diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f36655a --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +.python-version +.ipynb_checkpoints/ +spatio_flux.egg-info/ +__pycache__/ +out/ diff --git a/demo/particle_comets.ipynb b/demo/particle_comets.ipynb index 8999ade..2ebfbd3 100644 --- a/demo/particle_comets.ipynb +++ b/demo/particle_comets.ipynb @@ -2,52 +2,61 @@ "cells": [ { "cell_type": "code", - "execution_count": 17, "id": "11eeec03bc8a30ce", "metadata": { "ExecuteTime": { - "end_time": "2024-09-12T18:22:17.424053Z", - "start_time": "2024-09-12T18:22:15.995838Z" + "end_time": "2024-11-05T15:04:24.795841Z", + "start_time": "2024-11-05T15:04:24.779844Z" } }, - "outputs": [], "source": [ "import itertools\n", - "from process_bigraph import Composite\n", - "from spatio_flux import core\n", + "from process_bigraph import Composite, ProcessTypes, default\n", + "\n", + "from spatio_flux import register_types\n", "from spatio_flux.viz.plot import plot_time_series, plot_species_distributions_to_gif, plot_species_distributions_with_particles_to_gif\n", "from spatio_flux.processes.dfba import DynamicFBA, get_single_dfba_spec, get_spatial_dfba_state\n", "from spatio_flux.processes.diffusion_advection import DiffusionAdvection, get_diffusion_advection_spec, get_diffusion_advection_state\n", "from spatio_flux.processes.particles import Particles, get_particles_spec, get_particles_state\n", - "from spatio_flux.processes.particle_comets import get_particle_comets_state" - ] + "from spatio_flux.processes.particle_comets import get_particle_comets_state\n", + "\n", + "core = ProcessTypes()\n", + "core = register_types(core)" + ], + "outputs": [], + "execution_count": 3 }, { "cell_type": "code", - "execution_count": 18, "id": "968092e4-a12e-4f36-8a67-1e2e40461020", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:04:24.807940Z", + "start_time": "2024-11-05T15:04:24.805149Z" + } + }, + "source": [ + "core.process_registry.list()" + ], "outputs": [ { "data": { "text/plain": [ "['composite',\n", - " 'bounds',\n", " 'ram-emitter',\n", " 'console-emitter',\n", - " 'DynamicFBA',\n", " 'DiffusionAdvection',\n", - " 'Particles']" + " 'MinimalParticle',\n", + " 'Particles',\n", + " 'DynamicFBA']" ] }, - "execution_count": 18, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], - "source": [ - "core.process_registry.list()" - ] + "execution_count": 4 }, { "cell_type": "markdown", @@ -65,182 +74,65 @@ "## dFBA process" ] }, - { - "cell_type": "markdown", - "id": "cfc6d1f2", - "metadata": {}, - "source": [ - "### With the default values for model/metabolites" - ] - }, { "cell_type": "code", - "execution_count": 19, "id": "d1589355618d8afd", "metadata": { "ExecuteTime": { - "end_time": "2024-09-12T18:22:17.490642Z", - "start_time": "2024-09-12T18:22:17.424742Z" + "end_time": "2024-11-05T15:04:26.174650Z", + "start_time": "2024-11-05T15:04:24.881005Z" } }, - "outputs": [], "source": [ "total_time = 60.0\n", "\n", - "# get dfba config with all default values\n", + "# get dfba config\n", "single_dfba_config = {\n", " 'dfba': get_single_dfba_spec(path=['fields']),\n", " 'fields': {\n", - " # How to pass the initial state of the system?\n", " 'glucose': 10,\n", " 'acetate': 0,\n", " 'biomass': 0.1\n", " }\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "0286dd4f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'dfba': {'_type': 'process',\n", - " 'address': 'local:DynamicFBA',\n", - " 'config': {'model_file': 'textbook',\n", - " 'kinetic_params': {'glucose': (0.5, 1), 'acetate': (0.5, 2)},\n", - " 'biomass_reaction': 'Biomass_Ecoli_core',\n", - " 'substrate_update_reactions': {'glucose': 'EX_glc__D_e',\n", - " 'acetate': 'EX_ac_e'},\n", - " 'biomass_identifier': 'biomass',\n", - " 'bounds': {'EX_o2_e': {'lower': -2, 'upper': None},\n", - " 'ATPM': {'lower': 1, 'upper': 1}}},\n", - " 'inputs': {'substrates': {'glucose': ['fields', 'glucose'],\n", - " 'acetate': ['fields', 'acetate'],\n", - " 'biomass': ['fields', 'biomass']}},\n", - " 'outputs': {'substrates': {'glucose': ['fields', 'glucose'],\n", - " 'acetate': ['fields', 'acetate'],\n", - " 'biomass': ['fields', 'biomass']}}},\n", - " 'fields': {'glucose': 10, 'acetate': 0, 'biomass': 0.1}}" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "single_dfba_config" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "964feaf8", - "metadata": {}, - "outputs": [], - "source": [ - "# Make the simulation\n", - "# This isn't actually adding anything to the \n", + "}\n", + "\n", + "# make the simulation\n", "sim = Composite({\n", " 'state': single_dfba_config,\n", " 'emitter': {'mode': 'all'}\n", - "}, core=core)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "740f505c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sim" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "a4d6e9e9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created new file: out/single_dfba.json\n" - ] - } + "}, core=core)\n", + "\n", + "# save the document\n", + "sim.save(filename='single_dfba.json', outdir='out')\n", + "\n", + "# simulate\n", + "print('Simulating...')\n", + "sim.update({}, total_time)\n", + "\n", + "# gather results\n", + "dfba_results = sim.gather_results()" ], - "source": [ - "# Save the document\n", - "sim.save(filename='single_dfba.json', outdir='out')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "fa576b18", - "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Created new file: out/single_dfba.json\n", "Simulating...\n" ] } ], - "source": [ - "# simulate\n", - "print('Simulating...')\n", - "sim.update({}, total_time)\n", - "\n", - "# gather results\n", - "dfba_results = sim.gather_results()" - ] + "execution_count": 5 }, { "cell_type": "code", - "execution_count": 28, "id": "e6b3d8d3-8dcc-4315-939d-917f39a67a9a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Plotting results...\n", - "saving out/dfba_single_timeseries.png\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:04:26.380838Z", + "start_time": "2024-11-05T15:04:26.179913Z" } - ], + }, "source": [ "print('Plotting results...')\n", "# plot timeseries\n", @@ -249,112 +141,28 @@ " out_dir='out',\n", " filename='dfba_single_timeseries.png',\n", ")" - ] - }, - { - "cell_type": "markdown", - "id": "d9a477c5", - "metadata": {}, - "source": [ - "### With a Custom Model File and Non-Default Parameters" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "7e643143", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created new file: out/single_dfb_custom.json\n", - "Simulating...\n" - ] - } ], - "source": [ - "# Run a dFBA process with my own model/molecule definitions\n", - "total_time = 40.0\n", - "\n", - "# Get dfba config, but set non-default values to use in the spec\n", - "custom_dfba_config = {\n", - " 'dfba': get_single_dfba_spec(\n", - " model_file='iJO1366', # Use the full E. coli GEM that comes with cobrapy\n", - " kinetic_params = {\n", - " 'Glucose': (0.5, 1),\n", - " 'Acetate': (0.5, 2),\n", - " 'CO2': (0.5, 1)},\n", - " biomass_reaction='BIOMASS_Ec_iJO1366_WT_53p95M',\n", - " substrate_update_reactions = {\n", - " 'Glucose': 'EX_glc__D_e',\n", - " 'Acetate': 'EX_ac_e',\n", - " 'CO2': 'EX_co2_e'},\n", - " biomass_identifier='Biomass',\n", - " mol_ids=['Glucose', 'Acetate', 'CO2', 'Biomass'],\n", - " path=['fields']),\n", - " 'fields': {\n", - " # How to pass the initial state of the system?\n", - " 'Glucose': 10,\n", - " 'Acetate': 0,\n", - " 'Biomass': 0.1,\n", - " 'CO2': 0\n", - " }\n", - "}\n", - "\n", - "# Make the simulation\n", - "sim_custom = Composite({\n", - " 'state': custom_dfba_config,\n", - " 'emitter': {'mode': 'all'}\n", - "}, core=core)\n", - "\n", - "# Save the document\n", - "sim_custom.save(filename='single_dfb_custom.json', outdir='out')\n", - "\n", - "# simulate\n", - "print('Simulating...')\n", - "sim_custom.update({}, total_time)\n", - "\n", - "# gather results\n", - "custom_dfba_results = sim_custom.gather_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "a0fbafbc", - "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "saving out/dfba_single_custom_timeseries.png\n" + "Plotting results...\n", + "saving out/dfba_single_timeseries.png\n" ] }, { "data": { - "image/png": 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", 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" }, + "metadata": {}, "output_type": "display_data" } ], - "source": [ - "# Plot timeseries\n", - "plot_time_series(\n", - " custom_dfba_results,\n", - " field_names=['Glucose', 'Acetate', 'CO2', 'Biomass'],\n", - " out_dir='out',\n", - " filename='dfba_single_custom_timeseries.png',\n", - ")" - ] + "execution_count": 6 }, { "cell_type": "markdown", @@ -366,24 +174,17 @@ }, { "cell_type": "code", - "execution_count": 5, "id": "3e7670d2-89f7-403f-a272-385d3c39a623", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Making the composite...\n", - "Created new file: out/diffadv.json\n", - "Simulating...\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:04:26.508830Z", + "start_time": "2024-11-05T15:04:26.398204Z" } - ], + }, "source": [ "total_time = 50\n", - "bounds = (10.0, 20.0)\n", - "n_bins = (10, 20)\n", + "bounds = (20.0, 20.0)\n", + "n_bins = (20, 20)\n", "\n", "# get the config\n", "composite_state = get_diffusion_advection_state(\n", @@ -413,13 +214,40 @@ "\n", "# gather results\n", "diffadv_results = sim.gather_results()" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Making the composite...\n", + "Created new file: out/diffadv.json\n", + "Simulating...\n" + ] + } + ], + "execution_count": 7 }, { "cell_type": "code", - "execution_count": 6, "id": "0de01ce8-a20d-48d1-a489-ca0274b94b8b", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:04:41.922547Z", + "start_time": "2024-11-05T15:04:26.516526Z" + } + }, + "source": [ + "print('Plotting results...')\n", + "# plot 2d video\n", + "plot_species_distributions_to_gif(\n", + " diffadv_results,\n", + " out_dir='out',\n", + " filename='diffadv_results.gif',\n", + " title='',\n", + " skip_frames=1\n", + ")" + ], "outputs": [ { "name": "stdout", @@ -431,28 +259,18 @@ }, { "data": { - "text/html": [ - "\"\"" - ], "text/plain": [ "" + ], + "text/html": [ + "\"\"" ] }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "print('Plotting results...')\n", - "# plot 2d video\n", - "plot_species_distributions_to_gif(\n", - " diffadv_results,\n", - " out_dir='out',\n", - " filename='diffadv_results.gif',\n", - " title='',\n", - " skip_frames=1\n", - ")" - ] + "execution_count": 8 }, { "cell_type": "markdown", @@ -464,20 +282,13 @@ }, { "cell_type": "code", - "execution_count": 7, "id": "62815294-e12d-4c67-b987-905dfa72b9ff", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Making the composite...\n", - "Created new file: out/particles.json\n", - "Simulating...\n" - ] + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:04:42.945396Z", + "start_time": "2024-11-05T15:04:41.991896Z" } - ], + }, "source": [ "total_time=100\n", "bounds=(10.0, 20.0) # Bounds of the environment\n", @@ -521,13 +332,43 @@ "emitter_results = particles_results[('emitter',)]\n", "\n", "particles_history = [p['particles'] for p in emitter_results]" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Making the composite...\n", + "no representation for 20\n", + "no representation for 40\n", + "Created new file: out/particles.json\n", + "Simulating...\n" + ] + } + ], + "execution_count": 9 }, { "cell_type": "code", - "execution_count": 8, "id": "02d78f16-d50c-44e8-86de-cf8abaa6de16", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:05:24.643677Z", + "start_time": "2024-11-05T15:04:42.950209Z" + } + }, + "source": [ + "print('Plotting...')\n", + "# plot particles\n", + "plot_species_distributions_with_particles_to_gif(\n", + " particles_results,\n", + " out_dir='out',\n", + " filename='particle_with_fields.gif',\n", + " title='',\n", + " skip_frames=1,\n", + " bounds=bounds,\n", + ")\n" + ], "outputs": [ { "name": "stdout", @@ -539,29 +380,18 @@ }, { "data": { - "text/html": [ - "\"\"" - ], "text/plain": [ "" + ], + "text/html": [ + "\"\"" ] }, "metadata": {}, "output_type": "display_data" } ], - "source": [ - "print('Plotting...')\n", - "# plot particles\n", - "plot_species_distributions_with_particles_to_gif(\n", - " particles_results,\n", - " out_dir='out',\n", - " filename='particle_with_fields.gif',\n", - " title='',\n", - " skip_frames=1,\n", - " bounds=bounds,\n", - ")\n" - ] + "execution_count": 10 }, { "cell_type": "markdown", @@ -581,25 +411,13 @@ }, { "cell_type": "code", - "execution_count": 9, "id": "dd9600d789031061", "metadata": { "ExecuteTime": { - "end_time": "2024-09-12T18:22:17.635396Z", - "start_time": "2024-09-12T18:22:17.635332Z" + "end_time": "2024-11-05T15:06:25.209896Z", + "start_time": "2024-11-05T15:05:24.713316Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Making the composite...\n", - "Created new file: out/spatial_dfba.json\n", - "Simulating...\n" - ] - } - ], "source": [ "total_time = 100\n", "n_bins = (5, 5)\n", @@ -628,35 +446,31 @@ "\n", "# gather results\n", "dfba_results = sim.gather_results()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "27b8c3a7-2c98-4655-9a15-f27823488f8a", - "metadata": {}, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Plotting results...\n", - "saving out/spatial_dfba_results.gif\n" + "Making the composite...\n", + "no representation for 5\n", + "no representation for 5\n", + "Created new file: out/spatial_dfba.json\n", + "Simulating...\n" ] - }, - { - "data": { - "text/html": [ - "\"\"" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], + "execution_count": 11 + }, + { + "cell_type": "code", + "id": "27b8c3a7-2c98-4655-9a15-f27823488f8a", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:06:58.116622Z", + "start_time": "2024-11-05T15:06:25.215379Z" + } + }, "source": [ "print('Plotting results...')\n", "# make video\n", @@ -667,32 +481,40 @@ " title='',\n", " skip_frames=1\n", ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2f54705f-7a38-4204-995e-a8ac0516b93b", - "metadata": {}, + ], "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "saving out/spatial_dfba_timeseries.png\n" + "Plotting results...\n", + "saving out/spatial_dfba_results.gif\n" ] }, { "data": { - "image/png": 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", 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" + "" + ], + "text/html": [ + "\"\"" ] }, "metadata": {}, "output_type": "display_data" } ], + "execution_count": 12 + }, + { + "cell_type": "code", + "id": "2f54705f-7a38-4204-995e-a8ac0516b93b", + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:06:58.395907Z", + "start_time": "2024-11-05T15:06:58.177648Z" + } + }, "source": [ "# plot single traces from spatial dfba\n", "fixed_x = 0\n", @@ -704,7 +526,27 @@ " out_dir='out',\n", " filename='spatial_dfba_timeseries.png',\n", ")" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saving out/spatial_dfba_timeseries.png\n" + ] + }, + { + "data": { + "text/plain": [ + "
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0Wi2jR4+md+/eHD16lI0bN7J582YA9Hp9ke2Eh4cTGBiIVqs1le3evRtnZ2fq169vKmvbti1qtZo9e/bQo0ePYv3cdu/ezYgRI8zKOnTowJo1a8zKQkJCmDp1arHaLE2SyAshhBBCCCGEhaWnp7N06VKWL19OmzZtAFi8eDHe3t4P1O78+fPR6/WEhYVhY2MDQGBgoOn8jBkzGDNmDH369AFg2rRpbNu2jTlz5jB//nzi4+MJCAigWbNmqFQqfHx8TNfu3LmTvXv3cvnyZezs7EztrVmzhlWrVt1xOvv48eNN7319fRk5ciRhYWGMHj0ajUaDk5MT1tbWeHp63vXe4uLibvv5JCUl4e7ublZmbW2Ni4sLSUlJ9/pxmbXj4eFhVubh4XFbG97e3iQkJGAwGEptj/jikES+DNuXtI+03DRsrWyxs7Izvd76/tYylUpl6ZCFEEIIIYR45DQ2VkRP6WCxvovj7Nmz5OfnExISYirT6/VUq1btgfqPjIykefPmpiT+VmlpaSQmJtK0aVOz8qZNm5qmxw8YMIB27dpRrVo1OnbsSNeuXWnfvj0AUVFRZGRk4OrqanZ9dna2aep+UVasWMHcuXOJiYkhIyODgoICdDpdie8tOzsbe3v7El9XmjQaDQaDgdzcXDQazSPrVxL5MmxB1AL2JO0pdv2bib29lb3p1c7amOTffG9vZW/8bG1vVqax1hjLrO3RWN3y/ka5xkqDxlqDxkaDrdpWvjQQQgghhBCPDZVKVezp7WXJzRHgW5/Dv/ms+U0PmlzWrVuX2NhYNmzYwObNm3n55Zdp27Ytq1atIiMjAy8vL7Pn8W9ydnYusr3du3cTGhrK5MmT6dChg2m2wMyZM0scW/ny5Tly5IhZmaenJ5cvXzYrKygoIDk5+Z4j/P9s59KlS2Zlly5duq2N5ORkHB0dH2kSD5LIl2lVnKuQW5hLbmEueYV5f78ajK85BTko/P0f9c266aQ/1LjUKrUp+b+Z3GusNThYOxhfbRxue+9g7fD3exvj4WjtiKONo+mzjfr2bxGFEEIIIYR4Evj7+2NjY8O+ffuoXLkyAKmpqZw6dYoWLVoUeY2bmxsAFy9eJDg4GMBs4TuA2rVrs3TpUvLz828bldfpdHh7exMREUHLli1N5REREWYzA3Q6Hb1796Z379706tWLjh07kpycTN26dUlKSsLa2hpfX99i3eeuXbvw8fFh3LhxprK4uDizOra2thQWFt6zreDgYL744gsURTENJDZu3JiUlBQOHDhAvXr1ANi6dSsGg4GGDRsWK8ab7WzZssVsocFNmzbRuHFjs3pHjx41/ewfJUnky7D3Gr531/OKolBgKDAl8LceOQU5xmS/MMf0+ebrzbLcglyyC7KN5YU5xnMFOWQXZJs+33yfnZ9NniEPAINiIKsgi6yCrFK9X1u1LU62TjhYO+Bk64SjjaPpcLJxwsnGWOZka3zvZOuE1kaLo60jWhut6bONlXwhIIQQQgghHi9arZb+/fszatQoXFxccHd3Z+LEiajV6jvOdtVoNDRq1IipU6fi5+fH5cuXzZ4/Bxg2bBjz5s2jT58+vPfee+j1ev766y9CQkKoVq0ao0aNYuLEiVSpUoWgoCAWL15MZGQky5YtA2DWrFl4eXkRHByMWq1m5cqVeHp64uzsTNu2bWncuDHdu3dn+vTpBAYGkpiYyLp16+jRo4fZgnM3BQQEEB8fT1hYGA0aNGDdunWsXr3arI6vry+xsbFERkZSsWJFtFqt6Rn8W7Vu3ZqMjAyOHTtGzZo1AahRowYdO3ZkyJAhfPnll+Tn5zNs2DD69OlTovUG3n77bVq2bMnMmTPp0qULYWFh7N+/n6+++sqsXnh4uOlRg0dJEvknmEqlwsbKBhsrG5xweuj9FRoKjUl9QTbZ+dlkFWQZ399yZBVkkZWfZTqXlW98zczPNL3+832+wTg9KM+QR3JOMskkP1Cc9lb2aG21aG216Gx1pve3ftbb6dHZ6oyHnQ69rR6dnQ4Hawd5bEAIIYQQQjwUs2bN4o033jBtFTd69GgSEhLu+hz4okWLGDRoEPXq1aNatWpMnz7dLLF0dXVl69atjBo1ipYtW2JlZUVQUJDpufi33nqL1NRU3n33XS5fvswzzzzD2rVrCQgIAIxfMEyfPp3Tp09jZWVFgwYNWL9+vWla//r16xk3bhwDBw7kypUreHp60qJFi9sWirvp+eef55133mHYsGHk5ubSpUsXPvjgAyZNmmSq07NnT37++Wdat25NSkoKixcvZsCAAbe15erqSo8ePVi2bJnZlnjLli1j2LBhtGnTBrVaTc+ePZk7d67ZtSqV6o7tAjRp0oTly5czfvx43n//fQICAlizZo3pCwMw7giwa9cuvv/++zv++TwsKqUkGxs+JdLS0tDr9aSmpt7XoguidOUX5pNVkGVK7DPzM8nIzyAjP4Os/Cwy8jJMZZn5maTnpZvOZ+QZj/T8dLILsh84FmuVNTo7Y4LvbOeMs50zOru/3+vt9Ojt9JSzK4ezvbPpVR4LEEIIIYR4NHJycoiNjcXPz8/iC6E9qMzMTCpUqMDMmTMZNGiQpcN5LB0+fJh27doRExODk1PxBi9jY2MJDAwkOjra9IXF/RgzZgzXr1+/bZT+bu72+1mSPFRG5MVjz8bKBr2VMUF+EAWGAlOif+uRlpdmfJ+fTlqu8X1qXippuWmk5RmP1NxU8g35FCgFxlkBOSWbFaC10Zol9uXsyuGiccHFzsX4av/3Uc6+HHZWt08dEkIIIYQQT7ZDhw5x4sQJQkJCSE1NZcqUKQC88MILFo7s8VW7dm2mTZtGbGwstWrVKtY169evZ+jQoQ+UxAO4u7vfttf8oyKJvHhqWKutTSPmJaUoCjmFOaTlppGal0pqrvFIyU0hJTeFtNw00/tby1NyUzAoBtLzjV8UJKQnFKs/JxsnXDWuuNq74qpxpbymPK72xtfymvJ/l2lcZbRfCCGEEOIJMmPGDE6ePImtrS316tUjPDyc8uXLWzqsx9qdpsffyZtvvlkq/b777rul0s79kEReiGJQqVSmVfg9HIt+3qcoBsVAWm4a13Ovk5KbwvUc4+vNUf3knGSSs5O5nnud5Gzj5wKlwPRoQFxa3F3bV6GinH053DRulHcoj5vGzXg4uOGuccfdwXiU15THSl28PUyFEEIIIYRlBAcHc+DAAUuHIcoASeSFeIjUKjXO9s442zsXq76iKKTlpXEt5xrXsq/9/Xrj/dXsq1zL/vv11qn+J6+fvGO7ViorXDWueDp44uHogbuDOx4OHng4eODl5IWngyduDm5Yq+WvBCGEEEIIIR538q92IR4jKpXKNP3fX+9/17oGxUBKbgpXsq5wNfsql7Mum79mX+Zy1mWuZF2hUCnkcpbxM1eLbk+tUuPu4I6ngydejl54Onni6eBJBacKeDt5U8GpAg42Dg/hroUQQgghhBAlIYm8EGWUWqU2LZBXjWp3rFdoKCQ5J5lLWZe4lHnJ+HrzyLzExcyLXMq6RIGhgKTMJJIyk4i8EllkW852zqak3tvRG28nbypqKxoPp4rYWtk+pLsVQgghhBBC3CSJfFl2dgdkXwdbJ7B1AFvHG+8djYeNI9zY31E8vazUVrg5GJ+br1m+ZpF1DIqBa9nXSMpM4mLmRS5mXjS9T8xIJDEz0WwRv+hr0be1oUKFu4M7lbSVTEdFbUUqaStRWVcZna1s5SiEEEIIIURpkES+LAufCbE77l7H5pYE384JbLU3Xm+WaYs4dMZXs/M6sJJflyeVWqU2Jfu13IretiMjL4MLGRdMif2FjAtcSL/AhYwLJKQnkFWQZRrp339p/23Xu9i7UFlbGR+dj9lRSVtJpuwLIYQQQghRApKZlWUeNcFQAHkZkJd5y5EBisFYJz/LeGReefD+bBzBXmdM6m971d9yOBtfNc7m5TaaB49BWIyTrRPVXKpRzeX2afyKopCck8z5jPMkpCeQkJ7A+fTznE8/T3x6PFezr5oW5Stq2r6Hgwf+en/89H6mVz+9H+U15VGpVI/g7oQQQgghhCg7JJEvyzr+X9HligIFOcakPjf97+Q+NwPy0m+8Ztzymv73a2465KbdeL1RVpBtbDc/03ikX7y/eK3sQFPuxuFsfLV3vr3MwQU0Ln+/2jqCJHOPNZVKZdz3XuNKHbc6t53PzM8kPi2euLQ44tLiiE+P51zaOeLT4knJTTGN5O++uNvsOq2N1pTUV3WuStVyVanqXBUPBw9J8IUQQgjxVPL19WX48OEMHz7c0qE8FvLy8njmmWf49ttvadKkySPtu0+fPjRo0MAi+8lLIv8kUqmMo982GnAs/+DtFeYbE/qcVGOSn5MKOWk33qfdUpYKOSl/v8++5T0KFOZCRpLxKAkr278TewdX4+FY/sb78sbym58d3YyvVjYPft+i1DjaOFLDtQY1XGvcdi4lJ4VzaeeITY3lbOpZ0+uFjAuk56dz+OphDl89bHaN1kZLFecqpsS+qnNVAsoF4GLv8qhuSQghhBBCPCSTJk1izZo1REZG3rPul19+iZ+fn1kSn5yczH//+19+/fVX1Go1PXv25NNPP8XJyanYMRw7dowJEyZw4MAB4uLimD179m1fnowfP54WLVowePBg9Hp9sdsuDZLIi3uzsrmRRN9nkmQwGGcCZKcYF+fLufF62+frkHUdspMhK9n4WphnPEr6BYCmnDGpv+0oD04eNw4346tM+bcoZ3tnguyDCHIPMivPLcwlLi3OmNinnOVMyhnOpJwhLi2O9Px0Iq9E3jZN313jTqBLINXKVaO6S3UCXQLx0fpgpbZ6dDckhBBCCCEeCUVR+Oyzz5gyZYpZeWhoKBcvXmTTpk3k5+czcOBAhg4dyvLly4vddlZWFv7+/rz00ku88847RdapWbMmVapU4fvvv+fNN998oHspKUnkxcOnVv/9nHw5n+JfpyjGxwJuTeyzkiHzKmRdg6wbr5nXzD8rhr+/GLh66t792OnAyf1Gcu8OTp6g9QStF2g9brx6GuvJdO5Hxs7KjsBygQSWCzQrzyvM41zaOc5cP2NK7s+knCEhPYHL2Ze5fOEyOy/sNNW3t7KnqnNVqrlU4xnXZ3jW9VkCygXIVnlCCCHE00RRjOtGWYKNQ7H/DZmens4bb7zBmjVr0Ol0jB49ml9++YWgoCDmzJlzW/1z587h5+fHoUOHCAoKAiAlJYVy5cqxbds2WrVqBRhHl8eMGcOff/6JoigEBQWxZMkSqlSpgsFg4KOPPuKrr77iypUr1KhRg6lTp9KxY0fAOHV9xIgR/PTTT1y/fh0PDw/eeOMN3nvvPVN/I0eO5JdffiE3N5f69esze/Zs6tS5/XHLm8aMGcPq1as5f/48np6ehIaGMmHCBGxsbFiyZAmTJ08GMD1KuXjxYgYMGHBbOwcOHCAmJoYuXbqYyo4fP87GjRvZt28f9evXB2DevHl07tyZGTNm4O3tXaw/iwYNGtCgQQMAxo4de8d63bp1IywsTBJ5IUxUKuMK+3ZO4Fy5eNcYbiTxmVduHJeNif/Nzxk3yjIuQfol43T/3BuPB1w7c/e2bRz+TvB13jeOCsZX7Y3PTu4go78Pla2VbZEJfmZ+Jqevn+Zk8klOXD/BqeRTnE45TXZBNkevHeXotaP8dPonAKzV1gQ4B/CM6zOmI6BcAHZWdpa4JSGEEEI8bPlZ8H/FS+BK3fuJxjWfimHEiBFERESwdu1aPDw8mDBhAgcPHjQl6ffjwoULtGjRglatWrF161Z0Oh0REREUFBQA8OmnnzJz5kwWLFhAcHAwixYt4vnnn+fYsWMEBAQwd+5c1q5dy48//kjlypVJSEggISHB1P5LL72ERqNhw4YN6PV6FixYQJs2bTh16hQuLkXP6NVqtSxZsgRvb2+OHDnCkCFD0Gq1jB49mt69e3P06FE2btzI5s2bAe44bT08PJzAwEC0Wq2pbPfu3Tg7O5uSeIC2bduiVqvZs2cPPXr0uO+fZVFCQkL4+OOPyc3Nxc7u0f1bUhJ58WRRq8HR1XhQ/e51FcWYwGfcSOxvJvcZlyA9ybioX/qNKf05qcb/ASSfNR53orIyJvr6CqCvBPqKN45b3tvrZWT/IXC0cSTI3XyKfqGhkIT0BE5cP8GJayeIvhZNdHI0qbmpHE8+zvHk438n9yprAl0CqVW+FrXdalOrfC18dD6oVWoL3ZEQQgghnibp6eksXbqU5cuX06ZNG8A4El3cEeQ7mT9/Pnq9nrCwMGxsjOtIBQb+PSAyY8YMxowZQ58+fQCYNm0a27ZtY86cOcyfP5/4+HgCAgJo1qwZKpUKH5+/Z9ju3LmTvXv3cvnyZVMSO2PGDNasWcOqVasYOnRokTGNHz/e9N7X15eRI0cSFhbG6NGj0Wg0ODk5YW1tjaen513vLS4u7rafT1JSEu7u7mZl1tbWuLi4kJRUwrW6isHb25u8vDySkpLMfjYPmyTy4umlUv095b98wN3r5mUZE/r0JEhLNCb5aYmQduHG640ypRDSzhuPhD1Ft2WrBedKxlkGzj7G13I+f7/XOJf6rT6trNRW+Op98dX70tHXOD1MURQSMxONSf0tR0puiun9ipMrANDaaqlVvpYpua9dvjbO9s4WvCMhhBBC3BcbB+PIuKX6LoazZ8+Sn59PSEiIqUyv11Ot2u1b/5ZEZGQkzZs3NyXxt0pLSyMxMZGmTZualTdt2pSoqCgABgwYQLt27ahWrRodO3aka9eutG/fHoCoqCgyMjJwdXU1uz47O5uYmJg7xrRixQrmzp1LTEwMGRkZFBQUoNPpSnxv2dnZ2Nvbl/i60qTRGNfbysp6tI9uSCIvRHHYOoCLv/G4k8IC47T91AvGRD711iMBUhKMz/nnpcPlaONRFHv9jeTeD1z8/n518TdO5Zep+w9EpVJRwakCFZwq0M6nHWBM7i9mXuTw1cMcuXKEI1ePEH0tmvS8dHYl7mJX4i7T9X56P4Ldg01HZW1l2QpPCCGEeNypVMWe3l6WqNXGmYOKopjK8vPzzercTDTvV926dYmNjWXDhg1s3ryZl19+mbZt27Jq1SoyMjLw8vJi+/btt13n7OxcZHu7d+8mNDSUyZMn06FDB9NsgZkzZ5Y4tvLly3PkyBGzMk9PTy5fvmxWVlBQQHJy8j1H+O9HcnIyAG5ubqXe9t1IIi9EabGy/vvZeRoUXScv05jop8RDStyNIx6u33jNumqcxp90xHj8k9rGOHpf7kZi71oVXKsYX/UVJcm/TyqVCm8nb7ydvE0j9/mGfE5fP82RK0eMW+BdOWzaJi82NZafT/8MgIu9iympr+tel+qu1bFRy/aHQgghhCgZf39/bGxs2LdvH5UrG9eHSk1N5dSpU7Ro0aLIa24mjxcvXiQ4OBjgti3bateuzdKlS8nPz79tVF6n0+Ht7U1ERAQtW7Y0lUdERJjNDNDpdPTu3ZvevXvTq1cvOnbsSHJyMnXr1iUpKQlra2t8fX2LdZ+7du3Cx8eHcePGmcri4uLM6tja2lJYWHjPtoKDg/niiy9QFMU0sNK4cWNSUlI4cOAA9erVA2Dr1q0YDAYaNmxYrBhL4ujRo1SsWJHy5Uth2+8SkEReiEfJ1hHcAo1HUXIzjKP3188Zj+RYuB5rfC7/ehwY8o2L8hW1MJ+V3Y3k/kZi71oVyt/oS1PuYd7VE8lGbWNaCK83vQHjnveRVyI5dPkQhy4f4ujVoyTnJLMlfgtb4rcA4GDtQLBHMA08GtDAswHPuD6DtVr+qhVCCCHE3Wm1Wvr378+oUaNwcXHB3d2diRMnolar7zj7T6PR0KhRI6ZOnYqfnx+XL182e/4cYNiwYcybN48+ffrw3nvvodfr+euvvwgJCaFatWqMGjWKiRMnUqVKFYKCgli8eDGRkZEsW7YMgFmzZuHl5UVwcDBqtZqVK1fi6emJs7Mzbdu2pXHjxnTv3p3p06cTGBhIYmIi69ato0ePHmYLzt0UEBBAfHw8YWFhNGjQgHXr1rF69WqzOr6+vsTGxhIZGUnFihXRarVFLiTXunVrMjIyOHbsGDVr1gSgRo0adOzYkSFDhvDll1+Sn5/PsGHD6NOnT4nWG8jLyyM6Otr0/sKFC0RGRuLk5ETVqlVN9cLDw02PGjxSigXt2LFD6dq1q+Ll5aUAyurVq83OA0Ue06dPv2ObEydOvK1+tWrVShRXamqqAiipqan3c1tCPByFBYpyPU5RYrYryv7FivL7eEVZ3ldR5jVQlMmuijJRd+djelVFWdxFUX59R1H++lJRzmxVlNQLimIwWPquyrScghzl4KWDyjdHvlGGbR6mNP2hqVJzSU2zI+T7EOX1Ta8r3xz5Rjl8+bCSX5hv6bCFEEKIJ1p2drYSHR2tZGdnWzqUEktLS1P69eunODg4KJ6ensqsWbOUkJAQZezYsaY6Pj4+yuzZs02fo6OjlcaNGysajUYJCgpS/vjjDwVQtm3bZqoTFRWltG/fXnFwcFC0Wq3SvHlzJSYmRlEURSksLFQmTZqkVKhQQbGxsVHq1KmjbNiwwXTtV199pQQFBSmOjo6KTqdT2rRpoxw8eNAs5v/+97+Kt7e3YmNjo1SqVEkJDQ1V4uPj73ifo0aNUlxdXRUnJyeld+/eyuzZsxW9Xm86n5OTo/Ts2VNxdnZWAGXx4sV3bOvll182+/koiqJcu3ZN6du3r+Lk5KTodDpl4MCBSnp6ulmde7UbGxtbZC7asmVLU53s7GxFr9cru3fvvmM7/3S338+S5KGqGzdhERs2bCAiIoJ69erx4osvsnr1arp37246/89VBTds2MCgQYM4c+YM/v5FP6s8adIkVq1aZdqqAIyrFJZkqkNaWhp6vZ7U1NT7WnRBiEfOUGicmn8t5u8R+2un4copSL/Lwi52enCvYTw8nr3x/hlwKHqrEHF3BsXA6eun2X9pP3sv7mX/pf2k5aWZ1XGycaKBZwOaeDehsXdjecZeCCGEKGU5OTnExsbi5+dn8YXQHlRmZiYVKlRg5syZDBo0yNLhPJYOHz5Mu3btiImJwcnJqVjXxMbGEhgYSHR0NAEB91j0+i6++OILVq9ezR9//FHsa+72+1mSPNSi8z07depEp06d7nj+n4sR/PLLL7Ru3fqOSfxNxdmqQIgnitrqxoJ4fhDQ1vxcThpcPQ1XT8KVk3D1lPH1eizkpkLCX8bjVk6e4PGMMan3qAmetcCtGljJs993o1apqeZSjWou1QitEWpK7Pcl7WNf0j5TYr8tYRvbErYBUMGpAo28GtHEuwkNvRqityt6n1QhhBBCPPkOHTrEiRMnCAkJITU1lSlTpgDwwgsvWDiyx1ft2rWZNm0asbGx1KpVq1jXrF+/nqFDhz5QEg9gY2PDvHnzHqiN+1VmHty8dOkS69atY+nSpfese/r0aby9vbG3t6dx48Z88sknpgUjipKbm0tubq7pc1pa2h3rClHm2OugYj3jcauCXOPI/aUbK+hfPg6XjxlH9jOSjEfM1r/rW9mCW3XwrA2eN5J7j5qyXd5d3JrYv/LMKxQaCjmRfILdF3ezK3EXhy4f4kLGBX46/RM/nf4JtUrNs67P0qxCM1pUbMEzrs/IPvZCCCHEU2bGjBmcPHkSW1tb6tWrR3h4+CNfSK2sGTBgQInqv/nmm6XS7+DBg0ulnfth0an1t1KpVLdNrb/V9OnTmTp1KomJiXedIrNhwwYyMjKoVq0aFy9eZPLkyVy4cIGjR4+i1WqLvGbSpElMnjz5tnKZWi+eSrnpxhH7y9Fw6RgkHTWuoJ+bWnR9Zx/wDv778KojyX0xZeVnsf/SfnYn7mZ34m5iUs33W3W1dzUl9U28m+BkW7zpYkIIIcTT7EmaWi+ePKU1tb7MJPLVq1enXbt2JZ66kJKSgo+PD7NmzbrjcyVFjchXqlRJEnkhblIU40j9zW3xbh6p8UXXd/E3T+69g5/IvVtL26XMS+xK3MWf5/9k98XdZOZnms5Zq6yp61GXFhVb0LJiS3z1vpYLVAghhHiMSSIvHmdPxDPyxRUeHs7JkydZsWJFia91dnYmMDCQM2eK2K7rBjs7uyK3MxBC3KBS3di/3gdqdP27PCsZkg5D4qG/j5R443Z5yWfh6E83rleD+7NQsT5UbGA8XKuCWqaN38rD0YMeAT3oEdCD/MJ8Dlw+wJ/n/yT8fDjn0s6xN2kve5P2MmP/DKroq/Bc5edo49OGZ1yekQXzhBBCCCGeImUikf/mm2+oV68ederUKfG1GRkZxMTE8K9//eshRCbEU87BBfxbGY+bspLNE/sLB40r5186YjwOLDbWs9dDhXrGpL5SCFQMMT7PLwCwsbKhkVcjGnk1YnSD0cSnxfPn+T/ZcX4H+5P2E5MaQ8yRGBYeWYiXo5cxqa/chmD3YNm3XgghhBDiCWfRf+1lZGSYjZTHxsYSGRmJi4uLaXG6tLQ0Vq5cycyZM4tso02bNvTo0YNhw4YBMHLkSLp164aPjw+JiYlMnDgRKysr+vbt+/BvSAhhTO6rtjEeN6Ulwvn9cH6f8TXxEOSkGhfTu7mgnkptXDyvcmPwaWx81cruEzdV1lXmlWde4ZVnXiEtL40/z//J1vit7Lywk4uZF1l2fBnLji/D2c6ZVpVa0cG3Aw29GmKjlp0GhBBCCCGeNBZN5Pfv30/r1q1Nn0eMGAFA//79WbJkCQBhYWEoinLHRDwmJoarV6+aPp8/f56+ffty7do13NzcaNasGX/99Rdubm4P70aEEHen84ZnnjceAIX5xoX0LuyHhH3G7e+unzNO0086DHsXGOuV8/s7sfdtZvwsU8jR2ero6t+Vrv5dySnIYXfibrbEb2H7+e2k5Kaw5swa1pxZg7OdM2192tLRtyP1PepjpbaydOhCCCGEEKIUPDaL3T1OSrLIgBCilKQlQvxfEL/beCQdBf7x15OuIvi1AL/m4NscnCtZJNTHVYGhgIOXDvJH3B9sittEck6y6Vx5TXna+7Sno19H6rjVkW3thBBCPLFksTvxOHviVq1/nEgiL8RjICfVOFofvxviIoxT8g355nXK+RoTer8W4NcStB4WCfVxVGAoYF/SPjae28jmuM2k5aWZznk6etLJtxPPV3mequWqWjBKIYQQovQ96Ym8r68vw4cPZ/jw4ZYO5bGQl5fHM888w7fffkuTJk0ead+NGjVi1KhR9OzZs9jXlFYiL0MyQojHk70eAtpCmw/gtY0wNg5e+RmavQMV6oPKyjgd/9B38PMQmBkIXzSFPz6As9shP8fSd2BR1mprGns3ZnKTyWx/eTvz28ynm383HG0cScpMYvGxxfRY24OXf32ZZceXmY3eCyGEEEJY0qRJkwgKCipW3S+//BI/Pz+zJD45OZnQ0FB0Oh3Ozs4MGjSIjIyMEsWwcOFCmjdvTrly5ShXrhxt27Zl7969ZnXGjx/P2LFjMRgMJWq7NMjSxkKIssHW0XwRvZw041T82B0Q+6fx2fpLR43HrrlgrTE+V1/lOeM15QOf2ufrbaxsaFGxBS0qtiC3MJfw8+GsjVlL+Plwjicf5/je48zYN4NmFZvxQpUXaFGxBbZWtpYOWwghhBDirhRF4bPPPmPKlClm5aGhoVy8eJFNmzaRn5/PwIEDGTp0KMuXLy9229u3b6dv3740adIEe3t7pk2bRvv27Tl27BgVKlQAoFOnTgwePJgNGzbQpUuXUr23e5EReSFE2WSvg8D20OFjeCMcRsVAz2+gTj9w8oSCbDizCX5/D+aHwJxa8NsIOPUH5GdbOnqLsbOyo61PW+Y+N5etL29lbMhYnnV9lgKlgO0J23ln+zs8t/I5PvrrI04kn7B0uEIIIUSpUBSFrPwsixwleZI5PT2d0NBQHB0d8fLyYvbs2bRq1eqO0+jPnTuHSqUiMjLSVJaSkoJKpWL79u2msmPHjtG1a1d0Oh1arZbmzZsTExMDgMFgYMqUKVSsWBE7OzuCgoLYuHGj6dq8vDyGDRuGl5cX9vb2+Pj48Mknn5j1N3jwYNzc3NDpdDz33HNERUXd9T7HjBlDYGAgDg4O+Pv788EHH5Cfb3yEcsmSJUyePJmoqChUKhUqlcq0EPo/HThwgJiYGLMk+vjx42zcuJGvv/6ahg0b0qxZM+bNm0dYWBiJiYl3jetWy5Yt4z//+Q9BQUFUr16dr7/+GoPBwJYtW0x1rKys6Ny5M2FhYcVut7TIiLwQ4sngWB5q9TIeigKXo41b253ZAnG7IDUB9n9jPKw14N8KAjsYD523paO3iHL25QitEUpojVBiUmJYG7OW387+xuWsy6w4uYIVJ1dQ07UmvQJ70cmvEw42DpYOWQghhLgv2QXZNFze0CJ97+m3p9j/Dx0xYgQRERGsXbsWDw8PJkyYwMGDB4s9zbwoFy5coEWLFrRq1YqtW7ei0+mIiIigoKAAgE8//ZSZM2eyYMECgoODWbRoEc8//zzHjh0jICCAuXPnsnbtWn788UcqV65MQkICCQkJpvZfeuklNBoNGzZsQK/Xs2DBAtq0acOpU6dwcXEpMiatVsuSJUvw9vbmyJEjDBkyBK1Wy+jRo+nduzdHjx5l48aNbN68GQC9Xl9kO+Hh4QQGBqLVak1lu3fvxtnZmfr165vK2rZti1qtZs+ePfTo0eO+fo5ZWVnk5+ffdk8hISFMnTr1vtp8EJLICyGePCoVeDxrPJr81zgCHxsOpzbCqd8h7Tyc2mA8ALzqQGBHqNbZ+P4pnIJfxbkK79R7h7eC32JP0h7WnF7DpvhNHL12lKO7jzJ933S6+HehV2AvnnF9xtLhCiGEEE+c9PR0li5dyvLly2nTxvgo4eLFi/H2frABh/nz56PX6wkLC8PGxgaAwMBA0/kZM2YwZswY+vTpA8C0adPYtm0bc+bMYf78+cTHxxMQEECzZs1QqVT4+PiYrt25cyd79+7l8uXL2NnZmdpbs2YNq1atYujQoUXGNH78eNN7X19fRo4cSVhYGKNHj0aj0eDk5IS1tTWenp53vbe4uLjbfj5JSUm4u7ublVlbW+Pi4kJSUtK9flx3NGbMGLy9vWnbtq1Zube3NwkJCRgMBtTqRzfhXRJ5IcSTz0ZjnIYf2N44Wn/p2N9J/fl9cDHKeOyYBvrKUKOb8agUAk/Z3utWaiuaeDehiXcTknOSWXtmLatOryIuLY6Vp1ay8tRKnnF9hl6Bvejs1xlHG0dLhyyEEELck8Zaw55+eyzWd3GcPXuW/Px8QkJCTGV6vZ5q1ao9UP+RkZE0b97clMTfKi0tjcTERJo2bWpW3rRpU9P0+AEDBtCuXTuqVatGx44d6dq1K+3btwcgKiqKjIwMXF1dza7Pzs42Td0vyooVK5g7dy4xMTFkZGRQUFBwX7uFZWdnP5KdCaZOnUpYWBjbt2+/rT+NRoPBYCA3NxeNpnh/1qVBEnkhxNNFpQLPmsajxUjIvAqnN8HJ9XBmM6TGw1/zjYejO1TvYkzq/VqA1e3/A3ySudi7MKDmAPo/25/9l/az8tRKNsdtJvpaNFN2T2HW/ll0r9qdftX7UUlXydLhCiGEEHekUqmeyEfEbo4A3/oc/s1nzW960OSybt26xMbGsmHDBjZv3szLL79M27ZtWbVqFRkZGXh5eZk9j3+Ts7Nzke3t3r2b0NBQJk+eTIcOHUyzBWbOnFni2MqXL8+RI0fMyjw9Pbl8+bJZWUFBAcnJyfcc4S/KjBkzmDp1Kps3b6Z27dq3nU9OTsbR0fGRJvEgibwQ4mnnWB6C+hqPvCzjc/XHf4WTGyDzMhxYbDzs9cap9zV7Gp+vf4qSepVKRQPPBjTwbMD1nOusjVnLylMriUuL4/vj37Ps+DJaVmxJvxr9aOTVCNVT+GiCEEII8aD8/f2xsbFh3759VK5cGYDU1FROnTpFixYtirzGzc0NgIsXLxIcHAxgtvAdQO3atVm6dCn5+fm3jcrrdDq8vb2JiIigZcuWpvKIiAizmQE6nY7evXvTu3dvevXqRceOHUlOTqZu3bokJSVhbW2Nr69vse5z165d+Pj4MG7cOFNZXFycWR1bW1sKCwvv2VZwcDBffPEFiqKY/v3RuHFjUlJSOHDgAPXq1QNg69atGAwGGjYs2ToJ06dP5+OPP+b33383e+b+VkePHjX97B8lSeSFEOImWweo0dV4FOTBuXA4vhZOrIPMKxD1g/FwcIVnXoCavaByY3iEz0NZWjn7cvR/tj//euZf7ErcxbLjy9h5YSfbz29n+/ntVNFXoV+NfnT17/pEjnwIIYQQD4tWq6V///6MGjUKFxcX3N3dmThxImq1+o5fkms0Gho1asTUqVPx8/Pj8uXLZs+fAwwbNox58+bRp08f3nvvPfR6PX/99RchISFUq1aNUaNGMXHiRKpUqUJQUBCLFy8mMjKSZcuWATBr1iy8vLwIDg5GrVazcuVKPD09cXZ2pm3btjRu3Jju3bszffp0AgMDSUxMZN26dfTo0aPI5DcgIID4+HjCwsJo0KAB69atY/Xq1WZ1fH19iY2NJTIykooVK6LVak3P4N+qdevWZGRkcOzYMWrWrAlAjRo16NixI0OGDOHLL78kPz+fYcOG0adPnxKtNzBt2jQmTJjA8uXL8fX1NT1f7+TkhJOTk6leeHi46VGDR0oRt0lNTVUAJTU11dKhCCEeB4UFihK7U1F+e1dRpvkrykTd38fMGoqy8X1FuXBQUQwGS0dqEbEpscrHf32shHwfotRcUlOpuaSm0nh5Y2XGvhnKxYyLlg5PCCHEUyY7O1uJjo5WsrOzLR1KiaWlpSn9+vVTHBwcFE9PT2XWrFlKSEiIMnbsWFMdHx8fZfbs2abP0dHRSuPGjRWNRqMEBQUpf/zxhwIo27ZtM9WJiopS2rdvrzg4OCharVZp3ry5EhMToyiKohQWFiqTJk1SKlSooNjY2Ch16tRRNmzYYLr2q6++UoKCghRHR0dFp9Mpbdq0UQ4ePGgW83//+1/F29tbsbGxUSpVqqSEhoYq8fHxd7zPUaNGKa6uroqTk5PSu3dvZfbs2Yperzedz8nJUXr27Kk4OzsrgLJ48eI7tvXyyy+b/XwURVGuXbum9O3bV3FyclJ0Op0ycOBAJT093azOvdr18fFRgNuOiRMnmuqcP39esbGxURISEu7Yzj/d7fezJHmo6sZNiFukpaWh1+tJTU29r0UXhBBPsMICiN0BR38yTsHPTfv7nEsVqNPHeDhXtlyMFpKel84vZ35h+YnlJKQbt6WxVlvTxa8Lr9V8DX9nfwtHKIQQ4mmQk5NDbGwsfn5+j2QhtIcpMzOTChUqMHPmTAYNGmTpcB5Lhw8fpl27dsTExJiNlN9NbGwsgYGBREdHExAQcN99jxkzhuvXr/PVV18V+5q7/X6WJA+VRL4IksgLIYolPwfObDIm9Sc3QkH23+d8m0NQKDzzPNg+XSu7GxQD4efDWXJsCfsv7TeVt6rUikE1BxHkHmS54IQQQjzxynIif+jQIU6cOEFISAipqalMmTKF7du3c+bMGcqXL2/p8B5bS5YsoV69etSqVatY9efPn090dDTz589/oH5nzpzJK6+8goeHR7GvkUT+IZJEXghRYrnpxmfpI5cbR+xvsnWCZ7pDUD/wafLU7VF/+MphFh1dxNb4rSgY/3dT170ug2oNonmF5rIwnhBCiFJX1hP5wYMHc/LkSWxtbalXrx6zZs0qdoIqHn+SyD9EksgLIR5ISjxErYDIZXA99u/ycr7GUfrgV0BX/MVWngRnU8+y5OgSfj37KwWGAgCqOldlSK0hdPDtgJXaysIRCiGEeFKU5URePPkkkX+IJJEXQpQKRYH4v4wJ/bE1kJduLFdZQbVOUH8g+D/3VK16fznrMt9Hf8+Pp34kMz8TAH+9P2/UeYP2Pu0loRdCCPHAJJEXjzNJ5B8iSeSFEKUuL9O4ON6BpRC/6+9yZx+oN8A4Su/kbrHwHrW0vDR+OP4D30Z/S1qeccHAKvoqxoTetz1q1dPz5YYQQojSJYm8eJxJIv8QSSIvhHioLh+HA0sg8gfITTWWqW2M+9fXGwh+LZ6aZ+nT89JZdnwZ30Z/S/qNGQtV9FV4I8g4Qi8JvRBCiJKSRF48ziSRf4gkkRdCPBJ5WXBsNexfBBf+Xt0dtxrQ6A2o3RtsNJaL7xFKz0vn++Pf892x70jPNyb0VZ2r8u86/6adTztZFE8IIUSxSSIvHmeSyD9EksgLIR65i4fhwGLjInk3nh1H42J8jr7B4Kdmcby0vDSWRS/ju+i/E/qarjUZUX8EDTwbWDg6IYQQZYEk8uJxJon8QySJvBDCYrJT4ND3sHeBcfV7ALU1PNsDGv4bKtazaHiPSlpeGt9Ff8fSY0vJLsgGoHmF5gyvN5zAcoEWjk4IIcTjTBJ58TgrrUReHj4UQojHicYZmgyDtyKh9/fg0xQMBXBkJXz9HHzdzrgCvqHQwoE+XDpbHW8Gvcn6F9fTu1pvrFXWhF8Ip9faXozfOZ6kzCRLhyiEEEJYhK+vL3PmzLF0GI+NvLw8qlatyq5du+5duZQ1atSIn3766ZH3C5LIl2lZ+/aRsTOC/EuXkYkVQjxh1FZQoxsMXA+v/wl1+oGVLZzfCyv7w/wQOPgtFORaOtKHqrymPOMbjWdN9zW082mHgsIvMb/Q5ecuzNo/i9SbiwUKIYQQ4okxadIkgoKCilX3yy+/xM/PjyZNmpjKkpOTCQ0NRafT4ezszKBBg8jIyChRDD///DP169fH2dkZR0dHgoKC+O6778zqjB8/nrFjx2IwGErUdmmQRL4Mu7rgKxIGD+ZMy5acbtSYuH+9StKUD7ketoKsgwcpTE+3dIhCiNLgVQd6fAHDj0KL0WDvDNfOwNr/wqdBsOszyC3Z/5zKGh+dD7NazWJZ52XU86hHniGPxccW0/nnznwf/T35hnxLhyiEEEKIR0xRFD777DMGDRpkVh4aGsqxY8fYtGkTv/32G3/++SdDhw4tUdsuLi6MGzeO3bt3c/jwYQYOHMjAgQP5/fffTXU6depEeno6GzZsKJX7KQlJ5Mswm0oVsfXzA7WawtRUsvbt4/ry5SRNmkRcv1BONQjh9HPPkfDGv7kydy5pf/xBXkKCjN4LUVZpPeC5cfDOMWj/MWi9ID0R/hgHs5+Fbf8HWcmWjvKhqu1Wm8UdFjO/zXyqOlclLS+Nafum8dLal/jr4l+WDk8IIcRjSlEUDFlZFjlK8m/v9PR0QkNDcXR0xMvLi9mzZ9OqVSuGDx9eZP1z586hUqmIjIw0laWkpKBSqdi+fbup7NixY3Tt2hWdTodWq6V58+bExMQAYDAYmDJlChUrVsTOzo6goCA2btxoujYvL49hw4bh5eWFvb09Pj4+fPLJJ2b9DR48GDc3N3Q6Hc899xxRUVF3vc8xY8YQGBiIg4MD/v7+fPDBB+TnG7+UX7JkCZMnTyYqKgqVSoVKpWLJkiVFtnPgwAFiYmLo0qWLqez48eNs3LiRr7/+moYNG9KsWTPmzZtHWFgYiYmJd43rVq1ataJHjx7UqFGDKlWq8Pbbb1O7dm127txpqmNlZUXnzp0JCwsrdrulxfqR9yhKjdfEiQAYcnPJi4kh59Qpck+fJvfUaXJPnaLg0iUKEi+SkXiRjFv+Q1Y7OWFXvRr21WtgX6MG9jWqYxcQgMrGxkJ3IoQoETsn43P0IUMgKgwiPoXkGNgxDXbNg3oDoOnboPW0dKQPhUqlokXFFjT1bsrPZ35m3sF5xKTGMOSPIbSt3JaRDUZSwamCpcMUQgjxGFGyszlZ1zILxlY7eACVg0Ox6o4YMYKIiAjWrl2Lh4cHEyZM4ODBg8WeZl6UCxcu0KJFC1q1asXWrVvR6XRERERQUFAAwKeffsrMmTNZsGABwcHBLFq0iOeff55jx44REBDA3LlzWbt2LT/++COVK1cmISGBhIQEU/svvfQSGo2GDRs2oNfrWbBgAW3atOHUqVO4uLgUGZNWq2XJkiV4e3tz5MgRhgwZglarZfTo0fTu3ZujR4+yceNGNm/eDIBery+ynfDwcAIDA9Fqtaay3bt34+zsTP369U1lbdu2Ra1Ws2fPHnr06FHin6GiKGzdupWTJ08ybdo0s3MhISFMnTq1xG0+KEnknwBqOzvsn3kG+2eeMSsvTE0l9/Rpck6cJOfEcXKjj5N7+jSGjAyy9x8ge/8BU12VrS121aujqfks9s/WxL5mTeyq+KOyll8RIR5b1nZQrz8EvwLH10L4LEg6DH99btybvv4gaPYOOLlZOtKHwkptxUuBL9Hepz1fRH1B2IkwNsdvJvxCOANrDuS1mq+hsdZYOkwhhBCiWNLT01m6dCnLly+nTZs2ACxevBhv7wfbgnb+/Pno9XrCwsKwuTFwFxj49w4wM2bMYMyYMfTp0weAadOmsW3bNubMmcP8+fOJj48nICCAZs2aoVKp8PHxMV27c+dO9u7dy+XLl7GzszO1t2bNGlatWnXH6ezjx483vff19WXkyJGEhYUxevRoNBoNTk5OWFtb4+l590GJuLi4234+SUlJuLu7m5VZW1vj4uJCUlLJFstNTU2lQoUK5ObmYmVlxeeff067du3M6nh7e5OQkIDBYECtfnQT3iVLe4JZ6fU41K+Pwy3fRin5+eSejSX3xHFyjp8g58QJcqKjMaSlkXP4MDmHD5vqquztjSP2NWuiqV0LTVAQNhUrolKpLHE7Qog7UVsZt6d7pjvEbIEd0yFhD/w137g3fchQaPIWOLpaOtKHQm+nZ2zIWHoG9GTq3qnsTdrLl1FfsubMGkbWH0l7n/by95YQQjzlVBoN1Q4euHfFh9R3cZw9e5b8/HxCQkJMZXq9nmrVqj1Q/5GRkTRv3tyUxN8qLS2NxMREmjZtalbetGlT0/T4AQMG0K5dO6pVq0bHjh3p2rUr7du3ByAqKoqMjAxcXc3/jZGdnW2aul+UFStWMHfuXGJiYsjIyKCgoOC+tv3Ozs5+qFsMarVaIiMjycjIYMuWLYwYMQJ/f39atWplqqPRaDAYDOTm5qIp5p91aZBE/imjsrHBvlog9tUC0b/wAmCcKpKfkEDO0aNkHz1GztGj5Bw7hiEzk+xDh8g+dIjrN663cnFBU6eO8Qiqg33NWlg5OVruhoQQf1OpoGpbqNLGmNBv/RgSD0LEHNj3NTT6NzR+EzTlLB3pQxFQLoCv23/N5vjN/G/f/7iYeZGRO0YS4hnCB40+wFfva+kQhRBCWIhKpSr29Pay5OYI8K3P4d981vymB00u69atS2xsLBs2bGDz5s28/PLLtG3bllWrVpGRkYGXl5fZ8/g3OTs7F9ne7t27CQ0NZfLkyXTo0ME0W2DmzJkljq18+fIcOXLErMzT05PLly+blRUUFJCcnHzPEf5/UqvVVK1aFYCgoCCOHz/OJ598YpbIJycn4+jo+EiTeJBEXmD8i822cmVsK1dG17kzAIrBQN65OHKOHSX7yBGyo6LIiT5OYXIyGdu2kbFtm/FitRq7gAA0QUE41KuLQ716WHt7y+iXEJZ0a0J/6nfY9rFxyv2f/4M9XxmT+Ub/BvuSf/P9uFOpVLTzaUezCs1YcnQJ3xz9hr1Je+m5tidDaw/ltZqvYWMl64EIIYR4/Pj7+2NjY8O+ffuoXLkyYJzaferUKVq0aFHkNW5uxsfnLl68SHBwMIDZwncAtWvXZunSpeTn5982Kq/T6fD29iYiIoKWLVuayiMiIsxmBuh0Onr37k3v3r3p1asXHTt2JDk5mbp165KUlIS1tTW+vr7Fus9du3bh4+PDuHHjTGVxcXFmdWxtbSksLLxnW8HBwXzxxRcoimLKPxo3bkxKSgoHDhygXj3jughbt27FYDDQsGHDYsV4JzdH3m919OhR08/+UZJEXhRJpVZj5++Hnb8f+m7dAOOiernHj5MdFUV2VBRZkZEUJF4k9+RJck+eJGXFCgCsPT1xqFcPh/r10NSth11AVVSP8HkRIcQNKhVU6wiBHeDEb7DtE7h8DLb/H+xdAC3HQL2BYG1r6UhLncZaw7+D/k23Kt346K+PiEiM4LPIz9gQu4FJTSYR5B5k6RCFEEIIM1qtlv79+zNq1ChcXFxwd3dn4sSJqNXqOw6SaTQaGjVqxNSpU/Hz8+Py5ctmz58DDBs2jHnz5tGnTx/ee+899Ho9f/31FyEhIVSrVo1Ro0YxceJEqlSpQlBQEIsXLyYyMpJly5YBMGvWLLy8vAgODkatVrNy5Uo8PT1xdnambdu2NG7cmO7duzN9+nQCAwNJTExk3bp19OjRw2zBuZsCAgKIj48nLCyMBg0asG7dOlavXm1Wx9fXl9jYWCIjI6lYsSJardb0DP6tWrduTUZGBseOHaNmzZoA1KhRg44dOzJkyBC+/PJL8vPzGTZsGH369CnRegOffPIJ9evXp0qVKuTm5rJ+/Xq+++47vvjiC7N64eHhpkcNHilF3CY1NVUBlNTUVEuH8tjLS7qkpP7+u5I0dZpy9qWXlehnayrR1aqbHSdCGirxr7+hXP1mkZJ19KhiKCiwdNhCPJ0KCxXlyE+KMq++okzUGY9PgxTl6GpFMRgsHd1DYzAYlHUx65QWYS2UmktqKjWX1FQ+3P2hkpabZunQhBBCPATZ2dlKdHS0kp2dbelQSiwtLU3p16+f4uDgoHh6eiqzZs1SQkJClLFjx5rq+Pj4KLNnzzZ9jo6OVho3bqxoNBolKChI+eOPPxRA2bZtm6lOVFSU0r59e8XBwUHRarVK8+bNlZiYGEVRFKWwsFCZNGmSUqFCBcXGxkapU6eOsmHDBtO1X331lRIUFKQ4OjoqOp1OadOmjXLw4EGzmP/73/8q3t7eio2NjVKpUiUlNDRUiY+Pv+N9jho1SnF1dVWcnJyU3r17K7Nnz1b0er3pfE5OjtKzZ0/F2dlZAZTFixffsa2XX37Z7OejKIpy7do1pW/fvoqTk5Oi0+mUgQMHKunp6WZ17tXuuHHjlKpVqyr29vZKuXLllMaNGythYWFmdc6fP6/Y2NgoCQkJd2znn+72+1mSPFR14ybELdLS0tDr9aSmpt7XogtPM0NWFtmHD5O1/wBZB/aTHRmFkp1tVket1+PQoD6ODRvh0DDEuPWdTMUX4tEpLIBD3xpH6DNvPENWMQTafwSVH2zK2eMsNTeVmftnsvqM8Vt/N40b7zV8j7aV28rfQUII8QTJyckhNjYWPz+/h7oQ2qOQmZlJhQoVmDlzJoMGDbJ0OI+lw4cP065dO2JiYnBycirWNbGxsQQGBhIdHU1AQMB99z1mzBiuX7/OV199Vexr7vb7WZI8VBL5IkgiX3qU/HxyTpwga99+svbsIWv/fgyZmWZ1rFxdcQhpgGOjxjg2bYptRdn/WYhHIjfduO/8rnmQn2Usq9EN2k4G1yqWje0h2ntxL1P+mkJcmvF5vFaVWjG+4Xg8HD0sHJkQQojSUJYT+UOHDnHixAlCQkJITU1lypQpbN++nTNnzlC+fHlLh/fYWrJkCfXq1aNWrVrFqj9//nyio6OZP3/+A/U7c+ZMXnnlFTw8iv9vCEnkHyJJ5B8epaCAnGPHyPxrjzGxP3gQJSfHrI6try+OzZrh2KwpjiEhqJ/AFUaFeKykXTQ+N3/oe1AMoLaG+q9Bq/fAwcXS0T0UuYW5LDy8kG+OfkOBoQCtrZb3G75PF78uMjovhBBlXFlP5AcPHszJkyextbWlXr16zJo1q9gJqnj8SSL/EEki/+gY8vLIOXyYzL/2kLlrF9lRUXDLCpUqGxs09erh2LQJTs2aYVe9uvwjW4iH5fJx2DQRTv9u/KxxgTYToO6rxr3qn0Bnrp9hfMR4jl07BkA7n3aMbzQeF/sn8wsMIYR4GpTlRF48+SSRf4gkkbecwrQ0MvfsIXNnBJk7d5J/4YLZeWsPD5xatcKpVUscGzdGLX85C1H6zu6Aje8ZV7gH8AqCzjOgUgOLhvWw5Bvy+ebINyyIWkCBUoCLvQuTGk+ideXWlg5NCCHEfZBEXjzOJJF/iCSRfzwoikJ+XBwZN5L6zD17zBbOU9nb49iokTGxb90KmxI8myKEuIfCAtj/DWz9GHJTjWVBr0DbSeDkZtHQHpboa9GM2zmOMylnAHihyguMCRmD1lZr4ciEEEKUhCTy4nEmifxDJIn848mQm0vW3r1kbNtO+vZtFCReNDtv90wNtM+1QduuHXaBshK+EKUi4zJsngyR3xs/2+mh9fvQYDBYWVs2tocgtzCX+Yfms+TYEhQUPB09+ajpRzT0enJX8xdCiCeNJPLicSaJ/EMkifzjT1EUck+dJmPbNjK2bzc+W3/Lr7KNT2V07dujbdcO+1q1JKkX4kEl7IP178LFKONn92ehywzwaWLZuB6Sg5cOMm7nOM5nnAcgtEYo79R7BzsrOwtHJoQQ4l4kkRePM0nkHyJJ5MueguRkMrbvIH3zZjJ37kTJyzOds/b0RNuuHbr27dDUrYvK6slctEuIh85QCAe/hS2TIfu6saz+a8bp9vZ6i4b2MGTlZzFz/0x+PPUjANVdqvO/Fv/DV+9r2cCEEELclSTy4nEmifxDJIl82WbIzCTjzz9J++MPMnb8iZKVZTpn5eqKrkMHdF27oAkKQqVWWzBSIcqorGTYPNGY1ANovaDLTKjexbJxPSR/nv+T8TvHcz33OhprDR80+oBuVbpZOiwhhBB3IIm8eJyVViIvWYx44qgdHdF16kTF2bMJ3BVBxc/no+/eHbVeT+G1a1xfvpy4fqGcaduWyzNmkHP8OPJ9lhAl4OACz8+D/r+BSxVIvwhh/eDHVyH9kqWjK3UtKrZg1fOraODZgOyCbN7f+T7jdo4jKz/r3hcLIYQQpczX15c5c+ZYOozHRl5eHlWrVmXXrl2PvO9GjRrx008/PfJ+QRJ58YRT29ujfe45vKd+QuDOcCot/Ar9Cy+gdnSkIPEi177+htgeL3K2azeufP45eXFxlg5ZiLLDrzn8OwKajQCVFUT/AvMbGEfqn7Avx9wd3FnYbiH/CfoPapWatTFr6f1bb04mn7R0aEIIIcQTZ9KkSQQFBRWr7pdffomfnx9Nmvy9bk9ycjKhoaHodDqcnZ0ZNGgQGRkZ9x1PWFgYKpWK7t27m5WPHz+esWPHYjAY7rvt+yWJvHhqqGxscGreHO9pUwmI2EmFTz9F264dKltb8mJiuDp3HjEdOhLbuzfXw8IoTE21dMhCPP5sNNB2IgzdbtxvPicV1v4XlnaDazGWjq5UWamt+Hedf/N1+69x17hzLu0c/db1I+xEmMzqEUIIISxAURQ+++wzBg0aZFYeGhrKsWPH2LRpE7/99ht//vknQ4cOva8+zp07x8iRI2nevPlt5zp16kR6ejobNmy4r7YfhEUT+T///JNu3brh7e2NSqVizZo1ZucHDBiASqUyOzp27HjPdufPn4+vry/29vY0bNiQvXv3PqQ7EGWV2t4eXYf2VJw3l4CInXh98gmOzZqBlRU5UYdJmjSZ081bcGHECDLCw1EKCy0dshCPN6/aMHgLtP8IrDVwLhy+aAJ/fQkW+Jb6YWrg2YBVz6+iRcUW5Bny+HjPx4zYPoK0vDRLhyaEEOIOFEUhP7fQIkdJvuxNT08nNDQUR0dHvLy8mD17Nq1atWL48OFF1j937hwqlYrIyEhTWUpKCiqViu3bt5vKjh07RteuXdHpdGi1Wpo3b05MjPELd4PBwJQpU6hYsSJ2dnYEBQWxceNG07V5eXkMGzYMLy8v7O3t8fHx4ZNPPjHrb/Dgwbi5uaHT6XjuueeIioq6632OGTOGwMBAHBwc8Pf354MPPiA/Px+AJUuWMHnyZKKiokw54JIlS4ps58CBA8TExNCly9/r9Bw/fpyNGzfy9ddf07BhQ5o1a8a8efMICwsjMTHxrnH9U2FhIaGhoUyePBl/f//bzltZWdG5c2fCwsJK1G5psOgmwJmZmdSpU4fXXnuNF198scg6HTt2ZPHixabPdnZ33/pnxYoVjBgxgi+//JKGDRsyZ84cOnTowMmTJ3F3dy/V+MWTwUqrxblHd5x7dKfg2jVSf/2V1J9Xk3vqFGnrN5C2fgPW7u7oX3gBfY/u2BXxH7EQAuO+8k3+C9W7wq9vQ+wO2DgGTm2AFz4HfQVLR1hqytmX47PnPuO76O+YfXA2m+M3czrlNJ+2/pQqzlUsHZ4QQoh/KMgz8NXbOyzS99BPW2JjV7xdk0aMGEFERARr167Fw8ODCRMmcPDgwWJPMy/KhQsXaNGiBa1atWLr1q3odDoiIiIoKCgA4NNPP2XmzJksWLCA4OBgFi1axPPPP8+xY8cICAhg7ty5rF27lh9//JHKlSuTkJBAQkKCqf2XXnoJjUbDhg0b0Ov1LFiwgDZt2nDq1ClcXFyKjEmr1bJkyRK8vb05cuQIQ4YMQavVMnr0aHr37s3Ro0fZuHEjmzdvBkCvL3p3nPDwcAIDA9Fqtaay3bt34+zsTP369U1lbdu2Ra1Ws2fPHnr06FHsn92UKVNwd3dn0KBBhIeHF1knJCSEqVOnFrvN0mLRRL5Tp0506tTprnXs7Ozw9PQsdpuzZs1iyJAhDBw4EDA+M7Fu3ToWLVrE2LFjHyhe8eSzdnXFdcAAXPr3Jyc6mtTVa0j79VcKLl/m2sKFXFu4EE2dOjj37o2uU0fUGo2lQxbi8ePiB6/+Avu+hj8+gLPb4YvG0GUW1Opl6ehKjUql4tVnX6WuR11GbB9BXFoc/db146NmH9HOp52lwxNCCFHGpKens3TpUpYvX06bNm0AWLx4Md7e3g/U7vz589Hr9YSFhWFjYwNAYGCg6fyMGTMYM2YMffr0AWDatGls27aNOXPmMH/+fOLj4wkICKBZs2aoVCp8fHxM1+7cuZO9e/dy+fJl04DrjBkzWLNmDatWrbrjdPbx48eb3vv6+jJy5EjCwsIYPXo0Go0GJycnrK2t75kHxsXF3fbzSUpKum0A19raGhcXF5KSku714zK7t2+++cZstkNRvL29SUhIwGAwoH6EO2JZNJEvju3bt+Pu7k65cuV47rnn+Oijj3B1dS2ybl5eHgcOHOC9994zlanVatq2bcvu3bvv2Edubi65ubmmz2lpMj3yaadSqdA8+yyaZ5/FffQoMrZtJ3X1ajLCw8mOiiI7KopLU6ei7/4C5Xr3xq6KjMAJYUalgpAh4N8Kfh4KiQfhp0FwYp1xqzqHor+hL4tqlq9JWNcwRu0Yxd6kvYzYPoLBtQYzLGgYVurijcAIIYR4uKxt1Qz9tKXF+i6Os2fPkp+fT0hIiKlMr9dTrVq1B+o/MjKS5s2bm5L4W6WlpZGYmEjTpk3Nyps2bWqaHj9gwADatWtHtWrV6NixI127dqV9+/YAREVFkZGRcVt+lp2dbZq6X5QVK1Ywd+5cYmJiyMjIoKCg4L62/c7Ozn4oWwymp6fzr3/9i4ULF1K+fPm71tVoNBgMBnJzc9E8wkG+xzqR79ixIy+++CJ+fn7ExMTw/vvv06lTJ3bv3o2V1e3/OLp69SqFhYV4eHiYlXt4eHDixIk79vPJJ58wefLkUo9fPBnUtrboOrRH16E9BVeukLJ6DSkrVpB/4QLXv/2O699+h0ODBjj37o22fTvUtraWDlmIx0f5ABj0B4TPhB3T4djPEL8bXvgMqra1dHSlxsXehQXtFjDnwByWRi/l6yNfc/zacaa1mIberujpgEIIIR4dlUpV7OntZcnNEeBbn8O/+az5TQ+aXNatW5fY2Fg2bNjA5s2befnll2nbti2rVq0iIyMDLy8vs+fxb3J2di6yvd27d5ueO+/QoYNptsDMmTNLHFv58uU5cuSIWZmnpyeXL182KysoKCA5ObnYM71jYmI4d+4c3bp1M5XdXJne2tqakydPUuXGQF5ycjKOjo6PNImHx3zV+j59+vD8889Tq1Ytunfvzm+//ca+ffuK/EV5EO+99x6pqamm49ZnPoS4lbWbG+WHDqHKpj+otPArnNq0AbWarH37SBw5kjMtW3F5xgzyzp+3dKhCPD6sbKDVWBi8CVwDjPvOf98T1r0LeZmWjq7UWKutGdlgJNOaT8Peyp6IxAj6/NZHtqgTQghRLP7+/tjY2LBv3z5TWWpqKqdOnbrjNW5ubgBcvHjRVPbPqeC1a9cmPDz8tgQfQKfT4e3tTUREhFl5REQEzzzzjFm93r17s3DhQlasWMFPP/1EcnIydevWJSkpCWtra6pWrWp23Gkke9euXfj4+DBu3Djq169PQEAAcf/YAtrW1pbCYiw2HRwczIkTJ8y+yGjcuDEpKSkcOHDAVLZ161YMBgMNGza8Z5sA1atX58iRI0RGRpqO559/ntatWxMZGUmlSpVMdY8ePUpwcHCx2i1Nj3Ui/0/+/v6UL1+eM2fOFHm+fPnyWFlZcenSJbPyS5cu3fXbFzs7O3Q6ndkhxN2o1Gqcmjen0vzPqLp1C+WHDcPaw4PC69e59vU3xLTvwPn//pesfftkWyohbqpQD17/E0JuPC+372tY+BxcPm7ZuEpZZ//OfN/5eyo4VeB8xnn+teFfbIh99NvSCCGEKFu0Wi39+/dn1KhRbNu2jWPHjjFo0CDUajUqlarIazQaDY0aNWLq1KkcP36cHTt2mD1/DjBs2DDS0tLo06cP+/fv5/Tp03z33XecPGn8onnUqFFMmzaNFStWcPLkScaOHUtkZCRvv/02YFyD7IcffuDEiROcOnWKlStX4unpibOzM23btqVx48Z0796dP/74g3PnzrFr1y7GjRvH/v37i4w5ICCA+Ph4wsLCiImJYe7cuaxevdqsjq+vL7GxsURGRnL16lWzx6Bv1bp1azIyMjh27JiprEaNGnTs2JEhQ4awd+9eIiIiGDZsGH369Cn2egP29vbUrFnT7HB2dkar1VKzZk1sb5mBGx4ebnrU4FEqU4n8+fPnuXbtGl5eXkWet7W1pV69emzZssVUZjAY2LJlC40bN35UYYqnjI2nJ27D3qTqls1UnP8Zjk2agMFA+qbNxP3rVc717EXqL7+g5OVZOlQhLM/WATr/D/61Gpw84coJ+Ko1HPoenqAvvaq5VGNF1xU08W5CdkE2o/8czaz9szAoT9ZWfEIIIUrXrFmzaNy4MV27dqVt27Y0bdqUGjVq3PU58EWLFlFQUEC9evUYPnw4H330kdl5V1dXtm7dSkZGBi1btqRevXosXLjQ9Mz8W2+9xYgRI3j33XepVasWGzduZO3atQQEBADGLximT59O/fr1adCgAefOnWP9+vWmLxjWr19PixYtGDhwIIGBgfTp04e4uLjbHne+6fnnn+edd95h2LBhBAUFsWvXLj744AOzOj179qRjx460bt0aNzc3fvjhhyLbcnV1pUePHixbtsysfNmyZVSvXp02bdrQuXNnmjVrxldffWVW527b2hXXhQsX2LVrl2mh9UdJpVhwuDAjI8M0uh4cHMysWbNo3bo1Li4uuLi4MHnyZHr27ImnpycxMTGMHj2a9PR0jhw5YloVsU2bNvTo0YNhw4YBxoUT+vfvz4IFCwgJCWHOnDn8+OOPnDhx4o6/TP+UlpaGXq8nNTVVRufFfck9fZrk7743JvA3vkG0citPub59KdenD9Z32IpDiKdKxhX4eQic3Wb8XLuPcSE8OyfLxlWKCg2FzDs0j2+OfgNAm8pt+KT5J2isZccLIYR4WHJycoiNjcXPz++hLIT2KGVmZlKhQgVmzpzJoEGDLB3OY+nw4cO0a9eOmJgYnJyK92+I2NhYAgMDiY6ONn1hcT/GjBnD9evXb/uS4G7u9vtZkjzUoiPy+/fvJzg42PRMwYgRIwgODmbChAlYWVlx+PBhnn/+eQIDAxk0aBD16tUjPDzcbC/5mJgYrl69avrcu3dvZsyYwYQJEwgKCiIyMpKNGzcWO4kXojTYBQTgNWUyVbdvw+2dd7B2d6fwylWuzp3HmVatSRw/ntyzsZYOUwjLcnKDV36G5z4AlRoOh8HC1nDp2L2vLSOs1FYMrzecac2nYaO2YUv8Fl7b+BpXs6/e+2IhhBBPnUOHDvHDDz8QExPDwYMHCQ0NBeCFF16wcGSPr9q1azNt2jRiY4v/b+v169czdOjQB0riAdzd3fnwww8fqI37ZdER+ceVjMiL0qbk55P2+x8kL11Kzs2VNVUqtO3b4zp0CJpnn7VsgEJY2rkI4/Z06RfB2t44/T74X8Zt7J4QBy8d5O1tb5OSm4KXoxeft/mcquWqWjosIYR44pTlEflDhw4xePBgTp48aXpseNasWdSqVcvSoYlSUloj8pLIF0ESefGwKIpC9qFDXPv6GzK2bjWVOzZtiuvQoTiENLjjYiZCPPEyrxr3nI+5sc5JrZeh6yyw01o2rlIUlxbHm1veJC4tDicbJ2a1mkVjb1nDRQghSlNZTuTFk++JmFovxNNGpVLhULculT6fj9/aX9A93w2srMiMiCC+f3/i+vQlfetWFIMsiCWeQo7lIXQVtJkIKis48qNxIbwrT872bT46H77v9D113euSkZ/Bfzb/h59O/WTpsIQQQghRxkgiL4SF2AcGUmH6dKr8vpFy/fqisrUlOyqK8/95k9gXupO6bp0k9OLpo1ZD8xEwYB1oveHaaVjYBk4+Odu3Ods7s7D9Qrr4d6FAKWDS7knMOTBHVrQXQgghRLFJIi+EhdlWrIjnhAlU3bIZ1yGDUTs6knv6NInvjiT2he6k/fGH7EUvnj4+jY17zvs0hbx0+KEP7JgOT8iXW7ZWtnzS7BP+XeffAHxz9BtG7RhFbmHR++QKIYQQQtxKEnkhHhPWbm64v/suVbdtpfxb/0Wt1ZJ7+jQX3nqb2J49Sd+2TRJ68XRxcoNXf4EGQ4yft30MK1+F3HTLxlVKVCoV/wn6D//X7P+wVlvzR9wf/Gfzf8jIy7B0aEIIIYR4zEkiL8Rjxkqnw+0//6Hq5k24/vsN1A4O5EYf5/y//8O5Pn3I2BkhCb14eljZQJcZ0G0uqG3g+K/wdTtIPmvpyEpNtyrd+KrdVzjaOLI3aS+D/hhEck6ypcMSQgghxGNMEnkhHlNWej3ub79NlS2bcR08CJVGQ07UYRIGDybulX+RuWevpUMU4tGp1x8GrgcnD7hy3LgIXszWe19XRjTwbMA3Hb6hnF05oq9FM2DjAJIykywdlhBCCCEeU5LIC/GYsy5XDveRI6m66Q9c+vc3Lop34ADx/fuT8Pob5J45Y+kQhXg0KoXA0B1QoT7kpMD3PSFiLjwhM1SedX2WJZ2W4OnoSWxqLP/a8C9iU2MtHZYQQgjxWNuyZQs1atSgsLDwkfYbHR1NxYoVyczMfKT93iSJvBBlhHX58ni8N5YqmzZRrl9fsLYmY8cOzj7/Ahc/mED+5cuWDlGIh0/nZVzRPugVUAyw6QNY8x8oyLN0ZKXCX+/Ptx2/xVfnS1JmEgM2DiD6WrSlwxJCCPEEUalUrFmzpsTX+fr6MmfOnFKP50H7Gj16NOPHj8fKyspUtn37durWrYudnR1Vq1ZlyZIlJY4hJyeHN998E1dXV5ycnOjZsyeXLl0ynX/mmWdo1KgRs2bNKnHbpUESeSHKGBsPdzwnTMD/17Vo27UFg4GUlSuJ6diJK/M+w2ChbwWFeGRs7OGFz6DT/4z7zUcth2U9ITvF0pGVCi8nL5Z2WkoNlxok5yTz2u+vsS9pn6XDEkIIIR47O3fuJCYmhp49e5rKYmNj6dKlC61btyYyMpLhw4czePBgfv/99xK1/c477/Drr7+ycuVKduzYQWJiIi+++KJZnYEDB/LFF19QUFBQKvdTEpLIC1FG2fn5UXHePHyWfY+mTh2UrCyuzp/PmY4duf7jjygW+AtFiEdGpYKGQ6HfCrBxhNg/YVFHSEmwdGSlwsXehUUdFlHfoz6Z+Zn8e/O/2Z6w3dJhCSFEmaUoCvk5ORY5SrJI8caNG2nWrBnOzs64urrStWtXYmJizOqcP3+evn374uLigqOjI/Xr12fPnj2m87/88gt169bF3t4ef39/Jk+ebEo0fX19AejRowcqlcr0OSYmhhdeeAEPDw+cnJxo0KABmzdvNrXZqlUr4uLieOedd1CpVKhUKtO5nTt30rx5czQaDZUqVeKtt96663TzB+nrn8LCwmjXrh329vamsi+//BI/Pz9mzpxJjRo1GDZsGL169WL27Nl3+cmbS01N5ZtvvmHWrFk899xz1KtXj8WLF7Nr1y7++usvU7127dqRnJzMjh07it12abF+5D0KIUqVQ716+IT9QPrvv3N55izyExJImjCR5G+/xWPsezg1a2rpEIV4eALawWsbYNnLxkXwvm4LoT+CVx1LR/bAnGyd+KLtF4z6cxTbE7YzfNtwPmr2EV39u1o6NCGEKHMKcnOZ27+XRfp+a+kqbG5JNO8mMzOTESNGULt2bTIyMpgwYQI9evQgMjIStVpNRkYGLVu2pEKFCqxduxZPT08OHjyIwWAAIDw8nFdffZW5c+fSvHlzYmJiGDp0KAATJ05k3759uLu7s3jxYjp27Giajp6RkUHnzp35+OOPsbOz49tvv6Vbt26cPHmSypUr8/PPP1OnTh2GDh3KkCFDTPHGxMTQsWNHPvroIxYtWsSVK1cYNmwYw4YNY/HixUXe4/32VZTw8HD69etnVrZ7927atm1rVtahQweGDx9erD8DgAMHDpCfn2/WTvXq1alcuTK7d++mUaNGANja2hIUFER4eDht2rQpdvulQRJ5IZ4AKpUKXceOaJ97juthYVyd/zl5Z2JIGDwYbbt2eIwdg02FCpYOU4iHw6sODN4My1+Gy9GwqBO8tAQC21s6sgdmb23P7FazmbhrImtj1vJ++PsoikK3Kt0sHZoQQoiH4NYp4gCLFi3Czc2N6OhoatasyfLly7ly5Qr79u3DxcUFgKpVq5rqT548mbFjx9K/f38A/P39+fDDDxk9ejQTJ07Ezc0NAGdnZzw9PU3X1alThzp1/v4S/MMPP2T16tWsXbuWYcOG4eLigpWVFVqt1uy6Tz75hNDQUFOSHBAQwNy5c2nZsiVffPGF2Uj5g/ZVlLi4OLy9vc3KkpKS8PDwMCvz8PAgLS2N7OxsNBrNXdu82YatrS3Ozs63tZOUZL6rjLe3N3Fxcfdss7RJIi/EE0Rla4vLq6+i796dq/Pnk/z9MtI3bSIjPJzyrw/F5bXXUNvZWTpMIUqfcyV4bSP8+Cqc3Q4/9DHuP1//NUtH9sCs1dZ82PRD7KzsWHlqJeN2jgOQZF4IIUrA2s6Ot5ausljfxXX69GkmTJjAnj17uHr1qmmkPT4+npo1axIZGUlwcLApif+nqKgoIiIi+Pjjj01lhYWF5OTkkJWVhYODQ5HXZWRkMGnSJNatW8fFixcpKCggOzub+Pj4u8YbFRXF4cOHWbZsmalMURQMBgOxsbHUqFGj1PoqSnZ2dpFfFjxKGo2GrKysR96vJPJCPIGsdDo83nsPfc+eXPrwI7L27ePKp3NJWb0Gj/ffQ9uqlaVDFKL02euh30r4bThELoPf3oHrcdBmIqjL9pIwapWa8Y3GA0gyL4QQ90GlUhV7ersldevWDR8fHxYuXIi3tzcGg4GaNWuSl2fcneVeo8kZGRlMnjz5tkXZgLsmvCNHjmTTpk3MmDGDqlWrotFo6NWrl6nfu/X3+uuv89Zbb912rnLlyqXaV1HKly/P9evXzco8PT3NVpcHuHTpEjqdrlij8TfbyMvLIyUlxWxU/tKlS7fNEkhOTqZKlSoljv1BSSIvxBPMPjCQyt8uJW39ei5Pm05+fDzn3/g3Tq1a4fH+e9je4S9YIcosa1t4YT44+8D2/4OIOZCaAN2/NJ4rwySZF0KIJ9u1a9c4efIkCxcupHnz5oBxIblb1a5dm6+//prk5OQiR+Xr1q3LyZMnzabb/5ONjc1te65HREQwYMAAevToARgT9HPnzpnVsbW1ve26unXrEh0dfdf+/ul++ypKcHAw0dHm27Q2btyY9evXm5Vt2rSJxo0bFzvGevXqYWNjw5YtW0yPO5w8eZL4+Pjb2jl69Ci9ej369RfK9hCFEOKeVCoV+i5dqLJhPa6DB4GNDRnbt3O2azeuzJ2H4T6+/RTisaZSQasxxuRdbQ1Hf4KwvpD36Ke9lbabyfxLgS+hoDBu5zh+jfnV0mEJIYQoBeXKlcPV1ZWvvvqKM2fOsHXrVkaMGGFWp2/fvnh6etK9e3ciIiI4e/YsP/30E7t37wZgwoQJfPvtt0yePJljx45x/PhxwsLCGD9+vKkNX19ftmzZQlJSkmk0OyAggJ9//pnIyEiioqLo16+faVr/rdf9+eefXLhwgatXrwIwZswYdu3axbBhw4iMjOT06dP88ssvDBs27I73eb99FaVDhw63fdnxxhtvcPbsWUaPHs2JEyf4/PPP+fHHH3nnnXfu2M4/6fV6Bg0axIgRI9i2bRsHDhxg4MCBNG7c2LTQHcC5c+e4cOHCbYvrPQqSyAvxlFA7OuI+ciT+v6zBsUkTlLw8rn7+ObHde5B14IClwxOi9AX1hX4/grUGzmyG73tCTqqlo3pgN5P5XoG9JJkXQogniFqtJiwsjAMHDlCzZk3eeecd/ve//5nVsbW15Y8//sDd3Z3OnTtTq1Ytpk6dalp9vkOHDvz222/88ccfNGjQgEaNGjF79mx8fHxMbcycOZNNmzZRqVIlgoODAZg1axblypWjSZMmdOvWjQ4dOlC3bl2zvqdMmcK5c+eoUqWKadG82rVrs2PHDk6dOkXz5s0JDg5mwoQJty1Ad6v77asooaGhHDt2jJMnT5rK/Pz8WLduHZs2baJOnTrMnDmTr7/+mg4dOpjqLFmy5K7b2gHMnj2brl270rNnT1q0aIGnpyc///yzWZ0ffviB9u3bm/18HxWVUpKNDZ8SaWlp6PV6UlNT0el0lg5HiFKnKArpv/9B0scfUXjF+C1nuX59cRsxAisnJwtHJ0Qpi//LuD1dbqpxhftXVoOjq6WjemAGxcCHf33IqlOrUKHi42YfyzR7IYQAcnJyiI2Nxc/Pz+ILoYmHb9SoUaSlpbFgwYJiXzNx4kR27NjB9u3b77vfvLw8AgICWL58OU2bFn+757v9fpYkD5UReSGeQsbt6jpQ5bffcH7J+EzP9eU/cLZrN9K3bbNwdEKUssqNYMCv4FAeLkbB4k6QlmjpqB6YWqXmg0YfyMi8EEKIp9q4cePw8fG5bXr+3WzYsIHp06c/UL/x8fG8//77JUriS5OMyBdBRuTF0ybzr7+4OGEi+Te2/dB17oTHuHFYu5b9UUshTK6cgu+6Q9oFcK4Mr/4CLv6WjuqB/XNk/n8t/0cH3w73vlAIIZ5QMiIvHmcyIi+EKDWOjRrh/8saXAa9Bmo1aes3cLZzF1JWr0G+6xNPDLdA417zLv6QEg+LOsKl6Htf95i7OTLfM6AnCgpjw8ey68IuS4clhBBCiIdIEnkhBABqjQaPUaPw/fFH7KpXpzA1lYvvvcf5N/5NwZUrlg5PiNLhXBkGbgT3ZyHjEizpDOfL/mKPN5P59j7tKTAUMHz7cKKuRFk6LCGEEEI8JJLICyHMaGo+i9/KH3EbMQKVjQ0ZO3ZwttvzpP3xh6VDE6J0aD1gwG9QoT5kX4dvn4dzEZaO6oFZqa34pPknNPFuQnZBNv/Z/B9OXz9t6bCEEEII8RBIIi+EuI3KxobyQ4fgu2qVcXQ+JYULb71N4tj3KExPt3R4Qjw4Bxd4dQ34tYC8DFj2EsTttnRUD8zWypbZrWZT2602aXlpvL7pdc6nn7d0WEIIIYQoZZLICyHuyL5aIL4/rsB1yBBQqUhds4bYF7qTuXevpUMT4sHZaY37zPu3hvxMWNYLEsr+77aDjQOft/mcqs5VuZJ9haGbhnI1+6qlwxJCCCFEKZJEXghxV2pbW9zfHYHP999hU7Ei+YmJxPcfwKXp/8OQl2fp8IR4MDYa6LP875H5716E8/stHdUD09vpWdBuARWcKpCQnsAbm94gLS/N0mEJIYQQopRIIi+EKBaHevXwW7MGfa+eoCgkL1rEuV4vkXPypKVDE+LB2DpA3zDwbQ556fBdD7hQ9hfAc3dw56t2X+Fq78rJ6ycZtmUY2QXZlg5LCCGEKFVbtmyhRo0aFBYWPtJ+r169iru7O+fPW+YRNknkhRDFZuXkiPdHH1Hx8/lYubiQe+oU5156mes//CDb1ImyzdbRmMxXbgK5acZkPjHS0lE9sMq6yixotwCtrZZDlw8xYvsI8gvzLR2WEEIIC1KpVKxZs6bE1/n6+jJnzpxSj+dB+xo9ejTjx4/HysrKVLZ9+3bq1q2LnZ0dVatWZcmSJSWO4auvvqJVq1bodDpUKhUpKSlm58uXL8+rr77KxIkTS9x2aZBEXghRYtrnnsP/17U4tWqFkpdH0uQpXBgxgsKMDEuHJsT9s3OC0B+hUkPISYVvX4CLhy0d1QOr5lKN+W3mY29lz84LO5mwa4J88SaEEOKJsHPnTmJiYujZs6epLDY2li5dutC6dWsiIyMZPnw4gwcP5vfffy9R21lZWXTs2JH333//jnUGDhzIsmXLSE5Ovu97uF+SyAsh7ou1qysVv/gc99Gjwdqa9A0bie3Zk5zoaEuHJsT9s9NC6Cqo2AByUozJfNJRS0f1wILdg5nVahZWKit+O/sbX0Z9aemQhBBCFGHjxo00a9YMZ2dnXF1d6dq1KzExMWZ1zp8/T9++fXFxccHR0ZH69euzZ88e0/lffvmFunXrYm9vj7+/P5MnT6agoAAwjnQD9OjRA5VKZfocExPDCy+8gIeHB05OTjRo0IDNmzeb2mzVqhVxcXG88847qFQqVCqV6dzOnTtp3rw5Go2GSpUq8dZbb5GZmXnHe3yQvv4pLCyMdu3aYW9vbyr78ssv8fPzY+bMmdSoUYNhw4bRq1cvZs+efZef/O2GDx/O2LFjadSo0R3rPPvss3h7e7N69eoStV0aJJEXQtw3lUqF62sD8f3+O6y9vciPi+dc7z4y1V6UbfY6eOUn8K4L2cnGfeYvlf0vqJpXbM74RuMB+Dzqc36N+dXCEQkhxKOjKAqGvEKLHCX5N1FmZiYjRoxg//79bNmyBbVaTY8ePTAYDABkZGTQsmVLLly4wNq1a4mKimL06NGm8+Hh4bz66qu8/fbbREdHs2DBApYsWcLHH38MwL59+wBYvHgxFy9eNH3OyMigc+fObNmyhUOHDtGxY0e6detGfHw8AD///DMVK1ZkypQpXLx4kYsXLwLGpLxjx4707NmTw4cPs2LFCnbu3MmwYcPueI/321dRwsPDqV+/vlnZ7t27adu2rVlZhw4d2L374WwzGxISQnh4+ENp+26sH3mPQognjiYoCP+ffybxvffJ2LaNpMlTyNyzF68Pp2Cl1Vo6PCFKzl4P/1p9Y3p9pPF10O/g4m/pyB5Ir8BexKfHs/joYibumoiXoxf1Pevf+0IhhCjjlHwDiRN2WaRv7ylNUNla3bsimE0RB1i0aBFubm5ER0dTs2ZNli9fzpUrV9i3bx8uLi4AVK1a1VR/8uTJjB07lv79+wPg7+/Phx9+yOjRo5k4cSJubm4AODs74+npabquTp061KlTx/T5ww8/ZPXq1axdu5Zhw4bh4uKClZUVWq3W7LpPPvmE0NBQhg8fDkBAQABz586lZcuWfPHFF2Yj5Q/aV1Hi4uLw9vY2K0tKSsLDw8OszMPDg7S0NLKzs9FoNHdts6S8vb05dOhQqbZZHDIiL4QoFVbOzlT8fD7uY8YYp9pv3Ehsz15kHztm6dCEuD8aZ2My71ELMi8bF8BLv2TpqB7Y8LrDaefTjnxDPsO3D+dc6jlLhySEEOKG06dP07dvX/z9/dHpdKap7zdHqyMjIwkODjYl8f8UFRXFlClTcHJyMh1Dhgzh4sWLZGVl3bHfjIwMRo4cSY0aNXB2dsbJyYnjx4+b+r2TqKgolixZYtZfhw4dMBgMxMbGlmpfRcnOzi7yy4JHSaPR3PVn+7DIiLwQotSoVCpcBw7AITiI8yNGkB8fT1yfvnhOnIBzr16WDk+IknNwMU6zX9Qerp+D73vCwHXGEfsySq1S83/N/o9LmZc4fPUwb255k+87f085+3KWDk0IIR4alY0a7ylNLNZ3cXXr1g0fHx8WLlyIt7c3BoOBmjVrkpeXB3DP0eSMjAwmT57Miy++eNu5uyW8I0eOZNOmTcyYMYOqVaui0Wjo1auXqd+79ff666/z1ltv3XaucuXKpdpXUcqXL8/169fNyjw9Pbl0yfyL90uXLqHT6Up9NB4gOTnZNNPhUZJEXghR6kxT7d8fR8bWrVwc/wE50dF4vPceKhsbS4cnRMloPYwj8990gEtH4Ie+8MrPYGPZEYAHYW9tz6fPfcor618hPj2e4duGs7D9QmytbC0dmhBCPBQqlarY09st5dq1a5w8eZKFCxfSvHlzwLiQ3K1q167N119/TXJycpGj8nXr1uXkyZNm0+3/ycbG5rY91yMiIhgwYAA9evQAjAn6uXPnzOrY2tredl3dunWJjo6+a3//dL99FSU4OJjofyy03LhxY9avX29WtmnTJho3blzsGEvi6NGjtGrV6qG0fTcytV4I8VBYOTtT8bN5uL1t/Ib2+vIfiB/4GgXXrlk4MiHug4u/cWTeTgdxEfDTICgssHRUD6S8pjzz28xHa6Pl4OWDfBDxgSxSKYQQFlSuXDlcXV356quvOHPmDFu3bmXEiBFmdfr27Yunpyfdu3cnIiKCs2fP8tNPP5kWcpswYQLffvstkydP5tixYxw/fpywsDDGjx9vasPX15ctW7aQlJRkGs0OCAjg559/JjIykqioKPr162daQO/W6/78808uXLjA1atXARgzZgy7du1i2LBhREZGcvr0aX755Ze7LnZ3v30VpUOHDrd92fHGG29w9uxZRo8ezYkTJ/j888/58ccfeeedd+7YTlGSkpKIjIzkzJkzABw5coTIyEizreaysrI4cOAA7du3L1HbpUIRt0lNTVUAJTU11dKhCPFESNuyRTlRt54SXa26cqp1ayXr6FFLhyTE/YkNV5QpbooyUacoa95UFIPB0hE9sF0XdilBS4OUmktqKp8d+szS4QghxAPLzs5WoqOjlezsbEuHUmKbNm1SatSoodjZ2Sm1a9dWtm/frgDK6tWrTXXOnTun9OzZU9HpdIqDg4NSv359Zc+ePabzGzduVJo0aaJoNBpFp9MpISEhyldffWU6v3btWqVq1aqKtbW14uPjoyiKosTGxiqtW7dWNBqNUqlSJeWzzz5TWrZsqbz99tum63bv3q3Url1bsbOzU25NI/fu3au0a9dOcXJyUhwdHZXatWsrH3/88R3v8UH6+qdr164p9vb2yokTJ8zKt23bpgQFBSm2traKv7+/snjxYrPzixcvvmu7iqIoEydOVIDbjlvbWr58uVKtWrW7tvNPd/v9LEkeqlIU+fr9n9LS0tDr9aSmpqLT6SwdjhBPhNyYGM7/503y4uJQ2dvj9dFH6Lt2sXRYQpTc8d/gx3+BYoBmI6DtREtH9MB+Pv0zE3cZ7+PjZh/zfJXnLRyREELcv5ycHGJjY/Hz87P4Qmji4Rs1ahRpaWksWLCg2NdMnDiRHTt2sH379gfqu1GjRrz11lv069ev2Nfc7fezJHmoTK0XQjwSdlWq4LvyRxxbNEfJySFx5Egu/e9/KMV4/kmIx0qNrtDtU+P7nbNg93zLxlMKXgx4kUE1BwEwcddEIi9HWjYgIYQQopjGjRuHj4/PbdPz72bDhg1Mnz79gfq9evUqL774In379n2gdu6XjMgXQUbkhXh4lMJCrsz5lGsLFwLg2KwZFWbOwEpfdlcBF0+p8FmwZbLxfY+voE5vy8bzgAyKgZE7RrIpbhNuGjdWdF2Bm8OjX4VXCCEelIzIi8eZjMgLIcoklZUV7u+OoMLsWag0GjJ37uRc7z7k3cfeoUJYVLN3oNGbxve//AfObLZsPA9IrVLzUdOPqOpclSvZVxixfQT5hfmWDksIIYQQRZBEXghhEbpOnfD9YTnW3l7knTvHuT59yY6KsnRYQhSfSgXtP4JaL4OhAFYOhMvHLR3VA3GwcWBO6zlobbREXolk2r5plg5JCCGEEEWQRF4IYTH21avjGxaG/TPPUJicTFz/AaRt2mTpsIQoPrUaXpgPPk0hNw2WvwwZVywd1QPx0fkwtcVUVKhYcXIFq0+vtnRIQgghhPgHSeSFEBZl4+6Oz3ff4tSyJUpODhfeepvkb7+1dFhCFJ+1LfT+Hsr5QUo8hPWD/BxLR/VAWlRswX+C/gPAh399yJErRywckRBCCCFuZdFE/s8//6Rbt254e3ujUqlYs2aN6Vx+fj5jxoyhVq1aODo64u3tzauvvkpiYuJd25w0aRIqlcrsqF69+kO+EyHEg1A7OlJx/mc49+kNisKl//uEpP/7P1nRXpQdDi4QuhLs9XB+L/zyJpTxtWSH1h5K60qtyTfk8872d7iWfc3SIQkhhBDiBosm8pmZmdSpU4f582/fuicrK4uDBw/ywQcfcPDgQX7++WdOnjzJ88/fe2/bZ599losXL5qOnTt3PozwhRClSGVtjefEibiPfBeA699+x4XhwzFkZ1s4MiGKqXwAvPwdqK3h6CrYUbafL1er1Pxfs//DV+fLpaxLjNwxknyDLH4nhBBCPA4smsh36tSJjz76iB49etx2Tq/Xs2nTJl5++WWqVatGo0aN+Oyzzzhw4ADx91jd2traGk9PT9NRvnz5h3ULQohSpFKpcB08mAqzZqKysSF902biBgyg4JqMBIoywr8ldJllfL/9EziyyrLxPCAnWyc+fe5THG0c2X9pP7P2z7J0SEIIIYSgjD0jn5qaikqlwtnZ+a71Tp8+jbe3N/7+/oSGht4z8c/NzSUtLc3sEEJYjq5zZyovWYyVXk9O1GHO9elLXlycpcMSonjq9Ycm/zW+X/MfiN9j2XgekL/en4+bfQzA98e/59eYXy0ckRBCCPG3LVu2UKNGDQof8SOZ0dHRVKxYkczMzEfa701lJpHPyclhzJgx9O3bF51Od8d6DRs2ZMmSJWzcuJEvvviC2NhY/p+9+46rqv7jOP46l733EEWGooEg4N4jKVxkZupPzVVqrhxkluVOM0tx5cRwZIkNTXMlYm5zEYqAqMhwAA4QBEGEy+8P8uZVREDxgHyfj8d9dO8533PO+15J+dzv93y/rVu35u7du089Zs6cOZiYmKge9vb25fEWBEEoBf2GDXHYuBGtGjV4cOUK8e+9R86FC3LHEoSS8ZkBdbtA/v3Cye/S4uVO9Fw61OzAh/U/BGDGsRlE3Y6SOZEgCIJQVo/PTVZSjo6OLFy48IXned5rTZw4kcmTJ6OhoaHatn//fho0aICOjg61a9dm7dq1pbp+amoqH330EXXr1kVPT4+aNWsyZswY0tPTVW3c3Nxo1qwZAQHyjFarFIX8gwcP6NWrFwUFBSxfvrzYtp06daJnz57Ur18fX19fdu7cyZ07d/j555+fesykSZNIT09XPa5cufKi34IgCGWg4+yEY/BGdOrWJf/mLRL7DyD77Fm5YwnCsyk0oEcg2NaHe7fgp96Qk/7s4yqwkV4jaV29Nffz7zPhwAQyczPljiQIgiBUcYcPHyY2NpYePXqotsXFxdGlSxfat29PeHg448aNY8iQIfz5558lPu/169e5fv068+bN49y5c6qO4g8++ECt3eDBg1m+fDl5eXkv7D2VVIUv5B8W8QkJCYSEhBTbG18UU1NT6tSpw6VLl57aRkdHB2NjY7WHIAgVg6alJQ7r16Hn6Ul+ejqJgwaTdfyE3LEE4dm0DaDvJjCqBjfPw6/vg7LyrsSgkBTMaT2HagbVuHL3CjP/nklBJZ+ZXxAEoaLZvXs3rVq1wtTUFAsLC7p27UpsbKxam6tXr9KnTx/Mzc0xMDCgUaNGHD/+321cW7dupUGDBujq6uLs7MyMGTNUhaajoyMA3bt3R5Ik1evY2Fi6deuGjY0NhoaGNG7cmL1796rO2a5dOxISEhg/frxqZbCHDh8+TOvWrdHT08Pe3p4xY8YUO9z8ea71uODgYN544w10dXVV21asWIGTkxPz58/H1dWV0aNH8+6777JgwYJiPnl17u7u/Pbbb/j5+VGrVi1ef/11Zs+ezR9//KFWtL/xxhukpqZy4MCBEp/7RanQhfzDIv7ixYvs3bsXCwuLUp8jMzOT2NhYqlWrVg4JBUF4GTRMTKgZ9D36zZqhvHePK0OHcvevv+SOJQjPZmwHfYJBUw8u7S2cAK8SM9Ex4Zs236AhabArbhdbLm2RO5IgCEKJFBQUkJubK8ujNF96ZmVl4e/vz6lTpwgNDUWhUNC9e3eUSiVQWNu0bduWa9eusW3bNs6cOcPEiRNV+w8dOsSAAQMYO3YsUVFRrFy5krVr1zJ7duFcJydPngRgzZo1JCUlqV5nZmbSuXNnQkND+eeff+jYsSN+fn6qucY2b95MjRo1mDlzpmplMCgsyjt27EiPHj04e/YsmzZt4vDhw4wePfqp77Gs1yrKoUOHaNSokdq2Y8eO4ePjo7bN19eXY8eOlewP4SnS09MxNjZGU1NTtU1bWxsvLy8OHTr0XOcuC81nNyk/mZmZaj3lcXFxhIeHY25uTrVq1Xj33XcJCwtj+/bt5Ofnk5ycDIC5uTna2toAdOjQge7du6t+WCZMmICfnx8ODg5cv36dadOmoaGhQZ8+fV7+GxQE4YVRGBhgv3IF1/w/JjM0lKsfjcHu668x6dpF7miCUDw7L3hrMWweCge/BbsG8FpnuVOVmZe1F6O9R7MobBFzjs+hvmV9apvVljuWIAhCsR48eMBXX30ly7U///xzVe3yLI8OEQcICgrCysqKqKgo3N3d+emnn7h58yYnT57E3NwcgNq1//s7eMaMGXz22WcMHDgQAGdnZ7788ksmTpzItGnTsLKyAgpHLdva2qqO8/T0xNPTU/X6yy+/ZMuWLWzbto3Ro0djbm6OhoYGRkZGasfNmTOHfv36MW7cOABcXFxYvHgxbdu2Zfny5Wo95c97raIkJCRgZ2enti05ORkbGxu1bTY2NmRkZJCdnY2enl6x5yzKrVu3+PLLLxk2bNgT++zs7EiQYVJmWXvkT506hbe3N97e3gD4+/vj7e3N1KlTVd8yXb16FS8vL6pVq6Z6HD16VHWO2NhYbt26pXr9cKhJ3bp16dWrFxYWFvz999+qH1pBECovhY4ONRYuwPgtP8jL4/onn5C26enzXwhChVG/FzQpnCyOLR/C7dji21dw77u/Twu7FuTk5/DJwU/IzsuWO5IgCMIr4eLFi/Tp0wdnZ2eMjY1VQ98f9laHh4fj7e2tKuIfd+bMGWbOnImhoaHqMXToUJKSkrh3795Tr5uZmcmECRNwdXXF1NQUQ0NDoqOjn7n615kzZ1i7dq3a9Xx9fVEqlcTFxb3QaxUlOzu7yC8LXqSMjAy6dOmCm5sb06dPf2K/np5esZ9teZG1R75du3bFDjUpyTCU+Ph4tdfBwcHPG0sQhApM0tLC7uuvURgYcGdjMMnTpqHMvIvFY5OPCEKF8+YsSD4Licdg03vwQQjoGMqdqkwUkoLZrWbT84+eXLpzibkn5jK9xXS5YwmCIDyVlpYWn3/+uWzXLqmHI4sDAwOxs7NDqVTi7u5Obm4uwDN7kzMzM5kxYwbvvPPOE/uKK3gnTJhASEgI8+bNo3bt2ujp6fHuu++qrlvc9T788EPGjBnzxL6aNWu+0GsVxdLSkrS0NLVttra2pKSkqG1LSUnB2Ni41L3xd+/epWPHjhgZGbFly5Yi/yxTU1OpVatWqbM/L1kLeUEQhLKQFApsp05Fw9CI24GB3Ph2Hsqse1iN+UjuaILwdJra0HMtrGwDN6Jg20fwbhAUM4lPRWapZ8mc1nMYtmcYv138jWbVmtHRqaPcsQRBEIokSVKJh7fL5fbt28TExBAYGEjr1q2BwonkHlW/fn1Wr15Nampqkb3yDRo0ICYmRm24/eO0tLSeWHP9yJEjDBo0iO7duwOFBfrjHaba2tpPHNegQQOioqKKvd7jynqtonh7exMVpb4kavPmzdm5c6fatpCQEJo3b17ijFDYE+/r64uOjg7btm176hch586d49133y3VuV+ECj3ZnSAIwtNIkoT1x/5Y+fsDcGvZMm5+t1TmVILwDEa20Gs9KDQhcjP8vUzuRM+lWbVmDPEYAsD0Y9O5clcs3yoIglBWZmZmWFhYsGrVKi5dusS+ffvw//f3nIf69OmDra0tb7/9NkeOHOHy5cv89ttvqoncpk6dyvr165kxYwaRkZFER0cTHBzM5MmTVedwdHQkNDSU5ORkVW+2i4sLmzdvJjw8nDNnztC3b1/VBHqPHnfw4EGuXbumurX5008/5ejRo4wePZrw8HAuXrzI1q1bi53srqzXKoqvr+8TX3YMHz6cy5cvM3HiRM6fP8+yZcv4+eefGT9+/FPP87iMjAzefPNNsrKy+P7778nIyCA5OZnk5GS1Lxji4+O5du3aE5PrvQyikBcEoVKzHDYU608/BeDWd99xa/lymRMJwjPUbAa+/064tGcKxB8uvn0FN9JrJA2sG5D1IItPDnzCg/wHckcSBEGolBQKBcHBwZw+fRp3d3fGjx/Pt99+q9ZGW1ubPXv2YG1tTefOnfHw8ODrr79GQ0MDKCxst2/fzp49e2jcuDHNmjVjwYIFODg4qM4xf/58QkJCsLe3V81VFhAQgJmZGS1atMDPzw9fX18aNGigdu2ZM2cSHx9PrVq1VPOP1a9fnwMHDnDhwgVat26tmu/s8QnoHlXWaxWlX79+REZGEhMTo9rm5OTEjh07CAkJwdPTk/nz57N69Wp8fX1VbdauXVvssnZhYWEcP36ciIgIateurTZf25Ur/31pvXHjRt588021z/dlkQrEIrBPyMjIwMTERLXEgCAIFd/t1au5MW8+AFb+/lgOGypzIkEoRkEBbB4GET+DgRV8eLBwqbpKKjkrmXf/eJf0++kMcBvAJ40/kTuSIAhVWE5ODnFxcTg5OZX7RGiC/D755BMyMjJYuXJliY+ZNm0aBw4cYP/+/WW+bm5uLi4uLvz000+0bNmyxMcV9/NZmjpU9MgLgvBKsBgyBKt/h0zdDAjg9vdBMicShGJIEvgtAht3yLoJPw+AvNJP8lNR2BrY8mWLLwFYH7Weg1cPypxIEARBqCq++OILHBwcnhieX5xdu3bxzTffPNd1ExMT+fzzz0tVxL9Ioke+CKJHXhAqr5vLlnFr8RIAbCZ9hvm/66gKQoWUehlWtYOcdGg8BLrMlzvRc5l7Yi4bojdgqmPKlm5bsNSzlDuSIAhVkOiRFyoy0SMvCIJQBKuRI7EcORKAlDlfk7rhR5kTCUIxzJ3hncDC5ydXw9lf5M3znMY3HI+ruSt37t9h+tHpJVpGVhAEQRCE0hOFvCAIrxzLj0Zj8eGHAKTMmkXaxo0yJxKEYtTxhbaFEzayfXxhL30lpa2hzexWs9FSaHHg6gG2XNoidyRBEARBeCWJQl4QhFeOJElYjRuLxZAPAEieMZO0TT/LnEoQitFmItRsAbl34dcPKvX98i5mLnzk/RFQONT+WuY1mRMJgiAIwqtHFPKCILySJEnC6uOPMR80CIDkadNI/+MPeUMJwtNoaEKPQNA1hethsO9LuRM9lwFuA2hg3YB7efeYfHgyyoKST0AkCIIgCMKziUJeEIRXliRJWH86EbP33gPg+qTPyTx0SOZUgvAUJjWg23eFz48uhkuh8uZ5DhoKDWa1moWeph6nUk7xY7SYq0IQBEEQXiRRyAuC8EqTJAmbzydh3LUr5OVxdcxYssPD5Y4lCEVz9YNGhbeEsGU4ZN6QN89zsDeyZ0KjCQAsPL2Qy3cq773/giAIglDRiEJeEIRXnqRQYPfVbAxat6YgO5srHw7n/qVLcscShKL5zgZrN8i6UVjMl2Jd3IqmZ52etKzeklxlLp8f/pwHygdyRxIEQRCEV4Io5AVBqBIkbW1qLFqIrmd98tPTSRwylAfXr8sdSxCepKUH7waBph7EhsKx7+ROVGaSJDGj+QyMtI2IvB3J6ojVckcSBEEQXjGhoaG4urqSn5//Uq8bFRVFjRo1yMrKeqnXfUgU8oIgVBkKfX3sV6xAu1Yt8pKTSRwylLy0NLljCcKTrF2h45zC56Ez4NppefM8BxsDGyY3nQzAqjOriLwdKXMiQRCEqk2SJH7//fdSH+fo6MjChQtfeJ7nvdbEiROZPHkyGhoaqm379++nQYMG6OjoULt2bdauXVvqDB9++CG1atVCT08PKysrunXrxvnz51X73dzcaNasGQEBAaU+94sgCnlBEKoUTTMzaq4ORLNaNXIvX+bKh8NRyvRNqiAUq+EgcH0LlHmFS9LlZMidqMw6OXXiTYc3ySvI44tDX3A//77ckQRBEIRXwOHDh4mNjaVHjx6qbXFxcXTp0oX27dsTHh7OuHHjGDJkCH/++Wepzt2wYUPWrFlDdHQ0f/75JwUFBbz55ptqPf+DBw9m+fLl5OXlvbD3VFKikBcEocrRqlaNmt+vRsPUlJyzZ7k6ZiwFuZV33W7hFSVJ8NZiMLGHtDjYOUHuRGUmSRKTm03GQteC2PRYloQtkTuSIAhChbR7925atWqFqakpFhYWdO3aldjYWLU2V69epU+fPpibm2NgYECjRo04fvy4av/WrVtp0KABurq6ODs7M2PGDFWh6ejoCED37t2RJEn1OjY2lm7dumFjY4OhoSGNGzdm7969qnO2a9eOhIQExo8fjyRJSJKk2nf48GFat26Nnp4e9vb2jBkzptjh5s9zrccFBwfzxhtvoKurq9q2YsUKnJycmD9/Pq6urowePZp3332XBQsWFPPJP2nYsGG0adMGR0dHGjRowKxZs7hy5Qrx8fGqNm+88QapqakcOHCgVOd+EUQhLwhClaTj7Iz9qpVI+vpkHTnC9c8mUVCJJxUTXlF6ZtBjNUgacHYThG+UO1GZmemaMaPFDADWR63ndErlvV1AEITKp6CggPz8e7I8CgoKSpwzKysLf39/Tp06RWhoKAqFgu7du6P893eUzMxM2rZty7Vr19i2bRtnzpxh4sSJqv2HDh1iwIABjB07lqioKFauXMnatWuZPXs2ACdPngRgzZo1JCUlqV5nZmbSuXNnQkND+eeff+jYsSN+fn4kJiYCsHnzZmrUqMHMmTNJSkoiKSkJKCzKO3bsSI8ePTh79iybNm3i8OHDjB49+qnvsazXKsqhQ4do1KiR2rZjx47h4+Ojts3X15djx46V7A+hCFlZWaxZswYnJyfs7e1V27W1tfHy8uKQDMsba770KwqCIFQQevXrU2PxYq6MGEHGzp1oWlliM2mS3LEEQV3NZtBuEvw1C3Z+Ao4twbSm3KnKpK19W7rX7s6WS1uYfnQ6v771KzoaOnLHEgShClAqs9l/wEOWa7drG4GGhn6J2j46RBwgKCgIKysroqKicHd356effuLmzZucPHkSc3NzAGrXrq1qP2PGDD777DMGDhwIgLOzM19++SUTJ05k2rRpWFlZAWBqaoqtra3qOE9PTzw9PVWvv/zyS7Zs2cK2bdsYPXo05ubmaGhoYGRkpHbcnDlz6NevH+PGjQPAxcWFxYsX07ZtW5YvX67WU/681ypKQkICdnZ2atuSk5OxsbFR22ZjY0NGRgbZ2dno6ekVe85HLVu2jIkTJ5KVlUXdunUJCQlBW1tbrY2dnR0JCQklPueLInrkBUGo0gxbtcRuTuGkYqnr1pO2sfL2eAqvsNb+YN8Ucu/C1tGVekm6CY0nYKVnRXxGPIFnA+WOIwiCUKFcvHiRPn364OzsjLGxsWro+8Pe6vDwcLy9vVVF/OPOnDnDzJkzMTQ0VD2GDh1KUlIS9+7de+p1MzMzmTBhAq6urpiammJoaEh0dLTquk9z5swZ1q5dq3Y9X19flEolcXFxL/RaRcnOzi7yy4IXpV+/fvzzzz8cOHCAOnXq0KtXL3JyctTa6OnpFfvZlhfRIy8IQpVn0rULD65e5ebChSTPmo1WDXsMW7eSO5Yg/EehAW8vh+UtIe4AnPoemgyVO1WZGGsbM6npJPz3+/P9ue/p6NiR2ma1n32gIAjCc1Ao9GjXNkK2a5eUn58fDg4OBAYGYmdnh1KpxN3dndx/5/J5Vm9yZmYmM2bM4J133nliX3EF74QJEwgJCWHevHnUrl0bPT093n33XdV1i7vehx9+yJgxY57YV7Nm0aPHynqtolhaWpL22ApEtra2pKSkqG1LSUnB2Ni4VL3xACYmJpiYmODi4kKzZs0wMzNjy5Yt9OnTR9UmNTWVWrVqlTr78xKFvCAIAmDx4TBy4+NJ//13ro0fj+PGn9BxcZE7liD8x6IW+EyH3Z9CyFSo3QHMneVOVSY+NX1oZ9+O/Vf2M/3YdNZ3Wo9CEoMEBUEoP5IklXh4u1xu375NTEwMgYGBtG7dGiicSO5R9evXZ/Xq1aSmphbZK9+gQQNiYmLUhts/TktL64k1148cOcKgQYPo3r07UFigPzqpGxTeD/74cQ0aNCAqKqrY6z2urNcqire3N1FRUWrbmjdvzs6dO9W2hYSE0Lx58xJnLEpBQQEFBQXcv6++8sq5c+d49913n+vcZSH+1RQEQaDwH3jbmTPQa9QQZWYmV4aPIO/2bbljCYK6JsPAsTU8uAe/j6q0Q+wlSeKLpl+gr6nPmZtn+DnmZ7kjCYIgyM7MzAwLCwtWrVrFpUuX2LdvH/7+/mpt+vTpg62tLW+//TZHjhzh8uXL/Pbbb6qJ3KZOncr69euZMWMGkZGRREdHExwczOTJk1XncHR0JDQ0lOTkZFVvtouLC5s3byY8PJwzZ87Qt29f1QR6jx538OBBrl27xq1btwD49NNPOXr0KKNHjyY8PJyLFy+ydevWYie7K+u1iuLr6/vElx3Dhw/n8uXLTJw4kfPnz7Ns2TJ+/vlnxo8f/9TzPO7y5cvMmTOH06dPk5iYyNGjR+nZsyd6enp07txZ1S4+Pp5r1649MbneyyAKeUEQhH8ptLWpsWQJWjVr8uDaNa6OGo3yvljvWqhAFAro9h1oG0LiUTi+XO5EZWZrYMvYBmMBWBi2kOSsZJkTCYIgyEuhUBAcHMzp06dxd3dn/PjxfPvtt2pttLW12bNnD9bW1nTu3BkPDw++/vprNDQ0gMLCdvv27ezZs4fGjRvTrFkzFixYgIODg+oc8+fPJyQkBHt7e7y9vQEICAjAzMyMFi1a4Ofnh6+vLw0aNFC79syZM4mPj6dWrVqqSfPq16/PgQMHuHDhAq1bt8bb25upU6c+MQHdo8p6raL069ePyMhIYmJiVNucnJzYsWMHISEheHp6Mn/+fFavXo2vr6+qzdq1a4td1k5XV5dDhw7RuXNnateuTe/evTEyMuLo0aNYW1ur2m3cuJE333xT7fN9WaSC0qyHUEVkZGRgYmJCeno6xsbGcscRBOElu385jvj//Q9lRgbGXbpgN+/bYv+yF4SX7lQQbB8Pmrow/DBYVs7bQPKV+QzYPYCzN8/yuv3rLHp9kdyRBEF4BeTk5BAXF4eTk1O5ToQmVAyffPIJGRkZrFy5ssTHTJs2jQMHDrB///4yXzc3NxcXFxd++uknWrZsWeLjivv5LE0dKnrkBUEQHqPj7ESNxYtAU5OMHTu4tXSZ3JEEQV3DwVDrdcjLgS3DIT9P7kRloqHQYFrzaWhKmuy7so+9CXvljiQIgiBUMl988QUODg5PDM8vzq5du/jmm2+e67qJiYl8/vnnpSriXyRRyAuCIBTBoFkzbKdNBeDWd9+Rvn2HzIkE4RGSBG8tAR1juHYKji6WO1GZ1TGrw2D3wQB8dfwrMnIzZE4kCIIgVCampqZ8/vnnKBQlL21PnDhBkyZNnuu6tWvX5sMPP3yuczwPUcgLgiA8hVnPnpi//z4ASZ9/zr2wf2ROJAiPMKkBHb8ufL5/DqREFd++AvvQ80McjB24mX2TRafF8HpBEARBeBZRyAuCIBTD+mN/DDt0oCA3l6ujR/Pg+nW5IwnCf7z6Qp2OkJ8LWz6E/AdyJyoTHQ0dpjWfBsDPF34mLCVM5kSCIAiCULGJQl4QBKEYkoYG1b/9Bh1XV/JTU7k6dpyYyV6oOCQJ/BaBrikkn4VD8+VOVGaNbRvzjss7AMw4NoPc/FyZEwmCIAhCxSUK+UosL79yrh8sCJWNQl+fGkuWoGFiQk5EBCmzZssdSRD+Y2QLXf4t4A9+C8kR8uZ5Dv4N/THXNedy+mW+j/he7jiCIAiCUGGJQr4SG7spnEaz9tJrxTE+/fUsKw/EsicymUs37pKbJ4p8QXiRtGtUx27+fJAk7vzyC2m//CJ3JEH4j3sPcPUDZR78MRaU+XInKhMTHRMmNZkEQGBEIIkZiTInEgRBEISKSVPuAELZXb6Zxa3M+9zKvM+J+FS1fQoJ7M31cbI0oJaVIS7WhtT+92Gqry1TYkGo3AxbtcRq7FhuLlxIyswv0X3tNfQ8POSOJQiFQ+w7fQuXD8C104XrzDcZKneqMvF19GXLpS0cvX6Ub05+w3cdvpM7kiAIgiBUOFJBQUGB3CEqmoyMDExMTEhPT8fY2FjuOE91N+cBcbeyiLuVxeWbWVy+lUXcrUzibmaRlfv03hhLQx1qWxtQ29oQF2sjXKwNqWNrhKWhzktMLwiVU4FSydWPxpAZGopmtWo4/fYrmubmcscShEInAmHnBNA2gtEnwNhO7kRlcjn9Mj229iCvII+lHZbSpkYbuSMJglCJ5OTkEBcXh5OTE7q6unLHEQQ1xf18lqYOFYV8ESpLIf80BQUF3Lh7/9/iPpPYG1lcvHGX2BuZXE/Peepxloba1LExoq6tEa/ZGlHHpvBhoCMGbgjCo/Lv3iW+Zy9y4+PRb96MmoGBSJri/xOhAlDmw/dvFq4t7+oHvTfInajM5p2cx7qoddQ0qsmWblvQ1hCjyQRBKBlRyFctoaGhjB49mnPnzqGhofHSrhsVFcWbb75JTEwMBgYGJT7uRRXy4h75V5AkSdgY69K8lgX9mjow1c+NHz5oytFJHTg3w5eto1oyv6cnI9rVwsfVBgcLfSQJbmXmcjT2NmuOxPPpbxF0X3aUetP+pM03f/HhD6dYtPciIVEpXLuTjfj+R6jKNIyMqLFkMZK+PveO/c3NhQvljiQIhRQahbPYKzQh+g84v1PuRGU23HM4lnqWJN5NZH3UernjCIIgvDIkSeL3338v9XGOjo4sfEm/85TmWhMnTmTy5MlqRfz+/ftp0KABOjo61K5dm7Vr15Y5S0FBAZ06dXric3Nzc6NZs2YEBASU+dzPQ3QhVTGGOpp42pviaW+qtv1ebh4XUzKJSblLTPJdLqTc5XzyXW7evU9i6j0SU+/xZ2SKqr2JnhZu1YxxszPGrZox9aobU9vKEE0N8d2QUDXouLhg99Vsro0bz+3V36PrUR9j3zfljiUIYOsOzUfDkYWw8xNwagM6hnKnKjVDbUPGNxzPF4e/YNXZVfg5+2FjYCN3LEEQBKECOXz4MLGxsfTo0UO1LS4uji5dujB8+HB+/PFHQkNDGTJkCNWqVcPX17fU11i4cCGSJBW5b/DgwQwdOpRJkyah+ZJHZ4qqSwBAX7uwwO/VyJ4pXQt78E9+4UPYlDf4aWhTpnR1o0eDGrxma4SmQiI9+wHHLt/m+8NxfPzLGTouPIT79D/psfwo07dFsjnsKpdu3CVfKXruhVeXcceOmA8eDEDSpEncj42VOZEg/Kvtp2DqABlX4a+v5E5TZl2du+Jp5Ul2XjbzT8+XO44gCEK52717N61atcLU1BQLCwu6du1K7GO/X1y9epU+ffpgbm6OgYEBjRo14vjx46r9W7dupUGDBujq6uLs7MyMGTPIy8sDCnu6Abp3744kSarXsbGxdOvWDRsbGwwNDWncuDF79+5VnbNdu3YkJCQwfvx4JElSK2wPHz5M69at0dPTw97enjFjxpCVlfXU9/g813pccHAwb7zxhtoQ9RUrVuDk5MT8+fNxdXVl9OjRvPvuuyxYsKCYT75o4eHhzJ8/n6CgoCL3v/HGG6SmpnLgwIFSn/t5iUJeKJa5gTYtalnyQSsn5vfyZPe4NkTO9GX7R6345t36DGrhSBNHcwy0Nch5oOR0Qhprj8bj//MZfAIOUn/6n/RaeYxZ26PYfva6GJYvvHKsP/ZHv0kTlPfucfWjMeRnZsodSRBAWx+6/jvU7/hyuP6PvHnKSCEp+Lzp50hI7IrbxankU3JHEgShkiooKCArP1+WR2l+983KysLf359Tp04RGhqKQqGge/fuKJWFS0tnZmbStm1brl27xrZt2zhz5gwTJ05U7T906BADBgxg7NixREVFsXLlStauXcvs2bMBOHnyJABr1qwhKSlJ9TozM5POnTsTGhrKP//8Q8eOHfHz8yMxsXAZ0M2bN1OjRg1mzpxJUlISSUlJQGFR3rFjR3r06MHZs2fZtGkThw8fZvTo0U99j2W9VlEOHTpEo0aN1LYdO3YMHx8ftW2+vr4cO3asZH8I/7p37x59+/Zl6dKl2NraFtlGW1sbLy8vDh06VKpzvwhiaL1QajqaGrhXN8G9uolqm1JZwOVbWURcu0PE1Qwirt3h3LUMsnLzORGXyom4/5bHszHWwdveDO+apnjXNMOjugl62i9vYgpBeJEkTU2qLwgg7p0e5F6+TNKUKVQPCCj222NBeClq+4D7u3Du18K15YfsA43K98++m4UbPer04NcLvzLnxBw2dd2EpqLyvQ9BEOR1T6mk1sEIWa4d28YDgxJOwvboEHGAoKAgrKysiIqKwt3dnZ9++ombN29y8uRJzP9dNad27dqq9jNmzOCzzz5j4MCBADg7O/Pll18yceJEpk2bhpWVFQCmpqZqxamnpyeenp6q119++SVbtmxh27ZtjB49GnNzczQ0NDAyMlI7bs6cOfTr149x48YB4OLiwuLFi2nbti3Lly8vcrLBsl6rKAkJCdjZqa/QkpycjI2N+q1YNjY2ZGRkkJ2djZ6eXrHnfGj8+PG0aNGCbt26FdvOzs6OhISEEp3zRRL/EgovhEIhqdap7+5duC1fWUDszUzOXk3nzJU7hF+5Q3RSBikZ99kdmczuyGQANBQSrtWMaFDTjEaO5jRxNMfWRMwwKlQemhYW1Fi0kPj3+nN3127SW7bE9N135Y4lCNBxDlwKgaQzcGIVNB8pd6IyGeM9hj3xe7iQdoFfLvxCn9f6yB1JEAShXFy8eJGpU6dy/Phxbt26peppT0xMxN3dnfDwcLy9vVVF/OPOnDnDkSNHVD3wAPn5+eTk5HDv3j309fWLPC4zM5Pp06ezY8cOkpKSyMvLIzs7W9VL/jRnzpzh7Nmz/Pjjj6ptBQUFKJVK4uLicHV1fWHXKkp2dna5rEywbds29u3bxz//PHtEm56eHvfu3XvhGZ5FFPJCudFQSKol7N5tWAOA7Nx8zl1P55/ENP5JvENYYhopGfc5dy2Dc9cyWH+s8NusGmZ6NHE0LyzsncyoZWUoejiFCk3PywursWO4OT+A5Fmz0fP2RqdWLbljCVWdoTW8MbOwR37frMIl6Uzt5U5Vama6Zoz2Hs1Xx7/iu3++o6NjR8x0zeSOJQhCJaKvUBDbxkO2a5eUn58fDg4OBAYGYmdnh1KpxN3dndzcXIBn9iZnZmYyY8YM3nnnnSf2FVfwTpgwgZCQEObNm0ft2rXR09Pj3XffVV23uOt9+OGHjBkz5ol9NWvWfKHXKoqlpSVpaWlq22xtbUlJSVHblpKSgrGxcYl74/ft20dsbCympqZq23v06EHr1q3Zv3+/altqaiq1ZPidTxTywkulp61BY0dzGjsWfotYUFBAUnoOYYlpnE5I42R8KlHXM7ials3VtGts/ucaAGb6WjR0MKd5LQuaO1vwmq0RCoUo7IWKxeKDD7h37G+yjh7lmv/HOP68CYWOjtyxhKrOewCcCYbEY4Wz2PfZCJXwi9GedXry64VfuZB2gcX/LGZa82lyRxIEoRKRJKnEw9vlcvv2bWJiYggMDKR169ZA4URyj6pfvz6rV68mNTW1yF75Bg0aEBMTozbc/nFaWlrk5+erbTty5AiDBg2ie/fuQGGBHh8fr9ZGW1v7ieMaNGhAVFRUsdd7XFmvVRRvb2+ioqLUtjVv3pydO9WXXw0JCaF58+YlzvjZZ58xZMgQtW0eHh4sWLAAPz8/te3nzp3jXRlGYorJ7gRZSZKEnakeXevbMc2vHts/as3Z6b6sf78JY16vTTNnc3S1FKTde8De6BS+3B5F58WHaDgrhBEbTrP+WDyXbtwVE+gJFYKkUGA392s0LCy4HxPDjbnfyB1JEEChgK4LQaEFF3ZB9Da5E5WJpkKTSU0mAfDbhd+IvB0pcyJBEIQXy8zMDAsLC1atWsWlS5fYt28f/v7+am369OmDra0tb7/9NkeOHOHy5cv89ttvqoncpk6dyvr165kxYwaRkZFER0cTHBzM5MmTVedwdHQkNDSU5ORkVW+2i4sLmzdvJjw8nDNnztC3b1/VsP5Hjzt48CDXrl3j1q1bAHz66accPXqU0aNHEx4ezsWLF9m6dWuxk92V9VpF8fX1feLLjuHDh3P58mUmTpzI+fPnWbZsGT///DPjx49/6nkeZ2tri7u7u9oDCkcZODk5qdrFx8dz7dq1JybXexlEIS9UOIY6mrSpY4X/m3UJHtacs9N82TKyBZ91eo22dazQ19Yg7d4Ddp1LZurWSHwCDtLkq1A+2vgPwScSuX4nW+63IFRhmlZW2H39NQBpP/3E3UeWUxEE2Vi/Bq3GFT7f/Tnkvvx7+V6ERraN6OTUiQIKmHN8DsoC5bMPEgRBqCQUCgXBwcGcPn0ad3d3xo8fz7fffqvWRltbmz179mBtbU3nzp3x8PDg66+/RuPf0Qa+vr5s376dPXv20LhxY5o1a8aCBQtwcHBQnWP+/PmEhIRgb2+Pt3fh5FYBAQGYmZnRokUL/Pz88PX1pUGDBmrXnjlzJvHx8dSqVUs1aV79+vU5cOAAFy5coHXr1nh7ezN16tQnJqB7VFmvVZR+/foRGRlJTEyMapuTkxM7duwgJCQET09P5s+fz+rVq9XWkF+7du0LuW1348aNvPnmm2qf78siFcjYlXnw4EG+/fZbTp8+TVJSElu2bOHtt99W7S8oKGDatGkEBgZy584dWrZsyfLly3FxcSn2vEuXLuXbb78lOTkZT09PlixZQpMmTUqcKyMjAxMTE9LT0zE2Ni7r2xPKyYN8JWevpnMs9hbHLt/mVHwa9/PUf5mrZWVAmzpWtHGxoqmzOfra4i4S4eVK+eZbUoOCUJiY4Pz7FrSqVZM7klDVPciG75pAemLhOvPtP5c7UZmkZKXg97sf2XnZfNXqK/xq+T37IEEQqpScnBzi4uJwcnIql4nQhIrlk08+ISMjg5UrV5b4mGnTpnHgwAG1e91LKzc3FxcXF3766SdatmxZ4uOK+/ksTR0qa498VlYWnp6eLF26tMj933zzDYsXL2bFihUcP34cAwMDfH19ycnJeeo5N23ahL+/P9OmTSMsLAxPT098fX25ceNGeb0N4SXT0lDQ0MGM0a+78OOQZpyd/ibBw5oxpoMLDWqaopAg9mYWa47EM3jtSbxmhNA38G+W748l8nq6GIYvvBTW48ai6+GBMj2da598QkFentyRhKpOSw/e/LLw+ZFFcKf0swNXBDYGNgz1GArAkn+WcD//vsyJBEEQBDl98cUXODg4PDE8vzi7du3im2+e7xbIxMREPv/881IV8S9SmXrk8/Ly2L9/P7GxsfTt2xcjIyOuX7+OsbExhoaGZQsiSWo98gUFBdjZ2fHxxx8zYcIEANLT07GxsWHt2rX873//K/I8TZs2pXHjxnz33XcAKJVK7O3t+eijj/jss89KlEX0yFdu6dkPOHrpFgcv3uLghZtce2yova2xLu1fs6bDa9a0rG0p1rAXyk1uYiJx3d9BmZWF5ejRWI0eJXckoaorKIB1fhB/CNy6Qa/1cicqk5y8HLpu6UrKvRQ+bvgxg9wHyR1JEIQKRPTICxWZbD3yCQkJeHh40K1bN0aNGsXNmzcBmDt3rqrgfhHi4uJITk5WmzjAxMSEpk2bqiZzeFxubi6nT59WO0ahUODj4/PUYwDu379PRkaG2kOovEz0tOjkUY0573hw+NP27Pu4LdP93OjwmjV6WhokZ+Sw8UQiQ9afwmvmHgavOcEPfyc8UfALwvPSrlkT2+mFM2vfWraMeydPypxIqPIkCTrNBUkBUVsh7qDcicpEV1OX0d6FEymtilhF+v10mRMJgiAIwstV6kJ+7NixNGrUiLS0NLV1+Lp3705oaOgLC5acnAyAjY2N2nYbGxvVvsfdunWL/Pz8Uh0DMGfOHExMTFQPe/vKt8auUDRJknC2MmRQSye+H9SYf6a+wdrBjRnQ3IHqpnrcz1PyV8xNpvx+jpZf76PjwoPM3xPDuWtiCL7wYpj4+WHy9tugVHLtk4nkPbbWqSC8dDb1oNEHhc93fQr5lfO2Dz9nP1zMXLibe5dVZ1fJHUcQBEEQXqpSF/KHDh1i8uTJaGtrq213dHTk2rVrLyzYyzRp0iTS09NVjytXrsgdSSgnuloatKtrzcxu7hz+tD1/jmvDxI51aeRghkKC88l3WbLvEl2XHKbNt38xe0cUpxNSUSpFUS+Une2UyWg7OpKXnEzS5CniSyJBfu0/Bz0zuBEFp9fInaZMNBQa+DcsXJZp4/mNXL17VeZEgiAIgvDylLqQVyqV5OfnP7H96tWrGBkZvZBQULh2H0BKSora9pSUFNW+x1laWqKhoVGqYwB0dHQwNjZWewivPkmSqGtrxMh2tfl1RAtOTX6DgF6edKxni66Wgiup2QQeiqPH8mM0nRPK5N8jOHzxFg/yxXJHQukoDAyoHjAfSUuLzNBQ0jdvkTuSUNXpm0P7Lwqf75sF91LlzVNGLe1a0qxaMx4oH7DknyVyxxEEQRCEl6bUhfybb77JwoULVa8lSSIzM5Np06bRuXPnFxbMyckJW1tbteH6GRkZHD9+nObNmxd5jLa2Ng0bNlQ7RqlUEhoa+tRjBOEhcwNt3mlQgxX9GxI25Q1WvNeAt73sMNLR5Obd+2z4O5H3vj9O49l7+ey3sxy9dIt80VMvlJCumxtWY8cAkDJnDg+uX5c5kVDlNRwM1vUg505hMV8JSZKk6pXfGbeTyNuRMicSBEEQhJej1IX8/PnzOXLkCG5ubuTk5NC3b1/VsPq5c+eW6lyZmZmEh4cTHh4OFE5wFx4eTmJiIpIkMW7cOGbNmsW2bduIiIhgwIAB2NnZqa0136FDB9UM9QD+/v4EBgaybt06oqOjGTFiBFlZWQwePLi0b1WowvS1NenoXo2F//Pm9JTC++r7NLHHwkCbO/ceEHzyCn1XH6fZnFCmb4vkdEKaGC4tPJP54MHoeXmhzMwkafJk8TMjyEtDs3DiOygcXp8cIW+eMnK1cKWrc1cAAk4FiP+vBEEQhCqhzMvPBQcHc/bsWTIzM2nQoAH9+vVTm/yuJPbv30/79u2f2D5w4EDWrl1LQUEB06ZNY9WqVdy5c4dWrVqxbNky6tSpo2rr6OjIoEGDmD59umrbd999x7fffktycjJeXl4sXryYpk2bljiXWH5OeJp8ZQHH427zx5nr7IxIJj37gWpfdVM9/DzteMvTDtdqRkiSJGNSoaK6HxdHXPd3KMjJwXbaVMz69JE7klDV/TwQon4Hh1YwaHvhzPaVzPXM6/ht8SNXmcvSDktpU6ON3JEEQZCRWH5OqMhe1PJzZSrkX3WikBdKIjdPyeFLN/njTBJ7IpPJyv1v7og6Nob0aFCD7t7VsTYW/4AI6lLX/0DKV18h6enhvPV3tGvWlDuSUJXdSYTvGkNeDvRcC/W6y52oTAJOBbAmcg21TWvzq9+vaCg05I4kCIJMKnMh365dO7y8vNRuZX6co6Mj48aNY9y4cS8tV2UzZcoUUlJSWLXq5a5qsmLFCnbs2MEff/zx1DayFfLr168vdv+AAQNKc7oKSRTyQmll5+bzV8wNtoVfZ1/MDXLzCifEU0jQpo4VPRrU4A03G3S1xC+WAhQolSQOGsy9EyfQa9gQh/XrkDTEz4Ygo7/mwIGvwcQeRp0AbX25E5Va+v10Om/uTEZuBjNbzKS7S+X8QkIQhOf3qhfyN2/exMDAAH39yvd3dVnFx8fj