeo0NDgJhTy8853ty7zT85l7EjD3c7hSB9mVcKluzDRFJurChaTVteTn298+D8C2bRFyufKaC2MlkrVABiaSdcNiuoq77mrj2WeH2bo1Qnv7rPPm3DPY1Sh9XU3XxObNQjC6aZMfp1NjdDSD3++0yziGIdxBdd0gEnExM1PmE5/oWXZzuxbx88MPb+fQoSGeeWYEn8+Bz+egUKhSKFR58MFOvvzl93Py5Myyx2k2k9HEjh0RO0gyTZPu7hADA2lUVUxejsUKjIxkAbjzziiaptrvX2OjSV/fcRobPXR0NCx4XPv742QyFdxuBz6fQalUw+VSMU2VmZki4bCHD36wA1VV2L49woEDnXzxi68wNpZjaChtD9WLx4ucPZti796oPSDwSrleZdRLg+dCocrJk3HcbgebNzcQixVoaxOfb2Eu18CWLcG3laGiRAIyMJGsIxbTVbS1+enqCvH661Pce2/bkmf511L6upquidHRDN3dQvjqdGoEgy5mZkpomoKiaDgcqjWjxonX6+TRR3fwmc/cu+zmtlgGKJOpcPvtTRw6NLRoqURVVf7bf3uAb3zjHP/1v75ht/6+730b+fVfvxNVVecdp3qW6E//9DWOH0+QTBZpbPTQ0xPlgx/spK3Nx/nzs9kbRVFsTUl/fxy3W2P79gh+v5hMXA9K6sTjRTvzcOmgw/pxPXx4mM2bAzgcCm+9laBcLqOqTgxDBHgej0Zzs5fh4awdINx+exNPPtlPY6PopqlUxEyePXuaOXUqwYkT8av+LFyPMuqlAc/f//0FolHhwzIwkMLtdtjvoTCXS9Pa6l9zYaxEcqORgYlk3bCYrkJR4K67WkgkSmzfHrluQuCrEZxOThbZt6+FXE635uNoRKN+wCCTqVCp1Hj3uzfT09PM/fevfL0LnbVv2xahWKxy6lTCNvhaqoW4UKiyeXOQO+5osgObP/mTo/PuW88SPfPMEBMTOSYm8vb1w8NZXn99ittvbySTqdDU5LWPjaIoRKOiG+dTn9pPT0+U3/u9HxIIuC77XQYHhd6lUNAXPa71916YuxVIJkukUiUCARdtbX40TWF4OGsHosKhVePee1tJJsvk87plpR+ipcVHPF5cl5v53IBnYqKAaQqb/uPH47hcswGdyyV8Y0AKYyVvP2RgsgJ6e3t3Aj8NvA/oAeqz7GPAa8BXga/39fXVFn4GyUpYSldRKOh2x8j14moEp/U1t7b6aW31z7stFhMB1NWu+dKz9uPHY3zpS6/S1RVaNqOz0uxPf3+cZ54ZoljUOXkyYYtOQyE3sViebdvCnD49w86dTcsOHFzs/ctmdcBc8NjWj2tbm5fvfGeAWKyIy6WxfXuYdLpCMlkin6+wbVuLPaSvfiz+/u8vMDKSJZOpANDY6CEUctPS4rspNvNLRwkkEiWcTqFJqtv9w9vTUFHy9kYGJsvQ29v7V8AvL3LzZuvfQ8BLvb29j/T19V28YYu7xVhrU7mrEZxeyZrrJZPnnhumvz/GzEyJSMRLT08THR1By020uGB5xjRNnnrqLQYGkly4kCIQcBIKuUmnhUOqopj89V+/xRe+8D4ADh58i4GBFAMDKfx+p51JuDT789xzIlNy8mQCXTfRNBEEZrPCSG1oKMW2bRG8Xo1PfWr/kpqLxY6F3+8gHi/YFv0LHddNmxoYGMiwaZPfFuxGow7LEybPL//yLkxTDE08dGiUyckMQ0NZstnZ0lGxWLWs9FPce28bO3Y0rksL+DqXjhKYmBiz27zFgMDQ29JQUSKRgcnybLT+TwFfB54HBoAyInvym8BdwDuBQ729vXf39fXl12CdNz1rbSp3NYHRStc8K6wdYmwsz+RkziqZJHn55XGKxSpbt4bYt6/lsvIMwMGDJ/nudwdxOlW8XgfnziVJJsuEw27L3r3Kd787SE+PeL3vfGcQl0sMpJuZKTE5mae7O8SePdF52YTjxxNMTOTRNAVNU63/RUtxLqczPV1k794WpqaKy2ouFjsWbreD5mafbfi20HE9dGiYrq4Q8XjBFrLWJ/h2dQX5u787h8MhPFSGhlIUi1UymQqqCk6niqKIQYEej5Pz51P4/S5+/dfvXJcW8HUuHyXQwPnzSUB05SgKDA1l3naGihKJDEyW5yLwG8Bf9fX1FS+57Whvb+9B4H8BPwfcBvwO8Ic3dom3BmttKnc1gdFK11wvrXi9YvBbc7MXRVHIZisMDKRtAW0up9Pa6p9XcjEMk6997Qy6XiOREDNcCgXd6uzQqVQMHA6VaNTH3/7tWQA2bvSTTJZxOoUxmc/nYGAgTVub3+5sAUgmi/ZwvGKxak8EFkETFIv6iksJix2L3t69vPnmNIcOjSx6XA8ePMVddwmtzuBgilxOtyf4Fos6r702xcMPb+fllyds3xinU6FWg2rVQNNUFEVke+rH1TRZlxbwix2vhgYXu3Y12be1t/vfloaKEokMTJahr6/v48vcXu3t7e0FPgy4gEeQgclVs5amclcbGK1kzXVh7YULqXmTbNPpMg6HSjot/DrqnRj1ksuhQ0McPTrF2bMzOBwqtZpJLleiWjWtgEIlmSwSCLjYujVEf38MEOt58UVRGhDW7goul8aFCyI4qWd/Ghs9mKZpGZlVME2xwZsmgInH42RkJIvP5+BXfuV7l3XsXFoSWexY9PRE2bevddHjOqvVEUZtdUzT5NvfHiOf1zl8eISxsRyhkItyuTrHxE5BVUWQoigKmzYFcDpVnn9+ZN1awNd5O5ooSiTLIQOTVaCvry/R29vbD9wNbFvr9Uiunuu1UdSFtdmsPq/7olyuWcP1jHmdGCCEof39Cd54I0ZTkwenU6NWMxkd1VFVxepycVCtmuzZE6WlxUelYqIoxrz5NfXSSLVqMD6e47HH7rCzPz09UYaHs8Ri9fVV7K4cl0sjnS7h9zt4/XWdycnLO3YeeKBzSeO0lR7XhcpopikM2wYHM7S1+TBNk2rVYHRUzCEyDAOHQ4hFQyE3lYphaTREtkFawEskNycyMFk96n2SsjNHchn1jMCl3Rdut0Y6XcHnc8zrxAAhDBXmYk5SqQrlco1yuYrDoVoZE5Nq1WTXLjGvRmRFFEBkPepeI/XSiN/v4L77Ns7TVnzwg528/voU27aFGRpKMz1doFis4vFo6LpBc7OP226L8PLLE3b5yTRN4vEC27aFljVOW2kJYqEy2vBwhuPH42zeHMDjceB0arS2+hgZyaBpIphTFAO324FpQq1mEAy6SKUqPPbYLkZHM7aYuW79XneIVRSTd71rI6ZIDa1bgaxE8nZEBiarQG9vbwuw0/rx1FquRbI+qWcEOjtDTEzk7RJLKOQmkSgRCrnsTgyYFYZGIm4SiSLj43m83vqAQJVyWcfrFQZue/a0oCgKhmESiXjtx6uqYpdGDMNkaCjDY4/tmrfZ9vQ08+CDW3j22WG2bQuzd2/U1n8YhtjoL1xIzys/1ctC/f1xEoniksZpK808LVRGK5dr7N/fRiTi5siRcXw+0XLc2OhlejqP06mi6zUcDoNcroLH4yCVKvPgg5089NA2TpxI8Prr0zQ2mpw4EbezR06nQjxe4vTpGZ566i2AefqXtRDIrufuIYnkRiMDk9Xh95g9lv97LRciuf5czSZSzwg888wQzc2+OV05Ju3tfhKJEq2tXl57bZKJiTzVqsm+fVGcTo0zZ5IoighUPB7NymbUyOV0NmwIEAg4icUKZLM6jzyyA2BJoelcltLVPPXUScAkn59ffgJhADY+niccdi9pnLaSwGSh4/nRj+7k6adPYpomXq9zXllKZJVMUqkyGzc2US5XURSFrVtDPPbYLh56aJvlbiuO+de+doazZ2doavKg6yb5fJU77mhi9+5mWyy8b1/Lmglkr2YUgkRyKyMDk2ukt7f3J4Dfsn4cBf58DZcjuc5c7SYyNwA4dGiY/v44MzNFmpq83HFHI0NDGf7hHy6QTlfweh2Ewy7efDNGKlVGUYRItVw2KJV0DMOksdGLz1eluzuEpqnzxKTAkkLThda2kP5jwwY/584l8ftFy3G9/ATCAMw6Iksap63keD711Fv87d+eJZksoes1nE6N554boaHBRTjsxudzXlaWam728jM/s40nnnjfkpv2nj1R/uf/7CefF91L7e1+7r67hdZWP4qikEwWAWVNBbJXMwpBIrmVkYHJNdDb27sR+D+I42gAv7xAS7HkFuJaNpHFAoDjx2N89rMv0NDgoqMjiKIo5HIVZmZK1PfcYrGK2+3A43FTKAidyb59reze3czjj7/3stdaDQFvvfy0ZUuQycnZ8pNpisGE7e0+EonSksZpy3H8eIy+vuNksxXcbs0qU9UYHs6gqgqbNzfQ3OxdUVlqLnMDyGSyzIYNAUxTrGtqqmC79NbFwgtxowSyVzMKQSK5lXnbBiZWULHYN2e+r69vcJnHR4DvMWtP/7t9fX3fX70VStYj12MTOXx4mGSyOG+IWzJZxuHQcDhq1GpiCKDLJUo4QtPhpbMzOG/a8mowt6wyPp7HMAzGxoo0N3vndeW0tQXweh0oirqkcdpCzzu3/HXw4FvE42Kqbv13r/uuTEzk7CDkSg335gaQ09N5EokSfr9znp9La6vfFgsvxI2ygpfdQxLJfN62gQnweeBfLnLbD4D3L/bA3t7eAPBdYLd11R/29fV9eSUv2tvb+2vAr829zuVy8fnPf37e/d7znvfw3vdefiYsWVuuxyYyMVGgUjHn6Th0vYamKXi9DrLZCmCyeXODfRtALrc6VuWzVvlDHDo0yvR0ju7uCB0dDQSDLtLpCq2tPjo7g8zMlGhs9NLT08wHP9ixrHFa/fkXK3+99tokXq9m/T4VUqkylUoNl0vD4RAt0Y8//t4rNtybG0DOtXuvC3cHBtJEo77LxMJ1bqQV/NWMQpBIbmXezoHJVdHb2+sF/h54h3XVV/r6+j630sf39fX9BfAXc6/7/Oc/b372s59dvUVKrhvLbSLbtoU5fjx2RcLY9nYfLpdCuWzYOg6nU6NYrKKq4PE4qNVmRahCh6KtilX53KChbvfu9To4fTpBpVJjz54ozc0+hoYy/M7v3HPFxmlQz14M4fUKg7lsVrjWdnaGSCZLBIMuSqUCyWQZTQNNE862pVINVVXZvbv5irNQcwPISz1dFEUMWBwaylyxWPh6sNYzoiSS9cbbNjCxHF0/fiWP6e3tdSHm5bzfuuovERb0krcJS20imUyFYrHGl7706hUJYw8c6OS550YYHk7bZ/WRiJtstoyiKDQ1edi3r9Ue2Of1OvjMZ97Bww9vX1JjsZLOobklj7rdu9/vxDRNBgZSVsnDt2iZaiWGdM89N8zYWH7eHJxEosTERB6XS2V6Wlzv8cy2JKuqRqlURVHgxIn4FQcmcwPISz1dpqcLdHQE500rvhKx8Gqz1jOiJJL1xts2MLlSent7HYhW4J+0rvoboLevr89cu1VJbjRLbSK3397I6dMJtmwJXZEwtqenmUce2UFf33FLZCoCA00TRmqbNwfp7AzaU38feKBz2aBkpZ1Dc0seuVwFXa8xOlqyy0XHj09z//1b5pWprrRdur8/xuRkzjZog1kdiXhNE1U10HUVTYNazaRWM/D7Xeze3XxVup1LA0hFgdZWH9Gol6GhDJ/61P55z7mWtvBrPSNKIllvyMBkBfT29qrAXwMfsa76BvAv+/r6FpbzS25ZltpEDh0Sg9iuVBirKAqPPbaLO+9s4eDBkxw9OgUoPPBAJ+94xwZGR7NMTa18s7qSzqF6ycM0RcZnfDyH2+1A0xR0vca5cylaWmK0t/vZsSNyVe3SortIuex6RREaGr/fQSDgRNcNajUTh0MhEHCza1cTHR3Bq9Lt3GxZCDkzRyKZRQYmy9Db26sgNCG/ZF31PeAX+/r6qmu3KslastgmcvDgqasWxiqKwp49LXzhCy3XvL4r6RyqlzyyWWF573CoOJ0iiKjVVPx+jQsXklSrBr/xG3deUdBTz6xMTOSYnMxTLOpEIl7rGJnkchXi8ZK1Ljd+v/g6amhw0dUVpqXFRzxeuCrxp8xCSCQ3LzIwWZ4vAr9qXT4H/DtghxgovChn+vr69KXuILn1WC/dFVfSOVQveUxO5gkGRbanLkLVdYNIxEO5XCMUEmWVr3zl6IqCnrmZFYdDxe8XdvGpVJnGRg/VqnBu9fkcbNjgJ5OpYBgmW7eG7Lk/1yr+lFkIieTmRAYmy/Pzcy5vB15ZwWO6gKHrshrJumW9dFdcSYBUL3k88cQrOJ0qjY1uNE0hmSwRiXjYtCnAhg1+WlqEz8hKg565mRW/X7Q8R6M+kskyiUQJTVPo7g6i6wb33NPG5GSBCxeSnDyZsN1e12vZRSKRXF9kYCKRrBLrRddwJQFSveTR3x/jxRfHAOG2+t73bqalxWe31taN3FYa9MwtJ7W0+Ni6NczAQJrGRg+lUhXDEJORt26N0NIiAp+2Nj/9/dOMjOT48Ie7ZdlFInmbIgOTZejr69uy1muQ3BysF13DlQZIYt27mJwszNOOwOXBzEqDnrmZFaGfEe26AwNpxsayOJ0q99230Q5+AKsteSOqqvLJT95zPQ+RRCJZx8jARCJZRdaDruFqAqSVBjML3S+XqzA6miMYdHHw4Ena2/2YpkEuV7EzK4qi0NoqbODzeR0waW31XbYO6XQqkUhkYCKR3IJcaYB0JcHMnj1R+vtjHD06hWmC1yuM0UIhN6Zpcu5ckpGRLDMzJQ4c8KJps1b7hmESiXjsy9LpVCKRXIoMTCQSCbB8MHOph8ndd7cyPJzl1Vcn2bOnmWhUGKj5fE4aG70cPjzCm2/G6OhomJeBWQ828BKJZP0iAxOJRLIiFvIwSadLNDa6GRxM094uSjUAmqawd2+UQMDJhg2ByzIwsLY28BKJZP0iAxPJLcmV2qavF9bzuhcybstmddxuUcoZGEjbgQlAIOBcUsi61lociUSyPpGBieSW42ps01fzta82sFjLda9k/Qt5mDQ0OEkkSrhcqiVqBdOE6ekC/f3TuFxO/vRPX1s3wZVEIln/yMBEcstxJbbpq8m1BhZrte6Vrr+tzcf58/M9TLq6wkxMjGGaJk1NXkwTjh+PceFCknK5xn33RW54cCWRSG5u1OXvIpHcXKxkVsz1YG5gEY368PmcRKM+tmwJ8uyzw5w4EV+X666z3Po7OhrIZnUMY3agdkuLjy1bQszMlIlE3IyMZDh5Mg4o3HGHGMJ3JcdAIpFIZMZEcstxJbNiVpMrGZ63EGu17jrLrX9kJLOg10kw6OITn+jB69X4h38YIBr10dPTbJmnKfOeY7ljsNasZ42PRPJ2QQYmkluOtRqmd62BxVoPAVxu/VNTRT75yXuW9DqZnCximsaCv8ONCK6uhfWg8ZFIJDIwkdyCrNUwvWsNLNZ6COBK1r+c18laB1fXwlprfCQSiUBqTCS3HHXb9KGhDLFYgUJBJxYrMDSUua4GXgcOdF6mwYCVBxZrte4617r+1XqOtWKtNT4SiUQgMyaSW461GqZ3rdOF13oI4GpMR14vE5avhrXW+EgkEoEMTCS3JGsxTG81Aou1HAK4WutfDxOWr4abuQwlkdxKyMBEsm64FToi1sN04WthNda/kudYj+/1Wmt8JBKJQAYmknWB7Ih4+7Be3+ubuQwlkdxKyMBEsi6QHRFvH9bre30zl6EkklsJGZhI1gXXak4muXlYz+/1zV6Kk0huBWS7sGRdIDsi3j7I91oikSyFDEwk64L2dp89nfZS8nmdtjbfDV6R5Hoh32uJRLIUMjCRrAtuZmMuyZUh32uJRLIUMjCRrAvW2vV0tfjhD3+41ktY99wq7/WNQH6eJKvNzfCZkuJXybrgVumIeOGFF3jve9+71stY19wq7/WNQH6eJKvNzfCZkoGJZN0gOyLePsj3WiKRLIYs5UgkEolEIlk3yMBEIpFIJBLJukExTXP5e0muK729vTFAzlS/NdgJnFrrRUhuGeTnSbLarKfPVLyvr+8nL71SBiYSySrS29v7Wl9f3z1rvQ7JrYH8PElWm5vhMyVLORKJRCKRSNYNMjCRSCQSiUSybpCBiUQikUgkknWDDEwkktXlL9Z6AZJbCvl5kqw26/4zJcWvEolEIpFI1g0yYyKRSCQSiWTdIAMTiUQikUgk6wYZmEgkEolEIlk3yMBEIpFIJBLJukFOF5ZIVoHe3t6dwE8D7wN6gDbrphjwGvBV4Ot9fX21tVmh5Gaht7d3E/CbwIeBDqAKDALfAP5LX19fcg2XJ7lJ6O3tvRv4KeDdwC6gBfFZmgReBv66r6/ve2u3wsWRXTkSyTXS29v7V8Avr+CuLwGP9PX1XbzOS5LcpPT29v4kIogNL3KXMeBn+/r6jt6wRUluOnp7e38AvHcFd/0O8GhfX1/6Oi/pipClHInk2tlo/Z8C/gfwGPATwD3ArwCvW7e/EzjU29vrv9ELlKx/ent79wD/BxGUFIB/jzjbfT/wZaCG+Kz9Q29v74a1WaXkJqH+nTQF/P+Af4H4/nkH8K+Ac9btHwK+3dvbu65iAVnKkUiunYvAbwB/1dfXV7zktqO9vb0Hgf8F/BxwG/A7wB/e2CVKbgL+FPAjApCf6uvr++Gc237Q29v7OvAUokz4h8D/dcNXKLlZOA38PvB/+vr6qpfc9oqV5X0GuA+RWfkocPDGLnFxZClHIrkB9Pb2NgHjgAs43tfXt3eNlyRZR1h6gNesH5/s6+v7xCL3ew44gAheNvT19U3foCVKbjF6e3t7gOPWj9/u6+v72bVcz1zWVfpGIrlV6evrSwD91o/b1nItknXJw3MuP7nE/f6H9b8G/Mz1W47kVqevr68fSFg/rqvvJBmYSCQ3Dpf1v+zMkVzKu63/C8CrS9zv+QUeI5FcLU7r/3X1nSQDE4nkBtDb29sC7LR+PLWWa5GsS+6w/j+3gCbApq+vbxzIXvIYieSK6e3t3QcErR/X1XeSDEwkkhvD7zErNv/fa7kQyfqit7fXDTRbP66klXzU+n/z9VmR5G3C78+5vK6+k2RgIpFcZ3p7e38C+C3rx1Hgz9dwOZL1R8Ocy7kV3L9+n8B1WIvkbUBvb+8vMqtreg1h3rdukIGJRHId6e3t3YjwpnAABvDLC7QUS97eeOdcrqzg/uUFHieRrAjLL+cvrR8LwGN9fX3rqj1X+phIJItgBRWRRW7O9/X1DS7z+AjwPWbt6X+3r6/v+6u3QsktwtxA1bXovWZxL/A4iWRZent7twD/iPDLMYB/2dfXd3pNF7UAMjCRSBbn88C/XOS2HyAcORekt7c3AHwX2G1d9Yd9fX1fXtXVSW4VsnMur6Q8U7/PSso+EgkAvb297cCzzLrC9vb19f2fNVzSoshSjkSyyvT29nqBv0fYPwN8pa+v73NruCTJOqavr68MxK0fN63gIfX7jC55L4nEore3txkRlNT9Sn6nr6/vL5d4yJoiMyYSySL09fV9HPj4lTymt7fXBXyd2WzKXyIs6CWSpTiJsAbf3tvb61isZdiakROc8xiJZEl6e3tDCPv5XdZVn+vr6/vTtVvR8siMiUSySvT29joQbXc/aV31N4h06boSlknWJT+y/vcB+5e43/sXeIxEsiBWSfl7wD7rqif6+vrW/ZwuGZhIJKuANZ3zr4GPWFd9AyEsM9ZsUZKbia/PufyrS9yvPrivBnz7+i1HcrMzp6T8Tuuq/9LX1/eZNVzSipGBiURyjfT29irAXwC/ZF31PeAXl3LwlEjm0tfXdxT4vvXjx3t7e99z6X16e3sfBT5o/fjXcoCfZDGskvLfMZthexL47TVb0BUiNSYSybXzRWbPcs8B/w7Y0dvbu9RjzvT19enXe2GSm4rfBl5EtHJ+r7e393HgOcT39M8yu7FMMt+1UyK5lL8Bfsq6fAT4M2DXUt9JfX19J27AulaEDEwkkmvn5+dc3g68soLHdAFD12U1kpuSvr6+4729vT8PfBUIA//R+jeXMeBnrZk5Esli/Nycy+8Cjq3gMcp1WssVI0s5EolEsk7o6+v7HtCDyMKdAvJABrGx/Aegxyr7SCS3LIppyoYBiUQikUgk6wOZMZFIJBKJRLJukIGJRCKRSCSSdYMMTCQSiUQikawbZGAikUgkEolk3SADE4lEIpFIJOsGGZhIJBKJRCJZN8jARCKRSCQSybpBBiYSiUQikUjWDTIwkUgkEolEsm6QgYlEIpFIJJJ1gwxMJBKJRCKRrBtkYCKRSCQSiWTdIAMTiUQikUgk6wYZmEgkEolEIlk3yMBEIpHYKIrycUVRTOvflrVez3pCURSHoij91rH5xFqvZzkURfmctdbn1notEsmVIAMTiUQiWRm/DuwGBoG/uvRGRVHePyeo+w9LPZGiKBFFUV6Zc/+/UxTFtcrr/TMgBRxQFOWhVX5uieS6IQMTiUQiWQZFUfzA56wf/7Npmvo1PFczcBjYb131VeBfmKZZubZVzsc0zTTwFevHP1QURX7fS24K5AdVIpFIlqcXaAHiLJAtWSmKorQB3wfutK76n8DHTNOsXuP6FuPPAB24A/j56/QaEsmqIgMTiUQiWQJFUTTgN60fv3a12RJFUTYCPwB2WVf9P8CvmqZpXPsqF8Y0zRngu9aPv329XkciWU1kYCKRSCRLcz+wxbp88GqeQFGUTuCHwA7rqj81TfM3TNM0r315y/K09f99iqLsvAGvJ5FcEzIwkUgkV4SiKE5FUX5NUZRDiqJMKYpSURRlWlGU5xRF+XVFUZwreI4mRVG+oCjKGUVRitbzPFsXaa6z7qB/Yf0/bprmkSt9sKIoWxFBSbd11R+Zpvk7q7W4FfD3QF2/8tEb+LoSyVUhAxOJRLJiFEXZDLwO9AEfROgunEAUOAD8OfCmoigdSzxHD/AW8ClEBsFjPc/9wNcVRem7nr/DVfAB6/+Xr/SBiqLchghK6sfj35um+W9Xa2ErwTTNIvCm9eNP38jXlkiuBhmYSCSSFWF1pjyHaJkFoV14CLgH+AjwD9b1dwDPK4rSsMBzhIHvAa3WVV9FbJb3AL8IvAj8GqI1d81RFGUTs2WcV67wsbsRmpIN1lWfNk3zP67e6q6IelC1T1GU4BqtQSJZETIwkUgkK+XfAduty182TfOnTdP8pmmaR03T/JZpmh8Gvmjd3g38wQLP8e+Z3ag/Y5rmR03T/K71HP8beC/wLeAd1/H3uBLum3P59St43F7geUQAZgK/aZrmF5d+yHXlqPW/Auxbw3VIJMsiAxOJRLIslvnX/8f68QLwe4vc9d8CZ6zLv6ooinfOc7iBj1s/Hge+cOmDTdOsIVpzS9e+6lVh05zLU1fwuI8Azdbl3zZN87+u2oqujuk5l7sXvZdEsg6QgYlEIlkJ9wAR6/JfLdYya/lx/E/rxyDzMx/3AGHr8tOLdaSYpjkF/NO1LniViM65PHMFj5v7u33YCsrWkrlrb1uzVUgkK0AGJhKJZCXsnnP5pWXuO/f2nkWe4yhL89pSNyqK8jFFUf5CUZSjiqKUre6djy3znCiKsk9RlG8rijJjdQO9YXUYKYs8pGnO5dRyzz+H/4YQ+AI8APyflXQrWWvcMqcjKbbY4xRFabU6okxFUZYzaJsbmPhXsg6JZK2QgYlEIlkJjXMuTy96L8HkIo+LzLkcW+Y5lrv9DxGlpc2ssMSiKMp7gCPAPwO+g3BFdSI6jL6yyMPmZj48K3kdiziiy+is9fM/B/6XoiiOK3iOKqIc9DOL3P7LiPWvxDXWO+fyVdvpSyQ3AhmYSCSSK+VGmIItxyeATtM0W4D/sdydrYDgScANfMQ0zcdM0/w94C7gBeA3FUW5b4GHpuZcblzg9kUxTXMS0UI9YF31MPDXVzCz5iVEpuNXF7n9V4FjwNgKnmvu2lMrfH2JZE2QgYlEIlkJc0sBrYveSzBXwzD3cck5l+dqNxZiydtN0zxkmubIMs8xlw8gOoqeN02zbtGONTivPpzv1xZ43PCcy5EFbl8S0zTHEMFJfa2/BPyPJUpHcykjXFsftOzsbRRFeTdwGyLYWglz134lx00iueHIwEQikayEE3MuL9fKO/f2/jmX35pz+e5lnuOelSzqCni/9f8zC9z2IyA/5z5zmbvmHQvcviymaQ4jgpNx66p/Cfw/KwxOngQ0ZruZ6nyC2cBlJdw25/Jbi95LIlkHyMBEIpGshNeYzXg8tphWwrr+V6wfM8x3S30NSFuXH11sY1YUpRWhA1lN6kHFuUtvsFqUB4EOqy16Lq8zq+HYf7UvbprmBYRTbl2f82ssrmuZ+7hjCKHwr9SPl2WQ9gjwDWtI30qoB4szLHAMJJL1hAxMJBLJslglj/9u/bgD+E+L3PU/Abdbl5+07NDrz1EC/tr6cQ/w6UsfbOkv+rgyoelKCFn/pxe5PYMwHwvNvdI0zSyzXUb3XssCTNM8jQhOEtZVv6koymVeLgvwJLAVeJ/18y8BPlZexoHZtR+6QYMDJZKrRgYmEolkpfwnZs+2P2O13f6Moih3Wf9/G/iMdfsAwuX1Uv4Ds107jyuK8jeKovyk9Ry/gBCi/izz7d/XeiP9uvX/PkVRrkgAeymmaZ5AtA+nrKs+pSjKYkFenb8BisyKYH8VGEKMB1gWa6Jw3W33G1ewXIlkTZCBiUQiWRGmaeYQZ/x1vcmHEfbxR63/P2xdfxI4YGUbLn2OGeAnmW0H/iXEzJ2jwP9GWMD/v4isSZ3VcIGtZ0pCi9weRARAC2VUvooo5zgRJZRrwjTNNxDHoH58fl9RlEUH+5mmmQb+Dvg5q+V5P/A/ryDzUZ8onEG8TxLJukYGJhKJZMWYpjmKaLH9dcQZewzhixEHDgP/X+BOS/C52HMcQwz6+2NEBqZsPf554KOmaf4KIlCos1j55Uqo+4lsv/QGRVE0oAsYsUpWl653ktmsyaOrsBZM03wZMbwwb131eUVR/vUSD3kS4UXyVcBABG8rpR6Y/I+5pTWJZL2iyHKjRCJZbyiK8peIksWYaZqblrnvf0CUjR4zTfPgIvd5ANGR87xpmgcuue19wPcRVvsfX+Tx9wCvIrIqO03TPLPQ/VYDRVG2IMS4z5mmeb91nYIIrrYB/2Sa5k/Ouf8QsMk0zcsEyYqifAARMOrADtM0h67XuiWS1UJmTCQSybrCGvz3s9aPy9nfr5TnEdmZDyiK8lNzXsvFrJD3LxZ7sGmarwHfRghkP7fY/a4XVtnmEeAh4Dev4KH1tT4pgxLJzYLMmEgkkhuKoihbgYGFNBJWWeVJhNcHwM+apvntBe73CeDd1o93AnsRwtm6y+qPTNP8y0se815E1kRB6FkmgA8Bu4D/Yprmby2z7tsQviwqsOt6ZU0Wypgsc/8hFsiYWHqUHyK0JTus4YgSybrnSuY2SCQSyWrwb4B3K4ryVYTPySRCP7EXMf/mLut+zwN/v8hzvJvZ4KXOe6x/deYFJqZp/tCynf8DhFDXA5xB6GUWzZbMefwZRVF+GdEOvdF67HomgvhdX5NBieRmQmZMJBLJDWWOfmQpXgI+bJpm/AYsSSKRrCNkYCKRSG4oiqLsAH4O4eexBTEXx4kwHqu3DX/VNE1jrdYokUjWDhmYSCQSiUQiWTfIrhyJRCKRSCTrBhmYSCQSiUQiWTfIwEQikUgkEsm6QQYmEolEIpFI1g0yMJFIJBKJRLJukIGJRCKRSCSSdYMMTCQSiUQikawb/v8USvVptM4fLAAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -674,10 +690,17 @@ "ax.yaxis.set_label_coords(-0.13, 0.5)\n", "ax.xaxis.set_label_coords(0.5, -0.1)\n", "\n", + "x0,x1,y0,y1 = -4, 3, -3, 5\n", + "reg = LinearRegression().fit(km.reshape(-1,1), kcat.reshape(-1,1),)\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "#plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([x0, x1], [beta0 + x0*beta1, beta0 + x1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", "\n", "plt.xlabel(\"$\\log_{10}$($K_{M}$)\")\n", "plt.ylabel(\"$\\log_{10}$($k_{cat}$)\")\n", "plt.scatter(km, kcat, alpha = 0.5,s=60, c= \"darkblue\")\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"KM.png\"))\n", + "\n", "plt.show()" ] }, @@ -690,7 +713,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -739,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -749,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -793,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -805,7 +828,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -832,10 +855,15 @@ "ax.yaxis.set_label_coords(-0.13, 0.5)\n", "ax.xaxis.set_label_coords(0.5, -0.1)\n", "\n", + "x0,x1,y0,y1 = -5, 2, -2.5, 5\n", + "reg = LinearRegression().fit(flux.reshape(-1,1), kcat.reshape(-1,1),)\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([x0, x1], [beta0 + x0*beta1, beta0 + x1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", "\n", "plt.xlabel(\"$\\log_{10}$($flux$)\")\n", "plt.ylabel(\"$\\log_{10}$($k_{cat}$)\")\n", - "plt.scatter(flux, kcat, alpha = 0.5,s=60, c= \"darkblue\")\n", + "plt.scatter(flux, kcat, alpha = 0.5, s=60, c= \"darkblue\")\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"flux.png\"))\n", "plt.show()" ] }, @@ -848,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -883,7 +911,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -894,7 +922,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -908,7 +936,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/model_fitting/03 - Analyzing and plotting the results.ipynb b/code/model_fitting/03 - Analyzing and plotting the results.ipynb deleted file mode 100644 index e537e43..0000000 --- a/code/model_fitting/03 - Analyzing and plotting the results.ipynb +++ /dev/null @@ -1,2589 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", - "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", - "or from the matplotlib source distribution\n", - "\n", - "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", - "You probably need to get an updated matplotlibrc file from\n", - "https://github.com/matplotlib/matplotlib/blob/v3.5.2/matplotlibrc.template\n", - "or from the matplotlib source distribution\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "C:\\Users\\alexk\\projects\\kcat_paper\\code\\model_fitting\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib as mpl\n", - "from sklearn import metrics\n", - "from scipy import stats\n", - "from scipy.stats import wilcoxon\n", - "from sklearn.metrics import roc_auc_score, r2_score\n", - "import os\n", - "from os.path import join\n", - "import pandas as pd\n", - "\n", - "CURRENT_DIR = os.getcwd()\n", - "print(CURRENT_DIR)\n", - "\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "plt.style.use('CCB_plot_style_0v4.mplstyle');\n", - "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", - "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Plotting performance of different models:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (a) Pearson r" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "models = [\"str_fp\", \"diff_fp\", \"ESM1b\", \"ESM1b_ts\", \"ESM1b_ts_diff_fp\"]\n", - "model_names = {\"str_fp\" : \"str. FP\",\n", - " \"diff_fp\" : \"diff. FP\",\n", - " \"ESM1b\" : \"ESM-1b\",\n", - " \"ESM1b_ts\" : \"ESM-$1b_{ESP}$\",\n", - " \"ESM1b_ts_diff_fp\" : \"ESM-$1b_{ESP}$\\n + diff. FP\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "\n", - "\n", - "plt.rcParams.update({'font.size': 28})\n", - "plt.ylim(-0.01, 1)\n", - "plt.xlim(0.5, len(models) + 0.5)\n", - "\n", - "labs = [model_names[model] for model in models]\n", - "Boxplots = []\n", - "ticks = []\n", - "for i, model in enumerate(models):\n", - " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", - " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", - " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", - " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - " Pearson_test = stats.pearsonr(test_y, pred_y)[0]\n", - " \n", - " if i == 0:\n", - " plt.scatter(i+1, Pearson_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", - " else:\n", - " plt.scatter(i+1, Pearson_test, c='darkblue', marker=\"o\", linewidths= 8)\n", - " \n", - " Boxplots.append(Pearson_CV)\n", - " ticks.append(i+1)\n", - "\n", - " \n", - "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", - " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", - " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", - "\n", - "\n", - "\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=8)\n", - "\n", - "ticks1 = ticks\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "#ax.locator_params(axis=\"y\", nbins=4)\n", - "\n", - "\n", - "ticks2 = list(np.array(ticks)-0.01)\n", - "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", - "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", - "#ax.yaxis.set_major_locator(loc)\n", - "\n", - "plt.ylabel(\"Pearson r\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "#plt.legend(loc = \"upper right\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (b) MSE" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "plt.ylim(0.5, 1.4)\n", - "plt.xlim(0.5, len(models) + 0.5)\n", - "\n", - "labs = [model_names[model] for model in models]\n", - "Boxplots = []\n", - "ticks = []\n", - "for i, model in enumerate(models):\n", - " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", - " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", - " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", - " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - " MSE_test = np.mean(abs(test_y - pred_y)**2)\n", - "\n", - " \n", - " if i == 0:\n", - " plt.scatter(i+1, MSE_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", - " else:\n", - " plt.scatter(i+1, MSE_test, c='darkblue', marker=\"o\", linewidths= 8)\n", - " \n", - " Boxplots.append(MSE_CV)\n", - " ticks.append(i+1)\n", - "\n", - " \n", - "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", - " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", - " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", - "\n", - "\n", - "\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=8)\n", - "\n", - "ticks1 = ticks\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "#ax.locator_params(axis=\"y\", nbins=4)\n", - "\n", - "\n", - "ticks2 = list(np.array(ticks)-0.01)\n", - "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", - "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", - "#ax.yaxis.set_major_locator(loc)\n", - "\n", - "plt.ylabel(\"Mean squared error\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "#plt.legend()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (c) Coefficients of determination" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "plt.ylim(-0.01, 0.5)\n", - "plt.xlim(0.5, len(models) + 0.5)\n", - "\n", - "labs = [model_names[model] for model in models]\n", - "Boxplots = []\n", - "ticks = []\n", - "for i, model in enumerate(models):\n", - " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", - " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", - " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", - " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - " R2_test = r2_score(test_y, pred_y)\n", - "\n", - " \n", - " if i == 0:\n", - " plt.scatter(i+1, R2_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", - " else:\n", - " plt.scatter(i+1, R2_test, c='darkblue', marker=\"o\", linewidths= 8)\n", - " \n", - " Boxplots.append(R2_CV)\n", - " ticks.append(i+1)\n", - "\n", - " \n", - "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", - " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", - " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", - " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", - "\n", - "\n", - "\n", - "ax.locator_params(axis=\"y\", nbins=8)\n", - "\n", - "ticks1 = ticks\n", - "ax.set_xticks(ticks1)\n", - "ax.set_xticklabels([])\n", - "ax.tick_params(axis='x', which=\"major\", length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "#ax.locator_params(axis=\"y\", nbins=4)\n", - "\n", - "\n", - "ticks2 = list(np.array(ticks)-0.01)\n", - "\n", - "ax.set_xticks(ticks2, minor=True)\n", - "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", - "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", - "\n", - "plt.ylabel(\"Coefficient of determination\")\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "\n", - "leg = plt.legend(loc = \"upper left\", frameon=True)\n", - "leg.get_frame().set_linewidth(3.0)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### (d) Statistical tests" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['str_fp', 'diff_fp', 'ESM1b', 'ESM1b_ts', 'ESM1b_ts_diff_fp']" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "models" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[0] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[0] + \".npy\"))\n", - "errors_str_fp = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[1] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[1] + \".npy\"))\n", - "errors_diff_fp = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[2] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[2] + \".npy\"))\n", - "errors_esm1b = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[3] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[3] + \".npy\"))\n", - "errors_esm1b_ts = abs(pred_y-test_y)\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[4] + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[4] + \".npy\"))\n", - "errors_esm1b_diff_fp = abs(pred_y-test_y)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Difference between predictions with ESM1b/diff.fp and ESM1b_ts 0.004568738105303551\n", - "Difference between predictions with ESM1b/diff.fp and ESM1b 0.01628304601850678\n", - "Difference between predictions with ESM1b/diff.fp and diff.fp 0.04571626944538879\n", - "Difference between predictions with ESM1b/diff.fp and str.fp 0.0003341747266578134\n", - "Difference between predictions with ESM1b_ts and ESM1b 0.5411852307491818\n", - "Difference between predictions with diff.fp and str.fp (two-sided) 0.008892609024618676\n" - ] - } - ], - "source": [ - "d = errors_esm1b_diff_fp - errors_esm1b_ts\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and ESM1b_ts\", p)\n", - "\n", - "d = errors_esm1b_diff_fp - errors_esm1b\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and ESM1b\", p)\n", - "\n", - "d = errors_esm1b_diff_fp - errors_diff_fp\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and diff.fp\", p)\n", - "\n", - "d = errors_esm1b_diff_fp - errors_str_fp\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b/diff.fp and str.fp\", p)\n", - "\n", - "d = errors_esm1b_ts - errors_esm1b\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(\"Difference between predictions with ESM1b_ts and ESM1b\", p)\n", - "\n", - "d = errors_diff_fp- errors_str_fp\n", - "w, p = wilcoxon(d, alternative='two-sided')\n", - "print(\"Difference between predictions with diff.fp and str.fp (two-sided)\", p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Plotting predictions versus experimental values:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading predictions for the best model (ESM1b/diff. fp)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model = \"ESM1b_ts_diff_fp\"\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.704895592991433, 5.068688393051791)" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(abs(pred_y-test_y)), 10**np.mean(abs(pred_y-test_y))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 5.1, ymin = -3.5)\n", - "plt.xlim(xmax = 5.1, xmin = -3.5)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "ax.text(1.9, -2, r'$R^2=0.40$', fontsize=22, c = \"grey\")\n", - "ax.text(1.9, -2.4, r'$MSE=0.87$', fontsize=22, c = \"grey\")\n", - "ax.text(1.9, -2.8, r'Pearson $r=0.63$', fontsize=22, c = \"grey\")\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "\n", - "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n \\\n", - "for same enzyme-reaction pairs\", fontsize = 22)\n", - "plt.scatter(test_y, pred_y, alpha = 0.6, s=30, c=\"darkblue\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Comparison to the results of the DLkcat model" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "model = \"ESM1b_ts_diff_fp\"\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", - "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", - "data_test[\"y_true\"] = test_y\n", - "data_test[\"y_pred\"] = pred_y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (a) First, we need to calculate the maximal sequence identity for all proteins in the test set compared to all proteins in the training set:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (a)(i) Creating a fasta file for every sequence in the training set and for every sequence in the test set:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "'''for ind in data_test.index:\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", - " \"test_seq_\" + str(ind) + \".fasta\"), \"w\")\n", - " ofile.write(\"> seq_test_\" + str(ind) + \"\\n\" + data_test[\"Sequence\"][ind] + \"\\n\")\n", - " ofile.close()\n", - " \n", - " \n", - "train_sequences = list(set(data_train[\"Sequence\"]))\n", - "for ind, seq in enumerate(train_sequences):\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", - " \"train_seq_\" + str(ind) + \".fasta\"), \"w\")\n", - " ofile.write(\"> seq_train_\" + str(ind) + \"\\n\" + seq + \"\\n\")\n", - " ofile.close()''';" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (a)(ii) Calculating the maximal pairwise sequence identities (Calculations were done on a HPC):" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "'''from Bio.Emboss.Applications import NeedleCommandline\n", - "import os\n", - "from os.path import join\n", - "import pandas as pd\n", - "import sys\n", - "import time\n", - "import numpy as np\n", - "\n", - "\n", - "arg = int(sys.argv[1])\n", - "\n", - "CURRENT_DIR = join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\")\n", - " \n", - "def calculate_identity(fasta_file_1, fasta_file_2):\n", - " needle_cline = NeedleCommandline(asequence = fasta_file_1, bsequence = fasta_file_2,\n", - " gapopen=10, gapextend=0.5, filter = True)\n", - "\n", - " out = needle_cline()[0]\n", - " out = out[out.find(\"Identity\"):]\n", - " out = out[:out.find(\"\\n\")]\n", - " percent = float(out[out.find(\"(\")+1 :out.find(\")\")-1].replace(\" \", \"\"))\n", - " return(percent)\n", - "\n", - "\n", - "identities = []\n", - "for i in range(len(data_test)):\n", - " ident = calculate_identity(fasta_file_1 = join(CURRENT_DIR, \"test_seq_\" + str(arg) + \".fasta\"),\n", - " fasta_file_2 = join(CURRENT_DIR, \"train_seq_\" + str(i) + \".fasta\"))\n", - " identities.append(ident)\n", - "\n", - "\n", - "ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(arg) + \".txt\"), \"w\")\n", - "ofile.write(str(max(identities)))\n", - "ofile.close()''';" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (a)(iii) Mapping the results to the test DataFrame" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...structural_fpdifference_fpESM1bgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECy_truey_predmax_ident
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1......1100100000000000000000000000000001000001001000...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.020693962, 0.16804111, 0.0377352, 0.1768811...2.3404440.6656531.0000000.1146602.3404441.08239320.8
1Reaction_3629Sequence_3212[0.92][Q0PC20][1][0][0][False]MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL...{InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15......1100100100000000000000100010010001000001001100...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.07429815, 0.14984865, -0.08539086, 0.098546...-0.0362120.3407411.0000000.090196-0.0362120.37071535.3
2Reaction_375Sequence_26[21.0][Q0GYU4][0][1][0][False]MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL...{InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1......1100111100000001001000110110010001001111111100...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[-0.0272103, 0.2500836, 0.08181338, 0.03990136...1.3222190.1750000.1478871.0000001.322219-0.11979540.1
3Reaction_4312Sequence_3788[4.4][Q8ZNC4][0][0][1][False]MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT...{InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,......0000000000000000000000000000000001000001001000...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.079942256, 0.23130149, -0.012637342, 0.0787...0.6434531.0000001.0000001.0000000.6434531.04299425.9
4Reaction_2115Sequence_712[4.5][P53602][1][0][0][False]MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD...{InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15......1100110100000000000000110110010001000001111100...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.086191244, 0.21010432, 0.1960825, -0.041225...0.6532131.0000000.8490570.1125000.6532130.75596149.3
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5 rows × 26 columns

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" - ], - "text/plain": [ - " Reaction ID Sequence ID kcat_values Uniprot IDs from_BRENDA \\\n", - "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] [1] \n", - "1 Reaction_3629 Sequence_3212 [0.92] [Q0PC20] [1] \n", - "2 Reaction_375 Sequence_26 [21.0] [Q0GYU4] [0] \n", - "3 Reaction_4312 Sequence_3788 [4.4] [Q8ZNC4] [0] \n", - "4 Reaction_2115 Sequence_712 [4.5] [P53602] [1] \n", - "\n", - " from_Sabio from_Uniprot checked \\\n", - "0 [0] [0] [False] \n", - "1 [0] [0] [False] \n", - "2 [1] [0] [False] \n", - "3 [0] [1] [False] \n", - "4 [0] [0] [False] \n", - "\n", - " Sequence \\\n", - "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", - "1 MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL... \n", - "2 MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL... \n", - "3 MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT... \n", - "4 MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD... \n", - "\n", - " substrates ... \\\n", - "0 {InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1... ... \n", - "1 {InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15... ... \n", - "2 {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1... ... \n", - "3 {InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,... ... \n", - "4 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... ... \n", - "\n", - " structural_fp \\\n", - "0 1100100000000000000000000000000001000001001000... \n", - "1 1100100100000000000000100010010001000001001100... \n", - "2 1100111100000001001000110110010001001111111100... \n", - "3 0000000000000000000000000000000001000001001000... \n", - "4 1100110100000000000000110110010001000001111100... \n", - "\n", - " difference_fp \\\n", - "0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "3 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "\n", - " ESM1b geomean_kcat \\\n", - "0 [0.020693962, 0.16804111, 0.0377352, 0.1768811... 2.340444 \n", - "1 [0.07429815, 0.14984865, -0.08539086, 0.098546... -0.036212 \n", - "2 [-0.0272103, 0.2500836, 0.08181338, 0.03990136... 1.322219 \n", - "3 [0.079942256, 0.23130149, -0.012637342, 0.0787... 0.643453 \n", - "4 [0.086191244, 0.21010432, 0.1960825, -0.041225... 0.653213 \n", - "\n", - " frac_of_max_UID frac_of_max_RID frac_of_max_EC y_true y_pred \\\n", - "0 0.665653 1.000000 0.114660 2.340444 1.082393 \n", - "1 0.340741 1.000000 0.090196 -0.036212 0.370715 \n", - "2 0.175000 0.147887 1.000000 1.322219 -0.119795 \n", - "3 1.000000 1.000000 1.000000 0.643453 1.042994 \n", - "4 1.000000 0.849057 0.112500 0.653213 0.755961 \n", - "\n", - " max_ident \n", - "0 20.8 \n", - "1 35.3 \n", - "2 40.1 \n", - "3 25.9 \n", - "4 49.3 \n", - "\n", - "[5 rows x 26 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_test[\"max_ident\"] = np.nan\n", - "\n", - "for ind in data_test.index:\n", - " try:\n", - " with open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", - " ident = f.readlines()\n", - " ident = float(ident[0])\n", - " \n", - " \n", - " data_test[\"max_ident\"][ind] = ident\n", - " except FileNotFoundError:\n", - " pass\n", - "data_test.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (b) Using kcat values from the most similar enzymes from the training set as predictions:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...difference_fpESM1bgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECy_truey_predmax_identsim_pred
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.020693962, 0.16804111, 0.0377352, 0.1768811...2.3404440.6656531.0000000.1146602.3404441.08239320.82.024332
1Reaction_3629Sequence_3212[0.92][Q0PC20][1][0][0][False]MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL...{InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.07429815, 0.14984865, -0.08539086, 0.098546...-0.0362120.3407411.0000000.090196-0.0362120.37071535.30.188301
2Reaction_375Sequence_26[21.0][Q0GYU4][0][1][0][False]MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL...{InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[-0.0272103, 0.2500836, 0.08181338, 0.03990136...1.3222190.1750000.1478871.0000001.322219-0.11979540.11.910943
3Reaction_4312Sequence_3788[4.4][Q8ZNC4][0][0][1][False]MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT...{InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.079942256, 0.23130149, -0.012637342, 0.0787...0.6434531.0000001.0000001.0000000.6434531.04299425.90.817796
4Reaction_2115Sequence_712[4.5][P53602][1][0][0][False]MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD...{InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.086191244, 0.21010432, 0.1960825, -0.041225...0.6532131.0000000.8490570.1125000.6532130.75596149.30.815944
..................................................................
