diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml new file mode 100644 index 0000000..a9ac465 --- /dev/null +++ b/.github/workflows/python-package-conda.yml @@ -0,0 +1,38 @@ +name: Python Package using Conda + +on: [push] + +jobs: + build-linux: + runs-on: ubuntu-latest + strategy: + max-parallel: 5 + + steps: + - uses: actions/checkout@v4 + - name: Set up Python 3.10 + uses: actions/setup-python@v3 + with: + python-version: '3.10' + - name: Add conda to system path + run: | + # $CONDA is an environment variable pointing to the root of the miniconda directory + echo $CONDA/bin >> $GITHUB_PATH + - name: Download test data + run: | + git submodule init test_data + git submodule update test_data + - name: Install dependencies + run: | + # conda env update --file environment.yml --name base + - name: Lint with flake8 + run: | + conda install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + conda install pytest + pytest diff --git a/.gitignore b/.gitignore index 2f916c7..43e3738 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,4 @@ *.pyc neuroanalysis.egg-info +build/ +dist/ diff --git a/LICENSE.txt b/LICENSE.txt index d36f743..065e825 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,4 +1,4 @@ -Copyright (c) 2016 Allen Institute for Brain Science +Copyright (c) 2016- Allen Institute for Brain Science The MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: diff --git a/examples/exp_fitting.ipynb b/examples/exp_fitting.ipynb new file mode 100644 index 0000000..12f884e --- /dev/null +++ b/examples/exp_fitting.ipynb @@ -0,0 +1,592 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Testing algorithms for exponential decay curve fitting" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.optimize\n", + "from neuroanalysis.data import TSeries\n", + "from neuroanalysis.fitting.exp import exp_decay, Exp, normalized_rmse\n", + "import neuroanalysis.fitting.exp as exp_fitting\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def estimate_tau_Šimurda(data: TSeries, tau_guess: float = None):\n", + " \"\"\"data should be baseline of 0\"\"\"\n", + " # Šimurda 2020 method:\n", + " Δt = tau_guess or data.dt * 100\n", + " curve_start = data.time_at(data.data.argmax())\n", + " val_at = lambda _t: data.value_at(_t)\n", + " # threshold = val_at(curve_start) / 10\n", + " # later = data.time_slice(curve_start, None)\n", + " # first = later.data[later.data < threshold].argmin()\n", + " # if first >= 0:\n", + " # Δt = min(max(later.time_at(first) - curve_start, Δt), 60 * data.dt)\n", + " tmin = curve_start\n", + " tmax = tmin + 0.5\n", + "\n", + " def K(t):\n", + " ret = (val_at(t) - val_at(t + Δt)) / (val_at(t) - val_at(t + 2 * Δt))\n", + " # if np.isnan(ret):\n", + " # print(f\"Problem! K = {val_at(t):g} - {val_at(t + Δt):g} / {val_at(t):g} - {val_at(t + 2 * Δt):g}\")\n", + " return ret\n", + " # Ks = np.array([val for t0 in np.arange(tmin, tmax, data.dt) if (val := K(t0)) > 0.5 and val < 1])\n", + " Ks = np.array([K(t0) for t0 in np.arange(tmin, tmax, data.dt)])\n", + " # if K >= 1 or K <= 0.5:\n", + " # print(f\"Problem! K = {K:g} = {val_at(tmin):g} - {val_at(tmin + Δt):g} / {val_at(tmin):g} - {val_at(tmin + 2 * Δt):g}\")\n", + " tau = np.median(-Δt / np.log((1 - Ks) / Ks))\n", + " return tau" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# a function for generating a noisy exponential decay\n", + "\n", + "t = np.linspace(0, 0.1, 1000)\n", + "yoffset = -0.078\n", + "yscale = 0.01\n", + "tau = 0.01\n", + "noise = 0.001\n", + "\n", + "def mk_data(params, noise):\n", + " params = [params[x] for x in ('yoffset', 'yscale', 'tau')]\n", + " y = exp_decay(t, *params) + np.random.normal(0, noise, len(t))\n", + " return TSeries(y, time_values=t)\n", + "\n", + "params = {'yoffset': yoffset, 'yscale': yscale, 'tau': tau}\n", + "data = mk_data(params, noise)\n", + "data.t0 = 1\n", + "plt.plot(data.time_values, data.data)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Rough parameter estimation for initialization" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.078 0.01 0.01\n", + "-0.07764197628721943 0.005995787866906341 0.005105105105105201 1.0\n" + ] + } + ], + "source": [ + "def estimate_exp_params(data):\n", + " # wrap param estimation to look like other fitters used here\n", + " params = exp_fitting.estimate_exp_params(data)\n", + " # params['tau'] = estimate_tau_Šimurda(data, params['tau'])\n", + " return {'fit': params}\n", + "est_params = estimate_exp_params(data)['fit']\n", + "print(yoffset, yscale, tau)\n", + "print(*est_params)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# function for testing a fitting algorithm against a grid of exponentials varying in scale, offset, and tau\n", + "\n", + "def check_accuracy(method):\n", + " taus = 10 ** np.linspace(-3, -1, 5)\n", + " yscales = np.append(10 ** np.linspace(-3, -1, 5), -0.01)\n", + " noise = 0.001\n", + " results = np.zeros((len(taus), len(yscales)), dtype=object)\n", + " for i,tau in enumerate(taus):\n", + " for j,yscale in enumerate(yscales):\n", + " yoffset = np.random.uniform(-0.1, 0.1)\n", + " data = mk_data({'yoffset': yoffset, 'yscale': yscale, 'tau': tau}, noise)\n", + " result = method(data)\n", + " est_params = result['fit']\n", + " results[i, j] = {\n", + " 'nrmse': normalized_rmse(data, est_params),\n", + " 'params': est_params,\n", + " 'data': data,\n", + " 'result': result,\n", + " 'fit_data': exp_decay(data.time_values, *est_params),\n", + " }\n", + " \n", + " fig, ax = plt.subplots(len(taus), len(yscales), figsize=(15, 15))\n", + " for i in range(len(taus)):\n", + " ax[i, 0].set_ylabel(f\"tau={taus[i]:.3f}\")\n", + " for j in range(len(yscales)):\n", + " ax[0, j].set_title(f\"yscale={yscales[j]:.3f}\")\n", + "\n", + " result = results[i, j]\n", + " data = result['data']\n", + " ax[i, j].plot(data.time_values, data.data)\n", + " ax[i, j].plot(data.time_values, result['fit_data'])\n", + " ax[i, j].set_title(f\"nrmse={result['nrmse']:.3f}\")\n", + "\n", + " return results\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Check performance of rough estimation algorithm\n", + "(we expect this to be a mess, but to come reasonably close in all cases)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/luke/docs/AIBS/neuroanalysis/neuroanalysis/fitting/exp.py:15: RuntimeWarning: divide by zero encountered in divide\n", + " return yoffset + yscale * np.exp(-(t-xoffset) / tau)\n", + "/home/luke/docs/AIBS/neuroanalysis/neuroanalysis/fitting/exp.py:15: RuntimeWarning: invalid value encountered in divide\n", + " return yoffset + yscale * np.exp(-(t-xoffset) / tau)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "check_accuracy(estimate_exp_params);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test the fitting.exp.exp_fit() function" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# def logged_exp_decay(data):\n", + "# log = []\n", + "# def _exp(t, yoffset, yscale, tau):\n", + "# result = exp_decay(t, yoffset, yscale, tau)\n", + "# nmse = normalized_rmse(data, (yoffset, yscale, tau))\n", + "# log.append({'params': (yoffset, yscale, tau), 'nmse': nmse})\n", + "# return result\n", + "# return _exp, log\n", + "\n", + "def curve_fit(data):\n", + " # initial_guess = estimate_exp_params(data)['fit']\n", + " # bounds = ([-np.inf, -np.inf, 0], [np.inf, np.inf, np.inf])\n", + " # fit = scipy.optimize.curve_fit(\n", + " # f=exp_decay, \n", + " # xdata=data.time_values, \n", + " # ydata=data.data, \n", + " # p0=initial_guess, \n", + " # bounds=bounds, \n", + " # # ftol=1e-8, gtol=1e-8,\n", + " # )\n", + " # return {'fit': fit[0], 'result': fit}\n", + " result = exp_fitting.exp_fit(data)\n", + " params = result['fit']\n", + " return {'fit': params, 'result': result}\n", + "\n", + "results = check_accuracy(curve_fit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test fitting.exp.Exp()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/luke/miniconda3/envs/neuroanalysis/lib/python3.11/site-packages/lmfit/minimizer.py:141: RuntimeWarning: overflow encountered in multiply\n", + " return (r*r).sum()\n", + "/home/luke/docs/AIBS/neuroanalysis/neuroanalysis/fitting/exp.py:15: RuntimeWarning: overflow encountered in exp\n", + " return yoffset + yscale * np.exp(-(t-xoffset) / tau)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from neuroanalysis.fitting.exp import Exp\n", + "\n", + "def exp_fitter(data):\n", + " init = estimate_exp_params(data)['fit']\n", + " params = {\n", + " 'xoffset': data.time_values[0],\n", + " 'yoffset': init[0],\n", + " 'amp': init[1],\n", + " 'tau': init[2],\n", + " }\n", + " fitter = Exp()\n", + " result = fitter.fit(x=data.time_values, data=data.data, params=params)\n", + " fit_params = result.best_values['yoffset'], result.best_values['amp'], result.best_values['tau']\n", + " return {'fit': fit_params, 'result': result}\n", + "\n", + "results = check_accuracy(exp_fitter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test a simpler method: minimize only over tau\n", + "..while directly computing the optimal yscale / yoffset" + ] + }, + { + "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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from neuroanalysis.fitting.exp import exact_fit_exp\n", + "from neuroanalysis.fitting.exp import best_exp_fit_for_tau\n", + "\n", + "\n", + "def calc_exp_error_curve(tau, data:TSeries):\n", + " \"\"\"Calculate the error surface for an exponential with *tau* and noisy *data* \n", + " \"\"\"\n", + " taus = tau * 10**np.linspace(-3, 3, 1000)\n", + " errs = []\n", + " for i in range(len(taus)):\n", + " yscale, yoffset, err, exp_y = best_exp_fit_for_tau(taus[i], data.time_values, data.data)\n", + " errs.append(err)\n", + " return taus, errs\n", + "\n", + "test_params = {'tau': 40e-3, 'yscale': 5e-3, 'yoffset': -70e-3}\n", + "test_exp = mk_data(params=test_params, noise=1e-4)\n", + "\n", + "taus, errs = calc_exp_error_curve(test_params['tau'], test_exp)\n", + "\n", + "fit = exact_fit_exp(test_exp)\n", + "memory = fit['memory']\n", + "fit_yoffset, fit_yscale, fit_tau = fit['fit']\n", + "fig, ax = plt.subplots(2, 1, figsize=(10, 6))\n", + "\n", + "ax[0].plot(test_exp.time_values, test_exp.data, label='data')\n", + "ax[0].plot(test_exp.time_values, memory[fit_tau][3], label='fit')\n", + "ax[0].legend()\n", + "\n", + "ax[1].plot(taus, errs, label='error surface')\n", + "ax[1].set_xlabel('tau')\n", + "ax[1].set_ylabel('error')\n", + "ax[1].set_xscale('log')\n", + "ax[1].scatter(list(memory.keys()), [v[2] for v in memory.values()], color='r', label='tested taus')\n", + "ax[1].axvline(fit_tau, color='r', label='fit tau')\n", + "ax[1].axvline(test_params['tau'], color='g', linewidth=3, label='correct tau')\n", + "ax[1].set_xlim(taus.min(), taus.max())\n", + "ax[1].legend()\n", + "# for k,v in memory.items():\n", + "# print(k,v)\n", + "# print(taus[np.argmin(errs)])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/luke/miniconda3/envs/neuroanalysis/lib/python3.11/site-packages/scipy/optimize/_hessian_update_strategy.py:182: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", + " warn('delta_grad == 0.0. Check if the approximated '\n" + ] + }, + { + "data": { + "image/png": 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s3bp12Z07d7JHjx5lJ0yYwL755ptqhSJKJ/NkWZbdtm0bKxAI2CFDhrB79uxhIyMj2b///pudNWsWO378+DKff2FhIdusWTPW19eX3bFjBxsREcEOGDCAtbOzY8+cOcOxfffdd1mhUMjZlpKSwnp7e7MtWrRg9+/fz4aHh7Ndu3ZlXVxc2Lt373Jst23bxgJgv/jiC/b06dPs+vXr2WrVqrE9evRQG1d4eDi7Z88eduPGjSwA9qOPPmL37NnD7tmzh83LyyvzeRO6oblhfXNj3Lhx7Lfffsvu3r2bPXPmDPv333+zQ4YMYQGw33//fZnPmdAfmh+Wmx937txRXv/9/f1Zd3d35d937tzh2Pbo0YOtXr06u379evbUqVPsuHHjWADs9u3by3zOhH7Q3LDM3Dh06BDbr18/9o8//mAjIiLY8PBwdt68eWyNGjXYN954g83MzFTaLl68mAXA9uzZk42KilL7ISwDzY2yzY28vDzltf/bb79lAbBz5sxh9+zZw4aHh3NsDXnnoPtG+fC6zwdt6PudHD16NCsUCtknT54otxkyz548eaKcUz179mQBKP++cuUKx3bs2LGso6Mju2jRIvbMmTPs9OnTWYZh2AULFpjvgzADJO69BtSrV49t0qQJZ9vLly/ZUaNGsY0bN2YrV67MVqlShW3ZsiW7bNkyViKRcGy3bt3Kvv3226yTkxNbpUoVtk2bNuymTZuU+2NjY9kePXqwLi4ubPXq1dmPPvqITUhI0OvCwrIsGxkZyfbp04etUaMGa29vz9aqVYvt06cPu2fPHpOcf3JyMjtixAi2Ro0arJOTE9uhQwc2IiJCza5bt268VXQePXrEfvjhh2zVqlXZSpUqse+99x4bHR3N29fOnTvZli1bsg4ODqyXlxc7ceJENicnR82ubt26Zao0RJgGmhvWNTc2btzIdunShXVzc2Pt7OzYatWqsd26dWO3bdtmkvMlDIPmh2Xmx+zZszXeD0pXUs/JyWEnTpzIenl5sQ4ODmzLli3ZXbt2meR8Cf2huWH+uXH37l128ODBbN26dVknJyfWycmJbdy4Mfv999+z6enpvP1o+iEsB80N4+dGfHy8xu8wXxV1fd856L5Rfrzu80ET+n4nR44cyTtufeeZ4rz5fkaOHMmxLSoqYmfPns3WqVOHdXBwYN988012+fLlpjxti8CwLMvq6+VH2B43b95Eq1atsGrVKgQHB5f3cAjCaqC5QRCaoflBEPzQ3CAIfmhuEEQJNB+I8oDEvQrK48eP8fTpU0yfPh0JCQl49OgRKlWqVN7DIohyh+YGQWiG5gdB8ENzgyD4oblBECXQfCDKE0F5D4AwD/Pnz0ePHj2Qm5uLPXv20EWFIIqhuUEQmqH5QRD80NwgCH5obhBECTQfiPKEPPcIgiAIgiAIgiAIgiAIwkYhzz2CIAiCIAiCIAiCIAiCsFFI3CMIgiAIgiA0snr1avj5+cHJyQn+/v44e/asRtukpCR88sknaNSoEQQCASZNmqRmc+fOHQwaNAj16tUDwzAIDQ013+AJgiAIgiBeA0jcIwiCIAiCIHjZvXs3Jk2ahBkzZiAmJgZdunRBr169kJCQwGsvEong7u6OGTNmoFWrVrw2+fn5qF+/Pn755Rd4eXmZc/gEQRAEQRCvBZRzz0LIZDK8ePECLi4uYBimvIdDEAAAlmWRk5MDHx8fCATlo/XT3CCsEZobBCHn3XffRatWrbBs2TIA8rnRtm1bDBo0CL/88ovWY7t3747WrVtr9cyrV68eJk2axOvhpw2aH4Q1QvcOguCH5gZB8GPKuWFnojEROnjx4gV8fX3LexgEwcuzZ89Qu3btcumb5gZhzdDcIAggOjoaGzdu5Gw7c+aMRccgEokgEomUfycmJqJp06YWHQNB6AvdOwiCH5obBMGPKeYGiXsWwsXFBYD8H61q1arlPBqCkJOdnQ1fX1/l97M8oLlBWCM0NwhCnj+vcePGOH78ONq3bw+gZG6kp6dbdCwLFy7E3Llz1bbT/CCsCbp3EAQ/NDcIgh9Tzg0S9yyEwvW3atWqdDEhrI7ydE2nuUFYMzQ3iNeZ3NxcAECVKlXUvoOWnhvTpk1DSEiI8m/FwzDND8IaoXsHQfBDc4Mg+DHF3CBxjyAIgiAIglDDzc0NQqEQycnJavs8PDwsOhZHR0c4OjpatE+CIAiCIAhbgarlEgRBEARBEGo4ODjA398fERERavvatWtXDiMiCIIgCIIg+CDPPYIgCIIgCIKXkJAQDB8+HG3btkVAQABWrlwJABg9ejQAebhsYmIitm7dqjzm+vXrAORhvampqbh+/TocHByUBTCKiooQGxur/D0xMRHXr19HlSpV8MYbb1jw7AiCIAiCICoGJO4RBEEQBEEQvAwZMgTp6emYN28ekpKS0KRJEwBAnTp1AMiLbiQkJHCOadOmjfL36Oho7Ny5E3Xr1sWTJ08AyCsWqtosXrwYixcvRrdu3SxehZcgCIIgCKIiQOIeQRAEQRAEoZHg4GAEBwcDkBeycHV1Ve7bvHmzmj3Lslrbq1evnk4bgiAIgiAIQn8o5x5BVEBe5RVh2r6buJaQUd5DKTM7Lj1F6IkH5T0MgrAushKB7YOB3cPLeyQEYXWk7xyHjPUfIO/5nfIeCkFYF1f+ALYPAm78Wd4jIQiCqBCsOv0Im8/Hl/cwAJC4RxBWwYvMAmyNeoL8IolJ2pv9zx3suvwMA1dfMEl75cmM/bcReuIh4lJzy3soBGE9SAqBRxFA3JnyHglBWB0FD/9D9Rdn8TzpRXkPhSCsiqRH14FHJ5AUd6u8h0IQBGHzJGYWYNGx+5hzKBYyWflHJFBYLkFYAR+sOIdXeUV4+DIX8z9sXub2HqdUDCFMNWyrQCwtx5EQhJXBFK/NyWheEERpZMVr1yzND4Lg8ORVIbwBJKTlwru8B0MQBGHjFKg45pS/tEeeewRhFbzKKwIAnH2YWs4jsS7E0pLLpJ2ALlcEoUDCMgBIvCAIPthi8VsmpflBEKqwjLD4F1n5DoQgCKKCYQ25hOltmSAIq0UiK3n4FAqYchwJQVgXz7PlCwISqbicR0IQ1ger8NyTmibVBUFUGBRe3ywJ3wRRFgqKpCiSkEhOlGAFUbkk7hEEYb2oeu7ZC0ncIwgFAoE8q4aAvC8IQg1FWK6MBAyC4CKQe+4x5PVNEEZTKJaiyY9H0fnXU+U9FMKKkJHnHlFRMVVhCMI4yv/SYhokUvLcs2ZYloVIQi8I5QFT/IImZFjACh4mCMKakBWHHrIUlksQHBQh6ywtDBGE0dxPzgEApOSIytTO9WeZ+Dn8LvJE+r83m7pow/Vnmei+6DQiYl+atF2ifCBxz0qIT8vDoRsvDIrVvhz/CsfuJJtxVMYxY/8tNP3xGGISMsp7KISNI1G5gTEMiXvmgmVZFBpRsGTo+otoMec4sgooNNTSCIrFPQCUO4kgSqEUMMg7iSA4MMVzg6H7BkEYjaleST5cdR7r/4vDsogHetlfeJyGVvOO4+D1RL3sZTIWV5+80ioejt1yBU/S8zFu61W92iQ0Q557hJJ3Fp/B/3bFIPxWMnL1VO8/XheFL7dFIyE938yjM4wdlxIAAMtPPiznkby+VBQZTCylh09L8PWuGDSedRTPXhl2LbkU/wpFEhnO3E8x08gITbAClWL3MvKUJghVqFouQWig2KuVoZB1gjAaRuVNS5djjkzG4mJcOrILNS+EP0zJ1avfURuvIKdQgm/+vK6X/Y7LCRi8NgrDNlzUaJNfpN+1QCpjcf1Zpk2+m52IfYkPV51HfFqeWfuxAm2PxD1rY8LOa2g++xieZ+j/kv0yp9CMIyIsiam806zg2mISVOppWEUFoorKkZtJAIDtl56W80gIfREIVcU9ekkjCFUUFUFZEr4JggNb7PVNYbmENlavXg0/Pz84OTnB398fZ8+e1WofGRkJf39/ODk5oX79+li7di1vmwDg6ekJX19fTJ48GYWFhWo22vplWRZz5syBj48PnJ2d0b17d9y5c6eMZ2s4qq9rul5P/rzyDEPXX8SAVefNOyge9lx9BgC4+TxLo42+b56Ljt3Hh6vOY+reWyYYmWUZu/Uqrj/LxKTd183aD3nuERo5eP2F3rYCKw1XLP+vN2HrsPQtIgheGKHK7Zs8MAiCg6w49FBmgx4GBGFOKCyX0MXu3bsxadIkzJgxAzExMejSpQt69eqFhIQEXvv4+Hj07t0bXbp0QUxMDKZPn46JEydi7969SpsdO3Zgzpw5AIDLly8jLCwMu3fvxrRp0wzq97fffsPSpUuxcuVKXLlyBV5eXujRowdycnLM82FoQPXVWyHosCzLmw/vQHEI7ePUsnuNGfpeZEqtaW3kYwDA3mvPTdeohcnML9LLbtXpRxi6PsrglEVULZcwCVRngChNRflKqN6UrGAxpMLDVJhvTsWHYchzjyA0wSrCclny3CMIDhSWS+hg6dKlGDNmDMaOHYsmTZogNDQUvr6+WLNmDa/92rVrUadOHYSGhqJJkyYYO3YsRo8ejcWLFyttoqKi0L59ewBA3bp1ERgYiGHDhuHq1ZI8b7r6ZVkWoaGhmDFjBgYOHIjmzZtjy5YtyM/Px86dO834iaij+rwsY+VjG7jmAj5YcY4j8EmkMmTrkZe6PP10rC2nefTTDDx8aVmxtjSLjt3HxbhX2HdNv9yGSlh5sZU+y8/i5N3yKVBC4l4FwNomJVH+VBQdrKKcB0GYGiqoQRCaUYTlgqrlEgSXYs89um8QfBQVFSE6OhqBgYGc7YGBgbhw4QLvMVFRUWr2QUFBuHr1KsRiubDVuXNn3LhxQ7k/Li4O4eHh6NOnj979xsfHIzk5mWPj6OiIbt26aRybSCRCdnY258cUlPbcyyuSIiYhE7FJ2UjKLgk17rP8HO4l6xaqyvNN3ppUhJfZhRi05gJ6LPvPLO3r4yiSlV8ixhruucdiws5ruPMiG2O2XC3u07JvszYn7pk6B4BYLMa8efPQoEEDODk5oVWrVjh69CjHZuHChXj77bfh4uICDw8PfPjhh7h//77Jz80QVL8o5LlnfliWxc5LCbjxLLO8h/JaQXn2LIux6wS0wGB5GFVxjzz3CIIDVcslCA0o7h0k7hE8pKWlQSqVwtPTk7Pd09MTycnJvMckJyfz2kskEqSlpQEAhg4dihkzZgAAatasiQYNGuCdd97B1KlT9e5X8V9DxrZw4UK4uroqf3x9fXV+BvpQOuee6lPwfpWw1fsW8kB7nJqLrVFPUCThzmtbS2+0LjKuvIeAEZsuG32sjGWRpeKpGX4rCW3mR+DcwzRTDE0vbErcM0cOgJkzZ2LdunVYsWIFYmNjMX78eAwYMAAxMTFKm8jISEyYMAEXL15EREQEJBIJAgMDkZdn3oor2lCN6bbWnHsViVP3UjB9/y30L4dkqK8zqrcka9D5/rr6DD8djiXR0UrIyMjA8OHDlQ9tw4cPR2ZmptZj9EnGLBKJ8P333wMAvL290a9fPzx/XvKw9uTJE4wZMwZ+fn5wdnZGgwYNMHv2bBQVcXN5MAyj9sOXZNoYBAIGUlZ+7aeiAQTBpSQsl8Q9guAgkM8NAc0NQgulF21ZltW6kMtnr7r9zJkzyjDd//77D/v27cPhw4cxf/58g/s1ZGzTpk1DVlaW8ufZs2caz8EQuGG5LEfsW3z8gUn6MIT3lkTix4N3sOXCE852vV5XrEhG2Hg+Xvm76rvWq7wirIt8jBQTFhGVylhEPkjFq7wiZBeKsTXqCVJzRAY78nBSSJXaF7zjGjLzxfgs7FKZx6svNiXumSMHwLZt2zB9+nT07t0b9evXx1dffYWgoCAsWbJEaXP06FGMGjUKzZo1Q6tWrbBp0yYkJCQgOjrabOeqs6y2yv6KpO2tOv0IwTuiIbWGjJQqPHipX4ny0uQUirEt6olJL0a6ePAyB3eTTON2Xt4YIqKxLItXefolSjWWKX/fxB/n4nHhcbpZ+ykvbO1S8sknn+D69es4evQojh49iuvXr2P48OFaj9EnGfOkSZNw+PBhAPLrf25uLj744ANIi0P87t27B5lMhnXr1uHOnTtYtmwZ1q5di+nTp6v1t2nTJiQlJSl/Ro4caZJzZxgGUpB3EkHwUVItl+YGQajCUFguoQU3NzcIhUI1T7iUlBQ1jzkFXl5evPZ2dnaoWbMmAGDWrFkYMmQIAKBZs2YYMGAAfv75ZyxcuBAymUyvfr28vADAoLE5OjqiatWqnB9TwFdQQx9mHbiN43f4vQxNwXUjIsz4nv3Tc0Xl7sigKgV8tT0aC/+9h883XSlTm6qejH9dfYaRGy/jrfkRaDnnOH48eAcjNhrutaf6KVG1XAMwVw4AkUgEJycnjo2zszPOnTuncSxZWfJy0jVq1NBoY64YfwUccc/mXsk1s+jYfYTfSsZ/D1LLeygcjBVQZ+y/jVkH7+CTDZZT7APNlKegPOCuhmi/YH635ybemh+BSAt8dzL0rLZEmI+7d+/i6NGj+OOPPxAQEICAgABs2LABhw8f1pg2QZ9kzFlZWQgLC8NPP/0EAGjVqhW2b9+OW7du4cSJEwCAnj17YtOmTQgMDET9+vXRr18/fPfdd9i3b59an9WqVYOXl5fyx9nZ2STnL2AAKeQChkxKnnsEoYoiLJdy7hFEKSgsl9CCg4MD/P39ERERwdkeERGBjh078h4TEBCgZn/8+HG0bdsW9vb2AID8/HwIBFzZQSgUgmVZsCyrV79+fn7w8vLi2BQVFSEyMlLj2MwBy7L47WjJc6a8oIZ+x267+BRfbItWtmN439r32wnLpglceJyGiNiX8P/pBKb8fbNMbZUVVUefS/GvAAB3XphOTznGI7Ia4xyjqsmUDtEuD2xG3DNXDoCgoCAsXboUDx8+hEwmQ0REBA4ePIikpCTeNlmWRUhICDp37ozmzZtrHK+5YvxLxlHyu8BK/xXLIl4XGJjAko8JO6/hi61Xy3XlISJWXinnUYpxnn8AIJa+vg9ghvzLKUqzrzz10DyDIQAA+649x5Lj9znzqjxuZFFRUXB1dVVWXwOADh06wNXVVeOCjz7JmKOjoyEWi/Huu+8qbXx8fNC8eXON7QJyUZBvwefrr7+Gm5sb3n77baxduxYymWnmM3nuEYRmlJ57JGAQZqZ0LnBt94mkpCR88sknaNSoEQQCASZNmsRrt3fvXjRt2hSOjo5o2rQp9u/fb7oBF88NAc0NQgMhISH4448/sHHjRty9exeTJ09GQkICxo8fD0Ae6jpixAil/fjx4/H06VOEhITg7t272LhxI8LCwvDdd98pbfr27YuNGzcCkKc2iYiIwKxZs9CvXz8IhUK9+mUYBpMmTcLPP/+M/fv34/bt2xg1ahQqVaqETz75xFIfD64+zcAJlUqoLGtcZrtFx8qWv3/QmgvIKeRW4rUXGi4KqIY0f7LhEsZtlReC2BP9XNMhFuFWYibGbrmKWBMKes9eFSjfXxztdH9Wf13VHcat+lhvBY57tiPuKTB1DoDff/8dDRs2ROPGjeHg4ICvv/4an3/+ufJCU5qvv/4aN2/exK5du7SO01wx/gpUVWLKuadOTqEYR24m4XjsS6TkiMptHGVNZPrfg1Q0nPEvNqvkIHid4Hju6flRRj/NMM9gyonsQjGin2ZYRKTW51IS8tcNrDj1CNcSyvdzTk5OhoeHh9p2Dw8PrQs+gPZkzMnJyXBwcED16tU12pTm8ePHWLFihfIBVMH8+fOxZ88enDhxAkOHDsW3336Ln3/+WeM5GeLxzTCATCnukeceQaii9Nwj4ZswI3y5wAcPHqzRXiQSwd3dHTNmzECrVq14baKiojBkyBAMHz4cN27cwPDhw/Hxxx/j0iXTRIAw5LlH6GDIkCEIDQ3FvHnz0Lp1a/z3338IDw9H3bp1AchFatV8935+fggPD8eZM2fQunVrzJ8/H8uXL8egQYOUNjNnzsTXX38NAGjfvj3GjBmDoKAgrFu3Tu9+AWDKlCmYNGkSgoOD0bZtWyQmJuL48eNwcXEx98eiJD2X+14Zn5anFo5ZKJYiT6T92Wz1mcdlGkf00wz8sJfrXfd39HM8TjXMocQUMsLGc/FYflK7c0X00wz8e4vfeYqPQWuicOLuS/Rerr14qiosy2L6/ltYelyzcLo/JhEA4GDHr/Woci85R6eDjuq7PoXlGoC5cgC4u7vjwIEDyMvLw9OnT3Hv3j1UqVIFfn5+au3973//wz///IPTp0+jdu3aWsdrrhh/BdyCGiZt2ioo69wwdSEGS33EpfuZtPs6AGDOoVgLjcC6MEYctUS6RnNcu7MLxfj2rxtqIem9fz+LQWsu4IgBN0RL8CpPrNvICObMmcNbiEL15+pV+aoi38KOrgUfvuP0OUaTzYsXL9CzZ0989NFHGDt2LGffzJkzERAQgNatW+Pbb7/FvHnzsGjRIo19GOLxLSDPPYLQCOXcIywBXy7wWrVqabSvV68efv/9d4wYMQKurq68NqGhoejRowemTZuGxo0bY9q0aXjvvfcQGhpqmkEXzw0GNDcIzQQHB+PJkycQiUSIjo5G165dlfs2b96MM2fOcOy7deuGa9euQSQSIT4+Xm2x087OTlkZ9+XLl0hISMCqVatQrVo1vfsF5M9vc+bMQVJSEgoLCxEZGak1ks4clH4WHLD6AiLvc5/dW887jmazj5W5r5TsQkzYeQ2X4uS5viWlXnLCb6kvOr+3JFL5uz7vK7recZeffIg/zmquYiuVsZh3OBZLIx7gRWaBRrtBay7gqx3X8JCngvCrvCIcvV32XISPU3Ox81IClp96pNEm5K8biE/LQ0q2fvnw01TE3E3n4zFkXRRyVYRb1c9YZqQXpymxGXHPXDkAFDg5OaFWrVqQSCTYu3cv+vfvr9zHsiy+/vpr7Nu3D6dOneIV/kyNrsnIVYYrnrpX/lPDOjDEW0sRGvJ08QAkbf4Ghc9ua7WPjIxU3jRbtmypVsVTLBZj3rx5aNCgAZycnNCqVSscPXpUY7+KkJSzZ/VfYdEFXwWiQrEUU/fexEkVl3hz8jQ9DytOPuSUNjcHyyIeYO+152rJXJ9nyG+U4RYQ96whf+fXX3+Nu3fvav1p3rw5vLy88PKl+ncgNTVV64IPoD0Zs5eXF4qKipCRkaHRRsGLFy/wzjvvICAgAOvXr9d5bh06dEB2djbvuAHDPL7lOfeKxT3KK0YQXBiFdxLNDcI8aMoFrprSwRg05QvXFu5rCIyyWi557hGEMfA9Kf9vVwzn70KxYfNL0wLz9P23cORmEoasv2hQe38Xh9SW9W06KasASyMe4KcjdzXaqL6r6pNW61lGPv658QKfb7qMrHz5u9WHq85j/PayFyrV93Mfs+WKMo+fIcw9FItL8a84VYlV31Un7LiG1HKMGARsSNwDzJMD4NKlS9i3bx/i4uJw9uxZ9OzZEzKZDFOmTFHaTJgwAdu3b8fOnTvh4uKC5ORkJCcno6BAszptblTvyRXRc8/aMNZl2VLeuaqhIT6jlsOxdjOk7JkDSXYKr318fDx69+6tFMa//fZbTJw4EXv37lXazJw5E+vWrcOKFSsQGxuL8ePHY8CAAYiJKbmB8YWk9OrVi+OuXxb4Pr8/zsbhzyvPMGbLVZP0oYs+y89hScQDzDxQIpaaIxI+MaP8ridlxZSfh5ubGxo3bqz1x8nJCQEBAcjKysLlyyVi6KVLl5CVlaVxwUefZMz+/v6wt7fH6dOnlTZJSUm4ffs2p93ExER0794db731FjZt2qSWKJqPmJgYODk5qa1UKzDE41vAMCphuSRgEIQqirBcmhuEudCUC9zd3b1M7WrKF64pLQRgYBE/xb2KxD2C0Iv4tDxOKK6uSA99OPcwTS+7hFf5RrX/3Z4bnIIUpYlPy8PB64k6I1dyCnWnfTH0VVcklmHirhicvp+KZSceADD+PLWOS8tLeFxqnt7t8H06ql5/qg5XN55n6d2uubApcc8cOQAKCwsxc+ZMNG3aFAMGDECtWrVw7tw5zsvXmjVrkJWVhe7du8Pb21v5s3v3boude2lsIedeWXQtU4pituwFqO/IVUND7N18UeP9LyB0cUNOTDiv/dq1a1GnTh388ssvAICRI0di9OjRWLx4sdJm27ZtmD59Onr37o369evjq6++QlBQEJYsWcLbryIkxdfXF2vWrDH6nFVR/bdTXKSTsvRzozYVCtfry/HpKmOx6BAIHpo0aYKePXti3LhxuHjxIi5evIhx48bhgw8+QKNGjZR2jRs3ViYj1ycZs6urK8aMGYOZM2cCAG7cuIHPPvsMLVq0wPvvvw9A7rHXvXt3+Pr6YvHixUhNTVUu+ig4dOgQNmzYgNu3b+Px48f4448/MGPGDHzxxRdwdHQ0yWeg8NyTUc49guAioJx7hGXQlNvb1G1qewE3qIgfYyf/D0jcIwhdZOYXISj0P/j/dEJZ4NAUb91T93Fz5Z26V+KMcfxOMupNPYJlEQ/K1EfX306jSMJ/D3xn8Rl88+d1HLqZpPV8Apf9p7MfVU1Cn8/mqx3XlL+n5prWy+1JeoloJ2OhM++hviiqOivYEvVUpR/reim0K+8BGEpwcDCCg4N5923evFltmyIHgCa6deuG2Fjt+czKo9qqrh45E8k6tb0KRXmEK6ZkFyIzX3coqCI0RJHLQoGzXxuIEu/xHqMp7CMsLAxisRj29vYQiURwcnLitunsjHPnzmntNzAw0GThI3xTj77vZUefHHO2wI4dOzBx4kTld7lfv35YuXIlx+b+/fvIyipZSZsyZQoKCgoQHByMjIwMtG/fXi0Z87JlyyCTybB+/XoEBQXhvffew+bNm5WFlo4fP45Hjx7h0aNHavlXFfcLe3t7rF69GiEhIZDJZKhfvz7mzZuHCRMmmOTcKeceQWiGpbBcwsxoygWelqafR44mNOUL15RuApBHLoWEhCj/zs7O1ijwlYTl0twgCF2k5YpQJJGLehn5RfBwcYIegRo6KShSn3/3krPR2KsqvtgmD0/9/eRDNPSootw/6c8YtWO0kciT/y76aQYcVCrqTtxlWJt8lEUmEekRxmsI0/beUv7+y793seFsPLaPaV+mNlkAH6w4h5pV+BfmrUvas0Fxj5AjtTKV2NRYy9mxLAuRxPjVzbKch+LirgtNoSHCytUhzeMXtjWFfUgkEqSlpcHb2xtBQUFYunQpunbtigYNGuDkyZM4ePAgpMX5vTT1q0/4iEhUslKjNXxEBWv5TpQX4beSkZUvhmsle93GWrjwKA3BO69hwYct0KelN2efIXqfJRY9HqXkonZ1ZzjZ81e0qlGjBrZv3661jdLjVCRjnjNnjsZjnJycsGjRIqxfvx7Jyclq4bGjRo3CqFGjtPbbs2dP9OzZU6tNWRAwgIxl5MukMvLAIAgOCnGPhG/CTKjmAh8wYIByu2pKB2NQ5AufPHmyctvx48c1ppsA5Ckd9PUIZ5Rhua/7UxVB6Eaq8nileNS6najfe4s2+BbY80Tq9yvVWXrg+osy9ZldKMagNaZxvlBF16XkdmIW1v/HX5DjxF3+9FH6cOXJK1x7moEPWvngSvwrfNDSm5Pzb8PZeADAZ2FlqzT+8GUO7rzQ/G9eHk5g2iBxz0axsu+RybGWiRLy1w3sj0nEZx3qWLzv688yDbJXu1GwLLQ5SGsKJVFs//333zFu3Dg0btwYDMOgQYMG+Pzzz7Fp0yad7egKH5k7d66u0yk5BSvEHMPSR1ibdzgWSz5uZVT7mflFCN5xDRcey8OLJ+y8hj4t+3DHYFTL5vFsPX0vBZ9vvoLmtari8P+6mLx9W4dhGEggFzBkUvMWeyEIm4OhvGKE+QkJCcHw4cPRtm1bZWGl58+fK/dPmzYNiYmJ2Lp1q3Lb9evXAQC5ublITU3F9evX4eDggKZNmwIAvvnmG3Tt2hW//vor+vfvj4MHD+LEiRPKqImywhSH5QqoWi5B6EQ1b52kWN1bWsZwWYA/X/7ea88xde9N9R06KNTT+y0jr8jgtnUhlbGcUNjSHL2dhPHbNUdQloWP1kYBABb+K49Se56RXy7RXVpSG5YLNpVz73XCsGq51kmSlnLYtsL+mEQAwPaLpikQYQ40hYZI8zMhrFyN9xhNYR92dnaoWbMmAHlS6AMHDiAvLw9Pnz7FvXv3UKVKFWW1aE396hM+om9FUG7OPfl/raGia3nxKDXX6GNDTzxUCnu2wJ5o+ffCFCukFREBAyqoQRAaoLBcwhLw5QLfs2ePcn/pXOAA0KZNG7Rp0wbR0dHYuXMn2rRpg969eyv3d+zYEX/++Sc2bdqEli1bYvPmzdi9ezfaty9baJkCheceQ8I3QehE9X170JoL2Bv9XIu1/qTwVFTdeSkBD1O4z/mPUnQ/9x+5maRXn9oq3hqKIlR54q4Y9Pr9rHK7qnNHnkiil7AnM5E6tvj4A7O8I846eEfrfm2FS8oDEvdsFInUur5IClQTV5a+QBF6oOWaFP30FeYfjkV+ETc5qGpoiCqFT67DsVZj3rYUYR+qHD9+HG3btoW9PTfs08nJCbVq1YJEIsHevXvRv39/rf1GREToDB/RtyKoDWjY5cqRm0l4d8kZ3EvWLYBlF+rh3aVlySs+LQ8XHpUtl5Ah0L+9dhjKuUcQmhFQWC5hGYKDg/HkyROIRCJER0ejU6dOyn2bN2/GmTNnOPaKxOyqP0+ePOHYDB48GPfu3UNRURHu3r2LgQMHmm7AxXODCmoQhG5UhZuX2SJ8u+dGOY6GnyKpfnM5Ivalyfp8c+a/2HQ+Hkdu8QuLSVkFaDb7mF5tTdp93WTjKg/0Fff2xzzH2YepZh4NheXaLFkFJS/q1vQSLNbzAkMYzqA1cvdje6EAU3txRTvV0BBxWhFybhyFJDsVLq3lq8GlQ0PGjx+PlStXYvr06QDklXHDwsKwa9cuZZuXLl1CYmIiWrdujcTERMyZMwcymQxTpkzh7VcRkpKQkIDx48eb5JxZnr/Kqw6E6mqQtYSNT9gpXxH7384YRIR002pb1tWsdxaf4fyt7ydw41km9sckYnKPN+HqXLZ8gQQXtjj0UCKharkEwYFy7hEEL4xC3CPPPYLQiS3kuE/j8QK0BHMPqRckPXn3JcZ2qY8v9cwbDwD/3DAsl+AnGy4iVdM5W/AdsUgiQ1quSG9xb/LuG3irTjV0aehu1nGRuGfFHLyeiOUnH2LdcH+84eHC2ZddYJ05lkx1DSxrO5bWf7IKxLiWkIEub7jBTqUKkTmSs8XxhGYOGTIE6enpmDdvHl48T4SDW114fDQHdq4eANRDQ/z8/BAeHo6JEycCAH777TcsX74cgwYNUtoUFhZi5syZiIuLQ5UqVdC7d29s27YN1apV4+03KSkJzZs3R3h4OOrWrWuScy0qQzETU8NaQ0kPDROjgCffRqFYiq+2R+Odxh4YEVDPzAPTTP9V5wHIvXoXfWRcvkBt3HqehatPX2FkQD0I+JKYVGBkxQJGkdg67wcEUW4oiwaQuEcQqgiU1XKt5/mKIKwVU4WMmpMlJsgBaCp+OnIXYzr74ebzLLP1oS3FkCXfGz9cdR6xSdmY9H5DvY/haARmgsQ9K+abP68DAL7dcxMHJ3Ti7JPYwMXGmlkX+RiVHO0wvINmEUrjqgAPH6+Nwv2XOZjaqzHGd2tg/MD0+GfV5LkWHByM0eO+RONZRznbH6fmYvPmzWr23bp1w9mzZ+Hq6opbt26phcd269YNsbHqqzJ8/QYHB+seuBFsjXqi/N0GFs/KRFk86/i+E39eTsDp+6k4fT9Vo7gnlbE4UJxX0tw8KEOYfq/fz6J7I3f80FM9zLzvSnmScVdnewx8q7bRfdgkjABgAbGExD2C4MAovJNI3CMIDhSWSxB6Q6/bhmOoJ56tEpskT4kUeuKh3sfYWcAJgXLuWSmqXkKFRdpLY9sqUhmrUUAzp5dUclYhFv57D7MO3NbqSvvjwds623qSlofgHdG4/zIHAPBPGcuUl5W/eRK9vrckshxGYhoEDAMBZHBA+YsXpkjSej85Bxv+i7PIylJOoe5QzZ2XEzj5Q4yulqvHgYa2rSrm3k3Kxpozj7XaP3j5+uX4VBQNEIspLJcgVGGLBQxrSaFAENYCIyiulkueewShE2srlmALKJyTCHUs4blH4p6Nolq9x5ouO4aMZfTmK3h7wQlcefJKvR0znlSeSkEKbQ/+T9Pzdbb1xbarCL9VUi1WWFZFXo/DRVqEIX3LodsK72T8hTinz7DQfoPyu2XLgZdBof9hQfhdbDgbZ9J2jRUeL8VxXdsNyWdojjka/fQVVp1+pPFhKiOvCBINeT3LKxdjecIW38KlUhL3CEIVhjz3CIIXQXGuVvLcIwjdyGiBiDAhlvDco7BcW8VKrzWGrJJHPpBXjNka9RRv16thriEZjT5nEpeax/m7dM4vc3ggnrnPX2nnzossg0KJbQEp4wAAqATznxfLshi/PRpVnezNkhtOlZvPM83aPmB9Ypc+41EUjalZ2YF3f5v5EWhV2xUHv+6s3n6ZRmebKApqQEoCBkFwKM4rxlBBDYLgwFBYLkHoDXnuEaaExD1CI1aR3N+M6KsRSmUs5vxzB+38aqBvK5+S4800Ll0Iy0lheJyaiz7Lz5VP52akSOgMQC7uPU3Px67LCUjMLDRLX0/T83HsjrxM/MKBLSziOq2NibtikF+kn0cWn3BWeg7pFzpryBeY24FUxkIkkaKSQ9lvK495isYouKEhSa+1iZmWQMrIP2tWRp57BMFBUS2XPPcIgotQPjcoLJcgdGML1XIJ28HOAkIBiXtWiq5rie79LDLzxaiuwQPGXJjyEnjnRRbO3E/F2C5+cLQT8tr8cyMR2y4+xbaLTzninjZUp5Wx4y0US/HLv/fUCpuUOSzXSGISMsulX3MjFsjFPWdGhJFbr5q1L9UbOMOjFKkK6ua416t2WSiW8iak1eSZWd66FgNg4JoLuPEsE1dmvA93F0deG208e6U7DF77GMr7U7A8irBclryTCIKLwjuJBAyC4CAonhsC8twjCJ3YQrVcwnawE1DOPUIDuq413/51A23mR+DcwzStdizL4tCNF3iSlqfVTl9MJXqwAPosP4dFx+5jfaTm/GRpOUUGtXv+URo2X3hS0o+R4w07F89pR0FpUchcCz6//HsPc/65U9Kvebopd8QCJwCWCss1exd6oynHx4usQlx/lmmyfsp2ytxv3Y3icZ28+5LfWodrXZffTnNsDfVOfh0992QMiXsEwYvSc48EDIJQhRFQzj2C0BcKyyVMCVXLJTSimtuOL8/dvphEAMDK09rLMx+5lYT/7YpB98VnTDo+Q+Abv+o2RalpPjS90GsSaj794xK2Rj0tsTNS3tDkZSS0kMKwNvIxNl94gqSsAov0V16IhXJxz1mDuDc87BKyCkxfSZf/O2nyboxi95UEtW26hDPAHAIw/wdiio/JmAqX+nwGFQ2F5x5I3CMIDsq8YhSWSxAclNVySdwjCJ1QQQ3ClFR1tjd7HyTuWSl8l5JrCRm4lpChcb8xXH2SYaKWTIu1X0o16QiWDssVS+SfVEXVNSTCSgDkYbl8nH2YhlWnH1lkLJb8Tmp7ljDncwbDAPFpecgTGZ/DrbyegyroFNBKXrGunZ5TtpBmgqhwkLhHELwIij33KOceQWhGJmORklMIKU0TwoQ08nIxex8k7tkIBUVSDFx9AQNXX0ChWGqxF+gz91MwYec1ZOTpF/5a2hMu+ukr4zpWacYo4UrleGOFEmO8h0pXyyXKhiLnnraw3Kx8zZ57159lYv7hWOQU8tukZBciq0CMv648Q0Z+yXec719e19dBJJHidmKWXt8bReEOY+Br3lTfurtJ2Xhn8Rl0VQmR1QfVOVrRi/1YE4XFusW/t9TzMxLE6wxDOfcIgpcSzz26VxOEJibtvo52C04i8kFKeQ+FqEDUtEAtBCqoYSPkiErEiYIiqVHCkzGM2nQFAFDFwQ6/Dm6p+4BSwzp5NwX+dWuYYWTlDb+cYulquRVdSNEVlquLD1edBwBIpDLM7d+cs+9aQgYGrr6g/LuKo67LofbP+out0Yh8kIpa1Zyx5rO30LJ2NWOGrLMnff/NS1vxiuQ88xUA0vUQ81dq8JjUdGkydGoYeokTVFT3VS1Ii9fnKLyKILgoxD2qlksQXKigBkHoRlHU7q+rz8t5JERFwhIRfiTu2SAMw30nt4S8k5RdaNRxirHlFIqRmS+Gb41Kaja6KpOWroJ5MS4dsw/ewU8Dmpc+zPDxmfjDUyuoYdrmXzskQrnnniMjgRBSSMFfNVkXj1JzkV0oxu3nWWhfvyaEAgY7LnJz1+WqhKLyfS9Ut/EJbJEPUgEAiZkF6LfyPJ780segMXK837R8MXl3lZpCabkiLI14YFD/hnI7sSQXpozz2VgGlmU5iY5fQ20PsmJxT0gvaYSZWb16NRYtWoSkpCQ0btxYq21SUhK+/fZbREdH4+HDh5g4cSJCQ0PV7Pbu3YtZs2bh8ePHaNCgARYsWIABAwaYZsDkuUcQvAiEJO4RhHGwaMnEobPgNuoLkuCKPFRGAewZCRgADFgIwIIBC4B9LdPFENpxv/IW0CTMrH2QuGcrlHpjNkSUEktlkMpYONkbJ4woyMoX42ZiJjo2cNOoPGsalv/8EyiSyvDf9++gTk2uwHfnRZaaPac4Uamuhq6/CAD4aG0UZn3QVMM49PVuMk6KOP+IvwqxYqgKccbQKkt0I+CiCMsF5KG5OVAXh/X5N2TA4KM1Ubj/Mgez+zbF5538DP63t2jOPSP3KVh5Sr88hKU/gyINyUVkOr7Hv59QKdyj4eJkavFtxMbLiH1RIjC+jnNHSuIeYQF2796NSZMmYfXq1ejUqRNWrFiBGzdu4NmzZ2jWrJmavUgkgru7O2bMmIFly5bxthkVFYUhQ4Zg/vz5GDBgAPbv34+PP/4Y586dQ/v27cs8ZkaZV4w89whCFYY89wg9UF3QadasGUJDQ9GlSxeN9pGRkQgJCcGdO3fg4+ODKVOmYPz48cr93bt3R2RkJADA1dVVub137944cuQIAKBevXp4+vQpShMcHIxVq1YBAEaNGoUtW7Zw9rdv3x4XL140/mT1wB0ZWGa/Gp2Fd8zaD1GxycquZvY+SNyzVrSodwwYg4SJbr+dRlpeEW7ODjRa4GNZFv1XncOT9HzM7dcMIzvWM+h4hWhwMT5dTdyLS81Ts5+275byd8VLe06hGJUduF/Z8nihzykUI0FDtVxA/ll9suESouLSLTamiuq1xArsIWUZCBkWzhrEPX1gGOD+yxwAwIHrL/B5Jz+tKhnf/DJ3KLy+zeuTc08kMe0L7Z7oZ1r3Kz5bwHIi6NmHXIG9os4BbUiU4h4JGIT5WLp0KcaMGYOxY8cCAH755ResWbMGYWFhWLp0qZp9vXr18PvvvwMANm7cyNtmaGgoevTogWnTpgEApk2bhsjISISGhmLXrl1lHjPDkOceQfChFL5J3CM0UHpBZ926dejVqxdiY2NRp04dNfv4+Hj07t0b48aNw/bt23H+/HkEBwfD3d0dgwYNAgDs27cP6enpePPNN/HgwQMUFRWhVatW+Oijj5TtXLlyBVJpyfPM7du30aNHD44NAPTs2RObNm1S/u3gYN48ZpVQiG0Ov6Cx4BlErD1OytrgjqweXsEFeawzRLADVPz1ZGCK/fcYiuAiOIT4t4MeSc7KBIl7NsKhm0mcvw1xCHuRJQ+pjUvNQ1OfqkaP4Um6XNCa/c8dFIil+LJrfd6QWlPDMAziUnPx7pJIdHvTXa9jyiKU6CKnUHMlURZAaq7IYsJeRa/QzggEyIcTXFCASkwhr3Jk8GdQfIDBhxnY583nmUbn3dPmKWeMt2mhuGziz3EDCoBozrmn/7WCZQ3/9zH0WlQkkUHAAHZC260rJWPJc48wL0VFRYiOjsbUqVPV9l2+fNnodqOiojB58mTOtqCgIN7wXaOgarkEwYtQWFxQg4RvQgOlF3RCQ0Nx7NgxrFmzBgsXLlSzX7t2LerUqaO8fjdp0gRXr17F4sWLleJejRo1YGcn/+55enpi48aNqFSpEke4c3fnvuP98ssvaNCgAbp168bZ7ujoCC8vL5Odry4m2B1AY8EzvGSrYWjRLMSz3hbrm6hYfOnhb/Y+bO6tZvXq1fDz84OTkxP8/f1x9uxZrfaRkZHw9/eHk5MT6tevj7Vr13L2i8VizJs3Dw0aNICTkxNatWqFo0ePcmz+++8/9O3bFz4+PmAYBgcOHDD1aalRoPIy/iKzAPMPx5bsZLheROUh8Pzy7z01zxm+sZhibAyAP6/IPYcUec2U+8qoLVZwbczmYRigAI4AtFfMNYQbz+UVbQ3Na2fod7nfyvMGjqyE7/++qXFfZr4Yf119hg9WlFz7dAlbjWcd5eTIMxSTaPhW5FkXdi4eb878F51/Pa0z5NiaoYIahLlJS0uDVCqFp6en2r6XL42v+p2cnKzWpqenJ5KTkzUeIxKJkJ2dzfnRhKC4IihDc4MgOJSE5druvY8wH4oFncDAQM72wMBAXLhwgfeYqKgoNfugoCBcvXoVYrGY95iwsDAMHToUlStX1jiO7du3Y/To0WrPuGfOnIGHhwfefPNNjBs3Dikp5qto64xCjBBGAABmiT8nYY8oE3YWqLxpU+Kewk14xowZiImJQZcuXdCrVy8kJCTw2ivchLt06YKYmBhMnz4dEydOxN69e5U2M2fOxLp167BixQrExsZi/PjxGDBgAGJiYpQ2eXl5aNWqFVauXGn2c1Sw4Wy88vcckWZPMUNQ9fiJT8vDtH03tYaX6iIpq0BrH8ptZVT4GEazLlDWKcKyLHJFEmw6H897Psagr4cSy7L4fs8NzDsUq9tYB5N33yhzG9bIp+3rIp+Vi3vGVswF1MWvU/dSDPfcM7GKHvWY692pOsSIWM0vzafupWDK3ze1inW7LquH0cYmqdubY2HAFJ+TvmKitDAXaYeXIGHZx5jYqxWGDx+OzMxMreNiWRbfTp2B56tG4PLs3ujWvTvu3OHmUBGJRPj+++8BAN7e3ujXrx+eP+dWTKtXrx4YhuH8lPZuSkhIQN++fVG5cmW4ublh4sSJKCrSXYlYX6igBmEp+BYQyuq5r1aAimW1trlw4UK4uroqf3x9fTW3TQU1CIIXAYXlElrQtKCjbfFF02KNRCJBWpq6I0h0dDRu376t9Azk48CBA8jMzMSoUaM423v16oUdO3bg1KlTWLJkCa5cuYJ3330XIhH/O4Ihi0J8BAquwoUpwFOZByJk5ve6Iio2AgtEPNpUWK453IS3bduGGTNmoHfv3gCAr776CseOHcOSJUuwfft2APILSa9evcx2XssMrGrJMGV/KR+2/iKSDaiAy5/ni6fKbWnPPbD4j8fDz9zwfTxXnrzitZ3zzx38Hf0ca848xuUZ75t3YCo8e1WAPdFlK7Eem5SNem78q14VgTc8quBuseeeM1NktKtlacEpIvalwXPI0LBcXQzbcNHgirqaUJ2JKUZWtjYVUo1hufqj7+eb9s8iSHPS4PnxXADA9etbMHz4cBw6dIhj9zK7EP1WnsOQtr4QxxxA9pUDcOs9GXY1fOCRHYkePXrg/v37cHFxAQBMmjQJhw8fBgAcPXoUs2fPxgcffIDo6GgIhSV5S+fNm4dx48Yp/65SpYryd6lUij59+sDd3R3nzp1Deno6Ro4cCZZlsWLFCgM+Dc009K4GpAIeVWzqVk7YEG5ubhAKhbwvdR4eHka36+XlpdZmSkoKr4eggmnTpiEkJET5d3Z2tkaBjxGSuEcQfDDFYbm0KERow9DFFz57vu0AsHXrVjRv3hzt2rXT2F5YWBh69eoFHx8fzvYhQ4Yof2/evDnatm2LunXr4siRIxg4cKBaOwsXLsTcuXM19qOLrkJ5JM1hWQewtuUTRVghmgqSmhKb+Zaay01YJBLBycmJY+Ps7Ixz586ZcPTauf4s0+BjZGVUFwwR9spCTqEEIzcan5cHMF0030dro9S2sQDO3JeH+qbkmCbsU19RXixTf7B6kWnYv0vwjmvYd61sAqG1k68My+X/bBJe5evMKVc6hFwq0565jnd62UgES0EZ8+spyC4U4+zDVIMrPs8/HItZB26bZAzaEKc9Q2F8NGr2mgjHWk3gWKsJNmzYgMOHD+P+/fsc2xWnHuJltgi/n3yI0NBQuAYMQaVGHeHgXg+r1oUhPz8fO3fuBABkZWUhLCwMP/30EwCgVatW2L59O27duoUTJ05w2nVxcYGXl5fyR1XcO378OGJjY7F9+3a0adMG77//PpYsWYINGzYYvHKsCU9XeYEZx7IVQicIjTg4OMDf3x8RERFq+7S9mOkiICBArc3jx4+jY8eOGo9xdHRE1apVOT8aUXjukYBBEBwExcK3gGErfuJmwmA0LehoW3zRtFhjZ2eHmjVrqtnv27dPq9fe06dPceLECa02Cry9vVG3bl08fPiQd/+0adOQlZWl/Hn2THuBOC4sAgTy6KoLMvXK8ARhKEILeO7ZjLhnLjfhoKAgLF26FA8fPoRMJkNERAQOHjyIpKQkvib1xhA3YGP+nbn3Y8Nyh1mSnZe4IdNT/r6pljdPF4y2uFwNcHISarUzrF190HeofH0bI8yE/FUxQ3IV6ArLvRT/CoHL/jOoTRlrePioNT8Cq15Dxm29qvdx/97WnN9qyLqLGB52GX+cjTN4PNsuPlXbZuh1Tts/T6FYCtGLu2AcK8PRp5Fye4cOHeDq6qq24KNoS5L1EsnJyXD2a6PcZ+fggG7duimPiY6Ohlgsxrvvvqu08fHxQfPmzdXa/fXXX1GzZk20bt0aCxYs4ITcRkVFoXnz5pxV56CgIIhEIkRHR+v/QWhBUCxgsDIqGkCYj5CQEPzxxx/YuHEj7t69q6xwO3r0aADyl6cRI0Zwjrl+/TquX7+O3NxcpKam4vr164iNLUlB8c033+D48eP49ddfce/ePfz66684ceIEJk2aZJIxK+aGgApqEAQHAaOyGkSerUQpNC3oREREaFx80bRY07ZtW9jb26vZi0QifPbZZxrHsGnTJnh4eKBPH93RLenp6Xj27Bm8vflz4Rm0KFQKT2TAh3kFCSvANVlDvY8jCE0ILKC82Vwsj6ndhH///XeMGzcOjRs3BsMwaNCgAT7//HNOiW1jMMQN2Jj4a2OEBl1ahq7PUq8+9LAZufGyQeGI2kZU5mq9RnyQ2sXCsktAuSIJqjja3NQ0G4qCGpUZzZ6VhuaOZFkdnns6ckeyAP66+gw7Lj7F+hFtkVPInzB47qE7SMosxNB2vujeyPgQNkN48DLXJO3cLc7Rtz8mEbWrVzL4+BEbL6NWNSeddk/S8uBbw7D2J/15HdK8TAgruartc3N3x904/jys0twMAICgUjXlNpmMhaenJ54+lQuSycnJcHBwQPXq1TnHll5I+uabb/DWW2+hevXquHz5MqZNm4b4+Hj88ccfynZKLy5Vr14dDg4OGhekRCIRJ2+MLg8/QXF4FUjcI8zIkCFDkJ6ejnnz5iEpKQlNmjQBANSpUwcAkJSUpJb7uE2bEgE9OjoaO3fuRN26dfHkyRMAQMeOHfHnn39i5syZmDVrFho0aIDdu3ejffv2JhkzU1xQg/KKEQQXgUpqCZlUohTCCUJBSEgIhg8fjrZt2yIgIADr169HQkICxo8fD0C+oJOYmIitW7cCAMaPH4+VK1ciJCQE48aNQ1RUFMLCwrBr1y7e9vv06cPr0QcAMpkMmzZtwsiRI5XVdRXk5uZizpw5GDRoELy9vfHkyRNMnz4dbm5uGDBggAk/ATkNBYkAgKesJwqg+3mWIHRhibBcm1EQzOUm7O7ujgMHDqCwsBDp6enw8fHB1KlT4efnV6bxGpIbxph/59IiklTG8n5hDJGafjx4B/M/bM7fn46WFMKgqYsOAAAYzUUqNGl7qqPQ9vEOXnsBabmmCcdV9qe34Mj/WY3edAV/jQ8w3YBsnBzIhZ8qML74S2mkLKvX5CiSlLwYljafUlzRdmH4XZy+z++Nuun8EwDA0TvJJsuvx4e+RVwsyX96eOjuupyAaftuoX/rEu+2zHM7MOtX/gdCBQdHLJP/wjPXEtLzseFsPL5Ky4OfpnyUKsdJiysn65q3pW0mT56s/L1ly5aoXr06Bg8erPTmk3fDl5dUc18G54YpFvcY8k4izExwcDCCg4MByJ9nXF1LhPXNmzer2evzLDB48GAMHjzYZGNUhQpqEAQ/ykUhAFKZ1HZCuAiLUXpBp3nz5ggPD0fdunUBqC/o+Pn5ITw8HJMnT8aqVavg4+OD5cuXK/PbK3j06BEAYPjw4Rr7PnHiBBISEpSe4aoIhULcunULW7duRWZmJry9vfHOO+9g9+7dypzJpqQhI0979JCtbfK2zcGKYW2Q8Cofi47d121MlAsuTuqerKbGZsQ9VTdhVXU+IiIC/fv35z0mICBALbG6JjdhJycn1KpVC2KxGHv37sXHH39cpvE6OjrC0dFRL1tDvc/YUmkyBqy6gByRBJUchLg5OxB2QuNu1dsuPtUo7vFSPOzx26LxJD0Ph/7X2ah+dXfD6BXSdzcpG429XNS9NbUc8zDFNF5OnP40vNTsufoMfVv5wMle+yrpZQ2FP15Xslm5uOfCmKaaMVAclqtHOPvqM4/UtpUmVyRFVgG/554hWKNAB6C4Eqx52l5xUp4j5eD1F8ptLm99gJGffoLEzEJExfEX47Fz9YQ4NR7SvEzO9uSsQkjysyCsVA0X49LVxD1hFbk3niwvA6hSA4B8YUR1kcjLywtFRUXIyMjgHJuSkqI1H1iHDh0AyB9ea9asCS8vL1y6dIljk5GRAbFYrHFBypBFIQBAsXeSkDVNRXWCqCgo84qR5x5hZlavXo1FixYhKSkJzZo1w4IFC7TaR0ZGIiQkBHfu3IGPjw+mTJmi9IhSEBoaijVr1iAhIQFubm4YPHgwFi5cqJYj3BgEKu8IMindOwh+VBd0SsO3oNOtWzdcu3ZNa5tvvPEGAHDSnpQmMDBQ43uUs7Mzjh07prUPU9KQkXvuPWBrWazPsuDqbI92fjXKexiEBvq09IaPq/k9QG1qwaZ03pfJkyeruQmr5n0ZP348nj59ipCQENy9excbN25EWFgYvvvuO6XNpUuXsG/fPsTFxeHs2bPo2bMnZDIZpkyZorTJzc1V5o8BgPj4eFy/fl0tDMVYjPLcUxEmckTym3N+kRQXHqeXNjSIx6m5GL8tGrcTs7jNaGnn6J1k3EvOQfTTDLPksNt77TkeaRDhVD+6Xr+fxT83XvDaGYOx56LpsO//vollJ0oqI19/lqXBklAlu9hzryryTNamTMbq9e97+l6K8nfNYmD5Z+MzZ37Wu0nZiIh9WeZ2eKtr89gJK7nC3bc+qvnUhX1NX94fxs4Bjj5NwIryIHpRskI5eeUesKI8ONZqgmn7biFk93VO23aunnD38ETBkxjltsLCIkRGRiqFO39/f9jb2+P06dNKm6SkJNy+fVuruBcTI29TkfclICAAt2/f5uRvPX78OBwdHeHv78/bhsG5YQTyRSohyHOPIFRRhuWSVythRnbv3o1JkyZhxowZiImJQZcuXbR6o8bHx6N3797o0qULYmJiMH36dEycOBF79+5V2uzYsQNTp07F7NmzcffuXYSFhWH37t3KXJdlRSAo8euQSUn8JghN+DLyd4CnMq9yHol+MAwgkRr+TtK7heXPr25Nw9P9mINpvRqbvY/App7484sOWPXJW2VPJ6YHNuO5B5jHTbiwsBAzZ85EXFwcqlSpgt69e2Pbtm2oVq2a0ubq1at45513lH8rPCtGjhzJu3phKAbn3NNS4EpaRnXt801XkPAq36gwQnN+XTWKC6U+u91XnqF/61qcz8csocJa0NbdqbspmNZLnq/ouz2aC2Eo8p0RQI4ZPPeO3NJeMIe3WK6Gf9fyLlpjKzAM8OxVPkZtuoyxXepjWLs6Gm31+Ujt3Xzh5OeP9KMrUDPoawDA8ci1cG7wNuxrykMo9sUk4sjsoWjRfzzANATDMPDpNAg3w7fAvroP7Kr7IOTrLahUqRI++eQTAICrqyvGjBmDmTNnAgBu3LiBOXPmoEWLFnj//fcByItlXLx4Ee+88w5cXV1x5coVTJ48Gf369VPmIQsMDETTpk0xfPhwLFq0CK9evcJ3332HcePGGZTQWSvCYnGPPPcIggMjVOTcI3GPMB9Lly7FmDFjlFU9Q0ND8e+//+LBgwe89mvXrkWdOnUQGhoKAGjSpAmuXr2KxYsXK99NoqKi0KlTJ+U9qV69ehg2bBguX75skjELS4XlEgTBjzcjj6R6Af78gNaGi5M9cgsNfx7s3cIbtatXwvr/DC+gZyz2RkYZmpo3PU0fzl2a9SPamr0PVazjkzWA4OBgPHnyRFlxsGvXrsp9mzdvxpkzZzj2CjdhkUiE+Ph4Ndf3bt26ITY2FoWFhUhLS8PWrVs51Q0BoHv37vIE/KV+TCHsAaYtqJEvkuKDFWeNHoumwgTWKmAUFHEvYnwfZXya6Ty+9EFXfkJ9OGECT6mKgjk893TBJwizGvZb6dSwSmb/cwePU/Mwbd8tk7Tn1vc7OLjXw8u/ZuHlX7NQs05DuH3wLcfmwf37EOWXeP7WeWcoqrbth1fH1yBpy2QkJ73A8ePHOflali1bpqzSFhQUhEqVKuHQoUMQFof6OTo6Yvfu3ejevTuaNm2KH3/8EePGjeMkjxYKhThy5AicnJzQqVMnfPzxx/jwww+xePFik5w7AGVYrh2JewTBQeGdJKSwXMJMFBUVITo6GoGBgZzt2kIOo6Ki1OyDgoJw9epViMXy9B6dO3dGdHS0UsyLi4tDeHi4XpVD9UE1LJeV0b2DIPhh4c3Io+GSWf1CXf/8ooM5B6SVdvVqoFVtV0hkht/zZGV8kdk+xvBCWD2a8qensTQOduaVwro3cjdr+3zYlOdehcVgxz0WMg1q29/Rz3A7MZtjyznWyIq4fIJV6VYs4Wpamp/D73H+VnwsquP9Yms07s7vqXebIokUdkbWqmaV/8dPOXxENo/Cc68qY7qCGkbB8v5qcc9QW4VhgEJxiZfArssJZV40EDq7wK1vSZqFHi281bwyiyRSzPnnDm5dknt1s2BQrfOnqNb5UwDAtkld0ciLu3Ln5OSERYsWYf369UhOTlbztHvrrbdw8eJFneOrU6cODh8+bNS56YWQwnIJgg/GTuHVSnODMA9paWmQSqVqOVTd3TW/zPFVUff09IREIkFaWhq8vb0xdOhQpKamonPnzmBZFhKJBF999RWmTp2qsV1DKq1zw3JpfhAEH1WRj8qMfE4l6Snu8b3i1armjMRM00UeaeLHvk3BMAykWpS6rm+68xa7K8t7jKuzPTo3dMOmUW/j881XtNpOfPcNLD8lz2X+afs6WHPmsdH9mgo7M1ev/X1oG7O2z0e5ee7l5eXhv//+K6/urQqjPPc0zMMiLfkz0nJF6PjLKfzy7z2NNmVBIpPhxvNMs7RdFgrE+j+8FBRJ0WrucfT+3TjvxzP3U3WGfOoDyUUlKDz3XExYLVcXis//XnKOyjZ+dc9k/1ZluL+Uh7BuDKrDnLbvlkm8XFXRp73SNik5hSYdg0URUFguQfChCMsVguYGYV7UirjpeFHWZK/YfubMGSxYsACrV6/GtWvXsG/fPhw+fBjz58/X2ObChQvh6uqq/NFWiEmosngtpYIaBMGLV3FIbgZbBYXQr0AmH+d+eEe3kRF8H9SI87fisiLRIu5pujZJZazRryAKbeydxh46bT9qW3JdEppJVHurTjWD7O3N7Lnn6mz+6rilKTdx79GjR5w8dq8zhn6/WVZ/QUF1Hoedi0dSViHWRhqulOsj6v90+C6+3hmj29CKiXmWgUKxDPdf5ug21sDcQ7EmHBFRnp57Igm/WK7qOavvgtcfZy2Xy8IaYYr/p8rLbJEGa+MIv5Wstq30v0/pv3OMyE9iNRQLGHYkYBAEB6FS3CPPJMI8uLm5QSgUIjmZe99JS+Ov8g7Iq7GXtk9JSYGdnR1q1pTn9Zo1axaGDx+OsWPHokWLFhgwYAB+/vlnLFy4EDINIXfTpk1DVlaW8ufZs2cax8AwgISVv/6xVFCDIHjxZDIAAC/Z6nrZf/NeQ97t5lh8P/R1Z3zUtja3n+Lna22ee5reV8oitBlyfpUchMrf9RG9Jmr4TLWhK8T43cYeOBHSTfm3Q6ncf4s/aqV2jLlDd02NbY22gmKM556mGapNaDj7UPMDh4KyXINiragIhDHVggBuRU9NXkCWCMO0DT8sy1Aenns5hRJOCCkAiFW+U5ywXD3b/OnIXd7tLMsiPi2vTN+rivh9Mdc0u/OCe52y6ahqqpZLELwwQgcAlHOPMB8ODg7w9/dHREQEZ7tqpfXSBAQEqNkfP34cbdu2hb29/Hqen58PQanUMEKhUJnvmw9DKq0zDANp8eufjHLuEQQv1SF38shg9Su4MLnHm+YcjpL5HzZHi9quaovlivf3apXURbM+Lb1xZGJnje+1PZsbXy3XEF3Q3k6A45O74t9vuqCSg3kyw+l6l2roWYXzGdkJuSfg51ZSxff3oa3Rt5UPDk7oZNRYJr77hlHHlRWz5dyrUUN7fLqU8jwoMaJYrt7JL68+zTB4POVBTqEYLk5ld11lGODm80z0W3neqOP1SURqCTHAlvUGU1PiuVcAAWSQWWBNotMvp7SuKpmyGvO2i0/x48E7ZWrDFiivyGFdobqmDg22JIrQQyqoQRBcBHY0NwjzExISguHDh6Nt27YICAjA+vXr8fz5c+X+adOmITExEVu3bgUAjB8/HitXrkRISAjGjRuHqKgohIWFcYox9e3bF0uXLkWbNm3Qvn17PHr0CLNmzUK/fv2URZ3KihRCABIqqEEQPLAsi2qMvBBbBqrw2gR3b4DV5ZAzbniHurzbFc/YAfXVK/sOfqs2mvm4anx/dbTT/7ryead62HT+ifLv5rVc9T4WMKw6rTGvDTJW7hWYVSAvUFS7ujNa1HLFv7eLPaZZbp49VZG0bs1KqFOjsvLvejUrY8Uw43PmhQQ20m1kBswm7olEInz11Vdo0aIF7/6nT59i7ty55urepjAu5x7/DL3wOL3MY5EWt339WaZWW1O6Gm+NeoqmPppXGg1h9j/GCyWPU3J1G5WBF5n65fdaceqhWcdhS+SgZBWlCvKRreFGa2oUNwY+NBW0MYZlEQ/K3IZiKhZpCCO2NfZcfYZsC4XLlrVKWLlS7LknIM89guBAnnuEJRgyZAjS09Mxb948JCUloXnz5tizZw969+4NAEhKSkJCQoLS3s/PD+Hh4Zg8eTJWrVoFHx8fLF++HIMGDVLazJw5EwzDYObMmUhMTIS7uzv69u2LBQsWmGzcCs89qYTEPYIozcm7KagO+ftgJsv/zjGlZ2M1cc/Q9+KA+jUhEADnHxn+7l66K4VIxTcGxTtLWd9dpvVqjKSskvfYUR3rYcI7+nun2RtYrNKowEawiPy+O1rPk3tIf/3OGxjarg7qTT1SvB+wUwnFVQ1J3vx5O06fhugzjTxdypTSy5SYTdxr3bo1fH19MXLkSN79N27cIHGvGEND3b/fcwMd33Azy1hUJ/6Hq0q838z9/rvo2H0z96Af+rzol+XamCvS70FKbGRYcUWkCPYoZO3hxIhRlSlAtoYbrSXhVss1XVtlZdXpRyZsrfwwpbCn69/HpqsdF1fLtSNxjyA4CJT5KGluEOYlODgYwcHByr9VK9Vu3rxZzb5bt264du2axvbs7Owwe/ZszJ4926TjVEXuuUfiHkHwEX4rCS2KPfcyy+BQMKazn9b9Mz9ogmY+rkrhqSyoagkzejfBgvCSVECKd9tP2tfFxbhX8HBxREqOYTmvl37cCgPfqo05Kg40c/o149j80LMx4tNy8dfV55ztiz9qBaEAcHYwjeexKr2ae2FYuzoYsfEyAEAmA6pVclDu53vCr+Joh1Ed60EslcHL1anEljW+sMjOce3h/9MJI482LWYT9/r06YPMzEyN+2vUqIERI0aYq3ubonTcvC5O3kvByXspZhmLoe+51vZifP5ROtoYWClHFX3OxpbD+GyVTFSBFzJQDTl4DvfyHg5noljD90Hhsffv7bJXajYXtlLR15ZghJRzjyD4ENqR8E0QmpAy8pdsmbSonEdCENaHnZApCcs1wKHAW0UoWvlJG3zQ0kej7Y6x7dHMx7CQ1trVnZW/y0p5o6g+Yo/rWh8fv+2LVnOPy22L31n6tvRGQ48q8HOrjMazjhrUt59bZU5bfHzVvQEAqIl7g/1r85ljTt+mmFPGIpRrPvPn/K3LO1GhWyiEyYIi7jOCnWo1cQM0jppVHNG3lQ8O3Xih9zHmwmzi3vTp07Xu9/X1xaZNm8zVvU1hoJdq+aDh+21l2h4A4HZiltHHqoqV1nhuryvpbFV4MRmoyeTo7epW+sZnSlSbLrPnngmG+TAlFxKpzOCFAktSUCQxWwLdsmDKEGtLoxD3KK8YQXBRzA0Bw4KVScEITO8xQBC2isJzT0KeewShhoBhSsJyDfDc861RCWs/80f1SvZoz5P7TpVORkTgze5b4iknKfWOU9mR+3ytmjdc8ZjLMAyaeMtTYA32r42/o7kinDYUC/SmfGQe1ckPYinL8TIsK6XHp3grqlnZAel5RXi/iSd3f6nXJtXKuFKVPPyzPmiK+Ye1C5Fz+zWDq7MdUrJFGNa+jsFjNxXW96b1GmLLHi3W+FrMF9Kqr9Aj43hk8WPDWoDNks7Kb0Y1oZ9wK5Wx6LP8rNnGoyoClzXPpanILBCXW9EKfYhPy0czE+XVNCU2PZ8F5J1EEHwoPPcAQCaVQEjiHkEoUeTcY6Uk7hFEaQrEUqXnniLn3vJhbXDtaQY2X3ii9diyVJ7VhYtTiWwjVXmvDenxJrxdnfkOAcAfZSeRlspHq+f7g6mjlcZ1ra9R3DPGYUHTgv3p77vj+asCtfz+qnn1WAD2KtVzVfWEMZ390Kq2KwavjdLYd43KDvjpQ/5aE5bErOIey7I4ceIELly4gOTkZDAMA09PT3Tq1AnvvfeeTYtapsTQnHvlAd9k/m7PDXzYWrPLsTUh1qMKLsB90X+kobjGH+fiTDEkwgDSUSzuMdk6LOXEp+XhXrL5EpvGpeWZrC1ThbazrHUvFEj1nIOWxpbFPWW1XNALGkGoosi5BwBScRGE9o7lOBqCsC5kjBBgAYmEwnIJojRiqQyuOgpqAMB7jT1w8l4K3vTU7t23YlgbnLmfimsJGYg30ftD9coleeWCi8NhNcHn31La809fze6dRh7YfjEBlc2QP680ul5pFg1uqbatbs1KnL8Vp1XVyR5NfezV7EtrMKrFNiSlnIXa1KmufUBWgtkCQhMTE/HWW2+hV69e2L9/P+Li4vDo0SPs378fPXv2RNu2bZGYmGiu7m0KY6rlWgvGvheXjnE3N/oWqNDHavvFBN1GhElReu7pKe6Z26d0a9RTk7VlqpG+yCzA3SR9Px/LI5Gy5eJZqEu8s+WwXCqoQRD8CO1LXnxk5J1EEBwUYbnkuUcQ6tgJBKjCFAAAciAXi/gW4pd83Aoz+zTB9jHttbbXt5UPlnzcCg5C08kuVRztcGxSV5z8thtHkOKD7zlXamTqoncbe+CvLwPw35R39LKP/L67Tpsvu9VX/v6GR4lQ2rGB9tBmRYgxAOz9KgAD2tTCzwMN85xTrZbr7sJdBCztGCQUMIidF2RQ++WB2Tz3goODUaNGDTx79gze3t6cfUlJSfjss88wYcIEHDhwwFxDsBlsQdzT9P47dP1Fo9r78eDtMozGcNTcjzVgqRf9F5kFFumnopDOypPO1oR+4tWTtHxzDscq6a9S3doakciMr0JlTmxY2ivJuUfiHkFwEApLVuilEnE5joQgrA+F5x5VyyUIdbo0dEPle/Jqsrlw0mhXrZIDxnapr3G/uWnk5aKXHd+7rZrnnp4wDIN2fjX0tvesqvnzU7ap8nag+p7Qtl4N7P2qI3xrOKPdgpNqx6mel3/dGvCvqz4uXe8dDMPg1LfdUCSVoaoT17NPLFHXDkrnDq9VzRkft/XV0YtlMZvn3smTJ7F06VI1YQ8AvL29sXjxYpw4YR0lgwnjiX6aYdRxewxI4mkK9Pbcs9Cbvqncsl8X0gwMy32Sbhufr1TG2ra6ZADGrhKWFZ35QWz482eEcu8kCsslCC4CoQAyVv5YTxVBCYKLjDz3CEIjDGSoxMjFvXxWLk6196tZ5uiTsuar89GSV89Q3m3sAUCl8AbPuXlWLXs6C30+M9XPpX19uUDnWFzYwr9udXi4OGH7mPZ4v4kHhrUrKVShz2uFPp94ffcqaOxV4gXYwF1eGbi9n3bPQQDYNqYdvnm/oR69WA6zee45Ozvj1atXGvdnZGTA2dl0X1JbxgYc92weid459yzzpl/EsxpAaMbQsFxbCbVsMusoivT0KrV1JDIZbiVaX9iwrXxX+GCE8hc08twjCC5ChoEYQjhCQmG5BFEKGSO/d0hpbhCEGkJxSfRPHpww6K3a8HLV7YFmLvZ+FYBXeWLUKZVPThe1qjkjMbMAATzhrR+39YVbFUe08nXVePyFqe+hwfRwg8eril7RiSqP4dN6NUGdGpUQ1IxbmKRzQzd0buiG5ScfKreZy2ng2KSuEElkahWI+bDGNwizee4NHToUI0eOxN9//42srJIKl1lZWfj777/x+eef45NPPjFX9zaFMdVgLI0tvwAD+qn7htiVhWN3kvH55ivm76gCoRD33Bj9quXaytf1dRH2AKCqsz3SckUW71fXd8FGviq8lHjukbhHEKoIBYwyr5hUTJ57BKGKQtwj4Zsg1BFK5OKehBVABHvUqm4aZ6TeLeTRjHVqcEW69cP9IRQwCB3Smvc4/7o10KOpp8H9nf6uO67/2AMeLurCpFDAoEdTT959AODj6mQSdUKfNlSfwys72uGLrg1Qt2ZlflsVY3NpE3ZCgV7CHiAXUK0Ns3nuLVmyBBKJBJ9++ikkEgkcHOQvIUVFRbCzs8OYMWOwaNEic3VvU9iC554tvwAbgiVEzK+2R5u9j4rGS1ZeocgDmRBABpmOdYnX5ftqS2Tml0/eK13fBVsRgvkoqZYrBcuyVl0tmSAsCcMwkBbfJyj0kCC4yBj5vYOVUj5KgiiNnUReKTcfTgAYjO3iZ5J2J7zzBhp5uqjlrAts5oX783vCTijApN3XTdIXADjYCeBg56DbEICTXUn12xuzA+Fsb3w1XP+61ZVpu/R5LjUkak6qYisrp3Q/qjiV4XMyF2YT9xwcHLBmzRr8+uuvuHr1Kl6+fAkA8PLygr+/P6pWraqjhdcHW3gds+UXYED/C4etn2dFJQXVIWaFsGek8EAGkqE9D0JyVqGFRkZYO3P/uQMHO81isC17Jat67rGsbSwUEYSlEEPhnUQCBkGoQp57BKEZQbHnXh6ccHRSF2WhhfZ+NbHp/BOj27UXCtCrhXotAgDKird/ftEBf5yNw92kHCRasPji6M5+OHUvBR+09Fbm4TM2VdX64f7w/0leV0Evzz0DumnuU6IfWYG2Z5WYTdxTULVqVbz77rvm7oYwM7Y+f/S9cPyuEstPWA8yCJDE1kAdJhW1mDQks9rFvc0XnlhmYITVsyf6OT5tX0fjflu+tik89+whhYxlIbCJpSKCsAyKsFwZVcslCA4sQwU1CEITdsU590QCZ06hhaBmnlg33B9Nvc3noNShfk10qF8TK04+xJKIB8rCEubG1dkeh/7XmbPN2GiQmlUccSKkG+yFDAQCPTz3DGhbNTzZxcnsMpZNYtZPJS8vDzt37sSFCxeQnJwMhmHg6emJTp06YdiwYahcmT+e+nXjbrL1JZlXw4a9W4iKQSLrjjpIRW0mFdFso/IeDlFRsOFrG1McbiFgWEikUkBomYdAgrAFlDn3SNwjCA4sI79XkFcrQagjlOTJf3GowtnOMIxaoQdz8WW3BvCs6oSOb+iu2GoJDJX53vCootuoGEMewxmGwa+DWuB5RgGa19JcDGSwf22cf5SGvq189G9cT06EdMP7SyMxMqCuyds2BWZ7E4iNjcWbb76JKVOmICMjA3Xq1EHt2rWRkZGB77//Ho0aNUJsbKy5urcpCsXWn1T/xnP9ChlYK9mF1vMAQ3mxjCMRbgCAWkxaOY+EsDX+ufFC4z5bdutnhPbK35+kZJbfQAjCCpEW5xUrooIaBMGBLZ4bkJHnHsHP6tWr4efnBycnJ/j7++Ps2bNa7SMjI+Hv7w8nJyfUr18fa9eu5ezv3r07XF3lYoyrqysYhgHDMOjTp4/SZs6cOcrtih8vL66YxrIs5syZAx8fHzg7O6N79+64c+eOic5ajp20xHOvvHCwE+Djt31Ru7phFXLNhTkflVkDWx/ydh18G6jdyWPxR61w/od3UUXPwhiG8IZHFTz4qRfm9m9u8rZNgdnEvQkTJqBr1654+fIlDhw4gHXr1mH9+vU4cOAAXr58ia5du2LChAkGt2vqi41YLMa8efPQoEEDODk5oVWrVjh69GiZ+zUEknrMT5/l58p7CGYn59oRPF87Bk8XD0DS5m9Q+Oy2VvvIyEh07doVANCyZUu1uQEAoaGhaNSoEZydneHr64vJkyejsLAkn51EIsHMmTPh5+cHZ2dn1K9fH/PmzYNMZnrB+jkrF/dqk7hHGEhOoeYXGGNzilgDAhVxb8pfVKiHIFRR5BUrKiJxjyBUoZx7hDZ2796NSZMmYcaMGYiJiUGXLl3Qq1cvJCQk8NrHx8ejd+/e6NKlC2JiYjB9+nRMnDgRe/fuVdrs27cPDx48AAA8ePAAt2/fhlAoxEcffcRpq1mzZkhKSlL+3Lp1i7P/t99+w9KlS7Fy5UpcuXIFXl5e6NGjB3Jyckx2/opqueUp7hFlR5+QYGPRlsu7vDHbyC5duoRZs2Ypq+Sq4uDggOnTp+PSpUsGtWmOi83MmTOxbt06rFixArGxsRg/fjwGDBiAmJgYo/slCG2Y41KTd/c/vDq5Aa4BH8Nn1HI41m6GlD1zIMlO4bVXzI2OHTsCAL799lu1ubFjxw5MnToVs2fPxt27dxEWFobdu3dj2rRpSptff/0Va9euxcqVK3H37l389ttvWLRoEVasWGHyc0yQeQAA6jHJJm+bsE2khblIO7wECcs+RsKyj5F2eAlkhblaj2FZFpnnduD5qhFIWDIQv3w9TG3VVyQS4fvvvwcAeHt7o1+/fnj+/Lly/5kzZ9RWlxU/V65cUdrx7ecT0Y1FoFIF7VVOnsnaJYiKAEviHkHwwgqKvVlI3CN4WLp0KcaMGYOxY8eiSZMmCA0Nha+vL9asWcNrv3btWtSpUwehoaFo0qQJxo4di9GjR2Px4sVKmxo1asDTU54vzdPTExEREahUqZKauGdnZwcvLy/lj7u7u3Ify7IIDQ3FjBkzMHDgQDRv3hxbtmxBfn4+du7cabLzty8OyyVxrwRzOiLZ8Bq7VWI2ca969ep4+FBzcYJHjx6hevXqBrVpjovNtm3bMH36dPTu3Rv169fHV199haCgICxZssTofg2FojSJspJ95QCqtOwBl1ZBsHfzRY33v4DQxQ05MeEAgFP3XnLsFXPjl19+AQCMHDlSbW5ERUWhU6dO+OSTT1CvXj0EBgZi2LBhuHr1Ksemf//+6NOnD+rVq4fBgwcjMDCQY2MqHrC1AQBvCp7rsCReF9L+WYSil3Hw/HguPD+ei6KXcUg7vETrMdmX9iL7ygHUeH88vEYsRdUa7mqrvpMmTcLhw4cBAEePHkVubi4++OADSKVSAEDHjh05K8tJSUkYO3Ys6tWrh7Zt23L627RpE8du5MiRJjt/RiBU/m4PqcnaJYiKgCIsV0I59wiCg7KgBoXlEqUoKipCdHQ0AgMDOdsDAwNx4cIF3mOioqLU7IOCgnD16lWIxfzX37CwMAwdOlQt//7Dhw/h4+MDPz8/DB06FHFxccp98fHxSE5O5vTl6OiIbt26aRybSCRCdnY250cXQom8Sm0RQ+KeArOG5ZK6Z1LMJu6NGzcOI0eOxOLFi3Hjxg0kJyfj5cuXuHHjBhYvXozRo0fjyy+/1Ls9c11sRCIRnJycODbOzs44d+6c0f0ShCVhpWIUJT+Cs18bznZnvzYQJd4DAOSJuC/++syNzp07Izo6GpcvXwYAxMXFITw8nJMfo3Pnzjh58qTS1f7GjRs4d+4cevfurXG8xtxoAeARWwsyloEbk42asO0ckETZEac9Q2F8NGr2mgjHWk3gWKsJavb8HwoeX4E4nV8AZlkWOVcPwjVgCCo16ggH93r4fPpvnFXfrKwshIWF4aeffgIAtGrVCtu3b8etW7dw4sQJAHLvc9WV5Zo1a+Kff/7B6NGj1XJqVqtWjWPr7Gy6h0WBQAAxK39Js2esP3crQVgSWXFBDYYEDILgoMi5R9VyidKkpaVBKpUqvewUeHp6IjmZP3ImOTmZ114ikSAtTT2VTnR0NG7fvo2xY8dytrdv3x5bt27FsWPHsGHDBiQnJ6Njx45IT09X9qNoW9+xLVy4EK6ursofX19fLWcvRyAVAQAkAkedtkTZaednHUVDKgpmE/fmzJmDadOmYenSpWjTpg1q1aoFHx8ftGnTBkuXLsXUqVPx448/6t2euS42QUFBWLp0KR4+fAiZTIaIiAgcPHgQSUlJRvcLGC9gEBUfiYkz+EvzswFWBkElriessHJ1SPMyAKh7h+ozN4YOHYr58+ejc+fOsLe3R4MGDfDOO+9g6tSpymN++OEHDBs2DI0bN4a9vT3atGmDSZMmYdiwYRrHa8yNFgAK4YgnrHzMjQTP9DqGqLiIXtwF41gZjj4lSXUdazUG41gZosS7vMdIsl5CmpfBEcLtHbirvtHR0RCLxXj33XeVNj4+PmjevLnGBZ1//vkHaWlpGDVqlNq+r7/+Gm5ubnj77bexdu1ak+ajFDCApFjAsAe9pBGEKlLyTiIIfhRe3zQ3CA2UXqhkWVZrQUA+e77tALB161Y0b94c7dq142zv1asXBg0ahBYtWuD999/HkSNHAABbtmwxemzTpk1DVlaW8ufZM93vDwKZQtxTTy32umLOIMPeLbyw9jN/nJ3yjhl7eX0wazbAH374AS9evMDjx49x7tw5nDt3Do8fP8aLFy8wZcoUo9o09cXm999/R8OGDdG4cWM4ODjg66+/xueffw6hUKizHW39GitgEISxqH0dWRbaLse65saZM2ewYMECrF69GteuXcO+fftw+PBhzJ8/X3nM7t27sX37duzcuRPXrl3Dli1bsHjxYrUbsSrG3GgVPGDl86gp81TvY4iKiTQvE8JKrmrbhZVclaK22jG58u2CStWU22Qsy1msSU5OhoODg1raCG0LOmFhYQgKClK7zs+fPx979uzBiRMnMHToUHz77bf4+eefNZ6ToYtCDMNADLkHhj1DYbkEoYpS3CPvJILgQGG5hCbc3NwgFArVnndSUlLUnAIUeHl58drb2dmhZk11r6x9+/apee3xUblyZbRo0UKZ5ktROdeQsTk6OqJq1aqcH10IZfI8reS5ZxkYhkHP5l7wrWEdlYFtHdPXB+bBz88Pfn5+ZWrDXBcbd3d3HDhwAIWFhUhPT4ePjw+mTp2qHK8x/QJyASMkJET5d3Z2tkaBj6F6uUQZEFaqCjACNUFDmp8JYeVqANS/Y/rMjVmzZmH48OHKG3CLFi2Ql5eHL774AjNmzIBAIMD333+PqVOnYujQoUqbp0+fYuHChRpzizk6OsLR0bgb5jXZG+gpvIJ2gnv4Q9pHo13L2q64+ZxCd22RzHM7kHV+l1YbrxHL5L/wLrCwuhOZquxnWd2LNXI7fpvnz5/j2LFj+Ouvv9T2zZw5U/l769atAQDz5s3jbFdl4cKFmDt3rvaxqyBgAJFC3CPPPYLgoAjLhYxy7hEEB0VBDRL3iFI4ODjA398fERERGDBggHJ7REQE+vfvz3tMQEAADh06xNl2/PhxtG3bFvb29mr2IpEIn332mc6xiEQi3L17F126dAEg1xO8vLwQERGBNm3kERhFRUWIjIzEr7/+qvc56kIoLQQASBjy3Atq5omkrEI0r6W+mE5YJxYR9/g4ePAgsrKyMGLECL3szX2xcXJyQq1atSAWi7F37158/PHHRvcLlE3AIAhDYIT2cPB6AwVPrqPSmx2V2wufXIdzw/a8xyjmhqoXXum5kZ+fD4GA69wrFArBsqzSy0+TjSlDD1W5LGsCAHhbcB8MZGA1OB8LqEqNzeLy1geo3KSrVhs7V0+IU+MhzctU2yfNz4ZQxTNPFWEVuTeeLC8DqFIDgDxJsOpijZeXF4qKipCRwRXLU1JSlNWlVdm0aRNq1qyJfv366TgzoEOHDsjOzsbLly95F4cMWRQC5KudIsgfPp1AAgZBqKIoqEE59wiCi6JaLnnuEXyEhIRg+PDhaNu2LQICArB+/XokJCRg/PjxAOTPKomJidi6dSsAYPz48Vi5ciVCQkIwbtw4REVFISwsDLt28S/U9unTh9ej77vvvkPfvn1Rp04dpKSk4KeffkJ2drbSWYBhGEyaNAk///wzGjZsiIYNG+Lnn39GpUqV8Mknn5js/BNTM9EYQK5UqNO2orNueFu9FsAJ66HcxL0ffvgBDx8+1FvcA8xzsbl06RISExPRunVrJCYmYs6cOZDJZJywYV39EkR5U/XtD5F2eCkcvd6Ao08T5Nw4Ckl2KlxaywtbrFsyH7vZXLW5MX36dADyqtGl50bfvn2VOTPbt2+PR48eYdasWejXr58ybL1v375YsGAB6tSpg2bNmiEmJgZLly7F6NGjzXKet9l6yGMdUZ3JRTPmCW6z9Xntrj/LNEv/hPkRVnLlDbctjaNPE7CiPIhe3Ffm3RO9uA9WlAfHWk14j7Fz9YSwcnUUPImBg2cDAEBRkYiz6uvv7w97e3ucPn1aeVxSUhJu376N3377jdMey7LYtGkTRowYwbs6XZqYmBg4OTmhWrVq/OdkxKJQEWsHMIAjQ+IeQagiYxSeexSyThAcBIpiMzQ3CHWGDBmC9PR0zJs3D0lJSWjevDnCw8NRt25dAPJnooSEBKW9n58fwsPDMXnyZKxatQo+Pj5Yvnw5Bg0axGn30aNHAIDhw4fz9vv8+XMMGzYMaWlpcHd3R4cOHXDx4kVlvwAwZcoUFBQUIDg4GBkZGWjfvj2OHz8OFxcXk51/kSgfEAK3XoowxGSt2i4k7NkW5Sbu3bt3z+BjzHGxKSwsxMyZMxEXF4cqVaqgd+/e2LZtG+flS1e/BFHeVG7SFbKCHGSe/xPSvFdwcKsLj4/mwM7VAwCQlZ6K7NxUpb1ibkycOBEA8Ntvv6nNjZkzZ4JhGMycOROJiYlwd3dXinkKVqxYgVmzZiE4OBgpKSnw8fHBl19+aVCxHEOQwA6RslboLbyMD4QXcVvCL+4RFR97N184+fkj/egK1Az6GgCQfmwlnBu8DfuatZV2iRvGo3q3Eaj0ZkcwDAOXtv2RFbUH9tV9YFfdB1t/WctZ9XV1dcWYMWOUobM3btzAnDlzlAmeVTl16hTi4+MxZswYtfEdOnQIycnJCAgIgLOzM06fPo0ZM2bgiy++MKlXtwhyUdGJxD2C4KAMy5XS3CAIDhSWS+ggODgYwcHBvPs2b96stq1bt264du2a1jbfeOMNAOAULFPlzz//1DkuhmEwZ84czJkzR6etsTgWR0Ionq8IwpYoN3HPWEx9senWrRtiY2PL1C9BWAMub/WBy1v8eei++nEJhrarw9nWrVs3nD17Fq6urrh165Zaklk7OzvMnj0bs2fP1tyniwtCQ0MRGhpa5vHryz/SjugtvIz+wgtYIvlYWVCAeP1w6/sdMk6sw8u/ZgEAKr3RHjV6cD2qJa+eQybKV/5dtf0gsBIRXh1fA2lhLio1a6226rts2TLIZDKsX78eQUFBeO+997B582a1QkthYWHo2LEjmjRR9xS0t7fH6tWrERISAplMhvr162PevHmYMGGCKT8C5cOnA0sCBkGoogjLBUsCBmE+Vq9ejUWLFiEpKQnNmjXjLIDyERkZiZCQENy5cwc+Pj6YMmWKWiRQZmYmZsyYgX379iEjIwN+fn5YsmQJevfubZpBU1guQWhEKe6xlHOPsD3M/lb88OFDXLhwAcnJyWAYBp6enujYsSMaNmxo7q4JgiimInlUn5a1RgpbDd7MKwwUnsVuKZVOf10ROrvAre93Wm3q/nCY8zfDMKjW+VNU6/wpAODLdxqgefPGHBsnJycsWrQI69evR3Jyssbqajt37tTYb8+ePdGzZ099TqNMFBWLe44MvaQRhCqKiqAUekiYi927d2PSpElYvXo1OnXqhHXr1mHw4MEa7ePj49G7d2+MGzcO27dvx/nz5xEcHAx3d3dl5ERRURF69OgBDw8P/P3336hduzaePXtm0rBDhbhHc4Mg1FGkOSkkzz3CBjGbuKcolnHo0CG4urrCw8MDLMsiNTUV2dnZ6Nu3L7Zu3apXSeoKTwUSXgjrpCJVZBbBAeskH2CW/Xb8YLcLZ6St8BI1yntYhI0iEpun+IulELHFnnsoKueREIR1IVXk3JPS3CDMw9KlSzFmzBiMHTsWABAaGop///0XDx484LVfu3Yt6tSpo4x2aNKkCa5evYrFixcrxb2NGzfi1atXuHDhgjKXq8nTAFFYLkFoxKn4eUpRsIwgbAn+UpMm4H//+x/i4+MRFRWFjIwM3L9/Hw8ePEBGRgYuXLiA+Ph4/O9//zNX9wRBqFCRPPcAYKs0EHdkdVGDycVOhwV4g3le3kMibBSRxMbFPaXnHoXlEoQqUkY+N6haLmEOioqKEB0djcDAQM52TfnEACAqKkrNPigoCFevXoVYLL+G//PPPwgICMCECRPg6emJ5s2b4+eff4ZUqtnLTiQSITs7m/OjDUZRUINC1glCDcq5R9gyZvPc++eff3Ds2DG0b99ebV/79u2xbt06i4Qs2QIVTHchrJCKVulIDDt8KZ6MPx1+QgNBEo46TMVpWWuclzXHY9YHyWwN5LFOKIQDCuEACYRgwYAtPl7+O1P8O5S/02x8/RBJbDssSSnugcQ9glClRNwjzz3C9KSlpUEqlcLT05Oz3d3dXeMxycnJavaenp6QSCRIS0uDt7c34uLicOrUKXz66acIDw/Hw4cPMWHCBEgkEo3FyhYuXIi5c+fqP3ihwnPPthe3CMIcOCo891gS9wjbw6w597QJChVNbCAIa6YizrbnrAcGiubiJ/uNCBRGo4fwGnoItVfqIojS3HjRC4DuCm3WiiLnXg1HVoclYQ3kFIrxxdZo9Gnpjc86mDjUjuCgEPcEFJZLmJHS7zMsq/1arMlesV0mk8HDwwPr16+HUCiEv78/Xrx4gUWLFmkU96ZNm4aQkBDl39nZ2fD19dU8CEXOPfLcIwg1FJEQ5LlH2CJmE/f69u2LcePGISwsDG3btuXsu3r1KsaPH49+/fqZq3uCqMCwsIcU9pDAHhI4FP/XjpFy/raHBA6M/L+OhT4Aapf3wE1OCqrjC/G3aCh5jiDBFbQWPEItJg1eTAYqQUShioROWvlWK+8hlAlHJ2dADLx8lVXeQyH0YMPZeETFpSMqLp3EPTMjEZDnHmE+3NzcIBQKkZyczNmelpam8RgvLy81+5SUFNjZ2aFmzZoAAG9vb9jb23Oqszdp0gTJyckoKiqCg4N6HjBHR0c4OjrqPXYBFdQgCI0oIiEKKeceYYOYTdxbsWIFhg0bhnbt2qFatWrw8PAAwzB4+fIlsrKyEBQUhOXLl5ure4IwCAFkasKYPaMinEFaso9RFdWkKtvUBTeFreq+knalHBHODurinGpfyv2M4Q9jF195AGhj+g/OSnjI1sZDaW2g1EfDQAZHiGEPaXFQbvEKOVAcmFv6b+J142qvPuU9hDIhLn74pGq5tkFuoW3+O61evRqLFi1CUlISGjdurNM+MjISISEhuHPnDnx8fDBlyhSMHz9euV8sFmPhwoXYsmULEhMT0ahRI/z6668mTddSEpZLizyE6XFwcIC/vz8iIiIwYMAA5fbTp09rPCYgIACHDh3ibDt+/Djatm2rLJ7RqVMn7Ny5EzKZDAKBPDX6gwcP4O3tzSvsGYWQPPcIQhPKnHsUlkvYIGYT96pVq4Z///0X9+7dQ1RUlHKlysvLCwEBAXo9HL4uVMQIZQYy2EOqLlgxpbzNIFV6lylEMFVbjqhWSnBTPaakXXURza6UiFa6LztIIGRsN6RNxjIQww5FsIMYQohhJ/+btYOk+G+JsHJ5D7NcYCFAIRxRWN4DIawXJ9fyHkGZkAqKxT3KuUeYid27d2PSpElYvXo1OnXqhBUrVuDGjRt49uwZmjVrpmYfHx+P3r17Y9y4cdi+fTvOnz+P4OBguLu7KyuCzpw5E9u3b8eGDRvQuHFjHDt2DAMGDMCFCxfQpo1pFqKUYbkk7hFmIiQkBMOHD0fbtm0REBCA9evX4/nzkgJf06ZNQ2JiIrZu3QoAGD9+PFauXImQkBCMGzcOUVFRCAsLw65du5THfPXVV1ixYgW++eYb/O9//8PDhw/x888/Y+LEiSYbt0BYLHyz5LlHEBxYFs4MVcslbBez5twDgMaNG5OQZxJYCFW8y+xUPMY4YZjFopWdBu8yh9KiGI/gpiqWlfYuUxPVNHqy2fYDg5hViGTCYtHMDmLWrkQ4Q4lwVqSyXWmvIqyVPl7xt0TFVnNfQk6ffGOQQgBdWfUWe7ayzAdHEIRlsXMERPIE0BKpDHZCQXmPiKhgLF26FGPGjMHYsWMBAL/88gvWrFmDsLAwLF26VM1+7dq1qFOnDkJDQwHIQwqvXr2KxYsXK8W9bdu2YcaMGejduzcAuaBx7NgxLFmyBNu3bzfJuGUCyrlHmJchQ4YgPT0d8+bNQ1JSEpo3b449e/Yov9dJSUlISEhQ2vv5+SE8PByTJ0/GqlWr4OPjg+XLlyvnBQD4+vri+PHjmDx5Mlq2bIlatWrhm2++wQ8//GC6gVNYLkHwo3K/oJx7hC1idnEPAJ4/fw4fHx8IBALO74SczzNXYZbDTRWPMylKe5fZQwKBjXuXFYFHsOIIY8WCFasqaAnVhDGOaFZK7JLbCiHR4MkmLj2GUoKbYl9FK0FRsc6GIAgFMqE8z5IjxCgQS+FiQ+JeXGou3F0c4eJED9DWSlFREaKjozF16lS1fZcvX+Y9JioqCoGBgZxtQUFBCAsLg1gshr29PUQiEZycnDg2zs7OOHfunMaxiEQiiEQi5d/Z2dlaxy5h5F4XFJZLmJPg4GAEBwcr/1b9Xm7evFnNvlu3brh2TXvxr4CAAFy8eNFkYyyNoDgsV0BhuQTBRVIS60PiHmGLWETca9q0Ka5fv4769etzfifkeEiT0Uzw1ODjilREMKVgxSt2cUUusZrHmIo3GSvkFbsUwpiEx7ustC1fuzLYzgtnRcR2ZWGCILShCMt1YCQ2Nc/vvMhCn+XnUNXJDjfnBJX3cAgNpKWlQSqVwtPTU23fy5cveY9JTk5Ws/f09IREIkFaWhq8vb0RFBSEpUuXomvXrmjQoAFOnjyJgwcPQirV7Em0cOFCzJ07V++xM3bFIVUSkXZDgnjNYJTiHnnuEQQHSYnnXpFlZBKCMCkW+daqloXXVSL+deRvl+FYlvOumneZthDNiuhdRhAEQRiGVKDw3CsCKyvnwRjAqbspAIBsGy0wYQ4y8opQrZI9GCtMxMs3Jm3jLL1P8eyn2P77779j3LhxaNy4MRiGQYMGDfD5559j06ZNGtucNm0aQkJClH9nZ2fD19dXo72ieqhUQmG5BKGKQvgWsuTVShAcisNyi1h6zyZsE5KkrYA4h0aIkrmV9zAIgiAIG0NSXDTAEWKwNuS7Z+qRPnuVj2qV7G02xPe/B6kYsfEyBvvXxuKPrCdHqpubG4RCobIomioeHh68x3h5eanZp6SkwM7ODjVr1gQAuLu748CBAygsLER6ejp8fHwwdepU+Pn5aRyLo6OjUrDTBzsHZ/kv5LlHEBwExekchBSWSxBcitM4SEgiIWwUipW0AmzphYwgCIKwHsSM/CXNARLIXpNbSeyLbKRkl+TFeZqehy6/nYb/TyfKcVT6ocnZ7feTDwEAf0c/5zcoJxwcHODv74+IiAi1fe3ateM9JiAgQM3++PHjaNu2LeztueKrk5MTatWqBYlEgr1796J///4mG7vAjnLuEQQvdvJ5SOIeQZRCKr9fiCHEb4NblvNgCMJwSNyzAihSmSAIgjAGaXFFUCemCAVi28mfZOx973FqLnovP4t2P59UbrvwOB0AUCSx/rhkW7zfh4SE4I8//sDGjRtx9+5dTJs2DQAwevRoAPJw2REjRijtx48fj6dPnyIkJAR3797Fxo0bERYWhu+++05pc+nSJezbtw9xcXE4e/YsevbsCZlMhilTpphs3IydXPgWkLhHEByExcK3HWhuEIQqIpF84VAMO7TxrVa+gyEIIyBxzwqwwWd9giAIwgooEsgrjjpBhE6/nML5R2kW6ff8ozS8t+QMrjx5ZdTxxnqsX3uaobbtdcmKk5UvhkRqeQFzyJAhCA0Nxbx589C6dWucP38eAFCnTh0AQFJSEhISEpT2fn5+CA8Px5kzZ9C6dWvMnz8fy5cvx6BBg5Q2hYWFmDlzJpo2bYoBAwagVq1aOHfuHKpVq2aycQvsi/OKySjnHkGowhR77tmR5x5BcDh7X55SQgKhVea/JQhdUEC5NUDqHkEQBGEEhYw8r1hlyPOKzT10B8cndzN7v5/+cQkAMGRdFOIW9jF7fwoq6u1SV7GxhPR8dF10Gk29qyL8my4WGlUJwcHBCA4OBiAvZOHq6qrct3nzZjX7bt264dq1axrb69atG2JjY00+TlUE5LlHELwIi+cGiXsEwUVxvxCzdvCoqn+OV4KwFshzzwqgnHsEQRCEMYgEcnGvEiMPJbF03j1j+zNleGpFWFxPeJWv/F3G86F2XXQaABCblI0N/8XhaXqexcZmqwjtFQIGiXsEoYrCq5XCcgmCi2dluTQihhBVbbRAF/F6YxFx77PPPkPVqlXVfifk2GIOHoKwFJ+0l4d+fdjap5xHQhDWh0hQCQBQGXJxT5cHWFnhE56MwehWeA5kbCgwt7QQmZJdiIX/3kVarnroqEzGYtXpR7gYl87ZviD8Lnos+8+cw6wQCBVhuSTuEQQHhVerHchzjyA4FBfUENiRsEfYJhYJy12zZg3v74QcKal7BKGROX2boW9LH7xVtxoOXH9R3sMhCKtCVJxzTy7umdcPfNXpR1j/Xxz2BXdU2xeXmot91xIxprMfqld20NrOjWeZWF5cHdZQeM9Qi7b38GUOlp96hG/eewNveLgY1acpKX27/3pnDC6XyluoMDl8KwmLjt3nbceUxUNYlq2QuYWE9vK5QRVBCYKLIizXnuYGQXBgiheDJJS5jLBRKCzXCrB0GBVB2BIOdgIENKgJRzuhQcd93La2mUZEENaDIueePSOFAyRGe4IXSWQYtekyVp1+xLv/3MM0LDp2H1kFYvx85K7a/t7Lz2Ll6UeYuu+mzr76rzpv3CBhuKf7x+uicOjGCwxdf4mzXSZj8SKzQM3+zP0UxKdZLuS1tLAHlHhfPrHAOJKyChCw8BRWGCm2WjOKsFwhSwU1CEIVprharj157hEEB0YqnxMk7hG2itnFvdGjR2v9IUwX5kQQFZ1KDvoLfIIK6IlCEKURF4flAkAlFBodlht+Kwln7qdq9BT7LKxEHOObWoViuSfZtYRMrf2Y436nbaZn5MtX4dNyRZztU/fdRMdfTmF/zHPltuinGRi16QreWXzG5GPUBN9nqfiELHEFC414iOTsQiyJeGCB3iyLnULck4mRmU8CH0EosHMo9twjcY/QwOrVq+Hn5wcnJyf4+/vj7NmzWu0jIyPh7+8PJycn1K9fH2vXruXs7969u7IQk6urKxiGAcMw6NOnpCDXwoUL8fbbb8PFxQUeHh748MMPcf8+95lk1KhRymMVPx06dDDRWQMoLqghYUjcI2wTs4t7GRkZnJ+UlBScOnUK+/btQ2ZmpsHtmfpiAwChoaFo1KgRnJ2d4evri8mTJ6OwsFC5PycnB5MmTULdunXh7OyMjh074sqVKwaPXRMyCsslCL3YMrqd3rYCAYl7FYWfB7Tg3S4tzEXa4SVIWPYxEpZ9jLTDSyArzNXaFsuyyDy3A89XjUDCkoHo3r077ty5w7FZv3698oHT1dWV916VkZGB4cOHw9XVFa6urhg+fLiaXUJCAvr27YvKlSvDzc0NEydORFGRaUUGGSNEISvPDVMZhUaH5RaIpQZYGz+3Rm66bPSxAH+uPmNCSv+6Khf1Qk+UeKzdep5p5Kj0p/RQ+UaueCSwxJNBRU4LYu8oD8t1gATHY1+W82gIwnoQ2CkKakjBykwX4k9UDHbv3o1JkyZhxowZiImJQZcuXdCrVy8kJCTw2sfHx6N3797o0qULYmJiMH36dEycOBF79+5V2uzbtw8PHsgXkR48eIDbt29DKBTio48+UtpERkZiwoQJuHjxIiIiIiCRSBAYGIi8PK4Xe8+ePZGUlKT8CQ8PN9m5M8U596TkuUfYKGb/5u7fv19tm0wmQ3BwMOrXr29QW4qLzerVq9GpUyesW7cOvXr1QmxsLOrUqaNmr7jYjBs3Dtu3b8f58+cRHBwMd3d3DBo0CACwY8cOTJ06FRs3bkTHjh3x4MEDjBo1CgCwbNkyAMDYsWNx+/ZtbNu2DT4+Pti+fTvef/99xMbGolatWgZ+IuqQ4x5B6Mfb9Wrg96Gt8c2f13XakrZXcWjnV4N3e9o/iyDNSYPnx3MBAOlHVyLt8BJ4DJ6tsa3sS3uRfeUA3HpPhl0NH3jlnUWPHj1w//59uLjIc7Ll5+fjvffew7lz5zS288knn+D58+c4evQoAOCLL77A8OHDcejQIQCAVCpFnz594O7ujnPnziE9PR0jR44Ey7JYsWKFUZ+DJvLgBCeIUYkRQaTlfiKTsRpFb0OmS1mcYs8+TDP+YJi+AJXq4pqxTf919RnuJeVg1gdN8OxVAV5kFaBD/ZpGj8m8mRO5VOTLpMJzzx6SCn2eBGEo9sWeewKGhUQqgZ1Ae55U4vVi6dKlGDNmDMaOHQtA7gRz7NgxrFmzBgsXLlSzX7t2LerUqYPQ0FAAQJMmTXD16lUsXrxY+b5do0YN2NnJZQdPT09s3LgRlSpV4oh7iucpBZs2bYKHhweio6PRtWtX5XZHR0d4eXmZ9JwVMDL5AqyEMSwVEEFYC+WSc08gEGDy5MlK8UxfVC82TZo0QWhoKHx9fTUW6VC92DRp0gRjx47F6NGjsXjxYqVNVFQUOnXqhE8++QT16tVDYGAghg0bhqtXrwIACgoKsHfvXvz222/o2rUr3njjDcyZMwd+fn4mKw5i7uqGBFGR6N9aP0GdwnIrDnz/lOK0ZyiMj0bNXhPhWKsJHGs1Qc2e/0PB4ysQpz9XPwDya23O1YNwDRiCSo06wsG9HrZs2YL8/Hzs3LlTaTdp0iSEhIRoHM/du3dx9OhR/PHHHwgICEBAQAA2bNiAw4cPK0NIjh8/jtjYWGzfvh1t2rTB+++/jyVLlmDDhg3Izs4u2weiAsMA+WxJUQ1NnuD/PUhFq3nHceRmUtn7LHMLxrM28rHatrKMx5DbL8uyyBOph7FN+fsmNp6Px9mHaei66DSGrr+I2Bf8/8aq/R268YLX61BhQ1ewsmFX7Llnx8jAgLyTCEKBIh8lAEjEIi2WxOtGUVERoqOjERgYyNkeGBiICxcu8B4TFRWlZh8UFISrV69CLOavVh4WFoahQ4eicuXKGseSlZUFQC4MqnLmzBl4eHjgzTffxLhx45CSkqKxDZFIhOzsbM6PVmSUc4+wbcqtoMbjx48hkeif68FcF5vOnTsjOjoaly/LQ4Xi4uIQHh6uDMmSSCSQSqVwcnLitOPs7KzVq8OQi4mjvXWuDrg40oWNsF1I3Ks48P1bil7cBeNYGY4+jZTbHGs1BuNYGaJE9YIPACDJeglpXgac/dqUHOPoiG7dumm8j/ARFRUFV1dXtG/fXrmtQ4cOcHV1VbYTFRWF5s2bw8fHR2kTFBQEkUiE6OhovfvShzzI70+VmEKNYtWIjZeRUyjBhJ3X9G6XZVksi3iAU/e4IY3lObUSXuWbtD19xb241Fz4TQtHs9nHEJfKH/qdoZLX7aYeIb6rz6gLlYbCsiwWHbuHf25QJfHSODiUPLcJZPwvmATxOmLHEfcoHyVRQlpaGqRSKTw9PTnbPT09kZyczHtMcnIyr71EIkFamrq3fnR0NG7fvq30DOSDZVmEhISgc+fOaN68uXJ7r169sGPHDpw6dQpLlizBlStX8O6770Ik4hepFy5cqEyf4urqCl9fX419AgBTLO5JKeceYaOY/Ztb2vuBZVkkJSXhyJEjGDlypN7tmONi4+3tjaFDhyI1NRWdO3cGy7KQSCT46quvMHXqVACAi4sLAgICMH/+fDRp0gSenp7YtWsXLl26hIYNG2oc78KFCzF37ly9zm3x4Jbosew/vWwJoJGnC+6/zCnvYRBWDml7FQe+SFJpXiaElVzVtgsruUKal8HbjjRXvl1QqRpnu6enJ54+far3eJKTk+Hh4aG23cPDQ3k/4rv/VK9eHQ4ODhrvWSKRiPOAqq+HXz7kL2qVUajDUjN8GldE7Ev8zlNFlSnlU/ZYg9hlakoXxVBQlrmur+f893+XVAHeeSkBMz9oqtX+wctc3jBo1bEKBfzeeQVFUjjpueh37lEaVp2Wi4T9WvnosH69sHNwLvldavzcIIiKhp2dvfJ3aRHNDUKd0l7lLMtqzW/LZ8+3HQC2bt2K5s2bo107zXm0v/76a9y8eVPNkWbIkCHK35s3b462bduibt26OHLkCAYOHKjWzrRp0zhaRHZ2tlaBTxGWSzn3CFvF7N/cmJgYzt8CgQDu7u5YsmSJUdVyTX2xOXPmDBYsWIDVq1ejffv2ePToEb755ht4e3tj1qxZAIBt27Zh9OjRqFWrFoRCId566y188sknuHZNsweEIReThp4uWs64/LDWYOGRHeth+v5b5T0Mm+J1DP0mzz3bJPPcDmSd38XZVu9Xro3XiOKUDrz/xqxutcfA+wh/E3zhlNx29LFRxZBFIVVyWbmIUQUFGsNyBYzh+V1fZBbwbi89/IGr9fd61Ie0XBFqVHIAC0BYLI7dep6Fviv5veVVx3MpLh1NfarCxckehRqKhMQklIi/qh+JtsukajjuH+fi8e/tZPwyqAW6NHTntd94Ph5CATCjD1cEDDsXr/xdyDC8X9X2P5/EgwW99BItNQmeBCCws4eYFcKekYKRkIBBEAqEQgFErB0cGQmkEvLcI0pwc3ODUChUW4RMSUlRW7BU4OXlxWtvZ2eHmjXV88/u27cP8+bN0ziG//3vf/jnn3/w33//oXbt2lrH6+3tjbp16+LhQ/WFSEAeneHo6Mi7j4/nadloCCCt8PV7byIqBmYX906fPm2Sdsx1sZk1axaGDx+udA1u0aIF8vLy8MUXX2DGjBkQCARo0KABIiMjkZeXh+zsbHh7e2PIkCHw8/PTOF5DLybWyOsoCBEVB2EZKmp82a0+1kXGmXA0pmV+/2aYdfCObkMbxOWtD1C5SVfOtu1j2uOzsEvKv+1cPSFOjYc0L1PteGl+NoT/Z++845q43zj+uQQIe29lOUFxggPRunGvWre4J44qWuteraNqrXVSFdfPUatWaytVsI46cCHWvcEJKspwMULu90fIkZANARJ43q9XXpC77933e5d7bnzuGQU88yTwLe0AAKKPqYBlfg4XVdcRRbi6uuLVK/nqm2/evOHW4+rqiosXL8rMT01NRU5OjtK+tH3DzLWDOGeNLfMBSemZSP2YDTsL2QTpfB4DUa78OT1XxOLas1RkC+Vzkim7AhQUndI/54c8vnmfhZjbr9C2hub7U5qLj9+iz8YL8LA3Q+rHHExoVQWjm1fG7kuKq/QBsp6EfTZegK+rFY5O+gKbz8jb8KuMTPSQEiNZVtynp4O5VuN8kfYZoZGXkLi0k8y6pNl0JkFO3JNGWXGT7Fzd5od7+PoDRv/vCia2rqpxztKygJWpMT7DBMb4DFOQCEoQEhiGQQ6MIIAQudlkG0Q+JiYmCAgIQExMDHr06MFNj4mJQbdu3RQuExQUxBUTkxAdHY3AwEAYGxvLtc/KysLAgQPlprMsiwkTJuDgwYM4deqUyudsCW/fvsWzZ8/g5uamtq0mXHr4Ci2NASGrnymzCEIdpZZzT1ukTzbSxMTEoEmTJgqXCQoKkmtf8GTz6dMn8Hiyu4HP54NlWTlxy8LCAm5ubkhNTcWxY8eUnuQIgih9NPF6WdGrjsLpMzr44dKs1joekQ7RA6/EP8c3RdzsNljZW/E+VIYJX/Vlh29uA2MHD5lP1erVZb4zRiYQuPuBzfqIrJf3uGWzXt4Dm/URggp+CtdtZOMCvoUdPifme5RnZ2fj9OnTSq8jiggKCkJ6ejqXqxUALl68iPT0dG49QUFBuHnzJpKS8gtYREdHQyAQICAgQOF6BQIBrK2tZT7q8HW1RiprCUAs7gFAqx9PybWT9mT9dv91LP37LgBg3cmH6LkhFvMOy4vFyjz9CoblFmTkjisKp2vywuin4/cBAM/efcaHLCGW5I1TG+4mi9M2PE+V9zxM+ySbey05IxN9Nl5A0JITMtO//+s2foy+h14R55ElVOwBWFT4DKN2X6pDk3dwU/f9h0dvPiqsMq4Hp5Jiw8yEj2yIRW4jEQkYBCFNTp5/R66QbIOQJTw8HJs3b8aWLVtw584dTJ48GU+fPsWYMWMAiF9EDho0iGs/ZswYPHnyBOHh4bhz5w62bNmCyMhITJ06VeH6O3XqpNCjb9y4cdi5cyd2794NKysrJCcnIzk5GZ8/i6/lHz58wNSpUxEbG4vExEScOnUKXbp0gaOjo4wQWRSMIPbSzwGJe4RhUiIB5fv378dvv/2Gp0+fIjtb1v1bVWhrQcLDwxEaGorAwEAEBQVh48aNciebFy9eYMeOHQDEJ5u1a9ciPDwcI0eORGxsLCIjI7FnT37IV5cuXbBy5UrUq1ePC8udM2cOunbtCj5fbNjHjh0Dy7KoXr06Hj58iG+++QbVq1fH0KFDi7prOCxM+PiYXbQHiEqOFnic8lFHI9LPsNwiOGMR5QxNHpp9XZWHxOtzWO8XVR1LewioVVGc785YjVhXkL8mNkWIljlGFf0Wxo4eMPUJwNuja+DQbjwA4O2xtTCr3ADGDvlhHC82jYFd80Ewr9YEDMPAKrAb0mP3wdjOHUZ27hgyZAjMzc3Rv39/bpnk5GQ8fPiQ+37jxg1YWVnB09MT9vb28PPzQ/v27TFy5Ej88ssvAIBRo0ahc+fOqF5dXOAjJCQENWrUQGhoKJYvX453795h6tSpGDlypEainaaMbl4JG0+JxT07iMW91AIC1pazCciS8szbe+UZAGB6B19sOZeAgrzKyISNmTFuvUxX3GkhTUNVWPCGU48gMOLhwuN3Wq9XG1NlNbyybZYKn9W0wrCqdZ+69xqfClzjVZ1jRCIW156lqexvzT8PYGai/gHkU7bmxcvKGiIjMyA3HchRHGJOEOUVSTXQXCqoQRSgT58+ePv2LRYuXIikpCT4+/sjKioKXl5eAICkpCQ8fZrvTe/j44OoqChMnjwZ69atg7u7O1avXo2ePXvKrFdyXxUaGqqw3w0bNgAAWrRoITN969atGDJkCPh8Pm7cuIEdO3YgLS0Nbm5uaNmyJfbu3QsrK92kuDJixNfpHMq5RxgoxX7krl69GrNmzcLgwYPxxx9/YOjQoXj06BEuX76McePGabWu4jjZzJ49GwzDYPbs2Xjx4gWcnJzQpUsXLFq0iGuTnp6OGTNm4Pnz57C3t0fPnj2xaNEiha7GhUUXQpqutQh9jMo109PKwoT+oaXmJIe+Snsre9eBl4NFqY5hYbea3P/anneqFSLHqDIRxLHLVKQe/wWvfhPnRzWv0gj2bcfItBG+ew5RVn6FVetGPcEKs/AuegNyMz/gRXAQoqOjZW4MIyIiZHLfffGFOExYcoMJALt27cLEiRO5iuxdu3bF2rVruWX4fD6OHDmCsLAwBAcHw8zMDP3798eKFSu03n5VmBrzkcaKx27H5BcaWvL3HUxpWx0ilsXCv24rXFbsoS4/veeG87C3MMH154rFPU3ErnvJ72FrbgwX6/yKpcryAb79kIUfjmrvodf0hxMY3bwyrqsRwaR5laHcS0XZJS9HRZjs1H3/cf+LVETTDtl6WW4ajwelJ5oNpx/h+J3XylcI4MeY+0rnnbz7Ggv+vIUfe9dR+BtH30rG9efpKvdHWSCHJwByQeIeQRQgJ68aqIhy7hEKCAsLQ1hYmMJ527Ztk5vWvHlztQ47VapUAQC0atVK4Xx13v1mZmY4duyYyjZFxSTPc09I4h5hoBT7kbt+/Xps3LgR/fr1w/bt2zFt2jRUqlQJc+fOxbt32r+h1/XJxsjICPPmzcO8efOUtunduzd69+6t9Vi1ga+HXkKaejiUJAODvEp7CISBoInnnar7CG0LLJQU0mJJaXBhRmu42hRuDHGz2xRqOWUeu3wzKzh2URz2IcHr279kvjMMA9umA2DbdAAA4LRUzjQJ8+fPR3h4OGxsbJCenq7Q087e3h47d+5U2benpyf++usvlW10AReWi/yqtb+cfgwXK1P0bqA8Z5+y4/956meFIa3a0G6V2DtTOidd6kfFD5G5am7ofef8jcwceeXseepnzDl0U+EyLMvirYL+Bm+5pKC1alR5Ae+Pe652eWUPLKrygkoX3tCUmy/S4V9B7FE7dJtYTBy4+RIq2pnJtR31vzit12+I5PDE5yqWCmoQhAz5nntlW+AnCG0wAnnuEYZNsefce/r0KZeDyMzMDO/fiz0LQkNDZcJjyzsVFNx8a4uupTh99NwbFOQt892I4nQJJWgizqkSsOnIUoyFoPDesw6WhSwyRD+GSlKRF5bLfJCZ/uTtR5liFwURKcgtq2t2XnjC/a8orx8AGPNU34ooEvbUMeePm4i5LV/0pDBk5Yq4PH6qULYnle1iHqNcNhQWoqhG5zVnkS0UIe5J/ovTzzm5eviaruQQMuJzDpPzSU1Lgihf5DLi6CMhiXsEwWFMOfcIA6fYxT1XV1e8ffsWAODl5YULFy4AABISEqgaqxRGfP17es1VlSCplNC/vUToK5rovqo993Q3lrJEwV1WlIIA/hWsMaZ5ZbXtdJ3/sHdgRfWNDAhJWK5tAXFPxAKrjz9QuhwLICOzePOxzZbyrLv1MkPpOHTNzgvy1XVP339TqHUp8w4siKJ7mvDfrmHG7zcUtufzGKXnGWEhr79d155Fzw2xMtMevv6gpLUsZfGeTMjPe6GQQ557RPGwfv16+Pj4wNTUFAEBATh//rzK9qdPn0ZAQABMTU1RqVIlREREKG3766+/gmEYdO/eXcejBoR5Ybk5VC2XIDiM8zz3qFouYagUu7jXqlUrrjz28OHDMXnyZLRt2xZ9+vTRWWWbskBRw3K/rF9BRyPJp7APFyVJwd02oJFn6QyE0Ds0EYSU5QAD9DcstyzRsZYbpnfwVdtOl+Let+19Ud1VdwUt9IF3EIt79ngPaansfxeeIClDuaih6vgvDpT1V1KikrqQ3E9ZRRM6FW3F71dfcAVMCqLqui/MLdw+UedhmKCi6JYmIcaGhiQsl8mlnHuE7tm7dy8mTZqEWbNmIT4+Hs2aNcNXX32ltH1CQgI6duyIZs2aIT4+HjNnzsTEiRNx4MABubZPnjzB1KlT0axZs2IZu4g89whCDgrLJQydYhf3Zs2ahRkzZgAQV6/dtm0b/Pz8sGDBAkyfPr24uzcYiiIk1Klog+Vf1dHhaAyHgvttZLNKWi1/eHywLodTYnSq5VbaQ9B7NPLcUzGvOLW9QC+7Qi9blpxrNPX60+VPURYj+V+x4uPJnMmCNWTFm39VeKu9TCs5b6ZdF58ozeOnL4e0qgIVGqHlhvxz97VSe84uRFiuJrRccUrpvB2x4hDqD1lCnL7/RmUhEUMhN89zj0c594hiYOXKlRg+fDhGjBgBPz8/rFq1ChUqKH/ZHhERAU9PT6xatQp+fn4YMWIEhg0bJldoKTc3FwMGDMCCBQtQqZJ297WaIuLl5dzLJtsgCAnGDIXlEoZNsYt7VapUQVpaGve9d+/eWL16NQYMGABfX/UeG+UFVYm11eHjaCFeXl+ekIoZVXnStPXwcbMpWq5DP7fS8QCq6mJZKv0aEpoI5nbmJhispEhLcWpA1ma6q7RdWIY08dbJenQhgpoaq74U8XSoyJVFh8ywNv5IYcXnogrMW42XUyX06JpZB5WHtpYVwbow3u5ZwpIX0ERKxvkxz3PRf94xDN5yCauOF1Hs1AOEEs89EvcIHZOdnY24uDiuYroEZZVAASA2Nlaufbt27XDlyhXk5OTnR124cCGcnJwwfPhwjcaSlZWFjIwMmY86hIwJACCXQtYJgsOYq5ZL4h5hmBS7uKcs3ObDhw8wNS3dqo/6RFHCcstz+GDBLS/JXTG3cw0Mb+pTqGWLKgqWlYfh4kQTwdzH0QL1lXjRFZddda7tBmM9zLGpKcVx7Knz4NOltx1TpCyB+kkFOzO8ZB0AAO5MSimPRnv0sTJ7YRCKDMPTLUfJOD8UCEted/JRSQynWBHxxfeZfArLJXRMSkoKcnNz4eLiIjPdyclJ6TLJycly7V1cXCAUCpGSIj53nzt3DpGRkdi0aZPGY1myZAlsbGy4j4eH8irpEnLzbEOUTcVmCEKCMYXlEgZOsR254eHhAMQPyHPnzoW5uTk3Lzc3FxcvXkTdunWLq3uDQxc6Qtl4PNKOgvtN2/1YlP0+rKkP/rj2olDLFvXnLo+/tbYUVRCSXnzJl7WUJsXXFjcbUzx7p/5Bs05FG/z3PF0nfeqUYjj4PO3Nce+V8lxhuhRay+K7kDZ+zrhwyBG1kQB3LTz39IWMz8Vb1KOkmPuH4mrA+sZ/zxSfV16/L3u5t3KNxALGk1fv0KSUx0KUTQpen9TlEFXWnmEYvH//HgMHDsSmTZvg6Oio8RhmzJjBPXcBQEZGhlqBL5cnDllnyXOPIDj4koIa5LlHGCjFJu7Fx8cDEF+0bty4ARMTE26eiYkJ6tSpg6lTpxZX9wZHUcJyiXx0XVVTbyHXPbUU9ViQXtzJUlDE0ciiiaeSuhbbhjbA9AM3sKJXHQyMvMhNb1LZAecfaSbwOFsJtH6gNzORveHRhcVFhAaUWIhoWfR0tjU34Tz3Khig516/TRdKewjlit6/xKpvVEZIyktBKcxUXkiEIAqDo6Mj+Hw+kpOTZaZLPPAU4erqKtf+9evXMDIygoODA27duoXExER06dKFmy/K87Q1MjLCvXv3ULmyfIV5gUAAgUC7+xSJ5x5yyKuVICTwIba33OIPbiSIYqHYxL2TJ08CAIYOHYqff/4Z1tZlqzqhrimKEFH2HlU1p+B+03Y/lta+K6q+QNKeetr4uWDXxacqq0MCynVS6eBNZfu7qrMlHrz+UMgRFo0W1Z1xYWZruen1PG0xsXVV9N2oXjA5OC4YUdeTEHk2AckqqqoOaeKNQUFeYBgGJkba3/D0beCBQUHectMlIqePo4XK5XUblls2ecyKi+xUYwyv4umbMugxRugHrz+Lz1emyC7lkRBlDRMTEwQEBCAmJgY9evTgpkuefxQRFBSEP//8U2ZadHQ0AgMDYWxsDF9fX9y4IRslMHv2bLx//x4///yzRuG2miLixD3y3CMICZJqubksiXuEYVLsR+7WrVtJ2NMAewsT9Y2UUYaeVmd38kMFW82LXGiSc0+VMGCoIhk57qnHzISPE1Oaqy3WoAzpY0lZmE1hRdqiZH3TyOtPw+Ojgq0ZRn5RCdZmqt/zsCyLSk6WCkU4TfbBN+2qo4Z74a8DuvTILatO0ndEngAAP97TUh6JPN3WnSvtIRDllPRccfEic4YEDEL3hIeHY/PmzdiyZQvu3LmDyZMn4/nz/BcsM2bMwKBBg7jvY8aMwZMnTxAeHo47d+5gy5YtiIyM5CKZTE1N4e/vL/OxtbWFlZUV/P39ZaKgigprlOfpJyTPPYKQwMvz3KOwXMJQIVlaT5jdyQ8Nfeyxul+9Qq9DXZ4PQ6B2RVucm6680pg6Cj63DwrywuMlnZS2N9RdVlYS0Bc3DMNAXY57ZftSRtxTtv5CiHSaHnNFOTY1OT6aVc3P52NuolrcK0QRUBmUhcJquv800fYiBwfi++7+2gyrTHGXFYt7bsw72EF9pcSS5L9naaU9BEJDHqjIfWmIvBWKBQxLkLhH6J4+ffpg1apVWLhwIerWrYt///0X+/bt4+YnJSXh6dP8Fy4+Pj6IiorCqVOnULduXXz33XdYvXo1evbsWeJjFxnlvUgXkuc0QUgwYiRhuSTuEYYJiXt6grO1KX4bHYSuddy1Xrbs1X7UHDnRgAF61KvAfbUUULUjwHA9FIuC5MgQFVIl08SuCu25p2S5/o3EAs3kNtUKt2Jofj5o5evM/W9tZqyybWHE5J/71pUaU9HQxHOvtZ8LXK3VV2BnUTaLanyEGR6KxNePhry7pTwawlBp+9O/pT0EndKuXlUAgCVD3klE8RAWFobExERkZWUhLi4OwcHB3Lxt27bh1KlTMu2bN2+Oq1evIisrCwkJCRgzZozK9W/btg2HDh3S/cDzis3whCR8E4QESUENyrlHGCp05JYBJA+q5VnAyf/O4Kc+dTVevqgecIp0o02DAmFtaoSaKsIQi5xzrzz+2IVEnbinNOceo75NYZFenzE/v6Pvu/kjevIXmNi6ShE7UD17dic/GWFcndevas89xQdzDbf847+ox7umYbnl3SxOi+oAAFrz4kt5JAShH9SvJn5hYgkS9whCBmOxuMfkkrhHEBKooAZh6NCRSxgc0jqEnONege+qHvZ/7lu3WNSAplUc8d+8EMzs6Kf7lefhaW8uN+391SN4HjEcT1b0QNK2r5H57CY3T5E0cvr0aXzxxRcAgNq1ayMiIkKuzapVq1C9enWYmZnBw8MDkydPRmam7I3gixcvMHDgQDg4OMDc3Bx169ZFXFxc0TZQh2gbUurlIN63mshJuqi8OqCRF/c/j8egmouVyvWqExoZRv02F1y/unx4hQn5l+6iqN7FZTVPnq45LqoPAOjIvwhrUHVQgmAE4nObJfMZWcLcUh4NQegReWG5fBL3CIKDxD3C0KEjlygRtNVAutXVLDy5oEhRsBtVmkQ1FyvtBqUhDCMeV3AVR+Vtiih2fBVQUeb7xzv/4t0/m2AT1BvuQ1ZDULEmXu+bD2HGawDyGmZCQgI6duyIJk2aAACmTJmCiRMn4sCBA1ybXbt2Yfr06Zg3bx7u3LmDyMhI7N27FzNmzODapKamIjg4GMbGxvj7779x+/Zt/Pjjj7C1tS3S9pUkBY+RP8aJQ2pkjy0lefkK2acmxTqKM69iwT6/bl1VTfvC9MIo/LdQa9LUc0+DgZZlr9dYUQ3cFXnAksnETKNdIF9GorzDmIqv81b4jJAyFnJMEEWBMRGLe7xcyrlHEBLyxT3KuUcYJpSQrAxgyE4tk9tUw0/H78tNVyW8qXrOLygCqBJIWLboj75FEWA61XLDkRtJAAAbM2Okf87ReFkjPg/Nqznh9P03AICMy4dgWbstrOq0AwDYtxmFzwlX8T4+CnbNh8gtHxERAU9PTyxduhQbNmzA4MGDcevWLaxYsYJL7BwbG4vg4GD0798fAODt7Y1+/frh0qVL3Hp++OEHeHh4YOvWrdw0b29vrfZDsZF3KHzb3hc/HNU8B5mtuYn04qq7KITxaXrEFFaEYqD9cam+oIby9SnbB2Uxr53+w+A74UDsMlmCvkanUImXhL9zG+IZ64x01gJZMIYIDFjwwAIQ5f1lDfoqQhQXZcFzgRGI7yUs8RlP3pI3K0FI4OWF5RqJyHOPICRQzj3C0CFxrwzA5dzTYycNHsMgV8EATY0VnzxVCQPSq1EbrlcK+0RTUWNNv3qcuNe0iiP3v6ZINo3NzUF28kPYNP5KZr6ZTz1kvVAsasXGxiIkJERmWrt27RAZGYmcnBwYGxujadOm2LlzJy5duoSGDRvi8ePHiIqKwuDBg7llDh8+jHbt2qFXr144ffo0KlSogLCwMIwcOVLpuLOyspCVlf+mOCOjeCt7jmleCecepuDswxStlpP+HZWFuZaGgKXJIV3Uc0FB0b2o1XKVUZz77/KsNvhywzk8eyeba4sFYFWGC+2cE9XCNzmj8L3RFjTk3UND3r3SHhJhoKSw1gBGlfYwioTEc4/HsDAHeSgRhARenueeEXnuEQQHheUShk7ZfcIpQ7hamyI5Q/mbNUmIZ8dabog4/UgnfTpZCfDmve4u+Mqe4XWtGWirFUiLIM2qOuLMA+0EoMLCMOL8ame/bYnD/73EgEZeWot7EnI/ZQCsCDxzO5npfAs75H68qnCZ5ORkuLi4yExzcXGBUChESkoK3Nzc0LdvX7x58wZNmzYFy7IQCoUYO3Yspk+fzi3z+PFjbNiwAeHh4Zg5cyYuXbqEiRMnQiAQYNCgQQr7XrJkCRYsWFCobS0MDMPgq4CKhRD38o+mbKFIcZtCeDwVFN50bQOsDtZpbiIbjmBhon14Qmn4gklvt625MSramsuJewDQrW4FxNx+haDKDiU3uBJkX24LnMmthS/5Z1CP9whOTCps8RFGTC54ef56PLBg8j4EoYhU1grKk0sYBjxjcwhZHowYERXVIAgp+BJxjyVxjyAkSMQ9IUthuYRhQuKeAdAzoALWnVQv2k1uWxX+Faxhb26C/psvFqlPCxM++rWuitX/PCjSetShtFKphtIAv4DrnjYFNcTz81voojiCtlS0M0dYi8JVRpWrFKww4aDybZILYc77MSTTT506hUWLFmH9+vVo1KgRHj58iK+//hpubm6YM2cOAEAkEiEwMBCLFy8GANSrVw+3bt3Chg0blIp7M2bMQHh4OPc9IyMDHh4earZWe/hS29e1jjt4PAYT98hXEdVE2viYLdS4396BFfHblecat9c1IpYtVAEMaQoeSxNV5ORTdoRJH1+l4eGoqksTIx42DgossbGUBslwwPrc7gDVECCKQGJpD6CI8Pk8fIAZbPERVsyn0h4OQegNfBNx8TBjUXYpj4Qg9AeJuDemhepc1AShr5DPqQHQX6qapiIkD84CIz4613ZHkyqOqONhW+R+FVVkLSzF9XBvbWYs851XQOwT5cUTCowUH+rFEcqsSJgM8LJT0FI38M2tAYaH3I+pMtNzP6WBb2GrcBlXV1ckJyfLTHv9+jWMjIzg4CD2ZpozZw5CQ0MxYsQI1KpVCz169MDixYuxZMkSiETii5+bmxtq1Kghsx4/Pz88ffpU6XgFAgGsra1lPrqmd2BFOFgKuO88HoOudTQr0qKIT1mKFZKqzpZy03xd1W+PbEENxW0Ke2zmKnYyLBLS+1JTSlLPs8wLs5WtpM0ozD1YVOGTIAjDgWGADxB7KJHnHkHkwxeI7/FNyHOPIDiMGPH9fi2vshnZQZR9SNzTc2Z29EUFWzOs61+/tIdSLChL/K+pGNjAy17mO1/Jgsq8dKTFQVMpAdDH0QKjvqik2SCkGN+yCkwUCInFKXQwfGOYuFbB58RrMtMzE69BUMFX4TJBQUGIiYmRmRYdHY3AwEAYG4v3yadPn8DjyW4Ln88HK+UZFhwcjHv3ZHN63b9/H15eqgXpoqLq+JjdyQ/Lvqqj8bpUiT0SUbZjbTeF82d18tO4HwmB3sUn9ALi7SlJ+ao0PF4l/DY6CHU8bLF7ZKO8KVKeuCi+XIEEQRgGfIZBBmsBALBhqKAGQUgwJnGPIOQwyvPcYxgKyyUME4MT99avXw8fHx+YmpoiICAAZ86cUdn+9OnTCAgIgKmpKSpVqoSIiAi5NqtWrUL16tVhZmYGDw8PTJ48GZmZ+TnuhEIhZs+eDR8fH5iZmaFSpUpYuHAh571UnAiMxCeXTkrEBUCJ0FHksDwGDhYmRVpHwfUpQnlYrmbM7OSH2hVtuO88uWq5YpRti6VUYn3jAqJceNtqaOPnrLL/guOf2q66wnYFx6VrOcS6QXd8+C8aH65HIyflGd79swnCjDewqtsRALB77VKZMNkxY8bgyZMnmDlzJgDgf//7HyIjIzF16lSuTZcuXbBhwwb8+uuvSEhIQExMDObMmYOuXbuCzxcfl5MnT8aFCxewePFiPHz4ELt378bGjRsxbtw4HW+hLFdmtcEf44Jha26svnER+G10EK7PD0EFWzOF823MjPFlvQoar29d//ro4O8qM03XUpwuwnJ1QXFrfg4WJqhqy8D6QgSa1fSCjY0Nls2YAFHmh/z+FewGlmUxf/58uLu7w8zMDC1atMCtW7dk2mzcuBGdOnUCANjY2CAtLU1mfmJiIoYPH85dEypXrox58+YhO1s2vIlhGLmPomsQQRDFA49h8I4Ve1jb4X0pj4Yg9AcjU7HobUqFZggF6Pp5u0WLFrCxET+v2djYcPdEknstTfvV5B6uKPAk4h6/eJ8vCKK4MChxb+/evZg0aRJmzZqF+Ph4NGvWDB06dFAaApiQkICOHTuiWbNmiI+Px8yZMzFx4kQcOHCAa7Nr1y5Mnz4d8+bNw507dxAZGYm9e/dixowZXJsffvgBERERWLt2Le7cuYNly5Zh+fLlWLNmTbFvc2nBsixaVHfC8KY+Olmfts/5KqvlSv1vbWqEWR3zvafkcu5poHEMb+oDhgEmFcgrZmrMx+bBDVDJyUKDEatGl0KHonVZ+H0B+9YjkXbuV7zcNgFZz27Cudd8GNmIxcm0lNcyduLj44OoqCjuorls2TKsXr0aPXv25NrMnj0bU6ZMwezZs1GjRg0MHz4c7dq1wy+//MK1adCgAQ4ePIg9e/bA398f3333HVatWoUBAwboboMV4GApQB0PW4XHlba6lqrmfB4Da1PlF3hx6KfmdKrtpiDXoXbjUifcFUdYbmGQ3m/KwuILS9saLjg8oSn69++Pa9eu4ejRozh69Cge372FlL9+BKA8LPfo7o1YuXIl1q5di8uXL8PV1RVt27bF+/f5D/6fPn1C69atlfZ/9+5diEQi/PLLL7h16xZ++uknREREcGK5NFu3bkVSUhL3ka42TRBE8cIwQCrEFXMdGBL3CEKChZUtAMAMWRAJNc8rTJR9iuN5+/fff8f9+/cBiCN8bt68CT6fj169emnV77Jly9TewxUFviRRMc+gJBKC4DCoghorV67E8OHDMWLECABij7tjx45hw4YNWLJkiVz7iIgIeHp6YtWqVQDEucCuXLmCFStWcCJGbGwsgoOD0b9/fwCAt7c3+vXrh0uXLnHriY2NRbdu3bi3C97e3tizZw+uXLlSnJurBcXjIsMwDOZ0roH7r94rrCL75/im6LL2bJH6UCZUaFpQg2EYmdC7ggU2VHlFSebN6VwD0zv4wpiffyJnZBsWmYKee0XBzlyxF6JV/U6wqt9J4byw+SvRO1C2aEXz5s1x5swZ2NjY4MaNG3K574yMjDBv3jzMmzdP5Xg6d+6Mzp07a7EFuqM0Q0IBxZZXykPK89wr2jp0sV/NBXwcHh8MBgzngSzXTyHXPTTYGxlJiTh69CguXLiARo3EYblfz1uO8NAuyHkrLmgiV5mYZXF831bMmjULX375JQBg+/btcHFxwe7duzF69GgAwKRJk5CRkaG0qnP79u3Rvn177nulSpVw7949bNiwAStWrJBpa2trC1dX14KrIAiiBGAYBm9Z8bXNjsQ9guBwdnTi/k9LT4W9g5OK1kR5ojiet+3t7WFkJJYdXFxcsGXLFpibm8uIe+r6ZVkWq1atUnsPV1iEuSKuoIaxke6i1wiiJDEYWTo7OxtxcXEICQmRmR4SEoLz588rXCY2Nlaufbt27XDlyhXk5OQAAJo2bYq4uDhOzHv8+DGioqJk3ISbNm2Kf/75h3vj8N9//+Hs2bPo2LGj0vFmZWUhIyND5lNclJaYUKuijZyYpgxlY1T08F2Qgp5z8kVh85eRC8vVUOSQFvYKIpJaiX8FWQFM0/UX9QXQ9mENAQBWpkbYkfe/NrT3L5vigk4OfR1HsGpyTEiL14X10FOGqIRDch++/qBwOgMGtSvaopZU2LwuiY2NhY2NDSfsAUD1OgFgBBbIenEHgPy+Faa/Qsa7NzLXBYFAgObNmyu9jmhKeno67O3t5aaPHz8ejo6OaNCgASIiIkoknUN5R1ce50TZIJXN89xDBj5mkYcSQQAA38QU2az4xVvO5/RSHg2hLxTX83ZBIiMj0bdvX1hYWGjcb0JCApKTk4vlHg4AsoT54p7AhMQ9wjAxGHEvJSUFubm5cHFxkZnu4uIiV/VTQnJyssL2QqEQKSliT7S+ffviu+++Q9OmTWFsbIzKlSujZcuWmD59OrfMt99+i379+sHX1xfGxsaoV68eJk2ahH79+ikd75IlS2BjY8N9PDw8lLYtDko/45ZmKBuntEYXNbEZ97/AiCe3jPR3DbVGrZBef2F1E5OC4qGWimzzak5IXNoJN+a3g38F7cSSLUMCVYaWGjKKdmNJlpNQ9zMeD/8C41tWKZnB5JErYtG0qmOJ9ScsoTjggkWFkpOT4ewsnxOTb27DVY4uKJDmfhBP1+Y6ogmPHj3CmjVrMGbMGJnp3333Hfbt24fjx4+jb9++mDJlChYvXqx0PSX5UqgsEj+nLS7NbF1mz3eliXQepC+++EJte13kO9YVWSbiIkZ2zHtsPpOg8/UThKHyKa+SdO5n8molxBTX87Y0cXFxuHnzJuehp2m/kr/ajE2b+6rMnFxO3DMxNqjgRoLgMLgjVz5fFasyhExRe+npp06dwqJFi7B+/Xo0atQIDx8+xNdffw03NzfMmTMHgDgHwM6dO7F7927UrFkT165dw6RJk+Du7q40f9KMGTMQHh7Ofc/IyChxgU9TWlR3Qnjbaui69pzWy2rqXaQsLDW4iiNWxtxXuaypMR9zOtdAtlAEB0uB3PxcqbhcZb93UdCFJ5S3owVC7czxvwtPirwubeGX6bwRRVdziyIGMgyj8hjzdrBATXdrpfOBIte+kUPEskrDYDXtU5u9quz0W1iP4rSzu5B+bo/sun6QbXOncUxeH4o8dVmuc+UvD7S7jqji5cuXaN++PXr16iVzowqI81ZKqFu3LgBg4cKFMtOlWbJkidJQ4JKmjZ8zjt95XdrD0Aq7vMJJpR0aX9aQ5EFav349goODsWbNGvz333949uwZatasKddekn9p5MiR2LlzJ86dO4ewsDA4OTlxIVqSfMdbtmxBkyZNcP/+fQwZMgQA8NNPP+l0/Bk8ayAXsGfeI/VTtvoFCKKc8Ikxgy0+QPiZXiQRsuj6eVuaHTt2wN/fHw0bykciadKvNmPT5r4qUyiCZV7OPZ4RvSQkDBODEfccHR3B5/PllPnXr1/LKfgSXF1dFbY3MjKCg4MDAGDOnDkIDQ3lHspq1aqFjx8/YtSoUZg1axZ4PB6++eYbTJ8+HX379uXaPHnyBEuWLFEq7gkEAggE8kKUtmjykFLU55hKjpaoXdFW6XxdiA/KxhjgZafR8qrCrFSJb7rQTYqy/d9398eBq88xsVVV2FmYcOKeut/sl9AALD92T2nII6B62xwtTZDyoew/xOjiId6oGMXP0sgJWNJRn8q2UVWouwQbM/mbJ6v6nWHhJ+sZ9M+UFgCA1j+eAgBUquYL5t1TvHr1SqYdCxa5nzLAN7cVfy9gJHxL8fkmOTkZbm75FchVXUdU8fLlS7Rs2RJBQUHYuHGj2vaNGzdGRkYGXr16pbA/fXopVNnJ0uDEPQnSL3z0nUPjgrHzwhNYmRph67nE0h6OQgrmQVq6dCk2bNiAyMhIrFy5Uq69rvId64oMng2QCzgiHSY6KO6T/ikHd5Iz0MjHvtjO8S/SPuN9Zg58XVW/HCpuMnNyEXk2AS2rO6OGmhdVhOHxkTEDWCA3kzz3CDHF9bwtze+//46FCxdq3a8kf7E293Da3FflSIXlglH9kpwg9BWDcekxMTFBQEAAYmJiZKbHxMSgSZMmCpcJCgqSax8dHY3AwEAYG4sfKj99+gRegYd7Pp8PlmW5tw7K2uhL7qSy6KWgzQ2zqsc4VcKcStFOqnvpdtoKfQMbe+FgWDDnUaIJfB6DdjVd4Wptql1nUuwdHVToZQ0JRWHY2v5GnWq7qW+kgoLdSR+6xWGa6jZvSLA3ALFHLlC4SrUOlkXLNTKxQOXpgizrWRvd6rqjZ0BFuXl8cxsYO3jAwsULsUsH4fbq4fD19RWnRXDwgLGDBwQmpggKCkJ6erqMGHD3+lWwWR8hqCCuoF1wXxnZuMDa3knmupCdnY3Tp08rvY4o48WLF2jRogXq16+PrVu3yl0jFBEfHw9TU1PY2toqnC8QCGBtbS3zKS0aeMvnDywtxjSvrLbNvC41uP93X1Jc0U8fqethixW96sDZqvDn++JEWR4kAEqFOF3lO9YV7/jih0sXJhUmOsjd0WXtWfTdeAGHrr0o8rqUEbz0BNqvOoNXGboPU562/z8M2nIJIjUi+O6LT+E75yiWH7uHjqvP6HwcROnzmTEHALAk7hF5FNfztjRZWVkYOHCg1v36+PjA1dVVq3s4be6rWJaFEZP3bM8zGP8ngpDBoI7c8PBwhIaGIjAwkPOUePr0KZfnaMaMGXjx4gV27NgBABgzZgzWrl2L8PBwjBw5ErGxsYiMjMSePfkhX126dMHKlStRr149Lix3zpw56Nq1K/h8Ptdm0aJF8PT0RM2aNREfH4+VK1di2LBhJb8TFKBpZdnSRNNqsUZ8cTuVrQuoN5qE3hbF++677jUxbNsVTGxdFcdvF/QUymdK22qF70SKFb1qa9RO1T6q7GSpUTtDR9Gxr+1PbWpctLdz8kVhtB2Pbr2M/NzENy6r+9VDzK1XsDQ1wuj/xcm04al5wHVUEP6ujMK8XOjdwAO9G6j3SPN0MFc6z8/PD+3bt8fIkSPxyy+/AADWLpwGs8oNYOyQJxqyLF5sGgO75oNgXq0JGIZBq6+GYPHixahatSqqVq2KxYsXw9zcnPMgAsRvhR8+fMh9v3HjBqysrODp6Ql7e3u8fPkSLVq0gKenJ1asWIE3b95wbSVvlv/8808kJycjKCgIZmZmOHnyJGbNmoVRo0bpxKu7uGnt54yIgQEYszNOfeNixL+CNSrYmaltJ61TvHmfVYwjKl8oy4MEQM5zVoK6/Etubm7o27cv3rx5g6ZNm4JlWQiFQowdO1Ym33FBsrKykJWV/9tqmpPyo6kbkAlYMpnwslSc3F0bnr77BAD4678k9Kgn/4JClzx6/QEuRXjRp4jfroirid9OylCZw3fmwRs67ZfQP3L45oAIyPyYVtpDIfSI4njelqZTp04KPfrU9cswDCZNmqT2Hq6wiERSBZd45LlHGCYGJe716dMHb9++xcKFC5GUlAR/f39ERUXBy8sLAJCUlISnT/Pf2Pv4+CAqKgqTJ0/GunXr4O7ujtWrV3NhIYA4JxLDMJg9ezZevHgBJycnTsyTsGbNGsyZMwdhYWF4/fo13N3dMXr0aMydO7fkNt7Q0VAAsNIgEXpBKUSVA2Vh86lJD7eVrwtuL2wHcxMjxNxW/DADaCdylEVvS0IehlEsLAd62+HIjSSVyxY1HN7a1Bg9Ayri/MP8ZMajm1fCP3deo48KYc3UmIdqLlYa91OaLxd27dqFiRMncl5C9Zu1hahqb24+C0D47jlEWZ+4aSH9RqGOmznCwsKQmpqKRo0aITo6GlZW+dscEREhk6NFUkBg69atGDJkCKKjo/Hw4UM8fPgQFSvKPtxLXjYYGxtj/fr1CA8Ph0gkQqVKlbBw4UKMGzdO5/uhOGAYBu39XXFmWks0W3ayVMfSva47Ik49whfVHLHn0jO17XvUq4CD8cXnVVUeUeRNX9z5jgtS2JyUg7/wRcof1nBkMmCTnQygltbrUESOFuHfuSIWfA28BkUiFn/8J3XsKlnk/MMUnLr/BlNDqkPEslhx7B7a1HBB40ryD8zSSL8M1WW+169/jceL1M/YOzpIo+3UJcJcEXI1yDVLyCMysQJygA8ZVC2XyKc4nrcBcC9NQ0NDC9UvAEybNg2fP39WeQ9XWFhRbv4XEvcIA8WgxD0ACAsLQ1hYmMJ527Ztk5vWvHlzXL16Ven6jIyMMG/ePMybN09pGysrK6xatYrLH6NvKKwYqsVNW0lUF9XUc09S5VBV84Lbpirnnq5SL5mbGOX1LbtCWwU5w/SNsiwklvAzhEJUheUCgIVA/gYhtLEXBEZ8NK5kj43/Pi5ah2owM8nvf0YHP8zo4Kd2GSeronnudSliqLOm2NvbY+fOndz3w/+9xMQ98dx3EcvC69u/CizFYP78+Zg/f77S9c6fPx/h4eGwsbFBenq6XBjHkCFDuOT/ymjfvj3at2+v6aboLR725gj0ssOVJ6mlNgYrU2Oc/bYlGIZRKu5Jn5u71nEncU9HKMuDBEBhtWpAd/mOC1LYnJQ96lXAvT+d4MhmwPTjS7Xts4S5WHb0Hlr5OiO4iiM+Z+fKnEcl/Hv/DbKFIpgY8fAhS4j0zzmoYCvvZfrw9Qf0WHcOw5v5YFIb1R7+++Ke4dsD+R5zyl6e9N98EYA4N18NN2tsPpuAzWcTkLhUdVizbAEylU01hmVZ/HFNvF/3xz1Dnwae3HRNUqywLItlx+7h/KO3+LFXHVRxtlS7jDStfjyNdx+zcTCsCZytTGFjrvq+LDk9E9eepSKkhqtaL/ayDmtsAQDI/UziHiGLrp+3AaBKlSoAgFatWhWqX0D8gkjdPVxhYWQ89wxOIiEIAAaUc49Qjra3JvZa5H/TFZreP7naiMNPtNmm0syd3so3/+GmNIonlHcU7XNdV59VRMTA+tg9ohEAwNdV+dtChmHwRVUnfFm/AmZ3yhfVjPg89G/kiUpOlkrH6+eWv94TU5qrHI9Dnk2bKQgxruthiz6BHpgaopuw8YJI/wIHxgbh8qw2qKqF519xomjflsTLDH1lbIvK+K67f2kPo1CoO79aS3l9l8apePOgwCItr4tCDxJ0uf3K8iABUFjpENBdvuOCFDYnJcMwSBCKc0iej7umtv3284mIPJuAAZsvYtO/j+E39yiGbbussO2tl2JRpNGi4wheegLPUz/JtVn69x28zxJi1fEHuJz4DsuO3kWWMFeuHQCcf/S2wNhl5198/Bb9Nl7gvh+5noToW/lCKsuyiL6VjBdpn7lp6Z9z8D5THI6cWwwXyCxhfviERJh8nZGJJktPYGX0Pdx8kY6cXBE3voIcuZGEDace4b9naei+7hzefdS8GBjLsnj67hM+ZAnR9qd/UWdhtMz8Z+8+QZgrG97RfPlJjNl5FfviZF8UvHmfJTO+F2mfcfNFGRe9BGIbElHOPYIApD33qKAGYaCQuFcOaeOn+G27NNKeO70CxWFntVTkZlFEaynha3JeProv61dQ2r5/I0/uf60Kaqi4WbVT8wZXGZp6Gpb3t776SEk82Lf3d0OTKo4AgBHNfBCuIt8ij8dgZe+6GNGsklZ9zO9aE8OCffDXhKao5KTak2H3yMZo4+eM/WPlC6kwDIMfvqqN8a1UF7koLNK2wmMYrbz+VFGYQiAFzwUlIfQaEg297RHa2EvmZUtVZ0u1njK63o096im/DhRE00ITPaSuLdLHZH1PW437KgpfVHPSqr2pMQ+LeuQLrf0aeqCOh61OxjKnUw31jbQgPDwcmzdvxpYtW3Dnzh3MmDEDALi8wzNmzMCgQYO49mPGjMGTJ08QHh6OO3fuYMuWLYiMjMTUqVO5Nl26dMGGDRvw66+/IiEhATExMXL5jnVJIivOhVlB+BT/3HmF0/ffKG375G2+QLco6g4A4MTd1zj7IEWurUTY+pgtfii88PidXBvpF5C9ImKx/tQjpZWRC1Z6Lng567PxAmIfywqAN6QEqCM3kjDqf3H4Ii+UPkuYizoLolFrfjQ+ZQuLpZK09P6SsO7kQySlZ2L1iYfovOYspu77D8npmQhacgI/H38g03b87nxv6w9ZQtT/LgYJKR816lvVOf7f+2/QbNlJDN4qW/hF8ptJHwMH4p6jwaLjWPL3XW5a8NIT6LzmLJ69k9++MoOZuII8LzOtdMdBEHoAS557RBmAxD1CISt71+X+71rHHX9NaIrfFFRgVXWbaGmaf2Ls28ATZ6a1xIqv6mjUv+qwXNlejfnyh/HPfeuibQ0XjG1RRaP+CqKpuCeNVjn3tF67PKbGmpmvIRRcKSzS+3xkMx94O5jLiMS6xDGvgmzBfEICI77a6rCqUOZJZmtugrldaqhMeC6huqsVNg9ugJru2gnwukD6N9DVY2NFOzNsG6rYK4jQnt9GB2FelxpcBWVpjk36AuYKQg6l0aRoUUFMFJyXJazsXQc3F7TTaD3SAhgA2ChJhSB9HZA+JveNaYKrc9pq1FdR0PaS8e83LTGgUX4eIXMTI/wxLlgnYxnW1AfnprfCoCAv9Y0Btb9/nz59sGrVKixcuBB169bFuXPnAACenuJzrbL8S6dOnULdunXx3XffKcx3PGXKFMyePRs1atTA8OHD0a5dO644jq65JxKH71bnPcPw7VcweMslfMwSYs+lpxoLSQMjL8pNyxbKeoUpShOiyH72KKnoXHD5XzRI2yB97EffEucFzhWxqDn3KNadyC8MNGpHHLqsOct9v/48HXFP3nFjjH+aik/Z4ofbXRefqO1Xwr4r8qHy2bmy2/HHtZf4+Z8HSM7IxE/H72PTv4/xy+lHStd55PpL5IpYzP3jJv4oUJU4Kf0zt08V7e/NZx5DJGKxIzYRAHDuoVgMPXH3FbynH+HaSedrXvjXbQDAxn8f4/V72QrFd5I0K9xiiPAsxGHyJtmll3aBIPQG6ZMC5dwjDBQS9/Qc6ecFD3vFFQN1HQ46r0sNeNjnV6lkGAb+FWwU5pxRhfQ9F48R527ShadbwVu5FtWd0LyaEya0yhfyutWtgE2DAmEpKNybF2XjLElPoJkd/cDnMZjYSrFAObtTDVR2sii5Aekh0of+rE41cHJqC5nwPF2yc0QjtKjuhENhunkAl+CuIEeTKvQ5rLQwIpAizn7bCgFedkVej6LRlEdvvoY+9hga7MNdK6SvGTweo/ZlhrJdtriH8sIEu0Y2UjqPYRiNzs2tfZ3hZiNrH1uHNoCXgzk2qQiDlfUmLZmK4SX1CuX0Ny1Uzv8+L+y6gq2Zxsf6kCbeatuEhYUhMTERWVlZ+Pfff2Xmbdu2DadOnZKZJsm/lJWVhYSEBK7aoQRJvuOHDx/i8+fPePr0KdatWwdbW1vNBq0l99g8cY95BskRveHUI8z4/QZarhCPXZPzV0QBQSpLKMI5qaJFy47ew+2XYjFo/amH8J5+BCfvyXsJFvR2e576CVvOJiDqhmyuwhN3X3P/H72puAiT9PF++L/8nIIfs3OxWkrcO/swBY/e5AuZMw/eQM8NscjIzMFvV56hx/rz6L/pIt59zMasgzcV9gUAn/O8FK8kvkPnNWfk7pdeZWTKhcIC4EJzAbFH5JK/7yJVSQguj8fg5+P3sSP2Cb7+9RpWxtzHuN1XseHUIwQtOYH5h28BUBxm/P2ROwoLVg3bdkXmu7QwKP3bL/jztky7snzJ4FuJIxBMc0jcIwhWlJe+ALyynbCcKNOQuKeHKDuf7B0VhG/aVddoHQUFAHVv5osD6RsnbT3hVLUueC9nxOdh+7CGmBKi2b7RhMJokLrykJNsXw13a9z7rj3ClWyXh705/pnSQid9GioF97k6ofvfb1qigXfhRCNfV2tsG9oQtSqq9o7TNix19BeV0a+hB7YObVCocZU20rZdmvkvAflzgyTPYHF5c5YV1P1syjSP4s5KoMic63va4fQ3LdG2hovy5WTWwcisRzrcW1Hxg8Ki7NyjLJdeYV90eTkof6FTu6INBjbO99bT5xcBJc0j1h1ZrBFsmE/wZsQC2tqT+cLXpYR3qDH3GAZvuYQHrz4oXc9SqbBNQOy5N2BzvkdfyocsdFx9BoBY6FOFSMTityvP8PD1BzT94STnPaaIF2mfMWan4mT1n3MU5+/TlLjEVMz4XZwr79qzNEzb/5/idk/ewXv6EfjNPYqZB2/gq4hY3HyRIVcUqtHif7Av7rnc8umfc+Sm1ftOPpcjIL6uSAuTq/95gCPXk/DDUfH+3x77BL9ffa703DRhTzyO33mteGYe0bdfcf9Lr+ZWgTx76jTf9evXw8fHB6ampggICMD58+dVtj99+jQCAgJgamqKSpUqISIiQmb+pk2b0KxZM9jZ2cHOzg5t2rTBpUuXlKytaJhai725LYRlPLcgQWhCnudeLskjhAFDR68B4W5rhnEt5b24tH250L2ubL4jbTxZtOlLer26fAFSEo8rBUMv8/tW3ntxvOQxUhHapinejubqGxko2u5zTwdz1KloWyxjkeBsZYrtwxrigIL8d4owM+FjyZe10bK6+lyY+o6+ecW19nNB/Jy2WGSgRSS04cDYII3zyxU0G3UeS8rmqnppU1lNnkjNKNxJVZXIL70tC7vV5P7/++tmheqL61PL6Y6WuslNKU3Bl3iqxPaCx8rSL2W9MNvXdMUhHYUJ6wNjW9fANVZ8/xTEkxfRJuy5is85uTh9/w0uJcrnzVPG/jjF1Zs14ff4F5i2/zrarDytst3tlxmY8tu1QvejjqHbLsscK8pEsZ4bYrn/d19UHFasihgpMU0dN56rF5vm/XELV59q5nEmHY4rzZv3WZi2/z+8z8zPtZX49hNSPmRx30/czR93YspH/HHtBXfO3Lt3LyZNmoRZs2YhPj4ezZo1w1dffaV0HAkJCejYsSOaNWuG+Ph4zJw5ExMnTsSBAwe4NqdOnUK/fv1w8uRJxMbGwtPTEyEhIXjxQvcVwE1txPcd1mzZDT0mCE2ReO6JSB4hDBg6essJ0s9uTao44h811Td1gbTnntahw6oezkpAQVD20Kqrrkuisu7x8ObYPyYIFe3KsLhX2gNQQvNqTgjwsi/tYZQI0iahKP+RpnSt4w4AGNi48F52isR3OwuTclHJOsDLHqObV9aobcHdUdifTdlu/feblhpVZVfnOafuZ1vZW5zDtWBuucJsn5+bZpVXlcEwwHdSYqGEwuRvLSwFX0op2u7udd3hbmOKZV/Vlpnet6Gs3UWEBqCujgp86AN+blaIFYkLjTTnXZebLy3uaIOikFtAs/uUqfsUe8gVpOPqMwoLdZRlFIXVFuR9lhD9N8nnQdSGsF1x+O2KvJdh+G/5v81vV57De/oReE8/ghYrTuHrX69x4c8rV67E8OHDMWLECPj5+WHVqlWoUEF5waCIiAh4enpi1apV8PPzw4gRIzBs2DCsWLGCa7Nr1y6EhYWhbt268PX1xaZNmyASifDPP/8UaVsVYWUn9oC2Yd9DlFs0D1CCMHjybEAIyrdHGC4k7ukhJfEooBuvCtUURQgr7UdxZZ57qijtMRekirMlAr3LtsCkj6KNm41m1T0Li6qwPF1QlPDyotj88l61sXtkI8ztLC+QyPVT+G4IAE0qi/MsSY5VteGbSn5YZcKVp4PsCwV7CxOFhTBOfdMCzirC2DvXdlM5rC/rV8T56a2woKvsMaPyCC4mj3KGYRAa5I3RzWWrYiu6lKiqTnwwrAlGNPXhvqsqTFIQ+X0s/7v1beiJ8zNao4qzlcbrLQsIjPg4misu0tOKdxX2kPVU+pStW2Gj36YLOl0fUTxcTlTs+fevimrKAHDh8VtkZ2cjLi4OISEhMvNatWqldLnY2Fi59u3atcOVK1eQkyMftgwAnz59Qk5ODuztld/PZWVlISMjQ+ajCdYOYnGPz7B4nyZfDZogyhWs+DpAnnuEIUN1nvUdDZ4+NHkYL2w4bVHQ1otHeoiqHvhKAr4yzz0Vy+ihzlTm0add/ntYE6R9ypYpRqNL/prQFG/eZ5WIMK8N0h4qRcnxJTDic6JT4ceiSZvyIQ2am/CVChY/9amLHbGJ6Fm/IgDZAnF1PWxRzUWzY0zTlyA8hoGTtUAu55YxnwdLgRFev8+Sme7raoXvuvsjUIOiKooK0qjKZ6fJdcnGzFhhfjBNEBaoElpQAJ3buYZK0bKepx3qedph89kEAIARn4EmulNdD1vM6VxDZppIvqaBRpTFa5mJEQ93WU9cE1VCXd5jjDc6hIXCQcXWX3nztCtvCHNZpKSkIDc3Fy4usvk/nZzkq5JLSE5Olmvv4uICoVCIlJQUuLnJnxumT5+OChUqoE2bNkrXu2TJEixYsEDLrQBMBKZ4z5rBivmM9++SYOOgPJcpQZR5RGIP7lzy3CMMGJKm9R0NHkQ0uREvjefZooTota3hgjHNK2P9gPpy8wqzWltz7SqoFubhRhuPpzL47FQqVCyEkCYprtDKV7c57up72qGVb/HdGPtXsEFLHY9ZEZKCIGPyQjwHFwh5VIUh6GaGMEZdMKJZJXSq7YY1/erJzbO3MMGkNtU4IXpyW3HhkV4BFXFoXDCWfVVHpn0dJeGZBc+TPo4WCqvY8hhg/cD6aFbVEXtHNZaZJ7FH6UI31qbGaOBtX2jP3Gouyr3SNPn5Nw9WXolXgrKhFQzvZRigjV++3Q5r6gNna9169+4d1RiHxgXLVRamghr5SAqbrBT2AgAMMzqKwfxj4KGQCihRrhFKJSkseJ5S9wJJWXtF57tly5Zhz549+P3332Fqqvy8MWPGDKSnp3OfZ880zwX5hid+qfbpzRONlyGIMomIPPcIw4c898oAip4xCvMA6+1gjsS3n9DGT3OBQlU/RamcyTAMpnfwLfwKCuBhb44fetaCtakxxu5SXHFOGqUFNcqLMmAg/NCzFhYcvo3BTbw1XqaSkyVuLmgHi1KoIK3P/DqqMX6KuY/v8opPTGtXHd3quqO6CqGkICVlHp5KRF0yz3zMTfhY11/+5Ygi2tZwweVZbeBoqThP3rT2vrAzN0Gn2m4I+elfbnpBr7STU1soXJ7HMKjsZIn/DW8kN29YsA/qedqihpsN/OYeBaD9y5iC2JgZ48rsNlxlXEvT/Fsda1MjpH4Se+UVJQR9WjtfrnKnND3qVcDnnFzMOXQTgNiLcGLrqmord6piaLA3tp5LxOQ21WSme9qb49ikL2Cm5FymqT0UbOZuo7sqwvqC5Fj4V1QHm4QdMdIoCguMt2O00Z+IE1VDMmuPNNYSOeAjFzzk5v1ly8iruLKyHfqC9wsL2JqMAZ/PR3Jyssy8lBTl4a2urq5y7V+/fg0jIyM4ODjITF+xYgUWL16M48ePo3Zt2RyZBREIBBAIChfxkmrsAmQ/w7uXj9U3JoiyDOe5R+IeYbiQuKfvlGB8TPTk5sjIzNFZFT8LQfEcXoX1RujTwFNjcU5ZLikPe3M8evNR4byi/lQOFiZ4+zG7yKGJ5Qk3GzNEhAZovZxlMR2bhkzjSg7YOzq/wi+Px2hUZEDapIrirasJ0ZO/QOpH5aHPmvRO+p9inFSkQrAUGHHefdJoes5T1Y7HY7jiMxsG1Me284lYoKAwhbZIX8eM+TzEzW4DFkCHn89w0wXGim/gNdksZUIon8cgtLEXrj5JxcH4FxjfsgpqV7TF3M41UNFOe9GMZYHZnWqgbwNPVFWQq0+ZsAdo/4LtwNgmWHX8vlx4b1lAYJS/nxYL+yOZtccEo4NwZ97BnU/58QgtyQBuJrZFQEAAYmJi0KNHD27WyZMnlS4WFBSEP//8U2ZadHQ0AgMDYWyc/1Jj+fLl+P7773Hs2DEEBqr3JC4K2RbuQDbw4P5dNFbfnCDKLCwr9uSmsFzCkKEnXD2EYRitXFA0ecBS1UYieJkY8XQi7A1o5In0zzno6O+KP/MqiumSkvDOUZaz6YeetbHwz9ty1Rl1wbnprfA+U6jyIZsg9Jn6GuRIKwqqwi2BfO8comRC/wuGgRakbQ0XxNx+hRHNKqlsJ6FDLTd0qKW6iEZhcci7tklfPxr7OKBjLVe54hKKKozbmRtzHn+A+mI+y7+qjbEtKnOC3DCpIhma8E276lh+7B6WfVUbfB6D6q7aF8DIzpUPOZXe/toVbXD9eTq61xVX9wzwslPoXVkWMJcSQVnwEJnbEbtyW6MB7x78mCdwYDJgi48wYnLBgwhGyAUfIjD0OoBQQk1bB4SHhyM0NBSBgYEICgrCxo0b8fx5fvXdGTNm4MWLF9ixYwcAYMyYMVi7di3Cw8MxcuRIxMbGIjIyEnv27OGWWbZsGebMmYPdu3fD29ub8/SztLSEpaXuc+5mmIijdUw/vcT7zBxYmRbNc5ogDBaR+BovYuhekjBcSNzTcxQ9Puwe0QhrTjxE7OO34jYaqHu6EsQYqPd8GRTkjequVjh6M0k3nRagKJuibl9VsDXDi7TP6FTLVeF8F2tTrFOQB1CTdQOAEY+BUMQiqLKD3DxTYz5MjeltUWlSFhPJFzfSnrSl7RHZrqYrgqs4cJ5g0khsO6Rm+UgYXtxVlbvWcUeAGjF3Xf/6uP/qPWq6q/cALTnyj1cej8H6Afmev9GTv8D7zBy42pgidkYr3EnKQCMfB9x6mYFALzukfMhCw8X/aNSLEZ+nVoxWxbiWVTC4iXeRbCpbqLoSx+9jmyDts+689fUZRdfWTAhwRlQbZ6A65JEgFPFfheroU8Ufb9++xcKFC5GUlAR/f3/s27cPHTt2BAAkJSXh6dOn3DI+Pj6IiorC5MmTsW7dOri7u2P16tXo2bMn12b9+vXIzs7GV199JdPfvHnzMH/+fJ1vx3uB+H63IpOCzzm5JO4R5RZWRJ57hOFD4p6eo0jIalLFEU2qOKLzmjO4+SKDe+sus1wxvWwe3tQHm84koH1NxeIXkP+wX9wPl8XBkYlNcfNFBpooEN90wcmpLXDq/hv0CqhYLOsniJJGn/LcmRjxsGuE4sCif6Y0x9uP2aigoLpqWUH6t2hXzCLm0GBvAMDiHrUw8+ANhW1MjHjwr2BTrOPQFlWhqtJinJuNGeeZ2NBHLBbruhCGOooqlmcJ5T33pF9gGPF1461vCJhTjlUCQGhjL/zvgm4KR5jlCcZhYWEICwvjpmdkZHD/b9u2TW655s2b4+pV5bmfExMTdTI+TUk1F3sVV+U9R6aCcwZBlBeYvJx7VFCDMGTo6NVDNHUeOhgWjEuzWqNGCXpFTGvvi19HNcaqvnXVtvVzs0bEwPr4Y1ywTsegKzFB0XpszU3QtKqj0rBcVWiyhIe9OUIbe5GHHkGUMKbG/DIt7BWksJVmtV2/q41hiUNlqSiSup84R0FYbnnFQmCEcAV5I0sSZYW6SpoFXYue09JQaVtDdy89jPn68XsWlTSLShCxDByZDHT/4RDevM8q7SERROnAFdSgZzTCcCFxT89RdetgzOfB2apkPQmM+Tw0ruSgUpySrkDY3t8NdTxsdTqGwhbUKG4opNNwcbAQJ8cP8CzenHFlEf20RqK48VJS1ETfKU/H65zONWBlaiRTed7bAD3qdcXE1lURMVD7Aky6IlfbCifFRJ8GHqU9hFLD1JgP30Lkr1REcb9AKSkYE3MksmLR05f3FD9G3yvlERFEKcGKU1nkMiTuEYYLiXvlmMC8fEm9AnV7o6dOfJvcRvz2fG4hK/Lpq+NF2bjNK58cGNsEI5v5KM2nSCinuCvkEppTEp5BF2e2xqmpLWCXJ4gbmhdyeTpcfV2tcW1uCMY0r4yTU1vgj3HBcLUp2ReC+kZ7f1f8/XUzrZZxsZb1Tt2g5DrxQ89a+Lp1VYXz1AlKtStqH75+YGwQ3Avxexa02aZVHPFocUet12No+FewRgNvO/g4qhe4a0hVih/XsjJmdvRV0dqwaVbVEbdYbwBAIO8ePmQJcebBGyw6cpu8f4nyhUgs7lFYLmHI0NGrh5TUy8BfRzXG5VltSjwn0tdtquLSrNZy1QPLyEvQEmdIE+/SHoLB4+1ogVmdasClhHNqEYQuaV7NCbUq2KB/I89i68PF2hTeUg/HkoqzE5WIGvrGtPbVAaDIFc8N5XIlEXx9HC107kVvqPi6WmmVz7CyU36F0qkh1dDe3xXDC9y/3P2uPfo08MTkttXwc9+6MmJenYo2ODy+qdL1r+tfH4fHN8WhccGcF7k6Ls9qgwAve52kZeHzGPB5DP6aoHyMyjgY1kTjttamRpjXRflL3W/bywtoVqa6Sw2+sJs/GIZRea/ZqbYbhjf1Qafa+VW7v2nni1FfVNbZOPSNRpUckGwvrpLdhHcLLIDQyEvYdCYBey49Vb1wGaOgd61IxBZLKgeWZZGlpuCRMm69TMftlxnqG5YQLMvifWaO+oYGwCdbX0zNGY2dgt6lPRSCKDQk7pVjjPg8OFmVTr6kkg4nLglKK0RjfteauLmgHSpp8DaaIHRNefKE0ndMjHj4c0JTLO5Rq8T6lFScLe18ZpoyoJEXzk1vpdO8Y1/Wky9qRegvDMPgyuw2OB7+hUbtpV/6jGtZBQzDYE6ByANpb7hudSvg6KQvOGHqq0APmBjx5DwAJUi8n+t62OJ3DcUyyb1bTq7iE7A29wOS/t209AJ0sRagnqedTC7TLnXc0bK6k1zbtjVccH1+O7So7qx0fY0qyVc556m4r7r7XXutxsvPW5d06piCnpbr+teX+23LA336DAIA1Gce4NL129z0uX/cwufsfBHq8ZsPePr2k9zyKR+y8M+dVzgY/xxLou4oFMTeZ+bg9P03SPmQhY9ZQpXjeZn2Ga8zMvE+Mwev32cWdrM4svMKhYhELIRKvBGfp35C/e9isOTvOwDEQl/XdWfR9qd/NQqpZ1kWN1+k4+jNZHzKFnLr2BGbiDtJsmLc+N3xqD0/Gm/eZ+HozWQ0WHQcPxy9i7gnqUrXLxKxiH+aik6rz6Lj6jMyv0tBPmUL8WP0Pdx6mc6NTfL33cdssCyLG8/TZdYhErFYdfw+Tt17LbOu7/+6De/pR9Dup3/hPf0Ilh29KzN/8t5rqDU/Grdepht8TttMczfsz22OWKNGpT0Ugig0VC2XMDh0dfHQtRZXmp6HlgIjfFHNCY9TPmr85p8gCKI8oovCKiyA9QPqY/fFp5jR0a/ogyJKFFNjPqo4WyFhSUd8ys6FhcAI3tOPyLULbeyFia2rIvVTNvo19FT4Ek9ZOHzUxGa48uQdutURi7//G94I3+y/jv+epcm0M5Ja3svBAkODvbH1XKJG2yEU5QsVx8ObY8OpR+hRrwK2nU/A45SP3LwjE5ui38YLmKxChNc0rP/x4o44/eANaudFffCk3ATW9KsHAHj4+j2epX6Gl7059sc9x4hmlQCIPUgntakKO3MTzDt8CwDwZf0K6BXggfoKct5K7+7wttWwMuY+AGBsi8pqUwL8b3hDhEZe4r57SPKESq0zqLIDfv7ngUbbLU1ZEwBNnHxwRVQNgbz7GGD0D1YJv+Lm+c09iuVf1UaAlx1a/Xiam/5z37pISPmICa2qotvac3iR9pmb98u/jzG8qQ861nJFxmchvqjmhL4bL+CWlMeZvYUJFnX3x4GrL5AlzEXvQA908HdFTi6LJktPyIzvyMSmMObz8OulZwhrWZmr8s2yLCbvvYZD114CADYNCsTKmPtwshLg5z51YWdhgpdpn9Hup3/Rta47/rnzGskZmfi6dVV0qu0GT3tz7jhadOQO0j/n4JfTjzGjgx8S337EzRfi8VaeGYWxLSrD3JiPivZmaOXrAhszY9x8kY7IswmYElINJ+++xpw/bnFjTlzaCb9ffY65edNOTGku9hwFcORGEgCgwaLjXPsNpx5hw6lHWNe/PnZdfIKF3fxRxTnfa3j1iQdYdTz/WP3n7it0quWGR28+wsvBHMZ8sSGmfcpG0JIT+JyTizUnHqJ2RRvcTXqPc9NbYffFp/jp+H2ZfftocUc8ffcJoZEX8TxV/Bs29LGHu40ppoRUx+azCQCAe6/eAwDWn3qEFtWduSrykn3fafVZAECfQA9MaVfNIJ04JGmlyko+TaJ8YnDi3vr167F8+XIkJSWhZs2aWLVqFZo1U54/5fTp0wgPD8etW7fg7u6OadOmYcyYMTJtVq1ahQ0bNuDp06dwdHTEV199hSVLlsDUVHxi8vb2xpMnT+TWHRYWhnXr1ul2AyEuWpGTWzh3bQkl9e6kfU1XHL2VjJ71K+LA1ecl1GvR6FbXHUnpmTI5VXRBaV8Kvm3vi8rOlmjtq/zNOEGUJVJTUzFx4kQcPnwYANC1a1esWbMGtra2SpdhWRYLFizAxo0bkZqaikaNGmHdunWoWTPfk2vjxo343//+BwCwsbFBamqq3DoVXRe+/fZbLF26lPv+9OlTjBs3DidOnICZmRn69++PFStWwMSEBPiyQMdabuhYy019w2KitK85ZQGGYWCRF6JrYsRDtlAEV2tTJGeIvYUa+NjDyUqAbUMbyi3rYi3Aq4wshCipwOphb54vKAGo5mKFzYMCZR7obc2N0dpPdvl5XWqiqrMVZh68oXb8zao64dzDt7Aw4aOKsyV+7F0HALA9NlGmXU13G1ybGwJenoB3bW5b1F0YAyDfc4+nQtzr19ADey4949q1lPLAC/C0w7N3n2WEuCrOVqjiLA5NnlYg3HZSXt5libjXubYbgio7yPXp52YNL3tzHL2VDGtTI0xsXZUT95QN1VJghA9ZQqzsXUemeEwlRwvY5734lF5U2jOwqpSQoujZ3r+CNW6+yMAPPWuhT4PiS31QGgiMeNgqbI9Ak/sYxT+C6NxA3M7LwwcA3+y/LrfM179eAwDEP02TEfYkRJ5NQGSeMKSIdx+zMXbXVe77mQcpnA0WRCIcAcCWcwnoVMsNq/vVQ9e1Z2UEw5E7rgAA7iQB9b6Lwb/ftMTCv27hfZYQuy7mhxj//M8DTtS1MjXC+0xZT0JFQv+GU49kvrvZmCIpXXyeOBj/Qq79oC2X8O/9N9x3aWFUFeN2i/dJm5Wnkbi0E2Juv+K2S5rxu+PxT73XXN+darvh6pNUbkwSrj8Xe+9Jn3ekqTwzSm7apYR3APKFu4L0/iUWfQI9cDHhrdy8vVeewdXGVOWLBL0l7+GZrq2EIWNQ4t7evXsxadIkrF+/HsHBwfjll1/QoUMH3L59G56e8hfahIQEdOzYESNHjsTOnTtx7tw5hIWFwcnJCT179gQA7Nq1C9OnT8eWLVvQpEkT3L9/H0OGDAEA/PTTTwCAy5cvI1dKbLt58ybatm2LXr16Fct2bh/WEL0iYgHozhusgp0ZHr7+oJuVSbGyTx189bAimlZ1LDFxr6iOez/3raebgegZZiZ8hDYuWh4pgtCW0gzD6N+/P54/f46jR48CAEaNGoXQ0FD8+eefSpdZtmwZVq5ciW3btqFatWr4/vvv0bZtW9y7dw9WVuKH0U+fPqF169Y4e/as0vUAwMKFCzFy5Ejuu6Vl/sNhbm4uOnXqBCcnJ5w9exZv377F4MGDwbIs1qxZU5TNJvQAuvkvexwMa4KfYh5gWvvq+JAlxPVnaehSW7l4u39ME/xx7QVCG3tr3Ie0d1yvgIpY3quOwnZGCtSrDQPqY+yuq1jWszY3bXhTHzhbCdC4kqw4pkj8khbvbM3zXzD4u4s98FQd04t71IK3gwVqV7SVm7egqz8q2Jmhh5bh6SentsC95AwZoXBt/3qYsCceE1pWQVjLKvicnYtqrlZyoe+MktHeXNAOObkiGPN5sp5koflVktv7u+Kv62LPKR4DeDuYI/HtJ3Su7a5yvD/2qou7yRnooqadIcIwDK5ZtcCZTyfQjH8Tv5ksxI7cEFwQ+eEF64gM1hyfIQALBiIwYKU+5+8n6exhUiTU7MH02I1nqH5DLDarat9qeYzaNp8zhYUa/5v0DyqX08V+qTL9DwDKx/9n/BNu3rHr6veHLjlwJUFpf9YCw7xCSu5myXGPMGQMStxbuXIlhg8fjhEjRgAQe9wdO3YMGzZswJIlS+TaR0REwNPTE6tWrQIA+Pn54cqVK1ixYgUn7sXGxiI4OBj9+/cHIPbG6NevHy5dynfld3KSzR+ydOlSVK5cGc2bNy+OzUQDb/m8I0VlY2gAvj9yB+NaVtHpes1NjNCmwFtrVTlSdIHeZnSgqwFBlBh37tzB0aNHceHCBTRqJM6PsmnTJgQFBeHevXuoXr263DIsy2LVqlWYNWsWvvzySwDA9u3b4eLigt27d2P06NEAgEmTJiEjIwMLFixQOQYrKyu4uroqnBcdHY3bt2/j2bNncHcXPwz++OOPGDJkCBYtWgRra916DhMEUTRquttg8+BA7ruiMFFpPOzNMb6VdoVk+FL3Cao8WwK8ZfuuXdEGHWq54f73HWBilB8Ha8zn4cv6FeWW1+Q+7O+vm+HYrWSM+qISty5lMAyD0c0VF5WwMTfGN+20rybr42ghV7m2c213tKvpyo3F1JivMJ+nqs2TLCstcFqbGXP/S4uJPB6DA2Ob4GLCO7TxU+yBKaG6qxWqq6l6bMjsGNEI3X+chM3MCjTi3UWY0WGE4XBpD4soJdZfzsby81lIes+ipjMPq9qZopmXctngdKIQ4dGZuPVahHlbbJGzcIlcpFxaWhoAoFq1akhLS4OPjw9+/PFHdOwortatSaTckCFDsH37dpn5jRo1woULF4qyuQDynUeUvTwgCEPAYApqZGdnIy4uDiEhITLTQ0JCcP78eYXLxMbGyrVv164drly5gpwccWWfpk2bIi4ujhPzHj9+jKioKHTq1EnpOHbu3Ilhw4apjMnPyspCRkaGzEcbauXlMmmr5mZDE+p42KKSkyW2DGmAAC/VN6tFoWf9imhZ3UkmtKE4MPB8rQRRpigte4yNjYWNjQ0n7AFA48aNYWNjo/SakJCQgOTkZJnrgkAgQPPmzZUuo4offvgBDg4OqFu3LhYtWoTs7GyZ8fn7+3PCHiC+/mRlZSEuLk7rvgiiINIhn4RhIJ2fTtWps7KTJaInf4G42W1wdU5bHBgrLrQhLeypQjrMdWiwt8I2fm7WmNSmGsxNxA/spsZ8rO1fDz/3rSvT7sKM1hr1qStUiYwSXDUo/iEtpEqLndLekzyGgYOlAB1rucns2/L4cO9hZ473MEff7NkYkz0Jh3OD8FDkjnTWHLls+dsf5Zm9N3Mw6WgmZjUTIH60BZp5GqHDrk94mq64GElCqggdd39CM08jxI+2wLCuTTBx4kQcOHCAa5OdnY3u3bsDAHbs2IF79+5h06ZNqFAh3yv38uXLSEpK4j4xMWLPy4KRcu3bt5dpFxUlH1pcGPJz7ulkdQRRKhiM515KSgpyc3Ph4iIrdrm4uCA5OVnhMsnJyQrbC4VCpKSkwM3NDX379sWbN2/QtGlTsCwLoVCIsWPHYvr06QrXeejQIaSlpXGhu8pYsmSJWq8PVRwaF4zPObmwFBTuJ5IOlTs4VrPqa0VFkuulvELXAqI8Ulpae3JyMpyd5fNLOjs7q7wmAFB4XVD0tlgVX3/9NerXrw87OztcunQJM2bMQEJCAjZv3sz1VbAfOzs7mJiYKB1fVlYWsrKyuO/avhQiSo7SvPnfPyYI22OfYBYV8jA4pEUmdSkNqrkU3ktsQCMvGPF4MOIzWoXLSkJTJfnUKjtZaCSklRQbQwNw7mEKegd6qG0r/QJe2l6l/1eWu++rgIr4KeY+2irJp1gWMTHiYXGPWph58AaOihriqEg6zyQLAcROETyIwEj9ZaBY8CEMl4fnZ8Gqtg9W1xiB1QDQAhA+CEfghQZwbd5Prn3SxV3ItYrDiRYrcQLA7IH1MMxopUyk3JYtW5CaKq4G3LhxY1hbW8PLSzaVkKaRcgKBQGnURFEg5xGiLGAw4p6Egt5yLMuq9KBT1F56+qlTp7Bo0SKsX78ejRo1wsOHD/H111/Dzc0Nc+bMkVtfZGQkOnToIOONoYgZM2YgPDyc+56RkQEPD/U3IxL4PKbQwl5BVCVJNkQ0eKlbKtCbHoIoOvPnz1f7YuTy5csAFFc0U3dNULScJssUZPLkydz/tWvXhp2dHb766ivOm68w4yvqSyGifBDobY/AYkjfQRQ/0l5jJsV4M8PnMejfqPBFH34d1Rg/H3+A77r763BURSekpitCauY/1H8VUBH748T5nkfnhRcrQlkRDWXhy05WAtxc0A7G/PJ1Y9e/kSf6N/LElN/+K5BHm0EW9LcQVEU7M67Sa0mzqIc/9lx6itDGXvj2gPoiOO1quuDnvvVgasyHSMSikoKCFpogEWIVwWMAUQGhquA+WvZVbUxTUCiFzc3B5+QEWDbugwzkR2KZ+AQg48UjmEM+Ouv9i8fi+ZJ5xmZo164dIiMjkZOTA2NjYxw+fBgNGzZEYmIiqlSpAmdnZ/Tv3x/ffvst+Hz5yteSSLnw8HC5e6ZTp07B2dkZtra2aN68ORYtWqTwZS+g3UtTyS4r7vRSBFGcGIy45+joCD6fL+fx8Pr1aznvCAmurq4K2xsZGXEPX3PmzEFoaCiXx69WrVr4+PEjRo0ahVmzZoEnFUPx5MkTHD9+HL///rva8QoEAggEAq22kdCM0Mbe2Bf3HB38S69KoSLKYxgHQej6Tef48ePRt29flW28vb1x/fp1vHr1Sm7emzdvVF4TALFXnZtb/vlD1XVEUxo3bgwAePjwIRwcHODq6oqLFy/KtElNTUVOTo7Svor6UogoOegNP1EYTI35mNiqCj7n5MLZWn884grSuJIDGo+Sr2Crbyz9shaGBfvAy8Gcq3osgVXiV85T4tFXEE1DoMsi09pXR+Lbj/icnYvbSbJiyIGxQXj85iO+2X8dbfyccfzOawDA+gH10bGWG0QiFgv/uo1t5xNhxGMQP7ctas2P5pZv4G2Hhd38wTBAlzVn0aeBB3ZeeApNMTfh41O2uMjh8fAvYGVqDCdLAarN/hvCPEXLwcIE77OEsBQY4d1HcbqMfg09seeSuJ/fRgdh9P+uIPVTDrdeNxtT5OSySPmQBWX0CqiIfXH5oud33f0xoJEXBjQSe6AZ83lwshLgv2dpWBF9n2vnaGmClA/icUQMDODEKmnni0U9/DHvj1vcNkgwM+bjc454e/8c3xSJbz+ikY89nK1N0biSPSbvvYb/8iriSoib3RZ8PoM5h27Cy8ECTpYmGNjYCwv/uo3Uj9mY16Um7CxMZMS9JpUdcP7RW7T0MsN2VgSeuR3OT2+FI9eTsCjqDtoFVsehfVdRkOjJX6DN3s+wdHfDp7xpdSra4m22bKTc48ePkZiYCADYt28fkpKSMG7cOAiFQsydO1duvcoi5Tp06IBevXrBy8sLCQkJmDNnDlq1aoW4uDiFz93avDQVsRSWSxg+BiPumZiYICAgADExMejRowc3PSYmBt26dVO4TFBQkFzVxOjoaAQGBsLYWJxc99OnTzICHgDw+XywLCsXMrF161Y4OzsrzcenT/QO9MDCv26jrodtaQ9FYzQVx2zMjXH6m5bFPBrtsTQ1GHMiCJ2h7AGqsDg6OsLR0VFtu6CgIKSnp+PSpUto2FAcPnTx4kWkp6ejSRPFqQh8fHzg6uqKmJgY1KsnrpqdnZ2N06dP44cffijSuOPj4wGAEw2DgoKwaNEiJCUlcdOio6MhEAgQEBCgcB30Uoggyj7hIfLFfojCYcTnoYa7dsWJpINZ+GUsskVXuFibcnkeh2y9hFP33qCykwX2jGoMZytTBHjZo1deaPTYnXG4+TKdK1TC4zGY07kGalWwQQNve5kcikcmNkXNvOrMAPBgkbiQwrT2vuAxDHJFLDr+fAYv0j7DytQIN+a3w5kHbwCIc5HbmBnj3cdszPnjJnoFeqCKc37o+oWZrfHL6Udo7++GAC875IpY8HkM9xcA5nWpAVNjsZdY7IzWEIpYnLj7GtlCEb4KqAiWZfHg9Qf0/iUWw4J9MKypD+4lv8fqfx5gaLA3mldzwoWEt3j27jPGNK+M0MayYaWS4jbNqjph75VnePbuM3gMMKdzDS7UvaAX2p6RjfE+MwchNV3hZmOKpPRM9Gvgie2xiThw9TmW9KiNLmvPAgAqO1ugVsX8/VfJyRK/hwXj9ssMCEUi9N90ERNaV4GdhdjL8ue+9WT6mtelpsz35V/Vxjf7r2NRD7FI+SLtM0Qf3mH7BODguGC425ph5BeVEBrkhR+XXUO8gwVuLGyPlA9ZuPkiHe1quoLHY2BqzEdokDf6j2qBpPRMVHe1wrlHspFyIpEITk5OeP78OerVq4fmzZvj5cuXWL58uUJxT1mkXJ8+fbj//f39ERgYCC8vLxw5coQrlCaNVi9NJQU16LRAGDAGpUaEh4cjNDQUgYGBCAoKwsaNG/H06VOuGs+MGTPw4sUL7NixAwAwZswYrF27FuHh4Rg5ciRiY2MRGRmJPXv2cOvs0qULVq5ciXr16nFhuXPmzEHXrl1l3IRFIhG2bt2KGT0eDwABAABJREFUwYMHw8hI/3fbkCbeqFXRBjXcDKciY3AV9Q/0+sjCbjVxOTEVHf11n/+BIPSdhj6lEx7o5+eH9u3bY+TIkfjll18AAKNGjULnzp1lKuX6+vpiyZIl6NGjBxiGwaRJk7B48WJUrVoVVatWxeLFi2Fubs5VTAfEnn0PHz7kvt+4cQNWVlbw9PSEvb09YmNjceHCBbRs2RI2Nja4fPkyJk+ejK5du8LTUxwKFxISgho1aiA0NBTLly/Hu3fvMHXqVIwcOZIq5ZYBalag35Ag9BlHCwG8HcRFZ+zM80NKpcUVFyv99Z7UFzYMCMCNF+kI8LJTKIauH1AfIlZWKOXzGPQMEAtdIilPtIp2iosAWZvmVzPePqwBVsbcx4S8StTNqsrmYXOwFGD9APkXZI6WAszqVENmDNJ/AXDCnvT/Xevki0cMw6CaixWuzm7LedUFeNlh+7D8/IO/jw3Gybuv0aWO6vRM24Y2xNK/72Jiq6rwdjSHo6UJ6lS0lWsnXfymlW++V//QYB8MDfYBy7JoUd0J5iZ8rviNNHwewwl+1+eHaFSQRkKvQA+083fl9n8FWzNkmzvJRcqZGvPx+vVrVHBzhZkJHx725jIFnSSRcl4OFvByEFfALhgp5+bmBoZh8Px5vuejn58fkpOTkZ2dDROTfBvVJlLOzc0NXl5eePDggcL52rw05QpqUCQWYciwBsa6detYLy8v1sTEhK1fvz57+vRpbt7gwYPZ5s2by7Q/deoUW69ePdbExIT19vZmN2zYIDM/JyeHnT9/Plu5cmXW1NSU9fDwYMPCwtjU1FSZdseOHWMBsPfu3SvUuNPT01kAbHp6eqGWL8skp39mT917zYpEotIeSrlDH45LfRgDUTTO3H/DPk/9VOL9vn37lh0wYABrZWXFWllZsQMGDJA7dwNgt27dyn0XiUTsvHnzWFdXV1YgELBffPEFe+PGDZll5s2bx0L8DlfmI1lPXFwc26hRI9bGxoY1NTVlq1evzs6bN4/9+PGjzHqePHnCdurUiTUzM2Pt7e3Z8ePHs5mZmRpvH9mG/pGY8oGNfZRS2sMoVfTluNSXcRD6izBXxApz5e8t7yZlsPFPU4ulT304LvVhDNLcfpnO/vcstbSHUapk5eQazHNOw4YN2bFjx8pM8/PzY6dPn66w/bRp01g/Pz+ZaWPGjGEbN27MfZ8xYwbr6ekpc1yuWrWKdXNzk1uf5B4tJydH7VhTUlJYgUDAbt++XW1bllVtG8/efWT/F5vIRl1/qdG6CEJX6PKczbAsZY4pCTIyMmBjY4P09HTy2iD0Bn04LvVhDARREH04LvVhDARREH05LvVlHAQhjT4cl/owBsJw2bt3L0JDQxEREcFFym3atAm3bt2Cl5eXXKRcQkIC/P39MXr0aC5SbsyYMdizZw9XLffZs2eoUaMGPnz4gLi4OCQlJWHYsGGYOHEiZs2axfUtEong4+ODfv36YenSpTLj+vDhA+bPn4+ePXvCzc0NiYmJmDlzJp4+fYo7d+7Aykp9hXGyDUIf0eVxqf/xpQRBEARBEARBEARBFCt9+vTB27dvsXDhQiQlJcHf3x9RUVHw8hLnGExKSsLTp/lFUHx8fBAVFYXJkydj3bp1cHd3x+rVqzlhDwA8PDxw8OBBtG3bFk2aNEGFChXw9ddf49tvv5Xp+/jx43j69CmGDRsmNy4+n48bN25gx44dSEtLg5ubG1q2bIm9e/dqJOwRRHmAxD2CIAiCIAiCIAiCIBAWFoawsDCF87Zt2yY3rXnz5rh6Vb6arjSS4mevX79W6p0UEhIiV9BSgpmZGY4dO6ayD4Io75TfOu8EQRAEQRAEQRAEQRAEYeCQuEcQBEEQBEEQBEEQBEEQBgqJewRBEARBEARBEARBEARhoFDOvRJCkj8gIyOjlEdCEPlIjsfSLJpNtkHoI2QbBKEYfbAN6f7JPgh9Qh/sg2yD0EfINghCMbq0DRL3Soj3798DEFcLIgh94/3797CxsSm1vgGyDUI/IdsgCMWUpm1I+gfIPgj9hK4dBKEYsg2CUIwubINhS/vVazlBJBLh5cuXsLKyAsMwMvMyMjLg4eGBZ8+eKa0eRORD+0tz1O0rlmXx/v17uLu7g8crnSh9VbYB0O+tDbSvtEPV/iLbKFvQvtIOfbcNgO6rdAntL80x9Psq+q21g/aX5hi6bQD0e2sD7SvtKKn7KvLcKyF4PB4qVqyoso21tTUZhxbQ/tIcVfuqND0vAM1sA6DfWxtoX2mHsv1FtlH2oH2lHfpqGwDdVxUHtL80x9Dvq+i31g7aX5pj6LYB0O+tDbSvtKO476uooAZBEARBEARBEARBEARBGCgk7hEEQRAEQRAEQRAEQRCEgULinh4gEAgwb948CASC0h6KQUD7S3PKwr4qC9tQUtC+0g5D31+GPv6ShPaVdhj6/jL08Zc0tL80x9D3laGPv6Sh/aU5ZWFflYVtKCloX2lHSe0vKqhBEARBEARBEARBEARBEAYKee4RBEEQBEEQBEEQBEEQhIFC4h5BEARBEARBEARBEARBGCgk7hEEQRAEQRAEQRAEQRCEgULiHkEQBEEQBEEQBEEQBEEYKCTuFRPr16+Hj48PTE1NERAQgDNnzqhsf/r0aQQEBMDU1BSVKlVCRESEXJsDBw6gRo0aEAgEqFGjBg4ePFhcwy9RdL2vtm3bBoZh5D6ZmZnFuRklhjb7KykpCf3790f16tXB4/EwadIkhe1K8tgi29Acsg3tINsoP7YBkH1og6HbBkD2oQ1kG9ph6PZBtqE5ZBvaQbZRfmwDIPvQBr21DZbQOb/++itrbGzMbtq0ib19+zb79ddfsxYWFuyTJ08Utn/8+DFrbm7Ofv311+zt27fZTZs2scbGxuz+/fu5NufPn2f5fD67ePFi9s6dO+zixYtZIyMj9sKFCyW1WcVCceyrrVu3stbW1mxSUpLMpyyg7f5KSEhgJ06cyG7fvp2tW7cu+/XXX8u1Kclji2xDc8g2tINso/zYBsuSfWiDodtGYbahPNsH2YZ2GLp9kG1oDtmGdpBtlB/bYFmyD23QZ9sgca8YaNiwITtmzBiZab6+vuz06dMVtp82bRrr6+srM2306NFs48aNue+9e/dm27dvL9OmXbt2bN++fXU06tKhOPbV1q1bWRsbG52PVR/Qdn9J07x5c4Unk5I8tsg2NIdsQzvINsqPbbAs2Yc2GLptsCzZhzaQbWiHodsH2YbmkG1oB9lG+bENliX70AZ9tg0Ky9Ux2dnZiIuLQ0hIiMz0kJAQnD9/XuEysbGxcu3btWuHK1euICcnR2UbZes0BIprXwHAhw8f4OXlhYoVK6Jz586Ij4/X/QaUMIXZX5pQUscW2YbmkG1oB9lG/tjKum0AZB/aYOi2AZB9aAPZhnYYun2QbWgO2YZ2kG3kj62s2wZA9qEN+m4bJO7pmJSUFOTm5sLFxUVmuouLC5KTkxUuk5ycrLC9UChESkqKyjbK1mkIFNe+8vX1xbZt23D48GHs2bMHpqamCA4OxoMHD4pnQ0qIwuwvTSipY4tsQ3PINrSDbCO/fVm3DYDsQxsM3TYAsg9tINvQDkO3D7INzSHb0A6yjfz2Zd02ALIPbdB32zAq9AgIlTAMI/OdZVm5aeraF5yu7ToNBV3vq8aNG6Nx48bc/ODgYNSvXx9r1qzB6tWrdTXsUqM4joOSPLbINjSHbEM7yDbKj20AZB/aYOi2UZj+yrN9kG1oh6HbB9mG5pBtaAfZRvmxDYDsQxv01TbIc0/HODo6gs/ny6msr1+/llNjJbi6uipsb2RkBAcHB5VtlK3TECiufVUQHo+HBg0aGPRbAqBw+0sTSurYItvQHLIN7SDbyG9f1m0DIPvQBkO3DYDsQxvINrTD0O2DbENzyDa0g2wjv31Ztw2A7EMb9N02SNzTMSYmJggICEBMTIzM9JiYGDRp0kThMkFBQXLto6OjERgYCGNjY5VtlK3TECiufVUQlmVx7do1uLm56WbgpURh9pcmlNSxRbahOWQb2kG2kT+2sm4bANmHNhi6bQBkH9pAtqEdhm4fZBuaQ7ahHWQb+WMr67YBkH1og97bhlblNwiNkJRHjoyMZG/fvs1OmjSJtbCwYBMTE1mWZdnp06ezoaGhXHtJKenJkyezt2/fZiMjI+VKSZ87d47l8/ns0qVL2Tt37rBLly4tE6W3i2NfzZ8/nz169Cj76NEjNj4+nh06dChrZGTEXrx4scS3T9dou79YlmXj4+PZ+Ph4NiAggO3fvz8bHx/P3rp1i5tfkscW2YbmkG1oB9lG+bENliX70AZDt43CbEN5tg+yDe0wdPsg29Acsg3tINsoP7bBsmQf2qDPtkHiXjGxbt061svLizUxMWHr16/Pnj59mps3ePBgtnnz5jLtT506xdarV481MTFhvb292Q0bNsitc9++fWz16tVZY2Nj1tfXlz1w4EBxb0aJoOt9NWnSJNbT05M1MTFhnZycWF9fX+67t7c3O3/+fDY7O1ujsd27d4/98ssvWVtbW9bMzIxt2LAh+8cffyhsu3//frZJkyasnZ0da2NjwzZo0IDdsWOHTJuTJ0+yAJR+Ro8erfP9pagfLy8vmTYleWyRbWhOcdtGSEgIO3nyZLZ69erFbh/SDBgwgAXAdurUSWZ6Ue2DbKP82AbL6ve1g2VZ9saNG+xXX33FOjo6siYmJqyXlxc7duxYmTY3b95kx44dyzZu3Jg1NzdnAbAnT55UuD4vL69ye90ozDaUZ/vQV9u4cuUKGxYWxvr7+7OWlpass7Mz27p1a/aff/5R2F4kErFbtmxhGzRowJqbm7NWVlZsvXr12EOHDsm13bNnD1unTh1WIBCwbm5u7Ndff82+f/9eg71l+PZBtqE5+mobLMuy2dnZ7Pz587nxVa9enV29erXCtpo8c7Asy27fvp3t06cPW61aNZZhGLnjVB1kG+XHNlhWv+2DZem+ShNI3CPKNN9//z3LMAw7Y8YM9uTJk+yyZctYExMTduTIkWqXTUhIYO3t7dmaNWuyv/76K/vXX3+xnTp1YhmGkXkrwbIsGxkZyQJge/bsyUZFRbF///0327dvXxYAu3LlSq5deno6GxsbK/cZNGgQC4A9evSozvcBQSijpOxDmr/++ou1sLBgra2t5cQ9sg9CXyiKbbAsy544cYI1MzNjQ0JC2P3797OnTp1id+zYwU6ePFmm3bZt21g3Nze2Y8eObJcuXdTehAYHB8vZx+PHj4u6uQShMUWxjSlTprCBgYHsypUr2X/++Yc9fPgw27FjRxYAu337drn2o0ePZgUCATt9+nT2+PHj7NGjR9nly5ezu3fvlmm3c+dOFgA7YsQI9sSJE2xERARrY2PDtm3bVmfbTRDqKOp1Y8SIEaxAIGCXLVvGnjx5kp0+fTrLMAy7aNEimXaaPnOwLMu2adOG9ff3ZwcOHMhWqVJFa3GPIHQF3VeVDCTuETKIRCL206dPpT0MnZCSksKampqyo0aNkpm+aNEilmEYGVdYRYwePZo1NTVlnz9/zk0TCoWsn58f6+Hhwebm5nLTg4ODWS8vL5lpIpGI9fX1ZWvXrq2yH5FIxFaqVElueUL/IPvIRxv7kJCWlsZWqFCBXblyJevl5SUn7imC7MMwINvI5+PHj6ybmxvbqVMnViQSqWwrfUzv27dP7U2oJjZD6BdkG/m8evVKbppQKGRr167NVq5cWWb6wYMHWQDs3r17Va5TKBSybm5ubEhIiMz0Xbt2sQDYqKgolcsTpQfZRj43b95kGYZhFy9eLDN95MiRrJmZGfv27VtumjbPHNJtOnXqROKeAUH2kQ/dV2kOFdQoo8yfPx8Mw+DWrVvo168fbGxs4OLigmHDhiE9PZ1rxzAMxo8fj4iICPj5+UEgEGD79u3Ytm0bGIbBiRMnMHLkSDg4OMDa2hqDBg3Cx48fkZycjN69e8PW1hZubm6YOnUqcnJyZMawYcMG1KlTB5aWlrCysoKvry9mzpwp0yY5ORmjR49GxYoVYWJiAh8fHyxYsABCobDI++Do0aPIzMzE0KFDZaYPHToULMvi0KFDKpc/d+4c6tSpgwoVKnDT+Hw+OnTogGfPnuHSpUvcdGNjY1haWoLHyzcphmFgbW0NU1NTlf2cPHkSjx8/xtChQ2WWJ4oPso+StQ8JU6ZMgZubGyZOnKjxOMk+ShayjaLbxr59+5CUlIRvvvkGDMOobEvHtOFAtlF023B2dpabxufzERAQgGfPnslM//nnn+Ht7Y3evXurXOeFCxeQlJQkN6ZevXrB0tISBw8eVLk8UXTINopuG4cOHQLLsgqX//z5M44ePcpN0+aZg64xpQ/ZB91XlSRGpT0Aonjp2bMn+vTpg+HDh+PGjRuYMWMGAGDLli1cm0OHDuHMmTOYO3cuXF1d4ezsjMuXLwMARowYgS+//BK//vor4uPjMXPmTAiFQty7dw9ffvklRo0ahePHj+OHH36Au7s7wsPDAQC//vorwsLCMGHCBKxYsQI8Hg8PHz7E7du3uX6Tk5PRsGFD8Hg8zJ07F5UrV0ZsbCy+//57JCYmYuvWrVzb3NxcsCyrdnt5PB5n1Ddv3gQA1KpVS6aNm5sbHB0dufnKyM7Ohr29vdx0gUAAALh+/ToaN24MAJgwYQJ69eqFRYsWYdSoUWAYBtu2bUNcXBz27Nmjsp/IyEjweDy5Ex5R/JB9lIx9AMDx48exY8cOXL58GXw+X+1YJZB9lA5kG4W3jX///Zfru2nTprh06RIsLCzQvn17/Pjjj3B3d1c7HlXrtrKyQmZmJqpWrYrhw4dj0qRJWtkUUTTINgpvG4oQCoU4c+YMatasKTMtNjYWHTt2xMqVK/Hzzz/j+fPn8PLyQlhYGKZMmcI94En6rF27tsx6jY2N4evrW6gxEYWDbKPwtnHz5k04OTnB1dVVZrrkuJZevijPHETpQfZB91UlQqn4CxLFzrx581gA7LJly2Smh4WFsaamppxLKwDWxsaGfffunUy7rVu3sgDYCRMmyEzv3r27wpwOdevWZevXr899Hz9+PGtra6tyjKNHj2YtLS3ZJ0+eyExfsWIFC0DGRVdZwsuCn3nz5nHLjBw5khUIBAr7rlatmlwIR0G6d+/O2trayiVkbtasGQtAznX+0KFDrI2NDTcWMzMzdufOnSr7SE1NZU1NTdl27dqpbEfoFrKPkrWP9+/fs97e3uyMGTNkxqzOFZ7so+Qh2yi6bbRr144FwNra2rLTpk3jcoA5ODiwVapUYT9+/KhwOXXhI2FhYeyWLVvY06dPs4cOHeIK0wwcOFDleAjdQLZRdNtQxKxZs1gAMkUykpKSWACstbU1W7FiRXb79u3sP//8w44ZM4YFwM6cOZNru2jRIhYAm5SUJLfukJAQtlq1alqPidAOso2i20bbtm3Z6tWrK5xnYmIiF85YmGcOCsstHcg+6L6qJCHPvTJO165dZb7Xrl0bmZmZeP36NVxcXAAArVq1gp2dncLlO3fuLPPdz88Phw4dQqdOneSmR0dHc98bNmyItWvXol+/fujbty+Cg4Ph6Ogos8xff/2Fli1bwt3dXcblt0OHDpg6dSpOnz6NGjVqAAD+/PNPZGVlqd3egsq9KtdddW6948ePxx9//IFBgwZhxYoVsLCwwNq1a3H+/HkAsm6/R48excCBA9GrVy/07t0bRkZGOHz4MIYMGYLs7GylXke7du1CZmYmRowYoXbbCN1D9lEy9jF9+nQYGxtj7ty5ascoDdlH6UG2UXjbEIlEAIA+ffrghx9+AAC0bNkSrq6u6N69O3bv3l2oY3rdunUy37t16wY7OzusXbsW4eHhqFevntbrJLSHbKPwtlGQzZs3Y9GiRZgyZQq6devGTZfYUEZGBo4dO8Z5gbdq1QrJyclYuXIlZsyYAUtLS7V9azsmovCQbRTNNjRdvrDPHETpQvZB91UlAYl7ZRwHBweZ75KQuc+fP3PT3NzclC5fMOzOxMRE6fTMzEzue2hoKIRCITZt2oSePXtCJBKhQYMG+P7779G2bVsAwKtXr/Dnn3/C2NhYYd8pKSnc/zVq1NDYBViCg4MDMjMz8enTJ5ibm8u0e/fuHQICAlSuq3Xr1ti6dSumTJmCypUrc+P47rvvMHPmTC7XGMuyGDZsGL744gsZ1+o2bdogPT0dEyZMQO/evWFhYSHXR2RkJJycnGRuaomSg+yj+O3j0qVLWL9+PX7//XdkZmZy+0EkEkEoFCItLQ1mZmbcvpeG7KP0INsovG1I9l27du1kprdr1w4Mw+Dq1atqx6MpAwcOxNq1a3HhwgWDvAk1RMg2Cm8b0mzduhWjR4/GqFGjsHz5cpl5dnZ2YBgGVlZWMukdAPHD5qFDh3D79m00bNiQ+z3evn3LPSBLj0lR+giieCDbKNp149q1a3LTP378KJMGpSjPHETpQvZB91UlQfnOOEgAKL63mkOHDsX58+eRnp6OI0eOgGVZdO7cGU+ePAEAODo6IiQkBJcvX1b4GT58OLeuypUrw9jYWO1n4cKF3DKSuP4bN27IjCs5ORkpKSnw9/dXuw2DBw9GcnIybt++jQcPHuDWrVsAxPusWbNmAMQnxKSkJDRs2FBu+QYNGuDjx49ITEyUmxcfH4/4+HgMGjRI6cmUKH3IPpSjiX3cvn0bLMuiR48esLOz4z7Pnj3DsWPHYGdnhw0bNsitm+xD/yHbUEzB3F8F0WWyZ8kNdnlPIK1vkG2oZuvWrRgxYgQGDx6MiIgIuf1lZmaGqlWrKly24DGvbExCoRB3797VeExEyUC2oZhatWrhzZs3SE5OlpkuWZ9k+cI+cxCGAdmHYui+SnPIc48odiwsLNChQwdkZ2eje/fuuHXrFry8vNC5c2dERUWhcuXKSl2QJRTGBbh9+/YwNTXFtm3b0KhRI266pOpQ9+7dNRq/kZER/Pz8AADp6enYuHEjunXrBi8vLwDiN8ympqa4cOGC3LKxsbHg8XgK38RERkYCgMwJkyh/lHX7aN++PU6ePCm3XN++feHj44MlS5agSpUqcvPJPghDtY0ePXpg1qxZ+Pvvv9GjRw9u+t9//w2WZeU8kYrCjh07AECn6yT0H0O1DUnbESNGYODAgdi8ebPSh9mePXtiyZIlOH/+PJo0acJNj4qKgqWlJVeAo1GjRnBzc8O2bdvQp08frt3+/fvx4cMHfPnll2rHRJQdDNU2unXrhtmzZ2P79u349ttvZZY3MzND+/btART+mYMgAMO1D7qv0hwS94hiYeTIkTAzM0NwcDDc3NyQnJyMJUuWwMbGBg0aNAAALFy4EDExMWjSpAkmTpyI6tWrIzMzE4mJiYiKikJERAQqVqwIQL66jibY29tj9uzZmDNnDuzt7bk3EvPnz8eIESO43AGA2JCHDRuGLVu2YNCgQQCA169f48cff0RwcDCsrKxw9+5dLFu2DDweTyZGXyAQICwsDCtXrsSgQYPQp08f8Pl8HDp0CLt378bw4cPlXKYzMzOxe/duNGnShBNGiPJDebIPV1dXuepvAGBqagoHBwe0aNFCbh7ZR/mlLNiGr68vxo0bh/Xr18PKygodOnTA/fv3MXv2bNSrVw+9e/fmlv/06ROioqIAgHtYO336NFJSUribcADYvXs3fv/9d3Tq1AleXl5IS0vDvn378Ouvv2LIkCGoU6dOIfY2YUiUBdvYt28fhg8fjrp162L06NG4dOmSzPrr1avHhapNnToVu3btQq9evfDdd9+hYsWK2L9/Pw4fPowVK1bAzMwMAMDn87Fs2TKEhoZi9OjR6NevHx48eIBp06ahbdu2nChClF3Kgm3UrFkTw4cPx7x588Dn89GgQQNER0dj48aN+P7777nnCG2fOW7fvs1VRU1OTsanT5+wf/9+AOLwSulxEWWTsmAfdF+lOSTuEcVCs2bNsG3bNvz2229ITU2Fo6MjmjZtih07dsDJyQmAOK/AlStX8N1332H58uV4/vw5rKys4OPjg/bt26t9c6AJs2bNgpWVFdatW4cVK1bA1dUV06dPx6xZs2TaiUQi5Obmcgk7AbFH0rVr17B161akpaXBzc0N3bp1w9y5c+USkS5fvhx+fn745ZdfMHDgQIhEIlSuXBlr167FqFGj5Mb1+++/IzU1lQoFlFPKm31oC9lH+aUs2AYArFq1ChUrVsTmzZuxZs0aODo6om/fvli8eDGXJwcQi+S9evWSWXb+/PkAAC8vLy68qlKlSkhLS8PMmTPx9u1bGBsbo2bNmli/fj1Gjx5d5O0l9J+yYBtHjhyBSCTC1atXERwcLLfuhIQEeHt7AxA/DJ49exbTpk3D1KlT8fHjR/j6+mLLli1yBQMGDhwIPp+PpUuXYtu2bbC3t8egQYOwaNGiIm8vof+UBdsAgPXr16NChQpYs2YNkpOT4e3tjZ9//hkTJkyQaafNM8dvv/2GBQsWyEyTXHPmzZvHXW+IsktZsQ+6r9IMhtUkIyJBEARBEARBEARBEARBEHqHYWYKJAiCIAiCIAiCIAiCIAiCxD2CIAiCIAiCIAiCIAiCMFRI3CMIgiAIgiAIgiAIgiAIA4XEPYIgCIIgCIIgCIIgCIIwUEjcIwiCIAya9evXw8fHB6ampggICMCZM2dUtj99+jQCAgJgamqKSpUqISIiQmZ+Tk4OFi5ciMqVK8PU1BR16tTB0aNHZdosWbIEDRo0gJWVFZydndG9e3fcu3dPps2QIUPAMIzMp3HjxrrZaIIgCIIgCIIgiDxI3CMIgiAMlr1792LSpEmYNWsW4uPj0axZM3To0AFPnz5V2D4hIQEdO3ZEs2bNEB8fj5kzZ2LixIk4cOAA12b27Nn45ZdfsGbNGty+fRtjxoxBjx49EB8fz7U5ffo0xo0bhwsXLiAmJgZCoRAhISH4+PGjTH/t27dHUlIS94mKiiqeHUEQBEEQBEEQRLmFYVmWLe1BlAdEIhFevnwJKysrMAxT2sMhCAAAy7J4//493N3dweOVjtZPtkEUhVatWqFOnTr46aefuGkNGjRAp06dMH/+fLn2c+fOxd9//43Lly9z0yZNmoSbN2/i+PHjAIDq1atjypQp6Nu3L2cb3bt3h6WlJXbu3KlwHG/evIGzszNOnz6NL774AoDYcy8tLQ2HDh0q1LaRbRD6iD5cNwCyD0I/0Qf7INsg9BGyDYJQjC5tg8S9EuL58+fw8PAo7WEQhEKePXuGihUrlkrfZBuEPiOxjX79+iE2NhaJiYkK2z18+BBVq1bFjRs34O/vD0As7h06dAgmJiawtbVF8+bNsWjRIjg7OytcR1ZWFrKysrjvL168QI0aNXS+TQShC0rzugHQtYPQb+i+iiAUQ7ZBEIrRhW0Y6WgshBqsrKwAiH80a2vrUh4NQYjJyMiAh4cHd3yWBmQbRGFJSkqCr68voqOj0ahRI276ihUrsGfPHsTFxcktU79+ffTv3x9Tp07lpl28eBEhISG4d+8eXF1dMXz4cFy/fh3379+HhYUFYmJi8McffyA3N1fhOFiWRXh4OJo2bcoJewDQoUMH9OrVC15eXkhISMCcOXPQqlUrxMXFQSAQyK1nyZIlWLBggdx0sg1Cn9CH6wZA1w5CP9EH+yDbIPQRsg2CUIwubYPEvRJC4vprbW1NJxNC7yhN13SyDaKwfPjwAQBgaWkpc+wIBALw+XyFxxOPx4OZmZnMPHNzcwD5x+D69esxdOhQ3L9/H05OTqhcuTKGDh2KrVu3KhzH+PHjcf36dZw9e1Zmep8+fbj//f39ERgYCC8vLxw5cgRffvml3HpmzJiB8PBw7rvkYk+2QegjpR3SRNcOQp+h+yqCUAzZBkEoRhe2QQU1CIIgCIPE0dERfD4fycnJMtNfv34NFxcXhcu4uroqbG9kZAQHBwcAgJOTE3bv3g0AuHnzJu7evQtLS0v4+PjIrW/ChAk4fPgwTp48qdaV3s3NDV5eXnjw4IHC+QKBgLvhpBtPgiAIgiAIgiA0hcQ9giAIwiAxMTFBQEAAYmJiZKbHxMSgSZMmCpcJCgqSax8dHY3AwEAYGxvLtXd3d4dQKMSBAwfQrVs3bjrLshg/fjx+//13nDhxQqHwV5C3b9/i2bNncHNz02TzCIIgCIIgCIIgNILEPYIgCMJgCQ8Px+bNm7FlyxbcuXMHkydPxtOnTzFmzBgA4lDXQYMGce3HjBmDJ0+eIDw8HHfu3MGWLVsQGRkpl4Pv8OHDAIDz58+jffv2EIlEmDZtGtdm3Lhx2LlzJ3bv3g0rKyskJycjOTkZnz9/BiAOGZ46dSpXhOPUqVPo0qULHB0d0aNHj5LYNQRBEARBEARBlBMo5x5BEARhsPTp0wdv377FwoULkZSUBH9/f0RFRcHLywuAuOjG06dPufY+Pj6IiorC5MmTsW7dOri7u2P16tXo2bMn1yYzMxPff/89AGDAgAHo1KkT/ve//8HW1pZrs2HDBgBAixYtZMazdetWDBkyBHw+Hzdu3MCOHTuQlpYGNzc3tGzZEnv37i31QgQEQRAEQRAEQZQtSNwjCIIgDJqwsDCEhYUpnLdt2za5ac2bN8fVq1eVrq958+a4dOkSbGxskJCQoDD3HcuyKsdkZmaGY8eOqR44QRAEQRAEQRCEDqCwXD0gS5iLb/dfx983kkp7KIQBkpqaitDQUNjY2MDGxgahoaFIS0tTuQzLspg/fz6qV68OAOjUqRNu3brFzX/37h0mTJiA6tWrw9zcHJ6enpg4cSLS09OL3Le2bDmbgB+O3tXpOgnC4El7irQN7fH4507IFakWGgmivPHP6tE4s7Q7Eh/cLO2hEIRecezQTvy2YjxO/n2gtIdCEAShc+KfpmLSr/FITs8s7aGUCiTu6QG7Lz7F3ivPMHaXck8SglBG//79ce3aNRw9ehRHjx7FtWvXEBoaqnKZZcuWYeXKlVi+fDkAwNnZGW3btsX79+8BAC9fvsTLly+xYsUK3LhxA9u2bcPRo0cxfPjwIvetLQv/uo0Npx5hwZ+31DcmiPJCbg5sX8XC6V0cTt9/XdqjIQi9wjf1FJplnkRmKr00JQhpbJ+fQO8P/4Pg+ZnSHgpBEITO6bH+PA5de4kp+66V9lBKBQrL1QNev88q7SEQBsqdO3dw9OhRXLhwAY0aNQIAbNq0CUFBQbh37x7nmScNy7JYtWoVZs2aha5duwIAIiIiULVqVezevRujR4+Gv78/DhzIf6tbuXJlLFq0CAMHDoRQKISRkVGh+i4KW88lYmZHPxjz6Z0EQeTyTcEHYIpsfMzKLe3hEIRekc2YACyQm10+39wThDJERgIAAJObXcojIQiCKD4SUz6V9hBKBXpKJggDJjY2FjY2Npy4BgCNGzeGjY0Nzp8/r3CZhIQEJCcnIyQkhJsmEAjQvHlzpcsAQHp6OqytrWFkZFTovouKSE2eM4IoL2TCBABgzOTC0pjsgiCkyWHE9iHKIXGPIGTgi8U9Xi45FhAEUXZRlxu7rELiHkEYMMnJyXB2dpab7uzsjOTkZKXLAICLi4vMdBcXF6XLvH37Ft999x1Gjx5dpL4BICsrCxkZGTIfgiC045PImPvfjMkpxZEQhP6Rw4gFDFb4uZRHQhD6BcuXeO6RuEcQhsDWcwnoueE8MjLpXo9QD4l7ekA5FZYJFcyfPx8Mw6j8XLlyBQDAMIzc8izLKpwuTcH5ypbJyMhAp06dUKNGDcybN0/lOjTpe8mSJVwBDhsbG3h4eKgcJ0EQ8mSy+eIeL5e8kwhCGiFP7LnHkuceQchiZAoA4FFYLkEYBAv+vI24J6mIPJNQ2kMhDADKuUcQesj48ePRt29flW28vb1x/fp1vHr1Sm7emzdv5DzzJLi6ugIQe95VrlyZm/769Wu5Zd6/f4/27dvD0tISBw8ehLGxscx6tO0bAGbMmIHw8HDue0ZGBgl8BKElmUIRslhjCJgcgAQMgpBByIXlkncSQUjD5OXc44lI3CMIQyJLKNKoHcuymPvHLbjamGJcyyrFPCr9RZ2TS1mFxD2C0EMcHR3h6Oiotl1QUBDS09Nx6dIlNGzYEABw8eJFpKeno0mTJgqX8fHxgaurK2JiYjhxLzs7G6dPn8YPP/zAtcvIyEC7du0gEAhw+PBhmJqaFrlvQJzfTyAQqN02RZCXK0GIqepihfc8EwjYHDBCEvcIQprcPM89kG0QhCzG4vsvPoXlEoRBYcTTTKy69TID/7vwBADKtbhHOfcMhPXr18PHxwempqYICAjAmTOqS7mfPn0aAQEBMDU1RaVKlRARESHXJi0tDePGjYObmxtMTU3h5+eHqKgomTYvXrzAwIED4eDgAHNzc9StWxdxcXE63TaC0BY/Pz+0b98eI0eOxIULF3DhwgWMHDkSnTt3lqlW6+vri4MHDwIQv8mYNGkSFi9ejD///BMAMHbsWJibm6N///4AxB57ISEh+PjxIyIjI5GRkYHk5GQkJycjNzdXq74JgigesiFJjE4CBkFIk8vLe4FEXq0EIQMvLyyXLyJxjyD0iYevP+DoTeU5y/kainuZObm6GhJhgBiUuLd3715MmjQJs2bNQnx8PJo1a4YOHTrg6dOnCtsnJCSgY8eOaNasGeLj4zFz5kxMnDgRBw4c4NpkZ2ejbdu2SExMxP79+3Hv3j1s2rQJFSpU4NqkpqYiODgYxsbG+Pvvv3H79m38+OOPsLW1Le5NJgi17Nq1C7Vq1UJISAhCQkJQu3Zt/O9//5Npc+/ePaSnp3Pfp02bhkmTJmHKlCkAgKSkJERHR8PKygoAEBcXh4sXL+LGjRuoUqUK3NzcuM+zZ8+06psgiOIhmyHvJIJQRC6fbIMgFGIsEfcoOT9B6BNtVp7GmJ1xOP8wReF8TT33yoO/2ufsXNxJytCpd17ck1QM3HwR91+913iZ/XHP0WP9ObzO0J97DYMKy125ciWGDx+OESNGAABWrVqFY8eOYcOGDViyZIlc+4iICHh6emLVqlUAxJ5GV65cwYoVK9CzZ08AwJYtW/Du3TucP3+eyyfm5eUls54ffvgBHh4e2Lp1KzfN29u7GLaQILTH3t4eO3fuVNmm4MmPYRjMnz8f4eHhsLGxQVRUFKytrbn5LVq00OiEqUnfRaHgGMqphzVBKCQ7ryIonwQMgpBB4rmXk0XVcglCGs5zj6WcewShj9x4kY4mVRzxOTsXS/++w03n88tnDjlpRCIWPB6DHuvP4W7ye0QMDEB7f1edrLvnhvMAgKFbL+Pc9FYaLTN1338AgKV/38XKPnV1Mo6iYjCee9nZ2YiLi0NISIjM9JCQEJw/f17hMrGxsXLt27VrhytXriAnR/zG6vDhwwgKCsK4cePg4uICf39/LF68mAs9lLQJDAxEr1694OzsjHr16mHTpk063kKCIApSUMxjy8X7KILQDM5zj8JyCUKGV5/Ef+MTlIc4EUR5hGciFveMqKAGQZQ4v5x+hA4/n0HaJ+X2J3nS2XD6EbbHPuGma+q5V1xkZJaut++BuOeovSAaFx6/xd1ksXfdofgXOu8nKV37l4IZmUKdj6OwGIy4l5KSgtzcXLkqnC4uLkhOVnzzlpycrLC9UChESorY5fXx48fYv38/cnNzERUVhdmzZ+PHH3/EokWLuGUeP36MDRs2oGrVqjh27BjGjBmDiRMnYseOHUrHm5WVhYyMDJmPJrz9QDkwCIIgCPVIxD0eee4RhAzJn8SPRwJQ6CFBSMPPE/eMyXOPIEqcJX/fxZ2kDGz897Hatk/ffpT5ztOw+mtxSICn7r1G7fnRWPDnrWJYu2ZM2fcfPmQJMXLHFW6avjh9KIp2m/fHTaw6fr/Ex2Iw4p6EgmWNWZZVWepYUXvp6SKRCM7Ozti4cSMCAgLQt29fzJo1Cxs2bOCWEYlEqF+/PhYvXox69eph9OjRGDlypEybgixZsgQ2Njbcx8PDQ2lb6QMz4PvjSE6nBzWCAOTzRlBYLkHkk19Qg14KEYQ0WaxY+CZxjyBk4RubAQCMSNwjiFIjWygq0vK5ouJ7ILr/6j1W//MAn7LzvdGWRN0FAGw9l6h0PCuj7+HsA8X5ArXh7IMUDNl6CS/SlHjQ6eGzYMEhPX7zAdtjn2DV8QclXrXXYMQ9R0dH8Pl8OS+9169fy3nnSXB1dVXY3sjICA4ODgAANzc3VKtWDXw+n2vj5+eH5ORkZGdnc21q1Kghsx4/Pz+lhTwAYMaMGUhPT+c+0kUI1HH+UdENgyDKAnI590ppHAShj0g89xgKyyUIGbIgzqEsYEjcIwhp+Cbil0ImLNkGQRQnLMviQ1bhwzWVOS8din8BvzlHceLuq0KvWxUhP/2LlTH38WN0vteZOqfBg/EvsPrEQwyMvFjk/gdGXsSpe28wbf9/CudLPwsyGvopnnuYgsFbLuHZu09y85LSP+NA3PPCDDV/TAWeV4VS4mtxCrGKMBhxz8TEBAEBAYiJiZGZHhMTgyZNmihcJigoSK59dHQ0AgMDueIZwcHBePjwIUSifAX9/v37cHNzg4mJCdfm3r17Muu5f/++XOENaQQCAaytrWU+BEEUjZJ++0EQ+kxOXkENCsslCFkyIfHcI+8kgpDGyETsuWdMtkEQxcrYnVfhP+8YHmhRfVUaZbLVpL3XkJ0rwrBtV5S0yKcoz03Xn6dp3PapAtGsqCSnZ+JSwjv8+d9LmemabpN0qwGbL+L0/TeYsk9eMGy78l+F09UhkhLtCup30iHUQhL3lBMeHo7Nmzdjy5YtuHPnDiZPnoynT59izJgxAMTecoMGDeLajxkzBk+ePEF4eDju3LmDLVu2IDIyElOnTuXajB07Fm/fvsXXX3+N+/fv48iRI1i8eDHGjRvHtZk8eTIuXLiAxYsX4+HDh9i9ezc2btwo04YgCN1DUh5BKIfLuUeeewQhA+e5R2G5BCGDkUAs7pmQbRAKSE1NRWhoKJdWKjQ0FGlpaSqXYVkW8+fPh7u7O8zMzNCiRQvcuiWbmy0rKwvffPMNAHFEXNeuXfH8eb631KlTp8AwjMLP5cuXuXaK5kdEROhuB+iQo7fE0YPbYxO1Wo7TrgqRPC9LmItnqflCW1F8Ihgw2PTvY4RGXkSSupRhxeB8wTAMev8Siwl74mUE0qL09DpDvB0iEYuRO65g4Z+3C+1duV/K26/gmKSLn2TninDx8VssjrqDzJxcFDcGJe716dMHq1atwsKFC1G3bl38++//2Tvz8Caq9Y9/JukOpQVKW3YKymZBEBAKKrhQUATXK4qiKKAIqID+UESuVRSuioioyCKLCly5KigKloIIiLTsZd9pWbpQCt2XpE3m90eaNGn2Nmnacj7PkwcyOXPOSTqTzPnO933fHWzcuNHgoEtLSzMJlY2IiGDjxo1s27aNbt26MXPmTObPn89jjz1maNOyZUvi4uLYu3cvXbt25dVXX+W1117jrbfeMrTp1asX69at47///S+RkZHMnDmTefPm8fTTT1ffmxcIbkDMq+UKBAI9l/N1/+4+nWq7oUBwg6GShbgnqB4WLFhAREQEfn5+9OjRg127dlltm5aWxogRI+jQoQMKhYJJkyZZbDdv3jw6dOiAv78/LVu2ZPLkyRQXu+YmjrLMuecjl4hExgIzRowYQWJiIrGxscTGxpKYmMjIkSNt7vPxxx8zd+5cvvzyS/bu3Ut4eDgDBw4kL69ckJk0aRK///47ALGxseTn5/Pggw+i0ejEjr59+5KWlmbyGDNmDG3atKFnz54m4y1fvtyk3XPPPefiT8G1WDrN3HXmPbpgF5PXlLvQiko0PLdsD9/uSna6rz3J1/lw4wn+PpNJTpFnf0svG+Xfc/Rry5I2qg91Pngpm83Hr7DsnySzNraMdrIss2THeV774SCbjqWbbAc4lZ7Hmr0XTZ17GpnhixNYvOM83/xtv5BKVfFy+wguZvz48YwfP97iaytWrDDb1r9/fw4cOGCzz6ioKBISEmy2efDBB3nwwQcdnqdAIHA94jpUIChH704qLMz38EwEgpqFyLknqA7WrFnDpEmTWLBgAf369WPRokU8/vjjVturVCqaNGnC9OnT+eyzzyy2WbVqFW+99RbLli2jb9++nD59mlGjRgFY3ccZGtSrB4BCkskvKqJ+QECV+xTUDU6cOEFsbCwJCQn07t0bgCVLlhAVFcWpU6fo0KGD2T6yLDNv3jymT5/Oo48+CsC3335LWFgYq1ev5qWXXiInJ4elS5eyaNEiXnjhBW699VZWrlxJy5Yt2bJlC4MGDcLHx4fw8HBDvyUlJaxfv56JEyea5Z4LDg42aVsTMc7tVtmli6P55Iw5lppr8nz17otsP32V7aev8lzfNmi1MmqNFj9vpZUeaihGH6LWxmJwX/J1m93oP9FSje2CJqUaLV5KUw/c0ZQcnlu2h2sFupQG3sryv49+SoPm7QBg6uDycyUjr/zGzNkM91+v1yrnnkAguLG4UKEMvLDuCQTl6POK+YncSQKBCXpxT5wbAncyd+5cRo8ezZgxY+jUqRPz5s2jefPmVtu3adOGzz//nGeffZagoCCLbeLj4+nXrx8jRoygTZs2REdH89RTT7Fvn/38Wo7QsEGg4f/ZOZXLBSaom8THxxMUFGQQ9gD69OlDUFCQVUdqUlIS6enpREdHG7b5+vrSv39/wz779++npKSEe+65x9CmWbNmREZGWu13/fr1ZGZmGoRtYyZOnEhISAi9evVi4cKFJnnzK6JSqcjNzTV5uBtZlrnz47+Mnleun4pFLP48kcGHG46bbNNXqf3nrOVinLnFpje4Hl+4i8h3N7nMiVek1vD5ljOcSHf9d0lmvsridpWVSsPJmQU8vjDe8NzWx26tWIme42nmx8mrPxw0CHsAJRrjnHumoy3bWe4IzC4sMWpnc1iXIMS9Gogk6U7WK7kij5LgxmZ5JWzkAsGNQrFcLu6drmTCZoGgLlIq6QtqCOeewD2o1Wr2799vImoAJgJGZbjjjjvYv38/e/bsAeD8+fNs3LiRIUOGWN3HKQHDy9fwX1kUYxIYkZ6eTmhoqNn20NBQ0tPTLeyBYXtYWJjJ9rCwMMNr6enp+Pj40LBhQ6ttKrJ06VIGDRpEy5YtTbbPnDmTH3/8kS1btvDkk0/y+uuvM2vWLKvvafbs2Yb8gUFBQWb9uQNzMc81ik78+Wss+ds0jLTd2xuZv/UsT39juUptxbkcuJhNqVZm5xnLYqAjGAuGX/11ls+2nGbzcddX7jUWxawhG322pypcB9vKE2iv+q8lQVZrQ5mTZSgwyt2XmV8uAhrvVx0eFSHu1VBe+n4fvWf9yd9nrnp6KgKBx6j4RSoL655AYKBY706S1PzfT4c9PBuBoObw4j2dASHuCdxHZmYmGo3GTNRo0qRJlfp98sknmTlzJnfccQfe3t60a9eOu+++2yQXeEWcEjAkyeBs1ZYIce9GICYmxmqxCv1D7wy15GiSZdmu06ni647sY63N5cuX2bRpE6NHjzZ77Z133iEqKopu3brx+uuv8/777/PJJ59YHWPatGnk5OQYHpcuXbI5J1dQcaXirHNPv9apRD0Nq305sj01u4jvEy7Y7bNrTJwhx5wlh1vFKrsXrxXywoq9JJy/5sCMneNoSvn4Dn3ODn6ozq42ZWRWWDGkaKo5p5QQ92ooW05kALB0Z5KdlgJB3UVTUdwT2p7AAhUTmv/9998222/fvp0ePXrg5+dH27ZtzSqtlZSU8NFHHwG6O9a33norsbGxTo/rSAW5qqAyhOWWoLYSpiAQ3Ig0CwkGIEAhxD2Be7EkalSFbdu28eGHH7JgwQIOHDjA2rVr+f3335k5c6bVfZwVMNRlKddlIe7dEEycOJETJ07YfERGRhIeHs6VK+YOrKtXr5qJ2Hr0ue8qOvAyMjIM+4SHh6NWq8nKyrLaxpjly5fTuHFjhg0bZve99enTh9zcXIvzBl2IcIMGDUweriI9p9iim6vid4D+aZ6R423tgcvYw567rCIfx5402+ZMGOiQ+X8z45ejDrUtKtEQdyydrSczzF7LKSoxqQo79edDbD2ZwZOLbdc3qAwp2UVsO5XBhFUHyCq0n4ZDVaK7Vrb30cqyjCzrKupOWGW7dgPoPue8YstVdyuuZd1NrSuoIRAIbhxCipP4l/IfUuQQdmkjhW9PYIalhOb3338/x48fp1WrVmbtk5KSeOCBBxg7diwrV67kn3/+Yfz48TRp0sRQSf2dd97h+++/B2D37t3s2rWLRx55hF27dtG9e3eHx9VXkFuxYgXt27fngw8+YODAgZw6dYrAwECzuTmLcc69+r61LDmyQOBOvPwA8BHOPYGbCAkJQalUmokamZmVD3cDmDFjBiNHjmTMmDEAdOnShYKCAl588UWmT5+OQmHuy/D19cXX19dsuzXU+ABFIiz3BiEkJISQkBC77aKiosjJyWHPnj3cfvvtgO4aKCcnh759+1rcJyIigvDwcDZv3my4PlKr1Wzfvt1wk7RHjx54e3vz11/leejS0tI4evQoH3/8sUl/siyzfPlynn32Wby9ve3O+eDBg/j5+REcHGy3rSvZeCSN8asO8HC3Zsx7srvJaxW1HBmZz7ec4bMtpw3bshwIOXW2oMaCbefMtlm712BpuyNzMubF7/db3D5yqS6lQOvGAfz1+gBSjCrdGqPRyhSqSwn0s/93tsWo5XsB2HHafrSjfi72hFOtDFfzVYZw41l2chSmZhexJ8lyMQ/jfHxVvfnjCMK5VxOwWXK5+qZxI6HR6tT4L/484+mpCGzQPn8vn3gvZrhym6enIqihWEpo3rJlS77++muL7RcuXEirVq2YN28enTp1YsyYMbzwwgvMmTPH0Ob777/n9ddfB3QXri+//DKDBg3i008/dXjcihXkIiMj+fbbbyksLGT16tUuee+GnHuSGoWzt3gFgrpMWV4xIe4J3IWPjw89evRg8+bNJtuNBYzKUFhYaCbgKZVKg5PEFagNYbmWE9YLbkw6derE4MGDGTt2LAkJCSQkJDB27FgefPBBk0q5HTt2ZN26dYDOuTpp0iRmzZrFunXrOHr0KKNGjSIgIIARI0YAEBQUxOjRo3nnnXcAOHToEM888wxdunThvvvuM5nD1q1bSUpKshiS+9tvv7FkyRKOHj3KuXPn+Oabb5g+fTovvviiU+K2K5hftn78JTHV7LWKIa+xR9NNhD17uHLtv3C7ueBnzKXrhfz716N2q8xWxJqQZcyFa4X8cy7Tqkj5xKJ4usTEcc+cbSRlFpBdqCarwLL7zpG0THkqy845y9j17pk802plSm048C5nWRYwAYpLyiNrRM49gcBN/HUyg83Hr/DpZse/bAXVTzE690UAurvL1XHHQ1B7sJbQPDo62moFtvj4eLP2gwYNYt++fZSU6IQAlUpldqHo7+/Pzp07HR7XkQpyVUXv3POlxOnwDYGgTmNw7olquQL3MWXKFL755huWLVvGiRMnmDx5Mpcvl4fbTZs2jWeffdZkn8TERBITE8nPz+fq1askJiZy/Hh5BcyhQ4fy9ddf88MPP5CUlMTmzZuZMWMGw4YNQ6l0jUNbXVZwhlIh7glMWbVqFV26dCE6Opro6Gi6du1qiGTQc+rUKXJycgzPp06dyqRJkxg/fjw9e/YkJSWFuLg4kwiFzz77zFAUZtCgQQQEBPDbb7+ZHdNLly6lb9++dOrUyWxu3t7eLFiwgKioKLp27crnn3/O+++/b3LjtbqwlU+w4lIl10q4pjU+2XSKjNziarmue37FXr6Lv2BSZdYR9G45exSpNRbTxly6Xsj+C7ow7fOZBdw9Zxvd3t9M9LwdTs3DHs98s5vYo+ZFWxwpqGF80/z5FXttCni2GG8U1lsd61gRllsDWbGrPJmlkDLcQ5FRLgBBzaVI8gegnl7cc2Hf6lItPl7i/kZtxlpCc1sV2NLT0y22Ly0tJTMzk6ZNmzJo0CC++uorALRaLZs3b+bXX39Fo9E4PK6tCnIXLlhOWKxSqVCpyhdbNiseYhqW62z4hkBQl5HKnHteaEFTCkpxuStwPcOHD+fatWu8//77pKWlERkZyY8//sgDDzwA6EIPL168aLKPPnQRYP/+/axevZrWrVuTnJwM6NJCSJLEO++8Q0pKCk2aNGHo0KF8+OGHLpt3SZlzT+TcE1SkUaNGrFy50mabigKFJEnExMQQExNjdR8/Pz8++eQTFi9eTHp6utXcd7YiGwYPHszgwYNtzq0mUOzkGvObv8+z4UiaybbbZ/3pyimZof8Lns3Id+s4SoXptal+7fXIAss3ua/mWb7hkK+q3Lp959lMdp41TZWQlFlg94pZW0HcS7yUXanxzfqthvTYYmVbAzlkdAAJp5LgRqZYKnPuSXrnnmv6/f1wKu3f+YP/7bWcdLpUo2X/heuiSEEtwdkqbdYSoOu3f/7557Rr1w7Q5aqZOHEizz//vNkdZkfGdWZuTlU8BIpl3QLNF7Vw7gkExnj7lf9f5BUTuJHx48eTnJyMSqVi//799OvXz/DaihUr2LZtm0l7fXit8UMv7AF4eXnx7rvvcvbsWYqKirh48SJfffWVS3OK5Wt0v2W7Tqe4rE+B4EbC2iXXgYtZdHt/s5VXLfPBhhMcvJhd5TnVRBQKyeT6dMh8XeG5zHznXMNTfzrksjnN2XTKsUrOLhvRqN9qsG0JcU9Qazl8OZu0nMpZZAW1g6Iyca8erg0dmbj6IABTfz5s8fX//HGSx76O500rrwtqBtYSmlurwAa6qm2W2nt5edG4cWMAmjRpYrh7fPToUU6ePEn9+vWJiIhweFxHKshVxNmKhwbnniTEPYHABC9jcU+EHgoExugrrcefMs8XJhAI7GPpmuvQpWweteJIs8RXf52lzVsbXDirmkfFfNBnKukUNM5bV1WUCgmNHQvd8n+SGblst8vG1FMdni0h7glqJRevFTLsy3+Imr21UvuLhXDtoJAKzr1qClT/ZmcSAOsOirvaNRlrCc03b95stbJbVFSUWfu4uDh69uxpsTJbs2bNKC0t5eeff+ahhx5yeFzjCnJ69BXkrM3N19eXBg0amDxsIcJyBQLLSJIClVwWiiucewKBCaoy17coOCMQVA5L68j5ThZp/GTTKRfNxjFmbzxh+H91RQaqS7VmglaJxrNRUV4KiS+3nrXZJvZYOkdTbKfGqQyioIZAYIWL1wsN/3c2t4Gg9lCEac49kYRSUBFLCc0vXrzIuHHjAPOE5uPGjePChQtMmTKFEydOsGzZMpYuXcobb7xhaLN7927Wr18PwK5duxg8eDBarZapU6c6PK4jFeSqym0ROnegnyioIRCYoJAkVGV5xYS4JxCYoi5Lud6ygVgGCgSVwdIN1T9PZnhgJo6zaMf5ah9z7Hf7zMS8pxYnVPs8jCnRyvx16qpHxq4OTVVkGK7hiJR7lvH3Kb8g0dgoTS2o3RRWqJarkWXOZuTTspE/vl6uqRonqN1YSmi+ceNGWrduDZgnNI+IiGDjxo1MnjyZr776imbNmjF//nwee+wxQ5vi4mI++OADAJ5++mmGDBnC999/b5LzyN64oKsgV1RUxPjx48nKyqJ3795mFeSqwhN9O8BPOuee+LEQCMqRJMrEvSK06iJxJ1sgMMLLxx800KtFPU9PRSColdT2G6rbT1/loW7Nq2WsawWmVev3lVXJ9RS/HfJcOoJbWwS5fQwh7tUAxJKsalTm8xMhbLUDfc49H0mDN6WGMOxuLYP5ZUI/W7sKyvj9cCqtGgXQtUWwp6fiNsaPH8/48eMtvrZixQqzbf379+fAgQPmjY1e37NnD0FBQSQlJVkNj7U1LjhWQa4qSD46Z6tCkkV4lUBghM65pwtbP5lylc5NPTwhgaAGEVi/PuSAl1bkoxQIXEFtiyJbeyCFJoG+np7GDUezYH+3jyFuZtZwZGTUpVrOZuR5eio1FlFRuO5yc/PywgN69x64riR5XefgxSwmrj7IsC//8fRUBO7Au9x14SeL0EOBwBh9XjF1sSi8JRAYo1HqhG9Jo7bTUiAQWKKiRWTTsXSL7Woyi7ZXf5jujY7IuSdAluG5ZXu4b+4O1nvQRioQeIIebUMNC7R6CPHCWc5WsiqVp/k07hQzfjnq6WnUeJReXhSXnR/+CAFDINBTotGiNuTcE+eGQGCMVtKJewqNcO4JBJXCKC5XVapBXerZIhECx+nZuqGnp+BWhLhXw5FliD9/DYCVCRc8PJuaSaXCckVUbq2hAJ1tXF8xV1D3+WLrWb5PuEBSZoGnp1KjUUoShWXnh69WnB8CgR5VqdZQTVoqFQKGQGCMRqn73VCKsFyBoFIYLyP/88dJ/u+nwx6bi8A5PJnzrzqiDYW4V5sQ0acWkWXPhuZqtTIHLmbZzLdQoCpl3cHL5BSJvFjOoi+qIZx7nuFkei6F6lKPjF2xwpbAFIWi/PzwQyzSBAI9DQN8KJR1AoasFjcJBAJjNArh3BMIqoKxSWT5P8kem4egdiHCcgUmyELds8i+5OvcNnMzvxxM8cj4S3cm8eiCXYz5dp/VNlN/PszkNYd4eeX+apxZHUCCArmsYq4kLkKdparfGNtOZTB43t8Mmb/TJfNxBJFD03G8FAqDgOGnFaGHAoGe8CA/isqcexpVoYdnIxDULEqVuusqpXB8CwQCQfVRDUscIe7VcIwFPbHmNab8lsnob/eRVVjCpDWJHpnJ92Xh0jvPZlpts+FwGgC7zl1z2zw0WhmNtu4dJAXCuecx9Hk+KxseW1yi4dfEFK4XVC5pt/jOs41SgSEs9+Sl2pfMWVDzycrKYuTIkQQFBREUFMTIkSPJzs62uY8sy8TExNCsWTP8/f0ZMGAAx44dM7x+/fp1XnnlFTp06EBAQACtWrXi1VdfJScnx6VzD24QBIBGLcQ9gcAYTZm45yWcewKBQFBtVIdRy8vtIwhchq3D4WxGHr5eSlo2Cqi2+dRUZFnm5ZUHaFjPm9mPdrXYpq6l3NNqZQbN20GpRsufrw9Aqag779Dg3POwuCfLMkdScrgptD4BPuKr0xE+jj3Fsn+S6BAWyKbJdzm0jxD0HEchSRQazg8VOYUlBAV4e3hWgrrEiBEjuHz5MrGxsQC8+OKLvPTSSzb3+fjjj5k7dy4rVqygffv2fPDBBwwcOJBTp04RGBhIamoqqampzJkzh86dO3PhwgXGjRtHamoqP/30k8vmXizphO8L6Znc7rJeBYLaT6lC97uRk5vr4ZkIBLUTV6yyfFHTmFyCpAL8UeEnqfFDjT9qvClFgRaFJCMho0SLAhkFWpPnlZ9/VS62xYV6ZWmZchl62b6GqipihVoDsBWG5shCN7tQzX1zdwCQ/J8hrppWreXc1Xxiy0qSWxP36hp5xaWGyqhX81SEB/l5eEauQcIo556HC2qsO5jClP8dIrJ5A35/5U6PzsWTaLSyw+LxhiM659+pK3lVHrdIrcHfR1nlfuoSSkV5QY0ASUV2kVqIewKXceLECWJjY0lISKB3794ALFmyhKioKKv7yLLMvHnzmD59Oo8++igA3377LWFhYaxevZqXXnqJyMhIfv75Z8M+7dq148MPP+SZZ56htLQULy/XXJqez9ZyhxekXr3ukv4EgrrClSLdb7iXCMsVCCqF5ERlxgCK6aU4RQ/FKW6WUrhJSiVMuk4DSaRTudG4euEYIMS9Gxpjbc+aCHg568b7crD1nVqqNQ5llp36AvYU+apSiks0hNT3rdT+dTkfoz4s19POvR/3XQbgaEotutPt4sNiyY7zzN18mh/HRRHZPMj+8EbjF5doOHAhi55tGuHjZT0jhKUpf73tHB/FnmThMz0YHBleiZnXTRSSRJFe3KMYVakoQCJwHfHx8QQFBRmEPYA+ffoQFBRkNYQ2KSmJ9PR0oqOjDdt8fX3p378/u3btsur6y8nJoUGDBjaFPZVKhUpVHkaYa8d1pM+5J4rNCASmtG8eClcgQCGKvAkElcH+ylJmgOIQTyr/4h7FAXwky0UXVbIXOdSnSPahGB+K8EGFD2rZC22ZT0/3r+7/GoN3T6qidw/kKvgPq7LvjcxNIV1p4uYxhLhX03HgrK2uPGtX81TU81XW+JBEyegLR5ZtC4E1hch3NwGQ+O+BBAf4VKmv2vB+nUFfMKCehxdoNUFAlWWZrScz6BAeSIuG1R+C/+HGEwBM/+Uov07o59S+r/94iA2H0xjVtw0xw25xat+PYk8CMPWnQ0LcM0KpkIzC1lWoSoS4J3Ad6enphIaGmm0PCQmxKu6lp+tc82FhYSbbw8LCuHDhgsV9rl27xsyZM+2G+86ePZv33nvPkakDGAnfQtwTCIzxDagPgD+Vy4crENzo2FprdZXOEeP9Lbcpzhq2XZZDiNd05oTcmrNyMy7LTciUg8glgLqXLEpgjVk3dcHdMYWioEYtQgYK1aV88ecZThuFuWmqIUnV1TwVvT7cwm0zN7t9LFeireYEXj/sucg/FQpraJ0QX0+mVz18sa5hKKgh7OtsOZHB6G/3ccdHf1W6j9NX8njmm93sv5DlwpnZR19UZsWuZJvtRLVcx1EqjJx7UjHZRWKhJrBPTEwMkiTZfOzbp6v+bsn57sg5WnE/ay763NxchgwZQufOnXn33Xdt9jlt2jRycnIMj0uXLtlsX1R2Y8hfEueFQGCMwlt3c9BHFmG5AoGzaLUye5MtXUPLjFZu4GefGG5TnKVQ9mVp6f1Eqz7iDtXn/F/pOJZp7meH9lbOy83IpR5C2LuxEAU1BGbVcufGneabnUl8uvm0Ib+eM+JRZdELAcU10BkiSdZzE1r7ZNzlbntr7REATn0wGF8vXX6wCasPuGewG4R82R+AQDwr7lVFc3JVePju885VW7b0I/L88r2kZBex82xm5XN01nIBrlSjpVQr4+ddu3P4+XopjMLWVVzNEw4lgX0mTpzIk08+abNNmzZtOHz4MFeuXDF77do1699D4eE6Z216ejpNmzY1bM/IyDBz8+Xl5TF48GDq16/PunXr8Pa2nS/S19cXX1/HU1cYh+Xmq0qp7ysueQUCAMlHJ+75asVvhkDgLL8eSrGwVeYtrx8Y5/UbABs0txNT8hxXaVi9kxPUaKpj+SSce7UIGTh82TwUprrCcmsSjsokntIgvvrrnOH/fxxNd3i/yso/Fd/n//Ze4t5Pt3HhWkEle6w56O5sQQOp0Ol9ZVmuFvHbFl9uPUOf2X+Sml0znIcpNWQeFbleoOb7hAvkFLk/B9C9c7fT+d+xFKkt50CpLQT5exvC1gNQcQP+FAgqQUhICB07drT58PPzIyoqipycHPbs2WPYd/fu3VZDcgEiIiIIDw9n8+Zyl79arWb79u307dvXsC03N5fo6Gh8fHxYv349fn6uLwKld7X6o74hr5MEAmsofHTnm48IWRcInCb+nPkNrtHKjQZhb2bJ00woeU0IewIzquNKRIh7tQxLTpzqCMutaWWvjWdTURAzNkh5Kk+asw4rVzP158Ocu1rAjF+PeXQeVUWSJPLKnHsNcE6olGWZ4YsSGPrlTpcIfJXtYU7caa7kqvg07nSV51BjqKIL8X97zcPpRn+7lxm/HGXSmkSr+7nqbL5wrRCtDMdSrYsUtQFJMg3Lre40BIK6TadOnRg8eDBjx44lISGBhIQExo4dy+DBg03adezYkXXr1gG6Y3LSpEnMmjWLdevWcfToUUaNGkVAQAAjRowAdI696OhoCgoKWLp0Kbm5uaSnp5Oeno5G4zrBvVjWOff8UVGqqXlRBwKBp1D66m6a+sgqkQpDIHASb6WpfNJLOsk0r/8CMLPkGZZqhiDCbQUWqYbvWyHu1XBMjgErB4RWXLNaxfo5ZPqlW6rR8v5vx4k75rjLztBTDf7+VpXUbmcSlDv3Ap3Muacq1bIn+TrHUnO5lOW868+MKn4f18oLaGtTruJ7mfrzYdQVKrsevJgNwN9nMi3sIbCGPizXH7FIE7ieVatW0aVLF6Kjo4mOjqZr164sWrTIpM2pU6dM3HxTp05l0qRJjB8/np49e5KSkkJcXByBgYEA7N+/n927d3PkyBFuuukmmjZtanjYy6PnDAbnnqSmVDj3BAIDTUMaAeAnqyiu5Q52gaC6MRb3fFHzkfdivCQtazV3sFRzvwdnJqjpVIfpRoh7NZyKl6OShTsBxs499y3uapaC5eq3ue5gCsv+SeLF7/e7ZC6VnZ4r8rIZ91CThUdHyZV1uWEqOveKSzTsSbpuNdxK6Bzu+wwuZ1U9tLcyLrM6cDi7nCKjatJCvxC4mkaNGrFy5Upyc3PJzc1l5cqVBAcHm7SRZZlRo0YZnkuSRExMDGlpaRQXF7N9+3YiIyMNrw8YMABZli0+2rRp47K5FxrCclWUCOeeQGBAL7QrJRm1WoTmCsrJyspi5MiRBAUFERQUxMiRI8nOzra5jyzLxMTE0KxZM/z9/RkwYADHjpmKGIsXL2bIEF2e56CgIIt9OjL2xYsXGTp0KPXq1SMkJIRXX30Vtbp6iyZ5K8uvRkcr/6CtIp0MOZh3S0ZRU65Un7q9laenIPAQQtyr4RiLddbWbRVDDis6Yuo6tgQxR/WD9BwXVwyrIYtsS2JwbULCes698asO8MSieL7666yFPSv2U/XPoaoh3jXkkHAJecWlLunH2ZsRdekzdBUGAUMqFnnFBAIjghs0APRhueLcEAj0ePkGGP6vLsr34EwENY0RI0aQmJhIbGwssbGxJCYmMnLkSJv7fPzxx8ydO5cvv/ySvXv3Eh4ezsCBA8nLyzO0KSws5N57763S2BqNhiFDhlBQUMDOnTv54Ycf+Pnnn3n99der9qadRO/cq0cRL3r9DsCskhHkEWBrt2olyN92gSpB3UWIezWcA2WhagYsaBTGC7ojKTm0f+cP5mw65d6JeRzHLtQrCjKyLPP+b8f5Lj7ZDXOqedQt556puLf1ZAYAy/9Jsrifp/ItAuw8k8lTixNIynRvQRNPhWG64rOduPoAg+btcPnNCFWphl8TU7iWf2O4EQqNquWuP5TK3LhTIjxXIADeGnYboA/LvbFuegoEtpCUPpTKuiWgurj2F14TuIYTJ04QGxvLN998Q1RUFFFRUSxZsoTff/+dU6csrytlWWbevHlMnz6dRx99lMjISL799lsKCwtZvXq1od2kSZOYMmVKlcaOi4vj+PHjrFy5ku7du3Pffffx6aefsmTJEnJzc137Ydjg4nXdemSkcjPBUgHntE1Zr+1rZ6/qxdhdeKPSNqSep6fgEYS4V4uwtl4zDsv9cMMJAL50wM1keQyrSbYq1V91YOvrq+LbSbyUzbJ/kthVodJRTRTB9l/I4q8yAcsWWVlZvDTmeS5+9gQXP3uCCS++gLZYdyfW2vvSW+g7dOgAwJAhQ0ws9NevX+eVV16hQ4cOBAQE0KpVK1599VWTvErJycmMHj2aiIgI/P39adeuHe+++67L7fH6O2G+Ugm+mPftiDjkir+vM3rJM0t3E3/+Gq/+96DR/q49h64XqOk960/e+816/oaae9bClhMZnL6ST4KLi8/M3Xya135I5F8L413ab03FUC1XUrEn6Trzt54l3sMFfQSCmkCrsBAA/FBRIpx7AkE5koRK0hWcKVUJ555AR3x8PEFBQfTu3duwrU+fPgQFBbFr1y6L+yQlJZGenk50dLRhm6+vL/3797e6T2XHjo+PJzIykmbNmhnaDBo0CJVKxf79zqdVcpbiEg0n03P5/XAaElqe8doCwELNULQ1TFLxUdas+XiC6sq1+/KAdtUyjqOIv3wNwNEE8tbcMsaiQVVEjOX/JHH7rD85d7V2/9AbfwQVP7F8leVwwsroLrIsW63CWllnk/Hf77Gvd/H8ir2kZNvObzZixAiOHj5E2BPvEfbEexw9cpjM3z/V9WdF+tRb6D/55BMAQkNDTSz0qamppKamMmfOHI4cOcKKFSuIjY1l9OjRhj5OnjyJVqtl0aJFHDt2jM8++4yFCxfy9ttvV+q9W0KSIB8/tLLufQRi/lmoreRScrV5qTLdZdpxj13JLSanqKRS8/l2VzIZeSqW/5Ncqf1rA5U5j/44oiuKc97NrsmaQrlzrzy1wLX86s0/IxDUSLx1ldb9UYuwXIHbWLBgAREREfj5+dGjRw+bgkZaWhojRoygQ4cOKBQKJk2aZLFddnY2EyZMoGnTpvj5+dGpUyc2btzo0nkXl6V0KFFVPYeuoG6Qnp5OaGio2fbQ0FDS0y0XHNRvDwsLM9keFhZmdZ/Kjp2enm42TsOGDfHx8bE6lkqlMuSM1T8qw65zmXScEcvgeX8D0E9xjBZSJrlyAOs1Ncu1B+Dt5VmJZ+ebd9Mk0Nejc6gu4463omY5hIS4VwM4mZ5nvxE6scLS4WMsYlQlt9h7vx3nap6Kdy1WcqlZB66jwo27wtNkWebpb3bzwPy/Lea5kmW4eK2QGb8crfJY6TnWL7z0NvZ5Xy3Et3knfJt34tP5X1F0bi8l1y5b/GIzttAPGzYMgIULF5pY6CMjI/n5558ZOnQo7dq145577uHDDz/kt99+o7RUJ5AOHjyY5cuXEx0dTdu2bRk2bBhvvPEGa9eurfJ7NpkvCvLQLdIaSOaCjbEjY1/ydTYeSSvbz7VU5liyJTRnF+qcd7e+F1eleXmCuhL1WRMdu85icO5xY4QhCwQOUybueUsaSkrF+SFwPWvWrGHSpElMnz6dgwcPcuedd/L4449bba9SqWjSpAnTp0/n1ltvtdhGrVYzcOBAkpOT+emnnzh16hRLliyhefPmLp27StL9dpSKsNw6T0xMDJIk2Xzs27cPsJzHXJZluwX/Kr7uyD72+rDUj7Pzmz17tqFAR1BQEC1btnRqTnpGLNlt8vxx5XYA1mn6ocKnUn26ksUje5g89/WguBca6EuLhgHsefte+t3UuFJ9RDgYUtuonvXPflx/5xx1iyp8ho6iVNQsOa1mzUZgE0cW1K5YrO48m8mJtIp3Nmruat7We3bnrHedu8bJ9Dyrzrpnl+3m+4QLLh9XlmW++PMMsUfTDDb2nr1uN7ze8/beSL71UKWcsPhjV1kLfU5ODg0aNMDLy8tmm0aNGlXynVknVy4rqlEh715FHl8Yz/hVBzibYeo+dUlYrov3OZNRNYesu4Wp2pC3LTW7iHUHL9/QlTD1BTX8pBIU3Lifg0Bghnf54kBW2f7tEAgqw9y5cxk9ejRjxoyhU6dOzJs3z6YI16ZNGz7//HOeffZZgoKCLLZZtmwZ169f55dffqFfv360bt2aO+64w6oYWFnUZeKeRpwbdZ6JEydy4sQJm4/IyEjCw8O5cuWK2f5Xr141c8zpCQ8PBzBzzmVkZFjdx1o/9sYODw83GycrK4uSkhKrY02bNo2cnBzD49KlSw7PyRo+lHCvQpd25xfNHVXuz5jKinLtQuubPP9Xz8qJmK5AUbZAkSSp0oU9XryrrcXtPhU+nzcHd6BrC/Pv0lVjelPP1/p61ZG+HcWrhuU3rHXiXkX7+99//22z/fbt2+nRowd+fn60bduWhQsXmrVxxv4+e/ZsJEmyaqV3N7uTrpttc8cS/OGv/nFDr+Zk5qtIs+FMs4at92wserhLn7DXrwwkX7N/wSTLMh/HnjQ8d+TrYXfSdT7dfJpxKw9YtbErA4LQFGRZ7K8yFvpr164xc+ZMXnrpJavzOnfuHF988QXjxo2zOX9nLPJ6J6o+754l554lUu2EMtcEqsPFXdnj/48jadw2c7NZbkp3UJVT9J5PtzF5zSGrRVVuBPRhuQD1cHHVb4GgNqP0RlN2matVCXeSwLWo1Wr2799vcqMU4J577qlSv+vXrycqKooJEyYQFhZGZGQks2bNQqPRWN2nMqGHJWXinlZd86+XBFUjJCSEjh072nz4+fkRFRVFTk4Oe/bsMey7e/ducnJy6NvXcuhpREQE4eHhbN682bBNrVazfft2q/tYwpGxo6KiOHr0KGlpaYY2cXFx+Pr60qOHZdeVr68vDRo0MHlUlT6K4wRKRVyRg0mUXZtvrbLX7RWXFPV9vTg36wHaNK5aBd9Rfdswqm8bp/ZpXL/cTWcpus0RlFYWSRU3d2kejNbCh6ZUSBRYScVlDUUlXRNDujSt1H7uolaJe5bs7/fffz8XL1602D4pKYkHHniAO++8k4MHD/L222/z6quv8vPPPxvaOGN/37t3L4sXL6Zr165ue4+2sFrqwjgstxLHZYGq1Mz5oqpiBUtVqYbv4pNJtpH3SpZlen6whajZW63mwrPEx7EnGbm03B5tMxTZXeKei/pJOH+dBdvOmW2PiYnhwkcPcuGjB+nZprGJdT6qXQgXPnoQVdoZQHdnxNxlJYMk2TweHLXQ5+bmMmTIEDp37sy7775rsa/U1FQGDx7Mv/71L8aMGWPzPVfGIp+L5Yq51nCHq60yP7gmYbkV9rcXrqDRyhxLzbGa19HacS/LcpVddy+vOkBWYQnpuZbFIks/pBbnUqVZ2Ke4RPc99c3ihYabPgfnv0TxJdvh8MUXj5C24jVuv6mp1Zs+CxYsAHSid8uWLZk8eTLFxeWfR5s2bSyGtkyYMMHQZtSoUWav9+nTxxVv3YCPnz9qWQlAfQs5KQWCGxZJQlXmbNWWCHeSwLVkZmai0WjMbpQ2adKkSv2eP3+en376CY1Gw8aNG3nnnXf49NNP+fDDD63uU5nrKrVCL+4J4Vugo1OnTgwePJixY8eSkJBAQkICY8eO5cEHHzQU4APo2LEj69atAzAYXmbNmsW6des4evQoo0aNIiAggBEjRhj2SU9P5/Dhw4bnR44cITExkevXrzs8dnR0NJ07d2bkyJEcPHiQP//8kzfeeIOxY8e6RLRzlIEKXfGOLZoeyC6WUhy9vnYEpUJiYSVDTfXc1ymMHq0bOty+R+uGfPFUd8PzygbWeDnggFg9pjedmzVAa2EMpUKyKhBao1uLYKfa62lTw6ryul3cq3inaffu3ezYsYOSEueTyFuyv7ds2ZKvv/7aYvuFCxfSqlUr5s2bR6dOnRgzZgwvvPACc+bMMbRx1P6en5/P008/zZIlS2jY0PGDvLqxJXTN2XSK2RtPmGzLKy7hlnc3MeCTbS6dx9fbzvHvX48xYI5j/f6wx7JAWxGtVmbBtnOGRb1lyj+Dyha2sMdKF4XbXi8wTXyv13smTpxIszFf02zM1/y4Jd7EOr9g3XaajfkanyatrdrYNYW5KAOCLR4Nzljo8/LyGDx4MPXr12fdunV4e5vbq1NTU7n77ruJiopi8eLFdt9zZSzyubJO3AuUHBT3sCR4Vg1XH0mmwp957++uP8qQ+Tv5aNNJs9fAsoBZqtHywPydvPi966uG1dQw3YITO9j/wzzDTZ8GbbqQ8WMMpbmWK00nJSWR8VMMvi1uIfy5zy3e9Fm1ahUxMTEA7Nmzh6VLl7JmzRqmTZtmaLN3717S0tIMD/1d63/9618m4w0ePNiknauTooNEfllOyvqSEPcEAmP0ecVQC3FP4B4s3SitClqtltDQUBYvXkyPHj148sknmT59utW1DlTuuqpEoXN9a8W5ITBi1apVdOnShejoaKKjo+natSvff/+9SZtTp06Rk5NjeD516lQmTZrE+PHj6dmzJykpKcTFxREYGGhos3DhQu68807D87vuuovu3buzfv16h8dWKpVs2LABPz8/+vXrxxNPPMHDDz9ssq53BxVvst+l0ImUW7S3uXwsW98eSbMfsPqat1F13NtaBZf3V8VLd60sO2WY+PnlvrRtUh4i7IhYWVGD+/zJbjTwsxzOG2i0ve9NIVbHUEgSw25tZrbdFkEB3qx4vhc3VwhxdoT6ToYAuxO3iXtpaWnccccdhlxeWVlZPPjgg0RFRTFgwAAiIyNNbLX2sGZ/j46OtponLD4+3qz9oEGD2Ldvn0FcdNT+PmHCBIYMGcJ9993n8JxdjbULBmMBy9oJWKTW8OVfZ1m04zwZeeXuk0OXdF/OlvLGXc1T8djXu/hx3yUsBYyWaLQs25nEyXRz+/8eC+HDZvM2ejsfbDhhvSFw/mo+ZzPy0Vj6DGwZ91ygR8iyTG6xqRj97npLRUdM96kKISEheDduiXfjlrRp197EOt+y7U14N26J5OVjsLEf2LfXsO/kL35CVhXg27yTRXeYoxb63NxcoqOj8fHxYf369fj5+Zn1lZKSwoABA7jttttYvnw5CgeSijpjkddPPxfdXZEgHLvDLEmmP5DOJvV1FcbjVjwijO3flsx5KxN0gvei7ecdHu9wSg4n0nLZfPxK2ZiOH4ffJ1xgzV7HRHZX8s3fjr8/gCOXc9iTdN2wX+7eX2h3x1DDTZ92wyaiDAwh76BlEW3hwoUoA5vQ6L4X8Q5pafGmT3x8PL179wagdevWREdH89RTTxkSToPOnREeHm54/P7777Rr147+/fubjOfr62vSzuU5KWXIl3XiXqCDzlaB4EZBbRAwhPAtcC0hISEolUqzG6WZmZlV6rdp06a0b98epVJp2NapUyfS09NRqy1XQq9M6KGm7NyQxbkhMKJRo0asXLnSEN69cuVKgoODTdrIssyoUaMMzyVJIiYmhrS0NIqLi9m+fTuRkZEm+8TExBgEwZycHEOUiXE/jozdqlUrfv/9dwoLC7l27RpffPEFvr7urcpqvPZsyjVaKzLQyBJ7tR0stlcqpEqn3hlzZ4TV1yytZV655yZG9W1Dy0YBPHV7S0Lq+7LmpajKDW6B1o0DqOdTeeHqJgeEstmPdjF5/lA363lLn+3TGoBuLYMN2ywtuZUKCT9vJe8/dItjEy1jQIdQNk/pb79hDcZt4t6bb76JLMusW7eOpk2b8uCDD5Kbm8ulS5e4cOECYWFhNi3mFbFmf7eVJ8xSyeywsDBKS0sNP76O2N9/+OEHDhw4wOzZsx2er6tKb1eF3w+n8up/D1Kk1ph8MT305T/sTdaJb7YU9bmbT7P/Qhb/99NhLN1L+O+ei7z/+3FDWXBncVR2KNFouefT7dw3d7vT8fMVx/h2V7J5G9myBFJcokGrlZnyv0N0jYnjwMWsSo/rLvQ29smvjEeVchJVykl+nDcD/3a98G7cwqB7WrPQ//bbbwC8/PLLJhb6vLw8oqOjKSgoYOnSpeTm5pKenk56erpB+E5NTWXAgAG0bNmSOXPmcPXqVUMbV+HnrfuKypJ1Pw4NJccqS0u4Id+iizs0Fvc0Wp2AnJ7jeM40V8qVWQVqZvxylDd/PkJxifW8Pnpc+Un8fSaT1/93yKG2ecWlDP1yJ08siueDDSeQNSWo08/S9JbeJu38I7qjSrHseIyPj8c/ojxkIPZoutlNnzvuuINDh8rndP78eTZu3MiQIUMs9qlWq1m5ciUvvPCC2cXXtm3bCA0NpX379owdO5aMDMuOQqjc74ZMeU7KQOHcEwhM0BcNoESEHgpci4+PDz169DC5UQrw119/Vanffv36cfbsWbRGsWanT5+madOm+Pi4ripnqbJM3CsRvxsCgS1KNeVXvb0VOjPKETmCfCzns7vjphATJ50xvdpYj/57+4GO/F90B/7noDj364R+vB7dgZhhOgFr9qNd2ffOfSZjV2XpsmpMb1o3rsdd7ZvQspF/pfp47d6bGXtnBOvG9+WejuY54sFy1KG1IhUvD2jHkmd7suL5XoZtFnPulV2LhwaaG1PqOm4T97Zs2cKnn37K0KFDWbBgAfHx8bz77rs0b96cli1b8t577/HHH3843a+zpbat2eX12+3Z3y9dusRrr73GypUrLTqXrOGq0tumc7e/3fj9Tlx9kPWHUln2T5LJaZOWU8y/Fsbr9rUxXqHatpC29kCK03PVU6AqdTivgHH+v5ecDDes6KDbcsL6wtqY3OISOs6Ipdv7caw7qHufCy3kxnM95sfy7qTrNp2Aq1atotMtt3DlfzO48r8Z+DRpQ8iDrwPw50nd+7VmoX/9dV27tLQ0Ewv9/v372b17N0eOHOGmm26iadOmhoc+3CMuLo6zZ8+ydetWWrRoYdLGVdzWSvcjeF3W3YVu7KC4p1P3TJ96mop/Q+OvJq0s0zUmjj6z/yQzX1XpMSr7Pt/4sVzIslZ51p1RuT8fuFyp/TSFuSBr8W9Q7oaTJFDWa4imwFSMT84s4M2fDnMpJRVlvfKLqyn/SzS76aP/HQBo3Lgx7dq14+677+att96yOI9ffvmF7OxskzvQAPfffz+rVq1i69atfPrpp+zdu5d77rkHlcry37iyvxuGsFyRc08gMEEl6a7bJCFgCNzAlClT+Oabb1i2bBknTpxg8uTJXL5c/ns2bdo0nn32WZN9EhMTSUxMJD8/n6tXr5KYmMjx48cNr7/88stcu3aN1157jdOnT7NhwwZmzZplks/VFWjKxD1EPkqBwCbG18h6cW+3tpPV9vd1DkNt5Vr65rBAHu/Rwmz7nTeH8OJd7fBSKrg9wrEIj1uN3GtVRT9m06ByraNfWdirUiHx99R7SP6P5Rvctqjn68X0IZ3p3qohj3S37siryB03hdC3XWMa+Jm6Br2UCgZ2DiM4oPxGR+vG5jnv9IFkepOINZ66vRUAAzs7XtnZGH0ItKMRe87mAawMbgsQzsrKMhSlaNSoEQEBAbRu3drwert27ZwKy7Vmf7dVattSyeyMjAy8vLxo3LgxoLO/e3t7W7W/79+/n4yMDJMqPBqNhh07dvDll1+iUqlM9tUzbdo0pkyZYniem5vrEoHPmDd+PMTdHUJNww8ttDueZt39YUtgU9oJZUy8lG17glZIzixgwJxt3HlziNP7WqoWbD5L2cL/rGPpI9h5RrfIzy0uFzizC0tIyS5izV77uUwcLQ7kSLToJ5tOERFSjwesVOP5dn8moUP/j1ZtnrH4+vHUXAvCks5CP2XKFIKCgti4caNJGMeAAQPsflGNGjXKTMxwNXqx+ho60bERjjlgJSSHwtWdoaraVsX9jed04EK5EHU0JYcBHSzf3bK2f/m2yr1RvQgMlXufRWoN/j7m34NVxVFBUZIq/HjLMhW/GZ5bvocL1wpJzynGv0XF5qY3fbZt22YI092xYwfp6em89tprNG3alBkzZpiNv3TpUu6//36aNTPN7zF8+HDD/yMjI+nZsyetW7dmw4YNPProo2b9VPZ3Q5+TUuTcEwhM0ecVQxQNELiB4cOHc+3aNd5//33S0tKIjIzkxx9/5IEHdLmx0tLSzIr+de9e7hzfv38/q1evpnXr1iQnJwPQsmVL4uLimDx5Ml27dqV58+a89tprvPnmmy6de7m4J343BAJbbDhSrlf0UpwCYI+2o9X2I25vxYxfLBd2k2Xo3iqYn/ab3tR2V/qghvXKc9Q1DfIjzUqE0Pejb+dyVhGBvl48sSie4b1aOT1WRSGuKngpFawe24f/7bvE1J8O22w769FItnxomn9eL6L5etlem4wf0I7hvVrSqWmgzXYVebR7c5o39OeZPq3tNzbCkUIhVcVt4l5oaChpaWmGhcnEiRNNcg1lZWVRr57j1UWM7e+PPPKIYfvmzZt56KGHLO4TFRVlCDvUExcXR8+ePQ2FAfr168fq1avRarWGfGHG9vd7772XI0eOmPTx/PPP07FjR958802Lwh7o8l+4OgdAxeDRn/Zf5qf9l5lvVJXG0nfDhsNpZOZZcQPZWDwr3HQA/rcsr9ffZ6qWl0SPre9DR9yBzogZz3yzmyQbFYANfbrY5hR7NN0g7hnbl5MzC/j8zzM2961YtKM2onfuNbLi3Ov/yV+MvbOt4bknquWqS7WUarUE2MlNkZxZwOhv95oIeCO+Ka/+7OiRY+lC4PDl7PJ+KnkMOrKbcZv/7rnItLVH+PzJbjbzZFSGp7/ZzYNdrTtBlQENQFJQlHvNZLumMBtlvWCTbReulbkT/INNXH2ybH7TZ8aMGQwfPpwvvviCW265haioKAoKCnjxxReZPn26SW7JCxcusGXLFtauXWv3/TRt2pTWrVtz5ozlc7YyvxuyLBuceyLnnkBgilqhOzekUnFuCNzD+PHjGT9+vOG5cTqFFStWmLV35Lc5KiqKhIQEl8zPGgof3U2hUpU4NwQCR6hHEW0lndB3SHuT1Xa23VkyT/ZqRXGJlt4RjXjwi51OzWHB07cxftUB3hli3TloTNMgfz5/shuBfl7ceXMTbp5uOWrS10tJu7JCGNv+726H+n66dytW7dat6Q/MGEiAnZv8jqxKWjQ0Df91ZDkXGuhHsyA/Uo2EywBv3VrMx8u2c0+hkEzy9zlK9C3hDI4Md3o/a+HarsRt4l63bt2Ij4/n9ttvB+A///mPyes7d+6ka9euTvU5ZcoURo4cSc+ePQ2VOS9evMi4ceMAneshJSWF7777DoBx48bx5ZdfMmXKFMaOHUt8fDxLly7lv//9r6HPl19+mS+++ILXXnuNV155hTNnzjBr1ixeffVVAAIDA82SgtarV4/GjRubbfcUxhcK1k4CS443sJ1w313isq2Kvq7oz+S6ycWhhI4IewCpFgqUOII1Ucra27hv7vZKjVPbuC7bdu5duFbIO0Z3ySTglf8erI6pGbjr479Izy3m6HuDbLZ755ejnLtawLmrSZYbyPDP2coJ3//+tbzQiyy7NpTWWlfT1upufrz2Q6LLxb3rBWq+i7demVpSeuMTfhP7d21Hlv/PIHgWJyfif3NvJv1wkAe7NuM+I7u9b/OOFJ3dY3guI5vd9CksLDQrDqNUKg0JoI1Zvnw5oaGhVvPxGXPt2jUuXbrk0tB1GciTRc49gcASaqXu3FCq8z08E4GgZuHjpzNYaIS4JxA4RCfpAgpJJk1uRCZBlepDq9WJf6PvsF44wxYPdGnKifcHOxUt4+prcz0NjUJjG9Wznw/U6o0No7Xv6jF9KjWXzVP6k55bTOzRdHKLS2jVWPfb7+NiMW3LlLs4mpLLoFush/EuGtnDLI3YXe2bsOP0VaedfpXBbeLer7/+avP122+/3ayqoD0s2d83btxoCPetaH+PiIhg48aNTJ48ma+++opmzZoxf/58HnvsMUOb6rK/uwJHFurO2nq1llMC6PpyQoQr1WjxMjqBXOmcupJru8hARYHSWW1vw5E05m4+bdqHpR2deE+Z+a51y1lzIJY6Gv9by7muD8t1tKCGJLnMGVqi0eKtVNitPJtedpweTcmx2c5ewYqsQjVTjApMGJ9LPzmRm67SR0YVDql/zmYacnRUFw16PUzm73N544N5jHl8MOfWf0Vp7lUCuz3AL4mpLP98Fg/d7A/NdCGy9bvdT96B37n+5xICbx1McfoplsaZ3vQZOnQoc+fOBSA5OZkrV64wY8YMhg0bZuLW1mq1LF++nOeeew4vL9Of0/z8fGJiYnjsscdo2rQpycnJvP3224SEhJi4z12BcO4JBJZRKXUChkIU1BAITFD46ha/CuFqFQgcIlKRDMBRbRu7bacMbM/czacN/+qxt5bQU89HSYHa8nrBHWlwKoOjufPtYby81otyzlLP14t2Teoz4W5TR2Wj+q4rQgRwU2ggN4XaDuEddEs4t7YI4tDl8vXgwmduY/+FLPq0bezS+VjCbeKePXr16mW/kQUq2t+NsWR/79+/PwcOHLDZp7P2923btjnc1pWcybB/59lZTc3Wiblmn/3ccnq+i7/AC1buQiRlFhAR4ngItjFnM/KddqcZvyX9/3MKS7iSZ1kknLPplHkflr58PaijbTichkazn6G3NuPXROuFTOoq+rDc+lIxvqhRYfvLuqK4XBW36HfxF5y6w6a1JriWbbYnfFcMozauqptdWGL4v71+tFaqQNvj7k+3VWIvHU9/s7tSCXerQr1Od6EtymPpl3OY9/6beDVuRei/YvAK0oU9a/KzuHjxKlJz3feBd3A4oY/HkLX1G/IObsCrfmO+qHDT55133kGtVjN79mx69+5NkyZNGDp0qFmF9y1btnDx4kVeeOEFs3kplUqOHDnCd999R3Z2Nk2bNuXuu+9mzZo1hsI1riJfFgU1BAJLlJSF5WpVwrknEBgj+ehC8Lw04ndDIHAEvbh3TG5jsv3lAe34ukLRxVfuuYlHb2tO82B/2japx8TVumgiRz0ZO9+8h+4zN9tv6CQ/vxzF19vOOVxg0hbOrjGM1+edmzawWRNAT2iDqlW7bR7sz8rRvXlm6W77ja3grZQo0Tj3bsOD/EzEvQAfXVh0deBWcU+WZbZs2cKuXbtIT09HkiTCwsLo168f9957r9uSR97IOPuRusr4tfVkhlVxb/6fZ/josa54KyWn/+abjqXbb1QBY0lD//9eH26xWrno4nXzu5aWNM/KSSW2qfhp2Pp0Yo+lE1uJz6O2o7Myy5TISrwlDQ3JIx3bdz7MPlcnDrvc4hKT5wcuZDH6jgiHQ1w1Rg1TKhGeXXEcfXj8hWumzhN7gmVl76hVNUejVisb8nW6s8KuMYG3DcGv91CCS8zP8ZAhk9n2nyG0n/6H4TvAr1UXmo76HNBZ9seNu99kHy8vL9566y1mz57NlStXTIrNGBMdHW01zMDf359NmzZV5W05TC6ioIZAYIlDV0u52wtOXkilp6cnIxDUIBR+OnHPRyOcewKBI9wi6dLpHNWarnffHNzRTNyTJIkWDXXXZg92bcbM349zJVfFECvFESte0Tes58OGV+9gbtxpk6J3VaVH60Z881wj2ry1wbBt4TM9bOzhOozX0avG9DaIl7bWCndVovhmRe64OQSF5Jzm0TDAm6wyQ0XnZkEccrKI6MyHItl07Ir9hm7AbVn9UlJSuO2227j//vtZt24d58+f5+zZs6xbt47BgwfTs2dPUlJuPAeSO6jaAto9q2/jOeUWldDjg82M+Xaf0/1URv+15NyzJuw5NRcX5woE809fVVr1edY15LLKp1llobmNHQjNrcp9gwmrTJ2+f53KKMu15tj+Giu/HvofNXvHUUURWe/ce3udaWEfe+/R1cKaowU6bp+1hbd+tl3Zyh0UWxD2jPFS1s2bSbJc7tzTh+XeGMH6AoF99OdGPcl2eg+B4EZD6Vsm7mmFuCcQ2EOJhnZSKgAnZV2x0FfvuYmlz+luG9krJrFlSn9+f+UOBnRw3L11S7MgbgqrX8kZO8aovm0qVRgCqrbOUDi4UPOUEayhAzkEbRHawI+PH3OutoSrcJu4N378eBo1asSlS5dITExk06ZNxMXFkZiYyKVLlwgODmbChAnuGv4GxvGT4I0fD3HuqvN5aC5ZcLrpOZuRZ/b6nyczyCsudemdB0epiYvc+X+e4WyGZYHq0zjzEOGqEnsszX6jWsA1fVENyb6Nu+J54OgP0PHUXLNcfYVqDd/uSnb4WKpqDgpz557uvVxzMo9jdbnmKpKZr+aHvY6H9FcXSisXCO5w5FYnMjJ5+px7wrknEJhwZ2QbAMJ9Sz07EYGghqH0111T+WiF8C0Q2KOllIGPpKFY9iZF1rnJ/tWzJfd2sl5YwZhAP28imwc5LVa5w1hi0n8Vunf2+rl5cHk+PclIgerdthG3tQpmeM+WFvd7vl+bykzPBGev9J39WPy8zcVdhbsqk9rBbeLen3/+ydy5cy1WBWzatClz5sxhy5Yt7hq+VjFMsYsPvZbStuyOgLMYn1zOnKQ/7b/MJxbyzdnDuCqpMTmFJdw3dwd3fvyX032C5UIDjnypVXTsmDr3Krdwd+dyf+7m09w3dwdg/uWxNzmLNm9tMHNpVYWVCRftN6rB6P+E+rx71irmGlOZH6vc4hIemP+3xdc+2XTK4WPJWpEaw+72HHcVnmtkmYvXCs0cgf/546Th/wWqUkoquFNdlei2slT23HMb1ipR17BpVoZ8fbVcUVBDIDAhpFEjAAJEPkqBwARvP52456cV54ZAYA+9a++83Ay5TD6pDvFm2K3NALgp1LUOvp6tGwLweI8Wle/Eyevn2yMa8d6wW1g5urfJDXeFJLF2fD8+etx9Tjfja/3b2zSy216fH6++r2MZ7L55rictGvqz8JnbDNse6BJOREg9q6Klu3Bbzj1/f3+uX79u9fWsrCz8/f3dNXyt4knlVvoqj3NIbst5TTOn95+8xqiypisnZoVCteU74Kk55RcIttR8a8LLE4viWT/xjirNreLYlVm4F6pLPS5MrN5duwU5V6L/e14tKzsfKmXb3afiIWZ8TBxPtSwOZlUx15ye4lLL1a0qe0ipS7Xc9YltwfyWdzfRPNj0+/TQ5WyXKlefOOkqnbDadiEjgevQO/dEzj2BwBSfAN1NIR8hYAgEJvgE6MQ9fyF8CwR2aSvpoqDOyeWmpeowZnVu1oD4affQqIphohX54cU+ZBeVEFLft9J9eCud94g917cNAEVGlYDtLVXuat+E5f8ku+zzXjqqJ11i4my2eXNwR9o0DuDeTmFM/O9Bu312b9WQnW/eY7ItwMeLra/3r/bQYrc595588kmee+45fvrpJ3JyyquF5OTk8NNPP/H8888zYsQIdw1fqzgstwPgVul8lfuqjuPnbEY+09aa5tSSJNOxrSWttJaPDODw5RyK1Boe/OJvQ/9VzblXGWZtPFG1DhxEXaqtlr9XbUf/90yXdXdawqUsu/vY+iJde+Cyxe1eNn6knDmk9BWxrLEnyfpND6j88VuxeMdrPySatTmakmO2zVEWbXfu+2njkZpT/GX2H9bP6dpu3JNlI3FPLNIEAhN8A3Q3hfxEXjGBwASDuCeLsFyBwB565945udyEYy3dizP4eevWHnfaKBzRNMgfXy/bOf2cxUupqJSwp3cQeislxt7Zlo7hgfzfoA5O92OqGdi+Eh/Qvgmrx/Ym4e17nR7HEo5c9/v7KBnVL4KWjQJo0bDyZjRP5Ax0m3Pv008/pbS0lKeffprS0lJ8fHSKs1qtxsvLi9GjR/PJJ5+4a/haxeGyqjtdFC4Q96rBu5dVWMJ/95jn1DJOjqm1IuKp7RSMWHvwMkdTcjmaksvsR7uahB46ivGXRE5RCc6aYf88kUEvByy7VeW+udtpE1LP7ePUdvR/ziuyzkIeJtkWx8C2g/VkuuV8h152bgm5wgSXV6ESryVcFU4ry+b+2Qe/2GnyPLtQzZT/HaIyFKk1XMm1vSjIzFdVqm9Xs2j7eQL9LP/cedqlW1XeiO7Ago3ZANSTVCix7BwVCG5EfOrpnHv+shC+BQJjfMtcrX5SCZrSEpRe3h6ekUBQc2mnKBP3tOXinivCcv96YwB7kq5braJb01g+qhfvrj/Gi3e1JSjAm9hJd1WqH2fEPUmS6Nuu6lVz9RjrFfbWfgAxQ28B4OnbW7lsDu7EbeKej48PX3/9NR999BH79u3jyhVdOeDw8HB69OhBgwYN3DV0rUPv3OsoXcSHEtTUvh9YWTa1J1s7UWVkm8JLVUMj524+zfw/zxief/P3eeY92d2pPqxV8Cmxlkytkly8XshFG8VJBDr6tG1M/PlrTjr3TJ8bH447z5oWzNDjaOWmqrAv2f7cXYUjmtXyf5LZWslCN/fN3W7mFqzJ1FWT7Jg7I4g9fBHKDut6FPHtrmSiO4dZTPArENxI+PjrrjUDKEaWZY9V3hMIahr+9YIM/y8uyKVeUGMPzkYgqNnow3LPW3HuVfY+cdMgfx7q1rxKc6tOWjYKYNmoXlXux/izq+5r1fq+XjzTpxXqUi1hDfzstm8S6MtXI26z266m4DZxT0+DBg2455577De8gbksh3BNDqSxlEcn6QKH5Jsq3Zenrlu1FS6abUTf2qSqxTCMhT2ALSecFy6UVlT8gxezLW6XpLqRlL+mMm5AWz7bctrg3At3wLnnaqFOlqteVbW6q7I64gDMKqy8mF6bhD1b1PZTV5IkWjUJpuiqD/6SmgZSIfsvZPHVX2d5Pdr5UAmBoC7hW+bcq0cRqhINfj5uv+wVCGoFvr5+lMhKvCWNEPcEAhvUo4jGki7q54IcatjuqWqodQEvpYJ3hnQiX1VKs+Dqr8HwwcNdqn3M6sKtVzkFBQWsXr2aXbt2kZ6ejiRJhIWF0a9fP5566inq1RMhiTokjmjbMkB5iC6KJA5pKi/ueQqtbOrIs+rcs7OS1pqIe1WfV76qlMRL2U7to1RIQqyrQfh6KQn08yKtWHfhGUYWElpDtSpHyCkqsfvjUR3imyNjuCpMVCvbP4e+i7/gkrFqM3XlXM+mPv5cJ5h8LhPKbju5HQWCGwF96KFSkskrysfPJ9izExIIaggKpYI8/AiiAHWh5UJjAsGNTma+iuaSLjQiS65PPgGG14S2VzXG3NnW01Ook7itoMbx48dp3749U6dOJSsri1atWtGiRQuysrL4v//7Pzp06MDx48fdNXyt41BZaG4Pxekq9eOphapWrpBzz2pYrm2MxY/HF+5yxdR4+Kt/nGqvkJwTesR3e/VwlSC0soS3pKExlvPm6alo3JvtSO5GG39yGbnK55aj+7vqHK7tueQEzpEt626WBUsFHp6JQFBz8PILNPxfVSAEDIHAmCJJd9NTVWj7mkoguBH5Lj6Znh9soYV0FYAU2TTvm7VIL4HAk7hN3JswYQJ33XUXV65c4ZdffmHRokUsXryYX375hStXrnDXXXcxYcIEdw1f60jQdgIgSnGcqgSKeSos9+K1QtPkmFbS0+ly3ljvx1iPOGAlDNbdKCSJpKtigVzTKMWLTHQ5YuwV1ahYWOZcRr7Vtk8tTnBICKuqVCbjmHDnKklOaHum1OVcWxKQLetEjIZ2hG+B4IZCoaBQ1lUEFO4kgcCUIkmXb6qkSJwbAkFF/v3rMQCDuHdZbmLyenXk6ha4hu9euJ36vl7Mf8q5PPy1EbeJe7t372bGjBmGKrnG+Pj48Pbbb7N79253DV/rOKC9GZXsRbiURYSU7unpOE16brFDzj171AS3kapUy/ytZz09DYERbRrrXElpZUU1mknXbLav6LwstVEMJf78NZKvFVZL7jWHxD1XOfdc042gNiBBNrpzJEg49wQCEwrL3Elq4U4SCExQlZ0bpcXWb4AKBDc6+rDcy7L1iq3De7UEIKqtyF1ZE7mrfRMOvxvNsFub2W9cy3GbuNewYUPOnDlj9fWzZ8/SsGFDdw1f61DhwwFtewD6Ko5Vup8aoI0BoLFSUcN+WK7nuZqncqp9ZYuHVCdpObW7+MGCp3VVii6WJbJtJV2x2b7ieWDteNSjlOznWayq8OxwWK6LzgJZlk3mfDxV3JmvqygkiWy5PgDBiEWaQGBMkUHAEN+BAoExaqXu3NAUC+FbILCGNeeecZXXaQ90ZOlzPVnyXM9qnZvAcW6UAihuE/fGjh3Lc889x5w5czh06BDp6elcuXKFQ4cOMWfOHF544QVeeukldw1fK4nXdgYgqgrinicxFi+sOfcKVKWcTLN+EVETxElNTZiEi/n7dKanp1AlWjbSJbC9IIcB0MaOuFfx+MvMV5NVYL0yrEJRNVHtSm6xQ+0cGcFVh19FPfPZZXtc07GgxqGQdAU1ABpKQtwTCIxRKXWu1pxsUWRGIDDmSrGuruLeU5c8PBOBoObSosy5Z5xz7/l+bUza+HopubdTGPV9RUV2gWdxm7gXExPDtGnTmDt3Lt27d6d58+Y0a9aM7t27M3fuXN566y3+/e9/u2v4WslObSQAdymO4E2ph2djiiPuNGNxxJpAETV7K3+ezLDax5d/eT4cVl1qPYRT4Fn04l5ru+Ke+bYx3+2z2t5eUlxZti3MPb98r839y3ph+T9J9lu5rFpuRYHTOUdqXSOnqMTTU3AbEkbOPSHuCQQmyN66m0PF+TkenolAULMoRJdz7/KVqx6eiaCmkJWVxciRIwkKCiIoKIiRI0eSnZ1tcx9ZlomJiaFZs2b4+/szYMAAjh0zNaosXryYIUOGABAUFGTWZ3JyMqNHjyYiIgJ/f3/atWvHu+++i1ptemNekiSzx8KFC6v8vm1hybkn8u0JaipuE/cA3nzzTVJTUzl37hw7d+5k586dnDt3jtTUVKZOnerOoWsV4wfoKuUelG8iQw6mgVRY49x7juTQM25SF91vAs9zQeuYuGdJINt/Ictqe3thuapSrYm6dzw1l+ISTfnzNPvhXltOZLDrnO1cgeC6MG9VqRaNOA1vCBSKcueeCMsVCEwpknTi3j/Hkz07EYGghhEcFAxA93Dz/OiCG5MRI0aQmJhIbGwssbGxJCYmMnLkSJv7fPzxx8ydO5cvv/ySvXv3Eh4ezsCBA8nLK4/UKiws5N5777Xax8mTJ9FqtSxatIhjx47x2WefsXDhQt5++22ztsuXLyctLc3weO655yr/hu3gi5rGku59pMjl+fRukAhPQS2kWryjERERREREVMdQtZKHuzdnwbZzyCjYrOnB015/Mkixjx3aW53ua/9F6wJGVXC2ymdlC2oIBLbQO/eaS5l4UUqpla8wZwUyGfshs+czywsVPDD/bwL9vDgSM8i5gRzAlefO2gOXXdaXoOYiScK5JxBY41yekq5KKBUVQQUCExo1bAj54E/tzssscA0nTpwgNjaWhIQEevfuDcCSJUuIiori1KlTdOjQwWwfWZaZN28e06dP59FHHwXg22+/JSwsjNWrVxtScE2aNInc3Fzee+89i2MPHjyYwYMHG563bduWU6dO8fXXXzNnzhyTtsHBwYSHh7vkPdsjVNKtq4tlb3LLCpeBcO4Jai5ude7Z4tdff+W7777z1PA1CuO1fKy2FwDRyr0o0VjZwzrOFoNwFEcEh/zi8lBiG8VJBYJKk0EwRbIPXpLWYJO3hLOhrZXR0/KKS90S6upK1+sxUUTjhkACUVBDILBCrqxz7jWQCj08E4GgZqH10f1uKEtElXUBxMfHExQUZBD2APr06UNQUBC7du2yuE9SUhLp6elER0cbtvn6+tK/f3+r+zhKTk4OjRo1Mts+ceJEQkJC6NWrFwsXLkTrxkVnKNkAZMjB6K62dEhC3BPUUDwm7r355ps8//zznhq+RmGcqy5B25lMuQFNpFzuViR6blIVcETcm/3HCcP/84rrbn4rgeeQUXBebgpAe8m6Ky2r0LnjT0auVK67hdvOOb2PA5MRCJxCIUlkl91RFs49gcAUvduiAULcEwiMUZSJe14a4dwTQHp6OqGhoWbbQ0NDSU9Pt7oPQFhYmMn2sLAwq/s4wrlz5/jiiy8YN26cyfaZM2fy448/smXLFp588klef/11Zs2aZbUflUpFbm6uycMZQqVsADJoaLJdaHuCmorHxL2TJ0+i0TjvTKvrlODFT5q7AHhKudXDsylH48BNEeN8YrnFNasgyI1OVSrB1jROyq0A6ChZr+421kbxDEvIcuXceyo3FF8RIe0CZ1FIkCUHAjrnnoSwTgsEesqde8KdJBAYI/nqxD3vUnFu1GViYmIsFqIwfuzbp7tutuRIk2XZrlOt4uuO7GON1NRUBg8ezL/+9S/GjBlj8to777xDVFQU3bp14/XXX+f999/nk08+sdrX7NmzDcVBgoKCaNmypVNz0Yt7V+Rgk+0i556gpuIxcU9QTsW1/BrN3QAMUCTaDD2sToTgIKgpHNfqxL1Oigsu67OyR3eXFkEum4MeR4R0gcAYSZLIKXMnKSWZ+hR7eEYCQc2hUNIJGMK5J3AHCxYsICIiAj8/P3r06GEzFDEtLY0RI0bQoUMHFAoFkyZNstn3Dz/8gCRJPPzww66ddBkKP91NIV+tEPfqMhMnTuTEiRM2H5GRkYSHh3PlinnBuqtXr5o58/Toc99VdOllZGRY3ccWqamp3H333URFRbF48WK77fv06UNubq7FeQNMmzaNnJwcw+PSJevGAEvoc+5lyKbOPT8vpVP9CATVhdsLapw5c4Zdu3aRnp6OJEmEhYXRt29fbr75ZncPXWuoqJslyU35WxPJncqjjFOu553S0Z6ZmDFC2xPUEMqdexdd1ueW41cqJWDX93X9V6gQ0quXXm0a2m9Uw5EkUOFDkeyDv6QmWMqrVJi5QFAXyZfKwnJFzj2Bi1mzZg2TJk1iwYIF9OvXj0WLFvH4449bba9SqWjSpAnTp0/ns88+s9n3hQsXeOONN7jzzjtdPW0DigDdDUp/jUjnUJcJCQkhJCTEbruoqChycnLYs2cPt99+OwC7d+8mJyeHvn37WtwnIiKC8PBwNm/eTPfu3QFQq9Vs376djz76yKl5pqSkcPfdd9OjRw+WL1+OQmHfg3Tw4EH8/PwIDg62+Lqvry++vr5OzcMYfc69qxWce8/1a1PpPgUCd+I2515OTg4PPfQQHTp0YNKkSSxbtoxvvvmGSZMm0bFjRx5++GGn495vJL4ofQSAJ5TbaMo1242rgYQkz89BIAA4Webcay1lUM9FFd7eXX+M93477vR+GmfL8jrAil3JLu+zrpN3YAOXF47mwpxHSFvxGsWXjtpsX3zxCGkrXuPCnEfY9O9/sXDhQrM2CxYsAHR5Y1q2bMnkyZMpLi53xFkKc6lYvU2WZWJiYmjWrBn+/v4MGDCAY8eOueAdm6IsC325js6F0Zg89iZn8WtiisvHEtxYZGVlMXLkSENI08iRI8nOzra5jyPH/UsvvUS7du3w9/enSZMmPPTQQ5w8edIt76FQoc+5J9xJAtcyd+5cRo8ezZgxY+jUqRPz5s2jefPmVtu3adOGzz//nGeffZagIOvOf41Gw9NPP817771H27Zt3TF1AHwCggHwF849AdCpUycGDx7M2LFjSUhIICEhgbFjx/Lggw+aVMrt2LEj69atA3SRA5MmTWLWrFmsW7eOo0ePMmrUKAICAhgxYoRhn/T0dA4fPmx4fuTIERITE7l+/Tqgc+wNGDCAli1bMmfOHK5evUp6erqJI/C3335jyZIlHD16lHPnzvHNN98wffp0XnzxxSoJeLYI0zv3CDZs+/CRSBr4ebtlPIGgqrhN3HvllVdISkoiPj6erKwsTp06xenTp8nKymLXrl0kJSXxyiuvuGv4WoWlfGh75E4kaDvhI2l4y/u/HpiVKWsPiEWioGZwjSBS5MYoJJluirMenYtw2XmeghM7uP7nEoKinqDZqPn4triFjB9jKM3NsNi+JDudjJ9i8G1xC81Gzafbg8/z6quv8vPPPxvarFq1ipiYGAD27NnD0qVLWbNmDdOmTTPp65ZbbiEtLc3wOHLkiMnrH3/8MXPnzuXLL79k7969hIeHM3DgQPLy8lz6GbzUvx0AV2XdYjFEygHgtR8SXTqO4MZjxIgRJCYmEhsbS2xsLImJibz00ks293HkuNc7M06cOMGmTZuQZZno6Gi35GIuUJSF5QrnnsCFqNVq9u/fb1IlFOCee+6pct/vv/8+TZo0YfRoxyJ3Kls0wC9Q51yvJ4tzQ6Bj1apVdOnShejoaKKjo+natSvff/+9SZtTp06Rk5NjeD516lQmTZrE+PHj6dmzJykpKcTFxREYGGhos3DhQhMX6l133UX37t1Zv349AHFxcZw9e5atW7fSokULmjZtanjo8fb2ZsGCBURFRdG1a1c+//xz3n//fT799FN3fRw00RfUMHLuSYiEe4Kai9vCctevX8+mTZtMymnr6d27N4sWLWLw4MHuGr5O8EHJ0/zqM4OHlLv4UdOfndounp6SoJZS1zSovdoONFfu4nbFKf5x4ryYsibRpfNwh3NP4By5e3+hfteBBN46CIBG971IUdIB8g5upGH/UWbt8xP/QBnYhEb3vQhA51t60dknkzlz5vDYY48BEB8fT+/evdmxYwetW7emS5cuPPXUU+zZs8ekLy8vLzO3nh5Zlpk3bx7Tp0/n0UcfBeDbb78lLCyM1atX2xVInKFJoC9tm9QjM9tU3BMIqsKJEyeIjY0lISHBcC23ZMkSoqKirO7j6HH/4osvGvZp06YNH3zwAbfeeivJycm0a9fOpe+jUFEPNCLnnsC1ZGZmotFozPKKNWnSpEr9/vPPPyxdupTExESH95k9ezbvvfee02P51teJe/UprFIBBEHdoVGjRqxcudJmm4ppPyRJIiYmxnBT1BIxMTFMmTKFoKAgcnJyaNCggcnro0aNYtSoUTbHHTx4cLVrB+UFNcpTuNSlIoWCuodbC2rY+pEQPyDlWBNejspt+V4zEIA53gtpjFiwCQQAe7UdAeglORfGtfagax2oQtzzLLKmBHX6Wfwjupts94/ojirF8rGhSjlp0l6hgEGDBrFv3z5KSkoAuOOOOzh06JChzfnz59m4cSNDhgwx6evMmTM0a9aMiIgInnzySc6fP294LSkpifT0dBNXh6+vL/3797eZcL2y+CgVZJY595qU5YgRCKpCfHw8QUFBJjdp+/TpYzOcsDLHfUFBAcuXLyciIsJmJcPKupOKFDr3SKBUBJpSh/YRCBzFUpXQypKXl8czzzzDkiVLHMqRpqeyRQOUZWG5vlIpGrVr0pwIBHUFb0ppLOkc5xkVcu4JBDUVt4l7Q4cOZezYsYbS2sbs27ePcePGMWzYMHcNX2f4uPRJzmibEy5lscDnc/xQeXpKAoFH6BBWbu/fUybudVecxRe1p6YkwnI9jKYwF2QtigDTohjKeg3RFGRZ3qcgC2W98vb6Qk+lpaVkZmYC8OSTTzJ9+nQAGjduTLt27bj77rt56623DPv17t2b7777jk2bNrFkyRLS09Pp27cv167p8pPq88RUdHWEhYWZVZXTU1nxAsDHS8HVspwwwrkncAXp6emEhoaabbclOjhz3C9YsID69etTv359YmNj2bx5Mz4+Plb7nj17tiH3X1BQkE0h0Jiispx7AKhErmeBawgJCUGpVJod1/rfkcpw7tw5kpOTGTp0KF5eXnh5efHdd9+xfv16vLy8OHfunMX9fH19adCggcnDEbz9G6CVdeJkaaH43RAI9Jy+kkdDdMJeqawgm/oenpFA4BhuE/e++OILmjVrxu23306jRo3o2LEjnTp1olGjRvTu3ZumTZsyf/58dw1fZyjEj5dLXiNX9qe34iSLvecSQLH9HQWCOsZPL0cx7X6dqHdGbk6a3Ah/SU1fhesLFDiKRuuxoQVGmBnBZRkczImikCSD00LvwNi2bRtz5swBYMeOHaxdu5bff/+dmTNnGva7//77eeyxx+jSpQv33XcfGzZsAHQhiKZzM3d1WHOuV1a8APA2cu4JcU9gC0vFYCo+9DdmLR2rjjiTHDnun376aQ4ePMj27du5+eabeeKJJ0yK1lSksu4kbx8fCmRdsvXcHFEcTOAafHx86NGjB5s3bzbZ/tdff1W6z44dOxoKDegfw4YN4+677yYxMdGp3wRH8PJSko8fAKWFlm+ICQQ3Im/8eIhGZa69LOojuzfYUSBwGW7LuRccHMwff/zByZMniY+PN9zZCg8PJyoqio4dO7pr6FqHvevks3ILnldP5Xuf/3CX8ghrpXeZUPIq52TrFbkEAmPqgr8s0M+bl/q3Y/YfJwGJOE0PnvPaTLRiH39pu9vd3x1ohHPPoygDGoCkMHPpaQqzUdYLtrxPBVefn5eCjIwMvLy8aNy4MQAzZsxg+PDhfPHFF9xyyy1ERUVRUFDAiy++yPTp01EozC/y6tWrR5cuXThz5gyAIRdfenq6SULojIwMM1eTnmnTpjFlyhTD89zcXIcXcyH1fcrDcoW4J7DBxIkTefLJJ222adOmDYcPH+bKlStmr+ndqZZw5rjXi9g333wzffr0oWHDhqxbt46nnnrKYt++vr6VqogY6OdNbm496qHiypUrNGh6s9N9CASWmDJlCiNHjqRnz55ERUWxePFiLl++bHh92rRppKSk8N133xm26XPp5efnc/XqVRITE/Hx8aFz5874+fkRGRlpMkZwcDCA2XZX4K1QkEkADShCUyRcrQKBnkK1hiZ6cU8OtNNaIKg5uE3c09OxY0ch5NnBkcSc++UOPKOexiKfz+iouMRGn2ks0Qzhm9IHyEZ86QhuPOK0PXmOzQxS7uXd0lGoqf6y9FqRc8+jSEpvfMJvoig5kYD2fQ3bi5MT8b/ZvJgTgG/zjhSdLS+MoZAk4uLi6NmzJ97eumOosLDQTMBTKpXIsmzVtaRSqThx4oShGlxERATh4eFs3ryZ7t114rNarWb79u189NFHludWSfECIGbYLbx2fBsAISI/q8AGISEhDuXzioqKIicnhz179nD77bcDsHv3bpMqiRWpzHGvR5ZlVCrXpx6RJMiVA2gqXSdfOPcELmT48OFcu3aN999/n7S0NCIjI/nxxx954IEHAEhLS+PixYsm++jPC4D9+/ezevVqWrduTXJycnVOHQCFQiJPDgDpGqnp6QTdVO1TEAhqJLIs0wi9cy8QL4VEqbjmF9QCqsVjevnyZbRardn/Bc5xQG7Pg6oP2arphq9UykSvX9nl+yofey3iLsUhfCjx9BQFgmojQduZNLkRjaR8Biv2emQO764/xs/7L9tvKHAbDXo9TP6hOPIPx1GSeYnrfy6hNPcqgd10i6us7SvI/P1TQ/v63e6nNDeD638uoSTzEoe3rmPp0qW88cYbhjZDhw5l2bJlACQnJ7N582ZmzJjBsGHDUCqVALzxxhts376dpKQkdu/ezeOPP05ubi7PPfccoAtLnDRpErNmzWLdunUcPXqUUaNGERAQwIgRI1z+OTQN8icTEZYrcB2dOnVi8ODBjB07loSEBBISEhg7dqxZtcKOHTuybt06wLHj/vz588yePZv9+/dz8eJF4uPjeeKJJ/D39zeIIq4mlwAANCL0UOBixo8fT3JyMiqViv3799OvXz/DaytWrGDbtm0m7fU3iYwftoS9FStW8Msvv7hn8kAe/gD8+M9xt40hENQ2ZKBhmXPvunDuCWoRbnfuAXTu3JnExETatm1r8n+BDmci+67QiBdK/o9Bmn284rWOSEUyT3ht5wm2o5K9OCK35Yg2giQ5nGQ5nDS5MVlyIFnUR4PSfW9CIKhmNCj5ofRuJnv/zPNesaxXR+FonjVX8vqPh+w3EriNep3uQluUR/Y/P6ApuI5PSGtC/xWDV5CuEIAmP4vS3KuG9t7B4YQ+HkPW1m/IO7iBgkahzJ8/n8cee8zQ5p133kGtVjN79mx69+5NkyZNGDp0KB9++KGhzeXLl3nqqafIzMykSZMm9OnTh4SEBFq3bm1oM3XqVIqKihg/fjxZWVn07t2buLg4AgPdc6F4tayaWwOpCF/UqLBenEAgcIRVq1bx6quvGqrfDhs2jFmzZpkc56dOnTJx89k77v38/Pj777+ZN28eWVlZhIWFcdddd7Fr1y6LBTyqipdCIlfWFdWQi4TwLRAYkyfrhO8W/sIgIBAYY3DuyYHIwKBbwvjn7DUe7NLMsxMTCGxQLeKecRhTVUrEC/RIbNL2YpO6J7dLJxmqjGeQch+hUjY9pdP0VJy2uFe2XI9CfCmWfVDhQzE+FMs+lKBEgwKt4SGVPZfQojD8314yUXt/WtmG8GLrNd3rle9bAI2yxgKtPD0Nl7Nacy/jvH6ju+Is9ykOsEXbw9NTEniAwNuGEHjbEIuvhQyZbLbNr1UXmo76HIAnerZg3OO3mrzu5eXFW2+9xezZs3U5uixUHvzhhx/szkuSJGJiYoiJiXHgXVSdPPxRyd74SiU0kXK4LDex2laWZeKOX6Fz0wa0bBRQLfMT1D4aNWrEypUrTbZVrOJc8brO3nHfrFkzNm7c6NJ52uLDR7pw7EvdMS6psqttXIGgNhDUMARyoVX9Uk9PRSCoOchGzj0CkWWZhc/0oFQr460UxTUENZdqEfcEtqm83CmxR+7EntJOzCh9ntbSFXpKp7lZcZkIKZ02UjpNpGyCKUAhyQRLBQRT4Alzk8DD/FN4v6en4BauEsxyzWDGe60nxvtb9qg6kks9T09LUIuoW/ebJK4SRAsyaUI2l7Eu7m06doVxK/cDkPwfy8KoQFAXaB8WSHpIOGSBsliE5QoExij9gyAXvNR5np6KQFBj0MpyebXcMueeJEl4K8UiWlCzEeJeDcA1bkaJC3I4F+RwqJDSUIGWYPJpKOXhjwo/1PhLat2/qPGiFIUkl/n2ZJQGn54WZdk2vXfP+uj2ZueefXXUqdW5W+gc2N7TU3AbC0qH8aAinlaKq3zqvZCXS16jVHy1CRykrn17pMuNaCFlEi5dt/nm9iRdr75JCQQeRuXbEAAvIe4JBCZofXXh8kq1qJYrEOjRytAQkXNPUPuodSvgBQsW8Mknn5CWlsYtt9zCvHnzDNUJLbF9+3amTJnCsWPHaNasGVOnTmXcuHEmbbKzs5k+fTpr164lKyuLiIgIPv30U0Ni59mzZ7N27VpOnjyJv78/ffv25aOPPqJDhw5ufa+uQouC6zTgumwUWlbXVrQ3MJrifLK2LKLwzG4AAm7uTaP7XkLhV9/Q5vMQU3FPlmXee+89Fi1aBMCQIUNYuHAht9xyCwDXr1/n3XffJS4ujkuXLhESEsLDDz/MzJkzCQoKMpuDSqWid+/eHDp0iIMHD9KtWzc3vVtz8gngtZKJ/OAzk4HK/XzNPN4oGUcO9e3vLBDUMVLlxgA0kzJttpPEzWfBDUSJj07c81ELcU8gMEb20a0NvEvzPTwTgaDmIGPk3COwjkV5COoytSpofM2aNUyaNInp06dz8OBB7rzzTu6//36zMvN6kpKSeOCBB7jzzjs5ePAgb7/9Nq+++io///yzoY1arWbgwIEkJyfz008/cerUKZYsWULz5s0NbbZv386ECRNISEhg8+bNlJaWEh0dTUFBgUvel/i+EFSFzPWfoL5ynrAn3iPsifdQXzlvUh0U4N5OYSbPP/74Y+bOncsnn3wCQGhoKAMHDiQvT/dDlpqaSmpqKnPmzOHIkSOsWLGC2NhYRo8ebXEOU6dOpVkzzyWYPSjfzMslk1DJXgxUHmCL7/8xRrmBxojk6QLb1LULtnJxz7YzT2h7ghuJUj+duOerzvbsRASCGobspxP3fIS4JxAY0GpFtVxB7aRWOffmzp3L6NGjGTNmDADz5s1j06ZNfP3118yePdus/cKFC2nVqhXz5s0DoFOnTuzbt485c+YYKiMuW7aM69evs2vXLry9vQFMqsABxMbGmjxfvnw5oaGh7N+/n7vuuqvK76uuLS4F1UdJ5iWKk/YTPvJTfJvpnKSNB79C+so3KLl2Ge/GLQBQGK3kZVlm3rx5TJ8+nWHDhgG6c+Xmm29m9erVvPTSS0RGRpqI4O3atePDDz/kmWeeobS0FC+v8q+OP/74g7i4OH7++Wf++OOPanjXltmqvY3H1DF87v0V7RRpvOO9imleqzkmt+FoWQXpK3JDrhJMoexLMT4UlRWY0RoFnusC0SVkpLKiMpLhucC11ITPVCHXrSTiFZ17k9ck8p/HuuDrJaql38gkZRaQW1TCrS2DPT0Vj6D1awSAf2m2ZyciENQwJL9gAHxLRc49gUCPLMuGarnXEeKeoPZQa8Q9tVrN/v37eeutt0y2R0dHs2vXLov7xMfHEx0dbbJt0KBBLF26lJKSEry9vVm/fj1RUVFMmDCBX3/9lSZNmjBixAjefPNNlErLi6GcHJ0bqFGjRlbnq1KpUKlUhucVq8sZ06W5eZijQOAIqtQTSL71DMIegG/zjki+9VClnDCIe5KRiJKUlER6errJueHr60v//v3ZtWsXL730ksWxcnJyaNCggYmwd+XKFcaOHcsvv/xCQIDnK24eldsyWP0Rjyr/5mnlFroqkugqJdFVkeTpqQlqKHtTBwH/8/Q0XEZambjXVLoGwLqDKZzJyOP3V0zTV4iwXM8jyzJXclWEB/lVua+0nCKW/p3Es1FtaNXY/Lv47jnbAEiYdq9LxqttyAG68yKgVLi5BQJjpACdqzVAI8Q9gUCPt1aFv6QGME1rJRDUcKpF3HvmmWdo0KCB2f+dITMzE41GQ1iYaXhhWFgY6enpFvdJT0+32L60tJTMzEyaNm3K+fPn2bp1K08//TQbN27kzJkzTJgwgdLSUv7973+b9SnLMlOmTOGOO+4gMjLS6nxnz57Ne++959B78/GqVdHRghqEpiAbZYC5OKwMCEJTUJ5byHghrz9fLJ0bFy5csDjOtWvXmDlzponwJ8syo0aNYty4cfTs2ZPk5GSH5uyM8F0ZSvBijeZu1mjupinX6KY4S6QiiWbSNcLIIkTKwR81fpJK9y9qXdEYSVhoBbWfVDkEgOZl4h7A0RTzc0wS6p7Hmf/nWT7bcpq37u/IuP7tqtTX2O/2cTQll41H0tg17V6r7ZIyC25IcQ8jca+gWE09Px8PT0ggqBkoA3RGhfpaUVBDINDjX6pbQ6lkLwrx9fBsBALHqRZx7+uvv7b4/8pQcUEiy7LNRYql9sbbtVotoaGhLF68GKVSSY8ePUhNTeWTTz6xKO5NnDiRw4cPs3PnTpvznDZtGlOmTDE8z83NpWXLlrbfnNncRcjujUr2zlXk/PNfm23Cn/1M9x+Lx79sst1SE0fPpdzcXIYMGULnzp159913Ddu/+OILcnNzmTZtms15VsQZ4buqpNGYNG1j/tD2dqC1XFYlWjYKzC2vIi1wLfarYFcPQzq2openJ+FCUmXdQq2JlIMPJajxtthOSHue57MtpwH4zx8nqyzu6QXc1JziKs+rLqKopzsvlJLM5gOneLhvFw/PSCCoGSjr64TvBrJw7gkEUKYVFOeCL+RSD3HFJKhN1Jqw3JCQEJRKpZlLLyMjw8yBpCc8PNxiey8vLxo3LgtdatoUb29vkxDcTp06kZ6ejlqtxsen/O7uK6+8wvr169mxYwctWrSwOV9fX198faum9Nf38SJPVbfyQQkcI/C2B6nXyXY+R6+gMEquJqEpyDZ7TVOYizIg2PBcYSTahYeHAzoHX7t25QtKS+dSXl4egwcPpn79+qxbt86QlxJg69atJCQkmB3nPXv25Omnn+bbb7+1OG9XCN/uQSfnaQCNp6ciqDZKFHXrjmwWgRTJPvhLasKl61yULf8+VgelGi3XC9SENqg5TrF8VSn1fWvNpY/LuVENmw3q1SNX9qeBVIR/SbanpyMQ1Bi8y8Q9X9SgLgQfz6dYEQg8SVGJhgboimbmyuJ8ENQu3H6F+8ILL9h8fdmyZQ714+PjQ48ePdi8eTOPPPKIYfvmzZt56KGHLO4TFRXFb7/9ZrItLi6Onj17GkSKfv36sXr1arRaLQqFLjz29OnTNG3a1CDsybLMK6+8wrp169i2bRsREREOzbmq+Horhbh3g6IMCLIYblsR32adkFUFqFJPGfLuqVJPIasK8G3eydDOeD0XERFBeHg4mzdvNoh7arWa7du389FHHxna5ebmMmjQIHx9fVm/fj1+fqYL9Pnz5/PBBx8YnqempjJo0CDWrFlD797WnXJVFb4b1fPheoG60vsLBCbUDAOhC5FIlRvTTkqjuZRpIu5l5BUz/88zjLi9NQlJtqvpuoLHF8aTeCmb9RP70bVFsNvHs8cPey7y1tojxAztzKh+1fM7XhtIzS5i9e6LPNOndZ0N2b3j5hBS5UAaSEU0koRDSSDQ41c/mBJZibekQS68hiTEPcENjoREA6kQ0Dv3BILag9uTvWVlZZk8MjIy2Lp1K2vXriU7O9upvqZMmcI333zDsmXLOHHiBJMnT+bixYuMGzcO0DmCnn32WUP7cePGceHCBaZMmcKJEydYtmwZS5cu5Y033jC0efnll7l27RqvvfYap0+fZsOGDcyaNYsJEyYY2kyYMIGVK1eyevVqAgMDSU9PJz09naKioqp9OHbw8xa5+AS28Q5piV9ED67FfoEq5SSqlJNci/0C/3a9DMU0ACJv6cy6desAXTjupEmTmDVrlkH8fvnllwkICGDEiBGAzrEXHR1NQUEBS5cuJTc313DcazQ6X1urVq2IjIw0PNq3bw/oKuvac7ZWBR+lOC8EAltclEMBaC1dMdn+fz8eZmXCRR6Y/zeHLmW7fR6JZWP8tP+y28dyhLfWHgEg5rfjDrUv1WjdOR2PYCnVx3PL9vDlX2cZ893e6p9QNeGtVJCv1N0w81Vn2WktENw4+Pt4kV0mYBw6k+zZyQgENQTh3BPUVtzu3NMLCsZotVrGjx9P27Ztnepr+PDhXLt2jffff5+0tDQiIyPZuHEjrVu3BiAtLY2LFy8a2kdERLBx40YmT57MV199RbNmzZg/fz6PPfaYoU3Lli2Ji4tj8uTJdO3alebNm/Paa6/x5ptvGtro8wQOGDDAZD7Lly9n1KhRTr0HZ/D3tlytVyAwJmToG2RtWcSV/80AIOCm3jQaOM6kzalTpwxVngGmTp1KUVERr7/+OqA7d+Li4ggM1JV7379/P7t37wbgpptuMukrKSmJNm3auOvtCASCKpIshwOHiJDSDNtyCkvYfvqqx+Z0LDWHjFwVd3cMdWq/xEvZzNl0iulDOtGpafVVrHv316Os2XeJLVP606Jh3b64P5ORD1guvFKXKPZuCGqQitzvWhUIagv+PkrS5UCaSLmo8zI9PR2BwOPIyEbOvbr9+y+oe3gk8YxCoWDy5MkMGDCAqVOnOrXv+PHjGT9+vMXXVqxYYbatf//+HDhwwGafUVFRJCQkWH1d9lBVCz8h7gkcQOkfSMjQN2y2qXgMS5JETEwMU6ZMISgoiI0bN5pUsR4wYIDTx32bNm08dq4IBJWlLh6x5+WmALSVynPO3vp+nKemw+7z1/kuXleJO27yXbQPC3R434e/+geAEUsSOPjvaJttrxeoef+3YzzRqyV924VUfsLAt2XzXbLjPO89FFmlvmoS+px7V3KLCQ30vaGqJuude8piIe4JBHoC/bw5QX0AGpLv4dkIBJ5HlqEBZeKeLMJyBbULj8W3nTt3jtJSkU/OFiIsVyAw5wZaiwoElSKpTNxrI6XbaWmfzHwVWq3MluNXePOnwxSX6MLyZVlm8ppE/vPHSbt9nLpSnuMsKbOgUvPIKiyx2+b9347xS2IqI5bsrtQY7kaWZc5cyUOjrbqk/NP+y8SsP4a2En2tTLhA71l/MtuBv11dosArGADvIuFOEgiMKVTqbu6qhHNPIEAGGkhlYbllzr2erRt6cEYCgeO4XT2aMmWKyWPy5Mk8+eSTDB8+nOHDh7t7+FrN5Pvae3oKAoFA4DDiO6tmkKTVVcRuLaWjwH7euMtZujvUhepSViZcID2nGIB/zmbS84MtTPzvAcZ8t481+y6xdGcSAMfTcll3MIWF2885NTdFFdT5nMISZvxylIMXLedMu5Tlvjy4pRotJQ7m4MsrLkFVal5z++vt5xj42Q7e+eVIlefzxo+HWLErmb9OZTi1nyzDzN91OQcX7zhf5XnUJvK8dVVBfYo8F54uENRErpTo3ElbD9xYgr/AnKysLEaOHElQUBBBQUGMHDnSbo58WZaJiYmhWbNm+Pv7M2DAAI4dO2bSZvHixQwZMgSAoKAgi322adMGSZJMHm+99ZZJm4sXLzJ06FDq1atHSEgIr776Kmq1a4vsybJs5tz79IlbXTqGQOAu3C7uHTx40ORx+PBhAD799FPmzZvn7uFrNX1vCqHfTY09PQ230rdd3X5/AtcjIn9rLkH+Hsn0UCXqYih5Ko1Ryd74SBqaS/aFjGe+0TndPtxwgnd+OcojC3ShsHrhbuORcgegXvhTl1au2ERVjLczNxzn+4QLPLJgl8v7toYkSfxxJI2bpv/BzdP/sCvw5RaX0CUmjn7/2Wr22rzNZwD4755LLpufJUejVivb/PvcqO7nAh/d9YZvsXOCqEBQ18kqC8sN0OTYaSmo64wYMYLExERiY2OJjY0lMTGRkSNH2tzn448/Zu7cuXz55Zfs3buX8PBwBg4cSF5euWu/sLCQe++91+74+rz6+sc777xjeE2j0TBkyBAKCgrYuXMnP/zwAz///LMhf7ir0MqY5dxrVM/HpWMIBO7C7Suxv/76y91D1GkaBogvE4FAIBA4joyCZDmMDtJl2klpXJLDbLZPvqa7iN16Uid6pJUJeFoLwqdeGKpsrjZFFW4pnjEK760uZFnm5VXleXtTs4to3dh6Dh59FeLMfAtOgmoS1R5buIvT6Xnsfec+AnxML/NuVGEPIN+nCQD+xcK5JxAYkyOX5dyTRM69G5kTJ04QGxtLQkICvXv3BmDJkiVERUVx6tQpOnToYLaPLMvMmzeP6dOn8+ijjwLw7bffEhYWxurVq3nppZcAmDRpErm5ubz33ns25xAYGEh4eLjF1+Li4jh+/DiXLl2iWbNmgM4sNGrUKD788EOT3OFVQi6vlptXVi33RspPK6jdiKRuNZSuLXSJn6sSwiQQCATVibj4qTmclXUXvjdLlyvdh9ZBc54z7sd5W85QqK5cvl13eSxX/JPEO78cceh9GOfLyyksYe2ByxSodO9HXapFsqHgVdfZcfBiNgVqDXuTzcOX66BR1WEKy8Q9ZaFw7gkExuide0GioMYNTXx8PEFBQQZhD6BPnz4EBQWxa5dlx3xSUhLp6elER5cXvPL19aV///5W97HFRx99ROPGjenWrRsffvihSchtfHw8kZGRBmEPYNCgQahUKvbv3+/0WNYQ1XIFtZlqiaH66aef+N///sfFixfN4uLtVbK9UdFfgCsVYrEsEBgj9KOaS23829RVreO4tg1DlHvorLgA5unfLFJR+JEd/HRkWfe3/+tUBhqNzH2dw/g+Ptli28OXc/hs82mmD+lstb8f9lxEBp66vZXN+dlj6c4kvJUSz0a1sdku5jddDroHujS1W2XX2M344vf72J10naG3XuWdIZ2446OtVt32X287h6qSocyVxRGxsrLh1bWRxOu6v019qZj8vGzqBwZ7dkICQQ3hlnZt4CI0kqrfHS2oOaSnpxMaGmq2PTQ0lPR0ywW69NvDwkwjBMLCwrhw4YJT47/22mvcdtttNGzYkD179jBt2jSSkpL45ptvDGNVHKdhw4b4+PhYnZ9KpUKlUhme5+bm2p2HVoZAQ849Ie4Jahdud+7Nnz+f559/ntDQUA4ePMjtt99O48aNOX/+PPfff7+7h681/PPWPSx85jaz7bVwrewUN7KLQCCoa1QMAawN3B/Z1NNTcAvH5dYAdJYcu7h+dME/pOcWm2yz9P0sAQcuZrHhcGp5O0BVquH55XsZ890+copKmPHrMfOdyziSYj2vU76qlLfWHmHa2iPkFduvkGuNa/kqZv5+nH//esxQ4dceuUXm41V0oxqn3NuddB2A3w6lsnr3RUo0Mhl55YuIj2JPGqoDfxTrnkT1tq4R1iemWtxeXFL+Jtq/84fZ62k5RUxYfYBpaw9XdXo1ihLv+hTIvgA8OOsnl1QtFgjqAlFdOgIQohDiXl0kJibGrFBFxce+ffsAyxEYsizbjcyo+Loj+1Rk8uTJ9O/fn65duzJmzBgWLlzI0qVLuXbtmtVx7I01e/ZsQ3GQoKAgWrZsaXcevxxMMTj3crCehkMgqIm4XdxbsGABixcv5ssvv8THx4epU6eyefNmXn31VXJyROJWPc2D/RlstMisal6j2oKjzhCBQFCz6RgeSK82DT09DacZdIvtfHS1lRNaneutnZSKL/YryR24mG3yXJZlg3hlzLbTV3l0wS6W/J1k2KaVZUo15d/l9kQ5vaay6Vg63/xdXrFVq5VRGQlxFZ1u9n4vjH8uC9Xl/Tgq4jhSDNdaX5ZSaHy97Rx3z9nGyKW7HRrfUeLPXbPfCFh7MAUwdfBZquJbkdyiUjYcTmPz8SuVm2ANpWGAN1dk3XdUGNkUOSj6CgR1He9gXY6zELI9OxGBW5g4cSInTpyw+YiMjCQ8PJwrV8y/969evWrmmNOjz49X0TmXkZFhdR9H6dOnDwBnz541jFVxnKysLEpKSqyONW3aNHJycgyPS5fsF7R6//djhpx7wrknqG24Xdy7ePEiffv2BcDf399QOWfkyJH897//dffwtZ46ru3VeD56rAvb3hhQrWO+O9R6uJpAFJmpqXz0WFeb+cZqKnX1Bko6jbgu18dL0lYq796GI2kWt18oK75hzOB5O0zcePYc2Xqx6aXv9/PBhhMcvpzNi9/to+3bG1mw7ZzVfo6mmIbT2AopNd73x32OVafVyDJ/HEnjpe/3WW1jqcgIgK0MGn+fyXRofEd5akmCU+2Np3zggnkevoro32NdOzfq+XpxlWAAQiX7n4NA4AgLFiwgIiICPz8/evToYTPPWFpaGiNGjKBDhw4oFAomTZpk1mbJkiXceeedNGzYkIYNG3LfffexZ88eN74D8G+oMxcEoEJWCfdeXSMkJISOHTvafPj5+REVFUVOTo7J8bZ7925ycnIMa/mKREREEB4ezubNmw3b1Go127dvt7qPoxw8eBCApk11x2dUVBRHjx4lLa38+iQuLg5fX1969OhhsQ9fX18aNGhg8rBHPYpRSrrfwVzh3BPUMtwu7oWHhxvstK1btyYhQXdRmpSU5FQS7huVupByrzavD4b3akWbkOr9Yn+yVyv7jWzg51236+TMf6q7p6cgsIBSIdXqc73uIXFcqwvN7aJIstPWnJNpji/wzl0t4MnF5YKTvZ/2iua3q3kq4spcYkt3ls/VllPv98OptH/nD4eEO31OPXtotFpeXnWATcesO9asinu15Md6/tazNl9PzS7ibIYuqX4teUsO4++tJEMOBiBUyhZhuYIqs2bNGiZNmsT06dM5ePAgd955J48//rjV9iqViiZNmjB9+nRuvfVWi222bdvGU089xV9//UV8fDytWrUiOjqalJQUd70N/Os1oLAsZD3+sHtSCAhqPp06dWLw4MGMHTuWhIQEEhISGDt2LA8++KBJpdyOHTuybt06QHcTaNKkScyaNYt169Zx9OhRRo0aRUBAACNGjDDsk56ezuHD5akejhw5QmJiItev6yIE4uPj+eyzz0hMTCQpKYn//e9/vPTSSwwbNoxWrXTroujoaDp37szIkSM5ePAgf/75J2+88QZjx451XaVcoEFZvj2V7IUKbwB8lHV7bSWoO7j9SL3nnnv47bffABg9ejSTJ09m4MCBDB8+nEceecTdw9d6bDlhZj4cWY0zcQ9C3zXH16tqp+Ur99zsopnUTG4Kre/pKQgs4ElhL+/ABi4vHM2FOY+QtuI1ii8dtdm++OIR0la8xoU5j9C2bVsWLlxo1mbBggWALil0y5YtmTx5MsXF5TnpZs+eTa9evQgMDCQ0NJSHH36YU6dOmfQxatQos7w2+jCT6uCgrPsu6Kk47fS+3lW4kLUXPlvxxp613wG9yGSJiat1d/T/7yfLeeEqk/LBkbDcEo3jYbk1BWc+ib7/2cor/9V9tjX5PVUGP2+lISw3VMoiavafrNl70cOzEtRm5s6dy+jRoxkzZgydOnVi3rx5NG/e3Gr7Nm3a8Pnnn/Pss88SFBRksc2qVasYP3483bp1o2PHjixZsgStVsuff/7prrdBPV9vrsq6+Vy6lOy2cQQ1n1WrVtGlSxeio6OJjo6ma9eufP/99yZtTp06ZZJaa+rUqUyaNInx48fTs2dPUlJSiIuLIzAw0NBm4cKF3HnnnYbnd911F927d2f9+vWAzmG3Zs0aBgwYQOfOnfn3v//N2LFjTaL8lEolGzZswM/Pj379+vHEE0/w8MMPM2fOHJd+Bg0kXUhuHgGARO+IRvhUcW0mEFQXbs9+Pn36dMMP3bhx42jUqBE7d+5k6NChoqCGAyiV1i+uu7cMrr6JCKqNqjpAQgN9+X707Yxc6t4wjrqAQjJ3EQkqh6eEgIITO7j+5xIaRb+MX/PO5CX+QcaPMTQbswCvBuZV30qy08n4KYb6XQcxf9EylFdPM378eJo0acJjjz0G6C5uY2JiANizZw9paWmMGjUKgM8++wyA7du3M2HCBHr16kVpaSnTp08nOjqa48ePU69eudt38ODBLF++3PDcx6f6wsr3anV32ntJzjsxqnIha88RZUUfM2PEEsdy1Y35di/3dgozuRlWcQqOuLQ0Wvvq3pUKRUf0OPu1nV1oPw8iQEZeMWeu5NO3XWMkSbK437Kdtp2ZlY2SqGvi3lO3t+J/uxsB0Ey6RqFaw5s/H2F4Fd3yghsTtVrN/v37eeutt0y233PPPZw+7fwNFWsUFhZSUlJCo0aNXNZnRRQKiUyCaE0G7esVuW0cQc2nUaNGrFy50mabir8pkiQRExNjuG6yRExMDFOmTCEoKIicnBwzp91tt91miO6zRatWrfj999/ttqsK9dD9zufL/gB0aW5ZiBcIaiJul6FvuukmsrOzDc+feOIJ5s+fz9NPP03Hjh3dPXytp25dWledAB+lp6dQLViqnOwIvSMaMaxbszq3KHMXHzzcxdNTcBldW5RffLw+sH21j++pYy537y/U7zqQwFsH4R3Skkb3vYgyMIS8gxstts9P/ANlYBOeem0GLw67kzFjxvDCCy+Y3PmNj4+nd+/egC6dRHR0NE899ZShmhxAbGwso0aN4pZbbuHWW29l+fLlXLx4kf3795uM5+vrS3h4uOHhzgVaRQ5ob0YjS7RSXCUM8+IYtth1rvJ54qyFruoxc+5VeiQdW05kMG3tEZs/mI4UknBEA8svLrW43dnjf96WMw61u+M/f/H0N7vZciIDgGX/JJu8vu/Cdd7/3XrY8dU8FR9sOOHU3PTUtZ+RDuGB5Prqcje1kFybB1Fw45GZmYlGozFL5N+kSROXjvPWW2/RvHlz7rvvPqttVCoVubm5Jg9nyffW/TadOms7dF8gqOvUl3QCdyF+Hp6JQOA8bhf3rN0xzs/Px89PnDRVQQg4dYOO4YFm2+r7etvdb+itzegQFkhk8/K7X2teisLXSylEYQeIatuYxvXrRnGORSNNEwmPubNttc/B37t6hPfggPJzQ9aUoE4/i3+EaR5G/4juqFIsu9VUKSfxj+jOu0NvMRQMGDRoEPv27aOkRFfl9Y477uDQoUOGfc6fP8/GjRsZMmSI1XnpQ1Qqinfbtm0jNDSU9u3bM3bsWDIyMqz24YoFmjH5BHBc1uXdu13hnHuvKkUgrIWu6qmOdAwVrz3eW3/c7PV9ydfJKbJd2bdixd4SK+6+S1nmhUZskeWgc09dFiu84/RVwNxdmJpt2UmoZ/S3e1mxK9mpuempi9cYqvotAGguxD2Bi6hYeMaV+cQ//vhj/vvf/7J27Vqba6bZs2cTFBRkeLRs2dLpsS6qdNeiV9IcK0AkENRVAlABkC/EPUEtxG3i3pQpU5gyZQqSJPHvf//b8HzKlCm89tprDB8+nG7durlr+DqDrUuEunDd7eo13sJnLFdLqsk806d1pfb74qnubJp8F0oLB0Jdq3Joi/s6hdlvZIW68CmFN/Bj0C3hJoKJv4+ST/9lOVm3u2jVOKBaxjH+m2kKc0HWoghoaNJGWa8hmgLL1TA1BVko65m2DwsLo7S0lMxM3YL/ySefZPr06QA0btyYdu3acffdd5uFX+mRZZkpU6Zwxx13EBlZngv1/vvvZ9WqVWzdupVPP/2UvXv3cs8996BSqSz2U9UF2vqJ/RjQwdQ1kqDVVd/ur7Scm84dlNhJXlfR2bfRSmXeqlDxt2VNhcIbX/11lscXxvPgF38btln62vzvHtOcbKVWhMs1e51bEP9xJN3m68dSc4g7Zt7mp/2mlY+3l4l+1jh8Ocfm67aoawU1AK576X4vQqVsfHFMYBUILBESEoJSqSQ93fQ81f+OVJU5c+Ywa9Ys4uLi6Nq1q82206ZNIycnx/C4dMl5gS6zLOdeE7IrM12BoE5wvUBtcO4VyDpx7wZaUgnqAG4T9w4ePMjBgweRZZkjR44Ynh88eJCTJ09y6623smLFCncNf0NQE75sBt8Szrrx1kudV2Zx8PfUu62+Zq+7Fg39nR+wGgix4hAb2ac1w3s5f4fVHjXh2KguKnuXXJLcK4JW18I4vSwHWHGJadhhXU0laOlvZrZJlnFGutUfQ/q+t23bZgjT3bFjB2vXruX3339n5syZFvefOHEihw8fNkn8DDB8+HCGDBlCZGQkQ4cO5Y8//uD06dNs2LDBYj9VXaB1bRHMiudv5+h7gwzbtmp1rsa7FQdR4EDFCBdQXGJP3DN9vu6g66tA2vtamBOny4l16Xp5fqkzV6wX8NBTotFy6XqhoeiEHnuhyBVR2xFAh8zfyYvf7zfbfiXXsjDsDuqic69Q2YD8sgWbcO8JqoKPjw89evRg8+bNJtv/+uuvKvf9ySefMHPmTGJjY+nZs6fd9r6+vjRo0MDk4Sz1GzcDoIlU+RsCAkFt53qB2pBzr0A49wS1ELcV1ND/uD3//PN8/vnnLi1RfSOgv6S2tV6wVUm3ulg40rZT7sCMgXSfudlmm4q0bBRAx/BATqbnOT0fhSTRrWUwiZeynd7XnTzQpSnfxV8w216x4vGj3a1XWbOKhQVYXVyUWaOmilhtQupx/mpBtY33QJemfP7nGbqVFdpxZWhQTcL4yFYGNABJYebS0xRmo6wXbHF/S66+jIwMvLy8aNy4MQAzZsxg+PDhfPHFF9xyyy1ERUVRUFDAiy++yPTp01Eoyu+LvfLKK6xfv54dO3bQokULm3Nv2rQprVu35swZy/nWfH198fX1tdmHI9T3Lf9p36vtQI4cQGMpj27SWQ7I7s/HWFRiO79ddRybzoptAN/YKUwBupDjOz82X7zbC0W2x/HUXDo3s36dVJnqv1WlLv6MeHkpuSw3oaN0ieZSJuflZp6ekqAWM2XKFEaOHEnPnj2Jiopi8eLFXL5c7q6dNm0aKSkpfPfdd4ZtiYmJgC490dWrV0lMTMTHx4fOnXUu648//pgZM2awevVq2rRpY3AG1q9fn/r167vtvQzs3RU2Q4gQ9wQ3MAqpvKBGQVlBjW4tG9raRSCoUbg9597y5cuFsOcmasOFd3CAj20HhZXXfp3Yr1LjKWpQpfLhPcsdeY4uNIMDdA4/Z/62PhYqKteGY8NVhAf5cXubRtx5c4jT+7pVZKjmtfjLA9rx9dO38e0Lt3tieI8gKb3xCb+JouREk+3FyYn4NrdcsMm3eUeKK7SPi4ujZ8+eeHvr8vkVFhaaCHgASqUSWZYNx4wsy0ycOJG1a9eydetWIiIi7M732rVrXLp0iaZNmzr4DqtOKV5s03YD4H5l9VTQrugirYhWlh0qcOEsxl97Kdnuqfi486ztMNjK8sD8v22+XlIqM+bbvW4Z2xp19SZRiqz7rWghuedvKbhxGD58OPPmzeP999+nW7du7Nixgx9//NHwelpaGhcvmob2d+/ene7du7N//35Wr15N9+7deeCBBwyvL1iwALVazeOPP07Tpk0ND+OiT+7Au0E4AKFSFpedzCEqENQVlAqJevqwXPyYN7wbD3QJ9/CsBALHcZtzT+B+PJEPp0mgL1fz3B8W5OtVueT8NWkx0jqkPAeZOzWk2Y924blle5lw902GbcbHRsK0e903eA1AAta81AeAiGmWK6Ra3K/mHCouwc9byf1djEQjD6h71WEWrPh3a9DrYTJ/n4tv+E34NutE3qFYSnOvEthNt1jK2r4CTd41Qh58HYD63e4n78DvxEx/k8kTXyY+Pp6lS5eahNQOHTqUuXPnApCcnMyVK1eYMWMGw4YNQ6nUfTdNmDCB1atX8+uvvxIYGGhwVwQFBeHv709+fj4xMTE89thjNG3alOTkZN5++21CQkJ45JFH3P0xmbBeE8VDyl08otzJx6VPUuLmn3574l5+cSkPfG5bzKoqzy93jxD2z9lrbunXHj/uv2QWzuxuatLvqSu5LMQ9gQsZP34848ePNzw3LoRkKf2QvZuKycnJrpqaU/g0bgVAGFnszcynRcPqyaMrENQkJCSTsNwXKhNRJRB4kBrkcxI4j/MX3k0Cqxby5crF+5uDLTtr9PzwYh+8KiiY9nKkKSTM9qkJuHpN9vFj5cmVbwoN5J+37mFE71aGbcafU3hQ3csZYVwxFXTv19n8eRKSW/UvTzvnPBHGVz2U/50fva059TrdRaN7x5L9zw+krngF1aWjhP4rBq+gUAA0+VmU5pYv4r2Dwwl9PIb4nTvo1q0bM2fOZP78+Tz22GOGNu+88w4TJ04EoHfv3owePZpBgwaxaNEiQ5uvv/6anJwcBgwYYOKuWLNmDaBz+h05coSHHnqI9u3b89xzz9G+fXvi4+MJDDSvkO1qJt13s+H/27TdyJCDCZFyuUdxwO1j/7jvss3XU3OKOeeGkPW6esSDeZ7C6qAuansdwgK5LOuKzrSSrFeuFghuNJqEt6REVuIlaQlQC+FbcGMimYTl1r31k6DuI5x7tRhnL7ybB/uz9Y3+LNp+nrmbT1dyVOdXGP1uamzR7fBEzxZsPXnF6n592jbm7KwHaPOW5eTzlpAkiVmPduHpb3YT1sCXoym59ndyMT1bN2TGg53551x5sm5nRVFrf9o/XruTjuGBDoicdXBVZsTON+8h8t1NVepDkuzfQX/0tuasPeD6ZP/VgadT7t3TMZRmwX6sTLhov7ETGB/arRvVAyDwtiEE3jbEYvuQIZPNtvm16sKmBa/RLNhyAR4vLy/eeustZs+ezZUrVyymlrB37Pj7+7NpU9WO0arQo3V5jhgNSn7W3MnLXr8x2usPNql74c5a0TvPeqZQwcVrtTuULO5YOp/GnaZNSM1wzNTF35E3BnVgyh6dw7mtpKvS/MvBFAZ2DqOer7gkFtzAKJRcUzYmXJsBOZeBWzw9I4Gg2pEkqCfpxL18amaRRoHAFsK5V+OxvoB09sI7OMAbXy8lz/VtU/nZVEIw+PzJ7rx2782sHtvbZLs7KpVKQPuwQPa8fS/P9G5dpb62TLmrUvuN6teGW1sGmxQ88fN2zanWqWkDhz63mlo12FXUd9EizNMCmDvxxFszdgtaM9Aa56KsDBW7rYlO3ZrIitJBqGQvblecoo/ihKen4xb0laNrKy9+v59TV/LYdMz6Ta/qpEBd6ukpuJwgf28mPam7ERAhpSOhZdKaRGb8ctTDMxMIPM81pc7VKuXWzpuaAoErqI8u516hXPUCZwJBdSPEvVpMZZe0FbUha4lC906/z+L2Bn7OCSsh9X2ZPLA9LYKr7kaw9J6f7FUuFugFT0mSaBNSr0pj3RRatdA5Y6HjlXtuttHS9YTU9+X3V+5g6+v9q3VcT2AsYjkr+tVhbc9EuLy1RVC1j29NhI5s3oDPn+zmUB8/jouy+bojocc/v9yXZnUwNN0eFaupX6ER/9MMAGCa12oUaD0wK0Ft4kItd0Jao8stt6KWlQRIKppyHYB1iULMEAjOqYIBOHWqbt4AEgjsIcv/z955xzVx/3/8dQmQgEJQNiqIC1CcqIALbSvOWqu2WlvUuqvW/bNabaXOOurXttbaWlfrrFqtVUrROuoAJzhxMxRBBGXIhtzvj5CQy04IkMD7+XikNXfvz7jj3rm79+c9ABtGkls+lzz3CDOEjHsmjibPIkMd3xSbLejnqyTT3Lmu2vx8qkLZ5vfzgaudEPP7qc+jp/gizqDinlO/ju2Mr95pBce6VrC25HPmFtjEAf/Xx7tiAxiJ+nWsdJKT/U2N4Izk10CEJk51K96RGRE+vbte8pVaLFeHzn1cKy/3mry+VUe4GY9Rf3496pcb+j/vr/43w9pSubCO/O9eXYGFVvOev2c9rH2/rdL2unouUpgbnb3qK237ruRdZLPWaMt7jNH86gsZJohqhW+BRFayqNmU9wxAZQapE4T58FTsAAB4/SKxmmdCENVHnTLPvdeofQvDhPlDxj0zRtEzQ6s8I/2/YpEKZdlePs4q+2Ch+oV9cnBTRC14A5N6NNF7PhWhRwsnCCz4uDD/TcR82RtWFtxLeoB89dAqQt+/C1Ex5M+2h4N+3qFiLQa4yv5b/jYuQLuQgcgfWit35ZxxlQ0DRvUCgYLij+3qhXl9dTfCy/9NPgrUMfRexZ/ZTmipvLEGofhbCAAvUA+rS0YAkHjvdWAMzb1KEObNI9YdQHnevZqYX5Ag9OUZKzHuNWDSEZ9u/KJHBGHqiFkWdcsKauRRQQ3CDCHjnolTGZ57iimq9PFeYllWbSictGJpYx0NLKqqlbZRET7o66bdMGFlwYNQhZdPdUDvCFWLPs53p+b2lP2bYaq3Wq6rnbDC1at1HX9W7xaVNg5nTLlB3+3QABNVGPtbunG9FRmGUZs3T/G36aNAD45+CS35OnlIVkelUVNlZ+lbOFoaACumFFus1qI187i6p0QQVc5jVrLw15SReO6RcY8gyo17bkwGTt2latJE7UMSlksFNQjzhYx7pooOD5qGh+Vqb6juhVmd554u6NLOqa6ysaOpk1zuPBN7/u7azKG6p0BowNPBBts/7iT7rpgvUpthyBTf93QO5ZU7Nhsr3UJQJwc3NWRKKunWzFFp3J9C/eHvWV/Je1jdi7XiQgLLKv8E6PJzpEtuvtoDg3nFkxAjboZ6zGvstVqK9/mnULMzUBIElwfiBgAAb94TAEBRKeWgJIgB3STPSw2Z6ql4ThDVjbzn3qCOzap5NgShP2TcM3E0vZQaWm22ogYLo70C6jiPOSHesLHi45OexjM8qGJcNy/UsdLP+4/PU1ahjp71ANTsSqzmwp4JgWgpF5LKMAz8y/4+IztXrGprRTHU4MTXsTqsYu/qCudIOTa9G970VR2Obwiqfmf6tNI8B0UUdYgFZPnzFg1QzhWqDvLc45IHIT4qWoD/SlujDlOI1ZabcchqMd7iXYUFal6FVMJwXr16hdDQUIhEIohEIoSGhiIzM1NjG5ZlERYWBnd3d1hbW6Nnz564ffu2Wtl+/fqBYRgcPnzY+AeghttsYwBASyYRTFlxmdzCEsQ+ydTJG5ggaiItW0nur/WY17Aqzq7m2RBE1cOWFkPAFAMAPu3XvppnQxD6Q8Y9M8ZY1XJVPceqe7ZlWe3eTo3q6xaWK0m4r/0h2suxDm6G9cFnfX0q1XHvi4EtYWdd8TxcznaUo8FUcLe3VrJy7Z4QgBOzg9HXz01rzr3KxNB8frqGjykemrawdTuhpVLIflXAQP1CheJfh2WBLk0d8WB5P4zvrnt+T8vqODATJxfWGFP8GVYWf4B81grteQ/xi9U3uCiYiu8sv0coPxKBvDtwwUuQV1/tZeTIkYiNjUVERAQiIiIQGxuLSZMmaWyzevVqrFu3Dhs2bMDly5fh6uqK3r17IycnR0l2/fr1Bi9UVoRHrDsKWEvYMvnwZJ4DAN7bFIXBP5zHgatPq3w+BGEK2NqJkMbaAwDs8pKqdzIEUQ0wRXK5Jq1qV1FComZQs8sF1nD0zREjNSYoGhVUeRDZ26g2culijFs9rA2WHr2DMV28FMbhomr+Hcq8qhTR1Vupoug7SmWt8FNRDuMh/xfiMYDAgo9mzvrfsFcPbYN5B2/oPq6WS0OV+rrYCfA8u1BjO11VQfHa9HKoo0ZSfk7Gu+70uYbVSSoeg/S7JZ8nt017/wFNHNClqQMuPMrQeU61ATF4+Kn0bfxR2h3jLY5hCP8cnJgsDOJHYRA/SiZXzPKRiTrIZG2RiTooYK1QBEsUwULyf9YCxeADYCAGA1b2gezfKPu34n7CdIl/UYBVERGIjo5GQICk+M/mzZsRFBSktg3Lsli/fj0WLlyIIUOGAAB27NgBFxcX7N69m2MYvH79OtatW4fLly/Dza1qi1+Vgo841hPtmYfwYxKQwLrhTorEU+nA1ad4r2P1enYTRHVgZ22JONYVzkwmnjy6BeDt6p4SQVQpTNFrAEARLGBlYVXNsyEI/SHjnhljcM49DZ57Xw9pjZN30zC2G9cwx5HX0r+byBobP/RXbqfwFq7KYDdBD28cgtAFW7k8e4rea8Wlmq9m+Uv2/U6NOMY9gQUP8/r6YOnROwbNS5X6yhut1DfUTfEVQ1En9GiC5zkFeJlbhPCbqUryTrYCvMwt0qlvdcgPqc/vk7ysJZ9BcSkLH1dblZ57hsDnMdg9IRCN5x8zrIMazgvYY2XJh1hdMgKdePfQmbmL9rwH8GJS0ZB5AUumFE7IhhNDYVq1iW9T+BCJRDLDHgAEBgZCJBIhKytLZZv4+HikpqYiJCREtk0gECA4OBgXLlyQGffy8vLwwQcfYMOGDXB11S1cv7CwEIWF5Ysf2dmGX4+f9/fB7UhPtOc9hB8vAUfF5QZLMjoTtRVbgQUSxK4I4N2FfT557hG1D6ZYYtzLgzXItEeYI2TcM2NUvTs3drBBQkae7PuNsBC0CYvU2k7KkA4NMaKzh9r9rOw/FYdhuF252glhZVH5keIN7K2RnJmvZk6V7zHXtZkDcgtLMTJA+TyTv57xsbGywOGpXcFjlI17+UWlBvXp42qLvRMDYW9jZbBxz1D4Bl4kQks+lg1uDQAcI9eeCYFo5lwXQkt+tVSMZBjudT83xBtu9tbo1swR8em5HNmKFsZ4y9cFJ+KeV6gPc6dT43q4nPBK5b5S8BEtbolotATKVMMCJXBCFuyZ17BnXkOEXAhQBCumBAIUwwolsEIxLFAKBix4YMEw8r56ZdvK/PfK/08FDEydyJx7cHZWDqV1dHRUa9xLTZUsHLi4uHC2u7i4IDExUfZ91qxZ6NKlC9555x2d57Ny5Up89dVXOstrgscwuMVKFjGVKkazgFjMgkfh/EQtg2EYBHbsBMSehkMhhacTtZCysNw8CGFfvTMhCIMg456JI++povRSJvfc+XHXxniZW4T/vd8Oc/dfxx8xyQAkebRk4mXyii/wAkuekoz6CRlvVbs6DAnGxt+zHs4+UF1VTN3hOdUVYNd4bpLWAW3ccOxGCj4K9AQAiIyQ+48op10je5Xb84sNM+61dLODvY1kTU9gwUNhibKhYsk7rTBm22W1fagyJIe93Qrjf72ChvWs8fSVagO0oQU11OFQ1wpOtpIq1ULLihnXDQ1Tlz8X77RrAFeRJG/l4xevOXLaCmM0cayDxwoGQe44Bk2vxuDlWAedveqrNe6pogQWSIEDUlgHcmmqIWSe24Ws83s0yriO+h8KxJZwZqKV9umi54q/byzLyrYdOXIEJ0+eRExMjB6zBhYsWIDZs2fLvmdnZ6NRI8PCZ8Usi6viFgCA9ryHsEAJSsoeiS8lvESHZcdxeEpXNHbUns6AqD7kryvCOAhcmwMA3MXPqnkmBFH1MMUSB5l8UP50wjyhghomiqpHlf2Tu6BRfevyDXLP19PfaI5vR7RXudI8obtkdXp+Px9J3woibiJrfPpGM8zr6601LHBkoIfBL/CKrfgGPJDp+xBXWc98Qzs0hKeDDSb1aIoTs4PhpeIFQJ/TtOGD9ri7tK/sRaKlux2mv9EMK95tbawp11gqkvbQoU7Fne7VXWM9vZ0xp3cLvfp6q6ULboaF4LO+PmplhnZoqFNfuuppc7n8g82c6yp5lEqv7S5NHXTqT4qhYblSwx6guqCGJjaP7qh5HN2nVCOxNNTtk6hR2HYYCPfxP2r8WDl5YkCAD54/V/Z0zchQn7tSGmIr9eCTkpaWJvPmO3nyJB49egR7e3tYWFjAwkJiVBs6dCh69uyptm+BQAA7OzvOx1BKxcBD1h0v2bqwYQrRmonn7M/MK8bXf981uH+i8olPz0XnFf9i83+PtQsTOmPp3AwA4MGmQFxKHtZE7YIpliysFzCCap4JQRgGGfdMHMV32T8+6apSTv7l+O227gAAj7KqtQsHtMSdJX3QpaljmSyDhvWsOe3nhHhjSs9mGueyc1wA5oZ4G815o6KGt29HtDPKPPTl/Y4N8c37bXF6bk9YW0mKM6grQKIKVeePYRilkNHZId4qQ3cJ4/Fu+wYY29ULXZvpZ7jSVQc6e9XXe062Qs3X0pAODXFoShet/ehi2+vf2pVjMGcYBivebS377QCAw1O7Yvf4AHygIVxfX+RVn2EY9dVyFY5BW1iutorAtR0ew1CxHgJ8GxEsHRpp/DAWVvhoUG9kZWXh0qVLsrYXL15UG5ILAF5eXnB1dcXx48dl24qKinDmzBl06SL53Zo/fz5u3LiB2NhY2QcA/ve//2Hbtm2Vc9AKiFkWLHi4JPYFAATw4pRkSquxmjqhnWVH7+BFTiGWhyv/7QjDqePqjVKWgT2Ti/xM8t6rbbx69QqhoaEQiUQQiUQIDQ1FZmamxjYsyyIsLAzu7u6wtrZGz549cfv2bY7Mzz//jAEDBgAARCKRUp+nT5+WPQ8qfi5fLo+CUbV/06ZNRjl2AECZ514ByLhHmCdk3DNxfFxtOd+dbAX4YWQHbBvTSa0bSk9vJxyb3g3hM7rLttlYcSOwB5UZAPWhW3NHWPJ5FfKUkodhGE5fo7s01qv9O+0aGGcievBJz6ZYPawtAK4Xoao/RXsPe5V9yHsmERWnIkZiCz4PX77dEm/6uKjcL1+MQ+34GowlAU0c8Mso1d5khs6bxwDtPVRXldYXXXRZZG2JLs0cDa7OrZusapS8D7VVINY2Ti23a0kehKt7FoS54OPji759+2LChAmIjo5GdHQ0JkyYgL59+yrI+eDQoUMAJNfYzJkzsWLFChw6dAi3bt3CmDFjYGNjg5EjRwKQePf5+flxPgDg4eEBLy/1xbyMiTQVwSWxxEs6UIVxT6wtD4AJ8jDtNR4ppDOoqZSY4d/HHBBY10E8K6leHX3hv2qeDVHVjBw5ErGxsYiIiEBERARiY2MRGhqqsc3q1auxbt06bNiwAZcvX4arqyt69+6NnJzynK15eXl488031fbRpUsXpKSkcD7jx49H48aN0bEj9zl627ZtHLnRo0dX7KDluJMk8VYvoHIahJlidsa9jRs3wsvLC0KhEP7+/jh79qxG+TNnzsDf3x9CoRBNmjRRad3PzMzE1KlT4ebmBqFQCF9fX4SHh1doXGMxuktj/F8fb/w1rZts24A2bujl46z2RZdhGLRyF6GuoHJSKhqa1F6bIWFSj8qtlDuwjeRhZbaGUEljvvh2aeqIrWM64vTcngCAbWM6YUj7Bvj0jebGG4QwirFZ3d892NtJ5XaBHoVf3mqp2nCoOKaNFV/tPkOoaPEJRSrTKKSub8UjCGml+lxq64eQoEsxZoKQwucx2LVrF1q3bo2QkBCEhISgTZs2+Omnnzhy9+7d43jzzZs3DzNnzsSUKVPQsWNHJCcnIzIyEra2topDVBtD2ksWB8+LWwEAAnl3IEQhR0bMspi5NwZTdl01OB1JVVJQXIq31p3Bm9+cwdz917EqomaHFZv+X8Q8YRgGd1mJp/7FaDLu1Sbi4uIQERGBX375BUFBQQgKCsLmzZtx9OhR3Lt3T2UblmWxfv16LFy4EEOGDIGfnx927NiBvLw87N69WyY3c+ZMTs5URaysrODq6ir7ODg44MiRIxg7dqxSdIe9vT1H1traWk2v+nMu7gkAIKuEcp8T5olZPerv27cPM2fOxMKFCxETE4Pu3bujX79+SEpSXa49Pj4e/fv3R/fu3RETE4PPP/8c06dPx8GDB2UyRUVF6N27NxISEnDgwAHcu3cPmzdvRoMG5V5h+o5rTCz5PEzt1QytG4o0ylVWqNWqoZKcbz9+2EG2zU5L2KCh6FKZriIv718PbYOoBW9gqL9yzjLFMGW146udl+o9b/i4yPLo9fJxxrrh7SrN6EoYn17ezvgp1B8n5wQDkBTJ8HG1xewQ/XLpqUKqs3XKjHrqin4otdNRCczJqUHd75f8+/SByUHo08pV5z5butnhy4EtdRqntsBnavsZIPTBgs+gfv362LlzJ7Kzs5GdnY2dO3fC3t6eI8eyLMaMGSP7zjAMwsLCkJKSgoKCApw5c0bmnacOlmUxePBg4x+EGiz4PDxY3g+5ohZ4InaCkClGd95NjszrwhIcjn2G8JupSM0uqLK5GUpOQYns3weuPsWPpx+ZhVHSUGrysVU3cWKJcc+H9wR7L1X+uw5hGkRFRUEkEiEgIEC2LTAwECKRCBcuXFDZJj4+HqmpqQgJCZFtEwgECA4OVttGF44cOYL09HTOvUXKtGnT4OjoiE6dOmHTpk0Qi9XnhiwsLJTdv6QfTdhA8lufT2G5hJliVsa9devWYdy4cRg/fjx8fX2xfv16NGrUCD/++KNK+U2bNsHDwwPr16+Hr68vxo8fj7Fjx2Lt2rUyma1bt+Lly5c4fPgwunbtCk9PT3Tr1g1t27Y1eNyqgvNYo+cbm65GsuGdPHB/WT/0a+0m2/btiHZo5W6Hn0P99RtUBfo+mlXkxZSBpHiIPH4N7LBnQiCOfSoJYTbUeEgvzNVHZXtr9WnliiZOkqITo4IaI2JmDzjbVjy0WjrvI592w/huXlivYw5JXQ+3Ot57DB1S3d9QvjJwx8b1tRo25U1X2z7uhLHdqibEz1xgGIbcGwmd0bUyt7liyefBx02E42LJs0xv3lXO/qLS8l80U7EjFRSX4o9rT5HxulBpn6q/l7Z5v8wtQkklFU14lVuEeQeu41L8y0rp31ikqziXtZ27rKQKtS+ThPl/3NQiTdQUUlNT4ezsrLTd2dlZqUiSfBsAsoJJUlxcXNS20YUtW7agT58+ShXRly5div379+PEiRMYMWIE5syZgxUrVqjtZ+XKlbL8gSKRSGuFdWsUAQDyWQrLJcwTszHuFRUV4erVq5yVAQAICQlRuzIQFRWlJN+nTx9cuXIFxcXFACQrA0FBQZg6dSpcXFzg5+eHFStWoLS01OBxAf1XCgxB/qGtMt/XrBRCEJs52+LY9O4I0cOLRoKKp8wqfGJWdY4YMAhq6gCRjgUx1J1nel+uPkzlpctQmjrVxaKBLQ02GI5Rk6vS6GG5Ru1Nt779PeshsEl9fNBZ88OYzuPUcj3l88hzj9AdC57ZPCIajJhlZca9EP4VCMpe7ACguESz0SspIw/fnniAzLwijXLG5Ou/72L279cxcvNFpX2qbLHqioKwLIuHaa/RYelxDNsUZexpAgBWhMfh9ytP8f5PuvWfW1iCX6MSkJKVr5O8Me793/37AB2XncDO6ESFvs38waKCzA0dAgBoyiTDCsXVPBuiooSFhaktViH9XLlyBYDq6BCWZbUvrirs16WNOp4+fYp//vkH48aNU9q3aNEiBAUFoV27dpgzZw6WLFmCNWvWqO1rwYIFyMrKkn2ePHmicWxrpsy4R557hJliNk9u6enpKC0t1WtlIDU1VaV8SUkJ0tPTAQCPHz/GgQMHUFpaivDwcCxatAjffPMNli9fbvC4gP4rBbUBRa85U6AmvOxXpLKVt7c3AGDAgAGcylYvX77Ep59+Cm9vb9jY2MDDwwPTp09XWSXx2LFjCAgIgLW1NRwdHTFkyBCjHl9VUCGPUBWNvcpCsStjTMXx1HnX6PJuos/7iy664uVQB+0a2aN7c0dY8tU3UOxLk+fe3olBWDmkjd5zrAGqbXT0ccRq4qT9Gq5thE/vrl3IhJj+RrMKta/pnnuAxLh3UeyLZ2x92DO5HO+9IjmPNlU/le/8cA7/O3Ef7ZYcN7iIxbbz8fj2xAOd5cNvpgAA7j0vT1R/KzkLb607gxNxaUry4rIf+ay8Yvx3/wVKxSx2XUxEwIp/8fXfkiIisU8y9ZqzWMzi7IMX+DM2GXeeqV+4PnWvfD66GMuWHr2DL/+8jaCVJ7H5v8da5Y2xgLXu+H0AwKLDt2TbCopL0Xf9WUzbfa3C/ZsrzZv7IoO1hRVTilZMQnVPh6gg06ZNQ1xcnMaPn58fXF1d8fz5c6X2L168UHoPluLqKnH0UHwnTktLU9tGG9u2bYODgwMGDRqkVTYwMBDZ2dkq5w1IQoTt7Ow4H01Ic6/mU0ENwkwxG+OeFH1XBlTJy28Xi8VwdnbGzz//DH9/f4wYMQILFy5UCrnVd1x9VwoMwdieOZVNHYEFFr/dUrtgJaHKZ0XxeVObX4u6/dXpD1ORylbS1S5nZ2dOZatnz57h2bNnWLt2LW7evInt27cjIiJCaRXt4MGDCA0Nxccff4zr16/j/PnzsoqI5oT8ZfDr2M4V6uvE7B4VNgKIrHXPa2lKr988HoNDU7rg17GdZb+PoYGeWttVlf7UBGO+PuxQuJb1qZarzzVYW9DFw9uvgeYXh6pk+psVK95kUQuMe6ViFmLwcKC0BwBgOP+UbF+RnOeeqsq5r/LKPZre/OYM7qbqF6HBsiy++usO/nfiPmbti8WwHy+goLhUYxtVf5PJO6/iYdprzN1/XcUYkv8P3XQBo7Zewuazj7Hw0C2k5RSqNAbK82dsMvaU5VsTi1lceJSO7IJi/HDqIUK3XMKMvbHo/91Z5BSo9uxKf13u0XhRh9Dck3fL57M8PE6rQVA+zVapEZPMnoh7jnvPc3D0RorR+jQ3LCz4uCaW/H504OlufCZME0dHR/j4+Gj8CIVCBAUFISsrC5cuXZK1vXjxIrKystClSxeVfXt5ecHV1RXHjx+XbSsqKsKZM2fUttEEy7LYtm0bRo0aBUtL7ffcmJgYCIVCpVywhiINyy1gyXOPME/Mxrjn6OgIPp+v18qAq6urSnkLCws4ODgAANzc3NCiRQvw+eWVKn19fZGamoqioiKDxgX0XymoKPo+gleXMcrJtmI/lvq6eFfZcVbTO1BFK1tJV8U2bdrEqWzl5+eHgwcP4u2330bTpk3xxhtvYPny5fjrr79QUiJJ2l1SUoIZM2ZgzZo1mDx5Mlq0aAFvb28MGzasag6+kmjmXLeC7W1hLVf5Vh2aruVuzRwxvKNqb1/FdlVnsNJtIGmIhxQ7a+0FZIx1DIzaL9JNNd9YIU9wCyd8/0F72Xe+HifanAqyVBX6nD9TgFfB+dYGz738Iokx7ffSnihlGXTn30LLMk8leeNeiQ4K0Xf9WRSWlCI1qwC5hSVa5eX7PBSTjCuJr7QalPgqPKLzitQbBKWeew/TJJ6FX/+tWwXdUjGLGXtjseCPm3ieXYDdl5IwcvNFvL8pCt+UebtJaR0WqbW/V7nqQ5cLS0oR9SiD4ykpnYMm5Be4d11MVNr/PLsAZx+8UGskLFIIu07NKkBJqZhjtOWMV8tCda+JJYXDOvDuazU6EzUDX19f9O3bFxMmTEB0dDSio6MxYcIEDBw4UBbpAwA+Pj44dOgQAMkz38yZM7FixQocOnQIt27dwpgxY2BjY8NZ7E9NTcWNGzdk32/evInY2Fi8fMk1/J88eRLx8fEqQ3L/+usvbN68Gbdu3cKjR4/wyy+/YOHChZg4cSIEAuMY46wZ8twjzBuzMe5ZWVnB39+fszIAAMePH1e7MhAUFKQkHxkZiY4dO8pWA7p27YqHDx9yKu3cv38fbm5usLKyMmhcY2Bm7xA6o/isZmqPSuZ23quyslVWVhbs7OxgYSEx1ly7dg3Jycng8Xho37493Nzc0K9fP054ryqqIh9lRXC3t8a2jzvhjym66fdPBhaW0XSpMQyDVcPa4C1f5cTGqmRV8WGAB9xEQoztapyiEpp0w1ZDBWhdDGqG5mWRknPtGJ5uGgcPZ3ukbJ+Bgie3NMoXJN2Ev78/hEIhmjRpgk2bNinJbNy4EYAkBUOjRo0wa9YsFBQUKMl4eXlBKBTC398fZ8+e5eyXhr+7u7vD2toaPXv21KoflYlHfRudDZyNdKwgbkz6t9Y3j2vVoou3fKkYGBWk3Vu1KlClVr9PCtK5fW3w3HtdZoR7yjrjqFhybqZaHAYAToXcywkv8dVft1FUIsbk366iTdg/Kvvr+vVJBK78F52Xn9A4bmZeEbadj1faXlIqVmtEKi4Vo7ikfN+Fh+l4+/tzeKnBcGaokb5YztD2IqdQFrZ6NzVHXRONqMv9BwCf/3ELH2yORqaCUU2VQVX+3Mh3Ke/1J6XbqpMI3XKJ46GYnJmPvuv/w77LSdiqcP4DV/6LZgv/xp8xyUp9pWYVIGjlSaw/cV9pn+J9QNNzVEpKCkaOHAlvb2/weDzMnDlTpdzBgwfRsmVLCAQCtGzZUmZIqUpELSTPQP68Bzh3/0WVj09UD7t27ULr1q0REhKCkJAQtGnTBr/99htH5t69e5w0PfPmzcPMmTMxZcoUdOzYEcnJyYiMjIStra1MZtOmTejevTyqpUePHmjfvj2OHDnC6XvLli3o0qULfH19leZmaWmJjRs3IigoCG3atMG3336LJUuW4JtvvjHW4ZcX1KCce4SZYjbGPQCYPXs2fvnlF2zduhVxcXGYNWsWkpKSMHnyZACSUNhRo0bJ5CdPnozExETMnj0bcXFx2Lp1K7Zs2YK5c+fKZD755BNkZGRgxowZuH//Po4dO4YVK1Zg6tSpOo9bXbjaCRHYpD56tHBCXQ0v16aESwU99yqCMQx3agtqVLxrg6iqylYZGRlYunQpJk2aJNv2+LEkJ05YWBgWLVqEo0ePol69eggODlZaiZPHFPNRKv79enk7o4NHPZ3adm/uhN/GGRDKq8NFo4ujgLpr0t7GChfmv4EvDQiF79HCEQDgWFe3lctfRnfUe34cGZ1GUU1u3H94+e9miILex4lz0RA0bIW0/WFIVpEKIaSVC4ozU/HiQBi6d++OmJgYfP7555g+fToOHjwok9u1axfCwsIAAJcuXcKWLVuwb98+LFiwQCazb98+zJw5EwsXLkRMTAy6d++Ofv36ISkpSSYjDX/fsGEDLl++DFdXV074e1WzoL+P2r/HwU+4Rp+wQa1weGrXKphVObo6xiwd7Fe5EwHQvbkjNo/iXte6GEriUrKx5B0/fPNe20qame6oMpp39qqvc/va4LnXqXH5+fih5B0AwAD+JXRiuB5u8w7cwLbzCWix6G9E3E5FdoFqzzxpKGquGm+614UlmP17LNotOY4V4cpedCvC4+C1IByXE8rvoY9fvMaYbZfQfOHfHIPjyF8u4maych5ceXZcSMDtZ5plVCFvWBv4/Tmt8s8yuUUw/ozlGshUeeEduPoUZ+6/wMFrT1X2WSpmIRazuP4kE4UlpVjwx014LQjHG9+cxuWElxxT++l75can14UlYFkWxWXVjuVz/y356zbupubgs4M3EZP0SuW4VxLLt0sr/U7ZdRWp2QVYr5AfUdV9QFP0QmFhIZycnLBw4UK0bav6NyIqKgrDhw9HaGgorl+/jtDQULz//vu4eFG5iEplkmnfGsUsH67MK3y1M7xKxyaqj/r162Pnzp2yxfedO3cqhbyyLIsxY8bIvjMMg7CwMKSkpKCgoABnzpyBnx/3Ph0WFiYzCGZlZYFlWaV+AGD37t04f/68yrn17dsXMTExyMnJQW5uLm7evIkZM2bInA6MgSznHlXLJcwUszLuDR8+HOvXr8eSJUvQrl07/PfffwgPD4enp2SVPCUlhfNi5eXlhfDwcJw+fRrt2rXD0qVL8d1332Ho0KEymUaNGiEyMhKXL19GmzZtMH36dMyYMQPz58/XeVxj0rwsJHBQW3etsgzDYM+EQE5+K1Ons1d9LOjngy1lxgDpcTatpuTtip4YK99tDQCYG9KiOqYjQ76yVeKqgbJPdVS2ys7OxoABA9CyZUssXrxYtl3q7bpw4UIMHToU/v7+2LZtGxiGwf79+9WOWxX5KKsaXV+CA5uUv0jq0kL+6lz3fluVXoKaPLEM/V34vL8vlr7TCkemddNJvk1De/Vz0KF9RX6+CmOOoG6b3nDu2B/e3r6o/9ZE8G0dsX3Lz0qyg9q6o6f4Opp6Ncb69evh6+uL8ePHY+zYsVi7dq1MLioqSuYN6+npiZCQEHzwwQcynQOAdevWYdy4cRg/fjx8fX2xfv16NGrUSJavVT78fciQIfDz88OOHTs44e9Vjb2NldLfw9lWgBGdGsHfsz7Hy9OxrgDtGtljfDfjeH7qglhH615ooCfeaaf9HlkRfhsXgN4tuQsg9W24D/s7xwVAHZVVaLaiqS30wVyeKyrCZ/18sKCfDwDgPtsIu0t6AQCWW24xepXQpIw8TN8Tgz+uKXuGSZEaDd+Tq2A78berHOOVPqz55x5ikjK1yrEsi6m7ruGLw7dw/3kOLuuQI0+eLl+flIX+AsCMvbGc/TP2xiI+PRfPMvPx++Un+DfuOebuv47RWy9BHYUlYjT5PBzv/HAe0/fEyPL/PX6Ri/c2RckMb/JcTXwJv8X/YN6B8vC/xy9eo+OyE2g8/xj+uV2edF/+3+p4/6corAiPwzU151DVfaBBgwZq+2vcuDG+/fZbjBo1CiKRSKXM+vXr0bt3byxYsAA+Pj5YsGAB3nzzTaxfv17rfI1JPqxwjZXk3Qvm3dAiTRA1A2m13ALy3CPMFPNw95JjypQpmDJlisp927dvV9oWHByMa9c0V7wKCgpCdHS0weMakwOfdMHNp1kIauqgk7y5PXwzDINJwU1l3z/u6oUWLrZoq8E4wGlv5PmIuSlX0KWZI+4t6wuBhfacafL09HbGxfiXRvOgnDZtGkaMGAEASH6Vh/1Xn2J4x0Zwsy8PlWvcuDFu3LhRocpWTZuW/y1U5ZHMyclB3759UbduXRw6dIiT3NbNzQ0A0LJluWeYQCBAkyZNOEZ2RQQCgdFyYxiLiuYX0zXUUV5OF93t0dwRJ++mwcaKjyEdGqruswJKoS7M0MbKAqFBjbnjGDqIigkqni9Dj6GoqAh5zx6g73vj8Y2cl5m1V3tcvqj8m84wDJ7cjUW/vn042/v06YMtW7aguLgYlpaW6NatG3bu3Cnb//jxY4SHh2P06NGyca9evcpZBAKAkJAQWUiWtvB3eS/YyqR3Sxd4OtjIPFEt+FyrU/SCN8ErM06rMq5NCm6KX84phw+qo1szR5x7mG7QXEd09tDphRvghg1WFVYWPPRv7YrwmxIPZ0db5ZV9VYb+7z9oDwseg092Vbz65rLBfpj021Xtgiq49VUf7UK1jLoCC0wKboqVZbnoVpV8gBD+VbTgJSPMYjs+L5lgcN/SBbOC4lL8ePoRvv1Xv8IEv19+gvc6NuQYzQwhM0992K6Uh2mvcaysEu9v0cr563ThrXVnMK1XM8zt461y/1d/3cbVxFfIUeP1qMj7P5UbOHX5XXjyMg+r/pbkGt5/tdwbMPqxfoZKRX5WqNwrDQ0uLi5WeR944403cP++cviurkRFRWHWrFmcbX369Kly416j+jY4U9oWAby7CObdwIPnOWjsWAeWfLPyCyEIvbAp89zLI+MeYaaYnXGvpiOytkS35o7VPY0qg89j0KOFU7WNr+plVl/DHgCM7+4FN5EQAU10D3nShKOjIxwdJdeBD4A3gzqolJOvbNW5syQ0VJ/KVlLjnrSy1apVq2Ry2dnZ6NOnDwQCAY4cOQKhUMjpx9/fHwKBAPfu3UO3bhIPr+LiYiQkJFSKV6t6Kp65UapzLd1Mp9IlAIQGNYaTrRD+nupDhKsqck5zVXIN7VRsa6iQz83QxP/p6ekoLS3F50OD4O1qi/TXkocyfp16SHumOnF8amqqypD0kpISpKenw83NDSNGjMCTJ08wb948ODg4oKSkBJ988onsJU46rqbQdk3h74mJql+eCwsLUVhYKPtujHyUQks+Ts/tKfv7fRjogVUR5eeGJ3cBqfo9rMrITDeREDPebK6TEaRILvdYE8c6eJyeW5lTAyC5X8lXES4pVT5f0vBDeQP22zp44utKew97g9r9MLKD2aTvqE6yUBdzij/BNsvVGGlxCimsA74vHWJQX4UlYggt+dh4+hG+09OwBwDzDt7AvIMV95haG6nd0PRahwIgurDh1EO1xr1SMauzYQ+A3kbN93+KQkpWgXbBCjJsUxTELIvvBzVWeR9wcqrYM626e5S6tClA5dw7QoM8EXajB/BiH7rwbqHd/07izVYNscnAPMMEYQ5QWC5h7tDySy3FzBz+Kg1t1dgUUXfaLPk8DG7fAG6iqk1CX9HKVn/99RcASe5J+cpWOTk5CAkJQW5uLrZs2YLs7GykpqYiNTUVpaWSXEJ2dnaYPHkyFi9ejMjISNy7dw+ffPIJAOC9996rytNQYVq42OLcZ71w8BPDiuQYok+6NOHzGAxo4wZXkVCtjDlWgK1XxwoRM7vjzP/1NEp/SoZHLWHpqkLS5befPn1aFqb733//4Y8//sDRo0exdOlSrf0oVzPWLfwdqLx8lJzqxUJLWKqouAkAA9tIjFAe9W1k2ypacVUebeHrDBjUEei2uCLvubd3YiBn36QeTVS2+TnUH1YW5Y89dsKKGbt0DSM2Js62Qp2L/chjIfc3PzJNey7FcVUYjm1qnBG3xfISyb1wjuUBLLPYAgG0e78pIq0wejWxYp5jVYGmirvGorL1pSoMewBwNfEVYpIykZkvuSbU3U8qgj73DaBy7h0CCz4mvv8O0lh71GEK0ZV3ExG31RsYCaImIA3LpYIahLlCxj3CrOjrJwkpbVaWm7CiVMfLmbGpSGWrOXPmAJDkq5SvbHX16lVcvHgRN2/eRLNmzeDm5ib7yOfIW7NmDUaMGIHQ0FB06tQJiYmJOHnyJOrV060YhXHgPvC+5StZ8da3YmXDejawttLfa1N5BurRpdqmvlTEs0qfy9/QYdS9j/i42sHTQZJr09AwH0dHR/D5fCWPhtK8TDipKDQDSMLSFeXT0tJgYWEBBwdJOoQvvvgCw4cPBwC0atUK7777LlasWIGVK1dCLBarHVc+tF0+/F2djCJVlY9SnUG4s1d9nJgdjIiZ5RXtjGnca+ak+XebYQC+jsnqpF7SPAZwtlMwfquZci8fZ7jKycob+nSnvHNV1TxlUpVoc1cs9vOmj/aq2vKoy5Hp6VBu1K3tXn5bSgdgWfGHAICPLP5FuNUC9OFdAgPdw8GP3khBx2XHcf5hRmVN02gY07gXsEJ1pWBzOA/6UK++6vtAerphaQmkqLtHqbtvAJV377C04ONYqSS36Dt89VWACaKmYF3muVcA8twjzBMy7hFmxaIBLbFmWBslTw1D0du2Z4IujxWpbCXNCxMeHs6pbNWzZ09ZJSvFT+PGjWVylpaWWLt2LZ4/f47s7GwcP34crVq1qszDVQH3j/j9B+2xY2xnLBqgf5XYqsRol1IVXZMaQ281huVqn9+bvs5o01CE0XoaZK2srODv74/jx4+XjSWhICEWnQJU/0YEBQXJ5KVERkaiY8eOspySeXl54CkYmfh8vkwHFMeVcvz4cVk4vHz4uxRp+Lu6kHmBQAA7OzvOpzJY+76kSqO0kIA8zZzrwsZKzrCj5c9Xz8ZSs0AZs97SXqSIAdR6FSoyvlsTrBzSGmf+r5dO8oDEUMk1sFdMd8QVTdZZAeQNk4Pbq0/gr42OnvUwpktjNKxnzSmgY4K3uirnl9IBGFX0GV6wIjTlpeAnq/X4z2oW5lj8jgAmTmvBjUWHb8mq55o6nxkh/FfK8+xC7UI1AJbHV3kfOHXqVIX6VXePUnffACrv3mHF5+FIqWTcEN4VWcgiQdREnr7Kg3WZl3Y+S557hHlSu5dmazHm+txubcXHex2NE6oGAKU1wHOP4GJtxUdwFedxNKSwjbHCaU1dl3U5NQILvs6VeRWZPXs2QkND0bFjR/i28cfLfzejJPsFxoyTJMJfsGABkpOT8euvvwIAJk+ejA0bNmD27NmYMGECoqKisGXLFuzZs0fW59tvv41169YBABISEvD8+XN88cUXGDRoEPh8vtK4QUFB+Pnnn5GUlITJkyeXHXd5+Hvz5s3RvHlzrFixghP+Xl0MauuON3ycdfLOkvcMbVjPGk9f5XP2rx7WFhN+vQJt6OqcaaGj556VBQ8fdPaQfZfPu6dOt3iMAQs6Cgxq6449l5Lg5ViH4+k7pH0D/BGTzJmTvrzh44yTd9N0kuXLKVa+Dl5X6o6bYYCwQa2w+O2WnN8xH1fTyj9aXfwnbos3Cr/BBIujGMOPRCPeC3zKO4xPLQ6jhOUhkXXBI9Ydz9l6SGdFSIcIOawN8mGFfAhQwFqiAFYoBR+l4EEMBiyYsn+XfVgGYjAQ67HebpyEInLkAo563E3oyQkoKixUeR94+rS8mIfi/QcAYmNjAQCvX7/GixcvEBsbCysrK1lxshkzZqBHjx5YtWoV3nnnHfz55584ceIEzp07V6XHB0i8omPYZkgQu6Ax7zne4V9AbuEg1Knlnr1EzeT03VR8xEgWbfLJc48wU+jXmajRaDMs6BuWa+qGFKJ6qE4vl6oaW9M4mtSosqc3fPhwZGRkYMmSJUhJSQHqNYLze2Fo2EjiBZiSksKp3uzl5YXw8HDMmjULP/zwA9zd3fHdd99h6NChMplFixahqKgIK1euREBAAJycnPD2229j+fLlasf18/NDeHg4p5jMvHnzkJ+fjylTpuDVq1cICAjghL9XJ7qGXcqH5daxUm6j69+Xx2PQuqEI957nqJVhGIkXJw7p2KkcPVo4yYx76sLfGYbhXKuG6E5QUwecmB2MBvbWEFryMLFHE7iJhPigsweG+jdEx8bqUxKMDPDA7otJaOpUB49eKBcAsdIjPN1Czurqbq+c63WomuraikgNoVLDXvj07riVnIU+rdSHANZkgls44cz9F5xtObDBupL3sbHkHfThXcYb/Fh04d2GE5OFpkwKmiKlmmZLVCfxacdU3gf279+P/v37A1C+/wBA+/btZf++evUqdu/eDU9PTyQkJAAAunTpgr1792LRokX44osv0LRpU+zbtw8BAQFVdmxcGPxW+ha+4O3Cx/wIfHbgA2z4kIpqEDWPJqLyBTvKuUeYK2TcI2o1Yt3T5xCEUTGWUc6QnGjvtHPHn7HPMLln00qfg4dcHq/KYsqUKZgyZQoyXhfCfxk339P27duV5IODg3Ht2jW1/VlYWGD+/PlYuXIlnj9/rjbESTquOqTh72FhYTodhyki/7ctUfGDqevlx2cYfDGwJXIKivHP7edq5VzshLi+OARtv4oEAHz6RjMM6dAQvdae1th/RZPY169jhZe5knCcab2aaZSVz/n6eX9f2b+7NtNc6X75YD9Mf6M5DsUkcyoWS3mrpYvOCet7+jjjr+vPAAAOda3gWNdKFgK6/eNOCGziwJH3dlVjUFb4+7V0t0NL99rrtaep6EsBBPhT3A1/irsBYOGCV2jOS4YXkwInJhOOyIIjk406KIA1UwghiiFEIayZIvDLfPP4EIMHtuwjlvu/WGdDOaOH35yusvrcRXgM+e0BQHFZtWzF+4B8pVpV9x9dfquGDRuGYcOGVXySRmJ/aU/MtjgAH94T5N0Ox9NXvmhYr/Lv7QRRlbR1LffWK4RuKUcIwtQg4x5Ro7HQkr9J32q5BKEKfTxupBgSyquyHwParB/eDksH+8FOWPGHl70TAzUWJni7jTsSM/Lg71mVRVYIYyF/mcr/XM58qzmaOdfV2bjHYxiIrC3xWV8fmXFv1lst8L8T92Uy0ndekXX5dckwDLwc62jtX35uwS2c8NOZxyrl5F+s5acub8ScE6I9P6A2VOk3wzAqK197u9hi0UBfdG3qiLn7r+vU/+K3W8qMeywL+LrZ4ewDSSL/nt7lBTZOz+2J9NeFOp1DQh9vfgbPUR/PxfVxDq0rdU6E6TGhuxc+9W1e3dOoEoKaOCDqMfBbaQgmW/yFzyz2oseqdoic3RPNnKvfC50gjIWAleSUzGetwFJZAsJMoSuXqNG4iazxQWcPjO3qBaGlciVUfcNyWzcQGWtqRA2iTUMRBrR2w8QeTTTKtfcwvoHLEF1EoCMAAQAASURBVBshwzB6G/bU5TFT9BBShMdjMP3N5lq9mgjTRP76Ki4t99yb+VYLDGzjzpF1slUfxsIr84iSN6IN69gQZ/6vp+y7ql9jqbRDHc35b/q1llQndhMJ0aWp+muNU05D7tjkHbaMZXhXh2L3DetZo3tzJ9k5UkcDufBbXcOqGzvWQcfG9dXur0i17ZpIE8e66KQhtJogACC4hbNRFsfMgc2jO2LTR/7YWDIIr9i68OY9xcf8v/HWuv9w4VHFKgMThClhUVoAAMijkFzCjCHjHlHjWTmkNb58W3XlVF2Ne5GzemD98HaSfFAEoQDDMPjhww6cED15Tszugfn9fDDjzfKVfuMVy62at/PG5PlTK5E3xuUUlCjtlw/NCp/eXW0/Uidq+f54jCQcVoqq32Opx6e2y7xLU0f8PaM7Imf14Gy3FUqMYO097AGozw+pKRzTEPTpLWyQcoVxVfPZ/nGn8v4VdhsalWyswj7mzrcj2iG4hRNmvNkcv08Kwuphbap7SkQlsHSwn1H6UZfXsyZSV2CBvn6umPV2J6wuGQ4AmGfxO7yZJIzcfBGX4l9W8wzVU9F0DUQto1iSC5fy7RHmDBn3aivVWQHABJCGKL3po1vS8BYuthjcvkGVGVKImkUzZ1tMDm6q0nu0olTVJenlWAc7xnbG0U8Nq2pLmCfyl5fUOD22q5dsWwsXW3w7oh32TgzU6LknNVbJX68MGI6xT/497L//64UtozuiR1nla0sdQt993exgW+ZNs/3jTvjmvbb4d04w/q+PN34O7SgZQ81Lubq8kUPaNwAAXPz8Ta3jy9OjuWTebRsqe3srjtSovnLuqqAmDhjcjusZ2dylPARO3ijHMEAfP4nnoqudctgvoZ132jXAjrGdIbKxBMMwGNK+AT4MMLzyMWGayBei+X1SkMH91MaMLtaWfOwpfQMnS9tBwBRji9VaOOMV3v8pCln5xTJD2uGYZPwWnVhl82JZFvMP3sCPpx9xtocduY2glSfxqiyXqjHH05XDMcn449pTo8/BVLiW9AoP09QXyTI7ivMBAAUsVcolzBcy7hG1kn2TArH8XT+1Hn0EUdkYyyhXlZ43wS2c4Eeh6bUKeaNXt+aOiPmiN74YyPVQfaddA63h2dKFEcVQWHXGPQ8HG7zpW7748uNH/nCyFWD98HY6zbuntzOG+jeEs60QU3s1kxkeOdVyFQxkqlg3vB0eLO8HFz2NZiIbS9xd2heHpnRV2ic/1kg1BiSGAdaPaM8xRqjrAwBGdvbAto87IXyGeu9JXfohJFjweVj+bmtO8ZRFA3zho64wSRUxzF+3KsiEauT1v7NXfVhqycusDr9aWHRGkpaBwZziyXgkdkNDJh2/Wy1BE+YZ2n4VCa8F4bibmo2Z+2LxxeFbeJaZr1f/5x6k48NfopGYIfGeuvE0EyvC45BbWKLRoHbjaRb2Xn6CVRF3OXLbLyQgNbsAuy6qNzTqYqjLKSiW/TszrwhBK0/ii8O3tLbLyi/GzH2xmP37dbRfelx2XOrGZFkWBcWlAIBHL17jVnKW0v4DV58iLiVbVXMZadkFyC8qVdr+KrcI/7f/ukZPy7ScAmSXHe++y0n4/coT5BeV4lL8S6Uc5d+eeIAhGy/grXX/IfpxBn45+9j8PSXLjHv5IOMeYb6QcY+olTjbCvFhgCfq6Ji3iCCMjbFeqhvWs9YuZGSknq+dNeTyqg7kPWtrU9hUZSJ/nTawt0a9OlY6ezALLcsfMfgqcu4xDMPpX9PfrF0je1z6/E0MLvOkMzaaKj7r4jWoCqElX2UePXmj4op3NRdj2DyqIxgGWDWUK6fYK5/HoJe3MyfMWRfIuKcZ+ZfV8d2b4JiG0POqYI2Rw4U7lIWrq0I+H2ZNQfE35srC3hrluzZzwPQ3muGSnOduE6c6cKhb+8L2rK0kz8uvYIfRxZ/hidgJjXnPcdjqC3zA/xcMxOi7/qxMfsquaygsKcW91Bx8cfgW0rIl+czyi0pxLekVZu2LxS9nywsffbTlIs4/zEDwmtNIycrHoA3n8fN/j9Fq8T/oufY0cguV00LkF5VizLZLsu97Lz9BWnYBR2+LyqoaZ+YV4cLDdGS8LgTLsthxIQH+y07g9rMs3E3Nxj+3U1FSyq0Iv/H0Q7QOi0TELUkV84WHbyE1uwC/RSdixt4YxKfncuQTM3IxfscVTN19Df2/PcvZF7zmNHqsPoXe//sPFx6mK401Zdc1tP0qEgv+uIk3vzmDgd+fw77LSbL9ey49wdz919Hv27MY8N1ZzrmTkpKVj84r/kX31aeQll2Azw/dxN1UiTFw+M9R2H/1qcTTMq9Yqe35h+novPxftAmLxN5LSfjs4E3MO3ADH2+/hPd/isKGkw+x4I+bOHj1KRLScznFsEb8HI1lx+Lwb1yaeRv4rOvhklUALot9qnsmBGEwZNkgCIIwQ3aM7Yxria8woLVblY+9a3wA9l1+go8CPat8bKJqYRgG178MQVGpWO/FELHcu0tPb6ey/sq38Rjud23vBMZIiyA/RAtXW6SWvXCaamGJXj7OeLCsHywUDIzGq7ZtogduIihek8bOzQgAzZzr4mHaawDA1jEdMXb7FbWyDMPgxOweKCgW44s/byEmKbNCY/f1c8U1FX2097CHp0Pl5Vkd3M4dh2Ofyb63crfD7WeaPZIAYPf4ADRzrovOK/41aFzFcFqRjeaiGLvGB8r+HdLSBZF3nmNBP9W5dWs6b7d1w983U9ClmSOWHgXeLVqCH63+h068+1hpuQXj+eHYWtoPf5d2xkvYIfZJJrwXRcjaqwrVPRSTjPyiUtQVcu8tQStPcr4nZuRh89nHuP0sG8WlYmwd3Qlz91/HHzHJHLkFf9xUGuO7fx9g1lvN0W7JcZXHNeC7c7J/923liojbqdgwsj2y80uwOuIeAGDyzqtK7f6MfYY/Y5+he3NHjOvmhdzCUsz6PRZFJWIlWSlJL/MAACN/uQhAslAan56L7R93wt9lBsQ9l8oNep8dvIkG9jb4aMtFTj+3n2Xj9rNsuIqEWHDwJnIKSxDgVR8Xy7zy0l8XYsJvV3H9SSaO33mO5YP9cP/5a1n7tksi8VGgB6a/0RyOdQXg8Risjrgr2z9f7jxGP5b0KTXmyc9PkfG/XoGbSIgL898wzzRGDTtihWgxYrMzq3smBGEwZNyrpZjhTy5BqMRcFwn5vIo5Tge3cEJwWT6yqsbd3hqzereolrGJqkfbC7A65AtkuIkkHqbyxiQeoz7nXmUh71WwdlgbrI28h9DAxric8BJLjt5B12aaw4uNgb7vPIqGPYBrjKzIeTPH96+qZGSAB5Ydi0OAl/5eym4iIVKyCrTKHZgchANXn6JHCye0cFEd9jvMv6FsMaWZs0Rm/6QgrD/xABtOPVTZxlZggRwV3k7yvN3WHSvC7yptl1antuLzUKTgYWTF5+GjQE9sPR+v+cAAXPr8TQz8/hzScgo57VcNa4PIO8+RVxY+uG1MJ50MdoFNHLRWlZ7duwXWHb+vemcFlGXjhx2QklWgMkdmbUBgwceWMZKiPsEtHBGTlInhB2wxhv8PZlgcRFNeCpbztuIri+24xXrhmrg54lgPJIpdkMQ64yXsUATle8k36v5WCqw/8UD27xGbo/Uq5OG1IFwnuYjbEgPbtN0xOvd99kE6zj4wrGqw1PNvzLbLamUUDXvyyM/zosL5uP4kEwDwIqcQE39TNk7ujE7Czmj1hjpDSckqQHZBCUTWtaOaNEGYGmTcIwiCqEKWDvbDtyceKIXZEURNw8VOiGQNeZcYxZx7VRxK7WwnxOphbQFIPIc6eNarkpxqriL1+fsc61oh/XURerfUXOzJLL0izJCxXb3QtpE9WhmQY23zqI4Y+P05lfs+CvSQvVgLLPgY372Jxr7WvtdWaZsFnyerBq2KNe+1weSd1zT26yayxvXFIZiw4wouJZQbB6QebjweAIX0XQ51rfDl2y3x962UMmOXNcZ29cL2CwmwtuTjbmp5gn1nOyFGdPbAd/+WG2bASI75l1EdMfKXixjU1l2lAVuR1g1EGg17U3o2xZwQb/B5DEZ0boTOy7nGws6N66O9Rz2t40h5VyEFgAWfV2sNe4o0c7ZFM2db/HM7FVvj+uH30mAM55/CO/wLaMOLRzvmEdrxHim1y2MFyEIdZLM2KIIFimCJItYSxeCjEJYoAR9iMGDBAGDAAmDLvrMAxOBJ7hJPGbxvyYBly/cTpoP4binQflB1T8Mg6NZKmDtk3CMIgqhCQgM98VGAB72cEzWSPn6uOPcwHS52AmwZ0xGL/7yNOSHeKmUZhqnycFh1jjs8HoN2jeyrZA79/dwwKTgLHVQYGiJm9kBsUiZ6+ThXyVzod0gzPB6DTlpyi347oh1m7I1V2u7XQIRrX/RGh6XK4YATujdR6zWjq8cfALW53/w96yGkpatOfYisLbF3YiByi0qw7XwCfv7vMRb0k+ScKilVVpjism1HpnXDpfiXCGnlAks+D6GBnuAxDPyXHccruZxe6nJwdWnmiCuL3kJ9Gytk5nNzgJ2d1wtilsXSo3E4Efcck4Kb4LM+mvNgdWnqKAubdrblGtBvfdUHNmU5MH+fFAR3+/L964e3w8x9sfB0sMFbvi7o5+cKh7oCeJAhTyudGtfHibg0vIYNtpQOwJbSAWjIvIA/cw/teQ/RhEmBJ/McDZh0WDBi2DCFsEEh3Bjdve4I8+PIOS8MMlPjHkGYO2TcIwiCqGLohZqoqYzs7IGG9azRtqE96texwr5JQWplGUahCEpVhOVW/hBa4fEYtXm7HOsK8JYWrz1FNHlvaYN+iQxnTu8WeNPXBS3d7dDLxxmX41+ijsACR288Q38/SS5UxQInrRuI8OXbLeFub416NpawtuRDYMH1Wouc1QN3nmVj9u/XNXq+ApLcdV/9dRs5BSV4v2ND/H7lKQDg4CddOHJ+DexgK7BE1OMMAMD+yUGcYkw8HgNboSWmv9kcU3s1kxnJ3vR1xj+3n3P6klbsdbIVYECb8pyvUu+7bR93xmcHbmDhAMk1rlhlU/6acywzTiqeA6mH3IaR7RGTlIlOjetxvPa+/6A9Pt0jCUn8OdQfDMOgW3NHlefoh5EdUFcuX2hnhRDrwe0bVFqhnpqOj5uyR+tT1glPWSf8Ke4m28ZADFvkQcTkwh65sGXyYIViCFACS5TACsWwYkpggVIwYMGT89fj+vABPIhl28r3EaZEC4/qLTxUEeieSJg7ZNwjCIIgah70vF8tSKu2GoK4Cqx7Zl3JT4E1w9ogK7+4QqGCtM6gP31bueJifAZGd20MO6Ekr5Sd0BJv+kqMsoFNuHkbOzeuj0sJL+FkK8Bfn5YbPC5+/hYAKIWa2gotEdBEt9yPFnwebob1ASApWCA17ikS6OWAAW3c8O7GCxjYxk2jN6J80ZDVQ9siwOsplhy9AwBo21CE2VryrbZrZI9/ZvWQfVdlAFKkjsACH3dtjG3nE1BPLsen0JKPoKbK5+Lttu6w4DF4nl2AkFaqPRQX9vdFQkYu+rfWzYOR0J8ezR2xZlgb+LrZYcjGC0r5GaWw4CEbdZHN1sUTyQaj0ty5Lp5nS3K9EdXPg4H9qnsKBkOL74S5U7GM7gRBEARhItAjmXlTFWa3mmPaA97r2EhrrjZ1WJV5WXVrptrbiVDPjx91wKWFb8kMe9rY8GF7TApuggOTuV6sVhY8WFmofwzX1xCt6fePx2PQ3qMeYr/sje8/aK9znyIbS4zt5iX73svHWeOcVTGwtRuWDfbTKvflwJY4NKULTs3tqVO//Vq7YUxXL7X7J/RoguXvtqaX9UqEYRi817ER/BqI8MOHHdDewx4nZgfj6KfdML+f5jBqKR096+GDzh4491kvPF7RX7Z9QGs3+DWwgwWPwYhOjVS2tRVYIOHrATg+OxgXFryptP/op90wUM67VBXXvwzB6mFtZN8tFIztjR2UF0+m9mqqsU91DGitfi4/hfprbf9oRX98oyL/ppSPuzZGwtcDkPD1ADxe0R8nZvfAEAWv1DfkUj642pWHp0/t1RQ7xnbmyF5d9JbG+Wz8sAO6KBjfv/+gPSx1yKFJEETlQJ57tRR61iEIgiBMCV2NJUTFOfV/PXEpPgNvt3Gv7qmYHQzDwJKv+0OUs61QbRi2JlxFQjzTMfeeZF7q90kL19jbWKkX0kBnr/q4FP8Sg9vpH77K4zH4KNATiw7f0ijHMIxeBS8I06J3SxdOISC/BiKIrC3xIqcQfVq5YsOph6gr4GPPpScymTFdGiNsUCtOPz+M7IAz99OwbHBrmSGZZVnsvSxpd3xWD7zMLUJWfjEnFLuuwALh07vjn9up8HSwQR2BBfwaiLBhZAd8N4JFfnEpFh2+hd4tXbDw0E1ZXkiRjSXe79gI8w7cACAJNx/eqZGsOu/p/+uFDScfYG3kfYwO8kRokCeaOdvi//r4oKC4FMuPxeG36ETOMXRr5oiv3mkFN5EQh2KSkfG6CBcepWNKr6Y4djMFAPD3jO4oKC7Fn7HPEOzthJ4tnNSeWydbAZYP9gOfx2Cof0Ncf5qJV3nF+G5EOzAMg9P30vDX9RROflsej0EzZ1usG94OE3o0wel7LzC2W2MILPjILSxBblEJnG2FuP0sC9n5JTIP2dOfdoRfnw+Q9+AimvxogaYdg1HceQwEdWyR/roIgCQv6Px+Pujf2g39W7uBZVl8+n+fY9eOrXj/22wEBATghx9+QKtWkr/ty5cvsXjxYkRERAAAWrVqhXfffRdLly6FSCSSzfnVq1eYPn06jhw5AgAYNGgQvv/+e9jb28tkkpKSMHXqVJw8eRLW1tYYOXIk1q5dCysrw37bFKHXY8LcIeMeQRAEQRBVgrOtAN4utuDzGNiW5cFa935bvMgpRDPnupU+fg2Kyq0QDeyt8W77htU9DUID345oj0WHb2FSsG7emW/6uGAhbqGpUx3ZNqlRbph/xXLK7ZkQiNcFJRDZVNwAT4vLtYcPOnvI/v39B+1RKmbxbvuGiH6cgSPXn2Fqr2ZKbQa0cePkcgQkht/w6d3x4nUhmruor2je0t0OLVVUtubxGNQRWOB/w9sBAJo61cUvZx9jxlvNZTLbPu6EVX/fxZphbeHtaovmzrYyg9e0N5pjqH9DuNoJOZ6gQks+lrzTCs1d6qJNQ3ukZRfA182OkyrhwwBPAMD0N5vjycs82XaP+hIDpLxB+/GK/jj/KB33UnOw+1ISlgzyg5UFTylP5JJ3uJ6wPb2d0VNDOgxfNzv4yoXH1xFYoE7Z/beVu4gj+8n4MWgofo7/hYfD3sYKEydOhOfNbdi6ez/+e5COt9u6QWDB57RZvXo1fv35B2zfvh0tWrTAsmXL0Lt3b9y7dw+2trZ49uwZnj17hmXLlmHEiBHYuHEj5syZg2fPnuHAgQOyfkaOHImnT5/KjIATJ05EaGgo/vrrLwBAaWkpBgwYACcnJ5w7dw4ZGRkYPXo0WJbF999/r/b4CaI2wbA1KQGNCZOdnQ2RSISsrCzY2WnPP1LZXIp/ifd/igIAJHw9oJpnQ1QXpnBdGjqHrl+fRHJmPn4d2xk9NKx4ErWHwpJSeC+SPBTGLekLayu+lhbqMWfdMHXEZQn2FXONVQWrIu7ix9OP0L25I34bF1Dl49cETOW6NJV5mBJZecWwEfBlYXGlYhbZ+cWoV8c4Xi0VYez2yzh5Nw2TejTBgv76ezKaC6ZwXZrCHAhlcgtL0GrxPwCAh8v7yYrQmApxcXFo2bIloqOjERAguT9GR0cjKCgId+/ehbe3t1IblmXh7u6OmTNn4rPPPgMAFBYWwsXFBatWrcKkSZNksvLX5T///IOPPvoIubm5sLCw0Gnsv//+GwMHDsSTJ0/g7i7xOt+7dy/GjBmDtLQ0na51bbrxW3Qivjh8C82c6+LE7GD9TyJBGIAxf7PJc6+W0tmrPg5MDoJHBRJxE0R1EjmrB5Je5nFWI4najcCCjxOze0DMokKGPaJyqQ6jnpTZvVuga1NHdPC0r7Y5EERloehZx+cxJmHYAyThllcTXyGgifpiHgRRk6kjsMA/M3uAz2NMzrAHAFFRURCJRDLjGgAEBgZCJBLhwoULKo178fHxSE1NRUhIiGybQCBAcHAwLly4wDHuySM1YlhYWOg8dlRUFPz8/GSGPQDo06cPCgsLcfXqVfTq1UtpnMLCQhQWFsq+Z2dnazwHH3b2QFPHOmjVQKRRjiBMFTLu1WI6aqiWRhCmTh2BBRn2CCWaOasP2SEISz6Pk6eJIIiqwdqKT7pH1Hq8XU33GSU1NRXOzsrhvc7OzkhNTVXbBgBcXFw4211cXJCYmKiqCV6+fImlS5dyDH+6jJ2amqo0Tr169WBlZaV2fitXrsRXX32lcp8qeDwGXajQFGHGmN6yAUEQBEEQBEEQBEEQFSIsLAwMw2j8XLlyBQBUVpdmWVZr1WnF/ZravPfee2jZsiUWL16ssQ9V/eg7vwULFiArK0v2efLkiUo5gqgpkOceQRAEQRAEQRAEQdQwpk2bhhEjRmiUady4MW7cuIHnz58r7Xvx4oWSx5wUV1dXABKvOje38kIoaWlpSm1ycnIAAHXq1MGhQ4dgaWnJ6Ufb2K6urrh48SJn/6tXr1BcXKx2fgKBAAKBQOU+gqiJkOceQRAEQRAEQRAEQdQwHB0d4ePjo/EjFAoRFBSErKwsXLp0Sdb24sWLyMrKQpcuXVT27eXlBVdXVxw/fly2raioCGfOnOG0yc7OxrvvvgtAUgRDKBRy+tFl7KCgINy6dQspKSkymcjISAgEAvj7+1fgDBFEzYGMewRBEARBEARBEARRS/H19UXfvn0xYcIEREdHIzo6GhMmTMDAgQM5xTR8fHxw6NAhAJIw2ZkzZ2LFihU4dOgQbt26hTFjxsDGxgYjR44EIPHYCwkJQV5enux7amoqUlNTUVpaqvPYISEhaNmyJUJDQxETE4N///0Xc+fOxYQJE6gqNEGUQWG5BEEQBEEQBEEQBFGL2bVrF6ZPny6rfjto0CBs2LCBI3Pv3j1kZWXJvs+bNw/5+fmYMmUKXr16hYCAAERGRsLWVlI85OrVq5xw2hYtWsj+HR8fj8aNG+s0Np/Px7FjxzBlyhR07doV1tbWGDlyJNauXWvck0AQZgzDsixb3ZOoDWRlZcHe3h5Pnjyh1QXCZMjOzkajRo2QmZkJkah6yr6TbhCmCOkGQajGFHQDIP0gTBNT0A/SDcIUId0gCNUYUzfIc6+KkCYRbdSoUTXPhCCUycnJqbYbLekGYcqQbhCEaqpTN6TjA6QfhGlC9w6CUA3pBkGoxhi6QZ57VYRYLMazZ89ga2urVK5baq2lVQTdoPOlO9rOFcuyyMnJgbu7O3i86knBqUk3APp76wOdK/3QdL5IN2oWdK70w9R1A6DnKmNC50t3zP25iv7W+kHnS3fMXTcA+nvrA50r/aiq5yry3KsieDweGjZsqFHGzs6OlEMP6HzpjqZzVZ2eF4BuugHQ31sf6Fzph7rzRbpR86BzpR+mqhsAPVdVBnS+dMfcn6vob60fdL50x9x1A6C/tz7QudKPyn6uomq5BEEQBEEQBEEQBEEQBGGmkHGPIAiCIAiCIAiCIAiCIMwUMu6ZAAKBAIsXL4ZAIKjuqZgFdL50pyacq5pwDFUFnSv9MPfzZe7zr0roXOmHuZ8vc59/VUPnS3fM/VyZ+/yrGjpfulMTzlVNOIaqgs6VflTV+aKCGgRBEARBEARBEARBEARhppDnHkEQBEEQBEEQBEEQBEGYKWTcIwiCIAiCIAiCIAiCIAgzhYx7BEEQBEEQBEEQBEEQBGGmkHGPIAiCIAiCIAiCIAiCIMwUMu5VEhs3boSXlxeEQiH8/f1x9uxZjfJnzpyBv78/hEIhmjRpgk2bNinJHDx4EC1btoRAIEDLli1x6NChypp+lWLsc7V9+3YwDKP0KSgoqMzDqDL0OV8pKSkYOXIkvL29wePxMHPmTJVyVXltkW7oDumGfpBu1B7dAEg/9MHcdQMg/dAH0g39MHf9IN3QHdIN/SDdqD26AZB+6IPJ6gZLGJ29e/eylpaW7ObNm9k7d+6wM2bMYOvUqcMmJiaqlH/8+DFrY2PDzpgxg71z5w67efNm1tLSkj1w4IBM5sKFCyyfz2dXrFjBxsXFsStWrGAtLCzY6OjoqjqsSqEyztW2bdtYOzs7NiUlhfOpCeh7vuLj49np06ezO3bsYNu1a8fOmDFDSaYqry3SDd0h3dAP0o3aoxssS/qhD+auG4YcQ23WD9IN/TB3/SDd0B3SDf0g3ag9usGypB/6YMq6Qca9SqBz587s5MmTOdt8fHzY+fPnq5SfN28e6+Pjw9k2adIkNjAwUPb9/fffZ/v27cuR6dOnDztixAgjzbp6qIxztW3bNlYkEhl9rqaAvudLnuDgYJU/JlV5bZFu6A7phn6QbtQe3WBZ0g99MHfdYFnSD30g3dAPc9cP0g3dId3QD9KN2qMbLEv6oQ+mrBsUlmtkioqKcPXqVYSEhHC2h4SE4MKFCyrbREVFKcn36dMHV65cQXFxsUYZdX2aA5V1rgDg9evX8PT0RMOGDTFw4EDExMQY/wCqGEPOly5U1bVFuqE7pBv6QbpRPrearhsA6Yc+mLtuAKQf+kC6oR/mrh+kG7pDuqEfpBvlc6vpugGQfuiDqesGGfeMTHp6OkpLS+Hi4sLZ7uLigtTUVJVtUlNTVcqXlJQgPT1do4y6Ps2ByjpXPj4+2L59O44cOYI9e/ZAKBSia9euePDgQeUcSBVhyPnShaq6tkg3dId0Qz9IN8rla7puAKQf+mDuugGQfugD6YZ+mLt+kG7oDumGfpBulMvXdN0ASD/0wdR1w8LgGRAaYRiG851lWaVt2uQVt+vbp7lg7HMVGBiIwMBA2f6uXbuiQ4cO+P777/Hdd98Za9rVRmVcB1V5bZFu6A7phn6QbtQe3QBIP/TB3HXDkPFqs36QbuiHuesH6YbukG7oB+lG7dENgPRDH0xVN8hzz8g4OjqCz+crWVnT0tKUrLFSXF1dVcpbWFjAwcFBo4y6Ps2ByjpXivB4PHTq1MmsVwkAw86XLlTVtUW6oTukG/pBulEuX9N1AyD90Adz1w2A9EMfSDf0w9z1g3RDd0g39IN0o1y+pusGQPqhD6auG2TcMzJWVlbw9/fH8ePHOduPHz+OLl26qGwTFBSkJB8ZGYmOHTvC0tJSo4y6Ps2ByjpXirAsi9jYWLi5uRln4tWEIedLF6rq2iLd0B3SDf0g3SifW03XDYD0Qx/MXTcA0g99IN3QD3PXD9IN3SHd0A/SjfK51XTdAEg/9MHkdUOv8huETkjLI2/ZsoW9c+cOO3PmTLZOnTpsQkICy7IsO3/+fDY0NFQmLy0lPWvWLPbOnTvsli1blEpJnz9/nuXz+ezXX3/NxsXFsV9//XWNKL1dGecqLCyMjYiIYB89esTGxMSwH3/8MWthYcFevHixyo/P2Oh7vliWZWNiYtiYmBjW39+fHTlyJBsTE8Pevn1btr8qry3SDd0h3dAP0o3aoxssS/qhD+auG4YcQ23WD9IN/TB3/SDd0B3SDf0g3ag9usGypB/6YMq6Qca9SuKHH35gPT09WSsrK7ZDhw7smTNnZPtGjx7NBgcHc+RPnz7Ntm/fnrWysmIbN27M/vjjj0p97t+/n/X29mYtLS1ZHx8f9uDBg5V9GFWCsc/VzJkzWQ8PD9bKyop1cnJifXx8ZN8bN27MhoWFsUVFRTrN7d69e+yQIUNYe3t71tramu3cuTP7559/Ksl5enqyAFR+BAKB2v5TU1PZ+vXrswDY/fv36zQnfc+Xqjl5enpyZKry2iLd0J3K1o2QkBB21qxZrLe3t0H68eDBA/ajjz5iGzVqxAqFQrZJkybsrFmz2PT0dI7c7t272e7du7POzs6slZUV6+bmxg4cOJA9f/68Up/Z2dnsp59+yrq7u7NWVlZs8+bN2VWrVrElJSVGP1+kG+aNKd875Dl+/Ljs+nrx4gVn3+LFi/W6b+zZs4dt27YtKxAIWDc3N3bGjBlsTk6O1jmYu24Ycgy1WT9MWTcWLlzIDhgwgHV3d2cBsKNHj1Ypp69uSKHnKtINTZiybhQVFbFhYWGy+Xl7e7PfffedklxV3TdYlnSjNukGy5q2fsij6blKn3cOefS9d5iqbpBxj6jRLFu2jGUYhl2wYAF76tQpdvXq1ayVlRU7YcIErW3j4+PZ+vXrs61atWL37t3LHj16lB0wYADLMAxnVYJlWfbatWtsVFQU57Nv3z4WADtixAi1YwwdOlT2gKvrQyhBGIuK6EdaWhrr4ODAenl5sdu3b2dPnjzJfvPNN2zdunXZdu3asaWlpTLZ77//np0/fz574MAB9vTp0+yePXvYTp06sXw+nz19+rRMrri4mA0ICGDr1avHbtiwgY2MjGRnz57NMgzDfvrpp5VyDghCFRXRDXlycnLYxo0by37n1Rn3IiIiOPcPVavaO3fuZAGw48ePZ0+ePMlu2rSJFYlEbO/evSt0rAShDxXVDRsbGzYwMJCdPHkya2VlpdW4p4tuyEPPVUR1UVHdGD9+PCsQCNjVq1ezp06dYufPn88yDMMuX76cI0f3DcIcqarnKl3fORSpKfcOMu4RHMRiMZuXl1fd0zAK6enprFAoZCdOnMjZvnz5cpZhGI4rrComTZrECoVC9unTp7JtJSUlrK+vL9uoUSOO8UIVYWFhLAD2xIkTKvcfOHCArVu3Lrtjxw6z/yGpLZB+lLN582aV1/eKFStYAOy1a9c0ts/MzGQtLS05but79uxhASitVE2cOJHl8Xjs3bt3dTk0ohog3VDN1KlT2fbt27OLFi3SaNxT3K5ISUkJ6+bmxoaEhHC279q1iwXAhoeH6zwnomoh3eAi/+xUp04drcY9bbohDz1XmRekG+XcunWLZRiGXbFiBWf7hAkTWGtrazYjI0O2je4btQPSD9Voe65Shap3Dnlq0r2DCmrUUMLCwsAwDG7fvo0PPvgAIpEILi4uGDt2LLKysmRyDMNg2rRp2LRpE3x9fSEQCLBjxw5s374dDMPg5MmTmDBhAhwcHGBnZ4dRo0YhNzcXqampeP/992Fvbw83NzfMnTsXxcXFnDn8+OOPaNu2LerWrQtbW1v4+Pjg888/58ikpqZi0qRJaNiwIaysrODl5YWvvvoKJSUlFT4HERERKCgowMcff8zZ/vHHH4NlWRw+fFhj+/Pnz6Nt27Zo0KCBbBufz0e/fv3w5MkTXLp0SW1blmWxbds2NGnSBG+88YbS/pcvX2Lq1KlYvnw5PDw89DswosKQflRcP6TJckUiEWe7vb09AEAoFGpsb2trC6FQCAsLC9m28+fPg2EY9OvXjyM7cOBAiMViHDp0SGOfRMUh3ai4bkg5e/Ysfv75Z/zyyy/g8/kVmlN0dDRSUlKU5vTee++hbt26pBtVAOmGcXSDx6ucVw96rqo+SDcqrhuHDx8Gy7Iq2+fn5yMiIkLvOdF9wzQg/aj+5ypV7xxSatq9Q/kIiRrF0KFDMXz4cIwbNw43b97EggULAABbt26VyRw+fBhnz57Fl19+CVdXVzg7O+Py5csAgPHjx2PIkCHYu3cvYmJi8Pnnn6OkpAT37t3DkCFDMHHiRJw4cQKrVq2Cu7s7Zs+eDQDYu3cvpkyZgk8//RRr164Fj8fDw4cPcefOHdm4qamp6Ny5M3g8Hr788ks0bdoUUVFRWLZsGRISErBt2zaZbGlpKViW1Xq8PB5P9uB469YtAEDr1q05Mm5ubnB0dJTtV0dRURHq16+vtF0gEAAAbty4gcDAQJVtT5w4gcTERCxbtgwMwyjtnz59Ory8vDBt2jT8999/Wo+LqBxIPwzXj8GDB8PDwwNz5szBxo0b4enpiWvXruHrr7/G22+/DV9fX6U2paWlEIvFSE5OxsqVK8GyLKZOnSrbX1RUBB6Pp1RlS17niKqBdMNw3QCA/Px8jBs3DjNnzkSHDh1w5MgRjfKtW7dGWloaHB0d0adPHyxbtozzkCkds02bNpx2lpaW8PHx0WlOhHEg3aiYbuiLNt2QQs9V1Q/phuG6cevWLTg5OcHV1ZWzXfqbr6o93TfMC9KPqn2u0vbOIaXG3Tuq3lmQqAqkLturV6/mbJ8yZQorFApZsVjMsqwkuaNIJGJfvnzJkdu2bRsLQCnP1eDBg1kA7Lp16zjb27Vrx3bo0EH2fdq0aay9vb3GOU6aNImtW7cum5iYyNm+du1aFgDHRVdTwQr5z+LFi2VtJkyYoDa5bIsWLZTc1BUZPHgwa29vr5R0tnv37iwAJdd5eYYPH87y+XxOSK+Uo0ePspaWluzNmzdZlmXZU6dOmb0LsLlB+lFx/WBZln327BkbFBTEGeO9995jCwoKVMp7e3vL5Nzc3Nhz585x9q9fv54FwJ49e5az/YsvvmAB6DQnomKQbhhHN+bMmcM2adJEFlKjLozq119/ZZcvX86Gh4ezJ0+eZL/++mu2fv36rIuLC+f+sXz5chYAm5KSojRWSEgI26JFC61zIioG6YZxdEMeTWG5uuoGy9JzVXVDulFx3ejduzfr7e2tcp+VlRUnnJHuG+YF6UfVPldJ0fbOwbI1895Bnns1nEGDBnG+t2nTBgUFBUhLS4OLiwsA4I033kC9evVUth84cCDnu6+vLw4fPowBAwYobY+MjJR979y5MzZs2IAPPvgAI0aMQNeuXeHo6Mhpc/ToUfTq1Qvu7u4cl99+/fph7ty5OHPmDFq2bAkA+Ouvv1BYWKj1eN3d3TnfVXnN6bIPAKZNm4Y///wTo0aNwtq1a1GnTh1s2LABFy5cAKA+tOTly5c4fPgw+vbtywnpBYCsrCxMmjQJn332Gfz8/LQeD1G5kH4Yrh+vXr3CO++8g7y8POzatQuNGjXCrVu3sHTpUgwaNAjHjh1Tcn8/ePAgcnNzkZSUhE2bNqFfv344cuQIevbsCQD48MMPsWTJEkycOBHbtm2Dt7c3/v77b3z33XcAKi+ci1CGdMNw3bh06RLWr1+PiIgIWFtba5QNDQ3lfO/Vqxd69eqFoKAgrF69Gt9++61OY2ubE2E8SDcM1w190FU36LnKdCDdqJhu6Nqe7hvmCelH1TxXSdH2zlFT7x1k3KvhODg4cL5Lw9vy8/Nl29zc3NS2VwxLtbKyUru9oKBA9j00NBQlJSXYvHkzhg4dCrFYjE6dOmHZsmXo3bs3AOD58+f466+/lELwpKSnp8v+3bJlS51dgKU4ODigoKAAeXl5sLGx4ci9fPkS/v7+Gvt68803sW3bNsyZMwdNmzaVzWPp0qX4/PPPlQx3Unbu3InCwkKMHz9ead/ChQthaWmJadOmITMzEwDw+vVrAEBeXh4yMzMhEonohltFkH4Yrh+rVq1CbGwsEhMTZeeoe/fu8PHxwRtvvIFdu3Zh9OjRnDatWrUCIHnQGDx4MNq3b48ZM2bg+vXrAABHR0dERERg9OjRspB3BwcHrFu3DuPGjVOrc4TxId0wXDfGjh2LIUOGoGPHjrLfeekxZmdnQyAQwNbWVm37zp07o0WLFoiOjubMCQAyMjJkLwHyc1KVQoKoHEg3DNeNiqJKN+i5ynQg3TBcNxwcHBAbG6u0PTc3V22aIHnovmH6kH5U7XOVtneOmnrvIDcIotIu2o8//hgXLlxAVlYWjh07BpZlMXDgQCQmJgKQvMiHhITg8uXLKj/jxo2T9dW0aVNYWlpq/SxZskTWRhrXf/PmTc68UlNTkZ6erpOVfvTo0UhNTcWdO3fw4MED3L59G4DknHXv3l1lmy1btsDFxUVphQWQ5BxISEiAq6sr6tWrh3r16uHtt9+WjVWvXj1OclWi+iH9UE1sbCwaNGig9CDSqVMnAKrzw8hjYWGBDh064P79+0rt79y5g/j4eNy6dQvPnj2T5e/r0aOHxj6JqoV0QzW3b9/G/v37Zb/x9erVw6pVq2TzUXfvkIdlWc6Dsbo5lZSU4O7duzVq1bkmQLpReSjqBj1XmRekG6pp3bo1Xrx4gdTUVM52aX+66BbdN8wf0g/VVPS5StU7R029d5DnHlHp1KlTB/369UNRUREGDx6M27dvw9PTEwMHDkR4eDiaNm2q1gVZiiEuwH379oVQKMT27dsREBAg2y6tOjR48GCd5m9hYSEzLmRlZeHnn3/GO++8A09PTyXZK1eu4MaNG5g3b57Kijzr16+XrQ5IiY2NxaxZsxAWFobg4GDUrVtXp3kRNQNz1Q93d3f8+++/SE5O5njURUVFAQAaNmyosX1BQQGio6PRrFkzlfsbN24MQPKw+s0338Dd3R3vvfeexj6JmoW56sapU6eUtm3fvh07duzA4cOHtXqgRkdH48GDB5g+fbpsW0BAANzc3LB9+3YMHz5ctv3AgQN4/fo1hgwZou0QiRqEuepGRVGlG/RcRchjrrrxzjvvYNGiRdixYwc+++wzTntra2v07dtXY3u6bxC6YK76UdHnKlXvHDX13kHGPaJSmDBhAqytrdG1a1e4ubkhNTUVK1euhEgkknn2LFmyBMePH0eXLl0wffp0eHt7o6CgAAkJCQgPD8emTZtkBgLF6jq6UL9+fSxatAhffPEF6tevL1uRCAsLw/jx42W5AwDg119/xdixY7F161aMGjUKAJCWloZvvvkGXbt2ha2tLe7evYvVq1eDx+Phhx9+UDnmli1bAICzwiFPu3bt1M63VatWsjwARM2mJujH1KlTsWvXLvTu3Rvz58+X5dxbtmwZXFxc8OGHH8rad+nSBYMGDYKvry9EIhESEhLw448/4tGjRzh06BBnXgsXLkTr1q3h5uaGpKQkbN26FRcvXsSxY8d0zrNBmC81QTdU/Y6fPn0aAJRy3bRt2xYfffQRfH19IRQKcenSJaxZswaurq6YN2+eTI7P52P16tUIDQ3FpEmT8MEHH+DBgweYN28eevfurfXFjzB/aoJuAMCZM2fw4sULAJJqhomJiThw4AAAIDg4GE5OTgB01w16riJqgm60atUK48aNw+LFi8Hn89GpUydERkbi559/xrJlyzihl3TfIPShJuiHPs9Vur5z1NR7Bxn3iEqhe/fu2L59O37//Xe8evUKjo6O6NatG3799VfZg5ubmxuuXLmCpUuXYs2aNXj69ClsbW3h5eWFvn37al050IWFCxfC1tYWP/zwA9auXQtXV1fMnz8fCxcu5MiJxWJZyWwpFhYWiI2NxbZt25CZmQk3Nze88847+PLLL5USkQKSnAl79uxBjx490KJFiwrPnai51AT98Pf3R3R0NJYuXYqFCxfixYsXaNCgAQYNGqSkI126dMHevXuRkJCA3NxcODo6IigoCP/73//QpUsXzlivXr3CZ599htTUVNjZ2SE4OBgXL1406GGCMD9qgm7oQ8uWLfHzzz8jJSUFRUVFcHd3x4gRI/Dll18qhbx/9NFH4PP5+Prrr7F9+3bUr18fo0aNwvLlyw0+TsJ8qCm6sXjxYpw5c0b2/fTp07KXtFOnTsleqPTRDaJ2U1N0Y+PGjWjQoAG+//57pKamonHjxvj222/x6aefcuTovkHoQ03RD13R552jJsKwumREJAiCIAiCIAiCIAiCIAjC5KCCGgRBEARBEARBEARBEARhppBxjyAIgiAIgiAIgiAIgiDMFDLuEQRBEARBEARBEARBEISZQsY9giAIwqzZuHEjvLy8IBQK4e/vj7Nnz2qUP3PmDPz9/SEUCtGkSRNs2rSJs3/z5s3o3r076tWrh3r16uGtt97CpUuXODJhYWFgGIbzcXV15ciwLIuwsDC4u7vD2toaPXv2xO3bt41z0ARBEARBEARBEGWQcY8gCIIwW/bt24eZM2di4cKFiImJQffu3dGvXz8kJSWplI+Pj0f//v3RvXt3xMTE4PPPP8f06dNx8OBBmczp06fxwQcf4NSpU4iKioKHhwdCQkKQnJzM6atVq1ZISUmRfW7evMnZv3r1aqxbtw4bNmzA5cuX4erqit69eyMnJ8f4J4IgCIIgCIIgiFoLVcslCIIgzJaAgAB06NABP/74o2ybr68vBg8ejJUrVyrJf/bZZzhy5Aji4uJk2yZPnozr168jKipK5RilpaWoV68eNmzYgFGjRgGQeO4dPnwYsbGxKtuwLAt3d3fMnDkTn332GQCgsLAQLi4uWLVqFSZNmmToIRMEQRAEQRAEQXCwqO4J1BbEYjGePXsGW1tbMAxT3dMhCAASA0ROTg7c3d3B41WPIy/pBmEoRUVFuHr1KqZPn47s7GzZ9p49e+Ls2bOcbVLOnTuHnj17cvZ1794dW7ZsQUZGBiwtLQFwdSMvLw/FxcWoX78+p68HDx7A3d0dAoEAAQEBWLFiBZo0aQJA4iGYmpqKkJAQmbxAIEBwcDAuXLigk3GPdIMwRUzhvgGQfhCmiSnoB+kGYYqQbhCEaoypG+S5V0U8ffoUjRo1qu5pEIRKnjx5goYNG1bL2KQbhCnz5MkTrFy5Ev/88w9u3boFoVAIAPj777+Rl5eHFi1a4Pnz51i2bBnu3r2L27dvw8HBARcuXEDXrl2RnJwMd3d3WX8TJ05EYmIi/vnnH6WxCgsLUVhYKPuenJyMli1bVv5BEoQBVOd9A6B7B2Ha0HMVQaiGdIMgVGMM3SDPvSrC1tYWgOSPZmdnV82zIQgJ2dnZaNSokez6rA5INwhDSUlJgY+PD44fP47OnTvLtq9Zswb79u3DlStXlNp06NABH374IebMmSPbFh0djT59+uD+/ftwcXEBUK4bW7duxZ49e3D69GmZYQ8A+vXrJ/t369atERQUhKZNm2LHjh2YPXu2bJ/iyjDLsmpXi1euXImvvvpKaTvpBmFKmMJ9A6B7B2GamIJ+kG4QpgjpBkGoxpi6Qca9KkL6MmdnZ0c/JoTJUZ2u6aQbhKEIhULw+Xzk5ORwrp3s7Gy4ubmpvJ7c3d2RmZnJ2ZebmwsLCws0btxYFpYrZd26dThx4gTatGmjcS516tRB69at8eDBAwCQVc5NTU2Fm5ubTC4tLU1mQFRkwYIFHMOg9GZPukGYItUd0kT3DsKUoecqglAN6QZBqMYYukHVcgmCIAizxMrKCv7+/jh+/Dhn+/Hjx9GlSxeVbYKCgpTkIyMj0bFjR45h79tvvwUAHDx4EB07dtQ6l8LCQsTFxckMeV5eXnB1deWMVVRUhDNnzqidm0AgkD1w0oMnQRAEQRAEQRC6QsY9giAIwmyZPXs2fvnlF2zduhVxcXGYNWsWkpKSMHnyZAASbzhphVtAUhk3MTERs2fPRlxcHLZu3YotW7Zg7ty5MpnVq1dj2bJlAAAPDw+kpqYiNTUVr1+/lsnMnTsXZ86cQXx8PC5evIhhw4YhOzsbo0ePBiBZfZs5cyZWrFiBQ4cO4datWxgzZgxsbGwwcuTIqjg1BEEQBEEQBEHUEigslyAIgjBbhg8fjoyMDCxZsgQpKSnw8/NDeHg4PD09AUjy8iUlJcnkvby8EB4ejlmzZuGHH36Au7s7vvvuOwwdOlQms3HjRhQVFQEAWrRoIdu+ePFihIWFAZAkZf7ggw+Qnp4OJycnBAYGIjo6WjYuAMybNw/5+fmYMmUKXr16hYCAAERGRlZ7rjKCIAiCIAiCIGoWVC23isjOzoZIJEJWVhaFWhEmgylcl6YwB4JQxBSuS1OYA0EoYirXpanMgyDkMYXr0hTmQBCKmMJ1aQpzIAhFjHldUlguQRAEQRAEQRAEQRAEQZgpZNwzcX6NSsD8gzcgFpODJUE8evEak367gptPs6p7KgRR7Zx7kI5Pdl5FWk5BdU+FIEyKyb9dxTsbzuFuanZ1T4Wo4WzcuBFeXl4QCoXw9/fHhQsXNMqfOXMG/v7+EAqFaNKkCTZt2qQkk5mZialTp8LNzQ1CoRC+vr4IDw83ynx//u8RBm04h32Xk7QLEwRRKykVs5h34Dp2XUys7qkQekI590ycL/+8DQDo19oNwS2cqnk2hLlw7kE6nO0EaOFSs3J7jdt+GQkZefjn9nMkfD2guqdDENXKR1suAgB4DIMfPuxQzbMhCNMhLjUbiRl5eF1QUt1TIWow+/btw8yZM7Fx40Z07doVP/30E4YNG6ZWPj4+Hv3798eECROwc+dOnD9/HlOmTIGTk5Ms72tRURF69+4NZ2dnHDhwAA0bNsSTJ0+MlqvVPiECQ1JPAw/7Ap3GGaVPgiBqFsfvPMfvV57i9ytP8WGAp/YGhMlAnntmAj2gErryMC0HH225iJD//VfdUzE6CRl51T0FgjA5nmXlV/cUCMKk4DMMAICCHojKZN26dRg3bhzGjx8PX19frF+/Hg0aNFArv2nTJnh4eGD9+vXw9fXF+PHjMXbsWKxdu1Yms3XrVrx8+RKHDx9G165d4enpiW7duqFt27ZGmbNvQSzGWESifvo1o/RH1CxevXqF0NBQiEQiiEQihIaGIjMzU2MblmURFhYGd3d3WFtbo2fPnrh9+zZHpmfPnhCJRAAAkUgEhmEwYsQI2f7Tp0+DYRiVn8uXL8vkVO1X5f1KVIzs/OLqngJhIGTcI4gaxv3nr6t7CgRBVCHFpeLqngJBmBQ8nsS4V0rWPaKSKCoqwtWrVxESEsLZ/sYbb6htExUVpSTfp08fXLlyBcXFkpfpI0eOICgoCFOnToWLiwv8/PywYsUKlJaWqu23sLAQ2dnZnI9a6kqigASFGdoOkaiFjBw5ErGxsYiIiEBERARiY2MRGhqqsc3q1auxbt06bNiwAZcvX4arqyt69+6NnJwcjtzo0aMBAPfv30dKSgp++ukn2b4uXbogJSWF8xk/fjwaN26Mjh07cvrZtm0bR07aL0EQFJZLEDUOqn9NELWLW8nZePziNZo41a3uqRCESSD1Ooh9komgpg7VPBuiJpKeno7S0lK4uLhwtjs5qU+hk5qaqiTv4uKCkpISpKenw83NDY8fP8bJkyfx4YcfIjw8HA8ePMDUqVNRUlKCL7/8UmW/K1euxFdffaXTvJk6zgAAmyIy7hFc4uLiEBERgejoaAQEBAAANm/ejKCgINy7dw/e3t5KbViWxfr167Fw4UIMGTIEALBjxw64uLhg9+7dmDRpkkzWxsYGgOSaV6wIamVlBVdXV9n34uJiHDlyBNOmTQNT5oktxd7eniNLGB8W9DJprpDnHkEQZoPC/Z0giDK2nIuv7ikQhMmQllMIAFgVcbeaZ0LUdBQND6yWFVZ18tLtYrEYzs7O+Pnnn+Hv748RI0Zg4cKF+PHHH9X2uWDBAmRlZck+T548USvLt5MYRWxLXmqcJ1H7iIqKgkgkkhn2ACAwMBAikUhtoZj4+HikpqZyPFIFAgGCg4OV2vz+++8AgICAAMydO1fJs0+eI0eOID09HWPGjFHaN23aNDg6OqJTp07YtGkTxGL10Qt6ebUSMshRxHwhz70aBMuySg8NRO2jJq+2MEANPjqC0J20bG6FXAse/fYTBEFUFY6OjuDz+UhNTeVsT09PV9vG1dVVST4tLQ0WFhZwcJB4mLq5ucHS0hJ8Pl8m4+vri9TUVBQVFcHKykqpX4FAAIFAoNO8rewlxj2ROFMneaL2kJqaCmdnZ6Xtzs7OStetfBsAKj1SExPLK61++OGHcHZ2xuDBg/F///d/WLp0Ka5fv47jx4+r7HfLli3o06cPGjVqxNm+dOlSvPnmm7C2tsa///6LOXPmID09HYsWLVLZjz5erUQ59K5lvpDnXg0hv6gUvdaexrwD16t7KkQ1U5NXW8h4TRASXhdyiyzxeXQ7Jwgpo/j/YKHFTjgiq7qnQtRQrKys4O/vr2ScOHXqlNo2QUFBSvKRkZHo2LEjLC0tAQBdu3bFw4cPOd5I9+/fh5ubm0rDnr4I7d0AAPXZzJr9wEjICAsLU1usQvq5cuUKANXP2bo4j6jySJXfNmHCBPTq1QsAMGzYMBw4cAAnTpzAtWvKhV2ePn2Kf/75B+PGKVdzXrRoEYKCgtCuXTvMmTMHS5YswZo1a9TOSx+vVqIc+mkwX+htoIYQfjMFCRl5+P3K0+qeClHN0O8xQdR8rCy4t28+3c0JQsZ4fjgmWITDg3lOBWeISmP27Nn45ZdfsHXrVsTFxWHWrFl4+rT8OXzBggUYNWqU7PvkyZORmJiI2bNnIy4uDlu3bsWWLVswd+5cmcwnn3yCjIwMzJgxA/fv38exY8ewYsUKTJ061ShzrltfYtyzYkpQ9PqVUfokTJtp06YhLi5O48fPzw+urq54/vy5UvsXL14oeeZJkea+U+WRqq4NAHTo0AGWlpZ48OCB0r5t27bBwcEBgwYN0npsgYGByM7OVjlvQOLVamdnx/kQRE2GwnLNBG0OS2IysRO1APLbIwgJysY9su4RhJTnqAcPvIAL8wp3nmWjbSP76p4SUQMZPnw4MjIysGTJEqSkpMDPzw/79+9H//79AQApKSlISkqSyXt5eSE8PByzZs3CDz/8AHd3d3z33XcYOnSoTKZRo0aIjIzErFmz0KZNGzRo0AAzZszAZ599ZpQ517W1RTZrDTsmH3EPH6Jt+85G6ZcwXRwdHeHo6KhVLigoCFlZWbh06RI6d5ZcFxcvXkRWVha6dOmiso2XlxdcXV1x/PhxtG/fHoCkkvSZM2ewatUqtWPdvn0bxcXFcHNz42xnWRbbtm3DqFGjZN6smoiJiYFQKIS9vb1WWUJ3anKKp5oOGfcIwkAOXn0KN3shujTVfsMkjANF5RKEBIFcPiaAPPcIQp7nbD0AgAvzCnsvPyHjHlFpTJkyBVOmTJF9l0/Yv337diX54OBglaGI8gQFBSE6Otpoc5SHz2OQzopgx+SjIDOlUsYgzBNfX1/07dsXEyZMwE8//QQAmDhxIgYOHMiplOvj44OVK1fi3XffBcMwmDlzJlasWIHmzZujefPmWLFiBWxsbDBy5EgAwKNHj7Br1y706NEDgCQU/YsvvkD79u3RtWtXzhxOnjyJ+Ph4lSG5f/31F1JTUxEUFARra2ucOnUKCxcuxMSJE3XOOUkQNR16HSAIA4hLycac/dcxcvPF6p6KEtoqtREEYf6Q5x5BqCdNzri351KSFmmCqF0UCCSL0oKCjGqeCWFq7Nq1C61bt0ZISAhCQkLQpk0b/PbbbxyZe/fuISurPJ/pvHnzMHPmTEyZMgUdO3ZEcnIyIiMjYWtrC0CSn/Lff//FkCFDZPIhISE4ceIEp3AMICmk0aVLF/j6+irNzdLSEhs3bkRQUBDatGmDb7/9FkuWLME333xj7NNQ61BMX0GvkuYLee4RhAEkv8qv7inUShiql0sQACTGPQGK4IBsPIMjnr7Mq+4pEYTJIPXcc2YopxhBKJLNrwcUA/z8F9U9FcLEqF+/Pnbu3KlRRtGJgGEYhIWFISwsTKV8o0aNcObMGWRnZ0MkEiE2NlZt7rvdu3erHbdv377o27ev5gMg9Gb7+XiE/XUHv43rjO7NnQBUzptWQXEpLHgMLCo51CQrrxi5RSVwt7eu1HFMFVrqJwgDoPDQaoLOO0EAAPipsbgnHIMDgjAAwB8xydU7IYIwIWRhuSDjHkEo8tpCoh8WeenVPBOCIKqbsL/uAABm7o2ttDEKS0rhv/Q4eqxWX03cWLRdEokuX5/Ei5zCSh/LFCHjHkEYgCkb92qyK7UJn3aCqFrsGgIAXPEKViiu5skQhGnxHNKw3MzqnQhBmCDJJRKvqadPEqp3IgRBmCZGfplMzMhDblEpnmUVQCyumhfVW8+ytAvVQMi4Z0KIxazBF3wNtucQelKTKxyZslGVIKqUOo4o4gnBY1i4MxLvC8q3SRASPh/eC4Ak5x5BEFzicoQAAF4eheUSBKFMRZ4mrya+RN/1/yH6cXlOTz6v/AWuWCxW1czo1NZnYjLumQhiMYuQ9f+h/3dna+3FSBiHmnz5MOS7RxASGAZZVm4AgIZlxr2D15LxKreoOmdFECZBax8fAIAdkwdrFFTzbAjCtOjSRqIfjkzt9GwhCKLyGLYpCndTczDi5/KK33w574yS0qp5Ua0iG6LJQcY9EyEluwAP017jbmoOcgpLqns6hBlTk417BEGUkylwBwA0YtIAAHP3X0f7pcfxx7Wn1Tktgqh+BLZ4zUq8k8h7jyC4NPLwAgC48LKreSYEQZgihr5LPnmZp7KtvOdeSSmLp6/y8DAtByfuPDdwhtopreIXYlNxziLjnglCvkmmD3mQVQ8UlksQ5WQLpcY9bmjVl3/ero7pEIRJUV5UI7N6J0IQJgbP1hkAUI/NRGlpLXVvIYhaQFJGHh48z6my8d7bFKVyu7xx7/crT9Bt1Sm8te4/jP/1CqIfZ+DRi9fosvJf/BadaLS5VJWxLSuvGHsuJcF/2QlcTaz+xUQy7pkgDFkwTJ9K+BOVilmM2noJy47eqVA/prFuUDmQZhBEOdkCaVgu17hnKquHBFGdpEmNe+S5RxAcGntKPPcETAleZ1HFXIKoqfRYcwq9//cfsvL1K7ym7Tkyp6AYJ+8+R1EJd3EgNVt1Ggz53paHx3H23XyahS//vIVnWQX44vAtveapSGZeeWoaQ+t23ErOwqqIu1h29A7upmYjJSsfxWoWQbLyitF2SSQW/HETL3OLMPTHC7iWVL3PHGZj3Hv16hVCQ0MhEokgEokQGhqKzMxMjW1YlkVYWBjc3d1hbW2Nnj174vbtco+GhIQEMAyj8rN//35OX8eOHUNAQACsra3h6OiIIUOGGPX4KvwyRu9yZs+FR+n47/4L/HIuvkL90Is9QdQOsoUNACh77tEvAEEAz2EPAHAm4x5BcBDZ2iKbtQEAvM54Vs2zIQiiMpB/H3yuxujGkVfzb1WM2XYZY7dfwf9O3MeHv0Rj3PbLGt8/NRUMZRj1efhO3UvD/47fx6x9sVq94m4lZ6HdkuOy76UGWvcGfn8OP55+hF/OxaPv+rMIWnlSrUdi7NNMpW1DNl4waFxjYTbGvZEjRyI2NhYRERGIiIhAbGwsQkNDNbZZvXo11q1bhw0bNuDy5ctwdXVF7969kZMjcU9t1KgRUlJSOJ+vvvoKderUQb9+/WT9HDx4EKGhofj4449x/fp1nD9/HiNHjqzU4yUqTqmYxQ+nHuJKwkuj910ZHmTqVgW0oWj4/v7LGRAXvNbYRmr49vb2BgAMGDCAY/gGgEmTJqFp06awtraGk5MT3nnnHdy9e1epr8o2fMtDXq2EKjZu3AgvLy8IhUL4+/vj7NmzGuXPnDkDf39/CIVCNGnSBJs2beLs37x5M/r27QsA8PDwwFtvvYVLly5xZFauXIlOnTrB1tYWzs7OGDx4MO7du8eRGTNmjNLCUWBgoBGOWII0LLdhWc49KWTfJwi5sFwy7hGEEpk8ewBA3qvU6p0IQRCVgrxtiwGQX1SKq4kvMf/gDXy6J6ZCziBSQ9uPpx/h/MMM/Hs3DXlFpWrltQ3FU/N+9/G2y/j23wc4FJOMoT9qNpr9GpXA+S424sNw7JNMo/VV2ZiFcS8uLg4RERH45ZdfEBQUhKCgIGzevBlHjx5VepmSwrIs1q9fj4ULF2LIkCHw8/PDjh07kJeXh927dwMA+Hw+XF1dOZ9Dhw5h+PDhqFu3LgCgpKQEM2bMwJo1azB58mS0aNEC3t7eGDZsmFGPkV7GjM/vV55gzT/3MEyNtb0imJKRSdHwnXD/DtKPfqOxjdTwvWbNGgCAs7Mzx/ANAP7+/ti2bRvi4uLwzz//gGVZhISEoLS0/Me7qg3fpnPWCVNh3759mDlzJhYuXIiYmBh0794d/fr1Q1JSkkr5+Ph49O/fH927d0dMTAw+//xzTJ8+HQcPHpTJnD59GkOHDgUAnDhxAh4eHggJCUFycrJM5syZM5g6dSqio6Nx/PhxlJSUICQkBLm5uZzx+vbty1lACg8PN9qxZwklYblOTDaEKJRtZ8l3jyAoLJcgNJBSagcAuB6nvGhLEIT5o+i5NnrrJQz9MQp7Lz/BX9ef4cnLfLVtDbFLqGuy4eQD9FhzSmPbir5Wi8UsLsZznXlqq23FLIx7UVFREIlECAgIkG0LDAyESCTChQuqrbjx8fFITU1FSEiIbJtAIEBwcLDaNlevXkVsbCzGjRsn23bt2jUkJyeDx+Ohffv2cHNzQ79+/ZS8nIwJGTD051DMUwz8/iySM8t/qB6lafZeq07yi0oxc28Mwm+mVKgfVYbviQtXIf/RZRRnqK6YKW/4HjRoEABg06ZNHMM3AEycOBE9evRA48aN0aFDByxbtgxPnjxBQkICgKozfHMg5SAUWLduHcaNG4fx48fD19cX69evR6NGjfDjjz+qlN+0aRM8PDywfv16+Pr6Yvz48Rg7dizWrl0rk9m1axcmTJgAAGjRogU2b94MsViMf//9VyYTERGBMWPGoFWrVmjbti22bduGpKQkXL16lTOeQCDgLCDVr1/faMdeZGEnC62Sz7tXWx9oCEIe8twjCPVI9aNuEeXcI4iaRqmY5eR+G731Ei4pRLIpVpMtlsufZ8zHyLWR9zXuZxhGreeerhy4+hSJGXmcbYrGzT2XkjDgu7M6hSibM2Zh3EtNTYWzs7PSdmdnZ6SmqnYnl253cXHhbHdxcVHbZsuWLfD19UWXLl1k2x4/fgwACAsLw6JFi3D06FHUq1cPwcHBePlSfbhnYWEhsrOzOR9NPHmZp3G/KtJyCvDhL9EVNhDVBGbtu45bydn4soKJOKuKrefjcTj2GabsulahflQZvlv4dQAjqIPC5DiVbQwxfOfm5mLbtm3w8vJCo0aNAFSd4bukVEx5BAmVFBUV4erVq5xrGQBCQkLUXstRUVFK8n369MGVK1dQXKw64XBeXh6Ki4s1GuaysrIAQEnm9OnTcHZ2RosWLTBhwgSkpaWpam4wT1gnANy8eyWGZhEmiBqE1HjhDDLuEYQiDRpJimrYFL7QIkkQhLmx+p+7GPFztOz7syxlg5biu1VOYQlKSsVIzMhFYUl5lNb+K090GtPQdzUGAI9XMePeXzeUc4cqhuUu+OMmbj/LRq+1pys0liHkFBTjbmo2kjL0t/foS7Ua98LCwtQWtJB+rly5AkB1GCTLslrDIxX3q2uTn5+P3bt3c7z2AEAsllixFy5ciKFDh8pCFVUV3ZBn5cqVshxoIpFIZhBRRVGJGCN/uajxOFSOEX4X5x9mVNhAVJPI1LMakLFJzsxHdoH2ObzIKdQqowuqDN8sWPBtRCjNVf1Co4/he+PGjahbty7q1q2LiIgIHD9+HFZWVgCqxvBdUFyKoK9PYnjZDUpec0dujsZUuvZrNenp6SgtLdVrESc1NVWlfElJCdLTVXswzJ8/Hw0aNMBbb72lcj/Lspg9eza6desGPz8/2fZ+/fph165dOHnyJL755htcvnwZb7zxBgoLVeu/votCAPC0zLgn77lXKmbxf/uva21LEDWZVEiMe67MK7Biw3LaEkRNhbV1BQAIC4y74EQQROVTVCLG3dRstQa1n8481qkfxUIXR64/Q/Ca01gdUZ727P8O3EBSRh7+uZ2KXmtP492N51X2VZFlZV1teyzL4sbTTBQUq8/vVy6rerum3ICVxfmHGei7/ixm/R5b6WNVq3Fv2rRpiIuL0/jx8/ODq6srnj9/rtT+xYsXSi9pUlxdJTctxRe8tLQ0lW0OHDiAvLw8jBo1irPdzU2S06hly5aybQKBAE2aNFGb0wkAFixYgKysLNnnyRP1Vu8fTz9Su08Tr+TKPeubY+nG00yM3ByNW8lZBo0t5UrCS/walWAynlWGFqXQF1W/QSlZ+ej69Um0CYs0qE/5U1hRwzfAak1goIvh+8MPP0RMTAzOnDmD5s2b4/3330dBgWT1pyoM31cTX+FFTiEuleVRkJ/fhUcZOHYzxWSuPaL60HURR5O8qu0AsH79euzZswd//PEHhEKhyv6mTZuGGzduYM+ePZztw4cPx4ABA+Dn54e3334bf//9N+7fv49jx46p7Ecf3ZCiynMPAPZfVR2WTxC1BWnOPWumCHnZxi+sRRDmDM9OUpCpThF57hGEuTHptyvou/4s9l7WzatOHZ2Wn+B8P6Dm2TEtpwCTfruK+PRcxCRlqpRhDXwFZxj1BTUU2X/lKQZtOI8x2y5plVUMO64MLjzUNa1B2XtG5U1FRrUa9xwdHeHj46PxIxQKERQUhKysLE61wosXLyIrK4sTQiuPl5cXXF1dcfx4eUnkoqIinDlzRmWbLVu2YNCgQXBycuJs9/f3h0Ag4BTuKC4uRkJCAjw9PdUem0AggJ2dHeejjojbhlWqqkh8+pCNF3DhUQbHZVcTpWIWey8l4aFCHrthm6Lw5Z+3cfqeaTwcFJVUkXFPxam/lpipc3ttBqmKGL5ZFijNywbfxl5l3/oYvkUiEZo3b44ePXrgwIEDuHv3Lg4dOgSgagzfiqg672Tbq704OjqCz+frvIgDSK5/VfIWFhZwcHBQkl+3bh0iIyPRpk0blf19+umnOHLkCE6dOoWGDRtqnK+bmxs8PT3x4MEDlfsN0Y0nrMRztxFD3heE8VGsxh4aGorMzEyNbaTV2N3d3WFtbY2ePXsaXI29IhTCChmsLQDg7NUYo/ZNEOaOhagBAMCuOKOaZ0IQhL6cKnvv3nY+HgXFpbiVnKWUY04bLICM3CKtcgCw+6L69zophlanZaC70WvvZck8oh9rX7CrivfDn/5T7SGZoxDFJ51LVdTjNIuce76+vujbty8mTJiA6OhoREdHY8KECRg4cCC8vb1lcj4+PjLDA8MwmDlzJlasWIFDhw7h1q1bGDNmDGxsbJSqeT58+BD//fcfxo8frzS2nZ0dJk+ejMWLFyMyMhL37t3DJ598AgB47733jHJ8VnwFLxIt8ll5xXj6Kk9nF1ZVSHMyvS4s0Ul+3+UnmP/HTby17ozK/fHpuSq3Szkck4w/Y5M1yhiDqvLcU4UxK1RWxPD94FYM2MJcCBr4quxbX8O3PCzLykIKq8LwrYgaH0WilmJlZQV/f3/OtQwAx48fV3stBwUFKclHRkaiY8eOsLS0lG379ttvAUgqQnfs2FGpH5ZlMW3aNPzxxx84efIkvLy8tM43IyMDT548kRnGFdFbNxhG5rnXkDGNBRaiZqFYjT02NhaTJk3S2EZajX3Dhg24fPkyXF1dDarGXlGWDfbDM1ZisP/9X90WMgmitiCoJzHuOYgzUKRDiBtBEKZHYYkYPl9EYOD357Dw0E292qpyNFFnEPsjRvs7vMHGvbKINEWiHikvPNQVWiptUwcLFilZ+SqNnnsuJeHvmynIyi9G2JHbOK+zB55uLDvKzXsvnQFTBb57FpU+gpHYtWsXpk+fLkuEPmjQIGzYsIEjc+/ePVlScwCYN28e8vPzMWXKFLx69QoBAQGIjIyEra0tp93WrVvRoEEDpSTrUtasWQMLCwuEhoYiPz8fAQEBOHnyJOrVq2eUY7Pka7exyl8KbZdIwj7bNhQZZXxdkK+4owpN6pxTUIyZ+2IBAG/5uqCOoPIuu+LSqjH1qFJOQ1cI/rmdij6tXA1qK2/4/umnnwAAm1fMh3XTTrB0KPci8vHxwcqVK/Huu+9yDN8NGkge7j755BOO4fvx48fYt28fQkJC4OTkhOTkZKxatQrW1tbo378/AK7hu1GjRvD09MSaNWsAGM/wrQuSm5P6H8vwmyk4cec5VgxpDaElv8rmRVQNs2fPRmhoKDp27IigoCD8/PPPSEpKwuTJkwFIvOGSk5Px66+/AgAmT56MDRs2YPbs2ZgwYQKioqKwZcsWTkjt6tWrsWzZMgCAh4eHzNNPmn8SAKZOnYrdu3fjzz//hK2trUxGJBLB2toar1+/RlhYGIYOHQo3NzckJCTg888/h6OjI959913jHDzLyjz3PJk0SH6JqaQ0YRyk1dijo6NlRZs2b96MoKAgtW3kq7EPGTIEALBjxw64uLhg9+7dMsPgxIkTZW0aN26MZcuWoW3btkhISEDTpk2NMv8PAzxw/JgDWiMB7gx5JxGEPA09ywpqMIV4/ioDLioKFxIEYTqoSjkjXyF27+Un+Hqo6igTVRjbH6YitdxUmUI+2Ky8KCefI3Da7mvo5+eGAW1UL5ifjEvDwkO3ENLSBT+P4i7SL/iDawjdfiEBCV8P0GmuBcWlSM7MR1OnumplriRyPQvZcutepWM2xr369etj586dGmUULdAMwyAsLAxhYWEa261YsQIrVqxQu9/S0hJr167F2rVrdZ6vPlgoeu7paCW6qSFfXlGJGF/+eQs9Wjihf2vVF31VUCpmUVBc/utRWCJGHUHljlcVVNStVn6Wk367ioSvBxjcp6Lhu3WXNwGfERwZdYbvOXPmAABSUlI4hm+hUIizZ89i/fr1ePXqFVxcXNCjRw9cuHCBU8Cjsg3fiqdEZWEdLX1IC860cLXF5GDjvDQSpsPw4cORkZGBJUuWICUlBX5+fggPD5d5j6akpHDCxL28vBAeHo5Zs2bhhx9+gLu7O7777jsMHTpUJrNx40YUFUlCFVq0aCHbvnjxYtn95McffwQA9OzZkzOfbdu2YcyYMeDz+bh58yZ+/fVXZGZmws3NDb169cK+ffuUFpgqQlKZcc+OyUN95OAldPeEJQhNqKrGHhgYCJFIxLmfyKOtGrsqrz9V1diNAcMwSGYdAYCMewShQJ26dshi60DE5KI48xlAxj2CMFlaL/4HOYUl2P5xJ/T0No6uhh25rV1IDwz13MvMK8bVRN2q2p+T87A7eiMFR2+kYECbAch4rRxe/O9dSbqayDvKdRsqwuAfzuNuag5+G9dZrYzimZCem6pYfjcb415NRhfPPVUwDCMzBSvq097LSdh7+Qn2Xn6isyVa41gGtHnyMg991v+Hvn6GeaXVBIpKxNh6Ph49mjuhpbvml275v+H7m6Kwe0IALMqujbScAnyy8xo+DPDAkA7cvF6Khu9dFxOx8NAthb5VG75nz54NkUiE8PBwTgigu7s7wsPDtR5fZRu+FVF1Hep6M0k3UoViwvSYMmUKpkyZonLf9u3blbYFBwfj2jX1lZYTEhKQnZ0tM2KoCo/VtghjbW2Nf/75R/PEKwrDoBBWSGYd0IDJQGMmFS9ZMu4RxkFVNXZAkjZCnXFPUzX2xMREzraNGzdi3rx5yM3NhY+PD6cauyoKCws5laZ1qSadwtYHALiRcY8glHiBehAhF3kZTwC0q+7pEAShhpyyNFpjtl1G/Mr+Rukz6rHyfbEiKaYMNe7978R9g8eUUlCiObXAN5H3NO7Xh7upkhQjf1zTLd3Y6Xtp+HSPJO8v5dyrJVgpGPd0VQ1N10dadvUbMr779wHyiko5F78uXompWQVmVQFV01y3nIvH13/fRf/vziq00dznpYSXnNWJr8Pv4mriK8z+/boO89EqYpbcfpZFBTUIQgUJYskCihdjWHEmonZR0WrsutyfK1qNXRWGVJN+Jue5J64iz36CMBeeiSVRFgdPX67mmRAEAUhCPtf8cxdxKeoXryrzVlaRdypTvsV+f/Kh0fvMzi9Wv1PuXIzZVv77WhU598i4ZwKoenhW5JNd15SKVlSF9bciGFLNd++lJASu/Bdf/XWnEmZUOWj6Ibz1TH3otDbkQ4yzCzT8gNQSBnx3rrqnQBAmhfQXNoGVGPca88i4R2inItXYAUlhGHUYqxq7KgypJi0tqOGODGy/kKBVniBqE2mQGPd4r40btkaYL5VVIR2ArPifm5sb7O3t0bNnT+Tn5+s1dlJSEt5++23UqVMHjo6OmD59uiyNSk0gaOW/+OHUI/T79qxamRJx5RWPrIh9rroW0FiWrZLqiooLm9KwX30gz71aijpj0ReHuaGWuhgFqwpVK/mGTG9FuKS6jKk/hMsfGqvwfw7VsAJiwgsnRsB4hUwIoqYQz5LnHqE7FanGfvHiRbUhuYDxqrGrwpBK61LjnivzEsuO3tIiTRC1i+esPQDAhXmpWZCoNaiqkB4aGqqxjS4V0qOiomS5jU+ePInLly9j2rRp4PHKTRHaxi4tLcWAAQOQm5uLc+fOYe/evTh48KAsf7ipUipmsejwTRy5/kyr7Ks87c4clZlfviKRc8Z8H9PHUDjpt6t4rOAAZQjnH6ZrrJrrtSAcP//3SKe+WEjOpaJjlnwBlMqCjHtmRJFCWRue6dj2VGJI8QNTMlhqRG6aJhdCLDcfk5ubvihcDqouD0NzPBBETUHmuUfGPcKIyFdjj46ORnR0NCZMmIC+ffty5Hx8fGQed/LV2A8dOoRbt25hzJgxStXYV65ciatXryIpKQlRUVF4//33OdXYjUUa6qGE5cGSKYUjDPekJwh1bNy4EV5eXhAKhfD398eFCxc0yp85cwb+/v4QCoVo0qQJNm3apFZ27969YBgGgwcPNvKsJaSW5aR0YXRLZk/UbKQV0n/55RcEBQUhKCgImzdvxtGjR3HvnuqcZYoV0v38/LBjxw7k5eVh9+7dMrlZs2bJCir5+vqiefPmGDZsGAQCgc5jR0ZG4s6dO9i5cyfat2+Pt956C9988w02b96sUw7W6uLP2GTsjE7C9LK8a4o8fvEa3//7AK/L8uppo6QSjXsVMRyuirhrvHno8W5nrIIZH/5yER/+clGjzIpw3Y4xPj0Xb284h6E/cu8HyZn5aloYDzLumQA627MUrnNjxG3raiA0xOZmLnY6Rf53/D72XU7SLliGIT+DuiQsNXvDXCWg6pKis0TUduI5xj3SCMJ47Nq1C61bt0ZISAhCQkLQpk0b/PTTTxwZVdXYZ86ciSlTpqBjx45ITk5WWY29f//+aNasGd5//33UqVNHqRq7MRCDh+dloYcNGPUr8gRhCPv27cPMmTOxcOFCxMTEoHv37hg2bJha+fj4ePTv3x/du3dHTEwMPv/8c0yfPh0HDx5Ukk1MTMTcuXPRvXv3Spt/GivRDTLuEYDmCunqjNbaKqQDkrQMFy9ehJOTEwCgWbNmCA4Oxrlz5el2dBk7KioKfn5+cHd3l8n06dMHhYWFuHr1qsr5FRYWIjs7m/OpalRVcpWn9//+wzfH72PZUd1SUpWWVt5z3rWkTIPbHruZYpQ5sCxbqd6JVcWt5Gy8zK36kHGqlmuKaLie5Q0+8oY5Q1XAkLx4uvetvI1lJSsU7vbWEFryK21sQ7n9LAvf/j97Zx4WVdn+8c+ZAQZQFhUBcd/BfVdcUnMtTSvL0l7LUrOfWamZb2aLWumbmZqZaW5paruWqZFbmgsuqLiCC+KGoKAIKrLMzPn9MTDMMDsM+/O5rnMxc87zPOeew8xZ7ue+7+/OCwA8176WxXaGjtWcf4kjzjh7mho2ccTPl7dfaXWymsO8oEbpvwAIBPkh5/dwTfZHI0tUlNKpSgqJ+BarXYKyQ141djBVqbWkxj59+nSzY9qrxu4MpvRvzI1dOjVpoZgrcDbz5s1j1KhRjB49GoAFCxbw119/cf68efXHJUuWUKtWLRYsWADoIpgiIiKYO3euPmURdOmHL7zwAjNmzGDv3r02a57ll1cHdIbt8/GXCmd8QenCkkK6v7+/SR1Vwz5gXSH90qVLgE4UCeC3335jw4YN9OrVi9OnT9OwYUO79p2QkGCyn0qVKuHm5mbRvtmzZzNjxgyLn7kkkOPIOnLZvvT4bWctZ2mkZ2lK5PO1I/x95iaPNPIrbjNKLSJyrxRx+PId/jyZ6xV3hkujMJ175iIL915I5NEv9vD0YvMzQMXtiEqxpnxjgGTkWLX8nyiIpHh+MXzOKg9uL7vVpcuQk7M08EvENUZ9d4S0TPvSDAT5JxNX4rJVQUVqrkCQy7geDYjPrrsnnHsCZ5KZmcnRo0eNIpYAHn30UYt9wsPDTdr369ePiIgIsrJy7z9nzpxJ1apVGTVqlF225Dc6qVqNugAEkAyFWKRfULw4QyHdVtkkawrp2uzv1ssvvwxAy5YtmT9/Po0bN2blypUWxzC3b0fty48QU3Fhb7Daf387ZXHbkj321YQryeyIuqmvwS9wHBG5VwKx5hD6KjuqDKzXGrPXqWSvwyM/KcDmIvd+OqI7qZ61IvFdEOLuPuSXiGs8265m/gZwsjfM0r/Int2IgDRTzH0PZTvvR8XxLFre+fUkACv2xvJGr4bFbE3Z57IcSC0SqaNI4IgmuLjNEQhKDDeyHd/VhXNP4ESSkpLQaDQmkUQ5qYfmMBd5FBAQgFqtJikpiWrVqrF//35WrFhBZGSk3bbkNzrJo1IQWlnCVdKQde8Wrj6BDo8hKPmMHz+e559/3mqbOnXqcPLkSbMK6YmJiSbf2xwMFdKrVaumX2+okJ6zPjjY+N4kJCSEq1ev6sexte/AwEAOHTKuiZacnExWVpZF+1Qqlb6uX0nHGTXET8ellHrH2N9nEriXLgID8ouI3CtlXLh1X/86PSvXq7HtTP6iNeyJ3EtNz+KnCMdnOhRmvHtF4V9559eTRRotVHhOI9nMKzt6lSFBjbzOPLNpueUiPrH0Ym80rMBxDH8fQjFXIDDPjWzRABG5JygMzEUs5ae9JEncu3eP//znPyxbtgw/P/vT0vIbnVTB04MkfABYu/2g3fsTlC6coZBuSe3cHoX0OnXqEBQUxIULF4z6nj9/ntq1awPYte/Q0FBOnz5NfHxuFtu2bdtQqVS0bdu2gEep8LD3OeWBnYIa1giPuc23/14q8DjOQKmQuHUv3eF+wrFXMIRzrwSSH3/MP+cSbba5dS+dZf9eItmguOPDLA3hMbet3ox8HmZeIckWZqP9isgPk6Uuvw4f2cLrskop918KBE5BKOYKBObJidwLEs49gRPx8/NDqVSa1PpKSrIs3BIYGGjS/tatW7i4uFClShViYmK4fPkyTzzxBC4uLri4uLBmzRo2bdqEi4sLMTHmU+5UKhXe3t5Giz24uSi4kZ22Hn4s0q4+grKLJYX0gQMH0rhxY307RxXSJUninXfe0YsxxcTE8MEHHxAdHa1PPbdn33379qVJkyaMGDGC48ePs3PnTiZPnsyYMWPs/s6XZJJsCG/Yw4NMjRMscQ4uCklk+xcDwrlXArAUO+fsqKuXVh7h061RvPmjsRT3sGUH+SPyhsV+l5LuW9yWgzlT72eYRuzYCjk2PBY7zt4kS5PPs0Keg6rRyuy9kGgzisju+m2GfWTjv9aIuHyHQYv2cdwONaL8/vvLsrOrIGq5ouZe8VCGv47FjuF3OjdyzzlqZQJBWSHHeVFdSioTCnyCkoGbmxtt27Y1ilgC+Oeffyz2CQ0NNWm/bds22rVrh6urK8HBwZw6dYrIyEj9MmjQIHr27ElkZCQ1a+az5IwVcn4fwvktAPMK6d9//71RG0cV0gEmTJjAxIkTAejatSs7d+5k+/bt1K9f3+59K5VKtmzZgru7O126dGHo0KE8+eSTzJ07t7AOR7GSpdEiyzLxKQ+L25R8kaHWkqkW3r2iRtTcKwHkdTo449bT0MFzKfE+9apWJCq7zt3eC6azin+fSeDJ1tWdsOdcfo64bmqXA/1Hr4ng7T6NnFKva9X+WD7ZEkXjAC/+nvhIgcczTKuwKqiRZ9MzS8Lt3oex6q0DSrxW9l/auZFiGt7tjBoVAkFp55IcBEA9KQEFWrQoOH41mZBq3mw/e5M14Zf5algbAn3ci9lSgaBouSbrFBj9pFTSH6airOBTzBYJygqTJk1ixIgRtGvXjtDQUL799luuX8+99506dSpxcXGsWbMGgNdee41FixYxadIkxowZQ3h4OCtWrOCHH34AwN3dnWbNmhntw9fXF8BkvbO4IQRnBAaYU0jPi6MK6TlMmjSJGTNmEB8fbzbSzp5916pVi82bN1ttU1bYcjKeCT9FFrcZBeKRzy1PdggKB+HcK6GcvZHK9rOmhUXzw6Nf7GH35B5W26hciiaIU+1gJN7mk/H5cu7ldZj+HhkHwLmb9xweyxbCt1S82Hv8xf9JUJa5LlclXXbFXcqipnSLK3IgTy0+QLeGfvoJnY83n+XrF9oUs6UCQdFyD0+S5YpUku6jvXMFKrQobpMEZYTnnnuO27dvM3PmTOLj42nWrBm//PILjz/+OADx8fF6wQDQ1SfbunUrEydO5OuvvyYoKIiFCxcyZMiQ4voItGzWDKL/ooZw7gkEJYrS5NiT0OJJBh5koiITlZSFO5moyEIlZaHC8H0mSrTZiwYXg78KtLigQSmZXy8hI2XvT/dXNvgrI0l5t+XZbrJO91eBTN4QJMlM8E7eJLC8bUzfG3NBrg4McPDoOoZw7pVQHl+416njHbl8x+p2NyvOPWc6RU5cT7G6PW+hYQ83pf61Vitz5PIdQoK88XZ3dWi/+VH7tTqewXCy/q9zvUf5T8s1FOIo+x6t8vAZBQJzGJ7VtCi4KFenmXSZhlIcV7LTdA0jtVPThbiJoHxyTa5KJek+sRfP0LSmcO4JnMe4ceMYN26c/n1qaqr+9XfffWfSvnv37hw7dszu8c2N4Uxq1G4I0VBDKZx7AkFhYOl57lzCPd7//VTRGmMHbmQRKN3BjxT8pBSqSKn4oftbRUrFi4dUlB7iRVr234dU5CEKSTyP2aKitvBTrIVzrwRSHAqnl2+nIcuyiXOtqMm7dw/XXOfer0evM+W3k9T1q8A/NiIRT14zdiLm52MNXRrOW70a0qWBqWKZ0UO1LJN0P8PxHeQh7789P06rvDUKS3u0mj3/tyu30/D3sp1qqNbKHLp0m9a1Kll1ZgucS2n/DpYmLsjVaYbOubeDkqscJxAUNdfkqrQglhMnT9C057DiNkcgKDFIPro6fgGycO4JBEXJ6DVHuHaneOrpuZFFPSmextJV6ituUFNKpIaUSE0pkUApuUBjp8uuZOBKOm5kyK5k4EY6ur8ZsiuZuKDGBTUKNCj1fzWywmCdEg2K7EXXRosCWdbF5eXG4AEGr61vy4nPy4nZ07027WOMbGeAkLl2smy87jZefGfnccwvwrlXAsgbVWauJl6B92HDS3I49g6/RFxnaPv8FestrOgphYEP5o8TutTa2KQHNvst/TeGxgG5hVzz47I8HHuHF5Yf4vL/rIfP/t/ao+y/eJuh7WrkYy/2Yc/RnRMWzfK9sQxuFVRodhQ1uY4hGUv/xWeXhNv8HwF8d+Ay3x24zPPta/K/ISJyQ1D2uKCtDkpooLgOJUcwTSAodnLq7vlmCsEZgcAQyVdXb9ufZNBkgdKxzBiBQGAdc89wSfczisyxp0RDsHSVtorztFVcoIl0hbpSPC6S5VJZD2U3EmUfbuNDkuxDkuxNEj4ky16k4sk92ZN7eHBf9uAentl/PUjHjfw9dQuchXDulUCckWOf90RiTzTgdwcum3XuORp5czctE08353y18hv1YyKyUICIxA3HrrP+0FUW/6eN2Qix/Rd1s52GAiJ3HmRSuYJbgZye1j77tTtpqLUydf0q6Nct3h0DwC9HTYVMSit+F3/hb7ev+FvbjnnqoU4Z88cj1/jfkBbceZBJRZWLiOITlFpa1vQ1en9R1j2kNZTiisEagaBk8lizQK5F6Zx7HvevFbM1AkHJQunlT4bsgkpSk3HnOqqqdYvbJIGgzNPukx2FOn5tKYGeikh6KE7QQRGNp2SaYZYie3JOrslFbXWuyv5ck/25JlflulyVO3ghnHSlE+HcE+ixVI8pOS3T7jFupabTYdZOqvt62GxrLg04b1ppfnHUKajWaPl0axQarWnHST+fAOB/f0Uzb2gru8bbcOw6o7vVc8yIPORYkp6lYfe5RCNbu83RqQ+dmdGPCqqy+zOWtGoaK66TKDtX3fDanTS6zfmHen4V2GUjxVsgKKk80qgqHwxswsebzwJwQdZFDzeQbiChRUY4rgWCRcPb8PL7fwNQTb7Fw0yNUT1fgaA84+riQrxchTrSTWau28anE8YWt0kCgSAf1JHieVK5nycU4dRXGEepp8qeHNM25Ki2ISfl+kRra3KTSggHXtmj7HoFBA5zPfkh87adY1LfxkbroxPsV5jdd1GXUhx313aocftPd/Ddyx1oVj3XcZOarjZqY+ikc8RhZxK4Z6P9huNxrNp/2Wqb1Ie5zk97AwEN7TDnODTE0phf7rxg9D7TwAF650GmVedeaa93llq5OQAtFLFOdVbkKFFfsiPFWyAoyYzqWpe45Ies3B/LVVkXgeEpZVBdSuJ6dipiDsVdU1UgKA6UCol77tVBCzWlW3yz+6LJfY5AUF5xd1XonHvc5GHSleI2RyAo8ziztr4bWQxUhDPCZQetFRf167NkJUe0jflH24p/tS04L9cQE77lBPFfLifYexpZuOui7UZWcOTZMel+JhNtpCAbprU6ci7U5GlsaNc7v5zgenKa0fZbqem2x8x2zun+Ov6QvCb8stXtJoIa2Sv+PZ9oprV9LNt7Kd99SwIPfBqSLrviLaVRR7rptHEdiUZ1Ns6KThUIcsg5v2lQckmuBojUXIHAkM/HDEQrS1SQMth59GxxmyMQlBhULkqyvHS1mrsHFlwcTiAQWOfvMwV/nvEijfHKjexTvcU8tyW0VlxEI0vs1rTkrcxxtM5YyvCs91mmGcg5uZZw7OWD17rXL24T8oX4T5cTHJklsLetYSSa02rjOYm0TLWRQ9PQFffL0euM+i7CqL09Zmhk+GrnBZp99Dfnb1qPZjQ33q7oW7Z3kmeMLI2WMzdSbY5tiXnbz3M2T//ShEZy4bSsq//SUopx2rhfFdCJnV8+2XyWhtP+4pwD0bACgSPk1t0rO7U3BYKColJ5ZKcgQTVtQjFbIxCULPxr6B5iKzwUvw2BoLDZU4CgDRWZvKr8k39VE5js+gv+0l3i5crMyXqOThlfMzLrv/yh7cp9PJ1ocfnkte71iP64f3Gb4TDCuVcCKIpMqf/+dsruthuO2Y742H8xifrvbdW/v5udsppX+beg5Nf3dzoulRX7YvXv86ajnbPhnDOHVivzxfbzPMzSMGtLlF19CuK6lJHZc870AmDktLTjcBdnlFpBydLInNTqahe2VDjPuVdcLM/+Ts7ffr6YLRGUVc5rdXX3zEXuiaRcQXnFVangmlwVAI8H10UEtUBggOytmxTyyXRehoRAIHAuvRVH2aV6m/dcf6CSdJ8L2uq8mTmebhkLWKwZTCK+xW1imUJCwt219NXnFc49gQm/HbMd8fHC8kNG77/ZXTiOF0vOsc0nb9Dj83+KNCrNKMqwiJ6S7X0AcWb9hpJEplrLiWznXgtF6U4xLq8URDHaXhYvXkzdunVxd3enbdu27N2712r7PXv20LZtW9zd3alXrx5Lliwx2r5s2TL699fN1tWqVYvevXtz+PBhh/cryzLTp08nKCgIDw8PevTowZkzZwr4aU0J8FbpX+eIajRUiMg9gSAHF6XEtewalDWlRDafvFHMFgkEJQelby0AKqsdyzARCAS2MX1Ec+y+uCp3Wey6gOVuX1Bdus112Y/JWWPpl/kZm7SdUQsJhUJlQItqxW2CQwjnnqBYceT0ZugkGL/+OJdvpzHhp+N29XWGL86WIIYhMjKyLKO1s8+V2w/YetpY2ciSvy7y6l3960W7LhKf8pAtp+LNN6Z0R+tkajSckHXpIk2ly7igttFDUN746aefmDBhAtOmTeP48eN069aNxx57jKtXr5ptHxsby+OPP063bt04fvw47733Hm+++Sa//fabvs3u3bsZMmQIADt27KBWrVr07duXuLjcaDh79jtnzhzmzZvHokWLOHLkCIGBgfTp04d795yblv1iaB396/PZzr3G0nUUGE8OCD0NQXnFMHKvhnSLu2lZNnoIBOUHtyo1AfCX858uKBAIbJOepXEoK62b4iR/qd7lceVh1LKCb9RP0Dvjc37VdEdbCt04o7rWLW4T7Cf7nllZym6eS9+3QmATjVYukEpqxJXkfPd1+vc/+3P8dvQ6By/dMdmcpbHvg9qyy55RHFXrfXZJODsN6uxZU6rs/vluk3RoWTZv139W5EZN/njkGi8sO8RWK8690kymWstlOZAU2RN3KYvGoo6YIA/z5s1j1KhRjB49mpCQEBYsWEDNmjX55ptvzLZfsmQJtWrVYsGCBYSEhDB69GheeeUV5s6dq2+zbt06xowZA0CjRo1YtmwZWq2WnTt32r1fWZZZsGAB06ZN4+mnn6ZZs2asXr2atLQ01q9f79Rj4O6qxNtdN3N7WQ4kTVbhIWVSVyqb5wWBwFFclRJXtbrIvTrSTar7ehSzRQJByaFSkG4S1Ys0Uu8KB59A4AweZKiZtTWKOX9H69f9E33LrudJCS2TXH5mtetn+EmpRGlr8kTmp3ymHkY6KtsD5IOh7Wo4baxWNX3Nrn+kUVX96+BAL6ftrzAoiE9j9tPNnWeIgzjs3Ltw4QKrV6/ms88+Y86cOaxevZoLFy4Uhm3lBmc7xFrN2EbivfwrXmWqi7AWjR0nuPQsDW//csLsNheFfQfPVi3AmMT7NscwUuC1w+68TtLCSp29lPSgQM7ckozuuyiVqbp75Y3C/G5mZmZy9OhR+vbta7S+b9++HDhwwGyf8PBwk/b9+vUjIiKCrCzz0TxpaWlkZWVRuXJlu/cbGxtLQkKCURuVSkX37t0t2lYQJvRuBIAWBdGyLgojRDKOXixdc48CgfNQuSiJzVaSrqNIQO1AJL5AUNbx8anEbXwASIkrHsExgaCs8cEfp/n230sm98G2ytWoyGSR60LedPkdhSSzVt2LJzM/JkquXYjWgqebC25K58R9vT8gxOz67gbOvZJOzj1zfvw0xRntZ/d/MCUlhcGDB9O4cWMmTJjAypUrWb58ORMmTCA4OJgnn3yS1NTSq8pZlriXobarbp7T95uexZywc/nqe+TyHYYuCTdZLyNbvQl3sfckZOE3dis1nZ+PXOOPSNv1dwzTcjOKwAEq4yTHSCl+os85zsflBgC0VQghCkEuSUlJaDQaAgICjNYHBASQkGBe9S8hIcFse7VaTVJSktk+7777LtWrV6d379527zfnryO2ZWRkkJqaarTkh7Na3Q1gE8WVfPUXCMoaSoVEopsuKiFIuoOcmVbMFgkEJYubCt21KiNR1DcWCJzB5pOm2RO2fD7e3Ged2ywGKA+TKSuZmPl/vK8eRQZuTrHpg4FNrG53hk/qhY61CPRxL/hAxYy1jLvBrYKs9nVRlgLn3htvvEFsbCzh4eEkJydz7tw5zp8/T3JyMgcOHCA2NpY33nijMG0VlHBm/xVN3N2H+er77JJwDl82Tbu1haudPx5LrZ78ej9Tfjtp1xiGghqZNoQunOGTk2UZtdY+J6LVk3EpDlB4pq3uYeyINhiA9lK02XZ/RMaVKlGRUla+ocST9wIsy7LVi7K59ubWAyxYsIAffviBDRs24O5ufLNiz34dsW327Nn4+Pjol5o1a1r8DNbImd1tIhk796wdE4GgrLPl3cGkoEsDUqVeLl5jBIISRpKrLrJVcye2mC0RCMoGluquW3pcyXHstVOcJ0X25MWsqWzUdnOqTdZq3mllGUWe+8QKbvapxdap4ql/LUnYpTJrz2Nbk2reVrf3Dgmwut0ZmLtzXvBcK9rVrmSxT+UKznHG5ge7nXubNm1i2bJldOzY0WRbx44dWbp0KX/88YdTjRMUP444TM7EpTg+PhAVbzk6xdbuXQsYPnwjJd3utvbW9wPzdqsd6J/DWz9GOtynLOHrqTs5Htc2QCNL1FIkEoCpE/itHyMJO20+GqqsoNXK/HPuVoFS7ssafn5+KJVKk0i4W7dumUTM5RAYGGi2vYuLC1WqVDFpP2/ePLZt20aLFi0c2m9gYCCAQ7ZNnTqVlJQU/XLt2jWz7WxhKXJPuPYE5RkfD1eS3KoD4HHvcvEaIxCUMFLddb+NM2dOFbMlAkHp586DTIuZZ+bWevOAtW6zaa64TJLszbOZH3FQaz3KztnIMuStdvVGr4Z29c07eaxyMX0+z1uHrml16447gPcHmk/vzcHTTudjfshNyzW9e5YkyWqghrPSm/ODQ3t2JBJCYD+26sEVF7IsM2zZQbvbn79pu26dOR77cq9lG2z0tbvmnhMO8cNM+5Vat5wyTfMNv3Tbof2Vnji0wuc+npzNjkZqrzCf+h15/W4RWlT0bDwex8urjvDo3N2Fto/DsXd47Mu9ROQjirY4cHNzo23btmzfvt1o/fbt2+ncubPZPqGhoSbtt23bRrt27XB1ddWv+/LLLwH47bffaNeuncP7rVu3LoGBgUZtMjMz2bNnj0XbVCoV3t7eRkt+iJZropUl/KW7+OH4pItAUFaJytKJauw/dMhGS4GgfHFD0k06Vc6M50GG/fe7AoHAlO/DLZdFyRsA4oqaJa7zaaGI5bbsxfDMaZyX85e5URBkTDNL7K9tb4y5yL1hHWoBEDahG2O71+OjJ5paHfPcJ/0JDrR+H2ztWfnYB330rz8b4rjARc6hyPvZbKXkmuuUI3pXFNjt3HviiScYM2YMERERJtsiIiJ47bXXGDRokFONExQvKQ+zzCrUWkJbDGmRecOHLeEMB6rGgc93Os4J9SeddThLpu/YYSK0jQFoZ8G5t/d8EtEJZbfu565zOuXle4V40z10aThR8ak8Y6b+ZUll0qRJLF++nJUrVxIVFcXEiRO5evUqr732GqCLhnvxxRf17V977TWuXLnCpEmTiIqKYuXKlaxYsYLJkyfr28yZM4dPPvkEgFq1apGQkEBCQgL379+3e7+SJDFhwgRmzZrFxo0bOX36NCNHjsTT05Phw4cX6jF5iDuxsi5yMMQgek9MGAjKO+cycxRzy3akt0DgKIkuumtGTekWN1Ptz2oRCASmWHo8PX/zfp66+DKzXZfTWXmW+7I7IzKnFotjD3ROx7x2Gz76fvpUM8udJcOXEq5KBetHm2Z7AgQHejP1sRB8PFzNbgddpL3KRWkSSWhqs+U7W0OHmrV9WULvO8hjw5T+wcbbLfQN9M4t5TPnmZYAvNOvscN2OIrdbsSvvvqKYcOG0aFDB3x9ffH390eSJG7evElKSgr9+vVj4cKFhWmroAg5euUONSt72m5oQGE8ONpKC7Y3Is8ZkXt2lr8TFBKHtcG8zN90sODcOxufSv8Fe4md/biIJC5HPPfcc9y+fZuZM2cSHx9Ps2bN2Lp1K7Vr6yI94+PjuXo1VzW2bt26bN26lYkTJ/L1118TFBTEwoULGTJkiL7N4sWLyczMBKBRo0b69R999BHTp0+3a78AU6ZM4eHDh4wbN47k5GQ6duzItm3b8PLyKsxDAsBZuTb1iaeJdIW96FKKd0Xf4s8TN3iipR2zjgJBGeRyttO7riKeh5kaPAoxpUcgKE0kZAtq1JASiXEgU0UgEJhiySk1b7uxMOBryj95RvkvalnB61lvcVauU/jGWUDGNGimSwM/wHaNe3NbO2f3dZSPn2zGo8G6iThbz3PWvASGfd3MpAnbwtKu7VHClSSY1KeRvq5//2aBnJ3ZD0+3wo/gs/uT+vr68tdff3H27Fm++OILXnrpJV588UW++OILzpw5w9atW/H19S1EU8suPupEprqsQ4mmuE3RM+SbcDItKMLGp5gXzbDUviAcu3qXXdG3LG7XyjIpaVk2x3mQWfBjq7Gi2lsY2JJKN2pbDkJyciL3gqWreGFZ6XDP+cSiMklQQhg3bhyXL18mIyODo0eP8sgjj+i3fffdd+zevduofffu3Tl27BgZGRnExsbqo+1yuHz5MikpunTWlJQUZFlGlmW9Y8+e/YLuxmL69OnEx8eTnp7Onj17aNbMysynE4myUHfvjR+OF8n+BYKSyCVZJxpQR0pg7UGhJi0Q5FC3fiPUsgKVpIZ7piqfgvJBcnIyI0aM0At7jRgxgrt371rtk3N/FBQUhIeHBz169ODMmTMm7Q4fPgxAtWrV8PX1pUePHjx8qHumvHz5MqNGjaJu3bp4eHhQv359PvroI/1Eaw66WmfGy5IlS5zz4Z2IPUEG7aVoJrv8DMB09Uvs0bYsbLOskjdyr23tSjQJ8iZsQjeOTOtNhzqV7RqnoPEVIzrVprqvB2DsJH3j0QamjW08/77VqyG9gv3p3sjfqpiINezJ/hudZ2y/im4o83h4i8KxBw7W3AMIDg7m5ZdfZurUqUydOpWXX36Z4ODgwrCtfKBRM+n6W4x12cLLyrDitsYISwISk3854bR92CPY8aaVh9GDl+7QcuY2m2OcuHbXEbPMYqkwamFhr8NOk36fnUs+4Or8oVydP5SkzV+gTc9NHzR3Usq5EDdurHOYDRgwwORCPHbsWOrXr4+HhwdVq1Zl8ODBREcbq9WeP3+ewYMH4+fnh7e3N126dOGff/5x8JPaRyK+xGoDUEiyxdRcgNlbo/n9eJxT9inLMhdu3isUx7WjiFhEgSPk1KhsJgnlQ4Egh5zIvapSKgu3Hi1mawSCksP/9QwmTtZF2ihShOO7vDJ8+HAiIyMJCwsjLCyMyMhIRowYYbXPnDlzmDdvHosWLeLIkSMEBgbSp08f7t27p28THh6uz5DYtWsXR44cYfz48SgUOldEdHQ0Wq2WpUuXcubMGebPn8+SJUt47733TPa3atUq4uPj9ctLL73kxCPgHGw5uCqRykK3RbhIWjZourJW07toDLOKqVou6NJofT3daBjgxV9vdSPi/d4mghnNqvuYHXHmYF1dvQPvPpoviwztcVSkQgIm9mnEipHtUSokpj6WP3+Vpf+lt0eus+4VA+femG51aeBf+Bk6lnCalEdycjJr1qxx1nDlB6ULYZX/A8BEl18JIqmYDcrlfrr5sPzLSZajpkoTjigBQ/HUFLSHpE2fc/vKeQKGziBg6Awyb14iafMXVvvkXIg///xzAPz9/U0uxG3btmXVqlVERUXx999/I8syffv2RaPJjYIcMGAAarWaXbt2cfToUVq1asXAgQNNFEKdRXi2clRXxWmLbc7dvMeEnyK5l247otMWv0fG0Wf+v4xafaTAYwlsI8sy6w5dKTWCHiWZE9p6ANRXxOPNA6NtF2/lT/xIICjtPMCDW7IvIOruCQSGeLgpuaXUOb9JFs698khUVBRhYWEsX76c0NBQQkNDWbZsGZs3b+bcOfOT6rIss2DBAqZNm8bTTz9Ns2bNWL16NWlpaaxfv17fbuLEiYwdOxaAkJAQGjZsyDPPPINKpQKgf//+rFq1ir59+1KvXj0GDRrE5MmT2bBhg8k+fX19CQwM1C8eHh6FcDTyjy4wwNp9lsxnrsuoJt0hRluN97NewdlT+FMfC+aFjrUc6vNc+1q0rV1J/96cCERINW/8Kqr06bqgi1qbMci8OMaLoXW4/L8BBPnm739k+ORtLrXWWpZbXqecYTTlnCEt+OW1UKv7zmluKQ3X0B7D1y91rgNAHT/Hyps5C6c5965evcrLL7/srOHKFQe9+3NY25gKUgbTXVcXtzl6nli0r7hNKFQcTbNVa4o2esse67KSrpEee5Tuoz5AVT0EVfUQqvR/g4cxR8i6fd1sH8MLcY4IzpIlS0wuxK+++iqPPPIIderUoU2bNnzyySdcu3aNy5cvA5CUlMTFixd59913adGiBQ0bNuR///sfaWlpZsPxncE+rU7tqIsV514O9yw4px3huwO6G9y9F8w73a8np6Et4ojOssz+i7eZtvF0qRL0KKkk480Vra5mSXPFJaNtveftAeBS4n1CZ+9k9YHLRW2eQFBs5IjN1JVE6qFAYMiFrCoARJ6MLF5DBMVCeHg4Pj4+dOyYK4TQqVMnfHx8OHDggNk+sbGxJCQk0LdvX/06lUpF9+7d9X1u3brFoUOHqFq1KgANGjSge/fu7Ntn/TkzJSWFypVNU0HHjx+Pn58f7du3Z8mSJWitFEXPyMggNTXVaClsVu6/zEYrGURPKMLpqzxKpqxkfNabpOFusW1+Gdu9Pp8+ZZ9C7JwhLYh4vzetavry2ZAW1K7iia+nKx8/abmMzBfPtuTtPo3Y99+evD+wCb6eblTIrmHbM7tenjNwN3CaVfVSmWx3JO7G0EXXrLoP7etU5v0BIdSuYt4Jl5P5Zk9tXnPuv7a1K/P5My347f+sOxGdjd3Ovbw/jLyLYcSPwDFkScG0rFFkyUr6Ko/SV1Gyo4Ti7pqvuVfacDTNtshr7tmxu4wbUUiqCgQ2zD0Bq6oHI6kqkBEXZbaPPRfivDx48IBVq1ZRt25datbUqThVqVKFkJAQ1qxZw4MHD1Cr1SxdupSAgADatm1r2eYCXGgPaJuilSWCFdeoSrLVtrP/ira63R6sqTT9HHGNrp/9oy+WWtiUB5GQ2NsPbDcS2M0JuT4AraQYs9s/2nSG+JR0PtpUOM54gaAkEqvVOffqK+K5lCiiWAWCHK7Luody13vXitkSQXGQkJCAv7+pY8bf399iRk7O+oCAAKP1AQEB+m2XLukmGGfPng3Ab7/9Rps2bejVqxcXLlwwO25MTAxfffWVSU3kjz/+mF9++YUdO3bw/PPP8/bbbzNr1iyLn2n27Nn6+oE+Pj76Z5jC5JvdFy1uq0yqPpBnkfopouTaFtsWFa4uEn4VdY6zyhXc2PNOTyI/7EuNSpYjzypVcOONXg2N2uz976P88looPRpVzbcteWvXuSgVnPiwLyc+7Gs+cs/Ks3Le5yZzj1Gju9Vjzzs9LfTX/bXk3DPct6VntGfb1aRtbftqFToLhwQ1KlWqZHHJW0hcYD8ScEGuwTLNAAA+dV1JZQp/ZqG8cz3ZsfTioq65Zw+aB3dRevpwKzXDaL3S0wfNA53za+upeCOHrD0X4hwWL15MxYoVqVixImFhYWzfvh03NzdAdyLbvn07x48fx8vLC3d3d+bPn09YWJhVcZ2CXGjv4sXpbCWpLgrrDok/T9ywe1xLWHOnfblDd0Py61HzEZJWxy37fjpBCeCENtu5pzB17t26l05WEUcjCwQlgYtydQDqS3GMWHG4mK0RCEoOiip1AAh2KzklggQFZ/r06WaFKAyXiIgIwLyTQpZlmxPMebcb9smJrMvJ8GvZsiXz58+ncePGrFy50mSsGzdu0L9/f5599llGjx5ttO39998nNDSUVq1a8fbbbzNz5kx9iSFzTJ06lZSUFP1y7VrhO66tOZymua6linSPKG1NvtEMKtB+mlX3LlB/gKZB3jzWrFqBxwGdY7B9ncr5DkbY+mY33ns8xGS9j6crPp6uFoUtchyTtsivXW1rVTK73vD/bC0YpKixW7bDy8uLadOmGYXqGnLhwgV9Lr0gf3ypfpreiqM0UsTxP9dlvJo1ibJeRr843WVDvnEs9a8oa+7d3beOFz77wWqbwBfn615IEhFX8kax5UoefX/wCu3rVtYrD+Vg7UKcwwsvvECfPn2Ij49n7ty5DB06lP379+Pu7o4sy4wbNw5/f3/27t2Lh4cHy5cvZ+DAgRw5coRq1cxfLKZOncqkSZP071NTUx1y8O3XNqOFIpZuylP8ru1qte2GY4473gwpaLTcuYR7/BEZR63Knrg4WAhWUHC2nUkgXa1lUMug4jalWIjUO/cuojvb5n6fO3y6s3iMEgiKmRznXkMprsxkIgiKj8WLF/P5558THx9P06ZN+fTTT62237NnD5MmTeLMmTMEBQUxZcoUo+ikZcuWsWbNGk6f1pUfadu2LbNmzaJDhw6F+jkAHn+kC2yGapqCT44KSg7jx4/n+eeft9qmTp06nDx5kps3b5psS0xMNAkIyCEwUBcJnZCQYHTff+vWLX2fnPV5BThDQkK4evWq0bobN27Qs2dPQkND+fbbb218Ml3acGpqKjdv3jRro0ql0tf1K25aSxcYotSlIr+bNYYs+90wevZO6Um3OTrhQnudWpYIrVeFH17tVKAxnEmTIOvOSnOPZBP7NMLdVcGa8Cs8174mfef/a3WMIB93bj/IpF7VCjbtydldrxB/PnqiCTP+PGulbcnx19j9rWrTpg0A3bt3N7vd19fXYYECgTEZuDEh63V+d/uAvsqjDNXu5meN+VBRQcFJeeiY4IIl9eDCwKvNQKaOH8XCnebD1QFcfALISoxF8+CuyTZNWipKT1/9e8MZBcMLcf369fXrDS/EOeRE1zVs2JBOnTpRqVIlNm7cyLBhw9i1axebN28mOTkZb2/dCXnx4sVs376d1atX8+6775q1u6AX2r3a5vwff9JVcYq8Dou8TPq5YMrOBT1V91tg/SJTXtFoZc7fvEfjAC8UhTTdpdZoefV7nRpml/pVqFLAm6CSTpCvac2W03Jd1LKCqlIK1bhDPFWKwTKBoOSw7MV2TF+TCOhq7rlQ8NqsgvLLTz/9xIQJE1i8eDFdunRh6dKlPPPMMxbbx8bG8vjjjzNmzBjWrl3L/v37GTduHFWrVtUrie7evZthw4bRuXNn3N3dmTNnDn379uXMmTNUr169UD9P1do60bLKpJD1IBnXCuYjVgSlCz8/P/z8/Gy2Cw0NJSUlhcOHD+udyYcOHSIlJYXOnTub7VO3bl0CAwPZvn07rVu3BiAzM5M9e/bw2WefATrHYVBQkEkK7vnz53nsscf07+Pi4ujZs6de0C9HSdcax48fx93d3WrGUFFj7vlSQsuHrt8D8Iv6EU7IDfI1ds3KuWmwErD5ja5sPRVPWqaG76zUT3ZzUbB3irFPwZoYRWkg+uP+uLvqUmY/GNjErj57pvREo5X1/ayRE+AhSRJPt65h1bln+MBY3GWU7A4lGT58OO7ulgs+BgYG8tFHHznFqPLMWbkOX6iHAjDDZTUhklCsKo8oPX0IqtMA1yo1LS6SixuqoBDkjAdk3MhVscq4cQ454wGq6rmhzYYzCoYX4hxyLsSWLt45yLJMRoYuBTgtTZfWnPfiq1AorBa3LShHtY14KLsRIN0lRLpqu0MBMCcJb46rt/OnIL187yWe/Ho/qXYo+5acOSHHMDfp88mWszz25V4+Cyt4XURLaAz26wxxlZJO3yaBvNmrodG6DNyIlnVqabroPYGgfNOnSQBx+HFfdsdN0lBbMo1SEQjsZd68eYwaNYrRo0cTEhLCggULrDrglixZQq1atViwYAEhISGMHj2aV155hblz5+rbrFu3jnHjxtGqVSuCg4NZtmwZWq2WnTsLP9q6ShU/vZr0kaMRhb4/QckiJCSE/v37M2bMGA4ePMjBgwcZM2YMAwcOpHHjxvp2wcHBbNy4EdA5MiZMmMCsWbPYuHEjp0+fZuTIkXh6ejJ8+HB9m3feeYelS5cCunp6H3zwAdHR0YwaNQrQRez16NGDmjVrMnfuXBITE0lISDAqF/Tnn3+ybNkyTp8+TUxMDMuXL2fatGm8+uqrJSY6b92hK2bLOA1SHKC14iL3ZXfmqJ9z2v6aVfdhSv9gPG2IPjQL8ibAW+fHaRRQEYDBrQp3sqCwcctHRpSrUmGXYy8vXu4uNPSvSF2/CmaFPQwfF4s72M3uozJmzBjefPNNi9sDAgKEcy+f5PXwLtMMYI+mBR5SJktd5+GDKPhcHslU23aQufrVxL1uW26HfUVGXDQZcdHcDvsKj/rtca1SQ9/utcHdzF6I//zzTwD+7//+z+hCfOnSJWbPns3Ro0e5evUq4eHhDB06FA8PDx5//HFAN8NXqVIlXnrpJU6cOMH58+d55513iI2NZcCAAc4+HHoycGOfVicg0ktxrND2A9jtUbPHOWeOT7ZEEXntLiv2xto2pRi9e2qNllv30p023qr9lwFY+u8l6w0LgKFDu3TPTdqHQiExqU8jk/U5dfdamqm7JxCUTyQuyrpU/QZSHO8WkSiSoGyRmZnJ0aNHjcTJAB599FGLfcLDw03a9+vXj4iICLKyzN9HpKWlkZWVZVY1NAdnKYIqFJJeTXrb3v35GkNQulm3bh3Nmzenb9++9O3blxYtWvD9998btTl37hwpKSn691OmTGHChAmMGzeOdu3aERcXx7Zt2/Dy8tK3mTBhAhMnTgSga9eu7Ny5k+3bt+sziLZt28bFixfZtWsXNWrUoFq1avolB1dXVxYvXkxoaCgtWrTgyy+/ZObMmXzxxReFeUgscudBJkezyyJdSrzPnLBopm08bdLOBTWTXH4FYLF6EIk4PyLWkfvc3/6vMz+PDeW5doUvLmILlRmRDEvkfQwqjOeiIB93nm5TnZdCa6M0yCxSKCT+nvAIOyZ11683jHy0NxikKBBFoEogWhS8mTWeq9qq1FIkstB1EUo0xW2WoIj5eLOV8F8D/J6YjFvVOtz8+QNu/vwBblXr4DfwbaM21y/HmL0Qv/22rl18fLzRhdjd3Z29e/fy+OOP06BBA4YOHUqFChU4cOCAXknLz8+PsLAw7t+/z6OPPkq7du3Yt28ff/zxBy1btnTGIbDIDq1Ojbe3snCde/aeqgt6Tk9XG/++7zzI5HJSyVGOHbIknA6f7uR0XIrtxoXM4dg7rD/kWMRm3lm0W6npxT6zVlREZivmthaRewKBnouybvKroRTHj0eEMqjAcZKSktBoNCblTKpWtawUmZCQYFbMTK1Wk5RkXsTi3XffpXr16vTu3dviuM5UBM1Rk64ti7p75ZHKlSuzdu1avZN47dq1JimvsiwzcuRI/XtJkpg+fTrx8fGkp6ezZ88emjVrZjJ2Tr3t+Ph4Dhw4QNeuuXWzR44ciSzLZpcc+vfvz/Hjx7l37x4PHjzg1KlTvPXWW7i4OF67zhl0+2wXQ745wN4LiTy+cC+Ld5ufRH1KuY/ailskyt6s0vQv8H6DfHQReIZCGLZuaQ0DibzcXelQt3KhlcVxhLWjO1Ld14PlL7ZzuK+19NdhHWrlyx5Jkpg3tBUzBpt+fxUKycjhZ6SWm6+9FQ4O/RoePHjA+vXrOXDgAAkJCUiSREBAAF26dGHYsGFUqGC7OKHAPlKoyNisSWxw+4juypN8Iq9kqno0JevrU3DKyfN1oaL08MLviclW2/x1Kp7+zQL173MuxJMmTcLHx4etW7fq6+YBBAUFsXXrVpv7bteuHX///Xf+jbeDl7vU0Ud65bBL0xpcdSqgVUkulFkwKL5ouTYf61KmD7z7KEF5hFDyw50HmWw4dp3BraqbhJNfvZ3GI5//Y7X/iWt3AdhwLI5m1X0KbE9BGLpUJ4RTv2oFOtazXEPOUi2RzSdvMH79cYa2q8GcZwrXCV0SOKrVRfO1kmJwI4tMXIvZIoGg+LmgrQ5KaKiIA43uPFiriqftjgJBHsyJk+WnvbkH1Tlz5vDDDz+we/duq6WRCipUZkisrHMYtPPOK9QmEAgMeZCpm5jffS6R9Czz2VYuqHlDqcucWqp+godY/h0D1KjkQa3KnhyIuW2yrVtDXd3ErW9143RcKp3r594DWzrvvPd4MF/uuMCMQU1tf6BioH2dyux/13K0c36p7eD1/M1eDVm48wIzB+fvOJWgwD37I/fOnj1Lo0aNmDJlCsnJydSqVYsaNWqQnJzMO++8Q+PGjTl71r5II4F9RMm1eSvrdTSyxDCXf5iYHdIrEDhKCZicyTc1K5meoBPx1SuB9lIeL7R9F1WYtSWVpZPX7wLw69Hr/BGZ/1n0N344xidbonjluyMm24YtO2j3OCXp4nXljvU6h4b3OYa3PF9sOw/AzxEFU1IuLVySq5Eoe6OSsmghidRcgQDgQrZibiNJdx44EGM+akogsISfnx9KpdKoJhhgMQIPdPXJ87a/desWLi4uVKliPFk1d+5cZs2axbZt22jRooVVW1QqFd7e3kZLfmncVCeKoE0S0d4CgT1YuzV+WrmXWopEEmUf1mosR98a0tzMJPrXw9uw+AWduKmvpxtdG/oZRd5ZmlJ49ZH6nJzer9gn5p2BtQn9gjKpTyPOzOhHrxDzqtClCbude6+//jqPPPIIN2/e5Pfff2fp0qV8++23/P7779y8eZNHHnmE119/vdAMTU5OZsSIEfqQ8xEjRnD37l2rfWRZZvr06QQFBeHh4UGPHj04c+aMfvvly5eRJMns8ssvv+jbnT9/nsGDB+Pn54e3tzddunThn3+sR7o4i23a9ryvfgWAt1w28oryryLZr6BsUdzKPYXBdk12aq7iaKHto6Qctsm/FEz1d/9F3QzgKTNptXF3HxZo7OLCkX9N+Y4QljiiDQagg+KcjbYCQfkgx7lXT4pHiabEnOsFpQc3Nzfatm1rJE4GWH0+CA0NNWm/bds22rVrh6trblT1559/zscff0xYWBjt2jmerlYQtt7QTajWleK5drvklAcRCEobElpeVW4BYKl6IOnYFv3QaM3nnQxoUQ0vd8uZF9YihpWlOcLDgMoV3Ph+VIdCG7+CKv/p3SXpOcNu596hQ4f44IMPcHNzM9nm5ubGe++9x6FDh5xqnCHDhw8nMjKSsLAwwsLCiIyMZMSIEVb7zJkzh3nz5rFo0SKOHDlCYGAgffr04d69ewDUrFmT+Ph4o2XGjBlUqFDBSJp7wIABqNVqdu3axdGjR2nVqhUDBw40mX0rLH7Q9OKLrGcA+ND1e15TbiqS/QrKDmXjtG7MDq1uBqub4jQVyZ9arS0sRdTlt11pZ8W+WP46FW+y/vb9DIuiIiXoelduOaLVqdx1UBSeOrGgbFIYE6t52z722GNIksTvv//u/A9ghpY1fLguV+Wh7IZKyqKmdKvcnMMFzmXSpEksX76clStXEhUVxcSJE7l+PTcqfOrUqbz44ov696+99hpXrlxh0qRJREVFsXLlSlasWMHkybmlVebMmcP777/PypUrqVOnjl4x9P79ohHXe7Z3N7SyhLf0kMvXLhfJPgWC0oyl2nXdFSdooLjBPdmDHzU9TbYPaVPDZF16liZfNaFLknOpMPHxsK+0TFEcj5J6yO127lWqVIkLFy5Y3H7x4kUqVSqculdRUVGEhYWxfPlyQkNDCQ0NZdmyZWzevJlz58xHIsiyzIIFC5g2bRpPP/00zZo1Y/Xq1aSlpbF+/XoAlEolgYGBRsvGjRt57rnnqFhRJxOdlJTExYsXeffdd2nRogUNGzbkf//7H2lpaRZvVh3FnlvKrzRPsUD9NADvuv6YnaJbUr9WgpKGogxK55yTa3JBWx2VlEVfRUS+x9GakazPwd5oDkv13SyOa+eDZHFerAct2sfyvaZqtv+3zljE5H6Gmraf7KDF9G1FZRpgOxrV+NiV73PlYW0IAG0V51Fgvi5MxOU7Vn8LgvKJuYnVsWPHWu1ja2LVkAULFhR5ZPnPr4Uio9Ar5jaWrjt8DhcIAJ577jkWLFjAzJkzadWqFf/++69R5k98fDxXr+YKQNWtW5etW7eye/duWrVqxccff8zChQsZMmSIvs3ixYvJzMzkmWeeMVIMnTt3bpF8pn6t6hAn62p77TtYeEEbAkFZwdIVbFR2tt1Pmh7cx7TEUHCgl8m6h1mafN37l5fbt6ZBPrSo4UO/pubTZzvV06mKD2oVVOi2GP6fDKMj3ZTF+9Btd/zhmDFjeOmll3j//ffp06cPAQEBSJJEQkIC27dvZ9asWUyYMKFQjAwPD8fHx4eOHTvq13Xq1AkfHx8OHDhA48aNTfrExsaSkJBgJDmvUqno3r07Bw4cMHtzevToUSIjI/n666/166pUqUJISAhr1qyhTZs2qFQqli5dSkBAAG3btrVoc0ZGBhkZGfr31mTp7buvlVigfoZM2ZUprj/xlssGakiJTM0aXaoLpF+1UTdL4BzKZlSCxJ+aUCYpfuUJZTgbtI/ka5R6723l5S51+OgJ0yKqjj50arVyiVCfcgYnr6dw8noKo7vVs9quJKn6WsLSjdIfkXEMblW9aI0pBqLkWqTKHnhLDwmRrnBGrmvS5pkl4Xw2pDnPtc+fwpig7JEzsXrw4EH9/deyZcsIDQ212CfvxCrA6tWrCQgIYP369Ub3XidOnGDevHkcOXKEatWqWRrS6ahclEzo3ZDo3bVorrhMiOIKGvM+b4HAJuPGjWPcuHH694b3+999951J++7du3Ps2DGT9TlcvnzZmebli1g5kJokcudaVHGbIhCUfMzc9jeWrtJNeRqNLPGdBYVcV6Vpx2fa1jCZalpmh5JseZmgUiok/ni9i8Xns/WjO/EwS1OgFNv84O6q5M1HG5Cu1uLvbV00pbCx27U4ffp0pk6dyrx582jdujXVq1cnKCiI1q1bM2/ePN59910+/PDDQjEyISEBf39/k/X+/v4WU2Nz1puTnLfUZ8WKFYSEhNC5c2f9OkmS2L59O8ePH8fLywt3d3fmz59PWFiYiTS4Ic6UpTdksWYw07JeQS0rGKLcy1q3WVTGsuNQIABKdV6utcvVn1rdQ2ZXxWkqFeB3YKjGe/TKHdYevIIsy1YPm2HYvITE6gOXaTb9b45fta0wl5ap5vzN3CgWSz7E/1t3jKu3hQM8Pxje6KRlasxGpb31Y2QRWlR8aFHoVXOtpeb+diyuqEwSlAKsTaxawtbEag5paWkMGzaMRYsWERgYaG4oEzIyMkhNTTVa8kvTIB/OyrUBaCJd4V56FrvP3cpXOpRAUNa4lK2YW18yLcMhEAiMMRdA8aJSV1vzb217rstVTbb7e6moVMG01Nk7/YKNJqSjP+5Pnya2RR4M+xye1ssOq0sv1gIvFAqpyBx7fZrofFOVPHVBVpP6Nua9x0OKZN/WcChu8L///S83btwgJiaGffv2sW/fPmJiYrhx4wZTpkxxeOfTp0+3KGiRs0RE6NLtzP0jZVm2GVljTnLeXJ+HDx+yfv16Ro0aZdJ+3Lhx+Pv7s3fvXg4fPszgwYMZOHAg8fGWL3pTp04lJSVFv1y7ds2qnY6wTtObl7OmkCp70EFxjr9U7xKqcE6KsKBsUlSqr4WBtYetWLkap7R1cJU09FeaKsHmhyHfhPP+76fZcz7Rqh15rfpo0xnSMjV2iV/8cy6RvvP/tcuejzadNlnXf8G/zN5atmbULyc9YG+eY14QDP9dg7/ez1OL95P8ILPcPrzniGp0tFZ3r3weGoEFLE2s+vn5We0DtidWJ06cSOfOnRk8eLDd9jhz0rR3iD9ntXUACJGuMvuvaEauOsLmk8KZIRDEZKes15fiRLkGgcAGeR+xPEhnkFI3mfW9po/ZPstfMh+N5+PhajQ57e6qtMuGigYOLX+v4o0cKy8827Ymq0a2Z/uk7sVtihH5SgquW7euvvZd3bqm6T32Mn78eKKioqwuzZo1IzAwkJs3b5r0T0xMNLmBzCFnJtic5Ly5Pr/++itpaWlGhW8Bdu3axebNm/nxxx/p0qULbdq0YfHixXh4eLB69WqLn80RWfr8PGvu1bbgqcyZXNBWJ0C6yzrXWbzt8jOuqB0fTFDmKb2uPdv8qdFF7z2l3OfUcWMSH2CYYbvpxA2njm8vGWrTfLHohHss/de0Hh7oUoPnhEUTdto5gj/pWRqnjGOLHnN3s+2s6Xk+v+yIMh7rxPUUWn+8ncvlNBIyXNsEgFDFGZSY/5+Wl7SO8o4zJlZtYW1iddOmTezatYsFCxY4ZLczJ00lSSJK1qWg11Qk4o2uvMDeC86bYBAISishzdsD0FCKI+7uw2K2RiAo2eS9Sj6uOIyX9JArWn8Oas1Hcpnzmb/ZqyGQP7/Aq93rEVqvCrOeau54Z0G+UCgkegb741fRtgpyUeK0in9//PEHa9ascaiPn58fwcHBVhd3d3dCQ0NJSUnh8OHD+r6HDh0iJSXFKIXWkLp16xIYGGgkOZ+ZmcmePXvM9lmxYgWDBg2ialXj0Nm0NN2DoCKPIoFCoUCrdU6Rlgx1/h6eY+TqDMr8mB/VPVBIMm+4/M4Wt6m0lcyLjAjKL6U5cs+WhPvvmq5oZIkOinPUk5zngNNqjaN8D8feMdpu6eJb3O6RHVE3Wbw7htfWHjW7fdLPkQ45KoM/CDO7fs/5RP6z/BDXk3Nv/NMyTScXbN2kPMzM3/nP1je6vKTc2stJuR4psic+UhotJPOO4XIa1FjuKOjE6u3bty2Obc/E6q5du4iJicHX1xcXFxdcXHQRB0OGDKFHjx4Wx3Zk0tQeUqnA9WzhgBBJJ3rgWsyFsAWCkkCnjl0AqCXd4kpCUjFbIxCUbK7kmTQe6rIb0AlpyBZcLVpZNonKK0hmibe7Kz+82onhHUXd5PKO0+5i/vvf//Lyyy87azgjQkJC6N+/P2PGjOHgwYMcPHiQMWPGMHDgQCMxjeDgYDZu3AjoZmUnTJjArFmz2LhxI6dPn2bkyJF4enoyfPhwo/EvXrzIv//+y+jRo032HRoaSqVKlXjppZc4ceIE58+f55133iE2NpYBAwY45fM9zMq/k/Ah7ryrfpVxmW+SKHvTSBHHb6oZ/M/lW/yxXftLUD4oxb49nm1Xk3p+FSxuv0UldmlbAzBUudtp+9XmuchaO4bmts3fft6x/Wll9l9M4s6DTIf65SXxfobV7RuOxfHmD8eZt/08STbaWuOllYfZdzGJKb/mpiG/v9E0hdgWIR+GmbUj5WFWvm0TmKJByX5tMwC6Kk6ZbRNxJZmT1+8WoVWC4sAZE6uWsGdi9d133+XkyZNERkbqF4D58+ezatWqwvnQFjirza67p7gMCOeeQABQr04d7uKFQpK5c0WU/REIrLHlVG45h3rSDToqotHIEr9pLAv9ybJMr2B/+jfNrTmrESnwAifgtLuY6OhoNJrCS99at24dzZs3p2/fvvTt25cWLVrw/fffG7U5d+6c0U3nlClTmDBhAuPGjaNdu3bExcWxbds2vLyMpadXrlxJ9erVjQpA5+Dn50dYWBj379/n0UcfpV27duzbt48//viDli1bOuWzBXgVPJxzq7YTvTPm8qO6BwDPu+xmj2oib7v8jBflMw1NkEsp9u1RUeXCrsk9rLb5SdMTgCHKf52Wmq6xMYNmK4Xxy50X7N6XBGw8HscLyw/Rb4F9tfgsYW+U5sKdF5jghOi21PTc473huKkgQ3qWhpPX71qdkdwZZRod9I6N2oWl2WFdmLzWvT4ALWuYih7s0+rSNbopzTv3AAYt2l84hglKHZYmVvv3N1b+c3RiNTAwkGbNmhktALVq1SpQqRdHcVVKelGN3Mg9cWIRCJAkkivoriWud0Q2kEBgL08r9wKwW9uKm1S22E4rg4tSwZIRbelQV9fu6TY1AOjWUBdRbiNxSSAwS6mZoqxcuTJr167VK6StXbvWRK1WlmVGjhypfy9JEtOnTyc+Pp709HT27Nmjv4k0ZNasWVy7ds0k9TaHdu3a8ffff3P79m1SU1MJDw/nsccec9pnq+KkXO0UKvKu+lWezphOhLYRHlImb7j8zn7VG7zr8oOI5CvH2BKeKe38o23FTdmXqlIqvRTH8j3O1/9c1L8u6iLSf5/RpbEl3st/NB045sg9eMlyep01tjtQG++Xo9cZtGg/Px2xXB/LnNJY3pp51li8eDF169bF3d2dtm3b8tl3v1ttn371FPHfvcWVuU9Rr149lixZYrT9zJkz/Oc//wHAx8fHbG2wOnXqmK1V9vrrr+vbjBw50mR7p06d7P5c+eHdx4KJ/rg/rWtVMtn2b7Zzr410gYpWJn3Gfh9BdIJQYReYn1hdunSpUZv8TqyWBHIj964UsyUCQcnitofO0e77wHwZB4GgPJKepeH5b8ONnhdykXlCEQ7ABk03q+MYPmP8MKYTkR/2oYF/RQAeDfZn3eiOHJxatlVvBYWDw1rBFy5c4MCBAyQkJCBJEgEBAXTu3JmGDRsWhn3lAmcXMT8mN+KZzI/oq4jgbZdfaKy4zmsuf/KKcitbtJ34SdMzu8Bn2Xb4CHIp4749NCj5RdOd8S5/MNLlb8IyO+RrnM//zp2hTkhNt7w/rczNVAtOuHz+nJ31P3JknPyeecasiTC73tos47sbThFczZtWNX1NN5rpZ8u3mvM5f/rpJyZMmMDixYvp0qULcxYsYuqrwwgavRgXb1Olz6y7Cdz6dToVW/TDb+Bk3uvkwrhx46hatSpDhgwBdLVW69SpA5iqfuZw5MgRo2j106dP06dPH5599lmjdv379zdKNXRzc7P+wZyAJXW167I/sdoA6ipuEqo4y3atebW2v8/cJDzmNien9zNan5ap5lDsHTrXr4LKxT4FN0HpJmdi1ZDUVGPHb96o3JyJ1enTp9u9n+JSsc6J3GsoXccVNcv2xjKlf7BIzxWUe+5UqA9JkBl/trhNEQhKDL8evc7BS3c4eOmOybbmUiy1FbdIk1Xs0rayOo7hFU+pkPD1zL03lCSJLg0sq9ILBNaw++4lJSWFwYMH07hxYyZMmMDKlStZvnw5EyZMIDg4mCeffNLkhk9QnEhs07anf+b/eCVzMoe0wbhJGp5S7udHt0/Y7TaJN5QbqC+ZptEJyh5l3LcHwFp1b7JkJZ0UUTSzIBjg0HgHrxq9N4wu23bGuFh8QR1z9zPU/H3GeUqxxYUt8ZMnv3Z+yue8efMYNWoUo0ePJiQkhNHvzEDp5ce941vNtr8f+RdKr6pU7v0qrn41GT16NK+88gpz587Vt2nfvj2ffPIJoCvib46qVasSGBioXzZv3kz9+vXp3r27UTuVSmXUrnJly2kaRcFebQsAHlGctNrOMN06hzfWH+flVUf4eLN42BOUfmQZrstVSZU9UElqGmTfD127I0qZCATHHuomx2pprtpoKRCUH9KzLJcgG6jURe3t1LbmIe5Wx6lTxXItcYGgINjt3HvjjTeIjY0lPDyc5ORkzp07x/nz50lOTubAgQPExsbyxhtvFKatZZdCnLCWUbBL24bnMj/kiYxPWKfuxT3ZgzqKm7zt+is7Ve+ww20yk11+op0UjYuT6pUJShaKclC4IYEqbNbqUh5HufzllDEtHTVniz2sCbecEuZIQEvY6Xj++5vlemqFjT31/syle0751bqjyRxaLfxw8BJHjx41qZfqUbc1GXHRZvtlxEXjUbe10bp+/foRERFBVlb+/q+ZmZmsXbuWV155xSQFfvfu3fj7+9OoUSPGjBnDrVu3LI6TkZGhLz2Rs+QXS5FQe7Kde48qj+PoxWdntM72vI5vgaA0Us3XHZA4o9WlHzZTxAKiqLlAAHBOWx2A2tItUkTwhkAAWC5zJKFloPIgAJs1oVbHGNwqiEAf684/gSC/2O3c27RpE8uWLaNjx44m2zp27MjSpUv5448/nGqcwLmckusxTT2KDhlfMzlrLLs0rciUlTRQ3GC8yx/8qprJCdUYVrl+xhjlZtpI53GnYPW/BCWDsu/a07Fc/TgAAxUHCSR/9eQMyXFmgHF0nr2iFUXNa2vzX2/QGbgoJO6mZXL1tuXIl8tJD5yyr/WHr/LO9/vQaDQmqbPKCpXQPDBfY1TzIBllBeN6dAEBAajVapKSkvJly++//87du3eNar4CPPbYY6xbt45du3bxxRdfcOTIER599FEyMsyfV2fPno2Pj49+qVmzZr7sscZ+bTMeym5Ul27TVLJeZ+zTLWeJT3nodBsEgpLAipfaA3BC1gkHtJJiAMjSCOeeQPBYxxbckSuikGT2hguhJYHAGq2li1SXbnNP9mC31rrg5stdik44SlD+cKjmnrWi/GW9YH9hUtS3kQ9x51dNd37VdMeLNHoqIumjjKCz4gxVpHv0VJ6gp1KnVKmWFZyXa3JCW48zch1i5CAuaquTiA/lx2VU+ikvP88zcl3CNU0IVZ5ljMtWPlaPcNrYOYcw4vId3v/9tNG2wiwXZet/9+eJG5yOS+Htvo0dHzufNllCoZBoNXO71Ta/RFx3yr6OXsl13sXdzeOAkmUc+XQ5UW75vY6tWLGCxx57jKCgIKP1zz33nP51s2bNaNeuHbVr12bLli08/fTTJuNMnTqVSZMm6d+npqY63cGXjoq92ub0VR6ljzKCM+o6Ftsu2xvLodg7bBrf1ak2CAQlgUYBXvQOCSDyXLZzT6ErkJ6htpx2JRCUF55sXYPjf9agoxSN590LgPOEBAWCskZ/5REAdmjbkIH52sqDWgbxUufa5mtPCwROwm7n3hNPPMGYMWNYsWIF7doZF+GOiIjgtddeY9CgQU43UFC43MOTTdrObNJ2RkJLsHSNzorTdFJE0UoRQ1UphSbSFRMluRTZk4tydS7LgVyX/YiT/bih/1vF4olNUDyUNed78+o+nIpLMbttsWYQocqzvKDcwVL1QG5hqhpqjuV7rdfpWx1+hX7NAhm+7JDJtok/Rdq1j8LgjR+OA7D0X1P7ZVm26nhUOzn9zFbNPTCOhizw/jy9QVKweOtRnn08V1VMk3YXZQVf833MRPXdunULFxcXqlSp4rANV65cYceOHWzYsMFm22rVqlG7dm0uXLhgdrtKpbJY489RGgd6W9y2TdtO59xTHGUBz1gd5+R1878zgaAsUFGl5JBW59xrLF3DnQyRlisQAG4uCqSAppAYjTbhTHGbIxCUaHopdJkz2zTmhcoAqvm407Z28dZdFpR97HbuffXVVwwbNowOHTrg6+uLv78/kiRx8+ZNUlJS6NevHwsXLixMW8sslmojvRha22otLqfbgYIouTZRmtqs0AwAZKpxhxaKGFoqLtFIukYD6Qa1pFv4SGm0lS7QFvMPqSmyJ0myD7fx5rbszR3ZmyR0f+/JHtzHg3t4ck/W/b2f/TcT1yL7vOWJsuXagz/f6Eqdd7eY3bZX25wj2ka0V5zndZff+Uj9sl1jfrIlymYbc449gOiEe/rXMnDi2l279lnYjFt3zMi2wuZumnNrEdpCUrriFtiAG2cOG61PvxyJR0PTEhIAqurBPLxo3H7btm20a9cOV1fHzz+rVq3C39+fAQMG2Gx7+/Ztrl27RrVq1Rzej6M8174m99KzWBN+xSSycaemNRoXiaaKK9SQErkuV3Vo7DI2VyAox7i7KomnMjdlXwKkuzSVLgvnnkCQTWRmTToAbknCuVdeSE5O5s0332TTpk0ADBo0iK+++gpfX1+LfWRZZsaMGXz77bckJyfTsWNHvv76a5o2bWrU7vBh3b1XtWrVcHV1pVWrVvz11194eHgAUKdOHa5cMX7u/e9//8v//vc//furV6/y+uuvs2vXLjw8PBg+fDhz587Fza34gkrqSvHUV8STKSvZq21uuaG4dxIUAXY793x9ffnrr7+Ijo4mPDychASdWmRgYCChoaEEBwcXmpHlleKv6yURTxXitVX4W9tBv1ZFJvWkeBpIcdSUEqkuJREkJVE9e6kgZeAjpeEjpVGfeIf2mCG7kIEb6biRLruSjhsZuGa/d9O/z8CNDNkFDUrU2YsGJVko0aAgS3bR/c1erzZcZCUyOmemFgkZCW32gsFrWf9XgQxoDdrLSGhlyfh9drscZIOzuPFr03WY2W7Yxlx/S/swt90l6z7YGcFWWugV7G8hCkxinvpZfnD7lOeV/7BU/QQ3KFpJ+T3nE5021oEYy7UDT9mIqvrrdILV7WUB7/ZPcnHLPFauXEloaCgL53yJOjURr1a6+ovJe75Dc+82fgPfBqBiq8e4d2wzd3Yuw6tlf1auXMmKFSv44Ycf9GNmZmZy8uRJ/eu4uDgiIyOpWLEiDRo00LfTarWsWrWKl156CRcX48vp/fv3mT59OkOGDKFatWpcvnyZ9957Dz8/P5566qnCPiwoFRJju9cn8tpdE+deMt5EyI3pKEXTW3GU7zT9bY637lDRTTQJBEWFq1IBSJzQ1qev8iitFDEI355AoGPv/Wq8CjRRXEGr0aJQ2l2qXVBKGT58ONevXycsLAyAV199lREjRvDnn39a7DNnzhzmzZvHd999R6NGjfjkk0/o06cP586dw8vLC4Dw8HCGDBkCwK5du6hcuTInTpxAoTD+Ts2cOZMxY8bo31esWFH/WqPRMGDAAKpWrcq+ffu4ffs2L730ErIs89VXXzntGDjKo9lRe4e0IdzHs9jsEAjAwZp7AMHBwcKR52QKs15XYZCBmy7CT65tZquMNw+oKqVQhVSqSLrFT0qhMveoLKXixUO8pDS8eEhF6SFepFFRSgdAJalRocabNDHD4USuXPeA+s6rP1cSsOb7Dtc25YCmCZ2VZ5ns+jOTssYVmV2xSQ+Yt/18kezrWxupxGWFjzefxcNVyeR+pnUFK4Q8Qj0vmZkzZxIfH0+dhsH4PzsdFx9/ADT3k1Gn5jpbXX0D8X9mOsm7lnPv+BY+3lmDhQsX6m86AW7cuEG3bt0ASEhIYO7cucydO5fu3buze/dufbsdO3Zw9epVXnnlFRO7lEolp06dYs2aNdy9e5dq1arRs2dPfvrpJ/3NbnGyTdOWjopoHlMetuncS7qfwbSNuXUmxalZUFYIrV+F7w9eIVLv3LuItrTdlAkEhcQTvR9FHabAT0pl1fZDvNzfugqooHQTFRVFWFgYBw8e1AtoLlu2jNDQUM6dO0fjxqb3YLIss2DBAqZNm6avJbx69WoCAgJYv349Y8eOBWDixImMHTuWzz//nJCQELy9vWnYsKHJeF5eXgQGBpq1b9u2bZw9e5Zr167paxx/8cUXjBw5kk8//RRvb8vlSJzBxVv3+HjzWZP1vRS68ji7tK2t9pfE3ZOgCHDYuQdw/fp1goKCUCgURq8FzqX4I/fyg0QqFUmVKxJDdbvVQhRoqchDKvIQdykTdzJRkYW7lIkq5zWZuEtZqNBtd0WDq6RGiRZXNCjR4IIGV7LXSbp1httyFik7pk2BjELS6m1QZMfhKfTxeLrXxu+12X21Rm2V2eOYHhHZwuv8t8HBcVCWvxqIs9XD+UPxAU8r97FO3YujsuOCEyUdbTkIMblx9yEr9sUCWEwxbvzoMxxaOweA/ReTeGF5bvq034CJJu3dazWn2sgvAYj9n2k6bZ06dUhJScHHx4eUlBSLN4x9+/a1WFbBw8ODv//+28onKxos+Sm2ajrxges6OiqiqcZt4rFcbzAsTwRoWavhKSi/PNYskI51KxN5RReR21KKYejPJzj4Xi8bPQWCss8zHRty4a8gGkvX2fPvTuHcK+OEh4fj4+Ojd+wBdOrUCR8fHw4cOGDWuRcbG0tCQgJ9+/bVr1OpVHTv3p0DBw4wduxYbt26xaFDh/STqA0aNCAkJIRPP/2Url2NBbs+++wzPv74Y2rWrMmzzz7LO++8o0+5DQ8Pp1mzZkbiZf369SMjI4OjR4/Ss2dPpx6PvDy9+IDJOm8e0F5xDoCd2jZW+4tbJ0FRkC/nXpMmTYiMjKRevXpGrwX5w9LjuR216csMWhSkUoFUKuTNKxU4gc11y57apS0Hwym5Hj9pejDM5R+mu65mcOYnaClbkxDloTZUpjrXab4j6mYxWlK2iKcKh7TBdFRE84TyAN9qnrDYNq86dDm6NAnKOJIk0b9ZIPNidfewtRSJZKY6T/RHICjNKBQSZ+XaNOY6/ao4r9yIoGSSkJCAv7+/yXp/f399OS5zfQACAgKM1gcEBOjr5126pMsymT17NgC//fYbGzZsoFevXpw+fVofwffWW2/Rpk0bKlWqxOHDh5k6dSqxsbEsX75cv6+8+6lUqRJubm4W7cvIyCAjI0P/PjU11fpBsEJqutpk3SOKk7hKGi5oq3NVDjDTKxdx7yQoCvL1pGsYqWApakFQcISHX+AsyuJ3aYqZFM28zFUPJVX2pLniMi8odxSBVUWLphycf+35hGXw6+00PNyUFrf9oekCwGCl6Wy0Ncri+URQftFoZe7hyQVtdQDaKs6Le1uBIJtaTXRRXF53bYuOCUom06dPR5Ikq0tERARgfuJclmWbE+p5txv20Wp1k7Qvv6wTuGvZsiXz58+ncePGrFy5Ut9n4sSJdO/enRYtWjB69GiWLFnCihUruH07t/a0o/bNnj0bHx8f/VKzZk2rn8NRuilOAbBb29JmW3HvJCgKylYYSynF0j1k6UzLFZREykKdhzcebWD0vmGA7bplt/Hhc/VQAN51+YGaUtmK/CrrabmHY++QnqWx2U6cKvPHVk0HsmQlTRVXqC/F2d2vLJxPioprd9Ls+g4Lio/aVSoAcESrmzBqrzhXLqKiBQJ7yKjSBIAm0hUOxCQVszWC/DB+/HiioqKsLs2aNSMwMJCbN03vkxMTE00i5nLIqY+XN3Lu1q1b+j7VqlUDMKnZHxISwtWrVy3a3alTJwAuXryo31fe/SQnJ5OVlWXRvqlTp5KSkqJfrl27ZnF/jiPTRanLbNivbebEcQWC/COceyUYa7Mkq15uX4SWCEo7ZaEkpks+P8RaTW8OakOoIGUw13UpEuZrI5ZGynrk3tCl4Yz9/qjNdldup3H1dhparcz0TWeKwLLSgzVxgLt4sUfbAoDByv35Gj89SyOcVxY4HZdCtzn/0Hf+v8VtisAKvUN0aWiHtLoHzw6KKN7beKo4TRIISgwVa+tEAupINzl9yf5JIEHJwc/PTy+IaWlxd3cnNDSUlJQUDh8+rO976NAhUlJS6Ny5s9mx69atS2BgINu3b9evy8zMZM+ePfo+derUISgoiAsXLhj1PX/+PLVrmxNn1HH8uE6oIsc5GBoayunTp4mPj9e32bZtGyqVirZt25odQ6VS4e3tbbQ4i1rSLWpISWTJSg5rbYuNNrIjKEEgKChl4JG/9CNbSDyzVnPPpTwV5BMUmLIQaTOySx0a+ldkQm9TdS1ryCh4J+tVHsgqOiqiGavcXEgWFj3lIbrk6p00m21ikx7wyOf/sO1sAhdu3S8Cq0oPtm4mf9fo6nE+q/wXJXY66SRQa7Q8zNTQ9KO/aTFjW4G+i78evc5r3x8tc07CLad0DyD2fIcFxUfOROphbQgAzaTLbI64SFqmaX0lgaC8EVS9JvFyZRSSzM0LR4rbHEEhEhISQv/+/RkzZgwHDx7k4MGDjBkzhoEDBxqJaQQHB7Nx40ZAd/6cMGECs2bNYuPGjZw+fZqRI0fi6enJ8OHD9W3eeecdli5dCkBMTAwffPAB0dHRjBo1CtCJZcyfP5/IyEhiY2P5+eefGTt2LIMGDaJWrVqATsSsSZMmjBgxguPHj7Nz504mT57MmDFjCl0p1xxdFLqoveNyA9Jwt9r2g4FNeKJFkNU2AoEzEM69Eoy1tNyy4KwRFB1lwRfs4+HK9kndmdC7kcN9r8kBfKweAcA7Lj/RSWEqZV8asRaVVR6JTRJOlLyM7laXt3o15PfXu5jdvk3bjtuyF9WkO/RQRNo97iNz/iHkwzA0WplMtZb7Gfl3hEz+5QRhZxJYfeByvscoiRiedq8JB1+JpmPdysRThWvaqrhIWtooLtDkw7/LnMNZIHAUL3cXTmqzRRPjjhWvMYJCZ926dTRv3py+ffvSt29fWrRowffff2/U5ty5c6SkpOjfT5kyhQkTJjBu3DjatWtHXFwc27Ztw8srd3JxwoQJTJw4EYCuXbuyc+dOtm/fTv369QFdhN1PP/1Ejx49aNKkCR9++CFjxozhhx9+0I+hVCrZsmUL7u7udOnShaFDh/Lkk08yd+7cwjwkFumi0GWKHNA2tdqudhVPRnWti6IsPIwJSjz5UssVOBdLz+fW6kiJGlMCRxDfF/hR05P2imiGKPfxletXDMz4lJtULm6zCkR5iNxzhM/CoovbhBKHykXJxD6WHeKZuPKr5hHGumxhmHIXO7XmU1sMydJouZGSbrRO6YSb1tl/RbMz6harX+lgVQikNJKQmk7Nyp7FbYbAAouGt6H9pzs4LAdTk0Q6KKLYp23O3gtJ9GliXQFRICjLqFyUnNDWp58yglaKmOI2R1DIVK5cmbVr11ptk1dwSJIkpk+fzvTp0632mzRpEjNmzCA+Pt4k0q5NmzYcPHjQpn21atVi8+biz8CR0BKa7dzbr7Feb0/cqwuKEhG5VwJQZ6sIjehUm29eaKNfLwQ1BM5DfJdAYlrWKKK0NakqpfCd2xy8KN3RNOKGQeAMftQ8CkBPRSTVuG2jtfmzibPOMIcv3+GnI5YLbJcmDC/hRTVhr9HKqDVlp65oUVHVSwXk1t3rqNBNFIxZE1FsNglKB4sXL6Zu3bq4u7vTtm1bDhywrj6+Z88e2rZti7u7O/Xq1WPJkiUmbX777TeaNGmCSqWiSZMm+hTI4iJS1kVXtZSEc08gAAiWrlFFukearCJSbmC2TbeGfgBM6W+7Hp9A4Czy5dz7z3/+o/e4G74W5I8ste4BPdDHnYYBFfXrrablCl+NwAHKaiT4ipfaOdQ+HRVjsiZzS/YlRHGVpa7zcCOrkKwrfNTCuSfIB65K4xNCrFyNcE0TlJLM8y7/2Oxv7ltn6Zsoy7LJLL8tMtRl0TlV+CdhWZYZsHAvXT/7hyzh4MsXR7Kde62kGFRkAqZRKgJBDj/99BMTJkxg2rRpHD9+nG7duvHMM89YbB8bG8vjjz9Ot27dOH78OO+99x5vvvkmv/32m75NeHg4zz33HCNGjODEiROMGDGCoUOHcujQoaL4SGa55NoIrSxRS5GI5l5isdkhEJQUcsr7HNYGk2UhEXLNKx2IeL83g1qKWnuCoiNfzr1vvvkGPz8/k9eC/JFzE+6mVBil6FpNyxWRWAIHsKa8XJrpFRLAv+/0dKjPdbkqL2dO4b7sTmflWb52/bLUOvjUGvHQKXAcc+eDtZreAPxHuV3v1HAEcw4QWZYZseIwLyw/5JCDpCx+q4tigkUrQ3TCPRJS04lNelD4OyxjjOlWl1g5kFuyLyopizYKnbKjmEQRWGLevHmMGjWK0aNHExISwoIFC6hevbrF9kuWLKFWrVosWLCAkJAQRo8ezSuvvGJUM2zBggX06dOHqVOnEhwczNSpU+nVqxcLFiwogk9kns//05UYWeeguHB8d7HZIRCUFNopzgFYVcmVJAm/iqqiMkkgAERabokgM9u556qUjB5qymq0laDoKctfpfz4Lc/IdXg1axLpsit9lMdY5vpFvhwaxY2IzhHklwBv4xvOMG17rst+VJHu8bRyr9W+5vx0mWai7ZLTsth3MYkDMbdJvJ9RIHvtQa3RsvVUPIn3Cn9f9mB4nIqizIahA9Uex/8fkXFM+PG4EI3IZtqAJlTz8WCfVlc/qaviFCDOswLzZGZmcvToUfr27Wu0/tFHH7XYJzw83KR9v379iIiIICsry2obW+m+hUm3hlU5kZ2a+/e2rRy4mFRstggExY9MO8V5ACK0jov8CQSFicPOvVdeecXqInCcnBtHV5e8kXtl2SUjcBaa9Pskbf6Cq/OHcnX+UJI2f4E2/b5Rm7wPlrIsM336dL20/YABAzhz5ozZ8WVZ5rHHHkOSJH7//XejbcnJyYwYMQIfHx98fHwYMWIEd+/eddpns4f8/kwOaJvxctYU0mQV3ZUn+cHtE/xIsd2xBCEeOgX5pYLKOI1Eg5KV6scAGK3cioRj3622n+zg6JU7+vf30rPYfPJGbgMHgp/y85NOz9KwbG8s49Yd4/GF1p2TRYXWzkh8Z2F4iHNq+VrjrR8j+T3yBmsPXik8o0oZFVQu/KtpAcAjipNAbukUgcCQpKQkNBoNAQHGgitVq1a12CchIcGkfUBAAGq1mqSkJKttEhISLI6bkZFBamqq0eJsIrW5dfd+jrjm9PEFgtJCDSmRAOkumbJS7/QWCEoKDjv3kpOTjZZbt26xa9cuNmzYUOQP9WWFrOwZdlelAq2Bd89ZNfe+fL5Vfk0TlAKSNn1O5s1LBAydQcDQGWTevETS5i+M2uT9vsyZM4d58+bx+eefA+Dv70+fPn24d++eyfgLFiyw6GgePnw4kZGRhIWFERYWRmRkJCNGjHDOB7OTgkTEhGub8mLmf7krV6CN4iJ/qN6niXTZecYVMiJdTJAfJDDrbPtJ04NU2ZP6inh6K445PO77v+dOEEz4MZIP/8h978hX1VbTQ5dusyb8sj5S7fb9DII/CNOrJZeYyD3su547bX8GBy7n3HA56QGjV0dw9EqyxX5J90tf1HJhUVHlwv7syL3mistUJlWfXSEQmCPv/ZGtEgSW2huuN9fG2oT/7Nmz9ZOsPj4+1KxZ0y7bHeFEjnNPESNEewTlDsNSF+0lXUruGbkuGbgZtavk6VqkdgkEeXHYubdx40ajZfPmzVy6dInnn3+eTp06FYaNZR7DmnuGWEvLdeRBoV/TwHzZJSj5ZCVdIz32KFUeexNV9RBU1UOo0v8NHsYcIev2dX07w6+LLMssWLCAadOmMWjQIEBXByYtLY3169cbjX/ixAnmzZvHypUrTfYdFRVFWFgYy5cvJzQ0lNDQUJYtW8bmzZs5d+5c4XxgMxT0mTlCDuapzJnEaKtRXbrNRrePGKkMozRU/hI19wT5QQYea256XXiAB+s0vQB4w2Ujjv4GctI7b91LZ2f0LaNtkdcsO5cc5blvD/LhH2fYf1Gn7Lvt7E2nje1M7K2h67T9Gfy/cpS0/2/dMXZE3WTIN5ZT+oRgRC7jezYgEV/OamsDutTcV747wqFLtlWkBeULPz8/lEqlSURdTgSeOQIDA03a37p1CxcXF6pUqWK1Td5oPkOmTp1KSkqKfrl2zfmRdZ41W/BQdqOSdB/P1EtOH18gKMl89le0/nVOSu4RbWOTdu8+pqvBV7OyR9EYJhDkwSk19xQKBRMnTmT+/PnOGK7cseQ/bTn2QR/6Nwukgpt5xZ28OPKgUBQRA4LiIeNGFJKqAqqg3AuMqnowkqoCGXFR+nWGM76xsbEkJCQY1XRRqVR0797dqKZLWloaw4YNY9GiRQQGmjoCwsPD8fHxoWPHjvp1nTp1wsfHx2ptGGenjzhDXCZWrsZTmTPYpWmFSspiuusaVrt+Rg2pZKvCxd19WNwmCEopb/VqxJT+pjemy9QDeCCraKGIpa8iwqExH2Zq+Pd8Ih0+3Wmy7atdF/NtqyWu3klz+phbT8WzYMd5pzi8tAbhioUhghUec5uec3fr618ZRe5lO/6vF8IxKsv0bqJzoPyrzU7NVZ7iVFwKz317sDjNEpRA3NzcaNu2Ldu3bzda/88/lhXHQ0NDTdpv27aNdu3a4erqarVN586dLY6rUqnw9vY2WpzNd6O7EqltAIDyerjTxxcISjLbzuY63NtmO/eOmqm392zbmqwf05HN47sVmW0CgSFOE9SIiYlBrVY7a7hyRQWVC5UruOHuqqRWFU8m9G7IR080cdpMf36FOcKnWi4KLCgZaB7cRenpY7Je6emD5kFupIzhVyBnRthWTZeJEyfSuXNnBg8ebHbfCQkJ+Pv7m6z39/e3WhvG2ekjzhKeSaUir2S9wwdZI0mXXemuPMkOt8m8qdxQKsU2BAJLSICbi4IBzauZbLuDN6s0/QGY6PKrQ7X3HmSqGbnqsPl9lpI5pnHrjrFgxwXCY6xHamWoNVy9bdlx9snmsyzfF6t/r3Dgbmv+9vO8/fMJmw7GYcsOEpv0gOHLD5ls0ziQBy3i9kz5V9scgG6Kk+QcoR8PX2Xo0nAy1EKARKBj0qRJLF++nJUrVxIVFcXEiRO5fj03a2Lq1Km8+OKL+vevvfYaV65cYdKkSURFRbFy5UpWrFjB5MmT9W3eeusttm3bxmeffUZ0dDSfffYZO3bsYMKECUX50UzwcFNyWNZFJXVQRHO7CESSBIKSQs4l1ZsHNJJ0v3Fzzj2FQqJzfT98RHquoJiwL0zMgEmTJhm9l2WZ+Ph4tmzZwksvveQ0w8ozE3rrThZL98RYbFMUz0kersoi2IvAHHf3rSNl/w9W2wS+mB0pa/apWTZaby5601pNl02bNrFr1y6OHz9u1QZzNWBs1YaZOnWq0XkkNTW1YA4+p/4YJL7X9OWAtikfu6yis/Isk1x/5XmXXXyjHsTPmh4m9TUEgtKCX0U3ku5n0qFuZavtlqkH8KJyGyGKawxUHORPreWIEUPupVue4MtSy+w4e5OO9Srj5e5KeMxtjl1N5v+62y5GLcsyDzI1VMwjAjJ32zm6NvDjo03mxYAKgi1138GL9hOdcI+fx4aaHM9rd9KMHHvgWAT9lzsvAPBiaG1a1vS1u59xzT37nbIiLdcYlYuCo+pGPJTdCJDu0kS6wlm5Du9u0KnnNn4/jMv/G1DMVgpKAs899xy3b99m5syZxMfH06xZM3755Rcef/xxAOLj47l69aq+fd26ddm6dSsTJ07k66+/JigoiIULFzJkyBB9m86dO/Pjjz/y/vvv88EHH1C/fn1++uknoyyJ4iLKrRloN9BecY5vdsfw/sAmxW2SQFCktFJcRCHJXNYGkIRxcMWqke2LySqBIBeHnXt5H/YVCgVVq1bliy++EGq5JRShulv68GozkAohj1ht4+ITQFZiLJoHd022adJSUXr66t8bfgVyUmwTEhKoXz/3wdqwpsuuXbuIiYnB1zd3DIAhQ4bQrVs3du/eTWBgIDdvmta6SkxMtFobRqVSoVKprH42RyiMtPMYuTrDs6YxUHOQ91zXESTd4WPX7xjnsok16r78rOnObUwjJgWCksyG/+vCzxHXGNmlDmDsDDIkhYosUw/gbddf+a/rj2zPaEs6BfvNnrt5j9FrIgitV4UfXu3EsGW6NEdvD9uz26NXR7Az+ha73u5OvaoV9evvPMjkkc8tp8AVJtEJOvGhjcfjaFPLFxeDmrnmIrvyc5bKK+Sg0cocv5pMs+o+uJuZfDOsuefIdV/49ozZO6UnH206w95zzemrPEofxVHOauoUt1mCEsq4ceMYN26c/r1hqZHvvvvOpH337t05dsy6YNEzzzzDM8884zQbncUJuSFZspIaUhKHIiNBOPcE5Yzmkm7izpxKbvdGlpWyBYKiwuG03H/++cdo2blzJz/++COvvvoqLi4O+woFhczL2Q9xgtKF0tMH1yo1rS6SixuqoBDkjAdk3MgVsMi4cQ454wGq6iH6dYaPeXXr1iUwMNCopktmZiZ79uzR13R59913OXnyJJGRkfoFYP78+axatQrQ1YVJSUnh8OHcNLxDhw6RkpJitTaMsyk817XEZm0oPTPm8UHWSG7Ilakm3eG/rj8SrhrPQtev6KU4ihtZhWaBoPApT1FLtap4MrlfY/wq2nbULdMMIE6uQg0pibHKzU6zITyPMMFxKyquoFOoyxHn+OHwVTLVJUul8YfDVwn5MIxjV61/jvzMQeTt8tWuCzyzJJzx681HVOf3q1x+fgH24e/tzjf/acs2bTsA+ikdqz0pEJRVBrRtwBm5DgAtNGeL1xiBoBhoqdBl1Z3U1gPg06ea6bcpnFUnSCAoAE6ruScombwYWqe4TSiR1K9aobhNcAqufjVxr9uW22FfkREXTUZcNLfDvsKjfntcq9TQt+vSriUbN24EdBEdEyZMYNasWfz5558A/N///R+enp4MHz4c0EX3NWvWzGgBqFWrFnXr1gUgJCSE/v37M2bMGA4ePMjBgwcZM2YMAwcOpHFj00L9hUVhR6Zm4Mb3mr70yJjP5KyxRGrr4yZpGKQMZ4XbF0SoXmOe62IGKQ7gR0qh2iJwPuXIt2eCtZ9OOipmZb0AwGsuf1KdwhGXsXX4J/9yIretDN8diLXS2jG0Wpk3fzjOwp0XuJ+h5lo+xSeyNDJTfzvlNLtyyPv/WZmd6rsjyrw6sDafX+acblkaLf+cu0VqupiwANihaYNGlmiiuEIN6ZbtDgJBGWdyv8Yc0urq7jVXC+eeoPzRXKG7Duc491yEQ09QwshXqN2vv/7Kzz//zNWrV8nMNC40byvUXGA/1h68/L3c7RuDoqnPZ4u3ejXU1xEqCVTz8SAm8UGhjb/lza4MWLiv0MY3xO+JySTvWMrNnz8AwLNBRyr3ec2ozcUL50lJyXU8TZkyhYcPH/L2228Durow27Ztw8vLy6F9r1u3jjfffFOvvDto0CAWLVpUkI/jMI4Uji8Imbjyq6Y7v2q601SK5RnlvzymPEyglMzTyn08rdT9v6O0NTmkDeGkth4n5XpckoPQinmUEks59u3ZZIu2IyO0IXRSRPGx6ypeyXoHZ19RbEVO3k3LvcfQyhB57a7T9n0o9g6bTtwA4Nt/L3E/I7dm4J8nbvD3mQTmPtsSTztU7G3NMaRnabmfoTapG+gItiYyDI+kIxGpOem8X+26yMKdF2hZ05c/Xu+SHxPLFHfx4rA2hFDlWfoqjrJS81hxmyQQFCvurkpueLeGtC20k6K4dieNmpU9i9ssgaBIqEoy1aQ7aGRJH8Fa0rIJBAKH7zIXLlzItGnTeOmll/jjjz94+eWXiYmJ4ciRI7z++uuFYaPADLWqePLVsNa88YN1wYOCIJUIt2DhkN8IB3vxdHPhoyeaMOPPwp/ZVHp44ffEZKttbt/PoHKFXCEISZKYPn06kyZNwsfHh61bt+Lt7W11DHMPi5UrV2bt2rX5M9xJOFI43lmcketyRl2XmeoRtJEu0FcZQVfFaZoqrhCiuEaI4pq+7QNZxTm5JpfkIC5pqxEjB3FJrsYNuQoP8Chy2wXG6L7XZfdcVzAk3s96mS1u7/GoMpKnNXvZoLVeC9QZ3Lj7kGNXk2lTq5KRw8pZ5+27aZn8dToBT7fcunWGjj2AHVG6SC1vd1f+N6QFACkPs/BSuZhNvbFV+3PgVzrnf/TH/c3Wy8vB8DzraFSyrcPz6poIxnavTzUfd4J8c889Of1+O6pTADzhRAdqaWebtq3OuaeMEM49gQDo0WcQ2t8/pYHiBu3nbODI//5T3CYJBEVCC8UlAC7K1UlDF2QzsEUQn2yJor5BPWCBoDhx2Lm3ePFivv32W4YNG8bq1auZMmUK9erV48MPP+TOnTuFYaPAAk+0DLLp3JOk/NX6AXBROu+Bt6RpehR2Kp4syyhLUKh2ybHE+Rg6LYsaGQVH5cYcVevSkCuTSqjiLG0UF2iuuEQz6TIVpAzaSBdpw0XI80yfKnuQIFfWL0n4cFeuwF0qclfWLcl4kSJX4CEq0lChyTuIgHvHtpByeAOa+3dw86tFpV5jcK/ZzGL79KunSN61nMykqzT6uQZTpkzhtddyo13PnDnD1KlTAfDx8WH+/PlMmDDBaIzp06czY8YMo3UBAQEkJCTo38uyzIwZM/j2229JTk6mY8eOfP311zRt2tQJn7pouCjXYIH6Gf7r+iMfua5hX0ZzblHJaeObOxWvCb/CmvArhE3olu9z9fPfhrPw+db4e5tGuY/9/iiHYu/YdY7+8cg1Xu/ZALVWpufc3XSuX4X1YzqZtFPYGZx79U4ajQIsR0gbBiI7et6OS35odfu2szfZdlaX0ivUXu1ju7YdH/E97aVoqpAihJQE5Z66NWtwUq5LK+kS3RTOL0cgEJRUcpx7p2RdSu7hab2oVMGNEx/1xVUpMnQEJQOHnXtXr17VF8v38PDg3j2dYtyIESPo1KlTkafkCQqHj55oQoUCpA/lpV4Jm9Eo7Mg9KFm1vEqac9WZqFyUnPioLwoJmk/fVqy23MGbLdpObNHqHv4VaGkgxdFIuk5dKZ56injqSfHUlRLwltLwlh7iLcXRiDi795Ehu+gdfQ9l3d803MmSlWThkr1kv5bzvM9+rZGVaJHQokBGQoMi+72EnP1ak71Na7BNiwKtnLvO0P1glBJotF6ya3veNrbGzvl76exp9uzcQGi/AQTUqEX08QjO//IhT40ZTwUfUyfUvbvJ/PHrVzRs2Y7Ggx6nf8NKvPHmm1StWpUhQ4YAkJaWRp06dQCsKj83bdqUHTt26N8rlcaO1zlz5jBv3jy+++47GjVqxCeffEKfPn04d+6cwynwhYGHm32O4m81A+ivPExLxSW+cP2Gl7LeLZJU8483nyU2Kbd8giPn7YOX7vDJligWDmttsu1QrG4i0t6U/gMxSVy+nZb9+rbZNqfjUslQa3CzcYMvyzBixSH2XkgyG8WnzWfk3q7om7zynf3CD+dv3jOwyfQ4bD97kz5NLH/3ywNv92nEF9vhhLYeLRWXGKA8yBpNv+I2SyAoVur4VWCDdwda3b/E455RxW2OQFDopGdpAGgh6Zx7J7Lr7Xm7uwJYjcYXCIoah703gYGB3L59m9q1a1O7dm0OHjxIy5YtiY2NLVeqg6WF/KbWvtylrlPtqFnJgydaBvFndn2jwmJi70bM33HeZrvC/qZKkmTzQXRwqyD+iCzc41Fe8PFwLW4TzKJFwXm5JuflmroVmtxtFXhIoHSHACmZatwhULpDZekevtI9fHmAr3QfX+7jK93Hhwe4SLr0Y5WkRoUaXx6U7ZBMO+kYcZ9XWyv5psNu3YogCLmspv6JRczubRq19d8T6TTw0XB2wBngDJp6z3P8lVeYO3eu3rnXvn17GjduzFdffYVKZVlZ1sXFhcDAQLPbZFlmwYIFTJs2jaeffhqA1atXExAQwPr16xk7dmyBPrcz8Pdy5/0BIahclXzw+2mL7TQomZT1f/zp9j7dlKd5U97AAvUz+d7v6+tza/NaO03uv2jsSFsTfoUuDarYvZ+7D3OFISIu3yHA2z3f9aHscSxO33SGzSfiuZcnxTcvey8kATB+/TGWv9Te4n7y/ryt+fryloCIT0k3P0g228/minLk7NFw/DFrIsp9dN+4ng34Yvt5/tB0oaXiEk8q9wvnnkAAtHv0Gdj0I62yIlGr1bi4OC8YQCAoaSzceQGQ9WIap7KdeyoXEa0nKHk4fDZ+9NFH+fPPP2nTpg2jRo1i4sSJ/Prrr0REROgfYAQli8JWE7XXhtY1fQvVuffXW90IqeZtl3OvsL17sizbjNwb062ecO6VYx7gQYxcnRi5uh2tZdxQ40EGFUjHQ8rAk+xFSseDDFxR4yapcUGDK+rsJfu1pMYNDS6ocUONQh+zJ6NEi0KS9XF8Cn3snu61Ut/WeLuC3FqHkv6veceEJMkm2w3JWW+xv4XtWRotR29E8lzn2kRqfcn5Ybeud53t1x7yfPYNmCE7rl+gdT1vTmh1atJNfWvRr18TVqxYQVZWFq6u9juKL1y4QFBQECqVio4dOzJr1izq1dPtMzY2loSEBL3YDIBKpaJ79+4cOHDArHMvIyODjIwM/fvU1FS7bckvo7vp7LXm3AOIkavzXtYoFrgt5k3lRo5qG7FX2yJf+9xyMl7/2tEo6rwOP3uITkjlmSXhQP7TUQ3NPHTJvA0/HL5mdr0ldkTdIkujJSbxPo0DvJAkyWg/jly6r9w2VvqduuEUwzrUstjecOycfZaAW4USRU7a9p+aUKa5rKWN4iK1pJtclct3RKNAUKP5I9z/wx0/KYWN27fz1GOiHqWg7PLvhUQCuYOflIpaVhAl666tJeH5WiDIi8POvWnTplG9uu5h9LXXXqNy5crs27ePJ554gsfEyb3EUVLOOxKF608b37MBIdWsC0IYIhd67F7RpP7aSwkyRZAvJDJxJRNXUqiYN0+13KK+dxuN/BJz3MaxMDNEvz7F/Wfu39/J4MxPTPrE3XuVS3V6cyBzKADRj/Qn4Mgh1Go1SUlJVKtWza59d+zYkTVr1tCoUSNu3rzJJ598QufOnTlz5gxVqlTR197Lm9YbEBDAlStXzI45e/Zskzp+JYnftV3poI5muMsuvnZdyJDM6VyQaxS3WRaRgEW7LjB3mx0TPjYwTOF97tuD+R4n77XnrR+Ps/VUAjMHN+XF0DpG52pnCIHfSzcfRWgoACLrnefGNwz7LybRpYFfwY0o5STiy35tMx5RnmKwYj9facREtqB8o3BVEa5tQh/lMaL3/QHi+U9QhtFoIVhxFYAYOYgMiq/Wt0BgC4fjSRs0aMDdu3f174cOHcrChQt54YUXCA4OdqZtgjKEJJmv6+MsHK0N5IyHJmvkjcAQFC0uJUjMRFD4mExiOKCAq9bK+nOTI7Owjz32GEOGDKF58+b07t2bLVu2ALrUW2PbjMeUZdnifqZOnUpKSop+uXbNsUiwomCG+kWOaBvhLaWxym0OVUku0HiFeZrccz7RKY49KLzJmq2ndE7gJbtjTPbz5Nf7jdVzHRx73nbLn91wrLtpWTwy5x+u3jGO/nvThmBXeeJ3TRcAnlTuB2Qy1BrrHQSCMs5ebXMAuilOkqXR2mgtKC0kJyczYsQIfHx88PHxYcSIEUbP/eaQZZnp06cTFBSEh4cHPXr04MyZMybtDh8+DEC1atXw9fWlR48ePHyoE4LavXs3kiSZXY4cOaIfw9z2JUuWOO8A5CE6IZWo+FSaSDrnXrRsOSJeICgJOOzcs+SguX//Pu7upvWNBPnH0Xp5jtQiMqQo/CAStmvQFSVFYUtJ+rzlmZISvSpwPkpPb5AUaB4YO5g0aXdRVvA136dCJaP27/xyglu3buHi4kKVKvk7hwJUqFCB5s2bc+HCBQB9LT5D9VyAW7duWRTpUKlUeHt7Gy0ljQzcGJP5Npe0gdSQkljj9hmVKPz04eJGW8gzQpnZD8d5rxv3Der3GTqFb6am2xxTVyfIPIaRe5tPxps49vLur7ySc1/1t7Y9D2U36iviaSNd4J/oW8VsmUBQvOSUZeigiKb1tN9E3fUywvDhw4mMjCQsLIywsDAiIyMZMWKE1T454mGLFi3iyJEjBAYG0qdPH73oJkB4eLi+rvGuXbs4cuQI48ePR5EtNd+5c2fi4+ONltGjR1OnTh3atWtntL9Vq1YZtXvppZecfBRyeX2drkZwTuRetFY49wQlG7vTcidNmgTobvY+/PBDPD1zi1JrNBoOHTpEq1atnG6gwD56hwQwbUAIPefuNlpvz715YUex5dhRmNd9F6VjDyFFcQ9Skm5znKl8XBoI9HHnevLD4jZDUMhISlfcAhvw8HIkno0669enX47Eo2FHs31U1YN5ePFw7hgSbNu2jXbt2jlUby8vGRkZREVF0a1bNwDq1q1LYGAg27dvp3VrnWJrZmYme/bs4bPPPsv3fkoCd/FiZNZ/+c1tOiGKq6x3m8XwzPdIJh/OyJJ0orSCs66Tlq49mWotJ67d5cs8DjkZGLnqsF6VL4cen+8ukB323BsI3x54uOqunQ/wYJOmM8+57OYFlx2kPswVlJFlmUtJD6hbpQIKETUuKCfEyoHEaKtRXxFPd8VJ1NohuDp4Ly4oWURFRREWFsbBgwfp2FF3D7Vs2TJCQ0M5d+4cjRs3Nuljr3jYxIkTGTt2LJ9//jkhISF4e3vTsGFD/Thubm5GAmVZWVls2rSJ8ePHm0w0+fr6WhQzcybL914iJvEBACHZkXtRInJPUMKxO3Lv+PHjHD9+HFmWOXXqlP798ePHiY6OpmXLlnz33XeFaKrAGpJUsoUzJalwn+GaOFBvD4rmebKkTGLu+29P3MqJotO60R354tmWNArw0q8rKf8HQeHg3f5J7p/Yxv2T28hKusadnctQpybi1epxAJL3fEfS5i/07Su2egx16i3u7FxGVtI1ovdsYsWKFUyePFnfJjMzk5MnT+pfx8XFERkZycWLF/VtJk+ezJ49e4iNjeXQoUM888wzpKam6meQJUliwoQJzJo1i40bN3L69GlGjhyJp6cnw4cPL4pD4xA1K3s41P6qHMDzme+TKPtkO/g+JRDHxS4OX77jcJ/iwFmR2O9uOGV2faZGy+Cv97MrT0TY8r2x7D6XyKYTN7jzIFO//mFW4aeFKqTCS9Hq0aOHSWrV888/X4ifJn8Y+urWax4FYKDiEHtOnON+hpp/om+xYMcFen2xh5mbz1oYRSAoe6wf3YntWl1EVV9lBBlqkZpb2gkPD8fHx0fv2APo1KkTPj4+HDhwwGwfW+JhoMtYOHToEFWrVgV0Jb66d+/Ovn37LNqyadMmkpKSGDlypMm28ePH4+fnR/v27VmyZAlarfO/exdv3eOTLVG6z0Mm9SSdAGKUiNwTlHDsDuf5559/AHj55Zf58ssvS2S6UFlnyX/a8NraY2a3SZifZS8paTUSEr1DAvjfX9Em2+YMacGU304WbHwHP2dRTK57e5SMaLkalTxtNyoj5BR/33Iq3kZLQVmhQsgjaB/e4+7+H9E8uIObX238n52Oi48/AJr7yahTE/XtXX0D8X9mOsm7lnPv+BZSK1Vl4cKF+nQRgBs3bugj8BISEpg7dy5z586le/fu7N69G4Dr168zbNgwkpKSqFq1Kp06deLgwYPUrl1bP86UKVN4+PAh48aNIzk5mY4dO7Jt2za8vHKdzyWFVSM70HveHof6xMjVeT7zfda7fUqI4hobVR/xSuY7RMm1bXfOJvFehu1GTmTCj/mrI+esyL0T1+6aXZ9p4cHYWmptYSMhMXz4cK5fv05YWBgAr776qlmlZ0NyUrS+++47GjVqxCeffEKfPn04d+6c0Xd/zJgxzJw5U//ew8MxB3NRUKty7vXzhFyf09o6NFNcJjB2A6NXV+DgpVzn9HcHLjN9UNPiMFMgKHI6N/DjSuvBcOpPeioieZCWRkWVeDYszSQkJODv72+y3t/f36TEiGEfsC4edunSJUAnGgbw22+/sWHDBnr16sXp06eNIvhyWLFiBf369aNmzZpG6z/++GN69eqFh4cHO3fu5O233yYpKYn333/frH0ZGRlkZOTeZ6Sm2ldGJDktS/+6gRSHUpK5I1fkJpXs6i8QFBcOex9WrVpVGHYI7MDDrWQ4i/KDJEED/4ocePdR/jxxg5upGazcHwtQ5GksvUP8eaFTbV5edcR24wLwbNuaHLx0hz9P3DC7vTAjylQuCjGLKig3eLUZgFebAWa3+Q2YaLLOvVZzqo38Uv/+tdeM+9apU4eUlBR8fHxISUkxO5n1448/2rRLkiSmT5/O9OnTbbYtbhr4V8xXvxi5OkMyZ7DSdQ6NFHH84jaDd7PGsFkb6mQLncPvkebPx7Yo7HpSRR1gbM/HSU+8YjFFy/K49qVoAXh6ehZJalVBeLN3Q05cv8uRy8mAxDpNL2YrVvCCcie9Lj1GPkpXCwRlhn79niDxpA9VpRR+2/wrL494pbhNEphh+vTpzJgxw2qbHNEKc8ES1oTAcrAmHpYTWffyyy8zb948WrZsSbdu3di5cycrV67UO/1yuH79On///Tc///yzyX4MnXg55cBmzpxp0bk3e/Zsm5/dFk0UOielrt5eyQiaEQgsIe5KShEh1SxHe0gSZGlM79ZLyikop3h3kK8HY7vXZ2z3evptyiL+Flbz8aBnY3+W/KeNfl2nepULPO7IznWM3ru5KPhqWOsCj5sfRCaqQCDIL97ujk0kXZer8kzmdPZpmlJRSmeR21fMclmOikzbnUsJmkIuTlvU5QPsCXa/fy3KYoqWJexJ0cph3bp1+Pn50bRpUyZPnmxUfN0cGRkZpKamGi2Fjbe7K7+8llvP8w9NF1JlD+opEuipiCz0/QsEJZnKFd3ZrtHdSyvPbeWaGWEeQfEzfvx4oqKirC7NmjUjMDCQmzdvmvRPTEy0KARmj3hYtWrVAAgODjZqExISwtWrV03GXLVqFVWqVGHQoEE2P1unTp1ITU01azfA1KlTSUlJ0S/Xrl2zOWZegiVdH8OshGUvtrPUXCAoVoRzrxTh7+XOv+/05Oj7vU22SUikm6nBU0Kycq3aoVQUz9ewftXcSJUQB2v2maNvE/MXPmfRK9g0VF5gm56Nqxa3CQJBqeL9gU0c7pNKBV7Kepev1E+ilSWGu+xiq9tUOkpRhWBh0VMUwlNFSU4tIWtk3btjNkXLz8/PYh9rKVqGD38vvPACP/zwA7t37+aDDz7gt99+00f6WWL27Nn62n8+Pj4m6VqFSef6OtXcNNxZr9Hdg4112WzSzlJ6tUBQVtlmUHev+5ydxWyNwBx+fn4EBwdbXdzd3QkNDSUlJYXDh3NFxw4dOkRKSgqdO3c2O7aheFgOOeJhOX3q1KlDUFAQFy4Yl5k4f/68USkT0EX8rVq1ihdffNEukbPjx4/j7u6Or6+v2e0qlQpvb2+jxR4MJ9xCpOzIPTn3miPEYwQllVLj3Cusos4JCQmMGDGCwMBAKlSoQJs2bfj1118LvO/ColYVT6pUVJndVpLTMPOeAg3f25uV66hohi0MHY5SMcQ4yg7G13VtaPmBSmCMYQrd/Oda8f6AEOr6VShGiwSC0kNFlQv+XuavM9bQoOQL9VBGZL3LLdmX+op4flJ9zGcu31KVu843tIi4cTfdaYIaJYG7+9Zx5bOBVpeM+As6oS4LKVq2sJaiBbp6e71796ZZs2Y8//zz/Prrr+zYsYNjx8zXFQbnRGDkl6Uj2upfr1T3J1NW0lERTRvpvFG7TRbKcAgEZZXPJr9BiuxJoJRMR0XZmMwpr4SEhNC/f3/GjBnDwYMHOXjwIGPGjGHgwIFGSrnBwcFs3LgRsE88TJIk3nnnHZYuXQpATEwMH3zwAdHR0YwaNcrIhl27dhEbG2uyHuDPP/9k2bJlnD59mpiYGJYvX860adN49dVXUakcv2exl0aK60BOWq4Ol2IKTBEIbFFqvpnDhw8nMjKSsLAwwsLCiIyMZMSIEVb75BR1XrRoEUeOHCEwMJA+ffoYpX6MGDGCc+fOsWnTJk6dOsXTTz/Nc889x/HjuUW387PvokaSoGUNH1rV9CU4MDd9tzicVuYweUAweFulgn0n5OfaF94svVPK/pWMQy3Ig6+nG6O71cOvoptd7d98tEEhWyQQlEw+GNiEgS2q0bdJAF55UnMdmVzZr21O74zPWavuBcBzLrvZo5rI2y4/40XpS9v6cucFNh6PK24znIZXm4EEjf7G6uJWtTZuXlXMpjrdvm1ZFdmeFC1ztGnTBldXV5PIDkPyG4HhDLzccyNIblGJjRqd4M5rLn8atZv8y4kis0kgKAkEVPZhq0aXuj9Ysb/Q65MKCpd169bRvHlz+vbtS9++fWnRogXff/+9UZtz586RkpKifz9lyhQmTJjAuHHjaNeuHXFxcSbiYRMmTGDiRF0N5K5du7Jz5062b99O/fr1jcZesWIFnTt3JiQkxMQ2V1dXFi9eTGhoKC1atODLL79k5syZfPHFF848BEb4cg8/SVcC4qIcpF+vLOJ68QKBvZQKhYaoqCiLRZ3PnTtnNJuQg71FncPDw/nmm2/o0KEDoCvUOX/+fI4dO0br1q3zte/iQJLARang99e7cDouhYFfWZYXLw6speXW96/AzMFN+Sf6Fv+cS7Tc0AJPt66eL5sM7z9KSvqyNUqBiSUGcWspEDjOqK51gbom62c/3Zxn29agwbS/7B4rlQq8rx7FBk03PnBdS2vFRd5w+Z2XlNtYr3mU1ep+xFPFidYL7EXp6YPS03LdvBx8ajfhQnaKVs49Uk6KliUMU7Rat9bVnM1J0frss88s9jtz5gxZWVn62kwlER8PV1Ie6hQUv9UM4FnlHvoqj9JEfZmzch19u/QsDe6uymKyUiAoejKaDIHz//C48jBZGQ9xc/e03UlQIqlcuTJr16612iavA9de8bBJkyYxY8YM4uPjLU7OrF+/3mL//v37079/f6v7cBY5n7GBpJvYuy778RB3/Xbh3BOUVEpF5F54eLjFos55CzTnYG9R565du/LTTz9x584dtFotP/74IxkZGfTo0SPf+y5ujNJN7Tj3NK9u+ya/oJim5UpGr18MrUPXhvmrjfbJU83y1a8wHUCWDru90WP28ETL3Bmk2lXEjZRAIHAeo7rqRI8eDfZnWIdauORT+eiY3IinMmfwauZEzmur4y2l8ZrLZvaq3mKx6wJ6KY7igtqZpgucRMXAOmZTtPI+XDmaohUTE8PMmTOJiIjg8uXLbN26lWeffZbWrVvTpUuXIv+c9vLb/4XyZCvddTdGrs6f2YrQb7v8YtTuws37RW6bQFCcDH92GDfkynhLaUTv+624zREInEYDha7UQow2yGi9cO4JSiqlwrmXkJBgtqizv7+/SeqHYR+wXdT5p59+Qq1WU6VKFVQqFWPHjmXjxo36MOH87Buco+rmSDRZXmeZIyx7sR3jetQ3u23POz2c4vzzs1K/ydLnXDTcPqVZT7eCB6Daknh3FEuOwzWvdLSwxXEGNM+NcFCUhtDDIqQgWSEi6k8ggGEdavL3hEeMao2N6FTbSg9rSGzTtqdf5me8kjmZA5omuEhaHlceZoXbFxxUjWemyyoeUZwoUwq7pR0J8ylaOXWTcnA0RcvNzY2dO3fSr18/GjduzJtvvknfvn3ZsWMHSmXJjXhr4O/Fgudz70vmq4eglhX0Uh6nrXROvz5TU3LrHwsEhYGri5JNGp14wvU9a8gSvwFBGaFhduTeBdk4S6xpUNGVhRAIHKFYnXvTp09HkiSrS0REBGC5qLMtp4ytos7vv/8+ycnJ7Nixg4iICCZNmsSzzz7LqVOnLI5hz76dreo2/7mW1htYMMUel0+gjztT+geb3Va7SgWGdahldpu9TOzdCG93y4pHOTbmDfMe2CLItLETyPm3GaXlFsqerOPMsiTCtWcbUQbGeXzzQpviNkFQyEiSRONAL1wNIvZ6BhdMeVpGwS5tG4ZnvU//jP+xXP0YibI3flIqL7psZ43bZxxTjeVb1y8YqQyjqRSLElMVeEHRcCnpgT5FK2eScu3atSaqhLIsM3LkSP37nBSt+Ph40tPT2bNnD82a5UbY16xZkz179nD79m0yMjK4ePEiX375JZUrVy6iT+YcLsvV+FnTA4D/uv5IztRQXsXc41eTOZdwD4GgrCJJEr9rugLQW3GUk9GWa2cKBKWBnEeGnLTciwbOvSPTeovSC4ISS7HW3Bs/fjzPP/+81TZ16tTh5MmTZos6JyYmWizQbFjU2bCGi2FR55iYGBYtWsTp06dp2rQpAC1btmTv3r18/fXXLFmyhMDAQIf3DTpVt0mTJunfp6amFsjB91TrGna3dXYQlzVV1xUvtWPU6gir/YOreVnd7uyoOXsx+lzOMKE4nYXCu2cT4dtzHv7e7rYbCcoczhRoipZr8Yl6BLPVw+mmOElfRQSPKiMJlJLpqzxKX+VRAO7L7kRq63NWrkO0tibRci0uytXJxPKEkUBQVCxUP8XTyr10UJzjCUU4f2o7M3/7eULr61J2b6am89RiXQmXy/8bUJymCgSFSlKFhkRm1qeVIoZt6+fT9tMlxW2SQFBg6men5V40SMt1dy0ViY+CckqxOvf8/Pzw8/Oz2S40NJQUC0WdO3fubLaPPUWd09J0qn2KPHLWSqUSrVab732Drr5fYcpy58XiI5cTnsW0FrwiLgqJ7o3yF8lh6Fhztl+qR+Oq7LZDmMM4cq/gVsgWXlvCWqqyPXSqlxvloMzrIBWerHwjIvzsQRykckkhTCJoULJb25rd2taglmkqXaG74gTtFOdopziPt5RGV+UZunJG3ydLVnJd9uOa7M9V2Z8rcgBXZX9uypVJlH1IxFc4/wRFQgJV+Fo9mLddf2Wa6zp2ZbTm8OU7nEu4x+37GXoBDkHZIzk5mTfffJNNmzYBMGjQIL766iuTZwpDZFlmxowZfPvttyQnJ9OxY0e+/vprfYDBnTt3+Oijj9i2bRvXrl3Dz8+PJ598ko8//hgfn8Kvj10Qfvu/UBbN60UrRQzDlTvRaDQlOs1eILDED4evMnXDKTxJp4aUBBhH7lVUlQo9UkE5pVR8O0NCQvRFnXNqvbz66qsMHDjQSK02ODiY2bNn89RTTxkVdW7YsCENGzZk1qxZRkWdg4ODadCgAWPHjmXu3LlUqVKF33//ne3bt7N582aH9l3cGEa/GQlqFGJIl4x9td7MOkvMKNX+P3vnHR5F9TXgd3bTSQNCGiUJNYFQQwsdhBC6iAKiINJFREB/SFGJSpEi8gECFqqAoCKIiAEUiJSEJr33mgABkkCAtJ3vjyWb3WzJbnq57/Psk90755aZzNmdOXNK59peTP3zbI7XtXxgI56nqAj4NNzydeUjznZWbHq3OQv+ucg/5+5Z1Pf0Zx0ppfXjInLu6WLwlNP6h3ev6005J1vqVHDh/XXH8m1dAkFRJu+/ZSROy76cTvOFNJBQUUO6RT3FJWpINwlQ3CBAuo6L9BQ/6S5+6HvVpxMnl+K+7Mp92YU4HEmQHXiMAwmyAwmUevHXgafY8Vy24Rm2PMda670NSVjny14LijbfpXWll3IPvoq7vGe1kS9T+9Fx3r96cnFPk3F1yL2iWoKCpV+/fty6dYvwcPW15rBhw+jfvz9r1qwx2mfWrFnMnTuXFStWUL16daZOnUqHDh04f/48Tk5O3Llzhzt37jBnzhxq1qzJ9evXGTFiBHfu3OHXX3/Nr13LFj5lS7ElrSmfWK3GR3GPNz+ZzbxJY3FzzD9HB4EgN5j4mzo1V2VJ7bV3X3YmjowotIKKOBMIzKFIGPdAndQ5PekyqJ+QLVy4UEfGUFLnZ8+eMXLkSM0TMu2kztbW1mzdupUJEybQrVs3njx5QtWqVVm5ciWdO3e2aO6C4rWgCvxy5BYjWlfWtOW6Qc+IFczV3jpXQoDT1+vtas/MXrX5aMPJLHpkMZ4kYW9j2dNCU/sR5FOaI9cfZT2GkffG55SoV9GVAC9ni417pTI9NdJe/7phTRmw7KBF45U0Stla8UnXmlx/kKi3zVAYum9ZB649eJofSytwdoxtxY9R11kVed2oTEEbxgUFQ34/RJBRcE6uxLm0SjqtnjzER7pHJcVdKkn3NK9yUhzliMNWSsVVSsRVSqQat3O0hmeyDclYkYIVaShIwYpUWal5r/6rJDX9vZzxXoWE2kSpfslaf2VNuwL5xb6qZMMy2n91j4+k81f9Xn+7uTJkkkmU7QARSpoVSdjwWeoAltvMZohyK1vTmnBC1i9SVu/zHXz1Wl16BZmfZkVQODl79izh4eFERUXRpIm6UNr3339PcHAwFy8azjcnyzLz5s1j8uTJvPLKKwCsXLkSDw8P1q5dy/DhwwkMDGTDhoxqs1WqVGHatGm8+eabpKamYmVVuG/bkiQ7fktrwUCr7byp/JuGU2tzYWonbKxEGKOgaKDtCJBeTONypmIaAkFhpnD/SmiRntTZFJkLMqQndQ4LCzPap1q1ajo/pNmdu6CY9WodPutRy2jFWEvuxX4Y0JAJv53g6z71dNozh+WuHdKE6X+dZUbPOjpPL7rX9Sb8VIxZleKM2QYcbY2HUmX+/wKUy0Foq7mhwbNfrUO7ryLMGK/g0P4/+LmVKvFRk4bOFcNy5o039eXavLn0QA5WVHSo5uHEKw0qmDTuGQvVFxRvCsfDaokYyhIjl+VAWoCB7TIuJKoNfVI85YjDWXqKM4laf59pPtuThD1J2Ekp2JGEHcnYSBlFPOylZOwzV/AtFMch74mVRTVAQ5z9PJSWs3YS+yTjvNilqs/mtGC6KyOZa72YLsnTSULfS++DX47TvqYHDxOT1b/VgiJJZGQkLi4uGsMeQNOmTXFxceHAAcPXClevXiUmJkbjKADqFD6tW7dm//79DB8+3GC/+Ph4nJ2dC71hDyDif21Zuek53NhOB8VhfKQYlu69yjtt9I3dAkFhZNzPxzXvqypeFNNQ5U2BR4EgLyj8vxQCk0iSZNSwZynta3pwKKC9SXfjt5v70qyqG1vea6m37fXGlYi88oD7j5MybcnCEpCNG6WF/epzLvoxfRplv0iJbCA0OCdYKfL2js/U/0V7auFVZZgsosNNYlvCkuemZGGgd3UQ+cxKItqVc9O5OqMzfhO3FsBqjCERjyPxsiOX5Ox5SClJw45k7EjGXkrG5oWPnt5LSsOaNKxIxQrVi3b1e6WUhgQa/z0Fap1SvPDVk5C1fPZknTYp03uFlO67p72Xss5f9Xv97Znb0Wk33fcptryfnQNYzLG3UfLj4CZ0+r892FopsLNWEv8shU9S3qaJ4ixVFXcYb7WeL1L7G+xf97PtAPw9rhVV3U0XHBMUTmJiYnB3d9drd3d3N1iEL70PoFeMz8PDg+vXDT9Me/DgAV988YVRw186SUlJJCVlXHsnJCSYlM8rKpZxoF3r1uxaUZe2yuMMUW5l59XawrgnKDJsPJrh7V9FigbgsiyMe4KigzDuFXOyMjcNbemnK2/AgKTtBfVp15omx1vxdiO6zN9r9vosQXttXet407VONscx2JZzw5w5YQc5KSRiyhutcHjUFG5eaVCBozfidNoMHVNDh7mkHV47K9Nh7dU9nPgo1J+Z4efyaUWCwkAj3zI42Vnx+Hmqpq045p5JQ0ki9iRib/oJQAl4kCKMe4YJ8HJm+9hWeDjZ8SQ5leZf7iQeRz5KGcoKm9kMtvqLKFUAO1QNjY4RcSFWGPcKGWFhYXz22WcmZQ4dOgQYv17O6jsx83ZjfRISEujSpQs1a9ZkypQpJsecMWNGluvOL5xsrZmf1o22yuO8powgKnVYQS9JIMgWPpLaIH9V9irglQgE5lOy3FFKCJbca5UplXVYq0rHw8304LW8XRjU3M+kDBj3Lqvq7miiT+7cSaWPktuee3mdU8TaxPgu9taUd7XH3cmWck62BvPGlXTeaFxJr83co6TIY6/MwkZg+azD8XLzSXw7f30PiPzCroR5ZeYEpULiZFhHi/sIBMWR6h5OuDiof3vrVlBXMt2tqs/yVLWOfGW9GL8Xnh+GOH07I0f09tMxNJ72N5GXH+TtogUmGTVqFGfPnjX5CgwMxNPT06CH3v379w169AF4enoCGR586dy7d0/Pm+/x48eEhobi6OjIxo0bsbY27S0/ceJE4uPjNa+bN29astu5Sk1vZ6JUARxTVcFOSqH69Z8KbC0CQXaRUOErqXX8quxZwKsRCMxH3NUUQ+ytM7xucsOropFvmRyPYQrtJdbwdGLF24346339sN88XUMujGFjIGTN5JxmTjqyTRX6N/WhUhkHozIKSSLif23YN6EdSoVESlrJNu4ZsgMbMtCZay/OqpCAts4VByRJomnlvNX7gsTT2U7z/tOutQpwJcUflcgTICgBrB8erHlIMS31DQ6qauAsPeNb67k4YbgY029HbyPLMpfuPWHYj0e49ziJ/i9yu+bWw0yBZbi5ueHv72/yZWdnR3BwMPHx8Rw8mFG87MCBA8THx+vk4dPGz88PT09PduzYoWlLTk4mIiKCZs2aadoSEhIICQnBxsaGzZs3Y2dnZ2g4HWxtbXF2dtZ5FRRKhcSV6V1YktoNgLeV2xiwMJy9F2MLbE0CgaW4E4e9lEyqrOC27FbQyxEIzEYY94ohDlqVYtMMZL5/t22Gx405BqbaFVzY8E4wkRPbZWs9hq5RTXmWtanhToBX3l2YpO+yzhpMHAhzDaR22TTwZOVlNz7Uny9eDjRpiJIkCSulwmBOLIFlGDrMWZ0Bv74TbPE8+ye0o3nVshb3A7gyvXPWQjlkRGvLPfNWvN2InvXztqqYgxmVsPs39TG5vYp7RiJ7exuhM7mFm6N+AQFhoxCUBOyslfh7qkNsU7Hi3eTR3JVdqa64zXfWc7HNXJTlBZ/9cYb2czMKdqWqZFbsu0rj6f9w6d7jfFm7wHICAgIIDQ1l6NChREVFERUVxdChQ+natSvVqlXTyPn7+7Nx40ZAfZ02ZswYpk+fzsaNGzl16hQDBw7EwcGBfv36AWqPvZCQEBITE1m6dCkJCQnExMQQExNDWlqawbUURhQKiW2qhpxVVcJZekrTmDW8ufQAPx++yZOk1KwHEAgKGD+F2sP2plyOVJHFTFCEEHc1hZiuddQJPIN8SlvUz9HO+JfQgtfr87+O/prP5t54BfmUwcvaX9RRAAEAAElEQVTF3qRMug3MHFuYTkiseUvIdbJ707nlvRY6n1+pX54e9bxxd7ascq+hPH+zX81eIkER+aaL+Q6r5p0EmT33tI+3jVJBLW8XcyfU4Olsx+c9AilbyobJnQ1V/TSxnnz4h7ep4U5DA989/ZrohzfnJ82qZM8gqk1u5NgU6PNWsG+WMjU8RI4xQfFEOzXHfUozKHk8j2V7gpVnmGu9SFNURZsV+6/ptYX9cYb7j5OYvPEUsizzMDGZa7GJebl0QTZYs2YNtWvXJiQkhJCQEOrUqcOPP/6oI3P+/Hni4zPCr8ePH8+YMWMYOXIkDRs25Pbt22zfvh0nJ/X34pEjRzhw4AAnT56katWqeHl5aV4FGWqbHWQUzE7tDcDbynDK8Yjxv57gvbX/FfDKBIKs8XkRknv9RUhuMUwvLCimCONeIcbTxY7Tn3Xkl+GWeQXZWimZ3rM2H3cJoJyTrsHJHK+X/EA7D1NWIY+5RWU3tbdOt7pqo6kps866YU2Nbgssr2vImdunHv/Xt75OW3Z3qXYF40YiU2OK3xxdzDXcGpIzVPXYxV433422N2dmz8v2Abq5cwCqexjOJVmlnCOHP27P0FaVzVkuAKG18i/3hyGD9YchNczu72Rr3tNO7SPubOLhBJj3v3XKYgzduYX2WEp5V8MPesxRuw0jm+WpZ3ZBMqpt1YJegqAAeclf97v/tOzLsJRxJMlWdFEe5P+sF2KF+V5LB64+ZNCKQzT4Ygdt5uzmxK24LCuZC/KPMmXKsHr1ahISEkhISGD16tW4urrqyMiyzMCBAzWfJUkiLCyM6Ohonj9/TkREBIGBgZrtbdq0QZZlgy9fX9/82bFcZKeqPkdU1bCXkhltpfZg3HX+fgGvSiDIGj9NMQ2Rb09QtBDGvUJOKVurbHnp9GtSiSEtszYY5IZdLbhyWbxd7KhfyRWAV4Mq6Gw3dMPn7mRLt7re9GpQgVJmGgByytb3W7L7wzY0fJFDUCenTSaLgaXekpnJrldg1XKOGiOkJWQ2kKb/Lxr7Fd+8adkhPW9h1zrqyleG/k2DW+gWhJn9ah28XHVz3phSm9rl9Q20/p76xowMT1fzlfCXEcHM61svS7mKZUx72ZqLIcNXVvkF29QoB6gLVfzf6/XMm0drmuyGt2szIheLfQj0WT+8KW6ONoxpXy1r4UwoJFg1qHEerKrgcXUwnfReULwx9HAuUlWL0SnvkSwr6aaMYrH1/xkN0TWEtiGk+8J9vPzNPs3n1VHXGf7jYULn/UvI1xGcvhNvaAiBhYh8h7nDmc87AhKzUvoC0E/5DwHSdQCepxSdEGNBySS9Uu51Wf3QRjwGFhQVhHGvhJH5miU3rmHWDm3Cno/aYWulvikP8HLm8MftTfaRJIkFr9fnq951zZ7HwSZnRkA7ayW+Rgxn2T0MOTGOaow7Wj8ZVkoFO8a15stXauvJN/Itg4ezrV6etrTnT4j4bgouLi64uLjQv39/ZneryqTO/ix+o4HBuWVZJiwsjBo11F5YXbp04fTp00ZlO3XqhCRJbNq0SdN+7do1Bg8ejJ+fH/b29lSpUoUpU6aQnGz+jUteYSyP4V/vt2T72FY0r6pOjpv5/J/eszauDrp5w15rWFHvR137/555DEMFBAzm8ct08vw5uoUBKV0a+ZYxy/iVa/cmWkuc/3p9FrxeH/ssvH+71/Vm+duN+Pd/bbM1ZVZ5I83ZNWc700YWSzz7BPpUKO3AocntGdO+OgA+ZdVG89DArJ9wKySJck62HJ8SkqdrFAgKC9tUjRiWMo7nsjUdlEdYbTMdN7JniDt9JwFZlvn92G0+3nSKbafvci7mMRfuPmHE6iO5vPKSx75LsdT9bDtbTtwp6KUUedKv2Q/IAWxJa4pSkvnMegUg89JXESSK3HuCQozvC+PeNU1YrjDvCYoGwrgnyDGSJOmE2QK4OVqWf84cetT3pnX1ckzs5J+1sBmYMhJo742hMM2ssOQ3ILMhSqmQeK1hRcaH1mDDOxkV1Oyslez7qB2rB+tWYovdPJuHNy8QHh5OeHg4x44dY+zIIQxrVYWyRv4Ps2bNYu7cucyePRsAd3d3OnTowOPH+gm8582bZ/BH7dy5c6hUKr799ltOnz7N119/zZIlS5g0aZL5O5/PlLK1orpWzq/Mx95YPjlD+58e3j0sU0itTPaMvtoVXHNKVsa97ITLdq/rrdlnk30kibY13HHP5v5k/i7JTGavCjdHG+Qz27i1ZDDX5/QkesX77Nmzx+QYHUo/IHrF+1yf05MR3ZqxZMkSne2nT5/mzTffBMDFxYV58+bpjTFjxgwaNWqEk5MT7u7uvPzyy5w/f15HZuDAgUiSpPNq2tR4yH9RQlsnto9tRdTEl3R0y3g/9d/Moe7Fgez8VghKBrtV9Xk7ZTwJsgONFBf43fZjaknXsjVWtcl/8f66Y3rtT56nolLJPEs27BUly8a35ZTTd+J544cojt54lCfjp6lkvtp+nv5LD5Ccajg0Oe5pzh8s9l96gITnqYxae1TTduDKA1rM3Mmuc/dyPH5J449R6oeWU1PeIFG2pbHiPK8o9nA77hmnbgtPU0FhRcb3Rc69a8JzT1DEEMa9EkZBPHjIDS8iK4WErZWSlYMaMzwbVTwXvF5fr81dKx9h5jVaKRUMbenH640rUvFFKKcl5HSflQqJkW2q6oUHWykVOjfVKbE3eX71CC0HfUxwcDDBwcF8//33bNmyRc/QkLE2mXnz5jF58mS6d+8OwJIlS3j69Clr167VkT1+/Dhz585l2bJleuOEhoayfPlyQkJCqFy5Mt27d+fDDz/kt99+y9nO5wI5yblniMz37BISX71Wl99GNuODTDnoZFnW9/QzY47cfCqYVVjRwckZnrWVTJzflq4pN/bBUgPJw5O7ubl1MS7BvfEeOB/bCrXo1KkTqQmGb8RS4mLo37snthVq4T1wPr0GjWL06NFs2LBBI/P06VNNfiMPD/0cigARERG8++67REVFsWPHDlJTUzVVDrUJDQ0lOjpa89q6datF+1cUsLVS4ulinjE3v3KsFgQ2VoUjp62gcHFocnv83EoRqarFy8mfc1nlRXnpAb/ZfMpg5Z9IBgptmCJVZfj7XZIkei3ZT8Cn4TxMTOZO3DM2Hb1N6os8faPXHSPg03Cu3H9icvzEpFQ2Hb1NwvMUs9c0/tcT7Lv0gJ6L9pu/IxbQbcFeFuy8xJ6Lsfx1KlrTLssyey/GsiTiMvU+38H//X0xR/MYOrSvfx/FrUfPeHvFoRyNXRKpXcGFC1M7EUNZFqT2BOAT69WU4xF9voviTtyzAl6hQJDB/cdJAHjwCHspmVRZwS1ZnWqmQuncSXcjEOQ1wrhXwsjPVCIv1/PGp6wDLwW453is99pZntspndbVyxn0OKpQ2rTRbnKXmsx4xXT12oK+TU26cxbJthSeVTMSMjdt2hQXFxf27zd8kX316lViYmIICckIi7O1taV169Y6fZ4+fcrrr7/OwoUL8fQ0L6FsfHw8ZcoUnTx/5uqDntFKUldGbFCptJ6nmSxnz8iVm+dSVrulvbzFbzbIMo9edsmqwrYhsgr7zbxvd/dvoGyDUJzqdsTarSJl2g+jYsWK2F/6x2D/J8f+olKlSpRpPwxrt4qEvNKPQYMGMWfOHI1Mo0aNmDp1KqDWDUOEh4czcOBAatWqRd26dVm+fDk3btzgyBHd0DhbW1s8PT01r6KkH7lB/6Y+Bb2EXMNQPk1thOOeYFJn/cgCF3trypZSp3u4InvTM/kzdqQFYSul8on1GlZbz9Dkd8opR2/EAdDgix20+2o3Y9Yf01Tk/eO4OtR0pVaF3qfJ+qGRH204wZj1x6gTtp2kVPM8/WKfJGneZzef2pX7T+j9bST/XlDnGbwWm8i9hOcAnIlO0Mglp6qQZZnkVBXbz9zlzaUH+PKvcwB8/fcFo+Mfuf6Iz/44zWMLjJZg2OAnMB8bKwWfdK3JD2mdOanypbT0hC+tfwBkmn25k5O3hAefoHBw5Lra8zjda++WXI5U1JEuP7zViFbVy7HhHcuKXAoE+Y0w7gnyjHl967Prgza5kiA/rzGWoy275JdzSlpiHEoHF73CB+7u7sTEGL5ZSG/P7JHk4eGh02fs2LE0a9aMHj16mLWWy5cvs2DBAkaMGGFSLikpSVNdLv2V2xRkPmyVLPOGkfDe/CKr/c98fho7/3N6Gpuqipo57920noH4lHXgi5cDjfTQR05L4emdizhXDdJpDwkJoWzidToZyAGXdPucjmEboGPHjhw+fJiUFMtu+rSJj1ffoGQ23u3evRt3d3eqV6/O0KFDuXfPeGhXfuhGfnJ8SgiTuwTw1Wt1ecnfncEt/HRyKn7cJaAAV2cZdSq48Md7pvNiFmOnRIGZDGtVhRFa0QVrhzTBxkpBslaV2wQcGZoyjokpg3kq29JceZrtNuP5wOpn7EgyNKxZZD79nqeo5/zzZLSO8U2SJG7HPWPAsoPU/HQbvhP+1Gy7dO8JW05keMb9sOdqlvNevPuYx88zjIT+n4RrDHyZvcj3X4ql7ZzdRF5+oDXnY9rO2U27ryI4ePUhA5Yd5MGTJNrM2U3j6f/oGQsVksSon45S97PtbDhyS289c7ad13gdpqapWLz7MsduxtFr8X6W77vGiNVHuHhXPwWJ+thkvF+xL+t9F5jHG00qkYoV41JGkiRb85LyKH2VuwDotnAvJ27FoRJW1ELBo0eP6N+/v04e77i4OJN90vN4e3t7Y29vT5s2bXTyeF+7dg1JknBxUT8gc3Fx0aQq+eWXXyya+8aNG3Tr1o1SpUrh5ubG6NGjcy3Xd7r++yp0i2kAVHV3ZNWgxgT5lKwHtIKihzDuCfKU7FT6LQhkGWb20i9ikRU5CUM01TUsLEwvV1fm1+HDhzUD6RlrZDnLtWXert1n8+bN7Ny502CuMUPcuXOH0NBQXnvtNYYMGWJSdsaMGZofbhcXFypWrGjWHHmBtlGrT0Pz12HqyMrA5C4BLB/YyKK15KZhICtjdWZjcEFcUwd6a3tBSbzRxIeI/7XFr6zpatHa94ppTxNApcLaUTd83cPDg0ex91j8ZhCZSUt8ZNCwnZqaSmxsrMX7oV6TzLhx42jRogWBgRnGyU6dOrFmzRp27tzJV199xaFDh2jXrh1JSYZv4AuTbuQGLvbW2Fkr6RVUgaUDG/FJ15o628s5Gc/NuuW9Fno6NK5D9Szn3PJeC4583J5mVcpmKWsJhgrlZMZQdemijKHCToKs0f7+bfaieFNSSubQW4mf0l6ic/J0ItLqYCul8p7VJv61Hcvbyr8sqqibMa9hjt6Io+HUvzPkZJlXF+/XeMgBPEpM5udDN2k/N0Kn76V7hkN4j954RI+Fe5kVfo4OX//L00y5/GaFn2fYqsP0XLSfNK0fmH4/HOBqbCJv/BClafvwlxNcjdVNZ/DJ76c07/0/CdfZplRI/HkimmcpaWw/c1dvbQt3XWLalrMArD98k5nh53SqDO+79IAOX/9LSpqKqCsPGLbqMGfu6D9ICfvjDMdvxhncf4Fl2FkrOfpJBy7KFZiT+hoAU6xWaarndl+4j9D/+7cglyh4Qb9+/Th27JhOHu/+/fub7JOex3vhwoUcOnQIT09PnTzeFStWJDo6mgsX1J61Fy5c4LPPPqNUqVJ06tTJ7LnT0tLo0qULiYmJ7N27l3Xr1rFhwwY++OCDXNn39LQhlTLl2xMIihLCuCcQvECpMK4O/p7qRPGVjVTbBbDW6u+Rw+IIo0aN4uzZsyZfgYGBKEu5kpYYp5fH6v79+0ZzhaWH2Gb27Lt3756mz86dO7l8+TKurq5YWVlhZaX2surVqxdt2rTR6Xfnzh3atm1LcHAw3333XZb7NnHiROLj4zWvmzdvmnVM8gLt+/Uvs2HcNYSXix22Vkra+meEo2fXcJfdfqbsEO+0qaI/rhF5S+evUs64frzZVNeb0VjhjOzkZHN1yCjO8M8HrXUM1bN66YfWGzJsG2o3l1GjRnHixAl++uknnfY+ffrQpUsXAgMD6datG3/99RcXLlzgzz//NDhOYdKNgiawvAtNKus+IS9lRiGYwPIuRgsJmcK3rOk0DWZ5A+ezbW/pWw3NknNztMlWAZO8KIxVEjB0rhgLb70me/FWykcMTx7LTVU53KU4plj/yL+2Yxip3ERZC6rqPkw0zyC4MvI60fHPddo+3Xya8RtO6MluPHqbhTt189g9fp5Cz0X7OX4rnkW7LxucY+PRW2w/c5djN+M4G61vONN+oGQoNHjrSeNhyuZ8TZ94UazhXLRhDz2AlDQVfb+LYvuZu3Sev4dHicl6Kjxr2zmdz6IIRPYpXcqG6T1rszStMxFpdbCXkvnWei4uqA3IF+4+yTJfsCBvOXv2LOHh4fzwww/ZyuP9yiuvEBgYyMqVK3XyeCuVSjw9PTX3GB4eHmzcuJE+ffrg6Oho9tzbt2/nzJkzrF69mvr169O+fXu++uorvv/++1yJdEjX/4qS+sHHTTnnaaUEgvxGGPcERYK8DnfK6nJi2cBGDGtVmVWDG+u0ay9LoZA4GRbC8U9DzApFTvfy6Fm/PKAO+0rHzc0Nf39/ky87OzscK9VETkrEOyUjNOXAgQPEx8fTrFkzDOHn54enpyc7duzQtCUnJxMREaHpM2HCBE6cOMGxY8c0L4Cvv/6a5cuXa/rdvn2bNm3a0KBBA5YvX47ChIE0HVtbW5ydnXVehQFLDDuGRJcPbMTAZr683lg/JNec61VDXj/ZPe3n9amnk/9LO1fYR6H+euPmRlj6xpHNTOax9HbVzb9nzKs3q1NIe6VKB2dQKBhQz5VGvqVZNagxVco56hiqezeqyFvBGXnflKVKGzRsW1lZUbas5d5e7733Hps3b2bXrl1UqFDBpKyXlxc+Pj5cvGg46Xth1Y28wphevNNGHdaYWSfeaFKJltXcCKmp/t/mpmP4Px+0Mfk78167qmaNkx0PcGPYKE0rg4ONeVWvlQqJY5920OR9Mxdxm509DBkoXsvkGR418SWtTxLbVI1ol/wVE1KGcEt2w0OKY7z1z+y3fY951gtppTiOFfpGsNwiPR+fIeZsv6ATMnk34blR2XQePc06xUH6cUqz0HXcnAdA6YWZTP22fbHljM