diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..a4a7144 --- /dev/null +++ b/.gitignore @@ -0,0 +1,216 @@ +# Created by https://www.gitignore.io/api/linux,python,pycharm,windows,jupyternotebooks +# Edit at https://www.gitignore.io/?templates=linux,python,pycharm,windows,jupyternotebooks + +### JupyterNotebooks ### +# gitignore template for Jupyter Notebooks +# website: http://jupyter.org/ + +.ipynb_checkpoints +*/.ipynb_checkpoints/* + +# IPython +profile_default/ +ipython_config.py + +# Remove previous ipynb_checkpoints +# git rm -r .ipynb_checkpoints/ + +### Linux ### +*~ + +# temporary files which can be created if a process still has a handle open of a deleted file +.fuse_hidden* + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* + +# .nfs files are created when an open file is removed but is still being accessed +.nfs* + +### PyCharm ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/ + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +### PyCharm Patch ### +# Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 + +# *.iml +# modules.xml +# .idea/misc.xml +# *.ipr + +# Sonarlint plugin +.idea/**/sonarlint/ + +# SonarQube Plugin +.idea/**/sonarIssues.xml + +# Markdown Navigator plugin +.idea/**/markdown-navigator.xml +.idea/**/markdown-navigator/ + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# Mr Developer +.mr.developer.cfg +.project +.pydevproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +### Windows ### +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +# End of https://www.gitignore.io/api/linux,python,pycharm,windows,jupyternotebooks \ No newline at end of file diff --git a/notebooks/basics.ipynb b/notebooks/basics.ipynb new file mode 100644 index 0000000..a983888 --- /dev/null +++ b/notebooks/basics.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pyncov-19: Simulating the spread of SARS-CoV-2\n", + "\n", + "Pyncov-19 is a tiny probabilistic simulator for SARS-CoV-2 implemented in Python 3, whose only dependency is Numpy 1.18.\n", + "This simulator is used to learn and predict the temporal dynamics of COVID-19 that are shown in https://covid19-modeling.github.io. It implements a probabilistic compartmental model at the individual level using a Markov Chain model with temporal transitions that were adjusted using the most recent scientific evidence.\n", + "\n", + "This library is still a proof-of-concept and it's inteded only to be used for research and experimentation. For more information please read our [preprint](https://arxiv.org/abs/2004.13695):\n", + "\n", + "\n", + " Matabuena, M., Meijide-García, C., Rodríguez-Mier, P., & Leborán, V. (2020). \n", + " COVID-19: Estimating spread in Spain solving an inverse problem with a probabilistic model. \n", + " arXiv preprint arXiv:2004.13695. https://arxiv.org/abs/2004.13695\n", + "\n", + "\n", + "This model's main goal is to estimate the levels of infections (or the seroprevalence) of the population, using only data from the registered deaths caused by COVID-19. Although the model can be used to make future predictions (evolution of infections, fatalities, etc.), that's not the primary purpose of the model. Given the uncertainty about essential events that alter the course and dynamics of the spread (for example, the use of masks, lockdowns, social distance, etc.), it is tough to make accurate predictions, so we limit ourselves to use the model to reveal more information about what happened before (backcasting).\n", + "\n", + "\n", + "@author: Pablo Rodríguez Mier ([@pablormier](https://twitter.com/pablormier))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyncov-19 0.1.0\n", + "numpy 1.18.4\n", + "pandas 1.0.3\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "import sys\n", + "sys.path.append('../')\n", + "\n", + "# Extra libs: required for plotting and data manipulation only\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import pyncov as sim\n", + "import numpy as np\n", + "\n", + "print(\"pyncov-19\", sim.__version__)\n", + "print(\"numpy\", np.__version__)\n", + "print(\"pandas\", pd.__version__)\n", + "\n", + "try:\n", + " plt.style.use('seaborn-whitegrid')\n", + "except:\n", + " print('Using default style')\n", + "\n", + "figsize = (16, 6)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What's inside the model?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyncov-19 implements a compartmental model based on a Markov Chain with temporal transitions, consisting of six states:\n", + "- I1: Infected, incubating the virus\n", + "- I2: Infected, mild symptoms or asymptomatic\n", + "- I3: Infected, severe symptoms\n", + "- R1: Recovered, but still infectious\n", + "- R2: Recovered, cannot infect\n", + "- M0: Dead\n", + "\n", + "\n", + "People infected will move between the different states using the Markov Chain and scheduled on a daily basis. The simulator samples the transition and the time at which each person is going to transit each day. For example, a recently infected person in state I1 (incubating the virus) will move with some probability to I2 (mild symptoms or asymptomatic) or I3 (severe symptoms). If the person is determined to move to I2, it will transit to this state after a concrete number of days, which is sampled from a distribution. On average, people incubating the virus (I1) will present symptoms on the fifth day after contracting the virus. Still, in some situations, the incubation period is slower, and symptoms present after 9-10 days. \n", + "\n", + "\n", + "\n", + "\n", + "Each day, the simulator injects new infected people in the system, sampling from a $Poisson(\\lambda_t)$. The number of new infections entering the system is on average $\\lambda_t$, and it depends on two factors: the number of people who can infect $k_t$ at time $t$, and the dynamic individual reproductive number $Ri_t$. \n", + "\n", + "In our model, $\\lambda_t$ is defined as $\\lambda_t = k_t * Ri_t$. This dynamic individual reproductive number cannot be directly compared with the reproduction number $R_t$ used in traditional ODE-based models (SIR and variants). Its interpretation and analysis is more complicated.\n", + "\n", + "People infected will move between the different states using the Markov Chain, scheduled on a daily basis. The simulator samples the transition and the time at which each person is going to transit each day. For example, a recently infected person in state I1 (incubating the virus), will move with some probability to I2 (mild symptoms or asymptomatic) or I3 (severe symptoms). If the person is determined to move to I2, it will transit to this state after a concrete number of days, which is sampled from a distribution. On average, people incubating the virus (I1) will present symptoms on the fifth day after contracting the virus. Still, in some situations, the incubation period is slower, and symptoms present after 9-10 days.\n", + "\n", + "Distributions are adequately configured in Pyncov-19 using the current evidence in the scientific literature. However, that can be changed to model other situations or explore different scenarios.\n", + "\n", + "In order to explore the default configuration of the model, we can sample days using the timeSimulator in pyncov-19:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Configuration of the markov chain\n", + "n = 100000\n", + "bins = 100\n", + "m = sim.build_markovchain(sim.MARKOV_DEFAULT_PARAMS)\n", + "fig, ax = plt.subplots(2, 3, figsize=(16, 10))\n", + "fig.suptitle(\"Transmission times between the states of the model\", fontsize=24)\n", + "pd.Series(m.timeSimulator[sim.S.I1, sim.S.I2](n)).plot.hist(bins=bins, ax=ax[0,0], title='I1 to I2');\n", + "pd.Series(m.timeSimulator[sim.S.I1, sim.S.I3](n)).plot.hist(bins=bins, ax=ax[0,1], title='I1 to I3');\n", + "pd.Series(m.timeSimulator[sim.S.I3, sim.S.M0](n)).plot.hist(bins=bins, ax=ax[0,2], title='I3 to M');\n", + "pd.Series(m.timeSimulator[sim.S.I3, sim.S.R1](n)).plot.hist(bins=bins, ax=ax[1,0], title='I3 to R1');\n", + "pd.Series(m.timeSimulator[sim.S.R1, sim.S.R2](n)).plot.hist(bins=bins, ax=ax[1,1], title='R1 to R2');\n", + "pd.Series(m.timeSimulator[sim.S.I2, sim.S.R1](n)).plot.hist(bins=bins, ax=ax[1,2], title='I2 to R1');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dynamic individual infection rate $R_i(t)$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "rt_params = [0.8, 0.85, 12, 0.01]\n", + "params = rt_params + sim.MARKOV_DEFAULT_PARAMS\n", + "daily_ri_values = [sim.default_rit_function(i, rt_params) for i in range(60)]\n", + "pd.Series(daily_ri_values).plot(title=\"Dynamic individual infection rate $R_i(t)$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this configuration as an example, we can start doing simulations of the different trajectories that the epidemic can follow for a given population. The method sample_chains allows to generate C chains. It returns a matrix of shape (C, D, S) where C are the chains (simulations), D are the simulated days, and S are the states." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5b68ff6bedce43b1adfb659d216af20b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "num_chains = 1000\n", + "initial_infections = 1\n", + "susceptible_population = 999999\n", + "population = initial_infections + susceptible_population\n", + "num_days = 60\n", + "\n", + "simulations = sim.sample_chains(susceptible_population, initial_infections, m, daily_ri_values, \n", + " num_chains=num_chains, n_workers=4, show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 60, 6)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The dimensions are num_chains x num_days x states\n", + "simulations.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizing trajectories" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's represent the infections and fatalities of one of the possible trajectories" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=figsize)\n", + "pd.Series(simulations[0,:,sim.S.I1], name='Infections').plot.bar(ax=ax, legend=True);\n", + "pd.Series(simulations[0,:,sim.S.M0], name='Fatalities').plot.bar(ax=ax, color='r', legend=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What's more interesting is to plot all the simulated trajectories for different states of the model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_state(sims, state, ax, index=None, title=None):\n", + " df_a = pd.DataFrame(sims[:,:,state]).transpose()\n", + " df_b = pd.Series(np.mean(sims[:,:,state], axis=0))\n", + " if index is not None:\n", + " df_a = df_a.set_index(index)\n", + " df_b = pd.Series(np.mean(sims[:,:,state], axis=0), index=index)\n", + " df_a.plot.line(ax=ax, alpha=0.01, title=title, color='k', legend=False);\n", + " df_b.plot.line(ax=ax, color='r', linestyle='--');\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=figsize)\n", + "plot_state(simulations, sim.S.I1, ax[0], title=\"New infections over time\")\n", + "plot_state(simulations, sim.S.M0, ax[1], title=\"Total deaths\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulations with real data\n", + "## Madrid\n", + "\n", + "Let's have a look at the data from Madrid. We will use the data provided by [Datadista](https://github.com/datadista/datasets)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df_spain = pd.read_csv('https://raw.githubusercontent.com/datadista/datasets/master/COVID%2019/ccaa_covid19_fallecidos.csv')\n", + "df = df_spain.query('CCAA == \"Madrid\"').drop(['cod_ine','CCAA'], axis=1).squeeze()\n", + "df.index = pd.DatetimeIndex(df.index, freq='D')\n", + "\n", + "# Add leading zeroes to the time series\n", + "df = df.reindex(pd.date_range(df.index[0] - 17 * df.index.freq, df.index[-1]), fill_value=0)\n", + "df.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Parameters were obtained by fitting the stochastic model with CMA-ES. Training the model is out of the scope of this tutorial.\n", + "# NOTE: Remember that the Ri(t) function is not like the R(t) reproductive number of classical models.\n", + "\n", + "rit_params = [1.76206245, 0.73465654, 11.46818215, 0.01691976]\n", + "daily_ri_values = [sim.default_rit_function(i, rit_params) for i in range(len(df.index))]\n", + "pd.Series(daily_ri_values, index=df.index).plot(title=\"Dynamic individual infection rate $R_i(t)$ in Madrid\");" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bfca0b2894da4c0abb0225ef75568741", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1000.0), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Simulate using the population from Madrid (~6.680.000)\n", + "# Use 4 processed to parallelize the generation of the chains\n", + "\n", + "simulations = sim.sample_chains(6680000, initial_infections, m, daily_ri_values, \n", + " num_chains=1000, n_workers=4, show_progress=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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hhBBCCCGNgxWvYbohTLBWa5xR1JJQxJHVlSuZTKJYLMLzvJJlmAghhBBCCCGNQRzxKjoirts6FChqiROpmK7KJ2HIhBBCCCGEkPFNOQE7nOI1DIpa4iRK1NKtJYQQQgghZPyhhWs593U0zbFDUUuclJsMim4tIYQQQgghY5e4Albej2YoaskgolxagTMhE0IIIYQQMnaIErFjScC6oKgloZQTtYlEAp7nIZVKDWOpCCGEEEIIIXEQAVtuHOxYh6KWDCKu+0q3lhBCCCGEkNFDo4hYC0UtGYTv+7EmgJKbgqKWEEIIIYSQ4SVOOHGjtNEpakkJccbTahrlRiGEEEIIIWSkKefEyvtGg6KWlFDNjMacBZkQQgghhJDa06jhxJVCUUtKqDSUmONqCSGEEEIIqR0uIUsRGw1FLQmoNPRYp6WoJYQQQgghpDooZIcGRS0JqEbUVpOeEEIIIYSQRodCtnZQ1JISqr2BOK6WEEIIIYSQaChk6wNFLQmoNoSY42oJIYQQQghxQyFbfyhqSQnVilqA42oJIWQ0UiwWsXbtWjz33HNIpVLYuHEjjh07hve///1405veBAC45ZZbcPXVV2PLli3YvXs30uk0Ojo6MGvWLBw8eBCrV69GIpHAOeecg/Xr1yOZTDrTEkIIOYXv+/A8L/hMIVs/KGoJgKGHD/PmJISQ0cnjjz8OANi+fTu6urqwceNGvOtd78Jtt92G5cuXB+m6u7uxf/9+7NixA0eOHEF7ezt27tyJjRs3YuXKlbjwwgvR2dmJXbt2oa2tzZmWEEIaHevKUsgODxS1pISh3HAcV0sIIaOPyy+/HO985zsBAIcPH8Zpp52GZ599Fs899xx27dqFs846Cx0dHThw4ADmzZuHRCKBtrY2FItFHD16FN3d3Zg7dy4AYP78+di3bx+mTZvmTNva2jqCZ0oIISNDWHhxMpkcwVI1FhS1BEBtnFqOqyWEkNFJOp3GqlWr8Oijj+K+++7Dyy+/jJtuugnnn38+vvjFL+ILX/gCJk+ejKlTpwb7tLS04NixYyXPddnW29vrTGtFbU9Pz/Cc4AjS39/fEOdJwmEdaCz0701XdvRAUUsChnID8uYlhJDRzaZNm3DnnXfi5ptvxvbt23H66acDAK644gps2LABl112Gfr6+oL0fX19mDx5conT0NfXhylTpmDSpEnOtJYZM2bU8YxGBz09PQ1xniQc1oHGoqenB+eddx6F7Ahw4MCB0O/oiRMAtQkdFreWEELI6OE73/kOHnzwQQDAhAkTkEgk8MEPfhBPP/00AODJJ5/EW97yFsyePRt79+6F53k4fPgwPM9Da2srZs6cia6uLgDAnj17MGfOnNC0hBAyXpFJn4rFYhDBkkwmkUwmKWhHAXRqSUAtbkiKWkIIGV1ceeWVuPvuu7Fs2TIUCgV0dHTgjDPOwIYNG5DJZHDaaadhw4YNmDRpEubMmYPFixfD8zx0dnYCAFatWoV169Zh8+bNmD59OhYsWIBUKuVMSwgh4w1XiHEqlRrhUhELRS2pmRDluFpCCBl9TJw4EZ///OcHbd++ffugbe3t7Whvby/ZNm3aNDzyyCOx0hJCyHjBLscjjiwnfxqdUNSSgKEKUQpZQgghhBAyltFiluNlxw4UtaTmIcN0agkhhBBCyFjChhlzrOzYgqKW1BTe/IQQQgghZKxAMTs+oKglNXdWOVkUIYQQQggZzVDMji8oaklN4WRRhBBCCCFkNON5HsXsOIOitsHR05PXAj4UCCGEEELIaIRidvxCUUvqAp1aQgghhBAyGrAzGnNZnvEHRS0BUFuHlWKWEEIIIYSMBsSd5fI84xuK2ganXpM6cbIoQgghhBAyUmh3lqHG4x+KWlLzm1wmiyKEEEIIIWQ4YahxY0JR2+DUc+wrx9USQgghhJDhQC/Rw1DjxoOiltTFqQUoagkhhBBCSP3hrMaEoraBqWeIMB8mhBBCCCGknjDUmAgUtaSu4ceEEEIIIYTUGs5qTDQUtQ1MvZ1ailpCCCGEEFJLOKsxcUFR2+DU+0HAcbWEEEIIIaQW0J0lYVDUNjD1FJx8yBBCCCGEkFpAd5aUg6OpSV1hCDIhhBBCCKkWz/MoaElZ6NQ2OPV8MHBcLSGEEEIIqRYdbsyZjUkUFLUNynCITYpaQgghhBBSKQw3JpVCUUvqDieLIoQQQgghcRBBS3eWVAJFbYNT7/BjQgghhBBC4sBwY1ItrC0NynCGBTMEmRBCCCGEREFBS4YCnVpSV+jWEkIIIYSQMDh+ltQCdoM0MMP10KBTSwghhBBCLBS0pFbQqW1QhmvyJs6ATAghhBBCLAw3JrWkKlGbz+exevVqHDp0CMlkEhs2bEA6ncbq1auRSCRwzjnnYP369Ugmk9iyZQt2796NdDqNjo4OzJo1CwcPHhxyWjJ0hrM3jDMgE0IIIYQQgIKW1J6qatEPfvADFAoFbN++HStWrMDnPvc5bNy4EStXrsS2bdvg+z527dqF7u5u7N+/Hzt27MDmzZtxzz33AMCQ05KxA4UsIYQQQggRRNAmk0kKWlIzqqpJ06ZNQ7FYhOd56O3tRTqdRnd3N+bOnQsAmD9/Pp544gkcOHAA8+bNQyKRQFtbG4rFIo4ePTrktGRojEQ4MEOQCSGEEEIaGy1oaXyQWlJV+PHEiRNx6NAh/Mmf/AleffVVPPDAA/jRj34UVM6WlhYcO3YMvb29mDp1arCfbNehqNWkddHT01PNqYwp+vv7a3KeMig/lUrVoFTl0RMAkKFRqzpAxgb8vQkhhIwXKGhJPalK1H7lK1/BvHnz8NGPfhRHjhzBe97zHuTz+eD7vr4+TJkyBZMmTUJfX1/J9smTJ5eIm2rSupgxY0Y1pzKm6Onpqcl5joSo9X1/2I43nqlVHSBjA/7eI8eBAwdGugiEEDJuKBaLADjDMakfVVlnU6ZMweTJkwEAv/d7v4dCoYCZM2eiq6sLALBnzx7MmTMHs2fPxt69e+F5Hg4fPgzP89Da