845Reaction_3029Sequence_1106[1.14][Q8PDQ6][1][0][0][False]MSLAQLEHALQHDLQRLAHGGEPWVRPRVHPAGHVYDVVIVGAGQS...{InChI=1S/O2/c1-2, InChI=1S/C21H30N7O17P3/c22-......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.07993014, 0.11095398, -0.0057218825, -0.049...0.0569051.0000001.0000000.0271430.0569050.82321121.61.452899
846Reaction_3310Sequence_455[5.8, 5.9, 4.8][C7P8V7, C7P8V7, C7P8V7][1, 1, 1][0, 0, 0][0, 0, 0][False, False, False]MILFFEYAIASGFEDEGILEEGKMMFNTLLNQFLEIDNVTSLIHKD...{InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.15469086, 0.08214222, 0.006613599, 0.003951...0.7385071.0000001.0000001.0000000.7385071.02211321.10.696922
847Reaction_1253Sequence_1211[3.3333][O33289][1][0][0][True]MTERPRDCRPVVRRARTSDVPAIKQLVDTYAGKILLEKNLVTLYEA...{InChI=1S/C5H10N2O3/c6-3(5(9)10)1-2-4(7)8/h3H,......[0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0,...[0.095282555, 0.077073924, 0.1310218, -0.01710...0.5228741.0000001.0000004.2734620.5228740.74109922.81.854782
848Reaction_1626Sequence_783[18.9][P0AEP7][1][0][0][True]MAKMRAVDAAMYVLEKEGITTAFGVPGAAINPFYSAMRKHGGIRHI...{InChI=1S/C2H2O3/c3-1-2(4)5/h1H,(H,4,5)}...[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[0.07920394, 0.22367033, 0.120473295, 0.001293...1.2764621.0000001.0000000.9593911.2764621.35327828.91.545336
849Reaction_898Sequence_3700[800.0][P00387][1][0][0][True]MGAQLSTLGHMVLFPVWFLYSLLMKLFQRSTPAITLESPDIKYPLR...{InChI=1S/6CN.Fe/c6*1-2;/q;;;;;;-3, InChI=1S/C......[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...[-0.055920795, 0.26620504, 0.008486553, -0.058...2.9030901.0000000.7272730.6446412.9030902.41341487.02.706351
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850 rows × 27 columns

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" - ], - "text/plain": [ - " Reaction ID Sequence ID kcat_values Uniprot IDs \\\n", - "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] \n", - "1 Reaction_3629 Sequence_3212 [0.92] [Q0PC20] \n", - "2 Reaction_375 Sequence_26 [21.0] [Q0GYU4] \n", - "3 Reaction_4312 Sequence_3788 [4.4] [Q8ZNC4] \n", - "4 Reaction_2115 Sequence_712 [4.5] [P53602] \n", - ".. ... ... ... ... \n", - "845 Reaction_3029 Sequence_1106 [1.14] [Q8PDQ6] \n", - "846 Reaction_3310 Sequence_455 [5.8, 5.9, 4.8] [C7P8V7, C7P8V7, C7P8V7] \n", - "847 Reaction_1253 Sequence_1211 [3.3333] [O33289] \n", - "848 Reaction_1626 Sequence_783 [18.9] [P0AEP7] \n", - "849 Reaction_898 Sequence_3700 [800.0] [P00387] \n", - "\n", - " from_BRENDA from_Sabio from_Uniprot checked \\\n", - "0 [1] [0] [0] [False] \n", - "1 [1] [0] [0] [False] \n", - "2 [0] [1] [0] [False] \n", - "3 [0] [0] [1] [False] \n", - "4 [1] [0] [0] [False] \n", - ".. ... ... ... ... \n", - "845 [1] [0] [0] [False] \n", - "846 [1, 1, 1] [0, 0, 0] [0, 0, 0] [False, False, False] \n", - "847 [1] [0] [0] [True] \n", - "848 [1] [0] [0] [True] \n", - "849 [1] [0] [0] [True] \n", - "\n", - " Sequence \\\n", - "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", - "1 MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL... \n", - "2 MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL... \n", - "3 MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT... \n", - "4 MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD... \n", - ".. ... \n", - "845 MSLAQLEHALQHDLQRLAHGGEPWVRPRVHPAGHVYDVVIVGAGQS... \n", - "846 MILFFEYAIASGFEDEGILEEGKMMFNTLLNQFLEIDNVTSLIHKD... \n", - "847 MTERPRDCRPVVRRARTSDVPAIKQLVDTYAGKILLEKNLVTLYEA... \n", - "848 MAKMRAVDAAMYVLEKEGITTAFGVPGAAINPFYSAMRKHGGIRHI... \n", - "849 MGAQLSTLGHMVLFPVWFLYSLLMKLFQRSTPAITLESPDIKYPLR... \n", - "\n", - " substrates ... \\\n", - "0 {InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1... ... \n", - "1 {InChI=1S/C10H13N5O3/c1-4-6(16)7(17)10(18-4)15... ... \n", - "2 {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1... ... \n", - "3 {InChI=1S/C6H14N2O2/c7-4-2-1-3-5(8)6(9)10/h5H,... ... \n", - "4 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... ... \n", - ".. ... ... \n", - "845 {InChI=1S/O2/c1-2, InChI=1S/C21H30N7O17P3/c22-... ... \n", - "846 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... ... \n", - "847 {InChI=1S/C5H10N2O3/c6-3(5(9)10)1-2-4(7)8/h3H,... ... \n", - "848 {InChI=1S/C2H2O3/c3-1-2(4)5/h1H,(H,4,5)} ... \n", - "849 {InChI=1S/6CN.Fe/c6*1-2;/q;;;;;;-3, InChI=1S/C... ... \n", - "\n", - " difference_fp \\\n", - "0 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "3 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - ".. ... \n", - "845 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "846 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "847 [0.0, 0.0, 0.0, 0.0, 10.0, 0.0, 0.0, 0.0, 0.0,... \n", - "848 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "849 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n", - "\n", - " ESM1b geomean_kcat \\\n", - "0 [0.020693962, 0.16804111, 0.0377352, 0.1768811... 2.340444 \n", - "1 [0.07429815, 0.14984865, -0.08539086, 0.098546... -0.036212 \n", - "2 [-0.0272103, 0.2500836, 0.08181338, 0.03990136... 1.322219 \n", - "3 [0.079942256, 0.23130149, -0.012637342, 0.0787... 0.643453 \n", - "4 [0.086191244, 0.21010432, 0.1960825, -0.041225... 0.653213 \n", - ".. ... ... \n", - "845 [0.07993014, 0.11095398, -0.0057218825, -0.049... 0.056905 \n", - "846 [0.15469086, 0.08214222, 0.006613599, 0.003951... 0.738507 \n", - "847 [0.095282555, 0.077073924, 0.1310218, -0.01710... 0.522874 \n", - "848 [0.07920394, 0.22367033, 0.120473295, 0.001293... 1.276462 \n", - "849 [-0.055920795, 0.26620504, 0.008486553, -0.058... 2.903090 \n", - "\n", - " frac_of_max_UID frac_of_max_RID frac_of_max_EC y_true y_pred \\\n", - "0 0.665653 1.000000 0.114660 2.340444 1.082393 \n", - "1 0.340741 1.000000 0.090196 -0.036212 0.370715 \n", - "2 0.175000 0.147887 1.000000 1.322219 -0.119795 \n", - "3 1.000000 1.000000 1.000000 0.643453 1.042994 \n", - "4 1.000000 0.849057 0.112500 0.653213 0.755961 \n", - ".. ... ... ... ... ... \n", - "845 1.000000 1.000000 0.027143 0.056905 0.823211 \n", - "846 1.000000 1.000000 1.000000 0.738507 1.022113 \n", - "847 1.000000 1.000000 4.273462 0.522874 0.741099 \n", - "848 1.000000 1.000000 0.959391 1.276462 1.353278 \n", - "849 1.000000 0.727273 0.644641 2.903090 2.413414 \n", - "\n", - " max_ident sim_pred \n", - "0 20.8 2.024332 \n", - "1 35.3 0.188301 \n", - "2 40.1 1.910943 \n", - "3 25.9 0.817796 \n", - "4 49.3 0.815944 \n", - ".. ... ... \n", - "845 21.6 1.452899 \n", - "846 21.1 0.696922 \n", - "847 22.8 1.854782 \n", - "848 28.9 1.545336 \n", - "849 87.0 2.706351 \n", - "\n", - "[850 rows x 27 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def get_train_seq(ind):\n", - " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", - " \"train_seq_\" + str(ind) + \".fasta\"), \"r\")\n", - " return(ofile.readlines()[1].replace(\"\\n\", \"\"))\n", - "\n", - "data_test[\"sim_pred\"] = np.nan\n", - "\n", - "for ind in data_test.index:\n", - " try:\n", - " with open(join(\"..\", \"..\", \"data\",\"enzyme_data\", \"kcat_similar\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", - " ident = f.readlines()\n", - " indices = ident[0].split(\" \")\n", - " indices = [int(float(k)) for k in indices[1:]]\n", - " \n", - " kcats = []\n", - " Sequences = [get_train_seq(k) for k in indices]\n", - " for seq in Sequences:\n", - " kcats = kcats + list(data_train[\"geomean_kcat\"].loc[data_train[\"Sequence\"] == seq])\n", - " \n", - " data_test[\"sim_pred\"][ind] = np.mean(kcats[:3])\n", - " except:\n", - " pass\n", - "data_test" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "data_test = data_test.loc[~pd.isnull(data_test[\"sim_pred\"])]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (b) Comparing the results with predictions from the DLkcat paper:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (b)(i) Loading results from DLkcat paper" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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y_truey_predSequencemax_identsim_pred
0-2.207608-0.071899MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...22.8-1.486273
1-3.657577-2.707640MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...100.0-2.369079
20.9493900.831021MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...100.00.946618
31.6720981.513026MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...100.01.045579
4-1.790485-2.830310MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...99.4-1.733113
\n", - "
" - ], - "text/plain": [ - " y_true y_pred Sequence \\\n", - "0 -2.207608 -0.071899 MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI... \n", - "1 -3.657577 -2.707640 MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA... \n", - "2 0.949390 0.831021 MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG... \n", - "3 1.672098 1.513026 MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS... \n", - "4 -1.790485 -2.830310 MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW... \n", - "\n", - " max_ident sim_pred \n", - "0 22.8 -1.486273 \n", - "1 100.0 -2.369079 \n", - "2 100.0 0.946618 \n", - "3 100.0 1.045579 \n", - "4 99.4 -1.733113 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_test_DLkcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"DLkcat\", \"df_pred.pkl\"))\n", - "data_test_DLkcat.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.45244626585841485\n", - "0.44447253110852536\n", - "0.003277284826373785\n" - ] - } - ], - "source": [ - "help_df =data_test_DLkcat\n", - "\n", - "y_true = np.array(help_df[\"y_true\"])\n", - "y_pred = np.array(help_df[\"sim_pred\"])\n", - "abs_error_sim = abs(y_true - y_pred)\n", - "R2_sim = r2_score(y_true, y_pred)\n", - "print(R2_sim)\n", - "\n", - "y_true = np.array(help_df[\"y_true\"])\n", - "y_pred = np.array(help_df[\"y_pred\"])\n", - "abs_error = abs(y_true - y_pred)\n", - "R2 = r2_score(y_true, y_pred)\n", - "print(R2)\n", - "\n", - "d = abs_error- abs_error_sim\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.00655456965274757\n" - ] - } - ], - "source": [ - "d = abs_error- abs_error_sim\n", - "w, p = wilcoxon(d, alternative='two-sided')\n", - "print(p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### (b)(ii) Plotting performances for different sequence identities:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.24089033615537592\n", - "0.3947782909163574\n", - "0.0004027203915252622\n" - ] - } - ], - "source": [ - "help_df =data_test\n", - "\n", - "y_true = np.array(help_df[\"y_true\"])\n", - "y_pred = np.array(help_df[\"sim_pred\"])\n", - "abs_error_sim = abs(y_true - y_pred)\n", - "R2_sim = r2_score(y_true, y_pred)\n", - "print(R2_sim)\n", - "\n", - "y_true = np.array(help_df[\"y_true\"])\n", - "y_pred = np.array(help_df[\"y_pred\"])\n", - "abs_error = abs(y_true - y_pred)\n", - "R2 = r2_score(y_true, y_pred)\n", - "print(R2)\n", - "\n", - "d = abs_error- abs_error_sim\n", - "w, p = wilcoxon(d, alternative='less')\n", - "print(p)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% TurNuP: R2:0.2814016099629677, Similarity method: 0.01739891287801265, p = 2.1819560657739698e-07 \n", - "0-40% DLKcat: R2:-0.6072304105234347, Similarity method: 0.10837547424294935, p = 0.9957653015502506, p(two-sided) = 0.008469396899498782\n", - "40-80% TurNuP: R2:0.5011924207779686, Similarity method: 0.5002316521542809, p = 0.5802656158905746 \n", - "40-80% DLKcat: R2:0.34280134977895493, Similarity method: 0.1800891304717046, p = 0.5174532691949906, p(two-sided) = 0.9650934616100189\n", - "80-99% TurNuP: R2:0.6230368009214954, Similarity method: 0.7281503207725486, p = 0.9965843302704173 \n", - "80-99% DLKcat: R2:0.48622435213243465, Similarity method: 0.3392639181110536, p = 0.8129782958108247, p(two-sided) = 0.3740434083783506\n", - "99-100% TurNuP: R2:0.6649909290506475, Similarity method: 0.20650188480500864, p = 0.005403328503510895 \n", - "99-100% DLKcat: R2:0.5128517542754034, Similarity method: 0.48197442980722505, p = 6.379793252512187e-05, p(two-sided) = 0.00012759586505024374\n" - ] - } - ], - "source": [ - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "points1 ,points1_sim = [], []\n", - "points2, points2_sim = [], []\n", - "n_points1, n_points2 = [], []\n", - "n_points1_sim, n_points2_sim = [], []\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " R2 = r2_score(y_true, y_pred)\n", - " abs_error = abs(y_true - y_pred)\n", - " \n", - " \n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"sim_pred\"])\n", - " n_kcat_sim = len(y_pred)\n", - " R2_sim = r2_score(y_true, y_pred)\n", - " abs_error_sim = abs(y_true - y_pred)\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " R2_DLkcat = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat = abs(y_true - y_pred)\n", - " \n", - " \n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"sim_pred\"])\n", - " n_kcat_DLkcat_sim = len(y_pred)\n", - " R2_DLkcat_sim = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat_sim = abs(y_true - y_pred)\n", - " \n", - " \n", - " \n", - " points1.append(R2)\n", - " points1_sim.append(R2_sim)\n", - " points2.append(R2_DLkcat)\n", - " points2_sim.append(R2_DLkcat_sim)\n", - " \n", - " n_points1.append(n_kcat)\n", - " n_points1_sim.append(n_kcat_sim)\n", - " n_points2.append(n_DLkcat)\n", - " n_points2_sim.append(n_kcat_DLkcat_sim)\n", - " \n", - " d = abs_error- abs_error_sim\n", - " w, p = wilcoxon(d, alternative='less')\n", - " \n", - " d_DLkcat = abs_error_DLkcat- abs_error_DLkcat_sim\n", - " w, p_DLkcat = wilcoxon(d_DLkcat, alternative='less')\n", - " w, p_DLkcat_two_sided = wilcoxon(d_DLkcat, alternative='two-sided')\n", - " \n", - " print(\"%s TurNuP: R2:%s, Similarity method: %s, p = %s \" % (split, R2, R2_sim, p))\n", - " print(\"%s DLKcat: R2:%s, Similarity method: %s, p = %s, p(two-sided) = %s\" % (split, R2_DLkcat, R2_DLkcat_sim, p_DLkcat, p_DLkcat_two_sided))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% 0.2814016099629677 0.01739891287801265 -0.6072304105234347 0.10837547424294935\n", - "40-80% 0.5011924207779686 0.5002316521542809 0.34280134977895493 0.1800891304717046\n", - "80-99% 0.6230368009214954 0.7281503207725486 0.48622435213243465 0.3392639181110536\n", - "99-100% 0.6649909290506475 0.20650188480500864 0.5128517542754034 0.48197442980722505\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "points1 ,points1_sim = [], []\n", - "points2, points2_sim = [], []\n", - "n_points1, n_points2 = [], []\n", - "n_points1_sim, n_points2_sim = [], []\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " R2 = r2_score(y_true, y_pred)\n", - " abs_error = abs(y_true - y_pred)\n", - " \n", - " \n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"sim_pred\"])\n", - " n_kcat_sim = len(y_pred)\n", - " R2_sim = r2_score(y_true, y_pred)\n", - " abs_error_sim = abs(y_true - y_pred)\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " R2_DLkcat = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat = abs(y_true - y_pred)\n", - " \n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"sim_pred\"])\n", - " n_kcat_DLkcat_sim = len(y_pred)\n", - " R2_DLkcat_sim = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat_sim = abs(y_true - y_pred)\n", - " \n", - "\n", - " print(split, R2, R2_sim, R2_DLkcat, R2_DLkcat_sim)\n", - " points1.append(R2)\n", - " points1_sim.append(R2_sim)\n", - " points2.append(R2_DLkcat)\n", - " points2_sim.append(R2_DLkcat_sim)\n", - " \n", - " n_points1.append(n_kcat)\n", - " n_points1_sim.append(n_kcat_sim)\n", - " n_points2.append(n_DLkcat)\n", - " n_points2_sim.append(n_kcat_DLkcat_sim)\n", - "\n", - "\n", - "ticks2 = np.array(range(len(splits)))\n", - "labs = splits\n", - "ax.set_xticks(ticks2)\n", - "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", - "ax.tick_params(axis='x', length=0, rotation = 0)\n", - "\n", - "plt.ylim((-0.7,1))\n", - "plt.xlim((-0.2, 3.2))\n", - "plt.legend(loc = \"lower right\", fontsize=20)\n", - "plt.ylabel('Coefficient of determination R²')\n", - "plt.xlabel('Enzyme sequence identity')\n", - "ax.yaxis.set_label_coords(-0.15, 0.5)\n", - "ax.xaxis.set_label_coords(0.5,-0.13)\n", - "\n", - "plt.plot([-0.15,4], [0,0], color='grey', linestyle='dashed')\n", - "\n", - "\n", - "plt.plot([0,1,2,3], points1, c= \"black\", linewidth=2)\n", - "plt.plot([0,1,2,3], points2, c= \"orchid\", linewidth=2)\n", - "\n", - "for i, split in enumerate(splits):\n", - " points1.append(R2)\n", - " points2.append(R2_DLkcat)\n", - " \n", - " if i ==0:\n", - " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8, label =\"KCATpred\")\n", - " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8, label =\"DLkcat\")\n", - " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_points2[i], (i+0.06, points2[i]-0.01), fontsize=17, c='orchid', weight = \"bold\")\n", - "\n", - " else:\n", - " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8)\n", - " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8)\n", - " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_points2[i], (i-0.04, points2[i]-0.10), fontsize=17, c='orchid', weight = \"bold\")\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% 0.5340075669772718 0.017031398776714425\n", - "40-80% 0.7053270932283959 0.6016337360084704\n", - "80-99% 0.7994243130617344 0.7176680331371708\n", - "99-100% 0.8010763563536277 0.7399567971986073\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " pearson_r = stats.pearsonr(y_true, y_pred)[0]\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " pearson_r_DLkcat = stats.pearsonr(y_true, y_pred)[0]\n", - " \n", - "\n", - " print(split, pearson_r, pearson_r_DLkcat)\n", - " \n", - " if i ==0:\n", - " plt.scatter(i, pearson_r, c='black', marker='^', linewidths= 8, label =\"KCATpred\")\n", - " plt.scatter(i, pearson_r_DLkcat, c='green', marker='^', linewidths= 8, label =\"DLkcat\")\n", - " ax.annotate(n_kcat, (i+0.04, pearson_r-0.02), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_DLkcat, (i+0.04, pearson_r_DLkcat+0.015), fontsize=17, c='green', weight = \"bold\")\n", - "\n", - " else:\n", - " plt.scatter(i, pearson_r, c='black', marker='^', linewidths= 8)\n", - " plt.scatter(i, pearson_r_DLkcat, c='green', marker='^', linewidths= 8)\n", - " ax.annotate(n_kcat, (i+0.04, pearson_r-0.02), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_DLkcat, (i+0.04, pearson_r_DLkcat+0.015), fontsize=17, c='green', weight = \"bold\")\n", - "\n", - "\n", - "ticks2 = np.array(range(len(splits)))\n", - "labs = splits\n", - "ax.set_xticks(ticks2)\n", - "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", - "ax.tick_params(axis='x', length=0, rotation = 0)\n", - "\n", - "plt.ylim((0,1))\n", - "plt.xlim((-0.2, 3.2))\n", - "plt.legend(loc = \"lower right\", fontsize=20)\n", - "plt.ylabel('Pearson r')\n", - "plt.xlabel('Enzyme sequence identity')\n", - "ax.yaxis.set_label_coords(-0.11, 0.5)\n", - "ax.xaxis.set_label_coords(0.5,-0.13)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% 0.2814016099629677 -0.6072304105234347\n", - "40-80% 0.5011924207779686 0.34280134977895493\n", - "80-99% 0.6230368009214954 0.48622435213243465\n", - "99-100% 0.6649909290506475 0.5128517542754034\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "points1 = []\n", - "points2 = []\n", - "n_points1, n_points2 = [], []\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " R2 = r2_score(y_true, y_pred)\n", - " abs_error = abs(y_true - y_pred)\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " R2_DLkcat = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat = abs(y_true - y_pred)\n", - " \n", - "\n", - " print(split, R2, R2_DLkcat)\n", - " points1.append(R2)\n", - " points2.append(R2_DLkcat)\n", - " \n", - " n_points1.append(n_kcat)\n", - " n_points2.append(n_DLkcat)\n", - "\n", - "\n", - "ticks2 = np.array(range(len(splits)))\n", - "labs = splits\n", - "ax.set_xticks(ticks2)\n", - "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", - "ax.tick_params(axis='x', length=0, rotation = 0)\n", - "\n", - "plt.ylim((-0.7,1))\n", - "plt.xlim((-0.2, 3.2))\n", - "plt.legend(loc = \"lower right\", fontsize=20)\n", - "plt.ylabel('Coefficient of determination R²')\n", - "plt.xlabel('Enzyme sequence identity')\n", - "ax.yaxis.set_label_coords(-0.15, 0.5)\n", - "ax.xaxis.set_label_coords(0.5,-0.13)\n", - "\n", - "\n", - "\n", - "plt.plot([0,1,2,3], points1, c= \"black\", linewidth=2)\n", - "plt.plot([0,1,2,3], points2, c= \"orchid\", linewidth=2)\n", - "\n", - "for i, split in enumerate(splits):\n", - " points1.append(R2)\n", - " points2.append(R2_DLkcat)\n", - " \n", - " if i ==0:\n", - " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8, label =\"KCATpred\")\n", - " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8, label =\"DLkcat\")\n", - " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_points2[i], (i+0.06, points2[i]-0.01), fontsize=17, c='orchid', weight = \"bold\")\n", - "\n", - " else:\n", - " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8)\n", - " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8)\n", - " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", - " ax.annotate(n_points2[i], (i-0.04, points2[i]-0.10), fontsize=17, c='orchid', weight = \"bold\")\n", - " \n", - "\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Testing if differences in model performance is statistically significant using a one-sided Mann-Whitney U test" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0-40% MannwhitneyuResult(statistic=10975.0, pvalue=1.5135322688706623e-10)\n", - "40-80% MannwhitneyuResult(statistic=4132.0, pvalue=0.0017915422287357661)\n", - "80-99% MannwhitneyuResult(statistic=702.0, pvalue=0.0027672622913853277)\n", - "99-100% MannwhitneyuResult(statistic=13948.0, pvalue=0.07975196445872013)\n" - ] - } - ], - "source": [ - "from scipy.stats import mannwhitneyu\n", - "\n", - "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", - "lower_bounds = [0,40,80,99]\n", - "upper_bounds = [40,80,99,100]\n", - "\n", - "for i, split in enumerate(splits):\n", - "\n", - " lb, ub = lower_bounds[i], upper_bounds[i]\n", - " \n", - " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_kcat = len(y_pred)\n", - " R2 = r2_score(y_true, y_pred)\n", - " abs_error = abs(y_true - y_pred)\n", - " \n", - " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", - " y_true = np.array(help_df[\"y_true\"])\n", - " y_pred = np.array(help_df[\"y_pred\"])\n", - " n_DLkcat = len(y_pred)\n", - " R2_DLkcat = r2_score(y_true, y_pred)\n", - " abs_error_DLkcat = abs(y_true - y_pred)\n", - " \n", - " res = mannwhitneyu(abs_error, abs_error_DLkcat, alternative=\"less\")\n", - " print(split, res)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Comparing different sources of kcat values in test dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Source: from_BRENDA, no. of data points: 608, MSE: 0.8644825863653809, R²: 0.39205752485871626, pearson r: 0.6545772031701992\n", - "Source: from_Sabio, no. of data points: 51, MSE: 0.5050674667868178, R²: 0.48091695275256685, pearson r: 0.7252781571061273\n", - "Source: from_Uniprot, no. of data points: 193, MSE: 0.9401536294534142, R²: 0.3812980141526323, pearson r: 0.6361223052112547\n", - "Source: checked, no. of data points: 284, MSE: 0.9449160311750975, R²: 0.3480657772454745, pearson r: 0.6238625423159976\n" - ] - } - ], - "source": [ - "model = \"ESM1b_ts_difff_fp_separate\"\n", - "\n", - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", - "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", - "data_test[\"y_true\"] = test_y\n", - "data_test[\"y_pred\"] = pred_y\n", - "\n", - "\n", - "for ind in data_test.index:\n", - " data_test[\"from_BRENDA\"][ind] = np.mean(data_test[\"from_BRENDA\"][ind])\n", - " data_test[\"from_Sabio\"][ind] = np.mean(data_test[\"from_Sabio\"][ind])\n", - " data_test[\"from_Uniprot\"][ind] = np.mean(data_test[\"from_Uniprot\"][ind])\n", - " data_test[\"checked\"][ind] = np.mean(data_test[\"checked\"][ind])\n", - " \n", - " \n", - "columns = [\"from_BRENDA\", \"from_Sabio\", \"from_Uniprot\", \"checked\"]\n", - "for column in columns:\n", - " help_df = data_test.loc[data_test[column] == 1]\n", - " y_pred, y_true = np.array(help_df[\"y_pred\"]), np.array(help_df[\"y_true\"])\n", - " \n", - " MSE = np.mean(abs(np.reshape(y_true, (-1)) - y_pred)**2)\n", - " R2 = r2_score(np.reshape(y_true, (-1)), y_pred)\n", - " pearson_r = stats.pearsonr(y_true, y_pred)[0]\n", - " \n", - " print(\"Source: %s, no. of data points: %s, MSE: %s, R²: %s, pearson r: %s\" %\n", - " (column, len(help_df), MSE, R2, pearson_r))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predicting Proteom allocation" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (14,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "conditions = [\"$Petri_1$\", \"$Johan_1$\", \"$Johan_2$\", \"$Johan_3$\", \"$Johan_4$\",\n", - " \"$Joao_1$\", \"$Joao_2$\", \"$Joao_3$\", \"$Joao_4$\", \"$Joao_5$\", \"$Joao_6$\",\n", - " \"$Joao_7$\", \"$Joao_8$\", \"$Joao_9$\", \"$Joao_{10}$\", \"$Joao_{11}$\", \"$Tyler_1$\",\n", - " \"$Keiji_1$\",\"$Keiji_3$\", \"$Tyler_2$\", \"$Tyler_3$\"]\n", - "\n", - "\n", - "data = [[1.3958, 1.0951, 1.1511, 1.1255, 1.1380, 1.1731, 1.2599, 1.1834, 1.1232,\n", - " 1.2105, 1.1553, 1.2801, 1.1873, 1.2479, 1.1843, 1.1711, 1.3075, 1.3824, 1.1935,1.0210, 1.1693],\n", - " [ 1.3348, 1.0608, 0.9795, 1.1813, 1.1117, 1.0074, 1.0357, 0.9859, 0.9882,\n", - " 0.9457, 0.9748, 1.1171, 1.0519, 1.0512, 1.0897, 0.9635, 1.2411, 1.2818, 1.0986 ,1.0474, 1.0883]]\n", - "\n", - "X = np.arange(len(conditions))\n", - "ax = fig.add_axes([0,0,1,1])\n", - "\n", - "barWidth = 0.35\n", - "eps = 0.04\n", - "\n", - "plt.xticks([r + barWidth for r in range(len(data[0]))], conditions, rotation = 90, fontsize=26)\n", - "plt.yticks( fontsize=30)\n", - "\n", - "ax.bar(X + 0.00, np.array(data[0])**2, color = 'orchid', width = barWidth, label = \"DLKcat\")\n", - "ax.bar(X + barWidth + eps, np.array(data[1])**2, color = 'black', width = barWidth, label = \"KCATpred\")\n", - "\n", - "plt.ylabel('Mean squared error', fontsize=30)\n", - "plt.legend(loc = \"upper center\", ncol = 2, fontsize=26)\n", - "plt.ylim((0,2.2))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting orginal kcat values and log10-transformed kcat values" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"final_kcat_dataset.pkl\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "kcat_values = 10**np.array(df_kcat[\"geomean_kcat\"])\n", - "log10_kcat_values = np.array(df_kcat[\"geomean_kcat\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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kSVKjGdBKkiSp0QxoJUmS1GgGtJIkSWo0A1pJkiQ1mgGtJEmSGs2AVpIkSY1mQCtJkqRGM6CVJElSoxnQSpIkqdEMaCVJktRoBrSSJElqNANaSZIkNZoBrSRJkhrNgFaSJEmNZkArSZKkRjOglSRJUqMZ0EqSJKnRDGglSZLUaAa0kiRJarS1ZrsCkiStKvPnHzuj5S9ZcsiMli+pM3toJUmS1GgGtJIkSWo0A1pJkiQ1mgGtJEmSGs2AVpIkSY1mQCtJkqRGM6CVJElSoxnQSpIkqdEMaCVJktRoBrSSJElqNANaSZIkNZoBrSRJkhptrdmuQJPEGP8ROKx2aM+U0gV9ztkZOBR4IbA1cDdwNXAa8IWU0iMDXvslwMHArsCWwO+AK0oZXxvqhUiSJE0QA9oBxRh3BN495DkfAj7Cij3hWwCLy+NvYoyvSCnd1aOMAPwzcEhb0tbl8bIY45nA61NKDw5TP0mSpEngkIMBxBjXBE4g/wPw2wHPORCYIr/HtwAR2Bl4BXBWybY7cEaMsdfncCTLg9krgf2AFwCvAS4ux/cu9ZMkSVrt2EM7mHcBOwHXAN8APtQrc4xxE+DosnsbsEtK6Te1LGfHGE8ADiIPRdgPOLVDOU8B3l92rwD2SCndW/Z/XHpmzwJeCrwpxnhCSun7Q742SWM2f/6xM36NJUvav7SRpNWXPbR9xBifRO4lrYC3Ag8PcNqbgU3L9gfagtmWdwN/KNuHdUiHHEivXbbfUQtmAUgpLSt1erQcet8AdZMkSZooBrT9HQesT775atDez33L8z1Axxu2UkpLa2nPiTFuX08vY2f3Kbs3pJQupoOU0i3AeWX3z2KMGw5YR0mSpIlgQNtDjPFNwF7AHQzY+xljXJs8Vhbgh31u1Dq/tr1HW9oCYNuyfWGfy7bKWRd4/gDVlCRJmhgGtF3EGDcHjim7/yel9LsBT30ay8cmX9Mn73W17We1pdX3p1OOJEnSRDOg7e7TwObABSmlLw5x3ra17V/1yXtrbXu7GSpHkiRpohnQdhBj/HPgjcBD5JuuhrFRbXtpn7z19Paxr+MqR5IkaaIZ0LaJMW5AvhEM4OMppeuHLGK92vZDffLWx9eu15Y2rnIkSZImmgHtyo4k35B1I3DUCOffX9ue1yfvOl3OG2c5kiRJE82FFWpijM8H3ll2Dx1xKdl7atv9vv6vp7cPKxhXOY+JMR4MHFw/Nm/ePKamplbIt3DhQhYtWtTnkpIkSXODAe2KDgPWBK4FNo8xvqFDnufUtl8UY9yqbJ+TUvo9K97AVb+xq5P6DVy3tqWNq5zHpJSOB46vH5uamqoOP/zwPsVLkiTNXQa0K2p9df9M4F8GyP93te0dycvT3gAsI7+3/abQekZtu31qrvr+dMqRJEmaaEONoQ0hPBJCOGmAfCeEEJaNXq3mSik9DFxedneNMfYa/7q4tt2+EtjNwG1l+4V9Ltsq50Hgx30rKUmSNEGG7aEN5TFo3kZJKe3TL0+M8Qjgw2V3z5TSBR2ynQ7sTp5663XAlzuUs2FJA7gqpXRTW12qGOMZwNuBp8UY9+i0/G2M8YnAi8ruuWVJXUmSpNXGTM1ysCHw8AyV3QQnAXeV7Y/FGLfskOcYYOOy/cku5XyGPHwB4LNlSrHHxBjXIk8xtmafciRJkibWWMfQhhDWII8/fRH9V7eaWCml38cYDwNOJN/QdVmM8SjyGNstgAi8qmS/kA49uKWcm2KMHwf+ljxG99KyfwP5RrD3AHuU7F9KKV00M69IkiRp7uob0IYQHmk7tH8IYf8Byj51tCpNhpTSSWUGhNa8tsd3yHYJsG9K6dEeRf09eQnetwI7AKd1yHMm8JZpVViSJKmhBhlyEGqPqm2//bEMuAX4NCvOALBaSilNAbuRg/tbyDdt3UHulX0LsCildGefMqqU0iHAXsC/kW8Uewi4HTgHeF1KaZ8R58yVJElqvL49tFVVPRb0hhAeBU6pqurAGa3VHJZSOgI4Yoj8l7N81oPpXPdc4NzpliNJkjRphh1D+w/AT2eiIpIkSdIohgpoq6r6h5mqiCRJkjSKmZq2S5IkSVolhg5oQwgLQggphHBTCOG+snpYp8dquVKYJEmSVq2hhhyEEJ5NXqL1cfRfCaxxK4VJkjRd8+cfO+PXWLLkkBm/htQkw/bQTpFXt/p3YBdg46qq1uj2GHttJUmSpDbDznKwCLgZ+MuqqlbnpW0lSZI0Rwzbi7oO8CODWUmSJM0Vwwa0N5CHHEiSJElzwrAB7QnAohDCghmoiyRJkjS0oQLaqqr+L/CvwHdDCC8LIXjjlyRJkmbVsNN2/bxsLgC+BSwLIfwaeLRD9qqqqqdMr3qSJElSb8POcrCgth2AtYEndMlbjVIhSZIkaRjDBrRPmpFaSJIkSSMaKqCtquqWmaqIJEmSNApv6pIkSVKjGdBKkiSp0Uad5WAQznIgSZKkGTedWQ66qcgzIDjLgSRJkmbcuGY5WAN4IvBy4B3APwInTaNekiRJ0kDGOcvBL4ALQgg/AL4OXAQ4K4IkSZJm1NhvCquq6hvAlcCHxl22JEmS1G6mZjm4EfiTGSpbkiRJesxMBbRPZvjxuZIkSdLQxhrQhhDWDCG8j9w7+1/jLFuSJEnqZNh5aM/rkbwh8BRgE+BR4GOjV0uSJEkazLDDAhYPkOdnwAerqvrW8NWRJEmShjNsQLtnj7SHgNuqqvrlNOojSZIkDWXYeWgvnKmKSJIkSaOYqVkOJEmSpFVi5Km1Qgi7kcfUblMO3QZcUFXVpWOolyRJkjSQoQPaEMIC4DRg19ah8lyV9EuB/aqqunkM9ZMkSZJ6Gnbars2A84EnAkuBs4Cfl+QnA68EdgfOCyHsVFXVXWOsqyRJkrSSYXtoDyMHs18HDqmq6nf1xBLwHge8puT90DgqKUmSJHUz7E1hewO/Bt7UHswCVFV1J/CmkmefaddOkiRJ6mPYgHYB8P2qqh7slqGkfb/klSRJkmbUsAHtw8D6A+Rbr+SVJEmSZtSwAe21wJ4hhK26ZShpLyp5JUmSpBk1bED7ZWAD4LshhBe1J4YQ9gS+Q+7F/dL0qydJkiT1NuwsB8cBrwZeCJwbQlgC/II8B+2TyIssBPLUXseNsZ6SJElSR0MFtFVVLQshvBT4CPBWcgC7TS3LUnIg+3dVVT0ytlquQjHG9YG/AHYGng9sB2wObAj8AbgeOBc4IaV02wDlPQN4B7AX+b26H7gR+CpwbErpgQHrtTNwKPmfia2Bu4GryYtcfCGl1Mj3W5IkabqGHXJAVVUPVlX1PmBLYCHwhvJYCGxZVdX7es2C0ADPAr5Gnkd3T2B7YBNy8P948sIRHwaujzHu36ugGOMBwE/Jgej25JvlNgN2AY4BfhJjfFK/CsUYPwRcCuxPnj1iHWAL8tLDJwAXxRg3HeZFSpIkTYq+AW0IYbsQwnNDCH9UP15V1QNVVf2gqqp/LY8fABuXvNvOWI1XjV8DXwHeS14k4k/JS/2+GjgZeIA8lvgLMca/6FRAjHEv4ERgXeAO4D3AbsBLgC+WbM8Ezo4xbtitIjHGA4Ep8md1CxDJvcevIK/UBjnIPiPGOPQ/KJIkSU3Xc8hBCGFD4D+BtYGdBihvA+BC4L4QwvZVVd0//Squcj9NKc3vkX56jDEBF5Pfl48C365niDGuBXweWJM8DGOPlNL1tSzfjTHeRB668UxysHtk+4VijJsAR5fd24BdUkq/qWU5O8Z4AnAQeSjCfsCpA75OSZKkidCvR++N5PGjU1VV/bxfYSXPR8hjPP/X9Ku36g0yFjWldDlwXtndsUMP697AU8v2J9qC2ZajyGNpAd5VguB2bwZaQwk+0BbMtrybPLYX8jAJSZKk1Uq/gPaVwIPAsUOUeVw5Z58R69QU99S212lL27e2fXKnk1NKj7J86MGm5PGw7Vrl3EMe19upnKW1tOfEGLfvXmVJkqTJ0y+g/WPgR1VV3TtogVVV3QdcDjxvGvWa02KMWwAvLrt3pJR+15Zlj/J8Y0ppSY+izu9wTusaa5PHygL8MKXU60a7ruVIkiRNun7Tdm0BfH+Ecm9jeTA2EWKM6wLzgT8D3sfyoQCfbsu3IXmqL4Br+hR7XW37WW1pT2P55zOdciRJkiZav4D2YWDeCOXOA5aNcN6cEmOszyTQySnAJ9uOtRaXAPhVr/JTSnfGGO8jr6y2XVtyfaaInuUAt9a228uRJEmaaP0C2tuBZ4xQ7jOATjcwTYqbgJhSOq9D2ka17aUDlLWUHNC231g2TDn19K5TgEmSJE2ifmNofwg8M4Tw7EELDCE8h/y196XTqdgccSGwQ3k8nzwP7RfJy/x+qSyc0G692vZDA1yjNTZ2vbbjw5RTH1/bXo4kSdJE69dD+y/kqbuOCyG8uKqqnoFVCGFt8iwHVTm30VJK9wBX1Q79J3ke2i8BZ5MXVnhCSqk+h2x97t1Bhmu0Zkhon7N3mHLqsyw0ce5fSZKkkfUMaKuq+nYI4SLysrYXhBDeWlXVf3fKG0L4Y/L0XrsAF1dV9e1O+SZBSul7McbPkG8O+3CM8V9TSq0bs+rTeQ3y9X8rT/uwgmHKqad3HZ4QYzwYOLh+bN68eUxNTa2Qb+HChSxatKjPJSVJkuaGfj20AK8FLiEv/frTEMKVwI+A35b0LYEXkL+WD8DPgdeNv6pzzpnkgHYN8nyxR5Xjt5F7qAMr3ti1khjjZuTxs7DijV2w4o1g/ZYSrt8I1l7OY1JKxwPH149NTU1Vhx9+eJ/iJUmS5q5+Y2ipqup/yONHv0IO1J5LXsHqA+VxYDnWGmbwgqqqJvmGsJb/qW0/sbVRFjpoBZX9ptCq33DXPjXXDSyfKWI65UiSJE20vgEtQFVVf6iqaj/y3KjvAr4E/Ed5fLkce3pVVW+sququmanqnLNNbbv9a/6Ly/NTY4zze5SxuMM5AKSUHiYvUAGwa4yx1zjaruVIkiRNukGGHDymqqqfA5+dobo0zWtr21e2pZ0O/FXZPhD4aPvJMcY1gP3L7l3ABR2ucTqwO3kKr9eR/3loL2dDlg/xuCqldNNg1ZckSZoMA/XQrk5ijG8qQWKvPK8DYtn9A/DNtixnAjeW7ffHGJ/eoZgPknu8AT6dUuq0EMVJ5GAX4GMxxi075DkG2Lhsty/yIEmSNPGG6qFdTbwX+HyM8QzgInJgejewAXms6muAl5W8FfDOlNKd9QJSSstijG8Hvk2egeDiGOMUeW7eDYH9gANK9mvJQelKUkq/jzEeBpxIvjHsshjjUcAV5GWJI/Cqkv1COvTgSpIkTToD2s4eRx4OsH+PPHcC70gpfaVTYkrpOzHGg8hTmW0OfKpDtmuBl5cbyTpKKZ0UY9wKOBJYQNssBcUlwL4ppUd71FeSJGkiOeRgZfsC7wT+ldwT+mvgYeBe4BbgLOBtwFO6BbMtKaVTgB3JQe3PgAfIQwguI/cE75RS+kW/CqWUpoDdgFNLHR4E7iD3yr4FWNTeSyxJkrS6sIe2TUqpdePbWG5+KwsuHDqGci5n+awHkiRJKuyhlSRJUqMZ0EqSJKnRDGglSZLUaAa0kiRJajRvCpO0Wpk//9gZLX/JkkNmtHxJ0srsoZUkSVKjGdBKkiSp0QxoJUmS1GgGtJIkSWo0A1pJkiQ1mgGtJEmSGs2AVpIkSY1mQCtJkqRGM6CVJElSoxnQSpIkqdEMaCVJktRoBrSSJElqNANaSZIkNZoBrSRJkhrNgFaSJEmNZkArSZKkRjOglSRJUqMZ0EqSJKnRDGglSZLUaAa0kiRJajQDWkmSJDWaAa0kSZIazYBWkiRJjWZAK0mSpEYzoJUkSVKjGdBKkiSp0QxoJUmS1GgGtJIkSWo0A1pJkiQ12lqzXQFJkjS8+fOPndHylyw5ZEbLl8bJHlpJkiQ1mgGtJEmSGs2AVpIkSY1mQCtJkqRGM6CVJElSoxnQSpIkqdGctquDGONOwMuAPYBnA1sCy4DbgcuAU1NK5wxR3s7AocALga2Bu4GrgdOAL6SUHhmwnJcABwO7ljr9DriilPG1QesjSZI0SQxo28QYLwQWdUiaBzy5PP5XjPFs4I0ppT/0Ke9DwEdYsTd8C2BxefxNjPEVKaW7epQRgH8G2icF3Lo8XhZjPBN4fUrpwV71kSRJmjQOOVjZNuX5N+Qg8vXkHtFdgLcBN5b0lwPfjDF2fQ9jjAcCU+T3+RYgAjsDrwDOKtl2B87oVQ5wJMuD2SuB/YAXAK8BLi7H9wZOGOgVSpIkTRB7aFd2HfC3wNdTSsva0i6PMX4R+A45EF0E/BXw5fZCYoybAEeX3duAXVJKv6llOTvGeAJwEHkown7AqR3KeQrw/rJ7BbBHSunesv/j0jN7FvBS4E0xxhNSSt8f6hVLkiQ1mAFtm5TSK/qk3xtjfCvw3+XQa+kQ0AJvBjYt2x9oC2Zb3l3O3xg4jA4BLfAuYO2y/Y5aMNuqz7JSn5+Te4LfBxjQqnFcxlOSNCqHHIwgpXQl+YYsgO27ZNu3PN8DdLxhK6W0tJb2nBjjCmWVsbP7lN0bUkoX00FK6RbgvLL7ZzHGDfu9BkmSpElhQDu6Vq/pSjMUxBjXJo+VBfhhnxu1zq9t79GWtgDYtmxf2Kc+rXLWBZ7fJ68kSdLEMKAdQYxxR+BxZffaDlmexvLhHNf0Ke662vaz2tLq+9MpR5IkaWIZ0I7mb2vbX+2Qvm1t+1d9yrq1tr3dDJUjSZI0sQxohxRjfAPLx8f+GDijQ7aNattL+xRZT28f+zquciRJkiaWAe0QYozPBU4su/cBb0opVR2yrlfbfqhPsfXxteu1pY2rHEmSpIllQDugGOMC4NvABsCjwP4ppeu6ZL+/tj2vT9HrdDlvnOVIkiRNLOehHUCMcWvgXJavIhZTSl/vcco9te1+X//X09uHFYyrHABijAcDB9ePzZs3j6mpqRXyLVy4kEWLOq3+K0mSNPcY0PYRY9ycHMy25oh9d0rpxB6nwIo3cG3bNVdWv4Hr1ra0cZUDQErpeOD4+rGpqanq8MMP71O0JEnS3OWQgx5ijBuTl7l9djn0dymlTw9w6g1Aa9ncflNoPaO23T41V31/OuVIkiRNLAPaLspqW+cAO5ZDn0gpfXSQc1NKDwOXl91dY4y9xr8urm23rwR2M3Bb2X5hn8u2ynmQPPuCJEnSasGAtoMY43rAWcCu5dDnUkofGLKY08vzRsDrulxnw1raVSmlm+rpZQaF1rRgT4sxtq8k1irnicCLyu65ZUldSZKk1YIBbZvSm/pvLO/xPAl45whFnQTcVbY/FmPcskOeY4CNy/Ynu5TzGZYPX/hsjHGDtvquBRwHrNmnHEmSpInkTWEr+wrwsrJ9KfBZ4Nkxxq4npJSu6nDs9zHGw8jz1m4LXBZjPAq4AtgCiMCrSvYLgS93KfumGOPHyauT7QhcWvZvIN8I9h6g1XP7pZTSRQO/UkmSpAlgQLuyV9e2dwP+a4BzQqeDKaWTYoxbAUcCC2ibYaC4BNg3pfRoj/L/HtgceCuwA3BahzxnAm8ZoK6SJEkTxSEHMyylNEUOjE8FbiHftHUHuVf2LcCilNKdfcqoUkqHAHuRh0PcRl457HbyjWuvSyntk1J6sEcxkiRJE8ke2jYppY69rdMs83KWz3ownXLOJc+JK0mSpMIeWkmSJDWaAa0kSZIazYBWkiRJjWZAK0mSpEYzoJUkSVKjGdBKkiSp0QxoJUmS1GgGtJIkSWo0A1pJkiQ1mgGtJEmSGs2AVpIkSY221mxXQJIkzV3z5x87o+UvWXLIjJav1YM9tJIkSWo0A1pJkiQ1mgGtJEmSGs2AVpIkSY1mQCtJkqRGM6CVJElSoxnQSpIkqdEMaCVJktRoBrSSJElqNANaSZIkNZoBrSRJkhrNgFaSJEmNZkArSZKkRjOglSRJUqMZ0EqSJKnR1prtCkia2+bPP3bGr7FkySEzfg1J0uSyh1aSJEmNZkArSZKkRjOglSRJUqMZ0EqSJKnRDGglSZLUaAa0kiRJajQDWkmSJDWaAa0kSZIazYBWkiRJjWZAK0mSpEYzoJUkSVKjGdBKkiSp0daa7QrMRTHGTYAXADuXxwuArUvyhSmlxUOU9QzgHcBewDbA/cCNwFeBY1NKDwxYzs7AocALS13uBq4GTgO+kFJ6ZNA6SZIkTRJ7aDv7KfAd4KPAq1gezA4lxnhAKetQYHtgPWAzYBfgGOAnMcYnDVDOh4BLgf2BBcA6wBbAYuAE4KIY46aj1FGSJKnpDGg7C7Xt3wDfGraAGONewInAusAdwHuA3YCXAF8s2Z4JnB1j3LBHOQcCU+TP6hYgknuNXwGcVbLtDpwRY/TzlCRJqx2HHHT2eeAXwOUppVsBYozVoCfHGNcqZawJLAX2SCldX8vy3RjjTcBHyEHte4AjO5SzCXB02b0N2CWl9JtalrNjjCcAB5GHIuwHnDpoPSVJkiaBAW0HKaWj++fqaW/gqWX7E23BbMtRwF+XfO+KMR6VUlrWlufNQGsowQfagtmWdwOvBTYGDsOAVpIkrWb8inpm7FvbPrlThpTSoywferApeTxst3LuAb7WpZyltbTnxBi3H7aykiRJTWZAOzP2KM83ppSW9Mh3fodzAIgxrk0eKwvww5TSg6OUI0mSNOkMaMes3OC1Xdm9pk/262rbz2pLexrLh4RMpxxJkqSJZkA7ftuwfJaEX/XKmFK6E7iv7G7XlrxtbbtnOcCtte32ciRJkiaaAe34bVTbXjpA/lae9qm7himnnt51CjBJkqRJZEA7fuvVth8aIH9rbOx6bceHKac+vra9HEmSpIlmQDt+99e25w2Qf50O5w1bzjq17fZyJEmSJprz0I7fPbXtQb7+b+VpH1YwTDn19K7DE2KMBwMH14/NmzePqampFfItXLiQRYsW9bmkJEnS3GBAO363ARX5xrBte2WMMW4GrF92b21Lrt8I1rMcVrwRrL2cx6SUjgeOrx+bmpqqDj/88D7FS5IkzV0OORizstBBK6jsN4XWM2rb7VNz3QC0Vg6bTjmSJEkTzYB2Zlxcnp8aY5zfI9/iDucAkFJ6GLi87O4aY+w1jrZrOZIkSZPOIQcz43Tgr8r2gcBH2zPEGNcA9i+7dwEXdClnd/IUXq8DvtyhnA1LGsBVKaWbplNxNcv8+cfOaPlLlhwyo+VLkjQO9tDOjDOBG8v2+2OMT++Q54Pk1cAAPp1SWtYhz0nkYBfgYzHGLTvkOQbYuGx/csT6SpIkNZY9tB3EGJ8HPK9L8lYxxgPajp2TUrq9tZNSWhZjfDvwbfIMBBfHGKeAS8v+fkCrjGvJQelKUkq/jzEeBpxIvjHsshjjUcAVwBZABF5Vsl9Ihx5cSZKkSWdA29k+wIe7pD0d+ELbsT2B2+sHUkrfiTEeBBwLbA58qkNZ1wIvLzeSdZRSOinGuBVwJLCAtlkKikuAfVNKj3YrR5IkaVI55GAGpZROAXYkB7U/Ax4gDyG4DHgvsFNK6RcDlDMF7AacCtxCXhnsDnKv7FuARSmlO2fgJUiSJM159tB2kFI6AjhiTGVdBxw6hnIuZ/msB5rjvFlLkqRVxx5aSZIkNZoBrSRJkhrNgFaSJEmNZkArSZKkRjOglSRJUqMZ0EqSJKnRDGglSZLUaAa0kiRJajQDWkmSJDWaAa0kSZIazaVvJUnSrJrp5cLBJcMnnT20kiRJajQDWkmSJDWaAa0kSZIazYBWkiRJjWZAK0mSpEYzoJUkSVKjGdBKkiSp0QxoJUmS1GgGtJIkSWo0VwrTasXVaCRJmjz20EqSJKnRDGglSZLUaAa0kiRJajQDWkmSJDWaAa0kSZIazYBWkiRJjWZAK0mSpEYzoJUkSVKjGdBKkiSp0VwpTJIkrRZmerVIV4qcPfbQSpIkqdEMaCVJktRoBrSSJElqNANaSZIkNZoBrSRJkhrNgFaSJEmNZkArSZKkRjOglSRJUqMZ0EqSJKnRXCmsQWKM2wLvAF4JPAFYBvwCOAP4XErprlmsniRJq72ZXo0MXJGsE3toGyLG+FLgSuB9wDOBDYCNgecB/wBcGWPcadYqKEmSNEsMaBsgxvhc4OvAJsB9wIeBPYDFwKeAR4BtgG/FGOfPTi0lSZJmh0MOmuHT5B7ZR4CXpZQuqqVdGGP8CfAlYCvgo8CBq7yGkiRJs8Qe2jmuDCPYs+ye0hbMApBS+jJwXtn96xjjlquqfpIkSbPNHtq5b9/a9kk98p0MvAhYE3gVcOJMVmomzPRAegfRS5I0mQxo5749yvN9wI965Du/7ZzGBbSSJGkwdgKtyCEHc9+zyvONKaVl3TKllJYA97SdoxE89NBDs10FzXG2EQ3CdqJB2E7Gw4B2DosxrgNsXnZ/NcApt5bn7WamRqsHf7moH9uIBmE70SBsJ+PhkIO5baPa9tIB8rfybDgDdZEkSauZpgxtsId2bluvtj3Iv3APdjhPkiRpohnQzm3317bnDZB/nQ7nSZIkTbRQVdVs10FdlDG0D5Tds1NKr+iT/2ryDWG3p5S27pB+MHBw2+E/ZuXe3/8B7hip0pPhmcC1s10JzWm2EQ3CdqJB2E6Gc0dK6aXtBx1DO4ellB6MMd5BvjFs2wFOaeW5tVNiSul44PgxVW9ixRh/nFJ6/mzXQ3OXbUSDsJ1oELaT8XDIwdx3TXl+aoyx6z8gMcb5wOPazpEkSZp4BrRz38XleX3gBT3yLe5wjiRJ0sQzoJ37Tq9tv7lHvgPL8yPAN2euOpIkSXOLAe0cl1L6T+CCsntAjHFhe54Y4xuBF5fdU1NKv11F1ZtUjjNWP7YRDcJ2okHYTsbAWQ4aIMb4XOASYAPgPuDjwPfIN/XtDbwTWBO4HdipLIMrSZK0WjCgbYgY40uBfwE26ZLlNmDv0qOrOSbGuA3wauAvyFO0bAXcDVwOfCql9N1ZrJ5WoRjjjsA/AHuQF0G5DjgWOCGl5C/k1Zy/KzSKGOP7yZ1dAAtTSqvdvTQOOWiIlNI5wA7AJ8nz1d1L/iX3X8ARwA4Gs3PaO4DPANsD5wLHABcCfw6cG2N89yzWTatIGTJ0KflzPxv4LLA2kMjtQ/J3hYYSY3wO+Z/ke2e7LrPJHlppFYgx7gv8T0rp+23HF5KHjwA8MaX061VeOa0SZdq9a4CnAn+RUvr3cnwe8F1gIfCnKaVLZq+Wmm3+rtAwYoxrAz8EHgZuBPbDHlpJMyWldHr7H6hy/PvA+eReut1XecW0Ku1JDmbPbwWzACmlh4C/K7vtK/lpNePvCg3pcODZ5JmOHpnluswqVwrTnBZj3IQ8/+7O5fECoLWs74UppcVDlLUt+eu8VwJPAJYBvwDOAD6XUrprbBUfzrLyvFr/MppJc6Qdta7xnQ5pF5O/Lhy4Hhq/OdJOevF3xSybS20kxvgn5ID271NK18QYh3otk8YeWs11PyUHAB8FXsXyXxxDKTfVXQm8j3yjxQbAxsDzyGOProwx7jSG+g5bryeQp1y7H1ipV0ZjMxfa0dPK843tCSmlR8h/yJ5QhiBodsyFdtKtTH9XzA1zoo3EGNcBTgWuAP5xlDpMGgNazXWhtv0b4FvDFlCmPfs6eYaI+4APk+8wXwx8itzbsQ3wrbKE8CpRxj59CVgH+GhK6Xer6tqrobnQjjYuz3/ocom7Sz037pKumTcX2kmnMv1dMXfMlTZyJHkI09+Uf4hXew450Fz3eXLP1eUppVsBYozD3sn4afJ/v48AL0spXVRLuzDG+BPyH4utyP91H9heQIzxduCPhrjm36SUTumWGGNcAzgZWAR8g+XTrWhmzIl2pDlvzrUTf1fMObPeRmKMuwH/BzgipXTVKC9iEtlDqzktpXR0SunfWr84hlW+stmz7J7S9oujdY0vA+eV3b+OMW7ZoagbgeuHeHTrhSPGGIATyHejfhN4fUrp0aFfnAY2R9pRq01064F9HFDRo+1oZs2RdlIvz98Vc8xst5EyW8oXgf/Gf25WYA+tJt2+te2TeuQ7GXgRecW1VwEn1hNTSistOTyK0ttyIvA35D9Qry13uWtuG0c7uqE8P7X9pBjjmsCTgF/aHhptLL9vwN8VE2y6bWRDlv8OeajLjWDfL8d7flM4aeyh1aTbozzfB/yoR77zO5wzVm1/oL4BvMY/UI0xjnZ0QXneq0v5G9TyqJnG8vvG3xUTbbpt5EFyINzp0brh9Ftl/wZWI/bQatI9qzzfmFJa1i1TSmlJjPEeYKPaOWNT/kCdBBwAnA68IaX08LivoxkzjnZ0PvkPzp4xxpe1LazwkZLn+PFWW6vYtNuJvysm3rTaSErpfuCgTufEGE8h995+YnVcWMGAVhOrTGuyedn91QCn3Er+xbHdDFTn78l/oJaSly4+vMNXRReklC6YgWtrGsbVjlJKy2KMB5Gn/PlGjPGrwK+Bl5MnRv+cq4Q11xh/3/i7YkLNsb9JE8eAVpNso9r20gHyt/JsOAN1WVAr+/Ae+S6YgWtresbWjlJKF8UYdyfPM/lKYF3yTYRvxd7ZphtXO1lQO+7viskyl/4mTRwDWk2y9Wrbg4w/e7DDeWORUjqA3Oui5hlrO0op/YQczGqyjKWd+Ltios3o36TVve14U5gm2f217UFWX1qnw3mS7UiDsJ2oH9vIDDKg1SS7p7Y9yFc2rTyDfBWk1YftSIOwnagf28gMMqDVxEopPQjcUXa3HeCUVp6RJszWZLIdaRC2E/VjG5lZBrSadNeU56eWFVY6KutlP67tHKnFdqRB2E7Uj21khhjQatK15uJbH3hBj3yLO5wjtdiONAjbifqxjcwQA1pNutNr22/uke/A8vwIeZlJqc52pEHYTtSPbWSGGNBqoqWU/pPl8zUeEGNc2J4nxvhG4MVl99SU0m9XUfXUELYjDcJ2on5sIzMnVFU123WQuooxPg94XtvhL5Tn64GPt6Wdk1K6va2M5wKXABuQ18/+OPA98jzMewPvBNYEbgd2SiktGd8r0FxgO9IgbCfqxzYydxnQak6LMR4BfHiIU/bstCRkjPGlwL8Am3Q57zZg7/LfsyaM7UiDsJ2oH9vI3OWQA60WUkrnADsAnySvj34vcDfwX8ARwA7+4lA/tiMNwnaifmwj42cPrSRJkhrNHlpJkiQ1mgGtJEmSGs2AVpIkSY1mQCtJkqRGM6CVJElSoxnQSpIkqdEMaCVJktRoBrSSJElqNANaSZIkNZoBrSRJkhrNgFaSJEmNZkArSZKkRjOglSRJUqMZ0EqSJDVECGH7EMJxIYQrQgjLQghXzXad5oK1ZrsCkiRJGtizgZcDl5E7Ju2cBEJVVbNdB0mSJA0ghLBGVVWPlu1TgOdXVfWc2a3V7DOqlyRJaohWMKsVGdCq0UIIN4cQqgEei2e7ruNWe+0LZrsuTdHtPfO9VEvrd8ZMn7M6maSfrxDCgg5/X/7PbNermzLOtl7XC2a7TjPFMbSaFP8B3N4jvVea5pgQws3AE4EnVVV18+zWRpJWci/w9bI9l2/K+iZwBbAV8OezW5WZZUCrSfHxqqoumO1KrGIvBtYGbpvtikjSauaOqqoOmG4hIYSNga0HyPrLqqruG7b8qqr+vlxnMQa0kuaiqqp+Ntt1kCRNy18CXxgg357ABTNblWZzDK1WK2XuviqE8B8hhNAh/ZSSflY9vT5GLoRwcAjhpyGE+0IIvwshnB5C6HqHaQhhgxDC+0IIPwoh3B1CuD+EcHUI4YgQwoZdzqlf780hhMvKuVUIYZNyvNt40Pq5B4QQfhxCuDeEcHsI4aQQwhYlbd0Qwj+EEG4IITwQQvhlCGEqhLD2uF5LW11eH0K4NISwNIRwTwjheyGEPdryH1DyP7Ec+kXb+K8Ftby7hBA+WV7fb0IID4UQloQQvh5C2LXbaxhEGSf3SAjhzhDCel3yrB1C+HWp17MHLHcufTYjvX8hhKeHEL4YQrilnHNPaYtnhBBe3fYeViEPH+n7fvR4nzq2/1Fec+28HUp97yzv/09CCAd1yz+qEMI6IYSvlHpfEkLYfNzXGKFOzyj1+W239hRCWKvWtp9TOz62n7nptI+SNmx7H6jdjsMw16qq6pSqqsIAjwvGWceJVFWVDx+NfQA3AxWweMD865LHE1XAB9vSDijHfwls1pZWlcengEfI/yn/C3B9OX4vsEeH620LXF3y/JY81vdMYEk59l/Aph3Oa13vc+V6FwFfAX4MbNz22hd0OfcTwIPAd4B/A35du+aGwA+AO4EzgG+X11ABx3d574Z+LbW6HFlex4XAV4Fry/EHgd1q+fcATgGWlvSvl/3WY/Na3u8Cy8p1zyp5ryznLQNe26O9tL9nKx0HvlGOHdjl/XhDST9/iPY6lz6bUd6/HYC7S55rS92/DlwK3AecU8u7oOS7ud/7MWL7H/Vn64WlrhVwHfnn+IJynWO61WmQz7Xt2Cal3Ao4HVhvur/vxvUAfljqtXeX9JeX9B9Pt830+PmaTvsY6rMfpt0O8N71rPc4rzVgfU4Brhog3+JSpwtmu/3NWLue7Qr48DGdB0MGtOWcpwH3AA8Df1qOPYscNDx2rO2c1h/Ye4FFteMB+BjLA+F129IuYfkf5vVqaesBXyppp/S43u+Bnfu89gVdzr0deGbt+KbkP+BV+SP0fUpwUNKfV17/o8AT28oc6bXU6vI7YKfa8TWA40vauYO+trY8LwX+qMPxVwIPlWuuP+B7ttJx8hjlCvhRl+tfVNJfM0Tbm0ufzSjv38l0+GewpG3Iiv+cLGD6Ae3v6dD+p/Ga1wN+VdKOoszFXtJeyPJ/HFaq0yCfa23/iSwPuD4LrDFMeTP9AGKp2+ld0v+1pL99um2m6v7zNVL7GOWzH6bdDvDe9az3OK/Vow7rA68pj/PJf3ta+0/scs5iDGh9+Ji7j9ovyl6P33c4769K2q3AduS7VCvg/V2u0yrr6A5pawI/K+lvrB1/WTl2aac/aMAGwG/IgUq3ns0PDfDaF3Q59+AO57yrpD1CLaCqpZ9Z0v+67fhIr6VWl7d3OOePStoDwNqDvLYh2sVp5fyXD/iedTveCkp2bju+Qzl+G7DWEPWaM5/NiO/f2eX48wYoYwHTD2g7tv9ptMc3lfNuAtbscN4/davTIJ9r2d6R3FP4KPDeUdrvTD+AjYH7yd8SPL4tbdPyM/kgbd9UjdJmStpKP1+jto9RPvth2u102/U4rzVAHTo9DuhyzmImPKB1DK0mxX8AX+zy+Ep75qqqvgKcQP7q6kryUoLnAP/Y5zpf7lDWI+SvLSH/0mj5i/L8b1WHibCrqrqX/BXqWsALulzv9D716eWcDsduKs+3VFV1bYf0G8vz/Lbj030t3+pwzm+Au4B1gMd3OKevEMLmZSzq0SGEE0MeA30K0Br397RRyq35fHk+tO14a//4qqqWjVDunPhsRnj/Li/Px4UQXhJCWKdDPcepW/sf9TW/sDz/v/Jz2+5Lo1YUIITwUnLP/WbAG6qq+qfplDfA9fYJIbS3zb6qqvoDeUjNPOCNbclvIP9MnlVV1Z0drjnTP3P9jPLZr8p2O+PXqqrq5qr7WNtTxn29pnCWA02KUabt+t/AXuSvB38LvKkq/8r28Isux28uz9vWjj25PH8yhPDJPuVu0eX4LX3O6+VXHY4t7ZFWT1+37fh0X8svu+S9m9wj1H69vkIIkTzmcf0e2R43bLltTiUPKXl9COE9VVXdGUJ4HLAfuQfo+BHLnfXPZsT375PAQvJwjO8AD4YQriCPjf5yVVVX9rn+sLq1/1HbY+vns9/P8ajOIv9dfX1VVf86zbIGsQ/wfOD/1g+GEI4G2m9Au6OqqvoCAF8gB6/7k4dFtOxfnk9pv9gq+pnrZ5TPflW221X9M6LCgFars0XAE8r2ZsBTgTvGWP6a5flC+v+h7PiHu6qq+0e9eKfei5phl06c1mvpU5ehhRBeABxLvhHlMHIg8SvgvqqqqhDCUcAHyePtRlZV1b0hhJOBdwMHAkcDf00eC/e1qqp+PWK5s/rZjPr+VXkezD8LIexCHk/5p8BuwC7A+0IIH66q6shBKh1C6PsNYY/2P+2frRlyKrmdfDSEcGlVVbeuwmvXvYblM4W03ALUA9rvkj/zPwkh7FBV1ZUhhKeTP8vbafsWYVX9zNWu1619DP3Zj7Pd9rMqr6UVGdBqtRRC2Jr89WIg91T8DfD/QgjPq6rqrh6nLiDfQdvpOKy4yEHrj9nXqqr652lVePbNtdfyavJn99mqqo7ukL79GK/1z8A7gbeGEI4BDqkdnwtG+Wym9f5VVXUZcBlACGEeeUz6CcARIYSvVlV1PfkmIcjBfyftAdcwRm2PrZ/PBV3Sux0f1EHksalvAy4KIby4qqqfd8oYQtgN+AdgV/JncQ3wt1VVnVuCoQ+RvzLfmDxG/3NVVZ1QO/8USm9qbWqrL1ZVdUBVVX1fR1VVj4YQvkQOQg8A3lueAU7rMJRm3D9zo7aPkX8XDdhux2JVXkuZY2i12in/+Z8GbAl8pqqqA8k9K0+g/wTX7ePNCCGsSf7qDlac+Prfy/Nrp1PfOWJVv5bWH7tu/3RvVp5X6gELeS7Xl4yrIlVewOLfgaeQ74x/FnB1VVUXjusa0zTKZzO296+qqofKuL0fkgOe55ak/yF/jo8vZbb7iw7HBjVqe2x9Zm8oP7ftVvr5HkaVvZ38tfMCclD79PZ8IYQ/Jf+uWJccBL+afNNf6xujJ5JvenoL8AryGP3PhRDeVivmI+Qp3X5O7gHcrRwbxinl+Y0l6Nqv7XjduH/mRm0fY/ld1KPdjt2qvNZqbTbvSPPhY7oPRpu264hyzo+AeeXYBiyfG/WdHc5p3UG6lNp8s+RfTh8tab9ixSlk1iDfnFABx9HhjmHy+tpv6Xa9AV/7gkHPpc+drrX35oi24yO9ln6vo8drOL8cf2WX895T0v8T2LB2fCPyV6FVl9fR7Xodj9fS/7xWZgUcOmJ7nROfzTTev0OBp3co/8nkeXMrajNCkL/WrshjPOtTZO1BnuKp4/sxQLsZtT2uz/K5Sj/SoU5L+1170M+19nndDuzQlvYD8gwaK8200KHsQP7H7hjgv9vSTmGAOUj7lP+DUs/PlOcfd8k3Upvp9fM1SvsYsb0P1W77vF8L6D3LwdiuNc4Hq8EsB7NeAR8+pvOo/aI8hxUn4G9/7FXy70meFukPwJPbytqB5VPZPL8trfXL+lPl/PPJsye05g69D3hhh/ptC/x3yXM3eX7Rr5Dv3r6KPF7y9g7nzamAdtTX0u919HgN/7t2na8DJ5bH40v6puQbzSryDX2nkxch+B15kYKTOr2OHtfreLyWHmqf9d3ARiO21znx2Uzj/buiHP8ZuUfxNOB75J+ZCviXtvy719KuAb5Gvgv8EXJAOVJAO82frReRf84r8j+xXyH/PI91YYVy/LCSdgfwJ+XY+uVa7+tR3qbkAPNm8pjV1u+fB9ryncL0A9q31Mqv6DDF3nTaTJ+fu1Hbx7Dtfah22+f9WkDvgHZs1xrnAwNaHz7m9oPB5qGtyHN8bsny3pnXdSmvNeH4z4DH1Y63ygnk/8D/ixzEtlZz2qFHHdclj6m7sOR/qPwB+DH5q8ndO5wz5wLaUV5Lv9fR4zWsAfwtOeB4oPb+L6jl2RJI5K9cHyR/FXoCsHW319Hjeh2Pt+X5fMnz+Wm017n02Yzy/r2C3Cv2U/JXxg+Sg5zvkL8C7jQv6B7knrh7yAsXXE6Zr7nb+zFI+x/1Z6uc98fkYOMu8s/xFcBbh7n2EJ/r28gB1u/JwwK2Kfn361HemeQg+H+TA/DnkxcRqNryncL0A9rHsXzltJ5zz47SZvr9fI3SPob97Edptz3egwX0DmjHdq1xPlgNAtpQXqikHlo3XVRVNZY7eNUsZXzhL8mLQTy7qqprZrlKaqgQwgbkXsUPVlW10rzXIYR1yYHde6uq+nTt+OfIvaehduwU8rdJz2kvRzMjhLCAPO3bLdUAN9/NFSGExeRvIi6sqmrxrFZmhjjLgST19zZyMHuOwaymo8pTwV0K/HUI4Z+qlRd4WIf8DcWDrQMlyH11h+IeYoQ5nDUWm5d/KCAv1NFpsZRZF0I4knyz4VazXZeZZkArSR2Uu9MPI6/M9efkhRQ+MKuV0qT4AHAe8L0Qwj+Thz78CXnxg5NDCD8CPhhC+B05sH0veehNu2uBN4cQ3ghcX86/eVW8ALEByxehuIrOq//NBa8iD7GZeA45kAbgkIPVT+0rugdZPkfot2ezTpocIYTdyTOk7EK+Cepqchv7Xghhe/I4zF3JY2+PI7fDf2wbcvA48pjWl5CXj/5iVVUHrMKXIc0ZBrSSJElqNBdWkCRJUqMZ0EqSJKnRDGglSZLUaAa0kiRJajQDWkmSJDWaAa0kSZIazYBWkiRJjWZAK0mSpEYzoJUkSVKj/X91Gm4bnjEqDwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (10,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 699, ymin = 0)\n", - "plt.xlim(xmax = 5.1, xmin = -3)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "#plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", - "\n", - "plt.ylabel(\"Count\", fontsize = 22)\n", - "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.hist(log10_kcat_values, alpha = 0.9, color=\"darkblue\",rwidth = 0.95, bins = 20)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize= (8,8))\n", - "plt.rcParams.update({'font.size': 28})\n", - "\n", - "\n", - "plt.ylim(ymax = 2500, ymin = 0)\n", - "#plt.xlim(xmax = 5, xmin = -0.1)\n", - "\n", - "ax.tick_params(axis='x', length=10)\n", - "ax.tick_params(axis='y', length=10)\n", - "\n", - "ax.yaxis.set_label_coords(-0.18, 0.5)\n", - "ax.xaxis.set_label_coords(0.5, -0.1)\n", - "\n", - "plt.yticks([0,1000,2000,3000], [\"0\",\"1000\",\"2000\",\"3000\"])\n", - "plt.xticks([0,250,500, 750, 1000], [\"0\",\"250\",\"500\", \"750\", \"1000\"])\n", - "\n", - "plt.ylabel(\"Count\", fontsize = 22)\n", - "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", - "plt.hist(kcat_values[kcat_values<1000], alpha = 0.9, color=\"darkblue\", rwidth = 0.95, bins = 40)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Only considering predictions for Escherichia coli:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (a) Loading predictions:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", - "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", - "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", - "data_test[\"y_true\"] = test_y\n", - "data_test[\"y_pred\"] = pred_y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (b) Creating a txt file with all Uniprot IDs to the organism name for every data point:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "data_test[\"Uniprot ID\"] = [UIDs[0] for UIDs in data_test[\"Uniprot IDs\"]]\n", - "\n", - "IDs = list(set(data_test[\"Uniprot ID\"]))\n", - "f = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"UNIPROT_IDs_test_set.txt\"), \"w\") \n", - "for ID in list(set(IDs)):\n", - " f.write(str(ID) + \"\\n\")\n", - "f.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### (c) Using the UniProt Mapping service to map Uniprot IDs to organism names:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Uniprot IDOrganism
0F7YTI3Pseudothermotoga thermarum DSM 5069
1O05306Mycobacterium tuberculosis (strain ATCC 25618 ...
2A8IKD2Azorhizobium caulinodans (strain ATCC 43989 / ...
3Q8PDQ6Xanthomonas campestris pv. campestris (strain ...
4Q6XL56Fusobacterium nucleatum
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" - ], - "text/plain": [ - " Uniprot ID Organism\n", - "0 F7YTI3 Pseudothermotoga thermarum DSM 5069\n", - "1 O05306 Mycobacterium tuberculosis (strain ATCC 25618 ...\n", - "2 A8IKD2 Azorhizobium caulinodans (strain ATCC 43989 / ...\n", - "3 Q8PDQ6 Xanthomonas campestris pv. campestris (strain ...\n", - "4 Q6XL56 Fusobacterium nucleatum" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "UNIPROT_df = pd.read_csv(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"UNIPROT_results_test_set.tsv\"), sep = \"\\t\")\n", - "UNIPROT_df.drop(columns = [\"Entry\"], inplace = True)\n", - "display(UNIPROT_df.head())\n", - "\n", - "data_test = data_test.merge(UNIPROT_df, how = \"left\", on = \"Uniprot ID\")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.4869487064829419, 3.382004165523896e-05) 1.0180411592099263 0.2321770387743426\n" - ] - } - ], - "source": [ - "Ecoli_ind = [ind for ind in data_test.index if \"Escherichia coli (strain K12)\" in data_test[\"Organism\"][ind]]\n", - "\n", - "\n", - "y_test_pred = data_test[\"y_pred\"].loc[Ecoli_ind]\n", - "test_Y = data_test[\"y_true\"].loc[Ecoli_ind]\n", - "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", - "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", - "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", - "\n", - "print(Pearson, MSE_dif_fp_test, R2_dif_fp_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculating how much mean deviation we have between two measurements for the same enzyme-reaction pair:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ReactionSequencekcats
0Reaction_0Sequence_309[2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0]
1Reaction_1Sequence_309[1.2, 3.4, 0.61, 0.07]
2Reaction_2Sequence_3142[6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,...
3Reaction_4Sequence_3263[57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11....