7nQ9ce6smoMjlc9v42knZzdjPixyPEP8t+GoeSSr8mlYgY/xKjU0ZxQ1WOSor7zLdeqDm3Nx27XcArLNlERkbi4uJCkyZNNG25lcdbm6NHj3Ls2DEGDx5s0dyRkZEEBgbi7Z2RR71jx44kJSXp5TvODulfLRVeGPfSi2kIBEUJYdwTFHsye38YQpZNG1K8Xe2Z1DkgyyIcTnbWuDhY5iFR1d2JIx+357d3DBvjTBE18y0aNm/Lz19/QlRUFFFRUQwdOpSuXbtSo0ZGFVd/f382btwIqI1YY8aMYfr06fzxxx8AvPPOOzg4ONCvXz9A7d0XGBio8wKoVKkSfn5+gNpjr02bNlSsWJE5c+Zw//59YmJijOb6y0/MNVYZM8IaKjAxsVNGonRDhri2/u6Eda+lk1PMInJgqFg1qDGOWl5Nzaq6cWFqJ3Z92Iav+9Rl9ZAmlHe11xQ3MNeQae6SetTzpn6l0lnKfdOvgea9saq4hjz6jBk5JKU1Dt7VOHVwL7+MaEar6uoLsR07dugYt7XPBtvy/jqGbVA/DW7YsCHW1ubrrizLjBo1it9++42dO3dq9MIUDx484ObNm3h5eZk9T0lj86jmjO9Yw+A2O2slPw5uwncDGrLp3ebs/rCt0XEsfSCkVEi85G/8KX1oYNb/MwmoU8HVsolNMCoLg6K533MSGekcssJSA6BAn6GtKlO2lA1DW2Z8JwxvVVmTY9LJ1spgdekUrFiX1o62SXMZm/wOR1VVsZVSeVm5n1U2Mzlg+y7TrJbSVnEUe7I2sOUmkVce0H/pAbot2Mu/FyxPX5Capl8R+KWvIkhMSrU4R64xr29tTt6Op92c3ayOumFU5qeDup7Rw348opeiIi3T4p4mp3ElNpHw0zFcf6AbSiwwj4plHGjoX5kRKWN5JtvQWnlCU2BjwT+XCnp5JZqYmBjc3fV/B3Mjj7c2P/74IwEBATrXaebMHRMTozdP6dKlsbGxMTqXJbmM038iK2qMe25GZQWCwop5j30FgiLIv/9ry/7LsfQKMu1NU1Bo32dlJ4wMwNPFjm2bf2X06NGap2bdu3dn4cKFOnLnz5/XJPwHGD9+PM+ePdPkqYiOjmb79u04OTmZPff27du5dOkSly5d0vNYKiqhFVXdHRncwk8v/GzN0CZ89OsJPu2WkSNseOsqzHhRka8wJs7PfMytlAr83Erh9yKUfO9HbTU393qee0bDcnN3R7vU8eJddZSGjveF9jSGvDI+7VaTjzepczBl3k+PZr344YfZNGzYUBMafuPGDU1hl4kTJ/Jn1GloMhwAx3qduP7jXyj/+R6nuqGc2HmVpUuX6oTUJicnc+LECc3727dvc+zYMRwdHalaVW1weffdd1m7di2///47Tk5OmgtLFxcX7O3tefLkCWFhYfTq1QsvLy+uXbvGpEmTcHNzo2fPnjk5jMUGQ8apau5OZp139Sq6apLm5xaDW1Tm77P3sLNWaIoR7J/QDmetkNbL0zvz16loRq09anAMQ+dvi6pu7L1kuUHESpk7+pe+JHPU+fDH7fGbuDVX5i2puDvZcWhyex3vZCulgiEtK9PW391krklQG/k2qlqyMbkltaUrvKaMoLPyAG5SAm9Y/cMb/EOyrOSwqgb7VIEck6twQlWFx5h++JgT3vjhgOb9SQtDU7st3ItCkvhjlG5BmiuxiYT+37/cfPjMovFGrvnPLLkrsTk3vpmq5JqdFBICNa83rsSQc/cYlfIe31p/zavKf4mVnfky9nVNgZfwMS3x9yze3uv5RVhYGJ999plJmUOHDgGGr/tymsc7M7/++iuffPJJlmMYGsfS9c2YMSPLfdce25ZkyklqvU8Py10/rKlZ/QWCwoAw7gmKBA3M8ArKTKWyDlQqa17V0tyulpuflClThtWrV5uUyWwQkSSJsLAwxo0bh4uLC1u3bs0yBDDzGAMHDmTgwIHZWnNeY4ltMXOSf1CfbzvGtTbaJy8u6Q1dl5i7G64O1tSr5Mq+Sw9wsDHsjSgZMaaB8WIB5u6nqaq4xjDm4GjohsnUNWWZ2m34sHV5Pv/8c6KjowkMDGTr1q34+Ki9FKOjo4m/H63ZF2tXT7Zu3co7o0ZzadX7LCvvzfz58+nVq5dmzDt37tCyZUtA/aR4zpw5zJkzh9atW7N7924AFi9eDKCXg3L58uUMHDgQpVLJyZMnWbVqFXFxcXh5edG2bVvWr19vkRG9pKH9v87KQ8fUVm3v2rY1yrHr/H0T0mqCq5Tl0OT2nIlO4K1lBwFwd7LFSutkVSokutbx5oOfj5OUquuNJEkSqZnj+IBXGpTH3kbJjhfJ/5e8GcSI1bohRNoGRXMx98ZXyvRXm21jWtFxXkYi+9w26JdUjKUdqFLO0aJxTsqVOZlamc9SBxCsOEMnxUFaKU5QUXGfZsozNFOqQ0tVssRl2ZuTsh+XVOW5KKtfN2QPVAUcpCPLag+4zvP36G2z1LCX3zxLMZwrEczzIBQYJv278x9VEBNThzDb+jtGWG1BQmZGaj9AInTeHs59EWpWmhuBaUaNGkXfvn1Nyvj6+nLixAnu3tUvUmNuHm/tqATt9CiZefr0KQMGDNAbJ6u5PT09OXDggM72R48ekZKSYnSuiRMnMm7cOM3nhIQEo8XKyjnaakJyH8v2xKN+ON6kcu4W6BII8hJh3BMUav79X1su3X9Mi2p57BqdTdueuA8qmVh6A5wXhTG0qVPBla/71OO7iCu83iRrg7bZ689CbMt7Lfj34n0GNc86JDXzvhi7McrqhsnQMRk5ciQjR440KL9ixQo+2XSKH6Oua9pat27NmZPHjc7h6+tLfHw8Li4uxMfHGzR8Z+Wdam9vz7Zt20zKlHQMHULtcG0bKwWV3UoZ9cAx9zz+4a1GhM77l4tGKn++0qC85n05J1ts7+sa88xFAj2Dn3qdMPXlQFQqmQHNfGldXT+PT3UPJ05k8hLSPj79mlTi3wv3ufVIbQz5uk9dyhgJofVysdMpmJB+nF5rWIFvdukWQKjhadzQXFQ8sEsCaSjZq6rNXlVtQMZXiqGV4gQNFReoJ12ikuI+1aTbVOM2aNlCkmQrouWy3JbduCOX5TZu3JbduC+78FB25iHOPJCdeEbOioCVRIRxL/u0qVEOD2db7iYk8UtaGxx5xhTrHxlu9ScOJPFp6kBkFPh/Es61L7sU9HKLPG5ubri5ZX0fFRwcTHx8PAcPHqRxY3WOcUvyeNevXx/IyOM9c+ZMg306depEuXK6v4PmzB0cHMy0adOIjo7WGBK3b9+Ora0tQUFBBueytbXF1ta86KjA8i6ZQnKFjguKHsK4JyjUqL3v8i7URJvcCoEyF/GTkXcUxVvSnN5HuzvZ8bEBL0Rz5jU29YBgX37777ZBYwSoL4QCtYp1WIKxkCZD90u2Vjl7cl+UPXOLGlXKleLy/USc7Sy/vLCzVuh4yQF0DPRksZGKnObeWysVEjvGteabXZeYvU2/4t8bTXx0PttYZazBmAHR0BklSZBswLinkCQ8nO1YOrCRwbEmdw7gjxPGCxoATO9Zm6TUNGp8HK5eo9KwTvw8PBh/Lyc+23yGDf/d0tk2pn11PeOeoOBYO6QJ7/10lAdmVrnNQOKa7MW1NC9WpXUEwI146iouUUO6STXFbapJt6kq3cZOSsFXuosv+h4x2jyTbXiIEwlyKZ5gxxPZnkTseSLbqf9iR6JsxzNsScaaZNmKJKzV77EiSbYhGSuSseY51qSiJA0labKCNBSoUJCGpG5Du039KopXRCIsN/uUsrVi70fteJqURoevI1j+uBPPsGW61VL6W/2Np/SQsSkjeYID0fHP8HKxz3pQQY4JCAggNDSUoUOH8u233wIwbNgwg3m8Z8yYQc+ePXXyeFerVo1q1aoxffp0nTze6Vy+rP79eeutt7I1d0hICDVr1qR///7Mnj2bhw8f8uGHHzJ06NBcK0CWUUxDVMoVFE2EcU8gQH2j1rGWJwFezjTyNT8E2FBhBUHxJ0/+6wVofzJmWKxX0ZUjH7entEPuJdmvUNqeW4+e0a2uN1tORANQWqsIjSRJfN2nLo+fp/L4eSpRVx7Qva43H/6i9rTLjqFOOCDlH8sHNmbBzosMb105S1ntIiwNfUozt3c9i+bK/P1rZ61tlNOX93Y17J0U5KP7ne/pnD0vJkkCDwN9s/IwDPByztK4B7pG7hQDBQq+7R9EYz91AamvetfVGPfSK1CbW+ynV4MKnLwdR+saolJgXtKsqhv7JrTjl8M3aVPDHW9Xe6KuPCA6/jm3Hz3jt6O3aFbFjZ8OGi8KkU4sLvyjCuIfguBFJKkCFd7SA7yJVf+VYqkgqd+7SfGUlh5TlgTspBTspWTK84Dy0oM83mvDpMmSjsFPRnrxgvRfXBl02tPfp2/DxDadz7Kk+RXRnUcXOdP3S+bPNg9Xg3vDnO56icVaqcDFQUHkxJeoMmkr69LakSjbMcf6Wzoo/+M3aQrDUsYRPGMnc3vXJTTQ0+wK4YLss2bNmmzn8R45ciSPHj2iSZMmBvN4p6cQateuXbbmViqV/Pnnn4wcOZLmzZtjb29Pv379mDNnTs53/AUVJHV+XFFMQ1BUEd+SAgHgYKPEzlrJX++3zPWxW1Uvx78X7tOzfnmddpHbqORgjnEpv73LlAqJNJVM5XKlTMplt9iLMba+35Ir9xOpW8GFBa/XZ92hG4wP9deR6Vk/o0DLu21NVwyd2Ckgyzl7N6zImgNZ3yALck6lsg7Mfq2uWbJ+bqXYNqYVZUrZGC0yYMo7T/sr9MOQ6rwaZDiPTjrd65bnxK14GvmW0STlN3T+e7vas+iNBjoVqPXmNtLu51aKRW800En6b2ofutT2okU1N2ZrRXCPbFOFHvXK8/dZ495WmT0E3Z1s6VjL08haDS9g+dtqT8JpPQOZvPEUs16tA6gNg+YkUBfkHDtrJf2DfTWfm1fNuKF8v301AJ4mp/L7sayNv5lRoeCWXI5blDPx8EjGgSTKSAmUJQFn6SmleI6j9AxHnmnel+I5paRn2JGMDanYkoKtlKJ5b5P+ktSfrUhDieqF/54KK8l0PkmlJKMkDY1lMi/JpdM6Ws7dgj4lFe3w5j9UzbiR7M53NnOprrjNnzaT+Dx1AON+lpm+1Zbxof6E1PTANRcfOAp0yUke77CwMJP9pkyZwty5c1EoDD9oMmfuSpUqsWXLFpMyOaGCdA/IKKYhEBQ1hHGvhGFvJNl+SWXqy4FsPRnNkJZZe5kYxIyLxG/61effC7G083fn8n3DOZ8EucuUbjXpuWg/o9uZNgxll7KOll1Y2puREDq/vctOhXUkOU2V70/Cne2sqVfRFYBudb3pVtfbov7ax+nNppXMqoZdt6Ir7fzd2XnunkVzCfIeUznfAAY19+O3/27TvZ7p86RTbS88XUx73CkVElO61TJrXZ1re2UtlIl0I1rmvqY8vLvV1Z8n3di940yM0X5JBjz3jK7LwPR1KrjQtob65uWNJj70rF9e57tAGPYKD+W0HrD4lnXg2oOnuTi6xFPseCrbcQv3PPUgl1BhhQoFqheGP/V7qxc+e5o2SYUEer57up8z/OgkLb88ScdHDzCxzbjPHnrthuTmla6S/YMh0MHT2Y6YBHWe0ONyVbolTWOe9Tc0U55hpvX3dFAcJizxLcb/msx41DlHtR8CCgS5RQWdnHvwVrCPKXGBoNAhjHslhAmd/LkQ85hgUfFHhzeb+vBm07z94nays6ZLHf0bOHHrlHfU8nbhzGcd9fJ35ZQfBzfm6x0XmPFKHbPkP+1ak22nY+gf7MP6wzdNyuZ35Ki9jRJ7irax380Cr8IKpUXOnqJIWUdb9k9oZ9DYpN2U7e/TbCqesZx7hjArN6CBzqYM/mWNFNMwPH/WCxDhboUX7VDvb95oQJf5e03Ku9hbE/+s8HmVyShIMadybxFJo6CwE1XPc4udH7bmwZNkjt6Mw9nOik9/P80bDycxRLWV/1mtp73yKC0Up1ic2p1v07oydv1xvtp+gVuPnvH7u82p++KBoSFkWeZO/HO8XezEQwtBlmSE5Zbjq9fq6hTcEgiKArl75ysotIxoXYW5feqhENW9ChRxXZF/5LZhD6BltXL8NrJ5lt5G6Qxq4cf64cFmecxmpwBBUSK3Km9mdxiR/LzoYuyGzJRHXC3v7BV6yQtMnXqmzmdDm77rH8TwVpUJNRKCa3B+M9sEhZM3m/owrFVlfh4eTC1vF95pY9pj7N//tc2nlZVsRLXc3MPBxoqKZRzoXtebNjXc+Xd8W45+2pHv07rSOXkG+9JqYSelMNZ6A3tsxzBYuZXYR3EA9PhmH+GnMrycVSqZ5l/uZPRPRwGYu+MCzb/cydK9Vwti1wRFiaQnuEkJgNq45+UqDMKCokfxvpsUCAoZIrF/yUT7/x5cuSz1K7nqyVgpFZz5vCO9v43k1O2E/FtcCUEY94ofpv6l779UDVsrBSG1PEyOkd2vZEvOJlM3B6bmN/Q9EVLLkxADhj2T+yFO/SKNvY2SSZ0zcotaZWFUcnGwZt2wpvT9LkqnffRL1dh+OoZzMY+zvRafsg5cz9Ww4KKLUKu8xdXBhmtfdiFNJVNlUnk6px1gotVPVFTc5xPr1Yyw2sxPae34KfUlRqw+gpujDVYKBc9S0oh/lsLtuGf8r2MNFuy8BMDUP8/yRhMfi9ITHbsZx3f/XmZipwAqlnHIq10VFBbi1RE28bIDCZQS0W6CIonw3CuhtKymziXwcn3L8l0JdMnJxZ2wNZQkMm69v3+roV4BiXQcbKxwsM545jKrl3nhv4WZbnW9sVZK9G5outiBuWS38EgeOHIKChjtr9DMXjT2NkrGdqiepQefrVXunRjaRrzuWrkkTX3Vm3rg07JaOb7rH8TuD9sYlXm7uS8AEzsZ/k7Jan5B0aOMiZBsrxd5J5tWLktbrWrHDX1K8/5L1Qgf08ri+a7O6Kx5/8OAolMdNie5svo1qZS1kFCsfEH93S6xVdWUtslf8b+UYdxQlaOclMBoq03stR3N99ZzaPZ0NwkJcToh6S1n7dIZ68NfjnMuxvyHpy9/s4+tJ2MY9/OxXNobQaEmTl14Lb2YhvDaExRFxO1OCWXVoMac/TwULxeRh0ogyGu0b+CzjOTR2t67Ue4YxAqS+X3rcfqzUNydTRc7yGtESoLih5VSQb8mlehW15tKFnpVzH+9Pn5upZjXt1625jZkk3O0zfAImds7o2JwSppxC16dCqaNjyG1PPF1M17R+tOuNTk0uT2vNDCeXF54rRYvXm9s2PDUoaYHPw5uovms/RDp+wENNQbweibyk2WmuocjkiRxfmooURNfoppHzvPMZfaGeck/Z1Upx7yoKpyZWuWN69awVqaLqI3vWCNHaxLkDalY8UtaG9olf8U7ye+zP60mSkmmg/I/5tss5IjtCBZZz6O3chfexOr1//NkNKHz9lB98l9sPn6HQSsOse10DHcTnjNj61luaHml3n1R4APg2oOnnLodz18no3XGe5KUiizL/HzoJl0X7CHheQpHrj9i83Hj1a1lWebjTSf5ZtclVkVe4/7jpBwdE1mWOXDlQaHMs1nkcC7P96md+SMtuKBXIhBkGxGWW0KRJElUzs0FcnLPJJ4IlRxUOsa9LP7vRuwA1sq8O1+61vFiy4noPAlBkCQJG6vcW7vIuSfQZnrP2tnq172ut453XU74tGtNTt2Jp031DCOFds7PxORUvT4HJ79E3NMUTahXds9OSZIo52S6sIz2qf9qUAV+PXKLMe2rZ3NGQUFjZ6T6+uI3Guicd2laPzzafb7tH8SqyGt8s+uyyXna1CjH//WtD4CtlRJPl9y5ZtT2PDz8cXt2nr3HP2ZUMv+sey2mbD6t1z6qbVXebuZH3c+367Q3qFTa6Fj/61iDkJoeqGTo/W0kAL+NbMYri/YD6qIkWWEq56cgb0nFir9UTfhL1YQqqbfpqdxLV0UUvoq7dFYepLPyIFjDZZUXB1QBHJOrcExVlUtyeVQoSE5TaXLy7dQ693acucs/H7Rm57l7DF55WNN+/3ESXRdkFLKp6eXMmWi1B+DgFn6afH6tZ+3i0VO1kc3OSkENTycqlnbQebi491Isq6NuaD5/+vtpzk8NZdvpu2w4cotlAxvpeaLvuxTLttMxTO4SgK2VWg8fJiZz+k48/ZceBKCauyM+ZR0oW8qWma9mRH3cTXhOOUdbFAqJ1DQVD58m4+5kR5pKtihv5M2HT0lMTsXf05ljN+N476f/GNqyMv2b+rDtdAzBVdxITEolOVWFT1mHonmP4xnItNQ3C3oVAkGOEMY9gUAgyGO0Q0kL4/XOzF51eCnAnXb+pvOTFWWE454gN9E+nQa18DMp62rAUODuZIe7U/54s2obIWa/WoeJnfwpa0GlaUHhZkz7ajjYKPWKSKWkqTTvtcPPPZzt+F9Hfz3jnrbBAmDF243Nmn/52414e/khAAK8nOndsAKf/XEGgAaVXPnvRpzRvm6OtvQKqsCtR0+Z/yI3mjH6NKrIkojLRMc/p25FV3rW86ZzHS+slApcHBT8Pa4V7ef+q5Gv6u7I1tEtcXOy4V6CrnHGWqmgoW8ZZFmmX5NKVCrjQFV3R812cwwTttYi+Cm/8HKxIzr+OfUqujK2Q3VaVy9Hn28jOXD1IZfl8sxJ7cMcelNLukaI8jAtFSepK12miiKaKopo+rETgCeyHeflilxSleeS7M0luTxXZC+i5bIkY82V2ET8Jm7Ncj3aeqJdqCPdsAcw7McjgNqQ/G7bqsiyzMnbGcY4bdYeuKHRmQ5fRxBUqTTervZsOx2DlVLS5GH293TWhIy3mb2LhOcZD44u3nvCxXtPAHAtZU2Vco48fp7KF1vO0MSvDOuHB/P691EcuvYIgNIO1vz1fism/naCp8lprBnSRPMdcuX+E36Mus7yfdeoUq4U/3zQRhPi3K9JJdYeUBsnP/39NJ/+nmFwT39QPLCZL2Hda2V5HAUCQe4jjHsCQQ4QT24F5qDtbZblOWNkc16ea6VsrehZ33hYX2FC23GviZ/5nobKwmhVFRRrlrwZxNEbj2gfkLXRPC9PT+2xJUkShr1ihjEvTCetCuzmpCUY0tKPcT8ft3j+tjUyPFYrlytFKZuMeX8c3ISJv53UCVPMnDdVqZAYF1IjS+OenbWSPePbolRIBo1vVd2dcHWwJk7LwFLT2xnAqCFdkiQd798Dk14ymIfzRFgIdcLUnoFDWvhRxd2RtGdP6D90NJs3bwage/fuLFiwAIXCuNFPlmU+++wzvvvuOx49ekSTJk345ptvqFVL3xAiyzKdO3cmPDycjRs38vLLLxsdt7jz+6jmHLjykNBAT6xfGKCWvBnElpPRfPfvZW4+fAZInJb9OJ3qx9e8hjOJBCvOUF9xibrSZeooLuMoPSdIukiQ4qLeHPdkV+7IZbgjuxEtl+WB7MwDnHkkO/JQduIhzjyUnXiMA2mY78U6e9t5Zm87b1Im3bAHcOV+IlfuJxqUm7TxJBfvPaZuBVcdw15mvo24ovP5wNWH1P98u47x8dHTFJrO+EfzOTBsG89TVIxoXYUlERmG/8v3E/n50E3N53TDniG2nFCHLQd45Tx8XyAQZA9h3BMIskFlt1JciU2kXUDOcsUISgYW5dwzEnaa3UISxY3yrvZETXyJq7GJBFcx37gncu4J8pvQQE9CA/Ur2woEuUWF0sbzJld1d2JSZ388ssh3amulwN5GSfuaGUboGhbm1vttZDPWHbzB+FB//j5zV9NeytaK+a/Xp1tdb4auOmxihAxsrBQkp6oMbsvsnZiZ7nW9WRV5XVM0zlIMHas3mlTCydaK6h6OxD1N4aNO/lgrFXTq1Ilbt24RHh4OwLBhw+jfvz9r1qwxOv6sWbOYO3cuK1asoHr16kydOpUOHTpw/vx5nJx0j/m8efOKZmhjHuDuZEe3TGkUSpeyoX9TH/o2qsjINf+x48V5l14pOoFSbFM1YpuqEQAKVFSR7lBdukU1xS2qSneoIt3GT4rBTkrBXYrDXYqjHlf05s/MM9mGROxIlO1IxJ4nmvd2JGFDkmxFMtYvXlakaD5bkaTVloYCFQqdv3rvZQVpSJq2w/uvcBAFAZKEjIQMyC8e/mb+nNEmwTNwkUzIpqo/b46IxTvT9q827Mb9xWdDT6DlTG3udmlZHkOBQJA3COOeQJAN1g1ryp8no+kVVDS8nQQFi0rLuidyv2WPNUOasO7QTT7pWpMypWzwdLEspFF47glykx/easiQlYeZls2cf/mJpboiKDpklYN0WKsqRrf9/m5zzsUk0LthRVJVssYjCmDgiyrMWdGiqtqI1qBSaU2OO0c7/VuL9gHujGlfjToVXPjl8C2DY30/oCFfbDnD133qMXLNEe4mWF5oYEInfxr6ltGpFJxTPJ3tkCSJv95vhUpWH6ezZ88SHh5OVFQUTZqoi5h8//33BAcHc/GivlcYqD3x5s2bx+TJk3nllVcAWLlyJR4eHqxdu5bhw4drZI8fP87cuXM5dOgQXl5eubYvxRFrpYL5feuz5+J9WlRzw8FG9/z76/2WdPq/PahQcFGuwEW5AujYjmXK8Bgv6QHlpVi8pId4SQ8oSwKlpceUlR5TmseUkR7jLKkLbthLydiTjJtkfuXdksSNS2OhdlhBL0MgKJEI455AkA3cne14u7npPEsCgSGytDFl2t6zfnk2Hr3Nu22r5tmaigLNq7rRvGr2vDEA3LIoOiAQWELLauU483moRQnJ85uVgxqzfN9VZrySfQPko0ePGD1aN/Rw2rRpJvuYG3oYGRnJ5MmTOXDgANbW1tSrV4+//voLe3vj3mgCNWVL2fAgMZnmVbNfBKluRVfqvqicm7lgk8rMykXfDQjSawut5Um3ut4EVXLVtEmSpAkfNmbc61DTgw4vvAd/Hh7Myv3XWXfoBk+TzfcCcrCxyrVCOcvfbsS2UzEMaamurKtUSChf/EBHRkbi4uKiMewBNG3aFBcXFw4cOGBwvKtXrxITE0NISIimzdbWltatW7N//36Nce/p06e8/vrrLFy4EE9P4flrDvY2SkJqZRyrd9tW4Ztdl5nWM5AAL2cWvF6f914U0Pj3f22Z988FDlx5yO04dTivOuTWmdOy6et6a1Jx5CmlpOc48hwHnuMoPaMUzyklPacUz7ElGRtSsZFSsCEVW9R/bUjBRkrFOv09qShRoZRUSMjq96hQvPir/V7TJmm36froSRk+egY/Wy5rartpsvKwFQgEeYcw7gkEAkEeo32jZGmYzexX6zC4hR81vZxze1klileDKnDk+qMc3QwLBNrkpmEvL0yErauXo3X1nHkw9evXTy/0UNvDyBDmhB5GRkYSGhrKxIkTWbBgATY2Nhw/ftxkvjJBBr+Pak74qRj6Nq6UJ+PXreCapUzlcqX0vKRAfWO/4PX6RvuZYzf0KVuKT7vVpE+jinz4y3HGdqiWdadcpm0Nd518gtrExMTg7q6/zd3dnbt37xrooe4D4OGhm4PTw8OD69evaz6PHTuWZs2a0aNHD7PXmpSURFJShqdjQkLJ9ij7MKQGA4J9NWHWXet48cuRW9hbK6hYxp65vesBMPG3k6w7dIO6FVxp7FeG7/41HZKbghWPcKZ57Rqa/HKFJWNK+wB3/j6bddXp/CC8cUtyx8QuEAgsRRj3BAKBII8x0wnCIFZKBYHlXXJvMSUUa6WCOa/VLehlCARFBlOhh8YwN/Rw7NixjB49mgkTJmj6VquW/wacokqF0g4aj7LcZM/4ttyJe2byN6deRVeO3Yzj1WymJWlVvRzhp2OwMVC4IjM1PJ34470W2ZonO4SFhfHZZ5+ZlDl0SF0Z2NCDOlmWs3yAl3m7dp/Nmzezc+dOjh49asmymTFjRpbrLklIkqSTP1GSJFYN0q/+POOV2jqezR1qejBk5WHin6XoyWrzVe+69GtSiaM34vjj+B3OxTw2a12da3vyv47+tJ2zO0tZS4111koFzaqUZf/lB5q2s5+H8t+NR7zxg2Fv0ryitINNvs4nEAgyEI9IBQKBII+xxLZnrjNQgxchT80sKCohEAgKJ4Uxcb6p0ENjZBV6CHDv3j0OHDiAu7s7zZo1w8PDg9atW7N379682xmBWVQs40CTyqZ/U34c3JgfBzdmWDaNi30aVWTxGw2I+F+bbPXPS0aNGsXZs2dNvgIDA/H09DTooXf//n2DHn2AJsQ23YMvnXv37mm8+Xbu3Mnly5dxdXXFysoKKyu1D0avXr1o06aN0XVPnDiR+Ph4zevmzZtGZQXGaeRbhi3vtaBCaXvaB7hTsYw9KzMZBb/p1wBbKyXNqrjxbtuqhI9pxcHJLwFoDNblXe31Qt3PfN6RRW8E4edWiq/7ZDxonPVqHc37MqUyjGLlXe35c3QLxrSvRs/65Q2ut2IZe/w91d7QHWp6sHJQY7ZoGcPtbZR66UxGv1RNU5zko1B/9oxvi4NNRvXfrnV0czymV3vvUNODk2EhjG5XlQ3vNKNHPfUYs1+tQ2kHa4a2zAhp1t6PooabqCYvKOIIzz2BIB8xVdlOUHypVMbBbNmpLwfS97uoLHPsfT+gIb8fu2P0ok8gEBQd0sPGa3kXnvB7Y6GHbm5uxMfHG+0DpkMPr1xRh76FhYUxZ84c6tWrx6pVq3jppZc4deqUUQ8+EXpYOHCys6ZlteyHeysVEp1q53+RiF4NKrDhv1sMbOZrVMbNzQ03t6xzuwYHBxMfH8/Bgwdp3Fht/Dlw4ADx8fE6xnBt/Pz88PT0ZMeOHdSvrw5bTk5OJiIigpkzZwIwYcIEhgwZotOvdu3afP3113Tr1s3oemxtbbG1FUaJ3KBiGQf2ftROp21Qcz+uxD5h6VuNDKZjcHey40RYCPbWSqLjnlPG0YanSalM2Xyav07F0D7AQyeEvWf9CnSvW56bD5/i61YKe2slvx+7wxcv1yJ4xk4AxnaojquDDbW8XXiWnMbGo7dxtLUiMTmVRj5lGNqqMg0quSIDh689pJ2/B9YvIj3+HteKUrYZ833TrwG/HLnJrFfr4O6k9mj88pXaGpkzLzz8nO2s8XKxo2sdb346eAN/LycmdgogKTUNG6UCSZIYF1IDgCCf0vxfX/V5/FrDigAMbVkZJHSK8xQ1PgypzoTfThJYvvD8FgsEliCMewJBPuLqYMPf41pjZ110f/gEllOmlA1/j2uFvYH8RJmp6u7Eocnts/TkKetoy6AWoqiLQFAc6NuoItU9nDReGHlJboQeZoWp0EOVSl2qcvjw4bz99tsA1K9fn3/++Ydly5YxY8YMg2OK0ENBTpjxSm36Nq5IvRdFRHJCQEAAoaGhDB06lG+//RZQ56Ps2rWrjnHa39+fGTNm0LNnT3VRkTFjmD59OtWqVaNatWpMnz4dBwcH+vXrB6i9+wwV0ahUqRJ+fuL3vqD4tFvNLGWc7awBqFRW/TDX0daKhf0acOT6I4OGIqVCwtetFADd6nprvOmuzuis9/1pb6Pk6ozOADxPUWFrpUChZWQMDdQ1lld11/0d6VLHiy6ZPPK0jX+Aptq1ejxPQgMzzkNbKyXm4O5c9Cuz925YkWoejvh7CuOeoGgijHsCQT5T1d2xoJcgKAAyX2yZojCG6AkEgrxDkiSCfEpnLZgLjBo1ir59+5qU8fX15cSJEwZDDx88eGCghxrt0EMvr4ybSe3Qw/T2mjV1b5gDAgK4ceOG0bEnTpzIuHHjNJ8TEhKoWLGiyf0QCNKxsVLQyLdMro23Zs0aRo8erQlB7969OwsXLtSROX/+vI6X6/jx43n27BkjR47UVJLevn27ptCMoHihVEg09rPsnDN2/Zfebm9jnqFNkD0UCokgn9z7nhAI8hth3BMIBAKBQCAoIeRG6KExzAk99PX1xdvbm/Pnz+v0vXDhAp06dTI6tgg9FBQmypQpw+rVq/XatcPFM3u5SpJEWFgYYWFhZs9jjqesQCAQCAQgCmoIBAKBoIizaNEi/Pz8sLOzIygoiD179piUj4iIICgoCDs7OypXrsySJUt0tp8+fZo333wTABcXF+bNm5eteWVZJiwsDG9vb+zt7WnTpg2nT5/O/o4KBPmIduhhVFQUUVFRDB06lNDQUB05f39/Nm7cCKATerhx40ZOnTrFwIEDdUIPJUnif//7H/Pnz+fXX3/l0qVLfPLJJ5w7d47Bgwfn+34KBAKBQCAQFAeEcU8gEAgERZb169czZswYJk+ezNGjR2nZsiWdOnUyGt539epVOnfuTMuWLTl69CiTJk1i9OjRbNiwQSPz9OlTfH19Af3CAJbMO2vWLObOncvChQs5dOgQnp6edOjQgcePH+feARAI8pA1a9ZQu3ZtQkJCCAkJoU6dOpocY+kYCj0cM2YMI0eOpGHDhty+fVsv9HDMmDFMnDiRsWPHUrduXf755x927NhBlSpV8m3fBAKBQCAQCIoTkiz8vfOFhIQEXFxciI+Px9lZJOkUFA4Kw3lZGNYgKLo0adKEBg0asHjxYk1bQEAAL7/8ssHE/B999BGbN2/m7NmzmrYRI0Zw/PhxIiMjNW3p52WlSpUYO3YsY8aMsWheWZbx9vZmzJgxfPTRR4C62qeHhwczZ85k+PDhWe6b0A1BYaSwnJeFZR0CgTaF4bwsDGsQCDJTGM7LwrAGgSAzuXleCs89gUAgEBRJkpOTOXLkiCaheTohISHs37/fYJ/IyEg9+Y4dO3L48GFSUlJybd6rV68SExOjI2Nra0vr1q2Nri0pKYmEhASdl0AgEAgEAoFAIBBkhSiokU+kO0iKmzVBYSL9fCxIB16hG4LsEh0dTVpaGo6Ojjrnj4uLC3fu3DF4Tt25c4c2bdrobHN0dCQ1NZWrV69qqn2a0o3Y2FjS0tL0QnY9PDyIiYkB0Pw1JHP9+nWD+zNjxgw+++wzvXahG4LCRGH43dCeX+iHoDBRGPRD6IagMCJ0QyAwTG7qhjDu5RPpOZYqVqxYwCsRCPR5/PgxLi4uBTY3CN0QZJ/MHnTpGDunv/jiC7744gu99ho1aui1mfqhlSRJTzZzmzky6UycOJFx48ZpPt++fZuaNWsK3RAUSgrydyN9fhC/HYLCibiuEggMI3RDIDBMbuiGMO7lE97e3ty8eRMnJye9G7uEhAQqVqzIzZs3Rfy/GYjjZT5ZHStZlnn8+DHe3t4FsDo1pnQDxP/bEkrasUpOTsbT05OVK1fSrVs3TftHH33EyZMn2bp1q16fTp06UadOHWbOnKk5Xt999x0jR44kJiYGa2trIEM3WrRooTeGm5sbSqVS452Xzr179zSeeukegDExMXh5eRmUyYytrS22traaz46OjkI3cglxrCzD1PEqDL8bIK6rchNxvMynqF9Xif+1ZYjjZT5FXTdA/L8tQRwry8iv6yph3MsnFAoFFSpUMCnj7OwslMMCxPEyH1PHqiA9L8A83QDx/7aEknSsgoKC2LdvH2+88YamLSIigh49ehg8Bi1atOCPP/7Q2RYVFUXDhg0pW7asjqwx3bCxsSEoKIgdO3bQs2dPTfuOHTvo0aMHAH5+fnh6erJjxw7q168PqI2RERERzJw506x9E7qR+4hjZRnGjldB/26AuK7KC8TxMp+ifl0l/teWIY6X+RR13QDx/7YEcawsI6+vq4RxTyAQCARFlnHjxtG/f38aNmxIcHAw3333HTdu3GDEiBGAOtT19u3brFq1ClBXxl24cCHjxo3j9ddfB+DHH3/kp59+0oyZnJzMmTNnNO9v377NsWPHcHR0pGrVqmbNK0kSY8aMYfr06VSrVo1q1aoxffp0HBwc6NevX74dH4FAIBAIBAKBQFD8EcY9gUAgEBRZ+vTpw4MHD/j888+Jjo4mMDCQrVu34uPjA6iLbty4cUMj7+fnx9atWxk7dizffPMNADNnzqRXr14amTt37mi87QDmzJnDnDlzaN26Nbt37zZrXoDx48fz7NkzRo4cyaNHj2jSpAnbt2/HyckpLw+JQCAQCAQCgUAgKGEI414hwNbWlilTpujkWhIYRxwv8ykOx6o47EN+UVKP1ciRIxk5cqTBbStWrNBra926Nf/99x9JSUnMmDFDr6+vr69ZFatMzQtq772wsDDCwsKyHCs7lNT/d3YQx8oyivrxKurrz2/E8TKfon6sivr68xtxvMynOByr4rAP+YU4VpaRX8dLkguyHrVAIBAIBAKBQCAQCAQCgUAgyDaKgl6AQCAQCAQCgUAgEAgEAoFAIMgewrgnEAgEAoFAIBAIBAKBQCAQFFGEcU8gEAgEAoFAIBAIBAKBQCAoogjjnkAgEAgEAoFAIBAIBAKBQFBEEca9PGLRokX4+flhZ2dHUFAQe/bsMSkfERFBUFAQdnZ2VK5cmSVLlujJbNiwgZo1a2Jra0vNmjXZuHFjXi0/X8ntY7VixQokSdJ7PX/+PC93I9+w5HhFR0fTr18/atSogUKhYMyYMQbl8vPcErphPkI3LEPoRsnRDRD6YQlFXTdA6IclCN2wjKKuH0I3zEfohmUI3Sg5ugFCPyyh0OqGLMh11q1bJ1tbW8vff/+9fObMGfn999+XS5UqJV+/ft2g/JUrV2QHBwf5/fffl8+cOSN///33srW1tfzrr79qZPbv3y8rlUp5+vTp8tmzZ+Xp06fLVlZWclRUVH7tVp6QF8dq+fLlsrOzsxwdHa3zKg5YeryuXr0qjx49Wl65cqVcr149+f3339eTyc9zS+iG+QjdsAyhGyVHN2RZ6IclFHXdyM4+lGT9ELphGUVdP4RumI/QDcsQulFydEOWhX5YQmHWDWHcywMaN24sjxgxQqfN399fnjBhgkH58ePHy/7+/jptw4cPl5s2bar53Lt3bzk0NFRHpmPHjnLfvn1zadUFQ14cq+XLl8suLi65vtbCgKXHS5vWrVsb/DLJz3NL6Ib5CN2wDKEbJUc3ZFnohyUUdd2QZaEfliB0wzKKun4I3TAfoRuWIXSj5OiGLAv9sITCrBsiLDeXSU5O5siRI4SEhOi0h4SEsH//foN9IiMj9eQ7duzI4cOHSUlJMSljbMyiQF4dK4AnT57g4+NDhQoV6Nq1K0ePHs39HchnsnO8zCG/zi2hG+YjdMMyhG5krK246wYI/bCEoq4bIPTDEoRuWEZR1w+hG+YjdMMyhG5krK246wYI/bCEwq4bwriXy8TGxpKWloaHh4dOu4eHBzExMQb7xMTEGJRPTU0lNjbWpIyxMYsCeXWs/P39WbFiBZs3b+ann37Czs6O5s2bc/HixbzZkXwiO8fLHPLr3BK6YT5CNyxD6EaGfHHXDRD6YQlFXTdA6IclCN2wjKKuH0I3zEfohmUI3ciQL+66AUI/LKGw64ZVtlcgMIkkSTqfZVnWa8tKPnO7pWMWFXL7WDVt2pSmTZtqtjdv3pwGDRqwYMEC5s+fn1vLLjDy4jzIz3NL6Ib5CN2wDKEbJUc3QOiHJRR13cjOfCVZP4RuWEZR1w+hG+YjdMMyhG6UHN0AoR+WUFh1Q3ju5TJubm4olUo9K+u9e/f0rLHpeHp6GpS3srKibNmyJmWMjVkUyKtjlRmFQkGjRo2K9FMCyN7xMof8OreEbpiP0A3LELqRIV/cdQOEflhCUdcNEPphCUI3LKOo64fQDfMRumEZQjcy5Iu7boDQD0so7LohjHu5jI2NDUFBQezYsUOnfceOHTRr1sxgn+DgYD357du307BhQ6ytrU3KGBuzKJBXxyozsixz7NgxvLy8cmfhBUR2jpc55Ne5JXTDfIRuWIbQjYy1FXfdAKEfllDUdQOEfliC0A3LKOr6IXTDfIRuWIbQjYy1FXfdAKEfllDodcOi8hsCs0gvj7x06VL5zJkz8pgxY+RSpUrJ165dk2VZlidMmCD3799fI59eSnrs2LHymTNn5KVLl+qVkt63b5+sVCrlL7/8Uj579qz85ZdfFovS23lxrMLCwuTw8HD58uXL8tGjR+W3335btrKykg8cOJDv+5fbWHq8ZFmWjx49Kh89elQOCgqS+/XrJx89elQ+ffq0Znt+nltCN8xH6IZlCN0oObohy0I/LKGo60Z29qEk64fQDcso6vohdMN8hG5YhtCNkqMbsiz0wxIKs24I414e8c0338g+Pj6yjY2N3KBBAzkiIkKz7a233pJbt26tI7979265fv36so2Njezr6ysvXrxYb8xffvlFrlGjhmxtbS37+/vLGzZsyOvdyBdy+1iNGTNGrlSpkmxjYyOXK1dO9vf313z29fWVw8LC5OTkZLPWdv78efmVV16RXV1dZXt7e7lx48by77//nmW/N954QwbkLl266G3z8fGRAb3X8OHDzVqTpcfL0Fw+Pj46Mvl5bgndMJ+81o2QkBB57Nixco0aNSzWjylTphg8t9JfP/30k478r7/+Kjdr1kwuXbq07OLiIjdq1EhetWqVyTliYmLkMmXKyID8yy+/ZLkmoRslRzdkufD+dty4cUN++eWXZT8/P9nBwUF2dnaW69WrJy9YsEBOSUnRkTX2ewDItra2Grk7d+7IkydPlps2bSqXLVtWdnJykhs0aCB/++23cmpqaq4fq8KmG9nZh5KsH4VVN2RZlidPnix36dJF9vb2lgH5rbfeMih36tQp+Z133pGbNm0qOzg4yIC8a9cug7Lx8fHypEmT5GrVqsn29vayt7e3/Oqrr8qnTp0ya01FXT+EbphPYdaN5ORkOSwsTLO+GjVqyPPnz9eTW7t2rdyyZUvZ3d1dtrGxkb28vOSuXbvK+/bt05NNSEiQ33vvPdnb21u2sbGRq1WrJs+cOdOs3w1ZFrpRknRDlguvflhyXWXs/kT7miqdnOhHYdUNYdwTFGumTp0qS5IkT5w4Ud61a5c8a9Ys2cbGRh46dGiWfa9evSqXKVNGrlWrlrxu3Tp5y5YtcpcuXWRJknSeSmRmy5YtcqlSpWRnZ2ejxr3mzZvLkZGROq8rV67kaF8FAkvJiX7cvHlT7xyOjIyUAwMDZXt7e/nRo0ca2aVLl8qA3KtXL3nr1q3yX3/9Jfft21cG5Llz5xqdo1evXpobQHOMewJBbpET3Th79qw8YMAAedmyZfLff/8tb926VR41apQMyIMHD9aR/e+///R0aP369TIg9+3bVyP3xx9/yBUrVpQnT54s//nnn/L27dvlsWPHygqFQn777bdzff8FAmPkRDdkWZYdHBzkpk2byiNGjJBtbGyMGvdWrFghe3l5yZ07d5a7detm0rjXqlUr2cHBQZ41a5a8c+dOedWqVXLVqlVlJycnjSeFQJDX5FQ3hgwZItva2sqzZs2Sd+3aJU+YMEGWJEmeNm2ajtyCBQvkCRMmyL/++qu8e/du+aeffpIbNWokK5VKeffu3Rq5lJQUuUmTJnLp0qXlhQsXytu3b5fHjRsnS5Ikv/fee7m67wJBVuTXdVW6cS88PFzn2iqzt2Bx1Q9h3BPooFKp5KdPnxb0MnKF2NhY2c7OTh42bJhO+7Rp02RJknRcYQ0xfPhw2c7OTr5165amLTU1VQ4ICJArVqwop6Wl6fWJi4uTy5cvL8+dO1f28fExatwz1C4o/Aj9MM3Vq1dlSZLkN998U6e9efPmso+Pj47OqFQq2d/fX65Tp47BsX799VfZ0dFRXrlypTDuFQGEbmRN7969ZSsrK/n58+cm5cLCwmRA/vvvvzVtDx8+NPh0+91335UB+caNG9lakyDvEbqhi/bvQKlSpYwa97TlfvnlF6PGvYsXL8qA/PHHH+u079+/P8sHSIKCRehGBqdOnZIlSZKnT5+u0z506FDZ3t5efvDggcn+cXFxsrW1tU4o4E8//SQDet4/w4YNkxUKhXzu3Dlzdk1QQAj9yBpD11Xpxr379++b7Ftc9UMU1CimhIWFIUkSp0+f5vXXX8fFxQUPDw8GDRpEfHy8Rk6SJEaNGsWSJUsICAjA1taWlStXsmLFCiRJYufOnQwdOpSyZcvi7OzMgAEDSExMJCYmht69e+Pq6oqXlxcffvghKSkpOmtYvHgxdevWxdHREScnJ/z9/Zk0aZKOTExMDMOHD6dChQrY2Njg5+fHZ599Rmpqao6PQXh4OM+fP+ftt9/WaX/77beRZZlNmzaZ7L9v3z7q1q1L+fLlNW1KpZJOnTpx8+ZNDh48qNfngw8+wMvLi9GjR+d4/YK8Q+hHzvXDEMuWLUOWZYYMGaLTbm1tjaOjIwpFxk+OJEk4OztjZ2enN87Dhw959913mTZtGpUqVbJ4HYLsI3Qjb3QDoFy5cigUCpRKpVEZWZZZvnw5lStXpl27dpr20qVLG0xQ3bhxYwBu3bqVrTUJzEfoRu7ohvbvQG7IpeuFi4uLTrurqyuAwd8YQe4idCPnurFp0yZkWTbY/9mzZ4SHh5vs7+TkhJ2dHVZWVpq2ffv2IUkSnTp10pHt2rUrKpWKjRs3mrFngpwi9KNgr6uMUVz1wyprEUFRplevXvTp04fBgwdz8uRJJk6cCKhvwtPZtGkTe/bs4dNPP8XT0xN3d3cOHToEwJAhQ3jllVdYt24dR48eZdKkSaSmpnL+/HleeeUVhg0bxt9//83MmTPx9vZm3LhxAKxbt46RI0fy3nvvMWfOHBQKBZcuXeLMmTOaeWNiYmjcuDEKhYJPP/2UKlWqEBkZydSpU7l27RrLly/XyKalpSHLcpb7q1AoNBeEp06dAqB27do6Ml5eXri5uWm2GyM5OZkyZcrotdva2gJw4sQJmjZtqmn/+++/WbVqFYcOHcryS+bff//FycmJ58+fU61aNQYPHsyYMWOy9eUkyD5CP7KvH5lRqVSsWLGCqlWr0rp1a51t7733Hq+99hrTpk1j2LBhSJLEihUrOHLkCD/99JPeWKNHj8bPz49Ro0bx77//WrQOQe4gdCPnuiHLMmlpaTx+/Jjt27ezYsUKPvjgA52br8z8/fffXL9+nalTpyJJUpZz7Ny5EysrK6pXr27WmgQ5R+hG7v1u5AY+Pj706NGDr7/+mqCgIBo1asStW7cYPXo0lSpVom/fvvm+ppKK0I3s68apU6coV64cnp6eOu116tTRGV+btLQ0VCoVt2/fZsaMGciyzLvvvqvZnpycjEKh0HswpH0fI8g/hH7k73VV7dq1uXfvHm5ubnTs2JGpU6fqOAwUW/3Ib1dBQf6Q7pI6a9YsnfaRI0fKdnZ2skqlkmVZndzRxcVFfvjwoY7c8uXLZUAv5vzll182GOZQr149uUGDBprPo0aNkl1dXU2ucfjw4bKjo6N8/fp1nfY5c+bIgI6Lrqmk49qvKVOmaPoMHTrUYPJMWZbl6tWryyEhISbX9/LLL8uurq7y48ePddpbtmwpAzqu848fP5Z9fX3liRMn6qzZUPjtyJEj5WXLlskRERHypk2bNMU3MocyCvIOoR8514/M/PXXXzIgz5gxw+D2TZs2yS4uLpq12Nvby6tXr9aT27Jli2xtbS2fPHlSlmVZ3rVrlwjLzUeEbuSebsyYMUMzviRJ8uTJk7Ps06dPH1mpVOqkgzDGtm3bZIVCIY8dO9as9QhyhtCN3P/dMBWWq42psFxZVhciGDp0qM6669SpI1+9etWi9Qiyh9CNnOtGhw4d5Bo1ahjcZmNjoxfOKMuyXKNGDc1avLy85L179+psnzdvngzIe/bs0Wn/5JNPZMBifRVkD6Ef+XtdtWrVKnnatGny1q1b5Z07d8pffvmlXKZMGdnDw0Pn2qq46ofw3CvmdO/eXedznTp1eP78Offu3cPDwwOAdu3aUbp0aYP9u3btqvM5ICCATZs20aVLF7327du3az43btyYhQsX8vrrr9O3b1+aN2+Om5ubTp8tW7bQtm1bvL29dVx+O3XqxIcffkhERAQ1a9YE4I8//iApKSnL/fX29tb5bMrzISuviFGjRvH7778zYMAA5syZQ6lSpVi4cCH79+8HdENGJkyYgLW1NZ9++mmWa/zmm290Pvfo0YPSpUuzcOFCxo0bR/369bMcQ5A7CP3Ivn5kZunSpVhZWTFw4EC9beHh4bz55pu89tpr9O7dGysrKzZv3szAgQNJTk7WuOnHx8czfPhwPvroIwIDAy2aX5C7CN3IuW4MHDiQ9u3b8/DhQ3bu3Mns2bOJj49nwYIFBuUfPnzIpk2bCA0N1UkHYYj//vuP3r1707RpU2bMmGHWegS5g9CN3PvdyC3eeecdNm7cyNdff02DBg2IiYlh9uzZtGvXjl27duHj41Mg6yppCN3ImW5Y2n/Dhg0kJiZy48YNlixZQqdOndi8eTNt2rQB4I033uDzzz9n2LBhLF++nBo1avDXX38xf/58wPzQd0HuIPQjf66r+vfvr9Onbdu2tG3bluDgYGbNmsX//d//AcVXP4Rxr5hTtmxZnc/prqbPnj3TtHl5eRntnzks1cbGxmj78+fPNZ/79+9Pamoq33//Pb169UKlUtGoUSOmTp1Khw4dALh79y5//PGHwTxCALGxsZr3NWvWNNsFOJ2yZcvy/Plznj59ioODg47cw4cPCQoKMjnWSy+9xPLly/nggw+oUqWKZh1ffPEFkyZN0tx8HTx4kEWLFvHbb7/x/PlzzXFQqVSkpqYSFxeHvb295tgb4s0332ThwoVERUUJ414+IvQj+/qReS2bN2+mS5cueiElsiwzaNAgWrVqpRN60L59e+Lj43nvvffo3bs3pUqVYvLkyVhbWzNq1Cji4uIAePLkCQBPnz4lLi4OFxeXAruBLEkI3ci5bnh6emr0ISQkhNKlSzNhwgQGDRpk8Ht+9erVJCUl6eWszMzRo0fp0KED1apVY+vWrSZ/WwS5j9CN3PndyC3Cw8NZunQpv/zyC6+++qqmPSQkBF9fX8LCwnRCygR5h9CN7OtG2bJlOXbsmF57YmKi0TRBtWrVAtTGm5dffpn69evz/vvvc/z4cQDc3NwIDw/nrbfe0qQRKlu2LHPnzmXw4MFZPkQS5C5CP/L/uiqdxo0bU716daKiojRtxVU/iqZJUpCr5NWN8ttvv83+/fuJj4/nzz//RJZlunbtyvXr1wG1UoWEhHDo0CGDr8GDB2vGqlKlCtbW1lm+Pv/8c02f9Lj+kydP6qwrJiaG2NhYszyD3nrrLWJiYjhz5gwXL17k9OnTgPqYtWzZEoAzZ84gyzI9e/akdOnSmtfNmzfZtm0bpUuXZvHixSbnSf+SLKpPCYozQj+y5scffyQ5OdmgUeLu3btER0drEv9r06hRIxITE7l27Rqgzslx7do1PD09NXrUrVs3QK2LpUuX1kk+LChYhG5YRroOXLhwweD2pUuX4uHhofd0XpujR4/Svn17fHx82L59u14RAUHhQOhG/pFuEGnUqJFOu6urK1WrVi2QPIAC4wjdMEzt2rW5f/8+MTExOu3p42XV38rKigYNGuj9vjRq1IgzZ85w9epVTp06xZ07dwgICACgVatWJscU5D9CPywjq+sqbWRZ1rvPLo76ITz3BHlOqVKl6NSpE8nJybz88sucPn0aHx8funbtytatW6lSpYpRF+R0suMCHBoaip2dHStWrKBJkyaa9vSqQy+//LJZ67eystIoenx8PN999x09evTQhHmEhoaya9cuvX59+/bFz8+PGTNmULVqVZNzrFq1CkCnQIegZFDU9QPURglvb2+9ilOgrvJpZ2en87QsncjISBQKheZJ5bx58zQee+kcO3aMsWPHEhYWRuvWrXF0dDR7XYKiTXHQDW3SfycM/R4cPnyYEydOMH78eKMFN44dO0b79u2pUKECO3bsyHLfBcWX4qYbOSF9fVFRUTrhtw8ePODChQu89NJL+b4mQcFRVHWjR48efPzxx6xcuZKPPvpIp7+9vT2hoaEm+z9//pyoqCij9xu+vr6A2sDx1Vdf4e3tzWuvvZbFHgqKG0VVP4xh6rpKm6ioKC5evMjo0aMNbi9O+iGMe4I8YejQodjb29O8eXO8vLyIiYlhxowZuLi4aJ6ufv755+zYsYNmzZoxevRoatSowfPnz7l27Rpbt25lyZIlVKhQAdCvrmMOZcqU4eOPP+aTTz6hTJkymicSYWFhDBkyRJM7ANTGtUGDBrFs2TIGDBgAwL179/jqq69o3rw5Tk5OnDt3jlmzZqFQKHTy5mm7CGtjZ2dH2bJlNbkvANauXctvv/1Gly5d8PHxIS4ujl9++YV169YxcOBA6tata/F+CooexUE/0jlw4ACnT59m0qRJBqs929raMnLkSObOncuAAQPo06cPSqWSTZs2sXbtWgYPHqwJKahXr57R9daqVUtHlwTFk+KgG1OmTOHu3bu0atWK8uXLExcXR3h4ON9//z2vvfaawfCTpUuXAug8Hdfm/PnztG/fHoBp06Zx8eJFLl68qNlepUoVypUrZ/G+CooOxUE3ACIiIrh//z6grrp4/fp1fv31VwBat26tOY+fPn3K1q1bATQPhyIiIoiNjdXcoAK88sorfPrpp7zzzjvcunWLBg0aEB0dzezZs3n69Cnvv/++xfspKFoUB92oVasWgwcPZsqUKSiVSho1asT27dv57rvvmDp1qk7oZbNmzejevTsBAQG4uLhw7do1Fi9ezOXLl9m4caPOuiZPnkzt2rXx8vLixo0bLFu2jAMHDvDnn39ib29v8X4Kih7FQT8sua6qW7cub775JgEBAdjZ2XHw4EFmz56Np6cn48eP11lXcdQPYdwT5AktW7ZkxYoV/Pzzzzx69Ag3NzdatGjBqlWrNBduXl5eHD58mC+++ILZs2dz69YtnJyc8PPzIzQ0NFe8EiZPnoyTkxPffPMNc+bMwdPTkwkTJjB58mQdOZVKpSkpn46VlRXHjh1j+fLlxMXF4eXlRY8ePfj000/1EpGaS+XKlYmLi2PSpEk8ePAAa2tratWqxaJFixg+fHiO9lVQdCgO+pHO0qVLkSTJqFECYPbs2QQEBPDtt9/y5ptvolKpqFKlCgsXLmTYsGE53g9B8aE46EbDhg2ZP38+mzZt4sGDB9jZ2VGzZk2+/vpr3nnnHb25nj17xk8//USrVq2oXr26wfVERkby4MEDAE2oujbLly83WMxGUHwoDroB6pu0iIgIzefdu3eze/duQO2Fkf4Q5969e3qeE2FhYQD4+Pho0jk4OjoSFRXFtGnTWLJkCbdu3aJMmTLUr1+fxYsXi4iIEkBx0Y1FixZRvnx5FixYQExMDL6+vvzf//0f7733no5cs2bNWLduHdeuXSMxMRE3NzeCg4P5+uuvadasmY7so0eP+Oijj4iJicHZ2ZnWrVtz4MCBbBloBEWT4qAfllxX1axZk++++47o6GiSk5Px9vamb9++fPrpp3o5DYujfkiyORkRBQKBQCAQCAQCgUAgEAgEAkGhQ2TvFwgEAoFAIBAIBAKBQCAQCIoowrgnEAgEAoFAIBAIBAKBQCAQFFGEcU8gEAgEAoFAIBAIBAKBQCAoogjjnkAgEAgEAoFAIBAIBAKBQFBEEcY9gUAgEAgEAoFAIBAIBAKBoIgijHsCgUAgEAgEAoFAIBAIBAJBEcWqoBdQUlCpVNy5cwcnJyckSSro5QgEAMiyzOPHj/H29kahKBhbv9ANQWFE6IZAYJjCoBsg9ENQOCkM+iF0Q1AYEbohEBgmN3VDGPfyiTt37lCxYsWCXoZAYJCbN29SoUKFAplb6IagMCN0QyAwTEHqBgj9EBRuxG+HQGAYoRsCgWFyQzeEcS+fcHJyAtT/NGdn5wJejUCgJiEhgYoVK2rOz4JA6IYgp3z//ffMnz+fu3fv4u/vz5dffkmzZs2Myu/du5dJkyZx7tw5PD09ef/99xk8eLCOTLpubN++ncGDB9OjRw82bdqk2T5jxgx+++03zp07h729Pc2aNWPmzJnUqFFDIzNw4EBWrlypM26TJk2Iiooya7+EbggKI4XhdwOEfggKJ4VBP4RuCAojQjcEAsPkpm4I414+ke766+zsLL5MBIWOgnRNF7ohyAnr169n4sSJLFq0iObNm/Ptt9/y6quvcubMGSpVqqQnf/XqVV577TWGDh3KTz/9xL59+xg5ciSVKlWiV69eevKffPIJLVu21GuPiIjg3XffpVGjRqSmpjJ58mRCQkI4c+YMpUqV0siFhoayfPlyzWcbGxuz903ohqAwU9AhTUI/BIUZcV0lEBhG6IZAYJjc0A1h3BMIBAJBkWXu3LkMHjyYIUOGADBv3jy2bdvG4sWLmTFjhp78kiVLqFSpEvPmzQMgICCAw4cPM2fOHB3jXlpaGgATJ07k8OHDxMXF6YwTHh6u83n58uW4u7tz5MgRWrVqpWm3tbXF09MzN3ZVIBAIBAKBQCAQCAxSZKrlPnr0iP79++Pi4oKLiwv9+/fXu9nKjCzLhIWF4e3tjb29PW3atOH06dM6MsOHD6dKlSrY29tTrlw5evTowblz5zTbr127xuDBg/Hz88Pe3p4qVaowZcoUkpOT82I3BQKBQGAmycnJHDlyhJCQEJ32kJAQ9u/fb7BPZGSknnzHjh05fPgwKSkpmraZM2cCMGDAALPWEh8fD0CZMmV02nfv3o27uzvVq1dn6NCh3Lt3z+gYSUlJJCQk6LwEAoFAIBAIBAKBICuKjHGvX79+HDt2jPDwcMLDwzl27Bj9+/c32WfWrFnMnTuXhQsXcujQITw9PenQoQOPHz/WyAQFBbF8+XLOnj3Ltm3bkGWZkJAQjdfGuXPnUKlUfPvtt5w+fZqvv/6aJUuWMGnSpDzdX4FAIBCYJjY2lrS0NDw8PHTaPTw8iImJMdgnJibGoHxqaiqxsbEA7Nu3jx9//NHsdciyzLhx42jRogWBgYGa9k6dOrFmzRp27tzJV199xaFDh2jXrh1JSUkGx5kxY4bmAZaLi4tI+iwQCAQCgUAgEAjMokiE5Z49e5bw8HCioqJo0qQJoE6gHhwczPnz53USmKcjyzLz5s1j8uTJvPLKKwCsXLkSDw8P1q5dy/DhwwEYNmyYpo+vry9Tp06lbt26XLt2jSpVqhAaGkpoaKhGpnLlypw/f57FixczZ86cvNxtgUAgEJhB5hwVsiybzFthSD69/fHjx7z55pvMnz+fV1991az5R40axYkTJ9i7d69Oe58+fTTvAwMDadiwIT4+Pvz555+a3yVtJk6cyLhx4zSf0xPsCgQCgUAgEAgEAoEpioRxLzIyEhcXF41hD6Bp06a4uLiwf/9+g8a9q1evEhMToxN+ZWtrS+vWrdm/f7/GuKdNYmIiy5cvx8/Pz+QNVXx8vF7olUAgEAjyFzc3N5RKpZ6X3r179/S889Lx9PQ0KG9lZUXZsmU5ffo0165d0xjmypQpg0qlAsDKyorz589TpUoVTd/33nuPzZs38++//2ZZvt7LywsfHx8uXrxocLutrS22tramd1ogEAgEAoFAIBAIMlEkwnJjYmJwd3fXa3d3dzcZegWYFa61aNEiHB0dcXR0JDw8nB07dhitaHj58mUWLFjAiBEjTK65IHMnxT9LYcjKQ/xx/E6+zSkQ5Def/n6Kr7afL+hlCAoQGxsbgoKC2LFjh077jh07aNasmcE+wcHBevLbt2+nYcOGWFtb4+/vz8mTJzVeeHv37qV79+60bduWY8eOaR78yLLMqFGj+O2339i5cyd+fn5ZrvfBgwfcvHkTLy+v7OyuUS7de8KkjSc5F5PAkJWH2Cy++wUCLt59zFvLDnL0xqOCXopAUGj4ZtclQr6OYM2B6wW9FIFAICj23H+cxKAVh/jn7N18ma9AjXthYWFIkmTydfjwYcBwaeCsQq8M9TPU54033uDo0aNERERQrVo1evfuzfPnz/XGunPnDqGhobz22muayozGKMjcSQt3XuTvs/d476ej+TanQJCfXItNZFXkdRbsvFTQSxEUMOPGjeOHH35g2bJlnD17lrFjx3Ljxg3NA5iJEyfqFMUYMWIE169fZ9y4cZw9e5Zly5axdOlSPvzwQwDs7OwIDAykZs2aANSsWRNXV1ecnJwIDAzUPPh59913Wb16NWvXrsXJyYmYmBhiYmJ49uwZAE+ePOHDDz8kMjKSa9eusXv3brp164abmxs9e/bM1WPw2pL9rD1wg9B5e/j77D1Gi+9+gYA3lx4g4sJ9ei7az383HpGYlFrQSxIICpxHick8vXeVuzHiIZBAIBDkNZ9vOcPOc/cYvPJwvsxXoGG5o0aNom/fviZlfH19OXHiBHfv6ls779+/bzL0CtQefNpeEobCtdINcNWqVaNp06aULl2ajRs38vrrr2tk7ty5Q9u2bQkODua7777Lct8KMnfSg0RRyVdQvElOU2nem2PkFxRf+vTpw4MHD/j888+Jjo4mMDCQrVu34uPjA0B0dDQ3btzQyPv5+bF161bGjh3LN998g7e3N/Pnz6dXr14Wzbt48WIA2rRpo9O+fPlyBg4ciFKp5OTJk6xatYq4uDi8vLxo27Yt69evx8nJKWc7nYlHT1OyFhIIShAbj97ibkJG4ZpXFu2ndnkX/nivRQGuSiAoePrcmsrHtn/yy+3RQHBBL0cgEAhyjEolM2vbeepVdCU00LOgl6PDvQR9h7G8pEA999zc3PD39zf5srOzIzg4mPj4eA4ePKjpe+DAAeLj442GXvn5+eHp6akTfpWcnExERITRPunIsqxTzfD27du0adOGBg0asHz5chSKrA+bra0tzs7OOi+BQJA7aJvyXtRCEJRgRo4cybVr10hKSuLIkSO0atVKs23FihXs3r1bR75169b8999/JCUlcfXq1SzTLKxYsYJNmzbptMmybPA1cOBAAOzt7dm2bRv37t0jOTmZ69evs2LFClEgQyDIB8auP67XdvJ2fAGsRFDcWbRoEX5+ftjZ2REUFMT+/fuNykZHR9OvXz9q1KiBQqFgzJgxBuXmzZtHjRo1sLe3p2LFiowdO9ZgRFF2eOZaDQDX6H2oVOICSmCYzOf1nj17TMpHREQQFBSEnZ0dlStXZsmSJXoyv//+OwDlypWjZs2abNy4UWe7r6+vwSi+d999VyMzcOBAve1NmzbNhT0WFGW2nY5hScRlRqw+UtBLKXCKRM69gIAAQkNDGTp0KFFRUURFRTF06FC6du2qU0zD399f80UhSRJjxoxh+vTpbNy4kVOnTjFw4EAcHBzo168fAFeuXGHGjBkcOXKEGzduEBkZSe/evbG3t6dz586A2mOvTZs2VKxYkTlz5nD//n1N+JVAICh4ZGBW+Dle/y6KFC2PPoFAIBCUXCTE74Egb1m/fj1jxoxh8uTJHD16lJYtW5qssp6UlES5cuWYPHkydevWNSizZs0aJkyYwJQpUzh79ixLly5l/fr1TJw4MVfWfNauAQBNFWf4KepyrowpKF4YOq87deqkEwWhzdWrV+ncuTMtW7bk6NGjTJo0idGjR7NhwwaNTGRkJG+//TYA+/bto3///vTu3ZsDBw5oZA4dOkR0dLTmle6g89prr+nMFxoaqiO3devW3D4EgiLG3Xz2jivMFAnjHqh/7GrXrk1ISAghISHUqVOHH3/8UUfm/PnzxMdnPJkdP348Y8aMYeTIkTRs2JDbt2+zfft2TUiUnZ0de/bsoXPnzlStWpXevXtTqlQp9u/fryngsX37di5dusTOnTupUKECXl5emldhRUKEKApKDrIss2j3ZSKvPODvM/mTrFQgEAgEhZTUZOZbL+CQ7UiceVLQqxEUY+bOncvgwYMZMmQIAQEBzJs3j/LlyxuV9/X15f/+7/8YMGAALi4uBmUiIyNp3rw5/fr1w9fXl5CQEF5//XVNDvKcUrFmUx7JjjhJz9jwxx+5MqageGHovK5YsaImHUlmlixZQqVKlZg3bx4BAQEMGTKEQYMGMWfOHI3MvHnzaNu2LQDVq1dn4sSJvPTSS8ybN08jU65cOTw9PTWvLVu2UKVKFVq3bq0zn62trY5cmTJlcv8gCEocp27H0/zLnWw6ejtXx81v/+giY9wrU6YMq1ev1lSeXb16Na6urjoy2iFRoPbeCwsLIzo6mufPnxMREUFgYKBmu7e3N1u3buXu3bskJydz8+ZN1qxZo+MNOHDgQKPhVwKBoHCRLDz3BAKBoGRjZUMN6SZuUgKtFScKejWCYkpycjJHjhwhJCREp71du3Y5GrdFixYcOXJEk4roypUrbN26lS5duuRo3HSCq7mzT1ULgJaKk9x69DRXxhUUD4yd1yEhIUZDziMjI/XkO3bsyOHDh0lJSdHIZNaNjh07Gh0zOTmZ1atXM2jQIL282rt378bd3Z3q1aszdOhQ7t27Z3R/kpKSNLaD9JdAYIjR645yO+4ZY9Yfy7M5jlx/lGdjp1NkjHsCgUCQjvbvvDCzC0oyl+8LzySBIDO7VPUBaKs8VrALERRbYmNjSUtL0yvSV65cuRyN27dvX7744gtatGiBtbU1VapUoW3btkyYMMFoH0sMGJIk8dBdXUijhfIkD0URPoEWxs5rDw8PoympYmJiDMqnpqYSGxurkUmPijNnzE2bNhEXF6fjtAPQqVMn1qxZw86dO/nqq684dOgQ7dq108mVr82MGTM0hTNdXFxE3mOBUZJTMxxE8iof6ZoD1/NkXG2EcU8gEBRBMqx7aSIhtKCE8tkfp3npq4iCXoZAUOjYlVYPgDaKYyjQra4uEOQmmb2KcnqO7d69m2nTprFo0SL+++8/fvvtN7Zs2cIXX3xhtI+lBozTdkEA1Jcu8fxxXI7WKyieGDqvM7dlJZ+53ZIxly5dSqdOnfD29tZp79OnD126dCEwMJBu3brx119/ceHCBf7880+D40ycOJH4+HjN6+bNm0b3QSBIp9vCvVl+l8uyTGJSaj6tyHyEca8YYuK7VyAodvT5LkrzXty3CUoSy/ddK+glCASFkiNyNRJkB8pIT6grZRQNEA+DBLmFm5sbSqVSz/Mo3VMpu3zyySf079+fIUOGULt2bXr27Mn06dOZMWMGKpXh1COWGjD2PnDkmsoDK0mF7Z3IHK1XULwwdl7fu3dPzzsvHU9PT4PyVlZWlC1bViNz9+5dPRlDY16/fp2///6bIUOGZLleLy8vfHx8uHjxosHttra2ODs767wEAkNo209O30kgNYvrheE/HqHWlG1cuPtYb9vFu4/ZcuKO2kCYz5cdwrgnEAiKFE+TU1l3MKNi1/GbcQW3mAIm7mky30ZcJiZeVIkqaQgPJIHAOKlY8a+qDgBtlUc17WlCbwS5hI2NDUFBQZqKnuns2rUrR+M+ffoUhUL39kypVJrM922pAeN23DP2qdQ5yP/btZFL90R6B4EaY+f1jh07aNasmcE+wcHBevLbt2+nYcOGWFtba2Qy68b27dsNjrl8+XLc3d3NyjP54MEDbt68WagLXQqKBpm/XrUfBj5NTmX/5VhStXK7b39RxDHk63/1xurw9b+MWnuUfy/m7GFPdhDGPYFAUKSY9udZfth71eA2uYRl4Pvg5+PM+Osc/b6PylpYUKwQNgqBwDQ7X4TmtlMc07QJzz1BbjJu3Dh++OEHli1bxtmzZxk7diy3bt3SbJ84cSIDBgzQ6XPs2DGOHTvGkydPuH//PseOHePMmTOa7d26dWPx4sWsW7eOq1evsmPHDj755BO6d++OUqnMlXXP7V2XParaALRQnGLIykO5Mq6geGDovL5x4wYjRowA9M/rESNGcP36dcaNG8fZs2dZtmwZS5cu5cMPP9TIvP/+++zcuROACxcuMHPmTP7++2/GjBmjM7dKpWL58uW89dZbWFlZ6Wx78uQJH374IZGRkVy7do3du3fTrVs33Nzc6NmzZx4dDUFhRZZlrsUmGg3v3n85luZf7iTiwv1sja/SutAeuuow/b4/wDe7Lpvooc/+y7EcvPZQ81ki78MrrbIWEQgEgsLDznPGq2IVBJfuPeH+4ySCq5TN97l3nVcfiyuxifk+t6BgESYKgcA0Eaq6qGSJQMU13HnEPUpnGWYjEFhCnz59ePDgAZ9//jnR0dEEBgbyyy+/0LlzZwCio6O5ceOGTp/69etr3h85coS1a9fi4+PDtWvXAPj444+RJImPP/6Y27dvU65cObp168a0adNybd0965cn7OeaqGSJaorbPH8g8pAJMjB0Xm/duhUfHx9A/7z28/Nj69atjB07lm+++QZvb2/mz59Pr169NDLNmjVj2bJlvPXWWzRr1owqVaqwfv16mjRpojP333//zY0bNxg0aJDeupRKJSdPnmTVqlXExcXh5eVF27ZtWb9+PU5OTnl0NASFla93XGD+zkuMbFMFdydbve39vj8AwFvLDnLty6y9QDPbB99d8x/vtKlKY78y7Lv0AIC1B6/zfvtqZq/x24grZsvmFsK4VwwRKfcExZnCdn63n6suaLDzg9ZULudYwKsRlBSyCsu9GpuIn1upfFqNQFD4eIALJ+TK1JMu00Z5jJ/T2pKSajhnmUCQXUaOHMnIkSM1n7Ur1a5YsUJPPqvvbisrK6ZMmcKUKVNybY2ZkSQJd3dPTsT5UU+6QgvlKeKfpuDiYJ1ncwqKFpnPa20MndetW7fmv//+Mznmyy+/DKjzUhoLHQ8JCTGqI/b29mzbts3kHILiz82HTwk/FcP8nZcAWLT7MmHdaub6PLvO32fX+fs6hsH88LzLKSIstxginksLSiqWhirGPkli6d6rPExMzvHcBZGzxlTlMkHxJisHpLZzdufLOgSCwszONLWXVHpobtDUv0VorkCA+kHp3hehuc0Vpwj+8p+CXZBAIBCYQZf5e5i29axOW17eD83/J6NYy8PEZBbvvsy9hMKb61wY9wQCQZEiN7/Ah646zBdbzvDuGtNPGwWCwkZJyy8pEGSHnap6ALRQnMSGFABuPHxagCsSCAoHjnZWGuNeS8VJniWnFPCKBAKBQM3tuGe8u/Y//rvxSG9bwvNUi8cbtfY/Dlx5kK21zN1xQfM+OU3FzPBzDFh2MFtj5QfCuCcQCIoNlnruHb0RB0BkNr/wBYKCQhTUEAiy5rTsyz3ZFUfpOY0U5wBITLL8xkAgKG7MeKU2N0sF8li2x01KIFC6VtBLEggEAgDGrDvKnyeieWXR/lwZb8uJaPp8l3vFB8/FPM5WvweJSbm2BmMI414RIE0lM3f7efaaWU5ZBOoJBCUDoeslF2HcEwiyRkbBrhdVc19SHAUgSeTdEwjw93Rm3+RO7FUFAtBWq6q0QCAQFCRXY/POw/7I9UecvhOfZ+OHn4oxum33+exV7rUEYdwrAmz47xbzd17izaUHCnopAkGhRtg7BCUFEZYrEBhn+cBGmvd/qxoA0EFxBJBJTRPGPYEgnd0vQtfbKo/x4Enee5UIBAKBKaLjnxFr5Lso4XnO0gc8TEym1+L9dJm/V69wS24Vy/jk91O5Mk52Eca9IsBNkR9GUMK5+fCpJu9CYa0hIUwtgvxEeO4JBMZp6+9OxP/aALBHVZtnsg0VFfepKV0n4sJ9YeATCF6wO60uAHWly7Sf+hsqUXBGIBAUIMEzdhpsf5iYTJ2w7Tkau9/3GaG5ma+ji8tDc2HcK4RExz9j8saTXLirjucupLYMQSHh0aNH9O/fHxcXF1xcXOjfvz9xcXEm+8iyTFhYGDVq1ACgS5cunD59WrP94cOHvPfee9SoUQMHBwcqVarE6NGjiY/XdWO+cOECPXr0wM3NDWdnZ5o3b86uXbtyfR9bztrFK4v2czU2MdfHLsoUVkOnIO8pHpcgAkHeoVSovyCfY8u/qjoAhCgPs2j3ZWpN2cbu8/cKcnkCQaFg/GttOaPyQSHJtFKc4P31x7j1SDgVCASCwkV2C2Joo50rb/KmU5y6nTvhuV9tP58r4+QGwrhXCHl3zX+sOXCDrgv2qhvEHbzABP369ePYsWOEh4cTHh7OsWPH6N+/v8k+s2bNYu7cucyePRsAd3d3OnTowOPH6i+9O3fucOfOHebMmcPJkydZsWIF4eHhDB48WGecLl26kJqays6dOzly5Aj16tWja9euxMQYzzeQE85GJ5hUh8wu1gJBcUVlxrk+au1/bDlxJx9WIxAUPtKNewDb0xoCEKI4Aqjz7g1cfqhA1iUQFCZ6BVXgZtnmgDo094/jd3htSWQBr0ogEAjylp8OatlayFlY7oKdlzTvC/pWVBj3CiGn7iQAkPwi6bOlp5qwBZYczp49S3h4OD/88APBwcEEBwfz/fffs2XLFs6fN/wUQZZl5s2bx+TJk+nevTsAS5Ys4enTp6xduxaAwMBANmzYQLdu3ahSpQrt2rVj2rRp/PHHH6SmqisNxsbGcunSJSZMmECdOnWoVq0aX375JU+fPtXxAsxtcisngkBQlDHn4mHLiWhGrT2a94sRCAohSq2LoX9U9UmTJWoqrlNBEh57AoE2ES/y7rVWnECBiuj45wW7IIFAUKw5cyeBnefu6rUbSkX2za5L9F96gORCnk6j73eRbDhyq6CXIYx7hRFhuhCYS2RkJC4uLjRp0kTT1rRpU1xcXNi/33D58KtXrxITE0NISIimzdbWltatWxvtAxAfH4+zszNWVlYAlC1bloCAAFatWkViYiKpqal8++23eHh4EBQUlEt7qEtWBg3htycoMYiTXSAwSSlbK837OJw4qAoAoKPicEEtSSAolByVq5MgO1BaekJd6XJBL0cgEBRyLt17QkoOjG2d5+9h0IrDnI1O0Gl/6asIPdnZ286z52IsfxyPzvZ85pBT56ioKw/54JfjBe5kJYx7hRBFQZ8VgiJDTEwM7u7ueu3u7u5GQ2PT2z08PHTaPTw8jPZ58OABX3zxBcOHD9e0SZLEjh07OHr0KE5OTtjZ2fH1118THh6Oq6ur0TUnJSWRkJCg8xJkD+HFWHIxJyxXICjJlLK10qmau12lfugUosww7j1NTmVV5DXuxD3L9/UJBIWFZ2lo8lK2UR4r2MUIBIJCze/HbtN+bgRDVub8QVl6fQFQR5aZ8s5LSk0za0xZlpn25xmL15Jbl9WPc1jRN6cI414hJLNtT9j6Sh5hYWFIkmTydfiw+ktVMnCCyLJssF2bzNuN9UlISKBLly7UrFmTKVOm6MiPHDkSd3d39uzZw8GDB+nRowddu3YlOtr405UZM2Zoin+4uLhQsWJFk+vULnu+/UyMaaOGsHeUSBYtWoSfnx92dnYEBQWxZ88ek/IREREEBQVhZ2dH5cqVWbJkiVHZX3/9FUmSePnlly2eN71wjbe3N/b29rRp0ybXQtZlWUVrxXF6Kkzvq0BQkmnrn/HwKz3vXkPpPGVQP1T64OfjfPr7abov3Fcg6xMICgPvt6/GbpW6am5bxbGCXYxAICjULNt7FYCIC/d12hOTUrPMfb7vUiyh8/7VfE5Jy5Dv+12UoS4a9lyMNbpN+/Z1ddR1vt9z1eRY2tx/nJQjL8TMPE8p2PBhYdwrhOTUlie8eYo+o0aN4uzZsyZfgYGBeHp6cveufs6C+/fv63nmpePp6Qmg56V37949vT6PHz8mNDQUR0dHNm7ciLW1tWbbzp072bJlC+vWraN58+Y0aNCARYsWYW9vz8qVK43u28SJE4mPj9e8bt68afJYzPzrnOb978fucOuRcQ+LdYdumBwrt7j16CltZu9i5f5r+TKfwDjr169nzJgxTJ48maNHj9KyZUs6derEjRuGz4WrV6/SuXNnWrZsydGjR5k0aRKjR49mw4YNBuU/+eQTWrZsma150wvXLFy4kEOHDuHp6alTuCYnWF3ZyUqbmXxsvRobCvYpoUBQFLhNOU6pfFFKMi8p/wPgr1Pq38HYJ0kFuTSBoEDpWb8CEWlq414dxVXKEVewCxIIBIWWNAMGvJsPn1JryjbeWn6IQ9cesnj3ZVQqGZVKJmzzaX4/dhuAN344oFOxNlXLqHbg6sNcWd8nv1v2EL3RtL/5eOMp5GLiISKMe4WQzGG5mY11EzacoOuCPZqCG4Lih5ubG/7+/iZfdnZ2BAcHEx8fz8GDBzV9Dxw4QHx8PM2aNTM4tp+fH56enuzYsUPTlpycTEREhE6fhIQEQkJCsLGxYfPmzdjZ2emM8/SpOumpQqH7NaJQKFCpjJ+btra2ODs767xMcfHeE5PbtfnvRhwx8c95mJhsdp/sMO3Ps1x78JQpm/OucIhZCDs+c+fOZfDgwQwZMoSAgADmzZtHxYoVWbx4sUH5JUuWUKlSJebNm0dAQABDhgxh0KBBzJkzR0cuLU3t/j9x4kQqV65s8bzahWteeeUVAgMDWblypU7hmpyQ7NuaaLkMZaXHhCpE1U+BwBwyquaKvHsCgTb3ceW4Sv1b104pCjEJBALDpKbpG8F+eVFI4t8L93ltSSQzw8+x+fgdwk/HsGL/Nd5fd8zgWM9SzAu1zWvWH76JiVvXIoUw7hVGsgjLXXfoJqduJ+i5wwpKHgEBAYSGhjJ06FCioqKIiopi6NChdO3alRo1amjk/P392bhxI6AOxx0zZgzTp0/njz/+AOCdd97BwcGBfv36AWqPvZCQEBITE1m6dCkJCQnExMQQExOjMXoEBwdTunRp3nrrLY4fP86FCxf43//+x9WrV+nSpUvu7aSFD1KmbT1Lgy928MOeKxb1O3Ttoc4TJFMIw3rhIDk5mSNHjugUhwEICQkxWhwmMjJST75jx44cPnyYlJQMD7iZM2cCMGDAgGzNm53CNZbko1RJStantQGgn9U/RuUEgpLO0JZ+2FsrAdiuUhv3WilO4oCoCCoQpPPlK7XZkabOS9lBGL8FAoER0lS6N2YX7z5m/j8X9eSuxCZy/7Fpr/jP/jjDiVtxubm8bJNVSHFRQRj3CiGZnXGMOecUl5NQYB6X7j3h0j39cL41a9ZQu3ZtQkJCCAkJoU6dOvz44486MufPnyc+Pl7zefz48YwZM4YPPvgAgOjoaLZv346TkxMAR44c4cCBA5w8eZKqVavi5eWleaWH0bq5uREeHs6TJ09o164dDRs2ZO/evfz+++/UrVs31/bbUjfpP47fAWDqn2ct6vfakkhmbztvUR9BwRIbG0taWppFxWFiYmIMyqemphIbq87nsW/fPj0dsnTe7BSusSgfpQzrUtuSJks0VZylinTbuKxAUIKZ3KUmZz7vSINKrpyTK3JV5YGtlMJLiv8KemkCQaHBxkrBjhdFZ1ooTnH7nvH8VgKBoOSS2bjXdcFeo7LmFH+bZuH9Wl5hKNy4KCKMe4UQhcKyWLvD1x7y0a8niHuqDkUUBTiKH89T0mg/N4L2c//leSYX5jJlyrB69WqNp8/q1av1qtXKsszAgQM1nyVJIiwsjAsXLgCwdetWAgMDNdvbtGmDLMsGX76+vhq5hg0bsm3bNh48eEBCQgKRkZF06tQpV/c9p9+1dxOec8nM0N7vLfT2K2iEqqsxtziMKfn09sePH/Pmm28yf/78XJnXkrVZko9SBmIoy05VfQBeV+7Mcr0CQUlFkiS+7d8QkPhT1RSALsoDOjKf/n5KPDQVlFg61/bCq1oDbqjKYSelsOlX4w+4BAJBySWzESzJSDSTBKjM+EktLHaLXKypUaAI414hRM9zL4uT/tUlkaw/fJMvthQOy7cg90lMSjX4vrgjyzKHrz/K0RhNpv9D+7kR3HusDsGKiX/O5uN3zA7BFRRe3NzcUCqVZhWHScfT09OgvJWVFWXLluXy5ctcu3aNPn36AGrj+apVq9i8eTNWVlZcvnzZrHktKVyTjiX5KNOfhq5NewmAXso92GJersn4pyl6T14FguJOOSdbAP5MUxv32iqOUYqMAk2rIq+z79KDAlmbQFDQ2FkrWTGoCTtehK57RIsHRgKBQB/tnHvHb8aZlDXngVnUlYdmO2HkJcXl4Z4w7hVCtL06xv963Cw5gKuxasUw99wsLiexoPhy/FZ81kImOKhVeWl15HUA2s7ZzeifjrLyxWdtTHl7ZYVQp/zHxsaGoKAgneIwADt27DBaUCY4OFhPfvv27TRs2BBra2v8/f05efIke/eqwwz27t1L9+7dadu2LceOHaNixYpmzWtu4Zrskn6+Rajqckt2o7T0hE6Kg6Y7AdcfJFL38+30Wmw4759AUNw5K1fissrLYGhuwnNReVpQskkPzW2n+A85TeiDQCDQJVWr8kR0vOncteaE5QK0nxuRozWtP2Q80sVczF1rYUcY9woh2uaFnw/fYs72C2b1++9GnNlzrNh3lYZT/+bCXf0cbsWdi3cf887qI5yLMZ6svrBRPL5uLOdZcs6qKPX+NlLzfv7OS+oxX4Q1T/vzjJ58mkom6krBeW6kpqm4Hfcsa0GBhnHjxvHDDz+wbNkyzp49y9ixY7lx4wYjRowA1KGu2kUxRowYwfXr1xk3bhxnz55l2bJlLF26lA8//BAAOzs7AgMDqVmzJgA1a9bE1dUVJycnAgMDsbGxMWte7cI1Gzdu5NSpUwwcOFCncE1OSP9OUKFgXWpbwLzCGpuOqnNSHsviaatAUHyR+FPVBICuyiidLcXk2l4gyDaHVDV4JDtSRnrC9G9X5vg6TCAQFC/uJmQUyUg1UWJWkswLy80NTt/J+T39o6e59TBDphxxNJAu0EOxl1HKjcyy+pafrKcy1WppLs1hHKs8n0FgMaachza8KDWd3f7phP2hNmx8vPEU64c3zZHHUlHjjR8OcO9xEnsvxnLys44FvRyBCZQW5p+0BGM/OH2/i+Lal5ZX+80NFXpr+UH2XXrAykGNaV29XL7MWdTp06cPDx484PPPPyc6OprAwEC2bt2Kj48PoC4Wc+PGDY28n58fW7duZezYsXzzzTd4e3szf/58evXqlavzgrpwzbNnzxg5ciSPHj2iSZMmOoVrcoJK6wT+Oa0NY6w20FhxnmrSLS7KFYz2s7RAjUBQnHC2syLheSp/pjVltNUmWiuO48RTHuPw/+ydZ3gUVReA39mWhBZKKj303oJUqULooCIgINKLgDQRRT4VEIlIMRQRpCOiKIKFHnoLnVBDqKEmQCCFutky34+QZTdbspts+rzPM0+yM2funNmdcu+5p2S2ahISWQIdcnbra9FVfhCve7tYf6o1fRqUSnlHCQmJXIdxiG5yBIQc4w1nCXeeUla4RxlZJGWEpOUeJYSHuAmW0+QU0D9Ld70k414W4MCVh0TGvaRe6cKU9shr09D2yZ/Ww3RTw7GIx7Sas4//Pn6TPKrMuRzO341jdUgE4wMq4lXANd2P9+BVWe4n2TR3XW4yxKajbc/pOOP9lZTv6ZeQm3YZ9yQSGT58OMOHD7e4beXKlWbrmjVrxqlT9lfKtNRGSseF14VrJk+ebPexUsMDCrFT709b+XH6yIP5StvfqmzQzivpqouERFZm08dNaDpzD+FiCa7qi1JOdo9WspNs1DcBJOO3hARAsK4uXeUHaS07SedtYZJxT0JCAjBP6ZWQQv5yY/FfQiLSQaP0pxDxVJLdprJwiwrCbYMxz0Ow7i2oEwUiKcJtvRe3RU9ui57cEr24IfrybzrrK4XlZgGWHLjBhPVnOfmqcIAj9oxTt9JWbADg2sNn/Bt6L83tpJaO8w/yx4k7Tjdc5lRyU65ERytH26J73eI8fKJOWTALkBr77Zzgy0TGSSG9uYXkj4FVugAAusr3U4D0nxmUkMiOlCyS5KEnsMlQNfd1aG4uer1KSFikRnF39utroBaVlJbdx1ttnp9YIuezcOFC/Pz8cHV1xd/fnwMHDtiU37dvH/7+/ri6ulKmTBkWLVpkJvPPP/8A4OnpSZUqVdi4caPJ9smTJyMIgsmSVJwsCVEUmTx5MkWLFsXNzY3mzZtz4cKFNJ6txL7LD/n4t9PEPLNemG3Ub6fptOCgyTpbnnsPn740Kd725T9Z+3eSoaeCcJvOskN8rviNlcoZHHEZwWnXYfym+pavlL/wvmIv9WThBsPePbEwB3VVWa1tzWTNh3yY8BnN1HOopF7Fm+p59NT8jwnaoczXvcs/+jc5K5ZN9/OQPPeyAEn2i6TL35GB/UdrTpp8johO3aDOWf3ZJy81/B16j7ZVfQyV6WxxJ+a54f8r9zO/Uo5E1kLmRC/FfC5Kkxx8zsaZDpX2NiUYSc7bdYXt56PYPrap8xSRyLIk9zAK0Vfhkr4ElWS36S7fy1Kd46HlEhK5ic26BoxRbKCp7CwFeEY8eYl9nsDms5G0quKFi0Ke2SpKSGQ4vw1uwPzdVzl0uCot5aG0kR3nwZO+eOVP/8gaiazBunXrGDNmDAsXLqRx48YsXryYdu3acfHiRUqWLGkmf+PGDdq3b8/gwYNZs2YNhw4dYvjw4Xh6ehpSnoSEhNC/f2JUwaFDhwgODqZ79+4cPHiQ+vXrG9qqWrUqO3fuNHyWy02fw99//z1z5sxh5cqVVKhQgWnTptG6dWvCw8OdkvIkt9J3eWJBtjxKOTPeq2FR5t8z5o5AtnLurTlyy+q2zEekGNHUlF2jpuwatWTXqCbcIK9g2QkkQu/NJbEk4WIJrumLck305Yboy3Oy3nNRMu5lAZKG50lx6YKdQ3sBSO4NO+r301QtWsDmfpY8v3ROyng5ccM5Np2N5NcjN9k2xraR4UH8S96csccpx5XImcidHIJ8I5XG7+RYUisreHyE58ICObkV80e2wApdW2bIltBXvoPlunboXznnd1t0mPLe+elY3TfD9ZSQyKpcEYsTri9ORdkdAuQnWK9rZvAsGN68LBPaVspkDSUkMp68Lgo+b1eJCQffoKU8lHbyYwxfc4r1H6W9yrtE9mDOnDkMHDiQQYMGARAUFMT27dv56aefCAwMNJNftGgRJUuWJCgoCIDKlStz4sQJZs2aZTDuBQUF0aJFC3bu3EmFChWoW7cu+/btIygoiN9++83QlkKhMPPWS0IURYKCgpg0aRLvvvsuAKtWrcLb25u1a9cydOhQZ34NuZLIeNvVb5OjseG5l5VQoqW6cJ36skvUlYVTU3bNYljtU9GVMLEkYfpSXBJLcklfgnCxBM9wywStU4cUlpsFMORQe3V/pCUS8UF8ymGHlizv//v7PJ/8kfaw2O0XogC4FJWykeHsnTiTz1KuG+tko9RzTkXmxCeUM6+v9Dbk5aK0ihKpxNIkzT+6RsSI+Sghe0gr2Wuv7uMRMaw9eoteS4+ayJ+8GcNLjVQJUSJ3cWBCC9zdlABs0iWG5naWHTaR2XQ2MsP1kpDIStRq1RutKKOq7CYPb4VltjoSGURCQgInT54kICDAZH1AQACHDx+2uE9ISIiZfJs2bThx4gQajcYg07JlSzOZ5G1euXKFokWL4ufnx/vvv8/169cN227cuEFUVJTJsVxcXGjWrJlV3SQcw9rw49DVaIvrtSnk3MssXEigoewCo+V/8avyW866DGKDy2Q+U/7OW/LTeAjxaEQ5Z/V+/KJtxXjNUFqpv6eGeindEibzlbY/a3VvcUqskK0MeyAZ97IEr8NyX3nupWFkn9yAkVis44VhIKjTi/x2zLKb7F+n7hD9VJ1pOd0y0/PpqVrLv2fu8TRZkY3clN8up5Odfkp7vXclci9FC7qx6eM3Tda9xIXfdImd537y7Sm20fWnw1T6chsbTqVchV1CIqdQonAehjVLzHvzt74xAI1l5/HidQ7jnFzhT8K5JM9NZsvIEBkZSa9evahYsSIymYwxY8ZYlIuNjWXEiBH4+vri6upK5cqV2bJlSzqdgWV6tajNYX1VADrIjkr94VxCdHQ0Op0Ob29vk/Xe3t5ERUVZ3CcqKsqivFarJTo62iDj5eVls8369euzevVqtm/fzpIlS4iKiqJRo0Y8evTI0EbSfvbqplariY+PN1kkrGPNwah3ssnhJMIis8r3KVJJuMVg+SZ+UU7nrMtgflN9y1jlXzSWX8BNSOCxmI9tujf4RvMB76inUE29jM4J3/KldgDrdc24KhY3RLykFyp5+pvepLDcLEHinZSayNiU7IB9liXG0PesV4LAd2sw+vfTHLn+2Kp83Wk7ef+NEnzX1XK8vSPo9CI3op9R1tN2BWB72+r58xFKFsnDrG4106xbcsauCyX44n1aV/FmyYd1AYh5lkCHeQfoWLMoX7Sv7PRjGiOKYq6qgmsvWbUvac9PpdeLJOj0uCodz9tk76UgXTK5F1elnGrF3M3W/6JtzRD5JhrJL1JJe4tLonl+nOSM++MM79Ypnh5qSkhkSZKMd7dFb07oK1BXdpnO8sOGXJVZ9d0jkbWwlJvsvffesyqvVqvx9PRk0qRJ/PDDDxZlEhISaN26NV5eXqxfv57ixYtz+/btTMkntkVfn6byc7STH+XYjcfUL1Mkw3WQyBySj0lSGqdYkk++PqU227VrZ/i/evXqNGzYkLJly7Jq1SrGjRuXKt0CAwOZMmWKVb0lTLH0Pdryzvs7EwtyFuAZzWRnaCY/SxPZWbyFWJPtUWIhjuorc0xfiaP6SlwTiyJmsl+bQp7+AzfJcy8LYIjKTQrLTfOvYn7h/HbsNo+equ0KNfn9+G27jqLV6bl8/4nV2bzP/zpLqzn7WHbwhl3ticDfp+9y4V6c2bbQ2zEci3jM+pOvPUyeqrUmBTks8SD+JXEvNCkeO/jifZO/ACsO3eBe3Et+3n/d2m5O4at/ztPk+z08eWldT9HK/zmd7DzA+nD5MSp9uY1HT7NHhV6JnEEkRdimfwOAfvJtmayNRHYnJiaGPn364O7ujru7O3369CE2NtbmPvZUMxw6dChly5bFzc0NT09PunTpwqVLl9LxTMx1TGKjLtED9h356yqAzspDLJGzMc5NVrlyZYKCgihWrJhV+dKlSzN37lw+/PBD3N3NJ2cAli9fzuPHj/n7779p3LgxpUqV4s0336RmTedPbKfEdl1dtKKM6rIIgv7ckeHHl8h4PDw8kMvlZp5wDx48MPOYS8LHx8eivEKhoEiRIgaZ+/fvm8lYaxMgb968VK9enStXrhjaABzSbeLEicTFxRmW27ftG+NKvCYrvQ69iOEDeTCrlYGcdBnGfNUC3pPvx1uI5YWoYo+uJlM0fXhLPZMG6gWM1ozkV10rrorFM92wB9C5ZtF0P0bmn6WEwQV2+4UoNDp9mkLybBlDBq8+kep2LTH+zzME/LCf5YciLB7/z1eGuLk7r9jV3sMnasasC6XDvIOcvRNrss3SpMEb03by5ow93HxkuUhC7PME6k3fRc0p1jskvx69aXVbRj3LVofc5E7MC/48YT00zvh7zc4GL0dxZmhURoeUHHyVn2L7hfspSJojeeRJpIWV2jYAvCM/hCexmauMRLamV69ehIaGsm3bNrZt20ZoaGiKScuTqhkuWLCA48eP4+PjQ+vWrXny5HUuXn9/f1asWEFYWBjbt29HFEUCAgLQ6TImB6Sxd8JmXX0SRDlVZTepICQO/KLiXzLqt9MEbpVyjUlYxlpusuR5xRzl33//pWHDhowYMQJvb2+qVavG9OnTbd4b6RV6WL50aY7oEyNXasbvdUqbElkblUqFv78/wcHBJuuDg4Np1MhyUZWGDRuaye/YsYO6deuiVCoNMnv27DGTsdYmJF7XYWFh+PomFgPz8/PDx8fH5FgJCQns27fPajsuLi4UKFDAZMlJPE/Q8vP+a0Q4qWCgpbDczE5TUVx4yDD5v2xUfcUx1xFMU66gqfwcSkHHFX0xFms70DthIrXUP9Nf8xkrdO24JhYjq2Wsn9WtJl91qpLux8k2xr2sMHusVqupVasWgiAQGhrqpDN7nV9r3+WHzN99NU0De1u336lbsXa381Stpf3cA8wJvmxVJskVd+Geq7YbS8X5dF5wyOSzJcPMi1eJ4EOuPbLYhj1FPSZtPE90FvGssvXbGedSzE2FR3LCmabn75W1XlsSWYUTYkVO6cvhImjop5C89yRSR1hYGNu2bWPp0qU0bNiQhg0bsmTJErZts35NJa9mWK1aNVatWsXz589Zu3atQW7IkCE0bdqU0qVLU6dOHaZNm8bt27eJiIjIgDOD3vVLUtTdFYBY8rNHXxsw9d7798w9Fu+7jj4ruS1IZBms5Sbz9PRMU7vXr19n/fr16HQ6tmzZwv/+9z9mz57Nt99+a3WfwMBAw/jI3d2dEiVKpEmHJPSiyBZ9YtGZdvJjXLmfcr9aIvszbtw4li5dyvLlywkLC2Ps2LHcunWLYcOGAYnecB9++KFBftiwYdy8eZNx48YRFhbG8uXLWbZsGePHjzfIjB49mt27dwNw+fJlZsyYwc6dO03yTo4fP559+/Zx48YNjh49ynvvvUd8fDx9+/YFEidlxowZw/Tp09m4cSPnz5+nX79+5MmTh169emXAN5P1+H5bONO3XKLVnH1OatF8ZJEZtr1CxPOBPJj1qskcdBnN58rfqS1LtDec0pcjUNOTFurZtE6YSaC2N4f01VGjynhF7aR73eK851+cPKr0z4iXbYx7lmaP+/TpY3MfZ88eT5gwgaJFne9OaWzM23T2nkMD9uSyD5+orRbMcITfj93iYmQ883al7HUnMzLzZ0YXWGbFGmrvw+hFQjaoFplL43L1okhL2SnyYTv82h4y62tLVS5NyWxnxkuNjrDIeCmpt10ILNZ2AqCPfCd5eZHiHqtDItJZJ4nsRkhICO7u7tSvX9+wrkGDBlbDCSF11QyfPXvGihUr8PPzc5pRIiUK5lFx6POWfNQ8sbBGUmhuF/khBExDBbSScU/CBtZyjaUWvV6Pl5cXP//8M/7+/rz//vtMmjSJn376yeo+6RV6qBNFtuvqohMFasqu0z9ovVPalcja9OjRg6CgIKZOnUqtWrXYv38/W7ZsoVSpUkBiYZhbt16PNf38/NiyZQt79+6lVq1afPPNN8ybN4+uXbsaZBo1asTy5csN/69cuZJ169aZvF/u3LlDz549qVixIu+++y4qlYojR44YjguJY/ExY8YwfPhw6taty927d9mxY0em5KTMChy5nujg4qz3lKUhdUY5lajQ0FEWwjLlTI65JHro1ZVdRi8KHNZVYZJmAG+8/JF3E6ayWNeJG6JvhujlDKoXL5hhx8oWBTWSZo+PHDlieAgsWbKEhg0bEh4eTsWKFc32ST57DLBq1Sq8vb1Zu3atIaxkyJAhhn1Kly7NtGnTqFmzJhEREZQtW9awbevWrezYsYO//vqLrVu3OvX8jI1TMkGw2x0nPUP3HHlIyO1U5OqDJ2w5F8WAN/3I5+LYpWdTmzR+D1klBNJWhzC3Di18T8xkuepH1mmb85l2SMo7ZEFS1dG3cU1Gxr1g+K+n6NeodK4qwtJ76VFO3ozhhx41eae2VPwhJXbo/bmm96WsLJKe8t2GQgHW+OqfC3zYsDSQmNIgn4sCRQZU9ZLIuliqbgiJOZni4sxz4ybtA5arGd68aZoGY+HChUyYMIFnz55RqVIlgoODUamsz7yr1WrU6tee9mkNPRQEwTAJuEdfi3gxD0WFxzSQhRHyqkooSPn3JCxjLTdZUnXQ1OLr64tSqUQuf12Mq3LlykRFRZGQkGDxHnFxccHFxSVNx7VE8wpe/HArlqP6yjSSX6S97Cgxz3pTKG/W9ZCRcA7Dhw9n+PDhFretXLnSbF2zZs04deqUzTbffvttIPEesRQe+/vvv6eolyAITJ48mcmTJ6coK2HO8wQtEdHPqVLUcnhy0qji79N3+W7rJZZ8WJfIuJQniNNCWeEu78v30FW+n8LCU8P6c/rS/K1rzCZdQ+5TOF11sMS7dYqx4dRdp7SVkaO1bNFztzV7bG0m2Jmzx/fv32fw4MH88ssv5MmTx0lnZYTRLy53wLg35vdQ5+vyCmulsCGxkIaxwUJu7LlnwZCRtLXVnP3MCb7M99sSw57ttUvEv9Sw0iivn7muGW/guHL/CXvDH9gl+yD+JVvORdqsNpQSJjn3Ut1K9iO2WFP0okAPxV5ayU6mqa3Mcvhysm2Pyf9e4PStWEan4/2fFTl5MwZILA4kkTIiMhbrOgIwULEVJVq79rv9+Dm1pgbT5cdDKQtLZEsmT56MIAg2lxMnEnP0WppAsGfCwp5qhr179+b06dPs27eP8uXL0717d16+fGm1zfQIPUw6FzUqNukS+5hvy0yvfY0+9e9uiZyLtdxkyfOKOUrjxo25evUqeqPr7vLly/j6+to0fqcHSZ6tW/SJ90YH+VEOXUub8VJCQsJ5ODrG6L30KO3nHWD3Jcv5wLV6kYjoZ4xZF0pU/Es6LTjIkF/SNv6yhAoNXWQHWaeayi6XTxms2EJh4SmRYmHma9/mLfVMOiVMZ5muQ6YY9pxNRg5Bs4Vxz9rssZeXl9mMmfE+YHn2OPk+CxcuJF++fOTLl49t27aZzB6Loki/fv0YNmwYdevWtVtnR5LbGnd3HbDt8URt32AtNVgzmMW/1OA/bScfrXk9O+Nodd/Q27EOyU/ccI5tFyz/zmDbEGkP1ryfbDXb+of99FtxnIv3UvYceGvOPob/eorVIdaLdyRx8Eo0YZHmbZrk3MtF1r0nXvX4+ZXHUaByCYVJvadGWt3KN5y6w4hfT/FSYy2M23L7qfHcs+WRF/8i/e57R5ny3wUp4Xwm069Raavb/ta9SZRYCF/hMW8b5RKzxZZziRXVL9jxbJPInowcOZKwsDCbS7Vq1SxWNwR49MhynltwrJqhu7s75cuXp2nTpqxfv55Lly6xceNGq22nR+ih8dP571ehue3lR3HltYegVpeLXroSDmEpN9mdO6+LoyXPTQYQGhpKaGgoT58+5eHDh4SGhnLx4kXD9o8++ohHjx4xevRoLl++zObNm5k+fTojRozIsPNKQqVI7OBv1dVDK8qoKbvOrN+2SOkxJCSyCI6ObU6/yr+/8rDlMenuSw9oPmtvGrWyTiHiGSnfyEGX0cxVLaS+7BI6USBYV4cBCeN5Uz2X2drurwpiZA8alytCJZ+sExaeqcY9Z8wepxSWltbZ4/nz5xMfH8/EiRMdOjdHZpiNDWmXop5w7aH9FW/Sy2nNmnFv67lI4l5oTIxtKYXlJv++kz7N3mG9WIcxu8Nse8hZO7y9D7y0fIXh91MeAD95mWiM2ZOCp9+N6Gd8sOwo7eYeMNtm6rmXezpVelHkB+17XNKXwFOIZ7pyGZnluzjujzNsPhdJp/kH2ZnCNWmMs7W9/8S6Z0tG8iD+JSsORbB43/XskbcyhzK5c1Wr2xJQslzbFoCh8k1mucSSM2t7OFHxWeP6kkg/PDw8qFSpks3F1dWVhg0bEhcXx7Fjxwz7Hj161GpILqSummESoiiahN0mJz2qHhoXyzguVuSm3ov8wgvay44a1mslzz0JK1jKTfbnn38atifPTQZQu3ZtateuzcmTJ1m7di21a9emffv2hu0lSpRgx44dHD9+nBo1ajBq1ChGjx7N559/nmHnZcziPv64FvThoL46AG/LD3HoqnUDv4SERMaRWjv72TuxbDp7jxnbLvHwSfoXliwn3GG6YgkhLh8zXvknXkIskWJhZmveo7F6HoM149mtr4MOecqNZTFW9KvH1tFNDJ/HtCpvJpORMYaZmnNv5MiRvP/++zZlSpcuzdmzZy3OHj98+NBsJjgJ49njpBLaYH32OGkGuUGDBhQqVIiNGzfSs2dPdu/ezZEjR8xyWdStW5fevXuzatUqi8efOHEi48aNM3yOj4+3auBLU3XcdLJzWOvMWkr0n9qw2IsWPNRSQ1rDctOyuzP7/NdtGHVN6mnkHtseelFEjYpxmo/4W/UlbeXHeUd3kI36JinvnAxnfW9XHjy1ssXyhWQtXZMoilx58BQ/j7wok+U2s3ZJXrgXZ/M6yUjU2tcXf24yOGc31ureYqTib8rJ7hEgO8l2/RtWZRekVPk8GadvxZDPRUF576wzYynhPCpXrkzbtm0ZPHgwixcvBhLzFLdt29akYm6lSpUIDAzknXfeMalmWL58ecqXL8/06dNNqhlev36ddevWERAQgKenJ3fv3mXGjBm4ubmZGDkyAuO8kiIy/tA151PZH/RQ7GVDQlNA8tyTsE3y3GTGkTqWcpPZ4/XWsGFDjhw54hT90kqbqj60qerDmC+CaS4/QxfZIX48fYc3y3tktmoSErkefSoHN7HPNYxcexpI7MulF9WE64xSbCRA/jq094y+DMu07diir482C5d/sLe4oUwwdWTyzO/8/KeOkKmee86YPbY2E+ys2eN58+Zx5swZgxv9li1bAFi3bp3NsvSOzDCnxTSVXomep2+5ZHmDBWWNjWN2aeNkd8PopwmotaaeQ3suPWDihnN27Z+WyqQioNHpmbHtEoevJobUBu28zPMEx0MnbT2gjTuDuWqY8epkL4qlCdImVt2aolxJMR5molLWcCws9/fjtwn4Yb9JiLst4p5rGJos70XuKachkVqekodfdK0B+FixEWc9QR48eck7Cw/T+of9TmlPImvy66+/Ur16dQICAggICKBGjRoGQ18S4eHhJt58KVUzdHV15cCBA7Rv355y5crRvXt38ubNy+HDhy2mYElPhjQtQ1nPvLxdqyguChnrdU3RiQL1ZZfwExJD1KWCGhISsENfl+eiC36y+1w+vZ8xv5825MKVkJDIHGy9newNnz9y/bFzlDGitnCF5crv2eTyPwLkJ9GLAtt0b/Ce+iu6JHzDv/rGmW7Ya1/dJ0WZxuWKpChjj5NRRpYHyLrmUiOszR537NjRpFJueswelyxZ0kSXfPnyAVC2bFmKF3dOxca0eJ45q/S1vVjS9NrDZ+y7/JAaxdxT3UZa+GbTRVYevsGBCS0N6/qvPO7ko1hGL4qsPXqLn/Ze46e91wzrn6m1TOpQxaG2bD+gjf/PPQMN48t7sa4Tb8lP4y+7wnzVfLonfOXQiyGzvjVrP9eS/dcB2Blm7pVs6ZHQZ/lR7sQkq1iVida9XFSoN9uzVNuevvIdVJNF0Ep2ip16f7v2+2jNSYoXcjN7li3YfYVfj96yspdETqJw4cKsWbPGZF3yHMLJ30kpVTMsWrSoYaI0symcV8WuT5oD0P92LF1+PMQ+fU1aykPpLt/LDG1Ppm8J46cP7LtnJCRyKs9xJVjvTxf5Yd6WH2JqaFn+Dr1HxHe2K7FLSEikI1bGGFcfPKXVnH183q4Sw5qVzTB1agpX+UTxJ03liQ42WlHGP/rG/KjtwnWxaIbo0LKSF7svpZw+qUGZImw5Zz2nP8CCnnXYfekBcS80TN100aJM8vGQpXFfRg7ds0VBDbA8e/zLL7+YyGTb2eM0DJLjXmicp4cdWMtx2Hf5MdrNPWD3xfujA+Ff9oT83X6c+jLdaQuLFol4ZB4mefaOeU4irU5k/q4rVguK2Gu0e55CfjOdXmTEr6cc+o6zKsbejDrkjNaMJF7MQx3ZVcYr/shEzcxZf/KOxfXfbgmzWHRCZ+P3tnRJWrqmMpNcZGPO8gSPbWpzewwFWK1LrBw/RvEX9pq6t56PYsmBG2brZ+24TGSclJtPImeRdFes0zUHoKv8AHJ0bD0fxS9HbuaqiTUJieRUK1aAf3SJkU+d5CHIkXLtSkhkNtbeSq3m7APgu61WIvGcTGkhkh+VQfzj8hVN5efQiHLWaZvzVsIsPtF8lGGGPbDfaSqlV7qISKG8Krr6FyePynouQEu2kaRiRJlBtvDcA8uzx8nJiNnj0qVLO72Dl9accVkFa4nYLRkgZ24PT2917CYt374o2m/kCLn+iJDrj5gdfNniTKctJ0zjY0zaeI4/h1kPLd9/5SGbz0Wy+VwkI1qUs0+5LEryUOU7oiefaoayWPUDwxSbOKqvzB59bbvaSu1te+X+E77+90KKcraKbCzed51xrSvgonj9crAV6mX8onjyMmMN+BLZD3ty3i3RtqevfDvVZBG8JTvFLju99yQkcgu+7q4A7NbX4aFYAC8hlhayUHbq/fny7/P4FnClVRXLeZ4lJHI6P/X2p8X3j3ks5sNTiKOR7AIH9DUyWy0JiVxNZk86eRDHKMUGesp3oxR06EWBv3RNmKt7lztixqbZSEKeya5rIua2hYw09WQbz72cTFY37TX5fjfHbiTG46dW18NXow3/p9cF3mzmHmalxmhoTR87FLVaLMFxLWw+oI29F49H2M5x8jIHVS61lIdwu/4NVmjbADBb+RM+2Fe1bZeF8FdrPFNr6b30CEN/OUG/Fcc5fC3tleGSF1+x9T5OuvIStHqqT95B9ck70nx8S2R2p0DC+VS0YuhLrfceJBq4pbxjEjkd7wKJxj0NCjboEos29ZDvMWw/FuH8vEQSEtmFEoXzMKR5RTbrGgCJVXMlJCQyl8zqmSnRMkT+H3tdxvKhIhiloGO3rhbtEgL5VDssQw17DcuY5sWz5jQ1vLlpeHJ5r3w227WWkz/FXH2iSPVkqcqSF05MTyTjXhYgqzvu3X78gt5LE6t2pVbXXkuPGv5Pr9O9+ei5w9UewfLN+1KjY9OZe2brkw9wRUTLBpJUPG3t9dzLTVirUhio7cU5fWkKC09ZoJqPkpQLmDxwoNR781l7OXT1Edsv3OdurOMh35auieRhuPYYSx49s61zWu6ly/ef8Ma3O1l5yDzsMjlnbsdy9cETG3pk3EPM2pGi4l6i0TmxfHU2o8mryoV9GpayKrNE24FnogvVX3nv2UvrH/Yz/s8zVrdfvOecyucSEpnNma8CUMoF/ngVmttCFooniRNqP++/zrKDKT8vJSRyKuW88vG3rjEAbWTHccX+fpWEhITzMXaCuP34OQBhkZb7ZOFR1vvxjtBIdp6tqs/5Qvkb+YSXnNGX4f2E/zFAM4FwsWTKDTiZtYPrU7pIHsNna8a9CW0rmXxuVM6D2d1q2nUM4yZbG3nw1y1VyExWBCa2r2yyrnPNDAxLzrAjSVglO4Tlal4ZWZyhqrW8fZmFpZx+gVvCuB5tmktv+pYwakzebnh4gi3PPcetccn3uPrgKRtP32HT2XvcjnlucZ+cjrUKwgkoGakZRbzoRl3ZZb5SrHbqcR86YAi0hCW1tckMT7Zy7mWEreyLDeeIfprA5P8sJ4hNIvqpmi4/HqLVnIyvirrtfCTn76aca/DzabMoXqo0rq5u+Pv7c+DAAZvy+/btw9/fH1dXV8qUKcOiRYtMtm/YsIFmzZoB4OvrS61atcxyvJYuXRpBEMyWESNGGGT69etntr1Bgwb2nr5DLO1bl00fv0nv+tY7Vo+NvPfGKv5CwH5j6MbTd61u+/PkbfsVlZDIwrjnUbKwtz/XxGKc0FdAIejpLt9n2P6NlYTaEhK5gc41i3JSrMAtvSf5hJe0kR2XIgAkJDIR49uv26IQANrNtdwHPnwt2uJ6e/HhEQuUc1mrmk452T2ixQJ8qhnC2wlTOaJ3rIikPWwb08Tqtrnv1zL8LwgC7m5Kw2cX5WvzVn6XxAx0ZTzzWmynq39x9n/awiG9jB0aVg6oZ7ZdFCGfy+vMdz3rlcBVaT1nn7Ox27in0WiYMGEC5cqVo169eqxYscJk+/3795HLM07xnEQWs3VlC9K7M/Hf2UizdT/vv86zBB0Ldht5B4qWzXjHI2KIcjDhfPJzajVnH2PXnWHk2tP0WXbMobZyCraqQd8UfRitGYleFOij2Mn78t0ZqJltjlw3D+NVa/WM+PUUK155ytm6hjPCE86a4TQ5D+JfGzqtecalxpidEmduxzJszSk6zj9oU27dunXMnDIR94bd8ek7lyZNmtCuXTtu3bJczfXGjRu0b9+eJk2acPr0ab744gtGjRrFX3/9ZZApXLgw48ePB+DQoUP079+f/v37s337doPM8ePHiYyMNCzBwcEAdOvWzeR4bdu2NZFLryqhLgo51Yq5pzh58rO2A09EN6rJIuggO2pT1l6ML6XQ27F8tv4s0U8ljw6J7EmVogUAWKttCUAvxS5kDhjCJSRyKgq5jFFvVWC9LnHyq7t8H3vDH2ayVhISOZeU8m4b97+s5b5PSo2lkKV2bCHSU76LYJcJdJQfRScKrNQG0FI9iz91zRHTyVcsr8pyaYiCeZTIk51LPtfXssbn2bSCJ1tHN+G/kW9aPU7JInm4Nr293XoZd7ONjXhJiKKYbFyUsYYeu3+Nb7/9ltWrVzNs2DACAgIYO3YsQ4cONZGRZm9SS/ax7mVk+J012s09wI6L9udPS4n4F+YhnckfGsYY39Qi1kNmW8za65Ae0u1jTkqhq3v0tZmtTTSmTFWsoI5wOSPUShHjMPQk/g29x+ZzkUx55Sln69z+OnWHzgsOcuCK7Vm2jPCCNa4Q9VxtOZ9jely7Vx48tUtuzpw51Gj5DvlrtkHpUYKgoCBKlCjBTz/9ZFF+0aJFlCxZkqCgICpXrsygQYMYMGAAs2bNMsg0b96cTp06AVCmTBlGjx5NjRo1OHjwtaHR09MTHx8fw7Jp0ybKli1r8PhLwsXFxUSucOHCjn4VTiWGAizRJhb0+UTxBwo7Qtod4e0fD7HuxG3+t/G8U9uVkMgoihV0A2CzvgGPxXwUEx45FMYuIZGTGde6An/pmqAXBRrLL/DlqvSZsJKQyO0E7bxM9ck72HLO3OHEEZLGJHKZ40a4EsJ91iq/JVC5jPzCC07ry9Ep4Vsma/sRj+2cdelJcnuENfuEiEhl3wLktWCEM8bWuN9W+/aQ0U5cdv/Kv/76K0uXLmX8+PFMmzaNkydPsmfPHvr3728w6mW1cMvsQqoN6ZnAC03aizU4errJDQdhkfEM/eVkmvVIIqlcuDFyG9ey8XUuita9lhz9ruz1pEqJnHQbWsu5Z8yPui5s0dVDJehYpArCm6yZ9Dx51eiUDJdn78QxYf1Zh9pMD4xfeM8SnGsIsoU9z8WEhAROnjyJX82GJusDAgI4fPiwxX1CQkIICAgwWdemTRtOnDiBRmP+fYqiyK5duwgPD6dp06ZW9VizZg0DBgwwew/u3bsXLy8vKlSowODBg3nwwHpVZbVaTXx8vMmSFoJ61EIpN/8il+ra81AsgJ/sPu8bFQxwJtce2meclZDIqqhR8YcuMVynjzw4k7WRkMg69GrzJof0VQHoKttPz5+PZLJGEhI5j6CdVwD48m/TydLD16L5bP1Z4l9qzByr4q14+s3bdYXgi1F2H1tAT3/5VrarPqeR/CIvRBVTNX3omjCZi2Jpx04klVgbz4qi7bGu8VeSlqG1o+PpPg1KUaKwG139i5sct23VFApwOBm7jXt3796lWrVqhs9ly5Zl7969hISE0KdPH3S6nFOhM6PZet7+my0zmfLfBSZuOJfmdrKD8clezz29KDrNa+mClJTeDPsqdAqM1wzjkr4EXkIsy1SzyINjIdEZQfIQ4+ziqWmsp7XfIz1OxZ7nRHR0NDqdjjzuppWyvL29iYqy/FyNiorC29vbTF6r1RId/dpTMi4uMdefh4cHHTp0YP78+bRu3dpim3///TexsbH069fPZH27du349ddf2b17N7Nnz+b48eO0bNkStdpyyGpgYCDu7u6GpUSJEjbPPyWsPcee48p87TsAjFJsxM2B+8WSh77ktS+RU1mjewu9KNBUfg4/IW3eExISOYURLcrx56vQ3G6KfRy5LoXmSkikF8b94X9C79JryVHWnbjNrO3h3EuWAspahM2c4MvssTOE3pMYViln8LXyF/IIakJ0VWiTMIPlunboM7Bcg7U+rCiKZo5CyaPqkkiL44zJrnaMSb55uxr7P21BflelyfqkgncZhd2/kI+PD9euXTNZV7RoUXbv3s3x48fp27ev05XLLTx+lpDZKtjFikMRma1ChmHLsKA3MnDoRduGjbQWZsjt2Cw6YcRzXBmsGUe0WIBqsggWKOchJ2tNOCR/wdh7bpmNsd4ZqXJUnCP3jukNK4qiTU/y5NsseZ/nz58fgD179vDtt98ybtw49u7da7G9ZcuW0a5dO4oWNa2G1aNHDzp06EC1atXo1KkTW7du5fLly2zevNliOxMnTiQuLs6w3L6dtkIVtvKr/KZ7i5t6L7yEWAbIt9nd5si1p83WZY8rWULCce6IXuzR1wLgA/nOzFVGQiILsV3/BvFiHooL0TSQhWW2OhISOZjXfbnRv4ca/o94ZF5sMUGbtvywrWQn2e7yGU3l53ghqvifpj+9NF9wS/ROeWcno7ASRtykgqfZOH3Am36G/43HKnb5iNiB8eFsFUJNGkcY5+LL6MhWu417LVu2ZO3atWbrkwx8ERERztRLQoIf91wlMu6FU9q6kazybUrYGhT/fvz1gFtMwXNv1vZwh44rYYqtghrJuS16MyhhPC9EFS3loXyjWEFWMjsYG4VDrj0yuW6eZ2C4axL2fjOiyf9WPPdenczuS/eZ+t9Fs8rAqWHGtksW1xu/Iz08PJDL5TyLNc1N+ODBAzPvvCR8fHzMvPoePHiAQqGgSJHXHoCyV52KGjVq8Mknn/Dee+8RGBho1t7NmzfZuXMngwYNSvGcfH19KVWqFFeuXLG43cXFhQIFCpgsaUEuE6zmCdGgMOSrHKr4j0Kk7Dm86nAEmy3kfskmdmoJiVTxiy7RY7ebfB9uvOTivXinPOMkJLIzalT8q0tMidFdvjdTdZGQyMlYG5JaippISOW7yYUEpipWsFQ1m8LCUy7oS9Ex4VvW6FqnW8GMlLCUVubzdpWY/k51kk/qt6joZbGNOiULOV0ve2x1JQrn4Yv2lZjRtbrTj58Sdv9aX375Jd27d7e4rVixYuzfv5/ly5c7TTGJnIu9SSlnbg+nYeBu1GmchQDHi1vI7EyEmPhctT6yfZoBRpuYmBj69OljCOWbOfFj9C9t57sSRZHJkydTsWJFADp06MCFCxdMZIYOHUrZsmVxc3PD09OTLl26cOmSqcEl+bH79OlDbGys085N5+BLKlQsx6hXFXR7KXbzkfw/p+mSVow99XouMc1PU+Wr7RnuwWuvQcYRl/YBK0+w/NANNpy6m0qtLHPRSsi6SqXC39+fiLOm32dwcDCNGjWyuE/Dhg0NlW2T2LFjB3Xr1kWpVFrcBxLvGUvhtCtWrMDLy4sOHTqkdBo8evSI27dv4+vrm6KsM1BY6BgZ85++IRf0pSggvGCM4i+bsgBf/3shRRkJiZzGPn0Nbuq9KCA8p7M8hPbzDtB/5XH0ejHNXhISEtmV8l75DKG57WTHWBJ8ms4LDhL3PP1zAUtI5CasGZMs9c/VWsejlooLD/hLNZkPFYl945+1HXgnYSrXxGIOt+VMkofltqjoybBmZXF3U9rOuYfIrk+aMaVzVQYaefRlNEOalqXHGyUz/Lh2G/dKlSpFmzZtrG739fWVQnMl7COL59y7eC+eyFj7clCllHMvtae6/OANm9uNZ2s6vdud4ydPsW3bNrZt28b1SxeI3jQbgK//Oc+jp+YGie+//545c+Ywc+ZMALy8vGjdujVPnjwxyPj7+7NixQrCwsLYvn07oigSEBBgkl+zV69ehIaGGo4dGhpKnz59UnnW5jjiuZdEsL4uU7QfAvCZ8ne6yvY7TZ+0kFL+wL3h1gstZCbG19rcnVf46h/zKqjJz+yhhWsuLQxdc8LqtnHjxnF21waent2BJvo2Y8eO5datWwwbNgxIDHX98MMPDfLDhg3j5s2bjBs3jrCwMJYvX86yZcsYP368QSYwMJDdu3cDcPnyZebMmcPq1av54IMPTI6t1+tZsWIFffv2RaEwrcT19OlTxo8fT0hICBEREezdu5dOnTrh4eHBO++8k+bvxB5SqowmImOaNvGcest3UUFIXRiwNY9OCYmcgIiMNbpWAHwo3wGIHLgSzftLjlB76g6eqjPe81pCIrNZM6g+b73Vjsv6YrgKGiL2rubsnTiWHbLdf5WQkHAMa04xh64+MltnX67y1zSXhbJJNYlqsggeifnpk/A507W9ScD6ZHdGoZTLTIxzxuGtKY2vy3rmo2+j0qgUqfc6NDYgmh47axsyHDpjURS5ceMGWm1iRyYhIYF169axevVqk0TkEo4x870ama1ChnLsRtasZppE+3kH7K50G7j1km3jXirj7Kduumhz+5h1oQDsOXKKQ3t3Eld3IA0bNqRhw4aMmjyTF9eOo3l0h1UhN/lio2kRFFEUCQoKYtKkSXTu3BmARYsW8fz5c5PQ+yFDhtC0aVNKly5NnTp1mDZtGrdv3zaE4IeFhbFt2zaWLl1qOPaSJUvYtGkT4eHOCUce0NiPy9PaObzfKl0bftYmelJ9r1xMO9lRp+iTFlJ64WZVDxDj63vD6busDrlpVgk1+T3gYufL9HmClt2X7qc40xj/wvrgWVa2EQVbDib20O/cW/kx+/fvZ8uWLZQqVQqAyMhIbt26ZZD38/Njy5Yt7N27l1q1avHNN98wb948unbtapB59uwZn3zyCQCtW7dm/fr1rFmzxiz0dufOndy6dYsBAwaY6SWXyzl37hxdunShQoUK9O3blwoVKhASEmLI55dedKpZlEo++WlYpkiKhrcQfVW26t5AIej5UvELqQllT01Y7oMnL/nvzD00UnijRDbgT10zXogqqspuGvKLHbvxmGcJOg5dlfq/ErkP7wKufPxWeda9qijdW74LEHlpZ/9ZQkLCPhwZStpr2xPQM0q+geXKmRQUnhGqL0tH9XQO6LOOTUIhE6js+zo9jfHXYHN8ncr55lolCqZuxyyGImWRRMLDw2nTpg23b9+mTJky7Nixg27dunHp0iVEUSRPnjwcPnyY8uXLp6e+OZLCeVWZrYJENuOf0HvMfb82W3btQ3DJi0vRioZtlWvWRXDJi/puGMoixTly3dSYeuPGDaKioggICDCsc3FxoVmzZhw+fJihQ4eaHe/Zs2esWLECPz8/QwXPkJAQ3N3dqV+/vkGuQYMGuLu7c/jwYUPIb3LUarVJeGN8vPVcXzKZgEom4JnfxeHiJNO1vSjAM95X7GWucgEvNC7sfZUYPTNIKbz1cydUok4PLHUUErR6VHLrBjx7jXsj155m96UH9GlQim/ermZVzlY11k/XnyV/nQ7kr5NozD35nWl47MqVK832adasGadOnbLa5rRp05gwYQLu7u7cvHnTau67gIAAq7q5ubmxfft2q8dIT+b3rJ1iURFjpmt70VIWShP5eVrpTrFT7+/Q8VLTj+o47yAPnqgZH1CBkS2lfoNE1iaW/KzXNaWPYicD5Vs4oq9i2Ja15/AlJNIPQRBYr2vKp4p1VJHdpLZwlbjnGR+GJiGRk7FVwCE5lqK1kuPGS+YqfyRAfhKAX7St+EbbJ0t46xmTPCy3b6PSdu2X2liSFf3eIDjsPhPWn7Upl8H1MRzGbs+9zz77jJo1axIaGkrHjh3p2LEjxYsXJyYmhpiYGBo3bszUqVPTU9cci8LGIFki62PLMya97/+Y6IfI87ibrZfncUf3LAaAuBem+U+SigkkLzjg7e1tVmhg4cKF5MuXj3z58rFt2zaCg4NRqVSGdry8zBOYenl5mbVjTGBgoCFHn7u7u8FYaIuFveukKGOOwBfaQfyna4BK0LFI+QP1hcyr6Oaoq7wzUWt1HLwSnaoZdUvXt9mLLZmIvW7wuy8lhiL/cuRmYjMZVJkh7oWGX0Ii7OoEZVeSDHv2hA/cFr1Zqkv0kP2fYg0q0j9n0oNXxvrgi/fT/VgSEs5gua4delGgtfwUZYR7ma2OhESWoE3dSmzSJxbW+ECxk3Un0lblXUIiN/NUreX3Y7dSnYd74CrraWwAvHnMH6qpBMhPohaVfJIwjC+1A7KcYQ/MvfOaVvA0/G8rNX5qxxKF8qroXtfymNT4cI3LeQCYeBVmJey2Kh0+fJgpU6ZQvXp1pk2bRlhYGOPHj0epVKJSqfjss8/Yvz9r5LbKbthZu0Eii5Ie9ojYg79yc0ZHm8uJE68e4MkefvN3XwHEFKcWkj80LXn69O7dm9OnT7Nv3z7Kly9P9+7defnypdU2rLVjzMSJE4mLizMst2+n3BEsVThPijKW0CNjrGY4O3W1cRU0LFPNpF4mGfgyKvpwxNpTZqGOU/67yAfLjlLpy22sPXqLu7EvCL0dayIjiiLfbr7I78dumazXp0JvR2YZk1h56AYNA3dz/aHtYjDOYNy6UL7850KKnaCcgL358BZqu/BALEhp2X36ybc5dgwp5Z5ELuCG6MsufeJE0wD5VsP6Ib+clIoISORavn+vJmu0iTkpO8qO4E76v8MlJHIqEzec4/MN5+i/4phhnSCARqfn9uPnaWq7qhDB3y5fUV0WQbRYgJ4Jk/hL3zStKlvl7xGNU71vXpXc5vb09J4b2rQMeVVyRlmJKnF3UxI2tS2bPn4z/ZRIA3Yb954+fUrhwoUByJs3L3nz5jWp+Fe8eHHu35dm4FNDagbCEtaxNduhz2DvqdT+tPnrdKTooJ9sLtWqVaOwpxe6Z7Em+164F4/ueTzyPAUttu3j4wNg5l334MEDM28+d3d3ypcvT9OmTVm/fj2XLl1i48aNhnYs3fMPHz40a8cYFxcXChQoYLKkSBpuES0KRmhGc1BXlXzCS1aqvqeRzLwoRHrjSNXZtLD5bCT/hpp6law9+tpg98XGc/RYHGK234mbMSw5cMMsPNiS3gKCiUHHGQUVJv93kaj4lxYrsjr7m9v1ymMwuYEzJ2Jv4t9nuDFD8z4AoxQb8SZr50aVkMgIaibLwbNU2x6A9+T7KcTrlBJ7Lz9AK+WPlMilDOjxHhf0pXARNLwn35dmI4RE5rNw4UL8/PxwdXXF39+fAwcO2JTft28f/v7+uLq6UqZMGRYtWmQm888//wDg6elJlSpVDOOJJAIDA3njjTfInz8/Xl5evP3222Y5vPv164cgCCZLgwYN0ni2WYf/ziT238/ciTOskwkCvZccpcn3e1LdbgvZaf5QTcFXeMwVfTHeTpjKKbFCmvW1ha0cdgXzWPcUVMllnPyytc22bfVt0zpmmNi+Mmcnt6FkEeuOJW4quVnYcFbBbuNe0aJFTZKSf//99yYheQ8fPqRQoULO1S6XUDRsOUuVM2kuO40MqXOYVup8E2xxvV4v0nau871LbT1EUnvby/O4oyxSwuZy94mW2Px+iOpnqO+9fvmp74Ujqp/hUqyyxbb9/Pzw8fEhOPj195SQkMC+ffto1KiRTb1EUTTky2vYsCFxcXEcO/Z6duno0aPExcWl2E5Go0bFQM2n7NXVJI+gZrlyJk1lZzJUh53Jwg/T06afUvXGOzEvzNY54nmSXPd9lx8m2576k8vM8OUciQM/xQb9m5zUlyef8JKvlasdOEjqfzPp15bIiviXSuzP9nzDNETnqFiJc/rSuAqaVwUEEll64AbVJ+8wKzYkIZEb6FyrGJtdEw3fveW7aPL9bm4+epbJWkmklnXr1jFmzBgmTZrE6dOnadKkCe3atTOxAxhz48YN2rdvT5MmTTh9+jRffPEFo0aN4q+//jLIhISE0L9/fwAOHTpEnz596N69O0ePvi54t2/fPkaMGMGRI0cIDg5Gq9USEBDAs2em11Lbtm2JjIw0LFu2bEmHbyHrIAhwLCL1E67vyvazRDmbvIKa/brqdE2YzB3RPK1SejD6LVPvt1EtyzGlc1U2j2pidZ8CbgpclbY994oVcrO6zRm+FMkNd94FXNPeaAZht3GvVatWXLp0yfD5o48+Mqn4t2PHDurUSU1erFyOKOJz+VdayU+zUjWT/S5jGC7/G09iM1uzHMfj5wlcvp+6jvc3NqrXZlZI2luz97HjrgJXP38ebZvPkSNHOHLkCI+2zcet7BsoixQ3yFaqVMkwQyYIAmPGjGH69On8999/QOL9nCdPHnr16gXA9evXCQwM5OTJk9y6dYuQkBC6d++Om5sb7dsnduAqV65M27ZtGTx4sOHYgwcPpmPHjlaLaaQWZ5QdV6NiiGYcwbo6uAoalihn00p20gnavcaWZ+iTFAxuziSthsMXCTp+O3aL+/EvrXjumR7jr1N303ZAI9J6P529E8us7eG8SJAq9gE0N8pRkhIiMiZpBqIVZbSXH6OF7LRd+/127DanbsWkVkUJiSzHmoH1+WdEY3q8kTz/jsCSV5XY+yp24EJipMC5u3G80OiYvcM5leIlJLIbN3078ER0o4wsikayCzSbuTezVZJIJXPmzGHgwIEMGjSIypUrExQURIkSJfjpp58syi9atIiSJUsSFBRE5cqVGTRoEAMGDGDWrFkGmaCgIFq0SKysXKFCBSZOnMhbb71FUFCQQWbbtm3069ePqlWrUrNmTVasWMGtW7c4edK0r+7i4oKPj49hSYoszKmkpUs/QL6VOapFKAQ9f+maMEDzKfHkdZpuyfHzSGz7zVd56ca2fu0d+N271RkXUJG+jUpTrKAbg970M9k3Kafe8OblUjxOBe/8zH2/Fr8PMffaTI9heeNyRRjbqgKLPnCs4FxmYLdxb9GiRQwaNMjq9h49erB06VKnKJWrEAQut1zKUm07YsW8FBeimaD8g8MuH7NAOY+GsgtIvg3OIS1hkcsO3rC6zWZBjQwIufboNB6VZ2kCAgIICAhA5Vkaj46fmMiEh4cTF/faxXvChAmMGTOGTz5JlIuMjGTHjh0Gg72rqysHDhygffv2lCtXju7du5M3b14OHz5s4rH766+/Ur16dcOxa9SowS+//OL0c3TW15iAkuGaMWzR1cNF0LJI+QPd5Hud0zhQ5ost3I9/aXFblQxMvJrWr2vGtktM3HCOLgsOWayWm9LvIYoioigy/NeTfPybfQYiw76W7icHbt3OCw6xYM/VV7knJWZ2q+mQ/CWxJEt1iQb8b5QrcMPy9Zycdxce5vttl1IWTIalx/KOC1HsuGC9KI+ERHrjppJTs0RBk3f4xy0TBxxb9PW5JxbGU4jjbfkhk/22nJOuW4ncyUvBlY26xBxUH8h3ZrI2EqklISGBkydPEhAQYLI+ICCAw4cPW9wnJCTETL5NmzacOHECjUZjkGnZsqWZjLU2AcO4Jbnxbu/evXh5eVGhQgUGDx7MgwcPrLahVquJj483WbIbqRtLioxT/MFXysQx2TJtO8ZrhqJF4TS9JrQ1d+RYM6g+Y1tVYO77tcy2JT+NvC6muszqVoOd45rSv3Fpw7o8NnLvdalVjAZlihg+J4X6tqps3Sux7iuv/NolC1qVsYQgCIxuVZ621Xwc2i8zcNov7Ofnl7KQhEXUBcsyTduHmdoedJAd4QPFTurIrtJRfoSO8iNc0/uyXteMDbo3uU/Onp1IV9LLRpqKdh88eYlXfue4+Mrd8uPRaTwR33Vg1G+n+feMeRW/5JWDBEFg8uTJjBs3Dnd3d7Zs2WKS+65o0aJ2ubkXLlyYNWvWpP0kUsCZJlINCj7WfMxzlvCefD8zlT/jTQwLdG875UitZu+zuP5ipGmHQpue4aeCgFqr4+/Td3mzfMqeW3EvNAxa/bq4xJ7wxI5SVPxLK1WnzIuxGMt9uv4s83Zf4fbjxPDfb9+pRgHX1FfiSo1hPjzqSaqPl5Nwd1NS1jMv1x7aHyI1V/suHeVHKC5EM1qxke+0Pe3ab+Heaw7rl9yY+zxBy5BfEmfpL0xpY9b5k5DIaAY38eNOzAs61PBl/u6raFGwTNuOL5W/Mkz+L+t1TdFhO4RIQiKn07BsEdaEt+JDRTABshMUJZp/z9yjc82ima2ahANER0ej0+nMcmd7e3ub5epOIioqyqK8VqslOjoaX19foqKiTJwDUmpTFEXGjRvHm2++SbVq1Qzr27VrR7du3ShVqhQ3btzgyy+/pGXLlpw8eRIXFxezdgIDA5kyZYpd556Z3Hz0jAdP1Ba3OW7bE/lasZr+iu0AzNR050ddF9I6xlHJZSSkkFu2WEE3RreyXIgieRRW8lBXuSBQziu/ybqAKt60qepN7ZIpp37b/UlzwiLjaVS2iFWZRX38+evkHd6tU9yqTHbHoV7zs2fPWLt2LYcPHyYqKgpBEPD29qZx48b07NmTvHnTz80zJ5MU1q1GxQZ9UzYkNKWKEEEv+S7elh+irCySz2S/M16xjoP66qzXNWWHvi5qVJmreDYjM2x71h6j9b7dRcR3HZyqx0uNzqJhLyfgbA9IHXLGa4ZyXyzICMW/jFf+ibcQw9fafujtd2i2iL3htwna9MuvKQA/7r7KvN1XyWeHcWTmdlOPK+MiP9ZskCnZ25IMe/bIptSusQ56Pfy45yqNyhax62Uv4TgvcOVrTV+WqWYzUL6FjbrGhIslM+bYRuHULzU6ybgnkelM6lAFgKsPXk8Y/KZ7i5GKf/CT3aeD7Cj/6rNWnlkJiYymb6PSTNtcgsO6KjSSX+RDRTCjfvOQjHvZlOT9blEUbfbFLcknX+9ImyNHjuTs2bMcPHjQZH2PHj0M/1erVo26detSqlQpNm/ezLvvvmvWzsSJExk3bpzhc3x8PCVKJE+3kPnYCmN3bARkatj7n6Y/a3S2i1PYi0IuYJzxxuF592Qn0q1ucQ5cecjW84kGXkvXgkIuY3GfunY1XzivisavwoGt4ZHPhaHNytqnbzbF7lHsxYsXqVChAhMmTCAmJoaSJUtSvHhxYmJi+PTTT6lYsSIXL1rPSyZhnTKe+czWXRRL8z/tQOqrf+RTzRCO6ishF0Sayc8yX7WA4y7Dma5YSh3hMlLYrn188kf6FFCwNYvx8KnlWZj04JGNKsHZHePHfY+6znopC8zUvs9Xmr7oRYE+ip0sVs4hL+bFJrIbggD7r0QDKRfXAIiINq1sZ/x9W/Lcs9QXs/mST7Nx7/XKYxGPmbk9nHcWWg/lsERk3AsOX4t2aJ/cgLubZY/KXXp/tuneQCno+F75M3KkHIYSuRvjSY/nuLJc2xaA4Yp/EIyKoZX+fDPTt4RluH4SEpmJUi5j4/BGLNO1A6CnfBd57EzrIJF18PDwQC6Xm3nUPXjwwMw7LwkfHx+L8gqFgiJFihhk7t+/byZjqc2PP/6Yf//9lz179lC8uG0PK19fX0qVKsWVK5ZTsbi4uFCgQAGTJbtxP97esaSpYW+CZrDTDHsATcqbG85GtnidH++XgfUcak8plzG1S7WUBSUcwm7j3ogRI2jatCn379/n77//ZvHixfz888/8/fff3L9/n6ZNmzJixIj01DXHUjivim7+lh9ez3DjT11zeiR8RTP1HOZq3+GO6EEB4Tm9FLvZ4DKZ/aoxTFD8TmXhJpKhzzoHr6bPwH7z2Uir2w5csX5MvV7koRUX7NSg0+Xc397YmNS5lnNngVfr2jBCMwq1qKS1/BQbVF9TQrif8o5ZmHm7ruBIhXazsFejfY9HmBdKsNS0rasvqX21VkdUnOOd/dSky0xugGwYuJteS44Scu2R443lYJb2tT4j+pWmH3FiHmrKrjNUvikDtZKQyHokr563ShfAE9GNSrLbvJWs+MzP+68Tk4Mn3CRes3DhQvz8/HB1dcXf399mDrHIyEh69epFxYoVkclkjBkzxmbbv//+O4Ig8PbbbztX6XSidslC7NbX5obeG3fhOe/KD/D9tktoUwjlk8g6qFQq/P39CQ4ONlkfHBxMo0aWPZQbNmxoJr9jxw7q1q2LUqk0yOzZs8dMxrhNURQZOXIkGzZsYPfu3Xal/Hr06BG3b9/G19fXrvPLjtgzSW/JsPeHroVT9ZjRtYbZunGtK7B1dBOuTW9PkxTSAKU0LEn/LPW5A7uNe0ePHuXLL79EpTIPBVWpVHzxxRcm5awlHMOS915yboo+/KDtRhN1ED0TJvGXrgnPRBdKyh4yXPEvW10mslP1KaPlf1FWcF71Son0YdnBGwTtvOy09nSZVbY3AzDO0yBLhyIlW/X16Z7wJffFglSU3eFf1ZevitlkT+7Hqx0KZU5+6Rh/xzMsFEk4cCXaLFea5dx8pnSaf5AGgbtoNWcfOivxvpYKaqSlGE5yDl596LS2sgvG18KK/m+YbLNlBH5AIaZoPgRgtOIvKgi3na5bDn5sSeQwkr974snHGl0rAEYq/ib5FMdLreTtmtNZt24dY8aMYdKkSZw+fZomTZrw3nvvWZVXq9V4enoyadIkata0Xezo5s2bjB8/niZNmjhb7XRFRMYKXaJX6wD5Vn7ae4W/Tt3JZK0kHGHcuHEsXbqU5cuXExYWxtixY7l16xbDhg0DEkNdP/zwQ4P8sGHDuHnzJuPGjSMsLIzly5ezbNkyxo8fb5AZPXo0u3fvBuDy5cvMmDGDnTt3mhi4R4wYwZo1a1i7di358+cnKiqKqKgoXrxIjKh5+vQp48ePJyQkhIiICPbu3UunTp3w8PDgnXfeyYBvJqsiMlGx1mDY+ywdDHsABfOY24BkMoHKvgXMJr8sYWlcYrxbBtSgzBXYbdwrVKiQVZdXgKtXr1KokJT/KLU4ckGLyAjRV+UTzUfUVf/EiIRRbNO9gVpUUk52j7HKv9jl8ilbVBMZLv9HMvRlUVaFRLD22C2ntWfNWJIjMLo/FPL0efqfEcvRSf0tofoyFBKe8osykL7y7WRXb1hHPPeSG9RS2vfrf00Nn6Jo+1tK2nb5/lMArj54yu5LlqubpRSWm1ZeJORuDwKfZAmMFTLb3YAN+ibs1NXGRdAyS7kIBfbllHxs5LUUFfeSiOhnfP7XWT7+7bSJIVgy7klkFyxdq8u07XkpKqklu0Zj2fkU5SVyFnPmzGHgwIEMGjSIypUrExQURLFixazKly5dmrlz5/Lhhx/i7u5uVU6n09G7d2+mTJlCmTJl0kP1dGW9rhnxYh7KyKJoIQvl1uPnKe8kkWXo0aMHQUFBTJ06lVq1arF//362bNlCqVKlgEQP1Fu3Xo9f/Pz82LJlC3v37qVWrVp88803zJs3j65duxpkGjVqxPLlyw3/r1y5knXr1lG/fn2DzE8//URcXBzNmzfH19fXsKxbtw4AuVzOuXPn6NKlCxUqVKBv375UqFCBkJAQ8uc3LcSQmxgm/4+his0AfK4ZxLp0MOw5A0tDiyL5XOhSqyhv1ypq0Xgo4Th2Z6oePHgwffv25X//+x+tW7fG29sbQRCIiooiODiY6dOnp+heLmEdRwbixrzAlc36BmzWNyAfz2ktO0kneQhNZOeoIrtJFdlNJrCOa3pfdujrskNXl1CxLGIaiwZIpB2FTHBq598ez6nsirHx257ZodTygEL0SPiKQOVS3pUfZIpyFW/ILjFRM5gn5Em346YHyatS2cI8KjflfZPvY+vys3RtPrMSZmCpGWfarW8+sr9qbE4k+U9RqkhK17XAF5pBvCH7lBqyGwyVb+JH3dspHsc4B2iDwF0m2ya0qWinthISWQe5hYmlaNz5TdeS/ortfKz4m0MJ1Q3bOsw7wLFJrVDKpf5WTiQhIYGTJ0/y+eefm6xv2bIlly+nLSpj6tSpeHp6MnDgQA4cOJCivFqtRq1+neYlPj4+TcdPC38MbUj3xSH8pmvBUMVmBsi3cljslmn6SKSO4cOHM3z4cIvbVq5cabauWbNmnDp1ymabSeHl0dHRFnPfpTSOcXNzY/v27TZlchs95Hv4XPk7ANM0vfld1zLDjq1wcDxWMI/lHM9z36/tDHUkXmF3j2Py5MlMnDiROXPmULt2bYoVK0bRokWpXbs2c+bM4fPPP+err75KT11zNI4MxK3xlDxs1DdhgGYCb6gX8rlmEHt1NUkQ5ZSVRfKR4j82unzNEZeRTFMso6nsDEo7vTAknE/EI+fOZObssNzXOPoycRQ1KsZpPmKKpg8JopyO8qP8p5pEVeFGuh43MzEz7tnxFSe/2iyF0xrL7rxomsfQaliuA9excWVVe9llxWMwt1C0oKnnnj0zpQ8oxGRNXyAxPLeKEGHXsc7diWPxvmtm64f+ctKu/SUkshLFCrrxZccqhs/5XRXULlmQn7UdUYsKGsjCTNI5xDzXcP5uXGaoKpEBREdHo9PpzAoCeHrazjuVEocOHWLZsmUsWbLE7n0CAwNxd3c3LJlZDbSeX2EGvunHKm0btKKMN+UX2L1vD7ec3OeVkMjttJEdY7piKQALtZ1ZquuQocfv3aCUXXLfd61BnwalaFHRK501kgAHjHsAn332Gffu3ePatWscPHiQgwcPcu3aNe7du8eECRPSS8dcgbPjzGPJz++6lvTTfEYd9WJGJnzMv7qGPBHd8BZi+UCxi9WqGZx2GcLPytn0lu+kGLkvF1VOQp+Dow2N8zSkp+ee0RFZoWtHt4SvuSN6UFp2nw2qr/kwG4fp2iK5Ye5S1JM07W+2XYRBq0+YrLPmQefIt7sqJMIBaQkwNeYVK+hm934b9W+yQ+ePStAxT7kANzuqIHZacJDAreY5Gy9GvvYqyXl3k0ROZuCbfizvV5fSRfKwsv8beORzIZIirNW9BcB4xR8YX9XOTCkgkTVJnkcqLVEUT5484YMPPmDJkiV4eJhXprTGxIkTiYuLMyy3bzs/P6ojVCtWgHt4sE2fmON1iGITg1Yfz1SdJCQym+MRj3n7x0OcuR2b5rbqCWHMUy5ALoj8pm3B99oeaVfQAdpX9yGfi30BoN3fKME3b1dDliHjNwm7w3KN8fPzs6uCjYT9OJL83lGekodN+oZs0jdEhYYGsou0kZ2gtfwkXkIsAfKTBMhPghKu6IuxV1+TffqaHNNXIgHLLrQSWY/286yHbpyIeEzd0oUzUBvnYnx3ZGSI0xmxHO3V05mlXEyA/CRTlatoJTvFBM0QoiiSYXqkhpQMbsZoUxH3uu181OtjiSmE5VrQZd7uq5ZlHVBlx4WoFGWuP3zK2Tu523vG2tvFMcODwGeawdSUXaOc7B5fKtbwhXaQM9STkMhWtKzkTctKid5a8lce3Qu1nXlfvgd/2RWay0LZq08MM8rtOT5zMh4eHsjlcqKiTN9D0dHRqW7z2rVrRERE0KlTJ8M6/auZW4VCQXh4OGXLljXbz8XFBRcXl1Qf19mo5HIAFmk70VF+lM6yw8x5cB1olrmKSUhkIt0WhQDw/s9HCPumbarb8RMi+Vk1BxdByzbdG0zSDiQttWZLF8ljNZpsfEAFZu0wTzPgopCn+ngS6YvTRsn//PMPq1evdlZzuY6MMmYnoGS/viaTtAOpr15AB/W3zNR057i+AjpRoLzsLoMVW1ijCiTUZQjLlDMZKN9MFSECAamTml25+uBpZquQJoxt3+lRLdcW8eRjiGYcX2v68lJU0lR+ju0un9FFdpCs7HfkiL3u9K1Yh9u3VEXXKg7o4sg3esoOvVvO3seYdaEOtJrz8E5WRCMJRx1MYijAWM1w9KJAL8Vu2siOpVm31Hi5PFNrc3SOUYnsQ1LOyocUYpUuAIDxij9JepLtuBhlUlxGIuegUqnw9/cnODjYZP2ePXtS3WalSpU4d+4coaGhhqVz5860aNGC0NDQTA23dYSAqt7UKO7OebEM+3XVUQh6hso38eSlJrNVk5DIdF5oUl9JvSBPWKacSUHhGaf15RitGYE+jeYca84fC3rVNnM++qZLVcp75WNCWyl3clbFaca9zz77jP79+zuruVxHZjiqisi4IPrxo+5tuiVMprZ6McMTRvGHthn3xYLkEdS8JT/Nl8pf2eLyBSddhrFQGcQH8uBXFXilwVV2IScV0rUn516xgm7M7lbTiUcVWKVrQ4eE6YTqy+AuPGeuaiE/KufiSawTj5N9se2556SGJFLFjPdq0LSCJ6sH1ANeh7bXLJFYrbGCdz672zqsr8ZiXcfEdpVL8OWRk7W1zfm7cVT9ejufrj+boceVkLDE6FblDf8v1nbkqehKNVkEbWSJIYirQ25S55tgXqZhMCeRdRk3bhxLly5l+fLlhIWFMXbsWO7cuWPYPnHiRD788EOTfZKMdk+fPuXhw4eEhoZy8eJFAFxdXalWrZrJUrBgQfLnz0+1atVQqbJHNUmlXMa/I9/kyrftWKjrAkB3+T5aTv4jkzWTkMi+KNGyWPUDZWRR3BE9GJzwCWpS/0xISs1iyVC3dXQTOtYoara+T8PSBI9rhq+7/WldJDKWVIXlWuLSJQe8OCTMSM+wXHuJJx9b9A3Yom8AWpEqwk0ayS7QSHaBerJLFBae0l5+jPbyRG+N+2JBDuurckxfiRP6ilwVi0pVeLMo2T3vj3HBGXs99xqVc37Y7DWxGF0TpjBc/g+jFBvpID9GE9l5ZmjfZ62uZZa6/jPSs0kUbYcBh0VmXuU+icQOXJJhD2D7mCb8efIOQ5smhnetGlCP347dZt6uK3a1N1vbjYayC9SSXWeuagG9EiahdV53wiYL9yaGc68/eYdZTjXgS0g4Th7V6+s+hgIs07VjtGIj4xTrCU6oa/CouBv7go2n7tKkvAf1y2TtlA4S9tOjRw8ePXrE1KlTiYyMpFq1avz555+0b98egMjISG7dumWyT+3arytDnjx5krVr11KqVCkiIiIyUvUMQSmXUbxWa06d/506sqsMUGwl9HYHKvvml8L6JHI9Z+/EsvJwBBPaVLJDWiRQuZT6sks8Ed0YkPAp0bin6fgbRzTCK795ZMe41hWo7JtYzTgLmCckHCRjeuMSKZL1ckwKXBRLc1FXmqW6DijQUkO4TkPZRRrJLlBXdhlvIZZ35Id4R34IgFgxLyf1FTipr8BxfUXOimXSNKMg4Tyyewhb8pdL26o+bEsh35ozKlBbQoec+bp32a2vw3TlUmrKrvOtcjnvyg/whWYg4WLJdDmuo9gTsupMbF1i/VbYn0j7zJ044p6nPXRHFCEqLuWiD7mRcl75mdiusuGzr7sb41pXIPjifbsMsVoUjNaMZJNqEvVk4Xym+J1vtR84RTfRyv9JpNd9LSHhDJZp29NPvp2Ksjt0kR1io74JAGuO3GTFoQgW7LlKxHcZW9FQIn0ZPnw4w4cPN3yOj3/9DF25cqWZvKP9MUttZCfaVfdlYWgXlqpm84F8J41/3EH9KmVY8mHdzFZNQiJT6bwgcfy84dTdFGUHyLfxnnw/WlHGCM0oLotpD9G31p8a9Vb5FGUksi4OG/euXLnC4cOHiYqKQhAEvL29adSoEeXLl095ZwnrZHHTuBYFp8QKnNJV4Efd27iQQB3ZFRrILvKGEE4t2TUKCs94S36at+SnAUgQ5ZwTy3BCX4HT+vKc1ZfhHkXInCDk3E1OCsuFxEpsKRr30vkyuyCW5p2EqfSRB/OpYh3+sitsUk1itS6Audp3iMf+UEcJU77693ya29h16QG7Anc5QZvcw+oB9Xjj251Aorff3dgXVmVvij6M1wxlsSqIwYotnNaXS/T6TiPG415LY2BtsrLgVx88oYCb0uLss4RERhNPXhZpO/OZ8nfGK/9gi7o+alQZPtkiIZFVaFbBk0H62oTri1NRdocP5MEsvPh2ZqslIZFtqC+E8YXiVwC+1fZmv945UQvG46S6pQpx4mYMAVW8ndK2ROZhdwxZXFwcXbp0oWLFiowZM4bly5ezdOlSxowZQ6VKlXj77bdNZqskHCPree7ZRo2KEH1VftB2o5fmf9RQL6Gz+humavqwWVePB2JBVIIOf9kVhio2s0gVxGHXURx3+YilypmMkm+guSyUQkjXTEaQ3cNyk2NPGHtG3FJ6ZKzStaGVeiZbdW+gFHQMVGxln8s4PpRvR4E2A7TIfEREdE68xkJvxzok/+TUZu4sGsjNWe8QuXI0L2/bNg6+vHWOyJWjuTnrHcqUKcOiRYtMtm/YsIFmzRKr+vn6+lKrVi1++eUXE5nJkycjCILJ4uPjYyIjiiKTJ0+maNGiuLm50bx5cy5cuODQuWUknvldODChBX0blmLt4Popym/X12ORNjH/3vfKn1/lYnWMS1FP+HHPVYM3i3F4981Hz3iqNr2Htl+4b/j/9uPntJqzn3rfSkZciczDI59pldLlurbcFYtQTHjEAPk2wLSPN2t7OJBYyTtBKxUqk8jZKOQylHIFP2k7AzBQsZU8vOTn/dcyWTMJiYwjtRFU3jxmgWouCkHPRl1jVuhSX2U3OcbjpCUf1iXw3erM6m5qOMzivkcSFrDbuPfxxx9z48YNQkJCiImJITw8nMuXLxMTE8Phw4e5ceMGH3/8cXrqmqPJ7m6vWhScFcuyXNeOEZox1FP/SBP1D4xLGMZabUsu6EuhFWV4CvG0kp9mnHI9K1Xfc9p1GAdUo1mgnMsQ+X+8KTtHEeIy+3RyHDnJc09EpGMN35QFM/CWiqIIH2nG0ifhc8L1xSkkPGWqchXbVJ/TQnaa3FB85su/0+5tl4TOgQv2Wdh+Hu9agnvD7hTtNw+X4lV58OdktPEPLMprYqN4sH4yLsWrUrTfPL744gtGjRrFX3/9ZZApXLgw48ePB+DQoUP079+f/v37s337dpO2qlatSmRkpGE5d+6cyfbvv/+eOXPmsGDBAo4fP46Pjw+tW7fmyZMndp9fRlOicB6mdKlGqSJ57ZKfqe1BiK4K+YSXLFIGkQfHQ6Fnbg9nT/gD/jh+m5Brrwt0vLcohMbf7ba63/m70rtCIvNZNeAN6pYqxNpBiQZxNSpmaboD8JHiHwoTb1KRfMGeqwRfvE/L2fv4YNnRzFBZQiJD+XNYQ7bSiBt6b4oIT+gn3870LVKudoncw55w0z7pykM3UtxHhYafVEF4CvGE6UsyUTMIZw5ujB0lCuVV0bNeSQq4Kk1lnHY0iYzC7rDcf//9l+3bt1O/vvlsfv369Vm8eDFt2zrPmpzbyG6eeykjcFv05rbozQZ9UwBcSKCqEEEN2XVqyK5TU7hGWVkkJWQPKcFDOspfd3LviwUJ05ciTCzJRX0pLoqluCH6prncd24lp+Xcs8fwkBkG8wP6GrRPCKSnfDdjFespJ7vHCtVMTugrMFvbjRB91QzXKSMQRfgn9J5T27OX+ON/k69Ga/LXbANA4VZDeHHjFE9Ob6FQs35m8k9DtyLP70nhVkMAGDSoAydOnGDWrFl07doVgObNmxs80cuUKUOtWrVYtWoVBw8epE2bNoa2FAqFmbfe63MQCQoKYtKkSbz77rsArFq1Cm9vb9auXcvQoUPtP8ksjA45H2s+ZpPsC8rL7jJb+RPDNaMdLi7z7eYwrj18ZrY+7kXa8y9KSKQnVYu6s/6jRgCMbVWBH3Ze5m99Ywbqt1JNFsEoxQYma/uZ7PPr0ZsAHLvxOKPVlZDIcGqWKMjuCa2Y+X1XglQLGaLYxGpda7Q6PQq51K+XyPlsPmuaSmjyfxdT3Od/ijXUkV0lTszDUM1YXuKS4j7ORvLcy3449ES1FQqX3tVeY2Ji6NOnD+7u7ri7u9OnTx9iY2Nt7mNPSNTQoUMpW7Ysbm5ueHp60qVLF4uVfzdv3kz9+vVxc3PDw8PDMFhzFrnh5lGj4pRYgZW6tozTDOethNnUeLmEXglfMEPzPpt19biu90EvCngLsTSXn+EjxX/MVy1gl8unXHTpzz+q//Gd4mcGyLfSVHYGXx6RG7yi0oojnlBZEbnRDZLXJWvXAdIhZ42uNS3Uc1is7cBLUUld2WV+U33LWuU0/IXwzFYxy2Pv9SrqNCREXcXNr7bJeje/2qjvWvYKUN+9ZCbfpk0bTpw4gUZjbkgSRZFdu3YRHh5O06ZNTbZduXKFokWL4ufnx/vvv8/169cN227cuEFUVBQBAQGGdS4uLjRr1ozDhw9b1k2tJj4+3mTJDkTjzvCE0ahFBe3kx/lE8afDbVgy7GUUoihy+/HzbD8JIpH5FM6b6PUgIuNbbW8Aest34SdEmsjZW/VdQiKn4JXfhX/1jbim96Wg8Iz+8m0MWn0is9WSkMgQFA568bSXHeFDRTB6UWC0ZgS3ROfmwhvTqjyF86Zc9LJ73RIUzqviPf/iTj2+RPpht3GvU6dODB48mBMnzB/EJ06cYNiwYXTu3NmpyhnTq1cvQkND2bZtG9u2bSM0NJQ+ffrY3MeekCh/f39WrFhBWFgY27dvRxRFAgIC0Ol0Bpm//vqLPn360L9/f86cOcOhQ4fo1auXU88vvY2jWZV48nJYX42fdJ0ZoRlDy4Q5VFMv4131ZCZpBrBG+xan9OV4LrrgKmioKbvO+4q9fKX8hdWqGYS4fsw5l0H8rfqSmYpFDJH/R0vZKUoI9xGQctkkkc1teyjkMhb2rsOc7jXN8ht18y/OG6ULme1jfEu1qOiZ3iqaEU9eArW9aaoOYpW2NQminEbyi/zlMoVflNNpLDuHZJi2jL05InXP40HUI8tj+vvL8xZC9yzG8j7PYpDnNZX39vZGq9USHR1tWBcXlxjy6eHhQYcOHZg/fz6tW7c2bK9fvz6rV69m+/btLFmyhKioKBo1asSjR4lhpVFRUYa2kx8raVtyAgMDDRNY7u7ulCiR9mpoGcUpsQKfawYDMFLxD+/IDjit7UX7rvFSo0OrS59n+qrDETT5fg9f/5t18yFKZA+KFnQz/B+ir8ouXW2Ugo6JirUmcjkvWkNCwjZKuYzqxQsRpE30kB+s2MKp8IjMVUpCIoNYd+K23bLFhYd8p1wKwE+6TuzV1zaTaZ3GwhdjWlWwS65gHhXHJ7ViVjfnFPGQSH/sdoGZP38+PXv2pF69ehQsWBAvLy8EQeD+/fvExcXRpk0b5s2bly5KhoWFsW3bNo4cOWIIC16yZAkNGzYkPDycihUrmu1jb0jUkCFDDPuULl2aadOmUbNmTSIiIihbtixarZbRo0czc+ZMBg4caJC1dMy0IPXzXvMcV0Nl3iRk6Ckl3KeycJNKsluUF+5STrhHaSGK/MILagnXqCUzTc77QlRxQ/TlhuhNhOiTuOgT/z7Endz0reeEghrtq5vm2SvjmZfrD5/xTu1i+HnmZea2cDacfp3Q3/jX7dOwFHvCH2aQpqY8oBBfa/uzWNuJkYqNdJPvp4n8PE3k5zmvL81ibUe26OujQ54p+jkDZ19djl6vZnMjoogj93eSx5bxJEv+/PkB2LNnD0ePHmXcuHGUKVOG5s2bA9CuXTuDbPXq1WnYsCFly5Zl1apVjBs3zkg3Uz1EUbQ6mTNx4kSTfePj47OVgW+jvgnltHcZofiX75RLuJXgxUkx7e/K77Ze4rut6Zefaca2RG/a1SE3mdqlWrodRyLn07KSF+/WLmZ4FwVqe9JMdoYA+Uma6s4YqhzeiM48T1UJiczi7xGNKTMxhpH6v6kou8NAxVYOXGlBjWIFcc+jTLkBCYlswopDN3j6UsvHb5V3aD8FWuYqF1BAeM4pfTl+0L5nUS4jI7Lk0mxUtsJu417BggXZunUrly5dIiQkxOB54OPjQ8OGDalUqVK6KRkSEoK7u7tJvr8GDRrg7u7O4cOHLRraUgqJspTv6NmzZ6xYsQI/Pz/DgOrUqVPcvXsXmUxG7dq1iYqKolatWsyaNYuqVZ2XP0sK0bCNHtkrQ50vW/QNDOuVaCklRL0y9t2lvOwu5YW7lBEicRMSqCLcpAo3zdp7KrpyU/Tmxiuj303Rmxt6H26LXjygoMP5orI6lXzyZ7YKTmfLqCbcjX1BWc98AMzpUcswoPLM72JiQMkK99c9PPhCO5gftW8zSLGFHvK9VJNFMF+1gE/161iha8tfuqbEY18hgyyFk/sY0U8T7JKT5ykAgszMS0/3PBZ53oKW97Hg1ffgwQMUCgVFihQxrJPJEp8BNWrU4M033yQsLIzAwECDcS85efPmpXr16ly5cgXAkIsvKioKX9/XhukHDx6YefMl4eLigotLxudUcSaztN0pI0TSTn6cxaofeCdhCredHE4iIZFVEQTB5F10VSzOSl0bBim2MlmxirYJM0hAmalh6BISmYUgCIjI+EH7HotUQQyQb6XJsjbkLejFoc9bZrZ6EhJOQRRFprzKqdeuui/lvPLZve8YxV/4y64QL+ZhlOZjtFZMNZp0imSQyP44nLyqUqVK6WrIs0RUVBReXl5m6728vKyGN9kKibp509TYs3DhQiZMmMCzZ8+oVKkSwcHBqFSJcehJOZQmT57MnDlzKF26NLNnz6ZZs2ZcvnyZwoULWzy+Wq1GrVYbPqeUOykL2B6yJRoUXBWLc1V8lQvgVTS1DD0lhAeUFe7hJ0RRSrhPaSGK0kIUxYRo8gkvqSrcpKoFw59aVHBPLMJd0YM7oqfRkvj5AYWyXWGPijnQuOeqlBsMe0n8Oqg+P+29xrfvVDPx28pKs0538WSKti9zte/SRx5MP8V2Ssoe8rXsFyYo1vG3rjFrdK25IJbObFXt5lhE5iSFF+RKVD7leBERSp4KjQzrX0aE4lbevPgTgEuxSry4esxk3Y4dO6hbty5KpXXPAVEUTZ7pyVGr1YSFhdGkSRMA/Pz88PHxITg4mNq1E0MqEhIS2LdvHzNmzLD7HLMbIjLGaT6iuPCQ6rIIViu/472EyTzC3enHmr/7qlPaMX7/Ltx7leHNyzmlXQkJgLnarnSRH6aMLIoB8q0s0qVfChsJiazOp20qMmu7nov6UlSR3eQjxb8ExvZGrxeRZaG+moSEM7j1+Jndxr2GsgsMl/8LwOeaQdwRracUSp6iyB4+bVORmdvD+ah5WYf3lcg+pMpCcefOHfR6vdn/jjJ58mQEQbC5JOX4sxTGZCu8KQl7QqJ69+7N6dOn2bdvH+XLl6d79+68fPkSwHBukyZNomvXroYcfYIg8Oef1pOGO5o7KSt4FuUk9Mi4KfqwW1+HZbr2fKXtz4eaiTRNmEtl9UreUs9kQMJ4vtF8wC/aVuzXVeeW3hOtKMNF0OInu8+b8gu8r9jLeOWfBKkWst5lKkdcPybcpS/7VGP4Vfkts5SLGK9YxwfyYFrJTlJVuIEHcVku319uyenYuJwHawbVN6umK8+C5x9Lfubr3qWxeh6TNAMI05fATUigp2IPm12+YIPqK7rJ95KXF5mtapamwBtv8/TMDp6e3YEm+jaPdy1BG/+Q/LXaAxCzbyXRm2Yb5PPVaoc2/gGPdy1BE32b5cuXs2zZMsaPH2+QCQwMZPfu3QBcvnyZOXPmsHr1aj744AODzPjx49m3bx83btzg6NGjvPfee8THx9O3b18g8Z4bM2YM06dPZ+PGjZw/f55+/fqRJ08ep+dszWq8wJUBCZ9yW++Jn+w+K1Uz0uU6vhjp/IIj32+TCt4Yk17FzIxl27VrhyAI/P33384/gSzAE/IQqOkJwMeKjfjwKJM1kpDIPEa0KIeIjO+1PQDoJ99OceEhvx2/lcmaSUjYjyiKvEjQmaw7HvGY//19jviXWsO6BK3I7B0p9yvy8ZyZysXIBJHftC1MotQsMbF9JZpX9GRBr9p0qOFrUWZ+T9NcfdWLuXN5Wjs+a5uxTloSGUuqyk5WqVKF0NBQypQpY/K/o4wcOZL333/fpkzp0qU5e/Ys9+/fN9v28OFDq+FNjoREJXVay5cvT4MGDShUqBAbN26kZ8+ehn2rVKlikHdxcaFMmTLcumX9ReRo7qQsaHvIsSSg5JpYjGtiMbNtcnR4E0Nx4SHFhYcUE6IpLkQbPhcVHqEUdJQSHlCKB9aPIcp5QCGixMKvltf/PxALEo07D8WCPMGNjMj9lxsvL+N7KisbN1/iwq+6Vvyqe4u6Qjh9FDtpJztKHdlV6siuMkWxim36N9iga8JhfdVs5zWa3uSt3BT9iyfEHvod3bPHqDxK4dVtMgr3RG9v3dMYtPGv8y0qC/rg9d5kYnYv5cnpzXyzqzjz5s2ja9euBplnz57xySefANC6dWsqV67MmjVr6NGjh0Hmzp079OzZk+joaDw9PWnQoAFHjhyhVKlSBpkJEybw4sULhg8fTkxMDPXr12fHjh2GfH45mYcUoo/mc9arplBdFsFi5RwGaCaQQPrlVdLo9Oj0Iq5KOfdiX/Dz/uv0a1Sa0h62Q92z7tMh8+nVqxd37txh27ZtQGKeYktpTYxJKma2cuVKKlSowLRp02jdujXh4eFm135QUFCWfj47iw36JvTU7+YN2WUmKX/lY80ow7YXCTrcVNk356qERGrYq6/JIV1VGssv8IniD8Zu9MS/VCEq+RTIbNUkJFLkw+XHOHAlmqNfvIV3AVcAui0KMZMbtuakXe39T7GG4kI0N/VefKO1XTAUwCu/Kyv71wNg6znLUYy1ShQ0W6dSSGOInE6qjHuiUbJzMQ2J+j08PPDw8EhRrmHDhsTFxXHs2DHq1Uu8kI8ePUpcXByNGjWyuE9aQqKMw6/8/f1xcXEhPDycN998EwCNRkNERITJIC45juZOkjz3sgY65NzDg3uiB8fEymbbZehNjH++wmO8hcev/sbgKzzGgzhUgo7iJBoGbaEWlTzEnWjRnYfiq7+vPieuSzQERovuaTIE5sbrSzD6rrJSWK51BE6IlTihqcQ39KG7fC9d5fspK4vkXflB3pUfJFIszD+6xvyna/AqbDc7nFf6k79OB/LX6WBxm0eHsWbrXEtWx7ffXABufGe+37Rp05gwYQLu7u7cvHmTAgXMBxu///57inoJgsDkyZOZPHlyirI5kQjRl34JE/hdNY035ReYw0+M0oxMFwP1yZsxdP3pMEq5wLnJbRi8+gQX7sWz+Vwkxye1cvrxcgO2iplZw95iZgBnzpxhzpw5HD9+3GQSNidQt1QhTtw0zu0p8LWmH/+pJtFJfoS1urcI0SfmbR665iSrB9TjpUbHmiM3aVHJyyzlhIRETuLgZy3oNP8ggS96skn+P96RH2Kptj1tg+DIxLfwcXfNbBUlJGxy4Eri+O6/M/cY1MTUwem6gzlVW8hO875iL3pRYLxmGM9x7PoXrSS+VsilMUJuJFXGvYymcuXKtG3blsGDB7N48WIgcfa4Y8eOJsU0KlWqRGBgIO+8845JSFT58uUpX74806dPNwmJun79OuvWrSMgIABPT0/u3r3LjBkzcHNzo337xJCuAgUKMGzYML7++mtKlChBqVKlmDlzJgDdunVz2jnmRuNLdkSPjEiKECkW4bho2a1ZgRZP4vAVHhkMfsYGQC9i8BDiyS+8wEXQ2GUEhERDYAz5iBHz81jMTwz5iRHz8Zj8hnWx5EvcJubnMfl5gQsg5E7PUKNzlmeziapo3Fmo68JCXWdqC1d5V36ATvIQfIXHDFP8xzDFf9zSe7JFX59tunqEimWRDH0Szia/q4InRuElqeG8WIZhmrEsV35PR/kRXqLiU80Qpxct6vrTYQA0OpGrD55y4V5iyO7DJ9bzJErYxlYxs7i4OIv72FvM7Pnz5/Ts2ZMFCxYYIi1SwtFcxpnJwt51WHPkJvOM8kJeFEuzRteKvopgpiuW0jZhBmpU7L/8kJM3Y9h8NpLlh24wbXMYo94qz7jWFTLxDCQk0o/ihfJwfFIrfj9ekb83b+Ft+WG+UKylt+YLrjx4Ihn3JLINlnyc9A44PrnzlO+USwBYpmtndWzpqA4AClk2G/xIOIVsYdwD+PXXXxk1apShw9i5c2cWLFhgIhMeHm7S4UwpJMrV1ZUDBw4QFBRETEwM3t7eNG3alMOHD5sU8Jg5cyYKhYI+ffrw4sUL6tevz+7duylUqJDTzi+9HYv+GdGYLj8eSt+DSACgRWEwANqqIuqKGg8hDk/iEv8KcXgY/o/F49VnTyGOfMJLXAQNPsTgI8RYbzQZalHJY/Ljenkm1LVcTj2nYmzQzL7Gc4HTYnlOa8vzjbYPb8lO0UkeQgtZKCVlDxkm28QwxSbuikXYoavLbn1tjukroUaV2YpL5ACOT2pFgk5Pjck7DOtaVvJi9yXrKQkscVBfnVGaj1mgnMd78v1oRRkTtYPSrSp5GgIKXu2fcj7frMRLjQ5XpfPDOq0VM/Pw8LBq3LO3mNnYsWNp1KgRXbp0sVufwMBApkyZYrd8ZuJVwJVxARVNjHsAs7Q9aCM/gZ/sPmMUfzFDm5iLL8k4ncS8XVck455EjkYhl9G7fkma/NODdrJjNJZfoJnuLANXyrn8bbvMVk9Cwi4sec050geZolyJtxDLVX1RZmm7O1EzUCbz3Etj10gim5BtjHuFCxdmzZo1NmWShwinFBJVtGhRtmzZkuKxlUols2bNYtasWXbr6yjpPZCoWaIgI1uUY8Ee51QXlEg7L3HhjujFHbxSfOImGgLjKcgTCgtPKMhTCgtPKCQ8oVDS/zyhkPCUQsITCvMEF0GDi6DBl8fE5cLZG2vVct+tXYyBTfzos+wYj58lZLxiqSQBJVv19dmqr48bL2kmO0t7+VFayk5TTHhEf8V2+rOd56ILh/VV2KuvxV59LZvVtiQkbOGqlJsZjRb38efmo2e0mrPfoba26esxRjOCucoFvK/Yiw45k7QDSA+P0+Sd7dDbsRZzzyRhXngr++TB3XY+imFrTvJlxyoMfNPPrn0mT56copHs+PHjgPViZilhq5jZv//+y+7duzl9+rRd+ibhaC7jrMgT8vA/zQCWqmYzWL6ZTbqG2aoyuoSEMxEEgTuiJ6t0bRii2MwXil9pn1CN24+fU6JwnsxWT0IiRZJeh3q9UcoyO/d9S3aSt+WH0b0Kx03txLy1V3L2SEkk4Wxy34g/i5IR95+1mHyJrE+iIdCT82IZ9utr8q++MSt1bflB242vtP0ZqRlFb80k2icE0lC9gIrqlVR+uZzGL+fSUT0NbcnGmX0KmYqx596QZmWoWtTdZIBarKBbZqiVal7gyjZ9PUZpPsZfvYjBCeP4TduCKLEQeQQ1reSnmaZcwUGX0exUjecbxXI6yI7ggWVvGwkJe1HKZZTzyk+XWkUd3neTviGfaD5CLwr0VuxiimIl6TGXnLyje/ORY/lvdGl1/ctAxq4LBeCbTRft3mfkyJGEhYXZXKpVq4aPj4/FYmaPHlmv9mpczMwY42Jmu3fv5tq1axQsWBCFQoFCkTjP3LVrV5o3b261bRcXFwoUKGCyZEd26v3ZpKuPQtDznfJn5OhS3klCIgfzo7YLj8V8VJTdoY88mCbf72HYL/YVIpBIHxYuXIifnx+urq74+/tz4MABm/L79u3D398fV1dXypQpw6JFi8xk/vnnHwA8PT2pUqUKGzdudPi4jlRjzwiSegv7Lr8u3GbPBFgeXjJVuRKAJboOhIrlUq2DtTBgZXbLSSThFKRfPYuQEWGD2Wi8IpFmBF7gyl0SDYK4FsxshTIcY88RS7NXxrdD1aLZc5AIoEZFsL4uE7WDaaBeQDt1IDM073NUXwmtKKOc7B59FDv5UTWPE64fsUP1KVMVK2gvO4IX9od451R2hZkbLyRM+bxdYg6Yie1e54KZ0bUGg+z0FDPmb/2bTNAOQS8K9FUEM0OxBBl6p+kKjpsLkz8dHMmXk9mkZtLOw8ODSpUq2VxcXV1NipklkVTMzBrGxcySSCpmllQA7fPPP+fs2bOEhoYaFoAffviBFStWOHw+2ZEpmr7EinmpLotggHxrZqsjIZGpxJGPWdrEavTjFOspQhzbLliuACqR/qxbt44xY8YwadIkTp8+TZMmTWjXrh23bt2yKH/jxg3at29PkyZNOH36NF988QWjRo3ir7/+MsiEhITQv39/AA4dOkSfPn3o3r07R48edei4SdXYFyxYwPHjx/Hx8aF169Y8efIknb4N2yR1F2JfJJits8U4xZ8UEx5xW+/JXO276aKbQvLcy5VIxr0sQkZETeqzz3hFwslk35xzqcf4jI1PP2e/7ATCxFL8pOtMj4SvqKNexJCEsSzXtuWiPrG6dwXZXT5UBLNQNY9jriM47DKSH5VBDJZvoq5wCVdyVwGCzeciM1uFLM+wZmU5PqkVQ5uVNaxzVcp5s3zK1e4tsV7XjE81Q9GJAj0Ue5mvnIcKjbPUTbNxLvnul+8/4eCVlIseGRPzLIEfgi9z+/HzNOmSmRgXMzty5AhHjhxh8ODBtG3b1kSuUqVKBg8M42JmGzdu5Pz58/Tr18+kmJmPjw/VqlUzWQBKliyJn5/jBuPsyEMK8q22NwCfKP6kjHAvkzWSkMgcVg2ox1cdq/C7rgXn9aUpIDxnvOIPwD4PKAnnM2fOHAYOHMigQYOoXLkyQUFBlChRgp9++smi/KJFiyhZsiRBQUFUrlyZQYMGMWDAAJN0VkFBQbRo0QKAChUqMHHiRN566y2CgoLsPm7yauzVqlVj1apVPH/+nLVr16bfF2IDSxNsKfVBqgnX6S/fBsD/tAN44WB1XHMdLJPcsUG6n3IHqTIpffDBB4ZwCOP/JVJPehhfutctDkCTVwMw6abOveRC255ZQY0PG5aiQ3VfynrmM5PNqXdGPPnYoX+DqdoPaZ8QSK2XixmaMJYV2jZc1JdCJwoUFR7TQX6MScq1rHeZynmXgWxSfUGgYgkfyrdTV7hEPrKvgSIlcqPhOzV45ncxW5eW7+4vfVOGa0ajFhV0kB9jiXK20wzLj5+a5tJM8uK99eg5yw7e4HmC7QrAyTvmAT/s54NlR7n6wH7PgNrfBDN31xU6zEsMKdLo9Dx6mv0M57/++ivVq1cnICCAgIAAatSoweLFi01kLBUzGzNmDMOHD6du3brcvXvXpJhZbqKKr/X+8Z+6ZuzXVcdV0DBH+ZNZeO6KQzfSWz0JiUynWQVPBrzphx4ZX2v6AtBDvpfqLiyscAABAABJREFUwnW+2RTGg/iXmatgLiMhIYGTJ0+aVDwHCAgI4PDhwxb3CQkJMZNv06YNJ06cQKPRGGRatmxpJpPUpj3HTakae2ZgaWhta0whR0egcilyQeRfXUP26WumWYd8LpZLKAiCwB9DG6a5fYnsRaoKahhb7q1Z8SUcIz0GmFO7VKNlJS8al0s07pX3zn0d69yA7uVTYnYu5vmVRNf2POXrU7jVUGSur41YAuazN1OmTDEM0jp06MCiRYuoWrWqQWbo0KHs3LmTe/fukS9fPho1asSMGTOoVCkxNC8iIoJvvvmG3bt3ExUVRdGiRfnggw+YNGkSKlXmV2s1PmeBxPvBGrnF7h1Lfrbr32C7/g0gMedHDdl1aglXqS1LXLyEWKoJEVSTRZjsG6H3JkwsyUV9KcLFElwTi3JT9EabfeoyWSRHO3KmM2l9b23X12Og5lN+Vs6hmfwsa4RABiV8Qixpe1f9fOC6xfUBQft4qdFzJ+Y5X3eqalEGQKcXeanRMX/3FVpX8TGsv/rgKeW88rPz4n2eqrW8XbtYirrEv0w0JHaaf5BLUU/Y/UkzyliYYEgt6f3sslTMLD4+PpkOjhUzs0ROnXxc0f8NVh6OYE3ITZ6okxuVBSZohrBd9hm1ZNcYIf+HebrX4VlT/rtIv0al0YswZl0o1YsVYEjTskhI5EQWfeDP9gvF2HB+F+/KDzJVuZJ3D5Um9HYMG4bn7rzRGUl0dDQ6nc5ixfPkuVSTiIqKsiiv1WqJjo7G19fXYvV14zbtOa691diNUavVqNWvJ9aSv7/SA1uvs37y7VSXRRAn5mGq5kOnHO/zdpW4/vApZ+6Yp8yo51fYKceQyD5IYblZhLQMkqxZ7F2VctpW8yW/qxKAd2oXY1L7yqk+jkTWJPrfmSTcv4539yl4d59Cwv3rRG+abSIjJLvTk3JWzJw5EwAvLy+znBX+/v6sWLGCsLAwtm/fjiiKBAQEoNMlehdcunQJvV7P4sWLuXDhAj/88AOLFi3iiy++SN8TTgUp3145c2CZEs9x5Yi+Cot0nRmqGUc99Y80ejmPjxJGM1/7Njt1tbknJnYMSsvu005+nE+U6/lZ9QO7XD4lzKU/O1XjWaycwwTF73SV7aemcJUCPM3kM7MfyXMv9Tjjqzuor84HCROJE/NQV3aZDaqvKSWkLdeSWms5h99LTeL6kGvJCkIkOw+9CD/uucqPe67x9o+HTNYDDFp9gjHrQomKs9+j5FJU4rN163kpj1RuwruAK5+1rcTZya89Tca1rsDaQfUBiKIIX2oS81CNUmygumBqmH7j211sPH2X/87cY/qWSxmnuIREBtO2mg8/9KjFd5qePBVdqS27Snf5Pk7dis1s1XIltiqe2yuffL09bTpLJonAwEDc3d0NS1orrJ+/G8fha6/TdIiiyDO11sSgZ22yypMYxirWJ+ql7UU07jaPVcDVvslz7wKu/DPyTbtkJXI+DrtcDBgwwOb25cuXp1qZ3ExavEcGNynDDzsvpygnlwkMblqGb7eEpf5gElkKTfRtXt44iU+f2bgUrQhAkbYfE7VmPJpHd1AWSQzNNr68jHNWdO7cGUjMl1G+fHnWrl3L0KFDARgyZIhhn9KlSzNt2jRq1qxJREQEZcuWpW3btia5l8qUKUN4eDg//fSTSZ6NzML0PW9+g6WHSadIXhWPniWkLJhlEbiHB/f0HmzV1zesLcgTKstuUUWIoIrsJuWFu5QV7pFXUFNOuEc5zPNFxYl5uCV6cVv04pboxR3Rk1uv/r8reqLJIh5/tjqrErZx1ld3SqxA14TJrFR9TxlZFBtVXzEoYTynxAqpau/M7dg06SOKosEYZ4xeFE067THPE/BxdyxXTk71UJOwjfFzpmhBN6oWfT2o+1ffiADdSTrKj/CDciEdEqajJtH7PfqpmvF/nslwfSUkMosYeWF+0HblS+WvfKH4ld262pmtUq7Cw8MDuVxus+J5cnx8fCzKKxQKihQpYpBJXn3duE17jmtcjd3X19cu3SZOnMi4ceMMn+Pj41Nt4BNFkY7zD5qsu/rgKVW/3m6yzlL/AeAz5TryCS8J1Zdlna55qnSwRYcavmw+K+WRzu04PLqKiTGtrqjRaDh//jyxsbFmsfQS9pOWAaY0Ns29qO+FIbjkNRj2AFyKVUJwyYv6bthr457RRZJSzook454xz549Y8WKFfj5+dl8KcbFxVG4cNZzAbd0jyzuU5fui0OcehxZDo3xjCU/IfqqhFCV12mhRHx4TDnZPcoKr5dysrt4C7G4C8+pLkRQnQiz9vSiwEPciRILv1oKESUWSfzL63VpTTJsDzn0J8sQkof7Q+o7l1fF4ryjnsoS1Sxqya7zm+pbPtEMY5M+7fli5u68TOeaRa1uN6+WazmsRi8mhuwmkRo7XVa07T2If0nhvCoUcimYIz1ZPaAeR64/4p3axZIlOhf4n6Y/b8guUU52j4mKtUzW9sssNSUkMpVd45rTYqaWLvLD1JDd4Gvlak5EtOO9RSF8/14NutdNm+eVhG1UKhX+/v4EBwfzzjvvGNYHBwfTpUsXi/s0bNiQ//77z2Tdjh07qFu3Lkql0iCzZ88eM5mkKur2HNe4Gnvt2olG36Rq7DNmzLCom4uLCy4u5jmDU4N5agX4O9R8cttS9EAt4SrvyfcDMFnTF9GO4ElHuwsz36ths/+VBbsfEumAw8a9pIpoxuj1eoYPH06ZMmWcolRuxNYA00UhY1KHyrSv7kvdaTvNtktj09yL7lks8jzmbt3yPO7onr02xBtfX47krFi4cCETJkzg2bNnVKpUieDgYKv59K5du8b8+fOZPXu2xe1JZFT+C2ODnqV7pG6pQob/C+ZxTo7AnF2JNzkCURQhSl+Eg1Q32eLGS4oL0ZQU7lNCeEhJ4QElhIeUEB5QUnhAHkGNN7F4C7HUxHJ+NIB4MQ8PRXeiceeRWCBxoQDRounnh6I78eQlNU9DKSw39RQr6Ga27sdeddh8drPNfe7GvrC47SEFeT/hS+Yqf6SN/AQLVPOprL3JbG139GnIInLt4TOL6y/ff4JHPvNOf6IBz7wbLIqiSdV5S1XyUiKrVa0/dSuGdxcepkGZwvw+REq8nZ40reBJ0wqeFrfFkp8JmqGsUs2gn2IHIfqqhtyoltDq9JIxViJHUrJIHnTImagZzD+q/9FRfoQBP/8I1GHC+rOScS8DGDduHH369KFu3bo0bNiQn3/+mVu3bjFs2DAg0Rvu7t27rF69GoBhw4axYMECxo0bx+DBgwkJCWHZsmX89ttvhjZHjx5N06ZNAbh8+TK7du1i586dHDx40O7jGldjL1++POXLl2f69Okm1djTk7jnmlTtJ6BnsnIVAOt1TQkVy6VJj49bWt4/j0pBHpWc5wk6i9slcgdOiYuSyWSMHTuW5s2bM2HCBGc0meuw5fFTqkgePmxYmicvU/dQkch+xB78lbhDv9mU8fnwh8R/LBonRJP1lgwY9uSs6N27N61btyYyMpJZs2bRvXt3Dh06hKurqUfVvXv3aNu2Ld26dWPQoEE29Q4MDGTKlCk2ZTICmUxgw/BGqDV6dl+6n/IO9rQpGYoAeIErV8TiXBGLW9gqUoR4fITH+AqP8TEsMfjwCB8hBl/hEXkFNQWE5xQQnlOWlD3BtKKMePIQK+YjnrzEiXmJe/U3lnyGz/FG65+QBze9h/O/gFxCySJ5+ObtaizZf51bj51TUfklLnykGcNn4m8MVWxmhOJfqgkRjNKMJA7nFKIQRbj64AkBPyTOoru7KU22L953jZ1hDyzuZ1xJN1Wee1ls7vzXI7cAOHL9cSZrkvvo5l+cP0/eMXzep6/Jz9oODFFs5nvlYi4klOaOaG4MPHM7lm6LQhjbugIfNZcKbEjkTC6IpVmqa88wxSa+Ua7gqLoyzzCfUJJwPj169ODRo0dMnTqVyMhIqlWrxpYtWyhVqhQAkZGR3Lp1yyDv5+fHli1bGDt2LD/++CNFixZl3rx5dO3a1SDTqFEjli9fTt++fWnUqBFly5Zl3bp11K9f3+7jQmI19hcvXjB8+HBiYmKoX79+hlVjT63nfVf5AWrJrvFUdGWGpkeadAif1hYXhdzq9oJuSsm4l8txWtKja9euodWau6tK2Ic9Dj/WQnez1lBBwhnkr9ORvJWb2pRRuHujeXgD3bNYs2265/HI8xQ0fFYZzfAb56woW/b1wMBSzoqkBLTly5enQYMGFCpUiI0bN9KzZ0+DzL1792jRooVhli0lnJn/whbGL2FrRrc6JRO993aFOcm4JzlS2IHAI9x5JLpzQfSzIiOSnxd4CTF4EE8RIY4iQjweQjxFSPy/iBBPEeLxEOJwF56jEPQU5imFBceKeZy//RawIc1nlVvp06AUz9VaAre+TvbftU5xTt58TMQjU4NfFd8CxNsxSaVHRqC2Nxf0fsxQ/kwz+Vn+EyYxVDOOMLFUivtbQpfMZS7EhjFr6cEblvUSRRPjXmpwZPe4Fxp2XIiiTTUfCrgqLcqk9f2f1YyNuYkONXxNjHsA32t78IYsnNqyq8xXzqd7wldm+Um//Oc8CTo9M7ZdMjHu6fQiMkHKI5rRLFy4kJkzZxIZGUnVqlX59ttvrcpGRkbyySefcPLkSa5cucKoUaMICgoykVmyZAmrV6/m/PnzQGJxs+nTp1OvXr30PI0shXcBF+7HqwnSdqW97CglZQ8Zr/iDKdq+ma1armH48OEMHz7c4raVK1earWvWrBmnTp2y2ebbb78NJFbGLVCggMPHhdRVY3cWqXlf5uM5nyl+B2Ce9h0eUiiFPUwOaIYtwx7Akr51mbD+LJ+2qWi2zU1pe1+JnIHDxj3jQTkkevtERkayefNm+vaVHrqpxVZnLL+VTr1EzkWex91iuG1yXIpWRlQ/Q30v3JB3T30vHFH9DJdirysjG3uGGuesSDLupZSzIglRFE1Cau/evUuLFi0MlXVldli3nJn/whYquQyZkBgG51vQdu42Zw1vJc89ZyHwhDw8EfNwjWIp/kAqNBTkKe7CM9xf/S3Is8TPwjMK8IyCwlPck9a9+puf56jlzvEGy80k/3lmd6+JKIocu/GYkb+dZtRb5Yl7nkC3uiXotsj+PJf/6htxJaEYi5VzKCl7yAbV10zW9n2ViNqxe+2/M6Z5cS7eizP8H/fCPq94UYSnyXLuzNkRzr24l8x8r4ZdRhVHnjUf/3aa/ZcfsuVcJCv6556BfW7BkqFYi4KPNR+zWTWR2rKrjFesI1Db27BdFEWLV75Gpyfgh/34uruydnCDdNRawph169YxZswYFi5cSOPGjVm8eDHvvfeeVXm1Wo2npyeTJk3ihx9+sCizd+9eevbsSaNGjXB1deX7778nICCACxcuUKxYsfQ6lSzF+mGN+Cf0LgeuRPPFzUGsUQXSV76Drbp6hN5uTK0SBTNbRYlcSGrSanyk+BdPIY7reh9W6No5tO93XWswYu0pShbOY3d0RNWi7mwe1cRk3WdtK3H1wVPq+2W9nOgSzsdh497p06dNPstkMjw9PZk9e3aKlXQlrGPLKDCrW03AdCjjX6oQJ28m5lT7oEEp5gSnXC1XIueh9CiBq58/j7bNp0ibkQA82r4At7JvGIppAFSqVInAwEDeeecdk5wVSR3Fjz76yCRnxfXr11m3bh0BAQF4enpy9+5dZsyYgZubG+3btwcSPfaaN29OyZIlmTVrFg8fPjQcL8k7MDORyQQuTGmLXhRTnOlyFgXzqHB3U3L2TlzKwhJOIwElDyjEA/HVjKgDHbBWeT1Ymj5q5RoseaMJgkD9MkU49sVbJkYvV6Vj7q1hYik6JXzLXOWPNJefYYZyCU1k5/hCM/BVnkX7uBf3Os9f+P0nhN+3XM3OFiIw5b+Lrz+LMG/3VQD6NiyNj7srnvltT1wkVcu9G/uCmdsuMeBNP2oUL2hRdv/lxGfqnvCHFrfbw/YLUbgoZDSv6GVFoVQ3LZFG9OY51wG4I3ryqWYoP6t+YKhiMyf0FQnW1wXgjW93WjRGn78bx43oZ9yItpxfUiJ9mDNnDgMHDjSkIwkKCmLr1q1cvmy5T166dGnmzp0LwPLlyy3K/PrrryaflyxZwvr169m1axcffvihE7XPupQonIeRLcvTrW4J6k9/zO/a5ryv2Mts5SLa/liaKd0a8J6/pbQfEhLph6PV7r15zAD5NgACtb3MvLBTokMNX5pVbMOmM/f4fMM5h/Y1RkrfkLtwOIhsz549JsuuXbv4/fffGTJkCAqF06J8cx3WwnJbVPTEz8N8ANO1zuuXWuG8KpPiABI5h2/erpaijEen8ag8S3P/jy+5/8eXqDxL49HxExOZ8PBw4uJeG5wmTJjAmDFj+OSTRLnIyEiTnBWurq4cOHCA9u3bU65cObp3707evHk5fPgwXl6Jg8QdO3Zw9epVdu/eTfHixfH19TUsWQU3lZy8Ls57LvmXKkS7atYNl191rGx1m0TW5HykY2G8EubYClVN7s1WJK/jXrtx5KO/5lO+07yPRpTTUX6EzaovqCPYP6nljCq1elEk+ILlEP4fdl7mjW93svSA9QIxSXqsOHSDxt/t5u/Qe3RecCjZdpGI6Gf2DyJsiD16qmboLyfpt+K4WVgyQMyzBCk3TyZSv0zhZFVzX7ND/wbLtW0BmKP8ibLCXQCinyag0b3+LXdfuk/Pn48QGffSsM7RAahE6khISODkyZMEBASYrG/ZsqVTj/P8+XM0Gg2FC+c+rxvvAq7M6laTadoPuK33pITsIZMUaxj/5xlKf76ZB09eptyIhISTcPTJOlaxHjchgeP6CgTr/VN1zHxOHMNI5A6kKyaLYOy5V7VoAS7cS6wgOrH9a2OB8Rgpedy/s6p95lQ61PDlu3erM3fnFav5lLIipYvkSVFG7pYfj07jrW6f37M2nb4zvV6SclaMGzcOd3d3tmzZYpL/omjRomzZssXmcfv160e/fv1S1C874+6mNPGSGNDYD9+Crmw9H2VR3iu/7fDflPDI50L0U3XKghJOw9rgWsJ+HAn1SG3kuoiMRbrOHNFXYZ5yPiVlD/lDNZUfdW+zQPt2ijPix244oWCECBojdyvj9/DuS4kFOKZtDiP6aYKNJkQT77/kTN8SxpIDNxgfUCFVKur0ouGajjV6dulFEbmR/3/cCw21vwk22ffWo+eUtOOdI+Ec8rsquTClDfEvNNSbvsts+3RtL6rIbtJAFsbPyjm8nfANTzD9fQasPAFAyPVHhnVavYhSLj3X0pvo6Gh0Op1ZrmJPT8sVkVPL559/TrFixWjVqpVVGbVabZIyJT4+3qk6ZCbv+RenRvG2jA+6yW+qafRS7GGHvi579bWp9+0uIr7rkNkqSuQSHJk3KSfcoZt8HwCBml44mkrE5Lip3lMiN5Kq9O/r16+ne/fuNGjQgDp16pgsEqnDeMDzQ49a7BnfnGvT21PB+3X1H8HGgyE3j0+LursyqX1lKvtaTs4K0LKiF/ldlTSr6NxOV3rzZrm0V/LsVLOoEzTJ2Vh7YQ9607TgQ/vqKYcbJxk6ZAJ8/16NFMP0jFHk5hs5k5CMe2mnbunC/D6kASETU/ZYSWteylCxHB0SAvlH1wiFoGe0YgP/qL6kihBhc799l1Mf2pqEXhRNnhXW8u/8n737jmvi/AM4/skggCwHW5ElCooTVHAvcGvrbLXu1aq1alt/jrZi3aOtrdY66modta3auopbq3UruGcVJ7gFJyO53x+USCSBBMLS5/168VIuz909F+6b3H3vGXN3/2twG/o+a+bu/peY/7pTLtiT+vBpxhbdVolHYh7wPItWdr8fvUH5LyJZ/d8kDZndiJyNzXjzn6Q20E9UyDVWFgqc7a0Y0tgPAB+nlz01UlAyKGkIN6US+MpjmWnxPTKy/hvpa6Up5J5XWyebs+XktGnTWLlyJWvWrMHKyvDDw8mTJ2snQHNwcMiVScryU1kXOxo3f5tF6tTWrFMtFlAU04dWEIScMT62/6dchUImEamuzjEp84d1Wd2nicbYgilMTu5999139OrVC2dnZ6KioqhRowYlSpTg8uXLNG9u2kCRwkvpbzCVchnejjZ5ftMZ6lPC6LLuDoYvMlpVyttume5FrelXzwcb1es3C5CY9S5vGJoByzrdOeXtaGPU32N4WDk+axnAjo8b0CnYgwOjGputnoL5ieSeeYT4lMDNwTrLcnIzvN+PKcJHyYMYnPQhDyRbysuv8qfqc4Ypf8eClKw3kE2v5kxSspEM05d3mfLXORrM2JXpeh3m7qf3ksOZlvnkt+Mkpmj4+LfjbD2T+Qzg+v8K4g4iv3zYqAzzugXxS3/dyTDu48CApGG8kCxorIhimPL3LLeVpNbw/c5L7Lt0L7eqKwCOjo4oFAri4nRb8t+7Z573fcaMGUyaNIktW7ZQqVKlTMuOGjWK+Ph47c/169fNUoeCpG8dH6andOaipiQuskdMsfgR8Zkl5CVjk2zBsnOEKY6SIsmZntIpy/Lj2lTQ+d3OSsnKdBMjZTb0iSC8yuTk3pw5c5g/fz6zZ89GpVIxYsQItm7dypAhQ3TG9BJMk741g6GWDekX+znb6S2TE16OxnfHWdrb8Mx9s7vkbQvOtPcl83Gfcm//lUplPautUDh1qVnapPIyWWpCsG9dH7z+GyvTlOSRKeepmPXKPERuL28VL2Ku2d9lbNCEEpY4nU3qGljI1HykXMN61RiCZefMtA9drz4E6GDCzL+GtmGK9F0vs/Jq60Fj7g3E/UP+sVDIaVrBleJ6hlg5JfkwKjl1woYhyj9oK9+b6bbWH7/F9M3n6fLjwVypq5BKpVIRFBTE1q263dt37tyZ421Pnz6d8ePHExkZSXBwcJblLS0tsbe31/l53cjlMvaMbs7Q5IEkSQqaKQ7TTbEVr5Eb+fXI65fMFAoe474iJUZZrATgV3UD/pWynuG6uM3Lz30rCzknI5oS6vuywY34ahZMYXJy79q1a9SqVQsAa2trHj9ObRbdrVs3Vq5cad7avUHS32Aa023J3lrJoTGNORERnmVZYxlqlfRujYzN+wtii7L8+PBbP7gO49tmPemFULAZuqktolKmK5NaKDfP/PSDomdlWoeXT/L1TbqTlWFNdLsJxExpSZ9XuiEXFo+PbeTG3D5cnfE2sUs+4sX1U5mWf3HtJLFLPuLqjLfZP6kLc+fO1Xl9zZo11K9fHwA3NzeqVKnCzz//rFNm8uTJVK9eHTs7O5ydnXnrrbc4f/68TpmePXsik8l0fkJCdFvmvGlGtwighndxfLJxzupzHwcGJn/EoKQh3Jfs8Jdf53fLL5munEsJzPvA0Sy9HTPZxt3Hxo+3qfmvMoaShWqNlOk4vfq+w8UNRP6Ty2R6Hwit1dRlXkrq2GLTLOZTU3bW4Dau3BUz5uaV4cOH8+OPP7Jo0SLOnj3LsGHDuHHjhvb1UaNGZZjhNjo6mujoaJ48ecLdu3eJjo7mzJmX43BOmzaNzz77jEWLFuHl5UVcXBxxcXE8eSImf3K2t2LB//owOaULAJ8pl1FeFsOI30+w52LOh14QhMwY8wCsgfw4QfKLvJAsmJnS3uR96BuCq2VFNxRyGfXKFq6hpYT8YXJyz9XVlfv3U58ee3p6cuDAAQCuXLkiZujKgfQX2sbmzZztrLC3MlcrCMMtWII8i1PCRvdpsqNtwZnAI+2DMD9OP28nG1LE+DbCf/LyHPQs8TI5Ysq4fmk+auKXYZlKma1hWPPV07N/82D7AhxCO+He8zssS1Xgzm8RpCTc0Vs++VEcd36PwLJUBdx7fodPWDeGDBnC6tWrtWWKFy/OJ5+kTlLzzz//0KtXL3r16sXmzZu1ZXbv3s2gQYM4cOAAW7duJSUlhfDwcJ4+1b2xbtasGbGxsdqfrCaqed0521vx64BQ2lbJ+mn2qywNnp8yNmpCaJw4gxUpDQHoqPybHZYf01WxDbkR45QZwxzXOPP+NjybbvWJ24zaxrm4BKp8uYXvd14yWEatkUz+PBKXcPlPLpdl6JqbZkrKu2xS18BSlsI81df4yG7pLSf+jHmnc+fOzJw5ky+//JIqVarw999/89tvv2lfj42N5dq1azrrVK1alapVq3L06FFWrFhB1apVadGihfb1OXPmkJSURIcOHXBzc9P+zJgxI8+OqyBzL2rNYnUztqqDsJSlMMtiFjY8Z+Tqk/ldNeE1l3X3WImh/w2d8LM6jDsUM3rbVhap1zeVPTL2Bituo+LMl01Z2qu60dsT3lwm38k1atSI9evXA9CnTx+GDRtGWFgYnTt35u233zZ7Bd8UOt1yjegn9urnizka0ikMdQcm48Vi0SIqfukfQudgj2y3wDg4ujFfd6qcrXV1/FftzG680g4tN8bXys64S0Lhkxc3TNnuamumytlaFr4J1BMO/4FtpTDsKjfFwtGD4k36o7Bz5HGU/iTak+i/UNg5UbxJfywcPfCq3YrevXvr3Dg1aNCA1q1bA+Dj48NHH31EpUqV2Lv3ZXe4yMhIevbsSYUKFahcuTKLFy/m2rVrHD16VGd/lpaWuLq6an+KFxfdqQFq5EK38kfYMTqlH28njuOUxgsH2TMmWixio2oU9eTHc7x9TTYf5Gw/m/n4d6Z4kaym2cw9JLxIYfrm8ySrDbfcS+/Mraxnz8xJl2HBfKp7FWda+4xjrEnIGZY8kGOaMhSVPWWxxTS9rVMX7r2SF9UU/jNw4EBiYmJITEzk6NGj1K5dW/vakiVL2LVrl055SZIy/MTExGhfj4mJ0VsmIiIibw6oUJDxaXJ/bknF8ZXHMt5iMSnqzCccEoScyiq311AeTRX5ZZ5LKualtDZp2xs+rEPPWl58905Vva9bKhUFstecUPCYnNwbM2YMo0aNAuD9999nyZIlBAQEMG7cOEaOHGn2Cr4pdLvl6i+T2zFt6EPD0H5DfEowtUMldnzSINMJNgxxsbeiXbVSJq/3qrTqZXbflda6r6a38ZOGGLvvMs62GZb3qu1l1v1kh6u9FesH18nvarxRchqjDtbZa4lrrpvywjYpjaROJinuEtbeuhdD1t5VSbypf9y1xJvndMor5HKaNm3KkSNHSE5OzrgPSWL79u2cP3+eevXqGaxL2pizrybvdu3ahbOzM2XLlqVfv37cuaO/RSFAYmIiCQkJOj+vq1DfEpmO3aqPsTPtRkl+tEmawNjkHsRLRQiQX+cn1VR+sphMgOxqdqoLQMT6M1mWsbPKmCDvs/RItvf5qnNxxs0QeSY2gdazXiaj356zT+d1fW+laLlXcHQI0r02ShvbNxEVfZM+4arGGU/5HRaqpmPD8/yooiDkm23D6/MIO4YkDSZFktNOsZcmzzZRMWIzp2+J8d8F84p/lsz3Oy9lGMtWl8RQZWoPkJ/UYdzDuPHY3wtJHdu7jLMdEW0q4Gxv+v20IKRncnKvTJkyPHr0SPt7p06d+O677+jatSv+/v7mrNsbxZgJNfKyDunJZOBkm3m3vx/eC8LV3opv36mSCzXLXFq1M0tw5GbLvRJ63hsfp4wJv7w2tnV5KorJPoyS3e52qwx0n8oupSJ756e5bsotLQpXck/9LAEkDfIiul0fFDbFUD99qH+dpw9R2LwsL5eBi4sLKSkpOrMcpiXrHB0dadmyJbNmzSIsLEzvNiVJYvjw4dSpU4fAwJdjcDZv3pzly5ezY8cOvvrqKw4fPkyjRo1ITNQ/ttrkyZNxcHDQ/nh4ZBzv9HVSv6wTI5qVMzqpbcrHtwY5S9VNqZc4kwUpLUiSFNRTnGSjajQzLObiITNfa7r0Hr/Ivdl6wXALe32eJxtuySKe/xdscrmMye0qan9P35rjAfb0TP4fDyVbqsgvM9/iayxJMrita/efcePhs1ytryDkpTLOtrSq5MYRyZ/pKZ0BGKtcStnE03RZICaSEcyr3Q//MH3zedYd1z8UAkBj+TEqyy/zVLI0qdWek61I5gnmZXJyz9BN8JMnT7CyEidodqW/Xjd07a5vkE1zMthiEBnfd818BtzKHkU5MLpxtsZRMheN6B2rQzTCMN6rT8o+DivLjI66Xcb1ffQZ04XeFAp59sa8M9ffurDeAGb4zJQkjE1fyGWyl5OlpNuQnV3qjOQ7d+5k4sSJDB8+PEP3qjSDBw/mxIkTGSaV6ty5My1btiQwMJDWrVvz119/ceHCBTZu3Kh3O6NGjSI+Pl77c/366z8D4MAGZYj6PIyBDXyzLOte1Nrk7cdjy8SU92icNIP16hDkMokOir/ZqfqYqcr5lJIZbklZEK0+diPrQgaMW3+a+GcZW6emkSRIStHwIlnNxhOxxD83XFbIfZ2CPfi8VXk2fFhHO/t6miuSGz2S/sdjyZraitPMtpiFkoyJZa+RG6k3fSd1pu4UQ4gIr5XZXaqxuGd15qlbsUFdE5VMzQ+qb7F8fifTJIwgmOrfLCcpSt9qL5wH6J+tuliRjA8yRU9bwdyMHmBp+PDhQOrNzxdffEGRIkW0r6nVag4ePEiVKlXMXsE3hawgtNxLl6io6V2cg1ceAKkfPGWcbRn/ViCf/5H5LJT5QTuhRg63s2dEQ9QaiQYzdhm/b0N/KtG/qVDpU8ebrWduE339EQAfNPBFqcg60WbuhqDKbG7QXJMZPcnlVkfmpihiDzJ5hlZ66mePUNgU1b/OK636rC0U3LlzC6VSSYkSL7vty/9LtFaqVIk6depw9uxZJk+eTIMGDXS29+GHH7Ju3Tr+/vtvSpXKfJgBNzc3PD09uXjxot7XLS0tsbQ0fXKUwk4ul/Fp03IUUSmYseWCwXK2erq8Guu65MKHyUP4MaUFw5SraaA4TmflLtop9rBaXZfv1W25Lrlke/t5Zcm+mGyvu/ifGB48TeJbA2P67L10lxbf7dH+HuRZjNUf1Mr2/oScUchlmc5gfkLypW/SJyxVTSFMcZTpzGN48gdIBp7bJ6sllIWrcbYgZKqhvzMgY0TyAMrIbuEvv84Pqpm8u9KWRv6tOB+XgL+rPTaFcDxhofBoLD9GRXkMTyQr5v83q7k++0Y25tPfj7PhRGwe1k540xjdTCQqKoqoqCgkSeLkyZPa36Oiojh37hyVK1dmyZIluVjV11v6W/rMusfmah3Sbb9iyYzdOQvqbMjabrk5rJ9H8SJ4OdrQtELu3eAFuOl/miPkLysLBV9lMblLWrdv3Zmtzd1yL3vbM9eEzYVtsF6ZwgKVaxmex0TrLH8RE41lSf3DRFiW9OdFuvIWChlbtmwhODgYCwvD3UMlSdLpTitJEoMHD2bNmjXs2LEDb2/DN+Fp7t+/z/Xr13Fzc8uy7JtGJpMxuFHGGZzTe3Ucsuw4LpWhZ/L/aJcYwW51JSxkat5R7mKXajizLb6jkiyzMXUKv7QHGPpM2qQ7TuXRq/q7tgv5Y8fH9TMsOygF8EHyUJIlBW8r/mGCcjEyA7NDZz3ToyAUTs+won/ycOKlIgTJLzJOuYTAsZG0/2E/FcZuZvaOiwX2HkYo7CQGKf8EUmfIfWig1R6AtUqR4SF+4brqFgoDox9l7Ny5E4BevXrx7bffYm8vkhS5JTv39+bosmtwttz/lhf078WcXLiOav4yEWDKZgy974Y2kZOWXsWKWPAwky5VGepQwP9eBVlmSa70F4ivlnKyy1mrq2y33Ev3f5VCTlI2u19l1mp4SGM/vtuuv8VZfrKv/hb3NnyNpWsZLN0DeHw8kpSEu9hVaQHAw91LUD++j2OrjwGwrdKcx8c28GD7AuwqN+Pfuwc48PNCnS61kydPJiAgAIALFy7w999/89NPP/HDDz9oywwaNIgVK1bw559/YmdnR1xcHAAODg5YW1vz5MkTIiIiaN++PW5ubsTExDB69GgcHR3FzPLZ9G710oxZa57W48eksvRIHkm1lAsMUa6lgeI4rRQHaKU4wEGNP/NTWrJDU9VgK6jCrpDl8QUMj+W7U1OVj5M/YKbF93RVbkeGhjEpfTKcu2pxUSC8xq5JLgxJ/pBFFtN4V7mTGMmVeerUsc9mbLmAv6s9TcoX/NbZQuFSU3aOavJLJEoWLEppnmX5xBTd6/N2ZnhoKQjpmXzVunjxYpHYy2UGZ61N9//cuEYz2GJQu0/z7VTfuAOmGNTw5fhML1vuGS6f/j2NHFo3w3hqA+pnPd6TObxT3fQB8ud3C6J/PR9aVXLPhRoJxtI75l668yr6izAsc9jnqbJH0eytKElM71CJAfV9qOlTPMviPk42epdndsNfwkaVvbqZ6NcBoSaVtwmoR/HG/Xj0zy/cWvIhiddP4dwxAqWDMwDqJw9JSbirLW9R1BXnDhEkXj/FrSUfErVuEd999x3t27fXlnn69Ckff5yaDAwLC+P3339n2bJl9O3bV1vmhx9+ID4+ngYNGuDm5qb9WbVqFQAKhYKTJ0/Stm1bypYtS48ePShbtiz79+/XjucnmMbcY1xCapKvZ/L/aJY4hd/V9UiSFNSUn2Oh6iu2qT6lj2ITDjwx+37zy9PEFJLVGkR7gdfLOk0tPk5+H7Uko4tyJ1OVC5C/0oJPrZY4fSuen/fHMP/v17uFqvDmmPh2IHXKOBJe3oXdmsqMT+kGwCiLlbSQH9CWi7mf1bhpgmC6D5TrAPhVXZ+7FM2yvLXq5X3CmS+bUjIbYwkLQmbEIAQFkMEJNTK5865auiiRp+OM2v6sd6vyZ/Qttp3VnS3Q0HhGaQkMc6X2jn7WJEfjX5RzsePTpv58vzP14tSYMffSv3P+rvb4u9rzyW/H9ZY15TgN/UkMJRq71vSkvLsD7X/YZ/Q+wiu4El7BlbF/mtZipWrpoiaVFzKX9jc1ND6mnVXOEtYA7auVYtSak6bXDegYnJo47r7oUKZlK3sUZY6BCXJyIXdisuzMGGxXrSV21fSPc+LYcliGZValK+LW81sAmlVw5f1uQTqvT5gwgREjRuDg4MDVq1f1PtDK6mGHtbU1mzdvNvYQhHx2TirNJ8nvM51O9FRupqtiO77yWD6XL2OEchUbNCEsT2nMMcmPwpwYu/ckiYYzdhkcd08oPK5MbsHfF+9x69FzRq05yVpNXdTJCr6x+J5Oyt0oZGo+TX4fzX/P8f86FcfotS+/X5oHuuFRvIihzQtCodC1pidda3oCsOlkLAOXg6fsNr2Um/nG4gfikopzTCrLncf6Z6kXhOwqL4uhgeI4aknGfLXhsfbS+18zf2LuPeW9EE+KqEQaRjC/17O/yRuoV21vxrYuj5tD1jMWt67szidNy2p/H93Cn7p+jnQP9dRb3piWcZmZ3qGSzu8lbC2xssh+C6dXu9+m1S+zbrl53QXJ0I2/XC4jyLMYPWt55dq+G/s7s3VYvWzNLPkmS9+l1tjTxdznVXa75ZrSJf3PQbUNPilMn6wc0axctuqSUyojJjIxp2xOUCy8pm5TnKkp7xKaOItRyX04rfHEUpZMe8Ue1lhG8JdqJH0UG3HiUX5XNdtuPHye31UQzEAmk1G/rBOl0yXo1mlq8WHyh6RIctor9vKdxWxUpA7nseqI7uzbr3YPE4TCrkVFN9pXK8X4lG5sVVfDUpbMAtVXlJbdZv7fl1m490p+V1EoJM7HPeb0rfhMy7yvXA/ABk2o0RNyudhbsWZgbdpVE91xhdwhbmsKkcxu+1VKOb1qe+NrYEyWzHQP9eLnPjUNPkHQdss1ecupOgZ70C1Ef+IwO5pX1D8YfW50VQ7xKW5UwrQgqVfWCT8X0e3PVPZWFmweWo+dnzQwuvufuZN7hrbnap/5OWi2cz/d/oM9dbv35tVg1BZ5nNwrbJOICPq52Jt3luGnWLNS3ZiWSZNom/glv6bU54VkQYD8Op9bLOeA5SCWWkyhrXwv1rww677zgjjtCzebdF27Xp2IaZMmhEHJQ0iSFLRSHGCRxTRseM7xVyZTOXUzHo25ZmMShAKiiocDGuR8lDyYkxovSsge87PFZJx4yPgNZ/K7ekIhkKLW0HTm37T8bi9zdl3SW6a07DYt/+v2PTeldV5WTxAyJZJ7BYRdui6xlsqs/yxSDjvJmjRphJlmozVE38y8hnzatByDG5bR+1pm9dM38UW9sk4AtKqkmyxMv5lf+ofSOMA5y3qNbV1edxtADwMtIQFsLHM2NluLiq4GXxM3bdlXztUOb0f949Hpk/68MsfbbijR5FY0b5J76VvumRrv5cyUUM5Ot9z0BtT3Mam8oYmEhMLFHJNKGdrycakMI1IGUCPxez5L7sVRjR8KmUR9xQm+Vc3hsOVAvraYQ7j8MJYk5VI9zEstkjqvDX2zrG/W1KB38gieSpbUUZzmF9V4HNFthTJ0VTTz/r6MJElM2nSWFQevAbDn4l1O3si8xYogFFTW/zVUeIYVvZM+5arGGU/5HX5WTcGBJ9xJKHwPY4S8lX5SummR5/WW6a/YgEImsVNdmbOS+RqwCEJOieReAWFnZcHKfiH8OiDU4KD8xtyDZifpp2+7tcs4pi9h8jZNsWZgLSa3q2hU2RYV3VC9kvyUGTEmoL5j/L5LVb57typT21fK+KKJ6vo5ZVj2eavyekqm6l8vZxN4lComxskpCPKqS2dWEZj+3H+1bMtK+lu66pP+HtHUT5JSxczTDTynE3eMah5gUvkeudhFXjCfxb2qZ2u9XrW9zFaHBGxZpg6jfdI46id+zcyUdlzVOGMre0E7xV7mq74hynIA31vMpLV8HzYU3O6vY9aaPranUDDpS+4B7NVU5J2kz7kn2VNRHsPvqghKy3THWl6w5zI7zt1h/t+XGb32JNfuP6PbwkO0nr0XgBfJ6lyvvyCYU+vKbtTyLcHHYWW5SzG6Jo/mtlQUf/l1lqimMemPI/ldRaGAMzTBZJpiJNBB8TcAc1PaGLXNIY30N0wRBHMTyb0CJNS3BDW8s57pMjM5bcFzaHRjlvetSYNyL5NVOR1zLysWCjmuOej6mvYRbMq4Y5CaUG1T2V3P5B662wkrn3UruVe/ByQJlJl0L3SwtqB+2YwJQaFwyb3WQjC7S1U8ilszo2PlLBNthlrZTetQiVCfEkbvM6sLmrxQtEjezMoLcOSzJgR5Fsuz/QnGax748nN30tsVaVjOcAvqVx/4pOdslzvDKlyVXJmZ0oH6Sd/QLjGCH1Oac0NypIgskZaKQ8xSzeaY5fsstphKD8VmPGXGTXiVVy7cfn1mAH4Tpf/ET9/6eHK7iuwZ0VA7FMpJyYcOSWO5rnHCS36btaovCJad09nWB8uPaf9fb/pO7f+/2XoB/88jOXr1Qe4chCDkAkulghX9QviwsR+/DgjF0zeA95JG81Cypar8Eh0ujqDcyLV8/Otxrj94lt/VLXAePnxIt27dcHBwwMHBgW7duvHo0aNM15EkiYiICNzd3bG2tqZBgwacPn1ap0xiYiKffvopAG5ubrRp04YbN25oX4+JiaFPnz54e3tjbW2Nr68vY8eOJSlJtzW8TCbL8DN37lzzHLyR3lXswEqWzAmNNwcl/yzLr/6gFkOblM2ynCCYg0juFSK5NTZU+gSFs70Vtcs46uwrO8mztlXcAWjkn3WXVlNk9g5ocmls6PplnQw+GU9jZ2Cm4X51vc1WjzLpuj3mfwrmzZNVN1VzheenTcvRs5YXrSq5s2dEIzoEZT3obvqqpR+zToZxLfDk2iR1+m65r+wjqzoYsZ+spJ/UJC842ubt/gTjudhbETOlJTFTWtKlZmm9ZfxdUz8T53TRP/sz5HwIi6zJOCaVZUJKN+okfkvrxAnMSWnDZY0rlrJkGiqOM85iKbsth7NTNYyxyqU0kEcXmu67QsGX/vokvLwLHsWLMP6tQO2yGMmNdkkR2vHHVqgm0kGxG4AHT5NIMjCxxrfbLwIwdt1pniamkKwWE3AIhUsN7+Is7xvCd0PepWfSCJ5IVtRRnGaexTdsOHaZutN2subYjaw39Abp0qUL0dHRREZGEhkZSXR0NN26dct0nWnTpvH1118ze/ZsDh8+jKurK2FhYTx+/FhbZujQoWzYsAGAyMhInjx5QqtWrVCrU1sHnzt3Do1Gw7x58zh9+jTffPMNc+fOZfTo0Rn2t3jxYmJjY7U/PXr0MOM7kDkLUuiu3ArAopTmGHNHFljS3uixvAUhp0Ry7zVjbP4tfbmskhLGdHt91ZR2lfihazW+e7eqCWulsrLQPS1/7B6caXljkio5/Uit6lFU73Llf/0yne2sdC6m096r0S0CmN1F/3tg6i3nu9U9+LRpOdYMrGXimoI5pP290p9LufFdPahhGSLaVDBpnfQJjC9e7Q5uxIdC2s2hbrdcMSaXkPfSHpRkNdapUi4jcmg9zo1vRpPyxs1Sl/tknJR8mJbyDo2SviIscRqTk99lv7o8yZICb/lteik3s0Q1jROWfVml+pJhyt8JlZ8WyT7BJOk/1tOPD2Wox8BditEp6Qs2qmugkqmZYTGPkcoVyMk6YXfj4XMqjN1M2Ne7c1xvQcgPAW72HJfK0Df5E55JljRQHGeBxVdYksTwX4/nd/UKjLNnzxIZGcmPP/5IaGgooaGhLFiwgA0bNnD+vP6x5yRJYubMmYwZM4Z27doRGBjI0qVLefbsGStWrAAgPj6ehQsXMmHCBAAqV67MsmXLOHnyJNu2bQOgWbNmLF68mPDwcHx8fGjTpg2ffPIJa9asybDPokWL4urqqv2xtjbPsDAvj8nwa83lB3GVPeSOVJSNmhCjtqfMqzF8BAGR3BPIOvGlnS3XhHt9a5WC5hXdsM3Q5TVrp8c10/k9q25zxrQsNKVVlb7NTHxb/5iA6ZMh6WcEtvhvUgCZTGa21kFKhZxBDctQrXQx0XQvH6U/PTJ26c4dWY65l65SpUvojsdozLj5ack9nW7GJub2zDHhTm5OyGuXR38rIWf2jGjI2oG19I5jCmiHMxgentrFxcrC8ORExYpY5Oo5lTkZF6VSzFO35t3kz6iaOI8BScNYkdKQW1JxLGUp1JSf4yPlGlaqJnLCsh+/qMYzTPk7deQnsedpflVcKMDSZsmt7PFyIjK3dMOa2BvoRQDwHCsGJw/h25S3AXhfuYEfLWbgQOZdtB89SwYg5r5uF8ajVx9w5V7G8/Tfu09Yc+xGns2wLgjG+LRpOQ5oytMrKXWimXqKkyy0mI4ViWw8EStmjgb279+Pg4MDNWvW1C4LCQnBwcGBffv26V3nypUrxMXFER4erl1maWlJ/fr1tescPXqU5ORkGjVqpC3j7u5OYGCgwe1CalKwePGMw1UNHjwYR0dHqlevzty5c9GYueuW4YfbEn2UfwHwU0oYyRh3XSka7Ql5SdztFFKGrplyo7VNWgIjN1vypP/ce7ULrLEtC00dc88U5VztmNO1GgPTjU2Tft9pPmrsx67zd+gY5JHlNtNf+MplL5MwA+r5sP74Lb5obXhCjsyI75Dcoe/0ci9qzegW/thYKnOt2zzkrEusMTdYaU8Vc3IIWXVdB6hUyoETOZiFcX63IPr/fFTva33qeOPrZKv3tVq+JYhLeMHjuykAlCxqTbtqJbNdDyH3FC2iomppw+MuzusWxOlbCQZbU6d3YHRjFvx92Yy1y74nFGGzpjqbNdUhRcJLFkeI/Cwh8jOEys/gIntEiOwsIfKz2nUuakoSrfElSvIjSlOGC1Ip1ORspnVTZJYoEvLHug/rsPzANd5PNyu4m4M1fw6qTXEblc73UM9aXizZF6OzvoScb1I6clFTihkWc2mkiGajfDQfJA3lpGT8TONX7z+l/Q/7AYiZ0lLntcZfpbbwUyrktKnsbuohCkKu6FfXh90X7lK6eBg9j8lYrJpGHcVpFjGdvis+YRBWHB8bjoO1RX5XNd/ExcXh7Jyx1byzszNxcfrHjU1b7uKi24LexcWFq1evasuoVCqKFSuWoYyh7f7777/MmjWLr776Smf5+PHjady4MdbW1mzfvp2PP/6Ye/fu8dlnn+ndTmJiIomJidrfExIS9JZLz9BlczXZRSrLL5MoWbBC3TjL7aTJzfsDQXhVoWm5l1sDfA4YMABfX1+sra1xcnKibdu2nDunO9jwhQsXaNu2LY6Ojtjb21O7dm127txJQdS0Quog5C72mbcWS5+oM/Sh81nLADoHexDik/rUpCA8hNVX1bRFmQ8Hk/MPVmO2MCysLH8OroO1yrQbsFPjmmr/X7V0MfaNakyzQMOznL46kUOwmBQgz7x6HvSv50vXmp56y+aVzBJ4xjyM1peXe3W1V3cxoL4PE94KpHOwB00ruODtaJPpPqws5KwbXCfrymQivILhyW0+b1Xe4NhsoDvo/D8jG/FxeLkc1UXIH1YWCoI8ixk1fo2lUqFz3v7Q1fDYfHlLRozkxi/qRgxNHkzNxO9pkPgVI5P7slZdmxhN6k2Sn/wmHZV/M8liIX9ZjuKkZV9+U0UwTrmYdxQ7qCT7N1e78xqTsBfylq+TLV+0Lo+zve5EMZU9iuJRXLfV9qgWhgd636AJpV3SOGI0LpSS3eN3VQTvKbaS1aMktUZi8+k46k/fpV32PEnNoBXH+DP6pk7Z49cfGXVMgpAXVEo5vw4IZXrHysQWrUqPpP/xWLKmluIMK1QTKE4ClcdteS1bnEZEROidiCL9z5EjqbMI67snlCQpywTVq68bs46hMrdu3aJZs2Z07NiRvn376rz22WefERoaSpUqVfj444/58ssvmT59usF9TJ48WZs7cHBwwMMj68YXm07G6l3e+79We2vVtXmAfZbbAVjcq7pR5QTBXArNY9kuXbpw48YNIiMjAejfvz/dunVj/fr1BtdJG+BzyZIllC1blgkTJhAWFsb58+exs0sdiDsoKIiuXbtSunRpHjx4QEREBOHh4Vy5cgWFIjVB07JlS8qWLcuOHTuwtrZm5syZtGrVin///RdXV8M3m/mhe6gXHsWKULV0UaPXMfTR27eu7lPc/PrCM3ZGUrN1yzW+qNkUUaUPRdNr4GKfOzNCCi+Zq+VqROvyrIm6aVILttq+jkRde2TwdUOn/qsXTbs/baC3nL5xmjIL9w8blcmQHJu48YzhFcj5TLyrP8jZWJMiUfFmcbTN2Povs1l181dqsi9GnZrwA1JvNOX/UlV+kaqyS1SW/4u97DnVZReoLr+gXVMtybgsuXNG8uSspjQXpZL8K7lzXXLOcSs/ETOFm8rA+HtpzkhetEmawHSLeTRVHGGCxWKqy8/zWXJvHlNE7zrJag0DXmk9vXjfFTaeiGXjiVjaVhEtooWCb0mv6jT5+jnvJY1isWoaVeSX+U01ju5JI7l05wmRp+JIeJHMsLCyr1yfF06DBw/mnXfeybSMl5cXJ06c4Pbt2xleu3v3boaWeWnS7oPj4uJwc3vZKOHOnTvadVxdXUlKSuLhw4c66965c4datXSv7W7dukXDhg0JDQ1l/vz5WR5bSEgICQkJ3L59W28dR40axfDhw7W/JyQkZJng+/T3ExmWuXOP5vJDACxWN8vwuiENy5l3YklByEqh+MRKG+DzwIED2nEAFixYQGhoKOfPn6dcuYwtMF4d4BNg6dKluLi4sGLFCgYMGACkJgnTeHl5MWHCBCpXrkxMTAy+vr7cu3ePS5cusWjRIipVqgTAlClTmDNnDqdPn8635J6hG2WFXJZrg4sX1IdZaW+FuoCOl5Ebt0d1yjgyd/e/BnYobshyQ1qS2d/NjuI2KpyzObNrz9re7L9836Tk3uBGZZi985LB1zPtlpvu/54ldFvX9a7tzaJ/rjCqecYWHpklM/V9/mT1+ZC2hr7u7cbI6Wmd0+SiULjs/V9qksyhSOHsYvUAe3ZqqrJTkzohkwwNvrJblJddpbz8qvZfR1kCfrKb+HGTtoqXYxclSkquSG78K7lxSSrJv5rUpF+M5MJTjBt8XHQlKtyM+fslYMOA5GH01WxipHIlbRX7CJJfYHjSBxySAjKUv/s4McOyB0/0tx4VZ49QUJVxTm3gcVwqQ8eksSxVTcVXHstqywi6z3zBBSk1+XM29jHL+tbMbFOFgqOjI46OjlmWCw0NJT4+nkOHDlGjRg0ADh48SHx8fIYkXBpvb29cXV3ZunUrVaumfl8lJSWxe/dupk6dCqQ2pLGwsNDp9RYbG8upU6eYNm2adtnNmzdp2LAhQUFBLF68GLkRE1FERUVhZWVF0aJF9b5uaWmJpWXOxz7votyOQibxj7oC5yXDvUQEIb8ViuReVgN86kvuZTXAZ1pyL72nT5+yePFivL29tVn9EiVKEBAQwE8//US1atWwtLRk3rx5uLi4EBQUZLDO2enjb4wOQaW4nfACf1c7s2wPjL9pzknq7K2qJfn5wFXKuugfEyvTi9As6hfkmdptOLcHwzX31g2Om2jEjur4ObKiX026LDho3koJGUzvUIlJm84y573ULn2WSgUHRzfW6eZpKmNbo6axslDQJMCZbWfv6H09s1a1mb32easA3q/vk6GLVxoblYKnSeoMy2uXyfwicXqHSszZ9a/OYOtpDx1aVDTc3TxN6eJFuPbgWZblTCFaIb1Z0iba6FzdgwOX79OgbOF+ei4h55JUiktSKdZpamuXOvNIm+zzl1+jjOwWPrJbWMmS8Zddx5/rGbZ1X7LjmuTCNclZ+3Ndcuaaxpk4iqP5b8SWnHzGCYWJjB/VLTmm8WOmxfeUlt/lF9UE5qlb8XVKR51B40evPZlh7fRd5K/ef/mZr+/0ufXoOVfvPyPUt4R5D0EQTLR2YC3enrOPf6WSdEhMTfCVk9/gN9U4Pkgeyj5NIHsv3WPjiVhaVsr6uuV1EBAQQLNmzejXrx/z5s0DUhvBtGrVSude29/fn8mTJ/P2228jk8kYOnQokyZNws/PDz8/PyZNmkSRIkXo0qULAA4ODvTp00c7Lt7x48eJiIigYsWKNGnSBEhtsdegQQNKly7NjBkzuHv3rnZ/aQ1p1q9fT1xcHKGhoVhbW7Nz507GjBlD//79zZLAM8SCFDordgHwszos1/YjCOZQKJJ7uTXAZ5o5c+YwYsQInj59ir+/P1u3bkWlSu3SI5PJ2Lp1K23btsXOzg65XI6LiwuRkZEGnxJAah//cePGmXKYRpnRsbLZt2ns0/mctNwL8izGnhENcc5iLMCspE+K7Pi4Pv9cukfn6qlPUNSZdcs1YR+FZbyNWr76Eyzidsy8OgZ70CGolE6cWGTR3clUI5v741VCfzeoNK0ru2uTe38Oqq3zWuYTahh+TSaTGUzsASzpXYOOc/dr93FodGOuPXhGsFfG2cvS6xjswaaTsdrk3oB6PvSs7ZXpOukt71uTutN0xzW1MOIJriGShFFjtAmvH0ulgjldUx/EbT+bsbtR4SbjDsW4oynGLqrAf3l4ORrcZfcoI7uJr+wWZWS3KCNP/X9x2RNKyB5TQvaYqmRsDZwkKYiVSnBLcuRBigtIjURr8ELsm86VGbbqOJDaLT0pxfDgxMeksjRPmsIXyp/prNzFB8r11JOf4OPkDzj3X0uVPRfvZVhvfrpJa9KPxadPrSk7APh1QCg1vDP/HhGE3FS1dDH+ndQC39GbiKMEnZK+4EfVDKrLL/CTxRS+SOnFCnVjBq04hpdjHR48TaJkUWt8DEzc9bpYvnw5Q4YM0TaOadOmDbNnz9Ypc/78eeLjX/Y+GTFiBM+fP2fgwIE8fPiQmjVrsmXLFu0QWADffPMNGo2G+fPn07RpUxo3bsySJUu0Q2Bt2bKFS5cucenSJUqVKqWzv7T7MgsLC+bMmcPw4cPRaDT4+Pjw5ZdfMmjQoFx5L9KEyY/gJIvnjlSUbZqCMnavIOiXr8m9iIiILBNghw8fBnJ3gM+uXbsSFhZGbGwsM2bMoFOnTvzzzz9YWVkhSRIDBw7E2dmZPXv2YG1tzY8//kirVq04fPiwzvgC6WWnj39Bl9Mxx14d7DmnfJxsdb5kMx9zr/DcnBSO1OKbJbfPn/fr+2ZZJv3pXfmVmUINnfuyTF4zZn/VX0niOdtbZZoMNGRUi4zduwzsFdD/WRFY0rjBiw2p6lFUDPD+GksfotPaV8q/ihQAGuTckJy5ITmzi6o6r9nxDA/ZHTxkdyid7sdDdodSsruoZGo8ZXfw5A73NLEisVfIvV21FC72VszcepEJbwdS1sWOkzfiaT17r97yT7Hmfyn92aGpymSLBVSQX2W9agxz1a2ZnfIWiRieyfpVMfef0WD6Tj5o4Kt9CJtm5/k7VCtdFIVcZtL3a1KKho9/O0610kXZ9+99wgJc6FS9cF9bC/knfYv+eGx5L2k0UywW8LbiHyZZLMRPdoMJKe/R8ruX8XJ5UovX+mFh8eLFWbZsWaZlXm0EIZPJiIiIICIiwuA6VlZWTJ8+nfnz5xMXF4e9ve41Xc+ePenZs2em+23WrBnNmhk/3p2pbie84MDl+xmWd1VsB+AXdQNS0qVO+tX1ZsGeK7lWH0HIjnxN7uX3AJ9p0mbQ8fPzIyQkhGLFirF27VreffddduzYwYYNG3j48KH2g2jOnDls3bqVpUuXMnLkSL37N1cf/9ySnfv93GzQltnXpLHXfZpMZ8steMw1QYPwZsjsfHGwNjyuWHbPsoI0Rl2TAJccJ1g/bVqOokUsaJ7JLNTC68HQzX7677CNQ+ro3LABNCznxP+a+6PRwO9Hb7DoH92L9uaBrrwX4knXHwvvcAiPKcIZyYszkleG1+RocOUB7rJ7lJTdw9VWwai8r6JgZrV8HXVa+lcs5ZDlOps11TmWWIbxFktopjjMh8o/aCE/yOjkvhzUMxafPlvPpF63/2/1SdpVK6XT4v1OQiJ1p+3Eq4QNK/uHGH0sa6NusP74LdYfv6Xdh0juCTnhWaIIV+8/w83Bith4GJY8kIuakoyw+JVeys34yGL5MHkwCaQ2JnicmJLpNZdQeJ2+Fc9Hv0TrLPOR3aK24jRqScYvKY10XktW615hp28pLQj5JV+njnN0dMTf3z/THysrK50BPtOYMsBnmrQBPg2tk0aSJO14ec+epY779OqgnnK5HE1hyyblkFM2JxDIK2brlmtoeR7l4nK6nwKUkxHMqFKpohmWLewRTGWPoszsXDXjCv/J7vkU4mNal6lXd9PQP3UoBTtLczxDern1L9tWAOArE4cosLFUMrRJWcqZcbxSoeAw9Tz3c854HoxpGYC/qz3l3e2p7JExAfJN5ypZjjdZmGmQcwtHjkj+/Kmpw2aLxvldJSGX+DrZZFnmLsV4P3kYA5KGclsqiq88llWW45msXEBxTBtHutxnfzH/75eTgB28cp/Y+Bfsv3yfWdsvGj0cSsLzFJP2KwhZWfNBLRb2CCZyaL3/lsiYo36LAUlDeSZZUl9xgo2qMVSUpXY/b/zVbuKfJ+dfhYVco2887HcVqUMJ7NBU5Ra63//P041J3TzQlXp+TrlbQUEwQr4m94yVfoDPAwcOcODAAfr166d3gM+1a9cC6AzwuXbtWk6dOkXPnj11Bvi8fPkykydP5ujRo1y7do39+/fTqVMnrK2tadGiBZA6c1CxYsXo0aMHx48f58KFC3z66adcuXKFli1b5v2bkY86V/egS83SzOma++MNpJ94I/1HbWaJq8xmyzV3wqtvHW++72Lc+2CoxVFuJQtd7EzvNikUfL5Otmz4sA4HRr284W4c4MKfg2pTxtnwGDDZbSGqfGVcQVPHouxa05MfulZj28f1s7V/Q7qHenFufDOaV8yfmcqFwiv9R7H+j+Wshvkwa3UKvJj75p3URig4IofW49S4pkx6u2KWZTdrahCWOJ0V/7VaeVe5k52Ww+mpiESJcck2jQSTNp3T/p7+6+SrrRfYbmCyqFe9aTEo5L4StpY0DnDBRqXQWb5ZU4MOSWO5qnHGQ36X31URdFVs496TF1QetwWvkRtZ918LUuE18crniyVJdFTsBmC5OuPDru61PLX/L1nUmhK2luz6pAHj/3sILQj5oVAk9yB1gM+KFSsSHh5OeHg4lSpV4ueff9Ypo2+Az6FDhzJw4ECCg4O5efOmzgCfVlZW7NmzhxYtWlCmTBk6deqEjY0N+/bt007g4ejoSGRkJE+ePKFRo0YEBwezd+9e/vzzTypXNv/kFnnF0db0VngWCjmT3q5o1GyXOeVZIuunyvnps1bljZ49q1IpB6wtFAZnCn5VdpMxP3YPZkhjPxoHFO5ZId8E2b1BCSzpgKuDacnb/GpxqpDLaF7RDZdsjNGXlbSZUAXBFOkfAOnrdq5MN45SZmNVpo3/2CRA/7Ag2eUvWpUKecRCIcfWUom/m3HnXAI2jE7pS4fELzit8cRB9owIi5/YpBpFbXnGGXSz8mr4nb/92Kj1CtJwEWnmzJmDt7c3VlZWBAUFsW/fPoNlY2Nj6dKlC+XKlUMulzN06FC95VavXk358uWxtLSkfPny2oYLQu5RKuT0rOVFI/+X19BnJC9aJ01kizoIS1kKEy0W8Y3FHIrwAoAhK6NIeJHMmmM3ePxCtOYr7F79dGkpP0BR2VNuSI78rcl4z1/B3YFlfWrSOdiDj5r4AeDlaIOTaGQh5KNCk9xLG+AzISGBhIQEli1blmG2WkmSdAbjTBvgMzY2lhcvXrB7924CAwO1r7u7u7Np0yZu375NUlIS169fZ/ny5TqtAQGCg4PZvHkz9+/fJyEhgf3799O8efPcPNxc5+pgxdz3gljet2Z+VwXIeKGX/r7K2LG25r6X2pKudA4n7jB3MsTKQkH02DD++qieznJz7Uf94gnbfvic9qFlGdehOt27d+fRo0eZriNJEhEREdpzvWXLlpw+fVqnzIABA/D19cXa2honJyfatm3LuXPn9G2OxMREqlSpgkwmIzo62hyH9VrLq4SbTFZ4Zn8WhNxWoWRqV1snO0udgdTTpF+mL2zSuuz80j+UXweE0jWkdMZCOeCZxYzZmSlhY/xEB4KQplrpYiaVPyL50zppIqOS+3BfsqOs/CbLVZNZaDGdANlVo7dz4+HzDMteJKuz/L4qaPMYrFq1iqFDhzJmzBiioqKoW7cuHTp0MFg+MTERJycnxowZY7CBwP79++ncuTPdunXj+PHjdOvWjU6dOnHwYOEd67OwiGhTgYU9gqldpgQ1vIpzZXILErChf/JwJiW/S4ok523FP2xUjaKKLHW28Q+WHWX4r8cZ/uvLsdZi7j3lwdOk/DoMIZtevd/sokztkrsipREaAymTOn6OTO1QCTsrMQ6jUDAUmuSeYH7NAl0L7PhB2UlINAt048KE5rxbI+MNV34/7LVUKvTeTOpj6qHfWzede1fPExkZSWRkJNHR0XTr1i3TdaZNm8bXX3/N9OnTAXB2diYsLIzHj18+PQ8KCmLx4sWcPXuWzZs3I0kS4eHhqNXqDNsbMWIE7u7uplVcyBM5ze11rVkaV3srOgaLQcuFgsuYz/iSRa3ZM6Ih2w10Fc/qMzptH7aWSmp4F0dh5i+Wkc2NnVU6o1dn0DaXhw8f0q1bN+3EY926dTP64ZG7uzvW1tY0aNAgw8OjBg0aIJPJdH6ymmBNyB3j3wrMulA6GuSsVDemYeJXLEppRookp7Eiio2q0XxrMRtPWZzJdTh5Ix7/zyPxHrUp0+u/gjZL6ddff02fPn3o27cvAQEBzJw5k5IlSxos7+Xlxbfffkv37t1xcNA/scnMmTMJCwtj1KhR+Pv7M2rUKBo3bszMmTNz6SiE9GQyGcv61GTVgJB0yR4Z89Wt6ZI0hltScbzlt/ldFcFHitUcuJTapTxtAplTN+NpMGMX1cZvNbAHoTDwld0kWH6BFEnOb+qM1wytjOy9JQh5TST3hAKpey0vAGqXKWHSZBgqpVxvt1Z9g6S+DpLvXefFlaM07Pc5oaGhhIaGsmDBAjZs2MD58+f1riNJEjNnzmTMmDG0adMGgLlz5/Ls2TNWrFihLde/f3/q1auHl5cX1apVY8KECVy/fp2YmBid7f31119s2bKFGTNm5Npxvm7yKtksk2V/ttw0E9+uyL6RjfJkdrjsJiLrJB/V6Rb14vqpTMvv3r2boKAgrKys8PHxYe7cuTqvr1mzhvr1Uy/m3NzcqFKlSoZhICBjd6w9e/a8cjxZJzmEvOVRvAj2/z1ht7LQvQRSZtlyL/d83aky3o7ZH44ipy1065XVPxB4ly5diI6O1nl4NGDAgEy3lfbwaPbs2Rw+fBhXV9cMD48A+vXrR2xsrPZn3rx5OToGIXu6hXhyaaLpvVESsOXLlO6EJU1nvToEuUyirWIf21SfMlG5EDfuG72tyNMvE4LpJysY++cpJm06q/1dX0+OW48ytgKE1CTL3xfuGl2HhBfJJsVRUlISR48eJTw8XGd5o0aNDKxhnP3792fYZtOmTTPt7iuYV9oDB4BDoxvTI9QTeyslh6QAmiVO4U91LZQyDcMsVvO7ahxeslgAPvntOK1m7c1s00IBlv7TpYPibwB2aSpzF90Wzj1CPfnuHcMT2QlCfhLJPaFAeDX5Vr+sE3v/15ClvWrkeV0Mzpab4zSJcdszZS+Jt84is7TBO6CKdllISAgODg4GLwSvXLlCXFyczsWjpaUl9evXN7jO06dPWbx4Md7e3nh4vGzBdfv2bfr168fPP/9MkSLGdSlLTEzUdq9P+3ndWShSz29H24xd55b2zr1zXJLM0wW4oLWWSO/p2b/5ddYEnW5Rd36LICVB/wDtV65coUWLFtStW5eoqChGjx7NkCFDWL16tbZM8eLF+eSTTwD4559/6NWrF7169WLz5s3aMvq6YzVv3pxr165pyxib5BDyx+5PG/J5q/La33W65eop/+oycybp08bBnd8tyKjylUvptvrJaZjrO5Tke9eJjIzkxx9/1Hl4FBkZaXA76R8etWvXjsDAQJYuXZrh4RFAkSJFcHV11f4Yaskk5L5XJ1AyxRXJjQ+Th9AycRK71JWxkKnpqtzObsuhTFHO1yY+jLX62E26LTzIv3efsHT/Veb/fZld5+9w6MoDvefpewv1d1dtNWsv3Rcd4ur9p1nu8/SteCpFbGHwiiij63nv3j3UajUuLrpjbzo55WzGzLi4uAzbdHFxIS7OcIvIN/G6Kq8421sxrm0gJyKasv3j+lT39+Gj5MEMSRpEglSEqvJLRKpG8r5iHX8cjdG7jftPEsUQKYWIAjXtFKkPa/W12nNxsCrQ18XCm00k94QCq1SxIigVcpNvoPR+f76mn8GNvSwpYl+MT5vpjhPp7Oxs8EIwbbkxF49z5szB1tYWW1tbIiMj2bp1KypVaoIqbYzL999/n+DgYKPrPHnyZG0XLwcHB51k4etq7cDaNPJ3ZnnfEEA3KVDfQIsZc8lsYoBXvVPdA6VcRuvKpnexNiX5nZ3tG5Jw+A86v9dDp1uUws6Rx1Gb9JafO3cupUuXZubMmQQEBNC3b1969+6t0/K0QYMGtG7dGgAfHx8++ugjKlWqxN69L5/I6+uO5eHhwQ8//ACYluQQ8oeLvRVh6SbFUMpfXhLpi5tXB/M3Z4vwtE0bM1SGi70lfw6uo7Mss9niTdl/eom3zuLg4EDNmi/H5k17eGSIKQ+Pli9fjqOjIxUqVOCTTz7JMuktEhi5q0G5rL+L6mRyfp6WvOiZ/D86JX7OfnV5VDI17yh3sV31CbMsvjN6TL7xG86w5+I9Zm67qF3Wc/FhOs3bz2d/ZGyVffnuU6ZFniMxJeOQIQBXX5n1ece521y68wSAg5fv89kfJ7X72njStEQkZGxNaI4kjr5tZjb+9Jt4XZUffJ1saVU5tTvmOk1tmiVOYY86ECtZMiMtfuFP1ecEyi7rrLP7wl2CJmxj5GrTJ54R8lZa5NaVn8BF9ogHki07NNUylFOKxJ5QgInknlCoGJPoq6Jn7CFTPobz6umaod2kdeV7dTyiV3+OHDlC5VJFKVmsCM6vzMyU1YUgGHfx2LVrV6Kioti9ezd+fn506tSJFy9SZwmbNWsWCQkJjBo1yqTjHjVqFPHx8dqf69evm7R+YRRY0oFFPatT7r/ZMPN0Qg0TyjvbW3Hmy2Z8906V3KoSAL5O5pkNW1InkxR3iXoNG+sst/auSuJN/ZO/GOrydOTIEZKTM852J0kS27dv5/z589SrlzopjqHuWOHh4doERnZayIrkRd5Ln8RTKF5+/qX/Hvl1QCh/DKqdYUy+9B+Xn4SXzVE90hKFxozNqu/zw1Jp/ss59dNHODtnnH3d0dFwgsfYh0ddu3Zl5cqV7Nq1i88//5zVq1fTrl27TOsjEhi5a1GP6kR/EcbEtwOxs1JSzkV3Jt0dH9fn+67V+LRpOQNbSHVICuDd5M9olxjBNnVVFDKJ1ooD/GU5ip8sJtNIfgwZmizrs/74LaPrPmfXv8zYfJ6niSk8epbEunTrpo/xo1cf0nvJEZp8vRuAzvMPsOzANe14aaZwdHREoVBkeCh67949k7eVnqura4Zt3rlzJ0NMpfcmXlfll0D3lw83bsud6JY8io+T3ueRZEMF+VX+VH3OaOVybHjOi2Q1PRYdAmDVEfE3KSw6KlI/H/5U1yYZZYbXFfLMv2/ze5x34c2W8YwVhALG1ERI7TKOzO8WhJ+LHQ1n7MqVOuW2wYMHZzm4uJeXFydOnOD27YwXpXfv3jV4Iejq6gqk3oT5+vpql+u7eEy7ifLz8yMkJIRixYqxdu1a3n33XXbs2MGBAwewtLTUWSc4OJiuXbuydOlSvfu3tLTMsI6Qe0xNVquymSQwZTdmmyn6WQJIGpycdBMQCptiqJ8ee7m/dClOQ12eUlJSuHfvHm5uqU/l4+PjgZc3cHPmzCEsLAww3B0rfQIjsyTH1av6W7BMnjyZcePGGXfwgo5iRVTExr8web30N/7pn8aXdbFj7cBauNhb4V7UWu+66a/fs3tOO9pacu9JIhX/62b7autAY33RqgLbzurviv6qR3uXE//PSp1lr35au3b/BtA/xpkxnylZPTzq16+f9v+BgYH4+fkRHBzMsWPHqFYtY0sJSE1gDB8+XPt7QkKCSPCZkVwuo2gRFV1retKlRmkkCb7feYkr954ypLEfXv+NCTmwgS/TN+sf0ze9Y1JZ+iZ/SkDKVT5QrqOl/AD1FCeppzhJjMaFn9Th/Kauz2OyP0t0egv2XGHBnitULOnAyZvxesucuPHILPsCUKlUBAUFsXXrVt5++23t8p07d+Zou6GhoWzdupVhw4Zpl23ZsoVatWoZXEdcV+UdPxc7fh0QirOdJUWLWFDly62s1tRjd2JlvrD4iTaK/fRXbuQtxT+MGXsYGXWQ/mtL03vJYZoFuuLnbEvVdDNVP09SY61S5NchCekU5TFN5KnXj/q65IJouScUbCK5JxQI5n7KEV7B1bwbxPytrTLbnKOjY6atI9KEhoYSHx/PoUOHqFEjdey2gwcPEh8fb/BC0NvbG1dXV7Zu3apN7iUlJbF7926mTp2aeZ0licTERAC+++47JkyYoH3t1q1bNG3alFWrVul04xIyyrMJNZDlWStBU5hapayOIUMCQpLIrL2uoW5U6Zfb2aW2Wtm5cycHDx5k+PDh+Pj40KBBg0y38+oyU7pXieRF9s3qUpVPfzvOh439TFovfW/WVxNr6W++spLdMNv7v4Ykpmi0E9Zk956hdAnjEyR21VphE1BPZ1lNnxIcvPxyAgSlgwvJd6/ofXh0/77hiRLSPzxKS5RD1i2PqlWrhoWFBRcvXjSY3BMJjLyT2kMAvfGUVa+AV52VPBmS/CHTZJ3pptjKO4qdeMlv84X8Z4Yrf+NPdW1WqRtwQvLBHGOovJrYk4C4+Bf8dSqWhOcpWa7/4GkSW8/E0bKSO7aWmd8mDR8+nG7duhEcHExoaCjz58/nxo0b2tdHjRrFzZs3+emnn7TLoqOjAXjy5Al3794lOjoalUpF+fKp439+9NFH1KtXj6lTp9K2bVv+/PNPtm3bpjMshJC/angX1/7fzcEq9cGSrTNDnnzIWnUdxip/wkt+m69Uc3lPs42I5O4cl8qw49wddpxLfQizsl8IIT7FWXPsJh//dpwp7SryTo3S+XVIwn/aKvZhKUvhtMaTM5KX3jJpY+QKQkEkknvCG8HUi9G80LqyO4euPMjRDIkBAQE0a9aMfv36aWca7N+/P61ataJcuZddZ/z9/Zk8eTJvv/02MpmMoUOHMmnSJEqWLAnABx98QJEiRejSpQsAly9fZtWqVYSHh+Pk5MTNmzeZOnUq1tbWtGjRAoDSpXUvQmxtbQHw9fWlVKlS2T4mwbzMPRGMObjY678wMqWmFgo5iiL2IJPz9JFuN6jmZYpw9LE7iXrWM9TlSalUUqJECe0y+X/dLipVqkSdOnU4e/YskydPpkGDBga7Y6VPYGQnySGSF9nn62TLmoG1TV5PMtByzyjpimc3iW5locDK4mWLDWO65eaUoogDiiK64+YVdXPGIl43QVjMK5BLf+l/eGRI+odHVaumziZozMOj06dPk5ycrBMrwuvlhuTM5JSuzExpz9uKf+ipiKSs/CZdldvpqtzOWY0Hv6kbsFZdm4fYm2/HEnRZcIDL97KeWAOg15LDHL/+iH3/3ufbLGbE7Ny5M/fv3+fLL78kNjaWwMBAfvvtN+11UmxsrM4kS4A2LgCOHj3KihUr8PT0JCYmBoBatWrxyy+/8Nlnn/H555/j6+srHpoWYOsG1+FIzAMePktm9NqT7NRU5Z+kQHor/mKw8g+qyi/xp+UXrFXX5uuUDlyXUr//311wQGc7I9ecFMm9AiCtS66hVntda5ameaD5G5AIgrmIMfeEQiW7SbqCl9qDrjVKs7xvTf7Ixg1pesuXL6dixYqEh4cTHh5OpUqV+Pnnn3XKnD9/XueGbMSIEQwdOpSPP/4YSL0A3bJli7a1kpWVFXv27KFFixaUKVOGTp06YWNjw759+/SOwSQULGnJs1q+JfKs5V6PWl4AtKyU9c15p2AP3q2RsxZpFgo564Y0oEKlKuzdrdsNKvrA37Rr1kjvemldntLbsmULwcHBWFhYGNxf+lar6btjpbd161Zti9n0SY40aUmOzLpXCXnLx8kWP2dbgj2L5Wj2O3Ml0Y35jsuNkE6/18CS9vzUuwb7pnbXPjw6cOAABw4coF+/fjRr1kxnXX9/f9auXZu6nXQPj9auXcupU6fo2bOnzsOjf//9ly+//JIjR44QExPDpk2b6NixI1WrVqV27Zx9HwoF33OsWKFuTHjSNN5NGsMf6lokShYEyK/zhcXPHLQcxByLmTSVH8KSpBzvT0LSm9h7kax/Ao7j1x8B8Gf0LX7eH5Pl9gcOHEhMTAyJiYkcPXpU5xxesmQJu3bt0q2PJGX4SUvspenQoQPnzp0jKSmJs2fPZjkepZB/nOwsaV7RDc90raeTsGCuug2NEr9itbouAG8r/mG76hPGKRfjxCO923rwNOfnu5B9Ng/PEiiPIUlS8Kc643XavG5BTHy7opgpVyjQRMs9oUDI7GOy4LU7Mg+5XGbUzIhZKV68OMuWLcu0zKtjJMlkMiIiIhg+fDgODg5s2rQJe/uXT8rd3d3ZtEn/bKOGeHl55dlkJIWdOWfZ1OfvEQ15mqimuI0qz+LH29GGs182w8oi62dGFgo5k9tV4uPw1NalwRO2AaYn4St7FOXzUSMydIu6du0a77//Pit/OMXD3Us4sPsJ9P8LgPfff5/Zs2czfPhw+vXrx/79+1m4cCErV74cg2zy5MkEBAQAcOHCBf7++29++ukn7Uy4oL87Vtp+QTfJ4efnh5+fH5MmTdJJcgj5TyGXETm0Xra6w6aP4/QffUGexTh69WGW61f3Mr7bb25Ln1Oc0bEy/q6p3wfLly9nyJAh2olh2rRpw6RJk/D09NSW1/fw6Pnz5wwcOJCHDx9Ss2ZNnYdHKpWK7du38+233/LkyRM8PDxo2bIlY8eORaEQ404VBtuG16PJ139jo1JQ1tUOpVzG4Rjdc75NZXcu3XnCmVhDEwPJ2K+pwH5NBb7gCW0U++mo2E1l+WVaKA7RQnGIx5I1WzRBrFeHsldTkZRs3LYYuizx/zwyy3U///M03UK9TN6n8Oap5VuC8W0rIJfL6BzsQeiUHdx5XIyPkz9gcUpTRihXUU9xkh7KrXRU/M1idVMWpLTkES8nrqk2fiubh9bj0p0nJKnVvFWlpM4DnxM3HrHswFU+aVouw0R6Qs7ZxF/iqWTJbk1lva2Hm+bCkE+CYG4iuSe8EUxp8GdwFlvzVCXb+xdeH7ndVdZSqcBSmXqTrMnDE8rUAaFfHbckOzXV1y1q06ZN/yUfTqF+8pBnSQ+05b29vdm0aRPDhg3j+++/x93dne+++4727dtryzx9+lTbqjUsLIyAgACWLVtG586djdxvqqySHELBkN2usFU8iqJSyPEobq1z7s59L4hfj1w3OOmAUi5jXNsKNA/MXhfU3Ahpm3Rji6Ul9kD/w6NXZ3E29PAoIiJC7748PDzYvXt3Dmss5KcyznbETGmp/X3W9os6yb336/sypHEZniWptQ9vMpOALcvUYSxThxEgu0pbxT+0VuynpOw+7RV7aa/Yy0PJli3qYLZqgtirCeQFxg1hcMSIRHtm4p8na8fEBLj16DkO1hY6MSMIMplMJxH8z/8a0Xn+fqKuPeKU5EP35FGEqk8zQrmKqvJLDFKuo6diM8vVTfgxpQV3SH3Y03Tm39pt/H3hHr1qe1GpVFEA2sz+B4C7jxNZ3KtGnh3bm+KOV2va73DAAeO68AtCQSS+mYQ3giktpQriGGWCkG1vwOk8cOBABg4cqPc1x5bDCPEprrOsfv36HDt2TG95gAkTJjBixAgcHBy4evWqTqtWY/cLWSc5hMLNWqXgREQ4Fgo51x8847vtF6nuVQwnO0sGNSxjMLlXtIgFXWt66n0NYFHPYPr9dBS1xlDwGhfUqz+oRfsf9hlVdlTzAC7deUL3UMP1EgRDlIqXLbYvT2qh7bb2Illj8rbOSp6cTfFkaso7VJVdorViP60UB3CSxdNZuYvO7OKFZMEeTUW2aYLYoa7KXYoa3N4Pu/41uQ7pRaw7zTedq3Dx9mPCvklNvNhaKjk1rmmOtiu83lRKOSv6hhDwxcsWovs1FXg7aRxh8qN8pFxDoDyG/sqN9FBs5jd1feaqW3NDejn0zdqom6yNuqmTSAe4cPsJALsv3CU5RUOT8oYnKxJM8wwrniFaRQqFl0juCQVe+kHOi1hkr8tOAZxPwyCRXHz95Xa33PTE2SQIuSdtMgwvRxuOjw3PcnbNVJnHfyN/FzoFl2LloesA/P1pQ+pN35npOvoEeRrf7dfVwYqNQ+qavA9BAKhY8uUELenHo1Lk4OJLQs4xqSzHUsoyPqUbNeVnCZcfoYn8GB7yu4QpjhGmOAYWcFzjw15NIHs1FTmqKUsShsdPNdX+f++z7997fPrbCe2yJ4lZz7orCNYqBVcmt+DtOfuIvv4IC4WMd6p78vMBGVuTgqgvP8Eg5R/UkJ/nPeV23lXsYLOmOktSmnJI8iftu2L+3//i42ir3a5GkkhK0dBj0SEAor8Io2gRVX4coiAIBYxI7gkFQnFbw19KVhYKvulcmeQUiWI2uf/lJbrFCq+TwjAOorejDVfuPaVJgJisRSi80nfdy0ypYtYmbbd0iSJZFxKEfFTHz5Fv36lCGWdbneVyM03bp0GuHZ9vHN0pJ7tOE/kxwhRHqSL/l8ryy1SWX2YQ63guqTisKadN9p2VSiPlYP7AuIQXdFlw0DwHIrxxZDIZC3sEs3RfDB2DPfAoXoQ6fo4M+PkouzWV2Z1UmeqycwxW/kF9xQnteJNnNJ4sVjdlnboWkzad09mmRpL4/egN7e8nbsRTpXRR7K3Ml9QWBKFwEsk9oUDwd7VnZHN/3Bz0N4V+u2qpPK5R/ikEuRihEGld2Z0Fe67g42ST31UxaNWAELaduUPbKu56X89uSASWtOfUzQQ6BuVsZl5BMIdV/UNYsOcyY1tXyNF2xHeEUBC1rVIywzJ5upZ7df0c2XPxXoYyAxv48mnTclx78Iz603cZsScZ56XSnFeX5nv1WzjxkLryk9RWnKKu/BTOskfUU5yknuIksJIEqQhHNX4c1pTjiKYcxyVfEhGtnIS8U8LWkuH/TSAGqRMznJ/QjHKfpXbZPSz50yN5JGVTrtNDsYV2ij2Ul19lunw+o5QrWKOuyyp1Qy5KqfdCtxMSGb32pHZ73Rcdws5KyckI0VVcEN50IrknFBjv1/fNtW2b0jEkvwdJDvYsnnUhQTBSpVJF2TOiIU52xg0+nh+c7azoUrO02bf724BaXLzzWKfLmCDklRX9avLl+jO8W6M0zQNdcba3oqZPiXypS3EbFQ+eJuXLvgUBYGzrCjT5OuNEKjJZausmzxI2dAvx5OcDV03a7l2KsUZTjzWaeoCEn+xmarJPfooQ+RnsZc9oqDhOQ8VxAJIkBack79REn8aXk5I31yRnTJ+vXRCyz1KpYEbHyoxZe5I2ld357egNLkgejEnpw7SUznRW7KS7ciulZPfoq/yLvsq/iNb4skrdgPXqUJ6g26L78YsUwr7eTcfgUvSvl3o/deXeU2LuPaWhv+gVkZfEJ4mQn0RyT3gzmPBJO7Z1eW49ek7v2t46y3O7e2P0F2E8fJYsumAJZudRvPCdU5+El2XGlgs52oa1SqGdZU4Q8lotX0cih9bL1zpUcLfn9K0E/hhYmymRZ9l0Mi5f6yO8WSyVL7vDuhromZHe563K06qSG96ONny15QKrjlzPUOb9+r7M3W1okgwZF6VSXFSXYpG6OQrUBMiuEiy/QLD8PNXl53GRPaKa7BLV5Je0az2SbDip8eak5KP994bkiLhNF3JTh6BSvFXFncjTcfyWrpttPLbMV7fmR3VLGsij6aTYTWP5MarI/6WK/F++UP5MpKY669Wh7NFUIvm/2/mLd54wadM5LJUK3qpakoYzdgGwvG9NapdxzI9DFAQhj4nkniC8olSxIvkysHjRIioxIK4g/GdwI78cJ/cE4XXxS/8Q3pl/INMyv78fSoe5+3WWrRtch2S1BisLBXO6BuE1cmNuVlMQdCgVcnZ90oAUjYStpZINH9ah1ay9OmXSTzClUsq1rVvLudrp3Wbfut6ZJPd0qVFwSvLhlNqHJepmgISH7A7BsgsEyS8QKL9CgOwaRWVPqas4RV1OaddNkKy5IHlwXuPBeakU5zWlOS+V4hGp9VJrJBRykfwTckapkBOarkX3F63K8+WGMwAElirGjhvV2KGphiPxvKXYS2fFLvzkN3lb8Q9vK/4hXipCpLoGGzQh7NNUQI2CsetOM3bdae02u/54kLNfNsNalb1JCQXTlDRxXF1BMCeR3BPeCHk5O6kgZCWwpAMbT8bmdzUEQSgkQtLd/BlqQx7sVZxKpRw4cSNeu0whl6GQixs6If94Ob4c7zXQhCESlAr91205u5qTcV1y4brkwlpN6kNcC1IoK7tBRfllKskuU1F+mXKy69jLnhMsu0CwXPch0x2pKOc0HqTccENRulqOaiMIkDom3/EvwrFWKVAp5fSu401s/HOKFVHx4cootp65zT0c+FHdkh/VLagi+5c2in20VBzARfaIzspddGYX9yU7tqmD2Kapxl5NIM952Vo24ItIZnepSsNyzpkOP/Tw4UOGDBnCunXrAGjTpg2zZs2iaNGiBteRJIlx48Yxf/58Hj58SM2aNfn++++pUOHl+LKJiYl8+umnALi5udG4cWPmzJlDqVIvx1T38vLi6lXdbvn/+9//mDJlivb3a9euMWjQIHbs2IG1tTVdunRhxowZqFQFp3FEBXcHZnSsjHvRrFsrC4K5ieSe8EaQmSG3V8WjaM43IghAnzreyGRQz88pv6siCIIgCHnOs0QRniaq6VnbS+/r7aqV4vudl7idkJir9UhGyWnJi9NqL36hEZCa8POR3aKc7Abl5NcoK7uBv+w6HvK7OMse4ax4xPVnasRUTYK5OBTRnenWzSG19desd6vi/3nqxBsT3w5kzNpTREtliE4pw4SU96guO09rxT6aKw5RQvZYm+h7IVnwjyaQ7ZpqbFdX5TbFGbwiCoD+9XwA6BbiiUfxIqw7fgtJkmhbpSRdunThxo0bREam7rN///5069aN9evXG6z7tGnT+Prrr1myZAlly5ZlwoQJhIWFcf78eezsUlu6Dh06lA0bNgAQGRnJ2LFjadWqFUePHkWhePkA6ssvv6Rfv37a321tX87ArVaradmyJU5OTuzdu5f79+/To0cPJEli1qxZ2Xvjc0mHoDdnIkihYBHJPUEwkmcJG7YMq0cx0XVWyCGVUp6rE8gIglC4iRlxhdddROsK1C/rhNxA11ZbSyW7P22oTWykyYvQSEaZOiOvVBo0tbTLbXhOWdkNyspv0NHCQyT3hFxnZaHg6GdNUMhlFC2iok4ZR+2s0hrkHJQCOJgSwNiUnoTIz9BEfowm8mN4yO/SWBFFY0UUWMBZTWn2agL5RxPIsr9f8AwrNp6IpX89H20X3mv/XiQyMpJ9+/bzwMYTuQwWLFhAaGgop8+cpUL5AC7ffcLm07fZfeEOtX0dGdjAl5kzZzJmzBjatWsHwNKlS3FxcWHFihUMGDCA+Ph4Fi5cyLx58+jduzeVK1dm2bJleHh4sG3bNpo2fTnLr52dHa6urnrfiy1btnDmzBmuX7+Ou7s7AF999RU9e/Zk4sSJ2Nvb59rf4Y9BtXNt24JgTiK5JwgmKOuifwwYQRAEQcgLmU3uJJKCQmHh62RrMLGXxkIhz7Ds1XM8ZkpLNp6IZdKms9x89Fy73NnOkjuPzdvq7ynWREl+lAqsT7Cv/gSEIJhbCVtL7f89S9gwvm0Fvtl2UWcGdDUK/tFU5B9NRcbRnbKyGzSRH6WJ4hhVZP8SIL9GgPwa/dhEsqTgmOTHP48D2bC+PJb4kIiKcQvXIrO04d0/7wP3AZDLQGFlQ/1P5mFbKUynXgcuP2DO+n3ExcVRNfTl5FGWlpbUr1+fffv2MWDAAI4ePUpycjKNGjXSlnF3dycwMJB9+/bpJPemTp3K+PHj8fDwoGPHjnz66afaLrf79+8nMDBQm9gDaNq0KYmJiRw9epSGDRtmeO8SExNJTHz5OZCQkGDq2w+I3ltC4SGSe8IbQYy4JwiCILzupCzaNZVxtuXSnSf4Otnw792neVQrQXhp2/B63H+SROkSWc/irpDLaFjOiZ3n72qXqRRyvn2nCh//epw5XVPHvGtZyY1mga74jt4EwPQOlShdvAidDUxCU7GkAydvxut9zRgT2gZme11ByKluoV68F+LJF3+e5ucDV1n9QSgA7X9Im1BJxgXJgwtqD+ao36I4CdSSn6aW/BR15afwkN+lpuwcNeXnAEiUlJySvBn/PJmdRSwpTgIPSG0Fp5FAZu2A+ulDvXV5eC81NnuvushojRMDG5QBwMXFRTt+XlxcHCqVimLFiums6+LiQlzcyxncP/roI6pVq0axYsU4dOgQo0aN4sqVK/z444/a7bi4uOhso1ixYqhUKp3tpDd58mTGjRtn1PsqCK8DkdwT3ggycwy6JwiCIAgFWJMAF07dTMDJzlLv60t712DJP1foUcuLOlN35nHtBAHKONtRxtn48ot71QBgzq5LSFLq2GRtq5SkZUU3lOla9qVvBOjtaIOdlcWrm9Ja1qcms3Zc5Me9V0yuP4ClRcYWhYKQl2QyGV+2rcCIZuW053rMlJYs3ReDWiNR2aMoyw9eZc2xm1zeu56of1byfSbbO9wvkRryFM7Kkzlm9T7/atyIlnw5pfFmOklYyNRZVYhpkefxc7ajsb8zkiRlee/1aplhw4Zp/1+pUiWKFStGhw4dmDp1KiVKlNAed1bbSW/UqFEMHz5c+3tCQgIeHqJDvfD6Esk9QRAEoUDLrBuiILxp9HVVTDOwQRm8HW0ITTe7bnoli1ozpmX53KqaIOSatBZBaZSvxEH6m3uZDMq62PJ5q/KM33Amw7asVQoC3LI/PldmMSgIeUUmk2VIYveo5aX9f5BnMdpWKclHshfcDaiHfhJusgf8VPQBL6x3EvvkOAC+8lh8iaW9Yi/fPEtgssMa6qmOcVLy5pTGm9MaL85JHiTbprbG0zx9CLbF6ffTEb7uVJk7d+5oW9m5urqSlJTEw4e6rf/u3LlDrVq1MCQkJASAS5cuUaJECVxdXTl48KBOmYcPH5KcnJyhRV8aS0tLLC31P+wyljKL4QMEoSARyT3hjSAa7gmCIAiFhbOelndzulZj4sazfP9fV0R9VEo5bauUzM2qCUKhIJPJ6FPHW29yL6c36wpxsy8UEvXLOvFPRFsu333Kb0ev89P+1K6yXiWKEHP/GQD3KM06INnVj8eJH+B3eQR1SklUkl1GEXuc+MRj1PGQ4ye/iR83aafYq91+rHNRAm0tCL22CB/3MM5pSrPrhA27d+9m6tSpAAQFBWFhYcHOnS9bi8fGxnLq1CmmTZtmsO5RUamz+7q5uQEQGhrKxIkTiY2N1S7bsmULlpaWBAUFme9NA/4Z2Yh7jxP5Yde/DG5UJusVBKGAEMk94Y0gLsMEQRCEwmJAfV+u3H9Gy4ovB+1vUdGNFhXdtL//1LsGn/5+nKntK+VHFQWhwHKwVmn/H+Bmz9nYBBr7O7P93B0A5HJZpqNT1vQuzqNnyZy//Vhn+YyOlbGzErdOQuFiY6mkYikHlu6P0S77pX8o647fpEmAC5Gn42hdyZ3dF+7Sb0cQVyN/5EnTwZwpXp2oDTux9q3Oe7YfEZh0hYqyK3wz7xe+aGxHv4DnuMkfMaKmnMl7j/Oe4wV6lJDz0SwXihQpQpcuXQBwcHCgT58+fPbZZwAcP36ciIgIKlasSJMmTYDUyTIOHDhAw4YNcXBw4PDhwwwbNow2bdpQunRpAMLDwylfvjzdunVj+vTpPHjwgE8++YR+/fqZfabckkWtKVnUmrndzJs0FITcJr6hBEEQBEEQChAbSyWz3q2aaZl6ZZ04OLpJHtVIEAq+rztV5t6TRMo422qX/dIvhKjrD7FUKrTJPX361PFm4X9j8JVztePLtoF4jdyoU6ZDUKncqbgg5IFmFVz5/egNShcvgquDFf3r+QIvu7y/F+JJyK71VGnehdu/fs5TSyWNGzXltFcH7mLLTk1VdlKVG/cXMepJf759UQs/2U38alynflIk/TZd5MnzZHx9NWzZsgU7Ozvtvr/55hs0Gg3z58+nadOmNG7cmCVLlqBQKIDU7rOrVq1i3LhxJCYm4unpSb9+/RgxYoR2GwqFgo0bNzJw4EBq166NtbU1Xbp0YcaMGXn4LgpCwSaSe8IbQXTLFQRBEARBeH21q5Yx+eZQxIIG5Zx5/CIZgCKq1GRCPT9HbZluIZ583qq8Nrkn/++icV63IAb8fDS3qy0IeaJxgDNrBtbC18nWYJkypd2I3rkOa5WSkkWtATh69SEXbj9m1JqTAHj+bwMAz+UyojR+REl+UKsRtrXAFpjyTmUCA3Vj0crKiunTpzN//nzi4uIytLSrVq0aBw7on906vdKlS7NhwwZTDlsQ3igiuScIgiAIgiAIwmvLzsqC42PDsVSmTobhbG9F9BdhPE9W42pvpVM25L8JaZpWcKWunyN7Lt7L8/oKgrnJZDKqlS6WZbkyznY6vwd5FiPIsxiVSxWlxXd7ABj/ViDv1SyN96hNOmUnvh1I6yqihasg5BeR3BPeCD6Ohp9SCYJQMJV3s+dMbALtRVcoQTC7AfV8mPf3Zd6tUTq/qyIIecLBWndm0aJFVBRN9/ve/zXkbOxjmgQ4a5d1CvZgz8V7VC7lkDeVFIQCqry7PRcnNifheTIlbFMnfQryLMbRq6mz4B77PIziNqrMNlGgBbjaZV1IEAo4kdwTXmuHxzTheZKaYoX4y0YQ3lRrBtbi8t2nBLiJCy5BMLcRzfxpVcldxJcg/KdUsSKUKlZEZ1mrSm74ONlk2pVREN4UFgq5NrEH8NuAUG48fI5cTqFO7EFqa95dnzTAVkyaIxRi8vyugCDkJic7S0qXKJJ1QUEQChwrCwXl3e2RZTFo5pw5c/D29sbKyoqgoCD27NmTafndu3cTFBSElZUVPj4+zJ07V+f1BQsW0KxZMyB1fJcmTZpw6NAhnTJeXl7IZLIMP4MGDdKW6dmzZ4bXQ0JCTHkLBCHXKOQyKpZyQKkQl4KCYIhMJqOCuwNWFor8roogFDhyuYzSJTImxQsrL0cbHNMlLwWhsBFXdIIgCEKhtWrVKoYOHcqYMWOIioqibt26NG/enGvXruktf+XKFVq0aEHdunWJiopi9OjRDBkyhNWrV2vL7Nq1i/bt2wOwbds2SpcuTXh4ODdv3tSWOXz4MLGxsdqfrVu3AtCxY0ed/TVr1kyn3KZNuuPTCIIgCIIgCIIg5JRI7gmCIAiF1tdff02fPn3o27cvAQEBzJw5Ew8PD3744Qe95efOnUvp0qWZOXMmAQEB9O3bl969ezNjxgxtmeXLl9OvXz8AypYty4IFC9BoNGzfvl1bxsnJCVdXV+3Phg0b8PX1pX79+jr7s7S01ClXvHjxXHgXBEEQBEEQBEF4k4nkniAIglAoJSUlcfToUcLDw3WWh4eHs2/fPr3r7N+/P0P5pk2bcuTIEZKTk/Wu8+zZM5KTkw0m5pKSkli2bBm9e/fO0IV4165dODs7U7ZsWfr168edO3eMPTxBEARBEARBEASjiBEjBUEQhELp3r17qNVqXFxcdJa7uLgQFxend524uDi95VNSUrh37x5ubm4Z1hk5ciQlS5akSZMmerf5xx9/8OjRI3r27KmzvHnz5nTs2BFPT0+uXLnC559/TqNGjTh69CiWlhnHdElMTCQxMVH7e0JCgt79CYIgCIIgCIIgpCeSe4IgCEKh9mprOUmSMp2EQ195fcsBZs6cycqVK9m1axdWVlZ6t7dw4UKaN2+Ou7u7zvLOnTtr/x8YGEhwcDCenp5s3LiRdu3aZdjO5MmTGTdunMF6C4IgCIIgCIIg6COSe3kk7eZRtMQQCpK08zHt/MwPIjaE7FKpVCgUCi5fvkyFChW0y2/cuIGjo6Pec8rJyYlr167pvBYTE4NSqcTCwkK7PO3fr776iu3bt1OpUiW9dbh69Srbtm1jzZo1WdbXzc0NT09PLl68qPf1UaNGMXz4cO3v8fHxlC5dWsSGUKAUhO+N9PsX8SEUJAUhPkRsCAWRiA1B0M+csSGSe3nk8ePHAHh4eORzTQQho8ePH+Pg4JBv+wYRG0L2vffee3qXGzqn9+3bx9y5czMsd3R0zLDs559/Jjg42OC+Fy9ejLOzMy1btsyynvfv3+f69et6u/5C6uQb6bvrpn3Zi9gQCqL8/N5I2z+I+BAKJnFdJQj6idgQBP3MERsyKb8fvb4hNBoNt27dws7OLkPXr4SEBDw8PLh+/Tr29vb5VMPCQ7xfxsvqvZIkicePH+Pu7o5cnj/z62QWGyD+3qZ4E9+r1atXM2DAAL755htq1KjBkiVLWLp0KQcOHKB06dJEREQQGxvLvHnzgNRWeqGhofTq1Yv27dvTqFEjLCwsWLhwIW3btgVSu+JOnDiRWbNm0apVK21s2NraYmtrq923RqPB29ubd999lylTpujU68mTJ0RERNC+fXvc3NyIiYlh9OjRXLt2jbNnz2JnZ5flsYnYMB/xXpkms/erIHxvgLiuMifxfhmvsF9Xib+1acT7ZbzCHhsg/t6mEO+VafLqukq03MsjcrmcUqVKZVrG3t5eBIcJxPtlvMzeq/xseQHGxQaIv7cp3qT3qlevXjx//pxp06YRGxtLYGAgmzZtIjAwEIAHDx4QGxurfT8qVarEpk2bGDZsGAsWLABg6tSpdOvWTbvNRYsWkZSUxIABA3T2NXbsWCIiIrS/b9u2jWvXrtG7d+8M9VIoFJw8eZKffvqJR48e4ebmRsOGDVm1apVRiT0QsZEbxHtlGkPvV35/b4C4rsoN4v0yXmG/rhJ/a9OI98t4hT02QPy9TSHeK9Pk9nWVSO4JgiAIhdrAgQMZOHCg3teWLFmSYVn9+vU5duwYCQkJODg40KdPH53XY2JijNpveHi4wfExrK2t2bx5s1HbEQRBEARBEARByIn8608hCIIgCIIgCIIgCIIgCEKOiOReAWBpacnYsWN1BlIXDBPvl/Feh/fqdTiGvCLeK9MU9versNc/L4n3yjSF/f0q7PXPa+L9Ml5hf68Ke/3zmni/jPc6vFevwzHkFfFemSav3i8xoYYgCIIgCIIgCIIgCIIgFFKi5Z4gCIIgCIIgCIIgCIIgFFIiuScIgiAIgiAIgiAIgiAIhZRI7gmCIAiCIAiCIAiCIAhCISWSe4IgCIIgCIIgCIIgCIJQSInkXi6ZM2cO3t7eWFlZERQUxJ49ezItv3v3boKCgrCyssLHx4e5c+dmKLN69WrKly+PpaUl5cuXZ+3atblV/Txl7vdqyZIlyGSyDD8vXrzIzcPIM6a8X7GxsXTp0oVy5cohl8sZOnSo3nJ5eW6J2DCeiA3TiNh4c2IDRHyYorDHBoj4MIWIDdMU9vgQsWE8ERumEbHx5sQGiPgwRYGNDUkwu19++UWysLCQFixYIJ05c0b66KOPJBsbG+nq1at6y1++fFkqUqSI9NFHH0lnzpyRFixYIFlYWEi///67tsy+ffskhUIhTZo0STp79qw0adIkSalUSgcOHMirw8oVufFeLV68WLK3t5diY2N1fl4Hpr5fV65ckYYMGSItXbpUqlKlivTRRx9lKJOX55aIDeOJ2DCNiI03JzYkScSHKQp7bGTnGN7k+BCxYZrCHh8iNownYsM0IjbenNiQJBEfpijIsSGSe7mgRo0a0vvvv6+zzN/fXxo5cqTe8iNGjJD8/f11lg0YMEAKCQnR/t6pUyepWbNmOmWaNm0qvfPOO2aqdf7Ijfdq8eLFkoODg9nrWhCY+n6lV79+fb0fJnl5bonYMJ6IDdOI2HhzYkOSRHyYorDHhiSJ+DCFiA3TFPb4ELFhPBEbphGx8ebEhiSJ+DBFQY4N0S3XzJKSkjh69Cjh4eE6y8PDw9m3b5/edfbv35+hfNOmTTly5AjJycmZljG0zcIgt94rgCdPnuDp6UmpUqVo1aoVUVFR5j+APJad98sYeXVuidgwnogN04jYeFm31z02QMSHKQp7bICID1OI2DBNYY8PERvGE7FhGhEbL+v2uscGiPgwRUGPDZHcM7N79+6hVqtxcXHRWe7i4kJcXJzedeLi4vSWT0lJ4d69e5mWMbTNwiC33it/f3+WLFnCunXrWLlyJVZWVtSuXZuLFy/mzoHkkey8X8bIq3NLxIbxRGyYRsTGy/Kve2yAiA9TFPbYABEfphCxYZrCHh8iNownYsM0IjZeln/dYwNEfJiioMeGMts1EDIlk8l0fpckKcOyrMq/utzUbRYW5n6vQkJCCAkJ0b5eu3ZtqlWrxqxZs/juu+/MVe18kxvnQV6eWyI2jCdiwzQiNt6c2AARH6Yo7LGRnf29yfEhYsM0hT0+RGwYT8SGaURsvDmxASI+TFFQY0O03DMzR0dHFApFhizrnTt3MmRj07i6uuotr1QqKVGiRKZlDG2zMMit9+pVcrmc6tWrF+qnBJC998sYeXVuidgwnogN04jYeFn+dY8NEPFhisIeGyDiwxQiNkxT2ONDxIbxRGyYRsTGy/Kve2yAiA9TFPTYEMk9M1OpVAQFBbF161ad5Vu3bqVWrVp61wkNDc1QfsuWLQQHB2NhYZFpGUPbLAxy6716lSRJREdH4+bmZp6K55PsvF/GyKtzS8SG8URsmEbExsu6ve6xASI+TFHYYwNEfJhCxIZpCnt8iNgwnogN04jYeFm31z02QMSHKQp8bJg0/YZglLTpkRcuXCidOXNGGjp0qGRjYyPFxMRIkiRJI0eOlLp166YtnzaV9LBhw6QzZ85ICxcuzDCV9D///CMpFAppypQp0tmzZ6UpU6a8FlNv58Z7FRERIUVGRkr//vuvFBUVJfXq1UtSKpXSwYMH8/z4zM3U90uSJCkqKkqKioqSgoKCpC5dukhRUVHS6dOnta/n5bklYsN4IjZMI2LjzYkNSRLxYYrCHhvZOYY3OT5EbJimsMeHiA3jidgwjYiNNyc2JEnEhykKcmyI5F4u+f777yVPT09JpVJJ1apVk3bv3q19rUePHlL9+vV1yu/atUuqWrWqpFKpJC8vL+mHH37IsM3ffvtNKleunGRhYSH5+/tLq1evzu3DyBPmfq+GDh0qlS5dWlKpVJKTk5Pk7++v/d3Ly0uKiIiQkpKSjKrb+fPnpXbt2klFixaVrK2tpRo1akh//vlnhnKenp4SoPfH0tJSp2xCQoL04YcfSu7u7pJKpZL8/PykqVOnSikpKbnyfumrk6enp06ZvDy3RGwYL7djIzw8XBo2bJhUrly5bMXHxYsXpffee0/y8PCQrKysJB8fH2nYsGHSvXv3MpT9/fffpVq1aknFihWTHBwcpOrVq0s//fRThnJ9+vSRKlSoIDk4OEhWVlaSn5+f9Mknn0h37941+/slYqNwK8jfHelt3bpVe369eh6b8t0hSZJ09+5daciQIdrjdnZ2lpo1aybdv38/0zoU9tjIzjG8yfFRkGNjzJgxUsuWLSV3d3cJkHr06KG3nLGxsXPnToPlAGnAgAFmf78KWnyI2DBeQY6NpKQkKSIiQlu/cuXKSd99912GcmPHjjX6O8NQHBkTF5IkYuNNig1JKtjxkZ45rqtu3boljRkzRgoJCZFKlCgh2dnZSdWqVZPmzZtn1D15QY0NkdwTXmsTJkyQZDKZNGrUKGnnzp3StGnTJJVKJfXr1y/Lda9cuSIVL15cqlChgvTLL79IGzZskFq2bCnJZDKdpxKSJEnHjh2T9u/fr/OzatUqCZDeeecdbbnk5GSpZs2aUrFixaTZs2dLW7ZskYYPHy7JZDLpww8/NPvxC0JmchIfd+7ckUqUKCF5e3tLS5YskXbs2CF99dVXkq2trVSlShVJrVZryy5cuFACpPbt20ubNm2S/vrrL+mdd96RAOnrr7/W2e4777wjffvtt9LGjRul7du3S1OnTpXs7e2l8uXLS4mJiWZ/DwRBn5zERnqPHz+WvLy8tImMVy9Cjf3ukCRJunnzpuTj4yOVLVtW+vHHH6Xdu3dLq1evlgYPHizFxsbm+JgFwRg5jY0iRYpIISEh0vvvvy+pVCqDyT1jYyM+Pj5Duf3790vdu3eXACkyMtIchy0IWcppbPTt21eytLSUpk2bJu3cuVMaOXKkJJPJpIkTJ+qUS0vuRUZG6pzz+lpDeXp6SrVr184QH5cvXzbLMQuCsQraddX69eslDw8PacyYMdLGjRulLVu2SMOGDZPkcrnUq1cvsxxzfhDJPUGHRqORnj17lt/VMIt79+5JVlZWUv/+/XWWT5w4UZLJZDpNYfUZMGCAZGVlJd24cUO7LCUlRQoICJA8PDx0khf6RERESIC0bds27bKVK1dKQIZMfP/+/SW5XC6dO3fO2MMT8oGIj5cWLFiQ4fyWJEmaNGmSBEjHjh3TLqtdu7bk6empEzMajUby9/eXKlWqlGVd58yZIwHS9u3bjTk0IR+I2NBv0KBBUtWqVaXPPvtM70WoPvq+OyRJktq2bSuVLFlSevDggdH7F/KfiA1d6b8HbGxsDCb39DEUG6/SaDSSj49Phu8doWARsfHSqVOnJJlMJk2aNElneb9+/SRra2ud1tlpyT1jvk88PT2lli1bmnAkQkEh4kM/c11XPXjwQG+rwUGDBkmAdO3aNaPrVJCICTVeUxEREchkMk6fPs27776Lg4MDLi4u9O7dm/j4eG05mUzG4MGDmTt3LgEBAVhaWrJ06VKWLFmCTCZjx44d9OvXjxIlSmBvb0/37t15+vQpcXFxdOrUiaJFi+Lm5sYnn3xCcnKyTh1++OEHKleujK2tLXZ2dvj7+zN69GidMnFxcQwYMIBSpUqhUqnw9vZm3LhxpKSk5Pg9iIyM5MWLF/Tq1Utnea9evZAkiT/++CPT9f/55x8qV65MyZIltcsUCgXNmzfn+vXrHDp0yOC6kiSxePFifHx8aNSokc42ZTIZzZs31ynfqlUrNBoNa9euNeEIhewS8ZHz+EgbLNfBwUFnedGiRQGwsrLSKWtra4tc/vIrRyaTYW9vr1POECcnJwCUSmWWZYWcEbGR89hIs2fPHubPn8+PP/6IQqEwah1D3x0xMTGsW7eOfv36UaxYMaOPRTAfERvmiY303wOmMBQb+uzcuZPLly/Tq1evbO9PMJ6IjZzHxh9//IEkSXrXf/78OZGRkTmuo5A/RHwUzOuqYsWK6Z34o0aNGgDcuHHDqO0XNOJO6TXXvn17OnfuTJ8+fTh58iSjRo0CYNGiRdoyf/zxB3v27OGLL77A1dUVZ2dnDh8+DEDfvn1p164dv/zyC1FRUYwePZqUlBTOnz9Pu3bt6N+/P9u2bWPq1Km4u7szfPhwAH755RcGDhzIhx9+yIwZM5DL5Vy6dIkzZ85o9xsXF0eNGjWQy+V88cUX+Pr6sn//fiZMmEBMTAyLFy/WllWr1UiSlOXxyuVy7YXcqVOnAKhYsaJOGTc3NxwdHbWvG5KUlETx4sUzLLe0tATgxIkThISE6F1327ZtXL16lQkTJiCTyXS2KZfLM3yYpN+mkHdEfGQ/Pt566y1Kly7Nxx9/zJw5c/D09OTYsWNMmTKF1q1bExAQoC374Ycf0rFjRyZOnEj//v2RyWQsWbKEo0ePsnLlSr3bT0lJITExkejoaD7//HPq1KlD7dq1szxGwTxEbGQ/NgCeP39Onz59GDp0KNWqVWPdunVZrgOGvzv27NmDJEm4u7vz7rvvsn79elJSUggJCWHy5MmEhoYatX0h50Rs5Cw2sstQbOizcOFC5HJ5hhtJIXeJ2Mh+bJw6dQonJydcXV11lleqVEln++lVrFiRO3fu4OjoSNOmTZkwYQKlS5fOUO7vv//Gzs6OFy9e4Ofnp/1uMjY5IpiHiI+CdV1lyI4dO1AqlZQtW9ao7Rc4ed1UUMgbaU22p02bprN84MCBkpWVlaTRaCRJSh3c0cHBIUM3n8WLF0tAhnHg3nrrLb3jZFWpUkWqVq2a9vfBgwdLRYsWzbSOAwYMkGxtbaWrV6/qLJ8xY4YE6DTRzWxwzPQ/Y8eO1a7Tr18/vYPLSpIklS1bVgoPD8+0fm+99ZZUtGhR6fHjxzrL69atKwEZms6n17lzZ0mhUOh06ZUkSZo5c6YESHv27NFZ/vnnn0tAlnUSzEPER87jQ5JSB6MNDQ3V2UfHjh2lFy9eZCj7xx9/SA4ODtpy1tbW0rJly/Rud//+/TrbbNGihZSQkJBlfYScE7Fhntj4+OOPJR8fH22XGmO7URn67pg8ebIESPb29lLbtm2lyMhIafXq1VKlSpUkKysr6fjx41nWScgZERvmiY30TOmWayg2XvXw4UPJyspKatq0qUl1EbJPxEbOYyMsLEwqV66c3tdUKpVOd8affvpJmjhxorRp0yZpx44d0pQpU6TixYtLLi4uGeJj4MCB0qJFi6Tdu3dLf/zxh9S1a1cJkN57771M6yOYj4iPgnldpc/mzZsluVwuDRs2LMuyBZVoufeaa9Omjc7vlSpV4sWLF9y5cwcXFxcAGjVqZLCbT6tWrXR+DwgI4I8//qBly5YZlm/ZskX7e40aNZg9ezbvvvsu77zzDrVr18bR0VFnnQ0bNtCwYUPc3d11mvw2b96cTz75hN27d1O+fHkA1q9fT2JiYpbH6+7urvN7Zhn6rLL3gwcP5s8//6R79+7MmDEDGxsbZs+ezb59+wDDXUsePHjAH3/8QbNmzXS69AJ07dqVL7/8kv79+7N48WLKlSvHX3/9xXfffZfpNoXcIeIj+/Hx8OFD2rZty7Nnz1i+fDkeHh6cOnWK8ePH06ZNGzZu3KjtRhsZGcl7771Hx44d6dSpE0qlknXr1tGzZ0+SkpIytK6oWLEihw8f5tmzZ0RHRzNlyhTCwsLYsWMHRYoUyfI4hZwTsZH92Dh06BAzZ84kMjISa2vrLPedJrPvDo1GA0CpUqVYvXq1tsVFaGgoZcqUYdq0aSxbtszofQnZJ2Ij+7GRXZnFxquWL1/Oixcv6Nu3b67URTBMxEbOYsPY9bt166bzWsOGDWnYsCGhoaFMmzaNb7/9Vvva999/r1O2bdu2FCtWjNmzZzN8+HCqVq2aZb0E8xDxUbCuq1517NgxOnXqpO0RUViJ5N5rrkSJEjq/p3X/fP78uXaZm5ubwfVf7ZaqUqkMLn/x4oX2927dupGSksKCBQto3749Go2G6tWrM2HCBMLCwgC4ffs269ev19vfHeDevXva/5cvX97oJsBpSpQowYsXL3j27FmGhMCDBw8ICgrKdFuNGzdm8eLFfPzxx/j6+mrrMX78eEaPHm3wQ2LZsmUkJibqvbB0dHQkMjKSHj16aLv0lihRgq+//po+ffpk+cEjmJeIj+zHx9SpU4mOjubq1ava96hu3br4+/vTqFEjli9fTo8ePZAkid69e1OvXj2drgdNmjQhPj6eDz/8kE6dOmFjY6N9zcbGhuDgYADq1atHzZo1CQkJYd68eQwbNizL4xRyTsRG9mOjd+/etGvXjuDgYB49egSgPcaEhAQsLS2xs7PLsF5m3x1pf48mTZrodKVyc3OjcuXKHDt2LMtjFMxDxEb2YyO7MouNVy1cuBAnJyfatm2bK3URDBOxkf3YKFGiBNHR0RmWP3361OAwQenVqFGDsmXLcuDAgSzr/d577zF79mwOHDggknt5SMRHwbquSi8qKoqwsDD8/PzYtGmT9m9TGIlmQkKuPWnt1asX+/btIz4+no0bNyJJEq1ateLq1atAaqIrPDycw4cP6/3p06ePdlu+vr5YWFhk+fPll19q10nr13/y5EmdesXFxXHv3j0CAwOzPIYePXoQFxfHmTNnuHjxIqdPnwZS37O6devqXWfhwoW4uLhkeMKSpnr16pw5c4YrV65w6tQpbt26pR2frF69elnWSchbIj70i46OpmTJkhkuRKpXrw68HF/j9u3bxMbGageofbXs06dPiYmJyXRfwcHByOVyLly4kGk5IW+J2NDv9OnT/PbbbxQrVkz7M3XqVG19svPdkTbukj6SJIlW3wWMiA3zyuq6Kk1UVBRRUVF0797d4E2qkL9EbOhXsWJF7t69S1xcnM7ytO0ZE1vGfhekJWbE90bBI+JDv9y4rkoTFRVFkyZN8PT0ZMuWLRkmCixsRMs9IdfZ2NjQvHlzkpKSeOuttzh9+jSenp60atWKTZs24evrm+Xsf9lpAtysWTOsrKxYsmQJNWvW1C5Pm3XorbfeMqr+SqVSm3yLj49n/vz5tG3bFk9Pzwxljxw5wokTJxgxYkSWM3t6eXkBqV+yX331Fe7u7nTs2NGoOgmvj8IaH+7u7mzfvp2bN2/qtDjdv38/kNp9EFJno7KystL7NHn//v3I5fJMn1QC7N69G41GQ5kyZbI8RuH1UVhjY+fOnRmWLVmyhKVLl/LHH3/obaGd1XdHzZo1KVWqFFu2bEGtVmtb7926dYvjx4/TpUuXLI9ReH0U1tjIDlOuqxYuXAigcyMqvFkKa2y0bduWzz77jKVLl/K///1PZ31ra2uaNWuW6foHDhzg4sWLDBkyJMt6//TTTwAGJwUUXl+FNT5y47oKUhsqNGnShFKlSrF169Ysj70wEMk9IVf069cPa2trateujZubG3FxcUyePBkHBwdty54vv/ySrVu3UqtWLYYMGUK5cuV48eIFMTExbNq0iblz52oTBK/OrmOM4sWL89lnn/H5559TvHhx7ROJiIgI+vbtqx07AFK/6Hr37s2iRYvo3r07AHfu3OGrr76idu3a2NnZce7cOaZNm4ZcLs8whkUaYy4sx4wZQ8WKFXFzc+PatWssWrSIgwcPsnHjRpPGERAKr9chPgYNGsTy5csJCwtj5MiR2jH3JkyYgIuLC127dgVSux0MHDiQr7/+mu7du9O5c2cUCgV//PEHK1asoE+fPtouBRs2bGDBggW0adMGT09PkpOTOXLkCDNnzqRMmTJiDKU3wOsQGw0aNMiwzV27dgHoHesGsv7ukMvlfPPNN3Tq1Im2bdvywQcf8PTpU8aPH49KpdLOuie8vl6H2IDUhzV3794FUmddvHr1Kr///jsA9evXx8nJSWefxibsXrx4wYoVK6hVq5bObO3C6+91iI0KFSrQp08fxo4di0KhoHr16mzZsoX58+czYcIEna6XlStX5r333iMgIAArKysOHTrE9OnTcXV1ZcSIEdpyK1asYM2aNbRs2RJPT08ePXrEb7/9xi+//ELPnj2pXLlytt5voXB5HeIjN66rzp8/T5MmTQCYOHEiFy9e5OLFi9rXfX19M3wfFQYiuSfkirp167JkyRJ+/fVXHj58iKOjI3Xq1OGnn37SBoqbmxtHjhxh/PjxTJ8+nRs3bmBnZ4e3tzfNmjUzS/Z8zJgx2NnZ8f333zNjxgxcXV0ZOXIkY8aM0Smn0WhQq9XaQcshtcVedHQ0ixcv5tGjR7i5udG2bVu++OILvR8iz58/Z+XKldSrVy/T6bMfPnzI//73P+Li4rC3t6d+/focPHgwWx+WQuH0OsRHUFAQBw4cYPz48YwZM4a7d+9SsmRJ2rRpkyFGpk+fTkBAAPPmzeO9995Do9Hg6+vL7Nmz6d+/v7ZcmTJlUKlUjB8/ntu3bwOpLVz79OnDyJEjC31TeSFrr0NsmMrY744OHTqwdu1aJk6cSIcOHbC0tKR+/fqsWrVKOy6s8Pp6XWJj7Nix7N69W/v7rl27tDdpO3fu1LmJMzY2ANasWcPDhw/FQ6A30OsSG3PmzKFkyZLMmjWLuLg4vLy8+Pbbb/nwww91ypUvX5758+cTGxtLUlIS7u7uvPPOO3zxxRc6PSF8fHx49OgRo0eP5v79+1hYWFChQgXmzJnDgAEDcny8QuHwusSHKYz57ti/fz/3798HoHXr1hleX7x4MT179sx2HfKLTDJmRERBEARBEARBEARBEARBEAocMZKmIAiCIAiCIAiCIAiCIBRSIrknCIIgCIIgCIIgCIIgCIWUSO4JgiAIgiAIgiAIgiAIQiElknuCIAiCIAiCIAiCIAiCUEiJ5J4gCIIgCIIgCIIgCIIgFFIiuScIgiAIgiAIgiAIgiAIhZQyvyvwptBoNNy6dQs7OztkMll+V0cQAJAkicePH+Pu7o5cnj+5fhEbQkEkYkMQ9CsIsQEiPoSCqSDEh4gNoSASsSEI+pkzNkRyL4/cunULDw+P/K6GIOh1/fp1SpUqlS/7FrEhFGQiNgRBv/yMDRDxIRRs4rtDEPQTsSEI+pkjNkRyL4/Y2dkBqX80e3v7fK6NIKRKSEjAw8NDe37mBxEbQkEkYkMQ9CsIsQEiPoSCqSDEh4gNoSASsSEI+pkzNkRyL4+kNf21t7cXHyZCgZOfTdNFbAgFmYgNQdAvv7s0ifgQCjLx3SEI+onYEAT9zBEbYkINQRAEQRAEQRAEQRAEQSikRHJPEARBEARBEARBEARBEAopkdwTBEEQBEEQBEEQBEEQhEJKJPcEQRAEQRAEQRAEQRAEoZASyT1BEARBEARBEARBEARBKKREck8o0CRJYsTvx5m06Wx+V0UoRKb8dY7hv0YjSVJ+V0UoIObMmYO3tzdWVlYEBQWxZ8+eTMvv3bsXAGdnZ3x8fJg7d26GMo8ePWLQoEG4ublhZWVFQEAAmzZt0r4+efJkqlevjp2dHc7Ozrz11lucP3/evAcGXLv/jHfm72fn+Ttm37YgFFa7L9zlnfn7uXr/aX5XRRAKlB/3XCbg80iuP3iW31URCpmHDx/SrVs3HBwccHBwoFu3bjx69CjTdSRJIiIignLlygHQsmVLTp8+rVNmwIAB+Pr6Ym1tjZOTE23btuXcuXN6t5eYmEiVKlWQyWRER0eb47AEwSxWHrpG90WHeJqYkm91EMk9oUCLuf+MX4/cYP7fl/O7KkIhMnf3v6w5dpOzsY/zuypCAbBq1SqGDh3KmDFjiIqKom7dujRv3pxr167pLX/lyhU6duwIwJ49exg9ejRDhgxh9erV2jJJSUmEhYURExPD77//zvnz51mwYAElS5bUltm9ezeDBg3iwIEDbN26lZSUFMLDw3n61LzJhk9+O86Byw/otfiwWbcrCIVZj0WHOHD5AZ/+diK/qyIIBcapm/FM2HiW58lq6k7byU/7Y/K7SkIh0qVLF6Kjo4mMjCQyMpLo6Gi6deuW6TrTpk3j66+/Zvr06UDqQ9OwsDAeP355jR4UFMTixYs5e/YsmzdvRpIkwsPDUavVGbY3YsQI3N3dzXtggmAGo9ac5O8Ld1m090q+1UGZb3sWBCMkqzXa/0uShEwmy8faCIVNUrrzR3hzff311/Tp04e+ffsCMHPmTDZv3swPP/zA5MmTM5SfO3cupUqV4sKFC5QrV47q1atz5MgRZsyYQfv27QFYtGgRDx48YN++fVhYWADg6emps53IyEid3xcvXoyzszNHjx6lXr16Zju+e08SzbYtQXjdPHiWlN9VEIQC48v1p6krP8EBTXmSUfLFn6cJL++Kq4NVfldNKODOnj1LZGQkBw4coGbNmgAsWLCA0NBQzp8/r22Zl54kScycOZMxY8bQpk0bIPUay8/PjxUrVjBgwAAA+vfvr13Hy8uLCRMmULlyZWJiYvD19dW+9tdff7FlyxZWr17NX3/9lZuHKwjZ9li03BME/dKn8kQPS8FUoluukJSUxNGjRwkPD9dZHh4ezr59+/Sus3//fho1aqSzrGnTphw5coTk5GQA1q1bR2hoKIMGDcLFxYXAwEAmTZqk9ylzmvj4eACKFy+u9/XExEQSEhJ0foySg2ceIkaE152FQlzqCkKaAfcm87NqCl0U27XLniQm52ONhMJi//79ODg4aBN7ACEhITg4OBi8nrpy5QpxcXE612CWlpbUr1/f4DpPnz5l8eLFeHt74+HhoV1++/Zt+vXrx88//0yRIkXMdFSCkDueJqaw9+I9UvK4oYm44hEE4Y2UmGI4CSO8Pu7du4darcbFxUVnuYuLC3FxcXrXiYuLw8nJKUP5lJQU7t27B8Dly5f5/fffUavVbNq0ic8++4yvvvqKiRMn6t2mJEkMHz6cOnXqEBgYqLfM5MmTtePYODg46FzUZia7ub2JG88QMnk790XLP+E1plKIFv+CkOaKTWUAPlKuxp7UISKSUsRDHiFrcXFxODs7Z1ju7Oyc6fUUYNQ12Jw5c7C1tcXW1pbIyEi2bt2KSqUCUq+hevbsyfvvv09wcLDRdc72Q1NByAFJkui15DDvLTzIrB2X8nTfIrn3BpEkiVWHr3EurnB+sIlLD8FUhs6Z9cdvUe6zSH45pH/MNeH182qX/qy6+esrn365RqPB2dmZ+fPnExQUxDvvvMOYMWP44Ycf9G5v8ODBnDhxgpUrVxrc56hRo4iPj9f+XL9+PVvHZqwFe65wOyGRRf/k39gggpDbRMs9QXipYusPuagpSXHZEwYq/wSg/89H8rlWQn6KiIhAJpNl+nPkSOo5ou96w5hhk4y5BuvatStRUVHs3r0bPz8/OnXqxIsXLwCYNWsWCQkJjBo1yqRjy+5DU0HIqUNXHgDw6xHjruXNRVzxFDDn4x7zZ/TNXOkqtf5ELP9bfZJmMzOfJbIgSf+5L7qPCaYydMp8uDIKgJFrTuZhbYT84OjoiEKhyPCE+M6dOxmeJKdxdXXlzp07GcorlUpKlCgBgJubG2XLlkWhUGjLBAQEEBcXR1KS7hhfH374IevWrWPnzp2UKlXKYF0tLS2xt7fX+TFGTtslyXK8BUEouB6/SBHXD4Lwn2reTkxK6QJAL0UkpWR3uPHweT7XSshPgwcP5uzZs5n+BAYG4urqyu3btzOsf/fu3UyvpwCjrsEcHBzw8/OjXr16/P7775w7d461a9cCsGPHDg4cOIClpSVKpZIyZcoAEBwcTI8ePQweW3YfmgpCYSWSewVM05l/89Ev0ey6cNfs2z51M97s28x9L2868/rS/P6TRG4nvMjjvQrmJW7o3nQqlYqgoCC2bt2qs3zr1q3UqlVL7zqhoaHs3LlTZ9mWLVsIDg7WTp5Ru3ZtLl26hEbzciyNCxcu4ObmptONZPDgwaxZs4YdO3bg7e1tzkMDYMOJW1y888Sosvsu3ePn/TF5Pv6HIOSn87cfM23z+fyuhiAUCBYKOTs1VdirroClLIURylWAeID+JnN0dMTf3z/THysrK0JDQ4mPj+fQoUPadQ8ePEh8fLzB6ylvb29cXV11rsGSkpLYvXu3wXXSSJJEYmLqsCHfffcdx48fJzo6mujoaDZt2gTAqlWrDA6HAtl/aCoIhZVI7hVQZ26Zv+us+OI2nkYjETRhGzUnbedZUv7NeCPkjDjlBYDhw4fz448/smjRIs6ePcuwYcO4du0a77//PpD6ZLd79+7a8u+//7726e758+dZtGgRCxcu5JNPPtGW+eCDD7h//z4fffQRFy5cYOPGjUyaNIlBgwZpywwaNIhly5axYsUK7OzsiIuLIy4ujufPc95K4uaj57SdvZfBK6KMKv/4RTJdfjzI53+e5oPlx3K8f0EoyFYfvaHz+w+7/mXV4Wvi+1wQAJAxKaUrGklGG8V+qsgu0WrWXhp/tYtLRj4sEt48AQEBNGvWjH79+v2fvfOOr+n84/j73JFhxYzYe6SoLU2Nqv7s0ZYqpVEtWqNFVY3SGjVaM0XtXTVqtVRK7BkjiBmxgiAhIYOsu87vjytXbu7IvcnN4rxfr0vuOc9znuckZzzP5/kOTp48ycmTJxkwYACdOnUyypRbs2ZNg8WdIAgMHz6cadOmsXPnTkA/fsqXLx+9euktSG/fvs306dM5e/Ys9+7dIyAggI8//hhXV1c6dOgAQPny5aldu7bhU716dQCqVKli1SNCQiKnye65qCJ7m5PILDqdyCfLTuLmqmRpH9sDir4KZOfNoU3V2KO4ZCoVl26V3E6SWsvNx8+pVfrlqpyk7UkA9OjRgydPnjB58mTCw8OpXbs2fn5+VKhQAYDw8HDu3XsZf7FSpUps3ryZjh070qxZM0qXLs28efPo1q2boUy5cuXw9/fn22+/5c0336RMmTIMGzaM0aNHG8qkxN9r2bKlUX9WrVpF3759M3VOKo2OC/dtt8aOS3opauy9auxWk8GQfRISuRbf/ddNto3eeomgsFimd61jtN2WeFESEq8aV8WKbNU2p7viCOOU6+j+sAogMHHHFdb190q3vsTryZ9//snQoUMN2W+7dOnCggULjMqEhIQQG/tyfDJq1CgSExP57rvvAP2Yy9/fn4IFCwLg4uLC0aNH8fX1JTo6mpIlS9KiRQtOnDhhNoGHhISEZSTFIo9xOyqeUy8CNOp0IjKZ7QPSvDh4NYq5l0NSjWTxmDfwWXGKM3eimfnRmzndFYlcyODBgxk8eLDZfatXrzbZ1qxZM0AfS8aSG4e3tzcnT5602GZWPjssPc01Wh06EZwUxob50nNM4nUiv5P54e2eKxEGce95sobToU8YteUSo9vVoHsjKdC6xOvB0FZVmXfgJrM0H9NJfpLGsuu0lZ1hj64JWp30rpCwTNGiRVm3bp3VMmnHG4IgMHHiREaMGIGbmxt+fn5G46rSpUsb3GxtpWLFitK4RkLCDJJbbh4j9YPMXq0urz8E83j3JbKYM3eiAfjzlJQBV+LVR2bhBdBq9mG8pu1DbUdcvby37CMhYR1nhfnhrS7VQKLuJH++WB1I1PNkvt9yMbu6JiGR4wz/X3WcFDIeUZSl2o4AjFFsQImG/M7ydGpLSEhISFgjJzULSdzLpdgixElil4SEKSrNS1FDukckXlUsLe7ce5pAdIIav0vhRgmBrN4LedCqW0LCGg9izCfD0qWySkproZSg0vAsSZ2l/ZKQyA3IZAIhP7djXT8vlmo6ESm6UUn2iE/lezlx6wnJGm1Od1FCQkJCIgNI4p5ElqLR6rh4P0Yy85fINlJbLOV1a1UJCUukp8cN2xiE17T92dMZCYlcRuzzeBoL10y2W3slvPHTHupM9CdJLQkbErazcOFCKlWqhIuLCw0bNuTEiRNWyx8+fJiGDRvi4uJC5cqVWbx4sUmZmJgYhgwZQqlSpXBxccHT09Nut8X0EASBt6sUIx5X5mg+AmCoYjsKVSw1xu8mUaW/D1QaHQevPSY+WUpGIyEhIZHbyTPiXnR0ND4+Pri5ueHm5oaPjw8xMTFW64iiyMSJEyldujSurq60bNmSK1euGJWJiIjAx8cHDw8P8ufPT4MGDdiyZYvZ4yUnJ1OvXj0EQSAoKMhBZ5Zx8oJsMWHHFbosOM40v+AM1U89f5V0GglbuJEq05ulS0YyVJLI6+TFGKoSEtlCwlP+dfqBdU7TqCI8MNqls2EgkdriVULCGps2bWL48OGMGzeO8+fP07x5cz766COL5UNDQ+nQoQPNmzfn/Pnz/PDDDwwdOpStW7cayqhUKlq3bs2dO3fYsmULISEhLFu2jDJlyji8/zKZQK3ShfhL25IQXVmKCM/5RvE3ANvO6zNOz/YP4fPVZxgsZVmXkJCQyPXkGXGvV69eBAUFsXv3bnbv3k1QUBA+Pj5W68yYMYM5c+awYMECzpw5g4eHB61bt+bZs2eGMj4+PoSEhLBjxw4uXbpE165d6dGjB+fPnzc53qhRoyhdurTDz80e8pq+lRL/bMWx0AzVTz2BzbGEGjnSqkRWkhWySFySmvWn7vE0XmVXvRGbghi7TYr3JGEfduRSAqwvjkgy4etNWsujo0ePWi1/7NgxANzd3c1aHrVs2RJBEEw+HTt2NJSZOHGiyX4PDw/HnJBrER6IxXEWNExXLkfgpTW3Wiem63IoSHeEhI3MmTOHfv360b9/fzw9PfH19bUqwi1evJjy5cvj6+uLp6cn/fv354svvmDWrFmGMitXruTp06f8/fffNG3alAoVKtCsWTPq1q2bJeewa2hz3vUsxTRNbwA+k++hkhDOuO2X0Wh1rD+tH8cfvh6ZJe1LSEhIvMpkt36RJ8S94OBgdu/ezfLly/H29sbb25tly5bx77//EhISYraOKIr4+voybtw4unbtSu3atVmzZg0JCQmsX7/eUC4gIIBvvvmGJk2aULlyZcaPH0/hwoU5d854heq///7D39/f6AWcldhipRaRidXl/y6FZ7huTpGdlnuSlWDuQBRFfvz7MkuP3MpgffPbs8Lq6bu/LvDD9kv0X3PG5jrhsYlsO/+ADafDDC4wEhK24EgBQjICfH0xZ3nUvn177t0zn5goNDSU7t27A3D06FGzlkfbtm0jPDzc8Ll8+TJyudxQL4VatWoZlbt06ZJjTkoQGK/+gnjRmSayEHrJDxh2qTQ6Gk3Zx5LDlt8p0v0gYQsqlYqzZ8/Spk0bo+2tWrWyWCcgIMCkfNu2bQkMDESt1sd73LFjB97e3gwZMoSSJUtSu3Ztpk2bhlabdWOERZ825LCuLge09XAStIxX6LOhbj1332JyGgkJCQmJ3EeeeGIHBATg5uaGl5eXYdtbb72Fm5ubxdgWoaGhREREGL1EnZ2deeedd4zqNGvWjE2bNvH06VN0Oh0bN24kOTmZli1bGso8evSIAQMG8Mcff5AvXz6b+pycnExcXJzRx9HM3Xs9w3UH2WheHxoVn+HVuvvRCRmqJyGRQlBYDH+cvMs0v2t2Zf9MwdJqSVbM3fZefQTAuXsxNtfRaF/2L6csUyXyJnZb7knXl4QZzFkelStXjkWLFpktv3jxYsqWLQtAjRo1zFoeFS1aFA8PD8Nn79695MuXz0TcUygURuVKlCjhsPN6SHFmanoAMFqxgZI8Nex7lqRh+n+m8fgkJOwhKioKrVZLyZIljbZbu44jIiJMypcsWRKNRkNUVBQAt2/fZsuWLWi1Wvz8/Bg/fjyzZ89m6tSpFo+b2TmHUq6fDv6s8UEtynlPfp6WsiBGb71E1HP7vBEkJCQkJHKOPCHuRURE4O7ubrLd3d2diIgIi3UAsy/R1HU2bdqERqOhWLFiODs789VXX7F9+3aqVKkC6C2H+vbty8CBA2nUqJHNfZ4+fbohPqCbmxvlypWzua6tZEeSindnHeKzlacJCouxu+6l+7GZbt8o5l6mj2Y70kQ4d5CQyppt05kw+w9g4c8oc4Bpxr0nCZy8/STTx0lBcgWTsAsHuuVKvJ5Ysjxq06aNxYXTgIAAE8uktJZHaVmxYgU9e/Ykf/78Rttv3LhB6dKlqVSpEj179uT27dtW+2uPgFGpeH7WattwXleVQkIik5RrrB47NZLlnoQ9pPUESC+Rl6XyKdt1Oh3u7u4sXbqUhg0b0rNnT8aNG2dRcAfHzTlCxVKs0rYD4EfFHyiRkmhISEhI5CVyVNwzF3Ml7ScwMBAw70YnimK67nXmXqKpt40fP57o6Gj27dtHYGAgI0aMoHv37gb3kPnz5xMXF8fYsWPtOrexY8cSGxtr+ISF2SdMbDwTxp2oeJPtqccM2ZkJNDjc8ZaH9pJV53vj0TOi7YyTJpH9ZCTIucUrxgGTtxYzD9Jz6UmuPnTMvSEJyhL24FC3XElYfi2xZHmUdhE0NRERESaWSWktj1Jz+vRpLl++TP/+/Y22e3l5sXbtWvbs2cOyZcuIiIjg7bff5skTywsm9ggYzgoZOmSMUfdHLcppJz9DW9lpi+VTIwnhErZQvHhx5HK5yb1i7j5IwcPDw6T848ePUSgUFCtWDIBSpUpRvXp15HK5oYynpycRERGoVObHqpmdc6RmvuZDIsVCVJGF00fun+HjSEhISLyqnLnzlLHbLhKbYH5RMyfJUXHv66+/Jjg42Oqndu3aeHh48OjRI5P6kZGRJoPSFFICM5t7iabUuXXrFgsWLGDlypW899571K1blwkTJtCoUSN+//13AA4cOMDJkydxdnZGoVBQtWpVABo1asRnn31m8dycnZ0pVKiQ0cceHsQk0nLWIZPtqQUAe8efmRmwyu31AXMQqbXZrBhv33j0jNZzj1D/571G22NS3azSQD/38E/QAw6FPM70cRx5NV9+kHkLVQkJe7H3kTzvwI2s6YhEnie9RVBbypvbDnqrvdq1a9OkSROj7e3bt6dbt27UqVOH//3vf+zatQuANWssW9hlRMAIEcuzWNsZgMnK1RTCdNE0LdnhFSGR93FycqJhw4bs3Ws8fjx48KDFOt7e3ibl/f39adSoEUqlEoCmTZty8+ZNdLqXoUiuX79OqVKlcHJyMnvczM45AH7oUBOAZ+QzuLQPU2ylGNIYR0JCQiI13RcHsOF0GNP8gnO6KybkqLhXvHhxatasafXj4uKCt7c3sbGxnD79ctX11KlTxMbG8vbbb5s9dqVKlQyxXlJQqVQcPnzYUCchQR8TTiYz/jXI5XLDS3XevHlcuHCBoKAggoKC8PPzA/TuvNbiX2QH2Sk6KeWvpmXHydCnZrf77sv4RPh+dAJjt13k5uNn6ReWsJn70YkM2xhE31W2J6xIfY9Ex6sMSStSz0Hn7b9hNpmFKIqcuBnF42dJVq1GHWVxJ1lPSdiDrUlhYhJUfLbyNNvOPbByLEf1SiIvYcnyKPUiaFo8PDx4/PixSfnUlkcpJCQksHHjRhOrPXPkz5+fOnXqcOOG5XdvRgWMBZoPuKUrRUkhhjGKDemW10orehI2MmLECJYvX87KlSsJDg7m22+/5f79+4b9Y8eOpU+fPobvAwcO5O7du4wYMYLg4GBWrlzJihUrGDlypKHMoEGDePLkCcOGDeP69evs2rWLadOmMWTIkCw9lwHNK3NoZEsAtmjf4ZKuIoWERL5T/GUokxutVCQkJCRyitAn6S8YZjd5Iuaep6cn7dq1Y8CAAZw8eZKTJ08yYMAAOnXqRI0aNQzlatasyfbt2wH9xGf48OFMmzaN7du3c/nyZfr27Uu+fPno1auXoXzVqlX56quvOH36NLdu3WL27Nns3buXDz74AIDy5ctTu3Ztw6d69eoAVKlSxRBUOit5lmT5RWrv8DMzEzi5LPOXiiiKxCbqz0eTkeQIWTDetmT98jw543FGvlx7lg2nw/jwd/MxiyQyRtTz5AzXfRqvov7Pe2k8dR8ASeqX19+cvdf5bb/phNL/6iN6LT9Fk6n7+Xy17YKihER2YKvl3jS/4AwnRZJ4tbFkebR3716LC6fe3t4mlklpLY9S+Ouvv0hOTubTTz9Nty/JyckEBwdTqlQpO88ifZJx4ge1XmDspThAE8H6SrtOstyTsJEePXrg6+vL5MmTqVevHkeOHGHz5s2G/eHh4UaZpytVqoSfnx+HDh2iXr16/Pzzz8ybN49u3boZypQrVw5/f3/OnDnDm2++ydChQxk2bBhjxozJ0nMRBIGKxfOzdZA3OmRMVOu9k3rKD1FLCAXgr8CMu/tKSEhIvC7k5ChCkYNt28Wff/7J0KFDDYGfu3TpwoIFC4zKhISEEBv70nx81KhRJCYmMnjwYKKjo/Hy8sLf35+CBQsCoFQq8fPzY8yYMXTu3Jnnz59TtWpV1qxZQ4cOHbLv5KzwPFlDQRel2X32xqCzVxxLfXxFBtxynyUZC2QTdlxhbcBdiuZ3QqPVcWTUuxTOZ97FIAUja6YsuFMsWUtlJr7f1RfxCZ9lQiCU0JNZg6IUq7rz96IBy6Lt2bumFpyHQiLN/mzShjQPlMgBbLX0/CvwfrplJMO915cRI0bg4+NDo0aN8Pb2ZunSpdy7d4+BAwcCesujBw8esHbtWkBveZQy9goJCeHSpUusWLGCDRtMLeJWrFjBBx98YGLRBzBy5Eg6d+5M+fLlefz4MVOmTCEuLs5quJPMcEr0ZL3mXXopDjJduZwOqukkY378IWl7EvYwePBgBg8ebPieOtHL6tWrTcq/8847nDt3zuoxvb29OXnypMP6aA/1yxUB4KxYg3+0b/O+/AQTlGv5WPUTU/2CGdCico70S0JCQiK3YWn8bJwjIVu6YiDPiHtFixZl3bp1VsukFWQEQWDixIlMnDjRYp1q1aqxdetWm/tRsWLFbE1kkbap7LxYUsedsTfmXpJay6itF422rQ24C+itqAD+Pv+Avk0rWT2Occw9x59wdrijnbnzFPeCzlQolj/9whI28da0/dQrV5hFnzaw6p5o6z2SmXvJUlWdTkSWQ7EqJV59hGywu38ar6JIPqXNLsASeY8ePXrw5MkTJk+eTHh4OLVr18bPz48KFSoA5i2PNm/eTMeOHWnWrBmlS5c2sTwCfYywY8eO4e9vPiD//fv3+eSTT4iKiqJEiRK89dZbnDx50tBuVvCLphf/k5+niiycYYptzND0NFtOirkn8Tojkwk0qViU03eeMl39Ca1lZ2kiC6GzLICdurdRaXQ4KfKE45eEhITEa4f0dM7DZHV2TU1qcc/OyZ0tmU2zMyGIJSwq7g46/o1Hz+i+OIB3Zh5y0BElACLikth9JYIr6WSqTfk7pnftpOxefvQ232++kGm3rNCoeOpN9sd333Wb60jZciXsIbNy25+n7hp+nn/wJl0XHidB9dKydfflCBr8vJcf/7lsVO/8vWiOSG6+rxSDBw/mzp07JCcnc/bsWVq0aGHYt3r1ag4dOmRUvlmzZoA+qVloaKjByi811atXRxRFWrdubbbNjRs38vDhQ1QqFQ8ePGDr1q288cYbDjsnZ6XcZFsc+flR/TkAX8l38qZwy2xd3YsXRnYu5EpI5CY+bFAGgAiKsUijT0gzVrmefCQx2z8kJ7smISEh4VDuPonn4LXMJ2tMTU7O6SRxL5dj7dLI6nFndoxrk9RaVh8P5d6ThKxvzAwWNUsHnXt64pMjiI6OxsfHBzc3N9zc3Gjzfncu3LIcPB/0k5aJEycaYlZ27NiRK1euGPbfuXMHQRDMflLiydy5c4d+/fpRqVIlXF1dqVKlChMmTEClUmXdyaYhJp3gzimTs/T+nGfv6t12p+wKZvPZ+xy+Ybt4Ye4++fW/a8QlaTKVmEVCwhqZtaYbt/2laKfS6Dh3L8ZgXQ0wY881ANadvGdU78OFJ+iz8jQRsekv4EhI5BSzPnqT8kXz4VnKOOnGHl1jdmi9kQsis5SLccL0HaITRfqtPsP7vx+XrPgkXks+qFeGOmXcAFiq7USYrgSlhacMVWxnyZHbxCSoOHEzKkPxsyUkJCRyE+/MPMTnq89w8vYTu+taGoqvOn4nc53KBJK4l8uxmqUzq8W9bFCd5+69zsSdV3lvziEb+uN4bItblfGWs8ObrVevXgQFBbF7927mrvqLQyfO4N32Q6t1ZsyYwZw5c5g5cyYA7u7utG7dmmfP9Bl+y5UrR3h4uNFn0qRJ5M+fn/bt2wNw7do1dDodS5Ys4cqVK8ydO5fFixfzww8/ZO0JpyL+haWRpQdyeGwSn608bWRptPJYaLrH/eqPsybbstqKI1lt/yD53L1oQiKkrMyvI1nh8Z1asEvvcrfFOltCIqeoVrIgR0a9y6i2NUz2TVB/RqRYiOqyBwxTmIZl0epE9l97zMX7sVx/JD1fJV4/XJ3k7PymGTemticZJyZq9Bl/+8n9qCI8oN7kvfRafoolR27ncE8lJCQkHMOFsBi769ga/zo7kcS9XI61CVZWi2+pF6yzSqRKEWXU2vTPJUvEldx3T9pFcHAwu3fvZvny5Xh7e6MrUY1i7b4h8dYZQkLMu06Iooivry/jxo2jS5cuACxevJiEhATWr18PgFwux8PDw+izfft2evToQYECBQBo164dq1atok2bNlSuXJkuXbowcuRItm3bliXnmpJp2Rxbz5pPGjB22yUOX4/kj5MvLZIm/3s13bZUGh22irrm7kNb75fUl/SHC49bLHfvSQJ7rz7iy7WBLDx0E9BnD+668ARtfY/Y1pjEK0VWDChS4qHCS9dEgAPXHtF31WkeS4KeRF7DzG0STSHGq78AYKAZ99zg8JeCnr3xhiUkXiWUchk7v27Gfl1D9moboBS0/KxYRcr4aP2pe9YPICEhIZFHeFXCS0viXi4hSa3lwLVHdtVJT+t6Gq9i4B9n8b8SQUjEM5ZbsFi69ySBNnMPm6S4Tz25ywpdTRRJ904yTqjheCx75abfWqJKy8X7MTkalycgIAA3Nze8vLwA/e/UuUxNBOf8nDhxwmyd0NBQIiIiDJmnAZydnXnnnXcs1jl79ixBQUH069fPan9iY2MpWrSo1TLJycnExcUZfWzh4v1Yk21Z+RzecNr4frD0Z3bUn/+OFdf0FjMPMmBtIP5XHzFjt160TW05ldkYgSkcCnnMrD0hDjueRNYhCFCSp+TDcYJbTCoBPfXz/4vVgRwKieSnf66YqyYhkefYo2ti0T33h+2XDD9L2p7E606dsm6s7NuISZo+JIlK3pZfpYssAIAn8ck53DsJCQkJx5CZRfPQqHi2WDA0yW4kcS+XMHHHFb5YHZhuudRCQnrz7z8C7rL7SgRf/nGWfmvOWCz3047LXH/0nFFbjLPb5oZY0vb2YX/wI1rOPMj5e9E2lc9M3CqfFafosuA4mwNz7maOiIjA3d3dZLs8nxsREREW6wCULFnSaHvJkiUt1lmxYgWenp68/fbbFvty69Yt5s+fbza4emqmT59uiA/o5uZGuXLlrJa3Rk5k8Qx7msCG09ZXq7OjW6ktStQ6x8S96bvqDAsO3mTnxYcOOZ5E1iHE3GWz0ySWK2fhjGPiXB65Hkk73yMkqbVmn72Rz19O5ET01qP/BD0gWaN1SPsSEo7G2qM4PfdcAFmqh7lOJxIem+jgHkpI5H5a1SyJ71fvs0DzAQDjlOsoQAJJah3nbBxvS0hISLyqvDvrECM3X8jpbgCSuJdr2HgmzOx26+KWdeXrefLLlejIZ5ZX1xJV5idm2WGRZo8GYkt3+q0J5M6TBD5beTpT7dvSVuCLJAyps05a405UvE3lACZOnGgxoUXKJzBQLwabF7jEdIWvtPtF0XydxMRE1q9fb9Vq7+HDh7Rr147u3bvTv39/q+2OHTuW2NhYwycszPy1D/B3kPXEINkp7aVcEq1mH2Lstksm27Ob1Pe0xga3dnNsPH2PKf9eNbnXh20M4vGzV8sFc+HChVSqVAkXFxcaNmzI0aNHrZY/duwYoI9HWblyZRYvXmxSJiYmhiFDhlCqVClcXFzw9PTEz88vU+3aijzpKUWE57wtv8oS5VyziQEywrWIZ/x7MdymZ2D3xQEM2xjEnL22Z4WWkMhOyhbJZ3Ffeu65AOfuxZCk1o+Rvt5wDu/pB+i9/CTHbkRlTYclJHIpjSoWZZm2I6G6kpQUYhj+QhD/dlOQlHhGQkIiz3P2brTVEFAarY73Fxxj6Ibzhm22GHNk99NREvdyOSdvPyEuyfyFlt7kK/WKc7LGsmWPpQszOyz37LFwSs9V9lmq31OSjckJMmJhJYoiQ/48Z/iusiKspBbLWs46xJPntrkwfP311wQHB1v91K5dGw8PDx49eunOrXqRuUybEGdimZeCh4cHgImV3uPHj83W2bJlCwkJCfTp08fs8R4+fMi7776Lt7c3S5cuTffcnJ2dKVSokNHHEtHpZMPNifgIJvEhzdwotpp2ZyZuZmoXSXUGM9aN2XaJ5cdCOR361GTfyM0XzdTIm2zatInhw4czbtw4zp8/T/PmzWnfvj337pm3wAwNDaV79+4AHD16lB9++IGhQ4eydetL6x6VSkXr1q25c+cOW7ZsISQkhGXLllGmTJkMt2sXpRvwuep7EkRnWsovsEA5DwWazB8XvYWSzoYXQOiLBYs9l81b/NrLrovhTPjnsjRRlHAYVd0LsKBXff76ypvtg9+mWdXiRvutuecCjNx8geEbgwDwu6S/zo/ffMKnK05lS/8lJHIT37Z/kwmavgD0le+hhnCPu08S+HDh8RwNUSORfURHR+Pj42PwvvHx8SEmJsZqHVEUmThxIjVq6BMcdezYkStXjMN8fPXVV1SpUgVXV1dKlCjB+++/z7Vr10yOtWvXLry8vHB1daV48eJ07drVYecm8Wrz879X+emfyxafVbuvRNB5/jGL9c/ejebC/Vh2XMjd3k2SuJfLGbX1Ih/8/jLQviUxIODWE6b/F0xsgvrlRN9G4cOSEGHL5C6z2KXNpNMdI7diW889VbnpfsEGIdXaqT+KS2bXpXDDd40dwkqojdZ7xYsXp2bNmlY/Li4ueHt7Exsby+nTekvFmXtCSH4Ygpgcb9GFtlKlSnh4eLB3717DNpVKxeHDh83WWbFiBV26dKFEiRIm+x48eEDLli1p0KABq1atQiZz7CMlvT9jyt/PEVdq/zXW3eLDYxOZ7hfsgJYcw8OYl+5hKhuuwcdxSRbF5efJpqLQjVcoS+ScOXPo168f/fv3x9PTE19fX8qVK8eiRYvMll+8eDFly5YFoEaNGvTv358vvviCWbNmGcqsXLmSp0+f8vfff9O0aVMqVKhAs2bNqFu3bobbtQdBEAgUa9Jf/R3JopI28rPMVS5EhmNctM09A7Pa/WrI+nOsCbjL3+etW+xKSNhDpzdL06RSUeqXL8K6/l4m+1O7536r2GKyf/cVx4jXEhJ5na9aVOaIri5+2iYoBB2TlasBkYv3Yzkd+pQoGxewJfIuvXr1IigoiN27d7N7926CgoLw8fGxWmfGjBnMmTOHmTNnAnqPiNatW/Ps2ctxZsOGDVm1ahXBwcHs2bMHURRp06YNWu1L77KtW7fi4+PD559/zoULFzh+/Di9evXKmhOVeKWIT9aw4lgoawPuGiXNSsu9p5bjn2szqItktx2KJO7lAW5HmheEUsS3e08S+GTZSZYcvk3dyf40//UgYGy5Zw1LxVIbT2SVzGdPzLRdl8JJUms5c+epWUHtvwxYj6QWNpccuc10P9NVorT3clrR05rVVGZu6OfJGraevW/VRNjT05N27doxYMAAjh0/QfKDazzZPR/XKo0NK2QANWvWZPv27fo+CQLDhw9n2rRp7Ny5E4BBgwaRL18+k5fkzZs3OXLkiFlX24cPH9KyZUvKlSvHrFmziIyMJCIiwmLcvoyQkayzGWVfsPWENv3XBLLkyG3Tts0VzoYnuSbVDZpetulElZYm0/bTcMo+s8kyZK9w1HiVSsXZs2eNEsgAtGnTxmICmYCAAFq1amW0rW3btgQGBqJW6+/HHTt24O3tzZAhQyhZsiS1a9dm2rRphoFoRtq1h5Q/2Qldbb5SD0clyuksP8kM5VIEBwh85hZ3ssswQ5ogSmQnevdcfdiJr+T/0kgwHQdISEi8HLP/rPYhQXTGS3aNrjJ9qIkeS0/SaMq+nOyeRBYTHBzM7t27Wb58Od7e3nh7e7Ns2TL+/fdfQkJCzNYRRRFfX1/GjRtHly5dAP0CakJCAuvXrzeU+/LLL2nRogUVK1akQYMGTJkyhbCwMO7cuQOARqNh2LBhzJw5k4EDB1K9enVq1KjBRx99lOXnLZH3ST1nsjdO9NEbkczZe52MhjeX3HIlrCKaEdxSZ3YDiHiRRdPW+bolAWXbuZeJItKasMYmqPlh+yUC75i689mKvRf7pJ1XGbrhPN0XB+C774bVsimnFPU82aplXdpzvxoe96JvtvcuPWElNfac8zS/YL7bfIEBa61blP35559UqFaTFq3+x6O/fsSpREWKd/rOqExISAixsS+zzY4aNYrhw4fz3Xf6cuHh4fj7+1OwYEGjeitXrqRMmTImAgWAv78/N2/e5MCBA5QtW5ZSpUoZPo4iPffW7PQCuRZhfqUnpzxRUrsupmc9mjp+njkrP/mrkv/dDFFRUWi1WrsSyERERJhYqpYsWRKNRkNUlD7W1u3bt9myZQtarRY/Pz/Gjx/P7NmzmTp1aobbtSeTdOqFkUO6+nyj/gaNKOMj+RF+Vqwis8OJjHrGhkQ84360+ZVPW922JOcuiexmj64xW7QtkAkic5WLKIDl1XsJideZovmdCKcY8zQfAjBWuZ5C2B5TWiLvEhAQgJubG15eLy2g33rrLdzc3CwuWoaGhhIREWE0j3B2duadd96xWCc+Pp5Vq1ZRqVIlQ9K9c+fO8eDBA2QyGfXr16dUqVK0b9/exL1XQgJgxu5r/PJfqoW6TAwsfVacZt7+G+w0446bG6dPkriXh0mZJ1mKyWdr3C9L5aanuinSTvSm+QWz/tQ9PlocYFMbltu2Ttq5oP9VvXXVquOhVusla3RUG+dHoyn77OujmcnnhtOWEz6AdZfItDe9PULQ+lP6uFzm4qGlpmjRopT9cBTlv91M+W83U7zzSGQuBdK0K9K3b99U/RKYOHEi16/rA+H7+flRu3Ztk2NPmzaNsLAws+62ffv2RRRFsx9Hkd5DM7eEeElrDWfrsz639N9WK9+8jK0JZKyVT71dp9Ph7u7O0qVLadiwIT179mTcuHEmLrf2tJuZTNJ7dE0YoR6EThT4VLGf8Yp1ZE4ms79u5LNk2voeodkL6/HUnLnzlIZT9vFPOklyJCRyionqPtwXi1NOFsmPinVG++xJiCUh8Sqz4+umAKzQduCGrgwlhDhGKzbmcK8ksoOIiAjc3d1Ntru7u1tdLAVsWuhcuHAhBQoUoECBAuzevZu9e/fi5OQE6BdUQZ9wcPz48fz7778UKVKEd955h6dPLc+T7Fk0lXg1iEtSs/DQLRYfvsXRG5H6uakdY9rLD2LNbr8fkzcW/SRxL49gzk0pvcvUkZ52aQWb21HPHXLcrNQUUizqgsJirLSfZuJtpszKNEJiLtFjXgvSE52yIy5keiSqtbScdcji/sk7r2Z5HzLya0gtSDo4VGKuonjx4sjlcpsTyIA+6czjx49NyisUCooVKwZAqVKlqF69OnK53FDG09OTiIgIVCpVhtq1J5O0OXbomjJaMwCA/or/+F6xiYw+sdKz3DMn4t+OtPxeGLA2kKfxKoa9SE5g/djpFpGQcDjPyccIlV4g76E4RBvZGcM+a894CYnXiZQM1GoUjHuRbbq3Yr/kzp6HmThxIoIgWP0EBuq9iMwtTqa3WGqunrk6vXv35vz58xw+fJhq1arx8ccfk5Sk9zzRvfCJHDduHN26dTPE6BMEgc2bN1tsNzOLphK5myfPk1l+9LaJRqJJ5VHns+I0G06HGY1pBUEgLknNvP03zI5bO80/xtm7WRtjOit5had0rxaNpuwjSW3sIy6KIjEJKi7eN1WYbXnQjtl6kTtR8Ry7GZVu+9bmWskaLcuP3ubmY/sD8NtqXZhVpBVAU8QieyaXkc+SuWVlUiuRCdKz3DP8n3NqwJ4rESYBWFPfe2nF4ZzA3H2WOjDsq+yW6+TkRMOGDY0SyADs3bvXYtIZb29vDh40tj7z9/enUaNGKJVKAJo2bcrNmzcNA06A69evU6pUKZycnDLUrj2ZpC2xWduSH9V9ARii2MFIxV9kROBLzwL34LXHJtus1ZAy4ErkFhpVKGJx32nRk6XaTgBMVy6nBDEWy5pLRCQh8Tpw4Lt3AP39skHzLgDTlStwQs3TeFVOdk0iA3z99dcEBwdb/dSuXRsPDw8ePTKNTx0ZGWl1sRSwaaHTzc2NatWq0aJFC7Zs2cK1a9cM8cJTQv688cYbhvLOzs5UrlyZe/fuWTy3zC6aSuReBq07x5RdwXyZXviqU3dNxrTTdgUzZ+91/jfnsNk6h69HmmwzNyzOaR3DHJK4l4cIj00y2dZ31RkzJfUTqfSsnjaeCcNn5Smb2k5rIZX66+JDt5myK5j/zTmC777r6HSi7Rd7uuJN+hPCRJXWRPhMi0qjM8oUKooiv+27YZKEI6MWIx8sOG52u703fUjEM+KlCYPNGB7WOagbmNMszP3VHemubHLsDNRJLbbIX+GEGgAjRoxg+fLlrFy5kuDgYL799lvu3bvHwIEDAf3gr0+fPobyAwcONAwAQ0JCWLlyJStWrGDkyJGGMoMGDeLJkycMGzaM69evs2vXLqZNm8aQIUNsbjer+EPbhslqffa6rxX/MCoDFnzpaXHzDtw02WbtEk/vCrM3wLGEREbZMsi8uJ7CHM1HXNVVoJjwjF+Uy7B079SesIepu66yw0wcHgmJV5nKJV6Gfpmu+YRIsRDVZA/4Sr6TBj/vZe7e6znYOwl7KV68ODVr1rT6cXFxwdvbm9jYWE6fPm2oe+rUKWJjYy0uWlaqVAkPDw+jhU6VSsXhw4ct1klBFEWSk/Vzt4YNG+Ls7GyUuEOtVnPnzh0qVKhg8RiOWDSVyJ2cfhH3/9y9GKPtacebgmA6pj3/oo6lse68/dZj+9tDdnujSOJeHkYnihZdTjU6kRs2WNKFPU00+n469ClNfznAvqvGKzPWLsy5+16+xH333WDnRdsHupmVFJI1Wt6YsBuvafutlus8/xgNp+zjykO9leOp0KfM3XedXRfDjcrpbNCKzPX5mY2CnDWB58StKNr6HqHN3CM2Het1ID2BOhdoezY9tVcdD6XJtP3cfPzckIzm7N2MJ6NJS7JGi8+KUyw/aprNF8y7v6eOKSEIApfMWAC/KvTo0QNfX18mT55MvXr1OHLkCH5+foYBYXh4uNHKb6VKlQxuHs2aNePnn39m3rx5dOvWzVCmXLly+Pv7c+bMGd58802GDh3KsGHDGDNmjM3tZiUrte2ZqNYLloMVOxij2Iitd4qImCGXd2uLMamtWf8IuGO070JYDDXG77bpOBISWY0KJcPVg0kWFbwnP88n8gMWyy47GsrQDeezsXcSErmLOAow+cW75mvF31QWHvLb/hucu5d33dokzOPp6Um7du0YMGAAJ0+e5OTJkwwYMIBOnTpRo0YNQ7maNWsaLO4EQWD48OFMmzaNnTt3AvrF0Xz58tGrVy9AH09v+vTpnD17lnv37hEQEMDHH3+Mq6srHTp0AKBQoUIMHDiQCRMm4O/vT0hICIMGDQKge/fu2flrkMgFpJdIMDUCgtG4UhRFnBTZJ4FFPU/mhA1eko5CkW0tSWSatPNza3OvSTuv8m8a4coWei8/iVor0j+Nias9E7370YmUKOicbjm967D1MlcfWg98GvY0AVGE2ETzSUVSCHmkFzo7zjvGnV86EvnMNIZhSp8ciT3ejn6X9H+vBzGJ6ZR8fUjv1zdm2yX2BT9mX7Cpm0B2Ye6KSft3n/Qi7t5P/1ymdGFXtpy9z/pT9/iyRWWH9OGvM/c5eiOKozei6N/85TE1Wp2JQCqK8CguySjRjFwm0HnBMYf0JbcyePBgBg8ebHbf6tWrTbY1a9YM0LubWFrp9fb25uTJkxluN6tZrW2HDoHJyjUMVOwERH7RfIItyyr2PArTCwEBxs/oH/+5go93RcN3o4xmEhK5gOtiOWZoevCj8k9+VKzjhK4Wd0UPi+W1OvGVt4CWkLDETp033bRHaSm/wFTFSj5Rj6PrwhN837YGg1tWsekdIZE3+PPPPxk6dKgh+22XLl1YsGCBUZmQkBBiY18uGI8aNYrExES+++47QL+g6u/vT8GCBQFwcXHh6NGj+Pr6Eh0dTcmSJWnRogUnTpwwSuAxc+ZMFAoFPj4+JCYm4uXlxYEDByhSxHKoBYm8TbJGS9+VZ2hYoQgj274UkD9bddps+dCoeH5NM6YUBIwmayKglNv/TDpx64nZ7bbM23stP8WdXzra3WZGkMS9PIy1ydeG05bjD1hDrTV/UHs1r17LbHP3tea2+iAmkUF/njPfnxf/qzRZY+Fh7XyzyqbkVY57llXEJqrZeu5+jvbBnlhiogg3Hr+Mz7j0iHlLu/RImyHbnCu3WqujxYyDFMnnxBKfhobtq0/c4dfdxi++sKeWM0BptDpm+V/n7SrFaFG9hMVy/70Qp9vXKWXTOUhkD2u1bRER+Fm5moGKf5EhMk3Ti/QEPnuu69CoeHZeeIiLUm52/5+n7trTZbPP392Xw5EJAm1qWRZYJCQcyUpte96Tnedt+VV+U/7OR6oJaCwMm58naXDLpzTaFvksmYWHbtKrSXmqlSyYHV2WkMghBMZrPmevbBTe8qt01x1ms7YlM/eE8EapQrxb0zTDqkTepGjRoqxbt85qmbSGEoIgMHHiREaMGIGbmxt+fn5Gi6alS5fGz88v3baVSiWzZs1i1qxZGeu8RJ7j3wvhBNx+QsDtJ0bi3vGb5oW23stO8jBNGDMBY/dbUcRhlnsJKg1Nf7Fs3Z8TSG65eQzRSHnOPtcle9pylPXb9UfpuxWr7TDLtQVH+8Vbmj6LosiPf19mWSpxJzMrm7kxoKcjyAt6p73XjM4BiQUWHrxl3Acz9+fNx88Jj03iarix9WtaYQ/gGytuZZvP3mfx4Vv0WXnaoiVtfLKGQX+eY9Cf53iWZN2KNieZ/l8wfVaefu2SO/yhbcN49ecAfKnYxXjFOqwtU2w5e99ut9xvNpxngIWgxuO2X7brWGmJTVQzcN05vvzjbLrxVSUkHIWIjJHqgcSK+agnu8UIxRaLZe88iWf50dsGC9VL92NpPHUfq47fof1vR7OryxIS2UaTikWNvt8X3Zmr0Yeu+EGxnmLoLbc+X32GwDuOC0MiISHx+pA2E256pBX2ABAEk3mSUm6bBHY+nfACaeP95QYkcS8PEa/SsPrEHcP37AzQqHOshmbAmnhji67jaHHv5YQ2iywCX/wfFBbDHyfvMtUv2LBPcukxJS/8Rh4/M/MiscDdJ/FcepD52HYxCcbZ6MxpVandcTPzrLgf/dKqr8O8o2YTH6QWXJLU+nvyj4A7fLwkIMNiX3hsImO3XbJJ5LeFEzejWHL4NkeuR3L0hmkWrFedddrWjFN/AUB/xX9MUKzF0nPuzJ3oDMXccxRp3wupLVNVDn7mS7yepHbJ+a51dYvlHlKcMeoBAAyU76Sp7JLZcl0XnWDKrmBGb7kIYBTmQPOaLSZIvB4s69MI3x712P/dO3zSpDwAK7QduKKrQBHhOeOVL627UocBkZCQkEiLWqvj34sPjeZUoVHxTHdA2Ja0lnsg4mSDuBcS8YwPF57IdPvZjSTu5SGm/Bts5IKYncPFrGors5ZZjp7oOfo8LZ1fospUIJG0PVPSS6iRG4h6rjLZZqnXZleULKDV6TM6n7xtanouS3OxXIswtahLXUSbCaEmrbVeksq2e+7Hf65wOvQpy46GZqjdQevOseH0PTrPz3wswLtP4um1/GWoAI2F8AOvOn9q/8dYdT8APlfs4RfFMmSY/3taCtFgLxmx5M5BXVHiNWHboKY0r1acf79plq57zn86L9ZrWiETROYqF1EU0+dtijXwgWuPs6S/EhK5Dbd8Sj6oX4YqJQrwYydPALTIGavuj1YU+FB+nOayizncSwkJibzAimOhfL3+PO19X1q691pmHNP6w4XHufsk3u5jywTjseiyI6E2We619c2bCS4lcS8PEZB2kp+dlnt2zLbsCsSeSgbJiKucoyfpKedp7RwcIjeZOUhawUaCvGG650Bm7QlBFEW0OpFt5+4zd991ei41TdigSHOtXH5gOtlM7eadUTdUrU7kYIh9Vm46UTR6iSbYmEk6LSmiYrIm8wJ+aJTxYOB11o42aN9jpPortKJAT8UhflMuQEHG/ka2YOlZGnDrCcduZF/2MAmJ1NQp68Yf/byoXcaN4gXSTwA2WePDdV0Z3IUYZikXY+kpkpMWrxISOUU+JwVXJrXli6aVuChWYa1Wn2xhunI5+dEHm/8j4A5nJPdcCYkcQ6cTOR36NNeEzzkU8hjv6fs5fjOKA8H6hbEn8S8NJsLTGEScvxfDqC32LxgIgmA0Ft19JSJbs+VmN6/umb0GZOcg0tFZZFNIbZi15WxYmn02ZHR0dIcsHDAjKwVmD2+lw/ZYqakcIHjkBfJqLMGMxk9ccPAmtSfsoc3cw9xMlXgjLbZcK6ndvDP6rNBkwB+/re8Rui5K34z9t303GL7xvMVniyNjikqZ+ozZon2Hr9VDUYlyOstPslg5F2dMLVAzQ0qcFEvX3ifLTvLpilOcMmOZKiGRnbxfrzQ9G5djbo+6Fssk4cw36m9IEpW0kgfxhXy32XIancjjONsttCUkXhXyOysY9r9qAMzU9CBMV4KyQhSjFBsBvTV/98UBHLn++oXFkJDIDfwVGMbHSwKy1NV079VHeE/fz+nQ9IX8vqvOEB6bRO/lp0yMOczFBweISVCz5axpIsU7UZbn6RqtjjHbjEXBHRceptu/vIok7uVhstUt147GMtqvSw9iOXw90iSemDkyM1VPUmu5bCHumaWIe99uCjIpYxvme5pWtNLpRBYdumW2bFoOhTym+vj/WBtwx66e5EVeR00mXqXlVmQ8tyItv6hsic+YukhGxb0J/1yxu05MgprzNgSYnbvvOn8HPeTMHfPBah25niAZxZryn86LL9UjSBKV/E9+npXKmeTDcaJEoyn7gPTjjZ1NJ1hxXiAuSc2DmMSc7oZEBlHIZfzS7U0+rF/WarkQsTxTNJ8CMEaxnlqC+ZADTabtd3gfJSTyAm6uSm5MbU8CLozW6GNVfqbYSxPhZXzpcX+bj1spISGRtfwd9ADAqvFAZhmwNpDw2CT6rDyVfmErWJoThzx6xsjNF0y2f73hnMVjXbgfazG77quIJO7lYbLKms4caU1js4KNp8P4bOVpQyDqrJqP91l5miWpstSmxtLvNNLObD2WSLFGSita+V99ZPMxvl6vz2z6UwaEl7xGXtVkHNHvAs5yi/vSuuWa43Cq1fGMJsTZeCYs/UJkzjLOXIKO9Hgar2LEX0Fm4xGaI69agGaU/E6Wr53UHNLVp696NM9FF5rKr7DOaRqFcNyg70JYDG/8ZN7CKQWlLO8PQ+pO8qfpLwd4KAl8eZ5pH9axun+d9n/s1jbGSdAyT7nAoYK4hMSrQEosqxO62qzXvAvADOVSXNCPoyNikwi49fpMtCUkXkfs9TDL7CJ8RDboFJnm0VWIuJzlzeT9UfVrTHamX/5t/w2byy44eNPmsldSBetPsfAIe2r7BCkjzwJrpsKWjEzCniYyc881u2OX2ap52BOH5HWyQsqzlntZ3G9b4jOmFn9zcxyojAhvU/69yrZzD0ziER649oj3Fxzj5mPjDLuv0z0D0PqNkjaXPal7g09VPxAj5qeB7CYbnKZSjMxndAZ4//fjFp+pKZizQs3OhStHkNLdwLt53wrxdaeXV3nO/djaSgmB0eoBPBSLUkUWziTF6uzqmoREnmHy+7UAmK7pzUOxKBVlj/hOsRnQJ2v6ZNlJKSSDhEQ2k1uHVgqZkOlF+NyalV6Jhk6yADY5TYZF3mj3TcryNiVxT8Lh2KPWP43PWJynrLqFUyzrzE0ufz94i+3nHzhk4pn6EXYhLIYVx2zPKKqwIcPPq0JeyJZrjm3nHmT6GI68xh0t7iWoNEb3wcX7MTbVux+dkOF7PjV3nyaY3f7F6kAu3I81WLcaSHMZ5TXxyF5cnRR2lQ8Sq9JT9SORYiFqye7yl9NkypA9cZEUcsGu+Iqv+J9OIhdQNL+T1f2xFGC4aghaUaC74ghdZXkzo56ERFbRx7sicz6uyzPy8cOLDO395P9RX3hpKGCSJFBCQiJHiXqezNm72Z/0xpbMtekRk6DmdzuMi7Ka0kTxneIvTjh/wwKn+XjJrqERZdyK1oLOfo8le3h9VAKJPMWDmESbYso5WvuJibeeQehBtH1uV5a6l9qN8f3fj9t1zLwqeGWE1+hUTciMiPH3eWNx0ZELWlfCY3njpz2M3voyOO2CA+m/UJ/Gq2j260Ea/LzXpnYy0+XYROP7+HW6ZwBGtK5ud51rYnk+Vk3ggViMKrJwtjpPpIZwLwt6Z4xCJjO51q1e+7lY3MurovHChQupVKkSLi4uNGzYkKNHj1otf+yYPnSGu7s7lStXZvHixUb7W7ZsiSAIJp+OHTtmqt3cxGnRE19NNwCmKldSXbAewuDRiyQbGu3rkQxLQqJrA30My0O6+mzVNkcmiMxQLjUkb7ryMI5L9x1jJS4hIZE+6Y1QvKfvp9uiAI7fjMqW/qSglAs4IkLLzD0hmT9IJhDQ0Vx2kaXK2Rx1HsY3ir8pIcTySCyMr6YrTZPnMdFlFMhsC52TUSRxTyLHsDYP6jTvKPtepMXO6DEywrNkjf64FvYLgn1tpo1F5rPiNDcfP8+UaPU6uRgqdYl8JD/s8EyeeQGr+oaVnTEJKoanSgADEBweZ76wFR5ZyPiYsjL2V6BptipzLD8WyuO4JEIinqVf2AI9lgQQZsFazxxpfz+v0S3D9sFvU6Kgc4bqhoql6JY8kRBdWTyEaDY7TaaxYD5jmaOIT9ZwykqohL1XHzEr1YDNUVmUNVodSeqsXT3NC2zatInhw4czbtw4zp8/T/PmzWnfvj337pkXdkNDQ+nevTsAR48e5YcffmDo0KFs3brVUGbbtm2Eh4cbPpcvX0YulxvqZaTd3MgC7Qcc0dbBVVCxSOlLfiwv/nlN28+8/Td4Y8IegsJisq+TEhI5SJ0ybgBMVvsQKbpRTfaAoYptgP7Z3nnBMZ6/GHdLSEjkLGqtfnx15EbWeG4Eh8fRbdEJTtwyFg+dFLI8HRu7AAl8Lv+PA07f8YfTL7SRn0UuiBzX1mKgajhNk+fhq/mIRxQl2c5YhBlBEvckciXRCdYt6LISURQtBurP7KNHqxP56o/ATB3HbDKFvPtMtEq96H3MUi4hwPlrRis2ZJurYG7AmhWQNYHD3EB57Db7s9NZsvZLefkb98c6X284b7HPKUK3tfM9FfqU77eYZseyFVtiFL4qZFb6iqAY3VU/cUZXnUJCAuucptNGdsYhfTPHVL9gq/sHrA1kWypLVEct6LSee4SaP+4m/jWfWM6ZM4d+/frRv39/PD098fX1pVy5cixatMhs+cWLF1O2rN4ip0aNGvTv358vvviCWbNmGcoULVoUDw8Pw2fv3r3ky5fPSNyzt93ciIiM4eohhL+IvzdduRxrd+CcvddRaXR88PtxBv5x1iQ2qITEq8byzxrR9+2KxFKA8eovAPhK/i+1hZdJ7Z44KGGdhIRE7qbf6jOcvRtNr2XGmXQd4ZabE5QXHvGTYi0Bzt8wQfkHlWSPiBPzsUrTlveSZ9JbPY7duiZoeBkq52w2xGbOm7/NV4zwWCnDXkbJCm87UYQktXllXSYTMj25jIhNypzlno1Cxc3Hz5m1J4TYHBRKM4tG7sx9sThFhecMUuzkiPNwlijn4C27Qq72z3MAVs/Oyk57EtJkhNRJZZI1Wi4/iE03S+jZu9FW+3w/OoHGU/czP1XinrRinz2x+tIKiWnvmFf5yinkosz0MeIowKeqH9irbYizoGaR0pee8gMO6J1tZIdXbmhUPKCPefq6olKpOHv2LG3atDHa3qZNG06cOGG2TkBAAK1atTLa1rZtWwIDA1Grzb9rVqxYQc+ePcmfP3+G2wVITk4mLi7O6JPTPKUQQ1RDUYtyusgD+FS+z6Z6u69E0GfF6SzunYREzlKykAsTu9SiebXi7NE1Zqf2LRSCjpnKJTihf14sPXI7naNISEhkJ1llRff4mXkhXymX5aEwTCLesissVc7mkNMIvlDspqCQyA1dGcapv8AreQGTNJ9xSyyTYz2UxL1cwOit9lvVvAo4yr3K0aSXfMBSvwetO5urYi61nnuYBQdv8uM/WZ92O6u4UKQtLZJ9GaAawVFtbeSCSFt5IBucpuLvNIpP5XvJRx5If54BrFvuWeaTZSet7M08qTNSjdt+mU7zjxGeTgp6nShatAQUgBm7Q4h6nszsvdcN29MWt+fWMnHLzTujhkxRprArVd0LOORYyTgxUD2cjZqWyAWRX5TL+Vq+neyURp8lmYpFjn7GOvJouejxbxNRUVFotVpKljTOrlyyZEkiIiLM1omIiKBEiRIm5TUaDVFRpnF6Tp8+zeXLl+nfv3+m2gWYPn06bm5uhk+5cuXSPcfs4JxYnV80PQH4UfEHdQTbxIqH6Tw3JSReFf7o50U+JzkT1H2JEgvhKQtjuELvyv/nqXska6QQCRISWU4Oj1EsNa+UC7l+nO6Miu7yQ/znNJYNTlNpIz+LTBA5qK2Lj2oMrVUz+FP7PxJxyemuYl9KPeDGjRucOHGCiIgIBEGgZMmSvP3221SrVi0r+vdaEJ6O1curyjS/zMdyyorVhd8P3rLcnpWYe/9djuBRnPGqhLne6R9gGe932vaDwmK498Q0HllKuQs2ZjLNjQgC6JCxV9eIvbpGVNE84DO5P93kR6gue8AU2SpGKTayRfsOa7WtuSOWyukuOwydlbAMOSkia1N1bMtZ2+LuiaJ1MV9j7WRTjpHq5/TunrQt5fIxg8MY+E5lhx5Pi5wxmgFEUphvFH8zUrkZdyGGiZrP0GXh2qAowsnbT+i51FSoTv23vR35nOIFnR1iregIcuuCVXqkHVSLomh1oG2uvLntoLfaq127Nk2aNMl0u2PHjmXEiBGG73FxcblG4Fuh7UATWQht5YEsVP5GR9VU4nCM0C4h8SpwZNS7hEbFM25pCEuc5vKVfCf7tA04J1anxvjd9PIqz8AWVShX1DXXT/QlJCSsY+4eTj13Se0FlJvdcosQRx/5XnwUeyku6L0FEkRntmqbs1rbNkct9Cxhs7gXGxtLnz592LlzJ25ubri7uyOKIpGRkcTFxdG5c2fWrl1LoUKFsrK/EhJZztx91y3uExCsTt9Gbk4/LphGp8PXShvmCAqLoV65woCxZWHY0wQ+SCfbbl4eIqUVb2+JZfhJ8zkzNT3oJj9CH7k/lWURfKHYzReK3RzR1mG99j326RoYxTjIi+y+YtmCJSfRmIm5ZwvW9EitDel8Uw8KbOnBHwF3cFbI+bhxudcnW26q85zY+Q0m7rzqiIMyW/MxUaIbExRr6aPYS2khiqHqb0jIwhXK2f7ms56lXAbXHz2jzdwjuCrlBP/cLsPt5DVrO0dSvHhx5HK5ibXc48ePTazqUvDw8ODx48cm5RUKBcWKFTPanpCQwMaNG5k8eXKm2wVwdnbG2TljyWIyQ/3yhTl/LyadUgLfq7+ipnCPCrLHzFYuYYB6BOm9gTVaHYpcPLGRkHAUxQs4U7yAM9sbd2PruUC6yY8yR7mIDqrpJODC+lP3WH/qHl80rcRPnd/I6e5KSEhkAlEUCYl4xq3I53Sooze8SD3cajP3sOHneJWGI9dzV0z1skIk/eW76CE/hKugDwv0QCzGWk0bNmrfJTYXL97ZPKL45ptvCA0NJSAggOjoaEJCQrh+/TrR0dGcOHGC0NBQvvnmm6zsq0QOk5vM5hNUWmbtCWHmnqzN5JiW9DSCY2nSh5srn6TWcfSGfWnGZ+0JITZR76KWWty78YoH5Lb0+35GPlZr2/GeahafqUazX1sfnSjQQn6JxU6+BDh/w/eKjZQV0s+4nBfJSUFCY4MQZw5rtWwR9+wh8lkyP/5zhVFbL6LW6vK0wJ1R+jatRPDkdjgpHCMcrNG2ZYh6KEmikv/Jz7PJaTIlyPrAwGk5efsJz5LUhoFgYiYz3jrS2i6vCYVOTk40bNiQvXv3Gm3fu3cvb7/9ttk63t7eHDx40Gibv78/jRo1Qqk0tqD866+/SE5O5tNPP810uznJ1oFvU9OjIHVfLLBZIo78DFYPI1lU0lp+lkHyneke+/PVWZesRkIiN+J/JYJJ6j48FItSUfaIsYr1RvtXHg/NoZ5JSEikJuxpAp+vOs2Jm/bNWVNo63uEwX+eI+CWPkll6jHSrcj4VO3kHg9GT+EuvsoFHHL6lr4Kf1wFFZd0Ffla9Q0tkn1Zou2cq4U9sEPc27FjB8uWLcPLy8tkn5eXF0uWLOGff/5xaOckchczd5u3pMgpFhy8yYX7sdnapkyAn/+13RrGUSmvj92MYszWi4BxFtNzd2MccvzcSvrulzIO6+rST/09LVRz+V3ThUjRjRJCLEMUOzji9C1rldNpKzuNgtc7K6aj0Ggzdk1bdCUWbLTcS/VzouqloJNeQiKdKJpY7uU1ESajuDrJHSps/qfzopdqHFFiIerI7vC380/UEO45sAU91yLiOHPHvHD4zYbz9Fhy0mF/w9flWrDEiBEjWL58OStXriQ4OJhvv/2We/fuMXDgQEDvCtunTx9D+YEDBxIWFgZASEgIK1euZMWKFYwcOdLk2CtWrOCDDz4wseizpd3chEwm4De0OdsHpS88XhErMVGj/32NVGyiueyi1fJHb0Rx8/EzfFac4uzdpw7pr4REbqZEQRfiyM/36q8A8FHso4XM2OslN8WvlpB43UgZMn+3+QIHQyLptfyU9QrpEBxuX/KrR3HZHY9WnyRjjfIX/nMeywfyEygEHUe0deil+oHOqqn8q/NGizyb+5Ux7FrStycGi8Srx+oTd7j5+Bl9V53m/GuaYVBA4MA1263Bpu4Kdljb/12OIOp5stGgZ8HBm+nWy8v3pj19vy+6M1PTE+/k+QxUDeeItg4yQaSF/BJLnHw54TyUkYpNlBVyl+l3RsjJYa82g4Nuq5Z7ZnaaNJPqe4LqpVD7PMm6aHvpfizRCbZn2n3VcPTtf06szoeqSdzSlaKM8IQtTpPSFTDs5b/L1l3Sr4bHMdXv5bP1mw3n2XbOtviPEsb06NEDX19fJk+eTL169Thy5Ah+fn5UqFABgPDwcO7deyngVqpUic2bNwPQrFkzfv75Z+bNm0e3bt2Mjnv9+nWOHTtGv379MtRubkMmE5DJBMoXzZdu2Q3aVoZENPOV8yknPLJa/su1Zzl6I4puiwIc1V0JiVxLpzf1LnrHdXVYrdFnzJ6hXEohnhvK7LoUniN9k5CQeGlYEZGJpE+p52/Ljt7mebLtBhYfphNuylEI6GgrO80/Tj+ywWkq78gvohUFdmi96Zg8jT7qsZzQ1SavBbiyOShV586dGTBgACtWrKBRo0ZG+wIDAxk4cCBdunRxeAclcg8ancjnq8/kKvPZ7MbeifIDBydL+XDhcYtZRy0RGhXPnah4KhbP79C+5FY0KNita8JuXRPKaR7RU36Qj+WHcRdi+FrxD4PlOzimq80W7Tvs0TUiGaec7rLd5OSidobbthpzz76EGqnvgfTExo8Wv94T5qxIOhQmlqSrahJLnebgJbvGKuUMxmn6sUn7rsPbsoWdFx6y88JD2tbyIL+zfbE2X+dsuSkMHjyYwYMHm923evVqk23NmjUDIDIy0mKc5erVq6drfWOt3dzKyr6N+N+cI+mUEpig6UtNWRj1ZLdYqpxLV9VEi1n07ke/vmMqideP/s0rcTsynq3n7vOL5hOayy5RRRbOJOUavlUPAWDj6TA6vVk6h3sqIfF6k5nF4dTv//DYJKbust3rLaszycvQ0VF2kq8Vf1NDpl8YThSd+Ev7Dsu0Hbkvumdp+1mNzZZ78+fPp3Tp0jRp0oSiRYtSs2ZNPD09KVq0KF5eXpQqVYp58+ZlZV8lcgGvs7CXGwh7msjTePutkC4+yF735dxCmFiSmZqevJ08n0GqYUbWfPOcFnDGeTBTFSuoJ9wkx3PE5xEyKmDorFS0N0lH6mPZoAsC8OzcLu4v7sfdWR/yzcdtOHr0qNXyx44dA8Dd3Z3KlSuzePFio/2rV69GEASTT1LSy0GJRqNh/PjxVKpUCVdXVypXrszkyZPR2dppB9C+jgcA1Us6NkZILAXwUY1lu7YpCkHHr8plfK/YiED2nVtaFh2ynOk8O5CeIK8Ozi9iVSpkxrObqu4F2T28OVVKWF8sS8aJgarhRIqF8JTd4xflcixdIapUoQ4cvSAoIZHbcFbImdX9TT5pUo4knPlOPQitKPCh/DjtZXr3v6RMxlKVkJAwxZExhu0lJe5eTiJHy4eyo+x1+p75TguoIbtPnJiPeZoPaJo8jwmaz/O8sAd2iHuFCxfmv//+48qVK8yePZvPPvuMPn36MHv2bK5cuYKfnx+FCxfOwq5KSOQ82eXiao/5si3I8pZFscNRo+A/nRd91GNpnjyX3zRduS8Wp5CQQG/Ffv52/om9TqP4Sr4zR5IE2EtOvqAzyvpTL137Uq/oCQj2Z8tNVTxF6EuJSWmO+OAjPN2/DDfvjynddx61GnrRvn17I3fD1ISGhtK9e3cAjh49yg8//MDQoUPZunWrUblChQoRHh5u9HFxeWmd8+uvv7J48WIWLFhAcHAwM2bMYObMmcyfPz/d87WFgLGt2DDgLatlfn6/NtO71uHP/tbLZQQVSr5VD+Y3TVcAhih2sEj5G/nI7ngpejIijNx4ZJyUSBRF/K9E8CAmkZO3nxBjh0u3FCfq1eGvr7xpUqkoW8zE2avpUYj937WkccUiVo8RQTGGqIahFuW8Lz9BP7lfuu02/eUAOy88zHC/JSTyAoIgML3rmwAEiVVZqH0fgKnKFZQgmqRclMBPwpjo6Gh8fHxwc3PDzc0NHx8fYmJirNYRRZGJEydSo0YNADp27MiVK1eMynz11VdUqVIFV1dXSpQowfvvv8+1a8ZJE69fv877779P8eLFKVSoEE2bNjVJ8CSRO8nJ0ZECDd3lh9jvNJK5TouoIgsnRszPLHV3miX/xhzNxzzFvBdCXsQ+/xXA09MTT0/PrOiLhESuJ7tEsu/+CnLo8eR5OO6eowkTSzJX8xG+mq68JQumu/ww7WWnqSZ7wFjZBr5XbOKwri6bte9wQFcfFcr0D5rN5KxbbsYa358qVmVa61NrVn2Gdi2U14kioiiy8UyYxbpxZ/6mwJutKVi3LQADP32fkMBjLFq0iOnTp5uUX7x4MWXLluX69evUqFGDxo0bExgYyKxZs4xiiwmCgIeHh8V2AwICeP/99+nYsSMAFStWZMOGDQQGBqZ7vrZQys2VUm6uVsvkd1bwSZPyDmnPPAJzNR9xV+fOdOVy2snPUEF4RH/VdzygRBa2a8r28w8Y+l41nsYn07BCUZvqTNkVTP/mlQ3fd1x4yLCNQYbvpd1cODH2PUd3VSKXU7dcYf76yttqGWdF+sG1T4ue/Kz5lMnKNYxVbOCqWJEAXS2rdX4/eJPOdV+6JD5LUnMnKoE6Zd1s67yERB5jnqYrrWTnqSW7y2zlYj57MBpRFPN0zOhXlV69enH//n12794NwJdffomPjw87d1rODj5jxgzmzJnDwoUL8fHxwd3dndatWxMSEkLBggUBaNiwIb1796Z8+fI8ffqUiRMn0qZNG0JDQ5HL9c/ajh07Ur16dQ4cOICrqyu+vr506tSJW7duWR2LSWQ/giAYTVbs9dBxBE6o6SY/wmD5DsrJ9PHWn4gFWabpyB/a1sRjffycURpVKELg3ZwzFLEroYYoiuzdu5dJkyYxaNAgBg8ezKRJk9i3b5+0Yi3xWpBdw4w9V6wH4LYXaXxkioiMAF0tRqgH0zh5IaPVAwjUVUch6HhPfp7FTr6cch7CVMUKGgvXctTdMC2rT9zJsbYd8aRP3X9BMI6hZ+ldYs5aD/SZdhdacccUtWpUETdxrVTfsG3mnhDatGnDiRMnzNYJCAigVatWRtvatm1LYGAgarXasO358+dUqFCBsmXL0qlTJ86fP29Up1mzZuzfv5/r168DcOHCBY4dO0aHDh3MtpucnExcXJzRx5Es6t0Aj0IubBnoTdBPrR167G26FnyiGm9wQ/zH+UcaCdfSr+hg3p11iG6LArgTFZ+h+sduRBl9z+rYLxJ5Fxelqbg375P6JtvWatuwVdschaBjgXIeZbCe1CmtoNHO9yidFxzjYIjtybwkJPIC/wxpCui9K4aph5AkKmkhv8QX8t0MWneO58kaOs8/xrIjt3O4pxIAwcHB7N69m+XLl+Pt7Y23tzfLli3j33//JSQkxGwdURTx9fVl3Lhxhtj8ixcvJiEhgfXr1xvKffnll7Ro0YKKFSvSoEEDpkyZQlhYGHfu3AEgKiqKmzdvMmbMGN58802qVavGL7/8QkJCgokVoETmcMScMa1HTnaGnJCj5WP5QQ44f8d05QrKySKJFN2You5Ns+TfWKztkmXCHoA8h93lbBb3Hjx4QIMGDWjfvj3bt2/n9u3b3Lx5k+3bt9OuXTsaNWrEgwcPsrKvEhI5Tl4NfB2ToE6/0GvMc/KxSfsuH6km0ip5Fr9ruhAuFqWI8Jzeiv1sdp7MUefhjFJspLpg2ULsdcAR6zjzD7zM8iwAQamyb/9uQwbo1GOGDxeeYOYe84NKAG1CHIg6ZPleutDdioynQOFiRESYz8oaERFBiRLGVmclS5ZEo9EQFaUXf2rWrMnq1avZsWMHGzZswMXFhaZNm3Ljxg1DndGjR/PJJ59Qs2ZNlEol9evXZ/jw4XzyySdm250+fbrB1cXNzY1y5cql+7tITdUS1uPqta9TipM/vEejikUpnM84kUyzqsXtassc58TqvJ88hSu6ChQX4ljvNJWP5TnjMnM9lbvtjgsPaTnzIOfuZe1KqrTE+XpRyNXU+aVLXXNJAAR+UPfjkq4ixYRnLHGai6sV1/W004KUSdGui1IGUYlXi7rlCrNtsN71/aZYlimaTwEYpdjIvasnqT1hD5cexDLVL1iKw5cLCAgIwM3NDS8vL8O2t956Czc3N4uLpaGhoURERNCmTRvDNmdnZ9555x2LdeLj41m1ahWVKlUyjIOKFSuGp6cna9euJT4+Ho1Gw5IlSyhZsiQNGzZ04Fm+upgbv9+Jimf0lovcjnxuujOPIaCji+wEe52+Z4ZyGWWFKB6JhZmo7kOz5N9Yru1oMbGVI3E2s/CXndgs7g0ePJiiRYsSFhZGUFAQe/bswd/fn6CgIMLCwihcuDBDhgzJso5mxse/dOnSuLq60rJlSxN1PyIiAh8fHzw8PMifPz8NGjRgy5YtJsfatWsXXl5euLq6Urx4cbp27eqwc5OsqvIOy4+F5nQXMoR0jdnObbE0MzU9aZo8j16qH/hL8w7PRFfKClEMVuzA33k0/zmN4Uv5TjzI+QCx2U1EXNZaMs3yv252u4hITIKKoLAY1Fr7rSjT3gMarc6qy0/afSkWhSnb33rrLT799FPq1q1L8+bN+euvv6hevbpRPL1Nmzaxbt061q9fz7lz51izZg2zZs1izZo1ZtscO3YssbGxhk9YmG1C8o6vmzK7e128qxSzqbw5HBXH8SHF+Ug1gX+1XjgJWmYol/GTYi1ysndilvJ3ehqvYuiG89x5kkDXheYnEhISGWF0u5pUsjELfTJOfKUaQZRYiNqyO8xWLrZoDX41PI4nz5N5GJPIkD/PGbbbEptUQiKv0aB8EZpU1IdRWKf9H3u1DXEWNPym/B0Xkg3lHB2LWsJ+IiIicHc3TTjg7u5udbEU9AukqSlZsqRJnYULF1KgQAEKFCjA7t272bt3L05O+oVIQRDYu3cv58+fp2DBgri4uDB37lx2795tNeZ/VntE5HV6Lz/FpsAwei07lW7Z3Zcj6DjvKD//ezWXWZKLtJYF4uc0lnlOC6gsi+CJWJCf1b1pkezLam07knFK/zAOoExhVyZ3sR56I6uxWdzbv38/c+bMoVSpUib7SpUqxaxZs9i3b59DO5eaXr16ERQUxO7du9m9ezdBQUH4+PhYrZPi479gwQLOnDmDh4cHrVu35tmzlyv6Pj4+hISEsGPHDi5dukTXrl3p0aOHkXvV1q1b8fHx4fPPP+fChQscP36cXr16Oezcaqqv0UJ2AQXSi0tCIregQ8YJXW1Gab6iUfIiBquG4q9tiEqU4ym7xw/KDZxwHsoG5RR6yA9SiLy/6pXbaT7jIB/8ftwuS1R5vkIgyNDGG1ttRUU+NhlspuDh4cHjx8YDl8ePH6NQKChWzLyAJpPJaNy4sZHl3vfff8+YMWPo2bMnderUwcfHh2+//dZsnD/Qr2YXKlTI6GMLb5YtTLeGZW0qmx0k4sLX6qHMUX8EwBeK3axSzsjWeyRFmn2YndlHJe3ltaJkIRcOjmzJjG76xACV08mg+5DiDFQNRyXK6SA/zXDFVotlG07Zx9u/HGDXpZfWehpJ3MuVLFy4kEqVKuHi4kLDhg0tWiOlcPjwYRo2bIiLi4vZTOyp2bhxI4Ig8MEHHzi417mLDV++xYUJbdgwwJvR6gE8FgtTTfaAcYo/DWUipBAJWcbEiRMRBMHqJyVWsLlFUVviI5pbME27rXfv3pw/f57Dhw9TrVo1Pv74Y5KSkgzlBw8ejLu7O0ePHuX06dO8//77dOrUifBwy1bNmfWIeFX4KzDMbBy4FMtwcwv3af+iA9ed5crDOFYcC+XzVWeyopt2ItJcdpG/nX5kmdMcPGVhxIn5mKXuTotkX1ZoO2abqAd6S+TjY1pRMdWiX3qJt7ICmxNquLq68vTpU4v7o6OjcXXNGv/lFB//kydPGkyBly1bhre3NyEhIYbsO6lJ7eOfYmW3Zs0aSpYsyfr16/nqq68AvYnxokWLaNKkCQDjx49n7ty5nDt3jvr166PRaBg2bBgzZ86kX79+huObazOj9FBtpanTKaLFAvhrG+Gn8+K4rhYa+/OdSEi8UuQWi8NknPDTvYWf7i3ceE4H+Sk+kB/HS3YNb34Qv4YAAMCZSURBVPlVvOVX+VmxkmO6OvjpvPDXNiQO6y6SEvYhivAsyf4FEEGuxMmjKol3gshX/WXmyyOHDvJR1w/M1vH29uaff/4x2ubv70+jRo1QKs0nWBFFkaCgIOrUqWPYlpCQgExmvIYml8vR6XJP/MasQ2CetivXxbLMUS6ihfwSO4QfGaj+lmtiVib4eNG6YPx/dpAXs1hLZJ6PGpalTBFXapVOX4wPFGvyg6Y/s5RLGKbYzk1dGXbqTDPymkP7Wjw38habNm1i+PDhLFy4kKZNm7JkyRI++ugji+VDQ0Pp0KEDAwYMYN26dRw/fpzBgwdTokQJo2RNAHfv3mXkyJE0b948q08jx5HLBNxclSjlAk8pxAj1INY5TcdHsY/Durrs0zWk0/xjXJzYhkIuuS/JWV7n66+/pmfPnlbLVKxYkYsXL/LokWlM8MjISKuLpaC34KtSpYph++PHpgusKQJctWrVeOuttyhSpAjbt2/nk08+4cCBA/z7779ER0cbFj4XLlzI3r17WbNmDWPGjDHb/tixYxkxYoThe1xc3Gsn8CWoNIzactHm8nkhjUJ94QajlRt5SxYMQLzozCptO5ZqOubc/CuX/OJsttzr2bMnn332GVu2bCE2NtawPTY2li1btvD555871JotNVnp49+sWTM2bdrE06dP0el0bNy4keTkZFq2bAnAuXPnePDgATKZjPr161OqVCnat2/v0OCd4TIPIsVCFBGe00NxiDVOvxLoPIiZisW0lJ1HKVn0SUjkGmIpwAbte/RQ/cTbSfP4Rd2TYF05nAQtreRBzFIuIdB5ECuVM/hIfliy6EsHW7PhZeadWajxBzy/4M/zi/6oo8J4un8ZDx+EMXDgQEA/+OvTp4+h/MCBAw0usSEhIaxcuZIVK1YwcuRIQ5lJkyaxZ88ebt++TVBQEP369SMoKMhwTIDOnTszdepUdu3axZ07d9i+fTtz5szhww8/zPjJ5DF265rwkWoC98XiVJQ9YrvTT7wvO5bl7aq1Os7fi+bKg+xzwckl4zqJbEYmE2hatbhJHEtLbNG+w2JNJwBmKpdQV0g/zihYzzYYm6hGpZHEv+xmzpw59OvXj/79++Pp6Ymvry9lypSxWH7x4sWUL18eX19fPD096d+/P1988QWzZs0yKqfVaunduzeTJk2icuXKFo726lGmiN5I5JiuDks1+izzvyqXUgK9xdEnS0/mWN9eZYoXL07NmjWtflxcXPD29iY2NpbTp08b6p46dYrY2Fjeftv8IkWlSpXw8PBg7969hm0qlYrDhw9brJOCKIokJ+tdsxMSEgBMFkxlMpnVBdOMekS8Sqg19g1OrCWps8aT58npF8okFYVwflf6st15Am/JgkkWlazQtKdFsi+zND2yTNgrWyTrEnA4GptNw2bPno1Go6F3795oNBqDD7xKpUKhUNCvXz9mzpyZJZ10tI//3bt3Dd83bdpEjx49KFasGAqFgnz58rF9+3bD6sLt2/oMTRMnTmTOnDlUrFiR2bNn884773D9+nWKFi1qtv3k5GTDAwmw6uO/xKUfo+K600R2jQ6yU7SXn6aEEEt3xRG6c4Q40ZUjujc5oK3PIV09nvL6PZgkJHIjDynOYm0XFmu7UEV4QAfZKTrKT1FTFkYreRCt5EGoFHKO6eqwS/sWe3UNicO2GE2vC7fMBPF1dPb1/J4t0CU+I+b4RrTxT3EqXoE/N26jQoUKAISHh3Pv3j1D+UqVKrF582Y6duxIs2bNKF26NPPmzTOyrIiJieHLL78kIiICNzc36tevz5EjRwxW4ADz58/nxx9/ZPDgwTx+/JjSpUvz1Vdf8dNPPzn0/DKKZ6lCBIfr303OiqwLAHxVrEin5KnMUy6ghfwSvzktpJ7mFlM1vbPMQn3SzquEZ6Ebl0arY/PZ+7xVOeNxDiVeX2ZoelJVeMD/5OdZ6jSHLslTeIT58WQKlmLuPXmeTMMp+yjl5kLA2PeyorsSZlCpVJw9e9bEYqhVq1aGDOlpCQgIMDI6AH0m9hUrVqBWqw2W4ZMnT6ZEiRL069ePo0ePptsXe+YcuZlSbq7M6l6XkZsvMEvzMU1ll6klu8ts5WI+U4/mysO8eV6vCp6enrRr144BAwawZMkSQJ/ltlOnTkYebTVr1mT69Ol8+OGHCILA8OHDmTZtmkH4HjRoEPny5TMYBd2+fZtNmzbRpk0bSpQowYMHD/j1119xdXWlQ4cOgN6jokiRInz22Wf89NNPuLq6smzZMkJDQ+nYsWM2/ybyGNngwXDz8XP+N+dwlh2/KHEMVWyjt3w/SkGLThTYrH0HX003wsn6cVhGYn1Dziz62my55+TkxKJFi4iMjGTfvn2sXLmSlStXsm/fPiIjI1m4cKFB8LOV3ODjP378eKKjo9m3bx+BgYGMGDGC7t27c+nSJQDDasC4cePo1q0bDRs2ZNWqVQiCwObNmy22a4+PvyDo43ud1L3BT5rP8Ur+nR7JP7JG05rHYmEKCYl0kp9ijtNiAp0HsdVpAoPl/1BDuIcU5EdCm/ScqH9nc2/ux9yb+zFR/85Gl2QsmKR9uKQkm0l5GXfs2NHIGvXOnTsW70lz131ycjL16tVDEASCgoIcdm6ChTfSf8Nyn5vKLbEM87Vdaaf6lfeSZzJH/RHXUln0zXZaTKDzQFYoZ9JdfogiSINUgLHbLpls+2K142N5FGzQkbKDVlJh5N+U6vsbbpXr8jReBcDq1as5dOiQUflmzZoBeneT0NBQI4s8gLlz53L37l2Sk5N5/Pgxe/bswdvb27jNggXx9fXl7t27JCYmcuvWLaZMmWL3uzKrWP5ZIwBclDJGtK5usr+gs+OEtxgK0lc9mvmaDwD4XLGH9U5TDRYZjsYeYS8jYvLagLuM3XaJd2cdsruuLewPfsSiQ7ccLnRL5A50yBim/pprunKUFGJY5jTbKHmAOdQWxL2Tt/Uhc7JSzJYwJSoqCq1Wa2JEkDbTemoiIiLMGh2kzsR+/PhxVqxYwbJly2zuy6sUV+yjF/FjVSgZqv6aRNGJFvJL9JP/B8DZu9F899cFIp9lvZWQhCl//vknderUoU2bNrRp04Y333yTP/74w6hMSEiIkZffqFGjGD58ON999x2gX1D19/enYMGCALi4uHD06FE6dOhA1apV+fjjj8mfPz8nTpwwGPcUL16c3bt38/z5c1q1akWjRo04duwY//zzD3Xr1s2ms897HL0RSbNfD9hd78TNKO48STB8bz7D+jH+CXpgdxu24EIyg+V/c9j5W/oq/FEKWg5q69JeNZ3Rmi+zRdgDSFDlnWzddo/cCxUqxLvvvuuQxrPLxz91EpDUPv63bt1iwYIFXL58mVq19JlN6taty9GjR/n9999ZvHixoe4bb7xhOIazszOVK1c2svRIiz0+/mnH7jpknBI9OaXxZKLmM94UbtNKfp73ZOepLbtDQ+EGDWU3GMUmHopFOaatwzFdbU7oahOFm8U+SbyaRO2YifZZFCU/ngTAk90LiPp3Nu4fTTCUSauBpySbWbhwIT4+Pri7u9O6dWtCQkIoWLAg5cqVMwlQu3TpUmbMmEH79u1N+jBq1ChKly7NhQsXHHpuhfOZj63iWcqx1qs+b1Xgj5N30y9oI7fEMszTdmWetitVhAd0lJ2io/wkNWT3eU9+nvfk59EqBM6INfHXNsJf14j7ouUJwevGwZBIk22OFjkGrA3kzbJu7Pi6mUOPm5coU9iVO7/oV7wTzQxcfur8Bt/bEaclPXTImK35mIu6ysxWLqKJLIR/nccxWDWMs6Lj4tjay+nQp3jZaYEXeNc0BrEjr9B+a/QLmzsvPGRpn4aULZLPgUeXyErWftGEVcdDzT7HUhOPK/3VI/nHaTxvykKZrVzE1+qhiBbW3SUxI3diKbO6veUFQeDZs2d8+umnLFu2jOLFi9vch1c1rtgtsQw/a3yYplzBKMVGzuhq0G2Rft/DmETWD/CyObSHhGMoWrQo69ats1om7T0gCAITJ05kxIgRuLm54efnZ+QeW7p0afz8/NJtu1GjRuzZsydjHX9N8VlxOv1CZui13Dh7bthT68nJ1FbCRmQEGTq6yY/wnWIzHoJ+EfiSriLTNb04oavt0LZswRZxz9JvYFHvBgxKlfk+q7HZci89Hj16xOTJk+2qk9M+/pb891MHPG/YsCHOzs6EhIQY9qvVau7cuWNw6TKHo3z8RWRcEKsyV9OdTqppvJU0nx/U/dinrU+SqKS08JSPFYeZ5/Q7gS6D+M9pDOMU62gpC8IVaRX3VUcdFUZS6FmKtR+KcxlPnMt4UqzdNyTeOoP6yX2zdVInm+nSpQugjwOTkJDA+vXrAf094OHhYfTZvn07PXr0oEAB43gG//33H/7+/iYxYxxBv2aVHH5Mc2Tl2DBF6GurmsH/kmcwV92NK7oKyAWRt2TB/KT8g2POw9jlNJZh8q14CneRLHJNyYrfyMX7sekXek1wdZKzddDbbBv88p2a34GWe6nZq2vE+6ophOjKUlKIYaPTFD6T7yGnrvse2RzHKSgshugXVqPpcTU8jj4rMzY4l8gZWlQvwarPm7CsTyNWf97Yatn7Ygm+Un2LSpTTUX6a7xSWPUKCw+M4cSvKZLukbeQMxYsXRy6Xm4QHSrHAM4eHh4dJ+dSZ2G/dusWdO3fo3LkzCoUChULB2rVr2bFjBwqFglu3zMfCetXiitX0KGj4eb22Fbu0TXAStCxQzjfEMQ64/YSZe0IsHUJCQiIb0WTQbdUc3rIr7HL6gZnKpXgI0dwXizNUNYQuqik5IuwBVCxmusCqkNn28m1fp1T6hRyIw8S9iIgIJk2a5KjDGZHax//kyZOcPHmSAQMGmPXx3759O4CRj//27du5fPkyffv2NfLxr1mzJlWrVuWrr77i9OnT3Lp1i9mzZ7N3715D2vlChQoxcOBAJkyYgL+/PyEhIQwaNAiA7t27Z8n5WiOCYqzXvkd/9ffUTV6Gj2oMizWduKyrCICn7B4DFH6sdprBBecBbHP6iTGK9fxPdpbCPMv2/kpkLckPgxGc8+Nc+uV94FymJoJzfpIfBJutY2uymdScPXvWkDQgNY8ePWLAgAH88ccf5MvneMuS/M4K6pcv7PDjpiW75kY3xbL8pu1GR9V0miX7Mlntw0mdJ1pRoJbsLt8qt/Kf81iOOA1nvOIPmgjByJCCpIOUrCA7aFihCA3KFzF8L2JjgoCMECqW4kPVZHZq30IpaJmkXMPvyt8oSEL6lbOA4PA4NFodCWrT1dlHcaYLZeZCBogi7L36iC/XBhKTYF68O34zig9+P26Xm8ztyHiby0rkHlq/UZKWNdzZ+20Lq+UCxZqMVQ8A4GvFP/SUW742FpkJdC5pezmDk5MTDRs2NDIiADh48KDFOt7e3iblU2dir1mzJpcuXSIoKMjw6dKlC++++y5BQUGvhDWeLfzRz4tpH9Z5IY4LjFF/yV2dO+VkkcxULiVlIWjhoVskmXlmS0hIZC8aC2Ej7KGs8JiFSl82OE3FU3aPWDEfU9S9eS95Fjt0TS1atdtDu1oeGapXs1QhVvU1XqzTpZmY5JZ5is3L8hcvWnfNSW3ZlhX8+eefDB061CBIdOnShQULFpj0Ia2Pf2JiIoMHDyY6OhovLy8jH3+lUomfnx9jxoyhc+fOPH/+nKpVq7JmzRpDAE+AmTNnolAo8PHxITExES8vLw4cOECRIkXISZJx4qjuTY7q3gT0wSa9ZVdpJrtEc/klygpRNBBu0kB2E/gXgOu6MgTqanBaV5NAsfoLV0BpaJhX0cbHIM9n6ootz+eGNv5lLKvUE1Fbk82kZsWKFXh6ehpZyoqiSN++fRk4cCCNGjXizp07NvXZ3sDPtq6M5DXui+6s1LZnpbY9RYnjPfk52sjO0lx2kfKySPrL/qO/4j+eiAU5rKvLQW09DuvezLkU7xKvDTO6vcmtyOe8Vdl6gP/MkoAL36i/4byuGmMU6+koP01t4Q5D1EO5LGZvhsj2v1kOWv/ZytPsHm5doElhwFq9O23xPSFM+7COyf59wfoQI/F5KH6LROaoVrIgk7rUYsKOKxbLbNW1oJzmMcMV25iiWEmEWJRDunrZ10mJDDFixAh8fHxo1KgR3t7eLF26lPv3X3pNjB07lgcPHrB27VpAn4l9wYIFjBgxggEDBhAQEMCKFSvYsGEDoI89Vru2sWVK4cKFAUy2v8qUKOhML6/yAHSsU4pdl8IZoh7KVqeJtJUH0le3h9XadgDU/HE3f/RrQvNqUmgTCYnsRK3VoZTrBTeNlYzF6ZGPJAYpdvClfBfOghqtKLBO+z/maj4ihoLpH8AO3qlRgt1XzCdjTY93axond7VFz0wp4uaqJDZRnaF27cVmcS8lWL65WBIp27My7kFmfPwnTpxosU61atXYunWr1eMqlUpmzZqVJW6HjuQphdile4tdurdAI1JWiKSJcI1GshCayEKoKntIddkDqsse0Av9ynCUWIgLuipc1FXmgliFC7rKREvZeHOcmGN/Ent8g9UyHn3m6n8we9+JRttFM+5u6SWbSSExMZH169fz448/Gm2fP38+cXFxjB071mo/0zJ9+nS7rHxl2eBzlNMxW55SiM3almzWtsSVJFrILtFGfob3ZOcpJjyjq/wYXeXH0IgyzorVOaCtzwFdfW6IZXhdxHlz17BE1vBxY+vWIev6efHpilNWy9iOwEpte87qqrFAOZ8KssdsdZrINE1v1mjbkBuu72sRzxiy/hy1S7sxqGUVi+VSX6OPpOQGEqno412BRhWL8OHvJ1BZcF/y1XSjrBDFR/IjLFDO42PVT1wVKxqVufowjojYJDzcXLKh1xLp0aNHD548ecLkyZMJDw+ndu3abN682WAgYC4Tu5+fH99++y2///672UzsEsa8X680uy6Fc1mszDRNbyYp1/CD4k/O6qpz6cUi0Jitlzg+plUO91RC4vVic+B9ujUsg7NCjiYDMfcEdLwvO8EY5QZDXL3j2lpM1vgQIpZ3dHeB3GNdl5XYLO4VK1aMX3/9lffee8/s/itXrtC5c2eHdUwiswjcF925L7qzTae3OChKHI1kITSSXaexLITaQijFhThDgP8UwnQlDELfFbEiwbrykuCXzRRs0In8ntYtRRRuJVFHhqKNjzHZp02IQ56vsNl6qZPNVKnycqKaOtlMarZs2UJCQgJ9+vQx2n7gwAFOnjyJs7Oz0fZGjRrRu3dv1qxZY7Z9ewM/y19Ryz1LJOLCHl1j9ugao0BDQ+EGreTnaCULoprsAV7CNbxk1xjLBu6LxQ1CX4DuDZLJHVlYs4LX4YWcV6he0vHWoxfEqnRUTWOGcint5GeYpFzDW7KrjFZ/SRz5Hd6evey6GM6ui+EGcc+c2Jz6GtVKF6xEKgRBoFZpN5RyActGmwJj1f0pxROayq+w0mkmHyZPNsoG+CRexVvT9zO7e138LoUTl6Tmw/pls+UcJMwzePBgBg8ebPie2hth9erVJuXfeecdzp2zPbi6uWO8TqRe4F2jbcNbsqu0l5/hd+VvdFJNI478r904UeL1IDZRTcCtKN6t6Y6zQp7T3TFh54WH/LD9Et+3rWG3W25d4SYTlGtfeBfCXZ07UzW98dc1wp5F3W4NyrL1nPkY8+ZwpKFA44pFOHMnOv2C2YzN4l7Dhg15+PChxSQSMTExDs9m+LqgdYCfui08pRD+usb46/Q+486oeEO4y5uy29SV3aKucIsqsnDKySIpRySd5C+DjEeIRbimK0+wWJ5gXQWuiuUJFUuhJfc9bF4F5PnczLrbpsW5tCdicjzJD0MMcfeSH4YgJsfjXMbTbJ3UyWZSxL2UZDO//vqrSfkVK1bQpUsXSpQwdnmYN28eU6ZMMXx/+PAhbdu2ZdOmTXh5eVnus7OziSBoDUuDto8almXLWdsf6HkRDQpD5uzp9Kas8JhWsvO0kgXhLbtKWSGKPoq99GEviaITx3W1OKSrxxHdm9wTzWcSz6tIb5fcg7Mya577ceRnoHo4fXV7+EHxJ+3lZ6gt3OFr9TdcEKs6rJ3sGKtk02td4hWhpkdBrkU8Q42CQerhbBYmUUN2n1VOM+iumsAzjGPafrf5ZWb63Di5kJBwFMZjQIHR6i+pLdyhvCySX5TLGKwehqTtSbyK9F11mvP3YujXrBI/dnrDZP+zJDUjN1+gS90ydHwze5M2gD6pDcDMPSF0bVDGpjqFecYoxUZ6KfSxSeNFZxZoPmSltl2GDBRGtq1un7iXwbFZ37crmmz7qGHZXPn+tVnc++qrr4iPtxzUuXz58qxatcohnXrdSNbkTMD8ZJw4L1bjvLYavFhJLkQ8tWWh1BVu86bsFp7CPSrKHuEhROMhj6YlLweUyaKSG2IZbohluKkrwy2xNDfF0twVPVDbfmlJZAJl8XK4VGrIk93zKdb2awCe7FmAa5XGKIu9XM0f0b0VTnNn8uGHHxolmylTRv8wHjRokFGymRRu3rzJkSNHzKaoL1/e2GQ6JYtulSpVKFvWcZYElmLuFXZVOqyNvJJt8L7ozlptW9Zq2+JKEm/LrtBKFsS78vOUFp7yP/l5/vfCCveuzp2jujoc1b3JCV0tk8lhXiPyWXL6hSSylIHvVKGqewHcHHjvmSKwWtuOs7rq/K78jfKySDY7TWKGpicrtO0zHVB5z5UI2ryReeHbbEKNVD/rbFD3Dl57bBLDReL1RCF/eT3FkZ/PVaPY7vwTNWVhLFT68oV6lE3jqsA7T/Hdd4MJnd+gWknHxiqSkMgJGlUsgiC8nJTHkZ+v1d+w2WkSHeSn8dHt5Y8nbRj851nuPklg9sd1qekheRtJ5H3O34sBYMWxUBQygUEtq1A4VaKz+QdusufKI/ZceUTHNzvmUC/1HLz22Op+AR0fyY8wVrGeooI+4/VWbXN+VffkMRnPYWBuLGYNW7S99rU9+O/yy7h8BZwVNK5oHIPazVVpU9vli+rnXtk5z7RZgfnwww+t7i9SpAifffZZpjv0OpKsyT2BtePIzwldbU5Q2yD45SeRGkIYnrJ7eAp38ZTdo4YQRgEhidrCHWpzh9QGfBpRxl2xJLdfiH23xNLc1pXinliSKAqRG2IovUoU7zyS6H1LePSXPiZevqpeFG090KjMw7u3zCab+e677wB9XJjUyWZSWLlyJWXKlDHKrJvdWIq550jDGHtfDrmBRFzYr2vIfl1D0IjUFMJoJTtPC/lFGgrXqSB7TAXZfj5lPxpRRpBYlSPaNzmqq8NFsbJkdSthNx/WL0MNj+wRDC6JlemkmsYvymV0kJ9mvPJP3pFd4Dv1oEwNBL/64yy3p3VIv2AmsWSRn3rV+PPVZ9jxdVOqlyyIi4OsIWMT1Fy4H0PTqsUlV7U8REFnY8H8IcX5QjWKv5wm0Vx+mV9YxnfqgaQ3fvpocQCgv7aOjZZikEnkfQq6KLk6qR0jt1xg18VwQB/G4RdNL35S/sF4xTqCdFXxu6QvP2jdOQ6ObJlzHZaQyARHrkfy+8GbjGpX02j7kiO3CY9NYt4n9Q3bHsXlnti+0QmWk0XUFO4xRbmSRrLrAFzTleNH9eecEWtarGMr1oY5/ZpVYsWxUMP3t6sUS9d0r493BX7o4Ml/l3cbthUvYGpRKIqiiYtv6u8bv3yLv86EMf6FxWV2OrdK5lW5gJyy3LOVeFw5J1bnnLa6YZuAjnJCJDWFe1QVHlJF9pAqwgOqCOEUFBKpIoRThXBac9boWM9FF8JEd+6KJbkruqf6uSQPxWJopEvSbuSuBSneeaTVMjqdzihpREqymREjRuDm5oafnx+FCpmudE6bNo1p06bZ1I+KFStmq2t+2hTkrzcC18TyXNOWZ6H2ffKTiJcsmOayS7SQXaSKLJxGwnUaya4zgi3Eivk4rqv9wrKvDvdFyXpIIvcRR34Gq4fRS3eAHxV/0Fx+mT2y0YxRD2DPi/AS2c35e9HcfZJgfmeqZ5KtMfe6LDhO82rF+aOf5VAG9vDBwuOERsUzqUstPjPjRiKRO5nWtQ6LDt3kr8CX7kVXxIoMUQ9luXI23eRHiRQL84vmE5uOdz86Mau6KiGR7bg6yVnwSX2md63DkD/PcfRGFCu17fCSBdNWHsgiJ186JU8lhoKERsVTccwufutZj/fr2eYqKCGRW+iz8jQA3RadMNl36UGs0ffUSSxiElQsPXKbrg3KUNU9d1ht5yeRbxVb6Cvfg0LQES86M1fzEau1bR0337cg7o1sU51iBYxDQLWv7WGTYYiLUs6dXzpSccwuAFydTPsqYirYlSvy0kPqrcrFeKtyMXIChykp//zzD7GxsSZB9yXSR5XLxT1ziMi4J5bknlgSfzBY+YFISaKpIntIVeEBVQX9/xVkjyjFUwoISXgK9/DknskxNaKMcLEYDynGQ1H/CReL8UAsrt8uFiOOfEiWf/aT09lgM4OlB7Ejtb08/OsxSzyuHNA14ICuAQBlhUiayS7RXHaRZrLLuAkJdJCfpoNcP4i4LxbnhLYWAbo3CNC9QQQ580KSyN3kjCGYwHrte5zS1eQ35e/Ult1hidNc1mve5WeND4nYnzU0M4+ODxfqB9xlCrtaLWeLW24KR29EZaJHxoRG6cOn7LoYLol7eYhKxfPT5g0PI3EP4JCuPmM1/ZmpXMpAxU6eiAVZpu2UQ72UkMg5BEGgkIuSP/p5vZh0C3yv/orqQhiVZI/4Tfk7n6tHoXsRumHYxiDa1vJwmFW0hERWkqjS8vOuq1bLxCdrjL7vuhRu+Hnc9svsuhTOkiO3uWWDd4Ioilk4NxTpKDvFj8o/DFlwd2mb8LPax+HzC0veXWA6T9SJ6c/33q3x0tjht571mLv3Or496pk5uPFY8oN6pQ1WejmNw8S90aNHc+PGDUncywAqbd4T9ywj8IiiPNIV1bv2psIJNWWFSMoLj6ggPKaC8IhyL/4vLzzGRVBTTtAn87DEc9HFIPqFi0V5TGEei0WIFN2IFAvzGP3Pr3LWUAk9jkxE84ppeybcF0uwUduKjdpWyNBRV7hFc9klmssvUk+4RVkhio8Vh/mYwwDc1nkQoNOLfSd1bxBF+sldJF59qro7PkuurdwSy/ChajLfKTbzpfxfeikO4iW7xjD1EC6LlbO9Pw9iTC2jUj+Rcjxbrg0Ptd/23SA8NpHpXevk6QWgvMT3bWswcedVenuV589Txouclq6YzdqWFOUZY5UbGKdczxOxENt0LbK+sxISuZTdw5szdVcwR2/AQPW3/O30E+/ILzJM3MpcTXdDuS//OMuazxtLzzeJXM/SI7dZf8rU8CU1j58l8+R5solFGoDfZb3QZ+vc6P3fj7PiM8d7QJQmiinKlbSSBwEQqivJBE1fjujqOrwtsDzUEQTBxG1WJ4rILNQ48v27RMQl0bjiy7Av79crY9H6VwSjOHy+PeubLZcTOEzcu3btmqMO9dphbg7we68GDFl/Lvs7k4WoUHJbLM1tsbTJPgEd7sRQRoiijBBFaeEJpYQnlHnxf2khiqLCcwoISVQXHlCdB1bbihXz8VgswmOxMJG4GQTAaAryRCxEtFiQpxTkqViQ57jy6ss7eRdLrr72TJ5b1XTnQDrBXl8XdMgMiXTmabuSjyQay0Lwll3lLdkV6gihVJZFUFkWQW/2AxCiK/vCqq8WJ3WexJJzIo+jydrVy7zP6HY1uRAWw++9G2T691TKzYXw2IzHiFGj4BfNJxzWvckc5SKqyMLZ7jSBOZruLNF2MlhspEeVH0wTBDmC1I+k1JZ7B6494p+gh/z8QW0ztXKOufv08W8+fasCtctIAn520LdpJVrVLEm5oq782OkNfv73Kq1fJHjRWFnoXaLtRHEhlgEKP2YolxKtLshBnW2TiYjYJNxclbg6pW/BdOPRM3Qi2RZbU0IiI9T0KMTC3g2oM9GfELE8Y9T9+c1pIcMU27mgq2LwWjhyPZLbUfFUKfHqjFkkXk3Coi2E+0jD4euRdG1gmrTQ3vXEi/djaTx1n32VrCBDh498L6MUG8kvJJMsKlioeZ/F2s5ZanBjt+WemWU0j0IulC+Wj/LFbE88KIoiVd0LsHt4c0qYEVtzEinAWS6lVGH7XY3yMiIyvcWfWJRzYnWzZVxIprTw5MUnipJE4y7EGD4lhBhKEIOzoMFNSMBNSKBaOiIggEqUE/1C6HsqFnr584v/Y8X8xJGfODEfseQnTsxPHPlIwglJFMw57Invt+KzRlQaa3lC/zprOwm4cFhXl8MvVtUKEU9j2TXell3FW3aVN2R3qSG7Tw3Zffrij04UCBHLEqirwRldTU7rauRpN16tTjTKVClhzKCWVewqL5cJZleOp3etQ+e6pak9YU+m+xSgq0X75OlMU66gg/w0o5UbaSU/x0j1QO6KHpk+fkaZsOPKyy+pHipfrA4EyNYBoD1XdG6P+/uqkTKBcFHKmfphHcP2eJW15GoC0zS9KCrE0U1+jIXK3+it+sHieAkgNlHN+L8vs/PCQ9wLOrP/u3fI76RAZsG/XqXR0XruEQCuTm5LPjNxhiQkcgsFXZR0rV+Gbecf8I+uGfU1N+mr8GeuciGdVVO5J+pF8+l+11j+WaMc7q2EhHVsndIIApwOfUpIRJyVY2XvonU14T6/KpfSQHYTgNO6GoxV9+eWmPUxLwUB5vaoy7ebLpjsS/srfad6CY7eMPUOzMgcIMXdPzdm5rb7zX3jxg1OnDhBREQEgiBQsmRJ3n77bapVq5YV/XttqVgsf053IdeRhLNFy7+XiLgRT4kU0Y8Yw8/FhViK8owiwjOKCs8oyjPyCck4CVpKEkNJIcau/iSLCuLI90Ls04t/Kd9jyc8zMR/PcSFBdOE5LsTjSrzowvMX/8ej/0hJRKxj6X2ns3E+6qKUpfuSkyy3XhJH/pdZeIEixOElu4a37AresqtUlz3AUwjDUxaGD/pVvzBdCU6LNQjU1eC0ria3xNLkFeFbK4rSHehAbkxpT9D9GLouNA4G/UmT8g5tJ4aCDFYPo7vuMBMUa2ksu85/TmP5RdOTP7StEW204stOwuOSsk3gS++RlqR+KSRJj7/cQdtaJVl4MD+3X8RNTIuIjNHqLynCc1rJg1jlNIPuqglcF8uZLT9pxxV2XngI6N256kz0t5q8JUnz8pqITVRL4p5ErmdOj3qUKuzC7wdvMVXzKXVkoTSU3WCx0peuqokk4cy+4EdodaKUPVwiV5PWhdQaHy8JsLp/5p4Qk2y7WYETagYr/mGw/B+cBC3PRFd+1fTkT+172TYGExD4sH5Zs+JeWsW0qnsBjlw3FfcyMgayxRLeuCvZF6rF5jd3SrKMnTt34ubmhru7O6IoEhkZSVxcHJ07d2bt2rVmM25KWKdbg7JsPWccRFkujbYziEAsBYgVC3BTNDVbTosLyRThOUWFOIoKzyjCM4oJcXoB8IUQWIgE3IR4CpFAoRf/KwQdzoKGEsRRQrC8emILyaJSL/6JLwTAFz8/x4VEXEgUnUjEmUScSBadSOTFd9GJJJxJwsmoTBLOJKUqZ6urWm7F0vPQ1my57gWtW8FK4z3rRFOI3bom7NY1AaAYsTSShdBEFkJj2TVqCXcoJ9PHyuwmPwbAE7HgC6FPL/hdESvmWhE7p0OjvWrIZAINyhdJv6BDENisbUmArhYzFEt4W36Vyco1tJUFMlrzJffFEtnUD9vJygHe5VSZ9IR0xPV3Zx0y/Lx57Qq6rfid8PBwatWqha+vL82bN7dY99gx/X3u7u5O6dKlGTVqFAMHDjQqExMTw7hx49i2bRvR0dFUqlSJ2bNn06GDPtD3xIkTmTRpklGdkiVLEhERYdO5vooUdFFyYGRLQ4Y+c2hQMFg9jD+FaTSU3WCt0y98pJpo9lrfc8X0d+nI5C0SErmBYe9V5/eDt1CjYLBqGP86/8AbsrtMVa7kO/VAQCA4PE4KPSCRq7F1aBAalb777sJDt7Jc3GsgXOdX5TKqyfQecnu1DfhR/Xm2e/JYE0XN7cnsCKxwPiUxCWpa1XRPv3AOYfNs65tvviE0NJSAgAC8vIxX/U6dOsWXX37JN998w5o1axzeyVedAs6m6q+Qt/WYPEMSzoTjTLhYzI47XiQ/SQaxz414CgkJFCI+lQiYQEESyC8kUoAk8gtJ5CeJ/CSSX0iiAEk4C2oAnAU1zqgpJjzLknNMFhVwaQnU+ShLjp/VWLTcs/HvtfjThlb3C0J602CJ1DzBjT26Jux5IfblJ5H6sps0ll2jiRBCfdkNignPaCsPpK1c746YIDpzSazEOV01zuuqck5XXUrSIeEw7osl6K3+gU91+xir2EBT+RV2y0bzs8aHTdqW5IQVqbkWs6IXtyKfs+XsfQY0r0yn+cdsrpcS+zA++Aiz/pvLooULadq0KUuWLKF9+/ZcvXqV8uVNrS1DQ0Pp3l0fsP7o0aNcuHCBwYMHU6JECbp16waASqWidevWuLu7s2XLFsqWLUtYWBgFCxrHcatVqxb79r2M+SOXS1ktAd6tUYKDIZYTiyXhzBeq7/nLaTI1ZPf5UzmVj1U/8YiiRuWsu/lKSLwaOClklCjoTOSzZB5RlG/UQ1mnnEY3+VHO66qyTtuaTvOPcWtaB9RanZQ9VyJXYuvC37z9N7K4J9ZxRsX3ik18Id+NTBCJFAsxUd2XXTovHD3KaVurJHuuPHLoMc39nu2ZBe78uhl7rkTQy8ux3iiOxGZxb8eOHezZs8dE2APw8vJiyZIltGvXzqGde10wdztbCxApkdMILyzsXPWiIGRoKUCBhnwkGcS/Ai+Ev9QioCvJuKLCVVDh8uJnF0GFK8m4oMJVeLGfZFwElX4bKvIJyYZ2nAUNyJUOOvfcg62We1Xc9S7ua79oQp+Vp032C4Z/JDJCPK4c09XhmE4fO0qJhjrCbRq/sOxrJLtOYSEeL+EaXrKXiZfCdCU4J6aIfdUIFiugzgHrPluvI4mMY2/cvowgIuMPbRuO6N5klnIxjWX6VeV2stOMUQ8wET6ymqx4hYuiyPNkDQVdXj7PO/x2lGSNjluPn2eo/bgzf/NBj0/p378/AL6+vuzZs4dFixYxffp0k/KLFy+mbNmyXL9+nRo1atC4cWMCAwOZNWuWQdxbuXIlT58+5cSJEyiV+r5WqFDB5FgKhQIPj5yLkZhbaVKpmFVxDyCWAvRRjeEvp8lUkD3mT6dp9FD9yBMbFk1m+4dQtogrPRrrJydPniez8NAtOtQp5ZD+S0hkN7O71zWM707q3uBXTU/GKdczQbGW67qynBY9qfKDH4IAQT+1wc311RsTS+Rt8sJItL5wg1nKxVSR6TPzbta0YIrm0yxLsle3XGEjca9uucJcCIsxKmNpCC8I9sUxtJVyRfPRv3ll2yvkAHbNpKzFpZJiVmUccxef5Jb76qNBQRwFiKPAy6e6w57uIs6oDeJgQNX/OerA2Y7FbLm2mu69oEwRV7PbZYJg16qNhHXUKDgnVuectjpLtJ0R0FFFeEh92U0aCDeoL7tJdeG+wZX3fbk+NluSqOSSWInzumoGC7/sEGQkbS9jLO/TiF92X8PnrQrGiSTMMKptjWzqFdwVPeih+okv5P/xveIv3pVfwF82iqma3vyVQ1Z8KWR2nPTNhvP8ezGcXUObUau0XsRJSYZx7l5MmrbSP56oVaOKuIl3ix+Ntrdp04YTJ06YrRMQEECrVq24fv26YVvbtm1ZsWIFarUapVLJjh078Pb2ZsiQIfzzzz+UKFGCXr16MXr0aCPrvBs3blC6dGmcnZ3x8vJi2rRpVK6cuwfN2YGtCw6PKEpv9Tg2OU2mquwhfzj9Qk/VOP2YwgrzD+iDnqeIe2O2XWLv1UesOBZqKCM9FyXyEs2rFTcKqr9M25E6slC6yANY6PQb7yf/zANKIIr6BZHjY1rlcI8lJIzJimduxAsL/czijIpvFVsZIP8XuSASIRZhjLo/h2zM2O4oyhR24UKY8TZrYx1brSGzY1SYnTqZzc6fnTt3ZsCAAQQGBprsCwwMZODAgXTp0sWhnXtdMDeQk7Q9icwhkIwTMRQknGLg9OolaLF1ApQi3Flb3ZHIOkRk3BTLslnbkrGaAbRT/Urd5GX0Vo1ltvojDmjrES0WwEVQ01h2nS8Vu1js5Mspl6856TyEZcrZfCPfRktZEEXJXHxLc5iLSyWRPv97oyT7RrzDZ29XTLdsdi/+6ZCxXNuRDqppBOkq4yYkMEO5jPXKqVQQsufvbemM1wTczfAx/72oXy1fcTTUzF7jB5wtCxbahDgQdRQrbhyvzVrsu4iICEqUMC2v0WiIitLHc7t9+zZbtmxBq9Xi5+fH+PHjmT17NlOnTjXU8fLyYu3atezZs4dly5YRERHB22+/zZMnTyz2Nzk5mbi4OKPPq4hGa/ss775Ygk9VPxApuvGG7C5rnGaQn0Sb6o7/+xLxyRoTSwgJibyGIOiD6qfawij1l1zWVaS4EMdSpzm4ohc6HsQkcvymFHtSInfhaG3v3VmHmOoXnOnj1BZus9NpHAMVO5ELItu0zWiT/Gu2CHtp521pv39QrzSF8zlZrm/DMeHVM1Cz2XJv/vz5fPLJJzRp0oTChQvj7u6OIAg8evSI2NhY2rZty7x587Kyr68skluuhIT99GhczjDZtUZ6t5JMECSBL5t5Rj6O6+pwnDqgBRCpJES8sOy7QQPZTWoI9/AQovGQn6W1/Kyh7n2xOBd1lbmkq8wFsTKXdZWII+Pi9Yi/LtC1QfrJdyTyHrfEMnRTTaKvfDcjFZt5W36VPbLRzNF8xAptB7Rkb+yl7HzM2PNMSzuwFUXRLk+NlNXxlO06nQ53d3eWLl2KXC6nYcOGPHz4kJkzZ/LTTz8B0L59e0P9OnXq4O3tTZUqVVizZg0jRoww2+706dNNknC8imjtNOEIFUvRW/UDm5x+pr7sJiudZvKZajRJWM/MvO7kPdadvGe+D3ZaxktI5AY+blSWvwL1CQqTcOZL1Qj+cR5PLdldZiqX8LV6KCDQe/kp1vf34u2qxXO2wxISL3B0sq3QqHhCLWRetwUlGr5WbGeI/B8Ugo5IsRDj1P3w1zV2YC/tI/WvaHS7mumGfDH3KzWXgCM7xmbZmS3XZsu9woUL899//3H16lVmz57NZ599Rp8+fZg9ezZXrlzBz8+PwoULZ2FXX13M/b2lDJ4SEtZpXq0En76VfkDT9G4lQZBC7uU8AqFiKbbqWjBe048OqunUTl5Bt+QJTFb7sF3blFs6fTyoskIUHeSnGa3cyHqnaVx0GcBBp2+Zp5xPP/kumgjBFMA0m9izc7u4v7gfd2d9SPjqYSSFXbbao9QZQStXrszixYuN9q9evVqfjCXNJynJ2A3iwYMHfPrppxQrVox8+fJRr149zp49i0T2oEXOCm1H2qh+5ai2Ni6Cmh+UG/jH6UdqCXeyrF17V4Ltcp8xc+iMjBvl+QqBIONJ5GOj7Y8fP6ZkyZJm63h4ePD4sWl5hUJBsWL6GLSlSpWievXqRi64np6eREREoFKpzB43f/781KlThxs3LAcLHzt2LLGxsYZPWFiYxbJ5mQblC9td57pYDh/VGOJEV7xk11iinIsT6gz3ofmMg3y7KSjD9SUkcoIpH9Qx+v6Q4gxSDUclyukkP8Vg+T+GfaO3Xczu7klIGDFu+yU+XHgctVaXq0IhVBPu84/TjwxTbEch6PhX+xZtkmcYhL2h71XL4R6mT34nhc2We6/aJNDunKw1a9bk888/Z+zYsYwdO5bPP/+cmjWzNt3yq4/plSaX1D0JiXRJO5AzR3rvyyJWTLolco5EXDgr1mCltj3fqofwnmo2dZKW01M1nmnqT/hX+xZ3dfpU9JVkj+giD+BH5Z/85fwzl136c9hpOAuVvgyR/03Fa2uJ3r8UN+/ulO47D+eytXi8eSKauMdm206bEfSHH35g6NChbN261ahcoUKFCA8PN/q4uLgY9kdHR9O0aVOUSqXR4pi0EJb9hIkl8VGPZaT6K2LE/NSW3eEfp/GMUWzAGfOCU3bSctZB2wubXY02xhZxUZArcfKoysmjh4y27927l7fffttsHW9vbw4eNO6rv78/jRo1MiTPaNq0KTdv3kSn0xnKXL9+nVKlSuHkZP55m5ycTHBwMKVKWU7q4OzsTKFChYw+ryLvVC/BEp+GHBrZks+8TRORWOKyWJnPVaNIEJ15R36RBcp5KNFkuB/bzz/IcF0JiZzASSHjp05vAFDKTf8uDhRr8pPmcwBGKjbznky/uBb21Db3dQljoqOj8fHxwc3NDTc3N3x8fIiJibFa5//tnXdYFFcXh3+zlSJFOiIKYgFFRUEFG1bU2HtFjb0XLNFoYo0aNUrUWGI3tlg+E6MGe0sUW0CNGo0tNrALKsq2+f5YWXbZ2couLHDe59mH3Zl779wZ5szce+4pLMti+vTpqFBBGX+3ZcuWuHaNO1Yvy7Jo0aIFGIbBL7/8kutj2zJbzj1A0oM3+PP2C06LsryHRR/+QfwmmoKKvP/wii2G4ZJRGCEdhdfIft/mV14A9WukrwvR5T3RtYY/p7VcEdDtma7cA4BHjx6pBm3q3wnzMNX/+/j4BtbrDEHYGKauZvXNEQNMoFKUcze0uncEueUWEN7CAYmKivhR3hojpKMQLYlH2MdViJVMwnxpFyTIa+DxpwzWpXnP8Bn/PCYId+DDxV0YXI2HmzV/wTbfzfi2uRNcnR3BT9oBHrTfX1kZQQGgQoUKGDBgAPr164eFCxdqlGMYBj4+Phofdb799lv4+/tj/fr1qFmzJgICAtC4cWMEBVk/eyzBBYNd8mg0yVyI3+SREDAKDBH8hoOiL1CPZ30rDn3PmY9Sy46jjH2kOddoh/9t34R169bhxo0bGDt2LB48eIAhQ4YAUFrL9e7dW1V+yJAhKou5mzdvYt26dVi7di3Gjx+vKjN06FC8fPkSo0ePxq1bt7B//37MmTMHw4cPV5UZP348Tp48iXv37uHcuXPo1KkT0tPT0adPn9yffAGHYRg0q+SDAA9HTG9TCftG1jW67iW2AgZIxyGTFSKGfwlLhUtzpeAjiIJG39oBWNmrOn4dUQez2oUCALbLG2GTrCl4DIt44XKUZZSuu1/uuZqfXS2Q9OjRA8nJyUhISEBCQgKSk5MRGxurt878+fOxaNEiLFiwAIDSI6Jp06Z4+/atVtn4+Hidc2Bzjl0QYNn8T2LkiTdYL5yPGcKNsGOkOCGvimaZ87FfEalVVsC33UlTee9i2NivJuyExoddKWwx98xS7lWsWBH379/X+k6Yh6kC7eWkP44KQRQlJrUIRm8164as1dossh7aXHIW6ueMiiWcKVtuAeYNnHBaUQXL5e0wRDoWdTKXIuzjKvSQfInZ0p7YIYnCpScKNAkSwoNJR33+VQwV/IbYoLcISDmM1cLvtNrMygiqTrNmzXDx4kVIpdmubu/evUPp0qVRsmRJtGrVCklJSRp19u7di4iICHTu3BleXl6oVq0aVq9erfNcikrCgPzmBVwwUjoKAyTjkMK6IYD3FD+J5mGZ8Ht445VFjsH1RLHmUybnCrWxY1XHkPr4YvpczJw5E2FhYTh16hQOHDiA0qWVz9SUlBQ8eJAdly0wMBA7d+4EANStWxezZs3CkiVL0LFjR1UZf39/HDp0CBcuXECVKlUwatQojB49GpMmTVKVefToEbp3744KFSqgQ4cOEIlESExMVB2XUMIwDEL9XHBobH0cHlvfqDpnFKEYJI1DJitEc/6FfFXwnbn9Ao9ea4dJIAhrweMxaB7qCy8nO8RGlsastpUAADNlsTgrrwgn5gPWCL+DC95h67kHePIm24Iv+eEb/PfS/DhlhZ0bN24gISEBa9asQVRUFKKiorB69Wrs27cPN2/e5KzDsizi4+MxZcoUVeLNlStXIiMjA1u3btUoe/nyZSxatAjr1q2zyLELEsYmCbQGjXh/4XfxJDTkX0YmK8Q0aR/0lU7Ec7hylhfkk3ehJS6Rehs1AooDALrXNBziKbcE++adp4FZyj31QWReBggsrOSnQBOErWPIVH1IdBBmtg3l3GfoBWQLojeuaXmrtm8nNOsxX6B5AyecUYRijbwl4tJ6QM4CY4VT0TZzJiZL+2OzrDHkjh5IeQf8y2on0zAmI2hwcDA2bNiAvXv3Ytu2bbCzs0OdOnU0YobdvXsXK1asQLly5XDw4EEMGTIEo0aNwqZNmzj7PXfuXJW7iYuLC/z9/S14VWyD0u4O+d0FFUcU4WiaOR9rZC0gY3loxT+Ho+Lx6M/fD4GFlCGbE7Oz4xpaHT72z1MAwLzf/9Had/wfbhfyLHQ9ym6mvsXUX67iWbrumH7d+gzA/fv3kZmZiUuXLqF+/Wwl0oYNG3DixAmN8nXrKi3Jnj9/jnv37qms/NSJiopCYmIiPn78iDt37uDLL7/UiMG3fft2PHnyBBKJBI8fP8bu3btRsWJFvedYlCnv7YSyXsWMLn9SUVVDwWeui+69F+8hk5tnVXrh/iv0WHMOdb81weWcICxMr0jlgoEMAgyTjsIj1gMBvKdYIYyHEDLUnncMzeNP4dfkx2j3w5+IXnAifztsw5w9exYuLi6oVauWaltkZCRcXFxw5swZzjr37t1DamoqYmJiVNvEYjGio6M16mRkZKB79+5YtmyZlheEuce2NWRyBaRcz1Mmf+YjdsjELME6rBMthAeTjhuKUmgtmY2N8mbQtxyZV6HDcuqYjLlE6lW4rqm6zmVjv5rYNSQKn+fw+LIGS7pVQ/ea/vh9dD2rH6vozfpsEF03q4MobzP5EYQtkpsXnrHWK/lpkW3tY9cOomxwACBlhLjMlsU2eWNMlfXHFllj3Gd9sETWnrO8oYygkZGR6NWrF6pWrYp69ephx44dKF++PJYuXaqqo1AoUL16dcyZMwfVqlXD4MGDMXDgQKxYsYLzmEUhYcD6vvmXaY2Ld3DAbFksWku+wSVFORRjPuIr4Rb8JpqCcCb31gBTf9GfuEWdfhsu4vS/z7Hy5B2tfZ9vuKC17dC1VNX3nM/JrLu3WfwpbE58gLE7ko3uB2GbMAyDH2PDIeIbN3RXV/A14180S8HXcOEJlJ3yO95lmq4YvHj/tcl1CMLSMAyDxMmNAQCv4Yz+kvF4y9qjNv86ZgvWAWDxT+pbjN6enK/9LAikpqbCy8tLa7uXlxdSU1M5akC1PWeSJm9vb406Y8eORe3atdG2bVuLHRuwvEfE6/cSjNmehD9vvzCpHsuyaLzoJKLmHtNaMGEA5HWC8krMfewTTUGs4AgAYI2sBdpJZuIWa3hROb/yAqiPc4zpQeuqJbS2BXlmL5I5iASICHADLw/Ox8fFDnM7VEFIHljwkXLPBtClvNgxOEprW7sw7RuVIAozllrN4gyiagPeuNaO9VDUrauzMoLK32tONOUZb8BzLI4M2GnVMSYjaE54PB5q1KihYbnn6+urZY0UEhKi4eaoTkFNGLC8Z3UIeAyW9ahmsGwZz2J54gJhKjfY0ugkmYaJ0oF4zRZDCO8hdotnYL5gFdxg+mSAS6yNkfSkB2+Map9lWQz66ZLGb83jax7t+hPlOdx9/g4Hr+meCBG2TUwlH6ztG6H6HVWG+3mUhSUUfADwI4fCmSAKCj5q4VpusqUwUjoScpZBV8EJDOX/lo89sw2mT58OhmH0fi5evAiAe8zKsqzBsSzXgmnWtr179+LYsWOIj483qQ1jjm1pj4jZ+2/gl+Qn6LnmnEn1JHIF/nuZgRfvMvH4DVcyl7waqyuTZvxP9DXK8p7gKeuKXpLJmC2LRSaMSzCYX265xqB+FX1ctMf3zSp5Y1rritg9lDtpWGGAlHs2gC63w1A/F+wdUQcexbKFrbAFfSQIa2Ioll7WfLgwS1XRVu1lZwT9cD9ZY/vH+8kQ+3FnejcmI2hOWJZFcnKyRrbPOnXqaMWCuXXrVqGLK/ZZZV/8M6s5WlUp2ItPLHjYIW+IRpkLsU3WEADQRXASx8TjEMs/BD7kprVnhmL92pM0I/oJZEj09yXnMy1r7NDou5MYrKYU5OK3y0+w8OBNZMpMO18ib6hXzhOre0fghJHJ1Syh4Hv5PncZpR+8pLh7RP6yuGtVONkJ4FFMjBOKMEyXKZP3fCHcjhY8TUXN3N9vaNXPlMkhkRXOBJIjRozAjRs39H5CQ0Ph4+ODp0+fatV//vy5lmVeFlkutjmt6549e6aqc+zYMdy5cweurq4QCAQQCAQAgI4dO6JBgwaqdkw9NpA7j4iPUjna/vAnFhzMDpVhbAxRlmXxWsdzM+fQoO/6Czh3zzLxfvXhjHdYKYzHDOFGiBkZDsvD0TxzHv5QVDapnbywdOMm9zMahmHweZ1AhJcuboH+2Cak3LMF9NyrVUq6wqNYdgINBsZbGzUJ0TZfJoiChqnp4XWV1jvPzkelubUPXcQN9wAoM4K+u3wI764cgvTFQ7w6uhqy9OdwCvsMgHkZQWfMmIGDBw/i7t27SE5ORv/+/ZGcnKwRf2zs2LFITEzEnDlzcPv2bWzduhU//vijRtbQwoIgh6tgsI+TSfVtaSH4NZwxWTYQHTKn47qiNFyZ95gl3IADosmozTPOzZYBo+Vm87+kxwbrHbymPXnJiVzB4n0ON8mcYs4wwNVH2YrCV+8lOHqDu225Wkefv83EyG1JWHb8NkZsTeIsT+Q/TSt6I8DD0ej3Y04F3wrhYohhvMJu16VHePEu09zu4p9USg5E5C/tq5XElWkxODWxAQDgJ3kM1suaAQAWC5ejKnNbVXbVybsaz0W5gkWtOUdRa84Rje2FBQ8PDwQHB+v92NnZISoqCmlpaTh//ryq7rlz55CWlobatbktoQIDA+Hj44PDhw+rtkkkEpw8eVJVZ9KkSbhy5QqSk5NVHwBYvHgx1q9fDwBmHRvInUfEnqTHuPzwDX44brrl8sRdV1Bt1mGcuvXcqPJvP1o36VFV5jb2i6agOf8CJCwfM6SxGCiNw2uY7iHCz6M5U875i4Zbrg2NGW0NUu7ZACYl1DDhZna247YwIYiChHp8BAB5Eq9AF9ZITmHtTL2FbxhqOo4h9eHWeCDe/LkdTzaMRObDv+HVeToELsoFEHMygr558waDBg1CSEgIYmJi8PjxY5w6dQo1a9ZUlalRowb27NmDbdu2ITQ0FLNmzUJ8fDx69uyZR2eef5gSk4XHAKt7RxgumMf8xZZHa8lsTJV+jtdsMVTgPcJW0RysEi5CKUa/Ei7tgxQ91yRapV97Lz/Br8lPNLblnBicvfMSrZf9obGt/8aLnO21/eFP3Hn+DgCQ9iFb4XP4umFFI5G/jG6smZBp5xDtcC5ZnFRUxUBpHD6yQjThJ2GtcAHsoTvRijqZMgW6/Wjc/cyyLP5JTcdHabblJ72HCFuAYRg4iARoWUVpYT9LFouj8mqwY6RYI/oOfshWxEz95arq+6v3ErzJkOJ1hhRvMnJnxVqQCQkJQfPmzTFw4EAkJiYiMTERAwcORKtWrVChQgVVueDgYOzZsweA8pqPGTMGc+bMwW+/KV2ghw4dCgcHB/To0QOA0iovNDRU4wMApUqVQmBgoEnHtiRcyS+MfZbtvPQIALDkqDJUS/4ttLPoz9+PXaIZ8Oc9xwOFJzpJpmO9vAWMUSp80147YSEvnzRrY41IQJgzJm2LUO3kLIUdQX53gDBt0GOKIqBiCWejLAUIwpb5okUw+DwG4aWL4+qjNPSrG2hWO/osHIyVqqgy7jh+07hVOGOxvuUeTasAwKl6SzhVb8m5b8OGDVrb1DOCcq30Ll68GIsXLzZ43FatWqFVq1amdbaI0a1mKTQO8cbKXtUxZPNfAJQKP1swkJCDj83ypvhNHoUxgt2I5R9GM/5FNOAlY528BZbJ2uE97LXq3Xz61qr9+uaAttuYOu8NuO3mZOnRfxHfzXDMRMK2iApyR9JXTXHy1nNUL1Ucpdwd0LKyL/ZfTeEsf0pRFZ9LJ2KNcCHq8q9hEzMP/SQT8RaGs1jffvYO/z59C5mC5Vxkk8oVeP42E1cevVHJMUHYIsu6V8P+KylQgIdR0hHYxcxACO8B1ooWopNkGt7BAdvOP8TcDlUAaFqW28J7KT/ZsmULRo0apcp+26ZNGyxbtkyjzM2bN5GWlm05PnHiRHz48AHjxo0DoFxQPXToEJycTLPwN+bYloRzeG7i/z/LhVUji6vZPTINF7zDQuFKNOUrn8f75TUxSTrIqOd9Fo4ibVWRxMwM6rnh5uzmEAt0JxqN7xqG+CO3sLhrmMZ2e2HRS05qlhlKr169VJMd9e+EeZg697bTc3NnMblFMPrkQWpnSzG4fpn87gJhozjbCTGzbSjahvlhaquKKOGqPZHWiQHFmSrmnpEKNmu8kE1xtScsTzmvYoYLERYlIkeskyp+LgAAe7VB5PkpTfK0T4ZIQzHMkPVBc8k8nJJXhpiRYajgNxwXj0Mn/kkwKNixmH5JfoLvj/yLpcduGy5M2BTFHUVoV80PpdyVE7b5naroTW5zVlEJvSRfIo11QA3eLWwVzUZxI5PGNF18Ci2+P403GRJ8s/86tp1/gHN3XwIAeqxORO15xxC343LuT4ogrAjDMNjcvxYA4D3s0U8yAc9YVwTzHuIH4RIIPsWkzIqvpj72K+oLpm5ubti8ebMq8+zmzZvh6uqqUYZlWfTt21f1m2EYTJ8+Hbdu3QIAHDhwQGWdpwuWZdGuXTuTj21tTA0VlKUYzlnP2vdRNeZfHBBPRlP+X8hkhZgq/RzDpaNNUuwB3PMTU2NP1gx0U30f1iAI/m6G53HqIckAaCn2cho7tavmhxMTGqJCjpAwRVFazVLurVixAh4eHlrfCfMwyXKPMS6Q5eDoIAj5BcfruiD1lSiY+DhrZ03KwliLWGus2DIMjAqM7igyb/UpwN3RrHq5xUlcMAzDi6LJfl7QrJLyupbgyFbWvpof4ruGYXXvCHzTPhSdI5TZ69TjuLg5GM7a1rpq3ifwuM2WRG/pJPSXjMM9hTe8mDdYKFyF30RTjY7HZ6ssPnJLy92XKHg4igUGk9skseXQXTIVL1hnVObdx3bRbHjitd466lSbdRirT9/D5P9dRdcfE/HgZQYu3FfW50r2UsT1IYQNUrecB1bFhgMAUuCO/pLx+MCKEM2/gm8E6wCwqPvtcaRlSPH8bXa8SVlRN90rSpix8v4mQ4KUtOxsuFnzi5y3jfWeiSx68Q/jZ9FM+DEvcVfhg/aSGdgsbwpz0gdyueDamWgNp662cBQLUNpN97yknFcxRJVxx5lJjYqkYs4SkEbFBjBFe69PLK0RDyyvMCnuIEGYgauDCAdG1cPRcdFa+4y23LPCfcqAQWkDCriWlX1xeVqMSe22rKLMYJpfinOS6aLN0AZBWNajGn4dUVdrH4/HoF01PzSt6I2etUqr4vPx1G5VY2RSYIUsHBOaGRO7h8FRRTiaSeZjjrQ73rL2COXdx1bRHGwQfotg5oHhJggin7nOBqCr5CukssVRgfcIO0QzNWKO6SPn4/3ui3dW6CFBWJdmlXxUC79X2TIYKR0JOcugq+AExgp2AwCqzjyEFt+fVtXhisNGFB0MDW3DZh5G1Nxjqt9Z45qc84d/Ui0fusMOmfhOuAKzheshYuTYL6+JNpLZuM4GmN0ml3KvQ3U/NA72wuQWwWhakTtT8bAGQarvDBhU/BTK4bPKvpjepiLcHbkXcA/HRWPboEiIBAVXp5HfmGxa0a9fP737161bZ3ZniiqGpsCMkZqHTuElsTlRc1JxJK4+miw6ZWbP8g5jF8JcHYR4kyG1bmeIQkvFErkLIWANhZUx4u3pJNbKRmqIEi52Jq+uWZICo9ojn2irIOTzDFoP5UTdco9hGCzvWR3DtuRt7C7PHK4g+pBAiB/lrbFLHo2Rgj3oxT+CBvzLqM+7gt3yelgk64wUuFuxtwShm561SmHLOf2K5jusHzpLvsYW4RwE8p5ih3gmekm+xD3W16RjSeUF5olPEBocHFMfVWceAgAcUYTjK1k/zBGuxWjB/5DCumG7vJFGebrXizam/vezlGPq89yUtA/osfqc5ToFoBTzFCuF8ajI+w8ylod5su5YI/8M5ljrqcO1hmon5GNt3xoAgMEAAibt1yqj7h7LMMDeEXWQ/lEGt09KvYtTm6DLqrMqi29AOcdXp1GwFxYdvgUnO211FQ3ddWOyWvT169can2fPnuHYsWP43//+hzdv3lihi4Ufkyz3TLyZy3qZFqw0vzD2GjAA6pUjN3DCOIy9r4xV2inMWLA1Nqbb+s9rmN64HoxdFCjITGoRnN9dICxIzgy70eU98/T4kWXc0K6an8n1XsEZM2R90ESyAPvktcBjWHQWnMJxcRwmCrbDCRlW6C1B6Gd2u+yYVlX9XXWWe8h6o4vka9xR+MKPeYldoumowtwx6VgDN3FnYiYIW8fFQYj9o+qiZqAbGgd7Yau8Mb6XtQcAfCNYi0Y8zQWmWfuu50c3CRvBVA+ev/57jW3nH2hoBW9a2GqvIS8Jv4mmoCLvPzxnndFL+iXWyFsit4o9wHwlmvocxE7Ih4DPUyn2svarX8rL02Jw7svGGm2E+rngSFx9nJmkqWAn9GOycm/Pnj0an3379uHu3bvo1q0bIiMjrdHHQo8pzwl9scFMyaRraxirXGEYBpv61bRyb5TE6DA1JmwbloXK/NtY5YAxt5+dkKcREHda64oG63Sv6Y/KJV30lsl6ATas4AVfjvhkyjLKv2U8jI+fZ64LsXFuiYbJC7fcSrm0xAQsMfQhLEXOeLJ5neVsw+c1IRLwzB7M/sf6YIR0NNplzsQ5RTDsGCmGCfbipHgM+vF/hxgSy3aYIPSgPrmqHaTfgjQV7ugsmYbLijJwZ95im2g26vKuWruLBGETVCrhgh2Do1CnrNJ4YLGsE3bIosFnWPwgXIIwJjvR0Mlbz3H7GbmhFwWMGQpsOfcfzt97pXP/e4kck/93FWfuvFBtS0n7aIHeAQwUGCPYhfWiBXBhMvCXoixaZ36DRIXh+YkpR1GnblnDBjZNQrw0fs9oU4mznPoswcVeyJkNt6yXE5zshFrbCd1YxKGZx+Nh7NixWLx4sSWaK3KYpNyz4ZmoLsUEAHg56Xd1MiU+bV5ZJI1uUi5PjkNYnk39a2Jc0/L4pn1lo8obUoSNjymPkxMaatynZTz1W+QdiauPb9oZPj6j47tmmbwT/HbV/LBlQK1ct5MXMacL8oIGoQ0/x7Odx2NwemLDXLVZwdt06/XcJoNJZsuiq+Qr9JeMw78KP7gx7/C18CccF8ehO/+oKhMjQdgSr+CMHpIpOCWvDEcmE+uE89GGdya/u0UQeUa3mv6oFegGgMGXsv44Lq8Ke0aCtaIFCGRSVOUm7b6Sf50kbIYzd15gyp6/0WXVWYNl7754r/r+46m7uT62M95jnXABxgj+BwDYKGuKrpKvkWrlUCAz23Ir6tRZ3TtC47e/G3eG3tzEMXcsIEn78gOLRSu8c+cOZDIasJqDKWm19em18lvx16d2gM59fsX1p70eXL+MhXujRMg37qJwBfYkxUHBxaOYGCMbl4O3ngy5QPaqkSFFVEwlH3g725n0Iirr5WRUZmtj5DarzHQdq1/cdRij21eHzzCq1etckRfKPRLRQkU5b6XCXD2Qsr+bA05NaIggT22rVWP+/XYmZJnOup8GRwfpL2hcaziqCEdzyTxMkg5ACuuGEswrzBWuxVHReLTnnQYPFJidsC3ewx79pRPwq7w2RIwcS0TL8Dn/dwu0THHKCNvHQSTAz4OjAAAyCDBcOlplzbpROA8eSAMAXPzvNXZceJifXSXyAK4xpvqT7MFLzZAb+uYIiXdfWqhXQBnmCfaIvkZD/mV8YEUYKxmKabLPITU9lYJBck5jjInlzTDGzaDNMQKY1roiGgV7oUN140KoZHnh6UrgURgx+S6Ii4vT+M2yLFJSUrB//3706dPHYh0rShSWpJKBJrgM5sRLTQlTys0BD15xxygydS6vDGRq+AKf+7Ixyk7RHMDaiuJgVONyWHL03/zuRqHGWBdSa8iqMbdZ1gpV/fKeqFPWHX/eNm+QMLxhEEq42mPKnr9198dC970pixbmYomu2oqcE8qJ1eVpMRDlSB5Tyt2B013DKEzKRq+8GQbVL4MyHo4YaoFkHnLwsV3eCHvkddGDfxTDBL+iNO8ZFotWYJhiLxbJOiFBUQOs5dZaCSJXSCHAGOkwvGKd8LngIKYJf4IHk4YFsq4w96m770oKKvg452qcSBB5TQbs0E8yAbtF0xHAe4oNom/RXTIVb+GAibuvoEN1P5OTnREFG/UhRc7xo77hxul/X+jeaQINeMlYIlwGZyYDj1l3DJKMw7VcZMM1RE5vOWPHzIaMKwDzlnw+rxOIz+sEGl2+eagPdgyOMjr+eWHA5CdSUlKSxufKFaVp8nfffYf4+HhL969IYNrNrS/mXv6iL0adKX0TGGHtZCxcKbw5j8nnwSNHlkSa9BdMwvQEDteFsatHWRZtdcpazuydrzYw5HI5rxXohoH1sl9kduYqOWCcPFjqtlcf5LjYWyleBsloocPFXgh7Dmu76qVdrX7srFePkM9Di8qmZQs1RCZEWC9vgejMeHwr7YY3rCPK8R5jheh7/Caaioa8JJB1E5FXOBiwaGXBwwxZb3wr7QYAGC7Yi28Fq812Kd93JQUNF54wqy5B5Ccv4YI+0i/wnHVGKO8+1ogWquKnlp3yO45cf5rPPSSsBZf9mfpbWn3MLpUrrBxrmsUA/n6sFS6AM5OBC4ryaJM526qKvcp+LlpXwFivthoBxTGpRbCWi646uXHLNRaGYVAz0A3Fi5DlnsnKvePHj2t8jh49iu3bt2PQoEEQCMj/2RwslS03L7Nj/tRfO6kFwzC4MKVJrtsWGOlKawym6Al/HVEHTdUUlLbiltu6inKSWZRWHczh5IQGWP95DUQZCBzOhbEyOLJRWSzsXBUz24ZqbA/2MT2ul6uDEGIBDx0NmJb/PDjKrGCyNQLctLYZc5qWeo6oH8qYALzGkNOiyxLUCrRufBLCMkxsHoz2BjLZciXg4Fo99nPlDhORF+/QDNhhhbwN6mfG43tZB7xj7RDKu4/1ogX4RfT1p8yMpOQjLI96MrJyXsUwrXVFnbKghMEKeRtMkA6CnGXQVXACa4ULUcxC2Z8/SOTovPIMlh0jzwTCtlgVG64xbvmP9UEfySSks/aoxfsHy4XfqxTdAzZdxEMd3kZE4UHBYQWgPmJosOCE1WJNiyHBd8KVmCrcAj7DYrusAXpIpuIl9Cfsyy0MA5TPEbfYWK8chmEwJDpIY16dk8LiuWhrkC2xDWBatlzbxlNH4gxjJk01AooDALrVKKWzzOsM07INGmu5BygnfJNaBKt+24rlXjlvJ5yf0hj7R9XL767YNKXdHdGwgpfhghwYK4ICPg+dwksiKEcyDXNeUMlfx+Dm7BZwEOleFOFS6KofKmdGqizW9I7Quc+Q0trc+/63EXU1fltlBZPJ+TN3QrrBTGUwkfc42wkxyEBs1qvTY7S2zWoXisbBXtioptgowxG/D8jb92s6HLFY1gn1MuOxUtYKH1gRwnh3sE60EPtEU9CMdx4MxeQjLEhFtezig6OD8HmdQAyJNhzveKe8AQZJ45DBihHNv4KdohnwRe7jR+269BAX7r/GwkO3ct0WQViSZpV8sHlALbSuWkK17TobgH6SCfjAitCYn4TvhCtVz+h6848jLUOKH47fRsslp5H+UZpfXSesRJ/155Vf1Ma26nPMx28+WGXc64nX2C6ajY7805CxPEyT9sEk2UCrxNfjopS7A3YPra36begUr3CMw3RhXUvHootZyr1du3ahS5cuiIyMRPXq1TU+hOmYcmvnRuEUWUbbksdSHImrn+s2NvariV1DotBXT2IOU1dFTL1exmQuzQ+8nOw0gswTlkV9Ra7Up6xOlf2MXxGz1gtqy0D9WWsFPO17wsVeiCYVvU2yQqpaMvtczb3v7UWafcmLd3ZuFfANzFQGE7YBP4dptoDPw+AcygpvZzus7VsD0eU9DbaXHws6r+GMebIeqJf5PVbKWuE9K0Yo7z5WieLxu2gyWvHOUuINwuLUDDRtPHhUEY4ukq/wjHVFCO8h9oi/RiXmfq768FFK9zVh2zSrlG111LpqCVxkgzFUOgZSlo+2/DOYIdiIrFlc0sPXWHDwJq49SceGP+/nT4cJq3H63xe4+/xdDrdczTKWHveGMnfxm3gqqvFu4w3riD7SL7BR3gx5NUPNOkpZT+M8x4R8Bs4meBqRbs86mKwtWLJkCT7//HN4eXkhKSkJNWvWhLu7O+7evYsWLVpYo4+FHpPccnMh0GYHJOdA3eWvaUVvlPUy3S0xJw4iASIC3IzKMJqFlw5LwSxy42ZlzYlezUA3+LoYDjZK5A3qSuOtA2thWIMg/Ng73Oj6+uIXDfmUebNTeEn0itRtlcqFl5Pl7xGu+3pNnxqq76ZYuxpLyyqWiV+Ws2fW6Cthu6i/Klf3jsC4mApaZUY1KmewHXVlvHqA/7wMbZGTF3DBPFkP1M38Hktl7ZDO2iOY9xDLREtxWDQB7XmnwYc83/pHFHz4avd31rOztLu2FauTmNsi5G+2DNpnzsBNRUn4MK+xQzQDDXhJ1uksQdgATUK8EezjhM7hJRHfNQznpzRGxfodEScdCgXLoLfgMMYJdgIATt56rqqXKaNndWEg55Cg15pzGuOQnGPQvx68ttixY3gXsFM0Ez7Ma/yr8ENbySz8qahssfZNgVHTFunTWJg6hprWuiIAYFSjsmb0itCFycq95cuX48cff8SyZcsgEokwceJEHD58GKNGjUJaWpo1+ljosZTi2pBMGVKEbRsYqeG6pA/1VNjW0LxPbRliVDmhnhhcxR2EplvuMdax3ct5PgyAzuElOcvqS0ySRb1ylolhVtTJUqyrT/ZLFnfAxObB8FZTrBm6xxd2roqyOmIilvd2wo2ZzbGgUxWLyIqpAWhz3itcikj1GBrm6jdKFnfQua9FqI95jXLg75YdI4p0e0ULv+LZ//umFb3hw7FAoi4dP/Tg9ibQVBKG49yXjfHXV021ynUwEA/TGryGM76TdUHdzO+xWNoRaawDgngpWCxagaOi8ejOP6oK5k4QplDcUYRuNfzRNcIfbp+Ci9cr56Fl1dq0ku4xyGN4opNkOk7LQ+HIZGKtcCF68Q+b1Z93mYaTczx7+xHTfv0bN1PfmnUMgsgNdkI+fh9dDws6VwWfx8DLyQ4TmwfjN0VtfCX7HAAwUvALBvD3Y72atV7C36n51GPCmjxJ+6jxO+cYtOeacxY4Cov+/ANYKYyHPSPBSXkVtJfMwH+s5cbRRvPpBNWVmPrmIKbmw6xVxh3/zGqOOI6FWsJ8TFbuPXjwALVrK32v7e3t8fat8oUbGxuLbdu2WbZ3RQSTYu6ZMZldFRuOvrUD0FGHMimLqCB3o1yXDKEeoyILU7vtbEJ2zZyxvrJQsKYfV0O1xwCnJzZEtVKuJraSO5oYodxTR/7xHV7s+w4PFnfBg8Vd8GLfd1B8fKe3DsuymD59OipUUD5QW7ZsiWvXrqn2379/HwzDcH527typ0db+/ftRq1Yt2Nvbw8PDAx06dDCp/7ZAMQ5LBVNkrZy3E47ERcPZjtviwV7EN2pFa3hDw6tXhh4XOQ+TU6Y/q+xrlAJ5XgftFUIuWbg2oxkuT4vRUPhr98nwuRujsGYYYEVP4y0qicKFi70QJ8Y3wNnJjYwqX9Wf27Ve/Z0b5FkM3s52KmWHOou6hJnTTYuQjmL4Xt4RdTKXYL60K16yTgjgPcVc4Vr8IR6NYfxf4Az9z3mCyIov2fJT9ud5Havg205VVPsZhkHfOgEadQxZRL+FAz6XTsQOWTT4DIvZwvX4UrDFKPfx8/deqb5/f9RwIo1xOy5j49n/0Cz+lMGyBGENdI1ftsibYL60KwBgqnALuvKPq/bdef4eAHD9STqWn7hNlnwFFEMjV0tb+/MhxwzBBnwl3Awew2KLrDH6SSfgHXQvnucFxp6lOd40+uYOhHmYrNzz8fHBy5fKQLqlS5dGYmIiAODevXt5ktK4MBLs64RaRsY/0Sc2jjoC8zer5IPpbSpBYKpK/ROmxmYJ8dV20bWWhU1pdwdULumC4Q2DtPYpWNbkB696cQaAv5sDKqkFobYoOvpm6v/pxd4FkDy9C+8uM+DdZQYkT+/ixb7v9NaZP38+Fi1ahAULFgAAvLy80LRpU5Wy3t/fHykpKRqfGTNmwNHRUcP9fvfu3YiNjcXnn3+Oy5cv488//0SPHj1M6r8tMLB+GdQKdMPsdqGc+429jQzdb5Fl9Cdv6F7T3+AxTH3M5uyTkM/DjzlT06u1meX6362mcS7EjmIBXD4p46/NaKbabmoGYWPPK8Q3Wx7JcK/oEeDhCF8X3Rk+7dRik7o7clurezvrt2JXZ0DdQM7t8V3DjG4jN7yDA5bL26Ju5veYKY3FY9YdnkwaJgp34Kx4JKYKfkIJvMiTvhAFj99H18OfkxohVF8M2RzPXr4RLzwZBJgoG4QF0i4AgEGC/Vgt/M5gJt09SY8BaGeevPOcW1F97Um6wb4QRH6xXN4GK2WtAABzBWvQgZethH71XoLPlpzG/ISbWPfH/XzqIWFpNDxdLNiuIz5gtfA79BEchoJlMFvaE1Nk/SBH/im/ss5P/ZXANVb/rnNVFBMLsCbn3ILIF0xW7jVq1Ai//fYbAKB///4YO3YsmjZtiq5du6J9+/YW72AWr1+/RmxsLFxcXODi4oLY2Fi8efNGb50s66QSJUrA3t4eDRo00LBOAoDU1FTExsbCx8cHjo6OqF69Onbt2qVR5tatW2jbti08PDzg7OyMOnXq4Pjx47AUk1uE4OfBUUaV1ac8GFi/DCJKF8eMNpU495urei3j4ajThdRQy1+3qgiPYiJ8096ycQL+N6w22lQtge+6VNXdKzMs99Qxd0VmQjPD5sUscte3LDdS6YuH+HjvEtxbjILYLwRivxC4Nx+JD3cuQPryEfexWRbx8fGYMmUK2rRpAwBYuXIlMjIysHXrVgAAn8+Hj4+PxmfPnj3o2rUrihVTup/KZDKMHj0aCxYswJAhQ1C+fHlUqFABnTp1ysWZ5Q8u9kL8PDgKvSJLq7ap//+ddFjk5cSQXrZVFV+s7BWOP75oyLnf2jG/dKWw19iaiy44qllAhvm7Yn3fGjgSF21UXXOSkpBbLpETAZ+HM5Ma4fTEhrDP4YK+uncEekeVRpca2Up0QzI3tVVFldWTOu2q5a3L7gfYYZ28BaIzF2OMZBhuKPzhyGRigOB3nBSPxXfC5ajAPMjTPhG2j1jAh5+rbmU4Fxx5mnTA4Ad5O4ySjMBHVojG/CTsEU1DaUa3S+LbT1lEz99/pbH9zjOyQiUKIgzmybpjo6wpeAyLBcJVaM07AwAYvuUvValz93KfXZqwDfTF3DMXH7zETtFMNOIn4wMrwlDpaKyRt4Q5A/K6ZS0ftsnQeXYML4kr02JQ2wrHJkzHZOXelClTMHnyZADAkCFDsGHDBoSEhGDGjBmYNGmSxTuYRY8ePZCcnIyEhAQkJCQgOTkZsbGxeutkWSctW7YMFy5cgI+Pj4Z1EqB0J7558yb27t2Lq1evokOHDujatSuSkrKDBLds2RIymQzHjh3DpUuXEBYWhlatWiE1Ne9jKugbpLnYC7FraG300ZNt1jpoCr36g69f3UBcmNIE5XTEI9OJgXl+9VLFsaR7NZUFB5deQGm5Z9ph1ROWZH3rX7cMd2EdOOpJrqBxLDPeCX1rB8DTSYw+UQEAgMwnN8CIHSEuka1QFPsFgxE7IvPxDc427t27h9TUVMTEZKcrF4vFiI6OxpkzZzjrXLp0CcnJyejfv79q219//YXHjx+Dx+OhWrVq8PX1RYsWLbQU6DnJzMxEenq6xsdW+b5bGGa3C9VrKaSOIUUBwzBoHuqjNz6dIViN79o3vi3puxoGe+mMRZgTY3R7jFZKIVs6W8JWKOFqD383bRlrWtEbM9uGQqQnVisXtqRElkGAXxR10UIyD70lX+CMvCKEjBwd+X/goHgS1gu/RW3e37BcNF+iqNH70/jCWPYqaqOL5GukssVRjvcYv4q+QhSPexzwPlOGA1dT0G/DBY3tuuInFwSPoOXLlyMwMBB2dnYIDw/XOY7K4uTJkwgPD4ednR3KlCmDlStXauxfvXo16tWrh+LFi6N48eJo0qQJzp8/b81TIExEM4wDg+myPtgqawg+w2KxcDma887j7N1shV4mZYYuNKg/kiwxNqjE3Mcv4q9RkfcfnrPO6CaZioMK4+Lfc+HqYHxYq5wMiQ7CobH19ZbR9Ug2JRkmYV1MVu6VLVtWw2KuS5cuWLJkCXr27Ing4GBL9k3FjRs3kJCQgDVr1iAqKgpRUVFYvXo19u3bh5s3b3LWUbdO6tChA0JDQ7Fx40YN6yQAOHv2LEaOHImaNWuiTJkymDp1KlxdXfHXX8oVlxcvXuD27duYNGkSqlSpgnLlymHevHnIyMgwqMQwle5GuMH1rl3aYBld6BsjGRODSxeGHm5ZsdosgSkrEkpLoFy45X76rp5N0Rh3Lq4HHNf5m5P5eHqbSjg3uTHciykHFvL3b8B30Ha34Tu4QP6eO2tTllLa21vzf+7t7a1TYb127VqEhISo4m0CwN27d5V9mj4dU6dOxb59+1C8eHFER0fj1atXnO0AwNy5c1UWuC4uLvD3N+yKml+0DfPTsOZTp2RxbYVfs0+ByAPc8yY+BpdM65M19XtufEx5znbUqzfTE1jdVAw933RZFaqT89RsSelC5A9lPj2fQ/2sFD7BZmFwSlEVPaRT0SZzFvbJa0HOMmjIv4ytojlIEE1CN/4xSr5BmMSV6TEaoQ+MrscGoU3mbCQrguDKvMdPwrmI5R9CTiXz8ZvPMWzLX8iQaMYg4xfQieHPP/+MMWPGYMqUKUhKSkK9evX0ei/cu3cPn332GerVq4ekpCR8+eWXGDVqFHbv3q0qc+LECXTv3h3Hjx/H2bNnUapUKcTExODx48d5cUqEEZyZ1AgnxjfAuKbKcRQLHqbI+mOXvD4EjAJLhUvRmHdJVf4jxdwrkBgaY+b2sVWHdxU/f8qIe0vhh/aSWbjM5i5zbG7m2+Gli6O8d3ZIHa6mjBmrE/mLyco9Xato7969g52dduY6S3D27Fm4uLigVq1aqm2RkZFwcXHRuUJmrHVS3bp18fPPP+PVq1dQKBTYvn07MjMz0aBBAwCAu7s7QkJCsGnTJrx//x4ymQyrVq2Ct7c3wsMtG9hdVzB+ddkSC7itwsx9wESX98TBMfXxQ0/urIKAEco78w6twljLHn0U47h2LGv65F8z5p52ZU8nMfaN5E7gkd0GA49imkpArm7o69ubP7bgv29b4b9vW2kltODzeYgIcEdmyr96GjJ88jlfAKyOGIUfPnzA1q1bNaz2AEChUK5ETpkyBR07dkR4eDjWr1/PmXRDncmTJyMtLU31efjwod5+2ipBnsWwslc4dg/NVnh+1aoi5nWojB1DjHOzz2s0s+Iy3NvVyi/trvlcMEWccr4qdMUzzEJh5HghZ1zMnDjbCbB/lH4ZJQoPPw2ohaENgrCmdw2rHcPYwbKQnz8KiitsEEZIR6OR5DtslDXFe1aMYN5DzBOuQaJ4BCYKtsMHut3C3v61H49W9sd/C9sjZcNofHz4t97j/fHHHwCUsVq5LI8A4M2bNxg+fDh8fX1hZ2eHkJAQHDhwQKNMToun06dPm3H2hCVxttO2+hhhRKInAHiG4ugq+Qp75HUgYBSYJdyAbwTrIIThrLgCHbJj7VAVuWXRokXo378/BgwYgJCQEMTHx8PPT7fL/sqVK1GqVCnEx8cjJCQEAwYMQL9+/bBw4UJVmS1btmDYsGEICwtDcHAwVq9eDYVCgaNHj+bFKRFGYCfkI8DDEY1DshdAWfAwUToIv8prQ8jIsVz4PaJ5lwEA/6RQtufCgvpQNecihSm04Z3BeuF8FGM+4oy8IjpJpuMRm/uklrl5YurS8ahbVrs6aCcfI2wLo5V7cXFxiIuLA8Mw+Prrr1W/4+LiMHr0aHTt2hVhYWFW6WRqaiq8vLy0tnt5eem0NDLWOunnn3+GTCaDu7s7xGIxBg8ejD179iAoSJmggWEYHD58GElJSXBycoKdnR0WL16MhIQEuLq66uyzOa6HwxqURWQZN3zb0fj4dH6u9hhYL9CgGS0ATtdYFkAFHyedLhGAYXc5a4y9TF0Z6Fs7AHXLemBW2+x4gwqWNVnpqe6upeu8Qv1cODMrZsFnGHzVKsTgsXR1jWEAp+qtUGLACpQYsAI3btzQ+uw8chYiz9LgO7pC/v6NVhvyjHTwHVw52/fxUaZTzyk7z54905IXANi1axcyMjLQu3dvje2+vso4VBUrVlRtE4vFKFOmDB480B37SSwWw9nZWeNTUGke6oPw0sVVvx1EAnSrWQpeTtZZ6AAMuymNbVpe735TEAl4JifG0IVBywyjlXsM5/cs+tYJRKUS2tasllZgbNiwgTOb9MePHznbmzt3LhiGwZgxYwydImECfq72+KJ5MHxcrCdzxrxHVvYKx8kJ3LE084r/WB9Mk32OqMxlmC3tiYcKTxRn3mGYYC/+EI/GUuESVGduQV3Y3t84hVdHV8MlqgtK9F0CcclKeLZzOmTpzziPce/ePXTu3BkAcPr0aU7LI4lEgqZNm+L+/fvYtWsXbt68idWrV2soPbgsnlq0aKH33UFYB33jre+7haF6aVfV77V9lAHTRXweVsVqL3BnQoSx0mGYK+0OBcugp+AofhLNhTvS9PZh9SmlJ8Ctp2/xzf7rOHz9qbJvNuyWK5FIcOnSJQ0jAkAZm1wXZ8+e1SrfrFkzXLx4EVKplLNORkYGpFIp3Nx0J7crSOFOChMVSzhj/6i6qjjKCvAQJx2KA/KaEDMyrBIuQm3e3/gglWPhwZv47+X7fO4xYQqGPKzidlw2q93+/ANYIloGESPHPnkk+kq/QDocDVc0wCI9ceizWKCWLd0QWWfP5zH4bURd7B5aW5VAj7BdjFbuJSUlISkpCSzL4urVq6rfSUlJ+Oeff1C1alVs2LDBpINPnz6dc3Kk/rl48SIA7kmcLksjdQxZJ02dOhWvX7/GkSNHcPHiRcTFxaFz5864evWqqvywYcPg5eWF06dP4/z582jbti1atWqFlJQUncc1x/XQxUGI7YOi0LWGcVkqAaWb6JSWFVHWy/AEvLijCH9OaoSkr5oa3b4xmONeqo4pgfR1/bsdRAJsHlALsWqxYhSs/r75OCsng+rKC3UlpzkB/gGAz1O6c6pbJGopNvQ0zbJKt1qhuz+E7v4IDg7W+gQElQcjEEFcIgRs5ntkPsl2T898chNs5nuI/bgVjIGBgfDx8cHhw4dV2yQSCU6ePKnhdpvF2rVr0aZNG3h6aq4ohYeHQywWa7jGS6VS3L9/H6VLm+8+TpjP6YkNEavDjVgfPAMKM2uhHp/SKLdcA791oUuBoUuRYIwCAwCcnZ21skpzWbBfuHABP/74I6pUMX5QRVgP02OxGsbTSWyx4Nq5JR2OWCNviWjJYgyWjEWiIgQCRoHW/ET8Tzwdv4i+QnveaYghQfqFX1CsSlM4VW0GoYc/3JoMAt/JA2+TDnC2vXLlSpQsqUywVaFCBU7Lo3Xr1uHVq1f45ZdfUKdOHZQuXRp169ZF1arZEw8uiyd/f3+sWLHCuheHMAmG0Yx02jjEG6cnNsSV6TFoVslHVy2skrfGAOk4vGXtEcm7gd/EUxDG3NZ5nOM3n+PO83eIWXwKq0/fw8BNFxEwaT9eZ2grvN5kSGxC6ffixQvI5XKtRdGcYyV1UlNTOY0OZDIZXrzgzno9adIk+Pn5oUmTJjrbLUjhTgoblUq4aMRRloOPUdIROCwPhx0jxVrhQkTxrmHZ8duIXnACD17qzyht6+QmwWWFCsr44C1bttQZ2oplWbRo0QIMw+CXX35Rbb9//z769++PwMBA2NvbIygoCNOmTYNEkrehJ3Lz7GGgwCTBVnwl3AwAWC9rhpHSEZDAMgqzEq72BsMqdI7Q/WzIOf5XnxNXLumiYcxA2C5GK/eOHz+O48ePo0+fPvj9999Vv48fP46DBw9i1apVKFeunEkHHzFiBKdVkvonNDQUPj4+ePr0qVb958+fc1oaAcZZJ925cwfLli3DunXr0LhxY1StWhXTpk1DREQEfvjhBwDAsWPHsG/fPmzfvh116tRB9erVsXz5ctjb22Pjxo06z81WXQ/9XO1RXM3qjOshVTHHg8FwTD3jjj1HR8ZchbH+eCZi6AG8fVAk+kSVxtq+2e5c6q4hMjP7lfVwdFe7znweo7Vakpt5YFZdoYc/7ALD8TJhKTIf/4PMx//gZcJS2AfVgNA9O8NxcHAw9uzZo+rfmDFjMGfOHFXm66FDh8LBwQE9evTQOM7t27dx6tQpDBgwQKsPzs7OGDJkCKZNm4ZDhw7h5s2bGDp0KACoFCOE5dF3W3MlEQCyLVLrl+OedHg5idGmagl0Ci+JYmLjMgNbgv2j6qm+W+oxwCVWuhQYuhQJxigwAKUs5cwqnZN3796hZ8+eWL16NYoXp4FRQaSct2WsV/MaBXg4qKiBbpKv8FnmHOyU1UcmK0AY7y4Wi1bgpGA4ZKn/wj9QM2mUfWA1ZD7+h7PNs2fPalkm5bQ82rt3L6KiojB8+HB4e3sjNDQUc+bMgVyudGHSZfEUExNjMBkBkTeU8VRakdQt66H1UPV3c4Cd0HDisGOK6mgnmYk7Cl+UYF5hh2gGevKPQNfqZvyRfw22ueb0XYTNPIzBP10yWDav4DIiMKc818La/PnzsW3bNvzvf//TG/rIVuccRRUZBBguHYXj8qqwZyRYJ1yAOjyl0cjMfdfw79O3OHBVt4GILZObBJcLFiwAoPSIyJngMov4+HhOWfjnn3+gUCiwatUqXLt2DYsXL8bKlSvx5ZdfWubEuOAYUN430/pSABkWCldiiGAfAGCetBtmyHqDNT1Cmk4UChb96waqYkECgL+bvUkegYByvl6yuD3mdDCtHmEbmDyLW79+vcUO7uHhAQ8PwwkSoqKikJaWhvPnz6NmTWUGmXPnziEtLY3T0gjQtE6qVq0agGzrpG+//RaA0tQdAHg8TcHi8/mqeGK6yvB4PFUZLsRiMcRiw8kXcos1rGw29a+Jg9dSMWWPfre17D5o/tY1sOlRqxS+3HNVa7spSjRTFkwUrH53qgAPR8xoqxkHTH2VQibXfTB9Vz3LekO9toDHoHOEPybsuvJpn2GrU2PxaD0er4+swtMdXwEAHMrWglvTIRplbt68ibS0bLeYiRMn4sOHDxg3bhwAICUlBYcOHYKTk+Ykdt26dfDz89OahGWxYMECCAQCxMbG4sOHD6hVqxaOHTtGSgwbI/HLxnjwKgNh/q6c+xmGwZLu1Tj3RVfwxD+pb+HqILSqVZ8xq6E5j29Md1i5FJLU23CJ1Axybh9YTaciIUuBcevWLdW2Zs2aYe3atZBKpRAKlaus7969Q+nSpSGXyxEWFoZZs2ap3jdZDB8+HC1btkSTJk0we/ZsvX3NzMxEZmam6je5VtkGA+oF4qNUjpfvJdh6TrfbqI0Y7nFynQ3ABNkQzJN1R3f+MfQQHAU+vICCZbGm+HrIhMnYLG+Co4rq4DsWh/z9X5ztpKamqmISZ6FueeTr64u7d+/i2LFj6NmzJw4cOIB///0Xw4cPh0wmw9dff63T4klfUieA5MNaFOeIoXRwTH18lMrhZCfMlW/GHdYPbSWzsEC4Ci34F/CNcB3CmNuYKuuHTGge97fLTwy2N3v/DQDAoevaC/55jYeHB/h8vtY9q8sCD1AaHnAZHQgEAri7u2tsX7hwIebMmYMjR44YtPrOqzkHYTwSCDFEOhbL8T0a85OwTrgQg6RxOHKjKo7cUIY92DqgFmqbkCgwv8lKcJmYmKiKg7969WpERUXh5s2bKss8ddQTXLZp0waAcgG1XLly2Lp1KwYPHqwqe/nyZSxatAgXLlxQhf7Jonnz5mjevLnqd5kyZXDz5k2sWLFCa+HVmnw0I/OxAz5ihTAe0fwrkLE8fCEdhN0Kw+G0TKWsVzGIBDyMbFwO3x1Wjl/N8a7rUasUetQy3ouQsC0spy62IiEhIWjevDkGDhyIxMREJCYmYuDAgWjVqpXGg0SXddKePXvw999/o2/fvhrWScHBwShbtiwGDx6M8+fP486dO/juu+9w+PBhtGvXDoBSsVi8eHH06dMHly9fxq1btzBhwgTcu3cPLVu2zJPz71s7AABQr5z2CyC3cwmu+bRHMTF61jLetS/Xbrkcyj1LeVyYqozQUO7pUd7qgyt8oTUzwfHtneDRejxKjd2JUmN3wqP1ePDsNOMrsiyLvn37qn4zDIPp06erlBcHDhxAaKh2woM5c+bg4cOHWsrtLIRCIRYuXIinT58iPT0dhw8fRqVKlTjLEpbBnExVbo4inYo9Q4xtUh7zOlTG76PrGS6shjH9VC9hjuUe17Mnp8jLM9IBVgGeg6bCme9YXG/M1pyuVTldp4KDg7Fhwwbs3bsX27Ztg52dHerUqYN//822Ptm+fTv++usvzJ0716jzIdcq20Qs4GNcTAVUMyBDNqzbU/ESLlgmb496md9jnEQ5qWJZBvX5V/GjaDFOi0cjivkbAuh+/xmyPFIoFPDy8sKPP/6I8PBwdOvWDVOmTNGylDU2qVMWJB/WoVqp4hjbpDziu4aptgn5PDh9Sq5h7DjKTsg9TngHBwyVjsFcaXfIWQadBaewWzQdJRnuuI654Vn6R/RYnYjf88AqSiQSITw8XCPECaD0dNJFVFSUVvlDhw4hIiJCtWgEKBdOZ82ahYSEBERERFi244RVyIpHqU4mRBgqHYND8nCIGSl+FH6HRrzshZPrKQVrgcKaCS4zMjLQvXt3LFu2jNMLgou0tDS9sShtAVe8xVbRbETzryCDFWOAdLxVFHsA4OWsbd1ry4uOhHUoEMo9QJk9qnLlyoiJiUFMTAyqVKmCn376SaMMl3XSmDFjMGzYMERERODx48ca1klCoRAHDhyAp6cnWrdujSpVqmDTpk3YuHEjPvvsMwDKlbmEhAS8e/cOjRo1QkREBP744w/8+uuvGvFjrEnniJI4OKY+1vaxfDZAW0hpLbdi7BQdOimdqCvh9Fnu5aSyX3YAf664S1yZ4NSLdc0RA8FQdjobCDdD2AB5cRvYCfnoVrMUfF3srXocY85FK+Yex6BF12KDVlkDigRDCozIyEj06tULVatWRb169bBjxw6UL18eS5cuBQA8fPgQo0ePxubNm43OJE+uVXmFeaNdffeof3H7gqHd+4QcfJyxqwswPAx40xcrZK3xknVCCeYVyn34GzWKPcMK4WIg45VGPR8fHzx7pqmUyWl55Ovri/Lly4PPz3bdDAkJQWpqKiQSiU6LJ11JnbIg+bAeo5uUQ7tq3FlehUYuTjYO1v2/y4rD10v6JV6yTgjl3cc+0RRVNlFz2H8lBS/fZWpsm7nvOs7ceYmhW7gtTy1NXFwc1qxZg3Xr1uHGjRsYO3YsHj16pNo/efJkjWRkQ4YMwX///Ye4uDjcuHED69atw9q1azF+/HhVmfnz52Pq1KlYt24dAgICkJqaitTUVLx79y5Pzokwj8Yh3ljeszpmt9NcKJdAiOHS0fhdXgNiRoaVwsWI4V0AoLREffiq4MTgs2aCy7Fjx6J27dpo27atUX25c+cOli5diiFDhugtl5/JZrzwGjtEMxHGu4tXbDH0kEzBCUVYnh0/i5zj4mmtK1rV6ITIXwqMcs/NzQ2bN29WCebmzZu1stXqsk5KSUnBx48fcfLkSS3rpHLlymH37t14+vQp3r9/j8uXL2vFDoiIiMDBgwfx8uVLpKen4+zZs2jRooW1TlULhmFQwccJIoFt/LvUV3cBaE1oTFU8yc0zkDMKscBwXJicNAnxRrCPEyqV0A5KqktxUKVktnKPS2HA59Ayqrf1rVo8Pn2ZeAnLUhCVpAK1eymv+z/Ogpl4tdDjzp+TLAuRrLhQ6uQUP76DM8DwIH//WmO7POON3pithhQYOeHxeKhRo4bKcu/SpUt49uwZwsPDIRAIIBAIcPLkSSxZsgQCgUAVf0ydwpRJuijRsXpJeDnb5dqKPa9h+EKIfMri0f17+FbWHVGZyzBKMhx77/JQx5+PSsx9wM5Vo05UVJSWZVJOy6M6derg9u3bGqFLbt26BV9fX4hEIp0WT4cPH9YZagUg+cgvaga6oUZAcXSvqf0sXtw1e5F7aqsQzGqr33L/rKISWmXOQbIiCK7Me6wXzsdYwU7wof08zEnO0A3Dt/6FzivPAgAevc7As/SP2Hclb+OYde3aFfHx8Zg5cybCwsJw6tQp7Ny5U7U/JSVFI3FTYGAgDhw4gBMnTqhCOSxZsgQdO3ZUlVm+fDkkEgk6deoEX19f1ScvXQ8J8/issi96cSQ1k0KAkdKR2CePhIiR4wfhErTgnQMAjN9pvoLbUuR3gsu9e/fi2LFjiI+PN6q/T548QfPmzdG5c2fOmODqmGrxffH+K1z6T/nJDSWZZ9gpmoHyvMdIYd3QWTINyax+w4284vM6gTj3ZWOt7QVrBEPoIu8ipxM2iTnKgZyru7l9GHBlpdXVLVPNi+2NCPqckzV9Iox6UemCazGE/6mtyn4uuPo4DZ3CS2plgfu+WxiuP0lHdHlPXLiv/6Wiq2s8xnKJCQjbZEabSui55hwGR5fBqVu6Y/tYg9plPTC/UxVM/BQ78uAYy7kWKFhgTe8I/HDiNpIevFFtn9O+slacs+SvYyCVK+AgMvwKy1JgfLifDIfy2YoDQcrfqN27K2edqKgo/PrrrxrbuFyn1GFZFsnJyahcWRmAuHHjxqqs61l8/vnnCA4OxhdffKFh1UTkLWa7qeh4toaVcgWgXJjxKCYGwOLFu7zN4GcuzjXa4cW+RRD7lIW4RAg2XL6Od2nAH1W+xkMZH/5TpuDx48fYtGkTAKXl0bJlywAovSWuXr2KtWvXYtu2bao2hw4diqVLl2L06NEYOXIk/v33X8yZMwejRo1SlYmLi0NsbCwiIiIQFRWFH3/8EQ8ePDBohUHkPQI+DzuHcCtd24X5wcvJDiG+znBzFKFKSVeD7aXAHV0kX2OaYBN6Co5itGAPInk3MFIyEs+gO15v4GTtDM53X7zH249S1P1WtyustRk2bBiGDRum+q1uGbRhwwat8tHR0fjrL92Whffv37dk94h8IL5rGNb9eQ8excQ49o9yoVAGAUZLh0MGHtrxz2CpcCnGSBXYdy8Kiw7dRGxUADyd8idu4ogRI9CtWze9ZQICAnDlypVcJbgMCgpSbVe31D527Bju3LmjZbTTsWNH1KtXDydOnFBte/LkCRo2bKh6bxhi8uTJiIuLU/1OT0/XqeB7+CoDnT4tGABAqyq+nOUMEcQ8xmbRXPgyr/Cfwgs9pV/iEatt8ZhncIx5CqJxA2EcpNwr4OTWl55LscaFvlJaqzEm9kFuRW2UOco9IHeJSkJLfLLiUzutLPPnnwdH4p/Ut6jm74ofT93VqNc2zA9tw7jdYoxlZa9wDLKhLHKE5QnwcMSfk5TZKvNauQdAI5tuBZ/cZRFVt8RwshOgSUVvNKnojbbL/sDlR2naFT6JpZ2QrzNbI5fk5lRgvL2cAEXaM5UiYfLkySYrMGbMmIHIyEiUK1cO6enpWLJkCZKTk1WZ1p2cnLQsxR0dHeHu7s4Z35LIO8zX7el/V/F5DM5ObgSWBcpP/d2ktodEB2HlyTtm9sx8HEPqQ/HhLd78uR3y968g8igNr87T8dA5FA8VQIPUnVqWRzt37kTLli1Rt25dlChRQsvyyN/fH4cOHcLYsWNRpUoV+Pn5YfTo0fjiiy9UZbp27YqXL19i5syZSElJQWhoKA4cOIDSpY2P90vkPwzDoI5aQgBj3wkSCDFF1h/nFMGYI1yLWrx/8Lt4EuKkw3BSYVrIm0evP5hUniCsTbtqfmhXzQ9TciQRlIOPOOkwyCBAJ/4pfC9cBjuZBEuOAUuO3Ub/uoGoFeiG2ftvIL5bGKqXypvkdHmV4DJLuZczweWkSZO0LPAqV66MxYsXo3Xr1qptjx8/RsOGDREeHo7169frjAeujinJZu6+0MyEe/LWc6PqqVOJuYdNonlwZ97ilsIPvSRf6l20MJXIMm5IvGu8VSFZ4xU9SLlXwLEFN6Dc9sCayr2hDYNwfv0rfFbZBweuZsd2COJw5zMFLt3fn5Ma4eW7TAR4aLddzluZ4MJBJFC9rK0R5LRGgHZg2W41KOh4YaWUm0OeH9PoBQEjxXpFz+pYeeou5nXQnw0QMP9Zk1OB4eAdiN/VFAlcrlOGFBhv3rzBoEGDkJqaChcXF1SrVg2nTp1SDXiJokN5r+wERkKujErQb1Vdv7wnJrUIRoivE0ZvTzZ4vAnNKmDBwZvmdJUTp+ot4VSdO0EYl+VR3bp1ASitNXS5xkZFRSExMVHvcXNaPBEFHzshH9+0D8WUPX8bVX6vog6uSspgmXAJKvH+w0bRt1gua4NFsk6QGTlFufTfa8OFCMJGUICHCdJBkLJ8dBccx0LhKjghA+vlLbD2j3tY+8c9AEC/DReQ/HWMgdbyFvUEl6tWrQIADBo0iDPB5dy5c9G+fXuNBJd+fkoDhqFDh2okuPTx8eFMolGqVCkEBgYCUFrsNWjQAKVKlcLChQvx/Hm24s3YBBym4iQW4O1HmdHlI5h/sE60AM7MB1xWlEFfyUS8hmXDR7jYc3uP6IJhuDUFXIuVZMxXOCDlXhHHEma5piipGEb7mL4u2sHmdfXL1P42rOCFxMmN4eUkRpmrSpeOeuU8sLp37rKPtanqh3V/3lP9ZhjAz9Uefq7cSQeCPItxbteFuf8XJzttkZ7ZlqyECitjm5bTuA9NwVzlsqV18S0q+6JFZf2uD7UC3XDu3it0jjCsqNZ1XuoKDE8nMerXz3YpNkeBsXjxYixevNhgf9RRdy0h8o9gH+X/U+lCaz77RtbFnefvUKuMdhzGLhElseOiMrB+s0reuP8iAzefvuVsJ8uCVWCEFYK/mz1n0iaCsBV0Kbh1cY/1RQfJDEwRbEFvwWEME+xFTd4/GCkZiRRwxzhVZ+ovuhWJ156kYdCmS3iXKcOsdqFoU7WESX0jiNwwqH4ZbDn3AOGli6N6KVcMiQ5CyyV/IDX9IybLBuAd7DFQcADThD/BGRn4Xt4BWcuYbzKkePAyA6Xc834RVx9btmzBqFGjVNlv27Rpo/J0yIIrweWHDx8wbtw4AMoFVfUEl8Zw6NAh3L59G7dv30bJkiU19uWMx2kpTPHiqse7gh+Fi2DPSHBOEYz+kvF4B8v/78wx6uG8OqTJK7TYRoYGwnxyOca3hmzre8aqd3f30Cg0qOCJlbHhVuhFNj4uduCpBcILcHfU6dJnLBObV8CyHtVy1Ub7asqXU+Ngy8Vh4DEM1vWNQEu1OBE0D9Tmy8+CwecxmNOhcn53RYushCrGJNBxshPqVChbi9wMovaNrIuN/Uy3bFvTJwJrekdgYvMKhgsbAYlE0cZexMf1mc1w5pN7u7HkvPVD/Vx0hlKY36kqLk+LwXedq+K7LmF6n8NZ1rBNK+rLNqpkc/9anHFdCcJWMGd8lQkRvpZ9jqGS0Uhn7RHBu4UD4sloyruYq74M2XwJj998QNoHKUZtS4KCghITeUhpd0fcnN0cu4fWxpSWFeFeTIxv2mctuDP4RtYTC6WdAQBjhbvxlWAzGGQnIqq/4DgyJMZbjuUFuUlweevWLQDAgQMHDIYnYVkW7dq1U/3u27cvWJbl/FiLx2+Mc/lvzLuENcKFsGckOC6vij6SL6yi2AOA6AqeJpXXNVxw5rAApKFF4YCUewWcXAuiJSz3TCirbnEQXtoNGz6vyWnVpj4R+n10Pc7t5mKJNuyEfLSqkr0CbM5KiqeTGP/Mao41fbStCM39tzAM0CjYGwPqBqq2kZWHNoPqB+HmrOYIL5038UxMYdvASDQO9sL/hurOGpmfcGWRNpZQPxfUDsq2xDD2PneyE6JJRW+jsl/rWmmNLJPtsk7TO8JBJDA5A72p942LvRAdw0tqxKnkIiuprDH94fMYeqYTNk2LUE0XuSNx9Tkzm3Pxu6IWWkrm4LKiDIoz77BatAhzBKthj48m92P5idt4+Epzcr7rr0cmt0MQuSHnuEVTv8xgmbw9pkn7AAD6C37Ht4LVGtmje6w+lwe9JADz5tQxvAtYIYyHmJFhv7wmBknH4SOslxila4Q/vu8WZlIdrvOyE/JxbFw09o6oo9pW3NE0l1/CNiHlXhHHUIBwJaalN9df1rhy6gsxIb6WjVcQxeFCZS4tP7kT9qmtHQTcmGtrJ+TnKnlHTrjaomkgNwITXYfyigo+TljbtwZC/VzyuyuclPVywu6htXF6YkOz6uf1/dithj9aVvHFip7WtRAmCH1wPZuzYmZ+Vtn4eEECHo+ssQmbRt0tN6aiN8p6OcHJgIJbnYesNzpJpmOVrCUULIMeguPYL/oSVRjTEs7MT9COS3nxvjIQ/ZHrT7HvyhOt/defpOPM7bxPVEUUHeqW9YCXkxjR5bMtsDbKmyFOMgRylkEXwUksEy6BCFIAQPLDN5z3KpH/NOedxw/CJRAxcuyVR2GUdCSkVo54xuMxaBFqXhbfnJTxLIYqJV2xuGtVjGpUNs8SuBDWhWLuFXDMHeRnxbDqWSv3melydkGfUks5wckfu5k/JzXC9SfpaBJivhtszuu9rEc1LJBWgYPItkRJfSJJE0HC0hhj8ahLyo1SZlvwpq0R4IaO4ZrxWcitkTCH3Hj/cN1ze0fUwZVHaRrZRg3h6iBUZV/n4ui4aDT+7qQ5XSQIi5P1KI+u4MWdAV0HUggwV9YTJxRhWCRcgTK8VOwWTUe8rCNWyNtAYaZtQpaXxYBNSnffcl5OGhl+P1tyGgBwemJDHL3xFI5igVGxXgnCWOxFfPw5qREEPAaBk5WxwHvUKoWt5+rjvdQeS4RL0YJ/AY5YiCHSsciAHUZsTULVkq7wz4ckagQ3n/ESsUS4DAJGgT3yOhgvHQI5chfyyVhMGiIz4Ez0qE5WmCiicGCbpiuE1dnUvyYOjqmPtmHGBRcu760nIYQpCTWML2o2Y5qU0/ibhZ+rPZpW9M6VpVzOyR3DMFZR7JkyiRzVqCwAYOSnvzmxpGUgUXhwcxDldxd0Pw9yoUXJebur/17UpSrcHEVY0Yus+Ii8hesx7OogQv3ynnqVdepUK+UKOyFfr1uuqcmbCMIa9K0dAAAY1Vg5DhveMAhTW4bgy8+CtcqW9dJ9z55VVELzzHnYJ68FISPHBOEObBfNQknmuc46+lAmdct+v9x78Q4AkPZBit+vpqi2n7v3CtN/u44Ju65YNaYXUTQR8nkaY/P21fyQMKYeDipqoJ90At6zYtTnX8U20Wy4Q6kUf/EuE4/ffMCZ2y9w4f4ryOQKXc0TVqY174xKsbdbXg/jpENzpdj7ZXgdRBixaP51q4oATJtLM1Aucs/tUBk/D4o0r4NEgcK2zI0IkzEn1hugjAGhvlqpjwB3B/StHYgPEgXqlzfewoCLvIgVNLpxOXSsXhIli+dtooH8YmzT8ugYXlLl4kUQxtChekkk3n2J2iZYDZmCLknn8xi0rOyLtA9SBBpYTTQHF44gwVl0qF4S7av5kcKbMAvjwlhwo/6uXtGzOvxMeD/5ONuhZ61SGN5QuYBDlqeErTO9TSVMahGsSq4hFvAxoF4ZrDl9V6vsgVH1UH7q7zrbSkMxjJCOwnH5acwQbkBN3k0cEE3CNGlf7FHUhanLxmfuvFT79cmSb+MFXLj/WrX1fWZ2EgOWJQ8Iwjr8MrwO7jx7hxoB2TGB/1RURk/JFKwTzUdV3l3sFk1Hb+kktF9+RqPu8IZBmNBMW1lOWJe2vD+wSLgCfIbFTll9fCEbZLYlMaBctAvzd8WuobURf+QW4o/8q7Ns0KeFEGPGsN1rlsK28w8Q17SC6jdRNCDLvQKONQccPw+KxJDoIPStEwCRgIfRTcqhGoc/fk4Fo75FzlA/Zfw8IV9/x3M1iWIY+Ls5FOgJvCnnzzAMSrs7FujzJfIekYCH+G7V0MVKLkf67uAfelbH5gG1dN+zZt7LLav4onO4/vMhOSHMJTcGPOq3XYvKvqhS0tWoevZCPs5OboSRjcupsr6XyOMM2QRhDlxZc7msVAVGaasZ7FbURwvJXFxSlIMz8wGLRSuwSrgYHjDe3fe9RI6ea7ITFJy98wJr/7inodgDNBXoCrLcI6xEmL+rRtiQrDh8yWxZdJTMwEOFJwJ4T/E/0TRUZjQV4ytO3IGcsj/nKR14p1SKvW2yhpiYS8VeTkY1Kme4EIxbzpjTPhTnpzRGyyqWic9HFBxIuUfopFYZd0xqEWwwQ6Upc+Wl3auje81S2Deynt5yFS2cRCM/yM/xIKkvig79PmVGbhRsfixJa5ArS1IzhCfI0xE/9KhucgZUgsgLvJzszK6bUyFtrKz7u5ESkLAtciojynkVUymtjeEh640ukq/xnbQTpCwfzfgXcUg8Aa14Z2FMPOePUrnG741n/8Osfde1C6rJHKlPiLzC1SHb8+Ae64sOkhn4WxEADyYd20WzEM27rNqvYIGgLw9gfsI/+dHVIkd73mksFK4Cn2GxRdYYX8r6gzVBjVKnrOFkjjweo9f4JWuPcaGrmVyNO4iCC7nlFnBswQjFlC74uNhhbofKBstVK1Uca3pHoJS7bbma2sL1NoYQX2d4Oonh40wP9sJOvzoBqBnghvI+thFra+uAWjh0/SkG1S+T310hCJthdrtQSOUKVSwyY+nDUd6Q9en5KY3x9qMME3ddwcNXH0w6HkFYE4lanLCl3auhdpDhCW9O5OBjqbwDjiqqY6FwFSry/sMy0VJ8Jj+Hr6Sf4yV0Z5o3dt2ILPeI/KBsjpipz+GKrpKvsEIYj/r8q1gjXIhJ0oHYraivKrP8xB1MbE7uuZZA16u1JS8RC4UrwWNY/CRrgq9lfU1S7AEAn2e5hWfyQCH0QSYOhNmIP1nIRFfw1NhuqWFQk4reKO9tXFxAazOuaXkAyjgyeUIuL6JIwMPZSY3w6/A6lukPYbMwDIPKJV0MWtjmFbXLemB6m0qcLllGQwMXwgbJzWPZx8UOG/vVREMjre6+aB6MbQMjMT6mvFHlW1b2xbUZzQAorQQpsQZhi1Txc1V9b121BNyLiQ3WqV/ek3P7dTYAbSWzEC/rACnLx2f88zgknogWvHOc5QHgyI2nRvVTPdwM6faIvGJAvTKqJDRZvIc9+ksn4H/yuhAycnwnWolh/F+h/kY6fN24+5ownRjeBXwvXKZyxTVHsQfojwetjr7nDQ2NCWMg5R5hNqe/aIhN/WqiZeXC788/snE5/PtNC1TniDloqwj4PJPcXQiiMEKDIcJStK/mBz9Xe3S1UpxKdeyEPEQFuUPAN26YVtxRCEexpjMG3fqErVGnrDtW9grHsXHRGtud7LgdiRpW8NTr7SGFAPGyTmgnmYkbCn+4M2+xQvQ9lgmXwA3pZvdz3Z/3VN8fvSbrVyJvsBfxEde0PLrX1HzHSCHAOOkQrJS1BgBMFP6MOYK1EECZ+GXgpovouuoslh7VnYyBMJ0GvCQsEy75lBW3LqaY6IqbRVV/V0xtGYLiDsYp+AgiN5Byr4BjbrZcS+DlZIf65T2LjHmw0MhJliWghWKCMI3gQhCnk7BtiokFOD2xIb7tVCW/u0IQBRKGYdA81AdlcliWnpnUSKvspBbBWP95Tfi52qOVgaDw19hAtJXMxlJZO8hYHlrxE3FEPB7teadhzojq9rN3qu+jtiWZXJ8gcsPcDlVw+5sWGttY8DBP1h3TpH2gYBn0EBzDeuF8OOM9AODcvVf47vAt/PfyfX50udBRm/c3VgnjIWLk2CePxETpYLOTZ/w6vA68ne1wcEx9rX05LfX0Pa3yc85PFBxIuUdYHvJhAGBtBR094AkCAPaPqovP6wRgdtvQ/O4KUQSwVWtoeu0SBRknO02LlqPjojEkOkj1u1dkaYNtSCDEd7IuaP/Jis+NeYfFohXYJJwHf8Z8t8W7L94ZLkQQFkaX1fZGeTMMlMbhPStGPf7f2C2ajpLMM9X+6AUn8qiHhY8s5VlN5gbWChdCzEhxSB6OMdJhkCP3oW+8chkHnSXTD8IISLlXwCkiRnMFkgnNKgCAyQHUjYMe8AQBAJVKuGBa60oo7iji3E8rnURRgOuNQOMDorBgyq18lS2D1pJvMF/aFZmsEPX5V3FI9AUG8X8DH3LDDeRAQcMtwsY4qghHZ8k0pLBuKMd7jF9EX6M6c0u1/+W7TPzvr0c4dC01H3tZ8GDBohrzL9aJFsCekeC4vCpGSEdBlov8o4Eejqb1wcSVuvDSynBR9rmJc00UKihbbgHF3VGEl+8laBLind9dIXQQWcYdf89ohmJi08XM1Ic7QRAEUXiwhF6OXiNEQSXnvWuqgk0GAZbL2+KAoibmCNaiNv86vhRuQxv+WUySDsDfrPHZ3CUyheFCBGFFxjYpj8VHbkHAY7B5QC18lMrRdz3QNnMW1ooWoDLvPraJvsE46RDsU0QhfPYRVd3b37TA/5IegwHQOQ/ixRZkHF/+jY2ib1GM+Yg/5JUwRDoWEpgeJ6911RIY3bgcNpy5h8H1gwxXMBKuxeoJzSrg1tO3aFjBuGRdROGHlHsFlIQx9ZH04DUa26Byj+YT2Zij2DMOMskgCjGklSAIk7CGyHSo5mf5RglCB1Fl3HH27stPvzRvaIWOG7ysVzF4OYlx5s5Lzv33WV/0kE5BZ8VJTBFsQSjvPn4VfYV18hZYLOuEDJjuJvfZ96fx7O1HbOxXE5VKuJhcnyCM5YvmwUi8+xJDGwShY7gfnOyEqqyr2wZGovvqRHSRfI0lwh/QlH8Jy0RLESBNxTJ5O2TNE6rOOIT3EqXFaovKvlaclxR8RO8ewh6ZOKcIxkDpOGSC2yNEHxW8nbC4S1UI+DzMbqc7GZAuPIqJ8extpsFy3Wv649HrD6gZ4IbIMu4mH4covJBbbgHF00mMmEo+4Nto/B8id5BugyAIgjAWrqz1xrrlHhhVj3M7jS+IvGR1nwjV95xjIF3Wc+v71sDWgZEGWmawU94ATTIXYq88CnyGxUDBARwVj8dnvESYsiR9+t/nuJ6SjhfvJDROI6zO0AZB2NivJkQCHkoWd1Ap9gAgKsgdPw+KxAfYYbB0LFbLPgMAjBfuRLzwB4ghAQCVYg8AMqWmu6UXJZ6XbI7e0knoJ5mAD2Yo/gHg4Nj6Rme552LD5zURXro4NvaribplPTDlsxDVPvV3+twOVfBT/1o2GweYyD9IuUcQBEHYFhYMFkZxx4iCiLFZ6KPLeyJhTD3ULeeht5xYkD3cU58L7BlWW6eMkOwQeYlA7cbMqTfLtIBr7Au4YJR0JPpKJuCBwhO+zCssFy3BT8K5KMM80Vv31XsJ5if8g9i151XbeCQgRD5T65PFlgI8fCPrhS+l/SFjeWjHP4OdohnwhaZFqyXkqDBTyc8ZZxWV8B72+daHiiWcsXtobUSX98TmAbUwsL7xIQQIAiDlHmEFaDUz94iFhkSTLjJRiKGHCEEYhYOIj2AfZ4Pl1EVKXbr8XO1J3AibQF1XpmW5J+dWSogEpk9jTiiqoalkAeJlHZDJClGP/zcSRF9gomA77PGRs06decew/MQdjW1k2UrYAnuG1VZ93ypvjF7SL/GSdUIV3j3sFU9BOHNTtf8jWe7pxcspd9lszYFev4SlIeUeQdggg+oFoVIJZ0xuEZzfXSEIgiAKOKyOKQSrZx9B5CXqlnA578mcerSJzStgZKOy8HY2bzKeCRHiZZ3QVDIfx+RhEDFyDBPsxRHxBDTnnUfOKfcHDqVILjzvCMJiVCtVHFsH1FL9TlRURFvJbFxXlIYnk45totnoxj8GAMiQyEnBZ2NU9jO8OJcFLScQxkCvJsLi0EQh97g4CLF/VD0MjtaVZYke8UQhxgruTsuXL0dgYCDs7OwQHh6O06dP6y3/xx9/AAC8vLxQpkwZrFy5UmP/hg0bwDCM1ufjx2zLj7lz56JGjRpwcnKCl5cX2rVrh5s3b4IgLIWbo3EBv9WzjfLV5IvPY8hyj7AJ1O/LnPdkTEUfjd/DGpTFuJgKuT7mA9Yb/aQTMEAyDo9YD/gxL7FSFI9NwnkoyzzSW5fccglboXZZD4xoWFb1+xHriY6SadgnrwURI8c84RrMFKxHu6UnEPxVAkK+SsD6P+9h3u//4Pazd2DpJZBvfNGcjDgIy0LKPYIgCKJQ8/PPP2PMmDGYMmUKkpKSUK9ePbRo0QIPHjzgLH/v3j107twZAHD69Gl8+eWXGDVqFHbv3q1RztnZGSkpKRofO7tsS5KTJ09i+PDhSExMxOHDhyGTyRATE4P3799b72SJIsHKXtXRJMQbE5oZp+BQzzZqL+RjcP0y+LxOADyKia3VRYIwCX1uuSIBD31rBxjdlmmZnhkcUYSjSeYCfC9rj0xWgPr8q0gQTcJ0wQa44i1nLXLLJWyJnPfjB9hhhHQU5ku7QMEy6C04jC2iOXBDOj5I5Zjx23WsPHkHTRadRO15x/AsndslnbAsbcNKqL43r+QDJzuhntI5oEcOYQSk3CMIgiAKNYsWLUL//v0xYMAAhISEID4+Hv7+/lixYgVn+ZUrV6JkyZIAgAoVKmDAgAHo168fFi5cqFGOYRj4+PhofNRJSEhA3759UalSJVStWhXr16/HgwcPcOnSJeucKFFkaB7qizV9IuDqoNtyjwG3JRQLYPJnIZjWupIVe0gQpqGeRIbLKK5+eWXSGENKtVntQrGoa5jJx/8IMRbLOiNGMh8H5REQMAr0FRzCSfFY9OP/DiFkGuXJco+wJdpxKrQZLJe3w0BpHN6y9qjF+we/iacgjLmtUSol7SNm7LuO74/8i9Q0/Uq+169fIzY2Fi4uLnBxcUFsbCzevHmjtw7Lspg+fToqVFAuRrVs2RLXrl3TWbZFixZgGAa//PILZ5nMzEyEhYWBYRgkJyfrPbatsbhLmOp7yyraWe4JIreQco+wOGTdTRCErSCVSnDp0iXExMRobI+JicGZM2c465w9exaNGjXS2NasWTNcvHgRUqlUte3du3coXbo0SpYsiVatWiEpKUlvX9LS0gAAbm5u5pwKUYSwpt5AQS9pwkZpXbUEIsu4oYK3k9a+hhW8sHVALZyd3Iijpno5TwCAkG+eEP3H+mCwNA7dJVNwXVEaLkwGvhb+hIOiiWjMu4SseHxkuUfYEoEejvjrq6Y4Ehette+oIhztJDNxR+ELP+YldohmoBf/MNRjS+6/koLFR24hcu5Rvcfp0aMHkpOTkZCQgISEBCQnJyM2NlZvnfnz52PRokVYsGABAGW4k6ZNm+LtW22r2Pj4eIPZ4idOnIgSJUroLWOr8HgMLk+Lwc4hUWhFyj3CCpByjygQZD0Ah+qMQVe0qFrSBcE+TmgS4p3fXSEIm+btm9eQy+Xw9taUFW9vb6SmpnLWSU1Nhaenp1Z5mUyGFy9eAACCg4OxYcMG7N27F9u2bYOdnR3q1KmDf//9l7NNlmURFxeHunXrIjQ0lLNMZmYm0tPTNT5E0STQw9Gi7am73+ZU7oX4OqNKSRc42QksekyCMJWl3ath+6Ao8DgUZwzDoHZZD70ZLRsFe6FkcQcAgFzBrcTuHF7SqL6cVVRCK8k3+EI6EM9ZZ5ThpWKt6Dv8JJyLCswDUu4RNoebowhlvYrhhx7VtfbdYf3QVjILv8trQMTIMVu4HouFy3VmiObixo0bSEhIwJo1axAVFYWoqCisXr0a+/bt0xlPmGVZxMfHY8qUKWjTpg0ApXdERkYGtm7dqlH28uXLWLRoEdatW6ezD7///jsOHTqk5UmRn5j6KHCxF6JGgJtBJSZBmAMp94gCwdLu1fDXV01Ru6xHfnfFJhDwefh9dD2s7h2e310hiAJBzkEUy7J6B1Zc5dW3R0ZGolevXqhatSrq1auHHTt2oHz58li6dClneyNGjMCVK1ewbds2ncecO3euytXFxcUF/v7+Rp0bUXjYPbQ25rSvjLoWftdt7FdD9T2qjLvGvlUrV+Dqd7G4NbctUjaMxseHf+ttyxLJZqZPn661P6dbO0GYirtagpneUQGcZUxxp1WAh5/lDdEwcxFWyFojkxWgHv9vHBBNht2/+3LbXYKwCi2r+GJm20roXrMU7s75DIfH1gcAvIMDhkrHYLa0J2QsD+35f2KPaBoCmRSN+lvO/Ye0D1Ktds+ePQsXFxfUqpWdnTcyMhIuLi46PSHu3buH1NRUDe8JsViM6OhojToZGRno3r07li1bpvNd8PTpUwwcOBA//fQTHBwcjL8gVibM3zW/u0AQKki5R1gcazj8MAxjdFbAokLWhIggChteTpYL8u9c3A18Pl/LSu/Zs2da1nxZ+Pj44NmzZ1rlBQIB3N3dOevweDzUqFGD03Jv5MiR2Lt3L44fP66K5cfF5MmTkZaWpvo8fPjQ0OkRhYzw0sXRo1Ypiz/bK5VwwR9fNMTYJuXxnVrMH/VkM+v3HoO4ZCU82zkdsvRnnO1YKtkMAFSqVElj/9WrVy16zkTRQ11svvwshDMJR6ZMbnK77+CAb2Xd0USyAPvlNfEGxaAI0HZ/JAhboXdUAOZ2qAwej0E5bydMbRnyKQETgzXylughmYJnrCuCeQ+xVzQVzXjnVXWn7Pkb43Yka7WZmpoKLy8vre1eXl56PSEAGPSeGDt2LGrXro22bdtytsOyLPr27YshQ4YgIiLC0OmryAuPiNpBysU4c0MBGAtDGTUIIyAfDIIgCMKmmNU2FBKZwqTsiLoQCUUIDw/H4cOH0b59e9X2w4cP6xxERkVF4ddff9XYdujQIUREREAo5M5sxrIskpOTUblyZY1tI0eOxJ49e3DixAkEBgbq7atYLIZYTNlLCetQsrgDRjcpp7FNPdnMmdsv4NZkED7c+wtvkw6g3bjWWm1kJZu5desWKlSogBo1auDixYtYuHAhOnbsqCpnjCWeQCAgaz3CoqhPfkUCHqLLe2LDmfsaZTJlCrPbf8h6Y7h0DNyQjuP2rma3QxB5zYB6ZQAAUUHu6LD8DM6zIWiZ+Q2WiZbi91PJ+PHkTI3yawGs7Zv9+8KFCwC0vRrSMqR4kyFB+kdtSz919HlP7N27F8eOHdMbt3jp0qVIT0/H5MmT9R4nJ3PnzsWMGTNMqmMqIxqVhbeLHaLLeRouTBBWhiz3CIIgCJvCx8UOG/vVRMNg7RVic4iLi8OaNWuwbt063LhxA2PHjsWDBw8wZMgQAEqLud69e6vKDxkyRGU1d/PmTaxbtw5r167F+PHjVWVmzJiBgwcP4u7du0hOTkb//v2RnJysahMAhg8fjs2bN2Pr1q1wcnJCamoqUlNT8eHDB4ucF0HkBomEO9mMfWA1VGBSVNYI6lgy2cy///6LEiVKIDAwEN26dcPdu3ctdGYEoYTLAFbdLTemonlxi1/BGQzNoIgCSPVSxVUxWJ+jOHpKvoRT9Va4MdwRN4Y7YtfQiqg2cA5KDFih+tT5YhOCQypC4FgcT58+1Whv0v+u4MWLF9hxTTs5BgDVAo4+74ljx47hzp07cHV1hUAggECgtD3q2LEjGjRooCqTmJgIsVgMgUCAsmXLAgAiIiLQp08fneebFx4RdkI+YiNLo5S77bgKE0UXstwjLA4l4iMIwpbo2rUrXr58iZkzZyIlJQWhoaE4cOAASpcuDQBISUnBgwcPVOUDAwOxc+dOtGzZEnXr1kWJEiWwZMkSDcukN2/eYNCgQUhNTYWLiwuqVauGU6dOoWbNmqoyK1asAADV4DSL9evXo2/fvtY7YYIAYMiD58WLF5zJZviOxZGWyh0cPTU1Vet+Vk824+vrq0o2U7lyZaSnp+P7779HnTp1cPnyZZQrp7QcrFWrFjZt2oTy5cvj6dOnmD17NmrXro1r167pdH3PzMxEZmam6jclnCEMweXePrxhWdx+9g6dwktiYP0yOHrjKfpvvGhSu36u9nAU0RSKKJhs7FcDLZcoY6fKIMBSUT/csgvFAuGPCGYeoQm7BF9IByFBoRzPPAIQOus4pC8YpKWl4fz586qxzsHjf4DNfI+PxctyHiswMBA+Pj44fPgwgoKUSRElEglOnjyJb7/9FgAwadIkDBgwQKNe5cqVsXjxYrRurbQgX7JkCWbPnq3a/+TJEzRr1gw///yzRgzAnBQmj4jijtyeIwShDr2ZCIvDWiXqHqGL169fY9SoUdi7dy8AoE2bNli6dClcXV111mFZFjNmzMCqVasAAC1btsTKlStRqVIlAMD9+/d1uhDu2LFDFXPp1q1bmDBhAv78809IJBJUrlwZs2fPRsOGDS14hgSRe4YNG4Zhw4Zx7tuwYYPWtrp16wIAnj9/DmdnZ639ixcvxuLFi/Uek6WVDiI/MfL201KAWCDZTGRkpGp/nTp1UL16dSxduhRLliwBALRo0UK1v3LlyoiKikJQUBA2btyIuLg4zuPmhXsVUbDJeduGly4OAAjydETzUB98kChQsYQzDn5KMAAAjUO8sXtobXRcwZ0QgItDY+tTtlyiwFKphIvWtoOKmrgmCcQS4VJU593GSlE8fpI1wWxZL2RCGfNc6OEPhzLhaNe9N7asXwN7kQBP9sXDPqgGhO7Z8YSDg4Mxd+5ctG/fHgzDYMyYMZgzZw78/PwAAEOHDoWDgwN69OgBQGndxxWioVSpUqq5SKlSpTT2FStWDAAQFBSkN5ZxYeD7bmFISfuIYB/tsShB5ISMygmigNOjRw8kJycjISEBCQkJSE5ORmxsrN468+fPx6JFi7BgwQIAymC4TZs2xdu3SrN6f39/rWDoM2bMgKOjo8akrGXLlpDJZDh27BguXbqEsLAwtGrVSmdgXYLIayjnDEFw4+HhwZlsRp7xJs+SzWTh6OiIypUr6y1DCWcIQ+R83hcTC/DPrOY4OKY+JjQLxtetK3LWK+Wm7U7XsTq3wmBO+8pwFJNtBFG4aFbJG49YT3SRfI0VMqW1XKzgCH4RfY0g5rGqnFur8Ui380WL5s0RExMDsVcgPFqN02jr5s2bSEtLU/2eOHEixowZg3HjlOVSUlJw6NAhODk55cGZFXzahvlhSHRQfneDKCCQco8gCjA3btxAQkIC1qxZg6ioKERFRWH16tXYt28fbt7kdqtiWRbx8fGYMmUK2rRpA0AZJD0jIwNbt24FAPD5fNVKWtZnz5496Nq1q2q17MWLF7h9+zYmTZqEKlWqoFy5cpg3bx4yMjJw7dq1vLkABEEQBCc9I5WWDjUD3Dj3i0TZyWbU+Xg/GbVr1+asExUVhePHj2tsMzbZjK+vr86+ZmZm4saNG3rLiMViODs7a3wIQp3S7o5a2+yEfAj4+qc7Ag4rPF2W1z1qleLcThAFiX0j62JCswpY1zcCwxsGfcqkq3TT/VbWHb0lX+AF64wQ3gP8JpqKTvyTAFjw7Z3g0Xo8yk/cjWcvX8Ov/UTw7IpptJ2V2TYLhmEwffp03Lp1CwBw4MABhIaG6u0fy7Jo166dzv0BAQFgWRZhYWHmnL7JLOleDS1CKQEUYfvQ0hNhecgTLc84e/YsXFxcNOJNREZGwsXFBWfOnEGFChW06ty7dw+pqakaQdTFYjGio6Nx5swZDB48WKvOpUuXkJycjB9++EG1zd3dHSEhIdi0aROqV68OsViMVatWwdvbG+Hh4Tr7THGTCIIgrE+bqiUQ7OOMAA/dQb7j4uIQGxuLiIgI2JUIxqujqyFLf66RbObx48fYtGkTAGWymWXLlgFQWmdcvXoVa9euxbZt21RtzpgxA5GRkShXrhzS09OxZMkSrffH+PHj0bp1a5QqVQrPnj3D7NmzkZ6erjcwOkHoYtvASBy/+Qyf1wkwqz6fr63cU3Ao93xd7MxqnyBsjVA/F4T6Kd1zGwV74+GrDI39pxRV0SJzLhYLl6Mu/xoWClehLu8qvpZ+jnQ4Iv2jDMFfJWjUUShY8Aqpu3qbqiUQ4O6A3/8mzyTCtiHlHkEUYFJTU+HlpZ1R1MvLS6drbNb2nG5X3t7e+O+//zjrrF27FiEhIRrWHAzD4PDhw2jbti2cnJzA4/Hg7e2NhIQEvfH+KG4SQRCE9WEYBhV89Ls9qSebefIkBYybP7w6T7d6splHjx6he/fuePHiBTw9PREZGYnExETVcQnCFKKC3BEVxO0WbgxclnsKjoVqLoUfQRQGSha3R7NK3jh4LTsb7nMUR2/pZAxR7EWcYBfa8c+gBu8mxkuH4KyiklYbi4/cwrgYbaOCwgLXM4EgbA1yyyUIG2T69OlgGEbv5+JFZXY3rsDnrIGA6Fz1dNX58OEDtm7div79+2uVHzZsGLy8vHD69GmcP38ebdu2RatWrZCSkqLzuBQ3iSAIwnYYNmwY7t+/j+PXH8O37/ew8892l9qwYQNOnDihUV492cy9e/dUVn5ZLF68GP/99x8yMzPx7NkzHDx4EFFRURpltm/fjidPnkAikeDx48fYvXs3KlbkjodGENaGxzH24VLkkW6PKKwwDINVsRFa2xXgYbm8HTpLpuG+wht+zEtsEc7BZMEWiCDVKLv02G10WXkWOy8WznE9JUkjCgJkuUdYHHr05Z4RI0agW7duessEBATgypUrePr0qda+58+f6w2IDigt+LLS0gPKoOhcdXbt2oWMjAz07t1bY/uxY8ewb98+vH79WhX7aPny5Th8+DA2btyISZMmcR6/MKWlJ2wfBoXTRYQgLA3NW4iiipAjJh+XOJDlDlHY+eOLhtjz12N8d/iWxvYkthw+k8zFVMFP6CE4jsGC/ajPu4ox0mG4yWbHoTx//xUaBHvmdbfzBC75d7HnjjVLEPkFWe4RhA3i4eGB4OBgvR87OztERUUhLS0N58+fV9U9d+4c0tLSdAZEDwwMhI+Pj0YQdYlEgpMnT3LWWbt2Ldq0aQNPT82XdUaGMj4Hj6f5GOHxeFAoFGafO0EQBEEQRF7B5zFYFRuO77uFqbZxWemQ5Q5R2ClZ3AF9c8Su/H10PdQr54EM2OFL2UAMkIxTJdvYK5qK/vz9YJA97vdwLKwL+NryP6Jh2XzoB0HohpR7BFGACQkJQfPmzTFw4EAkJiYiMTERAwcORKtWrTSSaQQHB2PPnj0AlKb3Y8aMwZw5c/Dbb78BAIYOHQoHBwf06NFDo/3bt2/j1KlTGDBggNaxo6KiULx4cfTp0weXL1/GrVu3MGHCBNy7dw8tW7a04lkTBEEQBEFYjmaVfNA2zE/1O8zfFWcnN9IoQzH3iKKAk50Qf89ohuszm+Gvr5oixNcZdct6qPYfUYSjeea3OCqvBjEjw1fCLdgsnAtfvAQAuBcT5VfXrQqX5Z6BCEgEkeeQco+wOLSymbds2bIFlStXRkxMDGJiYlClShX89NNPGmVu3ryJtLQ01e+JEydizJgxGDduHABl0PRDhw7ByUkz+Pq6devg5+enkVk3Cw8PDyQkJODdu3do1KgRIiIi8Mcff+DXX39F1apVrXCmBGE6NPAiCIIgjOVIXDSmt66IvrUD4etir7GPRrdEUaGYWAAHkQBujkpF3YX7rzX2BwUGor90PL6U9kcGK0Yd/jUcFE9EJ/5JFHcoHK6qYoGmmkRBfvlEAaDAKPdev36N2NhYuLi4wMXFBbGxsXjz5o3eOizLYvr06ShRogTs7e3RoEEDXLt2TaPMnTt30L59e3h6esLZ2RldunTRimFmzrEJIq9wc3PD5s2bkZ6ejvT0dGzevFkrWy3Lsujbt6/qN8MwmD59Om7dUsbUOHDgAEJDQ5GTOXPm4OHDh1qut1lERETg4MGDePnyJdLT03H27Fm0aNHCYudGEARBEASRV5T1Koa+dQIh+jSxd7bLDk9Ok3uiqJIpk2v8HlCvDAAGW+WN0VIyB38pysKZ+YCFwlWoenlm/nTSwtQMdNP4TdJPFAQKjHKvR48eSE5ORkJCAhISEpCcnIzY2Fi9debPn49FixZh2bJluHDhAnx8fNC0aVO8ffsWAPD+/XvExMSAYRgcO3YMf/75JyQSCVq3bq0RM8ycYxMEQRAEQRAEUXD5qX8t1Xeuyf3y5csRGBgIOzs7hIeH48yZM3rbO3nyJMLDw2FnZ4cyZcpg5cqVWmWyskeLxWJUrFhRFVaFIPILdSu2I3H1IeRnu0XcY33RSTId86TdkMkKwQ9tlw89tDw1AjSVe3weuYIQtk+ByJZ748YNJCQkIDExEbVqKV+yq1evRlRUFG7evKkRWywLlmURHx+PKVOmoEOHDgCAjRs3wtvbG1u3bsXgwYPx559/4v79+0hKSlJl+1y/fj3c3Nxw7NgxNGnSxKxjF3XIK5cgCIIgChYs2SUQhBZV/V1V33OOb3/++WeMGTMGy5cvR506dbBq1Sp06tRJZ1v37t3DZ599hoEDB2Lz5s34888/MWzYMHh6eqJjx44AgLNnz6Jr166YNWsW2rdvjz179qBLly74448/VPMQgshr7IR81feyXk549PqDxn4FeFgpb4Pd8vq4UKZBHvfOcngUE+HFOwkAYHB0GbjYC1GvnDLeYPVSxVGnrDsCPRyxOfFBfnaTIHRSICz3zp49CxcXF42XWmRkJFxcXHSukN27dw+pqakascLEYjGio6NVdTIzM8EwDMTi7Kw+dnZ24PF4+OOPP8w+dlbbWW6SWR+CIAgib6F1VoIgCMIS5EyosWjRIvTv3x8DBgxASEgI4uPj4efnp6M2sHLlSpQqVQrx8fEICQnBgAED0K9fPyxcuFBVJj4+Hk2bNsXkyZMRHByMyZMno3HjxoiPj7fWaRGEQSZ/FoLKfi74rrMypjajFtD4+sxm6BVZCgDwHK750T2LsWNwFGIqemPfyLoQC/joUzsAZTyLAVBa7m0ZEInZ7Srncy8JQjcFQrmXmpoKLy8vre1eXl5ITU3VWQcAvL29NbZ7e3ur9kVGRsLR0RFffPEFMjIy8P79e0yYMAEKhQIpKSlmHxsA5s6dq4rR5+LiAn9/f+NOthDQsoovAMDfzd5ASYIgCOsSVso1v7tAEAWCir7O+d0FgrBJukSUBACMalxOtU0ikeDSpUtaCccaNdLMsKvO2bNntco3a9YMFy9ehFQq1VvGkLsvQVgTP1d7/DayLjqGK2UhtET2+8JBJMCkFiHoFVkKOwZH5VcXLUIZz2L4sXcEQv1c9JZzsVcmDWlQwTMvukUQRpOvbrnTp0/HjBkz9Ja5cOECAM0VgixYluXcrk7O/ep1PD09sXPnTgwdOhRLliwBj8dD9+7dUb16dfD5fJ1tGHPsyZMnIy4uTvU7PT29yCj4qpUqjhPjG8Db2S6/u0IQRBHlyvQYpH+QamU7JAiCG/diYpyZ1AiOogIRsYUg8oy5HapgYL0yKOtVTLXtxYsXkMvlWkYEnp66J/upqamcRgcymQwvXryAr6+vzjL6DAoyMzORmZmp+k3eQoS1yfm+KCYWFCmLtjOTGuHlOwlKuTvkd1cIQoN8HcGNGDEC3bp101smICAAV65c0cpgCwDPnz/XegFm4ePjA0D5IvX19VVtf/bsmUadmJgY3LlzBy9evIBAIICrqyt8fHwQGBioasfUYwNKF2B1d9+iRoCHY353gSCIIoyznRDOdsL87gZBFChKuJIynCBywucxKOftxLmPy4hAH7rKq2/XZ5jAxdy5cw0aSxCEpSnK7wtHsQCOYloII2yPfL0rPTw84OHhYbBcVFQU0tLScP78edSsWRMAcO7cOaSlpaF27dqcdQIDA+Hj44PDhw+jWrVqAJQm9CdPnsS3337L2RcAOHbsGJ49e4Y2bdqYfWyCIAiCIAiCIAonHh4e4PP5WhZ1L1680FnHx8dHq/yzZ88gEAjg7u6ut4w+g4Ki7C1EEARBZFMgYu6FhISgefPmGDhwIBITE5GYmIiBAweiVatWGtlqg4ODVeniGYbBmDFjMGfOHOzZswd///03+vbtCwcHB/To0UNVZ/369UhMTMSdO3ewefNmdO7cGWPHjlW1a+yxCYIgCIIgCIIo/IhEIoSHh+Pw4cMa248fP66zTlRUlFb5Q4cOISIiAkKhUG8ZfQYFYrEYzs7OGh+CIAii6FFg7Em3bNmCUaNGqYLMtmnTBsuWLdMoc/PmTaSlpal+T5w4ER8+fMCwYcPw+vVr1KpVC4cOHYKTk5NGncmTJ+PVq1cICAjAlClTMHbsWJOPTRAEQRAEQRBE0SAuLg6xsbGIiIhAVFQUfvzxRzx69Ei1f/LkyXj8+DE2bdoEABgyZAiWLVuGuLg4DBw4EGfPnsXatWuxbds2VZ3Ro0ejfv36+Pbbb9G2bVv8+uuvOHLkCP744488Pz+CIAiiYMGwhoJDEBYhPT0dLi4uSEtLoxU1wmawhfvSFvpAEDmxhfvSFvpAEDmxlfvSVvpBFG2WL1+O+fPnIyUlBaGhoZg9ezY+++wzpKWlYdSoUbh//z5OnDihKn/y5EmMHTsW165dQ4kSJfDFF19gyJAhGm3u2rULU6dOxd27dxEUFIRvvvkGHTp0MLpPJBuELWIL96Ut9IEgcmLJ+7LAWO4RBEEQBEEQBEHYCsOGDcOwYcNUv9Uz1W7YsEGrfHR0NP766y+9bXbq1AmdOnWyWB8JgiCIogEp9/KILANJSk9P2BJZ92N+GvCSbBC2CMkGQXBjC7KhfnySD8KWsAX5INkgbBGSDYLgxpKyQcq9POLt27cAQNmrCJvk7du3cHFxybdjAyQbhG1CskEQ3OSnbGQdHyD5IGwTencQBDckGwTBjSVkg2Lu5REKhQJPnjyBk5MTGIbR2JeVsv7hw4fk/28EdL2Mx9C1YlkWb9++RYkSJcDj5U/ybH2yAdD/2xToWpmGvutFslG4oGtlGrYuGwCNqywJXS/jKejjKvpfmwZdL+Mp6LIB0P/bFOhamUZejavIci+P4PF4KFmypN4ylL7eNOh6GY++a5WflheAcbIB0P/bFOhamYau60WyUfiga2UatiobAI2rrAFdL+Mp6OMq+l+bBl0v4ynosgHQ/9sU6FqZhrXHVfm35EoQBEEQBEEQBEEQBEEQRK4g5R5BEARBEARBEARBEARBFFBIuWcDiMViTJs2DWKxOL+7UiCg62U8heFaFYZzyCvoWplGQb9eBb3/eQldK9Mo6NeroPc/r6HrZTwF/VoV9P7nNXS9jKcwXKvCcA55BV0r08ir60UJNQiCIAiCIAiCIAiCIAiigEKWewRBEARBEARBEARBEARRQCHlHkEQBEEQBEEQBEEQBEEUUEi5RxAEQRAEQRAEQRAEQRAFFFLuEQRBEARBEARBEARBEEQBhZR7VmL58uUIDAyEnZ0dwsPDcfr0ab3lT548ifDwcNjZ2aFMmTJYuXKlVpndu3ejYsWKEIvFqFixIvbs2WOt7ucplr5WGzZsAMMwWp+PHz9a8zTyDFOuV0pKCnr06IEKFSqAx+NhzJgxnOXy8t4i2TAekg3TINkoOrIBkHyYQkGXDYDkwxRINkyjoMsHyYbxkGyYBslG0ZENgOTDFGxWNljC4mzfvp0VCoXs6tWr2evXr7OjR49mHR0d2f/++4+z/N27d1kHBwd29OjR7PXr19nVq1ezQqGQ3bVrl6rMmTNnWD6fz86ZM4e9ceMGO2fOHFYgELCJiYl5dVpWwRrXav369ayzszObkpKi8SkMmHq97t27x44aNYrduHEjGxYWxo4ePVqrTF7eWyQbxkOyYRokG0VHNliW5MMUCrpsmHMORVk+SDZMo6DLB8mG8ZBsmAbJRtGRDZYl+TAFW5YNUu5ZgZo1a7JDhgzR2BYcHMxOmjSJs/zEiRPZ4OBgjW2DBw9mIyMjVb+7dOnCNm/eXKNMs2bN2G7dulmo1/mDNa7V+vXrWRcXF4v31RYw9XqpEx0dzfkwyct7i2TDeEg2TINko+jIBsuSfJhCQZcNliX5MAWSDdMo6PJBsmE8JBumQbJRdGSDZUk+TMGWZYPcci2MRCLBpUuXEBMTo7E9JiYGZ86c4axz9uxZrfLNmjXDxYsXIZVK9ZbR1WZBwFrXCgDevXuH0qVLo2TJkmjVqhWSkpIsfwJ5jDnXyxjy6t4i2TAekg3TINnI7lthlw2A5MMUCrpsACQfpkCyYRoFXT5INoyHZMM0SDay+1bYZQMg+TAFW5cNUu5ZmBcvXkAul8Pb21tju7e3N1JTUznrpKamcpaXyWR48eKF3jK62iwIWOtaBQcHY8OGDdi7dy+2bdsGOzs71KlTB//++691TiSPMOd6GUNe3VskG8ZDsmEaJBvZ5Qu7bAAkH6ZQ0GUDIPkwBZIN0yjo8kGyYTwkG6ZBspFdvrDLBkDyYQq2LhsCs3tA6IVhGI3fLMtqbTNUPud2U9ssKFj6WkVGRiIyMlK1v06dOqhevTqWLl2KJUuWWKrb+YY17oO8vLdINoyHZMM0SDaKjmwAJB+mUNBlw5zjFWX5INkwjYIuHyQbxkOyYRokG0VHNgCSD1OwVdkgyz0L4+HhAT6fr6VlffbsmZY2NgsfHx/O8gKBAO7u7nrL6GqzIGCta5UTHo+HGjVqFOhVAsC862UMeXVvkWwYD8mGaZBsZJcv7LIBkHyYQkGXDYDkwxRINkyjoMsHyYbxkGyYBslGdvnCLhsAyYcp2LpskHLPwohEIoSHh+Pw4cMa2w8fPozatWtz1omKitIqf+jQIUREREAoFOoto6vNgoC1rlVOWJZFcnIyfH19LdPxfMKc62UMeXVvkWwYD8mGaZBsZPetsMsGQPJhCgVdNgCSD1Mg2TCNgi4fJBvGQ7JhGiQb2X0r7LIBkHyYgs3LhknpNwijyEqPvHbtWvb69evsmDFjWEdHR/b+/fssy7LspEmT2NjYWFX5rFTSY8eOZa9fv86uXbtWK5X0n3/+yfL5fHbevHnsjRs32Hnz5hWK1NvWuFbTp09nExIS2Dt37rBJSUns559/zgoEAvbcuXN5fn6WxtTrxbIsm5SUxCYlJbHh4eFsjx492KSkJPbatWuq/Xl5b5FsGA/JhmmQbBQd2WBZkg9TKOiyYc45FGX5INkwjYIuHyQbxkOyYRokG0VHNliW5MMUbFk2SLlnJX744Qe2dOnSrEgkYqtXr86ePHlSta9Pnz5sdHS0RvkTJ06w1apVY0UiERsQEMCuWLFCq82dO3eyFSpUYIVCIRscHMzu3r3b2qeRJ1j6Wo0ZM4YtVaoUKxKJWE9PTzYmJoY9c+ZMXpxKnmDq9QKg9SldurRGmby8t0g2jIdkwzRINoqObLAsyYcpFHTZMOccirJ8kGyYRkGXD5IN4yHZMA2SjaIjGyxL8mEKtiobzKeDEQRBEARBEARBEARBEARRwKCYewRBEARBEARBEARBEARRQCHlHkEQBEEQBEEQBEEQBEEUUEi5RxAEQRAEQRAEQRAEQRAFFFLuEQRBEARBEARBEARBEEQBhZR7BEEQBEEQBEEQBEEQBFFAIeUeQRAEQRAEQRAEQRAEQRRQSLlHEARBEARBEARBEARBEAUUUu4RBEEQBEEQBEEQBEEQRAGFlHsEQRAEQRAEQRAEQRAEUUAh5R5BEARBEARBEARBEARBFFBIuUcQBEEQBEEQBEEQBEEQBRRS7hEEQRAEQRAEQRAEQRBEAeX/RugOcKVos98AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "results = check_accuracy(exact_fit_exp)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def fit_linear(t, y):\n", + " \"\"\"Return slope, intercept, and mse of the best fit line to the data\"\"\"\n", + " result = np.polyfit(t, y, 1, full=True)\n", + " slope, intercept = result[0]\n", + " residuals = result[1]\n", + " mse = residuals[0] / len(y)\n", + " return slope, intercept, mse\n", + "\n", + "def fit_exp(t, y):\n", + " \"\"\"Return yoffset, yscale, tau\n", + "\n", + " Assume y = yoffset + yscale * exp(-t / tau)\n", + "\n", + " \"\"\"\n", + " # first estimate yoffset and yscale\n", + " yoffset = -0.09#y[-len(y)//10:].mean()\n", + " yscale = np.max(y) - yoffset\n", + " ymax = scipy.stats.scoreatpercentile(y, 50)\n", + " print(\"yscale\", yscale)\n", + " print(\"yoffset\", yoffset)\n", + " print(\"ymax\", ymax)\n", + " t = t - t[0]\n", + "\n", + " # first fine the yoffset that best linearizes the data\n", + "\n", + " log = []\n", + " best_result = None\n", + " def err(params):\n", + " nonlocal best_result, log\n", + " # params are just yoffset\n", + " yoffset = params[0]\n", + "\n", + " # take log of y, and normalize to 1\n", + " y_adjusted = y - yoffset\n", + " mask = y_adjusted > 0\n", + " ye = np.log(y_adjusted[mask])\n", + "\n", + " # fit a line to the log of the data\n", + " slope, intercept, mse = fit_linear(t[mask], ye)\n", + " tau = -1 / slope\n", + " yscale = np.exp(intercept)\n", + " params = (yoffset, yscale, tau)\n", + "\n", + " # calculate mse from the fit to original data\n", + " fit = yoffset + yscale * np.exp(-t / tau)\n", + " mse = np.mean((fit - y)**2)\n", + "\n", + " log.append((params, mse))\n", + " if best_result is None or mse < best_result[0]:\n", + " best_result = (mse, params)\n", + " return mse\n", + "\n", + " for yoffset in np.linspace(-0.0781, -0.0779, 11):\n", + " err((yoffset,))\n", + " # result = scipy.optimize.minimize(\n", + " # fun=err, \n", + " # x0=[yoffset-yscale], \n", + " # bounds=[(None, ymax)],\n", + " # tol=1e-12,\n", + " # options={'ftol': 1e-18, 'gtol': 1e-18, 'maxiter': 1000},\n", + " # method='L-BFGS-B',\n", + " # # method='Nelder-Mead',\n", + " # # method='Powell',\n", + " # # method='SLSQP',\n", + " # )\n", + " # print(result.message)\n", + " return best_result[1], log" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'y' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fit, log \u001b[38;5;241m=\u001b[39m fit_exp(t, y)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mshow_fit\u001b[39m(data, fit, ax1, ax2, ax3):\n\u001b[1;32m 5\u001b[0m fit_yoffset, fit_yscale, fit_tau \u001b[38;5;241m=\u001b[39m fit\n", + "\u001b[0;31mNameError\u001b[0m: name 'y' is not defined" + ] + } + ], + "source": [ + "\n", + "fit, log = fit_exp(t, y)\n", + "\n", + "\n", + "def show_fit(data, fit, ax1, ax2, ax3):\n", + " fit_yoffset, fit_yscale, fit_tau = fit\n", + " t = data.time_values - data.time_values[0]\n", + " fit_y = fit_yoffset + fit_yscale * np.exp(-t / fit_tau)\n", + " def map(y):\n", + " return np.log((y - fit_yoffset) / fit_yscale)\n", + "\n", + " ax1.plot(data.time_values, map(data.data), label='data')\n", + " ax1.plot(data.time_values, map(fit_y), label='fit', color='r')\n", + " ax2.plot(data.time_values, data.data, label='data')\n", + " ax2.plot(data.time_values, fit_y, label='fit', color='r')\n", + " err = (data.data - fit_y) ** 2\n", + " mse = np.mean(err)\n", + " ax3.plot(data.time_values, err, label='mse')\n", + " ax3.set_xlabel(f'mse: {mse:.2g}')\n", + "\n", + "n = 7\n", + "fig = plt.figure(figsize=(16, 10))\n", + "gs = fig.add_gridspec(4, n)\n", + "ax1 = fig.add_subplot(gs[0, :])\n", + "ax1.set_yscale('log')\n", + "# 10 small plots in second row\n", + "axs = [fig.add_subplot(gs[1, i]) for i in range(n)]\n", + "axs2 = [fig.add_subplot(gs[2, i]) for i in range(n)]\n", + "axs3 = [fig.add_subplot(gs[3, i]) for i in range(n)]\n", + "# plot mse over time in top\n", + "ax1.plot([x[1] for x in log])\n", + "# plot fits in bottom\n", + "for i,step in enumerate(np.linspace(0, len(log)-1, n, dtype=int)):\n", + " ax1.axvline(step, color='k', alpha=0.5)\n", + " show_fit(data, log[step][0], axs[i], axs2[i], axs3[i])\n", + " if i > 0:\n", + " axs[i].set_yticks([])\n", + " axs2[i].set_yticks([])\n", + " axs[i].set_xticks([])\n", + " axs2[i].set_xticks([])\n", + " axs3[i].set_xticks([])\n", + " axs2[i].set_xlabel(\"%0.2g\" % log[step][1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "neuroanalysis", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/release_model.py b/examples/release_model.py index 99935a8..802ed41 100644 --- a/examples/release_model.py +++ b/examples/release_model.py @@ -38,8 +38,8 @@ dynamics_types = ['Dep', 'Fac', 'UR', 'SMR', 'DSR'] model.Dynamics = {k:0 for k in dynamics_types} -print "Initial parameters:", model.dict_params -print "Bounds:", model.dict_bounds +print("Initial parameters:", model.dict_params) +print("Bounds:", model.dict_bounds) # Fit the model 5 times. Each time we enable another gating mechanism. @@ -48,10 +48,10 @@ model.Dynamics[k] = 1 fit_params.append(model.run_fit(spike_sets)) -print "----- fit complete -----" +print("----- fit complete -----") for j,params in enumerate(fit_params): - print params + print(params) for i,spikes in enumerate(spike_sets): x, y = spikes output = model.eval(x, params.values()) diff --git a/examples/test_event_detection.py b/examples/test_event_detection.py index 7779989..becd94b 100644 --- a/examples/test_event_detection.py +++ b/examples/test_event_detection.py @@ -1,5 +1,5 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import numpy as np from neuroanalysis.data import TSeries from neuroanalysis.ui.event_detection import EventDetector @@ -15,7 +15,7 @@ evd = EventDetector() evd.params['threshold'] = 5e-10 -hs = QtGui.QSplitter(QtCore.Qt.Horizontal) +hs = QtWidgets.QSplitter(QtCore.Qt.Horizontal) pt = pg.parametertree.ParameterTree(showHeader=False) params = pg.parametertree.Parameter.create(name='params', type='group', children=[ diff --git a/examples/test_psp_train_fit.py b/examples/test_psp_train_fit.py index c41b9bf..6fa58f8 100644 --- a/examples/test_psp_train_fit.py +++ b/examples/test_psp_train_fit.py @@ -1,5 +1,6 @@ -import pyqtgraph as pg import numpy as np + +import pyqtgraph as pg from neuroanalysis.fitting import PspTrain from neuroanalysis.ui.fitting import FitExplorer @@ -15,18 +16,18 @@ args = { 'yoffset': (0, 'fixed'), 'xoffset': (2e-3, -1e-3, 5e-3), - 'rise_time': (rise_time, rise_time*0.5, rise_time*2), - 'decay_tau': (decay_tau, decay_tau*0.5, decay_tau*2), + 'rise_time': (rise_time, rise_time * 0.5, rise_time * 2), + 'decay_tau': (decay_tau, decay_tau * 0.5, decay_tau * 2), 'rise_power': (2, 'fixed'), } for i in range(n_psp): - args['xoffset%d'%i] = (25e-3*i, 'fixed') - args['amp%d'%i] = (250e-6, 0, 10e-3) + args['xoffset%d' % i] = (25e-3 * i, 'fixed') + args['amp%d' % i] = (250e-6, 0, 10e-3) -fit_kws = {'xtol': 1e-4, 'maxfev': 1000, 'nan_policy': 'omit'} +fit_kws = {'xtol': 1e-4, 'maxfev': 1000, 'nan_policy': 'omit'} model = PspTrain(n_psp) -args2 = {k:(v[0] if isinstance(v, tuple) else v) for k,v in args.items()} +args2 = {k: (v[0] if isinstance(v, tuple) else v) for k, v in args.items()} y = np.random.normal(size=len(t), scale=30e-6) + model.eval(x=t, **args2) fit = model.fit(y, x=t, params=args, fit_kws=fit_kws, method='leastsq') diff --git a/examples/test_pulse_analysis.ipynb b/examples/test_pulse_analysis.ipynb new file mode 100644 index 0000000..7844de0 --- /dev/null +++ b/examples/test_pulse_analysis.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas\n", + "from neuroanalysis.data import TSeries\n", + "from neuroanalysis.test_pulse import PatchClampTestPulse\n", + "from neuroanalysis.neuronsim.model_cell import ModelCell\n", + "from neuroanalysis.units import pA, mV, MOhm, us, ms, pF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_cell = ModelCell()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "plt.close('all')\n", + "%matplotlib widget\n", + "# mpl.use('Qt5Agg')\n", + "import pyqtgraph as pg\n", + "%gui qt" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-21T00:20:47.749340Z", + "start_time": "2024-03-21T00:20:47.735882Z" + } + }, + "outputs": [], + "source": [ + "def test_test_pulse():\n", + " # Just test against a simple R/C circuit attached to a pipette\n", + " model_cell.enable_mechs(['leak'])\n", + " model_cell.recording_noise = False\n", + " \n", + " tp = create_test_pulse(pamp=-10*pA, mode='ic', r_access=100*MOhm) \n", + " check_analysis(tp, model_cell)\n", + "\n", + "\n", + "def create_test_pulse(start=5*ms, pdur=10*ms, pamp=-10*pA, mode='ic', dt=10*us, r_access=10*MOhm, noise=5*pA, cpip=0.5*pF, cmem=50*pF):\n", + " # update patch pipette access resistance\n", + " model_cell.clamp.ra = r_access\n", + " model_cell.clamp.cpip = cpip\n", + " model_cell.soma.cap = cmem\n", + " \n", + " # update noise amplitude\n", + " model_cell.mechs['noise'].stdev = noise\n", + " \n", + " # make pulse array\n", + " duration = start + pdur * 3\n", + " pulse = np.zeros(int(duration / dt))\n", + " pstart = int(start / dt)\n", + " pstop = pstart + int(pdur / dt)\n", + " pulse[pstart:pstop] = pamp\n", + " \n", + " # simulate response\n", + " result = model_cell.test(TSeries(pulse, dt), mode)\n", + " \n", + " # generate a PatchClampTestPulse to test against\n", + " tp = PatchClampTestPulse(result)\n", + " for ch in tp.channels:\n", + " tp[ch].t0 =0\n", + "\n", + " return tp\n", + "\n", + "\n", + "def expected_testpulse_values(cell):\n", + " if cell.clamp.mode == 'ic':\n", + " values = {\n", + " 'baseline_potential': model_cell.resting_potential(),\n", + " 'baseline_current': model_cell.clamp.holding['ic'],\n", + " 'access_resistance': model_cell.clamp.ra,\n", + " 'capacitance': model_cell.soma.cap,\n", + " }\n", + " else:\n", + " values = {\n", + " 'baseline_potential': model_cell.clamp.holding['vc'],\n", + " 'baseline_current': model_cell.resting_current(),\n", + " 'access_resistance': model_cell.clamp.ra,\n", + " 'capacitance': model_cell.soma.cap,\n", + " }\n", + " values['input_resistance'] = model_cell.input_resistance()\n", + "\n", + " return values\n", + "\n", + "\n", + "def check_analysis(tp: PatchClampTestPulse, cell: ModelCell):\n", + " measured = tp.analysis\n", + " expected = expected_testpulse_values(cell)\n", + " \n", + " # how much error should we tolerate for each parameter?\n", + " err_tolerance = {\n", + " 'baseline_potential': 0.01,\n", + " 'baseline_current': 0.01,\n", + " 'access_resistance': 0.3,\n", + " 'input_resistance': 0.1,\n", + " 'capacitance': 0.3,\n", + " }\n", + " \n", + " for k, v1 in expected.items():\n", + " v2 = measured[k]\n", + " if v1 is None:\n", + " assert v2 is None, f\"Test pulse metric {k} expected None, got {v2}\"\n", + " continue\n", + " elif v2 is None:\n", + " raise ValueError(f\"Test pulse metric missing: {k} expected {v1:g}\")\n", + " elif v2 == 0:\n", + " err = abs(v1 - v2)\n", + " else:\n", + " err = abs((v1 - v2) / v2)\n", + " if err > err_tolerance[k]:\n", + " raise ValueError(\n", + " f\"Test pulse metric out of range: {k} expected {v1:g}, got {v2:g} (err {err:g} > {err_tolerance[k]:g})\")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Just test against a simple R/C circuit attached to a pipette\n", + "model_cell.enable_mechs(['leak', 'lgkfast', 'lgkslow', 'lgkna'])\n", + "model_cell.recording_noise = False\n", + "\n", + "tp = create_test_pulse(pamp=-10*pA, mode='ic', r_access=10*MOhm)\n", + "tp._analyze()\n", + "\n", + "name, units = ('pipette potential', 'V') if tp.clamp_mode == 'ic' else ('pipette current', 'A')\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "for ax in axes:\n", + " ax.plot(tp['primary'].time_values, tp['primary'].data, label='primary')\n", + " ax.plot(tp.initial_fit_trace.time_values, tp.initial_fit_trace.data, 'g', label='initial fit')\n", + " ax.plot(tp.fit_trace.time_values, tp.fit_trace.data, 'r', label='final fit')\n", + " ax.set_xlabel('time (s)')\n", + " ax.set_ylabel(name + ' (' + units + ')')\n", + " ax.legend()\n", + "\n", + "# plot an expanded region near the pulse start\n", + "pstart = tp['primary'].t0 + tp.stimulus.start_time\n", + "axes[1].set_xlim(pstart - .1*ms, pstart + .3*ms)\n", + "axes[1].set_ylim(-.0755, -.0749)\n", + "\n", + "\n", + "# check_analysis(tp, model_cell)\n", + "\n", + "# print(\"Vm %g mV Rm %g MOhm\" % (model_cell.resting_potential()*1000, model_cell.input_resistance()/1e6))\n", + "\n", + "# # Have to test VC with very low access resistance\n", + "# tp = create_test_pulse(pamp=-10*mV, mode='vc', r_access=15*MOhm)\n", + "# tp.plot()\n", + "\n", + "check_analysis(tp, model_cell)\n", + "print(\"Test passed\")\n", + "\n", + "df = pandas.DataFrame()\n", + "for k,v in expected_testpulse_values(model_cell).items():\n", + " df[k] = [v, tp.analysis[k]]\n", + "# label df rows\n", + "df.index = ['IC expected', 'IC measured']\n", + "display(df)\n", + "df\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "check_analysis(tp, model_cell)\n", + "print(\"Test passed\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2024-03-21T00:24:52.654097Z", + "start_time": "2024-03-21T00:24:52.296217Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([-5.12652542e-10, 1.00000000e-05, 5.00000000e-03]), array([[ 9.28505271e-22, 1.37963833e-17, -4.76284139e-26],\n", + " [ 1.37963833e-17, 1.72043278e-12, -7.07353997e-22],\n", + " [-4.76284139e-26, -7.07353997e-22, 2.44313732e-30]]))\n" + ] + }, + { + "data": { + "text/plain": " baseline_potential baseline_current access_resistance \\\nIC expected -0.075 -4.331865e-14 1.000000e+07 \nIC measured -0.075 -4.315473e-14 1.564777e+07 \n\n capacitance input_resistance \nIC expected 1.000000e-11 5.998909e+08 \nIC measured 5.898797e-12 6.115075e+08 ", + "text/html": "
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baseline_potentialbaseline_currentaccess_resistancecapacitanceinput_resistance
IC expected-0.075-4.331865e-141.000000e+071.000000e-115.998909e+08
IC measured-0.075-4.315473e-141.564777e+075.898797e-126.115075e+08
\n
" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "ename": "ValueError", + "evalue": "Test pulse metric out of range: access_resistance expected 1e+07, got 1.56478e+07 (err 0.360931 > 0.3)", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValueError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[23], line 34\u001B[0m\n\u001B[1;32m 31\u001B[0m df\u001B[38;5;241m.\u001B[39mindex \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mIC expected\u001B[39m\u001B[38;5;124m'\u001B[39m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mIC measured\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[1;32m 32\u001B[0m display(df)\n\u001B[0;32m---> 34\u001B[0m \u001B[43mcheck_analysis\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtp\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mmodel_cell\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 35\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTest passed\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "Cell \u001B[0;32mIn[19], line 82\u001B[0m, in \u001B[0;36mcheck_analysis\u001B[0;34m(tp, cell)\u001B[0m\n\u001B[1;32m 80\u001B[0m err \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mabs\u001B[39m((v1 \u001B[38;5;241m-\u001B[39m v2) \u001B[38;5;241m/\u001B[39m v2)\n\u001B[1;32m 81\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m err \u001B[38;5;241m>\u001B[39m err_tolerance[k]:\n\u001B[0;32m---> 82\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[1;32m 83\u001B[0m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mTest pulse metric out of range: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mk\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m expected \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mv1\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124mg\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, got \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mv2\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124mg\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m (err \u001B[39m\u001B[38;5;132;01m{\u001B[39;00merr\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124mg\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m > \u001B[39m\u001B[38;5;132;01m{\u001B[39;00merr_tolerance[k]\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124mg\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m)\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n", + "\u001B[0;31mValueError\u001B[0m: Test pulse metric out of range: access_resistance expected 1e+07, got 1.56478e+07 (err 0.360931 > 0.3)" + ] + }, + { + "data": { + "text/plain": "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …", + "image/png": 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+ "text/html": "\n
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\n Figure\n
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\n ", + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "9bbed7533efe4ac0a698e5ec8ecd016a" + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Just test against a simple R/C circuit attached to a pipette\n", + "model_cell.enable_mechs(['leak', 'lgkfast', 'lgkslow', 'lgkna'])\n", + "model_cell.recording_noise = False\n", + "\n", + "tp = create_test_pulse(pamp=-10*mV, mode='vc', r_access=10*MOhm, cpip=5*pF, cmem=10*pF)\n", + "tp._analyze()\n", + "\n", + "name, units = ('pipette potential', 'V') if tp.clamp_mode == 'ic' else ('pipette current', 'A')\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "for ax in axes:\n", + " ax.plot(tp['primary'].time_values, tp['primary'].data, label='primary')\n", + " ax.plot(tp.initial_fit_trace.time_values, tp.initial_fit_trace.data, 'g', label='initial fit')\n", + " ax.plot(tp.fit_trace.time_values, tp.fit_trace.data, 'r', label='final fit')\n", + " ax.set_xlabel('time (s)')\n", + " ax.set_ylabel(name + ' (' + units + ')')\n", + " ax.legend()\n", + "\n", + "# plot an expanded region near the pulse start\n", + "pstart = tp['primary'].t0 + tp.stimulus.start_time + tp.stimulus.duration\n", + "axes[1].set_xlim(pstart - .1*ms, pstart + 1.2*ms)\n", + "rgn = tp['primary'].time_slice(pstart - .1*ms, pstart + .3*ms)\n", + "mnmax = rgn.data.min(), rgn.data.max()\n", + "axes[1].set_ylim(mnmax[0] - 0.1*(mnmax[1]-mnmax[0]), mnmax[1] + 0.1*(mnmax[1]-mnmax[0]))\n", + "\n", + "\n", + "df = pandas.DataFrame()\n", + "for k,v in expected_testpulse_values(model_cell).items():\n", + " df[k] = [v, tp.analysis[k]]\n", + "# label df rows\n", + "df.index = ['IC expected', 'IC measured']\n", + "display(df)\n", + "\n", + "check_analysis(tp, model_cell)\n", + "print(\"Test passed\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "neuroanalysis", + "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": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/neuroanalysis/__init__.py b/neuroanalysis/__init__.py index 5becc17..b1a19e3 100644 --- a/neuroanalysis/__init__.py +++ b/neuroanalysis/__init__.py @@ -1 +1 @@ -__version__ = "1.0.0" +__version__ = "0.0.5" diff --git a/neuroanalysis/analyzers/__init__.py b/neuroanalysis/analyzers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/neuroanalysis/analyzers/analyzer.py b/neuroanalysis/analyzers/analyzer.py new file mode 100644 index 0000000..56b12fd --- /dev/null +++ b/neuroanalysis/analyzers/analyzer.py @@ -0,0 +1,19 @@ + + +class Analyzer(object): + """Base class for attaching analysis results to a data object. + """ + @classmethod + def get(cls, obj): + """Get the analyzer attached to a recording, or create a new one. + """ + analyzer = getattr(obj, '_' + cls.__name__, None) + if analyzer is None: + analyzer = cls(obj) + return analyzer + + def _attach(self, obj): + attr = '_' + self.__class__.__name__ + if hasattr(obj, attr): + raise TypeError("Object %s already has attached %s" % (obj, self.__class__.__name__)) + setattr(obj, attr, self) \ No newline at end of file diff --git a/neuroanalysis/analyzers/baseline.py b/neuroanalysis/analyzers/baseline.py new file mode 100644 index 0000000..7c035fe --- /dev/null +++ b/neuroanalysis/analyzers/baseline.py @@ -0,0 +1,61 @@ +from neuroanalysis.analyzers.analyzer import Analyzer + +class BaselineAnalyzer(Analyzer): + + _settle_time = None ## (float) The amount of time (in seconds) to allow the cell to settle back + ## to baseline after the end of a stimulus. + + def __init__(self, sync_rec): + self._attach(sync_rec) + self.sync_rec = sync_rec + + self._baseline_regions = None + + @property + def settle_time(self): + if self._settle_time is None: + raise Exception("""%s._settle_time must be defined. + Should be a float specifying the amount of time (in seconds) + to allow the cell to settle back to baseline after the end of a stimulus.""" % self.__class__.__name__) + return self._settle_time + + @property + def baseline_regions(self): + """A list of (start,stop) time pairs where the recordings in this + sync_rec can be expected to be in a quiescent state. + + """ + raise Exception("Must be implemented in subclass.") + + +class BaselineDistributor(Analyzer): + """Used to find baseline regions in a trace and distribute them on request. + """ + def __init__(self, rec): + self._attach(rec) + self.rec = rec + self.baselines = [list(r) for r in rec.baseline_regions] + + def get_baseline_chunk(self, duration=20e-3): + """Return the (start, stop) indices of a chunk of unused baseline with the + given duration. + """ + for i, baseline_rgn in enumerate(self.baselines): + rgn_start, rgn_stop = baseline_rgn + if rgn_stop - rgn_start < duration: + continue + chunk_stop = rgn_start + duration + baseline_rgn[0] = chunk_stop + return rgn_start, chunk_stop + + # coundn't find any baseline data of the requested length + return None + + def baseline_chunks(self, duration=20e-3): + """Iterator yielding (start, stop) indices of baseline chunks. + """ + while True: + chunk = self.get_baseline_chunk(duration) + if chunk is None: + break + yield chunk diff --git a/neuroanalysis/analyzers/stim_pulse.py b/neuroanalysis/analyzers/stim_pulse.py new file mode 100644 index 0000000..1474c48 --- /dev/null +++ b/neuroanalysis/analyzers/stim_pulse.py @@ -0,0 +1,250 @@ +import numpy as np +from neuroanalysis.analyzers.analyzer import Analyzer +from neuroanalysis.stimuli import find_square_pulses, find_noisy_square_pulses, SquarePulse +from neuroanalysis.spike_detection import detect_evoked_spikes + + +class GenericStimPulseAnalyzer(Analyzer): + """For analyzing noise-free or noisy square-pulse stimulations.""" + + def __init__(self, rec): + self._attach(rec) + self.rec = rec + self._pulses = {} + + def _check_channel(self, channel): + if channel not in self.rec.channels: + if channel is None: + raise ValueError("Please specify which channel to analyze. Options are: %s" % self.rec.channels) + else: + raise ValueError("Recording %s does not contain specified channel (%s). Options are: %s" % (self.rec, channel, self.rec.channels)) + + def pulses(self, channel=None): + """Return a list of (start_time, stop_time, amp) tuples describing square pulses + in the specified channel. + """ + self._check_channel(channel) + + if self._pulses.get(channel) is None: + trace = self.rec[channel] + if trace.data[:10].std() > 0: + pulses = find_noisy_square_pulses(trace, std_threshold=10) + else: + pulses = find_square_pulses(trace) + self._pulses[channel] = [] + for p in pulses: + start = p.global_start_time + stop = p.global_start_time + p.duration + self._pulses[channel].append((start, stop, p.amplitude)) + return self._pulses[channel] + + +class PWMStimPulseAnalyzer(GenericStimPulseAnalyzer): + """For analyzing noise-free digital channels where pulse width modulation may have + been used to modulate amplitude.""" + + def __init__(self, rec, pwm_min_frequency=1000.): + GenericStimPulseAnalyzer.__init__(self, rec) + self._pwm_params = {} + + #### change the pwm_min_frequency to change what is considered pulse-width-modulation + ## - pulses with frequencies equal to or greater than self.pwm_min_frequency + ## will be considered pulse-width-modulation, and will be grouped into stimulation pulses with amplitudes ranging from 0-1 + self.pwm_min_frequency = pwm_min_frequency #Hz + + + self.pwm_min_delay = 1./self.pwm_min_frequency + 1e-6 ##(+ a buffer in case of floating point error) + + def pulses(self, channel=None): + """Return a list of SquarePulses found in the given channel of the recording. + If there is pulse-width modulation (higher than %s Hz), it will be grouped + into a single SquarePulse with lower amplitude. + Example: + _____|||||_______________|||||_____________|||||____________ + <---> <---> <---> + + The trace shown above has pulse width modulation would return a + list of 3 SquarePulses with an amplitude equal to the mean of the + trace during the periods indicated by the arrows. + + The trace shown below (no pulse width modulation) would also + return a list of 3 SquarePulses, but with an amplitude of 1. + + _____ _____ _____ + ____| |_____________| |___________| |___________ + + Parameters: + ----------- + + channel : str | None + The channel to analyze pulses from. + """ % str(self.pwm_min_frequency) + + self._check_channel(channel) + + if self._pulses.get(channel) is None: + trace = self.rec[channel] + + if trace.data[:10].std() > 0: + all_pulses = find_noisy_square_pulses(trace) + else: + all_pulses = find_square_pulses(trace) + + ## figure out if there is pwm happening + pwm = False + if len(all_pulses) > 1: + for i in range(len(all_pulses)-1): + if all_pulses[i+1].global_start_time - all_pulses[i].global_start_time <= self.pwm_min_delay: ## dealing with pwm + pwm = True + break + + ## convert pwm pulses into single stimulation pulses + if pwm: + pulses = [] + self._pwm_params[channel] = [] + ### make an array of start times + starts = np.array([p.global_start_time for p in all_pulses]) + + ### do a diff on the array - look for points larger than 1/min_frequency + breaks = np.argwhere(np.diff(starts) > self.pwm_min_delay) ## gives indices of the last pulse in a pwm pulse + if len(breaks) == 0: ## only one pulse + pulse, params = self._create_pulse_from_pwm(all_pulses) + pulses.append(pulse) + self._pwm_params[channel].append(params) + + + ### take the pulses between large diffs and turn them into one pulse with appropriate duration and amplitude + else: + start_i = 0 + for i, b in enumerate(breaks): + pulse, params = self._create_pulse_from_pwm(all_pulses[start_i:b+1]) + pulses.append(pulse) + self._pwm_params[channel].append(params) + start_i = b+1 + + pulse, params = self._create_pulse_from_pwm(all_pulses[start_i:]) + pulses.append(pulse) + self._pwm_params[channel].append(params) + + else: + self._pwm_params[channel] = None + pulses = [SquarePulse(start_time=p.global_start_time, duration=p.duration, amplitude=1, units='percent') for p in all_pulses] + + ### convert from SquarePulse to (start, stop, amplitude) + self._pulses[channel] = pulses + + return self._pulses[channel] + + def _create_pulse_from_pwm(self, pwms): + """Return a (SquarePulse, pwm_param_dict) where pwm_param_dict has 'frequency' and 'duration' for pwm pulses.""" + dt = pwms[1].global_start_time - pwms[0].global_start_time + duration = dt*len(pwms) + amplitude = pwms[0].duration / dt + return (SquarePulse(start_time=pwms[0].global_start_time, duration=duration, amplitude=amplitude, units='percent'), + {'frequency': 1. / dt, + 'duration':pwms[0].duration + }) + + + def pwm_params(self, channel=None, pulse_n=None): + """Return frequency and duration of pulse width modulation pulses for the given channel and pulse number. + """ + self._check_channel(channel) + + if self._pulses.get(channel) is None: ## we've not analyzed this channel yet, do it now + self.pulses(channel=channel) + + params = self._pwm_params[channel] + if params is None: + return None + + if pulse_n is None: + params = set(params) + if len(params) == 1: + return params + else: + raise Exception("Please specify pulse number to return params for. Found %i different param sets in %s channel"%(len(params), channel)) + else: + return self._pwm_params[channel][pulse_n] + + + +class PatchClampStimPulseAnalyzer(GenericStimPulseAnalyzer): + """Used for analyzing a patch clamp recording with square-pulse stimuli. + """ + def __init__(self, rec): + GenericStimPulseAnalyzer.__init__(self, rec) + self._evoked_spikes = None + + def pulses(self, channel='command'): + """Return a list of (start_time, stop_time, amp) tuples describing square pulses + in the stimulus. + """ + if self._pulses.get(channel) is None: + trace = self.rec[channel] + pulses = find_square_pulses(trace) + self._pulses[channel] = [] + for p in pulses: + start = p.global_start_time + stop = p.global_start_time + p.duration + self._pulses[channel].append((start, stop, p.amplitude)) + return self._pulses[channel] + + def pulse_chunks(self): + """Return time-slices of this recording where evoked spikes are expected to be found (one chunk + per pulse) + + Each recording returned has extra metadata keys added: + - pulse_edges: start/end times of the stimulus pulse + - pulse_amplitude: amplitude of stimulus puse (in V or A) + - pulse_n: the number of this pulse (all detected square pulses are numbered in order from 0) + + """ + pre_trace = self.rec['primary'] + + # Detect pulse times + pulses = self.pulses() + + # filter out test pulse if it exists + stim_pulses = pulses[1:] if self.rec.has_inserted_test_pulse else pulses + + # cut out a chunk for each pulse + chunks = [] + for i,pulse in enumerate(stim_pulses): + pulse_start_time, pulse_end_time, amp = pulse + if amp < 0: + # assume negative pulses do not evoke spikes + # (todo: should be watching for rebound spikes as well) + continue + # cut out a chunk of the recording for spike detection + start_time = pulse_start_time - 2e-3 + stop_time = pulse_end_time + 4e-3 + if i < len(stim_pulses) - 1: + # truncate chunk if another pulse is present + next_pulse_time = stim_pulses[i+1][0] + stop_time = min(stop_time, next_pulse_time) + chunk = self.rec.time_slice(start_time, stop_time) + chunk.meta['pulse_edges'] = [pulse_start_time, pulse_end_time] + chunk.meta['pulse_amplitude'] = amp + chunk.meta['pulse_n'] = i + chunks.append(chunk) + + return chunks + + def evoked_spikes(self): + """Given presynaptic Recording, detect action potentials + evoked by current injection or unclamped spikes evoked by a voltage pulse. + + Returns + ------- + spikes : list + [{'pulse_n', 'pulse_start', 'pulse_end', 'spikes': [...]}, ...] + """ + if self._evoked_spikes is None: + spike_info = [] + for i,chunk in enumerate(self.pulse_chunks()): + pulse_edges = chunk.meta['pulse_edges'] + spikes = detect_evoked_spikes(chunk, pulse_edges) + spike_info.append({'pulse_n': chunk.meta['pulse_n'], 'pulse_start': pulse_edges[0], 'pulse_end': pulse_edges[1], 'spikes': spikes}) + self._evoked_spikes = spike_info + return self._evoked_spikes diff --git a/neuroanalysis/data/__init__.py b/neuroanalysis/data/__init__.py new file mode 100644 index 0000000..6af2c9f --- /dev/null +++ b/neuroanalysis/data/__init__.py @@ -0,0 +1,2 @@ +from . import * +from .dataset import * diff --git a/neuroanalysis/data.py b/neuroanalysis/data/dataset.py similarity index 74% rename from neuroanalysis/data.py rename to neuroanalysis/data/dataset.py index 91b0931..f899a44 100644 --- a/neuroanalysis/data.py +++ b/neuroanalysis/data/dataset.py @@ -15,15 +15,12 @@ This abstraction layer also helps to enforce good coding practice by separating data representation, analysis, and visualization. """ -from __future__ import division +from collections import OrderedDict import numpy as np -import scipy.signal -from . import util -from collections import OrderedDict -from .stats import ragged_mean -from .baseline import float_mode -from .filter import downsample + +from ..baseline import float_mode +from ..filter import downsample class Container(object): @@ -31,12 +28,27 @@ class Container(object): This class is the basis for most other classes in the DAL. """ - def __init__(self): + def __init__(self, loader=None): self._meta = OrderedDict() + self._key = None + self._parent = None + self._loader = loader + + @property + def loader(self): + if self._loader is None: + raise Exception("No loader was specified upon initialization.") + return self._loader @property def parent(self): - return None + return self._parent + + # @property + # def top_parent(self): + # if self.parent is not None: + # return self.parent.top_parent + # return self @property def children(self): @@ -46,12 +58,15 @@ def children(self): def key(self): """Key that uniquely identifies this object among its siblings. """ - return None + return self._key @property def meta(self): return self._meta + def update_meta(self, **kargs): + self._meta.update(kargs) + @property def all_children(self): allch = [self] @@ -88,22 +103,59 @@ class Dataset(Container): a series of recordings made on the same cell almost certainly belong in the same Dataset, whereas recordings made from different pieces of tissue probably belong in different Datasets. + + Parameters + ---------- + data : list of SyncRecordings | None + A list of SyncRecordings in the dataset. If None, they will be loaded when needed with loader + meta : dict | None + An optional dict of metadata about the dataset. + loader : a class | None + A class which contains functions for loading data. Must include: + get_sync_recordings(self) - Return a tuple of (list of SyncRecordings, list of RecordingSequences) in this dataset + get_dataset_name(self) - Return the name of this dataset (str) + sequences: list of RecordingSequences | None + A list of RecordingSequences in this dataset. If None they will be loaded when needed with loader + name : str | None + The name of this dataset. If None, will be loaded when needed with loader + """ - def __init__(self, data=None, meta=None): - Container.__init__(self) + + def __init__(self, data=None, meta=None, loader=None, name=None): + Container.__init__(self, loader=loader) self._data = data + self._name = name if meta is not None: self._meta.update(OrderedDict(meta)) + + #self._loader = loader + #self._sequences = sequences @property def contents(self): - """A list of data objects (TSeries, Recording, SyncRecording, RecordingSequence) - directly contained in this experiment. + """A list of SyncRecording objects directly contained in this experiment. Grandchild objects are not included in this list. """ + if self._data is None: + self._data = self.loader.get_sync_recordings(self) return self._data[:] + # @property + # def sequences(self): + # if self._sequences is None: + # self.contents + # return self._sequences + + @property + def name(self): + if self._name is None: + self._name = self.loader.get_dataset_name() + return self._name + + def __repr__(self): + return "<%s %s>" % (self.__class__.__name__, self.name) + def find(self, type): return [c for c in self.all_children if isinstance(c, type)] @@ -147,13 +199,13 @@ def meta_table(self, objs): def trace_table(self): return self.meta_table(self.all_traces) - @property - def parent(self): - """None + # @property + # def parent(self): + # """None - This is a convenience property used for traversing the object hierarchy. - """ - return None + # This is a convenience property used for traversing the object hierarchy. + # """ + # return None @property def children(self): @@ -164,77 +216,112 @@ def children(self): return self.contents - -class RecordingSequence(Container): +# class RecordingSequence(Container): + +# For now, possibly forever, remove the concept of Sequences from datasets. +# It is too hard to decide programatically which sync recordings should make up a sequence, +# and it is likely that that will change or be highly specific to each type of experiment, +# and will likely involve manual annotation. +# So, I think that sorting sync recordings into sequences should be something that happens at a +# higher level than loading datasets. - # Acquisition? - # RecordingSet? +# # Acquisition? +# # RecordingSet? - # Do we need both SyncRecordingSequence and RecordingSequence ? - # Can multiple RecordingSequence instances refer to the same underlying sequence? - # - Let's say no--otherwise we have to worry about unique identification, comparison, etc. - # - Add ___View classes that slice/dice any way we like. +# # Do we need both SyncRecordingSequence and RecordingSequence ? +# # Can multiple RecordingSequence instances refer to the same underlying sequence? +# # - Let's say no--otherwise we have to worry about unique identification, comparison, etc. +# # - Add ___View classes that slice/dice any way we like. - """Representation of a sequence of data acquisitions. +# """Representation of a sequence of data acquisitions. - For example, this could be a single type of acquisition that was repeated ten times, - or a series of ten acquisitions that varies one parameter across ten values. - Usually the recordings in a sequence all use the same set of devices. +# For example, this could be a single type of acquisition that was repeated ten times, +# or a series of ten acquisitions that varies one parameter across ten values. +# Usually the recordings in a sequence all use the same set of devices. - Sequences may be multi-dimensional and may vary more than one parameter. +# Sequences may be multi-dimensional and may vary more than one parameter. - Items in a sequence are usually SyncRecording instances, but may also be - nested RecordingSequence instances. - """ - @property - def type(self): - """An arbitrary string representing the type of acquisition. - """ - pass - - @property - def shape(self): - """The array-shape of the sequence. - """ - - @property - def ndim(self): - return len(self.shape) - - def __getitem__(self, item): - """Return one item (a SyncRecording instance) from the sequence. - """ - - def sequence_params(self): - """Return a structure that describes the parameters that are varied across each - axis of the sequence. - - For example, a two-dimensional sequence might return the following: - - [ - [param1, param2], # two parameters that vary across the first sequence axis - [], # no parameters vary across the second axis (just repetitions) - [param3], # one parameter that varies across all recordings, regardless of its position along any axis - ] - - Each parameter must be a key in the metadata for a single recording. - """ - - @property - def parent(self): - """None +# Items in a sequence are usually SyncRecording instances, but may also be +# nested RecordingSequence instances. +# """ + +# def __init__(self, parent, name, sync_recs=None, meta=None, loader=None): +# Container.__init__(self, loader=loader) + +# self._parent = parent +# self._key = name +# if meta is not None: +# self.update_meta(**meta) + +# self._sync_recs = [] +# if sync_recs is not None: +# for sync_rec in sync_recs: +# self.add_sync_rec(sync_rec) + +# def __repr__(self): +# return "<%s %s>" % (self.__class__.__name__, self.key) + +# def add_sync_rec(self, sync_rec): +# if sync_rec not in self._sync_recs: +# self._sync_recs.append(sync_rec) + +# @property +# def type(self): +# """An arbitrary string representing the type of acquisition. +# """ +# return self._meta.get('type', None) + +# # @property +# # def shape(self): +# # """The array-shape of the sequence. +# # """ + +# # @property +# # def ndim(self): +# # return len(self.shape) + +# # def __getitem__(self, item): +# # """Return one item (a SyncRecording instance) from the sequence. +# # """ + +# def sequence_params(self): +# """ +# Return a dictionary of {param_name:values}. + +# ## maybe in the future: +# //Return a structure that describes the parameters that are varied across each +# //axis of the sequence. + +# //For example, a two-dimensional sequence might return the following: +# // +# // [ +# // [param1, param2], # two parameters that vary across the first sequence axis +# // [], # no parameters vary across the second axis (just repetitions) +# // [param3], # one parameter that varies across all recordings, regardless of its position along any axis +# // ] + +# //Each parameter must be a key under 'sequence_params' in the meta data for a single syncrecording. +# """ +# return self._meta.get('sequence_params') + +# # @property +# # def parent(self): +# # """None - This is a convenience property used for traversing the object hierarchy. - """ - return None +# # This is a convenience property used for traversing the object hierarchy. +# # """ +# # return None +# @property +# def contents(self): +# return self._sync_recs - @property - def children(self): - """Alias for self.contents +# @property +# def children(self): +# """Alias for self.contents - This is a convenience property used for traversing the object hierarchy. - """ - return self.contents +# This is a convenience property used for traversing the object hierarchy. +# """ +# return self.contents class SyncRecording(Container): @@ -242,11 +329,30 @@ class SyncRecording(Container): This is typically the result of recording from multiple devices at the same time (for example, two patch-clamp amplifiers and a camera). + + Parameters + ---------- + recordings : list of Recordings | None + A list of Recordings in this sync_recording. If None, will be loaded when needed with loader. + parent : Dataset | None + The dataset this sync recording is part of. + key : + A id for this sync recording that is unique among its siblings + loader : a class | None + A class which contains functions for loading data in the sync recording. Must have these methods: + get_recordings(self) - Return a dict of {device: Recording} + """ - def __init__(self, recordings=None, parent=None): + def __init__(self, recordings=None, parent=None, key=None, meta=None, loader=None): + Container.__init__(self, loader=loader) self._parent = parent - self._recordings = recordings if recordings is not None else OrderedDict() - Container.__init__(self) + self._recording_dict = recordings #if recordings is not None else OrderedDict() + self._key = key + if meta is not None: + self.update_meta(**meta) + + def __repr__(self): + return "<%s key=%s>" % (self.__class__.