2jrktGRoDHcosDy8GIJMCCHDT7FYxN13340lS5Zg2bJleP7553Hw4EHccsstWLp0KdavXx9E1GzZsgWLFi3CkiVL8PTTTwNARWkJIcRCQUuGg6qc2ve+973o6OjA0qVLkc/n8eEPfxjnn38+1q1bh82bN2P69OlYsGABUqkU5syZg8WLF8PzPHR2dgIAVq1aNaS0ZOjwoUIIIY3B448/DgDYvn07urq6sHHjRvi+j5UrV+LCCy9EZ2cndu3ahba2tmDCxiNHjqC9vR07d+4MJmyMk5YQQjQUtGS4qErUtrS04POf//yg7Y888sigbe3t7Whvby/ZNm3atCGnJcOPOK2VPpS0U8sHGiGEDC+XX3453vnOdwIADh8+jNNOOw27d+8umYRx3759mDZtWqwJG6PStra2jtRpEkJGGRS0ZDipStSSsU0l4tL3/eBP4MOJEELGFul0GqtWrcKjjz6K++67D48//nhdJne0orYRJjrjhG6EdWAw41nQ8vcenVDUNihxHjAywZOkTyQSVU8yJfsSQggZGTZt2oQ777wTN998MwYGBoLttZzc0dIIE51xQjfCOlDKeBa0AH/vkSRqEkeusdJgVCIspXc+lUoFDyZp3MhEIXEZjw81QggZC3znO9/Bgw8+CACYMGECEokEzj///LpM7kgIaWzGu6Alw4OOFPU8L/iLgk4tcRI1flZc12rGyHJcLSGEDC9XXnkl7r77bixbtgyFQgEdHR1485vfXJfJHQkhjQsFLYlCG2vWZItjupWrUxS1DUq5ihElapPJJIrFYkUClQ83QggZGSZOnDhskzsSQhoTCloCuIVrlGCVumJf7fdxoKhtMOKGH5cTrMlkMhhzW0mFo1NLCCGEEDJ+oKBtLFxiNUxfyJw8ul7Uq45Q1JJBSMXUk4NYpIJWMmkUH3SEEEIIIeMHCtrxyWgVrlFQ1DYg5Sqa53mxKmM1Y2s5AzIhhBBCyNiHgnbsY8Wrq52uQ4PDwoRHAxS1DUY5ARo1ltYiaeKKWi7rQwghhBAy9qGgHXuIESXvLdZxHWu/K0UtKSFK1Lq+q0aoclwtIYQQQsjYRJZWoaAdncR1X8eqeA2DorYBKefU6u/lwaVvCP0QqyQEebzcNIQQQgghjYhMEkpBOzrQwnUsu69DXe4HoKhtKMpVCuvE6tmNZdIoWQRZJoeqNAS50rSEEEIIIWTkoaAdWeIKWHk/WghzjMvpEtdY3igoakmAFbVW0Mp3xWIRnucF2ysJQR5NNxkhhBBCCCkPBe3wEyViR5OADXNZo2ZLltehrEtroahtQMIqjHZQo8bW2jVqK50FmZNFEUIIIYSMDShohw9pT4eJ2JG6/nHG6QojNVsyRW0DEUdMWpfWVQl1eHIqlaooBJkzIBNCCCGEjA0oaOtHHCd2OK95XOEaJlZHun5Q1BIAg8MF5AEWhsutrfR4I135CSGEEEKIGx2FxzZbbRgNTmy1syOP9jpAUdtgxHFSZcbjqLRS2bVbK/sN5fiEEEIIIWRkkYlB7dwqpDLC3NjhErFjfW3aSqI7KWobiCh31Dq1YevUagc3mUyiWCzG2reSshBCCCGEkJGBgnZouNxYuZb1avuWc19Ho3itdIKpclDUEgCnRKZrBmR7Y7pmPtZL/nC9WkIIIYSQsQcFbXWECdl6uLFjQcAOZRmfasfrUtQ2GOVCirWotevU6m16jIUOV47bu8LJogghhBBCRhfSpqOgjYd0Agi1FrJx16Yd6Uml7HtL1PjcWpWborZBiKpoYeHDdskeuWnlvWvm4zhL+3AGZEIIIYSQ0UWxWAQApFKpES7J6EbawdoIqpWojBoDG7W+az1wCdZyMyKP5GzIFLVkEMlksiSMQt9EUQK4khDkStIRQgghhJD6QYc2Gt0u1tdqqO3YOLMhD8dkUq5Xy2gIa46CorbBCJsASl61SPU8D+l0uqQC65tZu7V2LG6lZSCEEEIIIcMP16J1EzXhU7XiP8yJHQ6xWMlSPqNp/VmhXJQnRS0pCR3WocbycLMVW4tYmUyg3JThUcclhBBCCCHDD9eiLSXOhE+VXqdyedbjupcbiytlcL0OJ1FjcisdqkhR2yDEmW3MjosN67GTpXwk32rH1RJCCCGEkJGBMx2fQo+RBYY2TjZMUNZDxFbivsr74SBKoMbRJK7XclDUNjhRs5dFVaJkMjloJuRKx9VysihCCCGEkJGh0cfRWgd1qEI2LFRZ3teivPpYluEe8xp3LK4un+vVvq8WitoGIm6Fieq10yEqeokfPblU3HG1FLWEEEIIIcNPIwta1zI81VyHOKHKQymjPoZlJMbg2veu8tiyDGdkJkVtgxAmNu2NKJ+142ortR5r65oR2eZbTbkIIYQQQkjtacSJoWrtysowvKHmJfm5Xm3+8r6WDFW4Dkf9iaspKGobHB06rD/rCaOAU71Ysk1mPZbxtTYEuZxr2ygPUUIIIYSQ0UKjTQzlErNDdWUlorHWY26lfCM19tYeXz7Xg6GMuQ2DoraBiFMxfd8PBGuxWBx088t2PTuy7CdithLo1BJCCCGEDA+NMjGUFbPVuNJR42QruX5xJo4aicmjXK+1OrbruHE0wlAENUVtAxPVS6N7tKRXSl4zmUzwuVwIMmdAJoQQQggZeRphHK0dL1upmI0zTjauSVTOia0FcUOXayFeqx1uWO8JogSK2gYgrnuqQ409z4PneYPGDABAsVgMZj+W3iodgixp4xyXk0URQgghhNQXPY52PGKHzFUqHIc65jaOGztUKpn9uFbje+171/Hkvd4+ElDUNhC2krl6d1KpVMmNnUgkkEqlgtBiHXos+9gQZPm+nGAV4csQZEIIIYSQ+jCex9EOVczq9WkltLgSIRsWnizvq6Wek0fFHV+rjzNcolW77K7JaqOgqG1g9OROUkH1ZFDAKeEpjq2kKxQKSKfTg0KQXfHz4+0BSgghhBAyVhiP42iHImaH4srKcWs5+3FYuVz510PA1mt8rasMrvLEMcHiQFHbAERVFvnO1TOiZznWbmwqlUKhUAgekuLQ6hs8zrhanSeFLyGEEEJIbRmP42jtvC6ViNlqx9tqR7eaY4eVp5bjbisJT5bPQ8UK1WSg6XcAACAASURBVGrEqssJjhP+bKGobXBsCLCuhHo9WqC0UkmYsZ0FrppxtYQQQgghpLZIm268CFotLCsNE67G1Q1zdGVYXqWEhdMORcSWE7DyfijItdMdAvq9Lo89ntURLk2h87PaoZLzoKhtEMqNpwVOhR5rl1bfMPInN7OMrw0TsZWMqyWEEEIIIbVDr6c6lrGiNK6grEbMuoRsNcsBSV46T6GacOIoB3SoAlbn6RKwtgxy/XU7P0xn2Pe6zDovu92VZzkoahuAOOG92q3VYcV2gWkteCUMWfcEytq2kme541PUEkJI/cjn8+jo6MChQ4eQy+Vwxx134Pd///fx/ve/H29605sAALfccguuvvpqbNmyBbt370Y6nUZHRwdmzZqFgwcPYvXq1UgkEjjnnHOwfv16JJNJZ1pCyOhhPIQda4E5FIc1jigdyjjbqHxsXpWO+5X3mmrKFle46vylba9f4+Aq31DEalwoahuUsF4T/RCUh4iMq5X99HhaACVL+4QdK87DZKz3JBJCyGjju9/9LqZOnYp7770Xr776KhYuXIgVK1bgtttuw/Lly4N03d3d2L9/P3bs2IEjR46gvb0dO3fuxMaNG7Fy5UpceOGF6OzsxK5du9DW1uZMSwgZHYyHsONqQo2rEbNDmf047LiSVyUiXOejqVQQuyIsbZ66bmgBa00pbTyVO7daiFaXNqnE+KKobRCiKpiuyPrmBlAiaCUfcWjl5pc1beVBYG+WastFCCFkaFx11VVYsGBB8DmVSuHZZ5/Fc889h127duGss85CR0cHDhw4gHnz5iGRSKCtrQ3FYhFHjx5Fd3c35s6dCwCYP38+9u3bh2nTpjnTtra2jtRpEkL+f2yU3VijmlDjSsVsLVzZoQjZOC6svI9z/DDxqvNxCdew8a1RonUoYdiuz5W4v+WgqG1QdDiH7SHSoQauSiSurCtcWT67ennKlWcsPnwJIWQ009LSAgDo7e3Fhz70IaxcuRK5XA433XQTzj//fHzxi1/EF77wBUyePBlTp04t2e/YsWMlz2bZ1tvb60zrErU9PT11PsORp7+/vyHOk4QzmuqAtMFSqdRIF6ViKnVnqxGz1c5+rI/V39+P7u7uigSozkOworPc/nJ9bNhwVBnsUECb1rVvpeK+3HsX9hi1cHopasc5lfSA6LVoy4Wt2GV97FTnrnJEjaslhBBSH44cOYIVK1Zg6dKluPbaa/Haa69hypQpAIArrrgCGzZswGWXXYa+vr5gn76+PkyePLnk/0BfXx+mTJmCSZMmOdO6mDFjRp3OavTQ09PTEOdJwhktdUAET7UTG40UlbqzlYpZa7ZUOzZX9v/5z3+OmTNnVrWvLkPUvlLmciHJtu2t29vyaqMnhypao7RFPcSq5sCBA6Hfjd1ge1Iz7I0gr/ahYkM1bC9g2HTclZSBEEJI7XjllVewfPly3HXXXVi0aBEA4H3vex+efvppAMCTTz6Jt7zlLZg9ezb27t0Lz/Nw+PBheJ6H1tZWzJw5E11dXQCAPXv2YM6cOaFpCSEjx1gNO5YhbNLuLCdoJb2YL3aYnE1bLBZL2rVxHWB9HNlXVv8Ic0RlP1kdxLWv3V/2KRQKyOfzyOVyyOfzzjz09dHlA061y9Pp9KA/fdxyZZe/YrEYlMF1LF0mmTxWn6M95nDUSTq1DYK9gQQtRHXvnghWlwPrqqyukGP9YCrn1FLUEkJI7XnggQfw2muvYevWrdi6dSsAYPXq1fjkJz+JTCaD0047DRs2bMCkSZMwZ84cLF68GJ7nobOzEwCwatUqrFu3Dps3b8b06dOxYMECpFIpZ1pCyMih22hjAe3OViI0gWg31zVetppxubJfJWNz7fHCBKSefVi3sXW72jVEUDuvcUOf7bFdr5owN3ksdJRQ1I5zwsSidWXt0j2yTYc/AAiW89G9NDoEWbu3HFdLCCEjy9q1a7F27dpB27dv3z5oW3t7O9rb20u2TZs2DY888kistISQkUHPiTIWsA5kFFo41lrMukRpXIEtQ/bkWFEuqEvEuvZzCVhpV1ciYOOK12qEcT2x5Sz32UJR24DY+HgRnXppnkQigXw+X+LeyqusRSshDVq06kmj4jIabiRCCCGEkLHIWAk7rtSdjTNxVKXja205gHjjbPVxrBEUVm5XFKMWkVbEVurAjhXx6hqXG/VePttrR1FLnBXYCk8Rqtp9Fec1nS6tJhL7HxaCrIVuWOiECzq1hBBCCCHx0SJxNFOpO1su1NiVppJQ4UqFrD6OjB215xbmxOrP1YYR67b0aBKv5USqa8ij3d8OkXSFPcfptKGobUBcvVo6tCORSKBQKAA4NdZWk0qlUCwWUSgUkMlknCHIdlxtud4VillCCCGEkPiMhbDjStxZG2rsEjJWzMYRppW4suXGyeo85c/mrdvV+lz0WNs410HeW4ZDvFYiVoHBc/TIe90hIOj3YTMzVzOel6J2nFPO/ZRwYt17JrOe2TG2uqImk8lgZjTpsZJ8JGTZPhTiOLWEEEIIIaQ8oz3suJJ1Z8uljSN4XWmB6oWs3keL2Hw+H5g/Ul6dj/5d9Dw0YcfW+2rq7b5GhS+X+y5MsAJuB9qmqUedpahtAMIqjtx0wMkKKuK0UCiUfHZVRLmBdRiyDr+w4c1yjKgy6ocVIYQQQghxo92w0UjccONyocaVjJkNCxcuV0bBJWRlWRudRi+tY48XR8SGuZ31ELCVCFdXmXTZwl6rcVXDjleJdrBQ1DYYrnCKQqEw6CbW4RN6rSzf99HU1ASg1K2VcbeuEGTJm04sIYQQQsjQsI7laKKScONy7mxcp9eGGFdy3HJC1gpzPXZWRKzOx1W2ci7sUH/DSh3XMAPJFaEp23WaSspk37s+u9DHDnOELRS145iwSmMrtp7gSUKI5b3+TtzYXC6Hpqam4KYQUZtMJpHP5wdVRF0ho26kqO8JIYQQQsjoXZO2Gnd2KGI2Spy6julyceWzmDQanUZ3IqTTaaTT6apErLyvhjjiVd7rqEnrqoaJ1zhlG4pYDQtB1mXUnQlx8tRQ1DYY1qnV70WgSuixCNSmpqYSt7W/vx+FQiEQsoVCoWQ5IC2EXRU+7IahmCWEEEIICUcE01gVtDqdFVTlQpErTSdpw8SsNm/kO/lzOb/yp4V2PURslHgN+946mrbMcctSLjzZhctN1Z/LCVWd1qVT4nbiVC1qH3zwQTz22GPI5/O45ZZbMHfuXKxevRqJRALnnHMO1q9fj2QyiS1btmD37t1Ip9Po6OjArFmzcPDgwSGnJfFxVWBdwfTDUUKHRdxmMpmgkslfOp0OQpblwVAoFALxKw+KsMoZBUOUCSGEEELcjLbJoeKGG8d1Z6McV90WLefM2vGyup2bz+cHOZjaiJF2cdgxdJSjUG0osY2eLJfGbrNCu1LxWk64alPLOqv694jj2obpAuskxym/i6rUYVdXF37yk5/gG9/4Br72ta/hpZdewsaNG7Fy5Ups27YNvu9j165d6O7uxv79+7Fjxw5s3rwZ99xzDwAMOS2Jh6tC2RtCbkwdepxKpUrCFgqFQhCWIWJVTygl7ixQ+tAABvfQRInW0fKAJoQQQggZbYy2NWm1UJW5VFzIrMGJRGJQOjFC9FC3MDPGDokLE5t2VQ9p4+bz+ZLv9J9On0qlBk2WqvO2rnTYxKpR10za1Pq85DwljT0ecEqAp9NpZDKZ4E+XwbrIeubmsONK3mFllddCoRD86bxsG1+Lbf0n11aiPfW2qE6EchoCqNKp3bt3L84991ysWLECvb29+NjHPoZ/+Id/wNy5cwEA8+fPx759+zBt2jTMmzcPiUQCbW1tKBaLOHr0KLq7u4eU9oorrqim2MSB7vFzLcWjLX99w4lbm8/ng/fFYrHkhqrWdY0KUSaEEEIIaTS0QzkaiBNuXIk7G5aHS9BFpZF00q4VEau3a6xb6zoHV/iyTBAVRTkXtpxbKuWxa7lWmk/YPtJWd4UHu9zZMPfaCmnZZn+TsOthj2/Py5Wfi6pE7auvvorDhw/jgQcewIsvvog77rij5MRbWlpw7Ngx9Pb2YurUqcF+sn2oaV309PRUcypjiv7+/orO09WjJze3OK4yHra5uRm5XA6ZTGbQbMh6xjd5ld6VXC4XVDTpOcrn84HwFTGsyxDVm6WFMRlMpXWAjG34exNCCAFG1+RQlYhRV5o4IcvlxKxLbEo+YcvwSHotEuM4gzq8Vz6HEeUqukwj29Yu1wZ25aHRZXSJSHteWmfZstj3ugz2GPp6R4VL6+PYa2DP24r5ctqgKlE7depUTJ8+HU1NTZg+fTqy2Sxeeuml4Pu+vj5MmTIFkyZNQl9fX8n2yZMnl1ygatK6mDFjRjWnMqbo6emp6DytqJWHiA71kHVmRYxmMplg9jffPzmmVvLQYQyyTR4c6XQaxWIR2Ww2CO+QfMUBLtcbBqAkvJkMptI6QMY2/L1HjgMHDox0EQghBMDomRwqjhgdqjur948rZmUYnF21w4Y5u8aelss3ztjUOE6sS6APVcBKfjqdHfLnEq76mtroSp3edW56m2s/ea9f9XHCvtf5hwlyLZxdVHV3XHDBBfjhD38I3/fx8ssv48SJE7jooovQ1dUFANizZw/mzJmD2bNnY+/evfA8D4cPH4bneWhtbcXMmTOHlJbEI+oG059lDC2Akgmg5HvdU6JvBFt5ZX8RwNUsDE6HlhBCCCHkFKNhcijdrgsbPyvjNIHBgk0bKhLtZ/MXUarHYrq+lzwkBDifzwfD4BKJk0vuyD7AybZlpeNko0S7a7+o7+T4evyr6xrqfW3ech76GDLplWt8qxxTDCb5k236+uoxsZKP5CkRna6xynIMyVfOTx9LH0+HhevztON0Xecj5x9FVU7tpZdeih/96EdYtGgRfN9HZ2cn3vjGN2LdunXYvHkzpk+fjgULFiCVSmHOnDlYvHgxPM9DZ2cnAGDVqlVDSkuqw/b22J4XXVm002pj5WV2ZHFg8/l8UFnFtbU9PPbBFqechBBCCCGNzGiYHGoo4ca2LRjlvLrCgcO+9zwvmLBUi1zr1MZ1ZaM6DWx61xjUSp1Y175haXRngct1dQ0XtI5r2KStYW6uYMf1CtL5EJani7DwYv29LUOcfIWql/T52Mc+NmjbI488Mmhbe3s72tvbS7ZNmzZtyGlJPMJ6mYDSUN9CoQAAg24OHZagHxy2R61YLCKTyQTTpKdSqZL1a20vixW6urwUtYQQQgghIz95ZiXhwi53Nuo7LYbifi9iVuebSqVK2qhhItU1TjeukLXnJO1m2b/cDMg6rzChpo9pxaYVy3ofKy5d41s1+trYMus2v36vJ9yyebnyteV0YctZrtzlqFrUktFNVOixFo5acErFLRQKQciAa8pv3Rsmjq7uRRIhK+8lbSW9jCP9ECeEEEIIGUlG2qUNG58qiBCp1J3V7cqocbf6e5eYdQmquBNKRYUWu4SmvBeqEbFWKFrnV5dPL3Fj99Xpbfizdjrtn2zXv41rOR5dFv0+zLXV6LJZIRzXwbW/Y9zQe4raBkNuBvnTglT3CMl2+Sw3rxayWvQCp0KWXeLZhjZEObVR3xNCCCGEjHe08zgSyKSh5WYndgnaOM5tXLGrx15KJKAVOeXyijoPl+gN21+OLeNibT46P7vNnr8cS7exw8R9mGusz92KWVseu79to9sZocOuExBPrFbi4NaqvU9RO85x9X7Iq+/7JbMT64kI5LM4toKEeBQKhWAQeC6XK7kZJBTZ1QPkipEvV2ZCCCGEkEbCtpuGk7iC1n4fFaqsv7MixxUWLG3NKDFbLmQ5zJWNcmStkHVNahW2j3Y/5bMVsfY8dFu5nGvqWuLGJZxt3bGhxlGi1Yp5K4RtmayYDks7HFDUNgi20ssDS97bkAr57FpcOp1OI5fLoVgslszepm9eEcrSuyb5xHVgq4mlJ4QQQggZ64yUSxvlsgouwRvXnY3j3Goxa80VVzitzUfn5To/G/ZrRZu0a6P2l7ZtWJ6CFsXW1LFhw3ofKxZ1mjBnWfbV10ne22OGHdvlrtpy2Pf1wqUDymkDitpxSlRl0L1rNiRCbj55kMh39oaTSaEknb65gdJxtbrnKmxwu0aXixBCCCGkkRiJJXy0MHQZGmHiNK47GxairM9TO7PJZBJNTU2D3N0oURzHlbWf7XIzrvMOc3X1EDybl+t8bdvWOqg6vT22dUTDhKvLcXUJ1zCXtV51zp57uc/VQFHbYMiNkc/nA6Gpb0jt0orTKsgNkk6ng+V+ZPY3Sa9nQ5YxAvZBEPeGqSQtIYQQQshYZyTCjqsRtNW4s1akaWEsy/PI2FXBJTjjiFkt6nRYr6S1w+vs+YYJYXGS9XKW4u5K/lpYShmtOA+7voIeDqg/u1xXnY+k1ddPv9rvh0JYmLLrswuX+2uNrUqMLoracYxrnIFG38z2xpNJoPL5fLC/vBaLRWSz2ZKb2d788ioC17W+VpRTG/U9IYQQQsh4ZLhd2koErY7gCxO0Ye6saxIoCeOVSL5KwozDvo8KLxYhW86R1e1Xu03c2EwmE5TDOqNaxMr56nLYNrEVsDoPSWcjIiVN2PWqRf1xOb3lBKY9fhyR6ro2ru/LnRNF7TjFJQi1M2tvXhGyOu3AwEDgzAII1rOVHjWZ/a1QKCCZTAYTRukbXNxeHc6syxgGxSwhhBBCGonhdmnLCVqXQK101mMrRHW7UB/bCrO4YtYlZGW7pJfIQdf56z/bPtbH0mUUkRomYm35XTMeawfW5Qjbc9DXr1aua6WiNayzJUqoupzkWoQau6CobSBcYcC6gslNIkJVZjGWm1dEqu/7QfiyCF6gtCdKerjkeztGN85NWK9KTwghhBAy2hhOl7aWgjbOdjmnfD4fCDe7FE25iaTCxKwWpfJ9uWVyBBHYVgTbY+lj6vPSZXOJNn1eNkQ5ao1Z10zHleISq3FEqz4X13mVyzfMmY17LlFRnFFQ1I5j7INBsFON2xtNh1zo0AmdT7FYxMDAQNBjJGNo7X42b/3Akfzi9voQQgghhIxHhtOl1e0/l4PpWrLHtS2OO2tDjQUt9MotzRM2Lte6siJk7Tm53FztFGs3N0w0Szor0l3htdaFlVcdzizfRznUcYgSnBbbJnflFSZOy7XJdX72PMKOFfZdufzDoKhtMOzNqHvL5E9c2nQ6XRJmAQxe3zaXy6GpqSm0hwo4NeGUPED1zVTOtY3r6hJCCCGEjFWGy6WtVNBWIlz1/tad1Wn1PmEzJruOZ51SweXKWkEqgtIKWbvSh51jRnCJTu0K689yHnaCKn3cSh3YOOLV5Qbrc9HXw4p8V7tc/462nOVEaxS2U8D1vR1b6xpra6GoHYfYiu66sQH3QHX53vO8IPQYKL1R0ul0kIcIYEln1+2y42rjrrkWV/QSQgghhIxlhtOlteJSY9eg1SLPTi4q7TMdyRfmzopIBkrHlFoRqK+DK18dDajdVY1t80o7VUceyp9LINtIQxcyqZUV3LoN7BKxtRKwrrzCRGuYw6qvo2uCKrssp3wXRlxnFhgsUKPKWQkUtQ2E7dXSN7MOI7Yurd1fu7e+f3J8bXNzc/CdfQDJuFpX70+5MAlCCCGEkPHMcLm0IrpcE3daQVpunGyUOysCr1AoBKIOGDxJkj22S8xqk0QL2SiH14YXy1KU2rHUodAu0eYKERakDSwiW++rw6rj/J5xBKyrbHHDjl1lsLMxh6WNEqrWTa3GWbVh165rFtcMAyhqxzWum9PVmwScqjRyg8pDw4ZrAKceDsCpSaVcPX/64aErv3Vfo9zYWvTcEEIIIYSMRrSgqyfahdW43Ni4TqxLkMpykJ7nBQaJFXw2Py1otJjV5XAtx+NyZW17VZs4dhZiebXCUDvJWrhLeXQ4tXaLy/2GrtDfMHTHgku42vDisNBo2c8lSl3prfss5y6UE6t2XVxd36IEqut6WP1RDoracYgr/Ni+ZjIZ5HK5QWHHMuGThI3YGd4kTTabLXFsC4WCcwyuHM/2+sUJKy7n5BJCCCGEjGXsELB6ELamrEvQ2hBkm846ubINOCkqxZ3V67hacawND+vuWmdWu6y63DqdXR5Ii+5CoTDoWJKHXBsbEh1mAIm4zmQyQZs3inIhwLo97JoJOcq11SHCUe1qe976/OT8hSgBaX/HcmLVpUVcy/vEIU50J0BR2zDoniYAJQ8OGRsgN7482GTZHh3qkUqlMDAwgHw+H6xTm0gkkM/nkc1mQ8VsOp3GwMBAMK42rlMb53tCCCFu8vk8Ojo6cOjQIeRyOdxxxx04++yzsXr1aiQSCZxzzjlYv349kskktmzZgt27dyOdTqOjowOzZs3CwYMHY6clhFSGDV2tB65wYTm2FoIu4ar314LGbgtzZ62gck0CVamY1e1Ym18mkxkUFiztVy3mtMjVodiukGJXOLGrE6KcC2sFrL0egh17bPe1HQmu/cLKEyVa9XWKc576ves8wrCi2n4WXONuy0FRO06xldBV4fTsd3Ijy2d5WGSz2ZK1aFOpFNLpdODyptNpNDU1ob+/HwMDA4HQFYEMYFDFlW3lxKq+MSlqCSGkcr773e9i6tSpuPfee/Hqq69i4cKF+KM/+iOsXLkSF154ITo7O7Fr1y60tbVh//792LFjB44cOYL29nbs3LkTGzdujJ2WEFIZ9R5LW6mg1cJV76/bilb4xnVnbZiydlnLiVkdhivtU8lLwpIlLFjO164NK21cK2TtDMVSPtcat5q4Irac8NNltCHA+r2rk8DmXalodc2bY8+tkvBfLbxtfq787Wd7L7iuRxQUtQ2Arix6BjipfPqBJg+kfD4/aPyCVNR0Oh08PGS7CFlZ3keHUmgxK714VvCGQTFLCCHVc9VVV2HBggXB51Qqhe7ubsydOxcAMH/+fOzbtw/Tpk3DvHnzkEgk0NbWhmKxiKNHj1aUtrW1dUTOkZCxSL1d2qEKWhuGHOXO2lmIo5xeK2blOrjGzGqxpkOMRfxKufSsyJKHdWUlveSnv4s7uVOYsHalkfK7RJrrs52TRr9ap9UlNF2hwbpTQZdP0JNlheESp9ZxdqWVMtnzt+dszzHscxwoasch1tl0CUjdYyWfRXCKWNUTQkka4FSIRy6XQy6XQ3Nzc7C+baFQCF1XTB6Cuhxx3Ni4IQ2EEEJKaWlpAQD09vbiQx/6EFauXIlNmzYFz9uWlhYcO3YMvb29mDp1asl+x44dK3k2l0vrErU9PT31PL1RQX9/f0OcJwmnmjqgo+NqTZhQjSNoXW6sFaayTI7ko0VYnHxs2HCYmHWFGEtaEbkiVPV6s1IWXR4tpK0jG3UdXU6s/r31JKjyWon7ql/jilfXn6utH9Z+1qLfdgCUy0drB+v46s+u/e15huESx3EcY4racY4NIbAVWG7oXC4XPOAKhcKg2Y91fvJwymQyGBgYQC6XQyaTCR5yejyFTDwlPXl6MirZFoW+UQkhhFTOkSNHsGLFCixduhTXXnst7r333uC7vr4+TJkyBZMmTUJfX1/J9smTJ5c0+MqldTFjxow6nNHooqenpyHOk4RTaR2QNlm5ENdqqJWgtTMh68mgisViIDCFsHGrLncWOBXl5wp3lraioNPZPKyoc52PFbJRjmyYkNXf9fT04Nxzzw22WWGsBa1LwGqRZmdl1tdTv+o8ykU7aifVtU+YeA1z213Xy7bPo9rq+rexx7aCVTv8Lrq7u0OPU/vuITIqcIVD2G02LEJErIRpSEiHfQjo3jktfMXltfvI8aVcrgdJpb02hBBCyvPKK69g+fLluOuuu7Bo0SIAwMyZM9HV1QUA2LNnD+bMmYPZs2dj79698DwPhw8fhud5aG1trSgtISQe9RxLG0fQapfTCkARmyIudaSdngxKu3PWPdUGiAhUHSKcTqdL8pBjFQoFDAwMBO3MdDqNTCYzKG9XqLAOX5a8dJtWH9PVRpY2rx3na7/Tgt1eZ+3+Snl0OLRcw1wuF0Q3SueGlC+TySCTyQT5aBGqw7CtSJXtcq3l/OU4rrHIMi9ONptFNptFJpNBU1NTyUSwrshL7aLb6ybHc/3l8/lB5dGThNkOEbme+lpGQad2nBHWswRg0MPJ9qoBJ3vg5GbK5/MlU5br3jZZwsc6tLlcLnjg6ZtH97iIUytli3Jj44QnE0IIcfPAAw/gtddew9atW7F161YAwJo1a/CJT3wCmzdvxvTp07FgwQKkUinMmTMHixcvhud56OzsBACsWrUK69ati5WWEFIeG4pZS3S4rz6eFrA2lFj2czmrkpeILxFDUn7djrQTQWnBIwLFJZSkTWnT2XIIIsqt42rH3OqlfSwup9L1qh1XG0ps37tCiK3ja0OeXWHDUe5rmNtqnWGX2LbpwlxWfQ21wLTjb6PCi13HdYV5VxKNGSctRe04JyxEQx4IEhIsvS4SVixjY13jDdLpdND7o91a6QGzIcj65nOFNJd7sFPMEkJIdaxduxZr164dtP2RRx4ZtK29vR3t7e0l26ZNmxY7LSGkPPVyaW2YsN4OoGJBK+WTGYVt1J5rf9lHRLAcVwta4JQok+g+oFSIajFr8wYQrMqh3eS4QtYKo7BQXJ2H/s20cyimjS6HxjULsz5OlBHlci6tcBVHXYtHfc3CroE+L9luQ5XjCFUr7u31c11rW04Xel+rI6KgqB2n2EoovWUABt0AMhW77/uDHFbglMOrQ1dEnBaLxWBsrYhZCU/RvWfyMNUi2hX+EfaQj9uTQwghhBAyGqmXS6uNA523Hi4WJWhdE0IBCJZn1M5imJsr5XCtEau/FzErbU8JD5bjW3HucvvsWrJhoalWDJVzYy1auMmflFEmVRX0eeqJkqLEqbSVbVizFq+u6+ASsrbc+tiuOXVcafV11vm6RKbO324Lc1W1LnCJZZfYL3euGoraBsBWXrkhxXHVN5f0+sh+Ek6sqoTThwAAIABJREFUb1DP80rWo9UPv6amJgwMDAT5Sz6uG9pVzrCbU+9LCCGEEDLWqIdLq1026y4CpQ6ta6kdl6DVobzSLtSuo8vdFaNDu4i6TGKuiBiUcaQS2qrFmuRpXc5yrqwVW/q9q/3pEorWkXY5sdIutmHE+nuLnhjK1QaWc7a/Y5hTGnWeYY6zHMceT2PFqX5fThDbPCyS3naShJU1bP8wKGrHGa6KpXtqdKiCrqD2QZdOpzEwMAAAJQPrPc9DLpdDInFqkW3f9wMx6/t+4MbqcGQAwTZ5ODQ1NZUVugDH1RJCCCFkbFMvl9Y1MZQWrGHtPJegteNnJb0OC3atXyuT/mhnVh9LTzKlnVURs4Juo+rZl7WIdrmy2jWO2ga4RaL+k331xEpyvnr1D5cba0Weqwx2VmH7nS6XFsBaFOtjSFrb8aC3u8RnmCuqy2WjLqOun30v5+MqpyWqLILrt7RQ1I5zXKEW9mEnwtN+LhaLaG5uLpkUAEAw5hZAMDmU3EgyzlY/FPXEVDoE2RV+EAbFLCGEEELGKvVwaV0TQ7kErXVMwxxa3XYDSmc21gJNjmfXq7XjZvVsuMDJNqNuAwo6SlAvN6RFlp64VPK3zmGYUxkVYqud6TAB6grndU2o5HJhwyZs0miHXP9Gtsz61V7rqPBdnb/rWljTSNcZV521otmei+CaXMolal3X0kXYdoGidhziemDayuv7fuDG6tASSSPbXTMky0NOHlL6Bsvn88hms0HYsh5DK+WQbfLZFeLgolxlJoQQQggZbdTDpXVNDGWj3+R9uTG01oGV17BwY5c7q1e10DMaS1tQIvVcYtaKbu3oukSlvZ42X9c+Oj99HOvGukSwlMflwLqus76Oupx2X5d4tWHINjzXZVZZt90liO11tvmHHUefo/4s5df7WFEf5cra30eXKeyaloOidhxjwyIEcWVFlAKllVvGO+gHlM1TvpNZkkUky0NMhyDbm1C/t6EJYaLWVnJCCCGEkLFArV1aaQ/pUFMRS9p9jCtorUAOCzcGTs1sLOm1g6rHzUrbrqmpKfgOOGWMaJGlxarO07rL9hpYN9clZPXYVymfdWzl/FyCUdD76bG92vUNy0dfa7097Pj2d9afXe/tb21XLtF5hwlWl1DV7+MaS2EdOFY028+ufVwCOAqK2nGGK4RAXiWsWD/wbA+J3PBhU6Lrh48OMxYRK8ewIciCpAVOTSwVBwpZQgghhIw1au3SWvEqaNFYTtDqaDkdUqxFoMudlTaihAPraD4xNWw4skt0ymdXFKCeKdmKVi3C9T76HK3IlLJZIekSUNb11Nv0nzjTLrfRllu3y607Kt9Zp1lv1yJU72snW3Kll7xEhFuRbQ2jMBfVXiv9G4U5vfZ3tmLfdT+4hL9+T1HbgNgeDblZdKiE7iXTYRVS0WXmYkEeUhJqIuhxtAACMStLA+nQFHkvx9c9XZX0AFHgEkIIIWQsUGtRGzaOVo4RJWiB0vacFUt27VXZLmI1zJ2VJXqAU5OLugSzHMuKWe0sWoGnHeMwV9YeQ6fXx7Wh2rY8+rz1Prbs+vzCHNhyobPWIdWiVDuu+lzDhKsV0brjw4ZO2zJoKpmB2VUOK5K17rDr+EbdFy7hb8vkgqJ2nKMrlK7c8pCy4tbzvMA9lRASSSsVNZPJIJPJwPO8YF1aEbI2b5nlWOcv++kp3IHysxxXIn4JIYQQQkYSaTdVMi4wiqiJoVyiyApaOw5UCwYxJ3S4sYiRKHdWZjXWYleO41o71uUM67ICp0SNjM3V11G7yNqtdAlZ/b3FJWKt+ynppCxyHbSIty6uPZ41mPR