4Reaction_5Sequence_2101[2.98, 0.87]
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" - ], - "text/plain": [ - " Reaction Sequence \\\n", - "0 Reaction_0 Sequence_309 \n", - "1 Reaction_1 Sequence_309 \n", - "2 Reaction_2 Sequence_3142 \n", - "3 Reaction_4 Sequence_3263 \n", - "4 Reaction_5 Sequence_2101 \n", - "\n", - " kcats \n", - "0 [2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0] \n", - "1 [1.2, 3.4, 0.61, 0.07] \n", - "2 [6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,... \n", - "3 [57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11.... \n", - "4 [2.98, 0.87] " - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"merged_and_grouped_kcat_dataset_with_FPs_and_ESM1bs.pkl\"))\n", - "df = pd.DataFrame({\"Reaction\": df_kcat[\"Reaction ID\"], \"Sequence\" : df_kcat[\"Sequence ID\"],\n", - " \"kcats\" :df_kcat[\"kcat_values\"]})\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(0.75, 5.67)" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ind = 0\n", - "\n", - "deviations = []\n", - "\n", - "for ind in df.index:\n", - " kcats = df[\"kcats\"][ind]\n", - " if len(kcats) > 1 :\n", - " for i in range(len(kcats)):\n", - " for j in range(i+1, len(kcats)):\n", - " deviations.append(abs(np.log10(float(kcats[i])) - np.log10(float(kcats[j]))))\n", - " \n", - "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/code/model_fitting/03 - Testing additional ML models.ipynb b/code/model_fitting/03 - Testing additional ML models.ipynb new file mode 100644 index 0000000..9b39c5d --- /dev/null +++ b/code/model_fitting/03 - Testing additional ML models.ipynb @@ -0,0 +1,690 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n", + "\n", + "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import pickle\n", + "import pandas as pd\n", + "import os\n", + "from os.path import join\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn.metrics import r2_score\n", + "from sklearn.linear_model import ElasticNet, LinearRegression\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn import preprocessing\n", + "from scipy import stats\n", + "import xgboost as xgb\n", + "from hyperopt import fmin, tpe, rand, hp, Trials\n", + "\n", + "from tensorflow.keras import regularizers, initializers, optimizers, models, layers\n", + "from tensorflow.keras.losses import MSE\n", + "from tensorflow.keras.activations import relu\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.pyplot import figure\n", + "import matplotlib as mpl\n", + "plt.style.use('CCB_plot_style_0v4.mplstyle')\n", + "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", + "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading training and test data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3421, 850)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "\n", + "\n", + "data_train.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "data_test.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "len(data_train), len(data_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_indices = list(np.load(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_train_indices.npy\"), allow_pickle = True))\n", + "test_indices = list(np.load(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_test_indices.npy\"), allow_pickle = True))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "mean_y, std_y = np.mean(train_Y), np.std(train_Y)\n", + "train_Y = (train_Y-mean_y)/std_y\n", + "test_Y = (test_Y-mean_y)/std_y\n", + "\n", + "scaler = preprocessing.StandardScaler().fit(train_X[:, 2048:])\n", + "train_X[:, 2048:] = scaler.transform(train_X[:, 2048:])\n", + "test_X[:, 2048:] = scaler.transform(test_X[:, 2048:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Training and validation machine learning models" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (i) Performing hyperparameter optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_r2_linear_regression(param):\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + "\n", + " reg = ElasticNet(alpha = param[\"alpha\"], l1_ratio = param[\"l1_ratio\"]).fit(train_X[train_index], train_Y[train_index])\n", + " y_valid_pred = reg.predict(train_X[test_index])\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "\n", + "#Defining search space for hyperparameter optimizationhp.uniform(\"reg_alpha\", 0, 5)\n", + "space_linear_regression = {'alpha': hp.uniform('alpha', 0,5),\n", + " 'l1_ratio': hp.uniform('l1_ratio', 0,1)}\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "best = fmin(fn = cross_validation_neg_r2_linear_regression, space = space_linear_regression,\n", + " algo=rand.suggest, max_evals = 2000, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "#param = trials.argmin" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'alpha': 0.3960857176137572, 'l1_ratio': 0.003735725013911728}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "test_Y = (test_Y+mean_y)*std_y" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.542 1.014 0.293\n" + ] + } + ], + "source": [ + "reg = ElasticNet(alpha = param[\"alpha\"], l1_ratio = param[\"l1_ratio\"]).fit(train_X, train_Y)\n", + "y_test_pred = reg.predict(test_X)\n", + "y_test_pred = (y_test_pred+mean_y)*std_y\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(np.round(Pearson[0],3) , np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) Random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#create input matrices:\n", + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))\n", + "\n", + "\n", + "scaler = preprocessing.StandardScaler().fit(train_X)\n", + "train_X = scaler.transform(train_X)\n", + "test_X = scaler.transform(test_X)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def cross_validation_neg_r2_random_forest(param):\n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + "\n", + " reg = RandomForestRegressor(max_depth = param[\"max_depth\"],\n", + " min_samples_leaf = param[\"min_samples_leaf\"],\n", + " n_estimators = param[\"n_estimators\"]).fit(train_X[train_index], train_Y[train_index])\n", + " y_valid_pred = reg.predict(train_X[test_index])\n", + " R2.append(r2_score(np.reshape(train_Y[test_index], (-1)), y_valid_pred))\n", + " return(-np.mean(R2))\n", + "\n", + "#Defining search space for hyperparameter optimization\n", + "space_random_forest = {'n_estimators': hp.choice('n_estimators', [50, 100, 200]),\n", + " 'max_depth': hp.choice('max_depth', [5,6,7,8,9,10,11,12,13,14,15,16]),\n", + " 'min_samples_leaf': hp.choice('min_samples_leaf', [1,2,5,10,20])}" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "'''trials = Trials()\n", + "best = fmin(fn = cross_validation_neg_r2_random_forest, space = space_random_forest,\n", + " algo=rand.suggest, max_evals = 2000, trials=trials)''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Best set of hyperparameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#trials.argmin" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'max_depth': 15, 'min_samples_leaf': 1, 'n_estimators': 100}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (ii) Training and validating the final model\n", + "Training the model and validating it on the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.622 0.911 0.364\n" + ] + } + ], + "source": [ + "reg = RandomForestRegressor(max_depth = param[\"max_depth\"],\n", + " min_samples_leaf = param[\"min_samples_leaf\"],\n", + " n_estimators = param[\"n_estimators\"]).fit(train_X, train_Y)\n", + "y_test_pred = reg.predict(test_X)\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred)**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred)\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred)\n", + "\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Neural Network" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "train_X = np.array(list(data_train[\"DRFP\"]))\n", + "train_X = np.concatenate([train_X, np.array(list(data_train[\"ESM1b_ts\"]))], axis = 1)\n", + "train_Y = np.array(list(data_train[\"log10_kcat\"]))\n", + "\n", + "test_X = np.array(list(data_test[\"DRFP\"]))\n", + "test_X = np.concatenate([test_X, np.array(list(data_test[\"ESM1b_ts\"]))], axis = 1)\n", + "test_Y = np.array(list(data_test[\"log10_kcat\"]))\n", + "\n", + "mean_y, std_y = np.mean(train_Y), np.std(train_Y)\n", + "train_Y = (train_Y-mean_y)/std_y\n", + "test_Y = (test_Y-mean_y)/std_y\n", + "\n", + "scaler = preprocessing.StandardScaler().fit(train_X[:, 2048:])\n", + "train_X[:, 2048:] = scaler.transform(train_X[:, 2048:])\n", + "test_X[:, 2048:] = scaler.transform(test_X[:, 2048:])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def build_model(learning_rate=0.001, decay =10e-6, momentum=0.9, l2_parameter= 0.1, hidden_layer_size1 = 256,\n", + " hidden_layer_size2 = 64, input_dim = 1024, third_layer = True): \n", + " model = models.Sequential()\n", + " model.add(layers.Dense(units = hidden_layer_size1,\n", + " kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev= np.sqrt(2./ input_dim), seed=None),\n", + " activation='relu', input_shape=(input_dim,)))\n", + " model.add(layers.BatchNormalization())\n", + " model.add(layers.Dense(units= hidden_layer_size2,\n", + " kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev = np.sqrt(2./ hidden_layer_size1), seed=None),\n", + " activation='relu'))\n", + " model.add(layers.BatchNormalization())\n", + " if third_layer == True:\n", + " model.add(layers.Dense(units= 16,\n", + " kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev = np.sqrt(2./ hidden_layer_size2), seed=None),\n", + " activation='relu'))\n", + " model.add(layers.BatchNormalization())\n", + " \n", + " model.add(layers.Dense(1, kernel_regularizer=regularizers.l2(l2_parameter),\n", + " kernel_initializer = initializers.TruncatedNormal(\n", + " mean=0.0, stddev = np.sqrt(2./ 16), seed=None)))\n", + " model.compile(optimizer=optimizers.SGD(learning_rate=learning_rate, momentum=momentum, nesterov=True),\n", + " loss='mse', metrics=['mse'])\n", + " return model\n", + "\n", + "\n", + "\n", + "def cross_validation_neg_r2_fcnn(param):\n", + " \n", + " param[\"num_epochs\"] = int(np.round(param[\"num_epochs\"]))\n", + "\n", + " \n", + " R2 = []\n", + " for i in range(5):\n", + " train_index, test_index = train_indices[i], test_indices[i]\n", + " model = build_model(input_dim = 1280+2048, \n", + " learning_rate= param[\"learning_rate\"],\n", + " decay = param[\"decay\"],\n", + " momentum = param[\"momentum\"], \n", + " l2_parameter = param[\"l2_parameter\"],\n", + " hidden_layer_size1 = param[\"hidden_layer_size1\"],\n", + " hidden_layer_size2 = param[\"hidden_layer_size2\"]) \n", + "\n", + " model.fit(np.array(train_X[train_index]), np.array(train_Y[train_index]),\n", + " epochs = param[\"num_epochs\"],\n", + " batch_size = param[\"batch_size\"],\n", + " verbose=0)\n", + "\n", + " R2.append(r2_score( np.reshape(train_Y[test_index], (-1)),\n", + " model.predict(np.array(train_X[test_index])).reshape(-1) ))\n", + " return(-np.mean(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "'''space = {\"learning_rate\": hp.uniform(\"learning_rate\", 1e-6, 1e-2),\n", + " \"hidden_layer_size1\": hp.choice(\"hidden_layer_size1\", [256,128,64]),\n", + " \"hidden_layer_size2\": hp.choice(\"hidden_layer_size2\", [128,64,32]),\n", + " \"batch_size\": hp.choice(\"batch_size\", [8,16,32,64,96]),\n", + " \"decay\": hp.uniform(\"decay\", 1e-9, 1e-5),\n", + " \"l2_parameter\": hp.uniform(\"l2_parameter\", 0, 0.01),\n", + " \"momentum\": hp.uniform(\"momentum\", 0.1, 1),\n", + " \"num_epochs\": hp.uniform(\"num_epochs\", 20, 100)}\n", + " \n", + "trials = Trials()\n", + "best = fmin(fn = cross_validation_neg_r2_fcnn, space = space, algo=rand.suggest, max_evals= 500, trials=trials)''';" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "param = {'batch_size': 96,\n", + " 'decay': 8.925865617547346e-06,\n", + " 'hidden_layer_size1': 128,\n", + " 'hidden_layer_size2': 64,\n", + " 'l2_parameter': 0.0033008915899278156,\n", + " 'learning_rate': 0.006808549614442447,\n", + " 'momentum': 0.9054104435951468,\n", + " 'num_epochs': 62.68663708309369}" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "36/36 [==============================] - 1s 6ms/step - loss: 2.0003 - mse: 0.9252\n", + "Epoch 2/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.6849 - mse: 0.6270\n", + "Epoch 3/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.5191 - mse: 0.4923\n", + "Epoch 4/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.3920 - mse: 0.3958\n", + "Epoch 5/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.3207 - mse: 0.3543\n", + "Epoch 6/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.2364 - mse: 0.2991\n", + "Epoch 7/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.1669 - mse: 0.2581\n", + "Epoch 8/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.1248 - mse: 0.2438\n", + "Epoch 9/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.0808 - mse: 0.2267\n", + "Epoch 10/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 1.0345 - mse: 0.2062\n", + "Epoch 11/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.9915 - mse: 0.1887\n", + "Epoch 12/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.9617 - mse: 0.1836\n", + "Epoch 13/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.9230 - mse: 0.1688\n", + "Epoch 14/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8986 - mse: 0.1674\n", + "Epoch 15/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8757 - mse: 0.1667\n", + "Epoch 16/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8451 - mse: 0.1576\n", + "Epoch 17/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.8030 - mse: 0.1366\n", + "Epoch 18/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7789 - mse: 0.1331\n", + "Epoch 19/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7582 - mse: 0.1323\n", + "Epoch 20/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7438 - mse: 0.1367\n", + "Epoch 21/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.7180 - mse: 0.1293\n", + "Epoch 22/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6865 - mse: 0.1157\n", + "Epoch 23/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6692 - mse: 0.1160\n", + "Epoch 24/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6430 - mse: 0.1068\n", + "Epoch 25/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6295 - mse: 0.1097\n", + "Epoch 26/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6212 - mse: 0.1172\n", + "Epoch 27/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.6002 - mse: 0.1114\n", + "Epoch 28/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5760 - mse: 0.1018\n", + "Epoch 29/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5613 - mse: 0.1014\n", + "Epoch 30/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5434 - mse: 0.0974\n", + "Epoch 31/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5324 - mse: 0.0997\n", + "Epoch 32/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.5300 - mse: 0.1103\n", + "Epoch 33/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4996 - mse: 0.0924\n", + "Epoch 34/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4970 - mse: 0.1019\n", + "Epoch 35/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4783 - mse: 0.0948\n", + "Epoch 36/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4584 - mse: 0.0863\n", + "Epoch 37/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4553 - mse: 0.0943\n", + "Epoch 38/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4456 - mse: 0.0950\n", + "Epoch 39/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4338 - mse: 0.0934\n", + "Epoch 40/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4173 - mse: 0.0868\n", + "Epoch 41/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.4133 - mse: 0.0924\n", + "Epoch 42/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3981 - mse: 0.0865\n", + "Epoch 43/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3945 - mse: 0.0917\n", + "Epoch 44/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3867 - mse: 0.0925\n", + "Epoch 45/50\n", + "36/36 [==============================] - 0s 5ms/step - loss: 0.3825 - mse: 0.0965\n", + "Epoch 46/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3695 - mse: 0.0916\n", + "Epoch 47/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3576 - mse: 0.0874\n", + "Epoch 48/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3501 - mse: 0.0874\n", + "Epoch 49/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3387 - mse: 0.0833\n", + "Epoch 50/50\n", + "36/36 [==============================] - 0s 6ms/step - loss: 0.3314 - mse: 0.0830\n", + "27/27 [==============================] - 0s 1ms/step\n" + ] + }, + { + "data": { + "text/plain": [ + "0.3237192981547061" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model = build_model(input_dim = 1280+2048, \n", + " learning_rate = param[\"learning_rate\"],\n", + " decay = param[\"decay\"],\n", + " momentum = param[\"momentum\"], \n", + " l2_parameter = param[\"l2_parameter\"], \n", + " hidden_layer_size1 = param[\"hidden_layer_size1\"],\n", + " hidden_layer_size2 = param[\"hidden_layer_size2\"]) \n", + "\n", + "model.fit(np.array(train_X), np.array(train_Y),\n", + " epochs = 50,# int(np.round(param[\"num_epochs\"])),\n", + " batch_size = param[\"batch_size\"],\n", + " verbose=1)\n", + "\n", + "y_test_pred = model.predict(np.array(test_X))\n", + "r2_score(test_Y, y_test_pred.reshape(-1))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.593 0.969 0.324\n" + ] + } + ], + "source": [ + "y_test_pred = (y_test_pred.reshape(-1) + mean_y)*std_y\n", + "test_Y = (test_Y + mean_y)*std_y\n", + "\n", + "MSE_dif_fp_test = np.mean(abs(np.reshape(test_Y, (-1)) - y_test_pred.reshape(-1))**2)\n", + "R2_dif_fp_test = r2_score(np.reshape(test_Y, (-1)), y_test_pred.reshape(-1))\n", + "Pearson = stats.pearsonr(np.reshape(test_Y, (-1)), y_test_pred.reshape(-1))\n", + "\n", + "print(np.round(Pearson[0],3) ,np.round(MSE_dif_fp_test,3), np.round(R2_dif_fp_test,3))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/model_fitting/04 - Analyzing and plotting the results.ipynb b/code/model_fitting/04 - Analyzing and plotting the results.ipynb new file mode 100644 index 0000000..a0c954b --- /dev/null +++ b/code/model_fitting/04 - Analyzing and plotting the results.ipynb @@ -0,0 +1,2603 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "Bad key text.latex.preview in file CCB_plot_style_0v4.mplstyle, line 55 ('text.latex.preview : False')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n", + "\n", + "Bad key mathtext.fallback_to_cm in file CCB_plot_style_0v4.mplstyle, line 63 ('mathtext.fallback_to_cm : True ## When True, use symbols from the Computer Modern fonts')\n", + "You probably need to get an updated matplotlibrc file from\n", + "https://github.com/matplotlib/matplotlib/blob/v3.5.3/matplotlibrc.template\n", + "or from the matplotlib source distribution\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C:\\Users\\alexk\\projects\\GitHub\\kcat_prediction\\code\\model_fitting\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", + "from sklearn import metrics\n", + "from scipy import stats\n", + "from scipy.stats import wilcoxon, mannwhitneyu\n", + "from sklearn.metrics import roc_auc_score, r2_score\n", + "from sklearn.linear_model import LinearRegression\n", + "import scipy\n", + "import os\n", + "from os.path import join\n", + "import pandas as pd\n", + "\n", + "CURRENT_DIR = os.getcwd()\n", + "print(CURRENT_DIR)\n", + "\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "plt.style.use('CCB_plot_style_0v4.mplstyle');\n", + "c_styles = mpl.rcParams['axes.prop_cycle'].by_key()['color'] # fetch the defined color styles\n", + "high_contrast = ['#004488', '#DDAA33', '#BB5566', '#000000']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Plotting performance of different models:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (a) Pearson r" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "models = [\"str_fp\", \"diff_fp\", \"DRFP\", \"ESM1b\", \"ESM1b_ts\", \"ESM1b_ts_DRFP\", \"ESM1b_ts_DRFP_mean\"]\n", + "model_names = {\"str_fp\" : \"str. FP\",\n", + " \"diff_fp\" : \"diff. FP\",\n", + " \"ESM1b\" : \"ESM-1b\",\n", + " \"DRFP\" : \"DRFP\",\n", + " \"ESM1b_ts\" : \"ESM-$1b_{ESP}$\",\n", + " \"ESM1b_ts_diff_fp\" : \"ESM-$1b_{ESP}$\\n + diff. FP\",\n", + " \"ESM1b_ts_DRFP\": \"ESM-$1b_{ESP}$\\n + DRFP\",\n", + " \"ESM1b_ts_DRFP_mean\": \"ESM-$1b_{ESP}$\\n + DRFP (mean)\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "\n", + "\n", + "plt.rcParams.update({'font.size': 28})\n", + "plt.ylim(-0.01, 1)\n", + "plt.xlim(0.5, len(models) + 0.5)\n", + "\n", + "labs = [model_names[model] for model in models]\n", + "Boxplots = []\n", + "ticks = []\n", + "for i, model in enumerate(models):\n", + " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", + " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", + " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", + " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + " Pearson_test = stats.pearsonr(test_y, pred_y)[0]\n", + " \n", + " if i == 0:\n", + " plt.scatter(i+1, Pearson_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", + " else:\n", + " plt.scatter(i+1, Pearson_test, c='darkblue', marker=\"o\", linewidths= 8)\n", + " \n", + " Boxplots.append(Pearson_CV)\n", + " ticks.append(i+1)\n", + "\n", + " \n", + "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", + " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", + " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", + "\n", + "\n", + "\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=8)\n", + "\n", + "ticks1 = ticks\n", + "ax.set_xticks(ticks1)\n", + "ax.set_xticklabels([])\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "#ax.locator_params(axis=\"y\", nbins=4)\n", + "\n", + "\n", + "ticks2 = list(np.array(ticks)-0.01)\n", + "\n", + "ax.set_xticks(ticks2, minor=True)\n", + "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", + "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", + "#ax.yaxis.set_major_locator(loc)\n", + "\n", + "plt.ylabel(\"Pearson r\")\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "#plt.legend(loc = \"upper right\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (b) MSE" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "plt.ylim(0.5, 1.4)\n", + "plt.xlim(0.5, len(models) + 0.5)\n", + "\n", + "labs = [model_names[model] for model in models]\n", + "Boxplots = []\n", + "ticks = []\n", + "for i, model in enumerate(models):\n", + " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", + " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", + " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", + " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + " MSE_test = np.mean(abs(test_y - pred_y)**2)\n", + "\n", + " \n", + " if i == 0:\n", + " plt.scatter(i+1, MSE_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", + " else:\n", + " plt.scatter(i+1, MSE_test, c='darkblue', marker=\"o\", linewidths= 8)\n", + " \n", + " Boxplots.append(MSE_CV)\n", + " ticks.append(i+1)\n", + "\n", + " \n", + "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", + " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", + " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", + "\n", + "\n", + "\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=8)\n", + "\n", + "ticks1 = ticks\n", + "ax.set_xticks(ticks1)\n", + "ax.set_xticklabels([])\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "#ax.locator_params(axis=\"y\", nbins=4)\n", + "\n", + "\n", + "ticks2 = list(np.array(ticks)-0.01)\n", + "\n", + "ax.set_xticks(ticks2, minor=True)\n", + "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", + "#loc = plticker.MultipleLocator(base=0.02) # this locator puts ticks at regular intervals\n", + "#ax.yaxis.set_major_locator(loc)\n", + "\n", + "plt.ylabel(\"Mean squared error\")\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", + "#plt.legend()\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_MSE.svg\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_MSE.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (c) Coefficients of determination" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "plt.ylim(-0.01, 0.5)\n", + "plt.xlim(0.5, len(models) + 0.5)\n", + "\n", + "labs = [model_names[model] for model in models]\n", + "Boxplots = []\n", + "ticks = []\n", + "for i, model in enumerate(models):\n", + " Pearson_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"Pearson_CV_xgboost_\" + model + \".npy\"))\n", + " MSE_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"MSE_CV_xgboost_\" + model + \".npy\"))\n", + " R2_CV = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"R2_CV_xgboost_\" + model + \".npy\"))\n", + " pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + " test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + " R2_test = r2_score(test_y, pred_y)\n", + "\n", + " \n", + " if i == 0:\n", + " plt.scatter(i+1, R2_test, c='darkblue', marker=\"o\", linewidths= 8, label = \"test set\")\n", + " else:\n", + " plt.scatter(i+1, R2_test, c='darkblue', marker=\"o\", linewidths= 8)\n", + " \n", + " Boxplots.append(R2_CV)\n", + " ticks.append(i+1)\n", + "\n", + " \n", + "plt.boxplot(Boxplots, positions=ticks, widths=0.6,\n", + " medianprops={\"linewidth\": 2,\"solid_capstyle\": \"butt\", \"c\" : \"darkred\"},\n", + " boxprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " whiskerprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"},\n", + " capprops= {\"linewidth\": 1.5, \"solid_capstyle\": \"butt\"})\n", + "\n", + "\n", + "\n", + "ax.locator_params(axis=\"y\", nbins=8)\n", + "\n", + "ticks1 = ticks\n", + "ax.set_xticks(ticks1)\n", + "ax.set_xticklabels([])\n", + "ax.tick_params(axis='x', which=\"major\", length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "#ax.locator_params(axis=\"y\", nbins=4)\n", + "\n", + "\n", + "ticks2 = list(np.array(ticks)-0.01)\n", + "\n", + "ax.set_xticks(ticks2, minor=True)\n", + "ax.set_xticklabels(labs, minor=True, y= -0.03, fontsize = 22)\n", + "ax.tick_params(axis='x', which=\"minor\",length=0, rotation = 60)\n", + "\n", + "plt.ylabel(\"Coefficient of determination\")\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", + "\n", + "leg = plt.legend(loc = \"upper left\", frameon=True)\n", + "leg.get_frame().set_linewidth(3.0)\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_R2.svg\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"boxplots_R2.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (d) Statistical tests" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['str_fp',\n", + " 'diff_fp',\n", + " 'DRFP',\n", + " 'ESM1b',\n", + " 'ESM1b_ts',\n", + " 'ESM1b_ts_DRFP',\n", + " 'ESM1b_ts_DRFP_mean']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "models" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[0] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[0] + \".npy\"))\n", + "errors_str_fp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[1] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[1] + \".npy\"))\n", + "errors_diff_fp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[2] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[2] + \".npy\"))\n", + "errors_drfp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[3] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[3] + \".npy\"))\n", + "errors_esm1b = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[4] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[4] + \".npy\"))\n", + "errors_esm1b_ts = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[5] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[5] + \".npy\"))\n", + "errors_esm1b_drfp = abs(pred_y-test_y)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + models[6] + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + models[6] + \".npy\"))\n", + "errors_esm1b_drfp_mean = abs(pred_y-test_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Difference between predictions with ESM1b/DRFP(mean) and ESM1b_ts 1.177364363105206e-07\n", + "Difference between predictions with ESM1b/DRFP(mean) and ESM1b 0.0015088024807966155\n", + "Difference between predictions with ESM1b/DRFP(mean) and DRFP 0.004861912227347217\n", + "Difference between predictions with ESM1b_ts and ESM1b 0.40887357002408387\n", + "Difference between predictions with DRFP and str.fp (two-sided) 0.0002605996952975724\n", + "Difference between predictions with DRFP and diff.fp (two-sided) 0.06363682244584662\n" + ] + } + ], + "source": [ + "d = errors_esm1b_drfp_mean - errors_esm1b_ts\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b/DRFP(mean) and ESM1b_ts\", p)\n", + "\n", + "d = errors_esm1b_drfp_mean - errors_esm1b\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b/DRFP(mean) and ESM1b\", p)\n", + "\n", + "d = errors_esm1b_drfp_mean - errors_drfp\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b/DRFP(mean) and DRFP\", p)\n", + "\n", + "d = errors_esm1b_ts - errors_esm1b\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(\"Difference between predictions with ESM1b_ts and ESM1b\", p)\n", + "\n", + "d = errors_drfp- errors_str_fp\n", + "w, p = wilcoxon(d, alternative='two-sided')\n", + "print(\"Difference between predictions with DRFP and str.fp (two-sided)\", p)\n", + "\n", + "d = errors_drfp- errors_diff_fp\n", + "w, p = wilcoxon(d, alternative='two-sided')\n", + "print(\"Difference between predictions with DRFP and diff.fp (two-sided)\", p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Plotting predictions versus experimental values:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading predictions for the best model (ESM1b/diff. fp)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "model = \"ESM1b_ts_DRFP_mean\"\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_test[\"n_values\"] = [len(data_test[\"kcat_values\"][ind]) for ind in data_test.index]\n", + "n_values = np.array([len(data_test[\"kcat_values\"][ind]) for ind in data_test.index])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6850700979619714, 4.842505224693218)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(abs(pred_y-test_y)), 10**np.mean(abs(pred_y-test_y))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(0.06823529411764706, 0.027777777777777776)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "\n", + "\n", + "plt.ylim(ymax = 5.1, ymin = -3.5)\n", + "plt.xlim(xmax = 5.1, xmin = -3.5)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "\n", + "\n", + "reg = LinearRegression().fit(test_y.reshape(-1,1), pred_y.reshape(-1,1),)\n", + "reg.score(test_y.reshape(-1,1), pred_y.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([-3.5,5.1], [beta0 + -3.5*beta1, beta0 + 5.1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "\n", + "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "\n", + "for i in range(len(test_y)):\n", + " if n_values[i] <= 2:\n", + " plt.scatter(test_y[i], pred_y[i], alpha = 0.6, s=30, c=\"navy\")\n", + " else:\n", + " plt.scatter(test_y[i], pred_y[i], alpha = 0.9, s=30, c=\"darkorange\")\n", + " \n", + "\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"scatter_plot.eps\"))\n", + "plt.show()\n", + "np.mean(n_values > 2 ), np.mean(n_values[test_y < 1e-1] > 2)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "\n", + "\n", + "plt.ylim(ymax = 5.1, ymin = -3.5)\n", + "plt.xlim(xmax = 5.1, xmin = -3.5)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "ax.text(1.9, -2, r'$R^2=0.44$', fontsize=22, c = \"grey\")\n", + "ax.text(1.9, -2.4, r'$MSE=0.81$', fontsize=22, c = \"grey\")\n", + "ax.text(1.9, -2.8, r'Pearson $r=0.67$', fontsize=22, c = \"grey\")\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "\n", + "\n", + "reg = LinearRegression().fit(test_y.reshape(-1,1), pred_y.reshape(-1,1),)\n", + "reg.score(test_y.reshape(-1,1), pred_y.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([-3.5,4.9], [-3.5,4.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([-3.5,5.1], [beta0 + -3.5*beta1, beta0 + 5.1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "\n", + "plt.ylabel(\"Predicted $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.xlabel(\"Empirical mean of measured $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "plt.scatter(test_y, pred_y, alpha = 0.6, s=30, c=\"darkblue\")\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"scatter_plot.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Comparison to the results of the DLkcat model" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "model = \"ESM1b_ts_DRFP_mean\"\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_test[\"y_true\"] = test_y\n", + "data_test[\"y_pred\"] = pred_y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (a) First, we need to calculate the maximal sequence identity for all proteins in the test set compared to all proteins in the training set:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (a)(i) Creating a fasta file for every sequence in the training set and for every sequence in the test set:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "'''for ind in data_test.index:\n", + " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", + " \"test_seq_\" + str(ind) + \".fasta\"), \"w\")\n", + " ofile.write(\"> seq_test_\" + str(ind) + \"\\n\" + data_test[\"Sequence\"][ind] + \"\\n\")\n", + " ofile.close()\n", + " \n", + " \n", + "train_sequences = list(set(data_train[\"Sequence\"]))\n", + "for ind, seq in enumerate(train_sequences):\n", + " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", + " \"train_seq_\" + str(ind) + \".fasta\"), \"w\")\n", + " ofile.write(\"> seq_train_\" + str(ind) + \"\\n\" + seq + \"\\n\")\n", + " ofile.close()''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (a)(ii) Calculating the maximal pairwise sequence identities (Calculations were done on a HPC):" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "'''from Bio.Emboss.Applications import NeedleCommandline\n", + "import os\n", + "from os.path import join\n", + "import pandas as pd\n", + "import sys\n", + "import time\n", + "import numpy as np\n", + "\n", + "\n", + "arg = int(sys.argv[1])\n", + "\n", + "CURRENT_DIR = join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\")\n", + " \n", + "def calculate_identity(fasta_file_1, fasta_file_2):\n", + " needle_cline = NeedleCommandline(asequence = fasta_file_1, bsequence = fasta_file_2,\n", + " gapopen=10, gapextend=0.5, filter = True)\n", + "\n", + " out = needle_cline()[0]\n", + " out = out[out.find(\"Identity\"):]\n", + " out = out[:out.find(\"\\n\")]\n", + " percent = float(out[out.find(\"(\")+1 :out.find(\")\")-1].replace(\" \", \"\"))\n", + " return(percent)\n", + "\n", + "\n", + "identities = []\n", + "for i in range(len(data_test)):\n", + " ident = calculate_identity(fasta_file_1 = join(CURRENT_DIR, \"test_seq_\" + str(arg) + \".fasta\"),\n", + " fasta_file_2 = join(CURRENT_DIR, \"train_seq_\" + str(i) + \".fasta\"))\n", + " identities.append(ident)\n", + "\n", + "\n", + "ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(arg) + \".txt\"), \"w\")\n", + "ofile.write(str(max(identities)))\n", + "ofile.close()''';" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (a)(iii) Mapping the results to the test DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...ESM1bESM1b_tsgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECDRFPy_truey_predmax_ident