__name__, str(self.key)) @property def type(self): @@ -254,29 +360,35 @@ def type(self): """ pass + @property + def recording_dict(self): + if self._recording_dict is None: + self._recording_dict = self.loader.get_recordings(self) + return self._recording_dict + @property def devices(self): """A list of the names of devices in this recording. """ - return list(self._recordings.keys()) + return list(self.recording_dict.keys()) def __getitem__(self, item): """Return a recording given its device name. """ - return self._recordings[item] + return self.recording_dict[item] @property def recordings(self): """A list of the recordings in this syncrecording. """ - return list(self._recordings.values()) + return list(self.recording_dict.values()) def data(self): return np.concatenate([self[dev].data()[None, :] for dev in self.devices], axis=0) - @property - def parent(self): - return self._parent + # @property + # def parent(self): + # return self._parent @property def children(self): @@ -300,9 +412,25 @@ class Recording(Container): Each channel is described by a single TSeries instance. Channels are often recorded with the same timebase, but this is not strictly required. + + Parameters + ---------- + channels : dict | None + A {name : TSeries} dict of the channels in this recording. + start_time : ? | None + The start time of this recording. + device_type : str | None + The type of device that made this recording. + device_id : ? | None + The id of the device that made this recording. + sync_recording : SyncRecording | None + The parent sync recording this recording is part of. + loader : a class | None + A loader class for loading information not supplied at initialization. No required functions yet. + """ - def __init__(self, channels=None, start_time=None, device_type=None, device_id=None, sync_recording=None, **meta): - Container.__init__(self) + def __init__(self, channels=None, start_time=None, device_type=None, device_id=None, sync_recording=None, loader=None, **meta): + Container.__init__(self, loader=loader) self._meta = OrderedDict([ ('start_time', start_time), ('device_type', device_type), @@ -319,6 +447,7 @@ def __init__(self, channels=None, start_time=None, device_type=None, device_id=N self._channels = channels self._sync_recording = sync_recording + self._parent = sync_recording @property def device_type(self): @@ -332,7 +461,7 @@ def device_type(self): def channels(self): """A list of channels included in this recording. """ - return self._channels.keys() + return list(self._channels.keys()) @property def start_time(self): @@ -348,6 +477,12 @@ def device_id(self): def sync_recording(self): return self._sync_recording + @property + def stimulus(self): + if self._meta.get('stimulus') is None: + self._meta['stimulus'] = self.loader.load_stimulus(self) + return self._meta.get('stimulus') + def time_slice(self, start, stop): return RecordingView(self, start, stop) @@ -355,16 +490,37 @@ def __getitem__(self, chan): return self._channels[chan] def data(self): - return np.concatenate([self[ch].data[:,None] for ch in self.channels], axis=1) + return np.stack([self[ch].data for ch in self.channels], axis=-1) - @property - def parent(self): - return self.sync_recording - @property def children(self): return [self[k] for k in self.channels] + def __repr__(self): + return f"<{self.__class__.__name__} device:{self.device_type}, channels:{str(self.channels)}>" + + def save(self): + meta = self.meta.copy() + if meta.get('stimulus') is not None: + meta['stimulus'] = meta['stimulus'].save() + channels = {k: v.save() for k, v in self._channels.items()} + return { + 'schema version': (1, 0), + 'meta': meta, + 'start_time': self.start_time, + 'channels': channels, + } + + @classmethod + def load(cls, data): + from neuroanalysis.stimuli import Stimulus + + meta = data['meta'] + if meta.get('stimulus') is not None: + meta['stimulus'] = Stimulus.load(meta['stimulus']) + channels = {k: TSeries.load(v) for k, v in data['channels'].items()} + return cls(channels=channels, **meta) + class RecordingView(Recording): """A time-slice of a multi channel recording @@ -418,6 +574,13 @@ class PatchClampRecording(Recording): Should have at least 'primary' and 'command' channels. Note: command channel values should _include_ holding potential/current! + + Loader class needs methods: + load_stimulus(self) - Return a neuroanalysis.stimuli.Stimulus object + load_test_pulse(self) - Return a neuroanalysis.test_pulse.PatchClampTestPulse object or None + find_nearest_test_pulse(self) - Return the neuroanalysis.test_pulse.PatchClampTestPulse object recorded closest in time to this recording. + get_baseline_regions(self) - Return a list of (start, stop) time pairs when we can expect this recording to be in a quiescent state. + """ def __init__(self, *args, **kwds): meta = OrderedDict() @@ -428,9 +591,14 @@ def __init__(self, *args, **kwds): for k in extra_meta: meta[k] = kwds.pop(k, None) - self._baseline_data = None + Recording.__init__(self, *args, **kwds) self._meta.update(meta) + + self._baseline_regions = None + self._baseline_data = None + self._test_pulse = None + self._nearest_test_pulse = None @property def cell_id(self): @@ -442,7 +610,7 @@ def cell_id(self): def clamp_mode(self): """The mode of the patch clamp amplifier: 'vc', 'ic', or 'i0'. """ - return self._meta['clamp_mode'] + return self._meta['clamp_mode'].lower() @property def patch_mode(self): @@ -450,10 +618,6 @@ def patch_mode(self): """ return self._meta['patch_mode'] - @property - def stimulus(self): - return self._meta.get('stimulus', None) - @property def holding_potential(self): """The command holding potential if the recording is voltage-clamp, or the @@ -484,17 +648,38 @@ def holding_current(self): else: return self.baseline_current + @property + def bridge_balance(self): + """The bridge balance compensation applied during this recording. + """ + return self._meta['bridge_balance'] + + @property + def test_pulse(self): + if self._test_pulse is None: + self._test_pulse = self.loader.load_test_pulse(self) + return self._test_pulse # may still be None + @property def nearest_test_pulse(self): """The test pulse that was acquired nearest to this recording. """ + if self.test_pulse is not None: + return self.test_pulse + + if self._nearest_test_pulse is None: + self._nearest_test_pulse = self.loader.find_nearest_test_pulse(self) + + return self._nearest_test_pulse @property def baseline_regions(self): """A list of (start,stop) time pairs that cover regions of the recording the cell is expected to be in a steady state. """ - return [] + if self._baseline_regions is None: + self._baseline_regions = self.loader.get_baseline_regions(self) + return self._baseline_regions @property def baseline_data(self): @@ -506,6 +691,7 @@ def baseline_data(self): data = np.empty(0, dtype=self['primary'].data.dtype) else: data = np.concatenate(data) + data = data[np.isfinite(data)] self._baseline_data = TSeries(data, sample_rate=self['primary'].sample_rate, recording=self) return self._baseline_data @@ -553,20 +739,19 @@ def baseline_rms_noise(self): return self.meta['baseline_rms_noise'] def _descr(self): - mode = self.clamp_mode - if mode == 'vc': + if self.clamp_mode == 'vc': hp = self.holding_potential if hp is not None: - hp = int(np.round(hp*1e3)) - extra = "mode=VC holding=%s" % hp - elif mode == 'ic': + hp = int(np.round(hp * 1e3)) + return f"mode=VC holding={hp}" + else: hc = self.holding_current if hc is not None: - hc = int(np.round(hc*1e12)) - extra = "mode=IC holding=%s" % hc + hc = int(np.round(hc * 1e12)) + return f"mode=IC holding={hc}" def __repr__(self): - return "<%s %s>" % (self.__class__.__name__, self._descr()) + return f"<{self.__class__.__name__} device:{self.device_id} {self._descr()}>" class TSeries(Container): @@ -588,7 +773,7 @@ class TSeries(Container): Parameters ---------- data : array | None - Array of data contained in this TSeries. + Array of data contained in this TSeries where the first axis is time. dt : float | None Optional value specifying the time difference between any two adjacent samples in the data; inverse of *sample_rate*. See ``TSeries.dt``. @@ -609,18 +794,25 @@ class TSeries(Container): Optional string specifying the units associated with *data*. It is recommended to use unscaled SI units (e.g. 'V' instead of 'mV') where possible. See ``TSeries.units``. + channel_id : str | None + The name of the Recording channel that contains this TSeries + recording : Recording | None + The Recording this TSeries is part of. + loader : a class | None + A class containing functions for loading data. Required methods: + get_tseries_data(self) - return a numpy array of values meta : Any extra keyword arguments are interpreted as custom metadata and added to ``self.meta``. """ - def __init__(self, data=None, dt=None, t0=None, sample_rate=None, start_time=None, time_values=None, units=None, channel_id=None, recording=None, **meta): - Container.__init__(self) + def __init__(self, data: np.ndarray = None, dt=None, t0=None, sample_rate=None, start_time=None, time_values: np.ndarray = None, units=None, channel_id=None, recording=None, loader=None, **meta): + Container.__init__(self, loader=loader) - if data is not None and data.ndim != 1: - raise ValueError("data must be a 1-dimensional array.") + #if data is not None and data.ndim != 1: + # raise ValueError("data must be a 1-dimensional array.") if time_values is not None: - if data is not None and time_values.shape != data.shape: - raise ValueError("time_values must have the same shape as data.") + if data is not None and time_values.shape[0] != data.shape[0]: + raise ValueError("time_values must have the same length as data.") if dt is not None: raise TypeError("Cannot specify both time_values and dt.") if sample_rate is not None: @@ -650,6 +842,8 @@ def __init__(self, data=None, dt=None, t0=None, sample_rate=None, start_time=Non def data(self): """The array of sample values. """ + if self._data is None: + self._data = self.loader.get_tseries_data(self) return self._data @property @@ -788,7 +982,6 @@ def index_at(self, t, index_mode=None): inds = np.where(dif0 < dif1, inds0, inds1) if np.isscalar(t): inds = int(inds) - return inds else: # Be careful to avoid fp precision errors when converting back to integer index sample_rate = self._meta.get('sample_rate') @@ -807,9 +1000,10 @@ def index_at(self, t, index_mode=None): raise ValueError("index_mode must be 'round', 'ceil', or 'floor'; got %r" % index_mode) if np.isscalar(t): - return int(inds) + inds = int(inds) else: - return inds.astype(int) + inds = inds.astype(int) + return min(max(0, inds), len(self) - 1) @property def time_values(self): @@ -974,6 +1168,10 @@ def recording(self): """ return self._recording + @recording.setter + def recording(self, new_val): + self._recording = new_val + def copy(self, data=None, time_values=None, **kwds): """Return a copy of this TSeries. @@ -991,20 +1189,22 @@ def copy(self, data=None, time_values=None, **kwds): These include dt, sample_rate, t0, start_time, units, and others. """ + data, meta, tval = self._prepare_data_for_export(data, time_values, **kwds) + + return TSeries(data, time_values=tval, recording=self.recording, **meta) + + def _prepare_data_for_export(self, data=None, time_values=None, **kwds): if data is None: data = self.data.copy() - if time_values is None: tval = self._time_values if tval is not None: tval = tval.copy() else: tval = time_values - meta = self._meta.copy() meta.update(kwds) - - return TSeries(data, time_values=tval, recording=self.recording, **meta) + return data, meta, tval @property def parent(self): @@ -1089,7 +1289,7 @@ def resample(self, sample_rate): # bessel filter gives reasonably good antialiasing with no ringing or edge # artifacts - from .filter import bessel_filter + from ..filter import bessel_filter filt = bessel_filter(self, cutoff=sample_rate, order=2) t1 = self.time_values t2 = np.arange(t1[0], t1[-1], 1.0/sample_rate) @@ -1109,11 +1309,20 @@ def __truediv__(self, x): return self.copy(data=self.data / x) def __add__(self, x): + if isinstance(x, TSeries): + x = x.data return self.copy(data=self.data + x) def __sub__(self, x): + if isinstance(x, TSeries): + x = x.data return self.copy(data=self.data - x) + def concat(self, other): + data = np.concatenate([self.data, other.data]) + times = np.concatenate([self.time_values, other.time_values]) + return self.copy(data=data, time_values=times) + def mean(self): """Return the mean value of the data in this TSeries. @@ -1174,6 +1383,23 @@ def __repr__(self): units, ) + def save(self): + data, meta, tval = self._prepare_data_for_export() + + return { + 'schema version': (1, 0), + 'data': data, + 'time_values': tval, + 'meta': meta, + } + + @classmethod + def load(cls, data): + meta = data.get('meta', {}) + time_values = data.get('time_values') + data = data['data'] + return cls(data, time_values=time_values, **meta) + # for backward compatibility Trace = TSeries diff --git a/neuroanalysis/data/loaders/__init__.py b/neuroanalysis/data/loaders/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/neuroanalysis/data/loaders/acq4_dataset_loader.py b/neuroanalysis/data/loaders/acq4_dataset_loader.py new file mode 100644 index 0000000..ec9b596 --- /dev/null +++ b/neuroanalysis/data/loaders/acq4_dataset_loader.py @@ -0,0 +1,183 @@ +from datetime import datetime +from neuroanalysis.data.loaders.loaders import DatasetLoader +from acq4.util import DataManager +from acq4.analysis.dataModels import PatchEPhys +from neuroanalysis.data.dataset import Recording, SyncRecording, TSeries, PatchClampRecording +import neuroanalysis.stimuli as stimuli + + +class Acq4DatasetLoader(DatasetLoader): + def __init__(self, filepath): + + self._filepath = filepath + + self._dh = None ## directory handle + + @property + def dh(self): + if self._dh is None: + self._dh = DataManager.getDirHandle(self._filepath) + return self._dh + + def get_dataset_name(self): + return self._filepath + + def get_sync_recordings(self, dataset): + """Return a list of SyncRecordings.""" + + sync_recs = [] + sequences = [] + + for seq in self.dh.subDirs(): + if self.dh[seq].info().get('dirType') == 'ProtocolSequence': + params = self.dh[seq].info()['sequenceParams'] + #sequence = RecordingSequence(parent=dataset, name=seq, meta={'sequence_params':params}, loader=self) + for sd in self.dh[seq].subDirs(): + sdh = self.dh[seq][sd] + meta = {'sequence_params':{}} + for k in params.keys(): + meta['sequence_params'][k] = params[k][sdh.info().get(k)] + srec = SyncRecording(parent=dataset, key=(seq,sd), meta=meta, loader=self) + #sequence.add_sync_rec(srec) + sync_recs.append(srec) + #sequences.append(sequence) + elif self.dh[seq].info().get('dirType') == 'Protocol': + srec = SyncRecording(parent=dataset, key=(seq), loader=self) + sync_recs.append(srec) + elif self.dh[seq].shortName() == 'Patch': ## ignore this for now -- how should this data be represented? + continue + else: + raise ValueError(f'Not sure how to handle folder {self.dh[seq].name()}') + + #return (sync_recs, sequences) + return sync_recs + + def get_recordings(self, sync_rec): + """Return a dict of {device: Recording}""" + key = sync_rec.key + dh = self.dh + for k in key: + dh = dh[k] + + ## build a flat list of all the files in dh + ls = [dh[f] for f in dh.ls()] + files = [] + for f in ls: + if f.isDir(): + ls.extend([f[x] for x in f.ls()]) # -- broken - need to deal with str - fh conversion :/ + else: + files.append(f) + + recordings = {} + for f in files: + start_time=datetime.utcfromtimestamp(PatchEPhys.getParent(f, 'Protocol').info()['startTime']) + + if PatchEPhys.isClampFile(f): + meta = { + 'file_name': f.name(), + 'clamp_mode': PatchEPhys.getClampMode(f).lower(), + } + if meta['clamp_mode'] == 'vc': + meta['holding_current'] = PatchEPhys.getClampHoldingLevel(f) + elif meta['clamp_mode'] == 'ic': + meta['holding_potential'] = PatchEPhys.getClampHoldingLevel(f) + meta['bridge_balance'] = PatchEPhys.getBridgeBalanceCompensation(f) + else: + raise ValueError(f"dont know how to interpret {meta['clamp_mode']} clamp_mode") + + data = f.read() + dt = data.axisValues(1)[1] - data.axisValues(1)[0] + + rec = PatchClampRecording( + channels={'primary':TSeries(channel_id='primary', data=data['primary'].asarray(), dt=dt, units=data.columnUnits(0, 'primary'), loader=self), + 'command':TSeries(channel_id='command', data=data['command'].asarray(), dt=dt, units=data.columnUnits(0, 'command'), loader=self)}, + start_time=start_time, + device_type='patch clamp amplifier', + device_id=f.shortName().strip('.ma'), + sync_recording=sync_rec, + loader=self, + **meta + ) + rec['primary']._recording = rec + rec['command']._recording = rec + + else: + data = f.read() + time_axis = data._getAxis('Time') ## is there a way to do this without using a private MA function? + if 'Channel' in data.listColumns().keys(): + channel_axis = data._getAxis('Channel') + + #dt = data.axisValues(time_axis)[1] - data.axisValues(time_axis)[0] + + if data.ndim == 2: + channels = {k:TSeries(channel_id=k, data=data[k].asarray(), time_values=data.axisValues(time_axis), units=data.columnUnits(channel_axis, k), loader=self) for k in data.listColumns(channel_axis)} + elif data.ndim == 3: + channels = {'frames':TSeries(channel_id='frames', data=data.asarray(), time_values=data.axisValues(time_axis), loader=self)} + + rec = Recording( + channels=channels, + start_time=start_time, + device_type=f.name(relativeTo=dh).strip('.ma'), + sync_recording=sync_rec, + file_name=f.name(), + loader=self, + **data.infoCopy()[-1] + ) + + for k in rec.channels: + rec[k]._recording = rec + + recordings[f.name(relativeTo=dh)] = rec + + return recordings + + def get_tseries_data(self, tseries): + """Return a numpy array of the data in the tseries.""" + #### I don't think we need this because we hand TSeries their data when we instantiate them. + raise NotImplementedError("Must be implemented in subclass. -- This should only get called if we're using lazy loading.") + + def load_stimulus(self, recording) -> stimuli.Stimulus: + #### I don't know whether I should try to parse this from metadata, or just find square pulses in the command waveform. + ### I think finding square pulses would be simpler, but makes the assumption that pulses are square. Which is probably usually true. + ### what if I check the wavegenerator widget data for the function name (pulse) and then findSquarepulses, or raise an exception if it's a different function? + if not isinstance(recording, PatchClampRecording): + raise NotImplementedError('not implemented yet') + fh = DataManager.getFileHandle(recording.meta['file_name']) + seqDir = PatchEPhys.getParent(fh, 'ProtocolSequence') + if seqDir is not None: + dev_info = seqDir.info()['devices'][recording.device_id] + + if dev_info['mode'].lower() == 'vc': + units = 'V' + elif dev_info['mode'].lower() == 'ic': + units = 'A' + else: + units = None + + items = [] + + if dev_info['holdingCheck']: + items.append(stimuli.Offset(dev_info['holdingSpin'])) + + stim_pulses = PatchEPhys.getStimParams(fh) + + for p in stim_pulses: + if p['function_type'] == 'pulse': + items.append(stimuli.SquarePulse(p['start'], p['length'], p['amplitude'])) + elif p['function_type'] == 'pulseTrain': + items.append(stimuli.SquarePulseTrain(p['start'], p['pulse_number'], p['length'], p['amplitude'], p['period'])) + + desc = seqDir.shortName()[:-4] + return stimuli.Stimulus(desc, items=items, units=units) + + def load_stimulus_items(self, recording): + """Return a list of Stimulus instances. + Used with LazyLoadStimulus to parse stimuli when they are needed.""" + raise NotImplementedError("Must be implemented in subclass.") + + def load_test_pulse(self, recording): + """Return a PatchClampTestPulse.""" + raise NotImplementedError("Don't know how to programatically determine what is a test pulse in acq4.") + + def find_nearest_test_pulse(self, recording): + raise NotImplementedError("Don't know how to programatically determine what is a test pulse in acq4.") \ No newline at end of file diff --git a/neuroanalysis/data/loaders/loaders.py b/neuroanalysis/data/loaders/loaders.py new file mode 100644 index 0000000..6cd1c9f --- /dev/null +++ b/neuroanalysis/data/loaders/loaders.py @@ -0,0 +1,46 @@ +import numpy as np + +from neuroanalysis.data import PatchClampRecording, SyncRecording +from neuroanalysis.stimuli import Stimulus +from neuroanalysis.test_pulse import PatchClampTestPulse + + +class DatasetLoader(object): + """An abstract base class for Dataset loaders.""" + + def get_dataset_name(self) -> str: + """Return a string with the name of this dataset.""" + raise NotImplementedError("Must be implemented in subclass.") + + def get_sync_recordings(self, dataset) -> list[SyncRecording]: + """Return a tuple (list of SyncRecordings, list of RecordingSequences).""" + raise NotImplementedError("Must be implemented in subclass.") + + def get_recordings(self, sync_rec) -> dict[str, PatchClampRecording]: + """Return a dict of {device: Recording}""" + raise NotImplementedError("Must be implemented in subclass.") + + def get_tseries_data(self, tseries) -> np.ndarray: + """Return a numpy array of the data in the tseries.""" + raise NotImplementedError("Must be implemented in subclass.") + + def load_stimulus(self, recording) -> Stimulus: + """Return an instance of stimuli.Stimulus""" + raise NotImplementedError("Must be implemented in subclass.") + + def load_stimulus_items(self, recording) -> list[Stimulus]: + """Return a list of Stimulus instances. + Used with LazyLoadStimulus to parse stimuli when they are needed.""" + raise NotImplementedError("Must be implemented in subclass.") + + def load_test_pulse(self, recording) -> PatchClampTestPulse: + """Return a PatchClampTestPulse.""" + raise NotImplementedError("Must be implemented in subclass.") + + def find_nearest_test_pulse(self, recording): + raise NotImplementedError("Must be implemented in subclass.") + + def get_baseline_regions(self, recording): + raise NotImplementedError("Must be implemented in subclass.") + + diff --git a/neuroanalysis/data/loaders/mies_dataset_loader.py b/neuroanalysis/data/loaders/mies_dataset_loader.py new file mode 100644 index 0000000..c1e4141 --- /dev/null +++ b/neuroanalysis/data/loaders/mies_dataset_loader.py @@ -0,0 +1,466 @@ +import h5py +import numpy as np + +# import aisynphys.pipeline.opto.data_model as dm +import neuroanalysis.stimuli as stimuli +import neuroanalysis.util.mies_nwb_parsing as parser +from neuroanalysis.data.dataset import SyncRecording, PatchClampRecording, Recording, TSeries +from neuroanalysis.data.loaders.loaders import DatasetLoader +from neuroanalysis.test_pulse import PatchClampTestPulse + + +class MiesNwbLoader(DatasetLoader): + _baseline_analyzer_class = None ## make room for subclasses to automatically supply baseline analyzers + + def __init__(self, file_path, baseline_analyzer_class=None): + self._file_path = file_path + if baseline_analyzer_class is not None: + self._baseline_analyzer_class = baseline_analyzer_class + + self._time_series = None ## parse nwb into sweep_number: info dictionary for lookup of individual sweeps + self._notebook = None ## parse the lab_notebook part of the nwb + self._hdf = None ## holder for the .hdf file + self._rig = None ## holder for the name of the rig this nwb was recorded on + self._device_config = None + + @property + def hdf(self): + if self._hdf is None: + self._hdf = h5py.File(self._file_path, 'r') + return self._hdf + + @property + def time_series(self): + if self._time_series is None: + self._time_series = {} + for ts_name, ts in self.hdf['acquisition/timeseries'].items(): + src = dict([field.split('=') for field in ts.attrs['source'].split(';')]) + sweep = int(src['Sweep']) + ad_chan = int(src['AD']) + src['hdf_group_name'] = 'acquisition/timeseries/' + ts_name + self._time_series.setdefault(sweep, {})[ad_chan] = src + return self._time_series + + @property + def notebook(self): + """Return compiled data from the lab notebook. + + The format is a dict like ``{sweep_number: [ch1, ch2, ...]}`` that contains one key:value + pair per sweep. Each value is a list containing one metadata dict for each channel in the + sweep. For example:: + + nwb.notebook()[sweep_id][channel_id][metadata_key] + """ + if self._notebook is None: + self._notebook = parser.parse_lab_notebook(self.hdf) + return self._notebook + + def get_dataset_name(self): + return self._file_path + + def get_sync_recordings(self, dataset): + ### miesnwb parses sweeps and contents into nwb._timeseries -- this happens in a hidden way inside nwb.contents() + ## other classes (sync_recordings, etc) then use nwb._timeseries to look up their data by sweep number + ## So, possibly we save _timeseries here in the loader instead. + sweep_ids = sorted(list(self.time_series.keys())) + sweeps = [] + for sweep_id in sweep_ids: + sweeps.append(SyncRecording(parent=dataset, key=sweep_id, loader=self)) + return sweeps + + def get_recordings(self, sync_rec): + ### return {device: recording} + recordings = {} + sweep_id = sync_rec.key + + ### Hardcode this now, figure out configuration system when needed + device_map = { + 'AD6': 'Fidelity', + 'TTL1_0': 'Prairie_Command', + 'TTL1_1': 'LED-470nm', + 'TTL1_2': 'LED-590nm' + } + + for ch, meta in self.time_series[sweep_id].items(): + if 'data_%05d_AD%d' %(sweep_id, ch) in self.hdf['acquisition/timeseries'].keys(): + hdf_group = self.hdf['acquisition/timeseries/data_%05d_AD%d' %(sweep_id, ch)] + + ### this channel is a patch-clamp headstage + if 'electrode_name' in hdf_group: + #rec = OptoMiesRecording(self, sweep_id, ch) + device_id = int(hdf_group['electrode_name'][()][0].split('_')[1]) + + nb = self.notebook[sweep_id][device_id] + meta = {} + meta['holding_potential'] = ( + None if nb['V-Clamp Holding Level'] is None + else nb['V-Clamp Holding Level'] * 1e-3 + ) + meta['holding_current'] = ( + None if nb['I-Clamp Holding Level'] is None + else nb['I-Clamp Holding Level'] * 1e-12 + ) + meta['notebook'] = nb + if nb['Clamp Mode'] == 0: + meta['clamp_mode'] = 'vc' + else: + meta['clamp_mode'] = 'ic' + meta['bridge_balance'] = ( + 0.0 if nb['Bridge Bal Enable'] == 0.0 or nb['Bridge Bal Value'] is None + else nb['Bridge Bal Value'] * 1e6 + ) + meta['lpf_cutoff'] = nb['LPF Cutoff'] + offset = nb['Pipette Offset'] # sometimes the pipette offset recording can fail?? + meta['pipette_offset'] = None if offset is None else offset * 1e-3 + meta['sweep_name'] = 'data_%05d_AD%d' %(sweep_id, ch) + start_time = parser.igorpro_date(nb['TimeStamp']) + dt = hdf_group['data'].attrs['IGORWaveScaling'][1,0] / 1000. + + + rec = PatchClampRecording(### this makes TSeries when we make Recordings instead of waiting until recordings ask for their TSeries -- which is something I've been trying to get away from in the rest of this refactor + channels={'primary':TSeries(channel_id='primary', dt=dt, start_time=start_time, loader=self), + 'command':TSeries(channel_id='command', dt=dt, start_time=start_time, loader=self)}, + start_time=start_time, + device_type="MultiClamp 700", + device_id=device_id, + sync_recording=sync_rec, + loader=self, + **meta + ) + rec['primary']._recording = rec + rec['command']._recording = rec + + recordings[rec.device_id] = rec + + + ### Alice checked to see if there were pulses before labeling a trace as fidelity or ttl - if there weren't pulses she labelled it unknown -- is this necessary? -- I'm gonna say 'no' for right now + else: ### This is a pockel-cell recording + dt = hdf_group['data'].attrs['IGORWaveScaling'][1,0]/1000. + nb = self.notebook[sweep_id][ch] ## not sure if ch is the right thing to access this + meta = {} + meta['notebook'] = nb + meta['sweep_name'] = 'data_%05d_AD%d'%(sweep_id, ch) + start_time = parser.igorpro_date(nb['TimeStamp']) + #device = 'Fidelity' ## do this for right now, implement lookup in the future + device = device_map[meta['sweep_name'][-3:]] + + rec = Recording( + #channels = {'reporter':TSeries(data=np.array(data), dt=dt)}, + channels = {'reporter':TSeries(channel_id='reporter', dt=dt, start_time=start_time, loader=self)}, + device_type = device, + device_id=device, + sync_recording = sync_rec, + loader=self, + start_time=start_time, + **meta) + rec['reporter']._recording = rec + + recordings[rec.device_id] = rec + + ## now get associated ttl traces: + for k in self.hdf['stimulus/presentation'].keys(): + if k.startswith('data_%05d_TTL' % sweep_id): + ttl_data = self.hdf['stimulus/presentation/' + k]['data'] + dt = ttl_data.attrs['IGORWaveScaling'][1,0] / 1000. + + #ttl_num = k.split('_')[-1] + #device = self.device_config['TTL1_%s'%ttl_num] + ttl = k.split('_', 2)[-1] + device = device_map[ttl] + + meta={} + meta['sweep_name'] = k + + rec = Recording( + channels={'reporter':TSeries(channel_id='reporter', dt=dt, loader=self)}, + device_type = device, + device_id=device, + sync_recording=sync_rec, + loader=self, + **meta) + rec['reporter']._recording = rec + + recordings[rec.device_id]=rec + + + # rec.device_name = device_mapping['Wayne']['AD%d'%ch] + # return rec + # else: + # k = 'data_%05d_AD%d' % (sweep_id, ch) + # opto_rec = OptoRecording(self, sweep_id, ch, k) + # if opto_rec is None: + # return None + # else: + # opto_rec.device_name = device_mapping['Wayne']['AD%d'%ch] + # return opto_rec + + return recordings + + + def get_tseries_data(self, tseries): + rec = tseries.recording + chan = tseries.channel_id + + if chan == 'primary': + scale = 1e-12 if rec.clamp_mode == 'vc' else 1e-3 + #data = np.array(rec.primary_hdf) * scale + data = np.array(self.hdf['acquisition']['timeseries'][rec.meta['sweep_name']]['data'])*scale + + elif chan == 'command': + scale = 1e-3 if rec.clamp_mode == 'vc' else 1e-12 + # command values are stored _without_ holding, so we add + # that back in here. + offset = rec.holding_potential if rec.clamp_mode == 'vc' else rec.holding_current + if offset is None: + exc = Exception("Holding value unknown for this recording; cannot generate command data.") + # Mark this exception so it can be ignored in specific places + exc._ignorable_bug_flag = True + raise exc + #self._data = (np.array(rec.command_hdf) * scale) + offset + data = (np.array(self.hdf['stimulus']['presentation']['data_%05d_DA%d'%(rec.sync_recording.key, self.get_da_chan(rec))]['data']) * scale) + offset + + elif chan == 'reporter': + if 'AD' in rec.meta['sweep_name']: + data = np.array(self.hdf['acquisition']['timeseries'][rec.meta['sweep_name']]['data']) + elif 'TTL' in rec.meta['sweep_name']: + data = np.array(self.hdf['stimulus']['presentation'][rec.meta['sweep_name']]['data']) + else: + raise Exception("Not sure where to find data for recording: %s"%rec.meta['sweep_name']) + + else: + raise Exception("Getting data for channels named %s is not yet implemented." % chan) + + + if np.isnan(data[-1]): + # recording was interrupted; remove NaNs from the end of the array + + first_nan = np.searchsorted(data, np.nan) + data = data[:first_nan] + + return data + + def get_da_chan(self, rec): + """Return the DA channel ID for the given recording. + """ + da_chan = None + + hdf = self.hdf['stimulus/presentation'] + stims = [k for k in hdf.keys() if k.startswith('data_%05d_'%rec.sync_recording.key)] + for s in stims: + if 'TTL' in s: + continue + elec = hdf[s]['electrode_name'][()][0] + if elec == 'electrode_%d' % rec.device_id: + da_chan = int(s.split('_')[-1][2:]) + + if da_chan is None: + raise Exception("Cannot find DA channel for headstage %d" % self.device_id) + + return da_chan + + def load_test_pulse(self, rec): + if not isinstance(rec, PatchClampRecording): + raise TypeError(f"Can only load test pulses for PatchClampRecording, not {type(rec)}") + + if rec.meta['notebook']['TP Insert Checkbox'] != 1.0: # no test pulse + return None + + # get start/stop indices of the test pulse region + pulse_dur = rec.meta['notebook']['TP Pulse Duration'] / 1000. + total_dur = pulse_dur / (1.0 - 2. * rec.meta['notebook']['TP Baseline Fraction']) + start = 0 + stop = start + int(total_dur / rec['primary'].dt) + + return PatchClampTestPulse(rec, indices=(start, stop)) + + def find_nearest_test_pulse(self, rec): + sweep_id = rec.sync_recording.key + device_id = rec.device_id + + min_dt = None + nearest = None + for srec in rec.sync_recording.parent.contents: + if device_id not in srec.devices: + continue + if srec[device_id].meta['notebook']['TP Insert Checkbox'] == 1.0: + dt = abs((srec[device_id].start_time - rec.start_time).total_seconds()) + if min_dt is None or dt < min_dt: + min_dt = dt + nearest = srec[device_id].test_pulse + if min_dt is not None and dt > min_dt: + break + + return nearest + + def load_stimulus(self, rec): + if isinstance(rec, PatchClampRecording): + desc = self.hdf['acquisition/timeseries'][rec.meta['sweep_name']]['stimulus_description'][()][0] + return stimuli.LazyLoadStimulus(description=desc, loader=self, source=rec) + else: + raise Exception('not implemented yet') + #return stimuli.Stimulus(description=desc, items=self.load_stimulus_items(rec)) + + def load_stimulus_items(self, rec): + items = [] + + # Add holding offset, determine units + if rec.clamp_mode == 'ic': + units = 'A' + items.append(stimuli.Offset( + start_time=0, + amplitude=rec.holding_current, + description="holding current", + units=units, + )) + elif rec.clamp_mode == 'vc': + units = 'V' + items.append(stimuli.Offset( + start_time=0, + amplitude=rec.holding_potential, + description="holding potential", + units=units, + )) + else: + units = None + + # inserted test pulse? + #if rec.has_inserted_test_pulse: + # self.append_item(rec.inserted_test_pulse.stimulus) + if rec.test_pulse is not None: + items.append(rec.test_pulse.stimulus) + + notebook = rec.meta['notebook'] + + if 'Stim Wave Note' in notebook: + # Stim Wave Note format is explained here: + # https://alleninstitute.github.io/MIES/file/_m_i_e_s___wave_builder_8ipf.html#_CPPv319WB_GetWaveNoteEntry4wave8variable6string8variable8variable + + # read stimulus structure from notebook + #version, epochs = rec._stim_wave_note() + version, epochs = parser.parse_stim_wave_note(notebook) + assert len(epochs) > 0 + scale = (1e-3 if rec.clamp_mode == 'vc' else 1e-12) * notebook['Stim Scale Factor'] + t = (notebook['Delay onset oodDAQ'] + notebook['Delay onset user'] + notebook['Delay onset auto']) * 1e-3 + + # if dDAQ is active, add delay from previous channels + if notebook['Distributed DAQ'] == 1.0: + ddaq_delay = notebook['Delay distributed DAQ'] * 1e-3 + for dev in rec.parent.devices: + other_rec = rec.parent[dev] + if other_rec is rec: + break + #_, epochs = rec._stim_wave_note() + if 'Stim Wave Note' in other_rec.meta['notebook']: + _, other_epochs = parser.parse_stim_wave_note(other_rec.meta['notebook']) + for ep in other_epochs: + dt = float(ep.get('Duration', 0)) * 1e-3 + t += dt + t += ddaq_delay + + for epoch_n,epoch in enumerate(epochs): + try: + if epoch['Epoch'] == 'nan': + # Sweep-specific entry; not sure if we need to do anything with this. + continue + + stim_type = epoch.get('Type') + duration = float(epoch.get('Duration', 0)) * 1e-3 + name = "Epoch %d" % int(epoch['Epoch']) + if stim_type == 'Square pulse': + item = stimuli.SquarePulse( + start_time=t, + amplitude=float(epoch['Amplitude']) * scale, + duration=duration, + description=name, + units=units, + ) + elif stim_type == 'Pulse Train': + assert epoch['Poisson distribution'] == 'False', "Poisson distributed pulse train not supported" + assert epoch['Mixed frequency'] == 'False', "Mixed frequency pulse train not supported" + assert epoch['Pulse Type'] == 'Square', "Pulse train with %s pulse type not supported" + item = stimuli.SquarePulseTrain( + start_time=t, + n_pulses=int(epoch['Number of pulses']), + pulse_duration=float(epoch['Pulse duration']) * 1e-3, + amplitude=float(epoch['Amplitude']) * scale, + interval=float(epoch['Pulse To Pulse Length']) * 1e-3, + description=name, + units=units, + ) + elif stim_type == 'Sin Wave': + # bug in stim wave note version 2: log chirp field is inverted + is_chirp = epoch['Log chirp'] == ('False' if version <= 2 else 'True') + if is_chirp: + assert epoch['FunctionType'] == 'Sin', "Chirp wave function type %s not supported" % epoch['Function type'] + item = stimuli.Chirp( + start_time=t, + start_frequency=float(epoch['Frequency']), + end_frequency=float(epoch['End frequency']), + duration=duration, + amplitude=float(epoch['Amplitude']) * scale, + phase=0, + offset=float(epoch['Offset']) * scale, + description=name, + units=units, + ) + else: + if epoch['FunctionType'] == 'Sin': + phase = 0 + elif epoch['FunctionType'] == 'Cos': + phase = np.pi / 2.0 + else: + raise ValueError("Unsupported sine wave function type: %r" % epoch['FunctionType']) + + item = stimuli.Sine( + start_time=t, + frequency=float(epoch['Frequency']), + duration=duration, + amplitude=float(epoch['Amplitude']) * scale, + phase=phase, + offset=float(epoch['Offset']) * scale, + description=name, + units=units, + ) + else: + print(epoch) + print("Warning: unknown stimulus type %s in %s sweep %s" % (stim_type, self._file_path, rec.meta['sweep_name'])) + item = None + except Exception as exc: + print("Warning: error reading stimulus epoch %d in %s sweep %s: %s" % (epoch_n, self._file_path, rec.meta['sweep_name'], str(exc))) + + t += duration + if item is not None: + items.append(item) + + return items + + def get_baseline_regions(self, recording): + if self._baseline_analyzer_class is None: + raise Exception("Cannot get baseline regions, no baseline analyzer class was supplied upon initialization of %s." % self.__class__.__name__) + + return self._baseline_analyzer_class.get(recording.sync_recording).baseline_regions + + + + + +### Notes: +# -- Goal: make it so we don't have subclasses of the data model classes - ie, use Dataset, SyncRecording, Recording, PatchClampRecording -- instead move idiosyncracies about loading data into a DataLoader class +# -- one problem I'm running into is with all the extra functions implemented in MiesRecording - like nearest_test_pulse and baseline_regions +# -- on the one hand I wonder if that is the right place for those functions, and on the other I'm not sure where to put them +# -> well, I think maybe baseline regions should be an analyzer. Maybe nearest test pulse should be as well. +# -- part of why I'm running into the problems with the extra functions is that I don't know how to handle all the info in the lab_notebook, and those +# functions use the lab_notebook info +# -- solution (?): +# 1) Parse the notebook info and save it in the loader. +# 2) Use the notebook info to supply standard metadata about each recording and save the rest in rec.meta (anything should be allowed in rec.meta) +# 3) Write an AI specific analyzer that returns the nearest_test_pulse for a recording (and because this is AI specific it can raise errors if +# the correct metadata isn't there) +# 4) Write a baseline_regions analyzer, and use that to get baseline_regions instead of that analysis happening inside the Recording class. + + + + + + + diff --git a/neuroanalysis/event_detection.py b/neuroanalysis/event_detection.py index b75ed20..b29695e 100644 --- a/neuroanalysis/event_detection.py +++ b/neuroanalysis/event_detection.py @@ -1,6 +1,7 @@ -from __future__ import division import numpy as np +import scipy.optimize from .data import TSeries +from .fitting.psp import Psp def zero_crossing_events(data, min_length=3, min_peak=0.0, min_sum=0.0, noise_threshold=None): @@ -15,7 +16,51 @@ def zero_crossing_events(data, min_length=3, min_peak=0.0, min_sum=0.0, noise_th Returns an array of events where each row is (start, length, sum, peak) """ - + def measureNoise(data, threshold=2.0, iterations=2): + ## Determine the base level of noise + data = data.view(np.ndarray) + if iterations > 1: + med = np.median(data) + std = data.std() + thresh = std * threshold + arr = np.ma.masked_outside(data, med - thresh, med + thresh) + return measureNoise(arr[~arr.mask], threshold, iterations-1) + else: + return data.std() + + def fit(function, xVals, yVals, guess, errFn=None, measureError=False, generateResult=False, resultXVals=None, **kargs): + """fit xVals, yVals to the specified function. + If generateResult is True, then the fit is used to generate an array of points from function + with the xVals supplied (useful for plotting the fit results with the original data). + The result x values can be explicitly set with resultXVals.""" + if errFn is None: + errFn = lambda v, x, y: function(v, x)-y + if len(xVals) < len(guess): + raise Exception("Too few data points to fit this function. (%d variables, %d points)" % (len(guess), len(xVals))) + fitResult = scipy.optimize.leastsq(errFn, guess, args=(xVals, yVals), **kargs) + error = None + #if measureError: + #error = errFn(fit[0], xVals, yVals) + result = None + if generateResult or measureError: + if resultXVals is not None: + xVals = resultXVals + result = function(fitResult[0], xVals) + #fn = lambda i: function(fit[0], xVals[i.astype(int)]) + #result = fromfunction(fn, xVals.shape) + if measureError: + error = abs(yVals - result).mean() + return fitResult + (result, error) + + def fitGaussian(xVals, yVals, guess=[1.0, 0.0, 1.0, 0.0], **kargs): + """Returns least-squares fit parameters for function v[0] * exp(((x-v[1])**2) / (2 * v[2]**2)) + v[3]""" + return fit(gaussian, xVals, yVals, guess, **kargs) + + def gaussian(v, x): + """Gaussian function value at x. The parameter v is [amplitude, x-offset, sigma, y-offset]""" + return v[0] * np.exp(-((x-v[1])**2) / (2 * v[2]**2)) + v[3] + + if isinstance(data, TSeries): xvals = data.time_values data1 = data.data @@ -71,7 +116,7 @@ def zero_crossing_events(data, min_length=3, min_peak=0.0, min_sum=0.0, noise_th ## Fit gaussian to peak in size histogram, use fit sigma as criteria for noise rejection stdev = measureNoise(data1) #p.mark('measureNoise') - hist = histogram(events['sum'], bins=100) + hist = np.histogram(events['sum'], bins=100) #p.mark('histogram') histx = 0.5*(hist[1][1:] + hist[1][:-1]) ## get x values from middle of histogram bins #p.mark('histx') @@ -98,7 +143,7 @@ def zero_crossing_events(data, min_length=3, min_peak=0.0, min_sum=0.0, noise_th def threshold_events(trace, threshold, adjust_times=True, baseline=0.0, omit_ends=True): """ - Finds regions in a trace that cross a threshold value (as measured by distance from baseline). Returns the index, length, peak, and sum of each event. + Finds regions in a TSeries that cross a threshold value (as measured by distance from baseline). Returns the index, length, peak, and sum of each event. Optionally adjusts index to an extrapolated baseline-crossing. """ threshold = abs(threshold) @@ -109,6 +154,9 @@ def threshold_events(trace, threshold, adjust_times=True, baseline=0.0, omit_end ## find all threshold crossings hits = [] for signed_threshold in (-threshold, threshold): + if len(data1) == 0: + continue + if signed_threshold < 0: mask = data1 < signed_threshold else: @@ -158,6 +206,7 @@ def threshold_events(trace, threshold, adjust_times=True, baseline=0.0, omit_end ## Lots of work ahead: ## 1) compute length, peak, sum for each event ## 2) adjust event times if requested, then recompute parameters + last_adj = 0 for i in range(n_events): ind1, ind2 = hits[i] ln = ind2-ind1 @@ -169,7 +218,7 @@ def threshold_events(trace, threshold, adjust_times=True, baseline=0.0, omit_end peak_ind = np.argmin(ev_data) peak = ev_data[peak_ind] peak_ind += ind1 - + #print "event %f: %d" % (xvals[ind1], ind1) if adjust_times: ## Move start and end times outward, estimating the zero-crossing point for the event @@ -300,8 +349,8 @@ def clements_bekkers(data, template): See Clements & Bekkers, Biophysical Journal, 73: 220-229, 1997. """ # Strip out meta-data for faster computation - D = data.view(ndarray) - T = template.view(ndarray) + D = data.view(np.ndarray) + T = template.view(np.ndarray) # Prepare a bunch of arrays we'll need later N = len(T) @@ -309,7 +358,7 @@ def clements_bekkers(data, template): sumT2 = (T**2).sum() sumD = rolling_sum(D, N) sumD2 = rolling_sum(D**2, N) - sumTD = correlate(D, T, mode='valid') + sumTD = np.correlate(D, T, mode='valid') # compute scale factor, offset at each location: scale = (sumTD - sumT * sumD / N) / (sumT2 - sumT**2 / N) @@ -319,7 +368,7 @@ def clements_bekkers(data, template): SSE = sumD2 + scale**2 * sumT2 + N * offset**2 - 2 * (scale*sumTD + offset*sumD - scale*offset*sumT) # finally, compute error and detection criterion - error = sqrt(SSE / (N-1)) + error = np.sqrt(SSE / (N-1)) DC = scale / error return DC, scale, offset @@ -332,7 +381,7 @@ def exp_deconvolve(trace, tau): """ dt = trace.dt arr = trace.data - deconv = arr[:-1] + (tau / dt) * (arr[1:] - arr[:-1]) + deconv = arr[:-1] + (tau / dt) * np.diff(arr) if trace.has_time_values: # data is one sample shorter; clip time values to match. return trace.copy(data=deconv, time_values=trace.time_values[:-1]) @@ -348,3 +397,35 @@ def exp_reconvolve(trace, tau): for i in range(1, len(d)): d[i] = dtti * d[i-1] + dtt * trace.data[i-1] return trace.copy(data=d) + + +def exp_deconv_psp_params(amp, rise_time, rise_power, decay_tau): + """Analytical solution to exponential deconvolution on a PSP shape. + + For parameter documentation, see fitting.psp.Psp. + + The *decay_tau* parameter is used both to describe the psp shape and as the deconvolution + time constant. Under this constraint, the solution has exactly the same form as the original PSP + but with different parameters. + + Returns + ------- + deconv_amp : float + deconv_rise_time : float + deconv_rise_power : float + deconv_decay_tau : float + """ + # sympy helped us out a bit here: + # import sympy + # t, tr, td, rp, tdec = sympy.symbols('t tr td rp tdec') + # psp = (1 - sympy.exp(-t/tr))**rp * sympy.exp(-t/td) + # dec = sympy.simplify(psp + tdec * sympy.diff(psp, t)) + + rise_tau = Psp._compute_rise_tau(rise_time, rise_power, decay_tau) + deconv_decay_tau = 1 / (1 / rise_tau + 1 / decay_tau) + deconv_rise_power = rise_power - 1 + deconv_rise_time = Psp._compute_rise_time(rise_tau, deconv_rise_power, deconv_decay_tau) + old_max = Psp._psp_inner(rise_time, rise_tau, rise_power, decay_tau) + new_max = Psp._psp_inner(deconv_rise_time, rise_tau, deconv_rise_power, deconv_decay_tau) + deconv_amp = amp * new_max / old_max * rise_power * decay_tau / rise_tau + return deconv_amp, deconv_rise_time, deconv_rise_power, deconv_decay_tau diff --git a/neuroanalysis/fitting/__init__.py b/neuroanalysis/fitting/__init__.py index 17dc2d8..cc2a12c 100644 --- a/neuroanalysis/fitting/__init__.py +++ b/neuroanalysis/fitting/__init__.py @@ -3,4 +3,5 @@ from .gaussian import Gaussian from .sigmoid import Sigmoid from .exp import Exp, Exp2 -from .psp import Psp, StackedPsp, PspTrain, Psp2, fit_psp \ No newline at end of file +from .psp import Psp, StackedPsp, PspTrain, Psp2, fit_psp +from .fit_scale_offset import fit_scale_offset diff --git a/neuroanalysis/fitting/exp.py b/neuroanalysis/fitting/exp.py index ba9bf04..a0815d6 100644 --- a/neuroanalysis/fitting/exp.py +++ b/neuroanalysis/fitting/exp.py @@ -1,26 +1,178 @@ -from __future__ import print_function, division +import warnings import numpy as np +from scipy.optimize import minimize +from typing import Callable + +from .fit_scale_offset import fit_scale_offset from .fitmodel import FitModel +from ..data import TSeries + + +def exp_decay(t, yoffset, yscale, tau, xoffset=0): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return yoffset + yscale * np.exp(-(t-xoffset) / tau) + + +def estimate_exp_params(data: TSeries): + """Estimate parameters for an exponential fit to data. + + Parameters + ---------- + data : TSeries + Data to fit. + + Returns + ------- + params : tuple + (yoffset, yscale, tau, toffset) + """ + start_y = data.data[:len(data.data)//100].mean() + end_y = data.data[-len(data.data)//10:].mean() + yscale = start_y - end_y + yoffset = end_y + cs = np.cumsum(data.data - yoffset) + if yscale > 0: + tau_i = np.searchsorted(cs, cs[-1] * 0.63) + else: + tau_i = len(cs) - np.searchsorted(cs[::-1], cs[-1] * 0.63) + tau = data.time_values[min(tau_i, len(data)-1)] - data.time_values[0] + return yoffset, yscale, tau, data.t0 + + +def normalized_rmse(data, params, fn: Callable=exp_decay): + y = fn(data.time_values, *params) + return np.mean((y - data.data) ** 2)**0.5 / data.data.std() + + +def best_exp_fit_for_tau(tau, x, y, std=None): + """Given a curve defined by x and y, find the yoffset and yscale that best fit + an exponential decay with a fixed tau. + + Parameters + ---------- + tau : float + Decay time constant. + x : array + Time values. + y : array + Data values to fit. + std : float + Standard deviation of the data. If None, it is calculated from *y*. + + Returns + ------- + yscale : float + Y scaling factor for the exponential decay. + yoffset : float + Y offset for the exponential decay. + err : float + Normalized root mean squared error of the fit. + exp_y : array + The exponential decay curve that best fits the data. + + """ + if std is None: + std = y.std() + exp_y = exp_decay(x, tau=tau, yscale=1, yoffset=0) + yscale, yoffset = fit_scale_offset(y, exp_y) + exp_y = exp_y * yscale + yoffset + err = ((exp_y - y) ** 2).mean()**0.5 / std + return yscale, yoffset, err, exp_y + + +def quantify_confidence(tau: float, memory: dict, data: TSeries) -> float: + """ + Given a run of best_exp_fit_for_tau, quantify the confidence in the fit. + """ + # errs = np.array([v[2] for v in memory.values()]) + # std = errs.std() + # n = len(errs) + # data_range = errs.max() - errs.min() + # max_std = (data_range / 2) * np.sqrt((n - 1) / n) + # poor_variation = 1 - std / max_std + + y = data.data + x = data.time_values + err = memory[tau][2] + scale, offset = np.polyfit(x, y, 1) + linear_y = scale * x + offset + linear_err = ((linear_y - y) ** 2).mean()**0.5 / y.std() + exp_like = 1 / (1 + err / linear_err) + exp_like = max(0, exp_like - 0.5) * 2 + + # pv_factor = 1 + # el_factor = 4 + # return ((poor_variation ** pv_factor) * (exp_like ** el_factor)) ** (1 / (pv_factor + el_factor)) + return exp_like + + +def exp_fit(data: TSeries): + """Fit *data* to an exponential decay. + + This is a minimization of the normalized RMS error of the fit over the decay time constant. + Other parameters are determined exactly for each value of the decay time constant. + """ + xoffset = data.t0 + data = data.copy() + data.t0 = 0 + tau_init = 0.5 * (data.time_values[-1]) + memory = {} + std = data.data.std() + + def err_fn(params): + τ = params[0] + # keep a record of all tau values visited and their corresponding fits + if τ not in memory: + memory[τ] = best_exp_fit_for_tau(τ, data.time_values, data.data, std) + return memory[τ][2] + + result = minimize( + err_fn, + tau_init, + bounds=[(1e-9, None)], + ) + + tau = float(result.x[0]) + yscale, yoffset, err, exp_y = memory[tau] + return { + 'fit': (yoffset, yscale, tau), + 'result': result, + 'memory': memory, + 'nrmse': err, + 'confidence': quantify_confidence(tau, memory, data), + 'model': lambda t: exp_decay(t, yoffset, yscale, tau, xoffset), + } class Exp(FitModel): - """Single exponential fitting model. + """Single exponential decay fitting model. Parameters are xoffset, yoffset, amp, and tau. """ def __init__(self): - FitModel.__init__(self, self.exp, independent_vars=['x'], nan_policy='omit', method='nelder') + FitModel.__init__(self, self.exp, independent_vars=['x'], nan_policy='omit', method='least-squares') @staticmethod def exp(x, xoffset, yoffset, amp, tau): - return yoffset + amp * np.exp(-(x - xoffset)/tau) - + return exp_decay(x - xoffset, yoffset, amp, tau) + def fit(self, *args, **kwds): kwds.setdefault('method', 'nelder') return FitModel.fit(self, *args, **kwds) +class ParallelCapAndResist(FitModel): + @staticmethod + def current_at_t(t, v_over_parallel_r, v_over_total_r, tau, xoffset=0): + exp = np.exp(-(t - xoffset) / tau) + return v_over_total_r * (1 - exp) + v_over_parallel_r * exp + + def __init__(self): + super().__init__(self.current_at_t, independent_vars=['t'], nan_policy='omit', method='least-squares') + + class Exp2(FitModel): """Double exponential fitting model. @@ -38,4 +190,3 @@ def exp2(x, xoffset, yoffset, amp, tau1, tau2): out = yoffset + amp * (np.exp(-xoff/tau1) - np.exp(-xoff/tau2)) out[xoff < 0] = yoffset return out - diff --git a/neuroanalysis/fitting/fit_scale_offset.py b/neuroanalysis/fitting/fit_scale_offset.py new file mode 100644 index 0000000..a297e22 --- /dev/null +++ b/neuroanalysis/fitting/fit_scale_offset.py @@ -0,0 +1,18 @@ +# coding: utf8 + +def fit_scale_offset(data, template): + """Return the scale and offset needed to minimize the sum of squared errors between + *data* and *template*:: + + data ≈ scale * template + offset + + Credit: Clements & Bekkers 1997 + """ + assert data.shape == template.shape + N = len(data) + dsum = data.sum() + tsum = template.sum() + scale = ((template * data).sum() - tsum * dsum / N) / ((template**2).sum() - tsum**2 / N) + offset = (dsum - scale * tsum) / N + + return scale, offset diff --git a/neuroanalysis/fitting/fitmodel.py b/neuroanalysis/fitting/fitmodel.py index aaf2ce0..f193e99 100644 --- a/neuroanalysis/fitting/fitmodel.py +++ b/neuroanalysis/fitting/fitmodel.py @@ -1,12 +1,12 @@ -from __future__ import print_function, division """ Derived from acq4 and cnmodel code originally developed by Luke Campagnola and Paul B. Manis, Univerity of North Carolina at Chapel Hill. """ -import numpy as np import lmfit +import numpy as np from ..stats import weighted_std +from ..ui.fitting import FitExplorer class FitModel(lmfit.Model): diff --git a/neuroanalysis/fitting/psp.py b/neuroanalysis/fitting/psp.py index 343934f..51dd8c4 100644 --- a/neuroanalysis/fitting/psp.py +++ b/neuroanalysis/fitting/psp.py @@ -1,12 +1,14 @@ -from __future__ import print_function, division - -import sys, json, warnings +import json, warnings import numpy as np import scipy.optimize +from scipy.special import lambertw from ..data import Trace from ..util.data_test import DataTestCase +from ..baseline import float_mode from .fitmodel import FitModel from .searchfit import SearchFit +from ..util.jit import numba_jit +from ..util.lru_cache import lru_cache class Psp(FitModel): @@ -36,32 +38,34 @@ class Psp(FitModel): are re-expressed to give more direct control over the rise time and peak value. This provides a flatter error surface to fit against, avoiding some of the tradeoff between parameters that Exp2 suffers from. + + The results of this model are only valid when ``rise_time < decay_tau * rise_power``. """ def __init__(self): FitModel.__init__(self, self.psp_func, independent_vars=['x']) @staticmethod - def _psp_inner(x, rise, decay, power): - return (1.0 - np.exp(-x / rise))**power * np.exp(-x / decay) + def _psp_inner(x, rise_tau, rise_power, decay_tau): + return (1.0 - np.exp(-x / rise_tau))**rise_power * np.exp(-x / decay_tau) @staticmethod - def _psp_max_time(rise, decay, rise_power): + def _psp_max_time(rise_tau, rise_power, decay_tau): """Return the time from start to peak for a psp with given parameters.""" - return rise * np.log(1 + (decay * rise_power / rise)) + return rise_tau * np.log(1 + (decay_tau * rise_power / rise_tau)) @staticmethod def psp_func(x, xoffset, yoffset, rise_time, decay_tau, amp, rise_power): """Function approximating a PSP shape. """ rise_tau = Psp._compute_rise_tau(rise_time, rise_power, decay_tau) - max_val = Psp._psp_inner(rise_time, rise_tau, decay_tau, rise_power) + max_val = Psp._psp_inner(rise_time, rise_tau, rise_power, decay_tau) xoff = x - xoffset output = np.empty(xoff.shape, xoff.dtype) output[:] = yoffset mask = xoff >= 0 - output[mask] = yoffset + (amp / max_val) * Psp._psp_inner(xoff[mask], rise_tau, decay_tau, rise_power) + output[mask] = yoffset + (amp / max_val) * Psp._psp_inner(xoff[mask], rise_tau, rise_power, decay_tau) if not np.all(np.isfinite(output)): raise ValueError("Parameters are invalid: xoffset=%f, yoffset=%f, rise_tau=%f, decay_tau=%f, amp=%f, rise_power=%f, isfinite(x)=%s" % (xoffset, yoffset, rise_tau, decay_tau, amp, rise_power, np.all(np.isfinite(x)))) @@ -69,8 +73,24 @@ def psp_func(x, xoffset, yoffset, rise_time, decay_tau, amp, rise_power): @staticmethod def _compute_rise_tau(rise_time, rise_power, decay_tau): - fn = lambda tr: tr * np.log(1 + (decay_tau * rise_power / tr)) - rise_time - return scipy.optimize.fsolve(fn, (rise_time,))[0] + # rt1 = scipy.optimize.fsolve(Psp._rise_time_from_tau, (rise_time,), (rise_time, rise_power, decay_tau))[0] + + rt_over_td = min(rise_time / (rise_power * decay_tau), 0.99999) + + # lambert W returns real solutions for k=0 and k=-1, but we don't necessarily know which is better.. + denom = np.real(lambertw(-rt_over_td * np.exp(-rt_over_td), k=-1) + rt_over_td) + + rt2 = - rise_time / denom + return rt2 + + @staticmethod + @numba_jit(nopython=True) + def _rise_time_from_tau(rise_tau, rise_time, rise_power, decay_tau): + return rise_tau * np.log(1 + (decay_tau * rise_power / rise_tau)) - rise_time + + @staticmethod + def _compute_rise_time(rise_tau, rise_power, decay_tau): + return rise_tau * np.log((rise_power * decay_tau + rise_tau) / rise_tau) class StackedPsp(FitModel): @@ -84,10 +104,13 @@ def __init__(self): @staticmethod def stacked_psp_func(x, xoffset, yoffset, rise_time, decay_tau, amp, rise_power, exp_amp, exp_tau): + # print('xoff {:g} yoff {:g} rise {:g} decay {:g} amp {:g} exp_amp {:g} '.format(xoffset, yoffset, rise_time, decay_tau, amp, exp_amp)) with warnings.catch_warnings(): warnings.simplefilter("ignore") exp = exp_amp * np.exp(-(x-xoffset) / exp_tau) - return exp + Psp.psp_func(x, xoffset, yoffset, rise_time, decay_tau, amp, rise_power) + v = exp + Psp.psp_func(x, xoffset, yoffset, rise_time, decay_tau, amp, rise_power) + # print(" => ", np.linalg.norm(v)) + return v class PspTrain(FitModel): @@ -150,12 +173,12 @@ def double_psp_func(x, xoffset, yoffset, rise_tau, decay_tau1, decay_tau2, amp1, rise_exp = (1.0 - np.exp(-x / rise_tau))**rise_power decay_exp1 = amp1 * np.exp(-x / decay_tau1) decay_exp2 = amp2 * np.exp(-x / decay_tau2) - out[mask] = riseExp * (decay_exp1 + decay_exp2) + out[mask] = rise_exp * (decay_exp1 + decay_exp2) return out -def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline_like_psp=False, refine=True, init_params=None, fit_kws=None, ui=None): +def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline_like_psp=False, refine=True, init_params=None, decay_tau_bounds=None, rise_time_bounds=None, fit_kws=None, ui=None, max_nfev=500): """Fit a Trace instance to a StackedPsp model. This function is a higher-level interface to StackedPsp.fit: @@ -185,8 +208,14 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline If True, then fit in two stages, with the second stage searching over rise/decay. init_params : dict Initial parameter guesses + decay_tau_bounds : tuple | None + (min, max) values for decay_tau. Default bounds are the initial value for decay_tau 0.1 and 10. + rise_time_bounds : tuple | None + (min, max) values for rise_time. Default bounds are the initial value for rise_time 0.1 and 10. fit_kws : dict Extra keyword arguments to send to the minimizer + max_nfev : int + Maxmimum number of function evaluations Returns ------- @@ -195,6 +224,7 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline """ import pyqtgraph as pg prof = pg.debug.Profiler(disabled=True, delayed=False) + prof("args: %s %s %s %s %s %s %s %s" % (search_window, clamp_mode, sign, exp_baseline, baseline_like_psp, refine, init_params, fit_kws)) if ui is not None: ui.clear() @@ -210,7 +240,6 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline init_params = {} method = 'leastsq' - fit_kws.setdefault('maxfev', 500) # good fit, slow # method = 'Nelder-Mead' @@ -235,8 +264,12 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline # take some measurements to help constrain fit data_min = data.data.min() data_max = data.data.max() + if data_max == data_min: + return None data_mean = data.mean() + baseline_mode = float_mode(data.time_slice(None, search_window[0]).data) + # set initial conditions depending on whether in voltage or current clamp # note that sign of these will automatically be set later on based on the # the *sign* input @@ -247,6 +280,7 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline decay_tau_init = init_params.get('decay_tau', 50e-3) exp_tau_init = init_params.get('exp_tau', 50e-3) exp_amp_max = 100e-3 + yoffset_max = 200e-3 elif clamp_mode == 'vc': amp_init = init_params.get('amp', 20e-12) amp_max = min(500e-12, 3 * (data_max-data_min)) @@ -254,6 +288,7 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline decay_tau_init = init_params.get('decay_tau', 4e-3) exp_tau_init = init_params.get('exp_tau', 4e-3) exp_amp_max = 10e-9 + yoffset_max = 20e-9 else: raise ValueError('clamp_mode must be "ic" or "vc"') @@ -268,10 +303,12 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline raise ValueError('sign must be 1, -1, or 0') # initial condition, lower boundary, upper boundary + decay_tau_bounds = decay_tau_bounds or (decay_tau_init/10., decay_tau_init*10.) + rise_time_bounds = rise_time_bounds or (rise_time_init/10., rise_time_init*10.) base_params = { - 'yoffset': (init_params.get('yoffset', data_mean), -1.0, 1.0), - 'rise_time': (rise_time_init, rise_time_init/10., rise_time_init*10.), - 'decay_tau': (decay_tau_init, decay_tau_init/10., decay_tau_init*10.), + 'yoffset': (init_params.get('yoffset', baseline_mode), -exp_amp_max, exp_amp_max), + 'rise_time': (rise_time_init,) + rise_time_bounds, + 'decay_tau': (decay_tau_init,) + decay_tau_bounds, 'rise_power': (2, 'fixed'), 'amp': amp, } @@ -310,7 +347,9 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline prof('prep for coarse fit') # Find best coarse fit - search = SearchFit(psp, [xoffset], params=base_params, x=data.time_values, data=data.data, fit_kws=fit_kws, method=method) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + search = SearchFit(psp, [xoffset], params=base_params, x=data.time_values, data=data.data, fit_kws=fit_kws, max_nfev=max_nfev, method=method) for i,result in enumerate(search.iter_fit()): pass # prof(' coarse fit iteration %d/%d: %s %s' % (i, len(search), result['param_index'], result['params'])) @@ -333,10 +372,10 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline xoffset = [{'xoffset': ((a+b)/2., a, b)} for a,b in zip(xoffset_chunks[:-1], xoffset_chunks[1:])] # Search amp / rise time / decay tau to avoid traps - rise_time_inits = base_params['rise_time'][0] * 1.2**np.arange(-1,6) + rise_time_inits = base_params['rise_time'][0] * 1.2**np.arange(-3,6) rise_time = [{'rise_time': (x,) + base_params['rise_time'][1:]} for x in rise_time_inits] - decay_tau_inits = base_params['decay_tau'][0] * 2.0**np.arange(-1,2) + decay_tau_inits = base_params['decay_tau'][0] * 2.0**np.arange(-3,6) decay_tau = [{'decay_tau': (x,) + base_params['decay_tau'][1:]} for x in decay_tau_inits] search_params = [ @@ -355,13 +394,15 @@ def fit_psp(data, search_window, clamp_mode, sign=0, exp_baseline=True, baseline amp = [{'amp': (amp_init, -amp_max, amp_max)}, {'amp': (-amp_init, -amp_max, amp_max)}] search_params.append(amp) - prof("prepare for fine fit") + prof("prepare for fine fit %r" % base_params) # Find best fit - search = SearchFit(psp, search_params, params=base_params, x=data.time_values, data=data.data, fit_kws=fit_kws, method=method) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + search = SearchFit(psp, search_params, params=base_params, x=data.time_values, data=data.data, fit_kws=fit_kws, method=method) for i,result in enumerate(search.iter_fit()): pass - # prof(' fine fit iteration %d/%d: %s %s' % (i, len(search), result['param_index'], result['params'])) + prof(' fine fit iteration %d/%d: %s %s' % (i, len(search), result['param_index'], result['params'])) fit = search.best_result prof('fine fit done (%d iter)' % len(search)) diff --git a/neuroanalysis/miesnwb.py b/neuroanalysis/miesnwb.py index 7f71146..ed1ab87 100644 --- a/neuroanalysis/miesnwb.py +++ b/neuroanalysis/miesnwb.py @@ -3,7 +3,7 @@ from datetime import datetime from collections import OrderedDict import numpy as np -import h5py +from .util import h5py_wrapper from .data import Dataset, SyncRecording, PatchClampRecording, TSeries from .test_pulse import PatchClampTestPulse @@ -187,12 +187,7 @@ def contents(self): sweep_ids = sorted(list(self._timeseries.keys())) self._sweeps = [] for sweep_id in sweep_ids: - try: - srec = self.create_sync_recording(int(sweep_id)) - except Exception: - print("Skipping sweep %s:" % sweep_id) - sys.excepthook(*sys.exc_info()) - continue + srec = self.create_sync_recording(int(sweep_id)) self._sweeps.append(srec) return self._sweeps @@ -207,7 +202,7 @@ def open(self): if self._hdf is not None: return try: - self._hdf = h5py.File(self.filename, 'r') + self._hdf = h5py_wrapper.File(self.filename, 'r') except Exception: print("Error opening: %s" % self.filename) raise @@ -271,13 +266,13 @@ def test_pulse_entries(self): class MiesTSeries(TSeries): - def __init__(self, recording, chan): + def __init__(self, recording, chan, units=None): start = recording._meta['start_time'] # Note: this is also available in meta()['Minimum Sampling interval'], # but that key is missing in some older NWB files. dt = recording.primary_hdf.attrs['IGORWaveScaling'][1,0] / 1000. - TSeries.__init__(self, recording=recording, channel_id=chan, dt=dt, start_time=start) + TSeries.__init__(self, recording=recording, channel_id=chan, dt=dt, start_time=start, units=units) @property def data(self): @@ -298,6 +293,7 @@ def data(self): exc._ignorable_bug_flag = True raise exc self._data = (np.array(rec.command_hdf) * scale) + offset + return self._data @property @@ -345,20 +341,24 @@ def __init__(self, sweep, sweep_id, ad_chan): self._meta['notebook'] = nb if nb['Clamp Mode'] == 0: self._meta['clamp_mode'] = 'vc' + primary_units = 'A' + command_units = 'V' else: self._meta['clamp_mode'] = 'ic' self._meta['bridge_balance'] = ( 0.0 if nb['Bridge Bal Enable'] == 0.0 or nb['Bridge Bal Value'] is None else nb['Bridge Bal Value'] * 1e6 ) + primary_units = 'V' + command_units = 'A' self._meta['lpf_cutoff'] = nb['LPF Cutoff'] offset = nb['Pipette Offset'] # sometimes the pipette offset recording can fail?? self._meta['pipette_offset'] = None if offset is None else offset * 1e-3 datetime = MiesNwb.igorpro_date(nb['TimeStamp']) self.meta['start_time'] = datetime - self._channels['primary'] = MiesTSeries(self, 'primary') - self._channels['command'] = MiesTSeries(self, 'command') + self._channels['primary'] = MiesTSeries(self, 'primary', units=primary_units) + self._channels['command'] = MiesTSeries(self, 'command', units=command_units) @property def stimulus(self): @@ -469,11 +469,14 @@ def inserted_test_pulse(self): @property def baseline_regions(self): """A list of (start, stop) time pairs that cover regions of the recording - the cell is expected to be in a steady state. + the cell is expected to be in a steady (unperturbed) state. """ pri = self['primary'] regions = [] + # delay onset auto is time from the beginning of the sweep until the end of the test pulse + # (if any), including its flanking baseline regions start = self.meta['notebook']['Delay onset auto'] / 1000. # duration of test pulse + # delay onset user is extra delay time after the test pulse and before any stim can take effect dur = self.meta['notebook']['Delay onset user'] / 1000. # duration of baseline if dur > 0: regions.append((start, start+dur)) @@ -508,6 +511,12 @@ def __getstate__(self): state['_hdf_group'] = None return state + @property + def aborted(self): + """Bool indicating whether this recording was aborted early. + """ + return np.isnan(self.primary_hdf[-1]) + class MiesTestPulse(PatchClampTestPulse): def __init__(self, entry, rec): @@ -594,6 +603,7 @@ def __init__(self, nwb, sweep_id): self._chan_meta = None self._traces = None self._notebook_entry = None + self._aborted = None # get list of all A/D channels in this sweep self._channel_keys = self._nwb._timeseries.get(sweep_id, {}) @@ -604,7 +614,6 @@ def __init__(self, nwb, sweep_id): for ch in self._ad_channels: # there is a very rare/specific acquisition bug that we want to be able to ignore here: try: - hdf_group_name = self._channel_keys[ch]['hdf_group_name'] rec = self.create_recording(sweep_id, ch) except Exception as exc: if hasattr(exc, '_ignorable_bug_flag'): @@ -631,6 +640,19 @@ def __repr__(self): def parent(self): return self._nwb + @property + def aborted(self): + """Bool indicating whether this sync recording was aborted early. + """ + if self._aborted is None: + self._aborted = False + for dev in self.devices: + rec = self[dev] + if rec.aborted: + self._aborted = True + break + return self._aborted + class MiesStimulus(stimuli.Stimulus): """Stimulus that generates its children by parsing a MIES wavenote @@ -691,11 +713,11 @@ def _parse_wavenote(self): if notebook['Distributed DAQ'] == 1.0: ddaq_delay = notebook['Delay distributed DAQ'] * 1e-3 for dev in rec.parent.devices: - rec = rec.parent[dev] - if rec is self._recording: + rec2 = rec.parent[dev] + if rec2 is self._recording: break - _, epochs = rec._stim_wave_note() - for ep in epochs: + _, epochs2 = rec2._stim_wave_note() + for ep in epochs2: dt = float(ep.get('Duration', 0)) * 1e-3 t += dt t += ddaq_delay @@ -719,17 +741,27 @@ def _parse_wavenote(self): ) elif stim_type == 'Pulse Train': assert epoch['Poisson distribution'] == 'False', "Poisson distributed pulse train not supported" - assert epoch['Mixed frequency'] == 'False', "Mixed frequency pulse train not supported" assert epoch['Pulse Type'] == 'Square', "Pulse train with %s pulse type not supported" - item = stimuli.SquarePulseTrain( - start_time=t, - n_pulses=int(epoch['Number of pulses']), - pulse_duration=float(epoch['Pulse duration']) * 1e-3, - amplitude=float(epoch['Amplitude']) * scale, - interval=float(epoch['Pulse To Pulse Length']) * 1e-3, - description=name, - units=units, - ) + if epoch['Mixed frequency'] == 'True': + ptimes = [float(pt) * 1e-3 for pt in epoch['Pulse Train Pulses'].split(',') if pt.strip() != ''] + item = stimuli.SquarePulseSeries( + start_time=t, + pulse_times=ptimes, + pulse_durations=[float(epoch['Pulse duration']) * 1e-3] * len(ptimes), + amplitudes=[float(epoch['Amplitude']) * scale] * len(ptimes), + description=name, + units=units, + ) + else: + item = stimuli.SquarePulseTrain( + start_time=t, + n_pulses=int(epoch['Number of pulses']), + pulse_duration=float(epoch['Pulse duration']) * 1e-3, + amplitude=float(epoch['Amplitude']) * scale, + interval=float(epoch['Pulse To Pulse Length']) * 1e-3, + description=name, + units=units, + ) elif stim_type == 'Sin Wave': # bug in stim wave note version 2: log chirp field is inverted is_chirp = epoch['Log chirp'] == ('False' if version <= 2 else 'True') @@ -770,7 +802,14 @@ def _parse_wavenote(self): item = None except Exception as exc: print("Warning: error reading stimulus epoch %d in %s sweep %s: %s" % (epoch_n, rec._nwb, rec._trace_id, str(exc))) - + item = None + t += duration if item is not None: self.append_item(item) + + def save(self): + data = stimuli.Stimulus.save(self) + # don't use this class name when saving; just reload as Stimulus. + data['type'] = 'Stimulus' + return data \ No newline at end of file diff --git a/neuroanalysis/neuronsim/components.py b/neuroanalysis/neuronsim/components.py index 6cfa548..e56d1aa 100644 --- a/neuroanalysis/neuronsim/components.py +++ b/neuroanalysis/neuronsim/components.py @@ -32,19 +32,18 @@ def current(self, state): def name(self): if self._name is None: # pick a name that is unique to the section we live in - + # first collect all names names = [] if self._section is None: return None - for o in self._section.mechanisms: - if isinstance(o, Mechanism) and o._name is None: - # skip to avoid recursion - continue - names.append(o.name) - + names.extend( + o.name + for o in self._section.mechanisms + if not isinstance(o, Mechanism) or o._name is not None + ) # iterate until we find an unused name - pfx = self._section.name + '.' + pfx = f'{self._section.name}.' name = pfx + self.type i = 1 while name in names: @@ -144,7 +143,7 @@ def __init__(self, radius=None, cap=10*pF, vm=-65*mV, **kwds): self.cap = cap self.area = cap / self.cap_bar else: - self.area = 4 * 3.1415926 * radius**2 + self.area = 4 / 3 * 3.1415926 * radius**2 self.cap = self.area * self.cap_bar self.ek = -77*mV self.ena = 50*mV @@ -162,14 +161,8 @@ def add(self, mech): return mech def derivatives(self, state): - Im = 0 - for mech in self.mechanisms: - if not mech.enabled: - continue - Im += mech.current(state) - - dv = Im / self.cap - return [dv] + Im = sum(mech.current(state) for mech in self.mechanisms if mech.enabled) + return [Im / self.cap] def current(self, state): """Return the current flowing across the membrane capacitance. @@ -182,9 +175,8 @@ def conductance(self, state): This is for introspection; not used by the integrator. """ - g = 0 - for mech in self.mechanisms: - if not isinstance(mech, Channel) or not mech.enabled: - continue - g += mech.conductance(state) - return g + return sum( + mech.conductance(state) + for mech in self.mechanisms + if isinstance(mech, Channel) and mech.enabled + ) diff --git a/neuroanalysis/neuronsim/mechanisms.py b/neuroanalysis/neuronsim/mechanisms.py index 91e17b8..99a35af 100644 --- a/neuroanalysis/neuronsim/mechanisms.py +++ b/neuroanalysis/neuronsim/mechanisms.py @@ -7,10 +7,12 @@ """ from collections import OrderedDict + import numpy as np import scipy.interpolate -from ..units import * + from .components import Mechanism, Channel +from ..units import MOhm, us, ms, mS, pF, pA, mV, cm class PatchClamp(Mechanism): @@ -38,14 +40,12 @@ def queue_command(self, cmd, dt, start=None): else: last_start, last_dt, last_cmd = self.cmd_queue[-1] next_start = last_start + len(last_cmd) * last_dt - + if start is None: start = next_start - else: - if start < next_start: - raise ValueError('Cannot start next command before %f; asked for %f.' % - (next_start, start)) - + elif start < next_start: + raise ValueError(f'Cannot start next command before {next_start:f}; asked for {start:f}.') + self.cmd_queue.append((start, dt, cmd)) return start @@ -445,14 +445,14 @@ def derivatives(self, state): #gAlpha = 1e-3 * Area/cm**2 #EAlpha = -7e-3 # V -def IAlpha(Vm, t): - if t < Alpha_t0: - return 0. - else: - # g = gmax * (t - onset)/tau * exp(-(t - onset - tau)/tau) - tn = t - Alpha_t0 - if tn > 10.0 * Alpha_tau: - return 0. - else: - return gAlpha * (Vm - EAlpha)*(tn/Alpha_tau) * np.exp(-(tn-Alpha_tau)/Alpha_tau) +# def IAlpha(Vm, t): +# if t < Alpha_t0: +# return 0. +# else: +# # g = gmax * (t - onset)/tau * exp(-(t - onset - tau)/tau) +# tn = t - Alpha_t0 +# if tn > 10.0 * Alpha_tau: +# return 0. +# else: +# return gAlpha * (Vm - EAlpha)*(tn/Alpha_tau) * np.exp(-(tn-Alpha_tau)/Alpha_tau) diff --git a/neuroanalysis/neuronsim/model_cell.py b/neuroanalysis/neuronsim/model_cell.py index afd5344..67093d3 100644 --- a/neuroanalysis/neuronsim/model_cell.py +++ b/neuroanalysis/neuronsim/model_cell.py @@ -1,7 +1,7 @@ import numpy as np from . import Sim, Section, Leak, LGKfast, LGKslow, LGNa, Noise, PatchClamp from ..data import TSeries, PatchClampRecording -from ..units import um, mS, uV, mV, pA, cm, MOhm, ms +from ..units import mS, uV, mV, pA, cm, MOhm, ms class ModelCell(object): @@ -23,7 +23,7 @@ def __init__(self): # Add channels to the membrane self.mechs = { 'leak': Leak(gbar=1.0*mS/cm**2, erev=-75*mV), - 'leak0': Leak(gbar=0, erev=0), # simulate dying cell + 'leak0': Leak(gbar=0, erev=0), # simulate dying cell 'lgkfast': LGKfast(gbar=225*mS/cm**2), 'lgkslow': LGKslow(gbar=0.225*mS/cm**2), 'lgkna': LGNa(), diff --git a/neuroanalysis/neuronsim/sim.py b/neuroanalysis/neuronsim/sim.py index c80f304..7d2df1b 100644 --- a/neuroanalysis/neuronsim/sim.py +++ b/neuroanalysis/neuronsim/sim.py @@ -10,29 +10,148 @@ from collections import OrderedDict import numpy as np import scipy.integrate -from ..units import us +from ..units import us, ms + + +class SimState(object): + """Contains the state of all diff. eq. variables in the simulation. + + During simulation runs, this is used to carry information about all + variables at the current timepoint. After the simulation finishes, this is + used to carry all state variable data collected during the simulation. + + Parameters + ========== + difeq_vars: list + Names of all diff. eq. state variables + dep_vars: dict + Name:function pairs for all dependent variables that may be computed + difeq_state: list + Initial values for all dif. eq. state variables + extra: + Extra name:value pairs that may be accessed from this object + """ + + def __init__(self, difeq_vars, dep_vars=None, difeq_state=None, integrator='odeint', **extra): + self.difeq_vars = difeq_vars + # record indexes of difeq vars for fast retrieval + self.indexes = dict([(k, i) for i, k in enumerate(difeq_vars)]) + + self.dep_vars = dep_vars + self.state = difeq_state + + self.extra = extra + self.integrator = integrator + + def set_state(self, difeq_state): + self.state = difeq_state + + def __getitem__(self, key): + if isinstance(key, slice): + return self.get_slice(key) + # allow lookup by (object, var) + if isinstance(key, tuple): + key = f"{key[0].name}.{key[1]}" + try: + # try this first for speed + return self.state[self.indexes[key]] + except KeyError: + if key in self.dep_vars: + return self.dep_vars[key](self) + else: + return self.extra[key] + + def keys(self): + return self.difeq_vars + list(self.extra.keys()) + # return list(self.indexes.keys()) + list(self.dep_vars.keys()) + list(self.extra.keys()) + + def __contains__(self, key): + # allow lookup by (object, var) + if isinstance(key, tuple): + key = f"{key[0].name}.{key[1]}" + return key in self.indexes or key in self.dep_vars or key in self.extra + + def __str__(self): + rep = f"SimState {id(self)}:\n" + if self.state is not None: + for i, k in enumerate(self.difeq_vars): + rep += f" {k} = {self.state[i][-1]}\n" + else: + rep += " (no state)\n" + return rep + + def get_final_state(self): + """Return a dictionary of all diff. eq. state variables and dependent + variables for all objects in the simulation. + """ + return self.get_state_at_index(-1) + + def get_state_at_time(self, t): + index = np.searchsorted(self['t'], t) + return self.get_state_at_index(index) + + def get_state_at_index(self, index): + s = self.copy() + clip = not np.isscalar(self["t"]) + if clip: + # only get results for the last timepoint + s.set_state(self.state[:, index]) + + state = {} + for k in self.difeq_vars: + state[k] = s[k] + for k in self.dep_vars: + state[k] = s[k] + for k, v in self.extra.items(): + if clip: + state[k] = v[index] + else: + state[k] = v + + return state + + def get_slice(self, sl): + kwds = {'difeq_state': self.state[:, sl]} + for k, v in self.extra.items(): + kwds[k] = v[sl] + return self.copy(**kwds) + + def copy(self, **kwds): + default_kwds = { + 'difeq_vars': self.difeq_vars, + 'dep_vars': self.dep_vars, + 'difeq_state': self.state, + 'integrator': self.integrator, + } + default_kwds.update(self.extra) + default_kwds.update(kwds) + return SimState(**default_kwds) class Sim(object): """Simulator for a collection of objects that derive from SimObject """ - def __init__(self, objects=None, temp=37.0, dt=10*us): + def __init__(self, objects=None, temp=37.0, dt=10*us, integrator:str='solve_ivp'): self._objects = [] self._all_objs = None self._time = 0.0 self.temp = temp self.dt = dt - self.odeint_args = { - 'h0': 1*us, - 'hmax': 100*us, - 'rtol': 1e-6, - 'atol': 1e-6, - 'full_output': 1, - } + self.integrator = integrator + self._simstate = None if objects is not None: for obj in objects: self.add(obj) + def set_integrator(self, integrator:str): + if integrator not in {"odeint", "solve_ivp"}: + raise ValueError(f"Unknown integrator: {integrator}") + self.integrator = integrator + + def change_dt(self, newdt:float=100e-6): + newdt = np.clip(newdt, 5*us, 1*ms) + self.dt = newdt + def add(self, obj): assert obj._sim is None obj._sim = self @@ -47,9 +166,9 @@ def all_objects(self): for o in self._objects: if not o.enabled: continue - for k,v in o.all_objects().items(): + for k, v in o.all_objects().items(): if k in objs: - raise NameError('Multiple objects with same name "%s": %s, %s' % (k, objs[k], v)) + raise NameError(f'Multiple objects with same name "{k}": {objs[k]}, {v}') objs[k] = v self._all_objs = objs return self._all_objs @@ -58,63 +177,104 @@ def all_objects(self): def time(self): return self._time - def run(self, samples=1000, **kwds): + def run(self, samples:int=1000, **kwds) -> SimState: """Run the simulation until a number of *samples* have been acquired. - + Extra keyword arguments are passed to `scipy.integrate.odeint()`. """ - opts = self.odeint_args.copy() - opts.update(kwds) - + # print("Integrator: ", self.integrator) # reset all_objs cache in case some part of the sim has changed self._all_objs = None all_objs = self.all_objects().values() - + # check that there is something to simulate if len(all_objs) == 0: raise RuntimeError("No objects added to simulation.") - + # Collect / prepare state variables for integration init_state = [] difeq_vars = [] dep_vars = {} for o in all_objs: - pfx = o.name + '.' - for k,v in o.difeq_state().items(): + pfx = f'{o.name}.' + for k, v in o.difeq_state().items(): difeq_vars.append(pfx + k) init_state.append(v) - for k,v in o.dep_state_vars.items(): + for k, v in o.dep_state_vars.items(): dep_vars[pfx + k] = v self._simstate = SimState(difeq_vars, dep_vars) t = np.arange(0, samples) * self.dt + self._time - # Run the simulation - result, info = scipy.integrate.odeint(self.derivatives, init_state, t, **opts) - - # Update current state variables - p = 0 - for o in all_objs: - nvar = len(o.difeq_state()) - o.update_state(result[-1, p:p+nvar]) - p += nvar - - self._last_run_time = t - return SimState(difeq_vars, dep_vars, result.T, t=t) - - def derivatives(self, state, t): + opts = {"rtol": 1e-6, "atol": 1e-8, "hmax": 5e-4, "full_output": 1} + opts.update(kwds) + + if self.integrator == 'odeint': + # Run the simulation + result, info = scipy.integrate.odeint(self.derivatives, init_state, t, tfirst=True, **opts) + + # Update current state variables + p = 0 + for o in all_objs: + nvar = len(o.difeq_state()) + o.update_state(result[-1, p : p + nvar]) + p += nvar + self._time = t[-1] + # print(f" {self.integrator:s} final state = {str(result.T[:, -1]):s}") + # print(" start, finished at : ", t[0],t[-1]) + # print(" np.min(result.T): ", np.min(result.T), np.max(result.T)) + return SimState(difeq_vars, dep_vars, result.T, integrator=self.integrator, t=t) + + elif self.integrator == 'solve_ivp': + """Notes: + Different integrators were tried. + LSODA works ok; RK's seem to stall on AP. + Radau and BDF are very fast, but take some large steps, so the calculation of where the + pulse array is in the derivative via get_cmd are not always correct - + the algorithm might step a long time into the future, invalidating all the + commands in the queue, which are removed. Thus, only the first cmd + is executed. + Probably should not pop the queue in get_cmd until we are certain at THIS + level (or maybe in runner?) that the trigger arrays are actually finished. + """ + # in future we will need to implement this instead: + result = scipy.integrate.solve_ivp( + self.derivatives, + t_span=(t[0], t[-1]), + t_eval=t, + y0=init_state, + method="LSODA", # runs ok with LSODA + + dense_output=False, + # args=dep_vars, + rtol = opts['rtol'], #**opts, + atol = opts['atol'], + max_step = opts['hmax'], + ) + # Update current state variables + p = 0 + for o in all_objs: + nvar = len(o.difeq_state()) + # print("solve ivp state: ", p, nvar, result.y[p:p+nvar, -1]) + o.update_state(result.y[p:p+nvar, -1]) + p += nvar + self._time = t[-1] + # print(f"\n {self.integrator:s} {str(result.y[:, -1]):s}") + # print(" start, finished at : ", t[0],t[-1]) + # print(" np.min(result.y): ", np.min(result.y), np.max(result.y)) + + return SimState(difeq_vars, dep_vars, result.y, integrator=self.integrator, t=t) + + def derivatives(self, t, state): objs = self.all_objects().values() - self._simstate.state = state + + # bug: the integrators may trash their outputs later; + # copy them before that can happen + self._simstate.state = state.copy() + self._simstate.extra['t'] = t - - # Record the time of the last simulated sample. Note that this is NOT - # the same as the last _requested_ sample; the integrator may go farther - # depending on its time step - self._time = t - d = [] for o in objs: d.extend(o.derivatives(self._simstate)) - return d @property @@ -122,94 +282,26 @@ def last_state(self): """Return the last values of all state variables in a SimState object. """ return self._simstate - - -class SimState(object): - """Contains the state of all diff. eq. variables in the simulation. - During simulation runs, this is used to carry information about all - variables at the current timepoint. After the simulation finishes, this is - used to carry all state variable data collected during the simulation. - - Parameters - ========== - difeq_vars: list - Names of all diff. eq. state variables - dep_vars: dict - Name:function pairs for all dependent variables that may be computed - difeq_state: list - Initial values for all dif. eq. state variables - extra: - Extra name:value pairs that may be accessed from this object - """ - def __init__(self, difeq_vars, dep_vars=None, difeq_state=None, **extra): - self.difeq_vars = difeq_vars - # record indexes of difeq vars for fast retrieval - self.indexes = dict([(k, i) for i,k in enumerate(difeq_vars)]) - - self.dep_vars = dep_vars - self.state = difeq_state - self.extra = extra - - def set_state(self, difeq_state): - self.state = difeq_state - - def __getitem__(self, key): - # allow lookup by (object, var) - if isinstance(key, tuple): - key = key[0].name + '.' + key[1] - try: - # try this first for speed - return self.state[self.indexes[key]] - except KeyError: - if key in self.dep_vars: - return self.dep_vars[key](self) - else: - return self.extra[key] - - def keys(self): - return self.difeq_vars + list(self.extra.keys()) - - def __repr__(self): - rep = 'SimState:\n' - for i,k in enumerate(self.difeq_vars): - rep += ' %s = %s\n' % (k, self.state[i]) - return rep - - def get_final_state(self): - """Return a dictionary of all diff. eq. state variables and dependent - variables for all objects in the simulation. + def state(self): + """Return dictionary of all dependent and independent state + variables. """ state = {} - s = self.copy() - clip = not np.isscalar(self['t']) - if clip: - # only get results for the last timepoint - s.set_state(self.state[:, -1]) - - for k in self.difeq_vars: - state[k] = s[k] - for k in self.dep_vars: - state[k] = s[k] - for k,v in self.extra.items(): - if clip: - state[k] = v[-1] - else: + for o in self.all_objects(): + for k, v in o.state(self._simstate).items(): state[k] = v - return state - def copy(self): - return SimState(self.difeq_vars, self.dep_vars, self.state, **self.extra) - class SimObject(object): """ Base class for objects that participate in integration by providing a set of state variables and their derivatives. """ + instance_count = 0 - + def __init__(self, init_state, name=None): self._sim = None if name is None: @@ -218,7 +310,7 @@ def __init__(self, init_state, name=None): if i == 0: name = self.type else: - name = self.type + '%d' % i + name = self.type + "%d" % i self._name = name self.enabled = True self._init_state = init_state.copy() # in case we want to reset @@ -226,15 +318,15 @@ def __init__(self, init_state, name=None): self._sub_objs = [] self.records = [] self._rec_dtype = [(sv, float) for sv in init_state.keys()] - + # maps name:function for computing state vars that can be computed from - # a SimState instance. + # a SimState instance. self.dep_state_vars = {} @property def name(self): return self._name - + def all_objects(self): """SimObjects are organized in a hierarchy. This method returns an ordered dictionary of all enabled SimObjects in this branch of the hierarchy, beginning @@ -247,7 +339,7 @@ def all_objects(self): continue objs.update(o.all_objects()) return objs - + def difeq_state(self): """An ordered dictionary of all variables required to solve the diff. eq. for this object. @@ -259,12 +351,12 @@ def update_state(self, result): These will be used to initialize the solver when the next simulation begins. """ - for i,k in enumerate(self._current_state.keys()): + for i, k in enumerate(self._current_state.keys()): self._current_state[k] = result[i] def derivatives(self, state): """Return derivatives of all state variables. - + Must be reimplemented in subclasses. This is used by the ODE solver to integrate during the simulation; should be as fast as possible. """ @@ -272,6 +364,6 @@ def derivatives(self, state): @property def sim(self): - """The Sim instance in which this object is being used. - """ + """The Sim instance in which this object is being used.""" return self._sim + diff --git a/neuroanalysis/spike_detection.py b/neuroanalysis/spike_detection.py index eccd0f8..8344897 100644 --- a/neuroanalysis/spike_detection.py +++ b/neuroanalysis/spike_detection.py @@ -1,15 +1,10 @@ -from __future__ import division, print_function - -import os, pickle, traceback, warnings import numpy as np -from scipy.ndimage import gaussian_filter +import warnings from scipy.optimize import curve_fit -from scipy.stats import scoreatpercentile from .data import TSeries, PatchClampRecording -from .filter import bessel_filter -from .baseline import mode_filter, adaptive_detrend from .event_detection import threshold_events +from .filter import bessel_filter from .util.data_test import DataTestCase @@ -41,7 +36,7 @@ def detect_evoked_spikes(data, pulse_edges, **kwds): elif data.clamp_mode == 'ic': return detect_ic_evoked_spikes(trace, pulse_edges, **kwds) else: - raise ValueError("Unsupported clamp mode %s" % trace.clamp_mode) + raise ValueError(f"Unsupported clamp mode {trace.clamp_mode}") def rc_decay(t, tau, Vo): @@ -135,36 +130,37 @@ def detect_ic_evoked_spikes(trace, pulse_edges, dv2_threshold=40e3, mse_threshol dv_after_pulse = trace.time_slice(pulse_edges[1] + 100e-6, None).diff() dv_after_pulse = bessel_filter(dv_after_pulse, 15e3, bidir=True) - # create a vector to fit - ttofit = dv_after_pulse.time_values # setting time to start at zero, note: +1 because time trace of derivative needs to be one shorter - ttofit = ttofit - ttofit[0] - - # do fit and see if it matches - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - popt, pcov = curve_fit(rc_decay, ttofit, dv_after_pulse.data, maxfev=10000) - fit = rc_decay(ttofit, *popt) - if ui is not None: - ui.plt2.plot(dv_after_pulse.time_values, dv_after_pulse.data) - ui.plt2.plot(dv_after_pulse.time_values, fit, pen='b') - - diff = dv_after_pulse - fit - mse = (diff.data**2).mean() # mean squared error - if mse > mse_threshold: - search_window = 2e-3 - max_slope_time, is_edge = max_time(diff.time_slice(pulse_edges[1], pulse_edges[1] + search_window)) - if is_edge != 0: - max_slope_time = None - peak_time, is_edge = max_time(trace.time_slice(max_slope_time or pulse_edges[1] + 100e-6, pulse_edges[1] + search_window)) - if is_edge != 0: - peak_time = None - spikes.append({ - 'onset_time': None, - 'max_slope_time': max_slope_time, - 'peak_time': peak_time, - 'peak_value': None if peak_time is None else trace.value_at(peak_time), - 'max_slope': None if max_slope_time is None else dv_after_pulse.value_at(max_slope_time), - }) + if dv_after_pulse.duration > 0.5e-3: + # create a vector to fit + ttofit = dv_after_pulse.time_values # setting time to start at zero, note: +1 because time trace of derivative needs to be one shorter + ttofit = ttofit - ttofit[0] + + # do fit and see if it matches + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + popt, pcov = curve_fit(rc_decay, ttofit, dv_after_pulse.data, maxfev=10000) + fit = rc_decay(ttofit, *popt) + if ui is not None: + ui.plt2.plot(dv_after_pulse.time_values, dv_after_pulse.data) + ui.plt2.plot(dv_after_pulse.time_values, fit, pen='b') + + diff = dv_after_pulse - fit + mse = (diff.data**2).mean() # mean squared error + if mse > mse_threshold: + search_window = 2e-3 + max_slope_time, is_edge = max_time(diff.time_slice(pulse_edges[1], pulse_edges[1] + search_window)) + if is_edge != 0: + max_slope_time = None + peak_time, is_edge = max_time(trace.time_slice(max_slope_time or pulse_edges[1] + 100e-6, pulse_edges[1] + search_window)) + if is_edge != 0: + peak_time = None + spikes.append({ + 'onset_time': None, + 'max_slope_time': max_slope_time, + 'peak_time': peak_time, + 'peak_value': None if peak_time is None else trace.value_at(peak_time), + 'max_slope': None if max_slope_time is None else dv_after_pulse.value_at(max_slope_time), + }) for spike in spikes: assert 'max_slope_time' in spike diff --git a/neuroanalysis/stimuli.py b/neuroanalysis/stimuli.py index bbfa56d..82ca93e 100644 --- a/neuroanalysis/stimuli.py +++ b/neuroanalysis/stimuli.py @@ -1,9 +1,9 @@ # coding: utf8 -from __future__ import division, print_function from collections import OrderedDict import numpy as np from .util.custom_weakref import WeakRef from .data import TSeries +from .fitting import psp def load_stimulus(state): @@ -241,13 +241,22 @@ def save(self): """ state = OrderedDict([ ('type', self.type), - ('args', OrderedDict([('start_time', self.start_time)])), + ('args', OrderedDict([('start_time', float(self.start_time))])), ]) for name in self._attributes: - state['args'][name] = getattr(self, name) + state['args'][name] = self._save_value(getattr(self, name)) state['items'] = [item.save() for item in self.items] return state + @classmethod + def _save_value(cls, val): + if isinstance(val, np.floating): + return float(val) + elif isinstance(val, np.integer): + return int(val) + else: + return val + @classmethod def load(cls, state): """Generate and return a Stimulus instance given a state data structure that was @@ -271,6 +280,26 @@ def get_stimulus_class(cls, name): return cls._subclasses[name] +class LazyLoadStimulus(Stimulus): + def __init__(self, description, start_time=0, units=None, items=None, parent=None, loader=None, source=None): + if loader is None: + raise Exception("Use of a LazyLoadStimulus requires a loader to be specified upon init.") + if source is None: + raise Exception("Use of a LazyLoadStimulus requires a source to be specified upon init.") + + Stimulus.