5OTEP//8g5eXV7Ftk5OTqVOnDhERETg4OKi2L126VDVq29PTkyVLltCkSZNS5UhMTGTEiBH89ddfGBoaMnDgQObMmYOmpiYAubm5ODk5ERwcTOvWrYs8x4sq5DVLlZzCdeQf9eDBA+7du4e2tjb6+vqvRCEvCKWlp61BZ49qdPaoRnr2A3acTeK3sKucTkhjf8xN9sfcxFhXk66edrzbsAbe9qZi6H0VJikUVPvqK+Leeovs06dJXf8DFoMHyR1LqMpajoV/NkD6FTiyCNpPkjtRqZnomDCs/jDmnZrHd/98R0enjuhplu6WP0EQhMrAyspK7ggV2urVq2nRooVaEb9p0yb8/f1ZsWIFTZs2ZeHChfj6+hITE4O1tXWJzpufn0+XLl2wtbXl6NGjJCUlMWDAALS0tPjqq6+AwsnX+/bty+LFi59ayL8opZ7sLi0tTe2RmZlJTEwMrVq1YuPGjeWRURAqFRM9Lfo2rclvI1qw7+O2jG5fGzsTXTJy8vjpeCLvLDtKh4ADBB68TGpWrtxxBZlo16iO9WefAnBzwQLux8bKnEio0rT14c0vC58fWQjplXNN9j6v9aG6YXVuZN9gQ9QGueMIgiCUSV5eHqNHj8bExARLS0umTJmiNpGno6OjWo99YmIi3bp1w9DQEGNjY3r16qW2HPf06dPx8vIiKCiImjVrYmhoyMiRI8nPz+ebb77B1tYWa2trZs+erZYjICAADw8PDAwMsLe3Z+TIkWRmZqr2JyQk4Ofnh5mZGQYGBtSrV4+dO3cChTVjv379sLKyQk9PDxcXF9asWfPU97x7925atWqFqakpFhYWdO3aldhHfjdycnICwNvbG0mSaNeu3VPPFRwcjJ+f3xPvZejQoQwePBg3NzdWrFiBvr4+QUFBTz3P4/bs2UNUVBQbNmzAy8uLTp068eWXX7J06VJyc//7nd7Pz49t27aRnZ1d4nOXRakL+aK4uLjw9ddfP9FbLwhVnbOVIRN863L409f5cUhT3vGujp6WBpdvZjF7ZzTNvgplbPA/HL98W8y0XAWZ9uyJQevWFOTmcv2zSRTk5ckdSajK6nUHh5aF98r/NUfuNGWiraHNR94fAfD9ue+5nX1b5kSCIFQUBQUF3MvNk+VR2t/x1q1bh6amJidOnGDRokUEBASwevXqItsqlUq6detGamoqBw4cICQkhMuXL9O7d2+1drGxsezatYvdu3ezceNGvv/+e7p06cLVq1c5cOAAc+fOZfLkyRw/flx1jEKhYPHixURGRrJu3Tr27dvHxIkTVftHjRrF/fv3OXjwIBEREcydOxdDQ0Og8B71qKgodu3aRXR0NMuXL8fS0vKp7zkrKwt/f39OnTpFaGgoCoWC7t27o1QW3q564sQJAPbu3UtSUhKbN28u8jypqalERUXRqFEj1bbc3FxOnz6Nj4+P2nvz8fHh2LFjT830uGPHjuHh4YGNjY1qm6+vLxkZGURG/rf8aaNGjcjLy1P7LMtDqYfWP/VEmppcF+siC0KRFAqJlrUtaVnbkplv57Et/Do/nUjg3LUMtoZfZ2v4dWpbG9KnSU16NKiOqb623JGFl0CSJKrN+pLLfm+RExHB7dWrsRw+XO5YQlUlSfDGTFjdAc78BM1HgY2b3KlKrZNTJ9ZHrSfqdhQrzqzgi2ZfyB1JEIQKIPtBPm5T/5Tl2lEzfdHXLnnZZW9vz4IFC5Akibp16xIREcGCBQsYOnToE21DQ0OJiIggLi4Oe3t7oHBOs3r16nHy5EkaN24MFBb8QUFBGBkZ4ebmRvv27YmJiWHnzp0oFArq1q3L3Llz+euvv1SrfT06mZ6joyOzZs1i+PDhLFu2DCgcCdCjRw88PDwAcHZ2VrVPTEzE29tbVVA7OjoW+5579Oih9jooKAgrKyuioqJwd3dX3U5gYWGBra3tU8+TmJhIQUEBdnZ2qm23bt0iPz9frQAHsLGx4fz588XmelRycnKR53i47yF9fX1MTExISEgo8bnLotQ98tu2bVN7bN26lRUrVvDee+/RsmXL8sgoCK8UQx1N+jatyfaPWrNtdEv+19gefW0NLt3I5MvtUTT9KhT/TeH8k5gmd1ThJdCyscH2i88BuLl0GTml+AdFEF64Go3A9S0oUMLe6XKnKROFpODjhh8D8OvFX7meKToZBEGoXJo1a6Y2l1Lz5s25ePEi+fn5T7SNjo7G3t5eVcQDuLm5YWpqSnR0tGqbo6MjRkZGqtc2Nja4ubmhUCjUtt24cUP1eu/evXTo0IHq1atjZGRE//79uX37Nvfu3QNgzJgxzJo1i5YtWzJt2jTOnj2rOnbEiBEEBwfj5eXFxIkTOXr0aLHv+eLFi/Tp0wdnZ2eMjY1VhX9iYuKzPi41D4ezyz3JoZ6enupzKi+l7pF/++231V5LkoSVlRWvv/468+fPf1G5BKFKqF/DlPo1TPmiiyu/h1/np+OJRCdlsPmfa2z+5xqe9qa839KRzh7V0NJ4IXfCCBWQ8VtvkRESQubeUK5/+hlOv/yMpC1GZQgy6TANzu+Ai39C/GFwbCV3olJrUq0JTas15XjScVadXcX0FtPljiQIgsz0tDSImukr27XlpqWlpfZakqQitz0cyh4fH0/Xrl0ZMWIEs2fPxtzcnMOHD/PBBx+Qm5uLvr4+Q4YMwdfXlx07drBnzx7mzJnD/Pnz+eijj+jUqRMJCQns3LmTkJAQOnTowKhRo5g3b16R+fz8/HBwcCAwMBA7OzuUSiXu7u5q956XxMPh+2lpaapefEtLSzQ0NNTmDQBISUkptnf/cba2tqoh/o+e4+G+R6Wmppb7pISlrgyUSqXaIz8/n+TkZH766SeqVatWHhkF4ZVnpKtF/2YO7BzTii0jW9CjQQ20NRScuXKHscHhtJq7j+/2XeR25n25owrlQJIkqk2fjoapKfdjYri1YqXckYSqzLI2NBxY+DxkGlTS+TtGe40G4PdLv3Ml44rMaQRBkJskSehra8ryKO1KRY/fW/3333/j4uKCRhFL1bq6unLlyhWuXPnv77moqCju3LmDm1vZb486ffo0SqWS+fPn06xZM+rUqVPkbdT29vYMHz6czZs38/HHHxMYGKjaZ2VlxcCBA9mwYQMLFy586prut2/fJiYmhsmTJ9OhQwdcXV1JS1Mfmar9bwdHUaMSHlWrVi2MjY2JiopSO7Zhw4aEhoaqtimVSkJDQ2nevPmzP4x/NW/enIiICLVRCyEhIRgbG6t91rGxseTk5ODt7V3ic5eF6OIThApEkiS8a5oxv5cnRz57nfE+dbAy0iEl4z7z9lyg+df7mPjrGaKTMuSOKrxgmpaW2E6dAsCtwEAxi70gr7afgZYBXDsF0dvkTlMmXtZetKzekvyCfFacXSF3HEEQhBJLTEzE39+fmJgYNm7cyJIlS546qbiPjw8eHh7069ePsLAwTpw4wYABA2jbtq3ahG+lVbt2bR48eMCSJUu4fPkyP/zwAytWqP9dOm7cOP7880/i4uIICwvjr7/+wtXVFYCpU6eydetWLl26RGRkJNu3b1fte5yZmRkWFhasWrWKS5cusW/fPvz9/dXaWFtbo6enx+7du0lJSSE9Pb3Icz2cxO7w4cNq2/39/QkMDGTdunVER0czYsQIsrKyGDx4cIk/kzfffBM3Nzf69+/PmTNn+PPPP5k8eTKjRo1CR0dH1e7QoUM4OztTq1atEp+7LEo0tP7xD7I4AQEBZQ4jCMJ/rIx0GOvjwoh2tdgRcZ01R+I5ezWdn09d5edTV2nubMHwdrVo42Ip1qR/RRh16oTB1q1kHThI0rRpOKxfj6QQ37cKMjCygRaj4cBc2DsD6nYGDa1nH1fBjPIcxZFrR9h+eTtDPYbiaOIodyRBEIRnGjBgANnZ2TRp0gQNDQ3Gjh3LsGHDimwrSRJbt27lo48+ok2bNigUCjp27MiSJUueK4OnpycBAQHMnTuXSZMm0aZNG+bMmcOAAQNUbfLz8xk1ahRXr17F2NiYjh07smDBAqCwF3zSpEnEx8ejp6dH69atCQ4OLvJaCoWC4OBgxowZg7u7O3Xr1mXx4sVqS8xpamqyePFiZs6cydSpU2ndujX79+8v8nxDhgxh6NChfPPNN6o5AHr37s3NmzeZOnUqycnJeHl5sXv3brXJ6wYNGkR8fPxTz6uhocH27dsZMWIEzZs3x8DAgIEDBzJz5ky1dhs3bixyYsIXTSoowXoI7du3L9nJJIl9+/Y9dyi5ZWRkYGJiQnp6OsbGxnLHEQSgcNmUsMQ0go7Es/tcMvnKwv913aoZM7xdLTq726Ip7qOv9B5cu0ZsVz8KsrOx/XImZj17yh1JqKru34VFXnDvFnSZD42HyJ2oTD4K/Yj9V/fTxbkLX7f+Wu44giC8BDk5OcTFxeHk5CT7pGfCy1dQUEDTpk0ZP348ffr0KfFxbdu2pX379kyfPr3M146MjOT111/nwoULmJiYFNmmuJ/P0tShJSrkqxpRyAsV3fU72QQdjuOnE4ncyy28V6imuT7D2jjzbsMa6FaASVWEsru9Zi035s5FYWxMrZ070Cxm3VVBKFfHV8GuT8DAGsb8AzqGcicqtejb0fTa3gsJiS3dtlDLtHyHOgqCID9RyAvh4eFERETQv3//ErVPT0+nXr16nD9/HkPDsv9bt3fvXvLz8/H1ffrEiqKQL0eikBcqizv3cll/LIE1R+JIu/cAAEtDbQa3dOK9Zg6Y6FW+obACFOTlEd+rNzlRURh37kz1ALEiiCCTvFxY2gTS4qDd59DuU7kTlcn4v8azN3Evvo6+zGtb9IzJgiC8OkQhL1Rkshbyp06d4ueffyYxMfGJJQE2b95c2tNVOKKQFyqbe7l5/HzyCoGH4rh2p3D9TEMdTQY0d2Boa2fMDMRSZpVN9rlI4nv1AqUS+8BVGLZuLXckoao69xv8+j5oG8KYcDAs3+V0ysOFtAv02NYDgN/e+o06ZnVkTiQIQnkShbxQkb2oQr7UN9QGBwfTokULoqOj2bJlCw8ePCAyMpJ9+/Y99T4AQRDKl762JoNaOrH/k3Ys6O1JHRtDMu/nsWx/LK2/+Yv5e2JI/7fHXqgc9NzrYd7/PQCSp89Aee+ezImEKsutO9h5Q24mHPxG7jRlUsesDr6OhcMcl4cvlzmNIAiCIDy/UhfyX331FQsWLOCPP/5AW1ubRYsWcf78eXr16kXNmjXLI6MgCCWkpaGgu3cNdo9tw8r+DXGtZkzm/TyW7LtEq7n7CAi5QHq2KOgrC6sxY9C0q8aDa9e4uXSp3HGEqkqhAJ8Zhc9PBcHtyrk04kjPkUhI7E3cS/TtaLnjCIIgCMJzKXUhHxsbS5cuXYDCZQWysrKQJInx48ezatWqFx5QEITSUygkfOvZsuOjVqx4rwGv2Rpx934ei0Mv0mruPhbuvUBGjijoKzqFgQG2UwrXlk9du46caFF8CDJxbgu1fUCZB/tmyZ2mTJxNnens3BmAZeHLZE4jCIIgCM+n1IW8mZkZd+/eBaB69eqcO3cOgDt37nBPDP0UhApFoZDo6F6NnWNas6xfA+rYGHI3J4+Fey/S6ut9fLfvIvdy8+SOKRTDqH17jHx9IT+fpKnTKMjPlzuSUFX5TAckiNwM107LnaZMhtcfjkJSsP/qfs7dOid3HEEQBEEosxIX8g8L9jZt2hASEgJAz549GTt2LEOHDqVPnz506NChfFIKgvBcFAqJzh7V2D22Dd/19cbF2pCMnDzm7blAu2/389PxRPLylXLHFJ7C5vPPURgakhMRQdqPP8kdR6iqbD2gfu/C55W0V97RxBE/Zz8Avgv/TuY0giAIglB2JS7k69evT9OmTfHw8KBnz54AfPHFF/j7+5OSkkKPHj34/vvvyy2oIAjPT6GQ6Frfjt3j2rDof17Ym+tx4+59Pt8Sge/Cg/wZmYxYkbLi0bKxxnrCxwDcXLiQB0lJMicSqqz2k0ChCbH7IPG43GnK5EPPD9GQNDhy7QjhN8LljiMIgiAIZVLiQv7AgQPUq1ePOXPm4OrqysCBAzly5AifffYZ27ZtY/78+ZiZmZVnVkEQXhANhUQ3r+rs9W/L1K5umOlrEXsziw9/OE3PFcc4nZAqd0ThMaa9eqHn7Y3y3j2SZ82WO45QVZk5glffwuf7v5I1SlnZG9nzdu23AVgaLiaRFAShYmnXrh3jxo0rto2joyMLFy58KXkqqylTpjBs2LCXft3du3fj5eWFUln+I11LXMi3bt2aoKAgkpKSWLJkCfHx8bRt25Y6deowd+5ckpOTyzOnIAjlQEdTg/dbOXFgYntGta+FrpaCUwlp9Fh+jGHrT3HpRqbcEYV/SQoF1WbOAE1NMkNDyTx4UO5IQlXVekJhr/zl/ZBwTO40ZTKs/jA0FZr8nfQ3p1Mq5/3+giBUXSdPnpSlSJVTfHw8kiQRHh7+zLbJycksWrSIL774Qm370qVLcXR0RFdXl6ZNm3LixIlS5xgzZgwNGzZER0cHLy+vJ/Z37NgRLS0tfvzxx1Kfu7RKPdmdgYEBgwcP5sCBA1y4cIGePXuydOlSatasyVtvvVUeGQVBKGfGulp84vsa+ye053+N7VFIsCcqBd+FB5m29Rx37uXKHVEAdFxcMO/fH4CUr+ZQkCv+XAQZmDmA93uFzytpr7ydoR3da3cHIDAiUOY0giAIpWNlZYW+vr7cMSqs1atX06JFCxwcHFTbNm3ahL+/P9OmTSMsLAxPT098fX25ceNGqc///vvv07t376fuHzRoEIsXLy5T9tIodSH/qNq1a/P5558zefJkjIyM2LFjx4vKJQiCDGxNdPm6R33+HNcGH1dr8pUFrDuWQPt5hRPi5SvF/fNysxw5Ag0LC3Lj40n9YYPccYSqqvUEUGhB3EGIPyJ3mjIZ7D4YhaTgyLUjYl15QRAqlLy8PEaPHo2JiQmWlpZMmTJFbQ6jx4fWJyYm0q1bNwwNDTE2NqZXr16kpKSo9k+fPh0vLy+CgoKoWbMmhoaGjBw5kvz8fL755htsbW2xtrZm9mz1W/cCAgLw8PDAwMAAe3t7Ro4cSWbmf6M1ExIS8PPzw8zMDAMDA+rVq8fOnTsBSEtLo1+/flhZWaGnp4eLiwtr1qx56nvevXs3rVq1wtTUFAsLC7p27UpsbKxqv5OTEwDe3t5IkkS7du2eeq7g4GD8/PyeeC9Dhw5l8ODBuLm5sWLFCvT19QkKCnrqeYqyePFiRo0ahbOz81Pb+Pn5cerUKbX85aHMhfzBgwcZNGgQtra2fPLJJ7zzzjscOVI5/zEXBEGdi40Rqwc25schTXGxNiTt3gM+3xLBW98d5lS8uH9eThpGRlj7+wNwa9ky8m7elDmRUCWZ2kODwtEh7J8jb5Yysjeyp6NjRwBWR6yWOY0gCOWuoABys+R5lHIi4XXr1qGpqcmJEydYtGgRAQEBrF5d9N9TSqWSbt26kZqayoEDBwgJCeHy5ctP9BjHxsaya9cudu/ezcaNG/n+++/p0qULV69e5cCBA8ydO5fJkydz/Ph/E5kqFAoWL15MZGQk69atY9++fUycOFG1f9SoUdy/f5+DBw8SERHB3LlzMTQ0BArvUY+KimLXrl1ER0ezfPlyLC0tn/qes7Ky8Pf359SpU4SGhqJQKOjevbvqXvOHw+D37t1LUlISmzdvLvI8qampREVF0ahRI9W23NxcTp8+jY+Pj9p78/Hx4dixF3+LWM2aNbGxseHQoUMv/NyP0ixN4+vXr7N27VrWrl3LpUuXaNGiBYsXL6ZXr14YGBiUV0ZBEGTSsrYlO8e2ZsPfCQSEXCDyegbvrjjG2152fNbJFVsTXbkjVkkm3d8mLTiYnIgIbgQswG5O5RzeLFRyrT+GfzZA/CGIOwROreVOVGofeHzAzridhCSEEJ8ej6OJo9yRBEEoLw/uwVd28lz78+ugXfJayd7engULFiBJEnXr1iUiIoIFCxYwdOjQJ9qGhoYSERFBXFwc9vb2AKxfv5569epx8uRJGjduDBQW/EFBQRgZGeHm5kb79u2JiYlh586dKBQK6taty9y5c/nrr79o2rQpgNqke46OjsyaNYvhw4ezbNkyoHAkQI8ePfDw8ABQ66VOTEzE29tbVVA7OjoW+5579Oih9jooKAgrKyuioqJwd3fHysoKAAsLC2xtbZ96nsTERAoKCrCz++/P+tatW+Tn52NjY6PW1sbGhvPnzxebq6zs7OxISEgol3M/VOIe+U6dOuHg4MCSJUvo3r070dHRHD58mMGDB4siXhBeYVoaCga3dGL/hHb0aWKPJMHv4dd5ff5+lu2/xP28fLkjVjmSQoHt5MIJXNK3bCH7zBmZEwlVkkkNaDCg8Pn+OaXucaoI6pjVoV2NdhRQwJrIpw/5FARBeJmaNWuGJEmq182bN+fixYvk5z/5O1d0dDT29vaqIh7Azc0NU1NToqP/u23I0dERIyMj1WsbGxvc3NxQKBRq2x69Z3zv3r106NCB6tWrY2RkRP/+/bl9+zb37t0DCid+mzVrFi1btmTatGmcPXtWdeyIESMIDg7Gy8uLiRMncvTo0WLf88WLF+nTpw/Ozs4YGxurCv/ExMRnfVxqsrOzAdDVlbezSU9PT/U5lZcS98hraWnx66+/0rVrVzQ0NMozkyAIFZCFoQ5z3qlP3yYOTNt2jrDEO3yzO4ZfTl1ldnd3WtR6+nAp4cXT8/TEpHt30rdsIXnWbBw3BSMpnmvaE0EovVb+ELYeEo4U3i/v3FbuRKX2gccH7L+6n22x2xjhOQJbg6f39AiCUIlp6Rf2jMt1bZlpaWmpvZYkqchtD4eyx8fH07VrV0aMGMHs2bMxNzfn8OHDfPDBB+Tm5qKvr8+QIUPw9fVlx44d7Nmzhzlz5jB//nw++ugjOnXqREJCAjt37iQkJIQOHTowatQo5s2bV2Q+Pz8/HBwcCAwMxM7ODqVSibu7O7mlnNj34fD9tLQ0VS++paUlGhoaavMGAKSkpBTbu/88UlNTVdcvLyX+rW/btm1069ZNFPGCUMV51DDhtxEtWNjbC2sjHeJuZdE38Dif/HKGtCwxi/rLZO0/HoWBATkREaRv+V3uOEJVZFIdGg4qfF5Je+W9rL1oZNOIPGUe6yLXyR1HEITyIkmFw9vleDzSu14Sj96nDvD333/j4uJSZB3m6urKlStXuHLlimpbVFQUd+7cwc3NrWyfFXD69GmUSiXz58+nWbNm1KlTh+vXn/wixN7enuHDh7N582Y+/vhjAgP/WwnEysqKgQMHsmHDBhYuXMiqVauKvNbt27eJiYlh8uTJdOjQAVdXV9LS0tTaaGtrAxQ5KuFRtWrVwtjYmKioKLVjGzZsSGhoqGqbUqkkNDSU5s2bP/vDKKWcnBxiY2Px9vZ+4ed+lOi+EQSh1CRJ4m3v6uz9uC39mzkgSfDL6at0CDjA7/9cU5tZVSg/mlZWWI4aBcCNBQvIv3tX5kRCldRqPGjoQOKxwrXlK6GhHoX3nf528TfSctKe0VoQBKF8JSYm4u/vT0xMDBs3bmTJkiWMHTu2yLY+Pj54eHjQr18/wsLCOHHiBAMGDKBt27ZqE76VVu3atXnw4AFLlizh8uXL/PDDD6xYsUKtzbhx4/jzzz+Ji4sjLCyMv/76C1dXVwCmTp3K1q1buXTpEpGRkWzfvl2173FmZmZYWFiwatUqLl26xL59+/D/d2Lfh6ytrdHT02P37t2kpKSQnp5e5LkeTmJ3+PBhte3+/v4EBgaybt06oqOjGTFiBFlZWQwePLhUn8ulS5cIDw8nOTmZ7OxswsPDCQ8PVxs58Pfff6Ojo1MuXxI8ShTygiCUmbGuFl++7c6vw1tQ18aI1Kxcxm0KZ0DQCRJvl+99QUIh8/f6oe3kRP6tW9xatlzuOEJVZGxX6Xvlm9s1x9Xcley8bH46/5PccQRBqOIGDBhAdnY2TZo0YdSoUYwdO5Zhw4YV2VaSJLZu3YqZmRlt2rTBx8cHZ2dnNm3a9FwZPD09CQgIYO7cubi7u/Pjjz8yZ476KiX5+fmMGjUKV1dXOnbsSJ06dVQT4WlrazNp0iTq169PmzZt0NDQIDg4uMhrKRQKgoODOX36NO7u7owfP55vv/1WrY2mpiaLFy9m5cqV2NnZ0a1bt6dmHzJkCMHBwarbBAB69+7NvHnzmDp1Kl5eXoSHh7N79261CfAGDRpU7LJ2D8/t7e3NypUruXDhAt7e3nh7e6uNVti4cSP9+vVDX798b6mQCkTX2RMyMjIwMTEhPT0dY2NjueMIQqWQm6ck8NBlFoVeJDdPia6WgrEd6jCktRNaGuI7w/KUeegQV4YOA01NnLdtRaeYtU0FoVxkJMFiL8jLgfc2Q+0OcicqtT3xe/j4wMcYaRsR8m4IBlpiIl9BqKxycnKIi4vDyclJ9knPhJevoKCApk2bMn78ePr06VPi49q2bUv79u2ZPn16ma9969Yt6taty6lTp3ByciqyTXE/n6WpQ8Vv14IgvBDamgpGta/Nn+Pa0KKWBTkPlMzdfR6/JYc5d63o4U/Ci2HYujWG7dtDXh4ps78StzYIL59xNWj47/DEStor36FmBxyNHbmbe5dfYn6RO44gCIJQRpIksWrVKvLy8kp8THp6OrGxsUyYMOG5rh0fH8+yZcueWsS/SKKQFwThhXKyNODHIU2Z39MTM30tziff5e2lR1i09yIP8pXPPoFQJjaffYqkpUXWkSNk/vWX3HGEqqjVONDUhasn4VLoM5tXNBoKDd53fx+A9VHryc0Xk3cKgiBUVl5eXvTv37/E7U1MTLh69SqGhobPdd1GjRrRu3fv5zpHSYlCXhCEF06SJHo0rMFe/7Z09rAlT1nAgr0XeGfZUS6miAnZyoO2gwPm/07YkjLna5T378ucSKhyjGyh0QeFz/d/VSl75bs6d8VG34ab2TfZGrtV7jiCIAiC8FSikBcEodxYGOqwtG8DFv3PCxM9LSKupdNlyWFWHYwlX1n5fsmv6Cw/HIamtTUPrlwhdf16ueMIVVGrcaCpB9dOQ9wBudOUmpaGFoPqDQIgKCKIPGXJh2UKgiAIwsskCnlBEMqVJEl086rOnvFtaF/Xitw8JV/tPE/vlceIv5Uld7xXisLAACv/8QDcXhVIXppYRkt4yQytocGAwueHF8ibpYzecXkHUx1TrmZeZU/8HrnjCIIgCEKRRCEvCMJLYWOsS9Cgxszt4YGhjianEtLotOgQPxyLRyl6518YEz8/dF57DeXdu9xeuUruOEJV1GI0SBqFa8pfC5M7Tanpa+nznut7AKw+t1pMHikIgiBUSKKQFwThpZEkid6Na7JrbGuaO1uQ/SCfKVsjGbjmBDfvinu6XwRJQwPrjz8GIO3HH8m9ek3mREKVY1oTPHoWPq+kvfL/e+1/6GvqczHtIgevHpQ7jiAIgiA8QRTygiC8dPbm+vw4pCnT/dzQ1VJw6OItOi06xMELN+WO9kowaNUSgxbNKXjwgJuLFskdR6iKWo0r/G/0H3DroqxRysJEx4TedQtnHf7+3PcypxEEQRCEJ4lCXhAEWSgUEoNaOvHH6Fa8ZmvErcz7DAg6wde7zotl6p6TJElY/dsrn/HHH2RHRsqcSKhyrF2hbmegAI5Uzi+T+rv1R1OhyT83/uHcrXNyxxEEQRAENaKQFwRBVi42Rvw+qiXvNasJwIoDsfRccYwrqfdkTla56dWrh7GfHwA35s0T9/kKL1+rwokXORMMGdflzVIGVvpWdHLsBBSuKy8IgvAytGvXjnHjxhXbxtHRkYULF76UPJXVlClTGDZs2Eu/7ooVK/D79/ev8iYKeUEQZKerpcGstz1Y8V4DjHU1Cb9yh86LDrHjbJLc0So1q7FjkbS0uHfsb7IOH5E7jlDV2DcBh5agfADHlsqdpkz6u/UHICQ+hOSsZJnTCIIgFDp58qQsRaqc4uPjkSSJ8PDwZ7ZNTk5m0aJFfPHFF2rbly5diqOjI7q6ujRt2pQTJ06UKsOZM2fo06cP9vb26Onp4erqyqLHbmF8//33CQsL49ChQ6U6d1mIQl4QhAqjo3s1do5tTUMHM+7ez2PUT2FM2nyW7Nx8uaNVSto1qmPWrx8AN+bPpyBffI7CS/awV/70WriXKmuUsnC1cKWRTSPyCvLYeH6j3HEEQRAAsLKyQl9fX+4YFdbq1atp0aIFDg4Oqm2bNm3C39+fadOmERYWhqenJ76+vty4caPE5z19+jTW1tZs2LCByMhIvvjiCyZNmsR3332naqOtrU3fvn1ZvHjxC31PRRGFvCAIFUoNM302DWvG6Pa1kSTYeOIKb313mEs3MuWOVilZfDgMhZER98+fJ/2PP+SOI1Q1tX3Axh1yM+Fk5Zw07mGv/K8XfuXeA3HLjyAI5S8vL4/Ro0djYmKCpaUlU6ZMUbtF7vGh9YmJiXTr1g1DQ0OMjY3p1asXKSkpqv3Tp0/Hy8uLoKAgatasiaGhISNHjiQ/P59vvvkGW1tbrK2tmT17tlqOgIAAPDw8MDAwwN7enpEjR5KZ+d/vYwkJCfj5+WFmZoaBgQH16tVj586dAKSlpdGvXz+srKzQ09PDxcWFNWvWPPU97969m1atWmFqaoqFhQVdu3YlNjZWtd/JyQkAb29vJEmiXbt2Tz1XcHDwE8PbAwICGDp0KIMHD8bNzY0VK1agr69PUFDQU8/zuPfff59FixbRtm1bnJ2dee+99xg8eDCbN29Wa+fn58e2bdvIzs4u8bnLQhTygiBUOJoaCib41uXHD5pibaTDxRuZvL30CLvPiaGtpaVpZoblh4XD724uWozyvljmT3iJJOm/XvnjyyG38hXCbWu0xd7InozcDLbFbpM7jiAIZVRQUMC9B/dkeZR2npp169ahqanJiRMnWLRoEQEBAaxevbrItkqlkm7dupGamsqBAwcICQnh8uXL9O7dW61dbGwsu3btYvfu3WzcuJHvv/+eLl26cPXqVQ4cOMDcuXOZPHkyx48fVx2jUChYvHgxkZGRrFu3jn379jFx4kTV/lGjRnH//n0OHjxIREQEc+fOxdDQECi8Rz0qKopdu3YRHR3N8uXLsbS0fOp7zsrKwt/fn1OnThEaGopCoaB79+4olYUTID8cBr93716SkpKeKJ4fSk1NJSoqikaNGqm25ebmcvr0aXx8fNTem4+PD8eOHXtqppJIT0/H3NxcbVujRo3Iy8tT+yzLg2a5nv0FcHR0JCEh4YntI0eOZOnSJ++5W7t2LYMHD1bbpqOjQ05OTrllFAShfLSobcnOsa0Z/VMYf19OZfiG04xsV4uP36yLhkKSO16lYfbee6Ru+JG8pCTSNmzA4oMP5I4kVCVub0PoTLiTAP9sgKaV675ODYUG/Vz78fWJr9kQvYFedXuhkEQ/iCBUNtl52TT9qaks1z7e9zj6WiUfCm9vb8+CBQuQJIm6desSERHBggULGDp06BNtQ0NDiYiIIC4uDnt7ewDWr19PvXr1OHnyJI0bNwYKC/6goCCMjIxwc3Ojffv2xMTEsHPnThQKBXXr1mXu3Ln89ddfNG1a+Dk9Oumeo6Mjs2bNYvjw4SxbtgwoHAnQo0cPPDw8AHB2dla1T0xMxNvbW1VQOzo6Fvuee/ToofY6KCgIKysroqKicHd3x8rKCgALCwtsbW2fep7ExEQKCgqws7NTbbt16xb5+fnY2NiotbWxseH8+fPF5irO0aNH2bRpEzt27FDbrq+vj4mJSZE17ItU4f8lOnnyJElJSapHSEgIAD179nzqMcbGxmrHlPeHKAhC+bE01GHDB00Z2rpwSNWy/bEMWnOCtKxcmZNVHgpdXazGjgXg1spV5N+5I28goWrR0ISWYwqfH10C+Q/kzVMG3Wt3x0jLiISMBA5dLf8JjARBqNqaNWuGJP3XYdG8eXMuXrxIfhFz3URHR2Nvb68q4gHc3NwwNTUlOjpatc3R0REjIyPVaxsbG9zc3FAoFGrbHr1nfO/evXTo0IHq1atjZGRE//79uX37NvfuFY6uGjNmDLNmzaJly5ZMmzaNs2fPqo4dMWIEwcHBeHl5MXHiRI4ePVrse7548SJ9+vTB2dkZY2NjVeGfmJj4rI9LzcPh7Lq6uqU6rrTOnTtHt27dmDZtGm+++eYT+/X09FSfU3mp8D3yD799eejrr7+mVq1atG3b9qnHSJJU7Dc1j7t//z73HxlumpGRUfqggiCUG00NBV90caN+DVMm/nqWQxdv0XXJYVb2b4h7dRO541UKJm/5kbpmDfcvXODWylXYfDrx2QcJwovi9R7snwvpiXBuM3j2fvYxFYi+lj496vRgbeRafoj6gbb2T/8dRBCEiklPU4/jfct3qHNx15ablpaW2mtJkorc9nAoe3x8PF27dmXEiBHMnj0bc3NzDh8+zAcffEBubi76+voMGTIEX19fduzYwZ49e5gzZw7z58/no48+olOnTiQkJLBz505CQkLo0KEDo0aNYt68eUXm8/Pzw8HBgcDAQOzs7FAqlbi7u5ObW7qOm4fD99PS0lR1pKWlJRoaGmrzBgCkpKSUqmZ8KCoqig4dOjBs2DAmT55cZJvU1NQn6tgXrcL3yD8qNzeXDRs28P7776t9S/W4zMxMHBwcsLe3p1u3bkRGRhZ73jlz5mBiYqJ6PPqNliAIFYefpx2/j2qJo4U+1+5k02P5UX49fVXuWJWCpKGB9ScTAEjbsIHcq9dkTiRUKVq60GxE4fPDC+DfXxQrk76v9UVD0uB48nFiUmPkjiMIQilJkoS+lr4sj+LqlqI8fm/133//jYuLCxoaGk+0dXV15cqVK1y5ckW1LSoqijt37uDm5la2D4vCGdqVSiXz58+nWbNm1KlTh+vXrz/Rzt7enuHDh7N582Y+/vhjAgMDVfusrKwYOHAgGzZsYOHChaxatarIa92+fZuYmBgmT55Mhw4dcHV1JS0tTa2NtrY2QJGjEh5Vq1YtjI2NiYqKUju2YcOGhIaGqrYplUpCQ0Np3rz5sz+MR0RGRtK+fXsGDhz4xOSAD8XGxpKTk4O3t3epzl1alaqQ//3337lz5w6DBg16apu6desSFBTE1q1b2bBhA0qlkhYtWnD16tN/2Z80aRLp6emqx6P/IwiCULHUtTVi6+hWdHjNmvt5Sib8coYpv58jN6/yFQYvm0GrVug3a0bBgwfcXLzo2QcIwovU+APQMYab0XBxj9xpSq2aYTV8HAonSvoh6geZ0wiC8CpLTEzE39+fmJgYNm7cyJIlSxj77y1yj/Px8cHDw4N+/foRFhbGiRMnGDBgAG3btlWb8K20ateuzYMHD1iyZAmXL1/mhx9+YMWKFWptxo0bx59//klcXBxhYWH89ddfuLq6AjB16lS2bt3KpUuXiIyMZPv27ap9jzMzM8PCwoJVq1Zx6dIl9u3bh7+/v1oba2tr9PT02L17NykpKaSnpxd5roeT2B0+fFhtu7+/P4GBgaxbt47o6GhGjBhBVlbWE3OrFefcuXO0b9+eN998E39/f5KTk0lOTubmzZtq7Q4dOoSzszO1atUq8bnLolIV8t9//z2dOnVSm7zgcc2bN2fAgAF4eXnRtm1bNm/ejJWVFStXrnzqMTo6OhgbG6s9BEGouEz0tAgc0IjxPnWQJPjh7wT6rf6bVHHffLEkScJ6QmGvfMYf27l/6ZLMiYQqRdcEGr1f+PzwAnmzlNHDpeh2xu3kVvYtmdMIgvCqGjBgANnZ2TRp0oRRo0YxduxYhg0reqJQSZLYunUrZmZmtGnTBh8fH5ydndm0adNzZfD09CQgIIC5c+fi7u7Ojz/+yJw5c9Ta5OfnM2rUKFxdXenYsSN16tRRTYSnra3NpEmTqF+/Pm3atEFDQ4Pg4OAir6VQKAgODub06dO4u7szfvx4vv32W7U2mpqaLF68mJUrV2JnZ0e3bt2emn3IkCEEBwerbhMA6N27N/PmzWPq1Kl4eXkRHh7O7t271SbAGzRoULHL2v3666/cvHmTDRs2UK1aNdXj4YSCD23cuLHIiQlfNKmgtOshyCQhIQFnZ2c2b95c7B9cUXr27ImmpiYbN24sUfuMjAxMTExIT08XRb0gVHD7zqcwNjicuzl51DTXJ2hQY2pbG8odq0K7+tEY7oaEYNSpIzUWVM6CSqik7ibDwvqQfx8G7wKHFnInKrV+O/tx9uZZhnsOZ5TXKLnjCIJQhJycHOLi4nBycir3Sc+EiqegoICmTZsyfvx4+vTpU+Lj2rZtS/v27Zk+fXqZrx0ZGcnrr7/OhQsXMDEpeh6n4n4+S1OHVpoe+TVr1mBtbU2XLl1KdVx+fj4RERFUq1atnJIJgiCn11+zYcvIFtib65GYeo93lh3hyCXRU1Ycy9GjAbi7azc5MRdkTiNUKUa24NW38PnR7+TNUkYPe+V/jvmZ+/n3n9FaEARBeNkkSWLVqlXk5eWV+Jj09HRiY2OZ8O/IxbJKSkpi/fr1Ty3iX6RKUcgrlUrWrFnDwIED0dRUn2h/wIABTJo0SfV65syZ7Nmzh8uXLxMWFsZ7771HQkICQ4YMedmxBUF4SWpbG/H7yJY0cjAjIyePgUEn2HiidMuVVCW6detg1KkjALe+q5zFlFCJNRtZ+N+YnZB6Wd4sZeBT0wdbA1tSc1LZcXnHsw8QBEEQXjovLy/69+9f4vYmJiZcvXoVQ8PnG9Xp4+ODr6/vc52jpCpFIb93714SExN5//33n9iXmJhIUlKS6nVaWhpDhw7F1dWVzp07k5GRwdGjR59r1kZBECo+C0MdNgxpytteduQpC5i0OYLZO6LIV1aKu4deOqtRo0CSuBsSQs4jM7sKQrmzqgO13wAK4HjRMxhXZJoKTfq+Vjiq4IeoH6gkdygKgiAIr5hKc4/8yyTukReEyqugoIDFoZdYsLdwyPgbbjYs+p8X+tqazziy6rk24RMytm/H8PXXsV+2VO44QlVyKRQ2vAPaRuAfBbqV69/ajNwMfH7xITsvm5VvrKSFXeW7118QXmXiHnmhIqty98gLgiCUhCRJjPVxYXEfb7Q1FYREpdBzxTGS03PkjlbhWI4cCQoFmfv2kR1xTu44QlVS63WwrAu5dyH8R7nTlJqxtjFv134bEEvRCYIgCPIQhbwgCK+ktzzt2Di0GRYG2kRez6Db0sNEJ2XIHatC0XF2wsTPDxD3ygsvmSRBsxGFz4+vAGW+vHnK4D3X95CQOHztMJfTK9+9/oIgCELlJgp5QRBeWQ0dzPh9VEtcrA1JybhPr5XHOBGXKnesCsVy5AjQ0CDzwAGyz5yRO45QldTvDXpmkBYPF3bLnabUahrXpG2NtgD8EvOLzGkEQRCEqkYU8oIgvNLszfX5dXgLGjmYcTcnj/7fH2dvVIrcsSoMbQcHTN7uBsDNJaJXXniJtPWh4eDC538vlzdLGfV+rTcAWy9t5d6DezKnEQRBEKoSUcgLgvDKM9HX4ocPmtLhNWvu5yn5cMNpfj51Re5YFYbliBGgqUnW4cPcCwuTO45QlTQeAgpNiD8ESWflTlNqLexaUMOwBncf3GV3fOUbVSAIgiBUXqKQFwShStDT1mBl/4a827AG+coCJv56lhUHYuWOVSFo16iB6TvvAHBzyRKZ0whVikl1cHu78PnxFbJGKQuFpKBX3V4ABJ8PFkvRCYLw3Nq1a8e4ceOKbePo6MjChQtfSp7KasqUKQwbNuylX3fFihX4/Tv/UHkThbwgCFWGpoaCb9+tz4dtnQH4etd5Zu+IQinWmsdy+IegpcW9Y3+TdeKE3HGEqqTZyML/RvwCmTfkzVIGb9d+G22FNtGp0Zy7JVZ/EASh/J08eVKWIlVO8fHxSJJEeHj4M9smJyezaNEivvjiC7XtS5cuxdHREV1dXZo2bcqJUv6+c/v2bTp27IidnR06OjrY29szevRoMjL+m0z5/fffJywsjEOHDpXq3GUhCnlBEKoUSZKY1MmVzzu/BkDgoTgm/HqGB/lKmZPJS8vODtN3ewBwa/ES0bMovDw1GkKNJpCfC6eC5E5Tama6Zvg6+gIQHBMscxpBEKoCKysr9PX15Y5RYa1evZoWLVrg4OCg2rZp0yb8/f2ZNm0aYWFheHp64uvry40bJf8CWaFQ0K1bN7Zt28aFCxdYu3Yte/fuZfjw4ao22tra9O3bl8WLF7/Q91RknnK/giAIQgU0rE0t5vX0REMhsTnsGh/+cJrs3Mq3BNaLZPnhh0haWtw7dYp7x4/LHUeoSh4uRXdyNeTdlzdLGTwcXv9n/J/cybkjbxhBECq9vLw8Ro8ejYmJCZaWlkyZMkXtC/bHh9YnJibSrVs3DA0NMTY2plevXqSk/Dex7/Tp0/Hy8iIoKIiaNWtiaGjIyJEjyc/P55tvvsHW1hZra2tmz56tliMgIAAPDw8MDAywt7dn5MiRZGZmqvYnJCTg5+eHmZkZBgYG1KtXj507dwKQlpZGv379sLKyQk9PDxcXF9asWfPU97x7925atWqFqakpFhYWdO3aldjY/26BdHJyAsDb2xtJkmjXrt1TzxUcHPzE8PaAgACGDh3K4MGDcXNzY8WKFejr6xMUVPIvkM3MzBgxYgSNGjXCwcGBDh06MHLkyCd63/38/Ni2bRvZ2dklPndZiEJeEIQq692GNVjVvyG6Wgr2nb/BwKATZN7PkzuWbLRsbTHtXTgL903RKy+8TK5vgXENyLoJ536TO02peVp58pr5a9zPv8/W2K1yxxEEoQgFBQUo792T5VHaf0/XrVuHpqYmJ06cYNGiRQQEBLB69eoi2yqVSrp160ZqaioHDhwgJCSEy5cv0/vff88fio2NZdeuXezevZuNGzfy/fff06VLF65evcqBAweYO3cukydP5vgjX+QrFAoWL15MZGQk69atY9++fUycOFG1f9SoUdy/f5+DBw8SERHB3LlzMTQ0BArvUY+KimLXrl1ER0ezfPlyLC0tn/qes7Ky8Pf359SpU4SGhqJQKOjevTtKZeGIyYfD4Pfu3UtSUhKbN28u8jypqalERUXRqFEj1bbc3FxOnz6Nj4+P2nvz8fHh2LFjT830LNevX2fz5s20bdtWbXujRo3Iy8tT+yzLg2a5nl0QBKGC6+Bqw4YPmjJ47UlOxKcyMOgEawc3xkhXS+5osrAYOpQ7v/xCdlgYWUePYtiypdyRhKpAQxOaDIG90+HvZeDZByRJ7lQlJkkSver2Yuaxmfwc8zP93fqjkERfiSBUJAXZ2cQ0aCjLteuGnUYqxVB4e3t7FixYgCRJ1K1bl4iICBYsWMDQoUOfaBsaGkpERARxcXHY29sDsH79eurVq8fJkydp3LgxUFjwBwUFYWRkhJubG+3btycmJoadO3eiUCioW7cuc+fO5a+//qJp06YAapPuOTo6MmvWLIYPH86yZcuAwpEAPXr0wMPDAwBnZ2dV+8TERLy9vVUFtaOjY7HvuUePHmqvg4KCsLKyIioqCnd3d6ysrACwsLDA1tb2qedJTEykoKAAOzs71bZbt26Rn5+PjY2NWlsbGxvOnz9fbK6i9OnTh61bt5KdnY2fn98TX7Lo6+tjYmJCQkJCqc9dGuJfGUEQqrxGjub8OKQpxrqanE5I473vT5Ce/UDuWLLQsrHGtHfhMOHbK1fJnEaoUhoMBE09SI6AhCNypym1Lk5dMNQyJPFuIn8n/S13HEEQKrFmzZohPfJlZvPmzbl48SL5+U/eAhgdHY29vb2qiAdwc3PD1NSU6Oho1TZHR0eMjIxUr21sbHBzc0OhUKhte/Se8b1799KhQweqV6+OkZER/fv35/bt29y7dw+AMWPGMGvWLFq2bMm0adM4e/a/ZURHjBhBcHAwXl5eTJw4kaNHjxb7ni9evEifPn1wdnbG2NhYVfgnJiY+6+NS83A4u66ubqmOK40FCxYQFhbG1q1biY2Nxd/f/4k2enp6qs+pvIgeeUEQBKB+DVN+GtqM974/zpkrd3hv9XF++KAJpvrackd76Szef5+0jcHcO3GC7PBw9Ly85I4kVAX65uDVp3DCu7+Xg2MruROVir6WPn61/Nh4fiObzm+ihV0LuSMJgvAISU+PumGnZbu23LS01EcaSpJU5LaHQ9nj4+Pp2rUrI0aMYPbs2Zibm3P48GE++OADcnNz0dfXZ8iQIfj6+rJjxw727NnDnDlzmD9/Ph999BGdOnUiISGBnTt3EhISQocOHRg1ahTz5s0rMp+fnx8ODg4EBgZiZ2eHUqnE3d2d3NzcUr3Ph8P309LSVL34lpaWaGhoqM0bAJCSklJs7/7T2NraYmtry2uvvYa5uTmtW7dmypQpVKtWTdUmNTVVdf3yInrkBUEQ/uVe3YSfhjTD3ECbiGvp9A08TmpW6f4BeRVo2dpi8lbhJDG3VgXKnEaoUpr+O/Pv+R2QGidvljLoXbfwntT9V/eTnJUscxpBEB4lSRIKfX1ZHlIpbxV6/N7qv//+GxcXFzQ0NJ5o6+rqypUrV7hy5YpqW1RUFHfu3MHNza1sHxZw+vRplEol8+fPp1mzZtSpU4fr168/0c7e3p7hw4ezefNmPv74YwID//u9wcrKioEDB7JhwwYWLlzIqlVFj/S7ffs2MTExTJ48mQ4dOuDq6kpaWppaG23two6VokYlPKpWrVoYGxsTFRWldmzDhg0JDQ1VbVMqlYSGhtK8efNnfxjFePjFx/37/03UGhsbS05ODt7e3s917mcRhbwgCMIj3OyM2Ti0GZaG2kQlZdA38G9uZVa+WbSfl8UHQ0CSyNy3j5wLF+SOI1QVVnWhtg9QACcq360dtUxr0cimEcoCJb9e+FXuOIIgVFKJiYn4+/sTExPDxo0bWbJkCWPHji2yrY+PDx4eHvTr14+wsDBOnDjBgAEDaNu2rdqEb6VVu3ZtHjx4wJIlS7h8+TI//PADK1asUGszbtw4/vzzT+Li4ggLC+Ovv/7C1dUVgKlTp7J161YuXbpEZGQk27dvV+17nJmZGRYWFqxatYpLly6xb9++J4arW1tbo6enx+7du0lJSSE9Pb3Icz2cxO7w4cNq2/39/QkMDGTdunVER0czYsQIsrKyGDx4cIk/k507d7JmzRrOnTtHfHw8O3bsYPjw4bRs2VJtDoBDhw7h7OxMrVq1SnzushCFvCAIwmPq2hoRPKwZVkY6nE++S59Vf3Pjbo7csV4qHWcnjHwL18a+HVj0TLmCUC4eLkUX9gPcvytvljJ42Cv/28XfeKCsmnNtCILwfAYMGEB2djZNmjRh1KhRjB07lmHDhhXZVpIktm7dipmZGW3atMHHxwdnZ2c2bdr0XBk8PT0JCAhg7ty5uLu78+OPPzJnzhy1Nvn5+YwaNQpXV1c6duxInTp1VBPhaWtrM2nSJOrXr0+bNm3Q0NAgODi4yGspFAqCg4M5ffo07u7ujB8/nm+//VatjaamJosXL2blypXY2dnRrVu3p2YfMmQIwcHBqt5ygN69ezNv3jymTp2Kl5cX4eHh7N69W20CvEGDBhW7rJ2enh6BgYG0atUKV1dXxo8fz1tvvcX27dvV2m3cuLHIiQlfNKlArC/0hIyMDExMTEhPT8fY2FjuOIIgyOTyzUz6BP5NSsZ9nK0M2Di0GTbG5Td5SkWTExVF3Ds9QEODWrt3of3IRDqCUG4KCuC7RnD7EnRdAI3elztRqTzIf8Abv77B7ZzbzGs7D19HX7kjCUKVk5OTQ1xcHE5OTuU66ZlQMRUUFNC0aVPGjx9Pnz59Snxc27Ztad++PdOnTy/ztSMjI3n99de5cOECJiYmRbYp7uezNHWo6JEXBEF4CmcrQzYNa46diS6Xb2bxv1V/cyOj6vTM67q5YdCqFeTnc/v77+WOI1QVkgQN/x3qeDKosLCvRLQ0tHjH5R0Afo75WeY0giAIVY8kSaxatYq8vLwSH5Oenk5sbCwTJkx4rmsnJSWxfv36pxbxL5Io5AVBEIrhaGnApg+bU91Uj7hbWbz3/XHSqtAEeJYfFg7lS9+8hbybN2VOI1QZXn1BUxdSIuDqKbnTlFrPOj1RSApOJJ/g8p3LcscRBEGocry8vOjfv3+J25uYmHD16lUMDQ2f67o+Pj74+r6ckViikBcEQXgGe3P9f4fV63AhJZOBa05wN6dq3Puq16gRet7eFOTmkrpundxxhKpC3xzqFfZqc6ryjQapZliNNjXaAPDzBdErLwiCILx4opAXBEEogZoW+mz4oCnmBtqcvZrOB2tPkZ1b/BIorwJJkrAYVjhhS9pPG8l/yiyxgvDCNf6g8L/nNsO9VHmzlMH/6v4PgK2XtnLvwT2Z0wiCIAivGlHIC4IglJCLjRHr32+CkY4mJ+JTGfbDKe7nvfrFvGG7dujUqYPy3j3SfvpJ7jhCVVG9Idh6QP59CK98P3fN7Zpjb2RP5oNMdsXtkjuOIAiC8IoRhbwgCEIpuFc3Ye37jdHT0uDQxVuM2fgPefnKZx9YiRX2yhfeK5+6/geU90TvovASSBI0+rdX/lQQKCvX/2cKSUGvOr0A+OXCLzKnEQRBEF41opAXBEEopYYO5gQOaIS2hoI/I1OY+OtZlMrKNbN2aRl39EXL3p78tDTu/Pqr3HGEqsKjJ2gbQWosxB+UO02pvVX7LTQVmkTejiQmNUbuOIIgCMIrRBTygiAIZdDKxZKl/RqgoZDY/M81pm47R0ElWyarNCRNTSw+KOwdvR20hoLcqjNzvyAjHUPw7F34/GTlm/TOXNec9vbtAdhyaYvMaQRBEIRXiSjkBUEQyugNNxsCenkiSbDh70S+3n3+lS7mTbq/jaaVFXnJyaT/sV3uOEJV8XB4/fkdkJEkb5YyeLv22wBsv7yd3HzxBZggCILwYohCXhAE4Tl086rOnO4eAKw8cJll+2NlTlR+FDo6mA8aBMDtwEAK8l/9if6ECsDGDWo2h4J8CFsvd5pSa2nXEmt9a9Lvp7Pvyj654wiCUMG1a9eOcePGFdvG0dGRhQsXvpQ8ldWUKVMY9u/8Pi/TihUr8PPzeynXEoW8IAjCc/pfk5pM7uIKwLd/xrA57KrMicqPae/eKExMyI2P527IXrnjCFVFo/cL/xu2DvLz5M1SShoKDbrV6gbAlotieL0gCM/v5MmTshSpcoqPj0eSJMLDw5/ZNjk5mUWLFvHFF1+obV+6dCmOjo7o6urStGlTTpw4UeY8t2/fpkaNGkiSxJ07d1Tb33//fcLCwjh06FCZz11SopAXBEF4AYa0dubDNs4ATPz1LIcu3pQ5UfnQMDTAvF8/4N9e+Vf4VgKhAnHrBvoWkHENLv4pd5pS6167OwDHrh8jKbPy3R4gCELFYmVlhb6+vtwxKqzVq1fTokULHBwcVNs2bdqEv78/06ZNIywsDE9PT3x9fblx40aZrvHBBx9Qv379J7Zra2vTt29fFi9eXOb8JSUKeUEQhBfk046v8ZanHXnKAkZsCCPqeobckcqFWf/3kHR0yImMJPv0abnjCFWBpg54v1f4vBJOemdvbE9j28YUUMDvsb/LHUcQhAouLy+P0aNHY2JigqWlJVOmTFH74vzxofWJiYl069YNQ0NDjI2N6dWrFykpKar906dPx8vLi6CgIGrWrImhoSEjR44kPz+fb775BltbW6ytrZk9e7ZajoCAADw8PDAwMMDe3p6RI0eSmZmp2p+QkICfnx9mZmYYGBhQr149du7cCUBaWhr9+vXDysoKPT09XFxcWLNmzVPf8+7du2nVqhWmpqZYWFjQtWtXYmP/u13RyckJAG9vbyRJol27dk89V3Bw8BPD2wMCAhg6