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1......[0.020693962, 0.16804111, 0.0377352, 0.1768811...[0.83155197, 0.08632717, -0.42143562, 0.419359...2.3404440.6656531.0000000.114660[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...2.3404440.78154420.8
1Reaction_3629Sequence_3212[0.92][Q0PC20][1][0][0][False]MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL...{InChI=1S/H2O/h1H2, InChI=1S/C10H13N5O3/c1-4-6......[0.07429815, 0.14984865, -0.08539086, 0.098546...[0.13206507, -0.10826899, -0.31126085, 0.95038...-0.0362120.3407411.0000000.090196[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...-0.0362120.53721435.3
2Reaction_375Sequence_26[21.0][Q0GYU4][0][1][0][False]MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL...{InChI=1S/C4H8O2/c1-3(5)4(2)6/h3,5H,1-2H3, InC......[-0.0272103, 0.2500836, 0.08181338, 0.03990136...[0.3617253, 0.8765441, -1.0668296, 1.5401511, ...1.3222190.1750000.1478871.000000[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ...1.3222190.92722540.1
3Reaction_4312Sequence_3788[4.4][Q8ZNC4][0][0][1][False]MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT...{InChI=1S/p+1, InChI=1S/C6H14N2O2/c7-4-2-1-3-5......[0.079942256, 0.23130149, -0.012637342, 0.0787...[0.7798445, -0.7589981, -0.2779501, 0.2643281,...0.6434531.0000001.0000001.000000[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.6434530.95992925.9
4Reaction_2115Sequence_712[4.5][P53602][1][0][0][False]MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD...{InChI=1S/C6H14O10P2/c1-6(9,4-5(7)8)2-3-15-18(......[0.086191244, 0.21010432, 0.1960825, -0.041225...[-0.6100984, -0.054886594, -0.09893316, 0.2822...0.6532131.0000000.8490570.112500[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0.6532130.93309849.3
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5 rows × 28 columns

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" + ], + "text/plain": [ + " Reaction ID Sequence ID kcat_values Uniprot IDs from_BRENDA \\\n", + "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] [1] \n", + "1 Reaction_3629 Sequence_3212 [0.92] [Q0PC20] [1] \n", + "2 Reaction_375 Sequence_26 [21.0] [Q0GYU4] [0] \n", + "3 Reaction_4312 Sequence_3788 [4.4] [Q8ZNC4] [0] \n", + "4 Reaction_2115 Sequence_712 [4.5] [P53602] [1] \n", + "\n", + " from_Sabio from_Uniprot checked \\\n", + "0 [0] [0] [False] \n", + "1 [0] [0] [False] \n", + "2 [1] [0] [False] \n", + "3 [0] [1] [False] \n", + "4 [0] [0] [False] \n", + "\n", + " Sequence \\\n", + "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", + "1 MMKIAILGAMSEEITPLLETLKDYTKIEHANNTYYFAKYKNHELVL... \n", + "2 MASKTYTLNTGAKIPAVGFGTFANEGAKGETYAAVTKALDVGYRHL... \n", + "3 MTDSIMQNYNQLREQVINGDRRFQHKDGHLCFEGVDLDALARQYPT... \n", + "4 MASEKPLAAVTCTAPVNIAVIKYWGKRDEELVLPINSSLSVTLHQD... \n", + "\n", + " substrates ... \\\n", + "0 {InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1... ... \n", + "1 {InChI=1S/H2O/h1H2, InChI=1S/C10H13N5O3/c1-4-6... ... \n", + "2 {InChI=1S/C4H8O2/c1-3(5)4(2)6/h3,5H,1-2H3, InC... ... \n", + "3 {InChI=1S/p+1, InChI=1S/C6H14N2O2/c7-4-2-1-3-5... ... \n", + "4 {InChI=1S/C6H14O10P2/c1-6(9,4-5(7)8)2-3-15-18(... ... \n", + "\n", + " ESM1b \\\n", + "0 [0.020693962, 0.16804111, 0.0377352, 0.1768811... \n", + "1 [0.07429815, 0.14984865, -0.08539086, 0.098546... \n", + "2 [-0.0272103, 0.2500836, 0.08181338, 0.03990136... \n", + "3 [0.079942256, 0.23130149, -0.012637342, 0.0787... \n", + "4 [0.086191244, 0.21010432, 0.1960825, -0.041225... \n", + "\n", + " ESM1b_ts geomean_kcat \\\n", + "0 [0.83155197, 0.08632717, -0.42143562, 0.419359... 2.340444 \n", + "1 [0.13206507, -0.10826899, -0.31126085, 0.95038... -0.036212 \n", + "2 [0.3617253, 0.8765441, -1.0668296, 1.5401511, ... 1.322219 \n", + "3 [0.7798445, -0.7589981, -0.2779501, 0.2643281,... 0.643453 \n", + "4 [-0.6100984, -0.054886594, -0.09893316, 0.2822... 0.653213 \n", + "\n", + " frac_of_max_UID frac_of_max_RID frac_of_max_EC \\\n", + "0 0.665653 1.000000 0.114660 \n", + "1 0.340741 1.000000 0.090196 \n", + "2 0.175000 0.147887 1.000000 \n", + "3 1.000000 1.000000 1.000000 \n", + "4 1.000000 0.849057 0.112500 \n", + "\n", + " DRFP y_true y_pred \\\n", + "0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 2.340444 0.781544 \n", + "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... -0.036212 0.537214 \n", + "2 [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, ... 1.322219 0.927225 \n", + "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.643453 0.959929 \n", + "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0.653213 0.933098 \n", + "\n", + " max_ident \n", + "0 20.8 \n", + "1 35.3 \n", + "2 40.1 \n", + "3 25.9 \n", + "4 49.3 \n", + "\n", + "[5 rows x 28 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_test[\"max_ident\"] = np.nan\n", + "\n", + "for ind in data_test.index:\n", + " try:\n", + " with open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"kcat_ident\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", + " ident = f.readlines()\n", + " ident = float(ident[0])\n", + " \n", + " \n", + " data_test[\"max_ident\"][ind] = ident\n", + " except FileNotFoundError:\n", + " pass\n", + "data_test.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Using kcat values from the most similar enzymes from the training set as predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Reaction IDSequence IDkcat_valuesUniprot IDsfrom_BRENDAfrom_Sabiofrom_UniprotcheckedSequencesubstrates...ESM1b_tsgeomean_kcatfrac_of_max_UIDfrac_of_max_RIDfrac_of_max_ECDRFPy_truey_predmax_identsim_pred
0Reaction_3207Sequence_2150[219][B9W4V6][1][0][0][False]MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG...{InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1......[0.83155197, 0.08632717, -0.42143562, 0.419359...2.3404440.6656531.00.11466[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...2.3404440.78154420.82.024332
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1 rows × 29 columns

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" + ], + "text/plain": [ + " Reaction ID Sequence ID kcat_values Uniprot IDs from_BRENDA \\\n", + "0 Reaction_3207 Sequence_2150 [219] [B9W4V6] [1] \n", + "\n", + " from_Sabio from_Uniprot checked \\\n", + "0 [0] [0] [False] \n", + "\n", + " Sequence \\\n", + "0 MKYFPLFPTLVFAARVVAFPAYASLAGLSQQELDAIIPTLEAREPG... \n", + "\n", + " substrates ... \\\n", + "0 {InChI=1S/C7H5NO4/c9-8(10)5-1-2-6-7(3-5)12-4-1... ... \n", + "\n", + " ESM1b_ts geomean_kcat \\\n", + "0 [0.83155197, 0.08632717, -0.42143562, 0.419359... 2.340444 \n", + "\n", + " frac_of_max_UID frac_of_max_RID frac_of_max_EC \\\n", + "0 0.665653 1.0 0.11466 \n", + "\n", + " DRFP y_true y_pred \\\n", + "0 [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 2.340444 0.781544 \n", + "\n", + " max_ident sim_pred \n", + "0 20.8 2.024332 \n", + "\n", + "[1 rows x 29 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def get_train_seq(ind):\n", + " ofile = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"fasta_files\", \n", + " \"train_seq_\" + str(ind) + \".fasta\"), \"r\")\n", + " return(ofile.readlines()[1].replace(\"\\n\", \"\"))\n", + "\n", + "data_test[\"sim_pred\"] = np.nan\n", + "\n", + "for ind in data_test.index:\n", + " try:\n", + " with open(join(\"..\", \"..\", \"data\",\"enzyme_data\", \"kcat_similar\", \"test_seq\" + str(ind) + \".txt\")) as f:\n", + " ident = f.readlines()\n", + " indices = ident[0].split(\" \")\n", + " indices = [int(float(k)) for k in indices[1:]]\n", + " \n", + " kcats = []\n", + " Sequences = [get_train_seq(k) for k in indices]\n", + " for seq in Sequences:\n", + " kcats = kcats + list(data_train[\"geomean_kcat\"].loc[data_train[\"Sequence\"] == seq])\n", + " \n", + " data_test[\"sim_pred\"][ind] = np.mean(kcats[:3])\n", + " except:\n", + " pass\n", + "data_test.head(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "data_test = data_test.loc[~pd.isnull(data_test[\"sim_pred\"])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### (b) Comparing the results with predictions from the DLkcat paper:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (b)(i) Loading results from DLkcat paper" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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y_truey_predSequencemax_identsim_predsim_pred_1sim_pred_3
0-2.207608-0.071899MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI...22.8-1.486273-2.275724-1.486273
1-3.657577-2.707640MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA...100.0-2.369079-2.221849-2.369079
20.9493900.831021MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG...100.00.9466181.2304490.455934
31.6720981.513026MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS...100.01.0455791.6720981.045579
4-1.790485-2.830310MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW...99.4-1.7331130.995635-1.733113
\n", + "
" + ], + "text/plain": [ + " y_true y_pred Sequence \\\n", + "0 -2.207608 -0.071899 MSAIDCIITAAGLSSRMGQWKMMLPWEQGTILDTSIKNALQFCSRI... \n", + "1 -3.657577 -2.707640 MKEFYLTVEQIGDSIFERYIDSNGRERTREVEYKPSLFAHCPESQA... \n", + "2 0.949390 0.831021 MSPSKMNATVGSTSEVEQKIRQELALSDEVTTIRRNAPAAVLYEDG... \n", + "3 1.672098 1.513026 MKNVGFIGWRGMVGSVLMQRMVEERDFDAIRPVFFSTSQLGQAAPS... \n", + "4 -1.790485 -2.830310 MATSTETISSLAQPFVHLENPINSPLVKETIRPRNDTTITPPPTQW... \n", + "\n", + " max_ident sim_pred sim_pred_1 sim_pred_3 \n", + "0 22.8 -1.486273 -2.275724 -1.486273 \n", + "1 100.0 -2.369079 -2.221849 -2.369079 \n", + "2 100.0 0.946618 1.230449 0.455934 \n", + "3 100.0 1.045579 1.672098 1.045579 \n", + "4 99.4 -1.733113 0.995635 -1.733113 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_test_DLkcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"DLkcat\", \"df_pred.pkl\"))\n", + "data_test_DLkcat.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.45244626585841485\n", + "0.44447253110852536\n", + "0.003277284826373785\n", + "0.00655456965274757\n" + ] + } + ], + "source": [ + "help_df =data_test_DLkcat\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"sim_pred\"])\n", + "abs_error_sim = abs(y_true - y_pred)\n", + "R2_sim = r2_score(y_true, y_pred)\n", + "print(R2_sim)\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"y_pred\"])\n", + "abs_error = abs(y_true - y_pred)\n", + "R2 = r2_score(y_true, y_pred)\n", + "print(R2)\n", + "\n", + "d = abs_error- abs_error_sim\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(p)\n", + "\n", + "d = abs_error- abs_error_sim\n", + "w, p = wilcoxon(d, alternative='two-sided')\n", + "print(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### (b)(ii) Plotting performances for different sequence identities:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.239011719049261\n", + "0.43631116846784246\n", + "1.1460586039980551e-05\n" + ] + } + ], + "source": [ + "help_df =data_test\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"sim_pred\"])\n", + "abs_error_sim = abs(y_true - y_pred)\n", + "R2_sim = r2_score(y_true, y_pred)\n", + "print(R2_sim)\n", + "\n", + "y_true = np.array(help_df[\"y_true\"])\n", + "y_pred = np.array(help_df[\"y_pred\"])\n", + "abs_error = abs(y_true - y_pred)\n", + "R2 = r2_score(y_true, y_pred)\n", + "print(R2)\n", + "\n", + "d = abs_error- abs_error_sim\n", + "w, p = wilcoxon(d, alternative='less')\n", + "print(p)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0-40% TurNuP: R2:0.3282062960029778, Similarity method: 0.016279639392235312, p = 3.3778532715996525e-09 \n", + "0-40% DLKcat: R2:-0.6072304105234347, Similarity method: 0.10837547424294935, p = 0.9957653015502506, p(two-sided) = 0.008469396899498782\n", + "40-80% TurNuP: R2:0.5293866492629986, Similarity method: 0.4960913599652834, p = 0.5531581331990386 \n", + "40-80% DLKcat: R2:0.34280134977895493, Similarity method: 0.1800891304717046, p = 0.5174532691949906, p(two-sided) = 0.9650934616100189\n", + "80-99% TurNuP: R2:0.688850177486657, Similarity method: 0.7281503207725486, p = 0.9851054666995749 \n", + "80-99% DLKcat: R2:0.48622435213243465, Similarity method: 0.3392639181110536, p = 0.8129782958108247, p(two-sided) = 0.3740434083783506\n", + "99-100% TurNuP: R2:0.6749766930471568, Similarity method: 0.20645953288274277, p = 0.0023424625396728516 \n", + "99-100% DLKcat: R2:0.5128517542754034, Similarity method: 0.48197442980722505, p = 6.379793252512187e-05, p(two-sided) = 0.00012759586505024374\n" + ] + } + ], + "source": [ + "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", + "lower_bounds = [0,40,80,99]\n", + "upper_bounds = [40,80,99,100]\n", + "\n", + "points1 ,points1_sim = [], []\n", + "points2, points2_sim = [], []\n", + "n_points1, n_points2 = [], []\n", + "n_points1_sim, n_points2_sim = [], []\n", + "\n", + "for i, split in enumerate(splits):\n", + "\n", + " lb, ub = lower_bounds[i], upper_bounds[i]\n", + " \n", + " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_kcat = len(y_pred)\n", + " R2 = r2_score(y_true, y_pred)\n", + " abs_error = abs(y_true - y_pred)\n", + " \n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_sim = len(y_pred)\n", + " R2_sim = r2_score(y_true, y_pred)\n", + " abs_error_sim = abs(y_true - y_pred)\n", + " \n", + " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_DLkcat = len(y_pred)\n", + " R2_DLkcat = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat = abs(y_true - y_pred)\n", + " \n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_DLkcat_sim = len(y_pred)\n", + " R2_DLkcat_sim = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat_sim = abs(y_true - y_pred)\n", + " \n", + " \n", + " \n", + " points1.append(R2)\n", + " points1_sim.append(R2_sim)\n", + " points2.append(R2_DLkcat)\n", + " points2_sim.append(R2_DLkcat_sim)\n", + " \n", + " n_points1.append(n_kcat)\n", + " n_points1_sim.append(n_kcat_sim)\n", + " n_points2.append(n_DLkcat)\n", + " n_points2_sim.append(n_kcat_DLkcat_sim)\n", + " \n", + " d = abs_error- abs_error_sim\n", + " w, p = wilcoxon(d, alternative='less')\n", + " \n", + " d_DLkcat = abs_error_DLkcat- abs_error_DLkcat_sim\n", + " w, p_DLkcat = wilcoxon(d_DLkcat, alternative='less')\n", + " w, p_DLkcat_two_sided = wilcoxon(d_DLkcat, alternative='two-sided')\n", + " \n", + " print(\"%s TurNuP: R2:%s, Similarity method: %s, p = %s \" % (split, R2, R2_sim, p))\n", + " print(\"%s DLKcat: R2:%s, Similarity method: %s, p = %s, p(two-sided) = %s\" % (split, R2_DLkcat, R2_DLkcat_sim, p_DLkcat, p_DLkcat_two_sided))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MannwhitneyuResult(statistic=338596.0, pvalue=0.012594610167587074)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs_error_turnup = abs(np.array(data_test[\"y_pred\"])- np.array(data_test[\"y_true\"]))\n", + "abs_error_sim = abs(np.array(data_test[\"sim_pred\"])- np.array(data_test[\"y_true\"]))\n", + "mannwhitneyu(abs_error_turnup, abs_error_sim, alternative=\"less\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0-40% 0.3282062960029778 0.016279639392235312 -0.6072304105234347 0.10837547424294935\n", + "40-80% 0.5293866492629986 0.4960913599652834 0.34280134977895493 0.1800891304717046\n", + "80-99% 0.688850177486657 0.7281503207725486 0.48622435213243465 0.3392639181110536\n", + "99-100% 0.6749766930471568 0.20645953288274277 0.5128517542754034 0.48197442980722505\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", + "lower_bounds = [0,40,80,99]\n", + "upper_bounds = [40,80,99,100]\n", + "\n", + "points1 ,points1_sim = [], []\n", + "points2, points2_sim = [], []\n", + "n_points1, n_points2 = [], []\n", + "n_points1_sim, n_points2_sim = [], []\n", + "\n", + "for i, split in enumerate(splits):\n", + "\n", + " lb, ub = lower_bounds[i], upper_bounds[i]\n", + " \n", + " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_kcat = len(y_pred)\n", + " R2 = r2_score(y_true, y_pred)\n", + " abs_error = abs(y_true - y_pred)\n", + " \n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_sim = len(y_pred)\n", + " R2_sim = r2_score(y_true, y_pred)\n", + " abs_error_sim = abs(y_true - y_pred)\n", + " \n", + " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_DLkcat = len(y_pred)\n", + " R2_DLkcat = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat = abs(y_true - y_pred)\n", + " \n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"sim_pred\"])\n", + " n_kcat_DLkcat_sim = len(y_pred)\n", + " R2_DLkcat_sim = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat_sim = abs(y_true - y_pred)\n", + " \n", + "\n", + " print(split, R2, R2_sim, R2_DLkcat, R2_DLkcat_sim)\n", + " points1.append(R2)\n", + " points1_sim.append(R2_sim)\n", + " points2.append(R2_DLkcat)\n", + " points2_sim.append(R2_DLkcat_sim)\n", + " \n", + " n_points1.append(n_kcat)\n", + " n_points1_sim.append(n_kcat_sim)\n", + " n_points2.append(n_DLkcat)\n", + " n_points2_sim.append(n_kcat_DLkcat_sim)\n", + "\n", + "\n", + "ticks2 = np.array(range(len(splits)))\n", + "labs = splits\n", + "ax.set_xticks(ticks2)\n", + "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", + "ax.tick_params(axis='x', length=0, rotation = 0)\n", + "\n", + "plt.ylim((-0.7,1))\n", + "plt.xlim((-0.2, 3.2))\n", + "plt.legend(loc = \"lower right\", fontsize=20)\n", + "plt.ylabel('Coefficient of determination R²')\n", + "plt.xlabel('Enzyme sequence identity')\n", + "ax.yaxis.set_label_coords(-0.15, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.13)\n", + "\n", + "plt.plot([-0.15,4], [0,0], color='grey', linestyle='dashed')\n", + "\n", + "\n", + "plt.plot([0,1,2,3], points1, c= \"black\", linewidth=2)\n", + "plt.plot([0,1,2,3], points2, c= \"orchid\", linewidth=2)\n", + "\n", + "for i, split in enumerate(splits):\n", + " points1.append(R2)\n", + " points2.append(R2_DLkcat)\n", + " \n", + " if i ==0:\n", + " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8, label =\"KCATpred\")\n", + " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8, label =\"DLkcat\")\n", + " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", + " ax.annotate(n_points2[i], (i+0.06, points2[i]-0.01), fontsize=17, c='orchid', weight = \"bold\")\n", + "\n", + " else:\n", + " plt.scatter(i, points1[i], c='black', marker=\"o\", linewidths= 8)\n", + " plt.scatter(i, points2[i], c='orchid', marker=\"o\", linewidths= 8)\n", + " ax.annotate(n_points1[i], (i-0.06, points1[i]+0.05), fontsize=17, c= \"black\", weight = \"bold\")\n", + " ax.annotate(n_points2[i], (i-0.04, points2[i]-0.10), fontsize=17, c='orchid', weight = \"bold\")\n", + " \n", + "\n", + "\n", + " \n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"comparison_DLKcat.svg\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"comparison_DLKcat.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Testing if differences in model performance is statistically significant using a one-sided Mann-Whitney U test" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0-40% MannwhitneyuResult(statistic=10717.0, pvalue=4.3070137916732266e-11)\n", + "40-80% MannwhitneyuResult(statistic=4127.0, pvalue=0.0017400394232513574)\n", + "80-99% MannwhitneyuResult(statistic=617.0, pvalue=0.0003661062666722406)\n", + "99-100% MannwhitneyuResult(statistic=13408.0, pvalue=0.048009706357389804)\n" + ] + } + ], + "source": [ + "\n", + "\n", + "splits = [\"0-40%\", \"40-80%\", \"80-99%\",\"99-100%\"]\n", + "lower_bounds = [0,40,80,99,0]\n", + "upper_bounds = [40,80,99,100,100]\n", + "\n", + "for i, split in enumerate(splits):\n", + "\n", + " lb, ub = lower_bounds[i], upper_bounds[i]\n", + " \n", + " help_df = data_test.loc[data_test[\"max_ident\"]>= lb].loc[data_test[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_kcat = len(y_pred)\n", + " R2 = r2_score(y_true, y_pred)\n", + " abs_error = abs(y_true - y_pred)\n", + " \n", + " help_df = data_test_DLkcat.loc[data_test_DLkcat[\"max_ident\"]>= lb].loc[data_test_DLkcat[\"max_ident\"]<= ub]\n", + " y_true = np.array(help_df[\"y_true\"])\n", + " y_pred = np.array(help_df[\"y_pred\"])\n", + " n_DLkcat = len(y_pred)\n", + " R2_DLkcat = r2_score(y_true, y_pred)\n", + " abs_error_DLkcat = abs(y_true - y_pred)\n", + " \n", + " res = mannwhitneyu(abs_error, abs_error_DLkcat, alternative=\"less\")\n", + " print(split, res)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predicting Proteom allocation" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (14,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "conditions = [\"$Petri_1$\", \"$Johan_1$\", \"$Johan_2$\", \"$Johan_3$\", \"$Johan_4$\",\n", + " \"$Joao_1$\", \"$Joao_2$\", \"$Joao_3$\", \"$Joao_4$\", \"$Joao_5$\", \"$Joao_6$\",\n", + " \"$Joao_7$\", \"$Joao_8$\", \"$Joao_9$\", \"$Joao_{10}$\", \"$Joao_{11}$\", \"$Tyler_1$\",\n", + " \"$Keiji_1$\",\"$Keiji_3$\", \"$Tyler_2$\", \"$Tyler_3$\"]\n", + "\n", + "\n", + "data = [[1.3958, 1.0951, 1.1511, 1.1255, 1.1380, 1.1731, 1.2599, 1.1834, 1.1232,\n", + " 1.2105, 1.1553, 1.2801, 1.1873, 1.2479, 1.1843, 1.1711, 1.3075, 1.3824, 1.1935,1.0210, 1.1693],\n", + " [ 1.3348, 1.0608, 0.9795, 1.1813, 1.1117, 1.0074, 1.0357, 0.9859, 0.9882,\n", + " 0.9457, 0.9748, 1.1171, 1.0519, 1.0512, 1.0897, 0.9635, 1.2411, 1.2818, 1.0986 ,1.0474, 1.0883]]\n", + "\n", + "X = np.arange(len(conditions))\n", + "ax = fig.add_axes([0,0,1,1])\n", + "\n", + "barWidth = 0.35\n", + "eps = 0.04\n", + "\n", + "plt.xticks([r + barWidth for r in range(len(data[0]))], conditions, rotation = 90, fontsize=26)\n", + "plt.yticks( fontsize=30)\n", + "\n", + "ax.bar(X + 0.00, np.array(data[0])**2, color = 'orchid', width = barWidth, label = \"DLKcat\")\n", + "ax.bar(X + barWidth + eps, np.array(data[1])**2, color = 'black', width = barWidth, label = \"KCATpred\")\n", + "\n", + "plt.ylabel('Mean squared error', fontsize=30)\n", + "plt.legend(loc = \"upper center\", ncol = 2, fontsize=26)\n", + "plt.ylim((0,2.2))\n", + "\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"comparison_DLKCcat_proteome.svg\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting orginal kcat values and log10-transformed kcat values" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"final_kcat_dataset.pkl\"))\n", + "kcat_values = 10**np.array(df_kcat[\"geomean_kcat\"])\n", + "log10_kcat_values = np.array(df_kcat[\"geomean_kcat\"])\n", + "\n", + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "plt.ylim(ymax = 699, ymin = 0)\n", + "plt.xlim(xmax = 5.1, xmin = -3)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "#plt.yticks([-2,0,2,4], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\"])\n", + "\n", + "plt.ylabel(\"Count\", fontsize = 22)\n", + "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.hist(log10_kcat_values, alpha = 0.9, color=\"darkblue\",rwidth = 0.95, bins = 20)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.08, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "\n", + "\n", + "stats.probplot(log10_kcat_values, dist=\"norm\", plot=ax)\n", + "ax.set_title(\" \")\n", + "plt.ylabel(\"Sample quantiles\", fontsize = 24)\n", + "plt.xlabel(\"Theoretical quantiles\", fontsize = 24)\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"S1b.svg\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "plt.ylim(ymax = 2500, ymin = 0)\n", + "#plt.xlim(xmax = 5, xmin = -0.1)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.yticks([0,1000,2000,3000], [\"0\",\"1000\",\"2000\",\"3000\"])\n", + "plt.xticks([0,250,500, 750, 1000], [\"0\",\"250\",\"500\", \"750\", \"1000\"])\n", + "\n", + "plt.ylabel(\"Count\", fontsize = 22)\n", + "plt.xlabel(\"Experimentally measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.hist(kcat_values[kcat_values<1000], alpha = 0.9, color=\"darkblue\", rwidth = 0.95, bins = 