3Lrc1zEHWYt+KacnDTgRl64Sti1HCVR/fClT7Gub0ujoD7Dm62vz2vOJ2CFHUjiNcgk/3uAGlDzT9YBHHVd77vo/+/v4gxEJei8VisNSPCFt5wMix7CzHesp2/eCR8uj0UVDUEkIIIWSsUEuX1opQu007lkC0oNVD0aR9p/OSdpkeYqZDgz3v5PKOIjgzmUzJrMW6Lem6Fnacq8vc0DMkWydXv7fXxbY3JZ02V6yYl/dREY62/Hrcsf19XWOA5VjahXXtI2JZi25bD1yhvPa66utpcZVZz1TsCoXW19X1Puw4kk7/Vq7fxabX5xC37U9RO45xhTXYG19XHC0spRdq4sSJyGazAE6FFovglW0y67EWxfLA1CHIcqyocbW6jFHnVYt/EIQQQggh9aAeLq12NrXzJ209O75UC0ftNgrivEaFG8vMxdqdHRgYgOd5yGQywXeFQiEon3XitMsaJWZlm2soXJgwcoUHy3G1saMFu3V4tYh1OaB2YivtVmuTyJVG/2b6ekh72nVMm5cWstoAsu6oTudyVG3e9r2gxwvbffU+rt/Dlkt+I1c5XehyAaVr5ZYTtxS14xDbg+eqBLoHLJPJlEy7Lj1wqVSqRHDKDSjb+vv7gweaXkRbjq9DkGVtW5kRWW5MO642qkcm6gYkhBBCCBkt1NKltWHHLkEb5dBaQZtIJAYJWj3mVgSqtO8kn1wuFwxLy2azwVAyae9JG1C7nNrV1OXT18Y174vgatNa8WyFWzmRKWXQQ+u0MNaizhUaq6MQbd7WSdeOtxXYksblrso1cB3f1Va2UZlW8Mqrq27INdEiWru1Lmxnhd5uy221iFzDsHz1NaykU4iitgGQG0l635qamgaFdORyOQCn1qj1PA8TJkwI3gOnpnFvbm4OhO3AwEBwQ0slFfdWTwwl6J4dPa4WiCdWKwlDIIQQQggZCWrVAe9yfK3gsM6hFotW0OqZe+34WR1ubN3ZXC4XmBSZTAYAAoEr+VnhWYmYle1WxGghHuZUy7no/F2Oqk6j93WJWNdwPZ2vHl9sr7GEEIeF8brG7lr3WP/28l7no+uEFqM6T52HlFGfu32v0+o8rcuqCRO/rvxd9bSckHaFYIdBUTuOsL0g+lVXRD3uQdLIzVEoFIJJn/r7+4NleqS3LpFIoK+vD8BJhzefz+PEiROYNGlSyQzHEoIMnFrPS/cI6kmitPi1rq2FopYQQgghoxnXhE7VYIWbzlu+10LBOm9AaWSeFrR6MidxZ8Ul1YI2n88HBkZzc3PQvrMunRZ+euJQ1wRQUiYrwsLEmEv4uLZpkWmFrG1XSmizbX9qoa1Fr3aG5fzkusn56zz0qiAuASrvdUeAazImO9mVnsfGXnfbQaBdfH3+9pxdZdHl0C6+PQddNn1MV711ucku4ayx9SAKitpxhn0gAKVjFGxvkqSTB5lMDpVMJnH8+HG0tLRg4sSJwU0rIcR9fX1oaWkJZj8W8SuVVoerJBKJYNkfG3oh34nzG9e1rVUPKCGEEEJILamlSwsMDi0W7HwlLkGr8xAhp8fn6pUuZMIn2Vcmg0qn08G+0h7U7qw2NLTItiLIJWb1nz4/EeC6DWsFvORtlyJyCdmwcF7XmFsRsXZCKmkDnzhxAidOnAi+0+NdXSLUuuf62lgxJ2WwdcgKXo11QiW9Lr8rDy1ubQeMK9JSi1brersmr3KVV59nmKDWbramnKlFUdsA6B4gXYnk5tOOaqFQwKRJk/Daa68FlefEiRMlDyR5sPX29mLixIlIJpOBWytp5GGgRa2eTEBmTua4WkIIIYSMF2rl0tqxrtrNkvylHaTDiG2UnGyX+Uy0aJDoPB1OK+6sDEtramoqaavZkFvg1KShlYpZLda10Lb5yDmEhSqL4HQJWesG6nLofFyTWMlyRfpPC067n+SnxZ0ul8a60/p66jGx8irHEOdX1wnrdmrHWD7rccp2tmvrFmuhLm16F/r3tNfG1jX7m9q0LifWbqdT20DYnhE7UF5XcqmkuiKeOHECmUwmGEcrIjSTyQRp0+k0jh8/HtwQvb29yGQywY1re+30DerqudETTNlzCCNK9BJCCCGEjBS16HR3hVxaQWe3W0GrRaIVtIlEIhgjm0qlkMlkgvagTAYlk4Xq9p0IOG2U6KVtdFtPu5vWcXal121SHV6r25VaEMu2sLVp9THsb2MdVUmXz+eDa2rDprXwz2QyyGazgwShXvlD14E4kYe6w0CXy15P1zV2uZ/2uOLGy3tdF6xzayMC7HttWlmBLVjnVl9HXS5bL+356H3p1DY4coPJTZ9Op0segLoySQXV69Bms9mSiul5HrLZLAYGBoLQYxGw/f39mDhxYomo1ZVTQpB1bL7uZXT1HLkeBBS1hBBCCBlt6Ab4UNCCVD5rrKOoTYQoQSv7ybAxCSuWKDoRuul0OoioCyuLuLO6HHLu0g60rrV2/yStbovaEGPJW4tpK5S10NXXXV8z7S5bgaj/BHE29Rhk/Z1cQ/05ypxxubRahOu2unVYdd76fOzvrkW1dle1WLazVOu2tnbFrTDWaSVfqVc6ndUXNh+X8+xyaPU++vqUg6J2nKFvKv1AcAlBe0MDJ2cz9n0fzc3NgWgVN1WPmc1ms8jlcsGEUhMmTAgeNPr4ujfH87xgxjzbiyU3mj2HMFEb9T0hhBBCyHBjh3lVmwcQPY4WGBx6qtNqQWvzEzNCwop930culwvCjcXMEHGsx7YCpRNBWSdURwPKZy1cw8Ss3t86d5JOr+vqciZtO9clZKVsut2rr1eYiJUlfPRnOaY+b+t86u812g0VB9W6pVbc62sl+1iH1zri1km2glnva9fidQlq2ymg0+vj2vd2f9uJINfOXld73uWgqB2nSAUQh1b3tskNnU6ng7EUAwMDgUiVcGNdkXRYiOSbzWaDfEX0isC14cb6gWZv0lQqVdLrZh/SUedIUUsIIYSQkaYWLq11VF0hnVqg6U5+64y6IvMk7FQmhNKC1oYbA6dmCJa2W5g7K2WQ/awgtWJb2o1WkMq5SxpdZu0Q6utt3T8tsrQY1mJUrolrTKxLxOqQWXsMVxp9PnL97O8sr9JhoDsE9O+rh/OFtXutUaQ7IPS1kXz1+GddXslDR07KOdtj6Xa9Ph8bVaDFr6DfS0eCbLdjh+1Y7CgoascJYQJQVwI9o508FASZ/U7GVqRSqSA8RSYS0DdXU1NTEE6cyWSQy+UCMSs3rwhne4NK3q4y2hsrjDiilxBCCCFkOKiFqLVOr2typTiCVjurYYLWrj0r+8h+NmzZjp2VY2uzRAs+K0gk6k/ErB3apt1CSaf3lWtrRZJebkeOJ+vt2naiiDkrQLWYtOJYt33tsV3CXZ+zHYqnhaKcsx7rqstrjynHsyJVXxd9nhYR0PY3ssf2PK9kmSJb11wCVXdiWPFvO2V0Pvra2JBrrRPo1DYougLp0GJ9swKlFUQErX5wFQoFDAwMIJVKIZvNoqmpKXhw9ff3w/f9YJC8FsByg0pvn9yU4uTqMGP7WXqPpHxRAtf1YCOEEEIIGQmG2h5xhR1r7HYrxiSNnh1XhKoIhKamJiSTyUDQ6jBkvU6tFhni+sqxdTtSR+DZca+CFqqSnyuN552cpEqLSleotbQ7XSLYFSWohaf+ffRwOd3edE0gpffX4lkbOYLukJBXOwzQJeRs2K8r/FgLSF1GvU3vZ6+1rit2aU+dp+SjXVTt3urhhHpWbMlLztmKUb1msqS1HRL6NxCsEx0GRe04RSqevnG0ayoCVR5uIkpFvMrDbsqUKWhubi7Jx/O8YOytniF5YGAA2Wy25IaxvYuynI88QKVMkl4vUh0lWnWvEUUtIYQQQkYKK0grRTtX+jNQGpIZJWit+ynOqF6DNpE4ORmobGtqagKAIBrPGgth7qz+rI+rRYy0zbSYlf3tuYuYtaJNixztBsrx+/v7B4Xa6nT2+kob1opU+9tZU0VHIIqTmcvlBv02dlIj3RaX9q2r08AOzXOJbN0+1u1qHSJuBaXN0+Vu20mkbP2TPLVzasW8qy2u66Ju19sliayg1dutlikHRe04wfWQ0LOiCdLzoiuRDhtJJpPI5/PI5/PIZrNobm4edNO3tLQgkUigv78fhUIBTU1NwXHkQSmhyTL2Vs/opm8w2U9EritUIwyKWUIIIYSMNLVwaXVbS7fdXE6WtN30ce24xGKxiFwuh2QyGQhaWa5HRK4WDDacVUwL687asGCXmNWiVwteK9p0Xq7xwnrMq2yXZSeto6lDX6WsLrfQNSZWi0jdLtbjWrWQlMhCHa6t31uBbk0mG6Wo3WFX2K42laR8VgBqMa+vib6WWjzbSE7dOaB/K90ZYfWArn+6zur2vf49bAeJ5KfH+OrOHft7loOidhwilUIPctfuqTik0jumx1lI+IeIV9tzKDfGxIkTAQC9vb2BcE2lUsFkA/l8HplMJggJ0Q9LPaOyDZHQ5S33D8J10xBCCCGEDBdaqFWDbuDLZytUZJuIFCuewgStzHsiYlDmVNFC04oomQxKi0J5ryeMsmJPymPbcFbM6uFw1lEVgaeXf5R2qXWldRpg8BqrLgdXp5O8JJ1eCsnlOOvrLfvpsFiX0yrno9PpTgaNpNcTp8p2+Y102LUVxbatr91Vue72nPQ4aF1/XB0Q1gmXskka19JRLvfWdV01trPBXqMoKGrHEfbGs6EDyWQyGCere2lkeneZ8AkAJk6cWDKxlKDXmp04cSLy+TyOHz9eMjW87uWS/aUiy8NWegDlex3iYkOPywncofaQEkIIIYRUgxV2laDbOtYVBTCoPaTdOi0AgWhBqyeEkjIDpTPMythZLcisSWKXu7HOmy6HFrhSLivu5BpYV1YEtJ4wyopdew21kaMdTxu1KPmFiVg5BxsxqAWqPq6+frrjwTrI+pq4QoQlnfwGci0EK9ZlGKB18aVsepZo+W3teFYRojY03Lre1hWOEqdaIFvCtpX7DeJCUTtOcP3w+gaQXhpXaIZU8kKhgGKxGIyj1WEhOqxBnN5UKoWJEyeir68P+Xw+WNtWhHMul0NTU1PwIJWHrL75JHxZLxgdNcudxt5khBBCCCHDRa1cWisCtQjSQlCLIS16dPtMr2Shw3Vl/KwORxVE9Mj3VnwBpeHNcu5WlGtBqs9LQp7teEoRqlrYaFfWOrL62Lps+hpYsaxDiW155Zi6bap/G+tiS156yR/tnOrOCclDzxujyymOuQ6H1uHAMjGrvtbWNdW/pxWB2qmWNFrEynbr1Lqcapfjqn9HVxpdFqs97DbJsxzl7jOK2nGIHf8AnJowQPeYyWRQ0luTy+XQ0tKC5ubmIB+58ez4AgkvTqfTaGlpwfHjx5HJZALxms/nkUwmS2ZNtoP27c0j6eyNGxVuUO57QgghhJB6MBRRq10xoDTsWE/eEyVoreGgBa1MZiRtPC00dUisCDIt+kRU6ZBXOU8teIFT7q2Ezko6iQTUAloLdB2Cq11ZfU5W6NhhdS4hK2W3brPLIdbXXAtkHWmoEWGYTqeD8msHXfLVYlmukRxbH1fa4vq4VpxqAa5/A51evpMOAGm/684Al9tqxas1iWwb3V4LfT3t9jBsu12fi8a27eO09SlqxxFhFbFQKATiVMbPyk2lZz2W2Yt1GLIrBl9CkOVhNWHCBJw4cQK5XC4IbxDhLMeWG0+7xplMJoj39zyvJB4fiOfAlnNzCSGEEELqwVDaHlqQWMdR2knyWUSTdtbiClpZYlGLLO3cSf7WndUTNOky6yV8pA1ml3WR2ZTt/tqZ9f2TK2LoMaQ6nFofU7cN9bH18bXoD/utdH76emuRaa+tXCfP8wKDyIYPS1q7/I1cLxnmZ6+lPVdXaLUup/5N5Hjy3uVAWyGr83I5rC5n1eWS28/WFda/l6TRbXv7e5Zr+9s0YQxJ1P7mN7/BjTfeiC9/+ctIp9NYvXo1EokEzjnnHKxfvx7JZBJbtmzB7t27kU6n0dHRgVmzZuHgwYNDTktO4fqRbY8egJIHh1RCmZJcHnATJkwYFHYc1ksnx5G1bPv6+oLeq3w+H4QiT5w4MRizq3vPJG/9kLY3qD5H1z+OsBuWEEIIIaRe6NDhoeyrG/kiXKxIEAdUjzu1glYcxHw+H7TttKC1E/nocGMAgcgEUCI8pTzSdrOiVMolUXyyvxYtui3p+6UhxuIu2rBhLZZ0yKz+LkzIWvGor6+0O3VnglzLTCYzKBwYONV+1p0BEs4tv4Gkc5VJi00pv7jnVmDqa6EdXslH5xkVKWDdXZsurN7qsicSieDcXN/bumv1ge74cIlo6866tsdJL1QtavP5PDo7O4NQ1Y0bN2LlypW48MIL0dnZiV27dqGtrQ379+/Hjh07cOTIEbS3t2Pnzp1DTnvFFVdUW+xxj65g8idjYHWvkO7ZampqCsbEisMqaeT31TdEMpkMegDlYTtp0iQMDAzg+PHjaG5uDpb7kfzs2meukA/9kKnEtbWhDIQQQggh9aTaznQtLIHBYcf6ez0mUwtKSSsuYCqVCgStzGsi4g3AoPfSJpS2kw791e6hOMZ2TKYOMwZOiT6XmBXxrN1KOV9xiq2I1S62K7TYFUWonWaX4NPtW9kmE2dpgScus+wvnQUiNuXcJdLRhh3r49lOAUHycq0Xq/eNQqcJc2GtuxkmSMM+axFpJ9yyBpSrLPrauL7XDrPrfK3wts62pWpRu2nTJixZsgQPPfQQAKC7uxtz584FAMyfPx/79u3DtGnTMG/ePCQSCbS1taFYLOLo0aNDTktR60ZXYKkcduY0qUwDAwPo7+8PeosGBgYCl1Zoamoqubl1hctmsyVrhYnL+9prryGfz2PChAlBOLJeo1bfbFr06htNj92Nquyu86ZbSwghhJB6EuWQlcOGHdu8dPtHiybtaFpBKxFyAwMDJUv2yH46Ok721W0xOwbTNeGT3U+X0Yoyl5jV0YMiJuVaWrGlXV3ZVy/pY8ui99fCx15bPQ5Wu5B6fhlxa7UYLRQKwWzDvu/jxIkTg8YLu+qH/r3ketjrpPfRAl1/b6Ml7fu4YtW6qoKd90bqgxzHus92Ai3rwOr6obdZx12jr6W9DnGjIaoStd/61rfQ2tqKSy65JBC1+sdoaWnBsWPH0Nvbi6lTpwb7yfahpnXR09NTzamMKfr7+53nKTe99JANDAwgn8/jxIkT8LyTM9/JGAsRmSdOnMBvf/vbYLInAHj9618fPEik90wPOpceO+DUuIf+/v5gluNkMolf//rXyOfzwRjdZDKJbDaLlpYWAAhmOpYbRlxdfVyZEVm26Yds2D8Q/VAdz4TVATI+4e9NasVPf/pTfOYzn8HXvvY1dHd34/3vfz/e9KY3AQBuueUWXH311UMeLkRIo1CtqNXhrlpkaLEp3+vhWiJUrUMrAlFCjiWdFqmu8bM6nFiLSCmXzL+i24BaxFm3Vl8HcXOtk2knVrIiS7fhRMSKA2z30+61CEbt8rrajTaMGji1Jq+kk/LKmr5yLDtEzq71qx1uaVPb4XXlzBnrzto64nJQw77XHQL6d3WZX1I3dEi8FZfWvddY9zRM4MpvI1gRbw0tnU6fbxRVidqdO3cikUjgySefRE9PD1atWoWjR48G3/f19WHKlCmYNGkS+vr6SrZPnjy5RHhUk9bFjBkzqjmVMUVPT4/zPPVDScbJ5nK54KaUB1E+n0c2m0WxWERvby9+85vfYPLkyQCA5uZmTJkyJQhLEXEZNp5WjiciWiab+oM/+AP89re/DZxa4KSQbWlpCXoTtRvc3NwcLCWUyWRKehRlLIg8IKJEq74G45mwOkDGJ/y9R44DBw6MdBFqxsMPP4zvfve7mDBhAgDgZz/7GW677TYsX748SNPd3T3k4UKENALSBqqmE10LNN3BL4INOCVoRdCVE7Sy4oSehVjKJsO/RGhqMSliTY/btCJUi1dp81nHVJBjSNnlWLIKhj6uvha63aaXzJFroUW4Pj8RoyKgRbRqQ0QLpWKxGCxHqSd8EqNHi0J9XlJ+K/rkd5HrIq9aQOplkKy4tPXJCn39nVwbLVZ1h4U2nQQ77E+Oadev1eld5pHLVdVl16+6zLY8wODxumFusut7exwXVdlaX//61/HII4/ga1/7GmbMmIFNmzZh/vz56OrqAgDs2bMHc+bMwezZs7F37154nofDhw/D8zy0trZi5syZQ0pLBmMrnA6Z0HH98jCUXjhJl81mSx5cNtRDHhqyDJA+hh7vMGHChKDHR16lt04quS6rna5e8rM3tX6NOv9yFZ4QQhqNM888E/fff3/w+dlnn8Xu3buxbNkydHR0oLe3FwcOHIg1BOiJJ54ITUtIIzBUl1Y7hlow6PaPCFodqmkdU1mKUQSdzkOwglbadtrJleNLW00Em12LVT7bUGURijLjsrQT0+l00JEm86/o/MTIkLahRBlqsSntUrlWyWQyiAzUTradWElvz2azmDBhArLZbLByx7Fjx9DX14fjx4/j+PHjwe+inVs5N7ke2WwWzc3NaG5uDvKTUGWXeLWGkL7+0hYfGBgIfkN5lT+5lhL6rK+/DTeWsjY1NQV/qVQKTU1NyGazwauUOZPJlLyXqE1XR42UVcojpll/f3/w2v//sffuwZZe5Xnns6/fvp99zumLaJBMYwkjASIDGgk7mNjjEDFOnIlrxiZ2xZMay6mpjEcOf8SGgkhmCrsoohSTYGycspN/cGUSKCoeYseTCw7DmBiwcYxjLImLQELX7j6Xfb+dvff8cfRb59mrd3cf3TCtXm9VV5+z93dZ3/rWWud93ud53zUeazQaaTQaaTweazAYrHw3mUzC+43/MS48oIL5XDuOGvN529LnHe94h+655x594AMf0Cte8QrdeeedKhQKuu222/S2t71Ni8VC99577/NybLJLW5yEH8sViDaRT8tkzLJM0+k0TEwGmi+6XPfg4CBMABZAkueLxaIqlYp6vZ4ajYbG47Hy+bwmk4nK5XJY6DwSWalUVrYa8v3S4qhWHOHCrvR9smTJkl2rduedd+rRRx8Nv9966636kR/5Eb3mNa/Rhz/8Yf3yL/+yms3mc04X2trauuje14J8PqUJXFu2Lt3pSmMgBl/u48AargMbatl9AAAgAElEQVSXcWXgyWQiSSGlzAFt3CbApoM/QKWzmQ4mnG3lmi7/dLDGNQAlLnMG5Lo/6u1cB7LpW2m1gJADdicu+A5/krahNuQcwCL9DpvLMzihwj+ezdWK3JP3/cADD4R7xO86bu+6ujEEKfxYfPSY1Y1Buwcj8JtjiTEW16hhPPja7vnJ/h545nV1bmLA6f2z7r173/i78/uuY7Jjlvty9pxB7Uc+8pHw82/8xm9c9P3dd9+tu+++e+Wzs2fPPudjkx1ZPAB8oXAW1PfNmk6nKpVKmkwm2traCrKMfD4fFgAAqCe3s6eYbxbNhEJuUqlU1O/3NZvNgjTGc3qZVD6gfe80v26cY3A5O86AT5YsWbJr3d7ylreEVJ63vOUteu9736sf+IEfeM7pQuvsWpDPpzSBa8ccnLrfdaUx4GDSAUScl+lsplfclS4GtOTQxowvwM7lty7D5ZqAV3ywuPiStEqUOKCC8fSqyTCvzibyzA4+8RU5z2W1TrasA9PrQK4/L+fgw3I/fM84dxTg6lWYY7ntOpLogQce0Hd913ddpCqMcz9jSa2TSy6X9vY44Iuf28Gm+8sOOteB6lgK7Mf4ec50x6y/jwv/PO4vH4frCKd1suK4TW4xK/3QQw/pUva8MbXJvn3MByhgNR78LJz1ev2iIlKwsS5N4TNyX1lQPTGeBRm5B6BWkqbTqSqVykpE0gcpgNkBr0/2dazt5Z47sbXJkiVLtt7uuusu3XPPPbr11lv1+7//+3r1q1+t17/+9brvvvt011136cknn7woBeiOO+7Qpz/9ab3xjW/UDTfcsPbYZMle7BYDyOOeI128TUvss3jal+fRSgpEAvVO8L/WAVoKMsWA1otBxUyxy5q9XYAlZ4zn87nG43Hw5ZC6LpfLACaloyJM+KKAYPoklkA7g+s+KNfiPHxGB+6xjJV8YPxR+k5SAN/r2GN/Z5cCfvjDsL8xqMWf9ZxXB+texMp/joE078gDBOsAsv/uwN7vyzvnWZ1tj20dYI1Z2fi+cf85iI4Z6vjzy7HLPn6OYwnUvkgsBrLSxQwuA2YymQQQWy6Xw95mLEheVY5cA85n0lAIyqvfMZGXy8PcWsqfe5TRJci+aHrkDQOQr3uGS/XBuudOlixZsmRH9p73vEfvfe97VSqVdOLECb33ve9Vo9F4zulCyZK9mG0de3Uci4v5OBB1QBCzpvhV5KiyzaITCjFAAbi5rNeBjXQkNXX21J8rLgTl0tbpdBp8RNLQAHkQEw5mabtfcx1jTDvj4qTuG3ohLPxVWF9/znK5HNpHvziIjWW03qYrMa/87rm/65hH3qG/E+7vbC3n4Dv7OIPgcWm1g0Jn+f2a3rfel3FBK/rAgy2xjx3L0AHymPeX/36p4I0HESDCvF/itjqrvq59sSVQe5Vb/IJdauATg8F7cHCg8XgcwCIgE/PiBIPBQKVSKTCsTK5+v6/FYqFarabRaBSq6wGQR6ORarVakB7n8/nA2pKP6zKKg4ODEEmj7V7YIN7M+zhsbbJkyZIlO7KXvexl+uhHPypJevWrX61/+S//5UXHPNd0oWTJXszmfshxzZ1yZ0fdiZcUWMYY0AKeYoWcy0ABKoCnuJiQH8/nnjuLz+ekiLOzXNOLia4Dsy4BdjDrDJ0/G+ztuu0b4zbA3jpAdsbQc2FhquN8WK7pwCsGkpf6DrDoQJP3RZ94Tm4MmHnHXMM/5x5+fX9ffq0YFDpr7b5xHDThHbrEnPcZP3+sGvDAiQdovN0eJFn3zwMXDlDZ5YQ+9GvG7ffnv5QlUPsiM490sHAxgRigk8kkTHj2jS0UCppOp+HzwWAQGNc4ZyPLMk0mE/X7/SBt9nwN8mur1apGo5Gq1apms5nG47EqlUqQz3iUiYWXbYQY+LTNF93jgNrE1iZLlixZsmTJni97pn6F+yIOkuLgO4DWGTb8H3wiAGDMvq0DtE5OxNJTSSuVjf17Z/mcFQVYx/fxPGHABvJjfyb6wIsy4QMCQN2/c1mxS4od/HgfZFkW6r/E7ycGRc7+8X38u/8cA0VImSzLVKvVVlhOZ5D9Pp5SRxsuxTw6aPXfsZjYiRlW/Hhvj7O5LlF2EszZ+OVyGSTa3DNmc/19xuyzP1vMvvLzOmac72I/3gF9YmqvEfNFi0UgjsQgY5nP55pOp2o2myvRkNlspkqlEmTFlGKPo5OFQkGVSkWTyUSj0Ui5XE6TyWQlB2Q+nwfmlujUYnFYdZm9ch0sx/IKX9SxeIBfqh/82GTJkiVLlixZsudiz4aldWd8neyYY9YVhgIcOVh0NtP9sVKpFJi1S7FrgF/pCJD677TT8zzZcgVVH75aDFQcfALo/P74erCyMTPr15K0soWNM4D8D7j2HFVnA93cJ3SCx99PLDGm7c6+0m6Xy3p1Z/ztuJJ1zEA7KPV34ub968DTAaQDVfeJHbg6q+r1b/iOcZZl2VoGNPa1Y//bFaFxMMDZ29hoj5NV/B+D9vh9XskSqL3KbR3A84EUTzzkwqVSKSxS0lF0D0DrleBcluKRvEqlosFgEBbe0WgUmFgkztVqVb1eT1mWhcICSJE9ajefzwOg9n1umSw+4HnGK7G1yZIlS5YsWbJkz9Vi2eWVDJ/LQYh0JEPGYDZhALk+haFyudxFBZzw72AnY0Dr4MnZTSc68L+cRPBj8PfYngfig2cAvFEwyZleb6eDWSc+HJzjA3IdZ+k4nmeN91KNgd06ILjuuDg31bfwcZaaY6fTaWgXhbL4zAMMANWYxaRtMUvpPjDH8JnfP/6O+/FsLoP2tjg4dP/ZtzyKAX8sn/ZxH+MLv7/LjR0vuPl1GWsc7+80ZsiPG1RKoPZFZLHEIJYDTCaTlQUMLXuhUAgbIy8Wi7Ahs1/Xz2OBZXDPZrOQm8v2PUS0sixTv98PA9IrKBPN8gnMeV65jknmQPVyoJZzXAaRLFmyZMmSJUv2TO3ZsLTu0DtT6n4M/lAM9iiyybY5DhSd/QTQktfqTGvMpnIvZ2H9OgALgCUSW0ApfhttcllwzBa6vJV8TwdGPIvvHYv58+Gnxmxs/I97Auo9n5g+AzzRxxS5cmBIWxzAxiyugzrAthMvcW6y9098LQIMfh7tdZDoY8fzZ/nc33U8Ztfl7WLO0DsI9/bEx3FNCLNYaXkpP937z5n8uE38HgdI1h27zhKofRGYD5p48jAwmeSTySQMYM5BjkxRJ4+EeUQtXoT82MlksgJUkR37vmUsLNPpVFmWrQBlv6bnTsSD3Qf3lfoksbXJkiVLlixZsudizxTUxvJRAvYx+4dc1eW4Dio9fcxBcgxonV1zosDlvA5Y3E9zaTCqO/w6pMQAXEAv1/Nr0X5YY64LIHV/zEFjzMjlcod5q/zz/o9lw+sALPfneviX5XI5sOHep+7TOuhc5z96TmjMGPt9vU3rQKX3C9fz9sYMacyc+jlOXq0bp+5f83ksP3bf3skqvxfH+DPEADYGnX4/tzg/93LP7tdax/zGlkDtVW6XilzEkgLpcEEYDoeaz+dqNBphggJokcBgBwcH6nQ6AYwSlSuXy2q1WuEeWZZpNBpJOiyh7tXsFovDKsmdTmdFrsJiTUU/ZAgUrvIonz8Dfxwu9+xSKhiVLFmyZMmSJXvu9kz9CD8ewBkX0/Htb1wFB+Pq2/vErGsMaB0QcCzyUoCItMoY+y4UqPTw/1zyjL/lEuN1wNGBsrON9IGf6wDcpaxxsaeYjXXiZl0+J9eEgY0LRwFi3T/2QAPXcCYRcwBIzi/95qDar+GSZn9OL6BF0a44zS7On+Va67b3idlQ7zsHzHGla08n5Hu+4935MQ7qvU8dvHogx/vC50XMWseycO/P+FzHA+ssgdoXkTFJ44HL4B8OhxctaLC3JOUTfZrP59rf31e9XletVlspEtDtdrW3t6dWqxUmKtv3IGV2CbLn7vpCDMiN2dc4msh5l5M2xOaTfl0ifrJkyZIlS5Ys2eXM/ZLjmKvkCN7HzBQ+ULlcDkDAmVtAjqvcPG0McOaAw4EZ53leo6vrSO+aTCaBhEBq7GCa53FQ6mAW83xcfncpcKwexDxPdp0qz0Hrup8dPLss19nJ+Ph1bCz+YswsOhPsMmL6DnWjA3gHdvzPZwQy/N6w4/E7dPDL+4vfpQNRaTUflufn2tzTqxy7xNmf29+9v2vGrpv3recwe/95AGLdu/Xre6DDGejjBpYSqH0R2DopgC8kvrcXEz/LsjBgYE/der2eptOpqtVqyO/gX7lc1mw2097eXqiaRuRwOByGgk/cI5c7rJxHhWQkyMiVfQHhd18Q+Z2FOAbBx+mXxNYmS5YsWbJkyZ6JHUfyiLnCDB/MQQkMqG+hAgghNcz9ICcnkACT8+nAxsGZS4C5N9fzdLDxeKzlchnYWQfgtDP2IWkHbQKM+v0BNe7Dee4qcut18lsHOnGubsz++TW8v/Fn/XjO53163zsg5H+e2dtP7Rj6KMuyFT/agxOuMFwuD2W78b7AzsD7O6cNDkRjwicG5PzvTDuMtYNWH5NOgtEW5OU+9vndAaYDTQfA7pPHgRxXEziY9fzgeBx4sCGB2mvQ4qiUR1+oUEd0UDqavMiPa7WaCoWCOp1OYFuloz3BYGOHw2GIyvV6PZVKJVWr1TAh2eaHKCT3HI1GQWqBzIbrMtE51hlcng2LB//l2NorHZMsWbJkyZIlSxZbDByOe7x05LMAGACXgE0HZOPxOJwHc+fgo1AorBTj5Hqxws3vIR0BtWKxGAqDwjAiz3XygPPdl/QcXyTN8XYsMVPnYAT1H/vRxkCLvroUkPV3QHEnB7EOPumzWPrqbCkEDPelP/mHAcS5RqVSWQFaDs7wsbm2XweLi5cyDhzMet/5uKPtPi6cGXcg7eOHYATjindEHzlg9Xfv1Zvdf47VlQ50uU4cBPJnod0ECuJn9HvxvD5/6HOfL7ElUHsVmy+gvqjwP1Xq8vl8AKMMDhY4WFNkLZQpn81mqlarajabYfKwuNZqNU0mkzDIxuNx2JNWUsiZZd/bxeKwojLXIQoGcxtHj3zRQBItrUav/Lkv9wcnlnQkS5YsWbJkyZJdyZ4JqHXw5Ayf+yu+fQ+glqJJLld2Zgswt1wuV7aQcV/IGV4+A7xQPRhfz9lZlwS7vBUfDrBEW11i66lkmKd7AQghN5zVczDuWwIBrDAALP9fSn0Yg3wH1d62GMA6WMMHxud0qS/96X3ufqW33dnaePysk7C7dDsuquV+sec0e3ErCCKO52cHnH7fOBjB83NsvPVTPD7WpfMBVr0//Vwfyw5843cVE1H+u/fj3t7eRW3AEqh9EVgss/VFL47YuETh4OAggFgArZeSr1arKwsqkaDhcKharabRaKRCoaDhcChJarfbQa48HA4vql5XrVbV6XRUqVQ0mUxUqVRCVNEnHb97BI5J5VJlXxgv1zeSVs5NlixZsmTJkiW7nMWA4HLmIMlzG1127FJc/ClPuVrH1OF/OfDz68aA1oEM9UxQ47nkF4DibCc/8zv+m+e8AqRiuSnHwcp6bmcsEQYM45f6c9E/DpLYoSMuEkS/uYyY90YQwCstO0CnmrP7kfQhKkKu5W1xNaQHBZxM4jwHgZ6v6uPKASm1bSSF543l34wl6agwq6SLFI4xk0v7S6XSRYDeJdk+/hyQxvMgzoOOWVoHsj5O/PcY0MZjysk0D9hczhKofZGYT0qvJsyCwWQpl8th8HuRgEKhoMFgoEKhoHq9rizLVooY+GKZZZlms5myLAss72Qy0WAwCIsoCylRwvl8rmq1qn6/L0lBcuOl230CZ1kW2g9T6/kH0vFzZo8DfpMlS5YsWbJkyaSLi+RcztwnAZTEsmOqGjsbh//F985wUdAzBrSYAwHPdZQOgQt+3ng8Dtd2hg5A5LmokBqxVDhWAbrPBeDxasMAIGfnfG9bJ1mQVuOHev97sat191wul6FOCz4vz0QfrauC7EDe+9vfmzPAMfjl2jHh4vsNcy2/D+CcwIEzzh7UgC0GXOfz+ZDTGzP1gFTu7wDe69rwL85j9nfMeXFghT6LybKYQXWw78pMP5bjnY2OfXrMgwXOml/OEqi9im1dRGOd9GK5PJQIA2qZNDCzgNT9/X1tb28ryzJVq1VJR9WKPZrERJrP58qyTKVSSYPBIEQCmcjj8VjFYlG1Wi20ByDMQOW6vmjFf0RcguyyjliicSnzKGhia5MlS5YsWbJkl7Mr+RVuLqd0h57rwKYBegjqu7QUAzjAksaA1sGJAz4cf6obw1JKCiAVkgCiQzpKF6N4ZwxM41xXQJwD33USU+4HmHWfD8DulY89rzauNOzyVvxL5MSu6MP/9P13HfTzPrins6aeZ+vncEwMDslTdmmuvy/61IMBvhcs79LHmrO5zlpzL2fueQ7pCOQ76Pexgvn74fx1smoHk/5s8bPGsm/6O2ZrY3/dt31ax9JeSpF5HP89gdoXkfki4AOSRQJpBNJeJl+WZer1ejo4OFClUlG1WlWhUFjJv2AQEzli4ANUF4vDyseTyUTNZjOAVRZMIkyVSkXj8ThEnrxwgst1WHwBytLRhPIc3+OysC59Oe4fqmTJkiVLlizZtWUx6LicucPvvozLjgnMAz5gRMmpjSWfAEYUdjFIkI4KD/EzvhjnUU/F/SVADP6Zgy4AGoDKZa/eJ74lZAyqaKOrA50QKRaLqtfrKwWYYG8d1Hg/OqEBGRP7s85Ccx2CBgDBde/J2VH3ad0fXadWnM/nGo1GGgwGwZemP7kO+/zS9nU5