dCiDBw/Gzc2NFStWoK+vT1BQ0FPP8zTLly/nzp07TJgwocj9fn5+bNu2jezs7FKfuzREIS8IgvCCKBQS3/asTzNnczLv5zF47Qmu3Snfv8TloGlmhkm3wqHCt9eulTeMUHU0HFz439hQSI2TN0sZPOyV33ppK8oCpcxpBKHqKSgo4MH9fFkepR29tm7dOjQ1NTlx4gSLFi0iICCA1atXF9lWqVTSrVs3UlNTOXDgACEhIVy+fJnevXurtYuNjWXXrl3s3r2bjRs38v3339OlSxeuXr3KgQMHmDt3LpMnT+b48eOqYxQKBYsXLyYyMpJ169axb98+Jk6cqNo/atQo7t+/z8GDB4mIiGDu3LkYGhoChfeoR0VFsWvXLqKjo1m+fDmWlpZPfc9ZWVn4+/tz6tQpQkNDUSgUdO/eHaWy8O/Lh8Pg9+7dS1JSEps3by7yPKmpqURFRdGoUSPVttzcXE6fPo2Pj4/ae/Px8eHYsWNPzVSUqKgoZs6cyfr161Eoii6lGzVqRF5entpnWR40y/XsgiAIVYyOpgYr+zei54qjXEjJZFDQCX4d3gITfS25o71Q5gMHcOfnn8kM3UduYiLaNWvKHUl41Zk7Qa0OhYX86TXwxky5E5XKGw5vMOf4HK5lXuN40nGa2zWXO5IgVCl5uUpWjT0gy7WHLWqLlo5Gidvb29uzYMECJEmibt26REREsGDBAoYOHfpE29DQUCIiIoiLi8Pe3h6A9evXU69ePU6ePEnjxo2BwoI/KCgIIyMj3NzcaN++PTExMezcuROFQkHdunWZO3cuf/31F02bNgVQm3TP0dGRWbNmMXz4cJYtWwYUjgTo0aMHHh6Fk/46Ozur2icmJuLt7a0qqB0dHYt9zz169FB7HRQUhJWVFVFRUbi7u2NlZQWAhYUFtra2Tz1PYmIiBQUF2NnZqbbdunWL/Px8bGxs1Nra2Nhw/vz5YnM96v79+/Tp04dvv/2WmjVrcvny5SLb6evrY2JiQkJCQonPXRaiR14QBOEFM9HTYu3gJtga63LxRiZDfzjF/bxXa4Z3nVq1MGjTGgoKSF3/g9xxhKqi8b9L0f2zAfLuy5ullHQ1dens3BkQa8oLglC8Zs2aIUmS6nXz5s25ePEi+UWsFhMdHY29vb2qiAdwc3PD1NSU6Oho1TZHR0eMjIxUr21sbHBzc1PrVbaxsVG7Z3zv3r106NCB6tWrY2RkRP/+/bl9+zb37t0DYMyYMcyaNYuWLVsybdo0zp49qzp2xIgRBAcH4+XlxcSJEzl69Gix7/nixYv06dMHZ2dnjI2NVYV/YmLisz4uNQ+Hs+vq6pbquJKYNGkSrq6uvPfee89sq6enp/qcyovokRcEQSgHdqZ6rH2/MT2XH+NEXCr+P59hyf+8USikZx9cSVgMGkTWwUPc2bwZqzEfoWFsLHck4VXn4gvG1QsnvYvaCvV7yZ2oVLq7dGdTzCZCE0JJv5+OiY6J3JEEocrQ1FYwbFFb2a4tNy0t9ZGBkiQVue3hUPb4+Hi6du3KiBEjmD17Nubm5hw+fJgPPviA3Nxc9PX1GTJkCL6+vuzYsYM9e/YwZ84c5s+fz0cffUSnTp1ISEhg586dhISE0KFDB0aNGsW8efOKzOfn54eDgwOBgYHY2dmhVCpxd3cnNze3VO/z4fD9tLQ0VS++paUlGhoaavMGAKSkpBTbu/+4ffv2ERERwa+//gqgumXC0tKSL774ghkzZqjapqamqq5fXuT/qRIEQXhFvWZrzMr+DdHSkNhxNok5u6KffVAlot+8OTp16lBw7x53fvlF7jhCVaChCQ0GFj4/VfoJiuTmZu5GHbM65Cpz2XF5h9xxBKFKkSQJLR0NWR6P9q6XxOP3Vv/999+4uLigofHk8HxXV1euXLnClStXVNuioqK4c+cObm5uZfuwgNOnT6NUKpk/fz7NmjWjTp06XL9+/Yl29vb2DB8+nM2bN/Pxxx8TGBio2mdlZcXAgQPZsGEDCxcuZNWqVUVe6/bt28TExDB58mQ6dOiAq6sraWlpam20tbUBihyV8KhatWphbGxMVFSU2rENGzYkNDRUtU2pVBIaGkrz5iW/zem3337jzJkzhIeHEx4erpq34NChQ4waNUrVLjY2lpycHLy9vUt87rIQhbwgCEI5alHbkm/f9QQg8FAcQYcr3yRdTyNJEuYDC4uq1B82UPDggcyJhCqhwQCQNCDxGKREyp2mVCRJ4h2XdwD4/dLv8oYRBKHCSkxMxN/fn5iYGDZu3MiSJUsYO3ZskW19fHzw8PCgX79+hIWFceLECQYMGEDbtm3VJnwrrdq1a/PgwQOWLFnC5cuX+eGHH1ixYoVam3HjxvHnn38SFxdHWFgYf/31F66urgBMnTqVrVu3cunSJSIjI9m+fbtq3+PMzMywsLBg1apVXLp0iX379uHv76/WxtraGj09PXbv3k1KSgrp6elFnuvhJHaHDx9W2+7v709gYCDr1q0jOjqaESNGkJWVxeDBFe+KtwAAdXZJREFUg0v8mdSqVQt3d3fV4+FM+q6urlhbW6vaHTp0CGdnZ2rVqlXic5eFKOQFQRDK2dve1fm042sAfLkjitDolGccUXkYd+2ChoUFecnJZOzZI3ccoSowrgavdSl8HrZe3ixl0MWpC1oKLaJTo4m+/WqN0hEE4cUYMGAA2dnZNGnShFGjRjF27FiGDRtWZFtJkti6dStmZma0adMGHx8fnJ2d2bRp03Nl8PT0JCAggLlz5+Lu7s6PP/7InDlz1Nrk5+czatQoXF1d6dixI3Xq1FFNhKetrc2kSZOoX78+bdq0QUNDg+Dg4CKvpVAoCA4O5vTp07i7uzN+/Hi+/fZbtTaamposXryYlStXYmdnR7d/V88pypAhQwgODlbdJgDQu3dv5s2bx9SpU/Hy8iI8PJzdu3erTYA3aNCgYpe1K6mNGzcWOTHhiyYVlHY9hCogIyMDExMT0tPTMRb3fAqC8AIUFBTw+ZZzbDyRiKGOJptHtqCOjdGzD6wEbi5dyq0l36Hr4YHjz5tKPYRQEErt4l74sQfomcHHMYXrzFcinxz4hN3xu/lf3f/xRbMv5I4jCK+cnJwc4uLicHJyKpdJz4SKraCggKZNmzJ+/Hj69OlT4uPatm1L+/btmT59epmvHRkZyeuvv86FCxcwMSl6HpTifj5LU4eKHnlBEISXQJIkZnarR1OnwjXmh6w7RVpW6SZwqajM/vc/JG1t/t/efcfZVdf5H3+dc3uf3mdSJ70nEEILKlKkGFBRREF03V2lI+6KK5Z1lUXXii6I7s+OIAihKJ0QWmgJIb1nMr2X29s55/fHuXOTSSbJDEnmzkw+z32c/Z577rn3fiaekHnf7/d8v/GNG4m9+26uyxEngykfAF8FxHpg+z9yXc2w9a8p//e9fyeejue4GiGEGF8UReG+++4jnU4P+TV9fX3s3r2b22677Zg+u6WlhT/84Q+HDfHHkwR5IYQYITaLyj2fWUx1gYv67ihf/vM6Upp+9BeOctbCQgIfvRSA7t/+LrfFiJODaoEFnzb314295Q9PqziNck85oWSIF+tfzHU5Qggx7ixYsIDPfvazQz4/EAjQ2NiI1+s9ps8999xzOf/884/pPYZKgrwQQoygAo+d31x9Ch67hTV7uvjOE2Nrsq7DKbj6agBCL7xA8oCZc4U4YRZeZba7X4S+xtzWMkyqorJi6goAHtn1SG6LEUIIMSZJkBdCiBE2vczHTz+1EEWBP71Rzx/f2Jfrko6Zo7YWz5lngq7T/cex10MqxqCCyTDxLMCA9X/JdTXDtmLqChQU3mx5k8bQ2PoiQgghRO5JkBdCiBz48KxSvnr+dAC+/fhmXt/dmeOKjl3B5z4HQN/Df0MLhXJbjDg5LPyM2a7/E+hj6zaVCm8FS8uXAvDY7sdyXI0Q45PM6S1Go+N1XUqQF0KIHPnS8imsWFCBpht8+c/r2NcVyXVJx8Rzxuk4aqeiR6P0PvRwrssRJ4OZl4LDDz11sO/Vo54+2hy4prymazmuRojxw2azARCNRnNciRCH6r8u+6/T98t6PIoRQggxfIqi8N8fm8fezgjvNfbxT79/h0e+fDo+57H9hz1XFEWh4JpraPnGHXT/6Y8UXP1ZFKv8MyNOILsb5nwM1v4W3v0TTDo71xUNywdrPojf7qc10spbrW+xrGJZrksSYlywWCzk5eXR3t4OgNvtlqVRRc4ZhkE0GqW9vZ28vDwsFssxvd+oXkf+29/+Nt/5zncGHJs+fTrbtm077Gseeugh7rjjDurq6qitreWuu+7iIx/5yLA+V9aRF0KMpLZgnEt/8SptwQQfnFHCr69egkUdm79w6IkEuz7wQbTubip/8mP8F16Y65LEeNe4Fn7zQbA64bYd4DzxS/4cT99d813+uuOvXDrlUr535vdyXY4Q44ZhGLS2ttLb25vrUoQYIC8vj7KyskG/XBpODh31XSWzZ8/m+eefzz62HqF35/XXX+fKK6/kzjvv5OKLL+b+++9nxYoVrFu3jjlz5oxEuUIIMWylfif3fXYJV/xqDS9ua+fHz23nq+fPyHVZ74vqcJB/5ZV0/vKXdP3udxLkxYlXuQiKZ0LHVtj0N1jy+VxXNCyXTLmEv+74K8/te47/WPofuG3uXJckxLigKArl5eWUlJSQSqVyXY4QgDmc/lh74vuN+h75lStXsn79+iGd/8lPfpJIJMKTTz6ZPXbaaaexYMEC7r333iF/rvTICyFy4bH1Tdz0wHoA/u+aJXxoZmluC3qf0p2d7PrABzFSKSb85X7cCxfmuiQx3q35JTzzdahYBP+8KtfVDIthGFz06EU0hBq486w7uXjyxbkuSQghRI4MJ4eO+snudu7cSUVFBZMnT+aqq66ivr7+sOeuWbOGc889d8Cx888/nzVr1hzxMxKJBMFgcMAmhBAj7aMLKrlm2QQAbnlwPfVdY3OSHmtREf6LLgKg5y9jb1kwMQbN+ySoVmheB22bc13NsCiKkg3vT+5+8ihnCyGEEKZRHeSXLl3K7373O55++mnuuece9u7dy1lnnUXoMMsatba2Ulo6sAertLSU1tbWI37OnXfeSSAQyG7V1dXH7WcQQojh+I+LZrGgOo9gPM2X/ryWeGpszmSdf9WnAQg99TTprq4cVyPGPU8RTM/cxvHun3Nby/vQH+TXtKyhI9qR42qEEEKMBaM6yF944YV84hOfYN68eZx//vn84x//oLe3l7/+9a/H9XNuv/12+vr6sltDQ8NxfX8hhBgqu1Xlf69aRIHHzubmIN9+fGz1LvZzzZ2Lc+5cjFSK3of/lutyxMlg4WfNdsMDkE7mtpZhqvHXML94Prqh89Tep3JdjhBCiDFgVAf5g+Xl5TFt2jR27do16PNlZWW0tbUNONbW1kZZWdkR39fhcOD3+wdsQgiRKxV5Ln72qQUoCjzwdgMPvTM2v1zM/7TZK9/z4AMY2tgcWSDGkCkfAl85RLtgx9gLw9nh9XtkeL0QQoijG1NBPhwOs3v3bsrLywd9ftmyZbzwwgsDjj333HMsWybrsgohxpazaou55dxpAHxj5Sa2NI+9uTv8H7kQSyBAurmF8OrVuS5HjHcWK8y/0tx/90+5reV9uGDiBVhVK1u7t7KrZ/AOCyGEEKLfqA7yt912G6tXr6auro7XX3+dyy67DIvFwpVXmv9QX3311dx+++3Z82+66SaefvppfvSjH7Ft2za+/e1v884773D99dfn6kcQQoj37foPTOWc6cUk0jpf+vNa+mJja/kc1eEg8PGPAdDz5/tzXI04KSz8jNnueh6CzbmtZZjynHmcVXkWAE/seSLH1QghhBjtRnWQb2xs5Morr2T69OlcccUVFBYW8sYbb1BcXAxAfX09LS0t2fNPP/107r//fu677z7mz5/Pww8/zMqVK2UNeSHEmKSqCj+5YgGVeS72dUW57aH3GMUrhg4q/8orQVGIvPYaib17c12OGO8Kp8CEM8DQ4b2xt2LCJVMuAeDve/6Obug5rkYIIcRoNqrXkc8VWUdeCDGabGjs5eP3rCGp6Xztwhn86/IpuS5pWBr+5V8Jr15NwTXXUHr713Jdjhjv1t8PK78EBZPhhnWgKLmuaMiSWpJz/noOoWSI35z3G5aWL811SUIIIUbQuFpHXgghTnbzqvL45iWzAPjB09t4Y8/YWs4t/9Pm7VC9jz6KHovluBox7s36KNi90L0H9r2e62qGxW6xc/7E8wF4YrcMrxdCCHF4EuSFEGIMuGppDZcvrEQ34Ia/vEtnOJHrkobMc9ZZ2Kqr0YNB+p6UGbnFCWb3wJzLzf0xOOndJZPN4fXP7XuOWFq++BJCCDE4CfJCCDEGKIrC9y6bS22Jl45Qgq+OofvlFVUl/1OfAqDn/r+MmbrFGLbwarPdshIS4ZyWMlwLShZQ6a0kmo6yqn5VrssRQggxSkmQF0KIMcJlt3D3pxdit6qs2t7Bb1+ry3VJQxa4/DIUh4PE1q3E1q/PdTlivKtaAgVTIBWFbWNrFIiqqFw0+SJA1pQXQghxeBLkhRBiDJlR5uc/PjITgP9+ahubm/tyXNHQWPPz8X/kI4DZKy/ECaUoMO+T5v57D+S2lvehf3j9682v0xnrzHE1QgghRiMJ8kIIMcZcvWwC584sIanp3PiXd4km07kuaUjyP/1pAEJPP026a2xN2CfGoHlXmO3e1RBsOfK5o8zEwETmFs1FMzSe3vt0rssRQggxCkmQF0KIMUZRFH7w8fmU+Bzs7ojw3Se35LqkIXHNnYNz3jyMVIreh/+W63LEeFcwCapPM9eU3/RwrqsZtosnXwzAE3tk9nohhBCHkiAvhBBjUIHHzk8+uQBFgb+81cA/No6NHsf+peh6HnwAQ9NyXI0Y9+b3D69/MLd1vA8XTLoAq2JlS9cW9vTuyXU5QgghRhkJ8kIIMUadMbWIfzl7CgBf+9sGmnpH/1JV/gsvxJKXR7q5hfBLL+W6HDHezVoBFju0bYS2zbmuZlgKnAWcUXkGIL3yQgghDiVBXgghxrCvnDeN+VUBgvE0tzywHk0f3Uu7qQ4HeR//GAA9f74/x9WIcc9dALXnmftjcNK7i6eYw+v/vufv6Iae42qEEEKMJhLkhRBiDLNZVH5+5UI8dgtv1XXzixd35bqko8r71KdAUYi8/jqJvXtzXY4Y7+Z/ymw3Pgz62Lqd45yqc/DavLREWljbtjbX5QghhBhFJMgLIcQYN6HQw3dXzAHgZy/s4J267hxXdGT2qiq8Z58NQO8DY6+XVIwxteeBMw9CzVD3Sq6rGRan1cl5E80RBbKmvBBCiANJkBdCiHHg8kVVrFhQgW7ATQ+spy+WynVJR9Q/6V3fysfQE4kcVyPGNasD5lxu7o/BSe8umnQRAM/ve56UNrr/XgshhBg5EuSFEGKc+O6KOdQUuGnqjfGdx0f3xF6eM8/EWlGO1tdH6Nnncl2OGO/mZWav3/o4JKO5rWWYFpcupshVRDAZ5PXm13NdjhBCiFFCgrwQQowTPqeNn3xyAaoCj7zbxDObW3Nd0mEpFgt5l5uT3vU+9FCOqxHjXvVSyJ8IyTBs+3uuqxkWi2rh/InnA/BU3VM5rkYIIcRoIUFeCCHGkcUT8vnnzJJ0//HoRrrCo3fYet7HLgdVJfrWWyTr6nJdjhjPFGV/r/yGsTe8/oKJFwCwqn4VsfToX2ZSCCHEiSdBXgghxplbPlzLtFIvneEk31i5CcMYnUvS2crL8Zx1JgC9Dz+c42rEuNcf5He/COH23NYyTPOL51PhqSCajvJK49iasE8IIcSJIUFeCCHGGYfVwo+vWIBVVXhqUyuPv9ec65IOK/8TnwCg99GVGMlkjqsR41rhFKhcAoZmLkU3hiiKwgWTzF75p+ueznE1QgghRgMJ8kIIMQ7NqQxwwwdrAfjmY5tpC8ZzXNHgvMuXYykuQuvqIrTqpVyXI8a7/jXlN4y9ZQ8vnHQhAC83vkw4Gc5xNUIIIXJNgrwQQoxTX/7AFOZWBuiLpfja3zaMyiH2is1G3mXm0mAy6Z044WZfDqoVWt6D9m25rmZYpudPZ6J/IgktwaqGVbkuRwghRI5JkBdCiHHKZlH50RXzsVtVVm3v4KF3GnNd0qDyPm7OXh957TWSjU05rkaMa55CqD3P3B9jk94pipLtlZfh9UIIISTICyHEODat1MdXPjwNgP98cguNPaNvDW17TQ3uZaeBYdD3yN9yXY4Y7/onvdv4EOh6bmsZpv7Z619vep2+RF+OqxFCCJFLEuSFEGKc+6ezJrN4Qj7hRJp/e3gDuj76hthnJ7372yMY6XSOqxHj2rQLwBGAvgbY91quqxmWyXmTmZ4/nbSR5vl9z+e6HCGEEDkkQV4IIcY5i6rwo0/Mx2Wz8PruLv74xr5cl3QI77nnYsnLI93WRvgVWV5LnEA2J8z+qLk/Bie965+9/qm9T+W4EiGEELkkQV4IIU4CE4s83P6RGQDc+dRW9nZGclzRQKrdTmDFCgB6HxpbS4OJMWheZvb6LY9DKpbbWoapf3j9W61v0RHtyHE1QgghckWCvBBCnCQ+s3QCZ0wtJJ7Sue2h90bdEPu8T3wcgPDq1aTa2nNcjRjXapZBoAYSQdg+tnq2q3xVzCuah4HBs/uezXU5QgghckSC/BiWTmkYo+wXcSHE6KWqCj/4+Hw8dgtr9/XwpzdH1xB7x5QpuBYvBk2j79FHcl2OGM9UFeaaXxyxaexNsNg/vP7pvTJ7vRBCnKwUYzQuLJxjwWCQQCBAX18ffr8/1+Uc1hN3v0fDli7sLisOtxWH25ZpM/suKw6Pue/02HB4rDjd+1ub04KiKLn+MYQQI+wPa+r45mOb8dgtPHfrciryXLkuKat35UpavnY7tqoqpjz7DIoq3zeLE6R1E9x7Blgc8NWd4AzkuqIha4u08eGHP4yBwTMfe4YKb0WuSxJCCHEcDCeHWkeoJnECJGMpDAMS0TSJaBqID+v1qqoMCPpOb2bz2HB6rNljLq8Np8eefV5VJfwLMZZ9ZukEHlvfzNp9PXxj5Sb+75olo+ZLPf/559P2ve+TamwksmYN3jPOyHVJYrwqnQ3FM6BjG2x9EhZeleuKhqzUU8ri0sW80/YOz9Q9w7Vzrs11SUIIIUaYBPkx7KO3LMyGeHNLZfeTsRTx/uMR83g8kiIRSRGPpNHSOrpuEAuliIVSQ/9QBRxuKy6vHZfPDP0unx2XN9P6bbh9dnPfZ5fgL8QopKoK/335XC76+au8uK2dJza0cOn80dGjp7pcBC65hJ7776f3oYclyIsTR1Fgzsdh1X+Zw+vHUJAHuHDShbzT9g5P7X1KgrwQQpyEZGj9IMbK0PpjkU5qxCNm+I+HU8Qj+7dYOEUiPPBxPJzK9PoPk0I25Lv95ubKtJ7svgO3X0K/ECPtZ8/v5CfP76DQY+f5W5eT77HnuiQA4tu2sXfFZWCzUbv6JawFBbkuSYxXXbvh7kWgWOC2HeApynVFQ9YT7+EDf/0AmqHxxIonmBiYmOuShBBCHCMZWi+Oymq34LVb8OY7hvwaXdOJR9LEQkni4f6AnyQWThELZtpQkmjIfByPpMAg2+vf3Xzk5a4UVcHts+HJc+AOOPAE7NnWE3DgyTM3l9eGIoFfiGP2pXOm8PeNzexoC/Pdv2/hx1csyHVJADhnzMA5dy7xjRvpe3QlhV/4fK5LEuNV4RSoWAjN78LmR+HUL+a6oiHLd+ZzWsVpvNb0Gk/XPc2/zv/XXJckhBBiBEmQF0OmWtRsr/pQ6JqeCfdmsI+GkkSD/VvCPJbZYuEUhm4Q6UsS6UsCoSPUoQwI9t78TJvnwJPvwFfgxBOwo1pkkiwhjsRuVfnvj83jY/e8ziPrmlixoJKzpxXnuizAXIqudeNGeh9+mILPXztq7uEX49Ccj5tBftMjYyrIA1w48UJea3qNp/Y+xb/M+xf5eyKEECcRGVo/iJNhaP1oo2s6sVCKSF/CDPO9CaKZ/eiBx0JJGMIVqyjgDjjwFTjw5jvx5putr8CJr9DcHG6r/NIjBPDtxzfzu9frqMp38ewtZ+O25/47Xi0cYdfZZ6NHo0z40x9xL1mS65LEeNXXBD+ZDRhwy2YIVOW6oiELJUMsf3A5KT3F3y79G9Pyp+W6JCGEEMdAhtaLMUe1qNke9iPRNJ1oJtSHexJEes0t3N/2xAn3JNA1I/scBAd9L5vDkg312YBf4MRf5CJQ5MLhkaAvTg5fPX86z21po7Enxo+f3cE3Lp6V65KweD34LryAvr89Qu+jj0qQFydOoBImnA77XjN75c+4MdcVDZnP7uOsyrN4seFFntr7lAR5IYQ4iUiP/CCkR35sM3SDaChJuNsM9qFuM9yb+wlC3XFiweRR38fmtGRDva/ISaDIhb/Ihb/Iib/QhcUmQ/fF+LFqezvX/vZtVAUe/fIZzK/Oy3VJRNeuZd9Vn0F1u6l99RVUtzvXJYnx6u3fwN+/AuUL4F9W57qaYXl679N89eWvUumt5KnLn5IvoIUQYgyTHnlxUlPUzD30AQelkwb/C5BOaoS644S64tk22JV53BUj0pckFdfoagzT1Rg+9DMU8BY4CRS7CJS4zbbYRaDEDP5Wu+VE/5hCHFcfmF7CigUVrFzfzL//bQNP3HAmthzPM+FatAjbhBpS++oJPvMseZetyGk9YhybtQL+8W/Qst6cyb5wSq4rGrKzq87GZXXRFG5iS9cWZhfNznVJQgghRoAEeXFSstot5Jd5yC/zDPp8f9Dv64gR7IwT7IoR7IgR7IoT7IiRSmiZ0B+ncVvPIa/35jvIK3Vnt/xM6y1wyhJ7YtS64+JZrN7RwbbWEPe9vIfrPjA1p/UoikLeZZfR8dOf0ffIIxLkxYnjKYIpH4Bdz8PGh+Gcf891RUPmtrk5q/Isnt33LM/se0aCvBBCnCRkaP0gZGi9OBLDMIiFUvS1R+nriNHXEaO3PUpfe4y+9ijJuHbY11qsKoESF/mlbvLLPeSXuckv85BX5sYmvfhiFFj5bhM3P7geu1XlqZvOYkqxN6f1pFpa2PXBD4FhMOW5Z7FXV+e0HjGOrb8fVn4JiqbBdW+ZQ6/GiGfqnuG21bfJ8HohhBjjZGi9ECeQoijZZfjKp+YNeM4wDOLhFL3tMXrbIvS2Relti9HTFqWvI4qW1ulujtDdHIF3Ow54U/AXOjOjBMyQX1DhoaDcg90pf03FyPnoggoefbeJ1Ts6+OZjm/jTF5bmNBTYysvxnH46kddeo+/RRym+cexMRCbGmBkXg+Vm6NwBbZugbG6uKxqysyrPwmlxmsPru7cwu1B65YUQYryThCDEcaQoCi6fHZfPTvmUwIDndN0g1BWnty1KT2uEnlaz7W6JkIikzSH8nXH2beoa8DpfoZPCSi8FFR4KKzwUVnrJK3Vjscpke+L4UxSF7350Dh/+yWpe29XF4+8189EFlTmtKXD5ZURee43elSspuv56FFWufXECOP0w7TzY+oQ5vH4MBXm3zc1ZVWfx3L7neLbuWQnyQghxEpCh9YOQofViJPUP1e8P990tEXpazF776GFm11dVhbwyN4WVXoqqMlu1D7ffPsLVi/Hq7hd28qPndlDsc/DCV5bjd9pyVoueSLDzzLPQQyFqfvv/8CxblrNaxDi3+VF46HMQqIGbN4yp4fVP1z3NV1d/lSpvFf+4/B8yvF4IIcYgGVovxBhy4FD9ymn5A56LhZN0N0Xoao7Q3Rymu9ncT8bS2SH6O99uy57v8tvNUF/ppajaDPd5pW6ZYE8M2z8vn8yj7zaxpzPCj5/dwbcvzV0Pn+pw4L/oI/Q+8CC9jzwqQV6cONMuALsX+uqh8W2oPjXXFQ3Z2ZVn47Q4aQw3srV7K7MKZ+W6JCGEECeQ9MgPQnrkxWhmGAbhngRdTWE6M8vjdTaG6W2PwiB/m60OC8VVXoprfNktv8yNmuOlxcTo9+rOTj7zf2+iKvD49WcypzJw9BedILENG6i74pMoDge1r76CxefLWS1inHvkn2HDg3Dqv8BHfpDraobl1pdu5bl9z/GFOV/g5sU357ocIYQQwzScHCpBfhAS5MVYlEpodDXvD/adDSE6G8Okk/oh51ptKoVVXkom+Cmd6KNkop+8EjeK9NyLg9zwl3d54r1m5lfn8ciXTseSo2vEMAz2XHIJyV27KfvOd8j/5BU5qUOcBHY8A/dfAZ4SuHUrWMbO4MWn9z7NV1/+KtW+av5+2d9leL0QQowx42Zo/Z133skjjzzCtm3bcLlcnH766dx1111Mnz79sK/53e9+x7XXXjvgmMPhIB6Pn+hyhcgpm8NC2aQAZZP295rqukFva5SOhhAd+0Jm2xAiFddo2xukbW+QjZlz7U4LJRP9lEz0U5rZPHmO3PwwYtS446KZrNrWznsNvfzlrXo+c9qEnNRhril/Oe0//CF9jzwiQV6cOJM/AK58iLTDvldh8jm5rmjIzq46G4fFQUOogW3d25hZODPXJQkhhDhBRnWQX716Nddddx2nnHIK6XSar3/965x33nls2bIFj8dz2Nf5/X62b9+efSzfSIuTlaoq5jJ2FR6mLy0DwNAN+jpitNcHad8Xor0uSMe+EMm4RuO2Hhq39WRf78lzUDbJT9mUAGWTAxRX+7DYZEj+yaTE7+Qr503jO09s4QdPb+OCOWUUeXPzBU/g0kto//GPib33Hondu3FMmZKTOsQ4Z7XDzEth3e/N2evHUJB329ycVXkWz9c/z7P7npUgL4QQ49iYGlrf0dFBSUkJq1ev5uyzzx70nN/97nfcfPPN9Pb2vu/PkaH14mSjazrdLRHa9gZprwvSVheiuznMwf91UK0KJTU+yiabwb5sSgBPQHrtx7u0pvPRX77G5uYgly+q5MdXLMhZLQ1f+jLhVaso/OI/UfKVr+SsDjHO7X0Zfn8JOANw206wjp3/zj219yn+7eV/o8ZXw5OXPSmdGUIIMYaMm6H1B+vr6wOgoKDgiOeFw2EmTJiArussWrSI73//+8yeffgZlxOJBIlEIvs4GAwen4KFGCNUi0pRlY+iKh+zzzLXDE8lNDrqg7TuCdKyu4+2vX3EQila95jHoAEw17mvqM2jYmoeFbV5BEpc8ovjOGO1qPzXijlcfs/rPLKuiSuWVHPa5MKc1BK4bAXhVavoW/kYxTfdhGIdU/+MibFiwhngLYNwK+x+EaZfmOuKhmx51XIcFgf1oXq292xnRsGMXJckhBDiBBgzPfK6rnPppZfS29vLq6++etjz1qxZw86dO5k3bx59fX38z//8Dy+//DKbN2+mqqpq0Nd8+9vf5jvf+c4hx6VHXoj9DMMckt+2p4+WPUFad/cN2mvv8tupmBqgfKoZ7gurvLL83Tjx9Uc3cv+b9dSWePn7jWdht478bRZGMsnO5eeg9fRQ/at78S5fPuI1iJPEU1+DN++BOR+Hj/9frqsZlptX3cwL9S/wxblf5MZFN+a6HCGEEEM0Lmet/9KXvsRTTz3Fq6++ethAPphUKsXMmTO58sor+e53vzvoOYP1yFdXV0uQF+IokrE0rXv6aN7VS8uuPtr2BtHSA2fJtzstVNTmUTk9n8pp+RRVeWV2/DGqL5rigz96ia5Ikq9dOIN/XZ6be9Rbv/99ev7wR3znn0/Vz36akxrESaDxHfjNh8Dmga/uArs71xUN2T/2/IN/f+XfmeCfwBMrnpBRUkIIMUaMu6H1119/PU8++SQvv/zysEI8gM1mY+HChezateuw5zgcDhyOsXP/mxCjhd1lpWZ2ITWzzWHWWkqnbV+Qll29NO/so3V3L8m4Rt3GLuo2dgHg8FiprM2ncnoeldPyKajwyC+ZY0TAbeP2j8zktofe42fP7+TieeVU5Y98uMm7/HJ6/vBHwi++SLqnB2t+/ojXIE4ClYshrwZ662HnszB7Ra4rGrLl1cuxq3b2Bfexo2cH0wsOv9qPEEKIsWlUTz9tGAbXX389jz76KC+++CKTJk0a9ntomsbGjRspLy8/ARUKIQ5ksalUTM1j8QUTueSG+Xzhx2fziduXcPrlU5kwpxCbw0IikmbP+g5eeXAnD3z3LX77b6/yzG82seW1ZkLdskzkaPexRZWcOrGAWErjP5/YkpManDNm4Jg1EyOVIvjk33NSgzgJKArMvszc3/xIbmsZJo/Nw5mVZwLwTN0zOa5GCCHEiTCqh9Z/+ctf5v777+exxx4bsHZ8IBDA5XIBcPXVV1NZWcmdd94JwH/+539y2mmnMXXqVHp7e/nhD3/IypUrWbt2LbNmzRrS58qs9UKcGLqm014foml7D03be2jZ1Uc6NXAofn6Zm6qZBdTMLKBiWh5255gYOHRS2dEW4sKfvYKmG/z+86eyfFrxiNfQ/cc/0fa97+GYNZPJj4ytkCXGkOb1cN9ysLrM4fUOb64rGrIn9zzJ7a/czkT/RB5f8biMfBJCiDFg3Aytv+eeewA455xzBhz/7W9/y+c+9zkA6uvrUdX9Awt6enr44he/SGtrK/n5+SxevJjXX399yCFeCHHiqBaVskkByiYFWHzBRHMofl0fDVt7aNjaTXtdkJ7WKD2tUTauakRVFUon+6mZVUDN7EKKq31yf/0oMK3Ux+dOn8j/vbqX7zy+madvPnvEJ77zX3wRbT/4AYktW4lv24ZzhszMLU6A8vlQMBm698COp2Hux3Nd0ZCdU3UOdtVOXbBOhtcLIcQ4NKp75HNFeuSFyI14JEXTjh4atnTTsLWbYOfAofYuv50JswqomVNI9cwCnB5bjioVwXiKD/7PajrDCW6/cAb/koOJ7xpvvInQs89ScM3VlN5++4h/vjhJvPBdeOV/YPpFcOX9ua5mWG548QZeaniJf573z9yw8IZclyOEEOIoxuWs9SNJgrwQo0NfR5SGrT3Ub+6icVsPqYSWfU5RFcom+6mZXciEOYXmbPgydHREPfROA199eAMeu4UXbzuHUr9zRD8/9NJLNP7rl7AUFFC7+iUUm3yxI06Ats1wz+lgsZvD652BXFc0ZE/sfoKvv/p1GV4vhBBjxLgZWi+EOLkFit0Eit3MObsSLa3TsquXfZu62Le5m56WCC27+mjZ1cebj+3Bk+dg0rwiJs4vompaPhbbqJ7Lc1z42KIq7n+rnnfre7nzH1v56acWjujne888E0tREVpnJ+FXXsH3wQ+O6OeLk0TJLCiaDp3bYds/YMGVua5oyM6pPgebaqMuWMeu3l3U5tfmuiQhhBDHifymK4QYEyxWlaoZBZzx8Vo+/a2lfPa/lrH8ymlMnFeE1a4S6U2w6eUmnrz7Pf7vtld4+lcb2fZGC7FwMtelj1uqqvCfl85BUWDl+mberuse0c9XrFYCl14KQN+jj47oZ4uTiKLAnMvN/TE2e73P7uOMijMAeHbfszmuRgghxPEkQ+sHIUPrhRhb0imNxm091G3opG5DJ5G+/eFdUaBsSoBJ84qZvLCYQLErh5WOT7c/soG/vNXArHI/T9xwJpYRnJAwvmMHey/9KFit1L68GmtBwYh9tjiJdOyAX54CqhVu2wnusXOd9Q+vnxyYzGMrHst1OUIIIY5gODlUeuSFEGOe1WZh4twizrlqBtfceQafuH0JSz4ykcIqL4YBLbv6eP2RXfzpjjU8+L23ePvve+lqDiPfYx4ft503Hb/TypaWIPe/VT+in+2cNg3n7NmQTsua8uLEKZ4GpXNAT8O2J3NdzbD0D6/f07eHXT27cl2OEEKI40TukRdCjCuKqlAywU/JBD9LL51MsCtG3YYu9r7XQdOOXjobwnQ2hHnrib3klbqZvLCYKQuLKa7xyURQ71Oh18Ft50/nm49t5n+e2c5Fc8sp8NhH7PMDl11GfPNm+laupODqz47Y54qTzOzLoG0TbHoEFl19wj/OMAyi0Si9vb0Eg0F0Xc9++ThY6/F4qKysxOUaOOrIZ/dxesXprG5czTP7nmFq/tQTXrsQQogTT4bWD0KG1gsxPsXCSeo2dLL73Q4atnajp/f/589X4GTyomJqF5dSMlFC/XClNZ2L736Vba0hPr20hu9fNnfkPrunh51nL4dUikmPrcQ5XdbLFidA9x74+UJQLHDbDvAUHfNbptNp2tvb6erqore3l76+Pnp7e7P7qVRq2O9ZXFxMTU0N1dXVVFdXU1BQwJN7npTh9UIIMQbI8nPHSIK8EONfMpamblMne97tYN+mLtJJPfucv8jJ1MUlTF1cSlG1LGs3VG/u6eKT972BosAT15/JnMqRW6ar8YYbCT33HAWf+xylX/v3EftcMboZhoGR0jHiGno8jZEwWz2exohrGJqBxWfD4ndgCdhRvXaUI83x8Kvl0LIeLvoxnPKFYdUSiURobW2lra2N1tZWWltb6ezsRNf1I77O6/USCASwWs1BlP3/PTq47e3tpbv70Akn3W435VXlPN71OLs9u/nrZX+VXnkhhBilJMgfIwnyQpxcUkmN+s1d7FrbTt2GzgGhPlDionZJKVMXl1BY6c1hlWPDTQ+8y2Prm1lUk8fD/3o66ghNfBd6cRWNX/4ylqIiale9KGvKn0T0pEa6K066M0a6K2a2nTHSXXH0SAr0Yfyao4LFa0cNOLD47VgDDhxT8nDOLDAD/qs/hee/BRPPgs8d/l75eDxOU1MTjY2NNDY20traSigUGvRcp9NJSUkJeXl52S0QCJCXl4ff78c2jGs5HA7T0NCQ3Zqbm9E0Lft8xBqh8vRKbv7gzUN+TyGEECNHgvwxkiAvxMkrldCo29jJrrXt7NvUhZbaH+rzyz1MO7WUaaeU4i+S2e8H09oX54M/eoloUuNHn5jPxxZXjcjnGqkUO8/5AFpXF1X3/C++D3xgRD5XjBxD00m1x0g1hUk1h0m1Rkh3xtCCQ1hiUgHFYUV1WlCdVpRMi6qghZLofQm0UBIO8xuRpdCJ9/QKPFOSqPcuMN/wK9vAV4au63R0dGRDe2NjIx0dHYO+T0FBAaWlpZSVlWU3v99/wkb9pNNpWlpaaGho4KXXXyIZTmJgcN6Hz2PZsmWoqsx5LIQQo4kE+WMkQV4IAZCMp6nb0MnOd9qp39I14J768qkBpp1axtRFJTi90vt7oHte2s1dT2+jyOtg1W3L8TlH5s+n7c7/pvv3v8d33nlU/fxnI/KZ4sTQkxqplogZ2JsjJJvDpNoikB78VxbFZcVa5MJW6MRa5DK3Qheq347qtKDYLUcNy4ZuoIeTaH1JtGACLZgk3REj8m47Rixtfo7Tgt3+Mn2JJ2iadhn7tBIaGxtJJg/9MiEvL4/q6moqKyupqKigtLQUh8Nx7H8471NnsJPbf3U7lZFKAGpra1mxYgUej+d9v2dKS9Ed76Y73k1PvIfuRDcpbfj39QshxEjIc+TxgZrR/UW/BPljJEFeCHGwRDTF7nc72PFWG007erI9d6pFoWZ2IdNOLWXSvCKsdktuCx0FEmmNC3/6Cns6I3zxrEn8x0WzRuRz49u3s/ejK8BmM9eUz88fkc8Vx8bQDdKdMZL1IZINQZL1ITO0D3LruOK0YCv3Yq/wYKvwYi02A7vFc+K+LAr1BNn18ib2btpFS6KTTiWEoQz81clms1FZWUlVVVV283qPcitOKgYtG6BpLYSaT1j9B3p+3wsEu4KUxksBsNvsTJs+jcARftdJGzqbU728lexkc7KXLj1Bt56gR08QMtIjUrcQQhwPsxzFPPipF3NdxhFJkD9GEuSFEEcS7kmw8+02drzdSmdDOHvc5rQwdVEJM5aVUT4176SeJG/V9nau/e3bWFWFp28+m6klIzO/wJ7LLyexZSuld3yDgquuGpHPFMOjRVIkG0Ik64Nm2xDCiGuHnKf6bNgrvNgym73Cg6XAecL/XvX29rJv377s1tXVdcg5bsNBmZ5HmZ5HVVkltdecis3vPPyb6hp07oSmd8zg3rQW2jab69KPMjqw027jDaeTt1xO1jodRI4wBN9iGORpOvm6RoGm45BfK4UQo1SNPY+vfeGdXJdxRBLkj5EEeSHEUHU1h9nxVhs732oj1B3PHvcXOZl+WjkzTis7ae+n/6ffv83zW9s5q7aIP3z+1BH5YqP7D3+g7ft34pwzh0kPP3TCP08cWba3fV+QxL4gyX1B0h2xQ85TbCq2Si/2Gj/2ah/2Gh/WwIkfhm4YBp2dndTX12eDe19f3yHn9S/pNmHCBGpqanDffwPhpglEjQ+DruKYHKDoC3NRLJlrXEtD8zrYsxrqXoamdyE5yGR3nhKoWgIFk0E58ferJ/UUf9n2FzRD49JJl9Ld1E1HRzsdVo29BRb25VtYm+qm1xh4q4BfsXGqo4hF9kLKVBcFFgf5qoMC1YFfsaGexF9aCiHGEH8lnPavua7iiCTIHyMJ8kKI4TJ0g5bdfWx7o4Vda9tJHdDDWFGbx4xlZUxZVILdac1hlSOrrjPCeT95maSm86vPLub82WUn/DPT3d3mmvLpNJOfeBxHbe2w38PQNJJ1dcS3biO+dQuJbdvRDjPjuDiIbmAkNfSkhpHQMJIaxiCzxitWFcVuQXVk7l+3qTASYdAwSKVSJJJJkokEiWQCXTtoDL9iDjm3Oxw47HbsDjuqetAtM+F26K3HsAVIpcrAMLB4DCz2OMRDZmjXDxploKhg94DdbbY2D1jt5geOoH3BOoKJIAFHAItqJZQIktIH3teuKioemwev3YvX5sVpdY54nUIIcbw5Jk2i4q7/znUZRyRB/hhJkBdCHItUQmPP+g62rWmhcfv+++mtdpUpi0qYdUb5STP0/ofPbOOXq3YzscDCyi/NQNG7SSQ6SCY7SCa7MAwzQAz8h2j/I4vFjddTi8dTi8tVg6IcfQ6ChuuvJ/z8CxR84fOUfvWrRzzXSCaJb9tGfMtW4tu2Et+6lcT2HRjx+BFfJ4QQQoixxTl7NpP+9nCuyzgiCfLHSIK8EOJ4CXXH2f5mK9vWtNDXvn9IcaDExczTy5mxrBzPCAwhPtHS6QixWB3RaB3RWB3R6F7i8SbiiXZ6Q604rccejFXVgcc9FY+3Fo9nGl7PNLze6TidFQPOC73wAo3XXY+luIjaVatQrPtHQeiRCLH33iP6zlqi77xDbMOGQUO74nLhnD4dx8wZOGfMxFpcLB2SBqR7E6Tbo6Tbo6Tao+jhQ2coV9xWbMVurCVubCUurPlOsIzMH14ymaSzs4uOjnba2zvo7upC0wf2uNtsNoqLiykuLqakpJiCgkIsliEMa4+HoH0ztG6Etk0Qaj3oBCspJqBOW4Jl8gLInzgiw+UHYxgGzZFmtndvZ3v3dnb27iKpJQ4575SyU1hStoSpeVPZuXUnGzZspLCwgPPPO1+udyHEuGPx+XAvWZLrMo5IgvwxkiAvhDjeDMOgdU+Qra83s/OddtIJc9itoipMmFPIrDMrmDC7AHUogSJHDMMgkWglEtlBOLKDaGQP0dg+otG9JJPtR319UrPhcZXidhZjdxRjtxehKvb9JwwYoWDup5LdRCI7iUR3oeuHBhGAvLylTJl8K3l55j/ORjLJzuXnoPX0UPGj/0F1Os3gvnYt8c2bQRs45NmSn49z9mycM2fgnDkTx4yZ2CfUoFhO7hUI9HjanIyu//72+hBG4uDh4mAr82Cf4Mcx0Y+9xo8l3zFio036+vqor6/Pbm1tbYec4/F4sve2T5gwgdLS0qGtn54IQf0bsOcl2LvaDPAHUlQwdPCWYFz2Gzpf8pLYGcVS4KT0+gWo7pFdlrIp3MQbzW/wZsubvNn6Jt3x7gHP5zvyOa3iNJaVL+PpvU/zesvrfHHuF7lx0Y0ARCIRfvKTn5BOp7n22muZMGHCiNYvhBBCgvwxkyAvhDiRkvE0u9a2s/W1Flr37J9Yyx2wM+O0cmadWU6g2J3DCiGVCpqBPbydcGQHkUybTh86EVg/m60At2sCbvckXO6JuJzVOByl2OxF/Ov9dby6O8ZFcyv45VWLhl2PYWjEYvVEIjsJh3eYbWQH0egejMwSWAUFZzHR+znUbRG67ruPxI4dg9dZUYFryWLci5fgPmUJ9kmTTorbHI7EMAy0rjiJejOwJ/cFSbVGDr7nAcVuwV7jM4P7BD/2Gh/qCM37oGkabW1tNDQ0ZLfBJqYrKCigpqYmuxUWFg7tf9/+4F73CtS9Cs3rwTjoi4uSWTBpOUxeDsUz4e5F5jk3rEN31dD2i/Vo3XEc0/Ip+txsFPXEXVct4Rbebnubt1vf5p3Wd2gMNw543mV1sbh0MaeVn8Zp5adRm1+Lmhkh8I89/+DfX/l3Jvgn8MSKJ7J/Po8//jjr1q1jxowZfOpTnzphtQshhBicBPljJEFeCDFSulsibH2tme1vthIL7R+mXDUjn9lnVTJpfhEW64nrpTd72VsIhbYQCm8hnGnj8aZBz1cUCy7XJLzeaXjcU3C5J+J2T8LtmojNFjjs52xpDnLx3a+gG3D/F5dy+pSi41J7eMfbND73M6LvrMW+E6xdhwYn+8SJuJcuxb1kMe7Fi7FVVAzybicXPaGRbMwsAbfPXL9djxy6FJqlwIkjE9ztE/zYyjwnNJweKBqN0tjYmA3tTU1NpFIDh/IrikJ5eXk2tFdXV+Pz+Yb2AfEgNLx1QHB/99Dgnj8RJp4Fk8+BSWeDt2Tg83/6GOx6HpZ/DT5wO8nmMB33vIeR0vF9oJrA+RPf749/iOZwM++0vcPbrWZ4bwoP/DtqVazMLZ7LaeWnsbR8KfOK5mGzDD4qIJKKcPYDZ5PUkzx8ycNML5gOQHt7O//7v/8LwI033khBQcFxq18IIcTRSZA/RhLkhRAjTUvr1G3sZMurzdRv6c72hLr8dmYuK2fWmRUEio9tGTvD0IhG9xIKbTa38BZCoa2k072Dnu9wlOH1TsfrmY7HOx2vZxpu9xQslvd3T/83H9vEH9bsY3qpj7/feCbWYd5GoCeTxDdvJrbuXaLvriP27nq0g9b4NhSDVLVBcqqB4nZhfytB9Ze+Rf6VV76vmscDQzdId0RJNoRJNpg97oP1tmNRsPcvAZfpcbf47YO+5/GmaRrt7e00NjbS1NREY2MjnZ2dh5zncDiorq7ObpWVlTgcQ7weQ61Qv8bsdd/3unmfu3HQjPX5E2HimWZ4n3AG5FUf+T03/BUe+SLkT4Ib3wVFIbq+ne4HtgNQ+JmZuOYM/0sr3dDZ27eXde3reLftXda1rzskuFsUC7MLZ7OkbAmnlJ3CwpKFeGyeIX/Gzatu5oX6FwYMrwf405/+xK5du1i6dCkXXnjhsGsXQgjx/kmQP0YS5IUQuRTsjLHltWa2vtZCNLh/PefqmWYv/cT5RUednEvXU0QiuzKBfVMmvG9F1wdZw1ux4vFMxeudic83G593Jl7vzCP2sL8fvdEkH/ifl+iJpvjWJbO49oxJRzw/3dND7N31xN5dR3Tdu8Q3bsRIDlzfWrHZcM6fZw6TX7IEY1qAuo5f097+d/MEHfxbi1ly/esoOZp4bKRpwaQZ2PuDe2P40HvbAUvAgX2CzwzuNT7sFV6UEzj640DBYDAb2BsbG2lubj6ktx2gsLBwQHAvKioa2v3tug5du6DhDdi3xgzwPXsPPW+4wf1gyQj8sBZSEfjCc1B9KgC9T+wm/Fozit1CyfULsJUc+VaZpJZkS9eWbHB/t+Nd+hIDbxs41uB+sKf2PsW/vfxv1PhqePKyJ7PD63ft2sWf/vQn7HY7t9xyCy7XsX2BKIQQYugkyB8jCfJCiNFA03T2behi8ytN1G8d2Es/64xyZp9Via/AiaYliES2Ewxtyva2h8PbMYzkIe+pqi58vpn4vLPx+Wbh9c3C4659373sw/XnN/fxH49uwue08tJt51DoNT/XSCaJb99B7L33iG14j9h775HaV3/I6y35+bgWLcK9aCGuhYtwzpmNaj+01zgU3sbubT+gK7gagAn+zzF1yR0n9ofLAT2aItkUJtkYJtUYItkYRus7dFJAxaZiq/Jir/Zhr/bjqPFhGaHVEqLRKM3NzTQ3N9PU1ERzczOhUOiQ8xwOB5WVlVRVVWVbj2eIQTXeB01roeFtaMxs8d6DTlKgbA7ULMtsp4H/ONxm8ci/wIYHYMkX4OIfA2BoOh2/2URybx/WIhcl1y8YMJdAW6SNDZ0b2NBhbpu7NpM4aFZ5p8XJvOJ5LCxZyMKShSwoWXBMwf1g0VSUsx88m4SW4KFLHmJGwQyzdsPgf//3f+no6ODDH/4wZ5xxxnH7TCGEEEcmQf4YSZAXQow2wc4YW15tZtube9HVPTjz9+HMr8dX3ozqbAAO7XG1Wn2ZwD4bn28OPt9s3O6JQ1qL/UTRdINL736Fjl37uLYwyuWuPmLvvUd882aMxKEB1D55Mq5FC3EvXIRr0ULsEycOa2K6TT+6jLaFG8BQWLz4gezM9mORnkiTyoT2ZGOIZFMYrWuQZf0UsJW6sVX5zJ72aj+2EjfKCCwBl0gkaGlpGRDce3p6Di1RUSgpKRkQ2ofc266loXN7Jri/BY3vQMc2DrlXwOqEikUwYRnUnA7Vp4Dz+I4yAWD3i/DHy8CVD1/ZAVbziyUtnKT97neJBsM0LYywd3YP73W8x4aODbRFD51dv8BZkA3ti0oWMaNwBjb1xM58f8uqW3i+/nn+ae4/cdOim7LH161bx+OPP47f7+emm27CcpKv4CCEECNlODl0ZKaaFUIIMWypVI85CV1oE6HQFoyyLVR/eC+H3twMWtKHwzqD0sqF5BfOxe+bg9NZnfPZ2A3DIN3eTnzTJmKbNhHfuIkfbtiIEjSHDR+4QJYaCOCaPw/X/Pm45s3HNW8ulsCxBa8JS79C8JVriS3V2bTpRpYu/Ts2W/4xvedI0CIpUs1hkk1hUs1hUs0R0p2H3hYBYCl0mve2V/mwZVrVceKDVywWo6WlZcDWddCcBf3y8/OprKykoqKCyspKysrKhnZvu2FA9x5oWgfN68y2dQOkooeemzfBHNpedSpULYGyuXCYyd6Oq0nLwVsG4VZSO55mV2ktm7s2s6lzE5tqN7ArvBstpsM7+1+iKirT8qcxr2ge84rNbaJ/eF9SHQ/nTTyP5+uf55m6Z7hx4Y3Zz587dy7PP/88wWCQrVu3MmfOnBGtSwghxNFJj/wgpEdeCDGSDMMgHm8mHN5CKLw1Ozw+kWgZ9Hy7vQS/bw5WpZbOPaXsectPtNsPKKhWhamLSpizvIqyyf4RDQaGYZBuaSG+dSvxLVuJb9lCbNNGtI5DJy3TLBb2eMvorK7lsqvOx71g/lF723XdoCeapDOcpCOUoCMcpyucRNMP/8+YYRh0/u43BBd2YXjB7ZlKefnHGS2LzRmAEUujBZNowUSmTWLED51BHkBxWrEG7KgBB9aAA0vAjmI7saHdABLxOKFQKLsFQ0HisUFGA2AOkff7/fgDAbP1+7HbhhCoDR1/rJGi8DaKQtspDm2hOLQFR/rQYfhJi5tO30za/PNo88+lPTCXmL3wGH/S4dGMND2pRjqSe2hve4H25F46nXE0RT/kXGfaR5l9GuWB2ZQ5plFsn4Jdzf2950k9xT3r70Ez0nxm5mcoce+flX/37t3s2bMHv9/PqUuXjpq/M0II8X4VeR18bHFVrss4Ihlaf4wkyAshThRdT2QmodtKOLyVUNhs0+ngoOe7XDUHDI+fhdc3G4d94CzYqaTGzrfb2PxyE+379oeewiovc5dXUntKKfbjvNa3oWkk9+7dH9q3biWxdSvaIOt6Y7HgmDoV55zZuObMwTlnDsHyCZx79xpCiTTfvmQWnztjEoZh0BqMs7s9wq72ELs7IjT2ROkIJ+gIJeg8SmgXY4eVNLVKE3PUvcxS9jFbrWOmUo9POXTUQcKwsdmYwAZ9Mhv0ybxnTGGPUY7BCE5eqCRQna1YnM2ojuZM24aiHvqFi6E50WJVaPEq9HglWqwKI50HEoWFECKn5lYGeOKGM3NdxhFJkD9GEuSFEMfKMAySyXbC4W2ZbTvh8DYi0d0YxqG//CuKLTNz/IzMzPGz8flmYrUOcU3sjPZ9QTatbmLH221oKbNn0O60MH1ZOXOXV5JfNvzJstLd3SS2byexYwfx7TtI7NhBYtcujPggPbJWqxnaZ840tzlzcM6cgXrAzNfJtE59d4TfvLqXB95qwKoq1JZ4qe+OEkkeeq//wQo8doq9Dop9Dgo8dqxHuffbSCQJPvMM6SKN1CQdFJXCwuUnboi9AUZCQ4+n0WPmZsTS6IPMHN9PdVpR3VZUV6Z12+BE3tNuQDKZJBaPEY/HicfixOIxEonEYHduoCgKDqcDl9OFy+XE5XLjcruOfu+0YeDTuimP76EivpvyxB7KE7spS+zDahw6S31KsdPimEyjaxqNzloanDNocU5GV0bmTkDD0IkbnYSMRsJ6PWGjkZC+j6jRxmB/MFZceNUa/MpkAm19+IM2XGXnoBROzZ6jR1LEt/eAAu75xaCOrkDfvz69x+rhQzUfGvB9Q0NDA91d3eTlBZgwcWLOahRCiOOhKt/NrR+elusyjkiC/DGSIC+EGI50OkIkuotIeHs2sIcj20mlDp3kC8BqDZhLvPlmZpZ6m4XHMwVVPX5rdscjKbataWHT6ib6Ovb3clZOz2fu8sGXsNPCEZK7d5HYtZvEzp1mcN+xA22Q9bwBFLcb5/TpZmCfNRPHzJk4amuzs8j3xVLs7gizuz3M7o4Iu9rD7OkIs687etiedYuqMKHAzZQSL1OKvUwodFPqd1DsdVLsc1DotWMb5vrzAE23foW+f/ydyLfLCZbU43RWceopT2Czvf//xhu6gdaXINUeJd0WJdUaIdUWJd0exUgdOrwaQPVYsZV7sZV5sJVnthL3CVv2Tdd1+vr66OzspKOjg46ODtra2ujo6Bh0uTcAp9NJWVkZpaWllJWVUVZWRnFxMVbrUcJ0PAidO6B9q7m1bYK2zRAd/PrBETDvYy+fB+XzoWweFE0Dy8iE9t54Lzt7d7KzZyc7e3eyo2cHu3p2EU0Pcv89UOIqYXrBdGYUzGBm4Uxm5M+g0leJ2r+s4as/gee/bS5jd+0/sq8zDIPWH76D1h2n4NMzcM8rHoGfbuiiqSjLH1xOXIvz4MUPMqtwVva5trY27rnnHhRF4cYbbyQ/f/TPLyGEEGOZTHYnhBAngKYliEZ3E4nsJBzZQSS8g3BkB/F442FeoeJ2T8brnY7POwOvdwYez3SczooTfu+602Njwbk1zP9gNQ3butm0uom6DZ00be+hdXMzRUonU8qiFFl7MBr2kti9m3TL4PfkoyjYaqpxTpuGY9p0HNOm4ZhWi72mhpShUN8dZWNnhD0dYfZu28aejgh7OsN0hg9d/q6fx25hSomXYp+DVdva0Q34zqWzuPLUCdhPQKjN+9QnCf7jH3h/3kfyh5XE441s3fY15s755VH/tzB0g3R33AzrHZnQ3h4l3RHFSA4e2LGq5szxpW4ztJd5sJW5UX32E/K/vaZp9PT0DAjsHR0ddHZ2HjawWywWiouLKSkpobS0lJKSEkpKSvD7jzK3QrwPOrabM8W3bzPbjm0QbBr8fEWFgilQOgtK50DpbCiZZa7hfoL/HhiGQVe8iz29e9jdt5vdvbvZ07eH3b276Y53D/oaq2plSmAKtfm11ObXMj3fDO+FrqPcgz/3E/D8d2Dfa9CzD/InAOZoBve8IkIvNRJ7r2PUBXm3zc1ZVWfx3L7neKbumQFBvrS0lMmTJ7Nnzx7efPNNLrjgghxWKoQQ4kAS5IUQ4iCaFiUS2U0ksotIJrhHo7uJRvcBgwc3u70Er6cWr3cGXu90PN7pI7o++8EMTSPV1ERy7148e/eysGUv03t3E9+1BzW0f6RA+KDXWYuLsU+dgmPKVBzTp+GcPh3r5Cm0JhX2dEao64pQ1xmlbnUnezv3UX+E3nWAMr+TKSUephR7mZrpZZ9S7KXU78iGxbue3sY9L+3m3tV7+Nji6hMS5N2nnIJ98mSSe/YwoeXT7Cj8DR0dz9DY9Ceqqz4LmGuypzpipDtipDuj+/e7YqAd5me0KFiLXNhK9od2a5kHa4ET5QQMoY5Go3R2dtLZ2UlXV1d2v6enB10/zCgAVaWoqIji4mKKioqywT0/P//wQ+N1DfoaoHNnZtsBXbvMNnzo0mlZvnIong7FM80120tmQfEMsLuPw09/eCktRUO4gbq+OuqCddl2T98e+hKDzNuQUeGpoDa/lmn508zgnlfLhMCE97fsW6AKJp4Jda/Axofg7NuyT7nmFZtBfnsPeiKN6hhdv36dN/G8bJC/ceGNWNT918WyZcvYs2cP69at45xzzsHpdOawUiGEEP1G178kQggxQgzDIJnqIhrZkwnpe83h8ZFdxOOH6VnEHBbv9UzD451mtp5peL21OVnSzNB10u3tJOv2kdy3j2S92ab27SNZtw9jkJ7YbETOLyLiLqNLKSbqLifoKSVcMwH3omqMMgdNoQT7uiLsfa6bhu4mktphep4Bt93CpCIPk4u9TCryMKXYw+QiLxOL3PicRw9EN32olr9vaKG+O8r/PLOdb186+33+iRyeoijkXfEJ2n/4U1IP7WDCrddRF/kpO7f/F/qzXmxNZeiRwWeKB8we9hIzsFtL3GZb6jYD+/sY6n8ksViM7u5uuru76erqGrAfiw2+BB2AzWajsLCQ4uLiAdthA7thQKgVunZD925zmbeu3fsfpweflR4AX4UZ2Etm7g/uxdPMtdRPEN3QaY20Uh+qpz5obnVBM7A3hhrRjMHnIFBQqPZVMzlvMlMCU5iSN4XJeZOZ5J+E23acv2CY/ykzyG94EM76SnbEga3cg7XIRbozRnxLN+6FJUd5o5F1duXZ+Ow+msJNvNjwIh+e8OHsc1OnTqWoqIjOzk7effddli1blsNKhRBC9JN75Ach98gLMX5oWpxYbB/RaJ05LD66h2h0L9HobtKDLGvVz2YrwOOpxeOZgsc9FY/H3Oz2kpFd0i2ZJNXcTLKhkVRjA8mGRpL1+0jtqyfZ0DD4hHMZit2OfeJE7JMmYZ80iVRFNV0F5TR4i9gXV6jvirK7JcTetjBdyTTGEX4su0WlptDNxEIPEwvdTCzyMDkT3g/sXX+/XtnZwWf/7y0UBVZ++QzmV+e9r/cxDAM9mkbrjpvD4btjpDvjpLtipDui2bBuYNC84OeES97FFill4hvfRtVcWPx2rMUurMXubE+7tciFJc9x3HrYdV0nHA7T09MzYOsP7NHo4Pdo9wsEAhQWFlJUVJRti4qK8Pl8qOpBXypoaQg2Qk/d/q1rN3TvNYN7KnL4D7LYoXCquRXVmvevF9ZC0VRwBo71j2FQKS1Fc6SZxlAjDaEG6kP1NATNtjHUSFI//O0abqubCf4JTAxMZJJ/ktkGJjHRPxGndYR6keNB+J9a80uQL66CykXZp/qerSP0YgPOmQUUXXP8v6w6Vr949xf8asOvmFkwkwcvfnDA3+l33nmHJ598kry8PG644YajT3IohBDifZF75IUQJxVdTxKLNRCN1RGL1plBPVZHNFp32LXYTQpOZxUe9yTc7sm4PVPM8O6egt1eMCK1G5pGuqODVHMzqaYmczh8QwOphkaSjQ2kW9vgMEOmAbBYsFVVYq+ZgF5RRbionM68Euo9xexRPNT3JWnsidLYHSPaqgHdme0gCjgsKnm6gi9pENAU8nWViYVulp1awbKzq3F73sdw4yE6q7aYFQsqWLm+mdsf2cjj15+B9TA93UZKJ90bR+tJkO4xA7vWnQnr3XGM+JFnvtcTYVRHmknq19hu3EDS00b4Yy8xY/Z3UR3HJ6DEYjF6e3sHbAeG9nT6CL3/gNfrpaCggMLCQgoKCgbs2+0HTIpoGBBuh76d0LAPeusHhvbeBjhMTzVg3r+eV2Pew14wGQqnmPtFteZx9fgGNt3Q6Yh2ZMN6Y7iRplCT2YabaIu0YQw2bX6GTbVR5auixldDta+aif6JTAxMZKJ/IiXukf2SbVBOP0z/CGx+BDb8dUCQd88vJvRiA/EdPejRlLkywShy1cyr+MOWP7C1eyuvNb/GmZX7l2iaP38+L7zwAr29vWzZsoW5c+fmsFIhhBAgPfKDkh55IUafdDpENLaPWKyeWKyBWHa/nni8hcPduw5gtfpwuSbicU/B7Z5kBnb3ZFyuCVgsJ7anTo/HSbW0kG5tJdXSSqqlmVRT8/7g3toCqSOHOsNmJRUIEPX66XH76HD5aXIXUucqpM5ZRJfipg8XaeXoocurRwnoEfKJUWRJUmLXKHNBhUelyOvE6XGTiKl0Nmp0NqgYBEDxYHNYqF1SyuyzKimZ6DshgakznOBDP1pNMpbijrOncvnUEtI9+wO71hMn3ZNADx2+V7af6rNjLXCaW6ETa5ELa6GLZNNOGj53FYrTSe3Lq+lLb+Td9VejKBaWnvoUHs+Uo763rutEIhH6+voGbAeG9kQiccT3UBSFQCBAfn4++fn5FBQUZNuCggIcjszcCukkhJqhr8mcTK633tz6GjJt45GHwANYHObEa/kTIW9CJqxPNgN7Xg1Yj99qCUktSVukjZZIC03hJloiLTSHm7Nta7SVtH7k691ldVHpraTKV8UE3wRq/GZor/HXUOYuG3D/9qi04xm4/wrwFMOt2wbMwt/6k7Wk26Lkf7wWz5KyHBY5uB+8/QP+uOWPLCpZxO8v/P2A51avXs2qVasIBAJcf/312Gyj64sIIYQYD6RHXggx5mhagni8iXi8kVi8kXisIdM2Eos3HHYpt34WixuXayJu90TcmdaV2bfZCk5I8NRjMdJtbaTa2km3t5FobibR0ECysYl0Wyt6ZyeEDp5ObpD3ASJ2O0Gnix6nlw53Hi3uQhq9JezzltHsLiVudR31fTAMPFoEbzqCPx3Enw7tb1MhfOkQlkG+8DCApsw2GEWxkVTzeO/ZPDa8kI+vsIzaU6ax6MIlePO8R6/rwM9K62jBJFowgdaXQOtNmr3rvQm03gQrNQ82dHi5nc6X2w/7PopdxZLvxJpvhnXLAaHdku9EtQ8e9mxVC3HU1pLYuZO+xx6n4LOfoajoXDo7n2fXrv9m3rz7iEajBIPBQ7b+wB4MBtG0o6937/F4yMvLIy8vLxva+wN7IBDAYmgQajG3YDP0rYeGJjOcBzNtuJ1BF3Yf8IehmpPM5dVAoBoKJpmhvX/zlsHBQ+7fh4SWoD3aTnu0ndZIK62RVtqibQP2DzcT/IEsioVSdylVviqqfFVmaPdWUekz2wLnifn7OmKmfBDcRRDpgD2roHb//ebuecUEn9tHdEPnqAzy18y6hge2PcC69nWsbVvL4tLF2eeWLVvG2rVr6evrY82aNZx99tk5rFQIIYQEeSHEiEinw8TjzZmwnmkTmTbWRCJ5hJmwM2y2QtyuGlyuCbhcNbhc1Zl2AnZ70XH75V9PJkm3txNrbCTaUE+8qZFkSyvpjnb0zi6Mnl7UYBBL4ug9wwApVSXocNHn8NDl8tHmKqDVU0ijt5R93jLa3YXoytGDllM1KHJCkVOl1KNS6rFS7rNT7ndQnueg3O/E5XJiyaz3bRhGJgMamX2zNQyDdDJBIholEY2QzLT7H0eIBvvobW2hr6MNQ0+B1oGhdUAKepvg7SZ4e6UFb+FkpixewvzzzqKgsBw9lMwE9czWl9jf9iXQw4Mvhdavv48vhEHErjJhUp4Z0POdmeDuMIO62zrs/72TySThcJjIRy+l+cG/suulVdhLiolGF+Dzv0hn14vcffeX6e4uPep7KYqCz+cjEAhkt/7Qnuf3ErCmsCe6INQG4VYI7YHWFtjRbPawB1sOv776wSwOCFSCv3J/WM+rgbxM668Ey/vvHU3pKbpiXXTGOumIdtAR68gG9vZoO+0xsz3S7O8HclqclHnKqPBWUO4pH9BWeCoodhdjVcfxrx8WG8z5GLz1K3jvgQFB3jXfDPKJXT1okRSWE3i7yvtR6inlo1M/ysM7HuY3G38zIMjb7XbOPfdcHnnkEV555RUWLlyIz+fLYbVCCHFyk6H1g5Ch9UIMj64niMdbSSRaiSdaSMQzbaKFeLyFeLyJdProIcBiceN0VuFyVZutswqXqwqnsxqXqxqrdXi9vwNqTKWINjcRqa8n1thAorWFVFsH6c4O9J4ejL4gajiMNRLDeph1tweTVC0EHW56HR66XH7aXfm0eYpochfT5i6gw5VH2OY66nrZeS4bpX4nJX4H5QEnZQEXFQEnZQEnFXkuygJOfI7hh9djlU4m6atvpa++mUhzF5HWXqKtIYilcCoOnFYvLou5WYe6ZJdVwRJwmBPL5Tmx5DmymzXPwb5kmo/c+zpJTeeXn17ERfPKD/tWhmEQi8UIh8OEQiHC4fCgWygUIn6EiQEnT3mLysrthMP5vLvuI3g8Pvx+//7N56PArZJvS+JX47j1MGqsy+wxD7dDpN2cAT7cZvbEGkeY1+BAFjv4ysxZ4P3l5hJm/iqzDVSa+56iYa+3rhs6PfEeuuJddMW66Ip30R3rpiu+P7B3xjvpjHbSkzjyaJcDOSwOStwllHnKKHWXUuYpo8xdZraZzW8/yjr0J4PGtfCbD4LVBV/dCY79gbft5+tINUfIu2wq3qWHv7ZzpSHYwMUrL0Y3dP568V+ZWTgz+5xhGPzf//0fjY2NLFiwgBUrVuSuUCGEGIdkaL0Q4rgwDIN0Okgi0Uoi0ZbZWkkk27P78XgrqVTXkN7Pag3gdFbgdFZm2gqcjgqcLjO0D2cIfDqRINLUSLS5iXhzC4m2FpLtHaS7OtF6ejGCQZRwGDUawxpPYEulOfidLZlt0PdX1ExA99LjCtDlCtDuyKPdlUeXK0CnM0CXM0DE5jxiyAq4bNT6HBT7HJRk2lK/GdBL/U5KfWZ4d9pG7r5fQzPQoym0UBI9kkILp9DDyUy7f18LJdHDSdDBDtjxk4/f/JfjMB1xSS1OTAsT1yKofjsls6cSmFiOJWA3w3vAcdTe9AmpFP+yrIw/v7aTXzz2Kr7oRCxakkgkQiQSMXvVM/uRSOSw66cPxmq14vf7sXd14mnYQ1F5gIrTF+G3+Em0bcOaaOKDszfhUwvNHvO+DmjuMsO5PvQveFBU8JSAr9Qc2u4rNYe/+8rBX7G/dRcOKaTrhk4oGaIn3kN3vNtsE2bbH9j7n+uKddGT6EEf6pcJgFWxUugqpMhVRLGrmBJ3yaCbhPQhqlxkzvbftQu2PgELPp19yjWvmFRzhNiGjlEZ5Kv91Vww8QL+sfcf/Gbjb/jROT/KPqcoChdccAG/+c1vWL9+PaeeeioVFRU5rFYIIU5e0iM/COmRF+OdYeikUj0kk50kEu0kk+0kEu0kku0kEx0DWl0/ykRaGarqwOEox+kow+Esx+koz7b94f1wPeqpSIRoUxPx1hZira0kOtpIdnWR7u5G6+3DCAYxQmGUaBRLLI41mcSa1g4J5kejA2F7Jpw7/XQ5fPQ4fPQ4ffQ6fHQ7/XQ7zTZkcx82YHkdVoq8doq8DnPz2Sn0OCg6IKyX+MznTnRANwwDI6mjR1Lo0RR6NG2G80jKPNYf1Pv3IymM2JEnGxuM6rFi8dlRfXYsXjuqz4bFZ/aqK24rDfXdrH91Lc2716Ol9oJuLu1noFBSu5hTLr8MV0GAWCxGNBolGo0Si8WIRCJEo9FsG41GSSaHdstCtjZ08p1Q4FLId0KeXcdnTeO1JHErCZx6FKcexpYMocR7UKJdGNEulCPN5n44zoAZ0L2ZzVMC3uJMaC8Db6nZeooPO+N7SkvRl+yjL3HAlnncE++hN9FLb6J3wH5vondYwbxfniOPQmchha7C/a2rkGJXMcWuYorcRRS5ishz5KEO4fYOMQyrfwCrvgeTlsM1j2cPp7vjtP7gbVCg/OtLsfiO32SDx8vOnp1c/vjlKCisXLGSyYHJA55/5JFH2LBhA9XV1Xz+85+XL3eEEOI4GU4OlSA/CAnyYizS9ZQZzlPdpJJdJJNdJJMdJJOdZmBPdmSOdZJKdWEMI8RYrQEcjtLMVnbAfilORxl2exmWlAO9r494Rwfx9jbiHR0ku7tI9fSQ6u5G6wuih4IY4TBKNIYSj6MmklhTKSz6+//PUMjmImR30+Pw0Wf30Ofw0ufw0pvZ+uwes83s64OEK5/DSp7HRoHHQaHHToHHTqHHTqHXPvBYJryfiHBuGAZGSseIpdFjaTOQR1P792OZkB5LZ0O7lgntaO/jz08B1WND9djMgO6xYfHaUL32TGset/jsKB4raV0jFosRj8eJx+PZ/VgsNmALhyL0doUIh0Kk9ThYhvILvoHV0HCSxGmkcJDCiYbHqpBMJrDoGn67QZXPgsNIYzWSWLQESiqBmopDOoZhKBioGCiAgkHmsWE+Nj/lgB8e80sGQwPD5kHNKwGHn77EbpJKEmfeTHxFy8DhN5cUc/jNEG+xAQZJPUUsHSOeihNNR4mlY8QybTQdI5qKEktHD9g322g6QkI78oz2R+KwOPHavHjtXnw2H167B+8Brc/uxWf34bX78Nq8WIawkoE4QcJt8OQt5v5HfwGu/Uta9j21l3RnDM+pZTinj8xSl8P1m42/ZmPnRk4tW8pVM68a8Fw0GuXv//g7Wlrj9NNPp6amJkdVCiHE0DncViqn5ee6jCOSIH+MJMiL0UDT4qRS3Zlg3kMq1bP/caqHZDIT2FPdJJNdpNO9w/4MmzUPu1KEQy/CkQ5g0/xYky6UiBUjomCEdbS+FFpfmHQwiB4OoYcjEI1CLIaSSKImklhSqWH3jh/y8yoqIZuLoN1DyO42N5uboN1N2O7OBvGg3U3Q7qHP4SFkcw8I5lZVIc9tI89tJ89lO2Q/32OnwG0nz20G83yPjTyXHbv1+PREGikdPZ5Gj6cx4poZuvsfxzRzPxPUjQP2+7f3FcizP7yCxW1DddtQPdb9Id1jA5eFtMMgadNJ23RSqkYSjUQyYYbxaIxYJEE8liAeSxKPJknEkyTjKRLxFKlEGkMHxVBRDAsYCgqWzGOwGmDBwGKAaoAFBcXo31QUFDDU7GZgxcCCbvS3FnS500sIIYQQJ1BxjY8rvn5Krss4IrlHXohRRNeTpNJB0qleUuk+0qk+UqnezNZDKn3Afqov0/ai67GBb2SAkgIlAUoSlISSacGSAFtCRUkoWNNuLEkHatKGGrdBXEGJAnEdI6qZw6oTadRECjUZQzXqgXoA0pltMEe6n7xfSrEQsTkJ21xEMlvY5iRsdxO2uczN7iZ0YJvpUY9azXvNXTYLfpcVn9OG32nF77Lhc9oodFmZ7LIROGDz97dOM6R77JZhD/E0dMMM2gkNPaEd0KYPeqztD+gJzQzi2WNp9Lh2bEE8Q1cMDKcNzaGQtEPSDmlVJ2kxSKGTUgyS6KR0g7Suk9J00ppGOqWTSmlovTpap46WMtDTBrrGASHc3FRDgUwQx7AAKuY/B1bAk61FBVyZbcj1Z7bjQVE0VMXAQEczdHTFwGJRsFoV6P9eQMn8mSmgZ/9PM1tDM/cMDc3Q0A3z2X7G0ZZ1O1p9KFhUC6qiYlUsWBQrFlXNtBYsSmZT97dWxYJFtUpP+cki0gE9dea+t9RccUBRMDSDVJO5NKWt0osypJErI29P3x5CyRBFriIqvZUDnjMMg9bWVjRNy04KKYQQo1l+mTvXJRxXEuSFOArD0NG0CKlUkLQWIp0OkU4HSaf6SKeDJBN9pGM9aJEe0rEg6WgfWiyIFguhxcIY8YQZuFOKGcST7H+cBBJgJC2QUFCSCvakgiOhoCTdqElQUwaWpIE1NdR4lMhswxO1OjKbk5jVQdRmthGrk4jNSdTmJJrZjxywH7WawT3hcGNzO/G57HgcVnxOKx6HFa/DDOU+p5UahxWv03zsdVjxO83H/szzPqftsL3jhm5gJDWMpI6R1NBTZmtENIyeGEYqTCSpYSQyx5Ma+gHnG8kDAnnSbI2EhnHAn6thmNFOA3QjE0qNAx8baJnjmmGQwiBt6GaLThqDtGGQVkFXQVPMTe9/Tz3zPgYYuoJhmMfQFXMzVMxh30O9h90M5IN9xXL4Z47MQhKrksSiHNCSyu5blCQqKVCTKEoKQ01hKCkMi4FuBc2qoNnMNm1VSNkgZVVJWiFpMQgmwnT2dRDT4sQtGnEVkjYnCatCwpIkZYmRsidIqFF05Xh9JXAop8WJ2+bGGUpi7wrhTkCgqIKCGfNw2HSCHf/AqRrMmPgvFPmn47P7zCHrNnPout/ux2V1yb3B4sh0DZ69A974pfm4cAl84reQV0P7ve+RrAsSOKsC31mVR36fHHmzReOfnv0nHBYHT3/saYpcRQOe37hxI3/729+I2Gx8/Es3SJgXQogRJEFejEmGrmOk0xjJFEYqmWlTGMlk5nESPREnHQ+hxftIx8Po8TBaIowWj5COhUnFIqTicbR4jHQ8gZ5MosWT6MmU+X7JNCQ1SOmoKQMlDWrawJIyzDatm1vm/m4Fcw3sQxfgGuo6wQYMsYcwoVqJW+3ELQ5iVvsB+47MvnkslgnnsYO2pNWOZndgOJ3gcqG6XbiddjxOG16nHa/bgddpx2W34HVYKbdbcGdCucdhxWO34LZb8FgsuFUFj8WC3TAw0rp5n/fBbWbTkxp6XEPrS6IlYmgpHS2hoyXTBFM6PSkdLaGhpXW0lI6e0kmndPS0jq4Z2WCtc+C+GarTGGiGgYaBBmgYmRB+0OsMBR3MEJ1pQcncY73/fuqh63/N8Z8o7MBQbW4pVCWZ3RQlBUoKRU1iKElQzW+KdCWFoSbR1RSamkRTU6TVVLZNqWmSNkhZDRJWc8h9wqoTtxgkVYWEopBQwPyeySCBTsLQSOhpEsbwJ8obYGCn/9D+HAwrDtUJup1owoJu2JleWkSZzwzTHpsHj82Dy+rCbXPjsXlwW/e3/ccO3PrXMdcjEVrvvJO+h/8GNGAtjlP6jW9QPx3a2h4jT3uLRZNukcAu3h/VAhd8HyaeASu/BE3vwL1nwWX34p43n2RdkNiGjlEb5E8tO5V5RfPY0LmBP235EzcvvnnA83PmzOGtt96ioaGBF154gcsuuyw3hQohxEloTNwj/8tf/pIf/vCHtLa2Mn/+fO6++25OPfXUw57/0EMPcccdd1BXV0dtbS133XUXH/nIR4b8eeP1HnnDMCCdxtA0jLQGWv9+GjKtkU4PPCedwtA09FQKLZVGS6bQMvvZY6kUejLTZrc0ejpthuK0+dhIpzBSaTNwp82WbGueRyqJkjYfK+k0SjqNqvW3GqqWxqJpWIax3NRISikWUhYrcYudpMVK0mLL7NtIqjbiFpu5nzmesGZai42ExU5KtZC22EhbbOhWK4bNhmGxg92GYneg2h3YbA7sNisuqwWnxWxdVhtOixWn1YrDYsVpsWBDwWaADQWrYWDTFay6eR8zmoGh6ehpAy3T6poZlg3NQNMMc8i5bh5Pawa6rpuboaPpBoahZ4KwkR1ObRjmVxE6ma8lDAX9gJBsnICwe2KlUZUUipI2A7OSBjWZucchBUra7I1WzU1X0uhKEkNNoympTJBOo6lJ0mqatJpGU9Kk1BQpS4pkf6hW0yQtKRJKmoRVI25Jk1Q1UiqkMAO1MQpzpKqoOCwOnBYnDmumtTgG7LusLpxWp9lanDitmc3ixKqrNK1/j4Z31mFJ6Vh1C7VzT6esajl9DdC1KwZxKxZj4HfOccWg02awbHE5M2cVEShx4St04vTY3nfgjrz5Fq3f+hbJujoAHBctY9/Fa9CNJHPn/C8lJecf6x+XONn17IOHr4WmtQBoi26lZc0HwYCyfzsFa4EzxwUO7qWGl7jhxRvw2Dw8+/Fn8dsH/l7U1NTEr3/9awC++MUvUlk5Or+UEEKIsWBcTXb34IMPcvXVV3PvvfeydOlSfvrTn/LQQw+xfft2SkpKDjn/9ddf5+yzz+bOO+/k4osv5v777+euu+5i3bp1zJkzZ0ifOVaC/Iufuw7nrm0ouoai6SiGjqpr5mNdR9Uzjw1zf/+vtypG5pddI7vckJI5tr8d7Fi2RTHv88u0A89Rh/QaA3XgY0U96Bzzsfn6TM2HOaYpKppiQVctpFULumJBO7BVVXRlf2uoFjRFxVBUdNVi1qwoGOr+9+yviUydKApK5mfL7iuqGRwUBQVzXzH/YDHvoFWyf76gwIG9vtlwq2QmABtrIff4MEhjqBqGkkZXDm11NdMqaTRVQ1c0NDWFpmjmpmqkM8+l1TRpJU1a1UipGpqSRs+EZ01No2XeTxtwLG2+3yHH0qCM3v882lU7Nost29pUG3aLHbtqx26x739sseOwOA55vv94tlX3H3NYHNgstmxA7z/PaXEOPEd9/8H5QD2tzaz+4/9j9ztvAOD0+jj9iquYc8759LTGaN8XoqMhRMe+EF3NYfT04P+7WO0qvkIXvgIn/kInvszmCdhxuG3YXVYcbis2x+BzKeiJBJ333kvXr38D6TShyxRCH07gctZw2mlPo6qOY/5ZxUkunYTnv50dat+h3E0iNonAhRPxLa/ObW2HoRs6H3/i4+zs2ckNC2/gn+f98yHnPProo7z33nuyHJ0QQhyjcRXkly5dyimnnMIvfvELAHRdp7q6mhtuuIGvfe1rh5z/yU9+kkgkwpNPPpk9dtppp7FgwQLuvffeIX3mWAjyhmHw+8/+kphrCtmllA4J4JiBVIx5OjqGoqMrmRYNQzEyAVfHyLTZ5xUNg/7nDz7nwGMDH+sHnWMoOrp68PMH7/eHa+2QY2YQNwO32Vuto2WDubkd83T374P1gMnIrKo1O/mYVbVmn7MecMySPW495Byrkjl24KZYsVps2edsqm3A8zbVfsAxWzaIWzPH7GrmceZ4/3P7963j8hflhk0befnP/4+upgYACiurWfSRj2J37p9uT9d1OtujPPZaPYTTFCkWfKoFPTm8NeEVq4pqU1BtKopVHXAdKskk1u5OVC1I+SeexuKJ07drPvFu6WkUx4eqJbGkwhTYCyl3VxHXk7SlunJd1mGlDYOkkUYB7Mqhd2Vqik6Tpw8DyEs6seoymaMQYhTS7Fz9b/89qn+HGjez1ieTSdauXcvtt9+ePaaqKueeey5r1qwZ9DVr1qzh1ltvHXDs/PPPZ+XKlYf9nEQiQSKxf3KwYDB4bIWPgKiu015ow5M4cT1EBnrmjm0DIzNztDm894DHGGbn8gHH9j9vtv1zQxuKPvBY9j2N/jPMya36X3vA+3DQuQe+V3a///iAcw9qFSMbig/7Psr+53Vl4Lm6oh/wXua+fsC+Ga71A547eN844D0GPt9/DOXAGvRsZ775n5z+/98/CqD/cf8kaQduZL7Iydy/nf2iR82OhjBHSlgOGAGhZkchGIolM/rBgo45amH/vhVdcZivVfa/3rxPXM2+1/7P7f/MwZ7PfB6W/ecqlkPfK3PswH3zOcv+98i+LjPKAgsolgM+u//c4/aXZP+9BMdVKrOdhC78/MDHBnDQAg4EvPCRQ0dknRhXmE3tCH2cEEIIIU6Yj+s6Hsv4+LJxVAf5zs5ONE2jtLR0wPHS0lK2bds26GtaW1sHPb+1tfWwn3PnnXfyne9859gLHmGvLqrDkVoPmJN4ZUZrY/bIG5nj/b315vJM/ffZ6ijmsGElcw+zkskkCpmh32CoBwwDV/qHgWfen4OGxrN/RICRHbqvZHKOesDrzeM6Zg+Y3h9AM8/rmeCpZ1+zfyh9f8jUM+2Bz5nhkgGhlANqJfvN20Fh98Bh/wc+f8Drs6McBryPesj7DHyPA4PzgZOoKRwYqLPnj+JvBoUQQgghhBCjy6gO8iPl9ttvH9CLHwwGqa4enfeq9XOrKn/5zH/lugwhhBBCCCGEGBPc6vi57XhUB/mioiIsFgttbW0Djre1tVFWVjboa8rKyoZ1PoDD4cDhGFuTGCmKMm6GhQghhBBCCCGEGLpR/ZWE3W5n8eLFvPDCC9ljuq7zwgsvsGzZskFfs2zZsgHnAzz33HOHPV8IIYQQQgghhBhLRnWPPMCtt97KNddcw5IlSzj11FP56U9/SiQS4dprrwXg6quvprKykjvvvBOAm266ieXLl/OjH/2Iiy66iAceeIB33nmH++67L5c/hhBCCCGEEEIIcVyM+iD/yU9+ko6ODr75zW/S2trKggULePrpp7MT2tXX16MecK/D6aefzv333883vvENvv71r1NbW8vKlSuHvIa8EEIIIYQQQggxmo36deRzYSysIy+EEEIIIYQQYvwYTg4d1ffICyGEEEIIIYQQYiAJ8kIIIYQQQgghxBgiQV4IIYQQQgghhBhDJMgLIYQQQgghhBBjiAR5IYQQQgghhBBiDJEgL4QQQgghhBBCjCES5IUQQgghhBBCiDFEgrwQQgghhBBCCDGGSJAXQgghhBBCCCHGEAnyQgghhBBCCCHEGCJBXgghhBBCCCGEGEMkyAshhBBCCCGEEGOIBHkhhBBCCCGEEGIMsea6gNHIMAwAgsFgjisRQgghhBBCCHEy6M+f/Xn0SCTIDyIUCgFQXV2d40qEEEIIIYQQQpxMQqEQgUDgiOcoxlDi/klG13Wam5vx+XwoipLrcg4rGAxSXV1NQ0MDfr8/1+UIcQi5RsVoJ9eoGAvkOhWjnVyjYrQbK9eoYRiEQiEqKipQ1SPfBS898oNQVZWqqqpclzFkfr9/VF+QQsg1KkY7uUbFWCDXqRjt5BoVo91YuEaP1hPfTya7E0IIIYQQQgghxhAJ8kIIIYQQQgghxBgiQX4MczgcfOtb38LhcOS6FCEGJdeoGO3kGhVjgVynYrSTa1SMduPxGpXJ7oQQQgghhBBCiDFEeuSFEEIIIYQQQogxRIK8EEIIIYQQQggxhkiQF0IIIYQQQgghxhAJ8kIIIYQQQgghxBgiQX4M++Uvf8nEiRNxOp0sXbqUt956K9cliZPUnXfeySmnnILP56OkpIQVK1awffv2AefE43Guu+46CgsL8Xq9fOxjH6OtrS1HFYuT2X//93+jKAo333xz9phcn2I0aGpq4jOf+QyFhYW4XC7mzp3LO++8k33eMAy++c1vUl5ejsvl4txzz2Xnzp05rFicTDRN44477mDSpEm4XC6mTJnCd7/7XQ6cN1uuUTGSXn75ZS655BIqKipQFIWVK1cOeH4o12N3dzdXXXUVfr+fvLw8vvCFLxAOh0fwp3j/JMiPUQ8++CC33nor3/rWt1i3bh3z58/n/PPPp729PdeliZPQ6tWrue6663jjjTd47rnnSKVSnHfeeUQikew5t9xyC0888QQPPfQQq1evprm5mcsvvzyHVYuT0dtvv82vfvUr5s2bN+C4XJ8i13p6ejjjjDOw2Ww89dRTbNmyhR/96Efk5+dnz/nBD37Az3/+c+69917efPNNPB4P559/PvF4PIeVi5PFXXfdxT333MMvfvELtm7dyl133cUPfvAD7r777uw5co2KkRSJRJg/fz6//OUvB31+KNfjVVddxebNm3nuued48sknefnll/nnf/7nkfoRjo0hxqRTTz3VuO6667KPNU0zKioqjDvvvDOHVQlham9vNwBj9erVhmEYRm9vr2Gz2YyHHnooe87WrVsNwFizZk2uyhQnmVAoZNTW1hrPPfecsXz5cuOmm24yDEOuTzE6/Pu//7tx5plnHvZ5XdeNsrIy44c//GH2WG9vr+FwOIy//OUvI1GiOMlddNFFxuc///kBxy6//HLjqquuMgxDrlGRW4Dx6KOPZh8P5XrcsmWLARhvv/129pynnnrKUBTFaGpqGrHa3y/pkR+Dkskka9eu5dxzz80eU1WVc889lzVr1uSwMiFMfX19ABQUFACwdu1aUqnUgGt2xowZ1NTUyDUrRsx1113HRRddNOA6BLk+xejw+OOPs2TJEj7xiU9QUlLCwoUL+fWvf519fu/evbS2tg64TgOBAEuXLpXrVIyI008/nRdeeIEdO3YA8N577/Hqq69y4YUXAnKNitFlKNfjmjVryMvLY8mSJdlzzj33XFRV5c033xzxmofLmusCxPB1dnaiaRqlpaUDjpeWlrJt27YcVSWESdd1br75Zs444wzmzJkDQGtrK3a7nby8vAHnlpaW0tramoMqxcnmgQceYN26dbz99tuHPCfXpxgN9uzZwz333MOtt97K17/+dd5++21uvPFG7HY711xzTfZaHOzffrlOxUj42te+RjAYZMaMGVgsFjRN43vf+x5XXXUVgFyjYlQZyvXY2tpKSUnJgOetVisFBQVj4pqVIC+EOK6uu+46Nm3axKuvvprrUoQAoKGhgZtuuonnnnsOp9OZ63KEGJSu6yxZsoTvf//7ACxcuJBNmzZx7733cs011+S4OiHgr3/9K3/+85+5//77mT17NuvXr+fmm2+moqJCrlEhckCG1o9BRUVFWCyWQ2ZUbmtro6ysLEdVCQHXX389Tz75JKtWraKqqip7vKysjGQySW9v74Dz5ZoVI2Ht2rW0t7ezaNEirFYrVquV1atX8/Of/xyr1UppaalcnyLnysvLmTVr1oBjM2fOpL6+HiB7Lcq//SJXvvrVr/K1r32NT33qU8ydO5fPfvaz3HLLLdx5552AXKNidBnK9VhWVnbIROHpdJru7u4xcc1KkB+D7HY7ixcv5oUXXsge03WdF154gWXLluWwMnGyMgyD66+/nkcffZQXX3yRSZMmDXh+8eLF2Gy2Adfs9u3bqa+vl2tWnHAf+tCH2LhxI+vXr89uS5Ys4aqrrsruy/Upcu2MM844ZNnOHTt2MGHCBAAmTZpEWVnZgOs0GAzy5ptvynUqRkQ0GkVVB0YHi8WCruuAXKNidBnK9bhs2TJ6e3tZu3Zt9pwXX3wRXddZunTpiNc8XDK0foy69dZbueaaa1iyZAmnnnoqP/3pT4lEIlx77bW5Lk2chK677jruv/9+HnvsMXw+X/a+okAggMvlIhAI8IUvfIFbb72VgoIC/H4/N9xwA8uWLeO0007LcfVivPP5fNn5Gvp5PB4KCwuzx+X6FLl2yy23cPrpp/P973+fK664grfeeov77ruP++67DwBFUbj55pv5r//6L2pra5k0aRJ33HEHFRUVrFixIrfFi5PCJZdcwve+9z1qamqYPXs27777Lj/+8Y/5/Oc/D8g1KkZeOBxm165d2cd79+5l/fr1FBQUUFNTc9TrcebMmVxwwQV88Ytf5N577yWVSnH99dfzqU99ioqKihz9VMOQ62nzxft39913GzU1NYbdbjdOPfVU44033sh1SeIkBQy6/fa3v82eE4vFjC9/+ctGfn6+4Xa7jcsuu8xoaWnJXdHipHbg8nOGIdenGB2eeOIJY86cOYbD4TBmzJhh3HfffQOe13XduOOOO4zS0lLD4XAYH/rQh4zt27fnqFpxsgkGg8ZNN91k1NTUGE6n05g8ebLxH//xH0YikcieI9eoGEmrVq0a9PfPa665xjCMoV2PXV1dxpVXXml4vV7D7/cb1157rREKhXLw0wyfYhiGkaPvEIQQQgghhBBCCDFMco+8EEIIIYQQQggxhkiQF0IIIYQQQgghxhAJ8kIIIYQQQgghxBgiQV4IIYQQQgghhBhDJMgLIYQQQgghhBBjiAR5IYQQQgghhBBiDJEgL4QQQgghhBBCjCES5IUQQgghhBBCiDFEgrwQQgghhuVzn/scK1asyHUZQgghxEnLmusChBBCCDF6KIpyxOe/9a1v8bOf/QzDMEaoIiGEEEIcTIK8EEIIIbJaWlqy+w8++CDf/OY32b59e/aY1+vF6/XmojQhhBBCZMjQeiGEEEJklZWVZbdAIICiKAOOeb3eQ4bWn3POOdxwww3cfPPN5OfnU1payq9//WsikQjXXnstPp+PqVOn8tRTTw34rE2bNnHhhRfi9XopLS3ls5/9LJ2dnSP8EwshhBBjjwR5IYQQQhyz3//+9xQVFfHWW29xww038KUvfYlPfOITnH766axbt47zzjuPz372s0SjUQB6e3v54Ac/yMKFC3nnnXd4+umnaWtr44orrsjxTyKEEEKMfhLkhRBCCHHM5s+fzze+8Q1qa2u5/fbbcTqdFBUV8cUvfpHa2lq++c1v0tXVxYYNGwD4xS9+wcKFC/n+97/PjBkzWLhwIf/v//0/Vq1axY4dO3L80wghhBCjm9wjL4QQQohjNm/evOy+xWKhsLCQuXPnZo+VlpYC0N7eDsB7773HqlWrBr3ffvfu3UybNu0EVyyEEEKMXRLkhRBCCHHMbDbbgMeKogw41j8bvq7rAITDYS655BLuuuuuQ96rvLz8BFYqhBBCjH0S5IUQQggx4hYtWsTf/vY3Jk6ciNUqv44IIYQQwyH3yAshhBBixF133XV0d3dz5ZVX8vbbb7N7926eeeYZrr32WjRNy3V5QgghxKgmQV4IIYQQI66iooLXXnsNTdM477zzmDt3LjfffDN5eXmoqvx6IoQQQhyJYhiGkesihBBCCCGEEEIIMTTylbcQQgghhBBCCDGGSJAXQgghhBBCCCHGEAnyQgghhBBCCCHEGCJBXgghhBBCCCGEGEMkyAshhBBCCCGEEGOIBHkhhBBCCCGEEGIMkSAvhBBCCCGEEEKMIRLkhRBCCCGEEEKIMUSCvBBCCCGEEEIIMYZIkBdCCCGEEEIIIcYQCfJCCCGEEEIIIcQY8v8BJEyKcDV0ieUAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 13 }, { "cell_type": "markdown", @@ -716,10 +558,13 @@ }, { "cell_type": "code", - "execution_count": 12, "id": "cf39f578-5d5c-4bc2-b13b-0b4a880a20b2", - "metadata": {}, - "outputs": [], + "metadata": { + "ExecuteTime": { + "end_time": "2024-11-05T15:06:58.405486Z", + "start_time": "2024-11-05T15:06:58.403234Z" + } + }, "source": [ "comets_config = {\n", " 'total_time': 10.0,\n", @@ -734,24 +579,21 @@ " 'biomass': (0, 0.1)\n", " },\n", "}" - ] + ], + "outputs": [], + "execution_count": 14 }, { "cell_type": "code", - "execution_count": 13, "id": "757c67ba-6429-4365-8f3d-98540415c235", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Making the composite...\n", - "Created new file: out/comets.json\n", - "Simulating...\n" - ] + "metadata": { + "jupyter": { + "is_executing": true + }, + "ExecuteTime": { + "start_time": "2024-11-05T15:21:44.504472Z" } - ], + }, "source": [ "# make the composite state\n", "composite_state = get_spatial_dfba_state(\n", @@ -777,7 +619,7 @@ "}, core=core)\n", "\n", "# save the document\n", - "sim.save(filename='comets.json', outdir='out', include_schema=True)\n", + "sim.save(filename='comets.json', outdir='out', schema=True)\n", "\n", "# # save a viz figure of the initial state\n", "\n", @@ -785,7 +627,21 @@ "print('Simulating...')\n", "sim.update({}, total_time)\n", "comets_results = sim.gather_results()" - ] + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Making the composite...\n", + "no representation for 10\n", + "no representation for 10\n", + "Created new file: out/comets.json\n", + "Simulating...\n" + ] + } + ], + "execution_count": null }, { "cell_type": "code", @@ -825,6 +681,46 @@ " skip_frames=1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba2017c0-51c8-443e-9521-50c5fa0ca436", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c54ec58-0bb2-4fa2-8490-ce7a08175aff", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ff96cf8-ecb4-4c7c-aebb-c7e3ad5a6736", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92725785-e771-4bcc-a593-964edc1860bc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c37b0e61-af14-4a1d-8137-f961a29561a5", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 15, @@ -871,6 +767,14 @@ "## Particle-COMETS" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f82c824-f018-4290-b4ee-4bb8ba5a1ab2", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 16, diff --git a/spatio_flux/__init__.py b/spatio_flux/__init__.py index 386543d..9934e10 100644 --- a/spatio_flux/__init__.py +++ b/spatio_flux/__init__.py @@ -4,9 +4,7 @@ """ from process_bigraph import ProcessTypes - -# make type system -core = ProcessTypes() +from spatio_flux.processes import register_processes def apply_non_negative(schema, current, update, core): @@ -17,13 +15,26 @@ def apply_non_negative(schema, current, update, core): positive_float = { '_type': 'positive_float', '_inherit': 'float', - '_apply': apply_non_negative -} -core.register('positive_float', positive_float) + '_apply': apply_non_negative} + bounds_type = { 'lower': 'maybe[float]', - 'upper': 'maybe[float]' + 'upper': 'maybe[float]'} + + +particle_type = { + 'id': 'string', + 'position': 'tuple[float,float]', + 'size': 'float', + 'local': 'map[float]', + 'exchange': 'map[float]', # {mol_id: delta_value} } -core.register_process('bounds', bounds_type) + +def register_types(core): + core.register('positive_float', positive_float) + core.register('bounds', bounds_type) + core.register('particle', particle_type) + + return register_processes(core) diff --git a/spatio_flux/processes/__init__.py b/spatio_flux/processes/__init__.py index e69de29..9180c88 100644 --- a/spatio_flux/processes/__init__.py +++ b/spatio_flux/processes/__init__.py @@ -0,0 +1,13 @@ +from spatio_flux.processes.dfba import DynamicFBA +from spatio_flux.processes.diffusion_advection import DiffusionAdvection +from spatio_flux.processes.particles import Particles, MinimalParticle + + +def register_processes(core): + core.register_process('DynamicFBA', DynamicFBA) + core.register_process('DiffusionAdvection', DiffusionAdvection) + core.register_process('Particles', Particles) + core.register_process('MinimalParticle', MinimalParticle) + + return core + diff --git a/spatio_flux/processes/comets.py b/spatio_flux/processes/comets.py index eb08cf0..a6ad41d 100644 --- a/spatio_flux/processes/comets.py +++ b/spatio_flux/processes/comets.py @@ -20,6 +20,36 @@ 'initial_min_max': {'glucose': (0, 10), 'acetate': (0, 0), 'biomass': (0, 0.1)}, } +## TODO -- maybe we need to make specific composites +class COMETS(Composite): + """ + This needs to declare what types of processes are in the composite. + """ + config_schema = { + 'n_bins': 'tuple', + } + + def __init__(self, config, core=None): + # set up the document here + state = { + 'dFBA': { + 'config': { + 'n_bins': config['n_bins'], + } + }, + 'diffusion': { + 'config': { + 'something_else': config['n_bins'], + } + } + } + + super().