40)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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kSRoyHGHfmgykkiRpyHCEfWsykEqSpCFj221h1KiltznCvvkMpJIkqeMVCnDppXDSSTBv3pLtw4bBTjs5wr7ZHNQkSZI6WqEA++8P110HixcvvW/4cPjUpxxh32y2kEqSpI42fTrceOOyYRRgwQK4++7Br0lLM5BKkqSONnPm0rfpy40YYf/RVmAglSRJHW3cOBg9uvK+Lbaw/2grMJBKkqSOtv/+sNlmaVWmkhDSiPtbbrH/aCswkEqSpI5VKMBBB8Ejj6SJ8IcPT0uGXnIJ3HEHjBzZ7AoFBlJJktTBSiszlSbDX7Qorc40YoQto63EQCpJkjrW7be7MlM7MJBKkqSOVCjAZZelW/XlVljBkfWtxkAqSZI60rRpcP/9y27fZBNH1rcaA6kkSeo4hQJ89rMwf/6y+4480v6jrcZAKkmSOs706fDss8tuHz0att9+8OtRbQZSSZLUcaqtzrTOOt6ub0UGUkmS1HHGjYOxY5feNmoU/OAH3q5vRQZSSZLUcfbfP801Wm6FFWwdbVUGUkmS1HGuvhoWLFh624IFabtaj4FUkiR1nNtvhzlzlt42Z44T4rcqA6kkSeooTojffgykkiSpozghfvsxkEqSpI7hhPjtyUAqSZI6xrRp8NRTy253QvzWZiCVJEkdYcECOPlkWLhw2X1OiN/aDKSSJKntFQqw447w/PPL7hsxwgnxW52BVJIktb1p0+DuuyvvW289OOSQwa1H9Rle7wkhhJHA4hjjom7bA3AysDcwCpgB/CLGuLgRhUqSJFVz0UXLTvMEsNxyto62g7paSEMIJwFzgd9U2H0l8FPgGGAi8HPg8v6VJ0mS1HdrrmnraDuo95Z9qTvwb8s3hhAOBQ4qvp0C/BpYCBwcQpjUrwolSZJ6cMwxy7aCLrcc/Oxnto62g3oD6VbF51u6bf8AEIFvxRgnxRg/CpwCBOCD/StRkiSpukIhPd72thRCAUaNgvHjYeLEppamXqo3kK4JzI4xvt5t+z7F51+UbfsdKaSO61tpkiRJtRUKsP/+cOyx8OKLsHgxhACbbw7Tp9s62i7qDaTLk1o9/yWEsDmwGvBojPGJ0vYY41zgdWCV/pUoSZJU2fTpcOONKZiWxAgPPghXX928ulSfegPpi8AKIYR3lG0r9Sv9a4XjRwNv9KUwSZKknsycCfPmLbt93jy4885BL0d9VG8gvbn4fEZIVgf+nXRrfqn/h4QQ1ie1qD7b7yolSZIq2HbbNPF9d6NHw3bbDXo56qN6A+lPSLfsP0pq+XwKeCfwDHBpt2P3Lz7f0Z8CJUmSKikU4Ec/qrxU6K67ulRoO6krkMYYrydNfj8bGEuaAP8h4IgY4/xuh3+k+PzH/hYpSZLU3fTpcNNNy24fPhw+/WkHNLWTupcOjTGeB6wF7AxsAWwRY7y9/JgQwgjgO8ARwNQG1ClJkrSUav1HFy2qvoyoWlPdS4fCv0bQ31pj/0Lgir4WJUmS1JNx41Jf0e6h1P6j7afuFtJypYFNxQFMkiRJg2b//WGzzdK8oyXDhsFuu9l/tN30KZCGEHYNIUwF3gReAB7ttn+VEMKvQgi/DCGs0IA6JUmS/qVQgIMOgkceSfOODh8Oa68NF12U5h+1/2h7qTuQhhA+CdwAHAKMIY26X2qy/OJKTqsDHwaO6neVkiRJZaZPh5tvhtmz0/tFi2DWrDQFlGG0/dQVSEMIOwE/AhYDXwTWJ7WQVvJrUlA9qD8FSpIkdTdz5pIwWjJ7tpPht6t6BzV9lhQyz4wx/hdACKHasdcXn7fvW2mSJEmVbb116jsa45Jtyy0H22zTvJrUd/Xest+z+Pzzng6MMb4GvAWsW29RkiRJ1RQKcOaZsHjx0tvLw6naS72BdHXgzRhjb9enL/ThMyRJkqqaPh3uv3/Z7YsXO/9ou6o3LL4BrBhCGNnTgcV17lcGXupLYZIkSZXMnAkLFiy7feRI5x9tV/UG0n+Q+pDu2dOBwInFY2+u8zMkSZKq2nZbGDVq2e1bbOH8o+2q3kD6W1LI/FYIYWy1g0II+wNnARH4n76XJ0mStEShAD/+cZrmqSSEFFJvucUpn9pVvaPsfwd8EHgfcHMI4ZfAKIAQwqHABsAE4ABS2L0sxji9ceVKkqShbPr0FDwLhSXbRo2Cs85Kt+zVnupqIY0xRuAI0jr1WwDfI/UTBbicNEfphOJ1LwU+0KhCJUmSKs0/On++g5naXd0j4GOMs2KMRwD7AV3AY8A8YAHwFDAFmBBjPDrGOKeRxUqSpKFt3DgYM2bpbWPGOJip3dV7y/5fYox/Av7UwFokSZJqmjABdtoJbrwR5s2D0aPTewcztTfnCJUkSW2jUICXX156UJPan4FUkiS1hUIhtYbeddeSQDpvHtx8cxrspPZV9ZZ9COGDjfqQGONvG3UtSZI0NE2fDvfdt+z2OXPgzjvhkEMGvSQ1SK0+pL8hzSPaCAZSSZLUL9VWaBoxwkFN7a5WIL2BxgVSSZKkfimNsO8+7ZMrNLW/qoE0xjh+EOuQJEmqaf/9YeON4Z57Un/S4cNhyy1doakT9HnaJ0mSpMFSKMCBB6YBTSWLF8Pb3mYY7QSOspckSS1v6lS47rqlty1eDDfd5Aj7TmAglSRJLa1QgE98AmKFkS3z5qUR9mpvtaZ9+nPx5RMxxg9321aPGGN8X1+KkyRJmjYNXnyx8r7hwx1h3wlq9SEdX3y+v8K2ejhSX5Ik9UmhAJ/9bLo9X8mWWzrCvhPUCqQfLj6/UWGbJEnSgJs+HZ59tvK+DTeEW291UFMnqDXt0//2ZpskSdJAmTkz9RPtbq214IEHYOTIwa9JjeegJkmS1LLGjYOxY5feNno05LlhtJPUFUhDCH8OIVxUx/EXhBD+VH9ZkiRJqX/oTjulEArpebfdXLe+09Q7Mf544Pk6jt8FWL/Oz5AkSQLSoKaXX4ZFi5pdiQbSQN+yH4aj7CVJUh8UCrDjjml1plIgnTcPbr7ZyfA7zYAF0hDCKGBN4M2B+gxJktS5pk6Fu+9edvucOU6G32lq3rIPIawPbNht88gQwp5AqHYasApwAjASuLF/JUqSpKGm1upMw4Y5GX6n6akP6YeB07ttWxW4rhfXLgXWc+orCbIsWwXYEdip+NgReHtx9/V5no+v41rvAk4B9gfeAcwFHgKmAOfmeV5hMomK19kJ+ASwd7GWN4F7gPOBX+d5XujldfYDTiL1r10TeAW4s3iNXg8YkySpk02fnvqOVrL66k6G32l6CqSvA0+Wvd8AWAw8XeOcxSwJa7+KMV7bh7pmsmzLbN2yLDsROBcYXbZ5eWDn4uP/ZVl2cJ7nj/VwnS8DX2fpLg5rkAZ5jQc+nGXZIXmev1bjGgH4GfDxbrveXnxMyLLsCuC4PM/n9/jFSZLUwW6/vfpApp/9zMnwO03NPqQxxh/FGDcqPYqbXyrfVuGxcYxxXIzx/X0Mo7B0d4AXgGn1XiDLsv2BX5LC6MvAZ4Fdgf2A0gT/WwC/z7JsbMWLpOt8BDib9L16AshIrbaHAFcWD9sNuCzLslrfz7NYEkbvBt5Pavk9GvhrcftE4Be9/iIlSepAhQJccknlfdtsAxMnDm49Gnj1Tvv0NWDWQBTSzU+Bx4Bb8jx/CiDLsl6P1s+ybHjxGsNI9e6R5/kDZYf8Mcuyh0mtnluQwupZFa6zCvC94ttngJ3zPH+h7JDfZ1n2C+BjpFv57wd+W+E6GwNfKL69s1jP7OL724oto1cCBwIfyLLsF3me/6W3X68kSZ3k8ssrD2YCOPNMW0c7UV2j7GOMX4sxfn+giinJ8/x7eZ5fUgqjfTAR2LT4+jvdwmjJN0l9SQH+oxhiu/soqc8swBe7hdGSzwBvFF9/rko9/wGMKL4+pSyMApDn+SLgZFJ3B4DPV7mOJEkdbe5cOP746vvvvXfwatHg6dSlQ48se/0/lQ7I83wxS27dr0rqC1rtOm8BFQcc5Xk+q2zf1lmWbVK+v9h39PDi2wfzPP8rFeR5/gTw5+LbfWt1I5AkqRMVCrDVVtX7jg4f7uj6TtWnQBpCODCE8MsQwt9DCA+EEB6t8Xik0UX3wh7F54fyPH+2xnHlfVz3KN+RZdkIUl9RgL/3MNCo6nVIg7PWLb6+vsY1yq8zGtihh2MlSeoo06fDk09W37/llo6u71R19SENIYwgTZdU6k5cbS7ScoO6UlOxZXG94tueGvbvL3u9Zbd9m7Hk+9Of65S/r/c61/VwvCRJHeP221MraSVrrAG33mr/0U5V76CmL5BuP0fg98DlpME+vZrLc5C8gyVBudb0VOR5/mqWZXOAFVgSYkvWLXtd8zpAeV/XgbqOJEkdbeHCyttHjIAnnoCRIwe3Hg2eegPpv5HC6JdijP81APU0woplr3szI8AsUiDt3meznuuU7x+o60iS1LEKBTj//Mr7Pv95WH75wa1Hg6vePqQbkkaC/6TxpTRM+a/sgl4cX+ob2v1XvZ7rlPcvHajrSJLUsaZOhccfr7xvuU4dgq1/qbeF9HVgVIxx7gDU0ijltfWmcX9UhfPqvc6ostcDdR1JkjpSad36aobXm1bUdur9P8f1wMohhFbu3/hW2eve3PYuHdP9dno91ynfP1DXkSSpI02dCi9UmumbNIhp++0Htx4Nvnr/z/EN4FDgO8CkxpfTEM+Q+rkGlh5QtIwsy1Yj9R+FpQcUwdIDkGpeh6UHIA3UdZaSZdlJwEkjR47k7LPPBmDPPfdkr7326uEjJElqHXPnwnHHQawyJ89WWznV01BQVyCNMf4zhHA4MCWEMJ0UTG+NMc6ufebgyfN8VpZlTwHrs+wUTN29q+x19ymZHgQWkb5H/blO+fv+XGcpeZ6fB5x39tlnx9NOO62Hy0qS1HpKE+FXG13vVE9DR1237EMIBWAGsDKwP/An4M0QQqHGo8p6CwOqtBrSplmWrVPjuPEVzgEgz/OFwC3Ft7tkWVar/2fV6wCPk1ptIa13X0vpOvOB23o4VpKkttbTRPif+IRTPQ0V9fYhDX14NGNs3KVlrz9S6YAsy5YDPlR8+xqVJ6EvXWdF4Ngq1xlbtu+feZ4/XL4/z/MIXFZ8u1mWZd1XcipdZwNgn+Lba4pLkkqS1LFmzqw+Ef7w4bCDaxYOGfWGxY36+BhsVwAPFV9/IcuyzSsc8yXSakwA5+R5Xqkl91eksArwrSzL1qxwzA9ILcYA361Sz49It/8Bfpxl2ZjynVmWDQf+GyjdlKh2HUmSOsa4cbDCCpX37bmnfUeHkhCr9SJuoizLtgO267b518XnB4Bvd9s3I8/z57tdY3/gKlLIexk4G7iJNJL9/cCJxUPvA3aq1iKZZdlHgV8W3z4OfBO4E1gDyIDDivuuB/bJ83xxlet8HfhK8e3dxa/hQdJAps8CpZbT/8vz/IOVrlGJfUglSe2oUIALL4RJFYZIT54MRx9t39EOVXHZ+Vad2etw4Iwq+zZnSTgteS+wVCDN8/zqLMs+BpwLrA78sMK17gMOrnV7PM/zX2VZtjZwFmlhgPMqHHYjcGS1MFp0erGOk4FtgErrUVwB/L8a15Akqe0VCrDvvnDddZX333NPGnmvoaOj1z7I8/w3wDhSKH0EmEe6BX8zcCrwnjzPH+vFdc4GdgV+CzxBGnT0MqlV9P8Be+V5/moP14h5nn+cNBjsEtJApwWkID0DODbP88PzPJ9f4zKSJLW9qVOrh1GAG28ctFLUIvp8yz6EsCewO7AOMIYqTbBAjDF+tG/lqSfespcktZNCAdZZB158sfoxX/0qnHXW4NWkQdWYW/YhhK2BLmCrKh8Qu22LgIFUkiQxbVrtMDp6NHzlK9X3qzPVFUhDCG8nzT26Bmni9muAT5OWuTwHWIs0ddHGpFvaOUtGl0uSpCHuoouq71tuOXjpJeceHYrqbSH9T1IYnQFMjDEuDCF8GpgVYzy9dFAI4STgp8D2wCGNKlaSJHWuyZNh7NhmV6FmqHdQ04GkW/CnxRirLPQFMcbzgNOKx3+y7+VJkqROcvjhlbdvvTUceeSglqIWUm8g3QAokObhLInAqArH/ndxX6/n1JQkSZ2rUKg+WOlrX3Pe0aGs3kC6GHgjLj00fxawUghhqV+jGONbwJssWQ1JkiQNYZdfDnffXXnfvfcOailqMfUG0mdI4bP8vMeL19m2/MAQwsrAKoBdkyVJGuLmzq0+2f3w4bDddoNajlpMvYH0AdJAqC3Ktv2FNL3Tf3Y79uvFZ//PI0nSEFYowLvelZ4rWX11160f6uoNpFeTwmf5yPmfAAuB40MId4cQzg8h/IM0mCmSVkmSJElD1OWXw5NPVt//s5/Zf3Soq3fapynARsDs0oYY4wMhhA+R1njfiiUT5kfghzHGXzWiUEmS1H4KBfjYx6rvX3llmDhx8OpRa6orkMYYXwE+V2H75BDCH4EJwLrAG8AfY4wPNqRKSZLUlqZOhddfr77/F7+wdVR9WDq0mhjjy8D/Nep6kiSpvRUKcPLJ1fevv75zjyqptw+pJElSr9Ratz4EuP9+W0eVGEglSdKAqLVu/THHwPLLD14tam113bIPITzah8+IMcaN+3CeJElqY4sWVd83adLg1aHWV28f0g378Bmx50MkSVInmTsXLr648r4NNoBDDqm8T0NTvYH0vT3sXxnYEfgYMAL4FGl1J0mSNET0NBH+iSfad1RLq3fap+t7cdjUEMI5wB+BbwDv6UNdkiSpTV18cfWJ8EOAHXYY3HrU+gZkUFNxvtKPk27xnz4QnyFJklrPG2/A8cdX37/yyi4TqmUN2Cj7GOPfSSs6HT5QnyFJklrHrFmw6qq1jznvPG/Xa1kDFkhDCMNIXQLePlCfIUmSWsOCBbD66hBrDGV2InxVM5DzkB4EjAJeGsDPkCRJLeBrX4P582sf40T4qqZhS4cChBBGktaynwh8hTTl07RGfoYkSWothQL8+Me1j+nqciJ8VVfvxPhVJnCofDjwKA5qkiSpo02fDnPmVN+/5ZZw7LGDV4/aT7237EMvH08A/wXsEGP0lr0kSR3s5pth8eLK+9ZfH2bO9Fa9aqv3lv1GPexfBLweY5zdx3okSVIbKRTgJz+pvG/55eGhh2DkyMGtSe2n3onxnxioQiRJUvuZOjXNPVrJWmsZRtU7AznKXpIkdbBCAU4+ufr+nXcevFrU3gykkiSpT6ZPh5dqjBT59a8Hrxa1t3pH2f+5QZ8bY4zva9C1JElSE9x2W/WJ8Lfaymme1Hv1DmoaX/Y6kkbUV1JrX2m/JElqU7NmwU9/Wn3/4YcPWinqAPUG0g8Dq5HmFl0ZuAG4HnimuH8dYG9gL+B14CzgtUYUKkmSWsOsWbDiitX3DxsGpzsLuepQbyC9HLgVmA/sFWP8a6WDQgi7AZcAJwM7xRjf7E+RkiSpdRx4YO39n/+8o+tVn3oHNZ0ObAx8tFoYBYgx3gh8DNgM+Grfy5MkSa1kwQK48cbax5x55qCUog5SbyA9HJgbY/x9L469CpgLHFFvUZIkqTWdcUb1gUwAu+9u66jqV28gXQeosjjY0mKMESgUz5EkSW2uUIBzzql9zIwZg1KKOky9gfQVYEwIYfeeDiweMxZ4tS+FSZKk1jJtGsybV3nfyJHw1lswduzg1qTOUG8gvYo0ndOvQwibVDsohLAx8GvS9E69ub0vSZJa3JQp1fedeqphVH1X7yj7M0j9SDcG7g4hXEqa9unZ4v51SFM+HQmMBl4sniNJktrc009X3h6CA5nUP3UF0hjjcyGEvYGLgS2A44uP7gJwL3BMjPH5flcpSZKaqlCAu+6qvG+PPRzIpP6pt4WUGON9IYR3k4Lo0cD2wBrF3S8BdwAXAVNijIsaVagkSWqeadPgzSqziq+33uDWos5TdyAFKAbN3xUfkiSpgxUK8OlPV5/u6dhjB7cedZ56BzVJkqQhZupUeOKJyvtWWgkOOWRw61HnMZBKkqSaas09utpqae16qT8MpJIkqaoFC+DWW6vv33nnwatFnctAKkmSKlqwAFZfHebOrX7Mr389ePWocxlIJUlSRV/7Wlp9qZpTT4Xllx+8etS5DKSSJGkZhQL86Ee1j/nmNwenFnU+A6kkSVrG9Okwe3b1/bvv7mT4ahwDqSRJWsZtt9XeP2PG4NShocFAKkmSlnHPPdX3nXoqjB07eLWo8/VppaYQQgCOAPYD1gOWjzG+r2z/GOA9QIwx/qURhUqSpMGxYAFccknlfSHYd1SNV3cgDSFsClwKbAmE4ubui4nNA34FvDOEsHeM8a/9qlKSJA2ab3yj+jKhe+5p31E1Xl237EMIqwJ/BLYC7gZOB97sflyMsQCcSwqsR/W/TEmSNFj+WqMZ6dOfHrw61AZirD03WC/V24f0VNIt+j8AO8QYvwFUmy53avF5tz7WJkmSmmC11SpvX311mDhxcGtRi3rwwTRR7bveBR/9aL8vV+8t+4mk2/OnxhgX1TowxvhwCGEBsElfi5MkSYNrwQK49NLK+7bYwnXrh7Rnn4UpU6CrK03DEAKMHw+HHNLvS9cbSDcC5sUY7+3l8W8BK9f5GZIkqUnOOKN6/9E3l+mkp4732mvpfyhdXXDttemX4z3vge9/H447Dt7xjoZ8TL2BNAK9+r9RCGE4sBIV+phKkqTWUyjAD39YfX8DGsLUDubOhWnTUgi96qrUbL7ppnD66XDCCbD55g3/yHoD6WPAViGEd8YYH+3h2PcBI4D7+lSZJEkaVNOmwfz51feffvrg1aJBtmgR/OlPKYRedlkaqPT2t8MnPwmTJqVW0RB6vk4f1RtIfw9sDXwGOKXaQcV5SL9LalG9os/VSZKkQdPVVX3fHns43VPHiRH+/vf0g7/wQnjxRVh5ZTj22BRC99570DoN1xtIvw+cBHwihPAGsFTDfghhReBA4Cxgc+AZ0vRPkiSphS1YABdfXH3/Zz4zeLVogN1zTwqhF1wAjz0Go0fDoYemEDphAowaNegl1RVIY4wvhxAmAlcCXwK+QHFy/BDCq6Q+o6H4eBU4PMY4u6EVS5KkhvvGN2Dx4sr7Ro50uqe298QTMHlyCqJ33ZVaPvfdF848Ew4/HFZaqanl1b1SU4zxryGEdwPfBI4GSg34qxSfFwGXAF+MMT7RiCIlSdLAuuGG6vt22cXpntrSyy/DRRelEFpa7WDXXeEnP4FjjoG11mpufWX6tJZ9jPFJ4P0hhP9HWrP+7aRJ9l8AbosxzmpciZIkaSAtWAB/+1v1/a7O1EZmzYIrrkgh9Oqr02ClLbeEs8+G44+Hd76z2RVW1KdAWhJjnAu4Tr0kSW3sjDNSbqlkpZW8Xd/yFiyAP/whhdArrkjTNq2/Ppx6auoXus02AzpCvhH6FUglSVJ762nu0Q039HZ9S1q8GP7ylxRCL7ooTWD/trfBiSemELrbbrBcvSvEN4+BVJKkIWzq1Npzjx566ODVoh7ECHfemULo5Mnw9NMwZkwalDRpEuy3H4wY0ewq+6RqIA0hFBr0GTHGaPCVJKkFff/71fctt5yT4beEhx9OUzR1dcH998Pw4Wl6pu9+N/2PYcyYZlfYb7WCYqM6G7R2pwVJkoaoV1+tPZipq8vJ8Jvm+edhypT0Q7jllrRt773ThLBHHZVuz3eQWoF0o0GrQpIkDaq5c2tnmmHD4OijB68eAW+8AZdemkLon/+c+omOG5daQo87DtZbr9kVDpiqgdQ5RCVJ6lwf/GDt/bvt5mCmQTFvHlx1VQqh06alDr0bbwynnQYnnABbbNHsCgeFfTslSRpiCgW47LLax/zHfwxKKUNToQDXXptC6CWXwJtvpknqTz45DU7acceWn6ap0fodSEMIGwJrFN++FGN8vL/XlCRJA2fatJSJqllvPecebbgY4dZbl4yQf+GFNMnrUUelEDp+fBqsNET16SsPIWxEWsv+KJYsGVra9zpwEfCdGONj/axPkiQ1WFdX7f0PPODt+oa5774lI+QfeQRGjYKDD04h9OCDYfToZlfYEuoOpCGEY4BfA8tTeQT9qsD/Az4QQjgxxnhR/0qUJEmNMndumke9msmTYfnlB6+ejvTUU0tGyM+cmebPet/74CtfgSOOgJVXbnaFLaeuQBpC2BHoAoYBDwDfB64Hnikesg4wHvgMsAVwfgjh0Rjj7Y0qWJIk9U2hAJtvnu4eV+LI+n545ZXUH/T88+GGG9K2nXeGH/0Ijj0W1l67ufW1uHpbSL9CCqNXAxNjjN3XdngYeDiE8FtgKrA/8FXg8H7WKUmS+unCC1PjXTXrruut+rrMng1XXplaQmfMgIUL4V3vgq9/HY4/HjbZpNkVto16A+nuQAQ+XiGM/kuMcUEI4ROkgLpHP+qTJEkNUCj0PNXTpEmDU0tbW7gQrrkmhdDLL0+hdN1107QEkybBu9895EbIN0K9gXQ08EZvBivFGB8tDnAa1ZfCJElS41x4ISxaVH3/sGFw5pmDVk57WbwYbrwxhdALL0y351dbDd7//hRC99gj9RNVn9UbSB8B3hVCGFWrhRQghDAaGAPc29fiJElS//WmdfSVV1wmdCkxwt13pxB6wQXw5JNptNfEiSmEHnCA37AGqjeQ/oY0kCkDftzDsScBI4rnSJKkJpk6tXbr6HPPOfD7Xx57bMk0Tffck5qODzgAvvnNFEbHjm12hR2p3kB6DqlP6PdCCCsAP4oxzi0/oNgy+mng68Cl9BxcJUnSADrnnOr7Vl7ZAeC8+GK6Fd/VBTfdlLbtsQf8/Odp2oE11qh9vvqt3kD6K+BNYDZwNnBaCOE2lp72aQfSrfo3gLeAX4VlO/fGGONH+1q0JEnqvccfr77v3HMHrYzW8uabaVBSVxf88Y+pX8O228K3v51GyG+wQbMrHFLqDaQnkkbZlxLmGGDvKseuAnyoyr4IGEglSRpghQK89FLlfSNHpikyh4z582H69BRCr7wS5s2DDTeEL3wBTjgBtt662RUOWfUG0q8NSBWSJGlATJuWVmeqZOedh8C8o4UCXH99CqEXXwxvvJFuwX/sY2lw0i67OE1TC6grkMYYDaSSJLWRyZOr71tvvcGrY1DFCLffnkLo5Mlp1NbYsXDkkSmEvu99MLzu1dM1gPxpSJLUoQoFuOqq6vs77nb9Aw8sGSH/0EOpT8JBB6UQesghadomtSQDqSRJHeryy9PYnUpGjkwZre098wxMmZJC6O23p9vv731v6hd65JGw6qrNrlC90OdAGkJYF9gaWJU032hVMcbf9vVzJElS/QoF+GiN4cMrr9zG/Udfew0uuSSF0OuuS7fod9gBfvADOO44WGedZleoOtUdSEMIuwI/BHas4zQDqSRJg2j69DR+p5rx4wetlMaYMyeN0OrqSv0QFi6EzTaDM85II+Q326zZFaof6gqkIYQ9gGuA0lpZDwMvAIUG1yVJkvrh5ptr7//f/x2cOvpl0aI0R2hXF1x2GcyalVo/Tzkl9QvdfntHyHeIeltIzwZGATcCk2KMTza+JEmS1B+FAvy4xjqJu+zSwuN7YkyrJXV1pdWTXnoJVlklTVY/aRLstVcb9zVQNfUG0veQJrU/Icb41ADUI0mS+unSS6sPZgK45prBq6XX/vnPFEIvuCAtLTV6NBx2WAqhBx4Io0Y1u0INoHoD6VxgoWFUkqTWVCjAiSdW3/+2t6UpOVvCE08smabp7rtTy+d++8FZZ8Hhh8OKKza7Qg2SegPpHcA+IYSVYow1/u8lSZKa4dJL0/ifaj72scGrpaKXXoKLLkoh9G9/S9t22w1++lM45hhYc83m1qemqDeQ/hewL/A54KuNL0eSJPVVT62jkBofB91bb8EVV6QQevXVqdCttoJvfjP1Dd1ooyYUpVZS79KhfwohnAL8MISwNvDtGOMjA1OaJEmqx+WX124dPeKINCH+oFiwAGbMSCF06lSYOxfWXx8+97nUL3SbbQapELWDuuchjTH+PISwGnAW8JEQwjzS1E81Tokb97VASZLUO2ecUXv/+ecPcAGLF8MNN6QQevHFaQL71VeHD384hdBdd4XllhvgItSO6p2HdBQwBTi0tAlYHtiwxmmxT5VJkqS6PP109X2HHz5AUz3FCDNnphA6eXJaynPMmNQcO2kS7LsvjKi5oKNUdwvpl4HDgEWk1Zf+CLyIE+NLktRUCxbUXpmpq6vBH/jQQ0tGyD/wQAqdEybA978PhxySQqnUS/UG0veTWjxPjjH+zwDUI0mS+uDLX66+b7fdGtQ6+txzMGVKCqG33ppWSdprLzj1VDjqKFhttQZ8iIaiegPp24GFuDa9JEktY8GC1DBZzYYb9uPir7+e5pLq6oJrr039RMeNg+9+F447DtZbrx8Xl5J6A+mzwJoxxkUDUYwkSapfT4OZjj22zgvOnQtXXZVGQf3+9ynxbrwxnHYanHACbLFFn2uVKqk3kF4KnBpC2DXGeNNAFCRJkurzi19U37f88qlLZ48WLUotoF1dS9YeXXtt+PjH0+CkHXdMt+ilAVBvIP06aVDTr0IIB8cYHxuAmiRJUi89/DC88kr1/U8/nVbkrChGuOWWFEKnTIEXXoCVVoKjj04hdPz4GidLjVNvID0C+G/gDOD+EMJFwN3Ac7VOijHa51SSpAa799604FE1w4dXGWd0330phHZ1waOPwqhRcOihKYROmACjRw9YzVIl9QbS35BG2Zfa7E8oPnpiIJUkqYHuvhu23bb2MRMnlr156qk0T2hXF9x5Z5qg/n3vg69+Nc0ZuvLKA1muVFO9gfQGnOhekqSmKRTg5z+HT32q52P/75xXIL84hdAbbkgbd94ZfvSjNNJp7bUHtlipl+pdy378ANUhSZJ6UCjAHnvA3/9e/ZgVmM1hTOU77+5i+Y1mpMFK73oXfP3raYT8xq7mrdZT91r2kiSpOS6+uHIYHc5C9udqJtHF4VzOGOYQX1kXPvOZ1C/03e92hLxamoFUkqQ2sGBBauAsCSxmd/7GJLo4hotYnVd4hdXoWu4DfHDGJEa9b4/UT1RqAwZSSZLawOmnQ4yRbbmLSXRxAhewPk8xmxW4gol0MQn2259Lp41k5MhmVyvVp0+BNISwI3AysDuwDjCmxuExxmjwlSSprx59lBV+eAH/pIutuJeFDOcPHMAX+TZTOYzZjGXHHeGm6U4bqvZUd1AMIXwBOBvo7X0AO61IklSvF16ACy9MI+T//ndOB25gT07mXC7maF5h9X8duvHGcNNNhlG1r7oCaQjhvcC3gAJwOjANuAN4CdgVWAvYFzileMpHgbsaVawkSR3tzTfhsstSCP3jH2HxYhZs+W6+wneYzPE8xfoVT7vzTsOo2lu9LaSnkOYhPSPG+E2AkEbtFWKMjwKPAjeFEH4JXAf8ChjXsGolSeo08+bB9OkphF55JcyfDxttBF/6Em8cdAKr7F5jKSbglFNg7NhBqlUaIPUG0p2Lz+d1277U7fsY43MhhE8A1wBfBj7Zt/IkSepAhQJcd10KoZdcAm+8AWuuCSedlKZp2nlnFiwMrNqLFTy/970Br1YacPUG0tWB2THGl8u2LQJWqHDsn4G5wIQ+1iZJUueIEW67LYXQyZPh+edhxRXhyCNTCN1nn7T4fNEXvpBOqeWTn8QR9eoI9QbS14BRFbatHkJYOcb4RmljjDGGEBYDb+9njZIkta8HHkghtKsLHn44JciDD04h9OCDYfnllznl+efhnHN6vvQPftD4cqVmqDeQPg2MCyGMjTHOKm67F9gLGA9cUTowhPBu0nRQrzagTkmS2sfTT8OUKSmE3nFHWiVpn33gS19KLaKrrFL11Fdfhbf3oinn9ddtHVXnqDeQ3k4apLQz8KfitqnA3sD3QgjPAncC2wD/QxoAdX1DKpUkqZW9+mrqD9rVBddfn+6377gj/PCHcOyxsM46PV5i1ix429t6/qjnnoOVV25AzVKLqDeQXg78P+B4lgTSc0mT5G8KlK+wG4A5wJn9qlCSpFY1Z04aGd/VlUbKL1wIm20GZ56Z1vncdNNeX2ru3N6F0UMPhbXX7nvJUiuqN5D+gdT6uaC0IcY4L4SwN/Aj4DBSH9MI3AR8JsZ4d4NqlSSp+RYuTHOEdnWlOUNnz06tn5/6VOoXOm5cukVfhwULYKWVYNGino+dMqWPdUstrK5AGmNcDNxTYfvzwHEhhBGkkfhvlfUxlSSpvS1enJZC6upKqye9/HLqBzppUnrsuWe/Zqb/zGd6F0ZffLHiGCip7TV0jfkY40LguUZeU5Kkprn77hRCL7gAnngipcHDDksh9IADYFT3iWfqd+ed8POf93zcE0/AGmv0++OkltTQQCpJUtt7/PEUQLu64J//TC2f++8P3/gGTJyY5g5tgJdeSnPh98b998P6lVcNlTpCQwJpCOEU4CPAZqT+pXcC58QYr6h1niRJLeHFF+Gii1IIvfHGtG333eFnP4Njjml40+S998JWtVcE/ZeZM2HzzRv68VLLqRlIQwg7kgYyvQZsEWNcUOGYycAxpbfA8qRpoPYKIXw5xvidxpYsSVIDvPUWXH55CqHXXJOW89x6a/jWt+D442HDDRv+kXPnpkbWa67p3fHnnAPbbdfwMqSW01ML6T7AKsD5VcLoJODY4tsXSBPjzwYOBzYCvh5CmBpjvK9RBUuS1Gfz58OMGSmEXnllSogbbACf+1zqF7rNNgP20XPnwgqVFtquYtQo+Pd/H7BypJbSUyDdkzSF02VV9n+6+Pwk8J4Y4ysAIYSvAH8FtgM+CvxnvyuVJKkvCgW44YYUQi++OC1xtPrq8OEPpxC6666w3HIDXsZxx9V3/Asv9GvgvtRWegqk7yQF0pu77wghrA7sWNx/VimMAsQY54YQziS1mO7dsGolSeqNGNOSnV1dMHkyPPssjBkDRxyRQui++8KIEYNWzgMPpAbZ3th2W/jb32Ds2IGtSWolPQXStYE3Y4yzK+zbrfgcgUp/zEorOb2zj7VJklSfhx5KIbSrCx58MIXOCRNSCD300PrumTfAggXwwQ/2fjL7e+6BLbcc2JqkVtRTIB0DVJuqd8fi88Mxxpe674wxzgkhvAE0Zn4MSZIqefbZlPi6uuC229IqSePHp36hRx4Jq63WlLKefDJ1T+2t555zSVANXT0F0leAtUIIa8YYX+y2bxdS6+htNc4fSdkyo5IkNcTrr8Mll6QQeu216Rb99tvD976XRsi/4x1NLe/553sfRkeOhFde8Ra9hraeAuk/gP2B9wM/KG0s9h/ds/j2+konhhDWJk0B9VD/y5QkDXlz58K0aSmEXnVVuh++ySbw1a/CCSfAu97V7AqBVGY9efjFFw2jUk+BdApwAHB6COEx4PfAO4CfkVo/51N9BH4psP6zAXVKkoaiRYvgz3+G88+Hyy5Lc4euvTZ84hOpX+gOO6Rb9C3imWdg3XV7f/wTT8DKKw9cPVK76CmQ/h/wSeA9wMXd9kXgpzHGl6uce3zxmL/2q0JJ0tASI9x8c2oJnTIlNSGuvHJaMWnSpNQ/tMXmQ5o7N42Z+tOfej625Omnm96zQGoZNQNpjLEQQpgAnA/s1233b4EvVTovhPBO4LDi215OdCFJGtLuvXfJCPnHHkszwx96aAqhEybA6NHNrrCiegcvbb013HSTt+mlcj2uZV9sAT0ghLA5UFrC4vYY42M1TltMWq1pYYzx4X5XKUnqTE8+meYJ7eqCf/wjTVC/775wxhlpztCVVmp2hTXdfXeaN7S3nNZJqqzHQFoSY3wAeKCXxz4OPN63kiRJHe3ll9OKSV1d8Je/pG277AI//jEceyystVZz6+ulW26BnXfu/fEzZxpGpWp6HUglSeqzWbNg6tQUQv/whzRYaYst4BvfSCPk39k+a6i88UYq99VXe3/OddfBdtsNVEVS+zOQSpIGxoIFcPXVKYRecQXMmQPrrQef/WzqF7rtti01Qr436m0VhdQyahiVajOQSpIaZ/Fi+OtfUwi96KLUjLjaamn9zEmTYPfdUz/RNvPAA/VPc7rSSvDww7DGGgNTk9RJDKSSpP6JMQ1I6uqCCy5I8xmtsAIcfngKofvtl5YjakMPPwybblr/eXfdBdts0/NxkhIDqSSpbx55JAXQri647z4YPhwOPBD+67/gsMNgzJhmV9hnc+emHP23v9V/7kMPpQWkJPWegVSS1HvPPw8XXphC6M03p2177QWf/jQcfTS87W3Nra+fCgU45xz4z//s2/lOdi/1jYFUklTbG2+kZTu7utJSRIsXp1E6//VfcNxxsP76za6wIZ5/Ht7+9r6du/feMH06LL98Y2uShgoDqSRpWfPmwVVXpRA6bRrMn5/mOvryl9M0TR00oWZ/giik9eg7JJNLTWMglSQlhQJce20KoZdcAm++CWuuCVmWBifttFPbTdNUy513wrhxfT//Jz+Bj38chg1rWEnSkGUglaShLEa49dYUQqdMSc2FK64IRx2VQuh735sGK3WIuXNh4kS45pq+X+OHP4RTTjGISo3UOX/LSJJ67/77Uwjt6kqj5UeOhEMOSSH0oIM6qjNkI0IowDHHwO9+17YzWEktzUAqSUPF00/D5MkphM6cmSao32cfOO00OOIIWGWVZlfYUC+9lHoc9FcI8OCDTuUkDSQDaZNkWbYucApwKLA+sAh4DLgM+Eme5681sTxJneLVV+Hii1MIveGGdIt+p53S3EbHHtu/0TwtZsECOPHENDVqo1x9dZqPVNLAMpA2QZZlBwIXAKt027Vd8XFSlmUT8zy/fXArk9QRZs+GK69MIXTGDFi4EDbfHL72tTRCvsOa+m6/HXbYobHXXH55ePRRWHvtxl5XUmXtt6Bwm8uybFvgYlIYnQOcAewBjAd+CBSAdwDTsixbpzlVSmo7CxemaZre/35Ya60UPO+4I01Yf8cdaSWlr361I8LoG2+koBhCejQyjI4ZA889B3PmGEalwWQL6eA7BxhDCp4T8jy/oWzf9VmW3QH8H7A28A3gI4NeoaT2sHgx3Hhjagm98EJ45RVYddU0MGnSpLSC0nLt3+7QqL6gtdx2G7znPQP7GZKqa/+/qdpIlmXvAd5bfPubbmEUgDzPfwf8ufj2g1mWDfBfw5LaSoxw113wxS/CRhvBnnvCb34D++4LV1yRmvfOOw/Gj2/LMPrMM0taPkuPgQyj99+fvqWGUam5bCEdXEeWvf5VjeP+B9gHGAYcBvxyIIuS1AYeeyyN1unqgnvuSZNg7r8/nH12mtNoxRWbXWGvzZ0LBx+c5uBvhrvugm22ac5nS6rMQDq49ig+zwFurXFc+V/Te2AglYamF19Mt+K7uuCmm9K23XeHn/0sTYq5xhrNra+KZgfOSpZbDh5+ODUqS2o97Xc/p72VFn9+KM/zRdUOyvP8WeCtbudIGgrefBN++1s48EBYZ520JNCsWfCtb6VW0r/+FT7xiaaE0blz08JN3W+pd3+ssEJrhNGZM9Pt+BjTqqiGUal12UI6SLIsGwWsXnz7dC9OeYoURtcbsKIktYb582H69NQSeuWVMG8ebLABfP7zabR8g+8v33QT7LZbQy/ZMj78Yfjv/3Y1JandGEgHT3kHr1m9OL50zNgBqEVSEz35JGy0QYG9uZ5JdHEUl7Aqr/Mia3AhH6WLSdz0xK7wrQDfana1rc8+oVL7M5AOnvKFoRf04vj5Fc6rW6GQGl5mzoRx42DChDQWYqCuU+u4Svug9rZtt03v77or7d9//7Ryyu23p+sNGwbbbZeOufPOytsWLkwTXAO8851pf6GwZNuGG8Ljjy95/eijaaByjOn249vfns4rbV+8OC1+M2sWjB0Lq62Wzi1tGzMmvZ9V/C9F6Toxpvdz56YGsRBSK87yy6c5D2fPTtdebjkYMSIdv2hRqrV02zHGtH/x4iXXUzuJbM8d/Bvn8xSTWYfneIuxXMYRdDGJP/E+FjGi2UW2vPvvT/P8S+ocBtLBM7fsdW9uJo2qcF5dCgU44AC4+eYUdsaMgZ13hj/8ob5Q2tvr1DoOlt23005p+y23VN42a9aS6y9enPqljRyZlgecPXvJ5w4blsLZ4sW1t7WT+fOr7ysUBq8ONcamPMgJXMAkuticB1nACK7iILqYxDQOYS4rNLvEluRAJGnoMJAOnrfKXvfmNnzpmN7c3q9o+vQUAEstdbNmpffTp8MhhzT+OrWOg2X33Xhjej1vXvVt5eFr9uylg2hJpYBmaFOzrcMzHMcUJtHFDtzOYgLXMZ7v8jku4SheZ9Vml9gybr55yX9GJQ1NBtJBkuf5/CzLXiYNbFq3F6eUjnmq0s4sy04CTho5ciRnn302AHvuuSd77bXXv46ZOXPZADd7drqNXU8g7e11ah0X47L7SqGzp21Su1iF1ziKS5hEF+O5juWI3MZ7+CzfZwrH8SzvaHaJTXHjjbDrrs2uQlIrM5AOrnuBvYBNsywbXm3qp+Ia9iuVnbOMPM/PA847++yz42mnnVbxw8aNS7fBZ5W1sY4Zs6R/ZW/19jo9Hdd93+jR6bk8hFbaJrWy5ZnDIUxjEl0cxFWMZCEPsilncToXcAIP0rmdHQ2akhrFeUgH11+LzysAO9Y4bnyFc+o2YULqwzl2bBpAM3Zsel8aONTo69Q6rtK+3XZLj2rbIPUFHTYs7R8zJi3TvUK37nbLLbfsComVtkmNMoxFHMAM/pcP8gJrcSHHsRO38FP+nR24lc15gK9xZluE0fHj06C68oFzvX0YRiU1ii2kg+tS4MvF1x8Fbqpy3EeKzwVgal8/bNiwNKBo+vR023y77fo2yr631+npuEr7oPa20lQud9+d9pdG2d9xRxqBPnw4vPvd6Zh//KPytgUL0nzikAZHDB+ejitt22ADeOKJJa8fe2zZUfYbbbRkez2j7LuPhI8xjbJfUJxnoXyU/Zw5S2YJGD586VH2sGRkfWmWAEfZD7bILvydSXRxHFNYk5d4nZWZwnF0MYnr2ZvF1D+FxahR8Mgj8I6heTdfkgAI0X/VBlWWZdeSWkALwHvzPP9Lt/3/Bvyu+PbXeZ5/hBpq3bKX1AD33JMmrO/qSvODjR4Nhx4Kkyal/0GNGtXjJSRJ/xIqbbSFdPB9GrgRGAPMyLLs28CfSD+LicX9AM8DX2lKhdJQ98QTMHlyCqF33ZX6f+y3H3zta3D44bDSSj1eQpLUe/ayG2R5nt8FHA28TupLehbwN+B64LPAMOAZ4JDimvaSBsPLL8O558Kee6YVEr74xdT/4ic/gWefhRkz4IMfNIxK0gAwkDZBnuczgG2A7wL3AbOBN4F/AGcC2+R5fnvTCpSGilmz4Pzz4eCDU2fhT3widQY+++zUsfPGG+Hf/x3WWqvZlUpSR/OWfZPkef408PniQ9JgWbAgjbDr6oIrrkijzNZbD049NfUL3WabNJpNkjRoDKSSOt/ixfCXv6QQetFF8Npr8La3wYknphC6227OEyZJTWQgldSZYkxzh3V1wQUXwDPPpD6hhx+eQuh++8GIEc2uUpKEgVRSp3n44RRAu7rg/vvTpK4TJsD3vpemaypNFCtJahkGUknt77nn4MILUwi95Za0be+94TOfgaOOSrfnJUkty0AqqT298QZcemkKoX/+c+onOm4cfPe7cNxxaaCSJKktGEgltY958+D3v08h9Pe/h/nzYeON4bTT4IQTYIstml2hJKkPDKSSWtuiRXDttSmEXnopvPlmmhf05JPT4KQdd3SaJklqcwZSSa0nxtQXtKsLpkyBF15IKyQddVQKoePHp8FKkqSO4N/oklrHffelENrVBY8+CqNGpVWUJk1Kz6NHN7tCSdIAMJBKaq6nnoLJk1MIvfPONEH9PvvAV74CRxwBq6zS7AolSQPMQCpp8L3yClx8cQqhN9yQtu20E5xzDhx7bFpXXpI0ZBhIJQ2O2bNh6tQUQmfMSIOVNt8czjorjZDfZJNmVyhJahIDqaSBs3AhXH11CqGXXw5z5sA73gH/8R+pX+h22zlCXpJkIJXUYIsXw9/+lkLoRRel2/Orrgrvf38KoXvumfqJSpJUZCCV1H8xwl13pRB6wQVpoNLyy8PEiSmEHnAAjBzZ7ColSS3KQCqp7x59NAXQri64914YNiyFz299K4XRsWObXaEkqQ0YSCXV54UX4MILUwj9+9/Ttj32gJ//HI4+GtZYo7n1SZLajoFUUs/efBMuuyyF0D/+MfUT3XZb+Pa34fjjYYMNml2hJKmNGUglVTZvHkyfnkLotGnp/YYbwhe/mKZp2nrrZlcoSeoQBlJJSxQKcN11KYRecgm88Ua6Bf+xj6XBSbvs4jRNkqSGM5BKQ12McNttKYROmQLPPZcGIx15ZAqh73sfDPevCknSwPFfGWmoeuCBFEK7uuDhh9O0TAcdlELoIYekaZskSRoEBlJpKHnmmdQK2tUFt9+ebr+/972pX+iRR6YJ7CVJGmQGUqnTvfpq6g/a1QXXX59u0e+wA/zgB3DccbDOOs2uUJI0xBlIpU40Zw5ceWUKodOnpzXlN90UzjgjjZDfbLNmVyhJ0r8YSKVOsXBhmiO0qwsuvxxmzYK3vx1OOSX1C91+e0fIS5JakoFUameLF8NNN6UQeuGF8PLLsMoqabL6SZNgr73Scp6SJLUwA6nUjv75zyUj5J94AkaPhsMOSyH0wANh1KhmVyhJUq8ZSKV28fjjMHlyCqF3351aPvfbD77+dZg4EVZaqdkVSpLUJwZSqZW99BJcdFEKoX/7W9q2227w05/CMcfAmms2tz5JkhrAQCq1mrfegiuuSCH06qvTcp5bbQXf/GbqG7rRRs2uUJKkhjKQSq1gwQKYMSOF0KlTYe5cWH99+NznUr/QbbZpdoWSJA0YA6nULIsXww03pBB68cXw2muw+urw4Q+nELrrrrDccs2uUpKkAWcglQZTjDBzZgqhkyenpTzHjIEjjkghdN99YcSIZlcpSdKgMpBKg+Ghh+CCC1IQfeCBFDonTIDvfx8OOSSFUkmShigDqTRQnnsOpkxJIfTWW9MqSXvtBaeeCkcdBaut1uwKJUlqCQZSqZFefx0uvTSF0D//Od2iHzcOvvtdOO44WG+9ZlcoSVLLMZBK/TV3Lvz+9ymE/v73acT8xhvDV74CJ5wAW2zR7AolSWppBlKpLxYtSi2gXV2pRfStt2CtteDjH0+Dk3bcMd2ilyRJPTKQSr0VI9x8cwqhU6bAiy+m5TqPPjqF0Pe+Ny3nKUmS6mIglXpy770phHZ1wWOPwahRaWT8pElw0EEwenSzK5Qkqa0ZSKVKnnwyzRPa1QX/+EeaoP5974PTT09zhq68crMrlCSpYxhIpZKXX04rJnV1wV/+krbtvDP86Edw7LGw9trNrU+SpA5lINXQNmtWWju+qwv+8Ic0WOld74Kvfz2NkN9442ZXKElSxzOQauhZsACuvjqF0CuugDlzYN114TOfSf1C3/1uR8hLkjSIDKQaGhYvhr/+NYXQiy6CV19NKyV94AMphO6xR+onKkmSBp2BVJ0rxjQgqasrrSP/9NOwwgowcWIKofvvDyNHNrtKSZKGPAOpOs8jj6QA2tUF990Hw4fDAQfAd74Dhx0GY8c2u0JJklTGQKrO8PzzcOGFKYTefHPatueecO65aeL61Vdvbn2SJKkqA6na1xtvwGWXpRD6pz+lfqLvfndqCT3+eFh//WZXKEmSesFAqvYybx5cdVUKodOmwfz5sNFG8KUvpWmattqq2RVKkqQ6GUjV+goFuPbaFEIvuQTefBPWXBNOOikNTtp5Z6dpkiSpjRlI1ZpihFtvTSF0ypTUR3TFFeHII1MI3WefNFhJkiS1Pf9FV2u5//4UQru60mj5kSPh4INTCD34YFh++WZXKEmSGsxAquZ7+unUCnr++TBzZrr9vs8+8OUvpxbRVVZpdoWSJGkAGUjVHK++mvqDdnXB9denW/Q77gg//CEceyyss06zK5QkSYPEQKrBM2cOXHllCqHTp8PChbDZZnDmmWmE/KabNrtCSZLUBAZSDayFC+Gaa9LKSZddBrNnp9bPU05J/UK3394R8pIkDXEGUjXe4sVw442pJfTCC+GVV1I/0BNOSCF0r71g2LBmVylJklqEgVSNc9ddKYRecAE8+WQaEX/YYSmIHnggjBrV7AolSVILMpCqfx57DCZPTkH0n/9MLZ/77w9nnw0TJ6a5QyVJkmowkKp+L74IF12UQuiNN6Ztu+8OP/sZHHMMrLFGc+uTJEltxUCq3nnrLbj88hRCr7kmLee59dbwrW/B8cfDhhs2u0JJktSmDKSqbv58mDEjhdCpU2HePNhgA/jc59LgpG22aXaFkiSpAxhItbRCAW64IYXQiy+G11+H1VeHj3wkhdBdd4Xllmt2lZIkqYMYSJVWSbrjjhRCJ0+GZ5+FMWPgiCNSCN13XxgxotlVSpKkDmUgHcoefDBN0dTVlV6PGAETJqQQeuihsMIKza5QkiQNAQbSoebZZ2HKlBRCb7strZK0997wn/8JRx0Fq63W7AolSdIQYyAdCl5/HS65JIXQa69Nt+i33x6+9z047jhYd91mVyhJkoYwA+lQMGMGfOxjsMkm8NWvppWT3vWuZlclSZIEGEiHhsMOg1tugR12SLfoJUmSWoiBdChYYQXYccdmVyFJklSRE0pKkiSpqQykkiRJaioDqSRJkprKQCpJkqSmMpBKbe6GG25odgnqQP5eqdH8nVItBlKpzf3lL39pdgnqQP5eqdH8nVItIcbY7BrUD1mWvQQ80ew61FRbAPc1uwh1HH+v1Gj+Tgng5TzPD+y+0UAqtbksy27L83yHZtehzuLvlRrN3ynV4i17SZIkNZWBVGp/5zW7AHUkf6/UaP5OqSpv2UuSJKmpbCGVJElSUxlIJUmS1FTDm12ApL7LsmwL4CBgb2AbYO3irpeA24ALgEvzPC80p0K1kyzL1gVOAQ4F1gcWAY8BlwE/yfP8tSaWpzaRZdl7gAnAHsBWwJqk36XngZuB3+Z5PqN5FaoV2YdUalNZlv0v8MFeHPp34Jg8z58e4JLUxrIsO5D0H5hVqhzyDDAxz/PbB60otZ0sy64H9urFob8H/i3P8zcGuCS1CW/ZS+3rHcXn14H/AT4A7A7sAHwYuKO4fxfgj1mWjRnsAtUesizbFriYFEbnAGeQWrfGAz8ECqTft2lZlq3TnCrVJkp/L70A/Aw4jvR30M7AJ4GHivsPBqZmWWYOEeAte6mdPQ18HPjfPM/ndtt3e5ZlvwMmA0cBmwOfAb4xuCWqTZwDjCEFzwl5npcvOn59lmV3AP9H6hLyDeAjg16h2sX9wFeAi/M8X9Rt3y3FOztXA7uRWlInAb8b3BLVirxlL3WwLMveBjwLjATuyvP83U0uSS2m2N/vtuLbX+V5/rEqx/0J2IcUWtfJ8/zFQSpRHSbLsm2Au4pvp+Z5PrGZ9ag12FQudbA8z18B7i6+3aSZtahlHVn2+lc1jvuf4vMw4LCBK0edLs/zu4FXim/9e0mAgVQaCkYWnx1pr0r2KD7PAW6tcdy1Fc6R+mpE8dm/lwQYSKWOlmXZmsAWxbf3NbMWtawti88PVejz9y95nj8LvNXtHKluWZaNA1YqvvXvJQEGUqnTfYElgxenNLMQtZ4sy0YBqxff9mZasKeKz+sNTEUaIr5S9tq/lwQYSKWOlWXZ7sCnim+fAs5tYjlqTSuWvZ7Vi+NLx4wdgFo0BGRZdjxL+i3fRlp0QTKQSp0oy7J3kOaVHA4sBj5YYWooafmy1wt6cfz8CudJvVKc7/aXxbdzgA/kee5UPwKch1RqOcUwuWqV3bPzPH+sh/NXBWawZBnR/8zz/LrGVagOUv6flJFVj1piVIXzpB5lWbYhcBVpvtvFwIfyPL+/qUWppRhIpdZzNvChKvuuJ62eU1GWZWOB6cDWxU3fyPP8hw2tTp3krbLXvbkNXzqmN7f3JQCyLHs7cA1LVnHK8jy/uIklqQV5y17qEFmWLQ9cSVqiD+BHeZ5/tYklqcXleT4feLn4dt1enFI65qmaR0lFWZatTgqjpflGP5Pn+S9rnKIhyhZSqcXkeX4icGI952RZNhK4lCWtp78kLRUq9eRe0hKOm2ZZNrza1E/FNexXKjtHqinLspVJy4RuVdz01TzPz2leRWpltpBKbS7LsuGkqVMOLG7qIt0Sc7CAeuOvxecVgB1rHDe+wjlSRcXuQzOAccVN38nz/BtNLEktzkAqtbEsy5YDfgscXtx0GWmwwOKmFaV2c2nZ64/WOO4jxecCMHXgylG7K+s+tEtx00/yPP9iE0tSGzCQSm0qy7IAnAecUNw0Azi+1mo7Und5nt8OXFd8e2KWZXt2PybLsn8D3ld8+9s8z18cpPLUZordhy5hSYv6r4BPN60gtQ37kErt67ssadF6CDgd2CzLslrnPJDn+cKBLkxt59PAjaQpeWZkWfZt4E+kfyMmsiRQPM/Sq+xI3XUBE4qvbwJ+DGxV6++lPM//OQh1qcUZSKX2dXTZ602BW3pxzkbA4wNSjdpWnud3ZVl2NHABsApwVvFR7hlgYnFNe6mao8pe7wr8oxfnhAGqRW3EW/aSJPI8nwFsQ2p5vw+YDbxJChRnAtsUb+9LUsOFGB2IK0mSpOaxhVSSJElNZSCVJElSUxlIJUmS1FQGUkmSJDWVgVSSJElNZSCVJElSUxlIJUmS1FQGUkmSJDWVgVTSgAohbBhCiCEEV+GoUwjhuuL37sQm1+HPsIFqfT9DCGcW9/2mCaVJTeNa9pJq6kcIuT7GOL6RtXSKEMKGwInA6zHGc5pajBoqhHA4sB1wXYzxuqYWI7URA6mknrxQZftqwAhgHvBGhf2vDlhF7W9D4AzgCeCcGsc9CTxA5e+vWtPhwIeKr6+rcsxC0s9VUpGBVFJNMca1K20PIVwH7A1MiTGeOJg1DRUxxg82uwY1XozxGeBdza5DaiX2IZUkSVJTGUglDaoQwtYhhMkhhOdDCPNCCPeHEL4aQhjZw3l7FM97OoQwP4TwSgjhjyGEE0IIocZ5KxUHivwjhDCr+LgrhPC1EMLKVc7518CSEMJyIYR/DyHcEkJ4vbh9u27HHxpCuKL4NS0IIbwYQrgyhHBAhWs/DlxbfLtBaXBL2ePEsmNrDmoKyXEhhN8XP3t+COGZEMINIYTPhBDe1u34dUMI/xlCmBFCeCiEMCeE8GYIYWbx+7FKte9jf4QQRhd/xvcXf+bPFX+WW/YwwOfx4r7xNa5d+r5t2G37qBDCMSGE3xZ/9i8XP/uJEML5IYT31Ljmvz43hLBaCOEHIYTHyr6/vwghvL3bOeOLX0Ppdv0Z3X+2Zcf2a5BYX/4shBA2CiGcG0J4MIQwt/izf6L4O/alEMLqfalFapgYow8fPnzU/SD1j4vAb3o4bsPicRHYH5hTfP06UCjbd3mNa3yn7LhI6lO5uOz9BcByFc7bBHi87LjZxUfp/RPAphXOO7O4/3+By4uvFwGvFV9vVzxuBPC7CrWVv/9Ot2vfSupfG4tf//PdHsdV+B6fWKHGlYFryj5ncfG6c8u2ndjtnIvL9s0HXun2M3gYWLfWz7APvydjgb93+9zS92gWcEK1a5f97MbXuH7puht2235ID9+bhcAHqlyz9LnvL3s9m9RfunT+Y8CqZefsVvz5lT5jVvefbW++n2W/exX/XNGHPwvA9sCbZccsYMnvculxYLP/TvExtB+2kEoaTFOAK4GNYoyrACsBXyL9gzgxhHBQ9xNCCJ8GPk8aXHUSsEqMcWVgDHA86R/744EvdDtvJHAJsAHwFCkMjy0+9iUNGFofuCyEMKpKvUcCBwKfAFaKMa4KrAU8Wtz/X8C/kYLcscDYYm0rFc95C/h8COGE0gVjjDsWrwvwVIxx7W6PKTW/g0ucX/w65gKfBlaLMa4GrABsCZxFCh3l7gM+BWwGLB9jfBswGhhPCsobA3kvP7+3fgjsXKzzwyz5Hr27WM+5Df68klnAj4G9ip+5WoxxedLvwzmkMRTnhRDWr3GNn5C+h7vFGMeQfncmkv4ztSHpdxeAGOONMfW3Lv38vtf9Z9vfL6ivfxaA7wErAjcD28cYRxZ/l8cAO5K+Hw6cU3M1OxH78OGjPR/0rYX0aiBUOObK4v7/6bZ9FVKomwu8u8r1d2VJC9jIsu0fYElr0NYVztuquC8CH+m278yymk+q8rmbFj/3RWC9KsccX7zGP7ttH1/c/ngvv8cndtt+EEta/hrSskWaNeHF4jU3rPYzrPOaG7CkBfbEGp/Z8BbSXtT2q+J5Z9T43OeBt1XYf2px/6MV9v2muO/M3vyZqLCv9Lv3m27b+/NnoXRXYudG/K748DEQD1tIJQ2mb8cYY4Xtlxeft+62/ShSq9QfY4z/qHTBGONNFG+fAuX9Ao8uPl8RY/xnhfPuId3ChtS6WckrwP9U2fdBIJBmGXiqyjEXk25Rb9W9z2E/lUbf/yHGOKMRF4wxvgrcSPqadmvENUktwcsBzwK/rfKZA9VC2pMri8+71zjmvBjjKxW2X1583iiEMKahVVXXnz8LbxafG/k7KDWU0z5JGky3Vtn+TPF51W7bS8FonxDC8zWuu1rxeT3gpuLr7YvP19Y478+kPozbV9l/W4xxUZV9pdo+FEI4psZnjCir7bkax9Vjl+LzVfWeGELYCTiZVP+6pNu23a3T99KWUvq+/iXGuLjKMdc36LOWEUJYDfgkMAHYnNTvdli3w2p9rT39vkJquZzdxxLr0Z8/C1eRukv8NoTwc1Kgvj3GuHAgCpX6wkAqadDEGN+qsmte8XlEt+2lFp0Vio+elB+zRvH5mUoHFj1dfH5bCCFUaL19qca5pdpWLD7qqa2/1io+P1nPSSGE/yT1ey2NxC6Q+kguKL5fmdSntFGtfqWfwbM1jqn18+mzEMKWpP9wrFW2uXTLOwIjSf8BqvW1Vvx9jTHOKxvM3v13dqD058/C50iBfDdS/9IvAPNCCDcBF5G6B8xtYK1S3bxlL6mVlf6O+lGMMfTi8ZsK1xjdj88v9KK2z/Sytuv6UUe/hRC2Io3QDsBPSX1oR8U02Kc06KbUhaHqNFpt5NekMHoHaWDaijHGlWKMaxW/1lKrdrt8rX3+s1DsdrAHsB9poNdMUiB/L/Bz4J8hhHUH98uRlmYgldTKSsuW1hoJXU2pdbPWuaV/hF+p0re1lv7U1l+lz96gjnOOIv2d/4cY4ykxxntjjN0D91oVzuuP0s+g1m3xWvtK3SUq/qciVJ9Hdn1gJ9J/KA6LMf4hxjir22GN/loHWr9+32Lyxxjjp2OM2wOrAxlpANQ7SbMhSE1jIJXUykp94MaHEJav89w7is/vrXHMPt2OrUeptgP7cG6pP2VfW+f+XnxeZpqsGkrhe2alncXBObtU2tcPpe/rHtUmbCctP1vN68Xnaq13O1bZXjr+pZiW6axk3xqf2x/9/dlW058/C8uIMb4WYzwP+HJxU62fgzTgDKSSWtlFpAEjqwKn1zowhNB9QFTp9vOEEMK4CsdvxZKR+Bf2obbfkvoibhFCyOqsrTTquWILXy8/G2D/EEJvA3Fpnsltquw/jd71ha3HpaSA9g7SJPNLKX5fTq5x/t3F54kVzg0sO99mSelrXSuEsGaFc7cBJtX43P4o/WxXafB1+/RnIaSVxmqNFyn1Ha02F680KAykklpWse9bafLxLxaXbNystD+EsHwIYc8QwrmkKYvKTQHuKr6+PISwb6mVLoTwPtLI4xHAPaRJ5uut7V6W3Ob8eQjhW+X98EIIK4YQ9g8h/I4UJso9RFopaOUQwlH1fjYwvfgIwCUhhFNKy36GZMsQwvdDCIeXnXNN8fng4lKRKxSPXyOE8F3S97nSFEd9FmN8giXTZv13COGDIYQRxc/dBphB7T6+pf8oHBxC+EJpiqWQlgm9gKWnNip3H2nAWgCmhBA2KZ43IoRwJOl70f0WfqPcU3w+sJFTffXjz8JKwMMhhNNCCNuEEIYVj1+u+Ofg7OJxf2hUrVKfNHsiVB8+fLTngz5MjF/jmPHUmCge+ApLL484i9T3rXzZy8cqnNebpUM3q3Demb382oaRBoV0X8rx9W71Xlvh3P8t2/96sc7HgaMrfI9PrHD+KmX7Y/F78Qq1lw69pGxfaQL1Up2/pMqk7r35Gdb4HnVfOnRe8est/RyrLh1aoebSrACRNNn7/mX7Nux23hHdfj/eJM0JW/q5v7/a7xz9W7J09eLPoVTvc6WfbW++nz397lHnn4Xi70n57+eCYn2LyrY9QoUlY334GMyHLaSSWl6M8RukpSbPI7UuLkearuc5UsvO54E9K5z3cPG8s4DyyfH/CXwd2DbG+GA/6irEGD9BGsH8O1LQGUVq9XsSmAr8O0u6BpQ7GfgWcH/xnA2Kj7G9/OzXSX1gPwT8kRRKViSFjeuB/yh+frnjgC+SWhAXkloQ/wZ8KMb4sd58br1iGkw0nnSbufS9nkdqwd6JJX0jqzmB1J3gAVKIWkgKqbvEGK+u8bmXkb4/15CmbxpB+vl8DxjHkim/GirG+DKp3/KlpEFda7DkZ9uI69f7Z+FN4BDS8qC3FGtakfQfs1tJ39vtYowD8v2QeivEWO/AUkmSGqN4+/0xgBhju0zBJKnBbCGVJElSUxlIJUmS1FQGUkmSJDWVgVSSJElN5aAmSZIkNZUtpJIkSWoqA6kkSZKaykAqSZKkpjKQSpIkqakMpJIkSWoqA6kkSZKa6v8DA6xn768aKzkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (10,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "#plt.ylim(ymax = 2500, ymin = -100)\n", + "#plt.xlim(xmax = 2500, xmin = -100)\n", + "#plt.xlim(xmax = 5, xmin = -0.1)\n", + "\n", + "#ax.yaxis.set_label_coords(-0.08, 0.5)\n", + "#ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "stats.probplot(kcat_values[kcat_values<2000], dist=\"norm\", plot=ax)\n", + "ax.set_title(\" \")\n", + "plt.yticks([0,1000,2000], [\"0\",\"1000\",\"2000\"])\n", + "plt.ylabel(\"Sample quantiles\", fontsize = 24)\n", + "plt.xlabel(\"Theoretical quantiles\", fontsize = 24)\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"S1a.svg\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating how much mean deviation we have between two measurements for the same enzyme-reaction pair:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ReactionSequencekcats
0Reaction_0Sequence_309[2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0]
1Reaction_1Sequence_309[1.2, 3.4, 0.61, 0.07]
2Reaction_2Sequence_3142[6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,...
3Reaction_4Sequence_3263[57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11....
4Reaction_5Sequence_2101[2.98, 0.87]
\n", + "
" + ], + "text/plain": [ + " Reaction Sequence \\\n", + "0 Reaction_0 Sequence_309 \n", + "1 Reaction_1 Sequence_309 \n", + "2 Reaction_2 Sequence_3142 \n", + "3 Reaction_4 Sequence_3263 \n", + "4 Reaction_5 Sequence_2101 \n", + "\n", + " kcats \n", + "0 [2.8, 0.05, 0.11, 205.0, 2.3, 134.0, 360.0] \n", + "1 [1.2, 3.4, 0.61, 0.07] \n", + "2 [6.18, 14.5, 11.58, 13.12, 11.9, 13.98, 14.08,... \n", + "3 [57.1, 19.6, 5.96, 13.6, 26.4, 14.0, 41.1, 11.... \n", + "4 [2.98, 0.87] " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_kcat = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"merged_and_grouped_kcat_dataset_with_FPs_and_ESM1bs_ts.pkl\"))\n", + "df = pd.DataFrame({\"Reaction\": df_kcat[\"Reaction ID\"], \"Sequence\" : df_kcat[\"Sequence ID\"],\n", + " \"kcats\" :df_kcat[\"kcat_values\"]})\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.75, 5.67)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "deviations = []\n", + "x_value = []\n", + "y_value = []\n", + "\n", + "for ind in df.index:\n", + " kcats = df[\"kcats\"][ind]\n", + " if len(kcats) > 1 :\n", + " for i in range(len(kcats)):\n", + " for j in range(i+1, len(kcats)):\n", + " \n", + " deviations.append(abs(np.log10(float(kcats[i])) - np.log10(float(kcats[j]))))\n", + " x_value.append(np.log10(float(kcats[i])))\n", + " y_value.append(np.log10(float(kcats[j])))\n", + "\n", + " \n", + "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "deviations = []\n", + "x_value = []\n", + "y_value = []\n", + "\n", + "for ind in df.index:\n", + " kcats = df[\"kcats\"][ind]\n", + " if len(kcats) > 1 :\n", + " for i in range(len(kcats)):\n", + " for j in range(i+1, len(kcats)):\n", + " if np.log10(float(kcats[i])) > -2.5 and np.log10(float(kcats[j])) > -2.5:\n", + " deviations.append(abs(np.log10(float(kcats[i])) - np.log10(float(kcats[j]))))\n", + " x_value.append(np.log10(float(kcats[i])))\n", + " y_value.append(np.log10(float(kcats[j])))\n", + " \n", + " \n", + "np.round(np.mean(deviations),2), np.round(10**np.mean(deviations),2)\n", + "\n", + "x_value = np.array(x_value)\n", + "y_value = np.array(y_value)\n", + "\n", + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "\n", + "\n", + "x0, x1, y0, y1 = -3, 7, -3,7\n", + "plt.ylim(ymax = y1, ymin = y0)\n", + "plt.xlim(xmax = x1, xmin = x0)\n", + "\n", + "ax.tick_params(axis='x', length=10)\n", + "ax.tick_params(axis='y', length=10)\n", + "\n", + "ax.yaxis.set_label_coords(-0.18, 0.5)\n", + "ax.xaxis.set_label_coords(0.5, -0.1)\n", + "\n", + "plt.xticks([-2,0,2,4,6], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\", \"$10^{6}$\"])\n", + "plt.yticks([-2,0,2,4,6], [\"$10^{-2}$\", \"$10^{0}$\", \"$10^{2}$\", \"$10^{4}$\", \"$10^{6}$\"])\n", + "\n", + "\n", + "\n", + "'''reg = LinearRegression().fit(x_value.reshape(-1,1), y_value.reshape(-1,1),)\n", + "reg.score(x_value.reshape(-1,1), y_value.reshape(-1,1))\n", + "beta0, beta1 =reg.intercept_[0], reg.coef_[0][0]\n", + "plt.plot([x0,x1], [y0,y1], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.plot([x0,x1], [beta0 + x0*beta1, beta0 + x1*beta1], color='red', alpha = 0.6, linestyle='dashed')\n", + "''';\n", + "plt.xlabel(\"Measured $k_{cat}$-values [$s^{-1}$]\", fontsize = 22)\n", + "plt.ylabel(\"Additional measurment for $k_{cat}$-values [$s^{-1}$] \\n \\\n", + "for same enzyme-reaction pairs\", fontsize = 22)\n", + "\n", + "plt.scatter(x_value, y_value, alpha = 0.4, s=30, c=\"navy\")\n", + "\n", + " \n", + "\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"measured_vs_measured.eps\"))\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"measured_vs_measured.png\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculating kcat measurements and predictions for different EC classes:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_train.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "data_test.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "\n", + "model = \"ESM1b_ts_DRFP_mean\"\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_test[\"y_pred\"] = pred_y" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "EC_kcat =[[] for _ in range(6)]\n", + "\n", + "for ind in data_train.index:\n", + " try:\n", + " EC = int(data_train[\"ECs\"][ind][0][0])\n", + " EC_kcat[EC-1].append(data_train[\"log10_kcat\"][ind])\n", + " except IndexError:\n", + " pass\n", + " \n", + "EC_kcat_pred =[[] for _ in range(6)]\n", + "\n", + "for ind in data_test.index:\n", + " try:\n", + " EC = int(data_test[\"ECs\"][ind][0][0])\n", + " EC_kcat[EC-1].append(data_test[\"log10_kcat\"][ind])\n", + " EC_kcat_pred[EC-1].append(data_test[\"y_pred\"][ind])\n", + " except IndexError:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize= (8,8))\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "classes = [str(i) for i in range(1,7)]\n", + "#100 -8\n", + "for i in range(len(EC_kcat)):\n", + " \n", + " \n", + " \n", + " circle = plt.Circle((np.mean(EC_kcat[i]), np.mean(EC_kcat_pred[i]) ),\n", + " np.sqrt(len(EC_kcat_pred[i]))/500, color='navy', fill = True)\n", + " ax.add_artist(circle)\n", + " if i ==5:\n", + " ax.annotate(\"EC\"+ str(i+1), (np.mean(EC_kcat[i])+0.01, np.mean(EC_kcat_pred[i])-0.05), fontsize=17, c='black', weight = \"bold\")\n", + " else:\n", + " ax.annotate(\"EC\"+ str(i+1), (np.mean(EC_kcat[i])+0.03, np.mean(EC_kcat_pred[i])-0.01), fontsize=17, c='black', weight = \"bold\")\n", + " \n", + "\n", + "ticks2 = [0.6,1,1.4,1.8]\n", + "labs = ticks2\n", + "ax.set_xticks(ticks2)\n", + "ax.set_xticklabels(labs, y= -0.03, fontsize=26)\n", + "ax.tick_params(axis='x', length=0, rotation = 0)\n", + "\n", + "ax.set_yticks(ticks2)\n", + "ax.set_yticklabels(labs, y= -0.03, fontsize=26)\n", + "ax.tick_params(axis='y', length=0, rotation = 0)\n", + "\n", + "plt.ylim((0.5,1.9))\n", + "plt.xlim((0.5, 1.9))\n", + "plt.legend(loc = \"upper left\", fontsize=20)\n", + "plt.xlabel('mean measured \\n $k_{cat}$ value on $\\log_{10}$-scale')\n", + "plt.ylabel('mean predicted \\n $k_{cat}$ value on $\\log_{10}$-scale')\n", + "ax.yaxis.set_label_coords(-0.15, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.13)\n", + "\n", + "plt.plot([0.5,1.9], [0.5,1.9], color='grey', alpha = 0.3, linestyle='dashed')\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"EC_classes_mean_kcat.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparing different reaction similarities:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "model = 'ESM1b_ts_DRFP_mean'\n", + "\n", + "data_train = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "data_train.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "data_test.rename(columns = {\"geomean_kcat\" :\"log10_kcat\"}, inplace = True)\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "data_test[\"y_pred\"] = pred_y\n", + "data_test[\"y_true\"] = test_y\n", + "\n", + "\n", + "train_fps = [np.array(list(data_train[\"structural_fp\"][ind][:3276])).reshape(1,-1).astype(int) for ind in data_train.index]\n", + "test_fps = [np.array(list(data_test[\"structural_fp\"][ind][:3276])).reshape(1,-1).astype(int) for ind in data_test.index]\n", + "\n", + "\n", + "max_sim = []\n", + "\n", + "for fp in test_fps:\n", + " jaccard_sim = np.array([1- scipy.spatial.distance.cdist(fp,train_fp, metric='jaccard')[0][0] for train_fp in train_fps])\n", + " max_sim.append(np.max(jaccard_sim))\n", + " \n", + "data_test[\"reaction_sim\"] = max_sim\n", + "\n", + "data_test[\"reaction_sim\"]= (data_test[\"reaction_sim\"] - np.min(data_test[\"reaction_sim\"]))\n", + "data_test[\"reaction_sim\"] = data_test[\"reaction_sim\"]/np.max(data_test[\"reaction_sim\"])\n", + "\n", + "data_test[\"y_true\"] = test_y" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "'''all_fps = list(set(data_train[\"structural_fp\"] + data_train[\"structural_fp\"]))\n", + "\n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", 'all_structural_fps.pkl'), 'wb') as f:\n", + " pickle.dump(all_fps, f)\n", + " \n", + " \n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", 'all_structural_fps.pkl'), 'rb') as f:\n", + " all_fps = pickle.load(f)''';" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0 - 0.4 0.06430806812248102 0.28800920705781385 1.255855379466648 17\n", + "0.4 - 0.8 0.2007273222088296 0.47469369882327617 1.3067111634862465 129\n", + "0.8 - 1 0.4141624233212502 0.65263923852372 0.9083716908949246 350\n", + "identical: 0.571383822236111 0.7787732209816836 0.5057315733031261 354\n" + ] + }, + { + "data": { + "image/png": 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3Zo899uD222+vF4jdeOONnHjiiWy00UZL1l1cbbXV2H///ZkwYQLHHnts8z2RCrX4Fq4Wak7W/XK6CTNpyul+LFQOwENF0j5GbC5djrqB9g04544Hjl9++eUZNmwYECeTGzRoUIVVExERqdwpp5zCKaecUlbaHXbYgR122KF0wgL69u1LCEtGJXWo+kQ1oBauKnjvvwWmJQ9XLyNLJs2nRVM19Bn1x68VzO+9/zqrTt2LpBvhve/Xq1cvhg4dytChQxVsiYiIpEwBV/XeSrbrOOcKthQ653pTtzzRW4XS5eO9n0f9ZYvalsiSOb6oknJEREQkXQUDLjM7PLmtVCjNMu6pZLsCRbrwgMF58lQie8mgtQslSqaDyExV8XkV5YiIiEhKirVwjQRuJKfLzMz+bWaPm9n306xYK3B31v1jiqTLLFi9CLi/inLuyLr/8yLp9qFujrEG6zqKiIi0NBMmfMLBB99F796X06bNufTufTkHH3wXEyZ80txVq7lquhQHJ7dOxZMt3bz3LwHjkodHOucG5qZxzg0Bdkwe3uS9/zLneF/nXEhu43LzJx4CXkvun+6c2zxPOd8DhiUPvyUGyiIiIi3SggWLcO4BBgy4kVGj3mDSpLmEAJMmzWXUqDcYMOBGTjhhNAsWLD0jZIpdpfgtccqDFYukWdadDjxNDD7HOucuAh4nvq57J8cBJgO/r6YA7/1i59yJxDUZOwLjnXOXUXdV4o+As4DeSZah3vsvqns6IiIi6Tv11IcYMeLlomliuwZcd90eTVGl1BVr4cosWaNL2Arw3r8O7AfMJI7lOg+YAIwHziQOYv8c2KMxQZD3/hngAGAWMQA+NynnOeAqYrAVgHO895dXW46IiEjaJkz4ZEkwVYr3Ly013YvFWrgeAxwwzMy2At4FFmQdP8nMvsybs4gQwnmV5mnJvPdjnXMbA6cBewB9iOO1PgLuAa7y3s+oQTn3O+c2BE7NKmc54Ati69fwJAAUERFpsa6+urKFUK655gX69++TUm2ajmVNCFb/gNkawMvAKtSfCyozMLuq9Q1DCKWmNpAmMGzYsDB06NDSCUVERGqod+/LmTSp/HnAV1utM1988ctaFG2lk6SnYJdiCOFTYAviYsgTia1bRl2gZVXeREREZBk1eXJli65MmTIvpZo0raJL+yRB1/HZ+8xsMTHo2jiEUNFEnmkxsxWBNYnjm0q2oIUQNG2CiIhIM+jVq3NFLVw9ey4dkyK06rUUzewo4BRgU8pvPQu08uctIiLSWm2/fV9GjXqj7PSDB/dNrzJNqJp5uI4iTub5WamEaTGzNmY2itjduRnxeahbU0REpIU75ZRii7M0dPLJlaVvqSpu6Qkh/CONilToGOI0CQBTgb8DLwLTgcXNVSkREREprn//Pji3ZVlTQzi35VJxhSLUoGvNzNoTJ9/cGFg52T0d+C/wXAjh28aWkcdRyfZdYEAI4asUyhAREZEUDB++G0DRoMu5LZekWxpUHXCZWSfiBJzHAIUWuJ5tZn8Dzgkh1PIygw2IY7HOV7AlIiLSurRr15brrtuDww7bhGuueYFx4yYyZco8evbsxODBfTn55K2WmpatjKoCrmSOrn8Da1F8TFQX4ozre5vZj0MItRr3lbkSsUVcJSkiIiKV69+/z1IXWBVSccBlZssRF1ReO9n1LnGx5GeJawYC9CJ2Mx4FrAf8AHjQzDYPIdRiJcqJxFauLjU4l4iIiEiqqrlK8RjquvQuATYKIVwSQngyhPBecnsyhHApcVzXxUm+DZO8tXAnsWXtJzU6n4iIiEhqqgm49k+2o0MIZxVrsQohLAoh/A4YTQyQ9i+UtkJ/IbZynWZmG9bonCIiIiKpqCbg2pjYuvW3CvJcn2w3qaK8BkIIs4FdgUnAU2Z2ipmtXCKbiIiISLOoZtB812RbyQD4TNqajLkysw+Tuysk57wSuMLMpgHzS2QPIYS1S6QRERERqZlqAq4ZQHdgDeCVMvOsnmxnVlFePn1zHmdmkO9RRt5QOomIiIhI7VQTcP0X+DFwHHB/mXmOTbavV1FePi1htnsRERGRslQTcN0B7Aj81MwuBQoOnDezNsD5wJ7ElqU7qq1othDCUaVTiYiIiLQM1QRcfwdOJU7zkJnU9B/Eebi+JAZWPYnzcB0GrJvkeyPJKyIiIrJMqWbx6oVm9lPgceKEpmsD5xXJYsD7wE9rNOmpiIiISKtS1dI+IYRPzWwz4Gzi+KxuBZLOIE4JcX6N11JswMz6kGcB7RDCJ2mWKyIiIlJK1YtXhxDmA781s98D/YCNqB/svAG8GEJY0OhaFmBmBpxM7OL8QYE0/wOuAv4aQtAViiIiItLkqg64MpKA6pnk1mTMrCtxBvttM7sKJF2HGHAdZGZ7hBBmNUH1RERERJZodMDVjO4DtkvuTwduJ/8C2gcAqyRp7wV2aNJaioiIyDKvVQZcZnYwMJB4ReTtgEuW+8l1k5mdBVwHHAwMMrMDQwj/arraioiIyLKumrUUW4JDku2EEMLBBYItAEIIc0IIQ4AJxG7HQ5uigiIiIiIZrTXg2pLYujW8gjxXJtstal8dERERkcJaa8CVuRryfxXk+SDZrlLjuoiIiIgU1VoDrjnJtpzFqjO65+QVERERaRKtNeB6J9kOqSBPZtzX2zWui4iIiEhRrTXguo84AH6ImZ1cKrGZHU9c1zEQp4YQERERaTKtNeC6BvgsuX+VmT1hZkPMbG0zW9HMOif3DzGzx4BriQHaZ8l9ERERkSZT8TxcZvbH5O5fQwjTyszTjbj8DiGEYgtdlyWE8LWZ7Q48QRxAPyi5FawC8BWwewjh68aWLyIiIlKJalq4ziEuWl3JgPWVs/LVRAjhv8TFqu8AFhGDqny3RcTJUTcJIbxRq/JFREREytUqZ5rPCCFMAg40s17EJXvyLaD9RAhhcoFTiIiIiKSuqQKudsl2QRonTwKqf6ZxbhEREZHGaqpB85sl26lNVJ6IiLRCb731FkcccQQbbrghK6+8Mssttxxdu3Zlm2224fLLL+e7775bknbkyJGYWcHbCSec0OD806ZN44wzzmDNNdekffv29OjRgwMPPJA333yzKZ+mLINKtnCZ2eEFDu1tZv1KZG8PrA0cTZyS4YXKqiciIsuS119/nZtuuqnevlmzZvHcc8/x3HPP8cQTTzB69Oiqzj1p0iQGDBjAhx9+uGTf1KlTuf322xk9ejRjx45l4MCBjaq/SCHldCmOJAZL2Qy4oIJyDFhM3XqG5WUyW3LlYQjhyXz7q5F9LhERaTm6devGMcccw6BBg+jduzdz585lxIgRPPTQQwCMGTOG999/n3XWWadevjvuuINevXrV27faaqvVe3zGGWcsCbYGDx7ML37xCx588EG898yfP5/DDz+cd999l+WXXz7FZyjLqnLHcFmZ+/L5jtiydWEIYXyZeTLGEYO9QP26ZvZXI/dcIiLSQuyyyy7ssssu9fYNHjyYbt26LXk8e/bsBvn69etH3759C553ypQp3HXXXQC0adOGUaNG0bNnT/bcc0/Gjx/PO++8w8SJExkzZgz77LNPbZ6MSJZyxnCtmXVbK9kXgJ1zjuXe+gK9gE4hhIEhhAerrGNmeodC+6u5iYhICxdC4Msvv+SKK65Ysq9Xr15ssMEGDdIOHDiQ5ZdfnpVXXpmdd96ZBx+s/y/n6aefZtGiRQCsueaa9OzZEwAzY9ttt12S7skn1QEi6SjZ0hNC+Dj7sdmSeOWL3GMp2KHC/SIishTYaaedePzxx+vt23zzzRkxYgQdO3ZskP6zz+LiIzNmzODRRx/l0Ucf5ZJLLuHXv/41ABMnTlyStkeP+tNIZj/+6KOPavUUROqpuGsthNBkywEV6oKsomtSRERaufbt27N48eIlj9u1a8fuu+/OHnvswZprrslXX33F5ZdfzssvvwzA0KFDOeigg1hjjTWYN29evXzZssdszZ07N+VnIcsqjWUSEZEW54orrmD69OlMmjSJkSNHMnbsWJ599ll23HFHPvjgA3r06MGQIUMYMmRIvXy77bYbffv2Zfbs2SxYsICHH36YY489lk6dOi1Jkz21RO7jzp07p/vEZJnVKhevNrM/JrdVK8jTLZMvzbqJiEjjbbTRRgwaNIgDDzyQ0aNHLxkQP3fuXO69996C+bp168a666675PGXX34JUG9A/ZQpU+rlmTy5bjGSNddcs/GVF8mj6hauZEHqQ4HtiYPpVwTalsgWQghrV1tmlnOIA/fvBMpaQJu69RwD0OgFtEVEpPa+/vrrBmO0MhOZZsycOROA559/nq233rpe2unTp/Pee+8teZyZKmK77bajbdu2LFq0iI8//pjJkyfTq1cvQgg8++yzS9IPGtSoWYdECqoq4DKzvYG/A10zu8rMWu1UDiIisgzo168fW2+9NQMHDqRPnz7MnDmTkSNH1hvM3q9fnHP7gAMOoHfv3hx88MFssMEGTJkyhcsvv3zJtBEdOnRgt912A6Bnz578/Oc/5/bbb2fx4sUcdNBBnHnmmYwZM4Z3330XiK1gu+++exM/Y1lWVBxwmdkWwO1JXgMmAS8TF4teXCRrc0t1PUcREWm8efPmMXLkSEaOHJn3+OGHH86Pf/zjJY+feeYZnnnmmQbp2rRpw/Dhw+tNfnrFFVfw4osv8uGHHzJ+/HjGj6+7/mqFFVbgpptu0qSnkppqWrjOIgYv3wLHA7eEEFpDy9VmyVbrOYqItFC/+tWvGDNmDG+++SbTpk1j4cKFdO/enS222ILDDz+c/fbbb0naG2+8kTvuuIP//Oc/fPHFF8yePZvu3bszYMAAzjzzTLbZZpt6515ttdV47rnnuOCCC7jvvvv4/PPP6dq1K4MHD+bss89mww03bOqnK8sQqzRWMrMvgJ7ABSGEs1OpVcMyc9dzHEnsnvw98HmJ7NnrOa4C3BdC2LfWdWxthg0bFoYOHdrc1RAREWkqzTrxeTUtXCsn24dqWZESRtJM6zmKiIiINFY100JkrqddWMuKlCHf0jzlLuWzAJgA7KVJU0VERKSpVdPC9RRwELAx8GJtq1NQ9sQoBnxIbPHaBXi/SL4AfAN8FUJYlFblnHOrA6cCewJ9iMHoR8A9wHDv/YwUymxDDCKXDFLw3mudSBERkRaomhauPwOLgDPMrF2pxLUQQvg46zYx69AXOcdyb5+EEL5MOdjaFfgv8BtgfaAT0IU4SP9c4L/OuS1TKPpksoItEZGlzYQJn3DwwXfRu/fltGlzLr17X87BB9/FhAmfNHfVRCpWccAVQngJOAPYELjXzLrXulJl1KFNCKFtCOGtpi47m3NuE+Lkq12B+cDZwABgMPAXYmD6PWC0c653DctdAxhGbMHTVZcislRZsGARzj3AgAE3MmrUG0yaNJcQYNKkuYwa9QYDBtzICSeMZsGC1H5Li9RcNfNwZZbGeR7YDZhoZo8BbxODjqJCCEvTLO9XEFu0FgG7ee+fzDo23jn3MnAz0Is4wP/oGpX7V+LM/n8D1iHO9i8islQ49dSHGDHi5aJpvH8JgOuu26MpqiTSaNVMC7GY+lcMGhXMIB9CKLX8T6uQdBNmxrDd4L0/tkC6x4EfE4Oy3t77LxtZ7oHAKGLL1nrA3SQBVyVjuDQthIi0RBMmfMKAATeWnf6pp46if/8+KdZIliKtbloIaFjpZnsSZrY2sBewKbAq0LFEfUIIYccaFJ09l9cNRdL9nRhwtSXW82/VFuic60bdtBa/9N5Pd85VezoRkRbn6qtfqCj9Nde8oIBLWoWKA64QQjUD7WvOzFYArgEOI38AmG/eLvLsr9aAZDsfKPYN8UROnqoDLuAy4qSzT3jvb27EeUREWqTx4ydWlH7cuMrSizSXFhE8VcrisvH3AIcTn8NXwGvUBVX/Ad4iduNl9r0LjAeepDY2SLbve+8Lzknmvf8CmJOTp2LOuR2IY8C+BU6o9jwiIi3Z5MlzK0o/Zcq8lGoiUlutMuAC9gd+ktw/lzgofcnyPyGE7UMIGwPdgDOBecQZ8v8QQtihsYU759oTuy8BPisjy6fJdo0qy+sAjEgeXui9f6+a84iItHS9enWuKH3Pnp1SqolIbbXWgOuQZPtMCOHcEELuQH4AQgjzQghXADsSr+q728xqMT3Diln3y/k5lklT2TdJnbOBHwDvARdVeQ4RkRZv++37VpR+8ODK0os0l0YFXGa2g5ndbGb/M7O5ZrbQzDbISTPIzE4ys0MbV9V6+hEDrOvLSRxCeAG4ltgqdVoNyu+Ydf+7MtJ/mydfWZK5vn6VPDzRe/9tsfQiIq3ZKadsVVH6k0+uLL1Ic6kq4DKzjmY2CniM2Nq0FrAC+a8OXAxcDfzDzNaptqI5Mt15H2btW5Bdvzx5xiTbWkza8nXW/eXLSN8+T76SkuV7/ka8uOFm7/2/K8kvItLa9O/fh3IX53BuS12hKK1GtS1c/ySOozLiXFSXF0oYQniKOIAd6k+l0BiZQepzsvZl3++VJ8+sZFvVOKoc2WWV002YSVPZaFA4HdgKmA78ssK8IiKt0vDhu5UMupzbkuHDd2uiGok0XjUzze9NnE8qACeFEK5L9hcLCO4hXqG3PXBxFfXM9QWwNpC9rNBkYgtSB2AL4uLR2TKta9XOPbaE9/5b59w0Ykvb6mVkyaT5tGiqhs5Ktk8AOxaYc6tH5o5z7qDk7nfe+7vzJXbOHQ8cv/zyyzNs2DAABg4cyKBBgyqsmohIOtq1a8t11+3BYYdtwjXXvMC4cROZMmUePXt2YvDgvpx88lZq2ZJWp5rg48hkOyoTbJUhM0/V+lWUl89rxIBrY+BRiLOZmtlzxKDuJOCuTOJkke0zk4fv16gObwGDgHWcc8sVmhoiWUNxpaw8lch0Rf48uZXyz2Q7izgDfQPe+xHACM00LyItXf/+fRRYyVKjmi7FrYmtW7dVkGdSsu1RNFX5/k3sztw1Z//fk/2DzWycmZ1sZr8hrvuYGWh/e43q8FSyXYHY7VfI4Dx5REREZBlSTQtXZsD65xXkySzpXqtpKO4hDsTfwczWCiF8CBBCuMXMDiEGYgOTW7ZXgT/XqA53A/+X3D8GeKZAusyC1YuA+yspwHvftVQa59w4qlhLUURERJpONQFQZsB4voHphWTahL+qorwGQgiTgXZAh0ywlWUfYBgwhdjaZcQutmuAHUII39SiDj4uVT8ueXikcy43uMM5N4Q4BxjATbkLVzvn+jrnQnIbl5tfRERElg7VtHD9j9iFtiEwtsw8makY3qiivLySyU7z7f8W+APwBzNbmfgcp4YQarWGYrbTgaeBTsBY59xFwONJmXsnxyEO6P99CuWLiIhIK1BNC9dYYqvRqWZWMmAzs82BQ4njp8aUSF5TIYTpIYQvUwq28N6/DuwHzCSO5ToPmEBcs/FMoC2x63WPZE1FERERWQZVE3BdTexWXAO40czaF0qYTCExljg56DTghmoq2ZJ578cSr5a8FHibuG7jbOKVlOcAGyfdjyIiIrKMsmoaf8zsAOqmIJgKPEicLiIANxMDuf5AX2Jr2CJg9xDCI42usdSEpoUQEZFlTLNeWFbVJKAhhNvNbBFx2ZkewBHULR59WLLNPLHZwOFpBFtm1o3YXbk9cXmhFYndeMWEEMLata6LiIiISCFVz7oeQrjLzJ4gTjK6F7BZ1vkCcYD8fcCVIYRpjaxnA0l35d+BrpldZWZNZTyXiIiISCGNWuYmhDAduAC4wMzaACsTW5i+CiHknXm9FsxsC+IEpssRA61JwMvENQfzXr0oIiIi0lwava5gRjJNQ81bsgo4izgP17fA8cAtaV2JKCIiItJYNQu4mtgAYtfgJSGEm5u7MiIiIiLF1Gqpnaa2crJ9qFlrISIiIlKGgi1cZvbv5G4IIeyYZ3816p2rEaYAqwOpjRMTERERqZViXYqDk23u2KjByb5K5rPIpK/VOKungIOIE46+WKNzioiIiKSiWMD1JPkDpEL7m9Kfgf2BM8zslhDCgmauj4iIiEhBBQOuEMLgSvY3pRDCS2Z2BnAVcK+ZHRlCmNrM1RIRERHJq1VepWhmf0zuPg/sBkw0s8eIaxnOL5U/hHBeitUTERERqadVBlzERaEz3ZoB6AjskdzKoYBLREREmkxrDbig4aD9Zl2UUkRERKSQiufhMrONzOxDM3vfzL5XRvrVzex/ZvaBma1bXTXrCyG0acytFnUQERERKVc1wccQoC/wvxDC56UShxA+A95P8hxWRXkiIiIirVo1Adf2xHFToyvIcx+xy2+HKsoTERERadWqCbh+mGxfqyDPGzl5RURERJYZ1QRcKybbORXkyaRdqYryRERERFq1aq5SnEVcPLpHBXm6J9t5lRRkZh8md0MIYe08+6tR71wiIiIiaasm4PqAGHD9GHi0zDw/SbYTKyyrb7LNXUqoL9Vr7mWJREREZBlTTcD1GLA1cKKZXRtC+KRYYjP7PnACMdApN0DL+EeF+0VERERanGoCrmuBXxLHcj1uZoeEEF7Il9DMtgZuTdJ+B/y1koJCCEdVsl9ERESkJao44AohfG5mvwGuBNYCnjWzZ4D/AJOSZKsBg4BtMtmA/wshfNz4KouIiIi0LlUt7RNCGG5mHYFhQFtg2+SWy4BFwB9CCH+uupYiIiIirVjVy9yEEC4BtgBuA2YSg6vs20zgFmCLEMJFja2oiIiISGvVqMWrQwj/BQ41MyNeOZiZ/mEa8FEIIfUrAs2sDbABsXtzRWKLW1EhhJvSrpeIiIhIRqMCrowksPoouTUJM1sB+D1wLLBKBVkDoIBLREREmkxNAq6mZmadgSeIXZrWzNURERERKapVBlzElq0tk/vPAiOIazvOBBY3U51ERERE8ioYcJnZ35O7IYRwTJ791ah3rkbYj9g1+CCwdwhBQZaIiIi0WMVauI6kbhmcYwrsr4Ql+WoRcH0v2V6lYEtERERaumIB1yfkD6wK7W9KXwKrE6+GFBEREWnRCgZcIYS+lexvYs8TA64fAq80c11EREREiqp64tNm9pdke0oyB5iIiIhIi1Uw4DKzxWa20Mw2yNl/eHJbKf3q5RdCeBr4LbAdMMrMujZXXURERERKKTUtRL7Wo5HEMVwvAm/VukLlCiFcZmYfANcDn5rZo8B7wPwy8p6Xdv1EREREMooFXIuILWAtcq4uM+sB7AN0IdZz7wqyK+ASERGRJlMsmJpBXDJnbeD1pqlOecxsFeBJYB0007yIiIi0cMUCrheBXYE/mdk3xO66BVnHVzOzuZUWGEL4pNI8efwfsG5y/07gryQzzTfFgtkiIiIilSgWcF1DDLjWBUbnHDPgkSrKCyXKLNdeybluCSEcUYPziYiIiKSm4FWKIYQxwCnAbGKAlbllWJW3WsjMNN+YZYZEpBX67rvvOPfcc9l1113p1q0bZoaZ0bdv3wZpR48ezUknncQWW2xBz549adeuHb169WLffffl2WefzXv+Bx54gJ122omuXbvSvn171l13XYYOHcqcOXNSfmYisjSzUj1wZtYR6EcMctoDNxJbl/4AfF5pgSGEf1RezQZ1+iSpz1YhhJcbe75l0bBhw8LQoUObuxoiFZs5cybdunVrsP/73/8+EydOrLdvvfXW49133817nrZt23L33Xez1157Ldk3bNgwfv/73+dNv9lmm/Hkk0+y4oorVl95EWlOzTrmu2T3Xgjha+A/mcdmdmNy994QQnNNC/Ef4CBgI0ABl8gypE2bNmy99dZsu+22dOnShfPOK37R8YYbbsjRRx/NxhtvzMSJE/njH//I5MmTWbRoEaeffvqSgOuDDz7g7LPPBqBdu3ZceumlrLfeegwfPpwxY8bw6quvcvbZZ/PnP/859ecoIkufggGXmfVJ7n4eQliUdWg8sYVrXpoVK+Fy4OfAr8zs9hDCN81YFxFpQiuttBLPPfccAM8++2zRgOuqq67iJz/5CdkLUnTv3p199tkHgIkTJ/Lll1/So0cPxo4dy6JF8avupz/9KaeffjoAm2yyCb179wbghhtu4MILL6R9+/apPDcRWXoVW9pnIvAhcb3CbOOSW7MFXEk34rHEAf2PmNm6JbKIyDJo5513Jnf1r3XXrf91scIKKwAwa9asJfs6deq05H7nzp2X3J89ezavv96iZskRkVaimpnmzya2cN0JTKt5jcpgZpnB8m8BA4C3zOx1yptpPoQQjkmzfiLSct11111L7g8ePHhJQLXeeust2f/QQw/x0ksvsd5663HxxRfXy//pp5+y1VZbNU1lRWSpUSzgWgi0JQ6Ub2mOJAZ9JNs2wKbJrRhL0ivgElkGPfDAA5x//vlAbNm64oorlhzbc8892XDDDXnzzTeZMWMG/fr1y3uOb77RCAYRqVyxgGsa0BPYEHilaapTtk+oC7hEREq65ZZbOOqoo1i4cCEdO3bknnvuYdNN636jtWvXjkceeYTjjjuOBx98cMn+TTbZhAULFvD2228D0KVLlyavu4i0fsUCrqeBfYGLzWwl4F3qzzS/lZmtWmmBIYQnK82T5xx9G3sOEVl2XHXVVZxxxhmEEOjSpQv3338/gwYNapCud+/ejBkzhqlTpzJx4kR69OhBnz59WHvttZek2XjjjZuy6iKylCgWcP2ZuCB0L2B4zjGjuklHazLTfBIAAnynKxRFpJg//vGPS7oRe/XqxdixY+u1bOXTvXt3unfvDsC//vUvPvroIwA23XRT+vTpUyyriEheBYOfEMLTZrYvcQqGH+RJ0pwTiM0kBm+/BK5oxnqISDO48847AXj//feX7Js/f/6S/X379qVfv3784he/WDJOa4UVVuDiiy9mzpw5PPXUU0vybbXVVkumeTjxxBPp0KED2223HZ06deKpp57i8ssvX5L23HPPTfupichSquRM8wBmtgZxZvcOwL+pG3j+UaUFhhDGV5onT33mEwfzbxdCeK6x52ss59zqwKnAnkAf4gUHHwH3AMO99zMace52wI+BnwDbEKfp6Eq8GvNj4rxo3nv/RiXn1Uzz0prlTvWQ64gjjmDkyJH07duXjz/+uGjajz76aMmyQPvtt1+9qxiznXPOOUsmRhWRVqllzzQPEEL4FPgU6n3RPd+MM81PAvrSAgbOO+d2Bf5JDIKybZbcjnfO7e29f6mKc3cH3gZWyXN4JWDj5HaSc+4i770iKJFG2HvvvZk6dSrvvPMOM2bMoEuXLmy77bacfvrp7Ljjjs1dPRFpxcpq4aqXwWwkMdD5vxDCpDQqVUYdbiBODXFaCOGa5qgDgHNuE+LFBZ2ILU4XA48TA9m9gdOIU2tMBrb03n9R4flXJwl0gTeA+4BnkvN1IrZ8nQFkLpu6wHv/h3LOrRYuERFZxrT8Fq5sIYQjU6hHpa4GDiUu7XNLCGFWqQwpuYIY+CwCdvPeZ1+BOd459zJwM/HCgwuAoys8fwAeA8723j+d5/iTzrlbiUHfqsBZzrm/e+8r7uoVERGR9BRb2qdsZra6mfUzs0Fm1rEW5ywmhPAKcAqwOvAfMxuQdpm5nHNbAjskD0fmBFsAeO9vIY55AzjcOdejkjK89597739SINjKpHkfyCwmtxzws0rKEBERkfRVPUWDma0I/IrYatM769DGxCV3MukOBvYBZoUQjqu2vJyyM1NSvAtsBIw3s0+B14EZxBanQmq1tM++WfdvKJLu78Suv7bAXsDfalB2riey7ue7olRERESaUVUBl5mtDTxI/Oee3Seab0DYs8RuNTOzkSGECdWUmeNI6i/tY8SrA9coka+WS/tkWtXmAy8USZcdDA0gnYBr+az7xYJNkaXKhAmfcPXVLzB+/EQmT55Lr16d2X77vpxyylb076/5skSk5ai4S9HM2gOjgXWAr4FLidMh5BVC+AjIdLftVUUd8/kk5/Zxcsvdny/dJzWqwwbJ9n3v/cJCiZKB8nNy8tTa9ln3306pDJEWY8GCRTj3AAMG3MioUW8wadJcQoBJk+YyatQbDBhwIyecMJoFC/T7Q0RahmpauI4nzgU1H9g+hPASlJwX50FgMLBtFeU10NxL+zjn2hMHqQN8VkaWT4nBVqkWuGrq0ol4pSLAt8QrGUWWaqee+hAjRrxcNE1mJpbrrtujKaokIlJUNYPmf07slhueCbbK8FqyXaeK8lqiFbPuzy0jfSZN5xTqchmxOxXg6kqnnhBpbSZM+IRyp7Xz/iUmTKhVo7aISPWqCbg2TLYPV5BnWrLtVkV5LVH2lZjflZH+2zz5Gs05dzRwQvLwTaCsObhEWrOrry42ZLKha66pLL2ISBqq6VLMLBw9u4I87ZJtwbFOjWVmbYCVgRWAz0MIaQ7e+Drr/vIFU9VpnydfozjndgOuSx5OA/b13tfs/CIt1fjxEytKP25cZelFRNJQTcD1FdCT2I1VfBBFnfWS7ZQqyivIzNoChxOnptiKGNgFYBPqT02xBzCIODXFsBoUPSfrfjndhJk05XQ/luScGwTcRXy+s4BdvPfvlZn3eOD45ZdfnmHD4ksxcOBABg0aVIuqiaRu8uTK/oymTJmXUk1ERMpXTcD1OnEh5W2Be8vMcwgxEHq+ivLyMrNViYtDb0fp6fo/Ic4ZFsxsTAjh1caU7b3/1jk3jThwfvUysmTSfFo0VRmcc1sTrxLtCMwDdvfelxv44r0fAYzQ0j7SWvXq1ZlJk8oPunr27JRibUREylPNGK57iQHOiWbWvVRiMzsc2Dl5eGcV5eU7Zxvi1Xj9iYHcHcCphdKHEF6nbq6sn9WiDtS1oK3jnCsYuDrnelPXDduoxb6dc5sCY4mD9r8Ffua9r8W8ZiKtxvbb960o/eDBlaUXEUlDNQHXDcCHxDUEHzOzLXKOBwAz62NmfyHOtB6A10IIdzWmslkOJbawLQT2CiEcWMYi1vcTA8VaLQP0VLJdgdidWcjgPHkq5pxbH3iUeOHBAmB/7/1j1Z5PpLU65ZRif24NnXxyZelFRNJQccAVQlhAbCWaRVzG5wUz+zAryZ1m9gnwEXBaUsY0YL9G17bOwcQg7voQwoNl5sl0u/2wRnW4O+t+sZnrMwtWLyIGfRVzzq1NXMS6e3KeQ733D1RzLpHWrn//PsSlTEtzbkvNOC8iLUJVi1eHEN4A+gETiK1GfbMOr08cs2TJ7VngRyGED6mdzZPtvRXk+TLZrlKLCvg4EdC45OGRzrmBuWmcc0OAHZOHN3nvv8w53tc5F5LbuNz8SZo1gMeJ61UG4Bjv/e21eA4irdXw4buVDLqc25Lhw3drohqJiBRX9eLVSQA10Mz6A3sTA7AexEWapwGvAPeFEB6vRUVzZObzmlpBnlID66txOvA0sXt1rHPuImJwtBzxNTk9STcZ+H2lJ3fOrUJs2fp+susa4CXn3EZFss3z3n9UaVkirUm7dm257ro9OOywTbjmmhcYN24iU6bMo2fPTgwe3JeTT9ZaiiLSslQdcGUki1E39cDtmdRdIfhqmXnWSraVBGlFee9fd87tB/wT6Aqcl9yyfQ7sXeUM8BsD62Y9PiW5FTOe+uPGRJZa/fv3UWAlIq1CVV2KLcA7yXbzoqnq+3myLXsKhXJ478cSA6NLiQtHzyNOCvsacA6wsS93HRIRERFZKlkIoTYnMutEnOkdYHoIIbXZBs3s18DFxK7LNTNlmdli4jinjUMI2ROf7kxcQNuAY0III9OqW2uhebhERGQZk8bQorI1qoXLzDYxM59cpTgbmJjcZpvZh2Z2nZlt0vhqNnAtsWtwFeA+M+tVoH4dzOx04gSpltTtlhTqIyIiIlJQ1QGXmV0EvAQcS7xK0XJufYHjgJfN7MLGVjRbCGEusD9xPqodgIlm9u+sJFeY2ZPEFrA/E2dl/wbYP4SQ2nqOIiIiIvlUNWjezK4kDt7ONM+9Q5z+YXLyuBfwI+IUEQb8xsw6hhDOaFRts4QQnjSzwcBtxOBuMMmkq9RNxZCp38fEYKum47dEREREylFxwGVm2xKX0QnEQOv4EELeGdSTKSM8sAFwqpmNCiE824j61hNCeNbM1gUOpMjUFMBNyYStIiIiIk2umhYul2w/BfqHEGYUShhCmGBmA4lXBvYBTiC2hNVM0kV4a3ITERERaXGqGcM1iNi6dVGxYCsjSXMJsXtvUBXliYiIiLRq1QRcmSsCX6wgzws5eRvFzD4ysw/M7AcV5OmTXDn5QS3qICIiIlKuaroUvwPaJ7dyZdJ+V0V5+Xyf2Mq2fAV52hEH19dm4jERERGRMlXTwvVJst21gjy7JNuPqyhPREREpFWrJuB6hDge6xdmtlWpxGa2OfALYsvSw1WUVytdku38ZqyDiIiILIOqCbiuIE4i2hF4wsz+aGbfy01kZr3N7PfExZQ7JXmubERdG+vQZKtWNhEREWlSFY/hCiF8ZmbHAjcTg66zgbPN7AvgS2JLVk+gd5LFkn3HhBA+r6aSObPIZ7vRzEqt2dgeWIs4P1cgttCJiIiINJmqZpoPIdxmZtOBEcDqye7vJbdcnwHHhRAa0504mBgsZS88aUDJLs0cHwI1XWZIREREpJSqAi6AEMJYM1sL+BnwE2AjYOXk8HTgDeBR4N4arF/4JPWvLtw+efwSUKyFKxC7MicBTwOjQgilWsREREREaqrqgAuWzPJ+Z3JLTQhhcPZjM1uc3D0yhPBWmmWLiIiINFajAq5mdBOx9arkTPciIiIiza2sqxTNbKXk1rGSk5tZx0ze6qqXXwjhyBDCUSGESbU8rywdvvvuO84991x23XVXunXrhplhZvTt27deuokTJy45Vuw2bty4ZnkeIiKy9CgZcJnZL4gtSTOA3So8/27ATGCGmZ1Yce0qYGZtzGzVZAmftmmWJS3b/PnzOeecc3j44YeZOXNmo8/Xrl27xldKRESWaUW7FM2sAzA0eehDCHdXcvIQwt1mdi1wInCOmf0thLCguqrmrV9b4HDgaOIVi+2IXY2bAG9lpduDuHD2rBDCsFqVLy1TmzZt2Hrrrdl2223p0qUL5513Xt50q622Gv/5z38a7H/22Wf59a9/DUDv3r350Y9+lGp9RURk6VdqDNfPiFceziPOt1WNs4HDgFWT891R5XnqMbNVgXuA7ag/XUQ+nwC/AoKZjQkhvFqLOkjLtNJKK/Hcc88BMXgqFHC1b9+eAQMGNNh/1VVXLbl/4oknstxyrXWoo4iItBSluhR/mmzvDyFMraaAEMI04H5iULR7NefIZWZtgPuA/sQWrTuAU4vU4XXgheThz2pRB1k6ffbZZ9xzzz1ADMicc81cIxERWRqUCrj6EQOasY0sJ5O/XyPPk3EosC2wENgrhHBgCOGaEnkyQV/DJg2RxLXXXsvChXHauIMOOoju3bs3c41ERGRpUCrg6pVsJzaynMz6hb2LpirfwcRA8PoQwoNl5nk52f6wRnWQpcy3337L9ddfv+Txaaed1oy1ERGRpUmpgKtTsp3TyHLm5pyvsTZPtvdWkOfLZLtKjeogS5lRo0YxdWrsOe/fvz9bbLFFM9dIRESWFqUCrszEoqs2spxMkFOriUq7JdtKxpWVGlgvy7jhw4cvua/WLRERqaVSAdfkZNvYn/qZFqlaTVQ6M9muXixRjrWSbVWD/2Xp9vTTT/PSSy8BsPrqq7Pvvvs2c41ERGRpUup696eIc1r9HLikEeXsRxxzNaER58j2DnHw++bA6DLz/DzZvlw0lSwV7rwzLu/5/vvvL9k3f/78Jfv79u1Lv35113Bkt25pKggREam1Uv9VRgMnAf3M7IAQwu2VFmBmBxAnJQ2UHxyVMhoYCJxqZn8OIcwrUYediQFXIE4nIUu5/fffv8G+qVOnLtl/xBFHMHLkSAAmTZrEXXfdBUCHDh04/vjjm6yeIiKybCjapRhCGAu8Shz/dIOZbV/Jyc1sMPB3YqDzSnK+WriW2DW4CnCfmfXKl8jMOpjZ6cQJUo14teUtNaqDLCWuu+46FiyICyAcfPDBrLpqY4csioiI1GchhOIJzLYD/k1cNmcx8FfgyhDCh0XyrA2cTlzSpy3wLfDjEMIzNao3ZjYIeCSp1wLgaWAwMbh7HOhAHHvWkRhsfQ0MDCGoSxEYNmxYGDp0aOmEIiIiS4dmvXiu5ECVEMLTZnYcsaWqDXAKcLKZvQe8BEwhLv3TCegJbAmsS3xiBiwCjqtlsJXU68mkBe02oC91wRbAjsk28+J+DOyvYEtERESaQ1kjg0MIN5vZp8CtwGrEQOaHFJ5ENBPoTAKGhBDGNbKeher1rJmtCxwI7E2cyb4HsVVtGvAKcczWTbVcNFtERESkEmVfihVCGJd0FR4DHAlsRgxsci0ijvu6Efh7COGbRteyeL0WEgPBW9MsR0RERKRaFV37ngRP1wDXmNmKwMbEgesrEmej/wr4bwihsTPTizTahAmfcPXVLzB+/EQmT55Lr16d2X77vpxyylb079+nuasnIiLLkKonG0qCqqdrWBeRmliwYBGnnPIgI0bUH7I3adJcRo16g1Gj3sC5LRk+fDfatcvXSCsiIlJbmt1RljqnnvpQg2Arl/dxVvnrrtujKaokIiLLuFJL+zQrM1uUwm1hcz8vSc+ECZ8sCaZK8f4lJkz4JOUaiYiItPCAi7qpJWp9k6XU1Ve/UFH6a66pLL2IiEg1WnqX4rklju9OnAoC4E3geeK8YBDnBNsK2Ig4P9eLwIMp1FFakPHjJ1aUfty4ytKLiIhUo0UHXCGEggGXmf2RGGy9BhwfQsjbVGFmWwE+STsmhHBeGnWVlmHy5LkVpZ8ypegynCIiIjXR0rsU8zKzHYFzgHeBAYWCLYDk2EDgfeBsM9upSSopzaJXr84Vpe/Zs1NKNREREanTKgMu4DRiN+FFIYSSTRRJmouI47dOTblu0oy2375vRekHD64svYiISDVaa8CVGbf13wryvJ5st6pxXaQFOeWUyt7ek0/Wx0FERNLXWgOulZNt1wrydEm23WpbFWlJ+vfvg3NblpXWuS0147yIiDSJ1hpwfZFs96sgz/7JdlKN6yItzPDhu5UMujIzzYuIiDSF1hpwjSWOxzrOzA4rldjMhgDHE8d9aWqIpVy7dm257ro9eOqpozj44I1YbbXOtGljrLZaZw4+eCOeeuoorrtuDy3rIyIiTaZFTwtRxJ+AIcRFs0cmAdVI4AXgS2JglZmH6whgZ2KANhu4sBnqK82gf/8+6jIUEZEWoVUGXCGEz81sT+ABYCXgJ8mtEAPmAHuHED5vgiqKiIiILNFauxQJIfwH2Bi4C1hM4WV8FgN3A5uEEMY3T21FRERkWdYqW7gyQgifAvubWU9gB2IAlrmCcQZx2ognQgiTm6mKIiIiIq074MoIIUwBRiW3JuecW504oeqeQB9gIfARcA8w3Hs/o0blbA2cBGwPrEYck/YmcCtwo/d+US3KERERkdpqtV2KLYVzbldiS9pvgPWBTsQ5vzYjLr79X1fuxFDFy/k/4BniRQB9gfZAd2AwcD3wpHNOc4yJiIi0QAq4GsE5twlwJ3EC1vnA2cAAYhD0F2AR8D1gtHOudyPKORoYRny/PgYcsDWwB/HCAYDtgHucc3pPRUREWpilokuxGV1BbNFaBOzmvX8y69h459zLwM1AL+AC4OhKC3DOdQUuSx5+DvzIez8lK8kY59z1wLHErsZDgZsqLUdERETSo9aQKiXdhDskD0fmBFsAeO9vAf6dPDzcOdejiqKOoW45orNygq2MXwCzkvu/rqIMERERSZECrurtm3X/hiLp/p5s2wJ7NaKcOcAd+RJ47+dmHdvIOfeDKsoRERGRlCjgqt6AZDufOMN9IU/kyVMW51w74lgtgGe999+mUY6IiIikSwFX9TZItu977xcWSuS9/4LYOpWdp1zrUjfO7q0Sad/JUzcRERFpARRwVcE51x5YNXn4WRlZPk22a1RY1OpZ90uV82nW/UrLERERkRQp4KrOiln355aRPpOmc4rlZB+vtBwRERFJkQKu6nTMuv9dGekzY686Fk3VuHKyx3dVWo6IiIikSAFXdb7Our98Genb58lX63LaZ92vtBwRERFJkSY+rc6crPvldN9l0pTT/VhtOdnHyypn5MiRDfZtuOGGbLXVVixYsIBbb721wfHNNtuMzTbbjPnz53P77bc3ON6vXz822mgjZs2axT333NPg+LbbbssPf/hDpk2bxujRoxscHzRoEGuttRaTJ09m7NixDY7vuOOOrLHGGnz66ac8/vjjDY7vuuuu9OrViw8//JAnn2wwNRp77LEHq666Ku+++y7PPPNMg+P77LMPXbp04Y033uDFF19scPyAAw5ghRVW4NVXX+XVV19tcHzIkCG0a9eOF154gTfffLPB8SOPPBKAp59+mvfee6/esXbt2jFkyBAAxo8fz0cffVTv+AorrMABBxwAwGOPPcZnn9Uf1rfSSiux775xFpGxY8cyeXL9NdtXWWUV9txzTwAeeOABvvrqq3rHe/Xqxa677grA3XffzezZs+sdX3311dlpp50AuP3225k/f36942uuuSbbb789ALfeeisLFiyod3zddddlu+22A/TZ02dPn71s+uw1zWevuSngqoL3/lvn3DTiwPnVS6XPSvNp0VQNZX+ySpWTPVC+YDnOueOB45dffnkWLowXV3bt2pUuXbpUWDUREREpl4UQmrsOrZJzbjwwiDgPV5dCU0Mkayh+njz8h/f+yArKaJecfzngUe/9zkXSHgJkfpod5b0fWezcw4YNC0OHDi23KiIiIq2dNWfhGsNVvaeS7QrAVkXSDc6Tpyze+wXA88nDbZxzxcZxVV2OiIiIpEsBV/Xuzrp/TJF0mQWrFwH3N6KcFYED8iVwznXOOvaG9/5/VZQjIiIiKVHAVSXv/UvAuOThkc65gblpnHNDgB2Thzd577/MOd7XOReS27jc/IkbgBnJ/QsLLID9ZyAzCOvS8p+FiIiINAUNmm+c04GngU7AWOfcRcDjxNd17+Q4wGTg99UU4L2f6Zz7NfA34sD555xzfwJeBboDjrpFsccDt1T1TERERCQ1auFqBO/968B+wEziWK7zgAnEwOdMoC1xwPweyZqK1ZZzAzFgWwz0BUYQx3aNoS7YehrY13u/uNpyREREJB0KuBrJez8W2JjYlfc2MA+YDbwGnANsnHQ/NracYcC2wE3Ax8SZ5acRg7vjgEHe++mNLUdERERqT9NCLKM0LYSIiCxjNC2EiIiIyNJMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKRMAZeIiIhIyhRwiYiIiKTMQgjNXQdpBs65qcDHzV0PERGRJjLNe79rcxWugEtEREQkZepSFBEREUmZAi4RERGRlCngEhEREUmZAi4RERGRlCngEhEREUmZAi4RERGRlCngEhEREUmZAi4RERGRlCngEhEREUmZAi4RERGRlCngEhEREUmZAi4RERGRlCngEhEREUnZcs1dAWldnHO9gN8BewDfA2YBzwNXeO8fb+S5VwJ+A/wc+D7wNfAqcK33/s7GnLuxnHNtgGOBo4D1gbbA/4DbgCu999/VsKwzgL8kDz/23vet1bnLKDu19zennLbAc8CWya5zvffn1Or8VdRneeAM4BDgB8BC4G3gRuB6731oxLm3AE4DBgG9gQB8DjwJXOW9f7UxdU9Tyn/v+wDHED8DqwLfAB8AY4G/eO+nNOb8VdapDbA9sBXQL9n2TQ6f6L2/rqnrJEsPtXBJ2ZxzmwBvEP95rAV8S/yi3AN41Dl3ViPOvToxuBoKrAcsAlYCfgzc4Zz7a6Mq3wjOuXbAA4AHtgE6EgOuzYBLgKecc51rVNbqwPm1OFcVZaf2/uZxKnXBVrNKAv2ngYuBTQEjvsfbEN/z+51zVf04dc6dRAxQjgDWJAZbAGsTg/cXnXPHN+oJpCStz4Nzro1z7hbgbmB3oBfxx1VH4uv/W+At59xWjX4SlVsJ+Dfxs7A/dcGWSKMp4JKyOOc6AvcDqwCvABt577sA3YDLif+k/uSc27mKcxtwJ/Ef0kSgv/d+RWBFYovXYuBE59xxNXgq1bgA+CnxF/iRwApAJ2BPYDrxV7CvUVnDgc7E1p8mk+b7m6esTFD5MdDkrRh5XE8M/qYT39POxPf4SOJ7vgdwbqUndc5tAFxFDM4fBTZMzrsCsAkwLjl2tXNu7UY+h5pK+fNwHDAkuX8F0NN7vxLQAdgV+ARYGfhn0uLU1OYB/yG2Mh8CTG6GOshSSF2KUi5H7OabC+zpvf8cwHs/G/hV8g/jZ8CFwCMVnntv4EfEwGqfTBeL9/4b4FLnXG9id895zrl/1LL7rpSkS+X05OFvvff/yDo82jl3NHAvcLBz7mLv/euNKGsv4mt4D/A68TVpKmm+v7kyQeUQYkDSbJxzmwMHJA+P8t6PTu4vAv7hnOtKDAp+4Zy70nv/ZQWnP5AYUM0G9vXez8069l/n3N7AZ8QfFnsm5bQUaX4eDkm2//be/yKz03u/EHjYOXcE8ASxFXATYst3U5kFrOS9X5zZ4Zy7sAnLl6WYAi4pV+YX6W2ZL98clxK/gLdwzv3Qe/9uFed+rMB4lsuIQU8vYhfj2ArO3Vg/B9oTv4hH5B703t/nnHsPWJf4j6SqgCvpkrya+Ov6DODoKutbrTTf3yWygsrR3vv7nXPNGnBR98//Xe/9/XmOjyC2bnUB9gUqGcPTM9m+nxNsATF4cc69D2xBbDFtSdL8PGRel1cKHH8p636Tvi7JWL2qx+u1Rsn4xROJPxA2IL7m04kte08Bt3jvn8nJM4j43bgNsAaxq3km8DLwt0Jjbp1zI4nd6+cCfyL2YAwhBvfTiD9ez/bez0jSb0kcZrId8W/wDWCY9/7eEs/n+OT5bJg8n8nA48Cl3vu3y3tlak9dilKSc25F6sbbPFwg2bPEoARgxwqL2KHYuZMv/DeThz+u8NyNlanbk0mLWz6ZX/iNqdt5xC+u8733nzTiPBVrgvc3U04nYlD5NXEMV0uQeX/zttJ4778mdi9B5e/vxGS7Tr4xfsnYsXWShy9XeO7UNMHnYWKy3bzA8UzZ3wJvVXhuqUAyNvERYuvqtsQxbHOJXcmbACdR18KfydMZGE8c27c1sbX6a6A7sAtxzG2pIRbLA48Rhxb0JXZRr0H8XnjEOdchaQGeQAzsOyS3fsDdzrkD8pwT59xqxDGTw4EBxCDtW6APcczky865fUu9LmlRwCXlWJ/4BwF1gU89SRN85lfuBuWe2DnXg/jHXfDcicwXb9nnrpFMeeXUbf1kPFpFkm6t04hXxf250vw1kNr7m+N84pfqn7z3E6s8R80k79V6ycM0Pnu3EP8RrUT8J7GBc86S20bEX/MrAo947x+q8NxpSvvzcH2y/bFz7i/JdwDOueWcc7sAmW778zItHZKaQ4hXZc4HDgNW8N53I7bqfx84BXgtJ89i4pjbfYBVvPcrZY3vO4UYsB3vnNu/SLknEX9s7EFsgepMDKzmEIOqc4ifg1uB3t77rkAP4D7iZ/OK3AtZkoub7iNeePE4sVWsQzI+sDcxqOwA3NxcYybVpSjlWC3r/hdF0mWOrVYkTVOeuxYy5ZVTt87JbU65J08GBY8gjvU5yXu/oJpKNlLq70FWUPke8crOlmAl6rqsav68vfefJb+m/wn8hBi8ZFpJOxAvGBhGbN1sSVL9PHjv73LODSU+7zOAM5xzc4gXE7Qlvk5Hee9HVnJeqco2yfYm7/0tmZ3e+0XEixeuyc3gvZ9PvIIzd/9M4Brn3GzgJmJQdUeBcrsAe3vvx2ftu885dynxc/Fb4Anv/TFZ55/qnBsCTCJ+5rYjTq2ScQTxAqb/ALtlf5d67ycRx2F2JI5P/AUxOGxSauGScmSPo/i6SLr5ybaSKRLSPHctZOpXTt2g8vqdTPxFd6v3flyFeWsl1fcgCSo98Z/pKU150UMJqX/2vPdjicHWB8muTNdI5n5XYqDRkjTF3+SFxKtA5yWPVyR+PjLlr9pMVygua2Yn21r+kH0g2W6TzLeXzzM5wVbGY1n3G1ys4L2fR+zOBtgo5/ARyfbKIj9cb022PylwPFVq4RJpJsnVlxcQx8L8qpmrk6aTib88b/feP9rclWlKzrlzgT8SuyX3oG66jx8RW/pOBnZwzg1YVrrPkjFi/yTOwTWa2NX8NnEqiF2Jg6kvJY7lOriZqrmseIjYmrS3c+5+YCQw3nv/VbFMSXfeEcSWrk2J793yOck6ELsZp+U5xX8LnDr7KuA3CqTJTCXTLac+WycPvXOuQctcIhMArlHgeKoUcEk55mXd70jhLrPML/UGV2RVcO5CKj63c+5uYrNzrn9570/Psz+fecRWiHLqBpU99+HEbq3TvPfNOddPau9vVlA5h9iMXzPOuRfI/8V5mff+sjJOkdpnDyDp/vgj8Z/IoJx/YmOcc88SA7ENgLOI//hagjT/3iGOU9ydeFXynln75xD/Wb5DnBbiIOfcTeWOb6vB52GZ470f75z7I/FzumdyI3kPxsQk/v3sPMmg+Yep/936NTCVOL4L6q5E7UT+gGtSgSotyqpbqTTtsvZlB3yrUFqxv/fUKOCScmSP4+hN3WDZXL2TbaE/lHLOXeiXTzXnXpm6P/xsXSo4xxfEgKt3kTSZY3O992WN33LO7UCcZuBN4KY8V7Flvjws69g3yVxFtZbm+3shMaj8AzA7z/PMDM5ePnMs3xQKBXQn//tbbhfXbGJw0Yny3t9KnjfUXd11U74WA+/9V865m4FfEueiaykBV2qfh+TKzKOSh1fkS5MEAS8TW7j2JrbClKOxn4dlkvf+/GTm/wOBwcSrFddLbqc7547x3t+UleUPxGBrGvGzOzZ7frqkGzHzPVXxRURVyu5+3rylLpelgEvK8Q5xbhojzmvS4As4GW/xw+Rh2ZdyJwMhpxHncdmQwpehZ66EquTcg8tNW0SmBWLDImkydatkfpfvJ9sNifPXFNKHuhaGo4hN/rWW2vtL3fM8n+JLFv0uuUGZX9KNXWPSex+cc28Tx9CV8/5WOkXB+sn2oyJpPky2fSs8d5rS/DysQ123TqnXZUsqeF2acs3RpY33/iPgIuCiJGAaSJwraxDwV+dcdlCVGTB/qvd+VJ7T5Qt60/YVseWrLfE789VmqENJGpQoJSWtNi8mDwsNNvwRdS1HlS5q+0SxczvnvkfdP8SaLaBcpkzdBjrnOhRIk6l3U9etJprg/W3JSn32OhD/+UDlzzvTvdKnSJpMQFr2la1pS/nzsDjrfqt6XZYV3vtFyQU8ewALiC3A/bKSrJ5sC01cu1N6tcsvGSSf+czu1tTll0sBl5TrtmQ7JJlcLldm0PdLVcxCnjn3zs65TfMcP5P4a3sSdf8gm8rdxInzugLH5h50zu1J/KUfiAOBy+K9H+m9t0I36tbu+zhr/8jGPpkiUnl/vfeDSzzPj5Ok52bta0qZ92w959weeY4fRwwsviYuuVSJzPxFBxeY+LQzcFDysEnXzixDWn/v7xL/niC+tg0457Ygzr4PLe91Waoks7IX8h1146XaZ+3PTHi7cZ7zdSbODN8cRibbIwv8H1nCOdet2PG0qEtRyuWJc+Z8n7iG4GHe+7eSK47+QByPBPB/+TI75zLLZZzrvT8n5/B9xC/WHwH3OOcO8d4/65xrT5wr5Ywk3dlNPaWA936yc+5K4hIUlzjnZhGXO1nknPspcGOS9J8+zzqKWUtZfNzCuzzSfH9bLO/9K86524nrKY50zh3uvX8w6VYZAlycJP2Lz7OOonNuHHHiyPF5urCvJbaO9QHGOudOoy4I25S4jmSmlae5lzjKlcrnwXs/3zn3D+LSK/s6564nTnD6adKauAvxYpLliGPsRtb6iZXinOtC/QHZmYaJTs65VbP2z/Hef0vrdpNz7mtgFPB0Zgyqc64vsYuxA/HHxn+y8jxKvHr0z865qcRVOIJzbiviShLlDFpPww3E79ptgH87584iXiA1G5asi7sj8bP3BHFy1SalFi4pS7LEyd7EvvItgDeT4GMm8GtiC8/vvPcVL2zs4/pl+xHHdKwJPJNMhDiXuI5iG+A67/31hc+Sqt8DDxKvbLkJmOecm0e8imcV4AXghGaqW02k+f62AscR1+9bhXj14DziYPp/EN/z0cDZlZ7Ue/9P4MrkYf+kjMy5X0r2BeD3Le11Tfnz8EviGn0QW40/cc7NJb4u9xKvNJwD7O+9z3eFW9ruI15xl7llrny8LGf/0jBlRQfifGhjgVnOuRnJ5/8j4iD6RYDLeR9+TxwwvwYwDpifvH/PE1u9DqEZJN2KmeWAViZOKD3DOfdVUr9JxNUfBtFM62Uq4JKyee9fI042dxVxUGt74hfyGOAn3vuLGnHuz4DNiHPwvEP8hTuH+EvkAO/9iY2qfCMkf8h7EoOqZ4ldIoE4MPO3wIByr05sydJ8f1uy5BfwdsSpGV4jvrffEt9rB+xV7dWh3vsziL+q/0WcuTtjInESxv7e+2HV1j1NaX0ekqtQBxMXaH+EGLy0J7akvAH8Bdi4pQWhS6mziK33Y4nv8fLEgecfEFvvt/De35ydwXv/IXHOq1uIU560JQbitwJbNef7lrRCb09snX6Q+NlaMTn8DvEH8wHE1rsmZyEsUwuji4iIiDQ5tXCJiIiIpEwBl4iIiEjKFHCJiIiIpEwBl4iIiEjKFHCJiIiIpEwBl4iIiEjKFHCJiIiIpExL+4iIiEirlCxD9BFAM6zFWhEFXCIiItLiOOd+RlyBZJz3flyzVqYGFHCJiIhIS/Qz4oLUENdtzGcB8G5TVKaxFHCJiIhIq+S9/xxYr7nrUQ4NmhcRERFJmRavFhERWUo45yYC3wd2AN4HhgK7At8D3vbeb5ak6wycBuwDrAu0Bz4FHgIu9d5/mufcKwI/B34KbACsnuT7HPh3ku/9EvXbBjgJGAT0AuYCnwAPAyO99+865wYDTxQ7T2aAfDmD5p1zmwO/BLYHegBzgJeAEd77uwrkmUjd6/g68Hvia9UbmAY8CPzRez+pWD2zqYVLRERk6bMu8CpwItCTONYJAOfc+sAbwDCgH7ACsBD4AXAq8Jpzrn+ecx4B3AjsT+zGW0SMI9YGjgNecc7tlK8yzjlzzl0MPAMcRgxmvgWWBzYHzgJ+lyT/DpgCfJM8npc8zr6VxTl3PPAiMIQYIM4HugI7A3c65252zrUtcorVgZeBXxCDtUAMuo4FnnbOdSu3Lgq4RERElj6XA5OA/t77Tt77zsB+zrkuxNaZ7wN3AJsCHZLjawO3Ad2Au5xzXXPOOY0YpG0NrOC9XwXoAKwP3Ap0Am5zznXKU59fAb9J7v8V6Ou97+K9X4kYwJxAbJHDe/+0974X8K8k/WXe+17Zt3JeAOfcdsC1xFjnTmAN7303YsD1e2LwdCh1gV4+w4EZwHbe+05AZ2BvYCbQt0TeejRoXkREZOmzEPiJ935Ja5D3/n/OuQuIgcI/vfeHZGfw3n8IDHHOrUzshjwWuCzr+KjcQrz3AXjHOXcYsSVtJ2A/4B+ZNM65VYFzkocXeu//L+cckwBf9TMt7HxisDUBOMh7vygpby4wLAkMfwf81jl3lfd+dp5zfAvs5L3/Ksm7ELg/eR0vIz7X3+TJ14BauERERJY+N2UHW1ky0yxcXiTvbcn2J+UWlgReY5KHud2R+xG7LWcQg6DUJUHjDsnDCzPBVo6Lid2WnYnj0vIZkQm2ctybbNcs0KLXgFq4RERElj7P5O5wzq1BHJME8KBzrtBVc8sn2zXynGN14jivnYhdkCvSsPGmd87jbZLtE977r0tXvSY2B4zYbTg+XwLv/Szn3EvEAHELoEELHvBCgfN/nnW/K3GcWVEKuERERJY+U/PsWy3rfo8yzrFC9gPn3PbAaGKLUMYs6ga3dwRWIo7lytYz2X5SRpm10j3Zzkq6EAv5LCd9rjn5dnrvv3HOZR62K6dCCrhERESWPvm60LJborp572eWezLnXDvgFmKw9RhwHvCC9/6brDTHAH8jtiy1FO2buwIZCrhERESWDdljuvoQr7Qr17bE7sjpwN7e+/l50vTMsy+73O9XUF5jZVr4Ojrnunvv87X4QV0Xa6HjNaNB8yIiIssA7/1H1AU/u1WYPROYvFcg2II4riufZ5PtYOdcxwrKXJxsq2kxe4U4fgvqBs/Xk0yRsWXy8OUqyqiIAi4REZFlx8hk+yvn3PcKJUomKu2atWtWsl3HOdchT/qdKRDYEOfA+po4v9cfK6hrZpqGrsUS5eO9n07dbPW/dc7li3d+S5xHbC5xbrJUKeASERFZdlwEfAisSpwp/YDsVifnXJ9kdvaXgZ9l5ZtAnKV9FeAm59xqSfqOzrmjgbuAfNMn4L2fBpybPDzLOXe1c65PVpmrOefOdM7lBmNvJttdM+VV6A/EVrItgFHJFZY45zo75/6POLs9wEUF5uCqKQVcIiIiy4hkoPwuwNvEcVz/AuY456Y55+YDHxMnId2Mui65TL7MrOr7A18452YSW6FuAP5HXVCVzyXAFcn9k4GPnXMznXOzgC+I84KtlZPnHuKYsXWBz5xzk5xzE5N1Dst5rk8T121cnNT5E+fcdOLYtWHErspbiUFo6hRwiYiILEO89/8jzlN1ErHbbQbQhTg7/evACGB34lWJ2fmuAvalrrVrOeAd4GxgOwpMoZDkDd77XxAXrf4XcR6rjsSZ3F8G/kQMgrLzTCN2U95NHNTenTjwvuzB9957D2xFnMx1EvEqy1nAo8D+3vtDC0yKWnMWQqF5z0RERESkFtTCJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIAmNlEMwtmNrK569KcWtLrkNQjmNk5eY4Nzjo+uMkrl0ex+oos6xRwyVIl559Q7u1rM/vMzB4ys5PMrHNz11dERJYNCrhkWdIB+B6wK3AN8IaZbdq8VUqXmY1Lgs1xzV0XWba1xBY5kaa0XHNXQCRF1wJ/zXq8KvBD4ExgXeD7wENm9sMQwpxmqF+LEkLo29x1aAla0