p972GJQ6Yxyf6yCP56aNMVD0Mext4veYHKO9XNcBZsyoOsiMc23jNsV2qSBNHMjgnl7U6nKWQO1VbnGExCUovPzZbKbBYBAWLukQbA4GA1WrVR0cHKjf76/INKbTaSgxD7jlWkwWij9lWRaKPnEv8hUAsERmvLQ7Cw+Vk700OFILlxywCHlkzc+5XBQnjoglS5YsWbJkyZLFdinV2LrjOBbfxAEUvgy+FYQC/gosowMWjgNwxs6/pBUGDXDHPrPIjQGe/iz4cgAxKvbCBPIMAHEHRkiE3Vdz0Cet5oHii3I8zCxAF2Aay4qlw/xQ+mY2m63IpGFjnX2mn10eCxj29+SglHYDnDx1zmXAk8kkXB/5sCSdP39eW1tb4Tj3P10G7oDfc5QvxW67BPhS5v6sM6exYpNnjQt0xQw0fR+TRN6/3t510mEPsMSqUUkrMmy/rwcyYj/eJczH9dsTqL2KLR4YvHwmGJPPI4GAViZqsVhUv9/XcDjU9vZ2qFY8nU41GAxWoobS4SCn2rED6Hq9HthafkduwQLGAlIsFjUej0N15tlstvKHwCeWL+JesS8GqYmtTZYsWbJkyZI9F8OXOo7U0f0UBw98hsTXCzSRWxsXycQ3QiUXV591cOD/e00S/C18Ja4LGINB5ZhKpRJAF74jDC+WZVkAYXHOby6XW8mTdfa4WCyG69M/o9FoLRtHG7gG/inXIu+W514ul6GeC9fHd42BPwDOAbYXReJ90D/szEF/oE70fvH7IPd2Uggwizmj7VJjb5uDWZc/e9AkHneMM7+Xv0/a4r56PJ4cVMYgme+dRcfWfebPFDO1/mzeTgfyPCMEGN95QbPE1F4j5lG7eJBMp1ONRiO1Wq3Apo5Go5DbwL6yHulDukH0yxcvj/QxmYj01Wo1dbvdsNAul4cV3crlsqrVqubzeZAgEzmkoJRH2aSjXFokyCxKztDGwP5K0S0m1bqy5MmSJUuWLFmya9eOKz128AvAvJTsGBAFEMOH4j6AOwL9OPcxE8o5nj9bLpeD3FhSAH/cx9lZtmKEdXV5LN9xrlch9jbgO3kKG34hDCoAz+XF3kf+vOSeeoVfjvHdM7x6tO+7i1/o2x35M9BmB4r08cHBgUajUWhn3AbGAv6sV65uNpva3Nxc2XfX5cCxfNf9Ts+zBagBrhlXMdMcV1F2f9jBr78PD4xwjufQcow/Q3x/J4LiolIelOH8mIF3QM45sao0lrjzrmLgzfNfLuCUQO1Vbj7AMB8oSIBdtgGolRQALNE4Fo7ZbKbhcHhR3gQL1HQ6VZZl6vf7yuVyajabyrJMo9FIk8kkbNnDvSqVSlhI433LkKy4TFlSKGzluvpLRaw8InSc/rqSXDlZsmTJkiVLdm3ZcZVcON2xnJPvIARgOclfdFYQkABIm06nK7mDzmQ5EJIU/CjkxrGU1WupTCaTQB54cSPye10lV6lUVsAsnzvj68ws7Xfwzj3dV/O2oQZ0oMk9+B7wCYiJtwlyEMuzOEsJqQNonkwmGo/HkrTSNvch43xh3yeXZwBc1mo11ev18DngMB4XBDIArXwP0ObalUplRZ7L/+7fe7VkjHHhFsuofTx7wS3u7zJfl2h7bi2+vINl73/G/bqgULxdkDOwsR/uqlAUCC6fzuVyaZ/aF6Otk9vGJcEZYF7wyRe4QqGgTqcjSWo0GiFqRdQun8+rXq+vRPXK5XIoKsViulgsVC6XValU1Gg0NBwO1e/3g/RksVgEYMvkaTQa2t/fDyCbYlIM4FiCjCzE99hFsuwyh+Oytcc5NlmyZMmSJUt2bRg+x5UC3u6bOKDkcwAbdUfcN+M7BxdUK3ZA68DEQQN+GPeZzWYrOZqeFwugkw5BWFx0ie8kBX/NWTueC58QeTLgnOeLAe86Zg0m1PsCsMozxpLjSqUSrkOggJ8deLpCkV09YH+dTXbj2p7HzPW83Q6k437xlD9UifEY4hr40bFsl7ZTAIt7xRWxnfn0vo3HEECV4wHbjFvusw44A2Zj+bBLtqWjgIq3z1WbsWTamdh4bvGZA3B+dhDvCtTLWQK1LwKL9fcePUF6zGRFgsIgIZ+VbXnIhZUOgW6j0QgsrkfEBoOBhsPhCqvrUcf5fK5+v6+tra0gMWZfWpfkeDQLCXK8VxlRTr+/SyZ8gTgOUCWCl2TIyZIlS5YsWTJpNVfyOMfhd7nCzAPwngvo0mKXHHs+rEtvY0ALO1apVFaYUmqeuC+EWo52ZFkW/B4AL74S/p+zkM54wuR6USsYX4CHy58x2sTnnkOM78Zze5/TFs8D9v5y9ni5XGowGGg2m2k8Hof+dV+T+wHACQg4K4vf6vnAKAVhWj3nlD7mdweNsU/Ke/JiV1zDQayDYJcAuzQ5BsQO2PHvvWCW3wtQGufTev41/e/MfcwWO6h0mTXPQqDEwamPaR8fWCzbjgEz7ef7wWCgS1kCtVe58cK9Gp6ki/JlWYhYKFhop9Op2u22lstlOLZcLqter2tzc1ONRmNFViNJm5ubIb9gZ2cnLCIsdLVaTZPJRMPhMLCz3AvwSoQOOQNVkF1+wnOweEpakey4NCKWbVzJWCCSDDlZsmTJkiW7tm2dbHKdeaEbL8IJoEHlhi/jAXkAHuyg1x1xcMR9PD8XZtS3kPHAPqBrPB4HH69WqwWQ4rmOsG/OtPLsDmbxE50hJpcV0IwP5Qwm9wLQAVQAjoC0mMSQjsAv78TT1Sg2hRTWKxLznIVCQdVqNQB53oPnw7oa0Jlkl9o6A0u7YHSlVfAHUeT9ixoyBnLcg/Z4/qkHDwDq6xhPZ4yl1TxqGOg419XHUxyI8eNj+buzywRBHKh6IMDv6TJwNz8nZsD9GXn/ft46qXVsCdRepRazky6DcKA7n881HA61sbERIj5s79Pr9VSv11Wr1dTpdDSZTFSr1VStVkMlZKoZuzQB4FqpVJTL5fTwww9rNpup3+8ryzKVy+WwzU+n01G73VYul9NoNFKWZSqVSjo4OFC1WtVwOAwRNaKKcSI7A94/99+loyR8+uFKQNUnXZIhJ0uWLFmyZNeuHRfUxtJKz6OEWURqCkAhd5XrA/DIiQVk+j3wbwC0bHMDkAaM0Y6DgwMNh8OgYoMkcKYUn6pcLgdiAmCJfwhQhM0FgHshUU9vc1+L9Dbag0LQfVSAEec5m+iyYr5D0tzr9Va218FXLJfLajabkhS2knRAS+AgZgtjBpxn5ZrIcHmH9An90ul0tL+/fxHA5D7IuX0M4OcCWB3c4bs7W+6BApeOu9/r4zbeuodj/D04I485ceTPEysA/D6uEPX+456MSy826369k3DOcK97NvrjOJZA7VVusdzYfyaq5ZE58mHJt6hUKiGXlsjSiRMnVK1W1e/3V2QmTHjkHuVyWa1WS+12W/v7+yuSgFqtpvF4rH6/H1hjEvWZ7FybxXswGATJji947PNG9MwXc5/gz5St9QhWkiEnS5YsWbJk16YdJ1/PQYgze5ICY+h5ns60ugwUPwfJLOCSe+CT4JORL+lpV+4DUaBTUthpAtbPWT6X4TrI4ToAN+moKFK5XA7MLVJc6WjPVYAa/zgvZvi4J+aFr7zSMj7mcDgMbDGS1sVioUqlEthC+pF2OivLe3IAhS/sElx8P97RYrEIfquzyn4s14AVdWaZ90C/MEZ872A+c+kvLHxcldoLvMYA2tln2hfLfZ0IilljB8TO5vp4AWz7u/RK2b7NTnwPz4VdFyxa95n7756ieLlz3BKovYrNB75Hg1y7PxwOgxxjPB6r1+sFGbB0uJ8sC+JyudTp06dXqhpTXICBySBDXlwqldRutzWdTtXpdNTv9zWZTLS5ual6va7xeKxOp6PNzU3lcrlQXIqoH6wt+b9UVfZJFOcseDU0T2r3iXNc9tWlMgnYJkuWLFmyZNeWxXLIS1nM0gLcJAXZMb4KwXoHN6RdAVhhYdcBWoL5fqznz7r0Fra3UqmEXSxcRguYBfg5eEeqDFBxFnmxWITaKbSddsKgAoYBlvQN7XTg5qyy57Ki9ENSDOPtwJr7+88unfYthPCH/R1xP3xFyB0Hzfid+KVIxB0c44MOh0N1u91wHIyvp+vR17TXFYIAXICsdMSYxiAVH96rCDtQjf1dJ3gcFDpg5T14e501R3lwKVAZ/++y/JhdjvvDz4nZWD8gYLYAACAASURBVAfjbvGxl7IEaq9SiwGcD0oGOTkPTCKicEQImUBExLa3t1Wv14PkmIIELLQMqlKppGq1GhjefD6vra0tTSYTdbtdSQrgFHkNizdsLVKaUqkUyqyzGNFG2s1zEa3Msiws/F68wBnXZ8K+pvzaZMmSJUuW7Nq04wTB3dn3dCdJKyoyAKnnKHo+YqlU0mw2CyDSfRaICRhaSIq4IBSfDYfDACjZEgYWGd+IdDD8PWeWvRgTABF2DqAMOAHwOCsLKEXi64WBAGAue4Y1pN4LCkGvwrxcLle2COL6/JNW80UdWHNtr6js++T6PrT+3mMg6RWJvQCWPx/389xgB/30Ce10FpV37sDW82J9vHk+MW11Zpjx4H0S58/Gn62TfnMf2sd5DkLjPnM/fR0b7KDaZcfeZw5UXbbs5/Oz/38pS6D2KrZYWuFRFeQigNpKpRLkI9Lh4GFfWdjc7e1tjcfjsEDCxnI8A65YLKper4ecDApCnTp1SqPRSP1+PyyQrVYrfEYl5MFgoGq1GharcrkcpBWj0Ui1Wi1EOZmMAGAmdFwh2fM2nilb69GzBGyTJUuWLFmya8MceFzpOACj+wwwoqRUObiigBDgjPxb8mJdZeYAxQEh53sRI9hM/CIA7XK5DGBUkqrVakj3cmYNxRzX9krNpKwhO6YNXvjJWVl8JgdEgEdJF8mKx+NxYGQBsPh2qALZcQMfUzoClF5FmffAP/plPB6HfgAkeSEprkXbnaBBYs079H7jWfi5UqmsKAmdmYYVp1+8j7w4lKTA9LpyMPbvfYzyvfuqPi7jvvFzGYOxnxsDYJ8bPtb5nLbFVaG5lrffa/34s/g9fR66ZN7bn5jaa8B8EHpUD1Z0Pp+HRY1cWBaiYrEYpMcnT54MkmAA7d7e3koZdCbIYDDQdDpVo9EIC/VkMlGWZdrY2NBgMAhFoZAvE5lcLBZhUSO5v1araXd3V9JR+fM4MuXRJI84sVh61eT45+NYArbJkiVLlizZtWXrGKbY3GEHiPEZ4MxBIP4XQMVZRt9L1Bk0l6I6Kwqbx++j0SiwsBTrRE6LsY0P7Kzfx/dDLZfL4VnIBeV32Gfqrzi7BviStOJvuRSZY/AHAdKAI4qGOlAkX/ZSBYVgjbkfdWPom8lksgKikV07+zcajUIbffsb7uF7/nowwPfvLZfLarfbarfb4bt1Y8r9csaRS4GdtXS21QGvS5id8XWw7QxuvL1UPIYZr4xT718Hp/jY3MPfR9w37pNj8c/O0nrednxtf/fepuPMUymB2qvSPFrBYsakdmkJoJZBBOs6Ho9X9qwtFApqNptBMgGgzeVy2tzcDCXcJYV82G63q263q1zuMO+WCby1taULFy4EWUmpVFK9Xtfe3p7G47FqtVoAuOQIIDPx3A4idz6BPbrmfxTisvKxnOG4ADUB22TJkiVLluzaMJfFXuk4QIM71aR0OfCUFMCgy3M9Fcv3aYUR9II7OPIO0Mg7xQ/C76IiMn4PhaI8PxF/kLxfGFBALuldgB2ArHQEJGMZawwunTmcTCah2BN+abFYXMmHRaUXS4p5J05WABrZD5d8Xvraj0PezfH0EX3sObb4n57nKh2xy5A2LhumPz2gQH+5/+3MI23A73agGucNu6QZQojreBqg95MDw+FwuFa2G4NUPvN+JxDhvjRt92vFcmJXGjgg9/RAL7IVt91Z23jeYbEs+1KWQO1VbLE8wQfgfD5Xv9+XdChB8VLkTF72+jpz5ozq9XqYnL1eT/l8PuTNxnmrkrSxsaGDgwPt7Ozo4OBArVYrLOKbm5saj8fa399XlmWqVqvqdDpBppzL5TQYDFSr1VSr1SQpJObncrlwnC+amBciICrFM5NrS98cV64Q92kCtsmSJUuWLNmL244jPXYWC/CHv4VUFf8FPwmQyXfO3HohKUmBoaUdzpxRR2Q6nQZZMACI+3N8uVxWpVIJ3zngAuig2nNmFpDn2/n4+ZATzhxCpgDKaGe/3w/FSAGbgFmkuZVKJRQDRdIM4HQwVSwWA7vrWxlhgHDaT7vZ23Udawhw5H/aB/vqIDNmLD2lDx/Wd/zAXPLrzLKzvz7+HAgzhnzMcb8YpLr/H+85y3EOLmkP/rKDS8BzLHPmHTkz79fmHcdt8nZ6TnB8HYCu98k63532MA6p3bPOEqi9Ci1mah3QMgFZVNH1dzqdEE2azWaq1WoaDAYqFApqt9thYly4cGFlD1rycaWjxa/b7QYA22g0wnY+VDtuNBpqNpvq9Xra3d1Vu91Wo9FQr9dTv99Xs9nUdDrVcDgMwDnLstDmg4MDjcfjle19PILJAojch+gXk4bj1kWjjmMObD1qlSxZsmTJkiV7cZj7CZc7RjqqZgwQQCFGXmVMKgCU8Fd8a5h1+YOSVgBtuVwO7CqAFskuINRlyCjfpCOgTB0Vinbmcrmw04VXC/btbGgjz+ByXJ7Td58YDAbqdDoaj8chDY3iVPiPLjGO5aUuVYZ84R9G3q+DNOq/uOzaAw+00+/rebqAT5cioxiEvY5Bp6ezAda9mnTsKzrYhlTCZ3c216XcPIdLjv2d4uP7uQRXXN4cA8z4eRywot70senvhuflvHXPymecF7PCrogAuIIpeG5neh0U+znL5TKB2her+QTxf1SuI69V0kULxXw+V7fb1Y033qhKpRIY2kajEc5h0sYLf7VaDZX38vm8sixTr9eTdBSlOnHihPb29iRJw+FQGxsbYfNszqf6XbVaDTKU0WgU2kvCvDO2LNBEAj0CFrO18YR+JsCUCeeFEBKwTZYsWbJkya5+i53mdRZLHR2QwWA60wp4kRQktvgt0sW5he6beF4idUqGw2FgKKvV6gq7iW/kzCdtQPaby+VUr9dXfD/ArF/HwSzgm2q8LhPlXLZqpE4LwHp7ezv4jeTrel/zvDy7s58O7uk/znFmmT4iBxUZtVd/pq8gRvyZeaf4yfTpYnG4fZGzqfwP0+wsO0EEgKRXJKZtTozQdpdz0xcwpVwjzsF1oMnzu4rRfVV8Ydq8bozHjDj/OzHGePVtkzCXE/O7j+l4bDtzvo7Jjb9bx/geR1UhJVB71ZqzkPzvwI1iAq1WS8vlMuS4MniZvI1GI0T9hsOh2u12iAgyaFmwYXrJM2g2myGvtlwuq9/vq16vB/b0uuuu02OPPablcqlqtap6va5ut6vxeBxYXUDtfD5XrVYLQJncCbb+8QiWpBBZA3T7ohDnCDwbUIuxaDmATpYsWbKrzb74xS/qH/2jf6SPfOQjevjhh/XOd75TuVxON910k37+539e+XxeH/rQh/SpT31KxWJR73rXu3Trrbc+o2OTJbtaLC5ss85ilo7fPTfVGTfAq4NCl+JyXwd1XN8BD+wsIAe1nG+xSGqXs2KklAHonDn2gkQAWu7pDB/+ncuLYXQ7nU4gJ8gr3djYCKSEy5tpr/tNy+UynAtrDOCBXfUtg+I6Kt5H1Wp1hXXlZ38n/Ov3+yuADdDJs7n8OJbE0jcEBujTTqcT3pHnyq6TD/vewfl8PqQG+vFOnnjFZgeftMtlvw4YPYgQM6wcH7OoDhbXSX/je3Cst4H7xSDa54+fwzVcch3PCc53UBsrG9ZZArVXoTk9DyDlRSMlQUrBBB+PxyG5fnNzUzs7O0FmfHBwoMFgEKJaAFNkKj7QKYAgHVYtLpfLQfowm83U7XbVbrd1cHCgZrMZZM6dTkftdjvkIXhubaPRCDIOL5hAJJTy7gxuFgmPTDFxYWt9QaPtzxbY+mRLcuRkyZJdbfZrv/Zr+sQnPqFqtSpJet/73qe3v/3tuuOOO3Tvvffqk5/8pM6cOaPPf/7z+tjHPqYnnnhCd999tz7+8Y8/o2OTJbsa7DisjxMGboBU35tV0gp4RHLLjg/4YS4ddSAZA9perxcc/Wq1Go6FYMB3iyWozh572zxn0nNpYRgBePhVDkJoT7fbXZFVs60jz+rgk77lWam47EWNAJD0Z8x4u8+GD4gyEBY3y7Lgj/Is4/E49Al9G2+5g79JkS98TpeJew0aJzcAsABdzvF28Hyey8q1eA7voxh4AmJ9+yEH3C6n9tzsS5EuLuv1cR/Lkem3WN7sY5Z74Q/7fImvGxdWox84Jp5b3o517T+OD59A7VVqLj1gwfPo2HA4DJORDbpd2z+dTrW1tRUm9mQyUbvdDvJdl6WwADhwXiwWGgwGYdHt9XrKskzj8Vh7e3tqNpsqFotqtVra29vTaDRSq9VSrVZTp9MJm4sjzYFx5Q9FPp8PQJvvPGrn+RAsHhQrIKoa5xc8Fxkxi4i3I7G2yZIluxrshhtu0C/90i/p537u5yRJX/rSl3T77bdLkt785jfrM5/5jM6ePas3velNyuVyOnPmjObzuXZ3d5/RsVtbW39uz5gs2XHtSqA2ZoicZQUAAShilhbZLb4Ifoo7/R4g538Abb/fX5GlAqA4rl6vBxYYIAY7i+TUq8oCuACzABJnzSgcGu+W0el0ArmRZVnw67IsC5JnJw7cv5pOpyuSWvwy90MhKOhXZ00hOcjR5VxnpZFmexVp/LxY/kxfSkdVkSWtFJZycM0YIDfZfezFYqF+vx98YL7zvF8Yb/JSubYDV8C5tJqHyvnOYDqT6vnd1JbhOT2oAEmE/++BDB/f7tfymTPVPmf4LGZv4+fx8+PniuXstNdBdxz0WVcMa50lUHuVm0fwGOywqSwEw+FQg8FAi8VC1WpV/X4/MLIHBwfq9/srSe/kOLj0wSNwzqgSgWs0GhoMBiuLbKVSUaPRCPm0nU5HzWYztIl9agHEpVIpRCU9EuZ/EJjI/gchjuohA1kHZH0hf6bmC5+3IbG2yZIl+3a2O++8U48++mj43SPe9Xo9FPFj30X//Jkcuw7U3n///S/UY33b2Hg8viae88VisSw2tpgo8ArB1AXx75ELf/WrXw0MagyWAAoAWlelUekXQAvbms/nV9LGYrnxdDrVZDIJgX8HS8vlYd4voBaQ434RQJF+8K14UOCVy+WgyvNqwS7Hpi+8GnNMtMTASjoC2L6VDnvVekEsfD38Pfw438rH5bi8Q9hp/ELpiPUDvPp2krQ9JkYceHn160ceeSR8ji8IAHNJMrt2xP3mMl0Huw7e1hWU4vl8rMYKAAfCHLcu+EA7LsWCxoDarxfLph0Ux+fG4NZzkHmHfm2+ixnafD6vM2fOXNROLIHaq8x8sAOsnK1cLBYajUZhoWOhoSR6rVYLOa3VajVsXF2v1y+SgiDN8IWEhdKBLYsmUuHhcKjhcBgWwHq9rv39/RW2dm9vT7VaTYvFIuzdxiLrkpLRaKR6va75fB4WE+kwSger7FFJIo5EAONJwXM8F5bV83Q9YpXAbbJkya4G8/VvMBio1WqFwKR/3mw2n9Gx6+zmm29+AZ7g28vuv//+a+I5XwyGH3Op4Lb/XfdtZvCtyNsEJCF5/fKXv6zXve51yuVygRX1a7ok09mqfD6v4XAY8j5dPsw9qtXqyo4UEAqAJCcd8IGofBxXrfVzsMlkom63q16vp0qlope97GXKsky1Wi2QG+sqGONfkhvpElbPi+U8/Fe/DtsQ4d+5v8m7ctaSei9OXsCkxr6qs+n0Gz4z7fV+cODPP1hxwCrP8+Uvf1k333xzeF9+jfhZHZzTP/ihTsw4QI0DAy5ZdtWiVyj27zxfNw4i+HiIx6kz+ZwTM6SxjJjrOekUHx+D2zhYEH/nQScvbCUpFKFdZwnUXqXGAPC8Aa/gNpvNQpEoom5M5NlspmazqVwup9FoFKJj5GW4BNiT5j3fwvMw5vN5KOVeKBSCHBmgurGxocFgoG63q729PbVaLRWLRfV6PbXbbU2n08DWesQml8uFXGAiXb54OPj26KN0JHWJI0c+mZ4LCPWFyxeiBG6TJUv27W633HKLPve5z+mOO+7Qpz/9ab3xjW/UDTfcoPvuu0933XWXnnzySS0WC21tbT2jY5Ml+3a3K/3td1bM/84DVF0STJCfNKxCoRCIAgCQK7oAYtQ8kbSy3ykAydlCCiJxDdhZ35IHAEBamIMmfDlnBTkHufP+/r6kw4JUzWYzAFqePyYISG/jPvQpyj1nAyFXAJls94O/R+4qxUsB/fiB1EgBlFIzBsAf72/Lu+OaPLOzf76lj1c2dgDuua+0xUEfDLb3p3RURAym2Nvlclp/Lw4afZsbJ1Ac6HobvaCWpJViVs6E+3ExgMQAs86auu/s5mPK20Z/xcWi/Bz3w+kHnj321b2/YnC9zhKovcosZmo9micdTmbAZLlc1mAwCBE7pCzIgokMnj59+iLWFzlHtVoNwFU62rdqNBqFnAa+B3wyqYfDYZCV1Go17e7uajQaaXNzU9VqNex3Wy6XNRwOV7b34TqSwtZETEYmAOc6q8wEZtEDoPsfMgfAzxWArouIeRuTJUuW7NvN3vGOd+iee+7RBz7wAb3iFa/QnXfeqUKhoNtuu01ve9vbtFgsdO+99z7jY5Ml+3a2Szno/r0DEP6OAwgAPnzv+aaAVXwLB7QumfXPkByjUIvZTfwhZ2cBZQAQ/A/UeJAXAGHA82w2C+dNp1N1Oh31+30tFgvVarXAypKLCjNL28fj8UpRUvqJasbOfuNbAb7YRxdwDPni7DLFoFwiTX0YJ2/WSZ9jmTXBA38G3/aIdsLC0k+8NwCV3wf/jj4YjUbqdrtrAeu6Ak68NweL7pfGkl3O9+2BnKF2YOxKRO7BfX2cxDm9Pu49MOIsd1zRGT/cAxr+Oe0gWBETQJgD29hf9vat++5ylkDtVWoMEBZASWFBHA6HYTCNRiP1er0QdSTvqVQqaTweq9ForEQikSGT00ABgji6Qw4HYJaFhAV9Y2NDOzs72t/fV71e18bGhobDoXZ3d7W3txeij+PxONxnPB6H7X3Y8odolzPIPDuTmYkuHW2Qzh8PX6jot/iPzPNhLumhXxK4TZYs2beLvexlL9NHP/pRSdLZs2f1G7/xGxcdc/fdd+vuu+9e+eyZHJss2bezHQfUShfvBQrr5VJfwA3MI76Qs7Iub3X2TFLY65VCSOR1SodVfSuVSpAOk7OLv8K18aGQG/M9ZIQzoRAeOzs7Go1GWi6XoehUo9EILKqzbhSM8iKhsMWw1bQRwAwwrlQqIb+Y2ii+5ZFXIXZpMH5f3GcEDQDtkCe+5SP5q/Sps46QNbTVGXRnQh2s8TNjw8kQr3bNvZ3RjgsbeXXkWALMOQ4gIYsApzwjqspY8uz9FbOjjGmXNF9KseDP7qww1/fzHFg7k82xzjbH/vA6sOoS5hgIe4ABBn6dJVB7FZoPKCY4kw/5CZINIolsxjyfz9VsNsP+WuQzdDqdABrJYWWRJKeCSY90JsuyAD5hTVmYC4WCarWaut1ukOY0m01duHBBw+FQ9XpdWZaFvW3n87kGg4FqtVqQ8XguArkhlI9n4JNby8+wtZzL9w7I40jq8wVspYv3NkvFpJIlS5YsWbI/X7sUKxR/78e5gq1SqawAA2qLUNsDRs0BrfsADkRg+RzQ4p+w/ywqt8lkoslkEn53dmw4HAYyAx8jZo0hG/b398P+qKj1XAaMH8T9OFc69GtgZB2YO+MJkIUhns/n4X74hL63rLcvljE7+JrNZiF/mH6gvUiA8RcBf4CjGEj5OwEgcx/63xlW/ucfPnIMkj2H2NsP6OZ8+tPBdcxK+hjkWk7ixPmqPEOcWucKTg8OOHPscmaOy7IsnOftiaXD8ftyht1TI5krDlhjphjz+eXvyz+7kiVQe5WZD143FgUicLFMmEUEYEj0rlgsqtvtSjocwI1GI0TK8pK2HntMld1d5c+dU/7cOS1GI+3++I9rWK8HRhjWlkUd0OrVlKlsvL29rd3dXfX7/QBeO52Otre3Q7EEonuNRkPdbvcioO7gFPDqQJ/P2eIHKY5PDCapLxjPp8XRMT5L4DZZsmTJkiX71tpxWFoHBfgYsUpMUkiNyrJM+Xw+7I3qoMh/diA4HA5DDRGKc/J9pVJRrVZbkRu7ogy/BfUabCVgJC7eOZ/P1el01Ov1wlY87XY7SJrxkxw8+s4T5GxyH2flkAwjkeYccorpH2TF+F+TyUTD4TAAX/qJ9jobKyn0CzJm8jUp2EURUScueMdeAwYf2eXfDuj434Er1/PthpwQYWsgZxfjQlMEPXz8ubrSQSPt9DHKd7Dcy+VypWgq98Hnd1bVpb/rWOF41xTGnfvJMVB3i33q+H9nn9exsD4fvb0hMJHLaTmbKT+fKz+bKTeZKDedau8y9RsSqL0KzSUxRLuko72zptOpGo2GRqNRKBolHUYHT506FRbFer2+Auy8mnCWZXrphz+sE//0n67c+6DZ1Lm/9bcOZcKzmUZPT2r2qPUCBPl8PrCzg8FAGxsbqtfr2tnZUa/XC/KTyWQSnoPjiPz5REeqHOc/zGazUGLfGWmv0OzV91ze8UICW2l1020WyCRJTpYsWbJkyb51dim5Jd/FLK10tJ+p5yECGmu1WvB7pCMQ60yZB7Slw6JQDmhhKQuFghqNRvBvAJKc72wvIBjps3QExFC1zWYz9ft9dTqdwGa2Wq0gL0bxRroaajtyWlHyofTDd4JZhpWVFK5BgSekzy7VZktJ72MH0vizgGyuT9DA2Vfa6AWyeFeQKO5v+TvFb3TW0YErfR8DRmd+6W+KaQFCPejBZwQtnHhxYOhjjc99y6SY3eQajAUneLiGp+LFoNMBbDz+3fyZPVBQKBSk5VJaLJR/WjlQ+frXlRuNlB+NVJxOlR+Ptbj+ek1f+1ppNlPzV39V+fFYudFIufFYmky0/30/oJ3/7i/rYK+j1rv+gaazuQ7mCx3M5potltr7ob+u/e//AS2fOqf2e39Rk2JJo2KmcamiUSnTX/zg37/kPE+g9iqyeDL4ghdXJc6yTPv7+wFoAtra7baGw6Emk4na7XbYk3ZjYyMM+HqtpvlioS//1b+qvSzT+DWv0bDV0mxrS/PFQvPBQO18Xq/6qZ/S4Pbb9bUf+zEVW62wBy7ROJeaTKdTjUajsLju7u6GbYUoWtButzUajTQajULkzSsxU3gKxtb7hD8Mvpg5kzudTsMC6YuQdFTm/PmWImP+R5H7JHCbLFmyZMmSvfB2XJYWy+VyoeiS1w9ZLBYhuF4ul1dAqW/N4oAClpIqxzCXMLGws/g17Pjgkk8AJkwmbeR72M3xeKzJZKL9/X3l83nV63U1m83AdOIDTadTdbvdFRYWZZ8X2MzlcoE5JlUtlzvM0x0MBkGpB9iVjvxRADF+KuzyeDxekeLi0zWbzRUWFp/JtyTylDsvqsV98amonuygzMG5g2jvZ+nQH+Q5kV0j/XZmkyrM/s4dLDvQ9efy8RinxDkr6rnQTo5w/xhI+73ogwBqDw5UHo2U7/dVG42UK5U0ufFGFYtFNX/rt1R86inl+n3l+31pONTed36XHv+Rv6nRRGq++x4d7Hc1OVhoerDQdC7t3na7zv/w/6jpfKnqB/8vTXMFTYplTQpljUuZds7mtfOVqobTueb9V2lUyjRuZRpvlTUtlqXHJH3kK4eT7Xt+5uIJeSDpPzx++PP/8I6Lvv6La2fxoSVQe5Ua4M0T72FqmQgsWD4parWaOp1OkPhSHIoJdfJ3f1fbn/iE/vDeezUuFLT80R89XIRzOeUtv7X/5JPqnjih637915X98R/rz97/fhWaTZVKpSDNYZsfcmspBLW5uRkq73mlY/5AdLtdNRoNLZfLUDiKCBQLo3QU0ZIUFmIibl5lkHLmAFtfRDAHti+UTBhwm/JtkyVLlixZsm+NxbLU2BwwuALMZcj4S/l8Pmx3MxqNVtRk+B2kYgEser2ehsNhALT4ZZVKRfV6PfhjkBDS0dYu+EaepwjbSr4iaWbdblej0UiVSkXb29tB7UaeJAWn8KUAW77vLc+PdJg9ZJEWIymlmJWkAGJpo0uIHfyRBwvZ4Vv3oKxz4OlMpxda4p3CvtJf9BnPBEjkvcQy3Xg8wNbiw3qOKD4owQfaQYEo2u1SXgIOgGjeO8/h1/EteJxcybJMB7OZiuOxivv7Kg8GKs7nGj+9J3Lrt35L2Ze/rHy3q3ynI3W72n/J9fraO96t7ljK3vOLGj92TvvVpvYrDe1VWzr/srN64r+pqjOea/lwU5NlS5NiWePNTINTFS0Xeelfff2wAa//yfWT6rPnD///7r+pkhYq5ZYq5aSsIFXLBdXmC7UrOVVvuV7lUkHlQk7lQk6lgpQV8qqU8irlpHLx8PNiPqfS0/8Xc0uVSwUV8znltVS5kFNWkLJCTlnx8mRQArVXqTHpmRRIeNn+RlKIxOVyh/uHtVqtsNiwSPlkPPuJT+jshz+svVtu0aLTUeXkSZVKpRUZBAvCJJfTg/feq6de/3rd+oEP6Oaf+zl96X3vU+7pBZpS515MYTweq9frqVarqd1u68KFC6rX66rX65pMJur1eqGI1WAwUL1eD1WYkQGRc4tU2gEqxapYOFjIWLhZbP2ZnbHleK73QoFNl5MkSXKyZMmSJUv2wtiVWNp1Ek3yCr04FH4FJADAByCD/0BRHHyLfr+v4XAYwBxy21KppFarFeqdACoxwBDgkPvgk8HoonRj79oTJ06s+E7SYaVlFHrOTnqlYc9/9WJTEAIAUhhZ/DtJgZCAiXXFHEwsQBYQKF28LyngFwALGPRjAXxsgeQqO/xdB8mSVnJQ431cff9WB6PSUZEo3i05vHzPu2Z7oriWisvPAazFYlG52Ux6+v3UvvIVVb/yFeV3d1Xc31d+d1e5yURPvv/9Wi6Xesm7363S7/w7XSg3dKHe1jdrbZ277gY98L+d0d5oroM/W6gzfLkuNDa1c2pDnayuRS4vfewbhwPpDX9besPReK9rrlZxqdpopnopp+rZ0yoV8ypnRWXFvCoFqVrKqVbMqVbOKyvkVC0VngacUjkvVUt5GGooXAAAIABJREFUlQs51cpFlQo6zH219+j51z6vYpkzxriPZdFemMrnlBfBii2B2qvIfEAw2QFh5BI4SzkajVb2kaVSMdEsIoXFYlHVhx7Sjb/2a3ri9tv1R+98p2rttmq1mjY3N1eiW0hcWPDO/ZW/os9Pp7r9Qx/SjR/8oB742Z8Nkh3yOsiP8MV9a2tLe3t7Ib8EiTIRP7b9mc1moYS5M8DsecZC57IQj8rxGVXmiJJ5JUGXdsQS5hcKbPoETZLkZMmSJUuW7Pm3y4FaDy5LR4otLw7ljCNSW6S1BNPxQ/BRADH9fl+DwSCAQYprZlmmjY2NwEQCtDD8O79moVAIIJv9ZXu9Xti7ttVqaXNzM4BOcmMnk8mKug2QyjOTc4sf5jtqwPJCBHi7fJse8moB69VqNUidXQoMyOTe/uxeNIm2LpfLlTxjgCukDO0HKLv/xrujrbxn3hnP6qQH75SfeTYfP1yHbShhbnOLhbL9fZV3dlS6cEHj7/1eqVxW49//ezV+67dUOH9exZ0dFXZ2NBlP9Yef/qx2ZzMt/s1nNP3DP9FObUO79Q3ttc5qt9XW4//3N9SdLNS56W9rdNMatvSLHVWL0sYNt2ijkle7lNP1ZamZ5dUq59WqFlUv5dQq51Uv5dTM8qqVcspKxYvydPE749oznsdLX3k/xP6q/+7zzotFecVnvz7390JZ8fU5fm9v7+L+eNoSqL0KjUnluQSz2Uy9Xk+LxUKVSkW9Xk+j0UiSQn4GwNHlxpKUm8/1un/8