__init__(config, core=core) + + # TODO -- this could be in Process. + def get_default(self): + return self.core.default(self.config_schema) + def run_comets( total_time=10.0, diff --git a/spatio_flux/processes/dfba.py b/spatio_flux/processes/dfba.py index 93df3f7..5eaa5eb 100644 --- a/spatio_flux/processes/dfba.py +++ b/spatio_flux/processes/dfba.py @@ -7,14 +7,12 @@ import warnings -import cobra import numpy as np -from bigraph_viz import plot_bigraph +import cobra from cobra.io import load_model -from process_bigraph import Composite, Process - -from spatio_flux import core # import the core from the processes package -from spatio_flux.viz.plot import plot_species_distributions_to_gif, plot_time_series +from process_bigraph import Process, Composite +from bigraph_viz import plot_bigraph +from spatio_flux.viz.plot import plot_time_series, plot_species_distributions_to_gif # Suppress warnings warnings.filterwarnings("ignore", category=UserWarning, module="cobra.util.solver") @@ -22,10 +20,6 @@ "ignore", category=FutureWarning, module="cobra.medium.boundary_types" ) -# TODO -- can set lower and upper bounds by config instead of hardcoding -MODEL_FOR_TESTING = load_model("textbook") - - class DynamicFBA(Process): """ Performs dynamic FBA. @@ -33,18 +27,16 @@ class DynamicFBA(Process): Parameters: - model: The metabolic model for the simulation. - kinetic_params: Kinetic parameters (Km and Vmax) for each substrate. - - biomass_reaction: The identifier for the biomass reaction in the model. - substrate_update_reactions: A dictionary mapping substrates to their update reactions. - biomass_identifier: The identifier for biomass in the current state. + - bounds: A dictionary of bounds for any reactions in the model. TODO -- check units """ config_schema = { - "model_file": "string", - "model": "Any", + "model_file": "string", # TODO -- register a "path" type "kinetic_params": "map[tuple[float,float]]", - "biomass_reaction": {"_type": "string", "_default": "Biomass_Ecoli_core"}, "substrate_update_reactions": "map[string]", "biomass_identifier": "string", "bounds": "map[bounds]", @@ -53,9 +45,7 @@ class DynamicFBA(Process): def __init__(self, config, core): super().__init__(config, core) - if self.config["model_file"] == "TESTING": - self.model = MODEL_FOR_TESTING - elif not "xml" in self.config["model_file"]: + if not "xml" in self.config["model_file"]: # use the textbook model if no model file is provided # TODO: Also handle JSON or .mat model files self.model = load_model(self.config["model_file"]) @@ -63,23 +53,24 @@ def __init__(self, config, core): self.model = cobra.io.read_sbml_model(self.config["model_file"]) else: # error handling - raise ValueError("Invalid model file") + raise ValueError('Invalid model file') for reaction_id, bounds in self.config["bounds"].items(): if bounds["lower"] is not None: - self.model.reactions.get_by_id(reaction_id).lower_bound = bounds[ - "lower" - ] + self.model.reactions.get_by_id(reaction_id).lower_bound = bounds["lower"] if bounds["upper"] is not None: - self.model.reactions.get_by_id(reaction_id).upper_bound = bounds[ - "upper" - ] + self.model.reactions.get_by_id(reaction_id).upper_bound = bounds["upper"] def inputs(self): - return {"substrates": "map[positive_float]"} + return { + "substrates": "map[positive_float]" # TODO this should be map[concentration] + # "enzymes": "map[positive_float]" # TODO this should be map[concentration] + } def outputs(self): - return {"substrates": "map[positive_float]"} + return { + 'substrates': 'map[positive_float]' + } # TODO -- can we just put the inputs/outputs directly in the function? def update(self, state, interval): @@ -88,9 +79,7 @@ def update(self, state, interval): for substrate, reaction_id in self.config["substrate_update_reactions"].items(): Km, Vmax = self.config["kinetic_params"][substrate] substrate_concentration = substrates_input[substrate] - uptake_rate = ( - Vmax * substrate_concentration / (Km + substrate_concentration) - ) + uptake_rate = Vmax * substrate_concentration / (Km + substrate_concentration) self.model.reactions.get_by_id(reaction_id).lower_bound = -uptake_rate substrate_update = {} @@ -98,25 +87,19 @@ def update(self, state, interval): solution = self.model.optimize() if solution.status == "optimal": current_biomass = substrates_input[self.config["biomass_identifier"]] - biomass_growth_rate = solution.fluxes[self.config["biomass_reaction"]] - substrate_update[self.config["biomass_identifier"]] = ( - biomass_growth_rate * current_biomass * interval - ) - - for substrate, reaction_id in self.config[ - "substrate_update_reactions" - ].items(): + biomass_growth_rate = solution.objective_value + substrate_update[self.config["biomass_identifier"]] = biomass_growth_rate * current_biomass * interval + + for substrate, reaction_id in self.config["substrate_update_reactions"].items(): flux = solution.fluxes[reaction_id] * current_biomass * interval old_concentration = substrates_input[substrate] - new_concentration = max(old_concentration + flux, 0) # keep above 0 + new_concentration = max(old_concentration + flux, 0) # keep above 0 -- TODO this should not happen substrate_update[substrate] = new_concentration - old_concentration # TODO -- assert not negative? else: # Handle non-optimal solutions if necessary - # print('Non-optimal solution, skipping update') - for substrate, reaction_id in self.config[ - "substrate_update_reactions" - ].items(): + # print("Non-optimal solution, skipping update") + for substrate, reaction_id in self.config["substrate_update_reactions"].items(): substrate_update[substrate] = 0 return { @@ -124,57 +107,41 @@ def update(self, state, interval): } -# register the process -core.register_process("DynamicFBA", DynamicFBA) - - # Helper functions to get specs and states def dfba_config( - model_file="textbook", - kinetic_params=None, - biomass_reaction="Biomass_Ecoli_core", - substrate_update_reactions=None, - biomass_identifier="biomass", # TODO: How do we differentiate between biomass between models? Do we need to change the biomass identifier to be unique for each model? - bounds=None, + model_file="textbook", + kinetic_params=None, + substrate_update_reactions=None, + biomass_identifier="biomass", + bounds=None ): - if kinetic_params is None: - kinetic_params = {"glucose": (0.5, 1), "acetate": (0.5, 2)} - # TODO: Look for the biomass reaction in the model, so it doesn't have to be specified - # if biomass_reaction is None: - # biomass_reaction = get_biomass_reaction(model_file) if substrate_update_reactions is None: - substrate_update_reactions = {"glucose": "EX_glc__D_e", "acetate": "EX_ac_e"} - # TODO: Set the biomass identifier to something more specific to the model (e.g. Model Name + 'biomass') - # This will also be helpful once there are multiple models that would need to be named differently - # if biomass_identifier is None: - # biomass_identifier = get_biomass_identifier(model_file) + substrate_update_reactions = { + "glucose": "EX_glc__D_e", + "acetate": "EX_ac_e"} if bounds is None: bounds = { "EX_o2_e": {"lower": -2, "upper": None}, - "ATPM": {"lower": 1, "upper": 1}, - } - + "ATPM": {"lower": 1, "upper": 1}} + if kinetic_params is None: + kinetic_params = { + "glucose": (0.5, 1), + "acetate": (0.5, 2)} return { "model_file": model_file, "kinetic_params": kinetic_params, - "biomass_reaction": biomass_reaction, "substrate_update_reactions": substrate_update_reactions, "biomass_identifier": biomass_identifier, - "bounds": bounds, + "bounds": bounds } def get_single_dfba_spec( - model_file="textbook", - kinetic_params=None, - biomass_reaction="Biomass_Ecoli_core", - substrate_update_reactions=None, - biomass_identifier="biomass", # How do we differentiate between biomass between models? Do we need to change the biomass identifier to be unique for each model? - bounds=None, - mol_ids=None, - path=None, - i=None, - j=None, + model_file="textbook", + mol_ids=None, + path=None, + i=None, + j=None, ): """ Constructs a configuration dictionary for a dynamic FBA process with optional path indices. @@ -183,19 +150,9 @@ def get_single_dfba_spec( specification of substrate molecule IDs and optionally appends indices to the paths for those substrates. Parameters: - model_file (str, optional): The file path to the model file. Defaults to 'textbook'. - kinetic_params (dict, optional): Dictionary of kinetic parameters for each substrate. Defaults to - {'glucose': (0.5, 1), 'acetate': (0.5, 2)}. - biomass_reaction (str, optional): The identifier for the biomass reaction in the model. Defaults to - 'Biomass_Ecoli_core'. - substrate_update_reactions (dict, optional): Dictionary mapping substrates to their update reactions. Defaults to - {'glucose': 'EX_glc__D_e', 'acetate': 'EX_ac_e'}. - biomass_identifier (str, optional): The identifier for biomass in the current state. Defaults to 'biomass'. - bounds (dict, optional): Dictionary mapping reaction IDs to lower and upper bounds. Defaults to - {'EX_o2_e': {'lower': -2, 'upper': None}, 'ATPM': {'lower': 1, 'upper': 1}}. mol_ids (list of str, optional): List of molecule IDs to include in the process. Defaults to - ['glucose', 'acetate', 'biomass']. - path (list of str, optional): The base path to prepend to each molecule ID. Defaults to ['..', 'fields']. + ["glucose", "acetate", "biomass"]. + path (list of str, optional): The base path to prepend to each molecule ID. Defaults to ["..", "fields"]. i (int, optional): The first index to append to the path for each molecule, if not None. j (int, optional): The second index to append to the path for each molecule, if not None. @@ -206,9 +163,6 @@ def get_single_dfba_spec( if path is None: path = ["..", "fields"] if mol_ids is None: - # TODO: Change to get the names of all the external metabolites in the model(s) - # How am I handling multiple models here? - # Maybe I need to handle this outside of this function if there are multiple models mol_ids = ["glucose", "acetate", "biomass"] # Function to build the path with optional indices @@ -223,16 +177,13 @@ def build_path(mol_id): return { "_type": "process", "address": "local:DynamicFBA", - "config": dfba_config( - model_file, - kinetic_params, - biomass_reaction, - substrate_update_reactions, - biomass_identifier, - bounds, - ), - "inputs": {"substrates": {mol_id: build_path(mol_id) for mol_id in mol_ids}}, - "outputs": {"substrates": {mol_id: build_path(mol_id) for mol_id in mol_ids}}, + "config": dfba_config(model_file=model_file), + "inputs": { + "substrates": {mol_id: build_path(mol_id) for mol_id in mol_ids} + }, + "outputs": { + "substrates": {mol_id: build_path(mol_id) for mol_id in mol_ids} + } } @@ -242,91 +193,100 @@ def get_spatial_dfba_spec(n_bins=(5, 5), mol_ids=None): dfba_processes_dict = {} for i in range(n_bins[0]): for j in range(n_bins[1]): - dfba_processes_dict[f"[{i},{j}]"] = get_single_dfba_spec( - mol_ids=mol_ids, path=["..", "fields"], i=i, j=j - ) + dfba_processes_dict[f"[{i},{j}]"] = get_single_dfba_spec(mol_ids=mol_ids, path=["..", "fields"], i=i, j=j) return dfba_processes_dict def get_spatial_dfba_state( - n_bins=(5, 5), - mol_ids=None, - initial_min_max=None, # {mol_id: (min, max)} + n_bins=(5, 5), + mol_ids=None, + initial_min_max=None, # {mol_id: (min, max)} ): if mol_ids is None: mol_ids = ["glucose", "acetate", "biomass"] if initial_min_max is None: - initial_min_max = {"glucose": (0, 20), "acetate": (0, 0), "biomass": (0, 0.1)} + initial_min_max = {"glucose": (0, 20), "acetate": (0,0 ), "biomass": (0, 0.1)} initial_fields = { - mol_id: np.random.uniform( - low=initial_min_max[mol_id][0], high=initial_min_max[mol_id][1], size=n_bins - ) - for mol_id in mol_ids - } + mol_id: np.random.uniform(low=initial_min_max[mol_id][0], + high=initial_min_max[mol_id][1], + size=n_bins) + for mol_id in mol_ids} return { "fields": { "_type": "map", - "_value": {"_type": "array", "_shape": n_bins, "_data": "positive_float"}, + "_value": { + "_type": "array", + "_shape": n_bins, + "_data": "positive_float" + }, **initial_fields, }, - "spatial_dfba": get_spatial_dfba_spec(n_bins=n_bins, mol_ids=mol_ids), + "spatial_dfba": get_spatial_dfba_spec(n_bins=n_bins, mol_ids=mol_ids) } def run_dfba_single( - total_time=60, - mol_ids=None, + total_time=60, + mol_ids=None, ): single_dfba_config = { - "dfba": get_single_dfba_spec(path=["fields"]), - "fields": {"glucose": 10, "acetate": 0, "biomass": 0.1}, + 'dfba': get_single_dfba_spec(path=['fields']), + 'fields': { + 'glucose': 10, + 'acetate': 0, + 'biomass': 0.1 + } } # make the simulation - sim = Composite( - {"state": single_dfba_config, "emitter": {"mode": "all"}}, core=core - ) + sim = Composite({ + 'state': single_dfba_config, + 'emitter': {'mode': 'all'} + }, core=core) # save the document - sim.save(filename="single_dfba.json", outdir="out") + sim.save(filename='single_dfba.json', outdir='out') # simulate - print("Simulating...") + print('Simulating...') sim.update({}, total_time) # gather results dfba_results = sim.gather_results() - print("Plotting results...") + print('Plotting results...') # plot timeseries plot_time_series( dfba_results, # coordinates=[(0, 0), (1, 1), (2, 2)], - out_dir="out", - filename="dfba_single_timeseries.png", + out_dir='out', + filename='dfba_single_timeseries.png', ) def run_dfba_spatial( - total_time=60, - n_bins=(3, 3), # TODO -- why can't do (5, 10)?? - mol_ids=None, + total_time=60, + n_bins=(3, 3), # TODO -- why can't do (5, 10)?? + mol_ids=None, ): if mol_ids is None: - mol_ids = ["glucose", "acetate", "biomass"] + mol_ids = ['glucose', 'acetate', 'biomass'] composite_state = get_spatial_dfba_state( n_bins=n_bins, mol_ids=mol_ids, ) # make the composite - print("Making the composite...") - sim = Composite({"state": composite_state, "emitter": {"mode": "all"}}, core=core) + print('Making the composite...') + sim = Composite({ + 'state': composite_state, + 'emitter': {'mode': 'all'} + }, core=core) # save the document - sim.save(filename="spatial_dfba.json", outdir="out") + sim.save(filename='spatial_dfba.json', outdir='out') # # save a viz figure of the initial state # plot_bigraph( @@ -338,31 +298,31 @@ def run_dfba_spatial( # ) # simulate - print("Simulating...") + print('Simulating...') sim.update({}, total_time) # gather results dfba_results = sim.gather_results() - print("Plotting results...") + print('Plotting results...') # plot timeseries plot_time_series( dfba_results, coordinates=[(0, 0), (1, 1), (2, 2)], - out_dir="out", - filename="dfba_timeseries.png", + out_dir='out', + filename='dfba_timeseries.png', ) # make video plot_species_distributions_to_gif( dfba_results, - out_dir="out", - filename="dfba_results.gif", - title="", - skip_frames=1, + out_dir='out', + filename='dfba_results.gif', + title='', + skip_frames=1 ) -if __name__ == "__main__": +if __name__ == '__main__': run_dfba_single() - run_dfba_spatial(n_bins=(8, 8)) + run_dfba_spatial(n_bins=(8,8)) diff --git a/spatio_flux/processes/diffusion_advection.py b/spatio_flux/processes/diffusion_advection.py index 35b8577..05705eb 100644 --- a/spatio_flux/processes/diffusion_advection.py +++ b/spatio_flux/processes/diffusion_advection.py @@ -7,8 +7,6 @@ import numpy as np from scipy.ndimage import convolve from process_bigraph import Process, Composite -from bigraph_viz import plot_bigraph -from spatio_flux import core # import the core from the processes package from spatio_flux.viz.plot import plot_species_distributions_to_gif @@ -127,9 +125,6 @@ def diffusion_delta(self, state, interval, diffusion_coeff, advection_coeff): return updated_state - state -core.register_process('DiffusionAdvection', DiffusionAdvection) - - # Helper functions to get specs and states def get_diffusion_advection_spec( bounds=(10.0, 10.0), @@ -221,56 +216,3 @@ def get_diffusion_advection_state( } -def run_diffusion_process( - total_time=50, - bounds=(10.0, 20.0), - n_bins=(10, 20), -): - composite_state = get_diffusion_advection_state( - bounds=bounds, - n_bins=n_bins, - mol_ids=['glucose', 'acetate', 'biomass'], - advection_coeffs={ - 'biomass': (0, -0.1) - } - ) - - # make the composite - print('Making the composite...') - sim = Composite({ - 'state': composite_state, - 'emitter': {'mode': 'all'}, - }, core=core) - - # save the document - sim.save(filename='diffadv.json', outdir='out') - - # save a viz figure of the initial state - plot_bigraph( - state=sim.state, - schema=sim.composition, - core=core, - out_dir='out', - filename='diffadv_viz' - ) - - # simulate - print('Simulating...') - sim.update({}, total_time) - - # gather results - diffadv_results = sim.gather_results() - - print('Plotting results...') - # plot 2d video - plot_species_distributions_to_gif( - diffadv_results, - out_dir='out', - filename='diffadv_results.gif', - title='', - skip_frames=1 - ) - - -if __name__ == '__main__': - run_diffusion_process() diff --git a/spatio_flux/processes/particle_comets.py b/spatio_flux/processes/particle_comets.py index 6088476..c5af796 100644 --- a/spatio_flux/processes/particle_comets.py +++ b/spatio_flux/processes/particle_comets.py @@ -1,15 +1,9 @@ """ Particle-COMETS composite made of dFBAs, diffusion-advection, and particle processes. """ -from process_bigraph import Composite -from bigraph_viz import plot_bigraph -from spatio_flux import core -from spatio_flux.viz.plot import plot_time_series, plot_species_distributions_with_particles_to_gif - -# TODO -- need to do this to register??? -from spatio_flux.processes.dfba import DynamicFBA, get_spatial_dfba_state -from spatio_flux.processes.diffusion_advection import DiffusionAdvection, get_diffusion_advection_spec -from spatio_flux.processes.particles import Particles, get_particles_spec, get_particles_state +from spatio_flux.processes.dfba import get_spatial_dfba_state +from spatio_flux.processes.diffusion_advection import get_diffusion_advection_spec +from spatio_flux.processes.particles import Particles, get_particles_spec default_config = { @@ -101,63 +95,7 @@ def get_particle_comets_state( advection_rate=particle_advection_rate, add_probability=particle_add_probability, boundary_to_add=particle_boundary_to_add, - field_interactions=field_interactions, + # field_interactions=field_interactions, ) return composite_state - -def run_particle_comets( - total_time=10.0, - **kwargs -): - # make the composite state - composite_state = get_particle_comets_state(**kwargs) - - # make the composite - print('Making the composite...') - sim = Composite({ - 'state': composite_state, - 'emitter': {'mode': 'all'}, - }, core=core) - - # save the document - sim.save(filename='particle_comets.json', outdir='out', include_schema=True) - - # # save a viz figure of the initial state - # plot_bigraph( - # state=sim.state, - # schema=sim.composition, - # core=core, - # out_dir='out', - # filename='particles_comets_viz' - # ) - - # simulate - print('Simulating...') - sim.update({}, total_time) - particle_comets_results = sim.gather_results() - # print(comets_results) - - print('Plotting results...') - n_bins = kwargs.get('n_bins', default_config['n_bins']) - bounds = kwargs.get('bounds', default_config['bounds']) - # plot timeseries - plot_time_series( - particle_comets_results, - coordinates=[(0, 0), (n_bins[0]-1, n_bins[1]-1)], - out_dir='out', - filename='particle_comets_timeseries.png' - ) - - plot_species_distributions_with_particles_to_gif( - particle_comets_results, - out_dir='out', - filename='particle_comets_with_fields.gif', - title='', - skip_frames=1, - bounds=bounds, - ) - - -if __name__ == '__main__': - run_particle_comets(**default_config) diff --git a/spatio_flux/processes/particles.py b/spatio_flux/processes/particles.py index 3becf6d..345a7b6 100644 --- a/spatio_flux/processes/particles.py +++ b/spatio_flux/processes/particles.py @@ -6,21 +6,11 @@ """ import uuid import numpy as np -from process_bigraph import Process, Composite +from process_bigraph import Process, Composite, default from bigraph_viz import plot_bigraph -from spatio_flux import core from spatio_flux.viz.plot import plot_species_distributions_with_particles_to_gif, plot_particles -# TODO -- make particle type -particle_type = { - 'id': 'string', - 'position': 'tuple[float,float]', - 'size': 'float', -} -core.register('particle', particle_type) - - class Particles(Process): config_schema = { # environment size and resolution @@ -35,19 +25,6 @@ class Particles(Process): 'add_probability': {'_type': 'float', '_default': 0.0}, # TODO -- make probability type 'boundary_to_add': {'_type': 'list', '_default': ['left', 'right']}, # which boundaries to add particles 'boundary_to_remove': {'_type': 'list', '_default': ['left', 'right', 'top', 'bottom']}, - - # interactions between particles and fields - 'field_interactions': { - '_type': 'tree', # A dictionary of fields - '_value': { - '_type': 'map', - 'vmax': {'_type': 'float', '_default': 0.1}, - 'Km': {'_type': 'float', '_default': 1.0}, - 'interaction_type': { - '_type': 'enum[uptake,secretion]', '_default': 'uptake'}, # 'uptake' or 'secretion' - }, - '_default': {'biomass': {'vmax': 0.1, 'Km': 1.0}} - } } def __init__(self, config, core): @@ -59,10 +36,7 @@ def __init__(self, config, core): def inputs(self): return { - 'particles': { - '_type': 'list[particle]', - '_apply': 'set' - }, + 'particles': 'map[particle]', 'fields': { '_type': 'map', '_value': { @@ -75,10 +49,7 @@ def inputs(self): def outputs(self): return { - 'particles': { - '_type': 'any', - '_apply': 'set' - }, + 'particles': 'map[particle]', 'fields': { '_type': 'map', '_value': { @@ -93,23 +64,37 @@ def outputs(self): def initialize_particles( n_particles, bounds, - size_range=(10, 100) + fields, + size_range=(10, 100), ): """ Initialize particle positions for multiple species. """ + mol_ids = fields.keys() + + # get n_bins from the shape of the first field array + n_bins = fields[list(fields.keys())[0]].shape + # advection_rates = advection_rates or [(0.0, 0.0) for _ in range(len(n_particles_per_species))] - particles = [] + particles = {} for _ in range(n_particles): - particle = { - 'id': str(uuid.uuid4()), - 'position': tuple(np.random.uniform( - low=[0, 0], - high=[bounds[0], bounds[1]], - size=2)), - 'size': np.random.uniform(size_range[0], size_range[1]), + id = str(uuid.uuid4()) + position = tuple(np.random.uniform(low=[0, 0],high=[bounds[0], bounds[1]],size=2)) + size = np.random.uniform(size_range[0], size_range[1]) + + x, y = Particles.get_bin_position(position, n_bins, ((0.0, bounds[0]), (0.0, bounds[1]))) + # TODO update local and exchange values + local = Particles.get_local_field_values(fields, column=x, row=y) + exchanges = {f: 0.0 for f in mol_ids} # TODO exchange rates + + particles[id] = { + # 'id': str(uuid.uuid4()), + 'position': position, + 'size': size, + 'local': local, + 'exchange': exchanges } - particles.append(particle) + # particles.append(particle) return particles @@ -117,12 +102,13 @@ def update(self, state, interval): particles = state['particles'] fields = state['fields'] # Retrieve the fields - new_particles = [] + new_particles = {'_remove': [], '_add': {}} new_fields = { mol_id: np.zeros_like(field) for mol_id, field in fields.items()} - for particle in particles: - updated_particle = particle.copy() + + for particle_id, particle in particles.items(): + updated_particle = {'exchange': {}} # Apply diffusion and advection dx, dy = np.random.normal(0, self.config['diffusion_rate'], 2) + self.config['advection_rate'] @@ -132,70 +118,54 @@ def update(self, state, interval): # Check and remove particles if they hit specified boundaries if self.check_boundary_hit(new_x_position, new_y_position): + new_particles['_remove'].append(particle_id) continue # Remove particle