usQQrDmrkMlWlt9RZqSWrhkafZlCOGNrNu4EIIHNgb+naRZDTi++aooIiLLAgVcsswJIXwHnJO16yfNVBUREVlGKOCSZdXLWffXKJbQzDY2s2vM7C0zm21m883sf2Z2g5ltViLvaskA/TvN7H0zm2dm35rZ52Z2n5kdaGZl/R2a2epmNszMnjOzaWa2wMymm9kzZnaRmW2clXakmQVg+2TX9nkuIpiYc/6yrs4zs13N7F9m9qmZfWNmM8zsJTM7z8xWLZKvwRgeM/u5mT1qZl8m5/rAzK4ys17lvCYl6vkDM7vCzF43szlm9p2ZTU4e32pmh5vZinnyFXwdcp+DRceY2VNm9lXy+XjezA7Lybe8mZ1gZs8m79kcM5tgZgeUeA6NuurPzLYxswuSsXyTk9dgdvJZvtbMNiiRf2T2Z8XMepnZn8zsDTOblRz7WbH6mlnf5LP4RNapn8jzeTzSzNol9Qxm9nAZz69X8ncQzOyqCl+ezDn2NrO7zeyT5DM4L/kMPGNml5rZDiXyr2dmfzGzV5P3doGZTTWz8WZ2tpmtVSTv+mb2VzN718zmJmW/Z2bXmdmGJcqt91onn8dRZvaxxe+YmXnydDezcyx+h3yVfB6+MLN7s99HSUkIQTfdlpobMBgIye2cIuk6ZKV7tUAaAy4CFmWlzb0tBv5QIH/bEnkzt0eAziWe16nANyXOMzEr/cgyyp2YU8bEZP/IAnVoD/yrxDlnAjuV8d78GLipyHm+ANZpxOfg52W8XgHYNU/egq9DznP4CXB/kXNfmeTpBowvku7/ijyPgp/lnLoMznP8yDKe/0LgpCLlZz5HE4EfAV/mOcfPitUX6FtGPQJwZJL+4uTxImCNEu/zb7Pyb1rhZ6QtMKqMek0ukL8N8KfkNSyWf1yB/L8qkXch8NtyPhvAecTvonp/iznpDwBml6jrfUCnav/udCt+06B5WVZl/7KfWCDNlcRAB+A54Abgf8QvrQ2Ak4n/hM4zs+khhGty8mcGEP8beAj4LzAVWBFYCzgO2Jb4j/sa4Ih8lTCz3xD/CQHMAa4DHif+81sR2BTYA1gnK9tQ4DLgRqAf8CJwVM6pvyvwvAu5kfilDfBWcv7/AisBPwNOBLoAY8xsmxDCK0XOdT6wHTAmOe9HwCrEIOEQ4ti6vwMDK6wjZtYT+AcxQJxKvHDi6eR+B+Jr3z+pc2OcT3z/bwVuAyYTL8Y4h3hxxmlm9gDxM7Qd8SKOe4CvgM2S/L2Jn5/7QghvNrI+uZYDZhD/iT4JvA/MS8rcAjiNeCHJ1Wb2Tgjh34VOBHQG7gJWAC4k/kiYC6xP4b+fjM+J4ya3Ir6nAEcDL+Sk+yzZ/g34DTGgOZL4OhWS+Uy/FEJ4rUQ9cp0AHJjcfzop9wPi3/fKwEbATsT3Kp+riZ95iH+L1wBPEV/zbsTXeB9iIFOPmR0PXJo8nAFcQgzKIX7mf5vU4SIzmxNC+GvuObLsA2wCvAn8hfg32R7YOqu8nxODSwM+BYYn6ScTr9w+BDgI2Iv6f+dSS80d8emmWy1vlN/CdVtWukPyHN8p6/ipBc7RNus8s4GuOccN+EGJ+p6b5F9MnhYd4hdp5lfwB8CaRc7VoDUAGEeRX9k5aSdSuGVnt6zXYwLQMU+aPahr0XupxHtT8P0h/lOuqtUiyX90Vv6Ni6RrB6xY4euQ+xxOz5OmF3UtCV8m7+3PCry3mdfrygJ1LPhaUbqF63vACkWefxfgtST/fwqkGZlVxjxgixKvfdX1zUn7RNZn3gqk6Z91voKtdEXKeDLJ+xywXJF0q+TZ99Ossl/IlyYr7Ro5j1clBquZz0eD7wjij4LJSZr5QM8ir3VIXq8OhepPbHkOxO+r5QukOzHrfDtW+nrqVvqmMVyyzDCzVcxsgJk9CByc7H4KuD1P8rOS7YMhhOH5zhdCWERs5fqW2NK0f87xEEL4X4lqnQdMIwZne+U5/htiYAcwJITwUaEThRA+LVFWY5ySbBcDR4QQvs5T/mjiP2iALcysf5HzvUIMNvO5JOv+9hXWE2LAAzAjhPDfQolCCAtC46YDeS6EcGWe804mtmQBdAduDyHcmyfd68TPH1TRkldKCOHzEML8IsdnAX9MHg4ws1VKnPKSEMLLJdLUyt+S7VrEQC2fo5PtN8RAolKZz8mEEMLCQolCCF/l2f1/yfZbYL8CaTL5c/8ujwI6Jfd/m+87IoTwIfDr5GFH4NhC5yf+TR4dQvimwPFMy/NU4NgQLxrKV89rqWt1PDpfGmkcBVyyNDs7e1AuMbD5D7G15jtiF+FuuV+2ZrYSdV/ydxQrIIQwA3gjebhtsbRm1sbMepvZD81sIzPbiNglk+lK2TQ3PfGXNMAzIYRni50/LWa2HHWvx7gSQeSIrPvFrv68NSQ/q3OFEN4htgBA/IdbqS+SbTcz27uK/OUaVeRYdvdWOemqeZ4VMbNOyQD2DbM+fwuykmxaKG/ilhSrl+suYlcbwDG5B82sE3XdXneFEGZWUUbmc7KnFbnYI0/ZKxO7iAHuDiF8XGG5mb+LecA/i6T7F7GlNDtPPk8X+yEGZP4GHiwWgCeeTLZFv8ukOhrDJcuqd4ErQghz8xzbnLpWpRvN7MYyz9ngyjozM2AI8Z/Gj4i/VgvJ/dLvSxwLAjFQbC5rEcfuAJQK+l4m/hNvRxy3U8jbJc4zgzhuqMFVhGW4n7pxNPeY2XjgAeJr+Eqx1owKvVfk2MwK01XzPEtKAokziRcRrEPduMJ8igUdc0MIH9SybsWEEL4xs5uJ48z2NbMuSYtcxgHEzwfEH07VGElsQf0B8IGZ3Q08BjxVIojajLrXsZq/y42S7WtFWqUIIXxnZi8Tf+wU+1sqOHbNzNpSNwbtCDPLO040j0ZfJSwNqYVLlmbXEr+oNiYGUbsDnhgQbAyMN7Mf5snXo8ryVsh+YGYdiIPCbyZ+aRYLtshzvHvW/UlV1qkWVs66/2WxhCGEBcRB4bn5cpX6pb042bYtmip/HaYTx5N9QvzHOBi4HHgemGlmo83sACtzOo4iij2HxVn3y0lX8+9iM9sSeAf4HXEwf6lZ4It9PmfWqFqVuD7ZdqRuCEBGpsvrQ+I4xYqFEEYSu/QXEC/8OJLYijcxmRbi6qQVMFdj/y4zfxdF/5YSk5Nt1+THWz4zCuzPlFVNw0qp7yqpglq4ZGn2ZQjhjazHrwIPJleO3U/8MrrNzLZOxmNlZP+TP4368wcVMy/n8VBi9yXEK5CuIbYATQa+DiEsBjCzJ4ljeFrDsih5uwFbmhDC02a2LvFKxN2BQcSlnDolj3cHnjezPUIIU5utoikxs+WJYxNXIQYUw4lXK75HHNv2bZJuLeLAdCj++VtU5FgqQghvmNlzxJbho4lX52Jm6wADkmQ3FuqaLrOMs83sb8SAbkdiV2Fn4mflZOAkMzsvhHBO1U+kSPE1Ok+x9yb7u+xm6o+PlCamgEuWOSGEMWZ2HXAS8dLtI6nfLTEt6/7XOUFbWZJfo5mBrv8BfpwJsPIo1BKUXY/VKq1DDU3Put+zWEIza0f8J5+br8klQcW/khtmtgYxAD6JOF5pa2KL577NVccU/Zi6cWEnhRD+ViBdsVbIluB6YsC1lZltlPwtZlq3FlN3kUbVkkHtlwCXJF1wWxI/EycSW77ONrOXQwj3J1ka+3c5PclX9G8pkenam1llYPkVMbAzoE0132VSO+pSlGXVudS1SJ2dtAhkZC6Vh7pf0pVambovyzsKBVtm1pk4Z1M+H1HXXTCoynrU4lf0h9R1i/2oRNrNieO3IM4H1GKEED4NIYwgBlqZfzx7mtnS2H2SPUv5v4qk65d2RbJU81kcRZx7DuCYJCA6PHn8SAjhs/zZqhNCWBRCeD6EcBawa9ah7HmpXqHuuVTzd5n57G1qZu0LJUq+kzZPHlb1t5R08Wfmd+tfpFtSmoACLlkmhRC+JLZuQFza54isY1OJEyEC7G9m36uiiOzW404FU8VWsLwtzUmQNiZ5uI2ZbVNFPTKDcgt+sZeSDDIflzwcbGZrFkl+XNb9R6stM03JZfGZwc7LES+ZX9qU/PwlY9iOy3csJdkDxMv6PIYQsq/kOxTYkzhxK1Q/WL4sIYRnqPuhsWrW/unEuegA9jGz71d46szfRSfqJl7NZ3/qPpuN+Vu6L9n2pfGT/UojKOCSZdml1P0T+F0y/UFGZnbrFYC7i102bmZtzexQM1s9a/dU6gYaH5zvl6yZbUXxWbQzdcyM0bjVzPoWqUe+NSEzg3rXauSv26uTbVvilZv5ns9PqevueTmEMCE3TVMws13MrHeR4x2om/dqDvW7iJYW72fdP7JAmguJXepNJXuA+doV5MsMnl+VeCEMxPfs/vzJy2NmhyVd4IWOD6DuQpjcaRcuSrbtgTuTqSIKnSf37/JG6lrXL873N53suyx5+DV185JV40rqppcYYWZF33MzG2hm1cx/JyVoDJcss0IIk83sBuLg2DWJ0zf8Izn2sJldDvyS2AX1tpl54uD3qcQv4jWJg2z3JXYfbkwyp1YIYbGZ3ZqcexPgKTP7M/EfYRfi/FonEeeb+oJ4FVm+Or5uZn8grtm2FvB6Mv7sUeqWCdqIOGnqujT8R/Y0caLFHsCfzewWIHN5/YJy5xAKITxkZv8kDi7eHnjRzC4jdo+sRJzr52Tij7jvKD5RY9oOBh4ws8eBh4l1/IrYorAecWxO5uqz62s4TURL8jDxKrgewAXJP/B7iIHKD4gtWzsSW2qKTVBbMyGET8zsM2B14FfJ/Xep+0ExJd9EtCGEF83sNeK4u0w3/c2FJvCswE3AZWZ2L/F1+B8xuOlO7Co8OUm3kPrzy2XGgY4Ajid2y75tZtcQW04zU5JsRvxuWAjskJV3mpmdSWxh70X8W7qYulbXAcSJlzNjIX8VQphS7ZMMIUw1s8OBu4lB6zPJ98Bo4pW8bYljyrYitoBtSFyOanzeE0r1GjNNvW66tbQbZS7tk5V+DeJs0YH45d8m5/jvso4Xu31LzhIdxMDqlSJ5viJ+sY+jxPI7xIVuvytRh4l58nUmXoVWMj1Nu3j14BLvS9G6lMg7soz3KxCv4mtfSdnlPgfqLxzdt0i6czLpChwv+FkuVRdgF2IAUej5P0H855p5fGSR17LBZ6vS+ibHTyxSnwblZ+U7OSftRtV+R+Spa7HbfOIKD/nytwH+TMNFo3Nvef+uiTPJl1q8+qxqX+s86Xcl/kgr53kf3tjXV7eGN3UpyjItxCuU/pE8XJecMRUhhAuJE0b+iTiP01fEL8K5xEvs7yQugvu9kDMDe4gTNfYH/kAc9PpNku9tYnfBpiGEJylDCOEy4uD6y4iD+mcl9ZgOPJPUb9c8+eYSW+GuTMotNf9VsTp8G0I4kHil3x3E1rzvkrq8AlxADDofq7aMGvkFcbzPDcSlSjL1/JoYfN4G7BpCOCAk0yMsjUIIDxNbX24htqIuIP7DHU9smdmRhlOZpF2na4mTsD5CbIErt3XxVurmLHs+1OZqu42IS2fdTxxYPi2pzyziYu8XAeuFEG7NlzmEsDiEcCaxJeuvxL+vOck5phJ/SP0eOKxA/kuJreLXEb9L5ie394mtX5uGEC7Kl7caIYSxxFb5M4gt5JOIfxffEFu6HiYuWbReCOGmWpUrdSyJfEVERFqkZF3OzLqTLsSrTUVaFbVwiYhIS5dZT3EexdemFGmxFHCJiEiLlVz9e0jy8LYQwuxi6UVaKl2lKCIiLUoy990KxLmjLiResLEQLU0jrZgCLhERaWluJU4/ku1PuRemiLQmCrhERKSl+pp41d7VNG7yT5Fmp6sURURERFKmQfMiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpIyBVwiIiIiKVPAJSIiIpKy/wfs9JY/nozlQwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "train_reactions = list(set(data_train[\"Reaction ID\"]))\n", + "data_test[\"new reaction\"] = [r_ID not in train_reactions for r_ID in list(data_test[\"Reaction ID\"])]\n", + "\n", + "help_df = data_test.loc[data_test[\"new reaction\"]]\n", + "\n", + "\n", + "sim_bins_lb = [0.0, 0.4, 0.8]\n", + "sim_bins_ub = [0.4, 0.8, 1]\n", + "r2_scores, n_points, pearson_r = [], [], []\n", + "for i in range(len(sim_bins_lb)):\n", + " help_df2 = help_df.loc[help_df[\"reaction_sim\"] <= sim_bins_ub[i]].loc[help_df[\"reaction_sim\"] >= sim_bins_lb[i]]\n", + " pred = np.array(help_df2[\"y_pred\"])\n", + " true = np.array(help_df2[\"y_true\"])\n", + " r2_scores.append(r2_score(true, pred))\n", + " pearson_r.append(stats.pearsonr(true, pred)[0])\n", + " mse = np.mean(abs(true - pred)**2)\n", + " n_points.append(len(pred))\n", + " print(\"%s - %s\" % (sim_bins_lb[i], sim_bins_ub[i]), r2_scores[-1], pearson_r[-1], mse, len(pred))\n", + " \n", + "help_df = data_test.loc[~data_test[\"new reaction\"]] \n", + "\n", + "pred = np.array(help_df[\"y_pred\"])\n", + "true = np.array(help_df[\"y_true\"])\n", + "r2_scores.append(r2_score(true, pred))\n", + "pearson_r.append(stats.pearsonr(true, pred)[0])\n", + "mse = np.mean(abs(true - pred)**2)\n", + "n_points.append(len(pred))\n", + "print(\"identical:\", r2_scores[-1], pearson_r[-1], mse, len(pred))\n", + "\n", + "plt.rcParams.update({'font.size': 28})\n", + "\n", + "#plt.rc('font', **font)\n", + "\n", + "fig, ax = plt.subplots(figsize= (8,6))\n", + "\n", + "for i in range(len(sim_bins_lb) + 1): \n", + " plt.scatter(i, r2_scores[i], c='navy', marker=\"o\", linewidths= 8)\n", + " ax.annotate(n_points[i], (i-0.06, r2_scores[i]+0.03), fontsize=17, c= \"black\", weight = \"bold\")\n", + "\n", + "\n", + "\n", + "plt.xlabel('Reaction similarity score')\n", + "plt.ylabel('Coefficient of \\n determination R²')\n", + "ax.yaxis.set_label_coords(-0.13, 0.5)\n", + "ax.xaxis.set_label_coords(0.5,-0.23)\n", + "\n", + "ticks2 = np.array(range(len(sim_bins_lb)+1))\n", + "labs = [\"%s - %s\" % (sim_bins_lb[i], sim_bins_ub[i]) for i in range(len(sim_bins_lb))] +[\"same \\nreaction\"]\n", + "ax.set_xticks(ticks2)\n", + "ax.set_xticklabels(labs, y= -0.03, fontsize=24)\n", + "ax.tick_params(axis='x', length=0, rotation = 0)\n", + "\n", + "plt.ylim((-0.1,0.8))\n", + "plt.xlim((-0.5, 3.2))\n", + "\n", + "plt.plot([-0.49, 4], [0,0], color='grey', linestyle='dashed')\n", + "plt.savefig(join(\"..\",\"..\", \"data\", \"figures\", \"Reaction_Similarity_Score.eps\"))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "496 1.023881801116698 0.34994372195140233 0.6011238358501336\n", + "354 0.5057315733031263 0.571383822236111 0.7787732209816836\n" + ] + } + ], + "source": [ + "data_test[\"y_pred\"] = pred_y\n", + "data_test[\"y_true\"] = test_y\n", + "\n", + "pred = data_test[\"y_pred\"].loc[data_test[\"new reaction\"]]\n", + "true = data_test[\"y_true\"].loc[data_test[\"new reaction\"]]\n", + "\n", + "MSE = np.mean(abs(np.reshape(true, (-1)) - pred)**2)\n", + "R2 = r2_score(np.reshape(true, (-1)), pred)\n", + "pearson_r = stats.pearsonr(true, pred)[0]\n", + "print(len(true), MSE, R2, pearson_r)\n", + "\n", + "pred = data_test[\"y_pred\"].loc[~data_test[\"new reaction\"]]\n", + "true = data_test[\"y_true\"].loc[~data_test[\"new reaction\"]]\n", + "\n", + "MSE = np.mean(abs(np.reshape(true, (-1)) - pred)**2)\n", + "R2 = r2_score(np.reshape(true, (-1)), pred)\n", + "pearson_r = stats.pearsonr(true, pred)[0]\n", + "print(len(true), MSE, R2, pearson_r)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance for membrane proteins vs. non-membrane proteins" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "data_test = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))\n", + "\n", + "pred_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_pred_xgboost_\" + model + \".npy\"))\n", + "test_y = np.load(join(\"..\", \"..\", \"data\", \"training_results\", \"y_test_true_xgboost_\" + model + \".npy\"))\n", + "\n", + "data_test[\"y_true\"] = np.round(test_y,5)\n", + "data_test[\"y_pred\"] = np.round(pred_y,5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Creating input file for UniProt mapping service" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "IDs = []\n", + "for ind in data_test.index:\n", + " IDs = IDs+ data_test[\"Uniprot IDs\"][ind]\n", + "\n", + "IDs = list(set(IDs)) \n", + "f = open(join(\"..\", \"..\", \"data\", \"enzyme_data\", \"UNIPROT_IDs_test_set.txt\"), \"w\") \n", + "for ID in IDs:\n", + " f.write(str(ID) + \"\\n\")\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loading results from UniProt" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "63" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_location = pd.read_csv(join(\"..\", \"..\", \"data\", \"enzyme_data\",\n", + " \"uniprot-download_true_fields_accession_2Ccc_subcellular_location_for-2023.04.13-18.49.21.41.tsv\"), sep = \"\\t\")\n", + "\n", + "data_test[\"membrane\"] = False\n", + "\n", + "for ind in data_test.index:\n", + " IDs = list(set(data_test[\"Uniprot IDs\"][ind]))\n", + " for ID in IDs:\n", + " try:\n", + " location = list(df_location[\"Subcellular location [CC]\"].loc[df_location[\"From\"] == ID])[0]\n", + " if not pd.isnull(location):\n", + " if \"membrane\" in location.lower():\n", + " data_test[\"membrane\"][ind] = True\n", + " #print(location)\n", + " except IndexError:\n", + " pass\n", + " \n", + "is_membrane = np.array(data_test[\"membrane\"])\n", + "np.sum(is_membrane)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.3605761664082414 0.43880739645243383\n", + "0.6478576822890754 0.6724504396913856\n", + "0.683574459043685 0.8180548403855614\n" + ] + } + ], + "source": [ + "print(r2_score(test_y[is_membrane], pred_y[is_membrane]), r2_score(test_y[~is_membrane], pred_y[~is_membrane]))\n", + "print(stats.pearsonr(test_y[is_membrane], pred_y[is_membrane])[0], stats.pearsonr(test_y[~is_membrane], pred_y[~is_membrane])[0])\n", + "print(np.mean(abs(test_y[is_membrane] - pred_y[is_membrane])**2), np.mean(abs(test_y[~is_membrane] - pred_y[~is_membrane])**2))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1.0690475129835626, 1.4577078087170903)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.var(test_y[is_membrane]), np.var(test_y[~is_membrane])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/code/preprocessing/.ipynb_checkpoints/01 - Data preprocessing-checkpoint.ipynb b/code/preprocessing/.ipynb_checkpoints/01 - Data preprocessing-checkpoint.ipynb index bdd9fe2..e768189 100644 --- a/code/preprocessing/.ipynb_checkpoints/01 - Data preprocessing-checkpoint.ipynb +++ b/code/preprocessing/.ipynb_checkpoints/01 - Data preprocessing-checkpoint.ipynb @@ -5153,9 +5153,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2704 717\n", + "2001 703\n", + "1311 690\n", + "656 655\n" + ] + } + ], "source": [ "data_train2 = train_df.copy()\n", "data_train2[\"index\"] = list(data_train2.index)\n", @@ -5193,11 +5204,59 @@ "np.save(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_train_indices\"), train_indices)\n", "np.save(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_test_indices\"), test_indices)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding DRFPs:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_df = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "test_df = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle5 as p\n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", \"all_reactions_with_IDs_and_FPs.pkl\"), \"rb\") as fh:\n", + " df_reaction = p.load(fh)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "train_df[\"DRFP\"] = [list(df_reaction[\"DRFP\"].loc[df_reaction[\"Reaction ID\"] == R_ID])[0] for R_ID in train_df[\"Reaction ID\"]]\n", + "test_df[\"DRFP\"] = [list(df_reaction[\"DRFP\"].loc[df_reaction[\"Reaction ID\"] == R_ID])[0] for R_ID in test_df[\"Reaction ID\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_df.to_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "test_df.to_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -5211,7 +5270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/preprocessing/.ipynb_checkpoints/02 - Calculate reaction fingerprints and enzyme representations-checkpoint.ipynb b/code/preprocessing/.ipynb_checkpoints/02 - Calculate reaction fingerprints and enzyme representations-checkpoint.ipynb index 8d09208..00d47a0 100644 --- a/code/preprocessing/.ipynb_checkpoints/02 - Calculate reaction fingerprints and enzyme representations-checkpoint.ipynb +++ b/code/preprocessing/.ipynb_checkpoints/02 - Calculate reaction fingerprints and enzyme representations-checkpoint.ipynb @@ -12,6 +12,7 @@ "import os\n", "from rdkit import Chem\n", "from rdkit.Chem import AllChem\n", + "from drfp import DrfpEncoder\n", "CURRENT_DIR = os.getcwd()" ] }, @@ -71,8 +72,8 @@ " \n", " \n", " 2\n", - " {InChI=1S/C19H23N7O6/c20-19-25-15-14(17(30)26-...\n", - " {InChI=1S/p+1, InChI=1S/C19H21N7O6/c20-19-25-1...\n", + " {InChI=1S/C21H28N7O17P3/c22-17-12-19(25-7-24-1...\n", + " {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1...\n", " Reaction_2\n", " 1.000000\n", " \n", @@ -86,7 +87,7 @@ " \n", " 4\n", " {InChI=1S/C3H7O7P/c4-1-2(3(5)6)10-11(7,8)9/h2,...\n", - " {InChI=1S/H2O/h1H2, InChI=1S/C3H5O6P/c1-2(3(4)...\n", + " {InChI=1S/C3H5O6P/c1-2(3(4)5)9-10(6,7)8/h1H2,(...\n", " Reaction_4\n", " 1.000000\n", " \n", @@ -99,36 +100,36 @@ " \n", " \n", " 4434\n", - " {InChI=1S/O2/c1-2, InChI=1S/C34H58N7O21P3S/c1-...\n", + " {InChI=1S/C34H58N7O21P3S/c1-18(58-33-21(43)13-...\n", " {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C34H56N7O2...\n", " Reaction_4434\n", " 1.000000\n", " \n", " \n", " 4435\n", - " {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15...\n", - " {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-...\n", + " {InChI=1S/C11H19NO8/c1-4(10(16)17)19-9-7(12-5(...\n", + " {InChI=1S/C11H20NO11P/c1-4(10(16)17)21-9-7(12-...\n", " Reaction_4435\n", " 1.000000\n", " \n", " \n", " 4436\n", - " {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15...\n", - " {InChI=1S/p+1, InChI=1S/C8H16NO9P/c1-3(11)9-5-...\n", + " {InChI=1S/C8H15NO6/c1-3(11)9-5-7(13)6(12)4(2-1...\n", + " {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-...\n", " Reaction_4436\n", " 1.000000\n", " \n", " \n", " 4437\n", - " {InChI=1S/C16H12O4/c1-19-12-5-2-10(3-6-12)14-9...\n", - " {InChI=1S/p+1, InChI=1S/C17H21N4O9P/c1-7-3-9-1...\n", + " {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1...\n", + " {InChI=1S/C16H12O5/c1-20-10-3-5-11(14(18)7-10)...\n", " Reaction_4437\n", " 0.998668\n", " \n", " \n", " 4438\n", " {InChI=1S/C23H37FN7O17P3S/c1-23(2,18(35)21(36)...\n", - " {InChI=1S/p+1, InChI=1S/C2H3FO2/c3-1-2(4)5/h1H...\n", + " {InChI=1S/C21H36N7O16P3S/c1-21(2,16(31)19(32)2...\n", " Reaction_4438\n", " 1.000000\n", " \n", @@ -141,28 +142,28 @@ " substrate_InChI_set \\\n", "0 {InChI=1S/C8H8O3/c9-7(8(10)11)6-4-2-1-3-5-6/h1... \n", "1 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... \n", - "2 {InChI=1S/C19H23N7O6/c20-19-25-15-14(17(30)26-... \n", + "2 {InChI=1S/C21H28N7O17P3/c22-17-12-19(25-7-24-1... \n", "3 {InChI=1S/C16H28N2O11/c1-5(21)17-9-13(25)14(8(... \n", "4 {InChI=1S/C3H7O7P/c4-1-2(3(5)6)10-11(7,8)9/h2,... \n", "... ... \n", - "4434 {InChI=1S/O2/c1-2, InChI=1S/C34H58N7O21P3S/c1-... \n", - "4435 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... \n", - "4436 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... \n", - "4437 {InChI=1S/C16H12O4/c1-19-12-5-2-10(3-6-12)14-9... \n", + "4434 {InChI=1S/C34H58N7O21P3S/c1-18(58-33-21(43)13-... \n", + "4435 {InChI=1S/C11H19NO8/c1-4(10(16)17)19-9-7(12-5(... \n", + "4436 {InChI=1S/C8H15NO6/c1-3(11)9-5-7(13)6(12)4(2-1... \n", + "4437 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... \n", "4438 {InChI=1S/C23H37FN7O17P3S/c1-23(2,18(35)21(36)... \n", "\n", " product_InChI_set Reaction ID \\\n", "0 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... Reaction_0 \n", "1 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C17H21N4O9... Reaction_1 \n", - "2 {InChI=1S/p+1, InChI=1S/C19H21N7O6/c20-19-25-1... Reaction_2 \n", + "2 {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1... Reaction_2 \n", "3 {InChI=1S/C8H15NO6/c1-3(11)9-5-7(13)6(12)4(2-1... Reaction_3 \n", - "4 {InChI=1S/H2O/h1H2, InChI=1S/C3H5O6P/c1-2(3(4)... Reaction_4 \n", + "4 {InChI=1S/C3H5O6P/c1-2(3(4)5)9-10(6,7)8/h1H2,(... Reaction_4 \n", "... ... ... \n", "4434 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C34H56N7O2... Reaction_4434 \n", - "4435 {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-... Reaction_4435 \n", - "4436 {InChI=1S/p+1, InChI=1S/C8H16NO9P/c1-3(11)9-5-... Reaction_4436 \n", - "4437 {InChI=1S/p+1, InChI=1S/C17H21N4O9P/c1-7-3-9-1... Reaction_4437 \n", - "4438 {InChI=1S/p+1, InChI=1S/C2H3FO2/c3-1-2(4)5/h1H... Reaction_4438 \n", + "4435 {InChI=1S/C11H20NO11P/c1-4(10(16)17)21-9-7(12-... Reaction_4435 \n", + "4436 {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-... Reaction_4436 \n", + "4437 {InChI=1S/C16H12O5/c1-20-10-3-5-11(14(18)7-10)... Reaction_4437 \n", + "4438 {InChI=1S/C21H36N7O16P3S/c1-21(2,16(31)19(32)2... Reaction_4438 \n", "\n", " MW_frac \n", "0 1.000000 \n", @@ -219,6 +220,28 @@ " reaction_site = reaction_site + \".\" + Smarts\n", " return(reaction_site[1:])\n", "\n", + "def get_reaction_site_smiles(metabolites):\n", + " reaction_site = \"\"\n", + " for met in metabolites:\n", + " is_kegg_id = False\n", + " \n", + " if met[0] == \"C\":\n", + " is_kegg_id = True\n", + " \n", + " if is_kegg_id:\n", + " try:\n", + " Smarts = Chem.MolToSmiles(Chem.MolFromMolFile(join(mol_folder, met + '.mol')))\n", + " except OSError:\n", + " return(np.nan)\n", + " else:\n", + " mol = Chem.inchi.MolFromInchi(met)\n", + " if mol is not None:\n", + " Smarts = Chem.MolToSmiles(mol)\n", + " else:\n", + " return(np.nan)\n", + " reaction_site = reaction_site + \".\" + Smarts\n", + " return(reaction_site[1:])\n", + "\n", "def convert_fp_to_array(difference_fp_dict):\n", " fp = np.zeros(2048)\n", " for key in difference_fp_dict.keys():\n", @@ -228,34 +251,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - } - ], + "outputs": [], "source": [ - "df_reactions[\"structural_fp\"], df_reactions[\"difference_fp\"] = \"\", \"\"\n", + "df_reactions[\"structural_fp\"], df_reactions[\"difference_fp\"], df_reactions[\"DRFP\"] = \"\", \"\", \"\"\n", "df_reactions[\"#substrates\"], df_reactions[\"#products\"] = \"\", \"\"\n", "\n", "for ind in df_reactions.index:\n", @@ -269,9 +269,14 @@ " rxn_forward = AllChem.ReactionFromSmarts(left_site + \">>\" + right_site)\n", "\n", " difference_fp = Chem.rdChemReactions.CreateDifferenceFingerprintForReaction(rxn_forward)\n", - " difference_fp =convert_fp_to_array(difference_fp.GetNonzeroElements())\n", + " difference_fp = convert_fp_to_array(difference_fp.GetNonzeroElements())\n", " structural_fp = Chem.rdChemReactions.CreateStructuralFingerprintForReaction(rxn_forward).ToBitString()\n", + " \n", + " left_site = get_reaction_site_smiles(substrates)\n", + " right_site = get_reaction_site_smiles(products)\n", + " drfp = DrfpEncoder.encode(left_site + \">>\" + right_site)[0]\n", "\n", + " df_reactions[\"DRFP\"][ind] = drfp\n", " df_reactions[\"structural_fp\"][ind] = structural_fp\n", " df_reactions[\"difference_fp\"][ind] = difference_fp\n", " df_reactions[\"#substrates\"][ind] = len(substrates)\n", @@ -289,6 +294,17 @@ "df_reactions.to_pickle(join(\"..\", \"..\", \"data\", \"reaction_data\", \"all_reactions_with_IDs_and_FPs.pkl\"))" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle5 as p\n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", \"all_reactions_with_IDs_and_FPs.pkl\"), \"rb\") as fh:\n", + " data = p.load(fh)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -387,7 +403,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -401,7 +417,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/preprocessing/01 - Data preprocessing.ipynb b/code/preprocessing/01 - Data preprocessing.ipynb index 76bfce1..e768189 100644 --- a/code/preprocessing/01 - Data preprocessing.ipynb +++ b/code/preprocessing/01 - Data preprocessing.ipynb @@ -5204,11 +5204,59 @@ "np.save(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_train_indices\"), train_indices)\n", "np.save(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"CV_test_indices\"), test_indices)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adding DRFPs:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "train_df = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "test_df = pd.read_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle5 as p\n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", \"all_reactions_with_IDs_and_FPs.pkl\"), \"rb\") as fh:\n", + " df_reaction = p.load(fh)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "train_df[\"DRFP\"] = [list(df_reaction[\"DRFP\"].loc[df_reaction[\"Reaction ID\"] == R_ID])[0] for R_ID in train_df[\"Reaction ID\"]]\n", + "test_df[\"DRFP\"] = [list(df_reaction[\"DRFP\"].loc[df_reaction[\"Reaction ID\"] == R_ID])[0] for R_ID in test_df[\"Reaction ID\"]]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "train_df.to_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"train_df_kcat.pkl\"))\n", + "test_df.to_pickle(join(\"..\", \"..\", \"data\", \"kcat_data\", \"splits\", \"test_df_kcat.pkl\"))" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -5222,7 +5270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.7" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/code/preprocessing/02 - Calculate reaction fingerprints and enzyme representations.ipynb b/code/preprocessing/02 - Calculate reaction fingerprints and enzyme representations.ipynb index 8168273..00d47a0 100644 --- a/code/preprocessing/02 - Calculate reaction fingerprints and enzyme representations.ipynb +++ b/code/preprocessing/02 - Calculate reaction fingerprints and enzyme representations.ipynb @@ -12,6 +12,7 @@ "import os\n", "from rdkit import Chem\n", "from rdkit.Chem import AllChem\n", + "from drfp import DrfpEncoder\n", "CURRENT_DIR = os.getcwd()" ] }, @@ -71,8 +72,8 @@ " \n", " \n", " 2\n", - " {InChI=1S/C19H23N7O6/c20-19-25-15-14(17(30)26-...\n", - " {InChI=1S/p+1, InChI=1S/C19H21N7O6/c20-19-25-1...\n", + " {InChI=1S/C21H28N7O17P3/c22-17-12-19(25-7-24-1...\n", + " {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1...\n", " Reaction_2\n", " 1.000000\n", " \n", @@ -86,7 +87,7 @@ " \n", " 4\n", " {InChI=1S/C3H7O7P/c4-1-2(3(5)6)10-11(7,8)9/h2,...\n", - " {InChI=1S/H2O/h1H2, InChI=1S/C3H5O6P/c1-2(3(4)...\n", + " {InChI=1S/C3H5O6P/c1-2(3(4)5)9-10(6,7)8/h1H2,(...\n", " Reaction_4\n", " 1.000000\n", " \n", @@ -99,36 +100,36 @@ " \n", " \n", " 4434\n", - " {InChI=1S/O2/c1-2, InChI=1S/C34H58N7O21P3S/c1-...\n", + " {InChI=1S/C34H58N7O21P3S/c1-18(58-33-21(43)13-...\n", " {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C34H56N7O2...\n", " Reaction_4434\n", " 1.000000\n", " \n", " \n", " 4435\n", - " {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15...\n", - " {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-...\n", + " {InChI=1S/C11H19NO8/c1-4(10(16)17)19-9-7(12-5(...\n", + " {InChI=1S/C11H20NO11P/c1-4(10(16)17)21-9-7(12-...\n", " Reaction_4435\n", " 1.000000\n", " \n", " \n", " 4436\n", - " {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15...\n", - " {InChI=1S/p+1, InChI=1S/C8H16NO9P/c1-3(11)9-5-...\n", + " {InChI=1S/C8H15NO6/c1-3(11)9-5-7(13)6(12)4(2-1...\n", + " {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-...\n", " Reaction_4436\n", " 1.000000\n", " \n", " \n", " 4437\n", - " {InChI=1S/C16H12O4/c1-19-12-5-2-10(3-6-12)14-9...\n", - " {InChI=1S/p+1, InChI=1S/C17H21N4O9P/c1-7-3-9-1...\n", + " {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1...\n", + " {InChI=1S/C16H12O5/c1-20-10-3-5-11(14(18)7-10)...\n", " Reaction_4437\n", " 0.998668\n", " \n", " \n", " 4438\n", " {InChI=1S/C23H37FN7O17P3S/c1-23(2,18(35)21(36)...\n", - " {InChI=1S/p+1, InChI=1S/C2H3FO2/c3-1-2(4)5/h1H...\n", + " {InChI=1S/C21H36N7O16P3S/c1-21(2,16(31)19(32)2...\n", " Reaction_4438\n", " 1.000000\n", " \n", @@ -141,28 +142,28 @@ " substrate_InChI_set \\\n", "0 {InChI=1S/C8H8O3/c9-7(8(10)11)6-4-2-1-3-5-6/h1... \n", "1 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... \n", - "2 {InChI=1S/C19H23N7O6/c20-19-25-15-14(17(30)26-... \n", + "2 {InChI=1S/C21H28N7O17P3/c22-17-12-19(25-7-24-1... \n", "3 {InChI=1S/C16H28N2O11/c1-5(21)17-9-13(25)14(8(... \n", "4 {InChI=1S/C3H7O7P/c4-1-2(3(5)6)10-11(7,8)9/h2,... \n", "... ... \n", - "4434 {InChI=1S/O2/c1-2, InChI=1S/C34H58N7O21P3S/c1-... \n", - "4435 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... \n", - "4436 {InChI=1S/C10H16N5O13P3/c11-8-5-9(13-2-12-8)15... \n", - "4437 {InChI=1S/C16H12O4/c1-19-12-5-2-10(3-6-12)14-9... \n", + "4434 {InChI=1S/C34H58N7O21P3S/c1-18(58-33-21(43)13-... \n", + "4435 {InChI=1S/C11H19NO8/c1-4(10(16)17)19-9-7(12-5(... \n", + "4436 {InChI=1S/C8H15NO6/c1-3(11)9-5-7(13)6(12)4(2-1... \n", + "4437 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... \n", "4438 {InChI=1S/C23H37FN7O17P3S/c1-23(2,18(35)21(36)... \n", "\n", " product_InChI_set Reaction ID \\\n", "0 {InChI=1S/C17H23N4O9P/c1-7-3-9-10(4-8(7)2)21(1... Reaction_0 \n", "1 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C17H21N4O9... Reaction_1 \n", - "2 {InChI=1S/p+1, InChI=1S/C19H21N7O6/c20-19-25-1... Reaction_2 \n", + "2 {InChI=1S/C21H30N7O17P3/c22-17-12-19(25-7-24-1... Reaction_2 \n", "3 {InChI=1S/C8H15NO6/c1-3(11)9-5-7(13)6(12)4(2-1... Reaction_3 \n", - "4 {InChI=1S/H2O/h1H2, InChI=1S/C3H5O6P/c1-2(3(4)... Reaction_4 \n", + "4 {InChI=1S/C3H5O6P/c1-2(3(4)5)9-10(6,7)8/h1H2,(... Reaction_4 \n", "... ... ... \n", "4434 {InChI=1S/H2O2/c1-2/h1-2H, InChI=1S/C34H56N7O2... Reaction_4434 \n", - "4435 {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-... Reaction_4435 \n", - "4436 {InChI=1S/p+1, InChI=1S/C8H16NO9P/c1-3(11)9-5-... Reaction_4436 \n", - "4437 {InChI=1S/p+1, InChI=1S/C17H21N4O9P/c1-7-3-9-1... Reaction_4437 \n", - "4438 {InChI=1S/p+1, InChI=1S/C2H3FO2/c3-1-2(4)5/h1H... Reaction_4438 \n", + "4435 {InChI=1S/C11H20NO11P/c1-4(10(16)17)21-9-7(12-... Reaction_4435 \n", + "4436 {InChI=1S/p+1, InChI=1S/C10H15N5O10P2/c11-8-5-... Reaction_4436 \n", + "4437 {InChI=1S/C16H12O5/c1-20-10-3-5-11(14(18)7-10)... Reaction_4437 \n", + "4438 {InChI=1S/C21H36N7O16P3S/c1-21(2,16(31)19(32)2... Reaction_4438 \n", "\n", " MW_frac \n", "0 1.000000 \n", @@ -219,6 +220,28 @@ " reaction_site = reaction_site + \".\" + Smarts\n", " return(reaction_site[1:])\n", "\n", + "def get_reaction_site_smiles(metabolites):\n", + " reaction_site = \"\"\n", + " for met in metabolites:\n", + " is_kegg_id = False\n", + " \n", + " if met[0] == \"C\":\n", + " is_kegg_id = True\n", + " \n", + " if is_kegg_id:\n", + " try:\n", + " Smarts = Chem.MolToSmiles(Chem.MolFromMolFile(join(mol_folder, met + '.mol')))\n", + " except OSError:\n", + " return(np.nan)\n", + " else:\n", + " mol = Chem.inchi.MolFromInchi(met)\n", + " if mol is not None:\n", + " Smarts = Chem.MolToSmiles(mol)\n", + " else:\n", + " return(np.nan)\n", + " reaction_site = reaction_site + \".\" + Smarts\n", + " return(reaction_site[1:])\n", + "\n", "def convert_fp_to_array(difference_fp_dict):\n", " fp = np.zeros(2048)\n", " for key in difference_fp_dict.keys():\n", @@ -228,34 +251,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:20: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - "C:\\Users\\alexk\\anaconda3\\envs\\Predicting_Km\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" - ] - } - ], + "outputs": [], "source": [ - "df_reactions[\"structural_fp\"], df_reactions[\"difference_fp\"] = \"\", \"\"\n", + "df_reactions[\"structural_fp\"], df_reactions[\"difference_fp\"], df_reactions[\"DRFP\"] = \"\", \"\", \"\"\n", "df_reactions[\"#substrates\"], df_reactions[\"#products\"] = \"\", \"\"\n", "\n", "for ind in df_reactions.index:\n", @@ -269,9 +269,14 @@ " rxn_forward = AllChem.ReactionFromSmarts(left_site + \">>\" + right_site)\n", "\n", " difference_fp = Chem.rdChemReactions.CreateDifferenceFingerprintForReaction(rxn_forward)\n", - " difference_fp =convert_fp_to_array(difference_fp.GetNonzeroElements())\n", + " difference_fp = convert_fp_to_array(difference_fp.GetNonzeroElements())\n", " structural_fp = Chem.rdChemReactions.CreateStructuralFingerprintForReaction(rxn_forward).ToBitString()\n", + " \n", + " left_site = get_reaction_site_smiles(substrates)\n", + " right_site = get_reaction_site_smiles(products)\n", + " drfp = DrfpEncoder.encode(left_site + \">>\" + right_site)[0]\n", "\n", + " df_reactions[\"DRFP\"][ind] = drfp\n", " df_reactions[\"structural_fp\"][ind] = structural_fp\n", " df_reactions[\"difference_fp\"][ind] = difference_fp\n", " df_reactions[\"#substrates\"][ind] = len(substrates)\n", @@ -289,6 +294,17 @@ "df_reactions.to_pickle(join(\"..\", \"..\", \"data\", \"reaction_data\", \"all_reactions_with_IDs_and_FPs.pkl\"))" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle5 as p\n", + "with open(join(\"..\", \"..\", \"data\", \"reaction_data\", \"all_reactions_with_IDs_and_FPs.pkl\"), \"rb\") as fh:\n", + " data = p.load(fh)" + ] + }, { "cell_type": "markdown", "metadata": {},