jzWrVvWnf+/vqb65GfJdiZRwLAsyC3s+n9e5v/yX9dks0/i1r1X56UWaRYc9aBuNhhqNRpDhkFvrbO3BwYH6/b42NzcPJc79vlqtVog4sjANh8OwmfdKArsOI4YuifH9yKiGDPD1qBqT3KNw3wqJcJIkJ0uWLFmyZC+MxY5z/F1czAm/BgAIuCG/1AEfbB/HwCTm83n1er0Q1C8Wi0G6W6lUAkOLbNgltdIR2KN95MSOx+PAugKQq9Wqtra2VvJAAVwAP67pJEi1Wg3gE3kvwNHzZ11SzLPzs4P4RqMRVHTIrn37RQgRlxK7Kg6Q5MF+7xcAF4QGoAjfFKDOO4FZlY4Asuetwgzz3rzgkufH5vN5FaZTlc+fV3bunEpPPaXSuXM6d+utarVaav7rf636B39ZF6bSftbQo9WWdmstPZi/QRdKNU0eqWh48vu0+/K2dipN7ZVqGudL0m8+dvhiTn6f9N9/nySpXJAapZwa5ZyaeensRkEbp4pqZTltVYtqVwpqV4tqV/LaqBRUyh9tD0Q/038xQJSOQKsXrnL/2T9z0OqSaCd8vN8gkPy9unmusJ/PPPTx7/e5ktIitgRqrzKLE90dbHr0jUWRSdtoNMIgr1arQaa8XC5V2d1VodfTf/6xH1Pu1KlQWMDlsUwcopiA28Vioe3tbT31Pd9zCCjPn9fZ3/99PfmWtyjLsrBJ8ng8VrPZ1GAw0Hg8Vrlc1ubmZtg7jVxXgPhoNAobk7PIsqCzuBM1opADk4RIHn2AZETSRZ/7pHUJBIvrt4K1lVKV5GTJkiVLluz5tBiwujkriCH5RR2Gv7NYLNRoNILvIR1VlwX8er5rv99fAbQuXW40Gsrn8xoMBitFn9yngy2F4XVpb7/f12g0Coq3RqMRQBt5te4XouCTDgEQJAKSXAA7W/O4jNplzxTx9NzYRqMR2kjlYvqd9uAvkncsKUiR6XMHLPhyMOUAMRhxJNfOoOKvcf9yubyiKIRZj/NYYYJLo5FKjz2m6lNPqfz44yo+9ph6P/qjGp09q+Lv/Dst3/d/6lx9Uzu1ts7X29qptfVHlZx2n/yGzndv0f7//KsXD76HlirlB2pWX6LWq86oleV1Y5ZXsyy1srza1aI2q0U1S8tDsForqZ6Vwulxzi9j2JlSTw2kAFhcrOpS4959Xj5nvvC/XwMA6+M8No6Pv/MCXpgHKvxcZ4vj58UGg8HF/f20JVB7FZlPxLjS3ng8DvuDsfcrv7M4jkYjNZvNFZlIPp/XbqOhj7/nPSo3Grqx1VKWZWHgkkvBwsPn5MgCkk+ePKnz589r63d/V6/+pV9Str+vR972tpVCBJPJRBsbGzp//nxgazc3N/XEE0+slEfvdDpqtVqaTqcaDAZBIs0fGvJUyNOIo07OxLrkhKgVrC9RSY9AxSD3W8miJtY2WbJkyZIle37sOCyts1sUeOI7QBmAL86jdTkv4GIwGKjb7YZtcwBgVCKWFHwwb4czoJAGABVSsrjP5uamNjY2gg+HzNjTy+L9Ymu1mmq1WmCIITYAuIBO/Dok18iXc7lcYHAdAEsKkl4KXTkYRnaMPFg6yp/17xyMeWEm+hy/LgBR21kDH5VnAEgTNCgVCiqdP6/Ko4+q+thjKj3yiAbf//3q33qrZn/wx1q+41490jyhJ5rberJ5Qo+3T+vL/3mgxz73de1PbpLu+pWVsVPISc3cgc7kF/oLL63odL2gzUpeG5W8muVDwNooPZ17+nSxLp57Hejj+T0NLj7G/X+XSLuSknHsDK2f5+xrzMZ6u/x/fycuA+e6noMsaSXfdd094ueRVllZ3yc3Puc4lkDtVWYe0SOqyIIIUCSXlQmN1ATZBYvC8uBA3/lv/o3++PbbNS+V1H46H8MXAyaXyzdYLD1SRBTzsTe9SQ9/9rP6zl//dXVe+UodvPa1AVBPJpOQv8G2PZubm6Eycr1eD5HQRqOh4XCoQqEQck9gmFmoB4NBAKWewE/7YaJ9q56Dg4OwabdLczyvQjpiTh1YxlKlFwpsxqxtArbJkiVLlizZ8e1KLK3LW71oEr4S4Ii9VwFNgAgY1NFoFPyHwWCgfr8fwCaqsHq9rlarpeVyGWTBDkC8WjD+ludt7u/vazqdqlwu68yZM6H68sHBgfb29oJkWpJ6vV6Q4FYqFTUajVA8FNBMChkkwGAwWNkqB4AMg7uxsaFarXZRpV8vJOW1QeIcWpeo8nm1WpV0BIJiMOxVkSFhAGbcjzYCukuDgRoPP6zKww9resMNGr7+9ep84wkd/MzP6olKW49unNJjrVN6tH2DHv7jgp76029oMm9L/8sHw9jIa6l2Ja+T1ZxeW8vrVD2v0/WCtqsFbVQKalfy2qxn+upXv6qbbz4bxlDMMHpxqnDtKOWNdwgYd7XhOlBLfzpQXC6XK8W76G//HotBtTOyAGR+dkYX4/0ydxxAx6oHn4exNJp7xvcmpz1+Pm/DlRSMzwrUzmYzvetd79Jjjz2m6XSqv/t3/65uvPFGvfOd71Qul9NNN92kn//5n1c+n9eHPvQhfepTn1KxWNS73vUu3XrrrXr44Yef87HXmsXRFulI4oGEZDKZqFKpaDAYhFLzFFpiUHkVt9f8p/+kW//Fv9BThYL6P/iDoQgBA4oFMda/+xY5SFtKpZLq9bpOnDyp3/87f0ftRx/Va37xF/XZX/5lTZvNELF0cDoYDLS1taXt7W09/vjjKhQK4X79fl8bGxthP7Xt7W3l84f7rrFtEZFGFj8HtvyhQI5Cnq+k8PwAW57Z9zXzqCCLsU/4Fxrc+mKe5MjJkiVLlizZ8e1yLK3XJAFYAPb4+++yY88DBegCOgFp4/FYg8EgFEbCR6J+CMUuATExoOVvPmzYeDwOAf/FYqGtrS2dPHkyANFOpxPaWSgUwo4TuVwusML4RIBcwCzg2tnQfr+/osTbfLq2ShzUH41GKwAY38nlxy4Xpn/cvwSQUnnYGV8vZEr/eO5msVhUcXdXlelUkzNnVCoUtPm//rQe2xvrwUJDX9u6Xl/fOqOvXZAeeeAbmswl/cgvhHffKi60VS3oZDWnV1XzOvU0YN2q5rRdLWi7XlLp6ecBqMb72S4Wi1A0jLHmY8sZR8//deaafnKWmj7gXoxf92tpi5M1jgn8XcUAch1Y5nxnh+PaMvR9fA7HeIVq7se16C/GLe3zrZXc4mJi0hHp5M9yKXtWoPYTn/iE2u227rvvPu3t7emHf/iH9apXvUpvf/vbdccdd+jee+/VJz/5SZ05c0af//zn9bGPfUxPPPGE7r77bn384x/X+973vud07Fve8pZn0+wXjXlSP3kdOzs7kg5zCbrdrhaLhYbDYYi0AeRgKsuTiV77m7+pb95yi55685t1ysrUl8tltVqtUIKeQQkgrtVqoXx8LpdTt9sNC1Cj0dDwzBn97k//tP7ae9+rW/7hP9Qf/OIvhj8CVC4G2PZ6PbVaLe3v74ctfarVqrrdbig/PxwOQ7QRyQmLBlHRcrm88kdK0krUk0WW6Bk/uxQZBptFlT8EHrlygO/g9oUCnc7a8ocwWbJkyZIlS7benkkurTN9ON6AtkqlEopdujSUoDos2XA4VKfT0fXXXx8ksZPJRM1mU61WKwBc7kMb2YoRQAjY6/V6gT3NskzXXXddkCOPRqMAdjF8vUqloq2trfB8XsEYphP2eDKZhG1+YKgpEEpNFWcSXenn1wX0QRC4es77i+cB1DmIp60EElwdWCqV1Pqd31HjT/9Uo4e+qa/1FnqovKkHXvPf6s9uLeqx7oH2vvfnQl8UtdCp8lKnWiW9uZbTdfW8TtXyOlEr6HSjpKx46BNzbZ4JlSLjxseIg0QqSdMnHI+PS1CD86Sj7YjW5YcyBp24wt/D95YUxgm+qm9hFINaP8+ZVT+WNgFOY182lkl7kSmOC33+9PPG1/fnu9T8W0faxMevY3ovZc8K1L71rW/VnXfeGX4vFAr60pe+pNtvv12S9OY3v1mf+cxndPbsWb3pTW9SLpfTmTNnNJ/Ptbu7+5yPvZZBLYOKgeAV7bButxsSqZm4gFlyYF/z6U+rMhzqcz/4g2o+DWDn88MtfjY3N8MAY7H0yGaxWAwFEBhkw+EwTMJ6va7JzTfrP/3UT2n+kpcEKQkRNxjWwWCg6XSqZrOpzc1NDQYD7e/va3t7W7lcTp1OR1tbW2Fv2hMnTkiS6vW6ut3uShEAIqw8h0s7aCtA1av9AWy9KARRRya659q63MLBrX8eLwbPxzt31tbbkyxZsmTJkiU7suOwtA4M8H0AKhTcqVarociTM0wUHmLf2X6/H8AM4LXVaqnZbK4o6Wgbubr4J/gy4/FYnU4npFahZANYe60UAv6Ay3a7HfwESSHYLymkdVGcqtfrBb+i0WgEaTHH+x65EAgxm0gbADTItlEISlphKyWF6wMM+b9QKKjU62n7a19T7cEHVXnwQU1mc33y7/8fenBnpCful+5vfL+++b2nw3ts5Oc6PZnp1dsFna7m9bJWUdfVpJduZMppGXbvALwBtmNw6VJq2uJy4FhKze4atVot3MNBI76h57nCSNMO99ddms3nzla7UhAm3f1N3rUzp9L6qsgeUIklynwfty3+2QMR60Cmg3Nna5019v6K83O9Ro4HZ/z3y9mzArX1el2S1O/39TM/8zN6+9vfrve///2hIfV6PZQzb7fbK+f1er2Vh302x66z+++//9k8ylVjRPC+9KUvaTAYqNPpaGdnR5PJRKPRSF//+tc1Ho9VqVT06KOPamdnJ0hfGICwndV8Xj/xb/+tHnzpS/Vgu62TTz2lRx55RJVKRaPRSOfOnVupNOwSB5fJEP2bz+fq9/th8Sea9eBLX3q4/c6f/Im283kNsyws5iyGe3t7yrJMzWZT3W5XX/nKV/SSl7wkRD/PnTunSqWiJ554Qu12OxRfIEdXOpwkm5uboXgBET6iZrlcLkhuvHIzxjEsPCxgniPhkV8fk5eSJXPs8y1Nnkwm+rM/+7MkR75GbDwev+jXtmTJkiV7viyWXMbfxSlcMGD++3w+18bGRgiE850XbqJwFLJdz6Hd2NhQo9EIhZcoigTAZTseGFEA587OjhaLhWq1mk6fPq1Go6Hlcqlz585pOp2G/N3ZbLayVSJAA4DrAXf8M1hZ5Mn4S6jTuL73EX6N54KS6uXAxnfbcJY1y7KVfNsAwicT1b/6VQ1e//rDLYne8x71/sPv6YvX3aQvvuSV+sOXv1X3b92g+SfPS5JOfser9fJ2UXe0cnp5K6/rmwVt1ooBiONPwmx7MSMnJwDZ+GbOGMcgzxWBTpLg43nedQxUGUcOhvEPCQY4i40P6UEI3kFc4dgBobSq5vPfYzDr494BbOxL+nmejhgHA9ZdDwOEcqyTQN7+mEH2ceffx7715exZF4p64okn9NM//dP68R//cf3QD/2Q7rvvvvDdYDBQq9VSo9FYKb08GAzUbDZXOvDZHLvObr755mf7KFeFLRYL3X///XrVq16lbrerTqej/f19DQYDXbhwQaPRKAyE/f39AOC2t7fVbrcDsGu1Wjo1n2vnhhv0R9/zPXrd614XqrOdPHnykGWdTMI/9kdzqYJHnJi05XJZ0+k0RBKzLFOv19O5c+d088c/rls+8xn9fx/6kPrFYsjlyLJM29vbOjg40NbWlk6cOKF2u616va5Tp05pb29Py+VSm5ubyrJMW1tb2traCpKRTqezElHd2NgIciEWDSZSsVhU9jSoZsJ5cj3sLDkcPLNH93gP0pXBbTzhny8Ayhh4oXN6k3172P333/+iX9u+Xe0LX/jCn3cTkiVL9gzNma1137kjj/SWHR8AZlQJBnzytx4Qg7QW5pMtZKbTaQC0BwcHGo1GKxJRJwuQwAJmycc9efJkCNL3ej1duHAhkBqlUilsFXTy5MkA5lCc4cMAMtnPFl9oY2MjVDsm9QqGWFoFUzCX1FeBDfR8YJffUi8FRR6M93K5VGNnR+3Pflb1P/kTVf/rf1XnfEdfvO4m/T9/7x/owVFR33j5j2nwd35CklTOL/WKdkF3tnK6aauoV2zkdaKRBSDtuZgQFwBPCqhKWlHOcW7MZPq7iRWJAGAHofQR+a2e64oBQtnnl3EYKwD9GZyp9GAB93RfFlvnkxI88HMcvMbnOnMbzxHfYSW+vwN0v6ePIb8272bd3Iz9ZB+D647zdq+zZwVqL1y4oJ/8yZ/Uvffeq+/+7u+WJN1yyy363Oc+pzvuuEOf/vSn9cY3vlE33HCD7rvvPt1111168skntVgcJrs/12OvRXManoIDDCZKrjcajQAqPdEbiQuTc69U0r/6iZ/Q5uamWk8PjkajERZMgB/7oiFjQKojaUV+zEClEACS4Hq9rs3NTX3zllv0ht/+bd38oQ/pj3/2Z0OxhOl0qnq9rr29PQ0GAzUajZBfSwn63d3dEBXtdDqqVCpqNpsrFQklhbbGSfj0nbebfvS8YM9TQK7NpKbfHJzGeQW+aLzQ0mSu71LkBGyTJUuWLNm1bJdjaR08SAogtlwur+SNUtQIP4W/2ajM+NtO3Q92d1gul9rY2FCz2QyFnrhuLndY/JLrw/L2ej3t7Ozo4OBArVZLp0+fVr1e18HBgR599NEAqgGHuVxOp06dCuAoy7IQeJek0WgUtkIEqNbrdVUqFVWr1ZCGBbhG3uqAiIA+Kj/+kermzKXLYmljcTRS+4EH1PziF7X71/6azp94iR743Jf11H98UF+8/i/oi3/9bXqqsiFJyj+21A3NpW57aVmvaBf1yhOZbtgoqVw6qnyMOQNJPwKAAOmwthAV7iPxrPh01FJhvFB4yUGZ+1YEMvAx8VEdXMfSXcabFx3lGHx0nsuVgs7Sevoe13VwiL8eM+qSQtAilhnz/D5f1oHGmIldB0gdiLrigSAI9/MiYnHf+L14Zj5z5pbgwvO+T+2v/uqvqtvt6ld+5Vf0K79yuH/Tu9/9bv3CL/yCPvCBD+gVr3iF7rzzThUKBd12221629vepsVioXvvvVeS9I53vEP33HPPsz72WrWYBeTfYDAIg2YwGKwUHyBPJJ8/3Mz6Jd/8pvaLRfWeZsdZTGu1WmBGJa0UmVoul4HNJV+CvI58/nAfrl6vFyKcSIlns5mazaaefN3r9Advfavu+O3f1hO33abFX/pL6vV6oV21Wi1EIdvttvr9vnq9XgCuvlXRhQsXQjVnqs8xaTmHyKJLO1gIidqRw8IfNRY1FieCAAQRWPAuBW49AhiDWJeM+AR9LkDUJ/g6CUmyZMmSJUt2LdnlWFr+LksKLK0777HsGMWWf4+f1ev1NBqNQprUcrlUs9lUo9EIAXYH0fv7+1osFmGP1263G4pj5nI5XXfdddp8ekvFbrcbUsskhd0lGo2GKpVKYGad1SOvF1BbKpXUbDZVq9WCxJhtehzQODAiv9aBHECWYwn6e0GnQqGg0s6ObvjIR1T/whd04amO/uClN+vz179Gn/m9sR7Tk5JeKf2lV+pUVXp5K6/v38jrlScyvfJkVZVSIRRp8pxSzxn1olG53KF8Opat4sPxHvHvAE0O2HmnnhMsKfSry18BqzH7SFvwY+OiTT7unPTwgp8UDvOAgoNvZ0Cd6XTGVLo4iONssM+JWKbOua689DG/jg3mfydsuI/3qz9n/Dz+DLTPAT8Wp/NxrqcPxpZbxm/gKrQvfOELesMb3vDn3YwXzJgUDzzwgM6ePavd3d0gPe71enrooYe0s7Ojer2uBx98UE8++aRGo5E2NzdD0aVaraZyqaT//Z/9My0ODvTRd79b7c1NnTp1SqdPn9ZoNNLu7m4YXAA4ZCc+4WhTsVgM+STk11J0oNvthq135vO5nvjmN3XnL/yCNs+d06f+yT/R3tNy83K5rHK5rL29PUkK+9Y+/vjj2tzcDH8kyuWy2u22Dg4OdPr0aV133XWSDic6RRWI3nFeLpcLeRZeuQ0psnS0bxsFAZhsFH1gseEY6ShR3mXJcZQrlk44e/tc8m7XyVH9mom1fXFZkh//+dmL/e/Kt8KulT5M8/TP31wmGf8N5O8438O6ErxG7UblX9RunAuIKxQKYasdGN7pdKp2u61HH31UN910U2BAceqHw6EODg5Uq9U0n8/V7XZ14cKFAAa+4zu+I+TOPvnkk+r1eoEBXiwWajabAQwDwgAp+D/dblcHBwdqNBrhGRzMkffpOaHSUdFP/CN/ZvqKvqX/yuOxWv/lv6j5B3+g0c236I+++/v14CMdPfGJ/1ef/Y7X6Vz1kIWtFZa6aTOvGzcL+s52Ua86XVejfEhiAABhIdnpAj+Mtvk2jZjvZUtb3XwLHfzYuGiSF1ZyUoBARgxi6QP69ODgQPfff79uueWWlf6iTTDA/ruDPgdy+Ivrii/hw9I+rgegxryQGb6pg8oYWLuM3Jns+F3HZA39wvEeVKC9MU6In8tlzHGutqsdLxegqtfrl/y78qxzapN9680nGzadTrW3txciRZRNRyrhDN6rHnpIJx9/XL/5N/6GavW6sixTo9HQZDIJFfeogsekl4408T4AvaodBQAajYZ6vV5YiNlyp1gsqrW1pX/3Ez+h/+mDH1T5q19V4Q1vCJUDy+Wyms2m9vb2NBwO1Wq1NBqNtL+/r1qtFv7IDIdDFYvFsA0QQLpSqQTpMX1Ani1RTiYex7B4sgA6cPXqdyyILs/hOL6Po1tx/gKfe9VkP1Z6bmA0yZGTJUuWLNm1bJdirdwZ53d2g8CXCVsdPi0LBoTg38xms+BnUNwSQNtqtYJkeDweB4aVnNblchmqKPf7fZ0/f16LxUKtVktnzpxRpVLR7u6uzp8/HwAoxZ42NzeDrJacTEA47Cxto5BmtVoNEmPaLinUMimVSqrX6wEskX8L0ATgSgpy61wupzP//J9r4/d+T/raw/rP179Gn7zpDv1u55W68B/3JUmt136vvmuroLeeKOk1pzLd0C6raAWcIAHY2xf1n6SVWjn4XzwnLLm/W/w2gC9g8/9n782DLL2rK8Hz9uV739tyz6pSqQojkARILWkAI4mRje1gsQe8YJYGe8IMOHqM2xPRTMQ4psPGhE24O9oR7bEnHGNPu/Fg97QJ8AbYgLHYpEZIAgQS2tBeqsrtbd/b9zd/ZJ6b5/3qZakEeCiJ70ZkZOZ73778fvfcc++59FMJBhVwaQoyz0vVhfls6HNCcEb2V8mdarWK7e3tI2tq+fzwOBXcKRhU0ShgXulXGWgqRc9mM6vL1mNSxndR9p4CWJfl5nEpscPrzmvj1iS7Ksg0lw3XzxW0atDAZbE1M4DXZxELvshCUPscMAWXLITnQMxBLZPJWMSO6bi+72M0Ghnb+cNf/CIahQKevPFGFA5qJiKRfWGper1uqbisKVUQq9EZLY5XoBYEAVKplA1E+XzeBnbP8zC8+mp86Dd+A0nfR+4AbBKEUpmZyxYKBXS7XdRqNWxubiIej6PZbCKfz6Pf76NarWJtbV/enSnWTJnudrs2YDKPn0IQPA+mEnEiI1jVyYyRUEYTVUyKAJL3hoOWvrhad+sOmpqC/L0At2E6cmihhRZaaD+IpkHrRd+pD0P/hCCAvkAulzMgBcBa9zAw3uv10Gq1jKEdj8fwfR++71sdK7VO2NZnOp0inU6j0+mgVqshCAIkEglsbm6iXC5jPB7jsccesxIyZWaZJcef6XRqKcZklpPJJNbW1qz+dTAYGLHh1lqSIGBgn+cGYA7MDIdDZPf2ULztNqR3dvD0r/wKnqp18ZWtKL708nfhq294AcaRGNKxGa4qR/GTq0m8bD2NTT8+pzystaNatkVVaAU2Ku6kKeL0kdgPV9vrLGL2uA36eOq3KoiaTCYGDBWwKoOsTKHqxyj7yzRoZR+5DgGhbo+gkzXKSnboc6rAEIAdF8WtFDS6zLLWWHO7Ws+qx6D70dpYZaUV+CpTrUww1+e2NSCgIF3vuf6m6blo+rn7u1QqHTkWhKD2OWK8marupmIErKfl9/l8fu4lOVGv4/IzZ/D3P/ZjyBywnLFYzHrDuvLjAOYihJFIxNTyGAGczWY2uBL0MRUY2H+IC4WC1ZjkcjkMV1ZQr9Vw5Ze+hP5112FraQn9fh+9Xg+e56HdbqPZbKJYLCKXy6Fer6PVaqFUKqHRaJiycq/XQ71ex9LSEiaTibFyyowEAAAgAElEQVSyHMg6nc5cVI8TgA7iWlvCwVK3odE1larXlkHcHmsjlLl1U1f0ProRxO8VuNUUZx24QgsttNBCC+35aM/E0tIIXDiHE3yyXImAV1WEydBSB4Rgl31oyc6S8WTnCKoBdzod7O7uotfrIZPJ4LLLLkMikUCtVkO1WjV/gynG1AohwUB2s9vtzvlK6+vrljpMkMG0agbiU6mU1eEC+z4j63jp1wH7/o338MMof+pTKH/lK4g9dQZ3H78an33pq/H3f7+DnX4UuPIN2MgCP7GWwLVrCbx0w0MiFjXQpgJOJBQ0VZoMOet61UdR1pXAmz+qGKxptXpvFYzxvtG/47UBDvviEuhqjSjvF0kNrUvltrW902Sy3yqJx6D+pAI2ru/6Y3qOWp+sx8RnWIGnW0/Ka+cGCGhar8trpqnSSppRkIrnwON32WX9TFlifXdoDGboeSnDy/+VIefnrh/NY9DtuxaC2ueAaQqCyxKylc94PDYxAkbJyCzOZjNsbG+jl0rhsZtvRuog8jUejxEEgQ3kyiCm02n70Qc3kUhY7zHtP8aC/FQqZXW2kUgEvu+jVCqZwl+pVMJkbw/X/fVfo/+FL+AzH/gAJgfCAwTBrVYLsVgMy8vLCIIAzWbT6kTYDzcWi6Fer1sD7Hg8jnw+j2azaefSarXsxUin0xiNRiZyABymiXCgYto1rzWBvoJ9bfcTje4LUpHd1ToEF+DyPrrsrUYYv1fglgNCmI4cWmihhRba89kuxNJyDuQ8yOAzASwJAbbk0VpILjsajUy5GICVWvm+b6Kc3BZBJ8FmpVLB3t5+r9VSqYTNzU1Mp1Ps7u6iXq8bmGGmGkEtfRMC2Varhdlsv6/tsWPHLBON7C2PjedDVpb+DTP6FGROggBLd9+N6rXXYuR5qN51H+56tI9/+pH34u7lF2AYiSMWAV6UjeInXpDCK45nse7H5643f/MaKqNGMEnmm74pATzThvmbwFv9Xd4LAlkylsqwU7RUl1NjqdlsNjPGV8vrFMCp4jX9a7f+lc8Thb4YVKDvqOm9+j+vj2rUcL9a40tQSR+fvqAqNfN68nutu9X34iiATODJv5VJ1wxN+o4KMI9KLeZv9Wn5mf5PfLLo+i96r9UXfiZAC4Sg9jljGuli9KnX61mkiAMfX/ZisYhOp4OVlRVMp1PcffXVuPv0aVy+tGTtewho+UBTar5QKNjLpS+tCiOl02kbmIB9KXnWbHDyoPCU53nIZrMIgmA/lWRjA//48z+PN/7pn+IlH/0ovvm2txnrSeGofr+PTCaD1dVV7O3todFoYHV11cAqB4bd3V0cO3bMBhQqC7LdTxAEKJVKVsNBVhk4HBRisZh9zsGVdSCakqxy75p6zYFZhaUU4Gqdh8vU6mdHgVsFwM/meQnTkUMLLbTQQns+24VYWp1DOZe75UaFQsFSUDkHK1vVaDQs6D4cDq3Dg/Z4ZX0tNUJisRh2dnZQq9WQTCaxubmJXC6HZrOJWq1mZEQ6nTY2lYwqgfRgMDABqHQ6bbW79ANns9lcKZoqJAP7NbQEfrw+s3odq3feidKttyLx9W/i9uMvxd8Eq7grtY567hbgR2/BWgZ49WoC16wmcO0xDwkcgjkFbwpiee00k5D+EkWr6COp2rCSCmSYee8ikYgRJ/xOU3Y1xZmtGnn9CNS4LJ8DkhL1ev0Q3EtqMokMAkU1BYDT6RSZTAae580dr4I7gnkF2ipsqkDT7o+wsmTT6Q9ms1lbnn63y3pqxw+1o9Ly3TRwBZHq52oQg/tQ1lf9Wfe91JpYPc9FPzxWPRd3e7PZfo36URaC2kvc9EbqA8YBrdVqGQvJiB0HbartebMZ2pMJ0p5nUTUOmHzYWR/g+z4mkwlarRaAw3pVfZA50DPq5Xmefc+IIh/2nZ0dLC0toVwuo1AoWEue5i234N5vfQsv+eQnsXPDDTh3+jR6vR5GoxEKhQJ2dnbQbDYtvScIAsRiMRQKBXQ6HXQ6HZRKJXS7XTQajbkaWrb/YYpJtVrF8vKyDRJMzdEXmJEyCggweqiKgRwQGfkDzm/mTvDMz7VPnQtwaW5aigtutV7i2RoHaJ5rCGxDCy200EJ7vthRc6POzQCMqSRIYgtCMnLA/nypJV3dbhe9Xg/xeBz9ft8EmYB90Eiw1el0LDA+HA6xvb2Nfr8Pz/Nw7NgxRKNR82nof2Sz2TnRJvoknU7HGNp0Og3P82yf9CcIoJmySyJiPB6j2WzOnXsEQDyRQHxrC9e9/V/ijmNX4f+4/vX4zP/8bzCIJvZrY/MRvHEtiWvXUtjIH5ZpzcZDxA78vEwmc14qLsE1ACMECNT5v/odDCYo2KafRD9La12VBCAQJONK9pOtmRToazq01tq6RIOCRqY+qwAo19G0Z2b6DYdDC07wGLXel+espJEKR7lkhV5bzfLjMagPp9ef1xDAectz+y5o1b+5Lo9NU6L1HVPmVlPXuV9eY/1/EZDl3/xcmWG9DvoePxvfNwS1zxHTlFi+tEzznU6nqNVqVvyfSCSspnY0GuGXP/YxnC0Wces73wnf9y0tptVqYWlpCdHovlJwNptFr9ezmhMW9Wv6ACcEDiKMRmWzWRSLRcRiMdRqtbmHslKpAAAKhQKy2Sza7TZyuRzufOtbceLb38bL//AP8cn/8B8wPBg4BoMBisUims0mIpH9ZuPD4RBBENiASaDr+74B7Hw+j2g0Cs/zrLk4X6BGowHf941h5sDCAICmGvMceQ20HoUvt0rQ60tJAMtJimJYDEhwsNVBSwd8Hej0JdftP1twGtbZhhZaaKGF9nwzd46kqcMejc73pFU/KZVKmbCS1qbGYjG02220223L5CJ4BIB2u23aIvQzCIK3t7cxGAywsrJivsvOzo6xqkwxzmQy1sonEomg2Wyi0+kYqGFrQmAfQGndbCQSQT6ft76q7E/Lc05MJtj46ldR+sxn8OjSCfzjm96JR+oZ3P9rf45qLItMbIZXrsfwqmNJvGgpgWTiMPWX58+sOV5nAlJeO9agUsjKTR+ezfbbGQHzrW1Y7kZAqOyo+kZkhFUduNfr2boUM1VBpul0aqV16l+pyCeBtjKSTGMm8NIWQPTb6DcSsOdyOZTL5bkUXxcUKnhUUoi+JJcn+NZrp0yyK54FzKfx8tzouzOTkfdT3w89TtXQUeZY/X1+5wY0eAy8Rnrubkai6ycvYpIVACtbze3oMgwsLLIQ1F7iRnDIaBbTL5h+zDpRihgwLYSKwpfXarhsZwd3XnklPM9DJpNBt9tFtVq1lyKbzcLzPHS7XWvzE4/HkcvlTKlO1d8oRkDg12q10G63kU6nkc1mMRwO0Wjsy7zzBWXboXw+bwNIbmMD//DOdyLT66FzMAhxwOIx9Pt9+L6PpaUlnDt3DpVKBSdOnLDoKAffvb09xGIx+L6P6XRqk0G327WXTlN5yGJrmokyqhop5ISl7KxGBYFDoQGNCvP8CXB1gNJtKzjWgW9RREvTn58NOOX6YZ1taKGFFlpoz3VTh9k1BS2cc5lNRoDLNjucSwk6Y7EYut0u2u22pcBGIhGUSiXEYjHrMDEcDq1lYTKZtMy36XSKEydOmMbH7u4uAJh/EovFkM/nDSSNRiPzTSaTCXzfh3eQVacsJgPpnudZKRVFqugv5B96CLlP/xO+cW6AD5+8Hnde9ytopTzg/jHSsRleuJLFm4/F8YoTHtKJ2HmBeKa1KthiKnYqlUI8Hofv+7YccJhCTIabfg5ZbJIwPA/eB5IGTLsmOCM4VwCshIL2aOV6TLkeix+pzwLZdgJtrX8l4AVg90Qz7jQNlgCcx0rGeRFw1Sw7Bex8Jnnt6Dcq6aF+odawKnurLDnvn6uKDBz2tFVWmMvwuXTL41wmlstpCZ6en+tPukBWmV99XxW06jvN5fQ+aiZjrVbDURaC2ueA8SHiAMOHnyp2KgTAF5ARvFd985sYxGJ44LrrsO55GA6H2Nvbs5c/mUxauu5wOMTy8jI8z7OBhuCMtbscQDKZjEXpYrGY9bnVNJV6vW4RnV6vh2q1aoJOrD1pXHMNntzZQbbTwVoshtZBWslgMEAulzMZ/OXlZZRKJWxtbWF3dxfLy8s2GRQKBRPKikT2RRdGo5HVO3Q6Hfi+bzXIfHkZcSNop7ozz1OB6GAwmEtXSaVSc8wp2V2+yDq4cF0deHg/FWQqMNaXW+tqgcMB4tnWymrUM6yzDS200EIL7blq6tCruRlN2t+TjCdrU0kSMIgN7KcVU3CSIIMZbQSf3W4XnU7H/ICzZ8+iVquhWCyaxse5c+fQ7XYtcE6fjBltBILtdhuz2Qye5yGVSlmPWtZf0j8pFApzrYWAfXDkNRoI/ALu3APuezCOr1/xCxhfGccyBnjJchSnSzP8UCmOU0sZRABrD6R1rprSqorEFK/isfM6Eqzq3yQ8+v2+3Qc4+1N1YfqxCswJGoHze9FyHR4bgbMr1KkZdvRx0+m0facEjbKSDGxoivR5NcmzQxErtldSdpfHRV+LHTdUTZnLahslXicFg8r26g/Zc5fdVHCpoFYJEff9cVOI9X+XzeY+1WfUDEB3n7wWfE4V8GsQQN9hN03ZTaO+GCImBLWXuPGGkqVl9Io1F4w8tVotEzHgQBvvdnH9ww/jrhe8ANN8HpFIxIDdZDJBIpEwQSUqExcKBaRSKQRBcF5KLgFgp9OxlJN0Om3KfY1GA81mE8PhEJlMBoVCAbVazdJVms0mAODYsWOWNpzNZrG2toalW2/Faz/yEXzq3/5b7G5soNvtIh6PI5PJWMPzYrFotcDZbBaFQsHAdDqdNrYa2E91nkwmNohyewwGcCCk2JWCUuAw7YTXn58PBgMDpkyRAeYnUo1+Eqjy5dXWPzq48v5yeVekQNOpdKD7TlKKw3Tk0EILLbTQnqv2TCwtmVn2jdWyIjKO3W7X5ncuN5vN5vq8JhIJlMtlRCIRtNttq6PsdrsGbqrVKoIgQDKZxMbGBmKxGLa3tw0gMY03l8sZmG61WtZvNpVKwfd95PN5A7r01VhWRR+BABijEdbvvhvdL9yFT8Yvw0evey16iGEpv4ab80NcfzyCU4WUBfbpgxAws+wKwFzQndeGIJZsJAEsfUWynwSAagRsDOzz+o/HY9TrdQCHSr4EeEwbJmBT/4QML30ygkUC12w2ex5Y1ZIx7pv3VsHSIvBEFlZZRK03pV9HxWdNA2ZWH6+DrqPMq0ucKAuptcM8Nr2HXI7Hpll4ymzqu6Lgkv6jpkfr/wThun3drlsC515Prkfj+heqjXXPA5hndDUd+pnImBDUPoeMkTCmx3Q6HYseEuxwIE+n03jlI48gPR7jK9dei3K5jNlsZn3RWFvKbS4tLaFYLKLX6yEIAnu4mM7LQUEHC0arCI4ZgaxUKgiCALlcDvl8HvV63WpSOp2OiUdROCqXy6H60pdi8Hd/h5v/8A/xyd/6LeAgjZmDIwf6crmMbreLWq1mqSutVgvNZhPlchntdtsGHCqkMV2n2WzaYM6aDNa3ZLNZm6RcwMlrz+1ycgQOUzL4nRu5UuCoKcYc1DnIMUjAAZxpOhpFBTA3OPDldj+7GIDqpiOHrG1ooYUWWmjPBbsQS6spsOpsE8Sm02lrxaN1tNPp1Ni3wWCAZDKJ/AEZ0Gq1MBgMDNBSqOjs2bPo9Xool8sWmK5UKhiPx8a6JhIJlEol0xMJgsDm3KWlJUtfbrfbAPaZYi0DI5ExGo0QHwxw4q/+Bt/6dg1/8kOvxl0v/1eITye4NtPFD59K44pSHJlMac4f4TEow6kiU8ys4zIkT1zxJs1Yo9+QTqfPK82iX6hBeA1AEIzSt+L9pD+rQJD+H8W0uA3eNwJdHiPBN5fjMro811dWlH6dy37qeamYF1PIKeDF7SxiR+nLkegggFPigs8o2Wf17bg/sv4KePkeELS6zLP7nQJ5fkb/Vet8+Z3LAPNzBdF6rDx39Sf1Ouv5uuysyyi756jLUMtnkYWg9hI2jXYwgpfP5zEYDNDpdGzwpuQ8U2gI5r586hQ6sRiqp0/jWDRqjC5fKA5gq6urKBaLCILAInkEa9p/lg9bt9u1l5Qv22g0mosqVioVtNtt+ywIgrn6ing8jmKxCM/z9usdNjbwibe9DW/54z/Gyz/0Idz+nvfYIEq2tdfrIZFIYH193ZQES6USfN+3yYTAlqIOrE1JpVLWAxeAsbbNZtPqMXjuHBg0JZiDNQcm4DANWIvmOUC4g7yuz2U58ap4lAoaaC0Qf/hcuIOCm7ZxMQBV05H1nEMLLbTQQgvtUrSjWFpNaWQqqQb6GaRnqVU8Hje2kcCVwIjpvvF43EShqIRMwaK9vT30+32srKwgl8vh6aefxvb2toE9lkLl83kAwM7OjtWZMtOMNaRMayZY4jk2Gg3MplMU2m2cS+Zx29k4vrb0OtSO57E66+INGz28Yj2KlXwByWRyjiXl36rFQl8ol8vNZeHRv2TAvtfrGdBRoSGyvApWqVuiNZ7MBOQ5aXo3gRRwCBzZjmgymZg/pum3BHsavNdUZBp9Jx4vGV0GM5RJ5XEouFQ2VRlbZRt53GRq9VlzCQZNu+X1Vj8QgPnxLmPMa6AkCK8tj03ZW+7DZXgJxLmMCzr5Oa+PfqbpzLptAOcdq4JTl1zhNXCvEY37OSodWvej126RhaD2OWCz2cwk6PmbqnLD4RCtVstSZdgTbDKZIJjN8PUrr8TpYhGRSATb29tWN0vG0vd9FAoFSxtm5I3KgIziaWpDNpu1QZAF+GxazbqJtbU17O3tWd0JBao4uFE8gQ3ME4kE2jfcgC9++9u45dZbUT99Gt+45RYbcHmMjUbDVAW3t7exu7uL9fV1eJ6HVqtldS2WpgNYyg8jpkEQWOsjbZzued6ckp+mLXHw1bSPRZEqMrgaGOAgqvLy2suN+1BGV5WTdWLRY2Jkb1GE69mkFYfpyKGFFlpooT0X7CiWls6/lvQQcAyHQ0ttVQKA8/BgMLCyrkQigaWlJcxmM7Tbbfuc6cD9fh+7u7uYTCbY3Ny00qu9vT0rxWJQnerK1PugpgiBG2t3Z7OZlUyxc0NiPMaxz38ejTsewJ+94Gbcevn1AKK4cjmJn9ns4+qlJLKZtOma0GcguBgMBubTZbNZpNPpOVDF60Jgz/InBgIUfKq6LutINYDAAIGmHJPtVgHTTCaD2WxmPiQZVL2v/X5/ztfhvXNBk6b1KvOpAInXhqCb/hjPZzo9FJ7Slo28J2R+lSEFDvvNBkFg10dbCXE7BKAMlLgMpl5jFeyiz0nSgQJWmsbM6+z658rQukBUAbq+NwrY9V7o9XazDxe9gzRN33ZTuxextsou63nw+ijL/EwWgtpL2PTB4iAEHALZ6XSKIAgslVYjXK+791600mk8fOONiMfjaDabGAwGNrhFIvtNscvlskUoWZ/AKJ6KGAA476FkPSrl5Nknt9/vo1gsYmVlBfV6HUEQIJvN2kTDwXFnZwexWAzlchm1Wg3pdBoP/uzPYmN3F70DKf1kMmnMsO/7ltq8tLRk/eAqlQrW19eRyWTQ6XRQr9exsrJiKoWTyQSFQsEGimKxaJFXMracwKLRqAkj8HpqrQFfVmVNGbXktddJlUy3MrH8Xpdxo1I6qKlKIhUPVTiBk7lGFfW4LyYlWQfQMB05tNB+MOxNb3oTfN8HABw/fhxvectb8Du/8zuIxWK46aab8N73vhfT6RTvf//78dBDDyGZTOK3f/u3cfLkSdxzzz3nLRtaaP+cdiGWlkyhm57I0qV0Om1kANlLAMaU9no9pNNp+L6P2eywtpZZZul0Go1GA9VqFdFoFBsbG9Z5gRlwqVRqzsfa3d01kMjvyBxTzTibzdpxjEYjxOp1vOCT/4CHH6rg/S95Lb7xmtciNx3gR1b6eNUGcLycs4A5U5fpmxHUs62jqthSxImZdXrd1M/QOloNxCt7qMBTgQ/JA2b2kS3mvgiGut3uXPrwbLafJk3j9kiSqM/Fv1WNl+dJH4ZASLPPuL6qKgOYq6fW83HVhQk66RvRt+S5qc/E5chqa/o3fUTN0gNg95HX2CUtFBjSj1Zza3EVvLr3kMvzOBQoKwus75eep76Lug6A89KquV9Nb9bPFzG0alxesyTdOm61ENRe4saHQX9arZa1s6F4gbJ50W4XP3P//bjz+HFsHcivk6UtlUq2XdanMhKWz+eRzWZNmn063S/e56DDGg2+jGxYrinLQRBgOByiWq0ik8mgVCoZs0wFOqbzxONxbG1t4fjx48jn86jVavB8H59597vRarfhtdvIHSgxM4LKXrpBEKBcLmMwGKBWqyEej2NpaQme56HdbqPRaCCfz5so1mw2g+/7FgXL5XLIZDIWaVMg2Ol0LPLIAZVRUA6YHFAYpdSXWyNeWn+iwlscRHh9GTXUZdwBh+lFytzrYOmCWA5q7oB7IQtZ29BC+8EwOv4f/vCH7bM3vvGN+IM/+AOcOHEC73nPe/Ctb30LZ8+exXA4xF/+5V/innvuwe/+7u/ij/7oj/Cbv/mb5y179dVXf79OJ7QfANM5Tk1Lddimh1ocnO/5OQDzXUajEWq1mgE7ig51Oh0MBgOrf00kEtjZ2UEQBEgkEtZ9YWtrywAeS5yoHUIATZDreR76/T4ajYb5Mjyn4XCIQX+Ac/0Y7r2/h6+uvAG1kwWsoos3HevhhzdiKORyBkS0VIqp1AzEE5xqKvFwOLTjoS/H4DqBYBAE5t+xhlRTX+nHqE/ATD76dpqVRvaTmXjUVWGpGpclg06QruwpgR7ZSv7N365Pw7RpiqgSCKmPpUF7HqeKOimDqsyopswqM0z2V+u51ffi88njVb+KPrVLRLgspYJMnjvvJY9BWU4FvMqqKkvqpgHzPiuA57PB5TUwQVPw7AJp9Ytdc99h/d8Fz9weTVtKuRaC2kvY9AFlLUa/37d0Y6rJMcpF+fCbdneRGY9x+5VXIpfLodFooNVqWZueaDSK5eVlq1EgwIvH42g0GnMRF02fVcBDdpFpGBzUS6USOp0OOp2Ote0pl8sAYCrFTOkgeK5Wq1heXobv+/steopFjCcTrHzjG/iRz34Wn/q1X0Msn7c6DYL56XSKtbU1RCL7dcWz2QzlctmA+Ww2Qy6XMwZ2MBigUCjYMUSjURQKBROG4KDGiBLTdwgmqXTn1sxyQtT6W77QCijd68eBjdvmeqoo6ILb2Wxmy6pisqYdKcurA8PFglQdVEPWNrTQnp/24IMPotfr4Zd+6ZcwHo/xq7/6qxgOh7jssssAADfddBO+/OUvY29vDzfffDMA4Nprr8V9991nwVR32RDUhvbPZeo4qymYYOCff6smB5lR1q8mk0k0Gg0roSqVSibYRB+Gc+be3h5qtRoymQxWV1fR7XbRbDYxmex3WPA8z5jHWq2Gfr9v/VwJ+KrVKiKRiGXC0Q+YPfwYHrvrSfzt5nV4Mr2EWGQDLyk08YbNFl62koCXzSAWi1lpmDJ8yWTSFIt5Leh78VooEKGfQn+SPqWmrGrqLf0TZYbpg/EaA7CMM2WLuT8G3hkw4PlzG0oWqJ+hQE91RpRRZS0rz4mgEZhXzyUwVN8tEonMpUErUNYUWyUmFGASdKs6s7Ldbuov9+kSB/q57kvBqwJyZTu5fQWnCiYXvS8E3e5y3JYr4sVtL2KA9T7zmrjfuQw4Tf3Ti/Ev9XxDoajnsBHQ8MUZjUbodDoA9iPtbOWj6nA/8thjOJPLYe+KK7A0GKDRaFh6MtNwONBRZj4S2Zesz2QyFgXMZDI2INP4UHEw6fV6FglkhI1RSILvSGQ/5ZeqeLlcziKMk8nEZN6Xl5eNlS2Xyxgnk1g9dw4//id/gn/41/8akwMgXCwWkUgkLIq6srKCeDyOSqWCyWSCpaUlZLNZi7aS0WX6LsUbCATZloiTgaa3ELRrfaxK3nNw1pQVfdGBw0FRBwUVaKKioDKyHPAYbeU++CxwQOX/FN/SgUJTV2gXC1LdgS9kbUML7fll6XQa73rXu/DmN78ZTzzxBN797nebqA2wr0Vw5swZG7NpDCrqZ1x2kT3wwAP/fCdxiVi/3/+BOM/vpy2azwhsOCcylZT+TDwetzpYzrV0iBuNhgG6bDaLWq2GTqdjrCYD2hSE8jwPnudha2sL9Xrd1isWi2g2m+h2u7jrrrsA7LOzzWYT0Wh0LuOLLOlgMMDoqSqe3I3j8xvXoHf6Cpxqb+FVyadwebSK5XwWiXEC1UoC9ei8cBFBpYII6ptoT17O3wyI0z9hTbECUiUyCGq0nC2VSs2JISngUzYTOBRRYgCeQElBG9fRuk9VreY94rK89wQ26oso88gAP8FvPB6fa9WofpdeP2WgNRNAy8x0Xa736KOP2jlrSqym+/KY1Y/mNhcxmO5nLrjX7fJvPU79jt9rna/uW0Gqux33mNzrzXV5rXV/LlOt+9Lr656j/u0Cbt0eg6mLLAS1l6gp5U4peWA/XYYpswRhFAOYTqe4vNvFlbUa/vzaa1EoFjEcDtFoNJBKpWwgzGQy1ouKg22320UymYTneVb3wf3RNIrkNsZWoQUABmw7nc4csGUkMZ1Om/owj5EAlRPS4IYb8IndXbzxr/8aP/qnf4pP/8IvIBqNot1u23FSeW9lZQUAUKlUMJ1OsbGxYUCVNbbT6RSNRgPdbheTycSanZOdZc0La5Y5cDOyx7pmrY9lBJUDAq8LX3bgMDrKF10BIgdiYD79QyOKBK0uqOVAzOggAx8E3Iv2t+izC5mm44SsbWihPX/s1KlTOHnyJCKRCE6dOmW9w2mdTgf5fB79ft8CqQAsu0c/47KL7Morr/znO4lLxB544IEfiPP8fhnBhDtnqSYFy7BisZixrL7vWzsaao8A+4A2l8thNBqhUCigUCig3W4bS0uRo3PnzmF5edmC/0EQIBaLoVAoIJfLoVQqmUHWsNEAACAASURBVP/0wAMP4EUvehGy2SySyeSceCazsYajMe6tznDnIwHuz7wY6c0Bbuw8jisvK2BzNYtsNotU6nJjXznvanoxz5/1sf1+H7lczoCVzv1Uc9Y6Y9evYSrwdDq1Xrl6fYH5NFcF10wdpn+pXTIUBGqQHsBc31tNE14U/Of2tDZXa2YJwvl88H8G+sneZjKZuf1omZeyo3q+riASlxmPx3jooYdw8uTJOXaXfjX/d5lnfXb1e7demMeggJSfKSDXY3VTdnV/mpqs6cRuOrky/jwe/Zt+KJd3fy/62z1219yU60U+qd6rSCSCSqVy3jK0ENRe4uaytM1m09jEarWKwWBg0cjZbIZ4v4/7lpbw1auuQvHg5jPqxloQPsz5fB7RaBStVguZTMZqavmQUeRImT8AcwMGBxUKDbCWZDQaGXvc7XYRBAE8z0OpVMJkMjGRpkwmg16vZ+ls0WgU5XLZzufMj/4o/rHbxY9/+tN4TSyGz77jHZhMD9v7JJNJtFotS6mezWao1WrY2dlBPp+3Gtvt7W1ks1lLN6lWq+h0OjZZMUWJL3mxWLSoJiPBWo+gaoGtVst60nECcyNjXF9BLDAvngDMN6qmYp7WmvBzAHOS+ZrOo98rw6sDg0YOnwncKhjmvQ/BbWihPbftox/9KB5++GG8//3vx87ODnq9HrLZLJ566imcOHECt912G9773vdie3sbn/vc5/D6178e99xzD6644grkcjkkEonzlg0ttH8Oc5104NAPcVM5mXqr/WjpPwGwvvaj0QjZbNZ8hE6nY59HIhE88cQTBmAjkX3RJ863xWIRuVzOgCUzvqgwzGC7iThF47jznm18ZrCMnXEKxXQBv7BzB45ftwmvvG5ZcfSJCPYYNOd2SHAQoLt1ljxfAkbgsL4VgPmKJDdUDIjp2kzNVkBOsMjjUR0VBXoA5sS4SAQoWKXfoUwpA/NskePeZwBz+1Pfg+dAX5R/M6uO+yVDDcCAvsto81rqs6a+j/rDSmS4vhWPi/6eMszufhWUK/BTILroHVDQq0DV9T31OpG15/Hrb/1bdWIU7FPMS49Lz1vPX5lkl8DRZRedk7sN3c4zWQhqL1HTB0qBVa/XsxeSdaTT6dQiZg/4Pv73G2/ECzc20Gw2DUzOZjN4ngdgP225VCqZkILnefB9fw4QkYVVRTZNWdBaDQ6C0WjUUtJ6vZ6BvUQiAd/30Wq1kEwmDdiy5pZ1Lv1+Hzs7O9bDlpHWR9/0JiTGY6w0GogCiIjqL8+bNbarq6uIx+MIggDVatXANQBjtUulkl03shD5fB6ZTGZOtIADP+tlWGOrUvC8TxRE4IvL83ZThTjYcKJxWVyNigGYq4vRVkucLDTapjUjnNQ4iGmtr0YDObBeDEjlemFKcmihPfft537u5/Drv/7reNvb3oZIJIIPfvCDiEajeN/73ofJZIKbbroJ11xzDV760pfi9ttvx1vf+lbMZjN88IMfBAD81m/91nnLhhba99oU1NA4/xCIcC4k8NNMs+l0akCL7XWGwyEymYwBU7YLpF+xs7MDYL/lYDQaxc7ODiaT/f6yS0tLFkwno8uOCfSHCF5GozEeebyJT+x62Elv4nRrGz95eQxX+QP4Vx8Gh5Th7Pf7yGQyJiRFcSkFrEo8KHBkmjWvERlfAmP6Anr92EWBvgMzvXzfNwCsAXLunwCYwX8ylQDm/An1TVR8SUE5gLlWNsrGqp+kIJG/GfzntSIz6/onCoa5DI9TWeejQLOyprPZzEr1XIbUZWf5W9llXYfnrNdMQaBm1/HYXaCo29L9KXCkz+q+VxdieHn+LsDX91C3o7/1GrjH5x6Du57e30XbuZCFoPYSNd5MtsiZzWamxheJ7DcKZyuefD6P6XSKy3o91KNRRHwfyWQSu7u7Bk79g89YfxuLxebqSwm0VIJ9f1AezT24wKHasT7oOogw4sjoJ6ODZGhnsxmWl5exu7uLfr+PWCxmqcrD4RBnz54FAOTzeUudfuBnfxa3VSpI9fsoR6MYex5GByCfA3ar1bKetplMBpVKBdVqFb1eD6urqyYatbOzMwfk2SaH/XSpVqhqe7FYDJ7nIZfLzQUZqBLNlzAW25fDbx+0JALmBbcymYxdM058XE4Brlu7AcBqTXifFPhqjzTeP04SrMnVwcmdUDQF6JnArZuSzM9CcBtaaM8dSyaT+L3f+73zPv/IRz4y9380GsUHPvCB85a79tprz1s2tNC+l6YMl5qCR/ZZTSQSVscai8UsyExAy64Ig8HAdDRYzsW2PcPhELu7u0ilUvB9H4PBAHt7e9Yip1AomK7JeDy2VGMCS+1WsPdUFZ95dIZ78pfhxa3H8T+d/QLir7oKsaUystkly55TwEGATC0P9sdVVprghj4XAeVkMpkDoQys83P6BypqRB8km83O9XSlb8B9kTSgmrSbhkowTJ+J90sZX67HfariMIC5Xq70rXQ9BbwEvfRF1MchmAfm29YoI6zbcbPY+NzxM92vEgj0lZUpvRArqRorLlBdtK6SSPTjXZ+Q29ffvG8ugNTldRsuY6vA1mWPuZybqn0hdvWo83wmc8G/fn4hC0HtJWj6cLARNwUQCNxqtZp9lkqlMJ1O8a/uvx+r/T5+5x3vQLvdRr1eN1GlbDaLSOSwNc1oNLIUGr4AHBAZcev1egtfILbjAeblzFm3QXBUKBQQjUZtAuBAQwEp9o5lU3O2/gGAs2fPmpAV03sKxSJ6Tz+Nn/lP/wnbL3oRPvfzP49Z9FCIIZvN2uS0traG9fV17O7umhAV63A5SBLcUi252+2i2+2i0WjA932LxFH9GDh8odLptA1SFJYgE6oDGoMHHIQJ8jmhaIqMG1BwgSIHZvaQY8q3KiISZGuUk/eHDL/2yuVx6QB1MQysO+hptDEEt6GFFlpooX235jrewHzasQaVCbgICtmXlaxio9FAp9OB53koFAoYDocIggDtdtuWZStCz/OMOACAUqlkpVIUkMrlcojH4zYXEyx2x8Bnnori7vox5BNt/PLj/4i1648hsvLDVuqkbKKr7cFAPNN33RReEg2c3wlQuV2CO2Ce/aTwE4P22lJHs69UcGowGJyXUqudILgPF4CREHFJEQJA9ZN4D90UZQJ+ZVfpy2gGGp8J9Ue0VyrPkc8TfRT6SMrQEigraCMpoc+fXgvuzyUEeD7us+wymzRlVfV/vS78ToEx3wN3Gy54J1B2GVoXtF4skFzk5+lnesyaXu1u6yhwreer1+6ZSJcQ1F7ipvUge3t7ppRbr9ftJcxkMljtdHB9rYa/uOIKpDMZVKtV+54teyaTiUUVyTrywWFErd/vAzgczLQuADhUGwQOHzoCZdaVMk0GgMm4B0FgkTgKksxmM2OJOdmQkR4MBtje3sby8jLK5TJarRay2Sxmm5u49+qr8eovfhFetYpP/OIvYuZ5VmNbKBTQ6XQs2rq5uYlsNot6vY69vT2b0JjuQ3GrXC5nYluTyQTVatUmGjK4BJDJZHIOWEYiEfi+bwPteDyeqyvhC8k+bcPh0FKkOHjz2rHuQwdSrTNRpTmt9VU2nIIZjCLq5MfjUJbYZWg5CAHPvt5WB7IQ3IYWWmihhfad2CKWlg68Bng571BfhCCQgHY8HmN3dxetVgu+7yOfz1t/ewK4ZrOJZrOJbDaLTCaDer1u5VMUr2SnCbakYbCac14KETx56/34i+Ub0I0kcG2yip/InEHqmpcZo8t5N5lMGrAcj8cIgsD8HgJKztP0yxhsVjaWvtsilpHfs16XvgqvJ/1KEgH8YaCbtbMEelpuRSM4V9DB4+T/BFzxeNz8Ei3fcllbZUm5DfUnSASoT6rsMnCYCeeWc2karAb+eXzcntbuuj4S/WIy27pPHstR4Eu/53EsAnpclj4ft6++Gdd3l3cz/dxlF4Fpl7nV5/qoa6jL80dThl0Q7zK7i85V13OXW8T6LrIQ1F6CxptJtpQKdkw9brfbaLVa6Ha7xjz+1NNPYxKJ4L+9+MUY93oIgsAGpGQyiV6vh1KpZOnBnufNSb0T3PHhJHjTWg9NRWAqLGtJGQnkgM2XkWlBvu8jCAL0+32k02mUSiVryUMWudFomLpfp9NBr9ez/bHX7XA4xNd++qdRL5Xwkx//OH7+938ff/fLv4zR5qaJTzGtiOnMvu8jl8uhXq+jXq+bejJbCPX7fbTbbXS7Xbte2WzWmPBut2vpxLwmWgdDJlfBvu/7ds04KXGy0sbkTFfmxMoUZbLvKsalpgMPmXZOGGzZpGqQnCT4o84BI546GXEfF8vAagAkBLehhRZaaKF9N+aCGeAw7RiApd1GIhETZqJOCEHgYDBApVJBs9m0gPZoNMLu7q4FlykaSf9oa2sLvV4PnudhZWUFw+EQw+EQ8XjcSr3I8I1GI6RTKXRv/yZuHxzH/asvwIubZ3D96RkuL8SRTp80AMtMLJY2sSUju1gwm4rzLkEufSv6HTrHA/O9YKmQzDmd+4lEIiagxf8VbHJ9Eh1cT5le1VsBYP4LAaKSHASwZLRVdVjBo95f+hCaQQZgzmfRwDt/L0qXVXbXBV3KdtIU6CmjqawsfSz6b/TN9XlVH1mPT0Gs7s89Dz0XF7zxvFxG2E3t5X7oj+n23e3pNdLtucsv+luDEHocvG4uqHdZXO6b6+j3Wg+s61+MLxmC2kvQlNkjcGQKMgBLo+EggG4Xb9jexm0rK+iUSugdtGXgIM6oG6NbrKklazudTtFutzEej7G0tDRX98mHUwcGrb3VqGir1UI8Hke/3zegmkwmLV25XC5bS51kMmniUePx2NSH6/W6pcl0Oh1UKhV70anQ3O/3sfP61+O/lEp483/9r7jpYx/Dre99rw08FIFgSvF4PIbv+1hZWYHv+6hUKuj3+zhz5gySySTy+bylRvOaE+CTpeV1IPBkdJSAVllqprNo3St78/KFZx0QmVzWzfB68d5y/5wsWM/rMq3AYeRVpfoZzXbX0TQbBbcuq6vAFrg45tYVlLiY9UILLbTQQgsNWCwOpayfqvQ2m01j/gaDgfVsHY1GqNVqFuAvFosGaNlqsFqtYjweW9D83LlzmEwmKJfL5kNQt4OdHBi8H4/H6DyyhS88HcOXjv/3WOrU8fbOV3H8JStIp1NIp9PIZDIGGukDtNttVCoVK9UiWFRfj/Mls8O0hIzOPsWpCHStbdCBj0A2WtN1tQ6UIBk4BG0ATPuEf7tqyLPZbG6/ygAT5NA35LFyP4sYOfqhXH6RH6KMrm7HDcIryFWmU9lLbSPE50rZZm6fvpqynwpyKYZKP0rPTf1lbksZXGWD9fh5zRX08XoqMNZrS59OATUDAgpA9ZopIFWg7bKs7m8ek56bBhB4Hq7/54JYTZ9Wc4Gw+70LfhdZCGovMdPICgc4MomdTgfZbBbb29v2gCaTSbysVkN+PManr7gC0+kUzWbTUlsIvphKm8vlTBGPCsQqnKCF9ARVqpgGHAoUaFoBo4msSyWwZU0q04SWlpaQSqVQq9UMYHc6HXQ6HcRiMeTzefR6vbmBrl6vYzAYYG1tDYVCAZ7n7SsW3nADPlwqoQIg0mhgORJBP5HA4ACksd8uWdBOp4NkMomNjQ1TFGy1WtjZ2TEVQta3UPSB58PAAI+LE49GiDmoUyqf15+TCGtXAFi6MgcossJUV2Za8WAwsGuTyWSModfJyU2BYdoSo3U8Fp4TgxIqTMXBnufAycOdOL6TtOQQ3IYWWmihhXYx5rJt/IwBYAZhyZZxniOj2u12MRwOUa/XUavVTLF4Mpng3LlzBmgrlQoikQiy2SxarRaCIMBsti9iyfmTuh7cN7PQnu5E8Pm9LB7Ddcgvt/FTta8j7jdx+sorEI1GTTU5nU4jm83a/obDoWWQcX6kv8QgOMEpA9okILR1Iv0J4BAEtVqtufpb+m/cngJCgnL6Ngq63JIzZvwxi46gyWU8NVNLmVtl7tSfUHDEALzL0GnNLAEN7z39HAJUF7y6adEu66qqz1yX3yvgUyFPzUB0ewLTFjHGPAY3rVeXUTCvrKReC92XgmQF/3oci0Czuy0XjOpy7mfu8SmQdrfvmnte7ncXArTu9nd3d8/bBi0EtZeYaWSEgx2V8MgiNptNSz2eTCa4LZ/H217xCmRf+EI0azUTYaK6L8EPBRCA/YGw0+nYwEumkf9rKx89NkaNNPJGBWMyovF43I6TgzMH5NFohFQqhUKhYAJOTOPtdru2bKPRsMGbQG53dxe9Xg/r6+t2Hu3Tp4FWC6PhED/yoQ8hNxziU+94B5rHjhnbSlDf7XYNQPu+j/X1dZRKJZvQOp2O1dLmcjmLrM5mh3W+fPEIKgk2qb4HAM1m09KHGWmNRqNWX+yqBBLcT6f7Yl3aL44sbjQaRb/fR71eR6VSsZ543A7rZxRcM1UGgAk7cKAje6vgGDgc/F02HpiXtv9uwe2FBrjQQgsttNB+MO2otGMts+E8xXmXgWb6S6yhJaAdDAbY3d1Fs9m0lONYbL+jAcuS8vk8isXinPCRCkHNIjHc30jiwTMjPJTbQBJjvCK1hVcUavCX1/D4410rHWL/W6Y/t9ttC1hHo1ETp6KzTuDoAtlYbL8zhPodPDYKbnI+Z0kU9T8UeI5Go7lyLuCw1Q79hmw2a4KeWk/KZRVEKfhVEKvr0K/gvaTIk7KxWlPKwDqDFvqdgloFauq36N9KzACHoElVlBlYUP+G+yN7DpyfJr0IyJI84Gduza/r5+j10m2pv8T/CTwX/e3eTwWfLkh0WW89P+7rKFNwe9Ryi/DCUb+V0eZnLivr7utifcUQ1F6CRpaWOfv9fh97e3uWUtJqtSz1YzIaIZ3NIiiXkTgAvwSvLGRnjQV7yDLdGNhX9SOQpJgBcKiCy4GIx6WRNz7oBG6sAWYqbzQaNTBOEBaLxSxFd3l52WpKuP9Wq2U1t41GA9FodE6pkOnE6+vrlpbLKO1XbrgBb/zkJ/H23/s93PW61+FrN9+M0UFtB3CogNftdtFqtUw1OZ/Po1QqodfrWcCgUqmgXq9bio9OOkzzJchtNptzEVaN8hE4jsdjA+oc4Dn5MerK9CCCXO6D4JQTN1l7DqT5fN4isdqLTgcu1tEQVFOkgduORg+FJfQZVFaX0Uz90WfDHUTdZ9odiHUwvdC6oYUWWmihPf/NFZXhZwRv1IkgKOHy3W4Xg8EAvV7PwKvneSgWi+j3+9YFod1umz5GLBbD3t4eOp0OCoWCZakR2BHM9iNp3NXM44EghU40hcuH5/DOx/4Jyes2UcqlEYv5lt5cLpeRSqXQ6XSwtbVlAXkeryviSBCq3RSYtqy9YTWbi/4MzyGdTpvgJudZ1uiqVgaBFOd5dk2gWjR9AE1PJRCk0Yfg92SX6QsoeCXQJHvMFkuqiKztiYDDdFv1Q1wmkKCaz4b+r4DQ1QPR9Fueh1tep4EABeku0IxGoyiXyyiVSnZtNO1Xr6F7rPqd++y7TKsLeo8CfovYYi5/VIrvxaYa6/VRxlvN3YdrRwFSdz03AOCy2xdjIai9xIw3mb1TB4MB2u02arUaJpMJ9vb2LIIYj8fxu48/jmEqhT+77DJL02WbGr6QrMfgy9RoNObSafP5/NwAqi8dxY0WvdR80ChuAMD6uBK8RiIRA4u9Xs/65XLAKRQKSCQSCILA2Mxer2eCT9oOiOnNQRBgOBxieXnZGqiPx2NUbroJ/9fll+O1f/u3eNXHP46XfOlL+MS7342ttTVjkzUtmDU1BG2e52FpaQnLy8tWL8EUcKo1KzjnxMfBjPU8eo0ikYiBaV0+kUhYuwAV5WLauE566XTaJgamdDMtGdif0LkNqjcyUMFBiQOZMrScxDhQ8X6plL5GNrV+xv0BcB7oXWT6nYLbi1k3tNBCCy2056ep88o5QJlZ1pzGYjG0Wi0DIZ1Ox4L9LClKJpMoFAr7+hs7O2i32wiCwNoUMhU5EomgXC5bMJrW7/cRjSdwd6eEO4I8JtMIXvvwf8Nrn7gDletOI3jVyywwncvlkM1mLSC+t7dnPgoBJkWXAJjfRTFJrZtlthxBMEUkOf+ynEuVlMfj8RzrqOm9zLzjtSKQU19PWVleewJX+gdas8kuDQDmWirpMdAn5DoE8pHIoUDVIvZXs7k0XdllMulT8TnRUiqtP+U5q6k/5G6Dz5yCYM0y0xRu3gd9bhcxp27ate5TwRqv+aLAjp67gk+3ltc1TdnW7eg5ub79IsZVfwhuXXJCj9llWBedi66nx6L/u8dxMb5hCGovIdOHdTgcmtx8rVbDcDg0cMsWNKv9Pn682cRHTpwwGXoqDZORIxPJ1JK9vT1jbXO5HPL5vAEcKvxpiggHDx4XB0K+JO7AyAePrC8ZThbW7+zswPd9Ez2YzWaWRk1g6/u+ATbP80yZmCzncDhEo9FAv99HsVg0cBuPxxFEo/jEu96FO+69FzfdcQcej0YRH4+xNh6jlk6b2BYHf8r76/4jkX1RKqYikdkma0ppf43KUZGQ9TPA4YDvCjVpP1qtWSbI5EBMAO15ng2IZOF5bTmhtdttJBIJSykn2Nfaau6DEyEDEhqdnU6nVi+sdbeMsNKp4I9OCBqtBS6eveVzpJHKEOCGFlpoof3gGOcOZZJYu8i5JxLZ7/5Av4NzcavVQq1Wsw4Kvu+bIBMFJ1m3Op1O0Wg0LHBOoMWAfCqVwpNDD7c111GfJPHizlP4jx/993j4X1yJb7/99Ygmk/A8z+bhdDptzGyv1wMA6/fKcivOpQziM7OKqcL0hRj05m9un8F4+lpkPbme+iDaDlABnAJVBq816O2yZvRdWAalwpaahcbaVc7XPEZlihVIaVaYgkemehOE84f+0WQysXNT1lWJGFcwSY9pUfrrIuDqfk5TcEg/0QXiAIz1prmMrQbxF2WuuftXltQ9pkWfqf+lfx/Fph5FUri+mJ4T/3YFtrSWVtOhF21jkW+36Dy4HzX68YssBLWXkPGGckBkDWi1WsVgMLAIIMHTzz71FADgH06dQrVaxXA4RKlUMsDD3+xfytTebDaLXC5nywIwkEYQo71WgcWS24ziqaiQvuRuWi2jqvV63Vhbpg4nk0ksLS2hXq+bGjJFHyhu1Ww2bZus2SULfOzYMaTTaRSLxX0FwBtvxF9dfTVajQbiQYB3/cmfAJEI7nn1q3H/9ddjdDDYBkFgQJI9ZGezmQlZceCPx+M2IXGgZ5setg+o1Wqo1Wpz0U5OWBRCmM0O63P5omoEl+ws63HJXpMZbjab6Pf7dn8JQtPp9FzD+cFggCAIbCLlBMxoLwdoqi3zOHjOnFgZVVXgy1RoRnc5GWlU8tmwt3x23AkpBLihhRZaaM9/W8ROEURxXgNgwlCz2b7mCP2kWq02p4fRbrext7dn3/V6PcRiMbTbbfT7fQv6cl6jJsgglsU/Bet4bJTHSr+GG+NP4US2iY+/619i6vvISrCbmVePPfaYzbepVMo0MKbTqQHYYrFoGVjMpIpGo9Z6UP0rHg9ZQF4fOvLKutJHoO8GwNhUJSe0vlWBKNlVXl/tVUtwpkCOKsr8n74bj5HHw9I1glptG6QgFoAtQ6CrAFNFrLiugmMFa8pQ81x4XPxRv0KB6FHgkj7topTber1uytsuQKe5LLNul9+7DKyuq+fkbl+3oWDR7WvLvxeBebd+XY9BQeki0K3b0c8XpVFfaBuuPdNyR61HC0HtJWgc6Jj2WqvVrEF3v9/fFzDq9fDmIMAXSyXU83k0a7U5oYDZbGZMLLCfojqdTpHJZOba9nAw40BHMQM+rBywtK6AgyHrXDj4qDgBgTEA653GQT2RSKDdbmN3dxfFYtF67VLgiswjxQsIunK5nNXNxONxFItFNBoNVCoVUykkK80XKZlMotVo4LOveAVu/trX8GMf+xhu/OQn8a3rr8c3XvlK7B6ISJBJZp0J+8Cx7qPf78+9TIy+ptNpE6KIROZ7wY3HY7TbbdsGQRuPj/cpGo3OnddsNpubBNgqKZlMYmtrC4VCwYQoVCkRgAFaRrb5w/pgpqZzHe6T94u/mSrEc+Zyms7DCZDsLSdX4JCl1UmMn18IoLoDbAhwQwsttNCev+ayagBsXuFcqv8zW4v1qrVazbLUPM9DEARWV9tsNs2foSoys9Q4f0WjUURjcXxzsIyv9lYRmUzwv97+Z7hp52v4q3e8HVmvjEQqhVKpZGVb7XYb29vbNr9TyJLMbyqVsk4KrJlVbQpqjaiiMa+FzsecR5WpZQcGpiwT+Cl4VUBCdrXdbhuQ1jaABKEE4mRHCSZdgSdlSwnuebwMmLMNEOd/u84OY8p90u/R+Z1AjdeM69FnVX+UWXHcLo9BgZ8+b9zuIn9CAwDcj26Xvk6xWESxWDwPbNNcUKn71mUXgTQXNPL81Q9S9tIFo3yH3O0omF30Dqopu6znsGiZC52Du/6FfLdF23m2vl4Iai8hI4jq9Xro9Xo2cLL2kzLWqVQK/0O9jvJ0io+ur1sKztLSkikZs86DKanT6RSFQgHlchm+71utCgdT1q5y8GU00U0zBQ7Tal1mkBLr2kqIy3MAVrBDFrrdbiOfz9t3rCFlL1cykqwdjUajxpJ6nmeAPwgCNBoNFAoFS0mm6MLOj/84PvTKV6L80EN45Z134to77sC319YwyOVQ7