if it hits a boundary new_position = (new_x_position, new_y_position) - updated_particle['position'] = new_position + updated_particle['position'] = (dx, dy) # new_position # Retrieve local field concentration for each particle - x, y = self.get_bin_position(new_position) - local_field_concentrations = self.get_local_field_values(fields, column=x, row=y) - - # Interact with fields based on the config schema - for field, interaction_params in self.config['field_interactions'].items(): - local_field_value = local_field_concentrations.get(field) - vmax = interaction_params['vmax'] - Km = interaction_params.get('Km') - interaction_type = interaction_params.get('interaction_type', 'uptake') # Default to 'uptake' if not provided - - if interaction_type == 'uptake' and local_field_value: - # Michaelis-Menten-like rate law for uptake - uptake_rate = (vmax * local_field_value) / (Km + local_field_value) - - # Particle uptake rate is proportional to its size - absorbed_value = float(uptake_rate * particle['size']) - - # Update particle size based on the absorbed field value - updated_particle['size'] = max(updated_particle['size'] + 0.01 * absorbed_value, 0.0) - - # Reduce the field concentration in the environment - if local_field_value - absorbed_value < 0.0: - absorbed_value = local_field_value # Cap absorption to available field value - new_fields[field][x, y] = -absorbed_value + x, y = self.get_bin_position(new_position, self.config['n_bins'], self.env_size) - elif interaction_type == 'secretion': - # During secretion, use only vmax - secreted_value = float(vmax * particle['size']) + # Update local environment values for each particle + updated_particle['local'] = self.get_local_field_values(fields, column=x, row=y) - # Update particle size based on the secreted value - updated_particle['size'] = max(updated_particle['size'] - 0.01 * secreted_value, 0.0) + # Apply exchanges and reset + exchange = particle['exchange'] + for mol_id, exchange_rate in exchange.items(): + new_fields[mol_id][x, y] += exchange_rate + updated_particle['exchange'][mol_id] = 0.0 - # Increase the field concentration in the environment - new_fields[field][x, y] += secreted_value # Add secreted value to the field - - new_particles.append(updated_particle) + new_particles[particle_id] = updated_particle # Probabilistically add new particles at user-defined boundaries for boundary in self.config['boundary_to_add']: if np.random.rand() < self.config['add_probability']: + # TODO -- reuse function for initializing particles + position = self.get_boundary_position(boundary) + x, y = self.get_bin_position(position, self.config['n_bins'], self.env_size) + local_field_concentrations = self.get_local_field_values(fields, column=x, row=y) + id = str(uuid.uuid4()) new_particle = { - 'id': str(uuid.uuid4()), - 'position': self.get_boundary_position(boundary), + 'id': id, + 'position': position, 'size': np.random.uniform(10, 100), # Random size for new particles - # 'local': {} # TODO local field values + 'local': local_field_concentrations, + 'exchange': {f: 0.0 for f in fields.keys()} # TODO -- add exchange } - new_particles.append(new_particle) + new_particles['_add'][id] = new_particle return { 'particles': new_particles, 'fields': new_fields } - def get_bin_position(self, position): + @staticmethod + def get_bin_position(position, n_bins, env_size): x, y = position - x_bins, y_bins = self.config['n_bins'] - x_min, x_max = self.env_size[0] - y_min, y_max = self.env_size[1] + x_bins, y_bins = n_bins #self.config['n_bins'] + x_min, x_max = env_size[0] + y_min, y_max = env_size[1] # Convert the particle's (x, y) position to the corresponding bin in the 2D grid x_bin = int((x - x_min) / (x_max - x_min) * x_bins) @@ -207,7 +177,8 @@ def get_bin_position(self, position): return x_bin, y_bin - def get_local_field_values(self, fields, column, row): + @staticmethod + def get_local_field_values(fields, column, row): """ Retrieve local field values for a particle based on its position. @@ -247,7 +218,74 @@ def get_boundary_position(self, boundary): return np.random.uniform(*self.env_size[0]), self.env_size[1][0] -core.register_process('Particles', Particles) +class MinimalParticle(Process): + config_schema = { + 'field_interactions': { + '_type': 'map', + '_value': { + 'vmax': default('float', 0.1), + 'Km': default('float', 1.0), + 'interaction_type': default('enum[uptake,secretion]', 'uptake')}, + '_default': { + 'biomass': { + 'vmax': 0.1, + 'Km': 1.0, + 'interaction_type': 'uptake'}, + 'detritus': { + 'vmax': -0.1, + 'Km': 1.0, + 'interaction_type': 'secretion'}}}} + + + def inputs(self): + return { + 'substrates': 'map[positive_float]' + } + + + def outputs(self): + return { + 'substrates': 'map[positive_float]' + } + + + def update(self, state, interval): + substrates_input = state['substrates'] + exchanges = {} + + # Helper functions for interaction types + def michaelis_menten(uptake_value, vmax, Km): + """Michaelis-Menten rate law for uptake.""" + return (vmax * uptake_value) / (Km + uptake_value) if Km + uptake_value > 0 else 0 + + def calculate_uptake(field_value, vmax, Km): + """Calculate the net uptake value.""" + uptake_rate = michaelis_menten(field_value, vmax, Km) + absorbed_value = min(uptake_rate, field_value) # Limit to available substrate + return -absorbed_value # Negative for uptake + + def calculate_secretion(vmax): + """Calculate the net secretion value.""" + return vmax # Secretion value is directly proportional to vmax + + # Process each field interaction + for field, interaction_params in self.config['field_interactions'].items(): + local_field_value = substrates_input.get(field, 0) + vmax = interaction_params['vmax'] + Km = interaction_params.get('Km', 1) # Default Km to 1 if not specified + interaction_type = interaction_params.get('interaction_type', 'uptake') # Default to 'uptake' + + if interaction_type == 'uptake' and local_field_value > 0: + exchanges[field] = calculate_uptake(local_field_value, vmax, Km) + elif interaction_type == 'secretion': + exchanges[field] = calculate_secretion(vmax) + else: + exchanges[field] = 0 # No interaction by default + + # Return updated substrates + return { + 'substrates': exchanges + } # Helper functions to get specs and states @@ -258,25 +296,21 @@ def get_particles_spec( advection_rate=(0, 0), add_probability=0.0, boundary_to_add=['top'], - field_interactions=None, ): config = locals() # Remove any key-value pair where the value is None config = {key: value for key, value in config.items() if value is not None} return { - '_type': 'process', - 'address': 'local:Particles', - 'config': config, - 'inputs': { - 'particles': ['particles'], - 'fields': ['fields'] - }, - 'outputs': { - 'particles': ['particles'], - 'fields': ['fields'] - } - } + '_type': 'process', + 'address': 'local:Particles', + 'config': config, + 'inputs': { + 'particles': ['particles'], + 'fields': ['fields']}, + 'outputs': { + 'particles': ['particles'], + 'fields': ['fields']}} def get_particles_state( @@ -289,29 +323,34 @@ def get_particles_state( add_probability=0.4, field_interactions=None, initial_min_max=None, + core=None, ): if boundary_to_add is None: boundary_to_add = ['top'] + if field_interactions is None: field_interactions = { 'biomass': {'vmax': 0.1, 'Km': 1.0, 'interaction_type': 'uptake'}, 'detritus': {'vmax': -0.1, 'Km': 1.0, 'interaction_type': 'secretion'}, } + if initial_min_max is None: initial_min_max = { 'biomass': (0.1, 0.2), 'detritus': (0, 0), } - # initialize particles - particles = Particles.initialize_particles(n_particles=n_particles, bounds=bounds) - # initialize fields fields = {} - for field in field_interactions.keys(): - minmax = initial_min_max.get(field, (0, 1)) + for field, minmax in initial_min_max.items(): fields[field] = np.random.uniform(low=minmax[0], high=minmax[1], size=n_bins) + # initialize particles + particles = Particles.initialize_particles( + n_particles=n_particles, + bounds=bounds, + fields=fields) + return { 'fields': fields, 'particles': particles, @@ -322,77 +361,5 @@ def get_particles_state( advection_rate=advection_rate, add_probability=add_probability, boundary_to_add=boundary_to_add, - field_interactions=field_interactions, ) } - - -def run_particles( - total_time=100, # Total frames - bounds=(10.0, 20.0), # Bounds of the environment - n_bins=(20, 40), # Number of bins in the x and y directions - n_particles=20, - diffusion_rate=0.1, - advection_rate=(0, -0.1), - add_probability=0.4, - field_interactions=None, - initial_min_max=None, -): - # Get all local variables as a dictionary - kwargs = locals() - kwargs.pop('total_time') # 'total_time' is only used here, so we pop it - - # initialize particles state - composite_state = get_particles_state(**kwargs) - - # make the composite - print('Making the composite...') - sim = Composite({ - 'state': composite_state, - 'emitter': {'mode': 'all'}, - }, core=core) - - # save the document - sim.save(filename='particles.json', outdir='out') - - # save a viz figure of the initial state - plot_bigraph( - state=sim.state, - schema=sim.composition, - core=core, - out_dir='out', - filename='particles_viz' - ) - - # simulate - print('Simulating...') - sim.update({}, total_time) - - # gather results - particles_results = sim.gather_results() - emitter_results = particles_results[('emitter',)] - # resort results - particles_history = [p['particles'] for p in emitter_results] - - print('Plotting...') - # plot particles - plot_particles( - # total_time=total_time, - history=particles_history, - env_size=((0, bounds[0]), (0, bounds[1])), - out_dir='out', - filename='particles.gif', - ) - - plot_species_distributions_with_particles_to_gif( - particles_results, - out_dir='out', - filename='particle_with_fields.gif', - title='', - skip_frames=1, - bounds=bounds, - ) - - -if __name__ == '__main__': - run_particles() diff --git a/spatio_flux/processes/particles_dfba.py b/spatio_flux/processes/particles_dfba.py index 8cf1fb5..2e69b5f 100644 --- a/spatio_flux/processes/particles_dfba.py +++ b/spatio_flux/processes/particles_dfba.py @@ -1,14 +1,13 @@ """ Particle-COMETS composite made of diffusion-advection and particle processes, with a dFBA within each particle. """ - -from process_bigraph import Composite -from spatio_flux import core +import numpy as np +from process_bigraph import Composite, default from spatio_flux.viz.plot import plot_time_series, plot_species_distributions_with_particles_to_gif # TODO -- need to do this to register??? -from spatio_flux.processes.dfba import DynamicFBA, get_spatial_dfba_state +from spatio_flux.processes.dfba import DynamicFBA, dfba_config, get_spatial_dfba_state from spatio_flux.processes.diffusion_advection import DiffusionAdvection, get_diffusion_advection_spec from spatio_flux.processes.particles import Particles, get_particles_spec, get_particles_state @@ -36,7 +35,8 @@ } -def get_particle_dfba_state( +def get_particles_dfba_state( + core, n_bins=(10, 10), bounds=(10.0, 10.0), mol_ids=None, @@ -77,6 +77,7 @@ def get_particle_dfba_state( particles = Particles.initialize_particles( n_particles=n_particles, bounds=bounds, + mol_ids=mol_ids, ) composite_state['particles'] = particles composite_state['particles_process'] = get_particles_spec( @@ -86,56 +87,14 @@ def get_particle_dfba_state( advection_rate=particle_advection_rate, add_probability=particle_add_probability, boundary_to_add=particle_boundary_to_add, - field_interactions=field_interactions, + # field_interactions=field_interactions, ) + initial_fields = { + mol_id: np.random.uniform(low=initial_min_max[mol_id][0], high=initial_min_max[mol_id][1], size=n_bins) + for mol_id in mol_ids} + composite_state['fields'] =initial_fields return composite_state -def run_particle_dfba( - total_time=10.0, - **kwargs -): - # make the composite state - composite_state = get_particle_dfba_state(**kwargs) - - # make the composite - print('Making the composite...') - sim = Composite({ - 'state': composite_state, - 'emitter': {'mode': 'all'}, - }, core=core) - - # save the document - sim.save(filename='particle_comets.json', outdir='out', include_schema=True) - - # TODO -- save a viz figure of the initial state - - # simulate - print('Simulating...') - sim.update({}, total_time) - particle_comets_results = sim.gather_results() - # print(comets_results) - - print('Plotting results...') - n_bins = kwargs.get('n_bins', default_config['n_bins']) - bounds = kwargs.get('bounds', default_config['bounds']) - # plot timeseries - plot_time_series( - particle_comets_results, - coordinates=[(0, 0), (n_bins[0]-1, n_bins[1]-1)], - out_dir='out', - filename='particle_comets_timeseries.png' - ) - - plot_species_distributions_with_particles_to_gif( - particle_comets_results, - out_dir='out', - filename='particle_comets_with_fields.gif', - title='', - skip_frames=1, - bounds=bounds, - ) - - if __name__ == '__main__': run_particle_dfba(**default_config) diff --git a/spatio_flux/tests.py b/spatio_flux/tests.py new file mode 100644 index 0000000..d0e551a --- /dev/null +++ b/spatio_flux/tests.py @@ -0,0 +1,390 @@ +from bigraph_viz import plot_bigraph +from process_bigraph import Composite, ProcessTypes, default + +from spatio_flux import register_types +from spatio_flux.viz.plot import ( + plot_time_series, + plot_species_distributions_to_gif, + plot_species_distributions_with_particles_to_gif, + plot_particles +) +from spatio_flux.processes.dfba import get_single_dfba_spec, get_spatial_dfba_state, dfba_config +from spatio_flux.processes.diffusion_advection import get_diffusion_advection_spec, get_diffusion_advection_state +from spatio_flux.processes.particles import MinimalParticle, get_particles_state +from spatio_flux.processes.particle_comets import get_particle_comets_state, default_config +from spatio_flux.processes.particles_dfba import get_particles_dfba_state, default_config + + +def run_dfba_single( + total_time=60, + mol_ids=None, + core=None, +): + single_dfba_config = { + 'dfba': get_single_dfba_spec(path=['fields']), + 'fields': { + 'glucose': 10, + 'acetate': 0, + 'biomass': 0.1 + } + } + + # make the simulation + sim = Composite({ + 'state': single_dfba_config, + 'emitter': {'mode': 'all'} + }, core=core) + + # save the document + sim.save(filename='single_dfba.json', outdir='out') + + # simulate + print('Simulating...') + sim.update({}, total_time) + + # gather results + dfba_results = sim.gather_results() + + print('Plotting results...') + # plot timeseries + plot_time_series( + dfba_results, + # coordinates=[(0, 0), (1, 1), (2, 2)], + out_dir='out', + filename='dfba_single_timeseries.png', + ) + + +def run_dfba_spatial( + total_time=60, + n_bins=(3, 3), # TODO -- why can't do (5, 10)?? + mol_ids=None, + core=None +): + if mol_ids is None: + mol_ids = ['glucose', 'acetate', 'biomass'] + composite_state = get_spatial_dfba_state( + n_bins=n_bins, + mol_ids=mol_ids, + ) + + # make the composite + print('Making the composite...') + sim = Composite({ + 'state': composite_state, + 'emitter': {'mode': 'all'} + }, core=core) + + # save the document + sim.save(filename='spatial_dfba.json', outdir='out') + + # # save a viz figure of the initial state + # plot_bigraph( + # state=sim.state, + # schema=sim.composition, + # core=core, + # out_dir='out', + # filename='dfba_spatial_viz' + # ) + + # simulate + print('Simulating...') + sim.update({}, total_time) + + # gather results + dfba_results = sim.gather_results() + + print('Plotting results...') + # plot timeseries + plot_time_series( + dfba_results, + coordinates=[(0, 0), (1, 1), (2, 2)], + out_dir='out', + filename='spatial_dfba_timeseries.png', + ) + + # make video + plot_species_distributions_to_gif( + dfba_results, + out_dir='out', + filename='spatial_dfba_results.gif', + title='', + skip_frames=1 + ) + + +def run_diffusion_process( + total_time=60, + bounds=(10.0, 10.0), + n_bins=(10, 10), + core=None, +): + composite_state = get_diffusion_advection_state( + bounds=bounds, + n_bins=n_bins, + mol_ids=['glucose', 'acetate', 'biomass'], + advection_coeffs={ + 'biomass': (0, -0.1) + } + ) + + # make the composite + print('Making the composite...') + sim = Composite({ + 'state': composite_state, + 'emitter': {'mode': 'all'}, + }, core=core) + + # save the document + sim.save(filename='diffadv.json', outdir='out') + + # save a viz figure of the initial state + plot_bigraph( + state=sim.state, + schema=sim.composition, + core=core, + out_dir='out', + filename='diffadv_viz' + ) + + # simulate + print('Simulating...') + sim.update({}, total_time) + + # gather results + diffadv_results = sim.gather_results() + + print('Plotting results...') + # plot 2d video + plot_species_distributions_to_gif( + diffadv_results, + out_dir='out', + filename='diffadv_results.gif', + title='', + skip_frames=1 + ) + + +def run_particles( + core, + total_time=60, # Total frames + bounds=(10.0, 20.0), # Bounds of the environment + n_bins=(20, 40), # Number of bins in the x and y directions + n_particles=1, # 20 + diffusion_rate=0.1, + advection_rate=(0, -0.1), + add_probability=0.4, + field_interactions=None, + initial_min_max=None, +): + # Get all local variables as a dictionary + kwargs = locals() + kwargs.pop('total_time') # 'total_time' is only used here, so we pop it + + # initialize particles state + composite_state = get_particles_state(**kwargs) + + # TODO -- is this how to link in the minimal_particle process? + # declare minimal particle in the composition + composition = { + 'particles': { + '_type': 'map', + '_value': { + # '_inherit': 'particle', + 'minimal_particle': { + '_type': 'process', + 'address': default('string', 'local:MinimalParticle'), + 'config': MinimalParticle.config_schema, + 'inputs': default('tree[wires]', {'substrates': ['local']}), # TODO -- what sets this??? Particles + 'outputs': default('tree[wires]', {'substrates': ['exchange']}) + } + } + } + } + + # make the composite + print('Making the composite...') + sim = Composite({ + 'state': composite_state, + 'composition': composition, + 'emitter': {'mode': 'all'}, + }, core=core) + + # save the document + sim.save( + filename='particles.json', + outdir='out') + + # save a viz figure of the initial state + plot_bigraph( + state=sim.state, + schema=sim.composition, + core=core, + out_dir='out', + filename='particles_viz' + ) + + # simulate + print('Simulating...') + sim.update({}, total_time) + + # gather results + particles_results = sim.gather_results() + emitter_results = particles_results[('emitter',)] + # resort results + particles_history = [p['particles'] for p in emitter_results] + + print('Plotting...') + # plot particles + plot_particles( + # total_time=total_time, + history=particles_history, + env_size=((0, bounds[0]), (0, bounds[1])), + out_dir='out', + filename='particles.gif', + ) + + plot_species_distributions_with_particles_to_gif( + particles_results, + out_dir='out', + filename='particle_with_fields.gif', + title='', + skip_frames=1, + bounds=bounds, + ) + + +def run_particle_comets( + core, + total_time=10.0, + **kwargs +): + # make the composite state + composite_state = get_particle_comets_state(**kwargs) + + # make the composite + print('Making the composite...') + sim = Composite({ + 'state': composite_state, + 'emitter': {'mode': 'all'}, + }, core=core) + + # save the document + sim.save( + filename='particle_comets.json', + outdir='out') + + # # save a viz figure of the initial state + # plot_bigraph( + # state=sim.state, + # schema=sim.composition, + # core=core, + # out_dir='out', + # filename='particles_comets_viz' + # ) + + # simulate + print('Simulating...') + sim.update({}, total_time) + particle_comets_results = sim.gather_results() + # print(comets_results) + + print('Plotting results...') + n_bins = composite_state['particles_process']['config']['n_bins'] + bounds = composite_state['particles_process']['config']['bounds'] + + # plot timeseries + plot_time_series( + particle_comets_results, + coordinates=[(0, 0), (n_bins[0]-1, n_bins[1]-1)], + out_dir='out', + filename='particle_comets_timeseries.png' + ) + + plot_species_distributions_with_particles_to_gif( + particle_comets_results, + out_dir='out', + filename='particle_comets_with_fields.gif', + title='', + skip_frames=1, + bounds=bounds, + ) + + +def run_particles_dfba( + core, + total_time=10.0, + n_bins=None, + bounds=None): + + # make the composite state + composite_state = get_particles_dfba_state(core) + + composition = { + 'particles': { + '_type': 'map', + '_value': { + 'dFBA': { + '_type': 'process', + 'address': default('string', 'local:DynamicFBA'), + 'config': default('tree[any]', dfba_config(model_file='textbook')), + 'inputs': default('tree[wires]', {'substrates': ['local']}), + 'outputs': default('tree[wires]', {'substrates': ['exchange']}) + } + } + } + } + + # make the composite + print('Making the composite...') + sim = Composite({ + 'composition': composition, + 'state': composite_state, + 'emitter': {'mode': 'all'}, + }, core=core) + + # save the document + sim.save( + filename='particle_comets.json', + outdir='out') + + # TODO -- save a viz figure of the initial state + + # simulate + print('Simulating...') + sim.update({}, total_time) + particle_comets_results = sim.gather_results() + + print('Plotting results...') + n_bins = composite_state['particles_process']['config']['n_bins'] + bounds = composite_state['particles_process']['config']['bounds'] + + # plot timeseries + plot_time_series( + particle_comets_results, + coordinates=[(0, 0), (n_bins[0]-1, n_bins[1]-1)], + out_dir='out', + filename='particle_comets_timeseries.png' + ) + + plot_species_distributions_with_particles_to_gif( + particle_comets_results, + out_dir='out', + filename='particle_comets_with_fields.gif', + title='', + skip_frames=1, + bounds=bounds, + ) + + + +if __name__ == '__main__': + core = ProcessTypes() + core = register_types(core) + + run_dfba_single(core=core) + run_dfba_spatial(core=core, n_bins=(4,4), total_time=60) + run_diffusion_process(core=core) + # run_particles(core) + # run_particle_comets(core) + # run_particles_dfba(core) \ No newline at end of file diff --git a/spatio_flux/viz/plot.py b/spatio_flux/viz/plot.py index ed0e80f..6bd1d52 100644 --- a/spatio_flux/viz/plot.py +++ b/spatio_flux/viz/plot.py @@ -50,7 +50,7 @@ def plot_time_series( # coordinates = coordinates or [(0, 0)] field_names = field_names or ['glucose', 'acetate', 'biomass'] sorted_results = sort_results(results) - time = sorted_results['time'] + times = sorted_results['time'] # Initialize the plot fig, ax = plt.subplots(figsize=(12, 6)) @@ -59,12 +59,12 @@ def plot_time_series( if field_name in sorted_results['fields']: field_data = sorted_results['fields'][field_name] if coordinates is None: - ax.plot(time, field_data, label=field_name) + ax.plot(times, field_data, label=field_name) else: for coord in coordinates: x, y = coord - time_series = [field_data[t][x, y] for t in range(len(time))] - ax.plot(time, time_series, label=f'{field_name} at {coord}') + time_series = [field_data[t][x, y] for t in range(len(times))] + ax.plot(times, time_series, label=f'{field_name} at {coord}') # plot log scale on y axis # ax.set_yscale('log') else: @@ -243,7 +243,7 @@ def plot_species_distributions_with_particles_to_gif( ax.set_title(f'{species} at t = {times[i]:.2f}') plt.colorbar(img, ax=ax) - for particle in particles: + for particle_id, particle in particles.items(): ax.scatter(particle['position'][0], particle['position'][1], s=particle['size'], color='b' @@ -312,7 +312,7 @@ def plot_particles( ax.set_aspect('equal') particles = history[frame] - for particle in particles: + for particle_id, particle in particles.items(): ax.scatter(particle['position'][0], particle['position'][1], s=particle['size'], color='b')