vdRbDRQPX0avYO0H57jIlU/DgoUreL/WrNLJpVgt9vt2rVij+F6vY7JZGLLEiQT4FNUQhnjvb09FIvFOVDNFGWy8mwX1O/354Btt9tFvV63huhs48SJWic3Mq9MlebExnvOiY8DpdbBqBKgyt67LG4IcEMLLbTQfrBN5w7+TwDLOZBzGLAPaiga2Wg0sLe3h0wmg0wmYwxttVpFrVYzwMcgeaFQ2Bd/6vctI6ky9XBH4wQqUR+3PHY33nfbh/D1f3ElPvGG/xG5VAr5fN6C8t1ubpm7agAAIABJREFUF2fPnrX5nICb82Q8HkepVDJtCwb62blCz1V1TvQ6MLuJfg/negDmJ3Bu5vkxG0+ZV2ZUabqrMuKaYQccZowBhzofLhPMY+W+dX7X7alKM8Evz1HZPJ6/XhsF2PxffS+a+hX009Rf5XcaKFBwp2CaoN3NSOS10yxK1hNTdEzvHddZBF7d7S9iIDX1ntvleam5ANFlZN3lXN/L/V6Z70XfP9M2LuTDXWh77md6nkeB4EgkgkcffXTh/oAQ1F4ypi9Nr9dDp9NBu922wblSqQCAgYtP53LIp1K4p1jEoN22AZIgh4M81ZNZe5rP5y3VmMCMg2KpVLI0VU3LcQddHXCm06kN2P1+H91u1/q76mDDqBJFq7SuhOIKZBIJwJPJpNWyMvrJwU/rTKfTqQE5ikUEQYBisWi9dwnggmwWn7rqKvx9vY7udIpuEODld9+N1992G/rJJJ66/HI8/cIX4rHLLkPl+HH0RfRKBxiCbda18noyKsr2RBSI4Hl7nmeDGxWuef06nY41S1dpf42qtlotVKtVux4E0VRL5D74DLCWmgCc94gK2qzBVoEpAlbeZ6ZTcTLl80r2WiPE2i+O959BDE5WGoVVYPrdAFx3W6GFFlpooV3a5gJaBkI1KMusMLKBjUYDzWbTamg53xHgUuWYc9dwOLQ5UoPqsWQa35yewLcG68hMe/j9T/w7JDMd/M0vvgUzz0MhnzdGlwFhzp0E2BRyZPu/U6dOGRBkxh1wqFTslmrpNchkMpayTEaWTKwL1rQzAv0ObkuZM3alYKo0y6BUaIrHpUBQQeSiOlUXAJE95zY09XcRyON2lW3lOWo7SfUXXLbVBZ/8renT/Fy1VBaltyrYdRljngf/dokeHrt7PJo6rIBQz8NNK9brsgjULfKb1NzPLuQL6XEsWvaZ0nyPsotlYnW5Rend3+n+Q1B7iZim9bLuo1arIQgCDAYDnDlzxpYdDAaop1L4y3IZOJCJX1paMkDqeR4KhYIxgp7nYXl5Gdls1gAtI5+sQ+XEQCEF1onqAMnfClaZ/kpxBjYX73a7FsUiIGI9aiQSMfEDrs82Nd1uF4VCwQYKz/Msskommekz2uuVtTdMUaZKMlnrlZUVk90fjUZAuYx2u43ccIi7X/5yVEolnH7ySVxx5gyuePhhvDoaxQfe9z5MEwlc9eCDSPZ6qGxsYG95Gd3ZYW0sa3BZT0LhJda4Avsvb7/fnxvsOejFYjGUy2XEYjGL/vJcqLjMc2VUUVOwtK8cQbaCW7KyClzpJLAmie2B+Oz4vj/XZoAtiVRRUCc1Fa7Q9CQX4OokqrUcRwHbowCq+/lRLO6FthFaaKGFFtr3z1wAwbmNrQFZCsW5czweo9lsGnilOGI0GsXW1hb29vbQbDYN0LKtDQPEwCGoaaZW8PXOSdSSRWz0HsXp5jfx9ddcg0mxiEKhYEFyZoB1Oh0EQQAAc7oUhUIBuVwOhULBxJ3YwodMII9RWTSCVgaQVX9Dy7SUJex0Oub7cC52U5TJGtPnos/AYDqPH8Bc2jIwrwLMIAKAuQ4Y9AN4LpzftSSJ+2aGGe+xm9nFa6IsKYP9ejwuUGewndtQv0Trb7msAkoXrCvLrNvX9Vz1ZK7faDSwubk5t1393gXJF7JF/sx3Y24d8KL9LwKSz2b/+jw/22WP2veiY7mYz9RCUHuJGB8k7aG6tbWFRqOB7e1ta/9SiMXw/zYa+FCphIfjcQRBgHK5bLWS+Xwevu9bLUoul8Pq6qpFHCnWBGBOOZjMG+tkOcgwoqcPIVMwtF6CqSqpVArlctnqWzkZqKy91mtks1mbJNjmhvUxFEjib6b/RCIRS/uJRCLwfd8icUxZYoSTdTfNZtPYa6ZmE8gjn0f1+HGc6Xbx6X4fXqOB8tNPozkYYNLt4mVf/jJefCDKBQC1QgGPXn45/u6nfgrj8RgnqlW043E0fB+Tg2NgzYsCO2A+MscJq16vz0UKCWCpxqhpTrWD2mlGCxl5jcfj1i9Xv2ewQZvCa1o2e+exJzKZYILhXC5nqc0c4Be1DeCgznMHDsGvG110U7p10rkYoOvaIhbX/TsEuaGFFlpol4Zpyif9CKYZUzWY5TqcKxuNhmloNBoNy047d+6c+UjUpqC/wrmP2VSRZAaPDDdx3+wUjnV38Ruf/iA+/6qr4B1bQyydxurSEgBYa6Dd3V1jQpnZxMAvWwERiPHYmUGVTCYNmOt8py38tGSJgW4C8larZfOp6nIAh8CU9awMJBO8E6Tx+BTA8toTKA4GA2OWNR1aWddIJGKgW5ldLsvt8vhdtpRZYi5gUq0RBb1aG6zLq3aLS7IoEHVLx9SPUF9LMwTUbzjK3+C147FoMEavx8UCQ+7rqLpUXQ44BIM8dj1WVyHYZaPd71y2fdExu2TBom1eyD97tgB5EYP+bC0EtZeA8cYxMthsNi2Ntt1uY2try5SIf2U8xo+Ox/gzHDYcZ39S1lSS9Uun01hZWUE2m7WJgTLs6+vr1idNxRRU8EcnHDcSRcaULwfBF1NVKZTAlkLNZhP1et1qYXXQJ4CLRqPWmogiWazvZU0LBZ3IzvZ6PRN6IkjjCzIYDJDP521SYmovGUmmahNAl0qlfUC8vo7GiRMoH7S2+X/e+lb4OztY2d3FZr2OlUoFvdHIlI5/5iMfwVqjgXE0ilo+j3qxiAdOnsRtN9yAWCyG4vY22vk8WpH5xuXAYb2xDhpsqUOmnecWiURQKpVQKBTsXtbr9TmgqHXLHCzZ7igejyObzcL3fQuCsGdeuVy2VC9Gybe2thCNRu1a6ToMTnBQdaObPBfeC5103Aj9hUAuMN/zFzhfKl7tKBbX/VuXDUFuaKGFFtr/f3YUoKV/oEwtAW0QBNje3kaz2bQ2gDs7Ozh37pwRAQRU0WjUUoIHgwEy2SwasTXcO3oBmukc3nbPP+CFwTfwlde+EnnPQ7FYxHQ6RRAEqNfrqFQqFkhOJpNGFrC+lmCPGUoATB+DfhEAmytZLsWsNs51ZKG1I0Kv15vzuRi81hpXFZ5SNlGZSmU+lQFlKRC3z+VVB0XndWVmmWmnWiLKmhJsKlAlaAdgfoguq2KkWqLk+gyaAqxgdxEY5f/0/Vym0A12L0pLdpfl9eX/1HDR7ej+3dRrZU/detKjAKiuq+dwIZbUvQ6LfCJ3Pwr4F9lRZMKic3AJBPX1LmTfK18sBLWXgPHBZ3uabreLSqWCarWKra0tE4gqAvhfxmPc6nl4uFRCc2sLvu/D930kEgmUSiUTS0gkElZDG4lErOn40tISVlZW7LPhcDiXJqMDpYIvHqf+rZLzrN3gIMI0ataFlEol5PN56yfH1F1V+iMjG43uqwE3Gg2rG2EqC4/RrVchM8p0XA6GHNDH4zE8zzPlwW63OyeykMvlTHiJdabcp+d5wPo6mrMZ9g5SmwEgd9DO5y9f8xqUd3ex2m6j3GhgudVC/kAAazIe4wN//udIjcdop9OoFIuoFAq459Qp3PeiF+2/8PU6OgfpR6oSyAmFLDQBLwdgTRenY9BsNu3ecNJhfz4Adp9ZY0SQy2bwBL6coHu9HlqtlqVekVHP5/PWq48TlwZDeF/4W1OYXBCsWQBuupAqEeqgp/txmWC1Cw3oR4Fc/h1aaKGFFtr31rRFDOcttsbTrCHOfc1mE9VqFTs7O+Yz9Pt9bG1tmfJxt9s155l1rgxq9xN53JN8KaqJVVy9/QjedN8f4/FrTuORG25BwfcRieyLNrZaLWxtbRngI5ilNgc1LrR+VecxGjObKNpJZpF+DcEr53LNfNNuBqqXQV9E50wFPATC/E5TkYHzlXu5be1goKCLwJQsrZYVKVtKxpafuwFpBc7AfC2xBr0Xzbts6cRlea78jsu66ckuYwrAAg2LfIRFDKEKWbnHxWXYoUSfBV32KF/EvUZ6LVz2VNd9JrEoYF7ReZG5zPei41z0+VF+1KLlXHsmtnXRtp4JMBMTLbIQ1H6fTV+odrttIghPPPEEarUatra2TKX4fdgHtv/xoJ9rLBZDsVhEOp22VjZkYjc2NlAsFgHAmL+NjQ1jLjkIctBV0wFCazoWvaQ8Bw5oCrDG4zE6nY4NxGRDPc9Dt9u13roEXXxxCJQ4eVWrVUuN5mBMoMyBhTW3mUwGnU5nLtKpKR5kkRn5Ytsdts1hTSwnFp1kZrOZRWpjsRjy+fz++mtrqI9GePxg0iK4T/b7GE4m+M+33IKlZhMrQYDVVgsnn34aZ9JpVJeXUZ7N8O/+4i/QTqexWyjgXKGA7WIR3zp5EttLS5Y2ReCvasbJZNL2xclLWXmNAFOAgveUDK2qG/P+EOxqfS4nmNFoZCnKiUTCev1ls9m5Ol43TUrBKs9FBy4XvCqTzXupbK6yvYtA71FA96jBchHIddcPgW5ooYUW2nduylQpoKXQJDOvqPnQbDaxt7eHvb09C1i3222cOXMGtVrN6kyB/TE8l8sBOKg19crYnp3CA/5VmM3G2Ny6DSeHT+LRH3sFstksAJjYFNsCMivK8zysrKyYpge3qaJMWuKTTqfR6XSwsrIyBwBdBWfNLItEIuZbMBuKnRM4f3GOBw7FjsgScxsEr1xW/RtlUzWNORKJmD+lbK+KVNHvmM3m04EVmCob5zKaAGyf3K/ee/oUCrwJspXJ53a4jALIRcdw1JxNv0FBuJo7v7vaIfo5AKyurmJ5ednOidvgcbg+yIXAncuuLjK95os+P2p76uO4ac4Xs19ddhHLu+j/78V336mFoPb7bLypTI+t1WrY3d1FpVKxtBoA2IzH8WvDIf46lcK90SgGzaalFrOWdjAYIJVKYXNzE8ViEZFIBO0DZeTjx48jk8mYGJGmKmsUTBtzk+lcBAz0JeVy/E4HwVQqZWxwo9GwFONsNmtN0iuVCkajEdrt9tzgqSzgcDi0JuqavqwiC1T5ZSsdlYlnHS8ZTE6obuovB3sy2/xco8BkdAmwmLajfVy1tc7W5ia2I5G5yW04GCA9GmHc7eK/3HADVup1bDabeMmTT+Kmhx9GDcD9kQhO93r45dtvx9lSCWfyedwbiaCZSKAtQlTRaHQuUh2J7KchMzWdKVIEwwS7mtpDgHvu3DkDs57nmcgU2xMkk8k5Ve12u21RXrK8TIdnujInaj4XXFYnSD5zLouroFV/K9BdFCV+JkZXn2P9TN/Jo9jcRb9DCy200EI72lz2i4Cv2+2aKj8zuFhTevbs2Tn2tFar4cyZM2i327Yex37Wk8b8ZdQTp/FE+SpEZzO88b5/RC2zh5XTJ5DJ/BD6/T62t7dRq9X2s6kOUoxZKpXL5YwQUI0IZkcRiKbTaZvvksmklUvxvGazmYF1zmeJRALlcnmu7AmAsabcJwDTBOE26eNoui7nUu6LbDDnQmbTkbxgeRIA08nQrDt37lNBKmA+TVf9xqNIEDeAzeW5X5aucS52a2Pdulhu29XrWFTWpL6C7vMohlgDAHoN3Pmf94kM91HLPhNg0+NxwaV7Hy5mm3ouRy2n90aDE0dt/yjWV4MKz7TPRcdwlP+06FwvxDwvshDUfp+ND22n08Hu7i4ajQYefPBBVCoVPPHEE7bcuW4X/xuAuzMZNJtNrK2tGau2vLxsAwtTjpl2WiwWsb6+jlQqhVarZYBD01Z5HFp3oYOHGyHSF1EHEQUWWv+ggJLKxIx2pVIpnDx50tjSRqNh6TPK8DIaynpjpihxQCZ4SqfTBmz1f41UEsjG43EMh0ObWAh0mboEHCr/EbiRMSaocidqN4WIAyujoAS9rMuZzWa459gxmwRHoxES7TZGsxlikwkmvR7qsRhefPYsbnz0UbwVAL72Nfz2jTfi/ssuw0q1ihdWqzi7soInPA84mLRUoEKPlWy0MupM5ea15D1jgCOTycD3/bm2T0zXpiPBVKpWq2WDPqO/BMVM66ZDwGeG108nMGWf+RzxmWU0WRUa+VsZf0251mVc8OuC26OA7lETTQh0QwsttNCONneeZICVpUCczwlmK5UKdnZ2EASB+RJnzpyxmloCIc6rADCNpVApX4+t8tWIAPjp+z6Llfo38eTLXoSSt4FOp4Pt7W0EQYBWq2W1qlQ7LhaLlgVGFpFAlCQA/RBNu51O9/vgsuaXTCJBJYEyg8WaQUafi/OwiiDRRwEOS6x4LXkdSUzQp2CQnddmEZNK4Ebwy1Ix+nJkpI8CGGruXEq/jcuSCFiUqq3ASq+J2iJmVj8/qhZWwZqC2EXPpQvwuC93jleflsQA/eVF++RyR7HHLpmy6Px0/3ov3evhHq97DLod/lYi4UIgcxFDrNfC/W7R/TjqGBetezHbuZCFoPb7aLxho9HI2NnHHnsM9XodW1tb9sLEAExiMfyfkwnS/T6y2ay14WH6w3g8xurqKlZWVmzwW11dxfr6Okajkan/EhBoDy+NTtEWMWjuA0cQwsFY02NcAKGiAJwIyM4SuJbLZRQKBQO3ZDbJhLq1uZ1OB41GY47l1cE9nU6j1+vNtaThBERAR3EpphUROGlNC41AkZOGtvPh97FYzFJ0adFo1AA1j5HXiAJQnGDT6TSmhQJS8ThWZzOM1tbwf19xxf59qNWQeeQRvGQ6xUO+j0ajgdc89RTe9eCD+89AJIIzvo9HfR//+eqr0UunMZtMED9QQFR1PJ6ntgfgvaTDwIGL/XN5v6kiTZBaKpVMSIoOAJ8vBjEYaSebzUleRagYbCCry2eInxOMs28xz0ODJ0xlPwrs6sR6FBOs6y0Cu/oOhEA3tNBCC+1oWwRoB4OBqRW3223LwOr1enjyySdx7tw5K+UZj8d44okn0Gw2rdxIM6QisTjaq9dge/W/wzSWwuse+DyufuxzuO+aF6N9+VUYjUbYfuopBEGA4XBo82y5XDY9Dc3g6vf7VoJEEMtuAcA8IKDIJoOsBLy5XG6uJR59A85PClp1LgMOFYE1LZjHQqZVwTyAOZ9uNptZa0Veb2UqNeis8xrnRAWm7lyp8yTXc2tkGUxX8MYAOX0w+hdch74Ut+fWfuo8yu9d8SQ3VdgFYXqMmt7ssrfKNus6LpHT6/XmMvr0Wdd1eWxHsaAKao/yJ9zzuZj/dXsX+u57YXodXTvqnlzos+/m2EJQ+300BVm1Wg17e3s4c+YMzp07hwceeAAAcAzA5wD8m2QSH+/1MB6PDdCWSiVj1kqlEjY2Nuxh2NzcRKFQMNDIOg3tN6ovEwGOyz7qQKEvntaWAPMpM9yeKxTkppQwasnUZA6mFL/q9/vGzLqCVqVSCblczupqG42GqQWTkea+fd+3AMFgMJjr/0YJfoo3qKw9/+Zk5HmegXEO1IzmArDWARwYCXB5nRkVVVXHer0+l07LCZKTKqO98Xgc8XQaZ9bX0b/sMsT6/1975x4ld1Xt+U9VV6df1dWvdOedkIRnFDEBBK7GWT7wKpoLBBFEYMDFFR3XoAMijNyLjCugrhnX3HtFvXK9jk4AxwWIouhFI4YojyiBREICxjw7Sb+7091VXdVd1VXzx6/2r3edVFV3h5BKJfuzVq2q+j3P71f1O/t8997nnATzKit5OhJhy8KFzO/tZeHAAEujUd7W28tAKsVYLMbNr7/Oyp4edkYi7KqvZ2c4zM5wmK7aWiqzKUcSSdfXIv1tJBVMp6nr1GHAT1WWKYNk7mMRrhLRFaMWi8X8tC+dQqwH7NIjLYtzQfenFaMvZRO00ZXGg84gyNd31015ksbCVMSu9phqgyTvhQyTiV3DME50dFtCbKfMja7ns08mk/T09LB37156e3v9/To6Ojh48KC/j9jdUCjEjKpqKhpOpW/uRfRF2qju30XLgT/QW5XkxfPOJplM0rd7tx8BrqiooKGhgbq6OhoaGvxsoWAwSCKR8Ad2EsEsWUVin6PRaE47RPrfSneqZDJJW1ubL1TkGsTWa8e22F2xPeIM1uNciCDW2VWQG+WW4+aL7EmGmLZ78l2PsSHtQimX2CTdvQkmMtnEpurf10UfJ58Alm00OlVYX4+0iQpFY13Rme/Ysky3FfLZadfeu9cj28o9k3uk2xOaQhFMaccUun/5oqeuoyBfxHYyIVwMLcDd/ScTma54drWFrCvk/D+amKgtEfLDxuNxOjs76e7uZuvWrezbt48dO3Z4Q70DPwDmADuzD09TU5Mv6Orq6vxRfSUiW11dzezZs6mtrSUajfpeRsDvY5ovrQLI6XOh+yK6USrtIdPiVvqfyB9XBjnShiAUCvkDJYiQSKfT1NbW+tsNDQ35FXBzczONjY2+EYxGo75QFw+mRAwljUb65kh68uDgYE7KsU4Flnnc9IAO2niI0aiqqsqZ2Fz648r90JW1pC6JQEqn0zn9RkKhkC+mxcOrKykRvoODgzmCOJVK+fPuSjSzpqaGkdpaOpYs4cXAxITvMxIJGBvj9cZGWsbGWBqNclFPD0Ggr7KSj5x7LplMhsv6+gimUuypr2dXbS3xbMp0VVVVzm8tozXqilT+S6FQiN7e3sMEoqQtB4NBf77cpqYm38kiv4XMYReLxejt7T1McGpvughoPZCXNtjixRXj5XqW5f8mDgMdydXRXPnttLDVglj20+dxjZCbxmRi1zCMkwVX0Er0cHh4mGg06ovagYEBurq62L9/v5+KPDQ05Kcfi02X7KCq6mpqwotILH0vnc0LOLN7N1ds/N9sbg0RaGhgeDjBnvZ2X1BWVVXR0tLi94cVASpiMhwO+zZPHLHi8JaxKqQNJYNyig0T+y7XqyOOYif0bAMiXsVpLXYFJkb0lX7F0v6QY4vtEZurU5DlPmt7pB3R2mku7RUZX0OW6Uw7HQHVv6EslyCBzuSTbd19dXRYi1PZtpDo1efWgzzpiKm+1/p4epmOjLvXJNtp3PToYudxRaublquPX2xk4nziV9/HYu0Cd1/9Xgi9j/z/3PWFylrs+FNtt+hrm+zY+bYbGhoqeGwTtSVCRN/AwADt7e0cOHCA/fv3s3XrVn/6lM8B7wduArZlKxGJ0DY0NJBKpWhtbWXOnDmMj48TiUSYPXs21dXVJLJpypL6IoJRIqNS2Yu3Us8npqNbmnwPsPzh9Mh6eph6qUDdoeOl0pVKXabkEcEhFboIYEn/aWxsZGRkhFgs5g8yIRVAKBTyo9giHOPxuG8UJeoo4nRsbMyfRgfwo4Jy3XqUwHg8DuCLMBHB0v9WrlWMhBxf5q3TU+yk02mqq6t9oVudHfRJ7pNMTSTeU7kPlZWVvoc2Go36YlwGedBiTF4vLFrEH+bN88RhKsW8gQFq1GBaf9fRwbJsmhJAZyjEbxsa+PrcuWQyGc5KpeipqiKZ7VMk/1u5NzJXsJxfDJ54muX+6j7UMkCGOFxkPmM9lYEITElP6+3tBXLTiaXfr3jYJZ1ZGhx6Pl3ZVzzk8Xg8pz+vHoFRGgzyrOg+SnpUZymnfNdlk/Np4+A6iaYidt30cPc5NAzDON7QKa4y/6w4pvv6+nxxOzQ0xI4dO3JmQujq6qKjo8O32yKoIg2NJFpOI336++luWcTivv1c87tvsyt0iA2t9USjUQba2xkdHfUHLWxqasqZj13scyQS8e2NCExxGsdiMd9+yiwAtbW1OW0WacuIDZd+ozJolB5UUactw8Q4HfFs5p0WQmJzdHaSBBq0s1TaEmJbRTjmE1bF0oN1NFREuBatuj+rFnRyPH1cV7BKWaVMuj0py11xo0dcFvI5fLWAnyzS6S5zo8j6mG7mVaGIL0A4HPZH29biU8SrG/ksJgT1unzlK7Sfe+5i202XYs736TIdsSvnPlJM1JYAETaxWIzdu3dz8OBBtmzZwiuvvOIL2rOBrwE/Bf49u18wGGTu3LnevKl4UdvZs2cTDAZpamqitbXVT1UJh8O+GNPiUxr1klqj+6bIOeRdV4aafBWACFIROlIxSmUrU+7ogZckJVnOJ/1VdV9WOZ54FiXqJ4MwyRD5sVgsZ3oAERY1NTVUVVX5g2mJB1SityJyA4GA7/3R90sqfu35FOEo0UwRbrJOrlt7cGHCiMmxRbwCOYZJKrl0Ou3376mpqfG/RyIR/zrlPsl8aXpeXi3i5PfcU11NoKqK2uw2nzrnHBqGhjgtkWBxLMbpo6MMZcVzKpnke7t2Eclk6AsG2TVjBjtDIX5bW8v6SMSLCKdShNTgVDDhvZX/npRDBqYQcawNjIhT8ZrLZ4nOimjVI3br30UMiU5Rl/+R9pI3NDT4glkaHe7oyXJ8aUzIMeWzOD/k/6ojuPIs6VQvfXydASFG2X323HsjywoZb/3ufjYMwzhWaLuXyWT87CkZSGloaMiftnD37t10dHTQ19dHPB5ncHCQAwcO+FMbitOwPhzhrQuW07F8FeNNc4lHe3nPM//GwdF21oUqiA5Eiba3MzY2lpNeLGJWRzRramp8Z6e0OUQcSpRWsozE4Q/47Q9ZLzZFxGskEiGRSLB48WI/S0tsngwEpTPTxD5ICrNOaZY2gLRDpJ3kphmL40Aikdrmy/2XfbTQkvaJbsPJPpKW7Dpg5ZrcaKNO53W/FxKduv3oij83SqkHjxT0NWqh754rX9cg/bmQzdR210Uv11mQhSgUtZ3q9rq8xbbJdy2Tid3pMNl1TPcc+bbXjrA3gonaY4w8tMlkkt27d/OXv/yFTZs2sW3bNvbv3+9vtxroB/4++z0Sifhpx5WVlTQ2NjJr1iyqq6tpbW2lpaUFmPB0aYEGHBYRE6+l9rBJRSIvLWyl7JBbgeTzKul+krKPjkamUil/yh0RYxKZlEGA9ETjYkSkj6pU1CJeJRVbKmsZCEqis1oYi7CYNWtWTvpNMBj0y6O9sNroSfnc65Z9dWROjIx4b2UUYEnxkfuiU4Jkub4Puo+zeITF+ysGToytNmISPZW+wnrqHTGgcl/3pFJsraggVFNDKDsNUSUwnkxya1sbS8fGWJpKsXhsjPePjNAD/DSdpjZvfR9bAAAe+ElEQVSVYvuhQ3QFAuwNBtkTDLIvGOSpqio2VVRQXVlJTSDAsHJyAIyMjPj3TaK++j8ofZ71QBkiDGfMmOH335WX3As9Ub02vjqqKr+RLJMGjDwPMtWUHsFaHAfSQNLPjZRLD2imo7dyPe5nuff6GFoAy/3Rv73r0XXrlHwUMtwmfA3DOJro7K10Ok08HvcHfezp6fHnnD9w4ACvvfYag4ODHDp0iL6+Pn9UYrGPAIuaZvHWs97DweWXsCPczNKunRza8F2qel5jfSrpZ2tJl5S2tja/LSD1bEVFBY2NjTl1v9hAcZaKbdbdTfR0LXV1df56GaxJuqtIvSvitb+/3xdjgO+E1zZHt7EkCyuT8WbA0O0BHeXMl83j1vlaqEqGl9hWuS7Ab4/oVGXZV77rkZO1g1WuTV+f21bUtjxfKq+LmxGor0fQQk2XK98x8kWA3XaqbO/2Q85nFwv1A84X7S3meC5mp93j6+ssVh63TG8kwvlG9i/Unih2vDda1kKYqD3GiEDq6upi69atvPDCC2zbto2NGzcC2ZGOgf8BfA/oze63ePFif3qVlpYW2traaGho8Oc8k0paRpuVBrQMuCP9EN1UlmKVpnyH3D+tHkwJDq+U3Ma4VHq6n6aUWbyYiUTCT5OWvq0ihERUiDGRc1RXV/tCVqKE6XSa+vp6MpmMP0CUpMgGg8GciLH0wRQjBvipQtogyPVrr6kYJDHe0r9ZPKqS3q372+r568S4yXVqUSvpy8lkktraWt/QJZNJPwVb7o1MQSROARG00m9I+spEIhFf7Mt9l7IPDQ0RCoV8B4Pc70wmw4MZrz9RGghWVxOoqSGQTpMaHaUGWFNdzcJUilMyGd6RSrE6k2FvMsnvAgHmjY/zCtAHHFCv7wIvAg3AOcEgB9NpugBpzkg5potOE5b0s3A4nCNeJcoq27sDcog41iNui8AV0SsOFklhy2Qy/miX0hiRaaLkvy7PnE6t1kJbIgDSYJBoc3d3N7Nnz84rfLVTRT+DrhHVzqpCFBO8JoANwyiEtl9i44aGhvwpboaGhujv72ffvn3+yMa9vb309PTQ39/vj9IrVC04m+UrLqHntIvYWhHi7F2bmP0f/8xzna8Ry46IDF79LW0gmTZO2gXhcNivF8WOSr0t6cQyMBNMpPRKv1o9/2w6nfajqeLclnaHtKfcTB2pdyVbTgSyvOuopJ4HVwtTLRL1WCdutxndJ9J16LoOWI1rR+BwgVtIGMr+ep0uf6HIp7QLdbuykH1xgyeF2qhAzj10j1fMCVDs/MXsnrTd9IwSk6FFbyEbO5nQm6xM+T673ye73qlGgosxWZQ43+80Faayj4naY4iIic7OTl588UV++9vf8vLLL7N161YA/hNeg/8SYBeeAACYN2+eL5qam5uZO3cukUiE5uZmX/SIVxTw+xbqFBwdkdXeVN3/wn34C3nQxNvpVmJuRaXTdfU5pNKWClWEtwgzPSm7pC6Pj48Tj8dz+n7otCIxMMFg0Beeo6OjvlEKh8NkMpmcofulXyXgn0uih1pYiXGS9e6ACxJZdMWDRE21ARMjpiczl8ispPjIfnJdiUTCN6iyjx5VWbaViKH0KZZ9pb9QbW2t39dZyhGPx4lEIv75ZZ2kaUuacz6GgX90loWAinSacTwx+9+BBXijeM8D3o6XUg9wEfAr5XUcBLqB/ww8D5wDfAzPsdOnXluAeJ7yiGNDyiup/FNFBK88UzKoSDAY9KO21dXV/v9IR4+DwaDfn1caN7KvCGPJshDHiUznJL+LbKsH/mhvb/f/c/L/1inPerA0eS50ZFgP3KUbG+67K4ALMZngNQFsGCcH2qaJDRsZGaG7u5uuri4OHTpEb28vr776Kvv376ezs5Oenh527dqVc5wgcH7bEhaduZI/nflO0k1z6YwP87ZNT7B983/wi4GDh527oaGB1tZWIpGIX2dLFxNd94mAlW5Ium+tOCUl+0fXqXJdOjMMJoScHEPPqjA8PEx/f3+O2HEjloAvrsUBq+eu1RlE7kCFeryPTCaTk8Gkf498dbuOtrpiMd827u/r/uaQGzHU4z64g0fp9mK+/5AbzZXl+c6pt8vXttQBCvc8+rMruqcicLUwh4nswnznciPAbtvavc43026+0WMXag8UW16sHZFv+WRlnEzsa0zUHiOkP+fu3bt56aWXWL9+PU8++ST9/f0AXAGsxROzusHe2NjI3LlzaWpqIp1OM2/ePFpaWmhtbSWdTvtTo0ijOxwOM3PmzBzPpIgvXbG4gzVpoel62txKUo+CJ99FdGlBpwcbcCshEQZaYEvDXYRZfX19Tn9aieKK51Pmu5MorxbMcvxYLOYLVSmHK+5lQAjZXwSg7ociA0HpaxBBKinVcn/0deuRoXVUWFdmepAoWS7nEzKZjJ+y3dbW5nuAtdCVeyflEoMnxl3SrbUToqqqyh9UTI/2LNcoacsieEVgFyKVfQF04PULL8SLwEfwRvduU6++7Pq3ALcDlc5+bwNeAT4N3AcM4KXqH8q+/gvQA1wAnKuWD+EJ51eBfFcgjgw3cjAdAoEA9fX1/vRPMj2RjAItIlSLZxHB0r9KIg6hUIju7m72799POBxmfHzcHzBNxK2IamkgybF0GrYIXj09krx0ZFg7mtzGlEZHIvR1F7snU1k22XEMwyg9rpiNx+P09/fT19dHV1cX7e3t7Ny5kz179nDw4EH27dvHgQMHco4RmlHLhQvewqyFZ9N56oXsb55LT3qchr1b2PHc/2PktT+wJzV22Lmbm5t9MVpfX+87q6UeFMehpCGLiJVuKiJsdV9WmdlAuipJdpjYULH/4izXoxLr6KkMTCnnE+djIODN7a4Fq65r9XLdjhAbrAWjjt7q7/nElWwHHNb2c8Wrbs/pLDxXyAnFInD62PpYWuwViqZOdXmhcuhAS77juPtOxd7o7VwRXagtVMjZm89m5ivvVI9XbJkbZJoux2qfIzluX19fgS1N1L7pyB9/dHSUzZs3s27dOn7/+9+zbt06wBsQ6hvAxcAf8aK08nOFQiFfxC5YsIBUKuX3ox0eHvZTXMPhMG1tbUQiEb/yFGGjKxRdyUrFrqOeOhXFrZwgf98A14OlI7hasLoRQokiuqnMYqDc/h2hUMhP0ZRrk/4oeiCqVCrlR11HRkYOuwcy/6qUUZ9Le/jcFCBBhKn7kOmJ0EUgaIHvVmBuWrIuo9wjEZNy/wIBbzCmmTNn+sv1PZftgZwoq547d1Slb0m/1aqqqsOOJ+fUfXNkoCo5n9xfbeSnQy/wZJH1D2dfjUAL0Jx9Fz//NjxHUDPQlH3NY0KwfgT4hzzHDQMx4H8CNzMhdoeyr7/NbnclnoAeVq8BVea5eBVoNLsuiff7DQ0NFR1yfro0Nzf7QlX6EcvzIM4rPbWRpEbL/1tHKLQQ1n2GRSSL2BWhLOcTsazTsuVzvv7KriiWZ0Y3djSTCeTpCOPJ1hmGMX20LR8dHWVoaIiBgQH27t3Lvn37+Otf/8rOnTvZuXMn27ZtU7YzwIymuZw6+zQq5pxGz4K3MqNtMe3BCjpTSRa3v8KSjY/ypx0vsCuev95sbGwkEokQCARobGzMibDW1tb66ccyHoJ0NdHjI8DEGBZ60KXh4WFfnEq2D0wIR3F8S5tJR4BFkIoIXrx4sd/NSrettN2Xewnk2GxBtzG0yNXdoeT4OjLpRjDlGlxBp6/LjZTqsunzyPJ8wknOK8ctFhDR6/J1mSm2jSv+JrMJ0xGsha4xX4RY1jc3N/tj2bhMJ7I4nW2LcaQi9Hi3ldO9PyZq30QkOtve3s7LL7/M97//fZ566qmcbW4GVgD/FfhXJqJckl6zdOlSFi5cSFNTE93d3QwODvqRxKamJmbOnElDQ4PvtZMBgvSorzpFUhqgEhktVkkV844V2s+N6mjvoAyL704Jo/t4agGlB3mS8+qUaTEm7rQtsr94WMWAiQERISbn0CJdjJo+p4g2bWzcfsj6XfaT6xYD4ho0t1+t6z3Vv4WIb+1h1sZOphrSTgNxHMhxRJTqAaP03MKJRMKPgsv1SSq2fBbnRDqdZubMmX6qr0TM3WPrefuOFIm07nSWb8i+CvEV4F/wRHEjXh/eBjxBK/sHgUh2eT1Qpfb/AHAjXj93oQcvmgxwP3C5WjcGbMdLsQb4Jp7TKqZeO/CiywDXZMs1kn3F8aLbL2bXLwIyQLy/n2j23NN3HUyOCGbd7176JEtjTgbJknpFi2t5/iT9T7aXUUblXZ5XPXWTHEv/v0V4S4NQ1rvPgnx3BzUp5hWfzIgf7wbeMI4lOiIroxLv27ePnTt3sn37drZt28bevXvZtv01quaeTtW8txBsfQdNf/teWqvC1NdGGG49hXh1mBgwI5kgc/AvDD73Y+bte4WdHa/z1zwRWaG2tpaGhgYikQgNDQ1+PSVjS0gdVVdX50+votNx+/v7c1J7pW5JpVI5zjrJjNFzyUqdJ91EMpmJ6d60XRfbLu0LGfhJttNiTByNblBAzg8TUVFZJnWSdhYWaqNpcal/Q3fcBX1ujWyjB5HKJ/Km0zbU66bSFphOHVzseEcj0pivnetmKB7puQqd41iir+dEoSxEbTqd5p577uH1119nxowZrFmzhkWLFpW6WHmRCm5kZIQtW7awdu1aHnvsMfr6+jgFb+7Zy4D/hRfx+Yfs65A6xty5c1m0aBENDQ00NDQAE2KrqamJxsZGP3ojc5dK5EYqZTfdRTfq3ApW0NtN54+uBZy7r1Tq2tBo76EMtKOFok7b1YJXD9SgU3XFmMi+cnzxYOqR+iTlWcSvlEkPfe+mU+uoszu5ui6ziwhpLcZ19FbedTnkemUf8SzrQaD0qMw6Mu5Gx/XUArqM4tSQ+wCeiJe0LffYIv6lnK4Ylm10tFyuS77L/dSp4jrSXqjPzRshiScEewqs/3n2VYi/z75q8ASvK3r/Kbt/OLsujBexFeJ4orQVOCW7fi4TovZ2JgSw8DvgvdnPvwFOc9b/DK/+APgTniAfBRLZ16+Ar2bXfw8IZNfL6zngiez6W/CE+KgM0DY4yHZgK56QfyfePYzipXcnga7s5yBehDzpvI4W0lCVukzSrqurq/0+cjpSIpEbaYjqqaBEYMso1zqCI0JaBLpOBzcmp5xss1Ec3ViPxWIkEgk6Ojp49dVXee6FjWzZ28tfOgb97hlBoKU6TN0Zl7P0A28jVeVNNZhOJkgnYtQnopySGKZl+wYynTs42LGDl3v30ZXx7FChEQ/q6uqoq6ujsbGRpqYmv0uGOMSkK4c46AHfrgD+cy5dPMTpJtkqIhb1SPTyXdKLdfshFov5zjNZJ+hsFJgQonqZG7HVDjzd5nLbX7qtNhVnnbtuspTWfL+9e9zJlk2VQmJxqsecSvmORrmms15sTimYahT6WJzneKMsRO26desYGxvjxz/+MZs3b+ZrX/sa3/nOd0pdLN+D2d/fz+7du/nW/ffzxEMP0YLXgDyI1x/wYeCtwJnZ/V5hop/gIeeY8+fPZ86cOf70PY2NjcycOZOZM2dSX1/P/Pnz/UmfRYTIvK3SR8715kGu5829Bll+pF6bfBWOjkZOtr0Wvjr66JbPFXGyXkd3ZZkYODdyqaO3gh7YQItO97N+ybH1tbjL3WiuFr6uiNPrRATmO2cqlWLevHk56VNaIMuxdOqyjoDLsaRPskTC5b+s+/2KM0FPGSTHk/RUva+bxqx/G/msnQP57q2OlIvXW/9eEmU/lsSzr25n+WSR4i9Octy/AeqA2uyrDk+YCrfjpVtX4wnrGnKj1c8DM/GEdnX2XT/dFzEhxOVVgydqK4F/zlOmr+EN7hUBnsmz/i48UT4f2Jtn/S14Eeoz8QR0kol+1kngDuAxvAj2D5z148A9wO+BZckkXz50iHG1LoXnDNyIN4jYTdnl42r9vwL78NLGV+FFx4ey69N46ep9eP21L8ouk3VpvEHMYsAZwEMvSszcKMTxapuNXLQdGhwaZn9nN/sOdrF9x26eefZ5Nv5xE+PJNLWhSgYDQcYDQVoDARZE2kgsfCuH5r2L5Fm11Jzl1SH+cYHGQ52s3L6Bc/dspmXvFi5LRBnCe5624znFJiMUCvljgYgDSkYodmdukGwl3VVC2j8yt7nODHGnhJO2hohPnSGiu1AEAvm7aUGueJVl8Xic+fPnA4eLy3yZItMRDG4EtNAxpiv+yiH9tBhvdpSx2PGLtZff7Hs6les+0SKwU6UsRO2mTZtYuXIlAG9/+9v90YI1jzl/oh7gM9nP/4jXCBICeA2f/5b9/lW8RkyAiUbha8Cd2c/fBpbgRS9C2fc/Al/Irn8+u/7/MHFDf4SXXpjE6+f3OvAAXqQld+w/mDlzJrNmzWLRokX+57a2NlpaWpg1a5Y/8MyePXs455xz/EEIXE9fvnRiN/I6WXpFMaYTxZ0sxU+LuHwVaz4xrNN2dDkKiUtNscGN3PX6uHqZm3pS7HyFKjydsqK9wcXKostTVVXFGWeckfccEkGV/fNFcd0IrB6JWacN63edigwTg1i5x9GpyVrAalEry7UQ14JXf3dFuevQcCPfUkZ9D6Q8uh+xjkS790CXQ2cFyLUdSd/hQohYLsTPJtn/lknWv6XIuiRepLUKT+DOyL4GsuujwHvU8srsS2reAeCzal0o+74xu34Q+L9qP6k7u9T5D6j9KrLvEluoxatTQ+TWu9/Lrl8AfDy7TL9+hle3rwDW5Lnu3+CJ2vfipaa7nIInaq/Is844nKnY5lWXfynnewLYnP28DM+BoonhOX7Bc37UOeuH8QZ7A8+u1zjrD+HZb/D+B24spQ+vGwDA+eR2LwDPeSU2+kIOpxPYk93v/DzrDwDt2fOuyLN+H9BBgGrgba4oCQTYC3QToBZYJusDATIEyAQC7AkEGAgECQeCnBIMkg5WkA5UkA4GyQSCdAaDjAQC1AaCNAdDpIMVjAcrGK8IMVpZxWhltTpjM8z9MLWXfVgv8WkHTuvdy8Xb1rOsfSst3bv4TDrNATxH/bLUKHuH+/g+hw8K2JHn2iUKK6nE9fX1RCIRv00jYlbErZ6XXbIudCaa7n6kB0bUYwzoyKmbvaZFLRQeUChfe8RFMkJkn2MhFt2o8XTPeaSBDE0+sX2sKKUgL3eHwIlKWYjaaDTq95cA/D4Ren6u0519wurz/DzrNfPwGlDgeR4zTDTuYKI/nkQMkuT2bdsAvIyXlteXff+rWv836vP555/P+UuWUF9fT3NzM3PmzKGtrc33RkrFrVPhpFKdP38+g4ODk3r6yqHP2NGICL9ZHIvzHOk5FixYUFCgS9qlPn4+gT6dck22nyv2hWJiXYv6fAJ+smPlu3438l3M4eFG0vNF1HWk3o0454tASzm1M8A9thbb7nXpKLsuXyKR8PuL6/11P2x9fVqEa2eALpvrAND7tKTTNGbF/K48DoqxsTGqEwlG02m+PVa4L1wH8PmCaz3R8XdF1j/P4anZml/gRakL8UPgQXIFb5CJ9PDv40WMZbm8yxit/wasLnJ8w2MqtnmoeV7OPkkmMpVG8hxzVK2PcXg/8riz3s3b0OujHN7ISaj1w0w4UvKdP9+wRWNMOGDyrU9Osj6VLVMgk8mJYgbEWUfGX58g62jPZAiQIZhJE8hkCGTfa9LjBFNjBNNpb116nL5MhmQmTUUmzeL0OMF0isC4916RHOX5sTidyQQLxuK8ayxOZizBeDLO+FiC0dQYT5OhP5OmMZ1mZnyYzYlhnubwEeM3Azvq6mhesIBT5sxheTa7TPq86gHqpMuACFmduqlTcfOJT3kV60uar7+o1GnFMnveiJM/H+50RdPlaLfPjpf23olIKpVix44dk29oHFPKQtSGw+GcaTbS6XSO0QQ422kIn40nTgsx2frlwA1F1r+XydMLjzbbt2/nrLPOOsZnNY4n7D9wcmG/d+nYtGlTqYtw3DMV2/zMv3/2WBfrmGPPqWH/gZML+71LRzHbPPWe5CVkxYoVbNjg9V7bvHkzp59eLO5qGIZhGMabjdlmwzAM43ihLCK1F198Mc8++yxXX301mUyG++67b/KdDMMwDMN40zDbbBiGYRwvlIWoDQaDfOUrXyl1MQzDMAzDyGK22TAMwzheKIv0Y8MwDMMwDMMwDMPIh4lawzAMwzAMwzAMo2wxUWsYhmEYhmEYhmGULSZqDcMwDMMwDMMwjLLFRK1hGIZhGIZhGIZRtpioNQzDMAzDMAzDMMoWE7WGYRiGYRiGYRhG2WKi1jAMwzAMwzAMwyhbTNQahmEYhmEYhmEYZYuJWsMwDMMwDMMwDKNsCWQymUypC/FG2bRpU6mLYBiGYZxgnHvuuaUuQlljttkwDMM42hSyzSeEqDUMwzAMwzAMwzBOTiz92DAMwzAMwzAMwyhbTNQahmEYhmEYhmEYZYuJ2uOIjRs3csYZZ/DLX/4yZ/mqVau48847S1Qqo1Q88MADvOtd72J0dLTURTHeBOx5N4zywJ5VQ2O2+cTGnvfyxUTtccaSJUv4xS9+4X9//fXXicfjJSyRUSp+/vOfc8kll/Dkk0+WuijGm4Q974ZRHtizaghmm0987HkvT0zUHmeceeaZdHR0MDQ0BMATTzzBqlWrAHjwwQe5/vrrueaaa7j55psZGxvjJz/5CZ/4xCf4+Mc/zvPPP1/KohtHkY0bN7Jw4UKuvvpqHnroIQCuu+467r77bq677jquvfZaenp62LhxI1deeSXXXHMNP/3pT0tcamO6TPd5v+2221i/fj0AO3fu5FOf+lSpim4YJxVmmw0w23yyYLa5PDFRexxy8cUX85vf/IZMJsOf//xnli9fTjqd5tChQ/zgBz/g4YcfJpVK8corrwAQiUT40Y9+xEUXXVTikhtHi0ceeYQrr7ySJUuWMGPGDLZs2QLAihUrWLt2LR/60If47ne/C8Do6CgPP/wwl112WSmLbBwh03ner7zySh5//HEAHn30UT760Y+WuPSGcfJgttkw23zyYLa5/AiVugDG4axatYp77rmHBQsWcN555wEQDAaprKzk1ltvpba2ls7OTlKpFACLFy8uZXGNo8zg4CAbNmygv7+ftWvXEo1GefDBBwG48MILAc+APv3004D9/uXOdJ73Cy64gHvvvZe+vj6effZZbr311hKX3jBOHsw2n9yYbT65MNtcfpioPQ5ZsGABIyMjrF27lltvvZX29nai0Sjr1q3jkUceIR6Ps3r1amSK4WDQAu4nEk888QRXXHEFd9xxBwDxeJz3ve99NDU1sXXrVmbPns1LL73EqaeeCtjvX+5M53kPBAKsWrWKe++9l3e+851UVlaWuviGcdJgtvnkxmzzyYXZ5vLDnrjjlEsuuYSOjg7f01dRUUFNTQ2rV6/mxhtvpLW1le7u7hKX0ngzeOSRR7j00kv97zU1NXzgAx9g7969PP7441x77bWsX7+eT3/60yUspXE0mc7zvnr1an79619bepNhlACzzScvZptPPsw2lxeBjLgUDcM4rrnuuuu45557WLp0aamLYpSQrq4uvvjFL/LDH/6w1EUxDMM46THbbIDZ5uMBi9QahmGUCU899RQ33XQTt912W6mLYhiGYRgGZpuPFyxSaxiGYRiGYRiGYZQtNlBUiUkmk3zpS1/iwIEDjI2N8ZnPfIZTTz2VO++8k0AgwGmnncaXv/xlgsEgX//613nppZdIpVJcddVVfOxjH6O/v58vfOELJBIJ2tra+OpXv0pNTU2pL8swDMMwyhazzYZhGOWFRWpLzGOPPcZrr73GXXfdxcDAAJdffjlnnnkmN954IxdccAF33303K1eupL6+nrVr1/Ktb32LsbExPvzhD/Poo4/yzW9+k2XLlrF69WoeeOABZsyYwQ033FDqyzIMwzCMssVss2EYRnlhfWpLzAc/+EE+97nP+d8rKip49dVXecc73gHAu9/9bp577jmWL1/Offfd5283Pj5OKBRi06ZNrFy5MmdbwzAMwzCOHLPNhmEY5YWJ2hJTV1dHOBwmGo1yyy238PnPf96f80rWDw8PU1VVRUNDA8lkkjvvvJOrrrqKuro6otEo9fX1OdsahmEYhnHkmG02DMMoL0zUHgd0dHRw/fXXc+mll7Jq1aqcCbtjsRiRSASAwcFBbrrpJpYuXcrNN98MQDgcJhaLHbatYRiGYRhHjtlmwzCM8sFEbYnp7e3lk5/8JLfffrs/YfOyZcvYuHEjABs2bOC8884jkUhwww03cMUVV/DZz37W33/FihU888wz/rbnnnvusb8IwzAMwziBMNtsGIZRXthAUSVmzZo1/OpXv2LJkiX+srvuuos1a9aQTCZZsmQJa9asYe3atdx///2cddZZ/nb33XcfNTU13HHHHcRiMZqamvjGN75BbW1tKS7FMAzDME4IzDYbhmGUFyZqDcMwDMMwDMMwjLLF0o8NwzAMwzAMwzCMssVErWEYhmEYhmEYhlG2mKg1DMMwDMMwDMMwyhYTtYZhGIZhGIZhGEbZYqLWMAzDMAzDMAzDKFtM1BqGYRiGYRiGYRhli4lawzAMwzAMwzAMo2wxUWsYhmEYhmEYhmGULf8fyEvgU414hwkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=figsize)\n", + "plot_state(simulations, sim.S.I1, ax[0], index=df.index, title=\"New infections over time\")\n", + "plot_state(simulations, sim.S.M0, ax[1], index=df.index, title=\"Fatalities\")\n", + "df.plot(ax=ax[1])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/pyncov/__init__.py b/pyncov/__init__.py new file mode 100644 index 0000000..1106fa5 --- /dev/null +++ b/pyncov/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- + +__version__ = '0.1.0' + +from pyncov.pyncov import * diff --git a/pyncov/pyncov.py b/pyncov/pyncov.py new file mode 100644 index 0000000..3dd5df5 --- /dev/null +++ b/pyncov/pyncov.py @@ -0,0 +1,265 @@ +# -*- coding: utf-8 -*- +""" +SARS-CoV-2 probabilistic simulator +https://github.com/covid19-modeling + +This simulator was used to train the predictions available at: +https://covid19-modeling.github.io/ + +The method is described in detail in the preprint: + + Matabuena, M., Meijide-García, C., Rodríguez-Mier, P., & Leborán, V. (2020). + COVID-19: Estimating spread in Spain solving an inverse problem with a probabilistic model. + arXiv preprint arXiv:2004.13695. https://arxiv.org/abs/2004.13695 + +@author: Pablo Rodríguez-Mier (@pablormier) +""" + +import numpy as np +import multiprocessing as mp +import warnings +from collections import namedtuple +from enum import IntEnum, unique + + +RNG = np.random.Generator(np.random.PCG64(np.random.SeedSequence())) +Model = namedtuple('Model', ['transitionMatrix', 'timeSimulator', 'parameters']) +Schedule = namedtuple('Schedule', ['incoming', 'outgoing']) + +# TODO: Future versions will incorporate a way to define custom Markov Chains +# different for the one we used to model SARS-CoV-2 + +STATE_NAMES = [ + 'I1: Infected (incubating)', + 'I2: Infected (asymptomatic)', + 'I3: Infected (symptomatic)', + 'R1: Recovered (infectious)', + 'R2: Removed', + 'M0: Dead'] + + +# States of the Markov Model +@unique +class States(IntEnum): + I1 = 0 + I2 = 1 + I3 = 2 + R1 = 3 + R2 = 4 + M0 = 5 + + +S = States + +DYN_RI_DEFAULT_PARAMS = [ + 1.500, + 0.500, + 9.000, + 0.003, +] + +MARKOV_DEFAULT_PARAMS = [ + 0.8, # alpha: probability of going from I1 (infected) to I3 (symptomatic) + 0.06, # beta: probability of going from I3 (symptomatic) to dead + 5.807, # Gamma1 shape (time from I1 to I2) + 0.948, # Gamma1 scale + 5.807, # Gamma2 shape (time from I1 to I3) + 0.948, # Gamma2 scale + 6.670, # Gamma3 shape (time from I3 to M) + 2.550, # Gamma3 scale + 9., # Uniform1 lower bound to model transition times from I3 to R1 + 14., # Uniform1 upper bound + 5., # Uniform2 lower bound to model transition times from I2 to R1 + 10., # Uniform2 upper bound + 7., # Uniform3 lower bound to model transition times from R1 to R2 + 14. # Uniform4 upper bound +] + +DEFAULT_PARAMS = DYN_RI_DEFAULT_PARAMS + MARKOV_DEFAULT_PARAMS + + +def infectious(state): + return int(state[S.I1] + state[S.I2] + state[S.I3] + state[S.R1]) + + +def default_rit_function(day, params): + if len(params) != 4: + raise ValueError("Unexpected number of parameters for R0(t) function") + a, b, c, d = params + return d + a / (1 + b ** (-(day - c))) + + +# Markov chain model with temporal movements +def build_markovchain(params): + num_states = len(STATE_NAMES) + # Unpack params + alpha = params[0] + beta = params[1] + pGamma_I1_I2 = params[2:4] + pGamma_I1_I3 = params[4:6] + pGamma_I3_M = params[6:8] + pUniform_I3_R1 = params[8:10] + pUniform_I2_R1 = params[10:12] + pUniform_R1_R2 = params[12:14] + + # NOTE: can be a waste of space for spare data. Maybe worth it to change + # to a object model instead. + markovModel = np.zeros(shape=(num_states, num_states)) + markovModel[S.I1, S.I3] = 1 - alpha + markovModel[S.I1, S.I2] = alpha + markovModel[S.I2, S.R1] = 1 + markovModel[S.I3, S.M0] = beta + markovModel[S.I3, S.R1] = 1 - beta + markovModel[S.R1, S.R2] = 1 + + # Each transition has a distribution function to generate the times when + # the transitions are going to be made. + # TODO: Replace by a dict + distributions = np.full(shape=(num_states, num_states), fill_value=None) + distributions[S.I1, S.I2] = lambda amount, rng=RNG: np.ceil( + rng.gamma(pGamma_I1_I2[0], scale=pGamma_I1_I2[1], size=amount)) + distributions[S.I1, S.I3] = lambda amount, rng=RNG: np.ceil( + rng.gamma(pGamma_I1_I3[0], scale=pGamma_I1_I3[1], size=amount)) + distributions[S.I3, S.M0] = lambda amount, rng=RNG: np.ceil( + rng.gamma(pGamma_I3_M[0], scale=pGamma_I3_M[1], size=amount)) + distributions[S.I3, S.R1] = lambda amount, rng=RNG: rng.integers( + low=pUniform_I3_R1[0], high=pUniform_I3_R1[1], size=amount, endpoint=True) + distributions[S.I2, S.R1] = lambda amount, rng=RNG: rng.integers( + low=pUniform_I2_R1[0], high=pUniform_I2_R1[1], size=amount, endpoint=True) + distributions[S.R1, S.R2] = lambda amount, rng=RNG: rng.integers( + low=pUniform_R1_R2[0], high=pUniform_R1_R2[1], size=amount, endpoint=True) + + return Model(markovModel, distributions, params) + + +def simulate_day(day, schedule, model, rng, deterministic_transition=False): + max_days = np.size(schedule.incoming, 0) + remaining_days = max_days - day + for i in range(np.size(model.transitionMatrix, 1)): + destinations = np.where(model.transitionMatrix[i, :] != 0)[0] + incoming = int(schedule.incoming[day, i]) + # No destination from here + if np.size(destinations) == 0: + continue + # Nothing to schedule out today + if incoming == 0: + continue + # Get the transition probability for each state + probs = model.transitionMatrix[i, destinations].tolist() + # Optimize this step by multiplying by the probabilities. Add an option to switch to the expectation mode + average_assignation, assignations = None, None + if deterministic_transition: + average_assignation = [np.round(probs[i] * incoming).astype(int) for i in range(len(destinations))] + else: + assignations = rng.choice(destinations, size=incoming, p=probs, replace=True) + for j in range(len(destinations)): + dest = destinations[j] + # Amount of people designated to state j + if deterministic_transition: + amount = average_assignation[j] + else: + amount = np.sum(assignations == dest) + if amount == 0: + continue + # Get the time simulator from current to destination + time_dist = model.timeSimulator[i, dest] + sampled_times = time_dist(amount, rng).astype(int) + # Update the schedule table to indicate which days they are entering + counts = np.zeros(remaining_days) + # If there is only one person, no need to count. We just fill the corresponding day + if np.size(sampled_times) <= 1: + entering_day = sampled_times[0] + if entering_day < remaining_days: + counts[entering_day] = 1 + else: + c = np.bincount(sampled_times.astype(int)) + # Fill the corresponding days, discarding days ahead + # the remaining time in the simulation + s = min(remaining_days, np.size(c)) + counts[:s] = c[:s] + schedule.outgoing[day:, i] = schedule.outgoing[day:, i] - counts + schedule.incoming[day:, dest] = schedule.incoming[day:, dest] + counts + return schedule + + +def simulation(susceptible_population, initial_infections, model, daily_ri_values, rng=RNG): + num_states = len(STATE_NAMES) + num_days = len(daily_ri_values) + total_population = susceptible_population + initial_infections + matrix_size = (num_days, num_states) + state_counts = np.zeros(shape=matrix_size) + schedule = Schedule(np.zeros(shape=matrix_size), np.zeros(shape=matrix_size)) + for i in range(num_days - 1): + # Inject new infections into I1 each day in the main loop + if i == 0: + new_infections = initial_infections + else: + infectious_amount = infectious(state_counts[i, :]) + lambda_t = daily_ri_values[i] * infectious_amount + total = rng.poisson(lam=lambda_t, size=1) + new_infections = max(0, min(susceptible_population, total)) + susceptible_population = susceptible_population - new_infections + + schedule.incoming[i, S.I1] = new_infections + schedule = simulate_day(i, schedule, model, rng) + state_counts[i + 1, :] = state_counts[i, :] + schedule.incoming[i, :] + schedule.outgoing[i, :] + total = susceptible_population + np.sum(state_counts[i + 1, :]) + if total != total_population: + raise ValueError("Unexpected invalid state at day {}".format(i)) + return state_counts, schedule + + +def sample_chains(susceptible, initial_infected, model, daily_ri_values, num_chains=1000, + n_workers=None, pool=None, show_progress=False): + + if n_workers is not None and n_workers > 1 and pool is None: + pool = initialize_pool(n_workers, np.random.SeedSequence()) + + pbar = None + if show_progress: + try: + from tqdm.auto import tqdm as tq + pbar = tq(total=num_chains) + except: + warnings.warn("Could not load tqdm to show progress, please install it to use the option show_progress") + + simulations = np.zeros(shape=(num_chains, len(daily_ri_values), len(STATE_NAMES))) + if pool is None: + it = (simulation(susceptible, initial_infected, model, daily_ri_values) for _ in range(num_chains)) + else: + it = pool.imap_unordered(_fn_simulation, [(susceptible, initial_infected, model.parameters, daily_ri_values) + for _ in range(num_chains)]) + + for i, (st, _) in enumerate(it): + simulations[i, :, :] = st + if pbar is not None: + pbar.update() + + if pbar is not None: + pbar.close() + + if pool is not None: + pool.terminate() + return simulations + + +def _fn_simulation(args): + # Inject the random generator and build the model to allow pickle problems + # with the lambdas + s, i, p, v = args + m = build_markovchain(p) + states, schedule = simulation(s, i, m, v, rng=_fn_simulation.random_generator) + return states, schedule + + +def _process_initializer(fn, q): + fn.random_generator = q.get() + + +def initialize_pool(n_workers, seed_seq): + seeds = seed_seq.spawn(n_workers) + q = mp.SimpleQueue() + for s in seeds: + q.put(np.random.Generator(np.random.PCG64(s))) + # Pass the generators + return mp.Pool(processes=n_workers, initializer=_process_initializer, initargs=(_fn_simulation, q))