__init__(self, description, start_time=start_time, units=units, items=items, parent=parent) + self._loader = loader + self._source = source + + @property + def items(self): + if len(self._items) == 0: + items = self._loader.load_stimulus_items(self._source) + for item in items: + self.append_item(item) + return tuple(self._items) + + class Offset(Stimulus): """A constant offset in the stimulus. @@ -343,7 +372,7 @@ def mask(self, **kwds): return trace -def find_square_pulses(trace, baseline=None): +def find_square_pulses(trace, baseline=None) -> list[SquarePulse]: """Return a list of SquarePulse instances describing square pulses found in the stimulus. @@ -354,7 +383,7 @@ def find_square_pulses(trace, baseline=None): Parameters ---------- trace : TSeries instance - The stimulus command waveform. This data should be noise-free. + The stimulus command waveform. This data should be noise-free and nan-free. baseline : float | None Specifies the value in the command waveform that is considered to be "no pulse". If no baseline is specified, then the first sample of @@ -378,6 +407,69 @@ def find_square_pulses(trace, baseline=None): return pulses +def find_noisy_square_pulses(trace, baseline=None, std_threshold=5.0, min_duration=0, min_amplitude=0): + """Return a list of SquarePulse instances describing square pulses found + in the given trace. + + A pulse is defined as any contiguous region of the stimulus waveform + that has a value outside the amp_threshold or std_threshold from the + baseline. If no baseline is specified, then the first 200 samples in the + stimulus are used. + + Parameters + ---------- + trace : TSeries instance + The stimulus waveform. This can contain noise - for noise free data + see `find_square_pulse`. + baseline : numpy.array | None + Specify an array to use as the baseline (a region considered to be + "no pulse"). If no baseline is specified, then the first 200 samples of + *trace* are used. + std_threshold: float | 3.0 + How many stdev's the pulse must be from the baseline to be detected. + min_duration: float | 0 + If specified, the minimum duration of a pulse (in seconds). Pulses shorter + than min_duration will be discarded. + min_amplitude: float | 0 + If specified, the minimum amplitude of a pulse (absolute value). Pulses + with absolute value amplitudes smaller than min_amplitude will be discarded. + """ + if not isinstance(trace, TSeries): + raise TypeError("argument must be TSeries instance") + + if baseline is None: + baseline = trace.data[:200] + + threshold = baseline.std()*std_threshold + + sdiff = np.diff(trace.data - baseline.mean()) + changes = np.argwhere(abs(sdiff) > threshold)[:, 0] + + ### sometimes square pulses aren't quite square - only count the diff if the index before it is below threshold + real_changes= [] + for c in changes: + if (abs(sdiff[c-1]) < threshold) and (abs(sdiff[c]) > threshold): + real_changes.append(c+1) ## add one to get the first index at the new value (the start of the pulse, rather than the last point of baseline) + changes = real_changes + + pulses = [] + stop = 0 + for i, start in enumerate(changes): + if len(pulses) > 0 and stop >= start: ## this is the end of a pulse + continue + #amp = trace.data[start] - baseline.mean() + #if abs(amp) > threshold: ## should only be true at the start of pulses + else: + stop = changes[i+1] if (len(changes) > i+1) else len(trace) + t_start = trace.time_at(start) + duration = (stop - start) * trace.dt + amplitude = trace.data[start:stop].mean() - baseline.mean() + if duration > min_duration and abs(amplitude) > min_amplitude: + pulses.append(SquarePulse(start_time=t_start, duration=duration, amplitude=amplitude, units=trace.units)) + + return pulses + + class SquarePulseTrain(Stimulus): """A train of identical, regularly-spaced square pulses. @@ -431,6 +523,50 @@ def save(self): return state +class SquarePulseSeries(Stimulus): + """A series of square pulses of varying amplitude, duration, and timing. + + Parameters + ---------- + start_time : float + The starting time of the first pulse in the train, relative to the start time of the parent + stimulus. + pulse_times : float array + Array of starting times for each pulse relative to *start_time*. + pulse_durations : float array + Array of pulse durations in seconds. + amplitudes : float array + Array of pulse amplitudes. + description : str + Optional string describing the stimulus. + units : str | None + Optional string describing the units of values in the stimulus. + """ + _attributes = Stimulus._attributes + ['pulse_times', 'pulse_durations', 'amplitudes'] + + def __init__(self, start_time, pulse_times, pulse_durations, amplitudes, description="square pulse train", units=None, parent=None): + self.pulse_times = pulse_times + self.pulse_durations = pulse_durations + self.amplitudes = amplitudes + assert len(pulse_times) == len(pulse_durations) == len(amplitudes) + Stimulus.__init__(self, description=description, start_time=start_time, units=units, parent=parent) + + for i,t in enumerate(pulse_times): + pulse = SquarePulse(start_time=t, duration=pulse_durations[i], amplitude=amplitudes[i], parent=self, units=units) + pulse.pulse_number = i + + @property + def global_pulse_times(self): + """A list of the global start times of all pulses in the train. + """ + return [t + self.global_start_time for t in self.pulse_times] + + def save(self): + state = Stimulus.save(self) + state['items'] = [] # don't save auto-generated items + return state + + class Ramp(Stimulus): """A linear ramp. @@ -649,3 +785,62 @@ def mask(self, **kwds): chunk = trace.time_slice(start, start+self.duration, index_mode=kwds.get('index_mode')) chunk.data[:] = True return trace + + +class Psp(Stimulus): + """A PSP- or PSC-shaped stimulus. + + This shape is the product of rising and decaying exponentials. See ``neuroanalysis.fitting.psp.Psp``. + + Parameters + ---------- + start_time : float + The starting time (s) of the stimulus. + rise_time : float + Time (s) from stimulus start until the peak of the PSP shape. + decay_tau : float + Exponential decay time constant (s). + amplitude : float + The peak amplitude of the PSP shape. + rise_power : float + Exponent modifying the rising exponential (default is 2; larger values yield a slower initial activation, 1 yields instantaneous activation). + description : str + Optional string describing the stimulus. + units : str | None + Optional string describing the units of values in the stimulus. + + """ + _attributes = Stimulus._attributes + ['rise_time', 'decay_tau', 'amplitude', 'rise_power'] + + def __init__(self, start_time, rise_time, decay_tau, amplitude, rise_power=2, description="frequency chirp", units=None, parent=None): + self.rise_time = rise_time + self.decay_tau = decay_tau + self.amplitude = amplitude + self.rise_power = rise_power + Stimulus.__init__(self, description=description, start_time=start_time, parent=parent, units=units) + + @property + def duration(self): + return 15 * max(self.rise_time, self.decay_tau) + + def eval(self, **kwds): + trace = Stimulus.eval(self, **kwds) + start = self.global_start_time + chunk = trace.time_slice(start, start + self.duration, index_mode=kwds.get('index_mode')) + chunk.data[:] += psp.Psp.psp_func( + x=chunk.time_values, + xoffset=start, + yoffset=0, + rise_time=self.rise_time, + decay_tau=self.decay_tau, + amp=self.amplitude, + rise_power=self.rise_power, + ) + return trace + + def mask(self, **kwds): + trace = Stimulus.mask(self, **kwds) + start = self.global_start_time + chunk = trace.time_slice(start, start+self.duration, index_mode=kwds.get('index_mode')) + chunk.data[:] = True + return trace diff --git a/neuroanalysis/synaptic_release.py b/neuroanalysis/synaptic_release.py index 1fea735..2ff8e5b 100644 --- a/neuroanalysis/synaptic_release.py +++ b/neuroanalysis/synaptic_release.py @@ -1,3 +1,4 @@ +from __future__ import print_function from collections import OrderedDict import scipy.optimize as scimin from scipy.integrate import odeint @@ -253,7 +254,7 @@ def run_fit(self, spike_sets): self.data_y.extend(datay) #self.data_e.extend(dataz) lengths.append(len(datax)) - print self.Dynamics + print(self.Dynamics) params=lmfit.Parameters() @@ -320,14 +321,14 @@ def run_fit(self, spike_sets): for pm in self.order: #print 'pm0',pm if pm in self.Sel_gatings: - print 'variable pm',pm + print('variable pm',pm) self.freeparam=self.freeparam+1 fitmodel.set_param_hint(pm,vary=True,value=self.dict_params[pm],min=self.dict_bounds[pm][0],max=self.dict_bounds[pm][1]) else: - print 'fixed',pm + print('fixed',pm) fitmodel.set_param_hint(pm,vary=False,value=self.dict_params[pm],min=self.dict_bounds[pm][0],max=self.dict_bounds[pm][1]) pars=fitmodel.make_params() - print pars + print(pars) result = fitmodel.fit(numpy.array(self.data_y),spikes=numpy.array(self.data_x),length_array=lengths,dynamics=dynamics_vec,ode_variables=ode_variables_vec) print(result.fit_report()) #ci=lmfit.conf_interval(fitmodel,result) diff --git a/neuroanalysis/test_pulse.py b/neuroanalysis/test_pulse.py index b81749a..b1d13e6 100644 --- a/neuroanalysis/test_pulse.py +++ b/neuroanalysis/test_pulse.py @@ -1,51 +1,120 @@ +import contextlib import numpy as np +import warnings +import pyqtgraph as pg from .data import PatchClampRecording, TSeries -from .fitting import Exp -from .stimuli import find_square_pulses +from .fitting.exp import exp_fit +from .stimuli import find_square_pulses, SquarePulse -class PatchClampTestPulse(PatchClampRecording): - """A PatchClampRecording that contains a subthreshold, square pulse stimulus. +class LowConfidenceFitError(Exception): + pass + + +class PatchClampTestPulse(object): + """A PatchClampRecording of a sub-threshold, square-pulse stimulus. """ - def __init__(self, rec, indices=None): - self._parent_recording = rec - + def __init__(self, rec: PatchClampRecording, indices=None, stimulus=None): if indices is None: indices = (0, len(rec['primary'])) + else: + rec = rec.time_slice(*indices) + self._recording = rec self._indices = indices - start, stop = indices - - pri = rec['primary'][start:stop] - cmd = rec['command'][start:stop] - - # find pulse - pulses = find_square_pulses(cmd) - if len(pulses) == 0: - raise ValueError("Could not find square pulse in command waveform.") - elif len(pulses) > 1: - raise ValueError("Found multiple square pulse in command waveform.") - pulse = pulses[0] - pulse.description = 'test pulse' - - PatchClampRecording.__init__(self, - device_type=rec.device_type, - device_id=rec.device_id, - start_time=rec.start_time, - channels={'primary': pri, 'command': cmd} - ) - self._meta['stimulus'] = pulse - - for k in ['clamp_mode', 'holding_potential', 'holding_current', 'bridge_balance', - 'lpf_cutoff', 'pipette_offset']: - self._meta[k] = rec._meta[k] - + + pri = rec['primary'] + channels = {'primary': pri} + if stimulus is None: + cmd = rec['command'] + channels['command'] = cmd + # find pulse + pulses = find_square_pulses(cmd) + if len(pulses) == 0: + raise ValueError("Could not find square pulse in command waveform. Consider using `indices`.") + elif len(pulses) > 1: + raise ValueError("Found multiple square pulse in command waveform. Consider using `indices`.") + pulse = pulses[0] + pulse.description = 'test pulse' + stimulus = pulse + self._stimulus = stimulus + self._analysis = None - + # expose these for display and debugging + self._main_fit_region = None + self.main_fit_result = None + self.main_fit_trace = None + self.fit_result_with_transient = None + self.fit_trace_with_transient = None + + def save(self): + """Return a dictionary with all data needed to reconstruct this object. + """ + return { + 'schema version': (2, 0), + 'indices': self._indices, + 'recording': self._recording.save(), + 'stimulus': self._stimulus.save(), + } + + @classmethod + def load(cls, data): + """Reconstruct a PatchClampTestPulse from data returned by `dump()`. + """ + if 'schema version' not in data: + return cls._load_unversioned(data) + elif data['schema version'][0] == 1: + return cls._load_v1(data) + elif data['schema version'][0] == 2: + return cls._load_v2(data) + + @classmethod + def _load_unversioned(cls, data): + rec = PatchClampRecording( + device_type=data['device_type'], + device_id=data['device_id'], + start_time=data['start_time'], + channels={'primary': TSeries(data['data'], time_values=data['time_values'])}, + stimulus=SquarePulse(**data['stimulus']), + clamp_mode=data['clamp_mode'], + holding_potential=data['holding_potential'], + holding_current=data['holding_current'], + bridge_balance=data['bridge_balance'], + lpf_cutoff=data['lpf_cutoff'], + pipette_offset=data['pipette_offset'], + ) + return cls(rec, stimulus=rec.stimulus) + + @classmethod + def _load_v1(cls, data): + rec = PatchClampRecording( + device_type=data['device_type'], + device_id=data['device_id'], + start_time=data['start_time'], + channels={'primary': TSeries(data['data'], time_values=data['time_values'])}, + stimulus=SquarePulse.load({'type': 'SquarePulse', 'args': data['stimulus'], 'items': []}), + clamp_mode=data['clamp_mode'], + holding_potential=data['holding_potential'], + holding_current=data['holding_current'], + bridge_balance=data['bridge_balance'], + lpf_cutoff=data['lpf_cutoff'], + pipette_offset=data['pipette_offset'], + ) + return cls(rec, stimulus=rec.stimulus) + + @classmethod + def _load_v2(cls, data): + rec = PatchClampRecording.load(data['recording']) + return cls(rec, indices=data['indices'], stimulus=SquarePulse.load(data['stimulus'])) + + @property + def recording(self): + return self._recording + @property def indices(self): return self._indices - + @property def access_resistance(self): """The access resistance measured from this test pulse. @@ -93,93 +162,39 @@ def analysis(self): self._analyze() return self._analysis - @property - def parent(self): - """The recording in which this test pulse is embedded. - """ - return self._parent_recording - def _analyze(self): # adapted from ACQ4 - meta = self.meta - pulse_amp = self.stimulus.amplitude - clamp_mode = self.clamp_mode - - data = self['primary'] - pulse_start = data.index_at(self.stimulus.start_time) - pulse_stop = data.index_at(self.stimulus.start_time + self.stimulus.duration) - dt = data.dt - - # Extract specific time segments - nudge = int(50e-6 / dt) - base = data[:pulse_start-nudge] - pulse = data[pulse_start+nudge:pulse_stop-nudge] - pulse_end = pulse[int(len(pulse)*2./3.):] # last 1/3 of pulse response - end = data[pulse_stop+nudge:] - - # Exponential fit + pulse_amp = self._stimulus.amplitude + rec = self._recording + clamp_mode = rec.clamp_mode + + data = rec['primary'] - # predictions + pulse_start = data.t0 + self._stimulus.start_time + pulse_stop = pulse_start + self._stimulus.duration + + # Extract specific time segments + padding = 50e-6 + base = data.time_slice(None, pulse_start-padding) + pulse = data.time_slice(pulse_start+padding, pulse_stop-padding) base_median = np.median(base.data) - access_r = 10e6 - input_r = 200e6 - if clamp_mode == 'vc': - ari = pulse_amp / access_r - iri = pulse_amp / input_r - params = { - 'xoffset': (pulse.t0, 'fixed'), - 'yoffset': base_median + iri, - 'amp': ari - iri, - 'tau': (1e-3, 0.1e-3, 50e-3), - } - else: - bridge = meta['bridge_balance'] - arv = pulse_amp * (access_r - bridge) - irv = pulse_amp * input_r - params = { - 'xoffset': pulse.t0, - 'yoffset': base_median+arv+irv, - 'amp': -irv, - 'tau': (10e-3, 1e-3, 50e-3), - } - - fit_kws = {'tol': 1e-4} - model = Exp() - - # ignore initial transients when fitting - fit_region = pulse.time_slice(pulse.t0 + 150e-6, None) - - result = model.fit(fit_region.data, x=fit_region.time_values, fit_kws=fit_kws, params=params) - fit = result.best_values - err = model.nrmse(result) - - self.fit_trace = TSeries(result.eval(), time_values=fit_region.time_values) - - ### fit again using shorter data - ### this should help to avoid fitting against h-currents - #tau4 = fit1[0][2]*10 - #t0 = pulse.xvals('Time')[0] - #shortPulse = pulse['Time': t0:t0+tau4] - #if shortPulse.shape[0] > 10: ## but only if we can get enough samples from this - #tVals2 = shortPulse.xvals('Time')-params['delayTime'] - #fit1 = scipy.optimize.leastsq( - #lambda v, t, y: y - expFn(v, t), pred1, - #args=(tVals2, shortPulse['primary'].view(np.ndarray) - baseMean), - #maxfev=200, full_output=1) - - ## Handle analysis differently depending on clamp mode + prepulse_median = np.median(data.time_slice(pulse_start-5e-3, pulse_start).data) + + try: + main_fit_amp, main_fit_tau, main_fit_yoffset, fit_y0 = self.two_pass_exp_fit(pulse) + except LowConfidenceFitError: + main_fit_amp, main_fit_tau, main_fit_yoffset, fit_y0 = self.bath_fit(base_median, pulse) + + # Handle analysis differently depending on clamp mode if clamp_mode == 'vc': - hp = self.meta['holding_potential'] - if hp is not None: - # we can only report base voltage if metadata includes holding potential - base_v = self['command'].data[0] + hp - else: - base_v = None + base_v = rec.holding_potential + if base_v is None: + base_v = rec['command'].data[0] base_i = base_median - input_step = fit['yoffset'] - base_i - + input_step = main_fit_yoffset - base_i + peak_rgn = pulse.time_slice(pulse.t0, pulse.t0 + 1e-3) if pulse_amp >= 0: input_step = max(1e-16, input_step) @@ -189,52 +204,147 @@ def _analyze(self): input_step = min(-1e-16, input_step) access_step = peak_rgn.data.min() - base_i access_step = min(-1e-16, access_step) - - access_r = pulse_amp / access_step - input_r = pulse_amp / input_step - - # No capacitance in VC mode yet; the methods - # we've tried don't work very well. - tau = None - cap = None - - else: + + access_r = pulse_amp / access_step # pipette + input_r = (pulse_amp / input_step) - access_r # soma + # This uses the formula for a parallel RC circuit in series with an R. The tau in this case is + # proportional to the capacitance (it takes longer to fill larger capacitors), to Ra (it takes + # longer to fill capacitors through a constrained flow), and to Ri (the current has a harder time + # shifting onto a path with a larger resistor). + # https://electronics.stackexchange.com/questions/72185/what-is-tau-in-this-very-simple-circuit/72186#72186 + # See https://www.youtube.com/watch?v=4I5hswA45CM for full derivation. + cap = main_fit_tau * (1 / access_r + 1 / input_r) + + else: # IC mode base_v = base_median - hc = self.meta['holding_current'] - if hc is not None: - # we can only report base current if metadata includes holding current - base_i = self['command'].data[0] + hc - else: - base_i = None - y0 = result.eval(x=pulse.t0) - + base_i = rec.holding_current + if base_i is None: + base_i = rec['command'].data[0] + if pulse_amp >= 0: - v_step = max(1e-5, fit['yoffset'] - y0) + v_step = max(1e-5, main_fit_yoffset - fit_y0) else: - v_step = min(-1e-5, fit['yoffset'] - y0) + v_step = min(-1e-5, main_fit_yoffset - fit_y0) if pulse_amp == 0: pulse_amp = 1e-14 - input_r = (v_step / pulse_amp) - access_r = ((y0 - base_median) / pulse_amp) + bridge - tau = fit['tau'] - cap = tau / input_r + input_r = v_step / pulse_amp # soma + access_r = ((fit_y0 - prepulse_median) / pulse_amp) + rec.bridge_balance # pipette + # This uses the formula for a series RC circuit, effectively ignoring the access resistance and the + # voltage source. This is because, with current (change in charge over time) pinned at the source, any + # change in charge that the capacitor wants to do cannot go through the source, nor Ra. Thus, the + # perceived voltage drop will be the one described by a discharging capacitor in series with Ri. + # See https://www.youtube.com/watch?v=2m1emG-agbM for derivation. + cap = main_fit_tau / input_r self._analysis = { + 'start_time': rec.start_time, + 'steady_state_resistance': input_r + access_r, 'input_resistance': input_r, 'access_resistance': access_r, 'capacitance': cap, - 'time_constant': tau, + 'time_constant': main_fit_tau, + 'fit_yoffset': main_fit_yoffset, + 'fit_xoffset': pulse.t0, + 'fit_amplitude': main_fit_amp, 'baseline_potential': base_v, 'baseline_current': base_i, } - self._fit_result = result - - def plot(self): - self.analysis - import pyqtgraph as pg - name, units = ('pipette potential', 'V') if self.clamp_mode == 'ic' else ('pipette current', 'A') - plt = pg.plot(labels={'left': (name, units), 'bottom': ('time', 's')}) - plt.plot(self['primary'].time_values, self['primary'].data) - plt.plot(self.fit_trace.time_values, self.fit_trace.data, pen='b') + + def _analysis_labels(self): + return { + 'steady_state_resistance': ('Ω', 'Rss'), + 'input_resistance': ('Ω', 'Ri'), + 'access_resistance': ('Ω', 'Ra'), + 'capacitance': ('F', 'Cm'), + 'time_constant': ('s', 'τ'), + 'fit_yoffset': (self.plot_units, 'Yo'), + 'fit_xoffset': ('s', 'Xo'), + 'fit_amplitude': ('', 'Ya'), + 'baseline_potential': ('V', 'Vh'), + 'baseline_current': ('A', 'Ih'), + } + + def two_pass_exp_fit(self, pulse): + # start by fitting the exponential decay from the post-pipette capacitance, ignoring initial transients + main_fit_region = pulse.time_slice(pulse.t0 + 150e-6, None) + self._main_fit_region = main_fit_region + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + self.main_fit_result = exp_fit(main_fit_region) + main_fit_yoffset, main_fit_amp, main_fit_tau = self.main_fit_result['fit'] + self.main_fit_trace = TSeries(self.main_fit_result['model'](main_fit_region.time_values), + time_values=main_fit_region.time_values) + y0 = self.main_fit_result['model'](pulse.t0) + # TODO doing anything with this transient fit doesn't help pass any tests, and in fact causes a + # bunch of failures. not returning any of this for now, but it does plot well. + with contextlib.suppress(ValueError): + # now fit with the access transients included as an additional exponential decay + prediction = self.main_fit_result['model'](pulse.time_values) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') + self.fit_result_with_transient = exp_fit(pulse - prediction) + + self.fit_trace_with_transient = TSeries( + self.fit_result_with_transient['model'](pulse.time_values) + prediction, + time_values=pulse.time_values, + ) + if self.main_fit_result['confidence'] < 0.15: + raise LowConfidenceFitError(self.main_fit_result['confidence']) + return main_fit_amp, main_fit_tau, main_fit_yoffset, y0 + + @staticmethod + def bath_fit(base_median, pulse): + # no cell, no non-transient exponential decay, just ohm's law. + start_y = base_median + end_y = pulse.data[-len(pulse.data) // 100:].mean() + y_scale = start_y - end_y + y_offset = end_y + tau = float('nan') + y0 = base_median + return y_scale, tau, y_offset, y0 + + @property + def plot_units(self): + return 'A' if self._recording.clamp_mode == 'vc' else 'V' + + @property + def plot_title(self): + return 'current' if self._recording.clamp_mode == 'vc' else 'potential' + + def plot(self, plt=None, label=True): + assert self.analysis is not None + if plt is None: + plt = pg.plot(labels={'left': (self.plot_title, self.plot_units), 'bottom': ('time', 's')}) + plt.addLegend() + plt.plot(self._recording['primary'].time_values, self._recording['primary'].data, name="raw") + if self.fit_trace_with_transient is not None: + plt.plot( + self.fit_trace_with_transient.time_values, + self.fit_trace_with_transient.data, + pen='b', + name="fit w/ trans", + ) + plt.plot(self.main_fit_trace.time_values, self.main_fit_trace.data, pen='r', name="first fit") + if label: + self.label_for_plot(plt.getPlotItem()) + return plt + + def label_for_plot(self, plt): + asymptote = self.analysis['fit_yoffset'] + plt.addItem(pg.InfiniteLine( + (0, asymptote), + angle=0, + pen=pg.mkPen((180, 180, 240), dash=[3, 4]), + )) + abbrevs = self._analysis_labels() + text = "Estimated:
" + "
".join([ + f"{abbrevs[key][1]}: {pg.siFormat(val, suffix=abbrevs[key][0])}" + for key, val in self.analysis.items() + if val is not None and key in abbrevs + ]) + label = pg.LabelItem(text.strip(), color=(180, 180, 240)) + label.setParentItem(plt.vb) + label.setPos(5, 5) + return label diff --git a/neuroanalysis/test_pulse_stack.py b/neuroanalysis/test_pulse_stack.py new file mode 100644 index 0000000..0d25309 --- /dev/null +++ b/neuroanalysis/test_pulse_stack.py @@ -0,0 +1,113 @@ +from __future__ import annotations + +import json +import os + +import h5py +import numpy as np + +from neuroanalysis.test_pulse import PatchClampTestPulse + + +class H5BackedTestPulseStack: + """A caching, HDF5-backed stack of test pulses.""" + + def __init__(self, h5_group: h5py.Group, readable=True): + self._containing_groups = [h5_group] + self._readable = readable + self._test_pulses: dict[float, PatchClampTestPulse | None] = {} + # pre-cache just the names of existing test pulses from the file + for fh in h5_group: + self._test_pulses[float(fh)] = None + self._np_timestamp_cache = np.array(list(self._test_pulses.keys())) + + def __getitem__(self, key: float) -> PatchClampTestPulse: + if not self._readable: + raise ValueError("This stack is not readable") + if key not in self._test_pulses: + raise KeyError(f"Test pulse at time {key} not found") + if self._test_pulses[key] is None: + tp = next(grp[str(key)] for grp in self._containing_groups if str(key) in grp) + if tp.attrs.get('schema version', (0,))[0] == 2: + self._test_pulses[key] = self._load_test_pulse_v2(tp) + else: + self._test_pulses[key] = self._load_test_pulse_unversioned(tp) + return self._test_pulses[key] + + def _load_test_pulse_unversioned(self, tp): + rec = dict(tp.attrs.items()) + rec['time_values'] = tp[:, 0] + rec['data'] = tp[:, 1] + rec['stimulus'] = {k[9:]: v for k, v in tp.attrs.items() if k.startswith('stimulus_')} + return PatchClampTestPulse.load(rec) + + def _load_test_pulse_v2(self, tp): + state = json.loads(tp.attrs['save']) + state['recording']['channels']['primary']['time_values'] = tp[:, 0] + state['recording']['channels']['primary']['data'] = tp[:, 1] + return PatchClampTestPulse.load(state) + + def merge(self, other: H5BackedTestPulseStack): + """Merge another stack into this one.""" + if not self._readable: + raise ValueError("Only readable stacks can be merged into") + if not other._readable: + raise ValueError("The other stack is not readable") + self._containing_groups += other._containing_groups + # sort the groups by time to make append logic easy + self._containing_groups.sort(key=lambda grp: os.path.getmtime(grp.file.filename)) + self._test_pulses.update(other._test_pulses) + self._np_timestamp_cache = np.concatenate((self._np_timestamp_cache, other._np_timestamp_cache)) + + def __len__(self) -> int: + if not self._readable: + raise ValueError("This stack is not readable") + return len(self._test_pulses) + + def close(self): + for grp in self._containing_groups: + grp.file.close() + + @property + def files(self) -> list[h5py.File]: + return {grp.file for grp in self._containing_groups} + + def flush(self): + for grp in self._containing_groups: + grp.file.flush() + + def append(self, test_pulse: PatchClampTestPulse) -> tuple[str, str]: + """Append a test pulse to the stack. Returns the full path name of the dataset.""" + tp_dump = test_pulse.save() + rec = tp_dump['recording'] + pri = rec['channels']['primary'] + del rec['channels']['command'] + data = np.column_stack((test_pulse.recording['primary'].time_values, pri['data'])) + del rec['channels']['primary']['time_values'] + del rec['channels']['primary']['data'] + dataset = self._containing_groups[-1].create_dataset( + str(rec['start_time']), + data=data, + compression='gzip', + compression_opts=9, + ) + dataset.attrs['save'] = json.dumps(tp_dump) + dataset.attrs['schema version'] = tp_dump['schema version'] + + if self._readable: + self._test_pulses[test_pulse.recording.start_time] = test_pulse + self._np_timestamp_cache = np.append(self._np_timestamp_cache, test_pulse.recording.start_time) + + return dataset.file.filename, dataset.name + + def at_time(self, when: float) -> PatchClampTestPulse | None: + """Return the test pulse at or immediately previous to the provided time.""" + if not self._readable: + raise ValueError("This stack is not readable") + keys = self._np_timestamp_cache + idx = np.searchsorted(keys, when) + if idx == 0: + return None + if idx == len(keys): + idx -= 1 + return self[keys[idx - 1]] diff --git a/neuroanalysis/tests/test_analyzers.py b/neuroanalysis/tests/test_analyzers.py new file mode 100644 index 0000000..a93b88b --- /dev/null +++ b/neuroanalysis/tests/test_analyzers.py @@ -0,0 +1,79 @@ +import numpy as np +from neuroanalysis.data.dataset import Recording, TSeries +import neuroanalysis.analyzers.stim_pulse as spas +from pytest import raises + + +def test_generic_stim_pulse_analyzer(): + + ### test a recording with two pulses and noisy data + dt = 0.0002 + np.random.seed(54321) + + data = np.random.normal(0.002, 0.0015, 10000) + ## add a small 60 Hz sine wave + data += 0.004 * np.sin(np.arange(10000) * 2.0 * np.pi / (1/60. * 1/dt)) + + ## create a pulse an make the edges a little fuzzy + amp1 = 1 + data[2001:2200] += amp1 + data[2000] += (0.7 * amp1) + data[2200] += (0.2 * amp1) + + ## create a second smaller pulse + amp2 = 0.3 + data[5000:5500] += amp2 + + rec1 = Recording( + channels = {'reporter':TSeries(data=data, dt=dt, units='V')}, + device_id='test', + start_time = 0) + + spa1 = spas.GenericStimPulseAnalyzer.get(rec1) + + with raises(ValueError): + spa1.pulses() + + pulses = spa1.pulses(channel='reporter') + assert len(pulses) == 2 + assert np.isclose(pulses[0][0], 0.4) + assert np.isclose(pulses[0][2], amp1, 0.001, 0.001) + assert np.isclose(pulses[0][1], 0.44) + assert np.isclose(pulses[1][0], 1) + assert np.isclose(pulses[1][2], amp2, 0.001, 0.001) + assert np.isclose(pulses[1][1], 1.1) + + + ### test a noise-free recording with 3 pulses + data = np.zeros(10000) + data[5000:5020] = 1 + data[6000:6020] = 1 + data[7000:7020] = 1 + + rec2 = Recording( + channels = {'reporter':TSeries(data=data, dt=dt, units='V')}, + device_id='test', + start_time = 0) + + spa2 = spas.GenericStimPulseAnalyzer.get(rec2) + + pulses = spa2.pulses(channel='reporter') + + ## check values of pulses found + assert len(pulses) == 3 + for p in pulses: + assert isinstance(p, tuple) + assert np.isclose(p[2], 1) + assert np.isclose(p[1]-p[0], 0.004) + assert np.isclose(pulses[0][0], 1) + assert np.isclose(pulses[1][0], 1.2) + assert np.isclose(pulses[2][0], 1.4) + + ### check stim params + freq, delay = spa2.stim_params(channel='reporter') + assert np.isclose(freq, 5.0) + assert np.isclose(delay, 0.2) + + + + diff --git a/neuroanalysis/tests/test_event_detection.py b/neuroanalysis/tests/test_event_detection.py index 0796a45..f92bc8e 100644 --- a/neuroanalysis/tests/test_event_detection.py +++ b/neuroanalysis/tests/test_event_detection.py @@ -68,4 +68,29 @@ def check_events(a, b): assert(a.shape == b.shape) for k in a.dtype.names: assert np.allclose(a[k], b[k]) - \ No newline at end of file + + +def test_exp_deconv_psp_params(): + from neuroanalysis.event_detection import exp_deconvolve, exp_deconv_psp_params + from neuroanalysis.data import TSeries + from neuroanalysis.fitting import Psp + + x = np.linspace(0, 0.02, 10000) + amp = 1 + rise_time = 4e-3 + decay_tau = 10e-3 + rise_power = 2 + + # Make a PSP waveform + psp = Psp() + y = psp.eval(x=x, xoffset=0, yoffset=0, amp=amp, rise_time=rise_time, decay_tau=decay_tau, rise_power=rise_power) + + # exponential deconvolution + y_ts = TSeries(y, time_values=x) + y_deconv = exp_deconvolve(y_ts, decay_tau).data + + # show that we can get approximately the same result using exp_deconv_psp_params + d_amp, d_rise_time, d_rise_power, d_decay_tau = exp_deconv_psp_params(amp, rise_time, rise_power, decay_tau) + y2 = psp.eval(x=x, xoffset=0, yoffset=0, amp=d_amp, rise_time=d_rise_time, decay_tau=d_decay_tau, rise_power=d_rise_power) + + assert np.allclose(y_deconv, y2[1:], atol=0.02) diff --git a/neuroanalysis/tests/test_exp_fit.py b/neuroanalysis/tests/test_exp_fit.py new file mode 100644 index 0000000..f4ff82b --- /dev/null +++ b/neuroanalysis/tests/test_exp_fit.py @@ -0,0 +1,153 @@ +import numpy as np +import pytest + +from neuroanalysis.data import TSeries +from neuroanalysis.fitting.exp import exp_decay, exp_fit, best_exp_fit_for_tau + + +@pytest.mark.parametrize('tau', 10**np.linspace(-4, 0, 10)) +@pytest.mark.parametrize('yoffset', np.linspace(-0.1, 0.1, 3)) +@pytest.mark.parametrize('yscale', 10**np.linspace(-4, -1, 4)) +@pytest.mark.parametrize('yscale_sign', [-1, 1]) +@pytest.mark.parametrize('fn', [exp_fit]) # , exp_fit +def test_ic_exp_fit(tau, yoffset, yscale, yscale_sign, fn, plot_errors=False, plot_all=False, raise_errors=True): + noise = 5e-3 + duration = 0.2 + yscale *= yscale_sign + + _run_exp_fit_test(duration, fn, noise, plot_all, plot_errors, raise_errors, tau, yoffset, yscale) + + +@pytest.mark.parametrize('tau', 10**np.linspace(-4, 0, 10)) +@pytest.mark.parametrize('yoffset', np.linspace(-1e-9, 1e-9, 3)) +@pytest.mark.parametrize('yscale', 10**np.linspace(-13, -9, 4)) +@pytest.mark.parametrize('yscale_sign', [-1, 1]) +@pytest.mark.parametrize('fn', [exp_fit]) # , exp_fit +def test_vc_exp_fit(tau, yoffset, yscale, yscale_sign, fn, plot_errors=False, plot_all=False, raise_errors=True): + noise = 50e-12 + duration = 0.02 + yscale *= yscale_sign + + _run_exp_fit_test(duration, fn, noise, plot_all, plot_errors, raise_errors, tau, yoffset, yscale) + + +def _run_exp_fit_test(duration, fn, noise, plot_all, plot_errors, raise_errors, tau, yoffset, yscale): + rng = np.random.RandomState(0) + sample_rate = 50e3 + params = {'yoffset': yoffset, 'yscale': yscale, 'tau': tau} + fit, y = run_single_exp_fit( + duration=duration, + sample_rate=sample_rate, + params=params, + noise=noise, + rng=rng, + fit_func=fn, + ) + if plot_all: + plot_test_result(y, params, fit) + try: + check_exp_fit(y, params, fit, noise) + except RuntimeError: + if plot_errors and not plot_all: + plot_test_result(y, params, fit) + if raise_errors: + raise + + +def test_bad_curve(plot=False): + params = { + 'yoffset': -278e-12 * (500/560) - 28e-12, + 'yscale': -234e-12 * (560/500), + 'tau': 4e-3, + } + noise = 1e-12 + duration = params['tau'] * 5 # * 6 and this will pass + sample_rate = 1e5 + t = np.linspace(0, duration, int(duration * sample_rate)) + data = exp_decay(t, **params) + data += np.random.normal(0, noise, data.shape) + y = TSeries(data, time_values=t) + fit = exp_fit(y) + if plot: + plot_test_result(y, params, fit) + + check_exp_fit(y, params, fit, noise) + + +def run_single_exp_fit(duration, sample_rate, params, noise, rng, fit_func): + t = np.arange(0, duration, 1/sample_rate) + offset = params['yoffset'] + scale = params['yscale'] + tau = params['tau'] + data = exp_decay(t, offset, scale, tau) + data += rng.normal(0, noise, data.shape) + y = TSeries(data, time_values=t) + try: + fit = fit_func(y) + except Exception: + print(f"Error fitting {fit_func} {params}") + raise + return fit, y + + +def check_exp_fit(y, params, fit, noise): + # if fit['nrmse'] >= 0.05: + # raise AssertionError(f"Error too big: {fit['nrmse']}") + fit_y = fit['model'](y.time_values) + target_y = exp_decay(y.time_values, **params) + fit['err_std'] = (target_y - fit_y).std() + # assert np.allclose(fit['fit'], [params['yoffset'], params['yscale'], params['tau']], rtol=0.05) + print(f"tau: {params['tau']} vs {fit['fit'][2]}") + if fit['err_std'] >= noise * 0.3: + raise AssertionError(f"Params: {params} Error too big: {fit['err_std']} >= {noise * 0.3}") + + +def calc_exp_error_curve(tau: float, data: TSeries): + """Calculate the error surface for an exponential with *tau* and noisy *data* + """ + taus = tau * 10**np.linspace(-3, 3, 1000) + errs = [] + for i in range(len(taus)): + exp_y, err, yscale, yoffset = best_exp_fit_for_tau(taus[i], data.time_values, data.data) + errs.append(err) + return taus, errs + + +plot_window = None + + +def plot_test_result(y, params, fit): + global plot_window + import pyqtgraph as pg + + if plot_window is None: + plot_window = pg.GraphicsLayoutWidget() + plot_window.plt1 = plot_window.addPlot(0, 0) + plot_window.plt2 = plot_window.addPlot(1, 0) + plot_window.show() + + plt1 = plot_window.plt1 + plt1.addLegend() + plt2 = plot_window.plt2 + + plt1.plot(y.time_values, y.data, pen='w', label='data', name='data') + plt1.setTitle( + f"tau: {params['tau']:0.2g} yoffset: {params['yoffset']:0.2g} yscale: {params['yscale']:0.2g}" + f" nrmse: {fit['nrmse']:0.2g}") # err_std: {fit['err_std']:0.2g}") + plt1.plot(y.time_values, fit['model'](y.time_values), pen='r', label='fit', name='fit') + + target_y = exp_decay(y.time_values, **params) + plt1.plot(y.time_values, target_y, pen='b', label='target', name='target') + + taus, errs = calc_exp_error_curve(params['tau'], y) + plt2.plot(taus, errs) + plt2.addLine(x=params['tau'], pen='g') + plt2.addLine(x=fit['fit'][2], pen='r') + if 'memory' in fit: + plt2.plot(list(fit['memory'].keys()), [m[2] for m in fit['memory'].values()], pen=None, symbol='o', symbolPen='r') + pg.exec() + + +if __name__ == '__main__': + test_bad_curve(plot=True) + # test_ic_exp_fit(plot_all=False, plot_errors=True, raise_errors=False, fn=exact_fit_exp) diff --git a/neuroanalysis/tests/test_release_model.py b/neuroanalysis/tests/test_release_model.py index b6e8218..389d109 100644 --- a/neuroanalysis/tests/test_release_model.py +++ b/neuroanalysis/tests/test_release_model.py @@ -1,3 +1,4 @@ +from __future__ import print_function from collections import OrderedDict import numpy as np from neuroanalysis.synaptic_release import ReleaseModel @@ -48,6 +49,6 @@ def test_release_model(): for k in params: if abs(params[k] - expected_output[i][k]) > max(1e-12, abs(expected_output[i][k] / 1e3)): test_pass = False - print "Parameter mismatch: %d %s\t%g\t%g\t%g" % (i, k, params[k], expected_output[i][k], params[k] - expected_output[i][k]) + print("Parameter mismatch: %d %s\t%g\t%g\t%g" % (i, k, params[k], expected_output[i][k], params[k] - expected_output[i][k])) assert test_pass diff --git a/neuroanalysis/tests/test_spike_detection.py b/neuroanalysis/tests/test_spike_detection.py index acbe80d..34d1960 100644 --- a/neuroanalysis/tests/test_spike_detection.py +++ b/neuroanalysis/tests/test_spike_detection.py @@ -104,5 +104,5 @@ def test_pulse(amp, ra): test_pulse(amp, ra) # redraw after every new test - pg.QtGui.QApplication.processEvents() + pg.QtWidgets.QApplication.processEvents() diff --git a/neuroanalysis/tests/test_stimuli.py b/neuroanalysis/tests/test_stimuli.py index e219bd9..95f714a 100644 --- a/neuroanalysis/tests/test_stimuli.py +++ b/neuroanalysis/tests/test_stimuli.py @@ -1,6 +1,8 @@ +import os from collections import OrderedDict import numpy as np import neuroanalysis.stimuli as stimuli +from neuroanalysis.data.dataset import TSeries def test_stimulus(): @@ -292,3 +294,50 @@ def test_chirp(): # test analytically determined frequencies freqs = f0 ** np.linspace(1, np.log(f1) / np.log(f0), len(t)+1)[:-1] assert np.allclose(freqs, stim.frequency_at(t)) + +def test_find_noisy_square_pulses(): + dt = 0.0002 + np.random.seed(54321) + + data = np.random.normal(0.002, 0.0015, 10000) + ## add a small 60 Hz sine wave + data += 0.004 * np.sin(np.arange(10000) * 2.0 * np.pi / (1/60. * 1/dt)) + + ## create a pulse an make the edges a little fuzzy + amp1 = 1 + data[2001:2200] += amp1 + data[2000] += (0.7 * amp1) + data[2200] += (0.2 * amp1) + + ## create a second smaller pulse + amp2 = 0.3 + data[5000:5500] += amp2 + + tseries = TSeries(data=data, dt=dt, units='V') + + pulses = stimuli.find_noisy_square_pulses(tseries) + + ## make sure we found correct number and that they are SquarePulses + assert len(pulses) == 2 + assert isinstance(pulses[0], stimuli.SquarePulse) + + + ## check parameters + assert pulses[0].start_time == 0.4 + assert np.isclose(pulses[0].amplitude, amp1, 0.001, 0.001) + assert pulses[0].duration == 0.04 + assert pulses[1].start_time == 1 + assert np.isclose(pulses[1].amplitude, amp2, 0.001, 0.001) + assert pulses[1].duration == 0.1 + + + + + + + + + + + + diff --git a/neuroanalysis/tests/test_test_pulse.py b/neuroanalysis/tests/test_test_pulse.py index f016962..e1e2fe2 100644 --- a/neuroanalysis/tests/test_test_pulse.py +++ b/neuroanalysis/tests/test_test_pulse.py @@ -1,110 +1,454 @@ +import os + +import h5py +from typing import Literal + import numpy as np -from neuroanalysis.data import Recording, TSeries +import pytest +from neuron import h +import pyqtgraph as pg + +from neuroanalysis.data import TSeries, PatchClampRecording from neuroanalysis.test_pulse import PatchClampTestPulse -from neuroanalysis.neuronsim.model_cell import ModelCell -from neuroanalysis.units import pA, mV, MOhm, pF, us, ms +from neuroanalysis.units import pA, mV, uV, MOhm, pF, uF, us, ms, cm, nA, um, mm +from pyqtgraph.parametertree import ParameterTree, interact +h.load_file('stdrun.hoc') -def test_test_pulse(): - # Just test against a simple R/C circuit attached to a pipette - model_cell.enable_mechs(['leak']) - model_cell.recording_noise = False - - tp = create_test_pulse(pamp=-10*pA, mode='ic', r_access=100*MOhm) - check_analysis(tp, model_cell) - - - -model_cell = ModelCell() +@pytest.mark.parametrize('pamp', [-100*pA, -11*pA, 12*pA]) +@pytest.mark.parametrize('r_input', [103*MOhm, 200*MOhm, 499*MOhm]) +@pytest.mark.parametrize('r_access', [5*MOhm, 10*MOhm, 15*MOhm]) +@pytest.mark.parametrize('c_soma', [50*pF, 100*pF, 200*pF]) +@pytest.mark.parametrize('c_pip', [1*pF, 3*pF, 10*pF]) +# @pytest.mark.parametrize('only', ['input_resistance', 'capacitance', 'access_resistance']) +def test_ic_pulse(pamp, r_input, r_access, c_soma, c_pip, only=None): + tp_kwds = dict(noise=10*uV, pamp=pamp, pdur=200*ms, mode='ic', r_access=r_access, r_input=r_input, c_soma=c_soma, c_pip=c_pip) + tp, _ = create_mock_test_pulse(**tp_kwds) + if only: + only = [only] + check_analysis(tp, tp_kwds, only=only) - -def create_test_pulse(start=5*ms, pdur=10*ms, pamp=-10*pA, mode='ic', dt=10*us, r_access=10*MOhm, c_soma=5*pF, noise=5*pA): - # update patch pipette access resistance - model_cell.clamp.ra = r_access - - # update noise amplitude - model_cell.mechs['noise'].stdev = noise - - # make pulse array - duration = start + pdur * 3 - pulse = np.zeros(int(duration / dt)) - pstart = int(start / dt) - pstop = pstart + int(pdur / dt) - pulse[pstart:pstop] = pamp - - # simulate response - result = model_cell.test(TSeries(pulse, dt), mode) - - # generate a PatchClampTestPulse to test against - tp = PatchClampTestPulse(result) - return tp +@pytest.mark.parametrize('pamp', [-85*mV, -75*mV, -55*mV]) +@pytest.mark.parametrize('r_input', [100*MOhm, 200*MOhm, 500*MOhm]) +@pytest.mark.parametrize('r_access', [5*MOhm, 10*MOhm, 15*MOhm]) +@pytest.mark.parametrize('c_soma', [50*pF, 100*pF, 200*pF]) +@pytest.mark.parametrize('c_pip', [1*pF, 3*pF, 10*pF]) +# @pytest.mark.parametrize('only', ['input_resistance', 'capacitance', 'access_resistance']) +def test_vc_pulse(pamp, r_input, r_access, c_soma, c_pip, only=None): + tp_kwds = dict(noise=1*pA, pamp=pamp, pdur=20*ms, mode='vc', hold=-65*mV, r_input=r_input, r_access=r_access, c_soma=c_soma, c_pip=c_pip) + tp, _ = create_mock_test_pulse(**tp_kwds) + if only: + only = [only] + check_analysis(tp, tp_kwds, only=only) + + +def test_pulse_in_bath(): + tp_kwds = dict(noise=1e-13, pamp=-10*mV, mode='vc', c_soma=False, c_pip=3*pF, r_input=False, r_access=10*MOhm) + tp, _ = create_mock_test_pulse(**tp_kwds) + assert np.isnan(tp.analysis['capacitance']) + assert np.isclose(tp.analysis['steady_state_resistance'], tp_kwds['r_access'], rtol=0.3) + + tp_kwds = dict(noise=1e-6, pamp=-100*pA, pdur=100*ms, mode='ic', c_soma=False, c_pip=3*pF, r_input=False, r_access=10*MOhm) + tp, _ = create_mock_test_pulse(**tp_kwds) + assert np.isnan(tp.analysis['capacitance']) + assert np.isclose(tp.analysis['steady_state_resistance'], tp_kwds['r_access'], rtol=0.3) + + +def test_leaky_cell(): + tp_kwds = dict(noise=0, pamp=-10*mV, mode='vc', r_input=80*MOhm, rmp_soma=-30*mV) + tp, _ = create_mock_test_pulse(**tp_kwds) + check_analysis(tp, tp_kwds) + + tp_kwds = dict(noise=0, pamp=-100*pA, pdur=200*ms, mode='ic', r_input=80*MOhm, rmp_soma=-30*mV) + tp, _ = create_mock_test_pulse(**tp_kwds) + check_analysis(tp, tp_kwds) + + +def test_with_60Hz_noise(): + assert False # TODO + + +def test_with_12kHz_noise(): + assert False # TODO + + +def test_clogged_pipette_with_soma(): + shared_kwds = dict(noise=0, c_soma=80*pF, c_pip=3*pF, r_input=100*MOhm, r_access=50*MOhm) + tp_kwds = dict(pamp=-100*pA, pdur=200*ms, mode='ic', **shared_kwds) + tp, _ = create_mock_test_pulse(**tp_kwds) + check_analysis(tp, tp_kwds) + + tp_kwds = dict(pamp=-20*mV, pdur=10*ms, mode='vc', **shared_kwds) + tp, _ = create_mock_test_pulse(**tp_kwds) + check_analysis(tp, tp_kwds) + + +def test_clogged_pipette_in_bath(): + tp_kwds = dict(noise=0, pamp=-10*mV, mode='vc', c_soma=False, c_pip=3*pF, r_input=False, r_access=30*MOhm) + tp, _ = create_mock_test_pulse(**tp_kwds) + assert np.isnan(tp.analysis['capacitance']) + assert np.isclose(tp.analysis['steady_state_resistance'], tp_kwds['r_access'], rtol=0.3) + + tp_kwds = dict(noise=0, pamp=-100*pA, pdur=200*ms, mode='ic', c_soma=False, c_pip=3*pF, r_input=False, r_access=30*MOhm) + tp, _ = create_mock_test_pulse(**tp_kwds) + assert np.isnan(tp.analysis['capacitance']) + assert np.isclose(tp.analysis['steady_state_resistance'], tp_kwds['r_access'], rtol=0.3) + + +def test_cell_attached(): + tp_kwds = dict(noise=0, pamp=-10*mV, mode='vc', c_soma=0.1*pF, c_pip=3*pF, r_input=1000*MOhm, r_access=10*MOhm) + tp, _ = create_mock_test_pulse(**tp_kwds) + assert np.isclose(tp.analysis['steady_state_resistance'], tp_kwds['r_access'] + tp_kwds['r_input'], rtol=0.3) + + tp_kwds = dict(noise=0, pamp=-100*pA, pdur=200*ms, mode='ic', c_soma=0.1*pF, c_pip=3*pF, r_input=1000*MOhm, r_access=10*MOhm) + tp, _ = create_mock_test_pulse(**tp_kwds) + assert np.isclose(tp.analysis['steady_state_resistance'], tp_kwds['r_access'] + tp_kwds['r_input'], rtol=0.3) + + +def capacitance(section): + """Return the capacitance of a soma in F.""" + # its units are (µF / cm²) + return (section.cm * uF / cm ** 2) * section_surface(section) + + +def trunc_cone_surface_area(base_radius, tip_radius, length): + return np.pi * (base_radius + tip_radius) * np.sqrt((base_radius - tip_radius)**2 + length**2) + + +def section_surface(section) -> float: + """Return the surface area of a section (truncated cone) in m².""" + # its units are um * um + a_r = section(0).diam * um / 2 + b_r = section(1).diam * um / 2 + length = section.L * um + return trunc_cone_surface_area(a_r, b_r, length) + + +def resistance(s): + """Return the resistance of a soma in Ohms.""" + # its units are S/cm² + return 1 / ((s(0.5).pas.g / cm**2) * section_surface(s)) + + +def set_resistance(s, r): + """Set the resistance of a soma in Ohms.""" + s(0.5).pas.g = 1 / (r / cm ** 2 * section_surface(s)) + + +def set_pip_cap(v): + # TODO we need to build https://www.neuron.yale.edu/phpBB/viewtopic.php?t=203 + # mech.c = h.Matrix(1, 1, 2).from_python([[v * uF]]) + cmat = h.Matrix(2, 2, 2).ident() + cmat.setval(0, 1, v * uF) + gmat = h.Matrix(2, 2, 2).ident() + y = h.Vector(2) + y0 = h.Vector(2) + b = h.Vector(2) + + return h.LinearMechanism(cmat, gmat, y, y0, b), cmat, gmat, y, y0, b -def expected_testpulse_values(cell): - if cell.clamp.mode == 'ic': - values = { - 'baseline_potential': model_cell.resting_potential(), - 'baseline_current': model_cell.clamp.holding['ic'], - 'access_resistance': model_cell.clamp.ra, - 'capacitance': model_cell.soma.cap, - } +def _make_ic_command(connection, amplitude, start, duration): + ic = h.IClamp(connection) + ic.amp = amplitude / nA + ic.dur = duration / ms + ic.delay = start / ms + return ic + + +def create_mock_test_pulse( + start: float = 5*ms, + pdur: float = 200*ms, + pamp: float = -10*pA, + hold: float = 0.0, + rmp_soma: float = -65*mV, + mode: Literal['ic', 'vc'] = 'ic', + dt: float = 10*us, + r_access: float = 10*MOhm, + r_input: float = 200*MOhm, + c_soma: float = 100*pF, + c_pip: float = 5*pF, + plot: bool = False, + noise: float = 0.5*pA, + assert_valid: bool = False, +): + settle = 500 * ms if mode == 'ic' else 50 * ms + pulse = np.ones((int((settle + start + pdur + settle) // dt),)) * hold + pulse[int((settle + start) // dt):int((settle + start + pdur) // dt)] = pamp + + pip_sections = _create_pipette(r_access, c_pip) + + if c_soma and r_input: + soma = h.Section() + soma.insert('pas') + soma.cm = 1.0 # µF/cm² + soma.L = soma.diam = (500 / np.pi) ** 0.5 # um (500 um²) + soma.cm = soma.cm * c_soma / capacitance(soma) + set_resistance(soma, r_input) + pip_sections[-1].connect(soma(0.5), 1) + for seg in soma: + seg.pas.e = rmp_soma / mV + else: + # connect the pipette to ground + ground = h.VClamp(pip_sections[-1](1)) + ground.dur[0] = 1e9 # clamp forever + ground.amp[0] = 0 + + clamp_connection = pip_sections[0](0) + + def run(): + vinit = -60 # mV + + h.init() + h.finitialize(vinit) + + h.dt = dt / ms + h.continuerun((settle + start + pdur + settle) / ms) + + if mode == 'ic': + pre_ic = _make_ic_command(clamp_connection, hold, 0, settle + start) + pulse_ic = _make_ic_command(clamp_connection, pamp, settle + start, pdur) + post_ic = _make_ic_command(clamp_connection, hold, settle + start + pdur, settle) + + pip_rec0 = h.Vector() + pip_rec0.record(clamp_connection._ref_v) + + run() + out = pip_rec0.as_numpy() * mV else: - values = { - 'baseline_potential': model_cell.clamp.holding['vc'], - 'baseline_current': model_cell.resting_current(), - 'access_resistance': model_cell.clamp.ra, - 'capacitance': None, - } - values['input_resistance'] = model_cell.input_resistance() + vc = h.SEClamp(clamp_connection) + vc.rs = 0.01 / MOhm # just get out of the way of our segmented pipette + + vc.dur1 = (settle + start) / ms + vc.amp1 = hold / mV + vc.dur2 = pdur / ms + vc.amp2 = pamp / mV + vc.dur3 = settle / ms + vc.amp3 = hold / mV + + vc_rec = h.Vector() + vc_rec.record(vc._ref_i) + + run() + out = vc_rec.as_numpy() * nA + + out = out[int(settle // dt):int((settle + start + pdur + settle) // dt)] + if noise: + out += np.random.normal(0, noise, out.shape) + pulse = pulse[int(settle // dt):int((settle + start + pdur + settle) // dt)] + + tp = PatchClampTestPulse(PatchClampRecording( + channels={ + 'primary': TSeries(out, dt=dt), + 'command': TSeries(pulse, dt=dt)}, + dt=dt, + t0=0, + clamp_mode=mode, + bridge_balance=0, + lpf_cutoff=None, + pipette_offset=0, + holding_current=hold if mode == 'ic' else None, + holding_potential=hold if mode == 'vc' else None, + )) + if plot: + tp.plot() + # pg.plot(pulse, title=f'{mode} command') + if assert_valid: + try: + check_analysis(tp, locals()) + except AssertionError as e: + print("assertion failed", e) + return tp, locals() # NEURON blows up if GC deletes objects before we're done + + +def _create_pipette(r_access, c_pip): + pipette_halfangle = np.deg2rad(20 / 2) + base_radius = 0.5 * 0.86 * mm + tip_radius = 0.5 * 1.0 * um + length = base_radius / np.tan(pipette_halfangle) + resistivity = np.pi * base_radius * tip_radius * r_access / length + n_pip_sections = 15 + pip_sections = [] + # make a series of connected sections to approximate a truncated conic conductor. + # sections will have progressively smaller radius. section lengths are chosen + # such that all sections have equal resistance given a constant resistivity. + axial_resistance_per_section = r_access / n_pip_sections + next_radius = base_radius + for i in range(n_pip_sections): + r = next_radius + + # solve for length of truncated cone with specified resistance + # R = ρ * l / (pi * base_r * tip_r) + # l = (pi * base_r * tip_r) * R / ρ + # tip_r = base_r - l * tan(halfangle) + # l = (pi * base_r * (base_r - l * tan(halfangle))) * R / ρ + # l * ρ / (pi * base_r * R) = base_r - l * tan(halfangle) + # l * ρ / (pi * base_r * R) + l * tan(halfangle) = base_r + # l * (ρ / (pi * base_r * R) + tan(halfangle)) = base_r + # l = base_r / (ρ / (pi * base_r * R) + tan(halfangle)) + l = r / (resistivity / (np.pi * r * axial_resistance_per_section) + np.tan(pipette_halfangle)) + + # now choose an effective cylinder radius that gives the same resistance + # R = ρ * l / A + # A = ρ * l / R = pi * r^2 + # r = sqrt((ρ * l) / (pi * R)) + cyl_r = np.sqrt((resistivity * l) / (np.pi * axial_resistance_per_section)) + + sec = h.Section() + sec.nseg = 1 + sec.L = l / um + sec.diam = 2 * cyl_r / um + sec.Ra = resistivity / cm + if i > 0: + pip_sections[-1].connect(sec(0), 1) + pip_sections.append(sec) + next_radius -= l * np.tan(pipette_halfangle) + + total_surface_area = np.sum([section_surface(sec) for sec in pip_sections]) + pip_cap_per_area = c_pip / total_surface_area + for sec in pip_sections: + sec.cm = pip_cap_per_area * cm ** 2 / uF + return pip_sections + + +def expected_testpulse_values(tp_kwds): + values = { + 'access_resistance': tp_kwds.get('r_access', 10*MOhm), + 'capacitance': tp_kwds.get('c_soma', 100*pF), + 'input_resistance': tp_kwds.get('r_input', 200*MOhm), + } + if tp_kwds.get('mode', 'ic') == 'ic': + # values['baseline_potential'] = 0 # TODO + values['baseline_current'] = tp_kwds.get('hold', 0) + else: + values['baseline_potential'] = tp_kwds.get('hold', 0) + # values['baseline_current'] = 0 # TODO return values -def check_analysis(tp, cell): - measured = tp.analysis - expected = expected_testpulse_values(cell) +def check_analysis(pulse, tp_kwds, only=None, tol_override=None): + measured = pulse.analysis + expected = expected_testpulse_values(tp_kwds) # how much error should we tolerate for each parameter? err_tolerance = { - 'baseline_potential': 0.01, - 'baseline_current': 0.01, - 'access_resistance': 0.3, - 'input_resistance': 0.1, - 'capacitance': 0.3, + 'baseline_potential': (0.01, 5*mV), + 'baseline_current': (0.01, 1e-12), + 'access_resistance': (0.3, 5e4), + 'input_resistance': (0.1, 1e5), + 'capacitance': (0.3, 1e-13), } - - for k,v1 in expected.items(): + if tol_override: + err_tolerance.update(tol_override) + mistakes = False + if only: + expected = {k: v for k, v in expected.items() if k in only} + for k, v1 in expected.items(): v2 = measured[k] if v1 is None: - assert v2 is None + print(f"FAILURE: expected None for {k}, measured {v2}") + mistakes = mistakes or v2 is not None continue - err = abs((v1 - v2) / v2) - if err > err_tolerance[k]: - raise ValueError("Test pulse metric out of range: %s = %g != %g" - " (err %g > %g)" % (k, v2, v1, err, err_tolerance[k])) + elif v2 is None: + print(f"FAILURE: expected {v1} for {k}, measured None") + mistakes = True + continue + abs_err = abs(v1 - v2) + if v1 == 0: + rel_err = abs_err + else: + rel_err = abs((v1 - v2) / v1) + rtol, atol = err_tolerance[k] + if rel_err > rtol and abs_err > atol: + print(f"FAILURE: expected {v1:g} for {k}, got {v2:g} (rel_err {rel_err:g} > {rtol :g}) " + f"(abs_err {abs_err:g} > {atol:g})") + mistakes = True + else: + print(f"success: expected {v1:g} for {k}, got {v2:g} (rel_err {rel_err:g}) (abs_err {abs_err:g})") + assert not mistakes, ', '.join((f'{k}={v}' for k, v in tp_kwds.items())) + + +def test_load(): + tp, _ = create_mock_test_pulse() + new_tp = PatchClampTestPulse.load(tp.save()) + for k, v in tp.analysis.items(): + if isinstance(v, (np.ndarray, int, float)): + assert np.allclose(v, new_tp.analysis[k]) + else: + assert v == new_tp.analysis[k] + + +def test_unreadable_stack(): + from neuroanalysis.test_pulse_stack import H5BackedTestPulseStack + + f = h5py.File('test.h5', 'w') + try: + stack = H5BackedTestPulseStack(f.create_group('test_pulses'), readable=False) + with pytest.raises(ValueError): + stack[0] + with pytest.raises(ValueError): + len(stack) + with pytest.raises(ValueError): + stack.merge(stack) + stack.append(create_mock_test_pulse()[0]) + finally: + f.close() + os.remove('test.h5') + + +def test_bath_ugly(): + from neuroanalysis.test_pulse_stack import H5BackedTestPulseStack + + fn = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'test_data', 'bath-ugly.h5') + print(fn) + f = h5py.File(fn, 'r') + tps = H5BackedTestPulseStack(f['test_pulses']) + assert len(tps) == 1 + tp = tps.at_time(float('inf')) + assert tp.analysis + assert np.isnan(tp.analysis['capacitance']) + assert tp.analysis['steady_state_resistance'] < 10e6 if __name__ == '__main__': - import pyqtgraph as pg - - # Just test against a simple R/C circuit attached to a pipette - model_cell.enable_mechs(['leak']) - model_cell.recording_noise = False - - tp = create_test_pulse(pamp=-10*pA, mode='ic', r_access=100*MOhm) - tp.plot() - - check_analysis(tp, model_cell) - - print("Vm %g mV Rm %g MOhm" % (model_cell.resting_potential()*1000, model_cell.input_resistance()/1e6)) + params = interact( + create_mock_test_pulse, + rmp_soma={'siPrefix': True, 'suffix': 'V'}, + r_access={'siPrefix': True, 'suffix': 'Ω'}, + r_input={'siPrefix': True, 'suffix': 'Ω'}, + c_soma={'siPrefix': True, 'suffix': 'F'}, + c_pip={'siPrefix': True, 'suffix': 'F'}, + pamp={'siPrefix': True, 'suffix': 'V/A'}, + hold={'siPrefix': True, 'suffix': 'V/A'}, + ) - # Have to test VC with very low access resistance - tp = create_test_pulse(pamp=-10*mV, mode='vc', r_access=15*MOhm) - tp.plot() - - check_analysis(tp, model_cell) - + app = pg.mkQApp() + tree = ParameterTree() + tree.setParameters(params) + tree.show() + pg.exec() - \ No newline at end of file + failures = [ + "dict(pamp=-0.02, pdur=0.01, mode='vc', noise=0, c_soma=8e-11, c_pip=3e-12, r_input=100e6, r_access=100e6)", # clogged pipette + "dict(noise=0, pamp=-0.01, mode='vc', c_soma=1e-13, c_pip=3e-12, r_input=1000e6, r_access=10e6)", # attached + "dict(noise=10e-06, pamp=1.2e-11, pdur=0.2, mode='ic', r_access=5e6, r_input=103e6, c_soma=5e-11, c_pip=1e-11)", + "dict(noise=10e-06, pamp=-1.1e-11, pdur=0.2, mode='ic', r_access=5e6, r_input=200e6, c_soma=5e-11, c_pip=1e-11)", + "dict(noise=10e-06, pamp=-1e-10, pdur=0.2, mode='ic', r_access=15e6, r_input=103e6, c_soma=5e-11, c_pip=1e-11)", + "dict(noise=10e-06, pamp=-1e-10, pdur=0.2, mode='ic', r_access=15e6, r_input=499e6, c_soma=5e-11, c_pip=1e-11)", + "dict(noise=10e-06, pamp=-1.1e-11, pdur=0.2, mode='ic', r_access=15e6, r_input=499e6, c_soma=5e-11, c_pip=1e-11)", + "dict(noise=10e-06, pamp=1.2e-11, pdur=0.2, mode='ic', r_access=10e6, r_input=200e6, c_soma=1e-10, c_pip=1e-11)", + ] + for _kwds in failures: + title = _kwds[5:-1] + _kwds = eval(_kwds) + _tp, vc_locals = create_mock_test_pulse(**_kwds) + print(title) + print(_tp.recording.clamp_mode, _tp.plot_units, _tp.analysis['time_constant']) + plt = _tp.plot() + plt.setTitle(f"tau: {_tp.analysis['time_constant']}, nrmse: {_tp.main_fit_result['nrmse']}") + print(f"tp.analysis: {_tp.analysis}") + pg.exec() + check_analysis(_tp, _kwds) diff --git a/neuroanalysis/ui/baseline.py b/neuroanalysis/ui/baseline.py index 3f841d8..f0af600 100644 --- a/neuroanalysis/ui/baseline.py +++ b/neuroanalysis/ui/baseline.py @@ -1,5 +1,5 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import pyqtgraph.parametertree as pt from ..baseline import mode_detrend diff --git a/neuroanalysis/ui/event_detection.py b/neuroanalysis/ui/event_detection.py index 466aab0..7203d21 100644 --- a/neuroanalysis/ui/event_detection.py +++ b/neuroanalysis/ui/event_detection.py @@ -1,5 +1,5 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import pyqtgraph.parametertree as pt import numpy as np import scipy.ndimage as ndi diff --git a/neuroanalysis/ui/filter.py b/neuroanalysis/ui/filter.py index b51e5a7..b0bb2bd 100644 --- a/neuroanalysis/ui/filter.py +++ b/neuroanalysis/ui/filter.py @@ -1,5 +1,5 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import pyqtgraph.parametertree as pt from scipy.ndimage import gaussian_filter diff --git a/neuroanalysis/ui/fitting.py b/neuroanalysis/ui/fitting.py index 8a1631c..752e6df 100644 --- a/neuroanalysis/ui/fitting.py +++ b/neuroanalysis/ui/fitting.py @@ -1,24 +1,22 @@ from collections import OrderedDict import numpy as np import scipy.stats -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import pyqtgraph as pg -import pyqtgraph.parametertree import lmfit.minimizer -import sys -class FitExplorer(QtGui.QWidget): +class FitExplorer(QtWidgets.QWidget): def __init__(self, fit=None, model=None, data=None, args=None): - QtGui.QWidget.__init__(self) + QtWidgets.QWidget.__init__(self) self.model = model if model is not None else fit.model self.args = args self.data = data self.fit = None - self.layout = QtGui.QGridLayout() + self.layout = QtWidgets.QGridLayout() self.setLayout(self.layout) - self.splitter = QtGui.QSplitter(QtCore.Qt.Horizontal) + self.splitter = QtWidgets.QSplitter(QtCore.Qt.Horizontal) self.layout.addWidget(self.splitter) self.ptree = pg.parametertree.ParameterTree(showHeader=False) self.splitter.addWidget(self.ptree) diff --git a/neuroanalysis/ui/nwb_viewer/analyzer_view.py b/neuroanalysis/ui/nwb_viewer/analyzer_view.py index 3755c86..9f0ecc7 100644 --- a/neuroanalysis/ui/nwb_viewer/analyzer_view.py +++ b/neuroanalysis/ui/nwb_viewer/analyzer_view.py @@ -1,17 +1,17 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore from ..plot_grid import PlotGrid -class AnalyzerView(QtGui.QWidget): +class AnalyzerView(QtWidgets.QWidget): """A sweep analyzer of unspecified function. """ def __init__(self, parent=None): self.sweeps = [] - QtGui.QWidget.__init__(self, parent) + QtWidgets.QWidget.__init__(self, parent) - self.layout = QtGui.QGridLayout() + self.layout = QtWidgets.QGridLayout() self.setLayout(self.layout) self.layout.setContentsMargins(0, 0, 0, 0) diff --git a/neuroanalysis/ui/nwb_viewer/sweep_view.py b/neuroanalysis/ui/nwb_viewer/sweep_view.py index 40f3c61..f754f07 100644 --- a/neuroanalysis/ui/nwb_viewer/sweep_view.py +++ b/neuroanalysis/ui/nwb_viewer/sweep_view.py @@ -2,19 +2,19 @@ from scipy.ndimage import gaussian_filter import pyqtgraph as pg import pyqtgraph.reload -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore from ..plot_grid import PlotGrid from ...miesnwb import MiesNwb -class SweepView(QtGui.QWidget): +class SweepView(QtWidgets.QWidget): def __init__(self, parent=None): self.sweeps = [] self.chans = None - QtGui.QWidget.__init__(self, parent) + QtWidgets.QWidget.__init__(self, parent) - self.layout = QtGui.QGridLayout() + self.layout = QtWidgets.QGridLayout() self.setLayout(self.layout) self.layout.setContentsMargins(0, 0, 0, 0) diff --git a/neuroanalysis/ui/nwb_viewer/viewer.py b/neuroanalysis/ui/nwb_viewer/viewer.py index 670d813..1207554 100644 --- a/neuroanalysis/ui/nwb_viewer/viewer.py +++ b/neuroanalysis/ui/nwb_viewer/viewer.py @@ -1,5 +1,5 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore from neuroanalysis.miesnwb import MiesNwb from ..signal import SignalBlock @@ -8,7 +8,7 @@ from ...util.merge_lists import merge_lists -class MiesNwbExplorer(QtGui.QSplitter): +class MiesNwbExplorer(QtWidgets.QSplitter): """Widget for listing and selecting recordings in a MIES-generated NWB file. """ selection_changed = QtCore.Signal(object) @@ -16,30 +16,30 @@ class MiesNwbExplorer(QtGui.QSplitter): check_state_changed = QtCore.Signal(object) def __init__(self, nwb=None): - QtGui.QSplitter.__init__(self) + QtWidgets.QSplitter.__init__(self) self.setOrientation(QtCore.Qt.Vertical) self._nwb = None self._channel_selection = {} - self._sel_box = QtGui.QWidget() - self._sel_box_layout = QtGui.QHBoxLayout() + self._sel_box = QtWidgets.QWidget() + self._sel_box_layout = QtWidgets.QHBoxLayout() self._sel_box_layout.setContentsMargins(0, 0, 0, 0) self._sel_box.setLayout(self._sel_box_layout) self.addWidget(self._sel_box) - self.sweep_tree = QtGui.QTreeWidget() + self.sweep_tree = QtWidgets.QTreeWidget() columns = ['ID', 'Stim Name', 'Clamp Mode', 'Holding V', 'Holding I'] self.sweep_tree.setColumnCount(len(columns)) self.sweep_tree.setHeaderLabels(columns) - self.sweep_tree.setSelectionMode(QtGui.QAbstractItemView.ExtendedSelection) + self.sweep_tree.setSelectionMode(QtWidgets.QAbstractItemView.ExtendedSelection) self._sel_box_layout.addWidget(self.sweep_tree) - self.channel_list = QtGui.QListWidget() + self.channel_list = QtWidgets.QListWidget() self.channel_list.setMaximumWidth(50) self._sel_box_layout.addWidget(self.channel_list) self.channel_list.itemChanged.connect(self._channel_list_changed) - self.meta_tree = QtGui.QTreeWidget() + self.meta_tree = QtWidgets.QTreeWidget() self.addWidget(self.meta_tree) self.set_nwb(nwb) @@ -66,6 +66,12 @@ def update_sweep_tree(self): V_holdings = '' I_holdings = '' for rec in sweep.recordings: + if not hasattr(rec, 'clamp_mode'): + modes += "-" + V_holdings += "-" + I_holdings += "-" + continue + if rec.clamp_mode == 'vc': modes += 'V' else: @@ -82,12 +88,12 @@ def update_sweep_tree(self): else: I_holdings += '?? ' - item = QtGui.QTreeWidgetItem([str(i), stim_name, modes, V_holdings, I_holdings]) + item = QtWidgets.QTreeWidgetItem([str(i), stim_name, modes, V_holdings, I_holdings]) item.setCheckState(0, QtCore.Qt.Unchecked) item.data = sweep self.sweep_tree.addTopLevelItem(item) - self.sweep_tree.header().resizeSections(QtGui.QHeaderView.ResizeToContents) + self.sweep_tree.header().resizeSections(QtWidgets.QHeaderView.ResizeToContents) def selection(self): """Return a list of selected groups and/or sweeps. @@ -145,7 +151,7 @@ def _update_channel_list(self): # add new items to the channel list, all selected for ch in channels: - item = QtGui.QListWidgetItem(str(ch)) + item = QtWidgets.QListWidgetItem(str(ch)) item.channel_index = ch self._channel_selection.setdefault(ch, True) # restore previous check state, if any. @@ -158,11 +164,6 @@ def _update_channel_list(self): def _selection_changed(self): sel = self.selection() if len(sel) == 1: - #if isinstance(sel[0], SweepGroup): - #self.meta_tree.setData(sel[0].meta()) - #else: - - #self.meta_tree.setData(sel[0].meta(all_chans=True)) sweep = sel[0] self.meta_tree.setColumnCount(len(sweep.devices)+1) self.meta_tree.setHeaderLabels([""] + [str(dev) for dev in sweep.devices]) @@ -178,15 +179,15 @@ def _selection_changed(self): def _populate_meta_tree(self, meta, root): keys = list(meta[0].keys()) for m in meta[1:]: - keys = merge_lists(keys, m.keys()) + keys = merge_lists(keys, list(m.keys())) for k in keys: vals = [m.get(k) for m in meta] if isinstance(vals[0], dict): - item = QtGui.QTreeWidgetItem([k] + [''] * len(meta)) + item = QtWidgets.QTreeWidgetItem([k] + [''] * len(meta)) self._populate_meta_tree(vals, item) else: - item = QtGui.QTreeWidgetItem([k] + [str(v) for v in vals]) + item = QtWidgets.QTreeWidgetItem([k] + [str(v) for v in vals]) root.addChild(item) def _tree_item_changed(self, item, col): @@ -199,25 +200,25 @@ def _channel_list_changed(self, item): self._channel_selection[item.channel_index] = item.checkState() == QtCore.Qt.Checked -class MiesNwbViewer(QtGui.QWidget): +class MiesNwbViewer(QtWidgets.QWidget): """Combination of a MiesNwvExplorer for selecting sweeps and a tab widget containing multiple views, each performing a different analysis. """ analyzer_changed = QtCore.Signal(object) def __init__(self, nwb=None): - QtGui.QWidget.__init__(self) + QtWidgets.QWidget.__init__(self) self.nwb = nwb - self.layout = QtGui.QGridLayout() + self.layout = QtWidgets.QGridLayout() self.layout.setContentsMargins(0, 0, 0, 0) self.setLayout(self.layout) - self.hsplit = QtGui.QSplitter() + self.hsplit = QtWidgets.QSplitter() self.hsplit.setOrientation(QtCore.Qt.Horizontal) self.layout.addWidget(self.hsplit, 0, 0) - self.vsplit = QtGui.QSplitter() + self.vsplit = QtWidgets.QSplitter() self.vsplit.setOrientation(QtCore.Qt.Vertical) self.hsplit.addWidget(self.vsplit) @@ -229,10 +230,10 @@ def __init__(self, nwb=None): self.ptree = pg.parametertree.ParameterTree() self.vsplit.addWidget(self.ptree) - self.tabs = QtGui.QTabWidget() + self.tabs = QtWidgets.QTabWidget() self.hsplit.addWidget(self.tabs) - self.reload_btn = QtGui.QPushButton("Reload views") + self.reload_btn = QtWidgets.QPushButton("Reload views") self.reload_btn.clicked.connect(self.reload_views) self.vsplit.addWidget(self.reload_btn) @@ -318,11 +319,11 @@ def create_views(self): -class AnalysisView(QtGui.QWidget): +class AnalysisView(QtWidgets.QWidget): """Example skeleton for an analysis view. """ def __init__(self, parent=None): - QtGui.QWidget.__init__(self, parent) + QtWidgets.QWidget.__init__(self, parent) # Views must have self.params # This implements the controls that are unique to this view. diff --git a/neuroanalysis/ui/plot_adapter.py b/neuroanalysis/ui/plot_adapter.py deleted file mode 100644 index 799446d..0000000 --- a/neuroanalysis/ui/plot_adapter.py +++ /dev/null @@ -1,3 +0,0 @@ -class PlotAdapter(QtCore.QObject): - """ - """ \ No newline at end of file diff --git a/neuroanalysis/ui/plot_grid.py b/neuroanalysis/ui/plot_grid.py index bfd8b42..22dfd85 100644 --- a/neuroanalysis/ui/plot_grid.py +++ b/neuroanalysis/ui/plot_grid.py @@ -1,16 +1,16 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from . import qt -class PlotGrid(QtGui.QWidget): +class PlotGrid(qt.QWidget): def __init__(self, parent=None): - QtGui.QWidget.__init__(self, parent) + qt.QWidget.__init__(self, parent) self.rows = 0 self.cols = 0 self.plots = [] - - self.layout = QtGui.QGridLayout() + + self.layout = qt.QGridLayout() self.layout.setSpacing(0) self.layout.setContentsMargins(0, 0, 0, 0) self.setLayout(self.layout) diff --git a/neuroanalysis/ui/psp_fitting.py b/neuroanalysis/ui/psp_fitting.py index 755160e..c490929 100644 --- a/neuroanalysis/ui/psp_fitting.py +++ b/neuroanalysis/ui/psp_fitting.py @@ -17,7 +17,7 @@ def __init__(self, title=None): self.console = pg.console.ConsoleWidget() - self.widget = pg.QtGui.QSplitter(pg.QtCore.Qt.Vertical) + self.widget = pg.QtWidgets.QSplitter(pg.QtCore.Qt.Vertical) self.widget.addWidget(self.pw) self.widget.addWidget(self.console) self.widget.resize(1000, 600) diff --git a/neuroanalysis/ui/qt.py b/neuroanalysis/ui/qt.py new file mode 100644 index 0000000..4a4a383 --- /dev/null +++ b/neuroanalysis/ui/qt.py @@ -0,0 +1,6 @@ +from pyqtgraph.Qt import QtGui, QtWidgets, QtCore + +# copy all of the Qt classes into the local namespace to work around version differences in Qt bindings +for mod in [QtGui, QtWidgets, QtCore]: + for k,v in mod.__dict__.items(): + locals()[k] = v diff --git a/neuroanalysis/ui/spike_detection.py b/neuroanalysis/ui/spike_detection.py index 28c8cfe..c18292d 100644 --- a/neuroanalysis/ui/spike_detection.py +++ b/neuroanalysis/ui/spike_detection.py @@ -19,7 +19,7 @@ def __init__(self, title=None): self.console = pg.console.ConsoleWidget() - self.widget = pg.QtGui.QSplitter(pg.QtCore.Qt.Vertical) + self.widget = pg.QtWidgets.QSplitter(pg.QtCore.Qt.Vertical) self.widget.addWidget(self.pw) self.widget.addWidget(self.console) self.widget.resize(1000, 900) diff --git a/neuroanalysis/ui/sta_analyzer.py b/neuroanalysis/ui/sta_analyzer.py index d8b71ab..1cf0b40 100644 --- a/neuroanalysis/ui/sta_analyzer.py +++ b/neuroanalysis/ui/sta_analyzer.py @@ -1,6 +1,6 @@ import numpy as np import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import pyqtgraph.parametertree as pt from .cell_selector import CellSelector from .event_detection import EventDetector @@ -8,7 +8,7 @@ from ..data import TSeries -class STAAnalyzer(QtGui.QWidget): +class STAAnalyzer(QtWidgets.QWidget): """Analyzer for running spike-triggered averaging on BOb calcium imaging data. Features: @@ -44,11 +44,11 @@ def __init__(self, boc, expt_id, cell_id): # make stimulus frame locations easier to look up self.lsn_id = None - QtGui.QWidget.__init__(self) - self.hs = pg.QtGui.QSplitter() + QtWidgets.QWidget.__init__(self) + self.hs = pg.QtWidgets.QSplitter() self.hs.setOrientation(pg.QtCore.Qt.Horizontal) - self.vs1 = pg.QtGui.QSplitter() + self.vs1 = pg.QtWidgets.QSplitter() self.vs1.setOrientation(pg.QtCore.Qt.Vertical) self.params = pt.Parameter(name='params', type='group', children=[ @@ -66,7 +66,7 @@ def __init__(self, boc, expt_id, cell_id): self.vs1.addWidget(self.expt_imv) - self.vs2 = pg.QtGui.QSplitter() + self.vs2 = pg.QtWidgets.QSplitter() self.vs2.setOrientation(pg.QtCore.Qt.Vertical) self.plt1 = pg.PlotWidget() @@ -84,7 +84,7 @@ def __init__(self, boc, expt_id, cell_id): self.hs.addWidget(self.vs1) self.hs.addWidget(self.vs2) - self.layout = QtGui.QGridLayout() + self.layout = QtWidgets.QGridLayout() self.setLayout(self.layout) self.layout.addWidget(self.hs) diff --git a/neuroanalysis/ui/triggered_average.py b/neuroanalysis/ui/triggered_average.py index 3f04964..cdce1fb 100644 --- a/neuroanalysis/ui/triggered_average.py +++ b/neuroanalysis/ui/triggered_average.py @@ -1,5 +1,5 @@ import pyqtgraph as pg -from pyqtgraph.Qt import QtGui, QtCore +from pyqtgraph.Qt import QtWidgets, QtCore import pyqtgraph.parametertree as pt import numpy as np import scipy.ndimage as ndi diff --git a/neuroanalysis/ui/user_test.py b/neuroanalysis/ui/user_test.py index 1480544..7f3c12a 100644 --- a/neuroanalysis/ui/user_test.py +++ b/neuroanalysis/ui/user_test.py @@ -6,10 +6,10 @@ class UserTestUi(object): def __init__(self, expected_display, current_display): pg.mkQApp() - self.widget = pg.QtGui.QSplitter(pg.QtCore.Qt.Vertical) + self.widget = pg.QtWidgets.QSplitter(pg.QtCore.Qt.Vertical) self.widget.resize(1600, 1000) - self.display_splitter = pg.QtGui.QSplitter(pg.QtCore.Qt.Horizontal) + self.display_splitter = pg.QtWidgets.QSplitter(pg.QtCore.Qt.Horizontal) self.widget.addWidget(self.display_splitter) self.display1 = expected_display @@ -17,15 +17,15 @@ def __init__(self, expected_display, current_display): self.display_splitter.addWidget(self.display1.widget) self.display_splitter.addWidget(self.display2.widget) - self.ctrl = pg.QtGui.QWidget() + self.ctrl = pg.QtWidgets.QWidget() self.widget.addWidget(self.ctrl) - self.ctrl_layout = pg.QtGui.QVBoxLayout() + self.ctrl_layout = pg.QtWidgets.QVBoxLayout() self.ctrl.setLayout(self.ctrl_layout) self.diff_widget = pg.DiffTreeWidget() self.ctrl_layout.addWidget(self.diff_widget) - self.pass_btn = pg.QtGui.QPushButton('pass') - self.fail_btn = pg.QtGui.QPushButton('fail') + self.pass_btn = pg.QtWidgets.QPushButton('pass') + self.fail_btn = pg.QtWidgets.QPushButton('fail') self.ctrl_layout.addWidget(self.pass_btn) self.ctrl_layout.addWidget(self.fail_btn) @@ -44,7 +44,7 @@ def fail_clicked(self): def user_passfail(self): self.widget.show() while True: - pg.QtGui.QApplication.processEvents() + pg.QtWidgets.QApplication.processEvents() last_btn_clicked = self.last_btn_clicked self.last_btn_clicked = None diff --git a/neuroanalysis/util/h5py_wrapper.py b/neuroanalysis/util/h5py_wrapper.py new file mode 100644 index 0000000..6acbdfc --- /dev/null +++ b/neuroanalysis/util/h5py_wrapper.py @@ -0,0 +1,43 @@ +import h5py +from h5py.h5t import check_string_dtype + + +def File(*args, **kwds): + return H5pyWrapper(h5py.File(*args, **kwds)) + + +class H5pyWrapper: + """Wraps h5py objects to preserve string behavior from version 2. + + This allows the same code to use h5py version 2 or 3 without changes. + """ + def __init__(self, obj): + self.__dict__['_wrapped_obj'] = obj + + def __getattr__(self, name): + + return getattr(self._wrapped_obj, name) + + def __setattr__(self, name, value): + setattr(self._wrapped_obj, name, value) + + def __getitem__(self, name): + item = self._wrapped_obj[name] + if isinstance(item, h5py.Dataset): + if check_string_dtype(item.dtype): + return item.asstr() + else: + return item + else: + return H5pyWrapper(item) + + def __setitem__(self, item, value): + self._wrapped_obj[item] = value + + def __repr__(self): + return f"" + + +if h5py.__version__.split('.')[0] == '2': + def H5pyWrapper(obj): + return obj diff --git a/neuroanalysis/util/jit.py b/neuroanalysis/util/jit.py new file mode 100644 index 0000000..0a3953b --- /dev/null +++ b/neuroanalysis/util/jit.py @@ -0,0 +1,28 @@ +import warnings +try: + import numba + have_numba = True +except ImportError: + have_numba = False + + +no_numba_warn = True +use_numba = True + + +def _fake_jit(fn): + return fn + + +def numba_jit(*args, **kwds): + """Wrapper around numba.jit that fails gracefully if numba is not available. + """ + global use_numba, have_numba, no_numba_warn + if use_numba and have_numba: + return numba.jit(*args, **kwds) + else: + if use_numba and no_numba_warn: + warnings.warn("Could not import numba; falling back to slower implementation.") + no_numba_warn = False + return _fake_jit + diff --git a/neuroanalysis/util/lru_cache.py b/neuroanalysis/util/lru_cache.py new file mode 100644 index 0000000..82fa271 --- /dev/null +++ b/neuroanalysis/util/lru_cache.py @@ -0,0 +1,7 @@ +try: + from functools import lru_cache +except ImportError: + # fake decorator; lru_cache only on python 3 + def lru_cache(*args, **kwds): + return lambda fn: fn + diff --git a/neuroanalysis/util/mies_nwb_parsing.py b/neuroanalysis/util/mies_nwb_parsing.py new file mode 100644 index 0000000..06b2785 --- /dev/null +++ b/neuroanalysis/util/mies_nwb_parsing.py @@ -0,0 +1,192 @@ +from collections import OrderedDict +import numpy as np +from datetime import datetime + + +def parse_lab_notebook(hdf): + """Return compiled data from the lab notebook in the given hdf. + + Parameters: + ----------- + hdf : HDF5 file (needs to have a labnotebook field) + + Returns: + -------- + notebook : dict + Contains one key:value pair per sweep. Each value is a list + containing one metadata dict for each channel in the sweep. + For example:: + + notebook[sweep_id][channel_id][metadata_key] + + """ + + # collect all lab notebook entries + sweep_entries = OrderedDict() + tp_entries = [] + device = list(hdf['general/devices'].keys())[0].split('_',1)[-1] + nb_keys = hdf['general']['labnotebook'][device]['numericalKeys'][0] + nb_fields = OrderedDict([(k, i) for i,k in enumerate(nb_keys)]) + + # convert notebook to array here, otherwise we incur the decompression cost for the entire + # dataset every time we try to access part of it. + nb = np.array(hdf['general']['labnotebook'][device]['numericalValues']) + + # EntrySourceType field is needed to distinguish between records created by TP vs sweep + entry_source_type_index = nb_fields.get('EntrySourceType', None) + + nb_iter = iter(range(nb.shape[0])) # so we can skip multiple rows from within the loop + for i in nb_iter: + rec = nb[i] + sweep_num = rec[0,0] + + is_tp_record = False + is_sweep_record = False + + # ignore records that were generated by test pulse + # (note: entrySourceType is nan if an older pxp is re-exported to nwb using newer MIES) + if entry_source_type_index is not None and not np.isnan(rec[entry_source_type_index][0]): + if rec[entry_source_type_index][0] == 0: + is_sweep_record = True + else: + is_tp_record = True + elif i < nb.shape[0] - 1: + # Older files may be missing EntrySourceType. In this case, we can identify TP blocks + # as two records containing a "TP Peak Resistance" value in the first record followed + # by a "TP Pulse Duration" value in the second record. + tp_peak = rec[nb_fields['TP Peak Resistance']] + if any(np.isfinite(tp_peak)): + tp_dur = nb[i+1][nb_fields['TP Pulse Duration']] + if any(np.isfinite(tp_dur)): + next(nb_iter) + is_tp_record = True + if not is_tp_record: + is_sweep_record = np.isfinite(sweep_num) + + if is_tp_record: + rec = np.array(rec) + next(nb_iter) + rec2 = np.array(nb[i+1]) + mask = ~np.isnan(rec2) + rec[mask] = rec2[mask] + tp_entries.append(rec) + + elif is_sweep_record: + sweep_num = int(sweep_num) + # each sweep gets multiple nb records; for each field we use the last non-nan value in any record + if sweep_num not in sweep_entries: + sweep_entries[sweep_num] = np.array(rec) + else: + mask = ~np.isnan(rec) + sweep_entries[sweep_num][mask] = rec[mask] + + for swid, entry in sweep_entries.items(): + # last column is "global"; applies to all channels + mask = ~np.isnan(entry[:,8]) + entry[mask] = entry[:,8:9][mask] + + # first 4 fields of first column apply to all channels + entry[:4] = entry[:4, 0:1] + + # async AD fields (notably used to record temperature) appear + # only in column 0, but might move to column 8 later? Since these + # are not channel-specific, we'll copy them to all channels + for i,k in enumerate(nb_keys): + if not k.startswith('Async AD '): + continue + entry[i] = entry[i, 0] + + # convert to list-o-dicts + meta = [] + for i in range(entry.shape[1]): + tm = entry[:, i] + meta.append(OrderedDict([(nb_keys[j], (None if np.isnan(tm[j]) else tm[j])) for j in range(len(nb_keys))])) + sweep_entries[swid] = meta + + # Load textual keys in a similar way + text_nb_keys = hdf['general']['labnotebook'][device]['textualKeys'][0] + text_nb_fields = OrderedDict([(k, i) for i,k in enumerate(text_nb_keys)]) + text_nb = np.array(hdf['general']['labnotebook'][device]['textualValues']) + entry_source_type_index = text_nb_fields.get('EntrySourceType', None) + + for rec in text_nb: + if entry_source_type_index is None: + # older nwb files lack EntrySourceType; fake it for now + source_type = 0 + else: + try: + source_type = int(rec[entry_source_type_index, 0]) + except ValueError: + # No entry source type recorded here; skip for now. + continue + + if source_type != 0: + # Select only sweep records for now. + continue + + try: + sweep_id = int(rec[0,0]) + except ValueError: + # Not sure how to handle records with no sweep ID; skip for now. + continue + sweep_entry = sweep_entries[sweep_id] + + for k,i in text_nb_fields.items(): + for j, val in enumerate(rec[i, :-1]): + if k in sweep_entry[j]: + # already have a value here; don't overwrite. + continue + + if val == '': + # take value from last column if this one is empty + val == rec[i, -1] + if val == '': + # no value here; skip. + continue + + sweep_entry[j][k] = val + + return sweep_entries + #self._tp_notebook = tp_entries + #self._notebook_keys = nb_fields + #self._tp_entries = None + +def igorpro_date(timestamp): + """Convert an IgorPro timestamp (seconds since 1904-01-01) to a datetime + object. + """ + dt = datetime(1970,1,1) - datetime(1904,1,1) + return datetime.utcfromtimestamp(timestamp) - dt + +def parse_stim_wave_note(rec_notebook): + """Return (version, epochs) from the stim wave note of the labnotebook associated with a recording. + + Paramenters: + ------------ + rec_notebook : dict + A labnotebook dict for a recording, as returned by parse_lab_notebook(hdf)[sweep_id][channel] + + Returns: + -------- + (version, epochs) : tuple + version is an int, epochs is a list of dicts + + """ + + sweep_count = int(rec_notebook['Set Sweep Count']) + wave_note = rec_notebook['Stim Wave Note'] + lines = wave_note.split('\n') + version = [line for line in lines if line.startswith('Version =')] + if len(version) == 0: + version = 0 + else: + version = float(version[0].rstrip(';').split(' = ')[1]) + epochs = [] + for line in lines: + if not line.startswith('Sweep = %d;' % sweep_count): + continue + epoch = dict([part.split(' = ') for part in line.split(';') if '=' in part]) + epochs.append(epoch) + + return version, epochs + diff --git a/neuroanalysis/util/optional_import.py b/neuroanalysis/util/optional_import.py index f62c79a..ed1c9e3 100644 --- a/neuroanalysis/util/optional_import.py +++ b/neuroanalysis/util/optional_import.py @@ -1,18 +1,65 @@ import importlib -def optional_import(module): +def optional_import(module, names=None, package=None): """Try importing a module, but if that fails, wait until the first time it is accessed before raising the ImportError. + + Parameters + ---------- + module : str + Name of module to import (or import from) + names : str | list of str | None + Optional name or list of names to import from *module*. If string, then + the single imported object is returned. If list, then a list + of imported objects is returned. + package : str | None + Optional base package from which relative *module* names are constructed. + This argument is required if the *module* is relative (begins with `.`). + + Examples:: + + # import numba + numba = optional_import('numba') + + # from numpy import array, zeros + array, zeros = optional_import('numpy', names=['array', 'zeros']) + + # from ..mypackage import myname + myname = optional_import('..mypackage', names='myname', package=__name__) + """ + returnlist = isinstance(names, (list, tuple)) + if not returnlist and names is not None: + names = [names] + try: - return importlib.import_module(module) + mod = importlib.import_module(module, package=package) + if names is None: + return mod + else: + ret = [] + for name in names: + if hasattr(mod, name): + ret.append(getattr(mod, name)) + else: + ret.append(OptionalImportError(ImportError("cannot import name '%s' from '%s' (%s)" % (name, module, mod.__file__)))) + return ret if returnlist else ret[0] except ImportError as exc: - return OptionalImportError(exc) + mod = OptionalImportError(exc) + if returnlist: + return [mod] * len(names) + else: + return mod class OptionalImportError(object): + """Dummy object that just re-raises an ImportError when it is accessed. + """ def __init__(self, exc): self.exc = exc def __getattr__(self, attr): raise self.exc + def __call__(self, *args, **kwds): + raise self.exc + diff --git a/neuroanalysis/util/tests/test_optional_import.py b/neuroanalysis/util/tests/test_optional_import.py new file mode 100644 index 0000000..15e5fad --- /dev/null +++ b/neuroanalysis/util/tests/test_optional_import.py @@ -0,0 +1,63 @@ +import inspect +from pytest import raises +from neuroanalysis.util.optional_import import optional_import, OptionalImportError + + +def test_optional_import(): + # import numpy + + np = optional_import('numpy') + assert inspect.ismodule(np) + + bad_mod = optional_import('aewhfjarelgsg') + assert isinstance(bad_mod, OptionalImportError) + with raises(ImportError): + bad_mod.test + + # from numpy.random import normal + + norm = optional_import('numpy.random', 'normal') + assert isinstance(norm(), float) + + bad_name = optional_import('numpy.random', 'aergjkseorser') + assert isinstance(bad_name, OptionalImportError) + with raises(ImportError): + bad_name() + + # from numpy.random import normal, random + + norm, rand = optional_import('numpy.random', ['normal', 'random']) + assert isinstance(norm(), float) + assert isinstance(rand(), float) + + bad_names = optional_import('numpy.random', ['aergjkseorser', 'agfawreaerges']) + for name in bad_names: + assert isinstance(name, OptionalImportError) + with raises(ImportError): + name() + + # test again using package: + + norm = optional_import('.random', 'normal', package='numpy') + assert isinstance(norm(), float) + + bad_name = optional_import('.random', 'aergjkseorser', package='numpy') + assert isinstance(bad_name, OptionalImportError) + with raises(ImportError): + bad_name() + + norm, rand = optional_import('.random', ['normal', 'random'], package='numpy') + assert isinstance(norm(), float) + assert isinstance(rand(), float) + + bad_names = optional_import('.random', ['aergjkseorser', 'agfawreaerges'], package='numpy') + for name in bad_names: + assert isinstance(name, OptionalImportError) + with raises(ImportError): + name() + + + + + + diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9ac9b91 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,6 @@ +[build-system] +requires = [ + "setuptools >= 40.9.0", + "wheel", +] +build-backend = "setuptools.build_meta" diff --git a/setup.py b/setup.py index 8394964..8f978ee 100644 --- a/setup.py +++ b/setup.py @@ -1,22 +1,40 @@ -import os +from os import path + from setuptools import setup, find_packages -packages=find_packages('.') +this_directory = path.abspath(path.dirname(__file__)) +with open(path.join(this_directory, "README.md"), encoding="utf-8") as f: + long_description = f.read() setup( - name = "neuroanalysis", - version = "0.0.1", - author = "Luke Campagnola", - author_email = "lukec@alleninstitute.org", - description = ("Functions and modular user interface tools for analysis of patch clamp experiment data."), - license = "MIT", - keywords = "neuroscience analysis neurodata without borders nwb ", - url = "http://github.com/aiephys/neuroanalysis", - packages=packages, + author="Luke Campagnola", + author_email="lukec@alleninstitute.org", classifiers=[ - "Development Status :: 3 - Alpha", + "Development Status :: 4 - Beta", + "Environment :: Other Environment", + "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", + "Programming Language :: Python :: 3", + "Programming Language :: Python", + "Topic :: Scientific/Engineering", + "Topic :: Software Development :: Libraries :: Python Modules", ], + description="Functions and modular user interface tools for analysis of patch clamp experiment data.", + extra_require={ + "ACQ4": ["acq4"], + "jit": ["numba"], + "MIES": ["h5py"], + "test": ["pytest"], + "ui": ["pyqtgraph"], + }, + install_requires=["lmfit", "numpy", "scipy"], + keywords="neuroscience analysis neurodata without borders nwb", + license="MIT", + long_description=long_description, + long_description_content_type="text/markdown", + name="neuroanalysis", + packages=find_packages(), + url="https://github.com/AllenInstitute/neuroanalysis", + version="0.0.5", ) - - diff --git a/test_data b/test_data index d838553..4a2f6c8 160000 --- a/test_data +++ b/test_data @@ -1 +1 @@ -Subproject commit d838553653806f0f469c7b680e57e83889664950 +Subproject commit 4a2f6c8c24ebaf72d73406fec433184a7472f0f8 diff --git a/conftest.py b/tools/conftest.py similarity index 100% rename from conftest.py rename to tools/conftest.py diff --git a/tools/import_psp_fitting.py b/tools/import_psp_fitting.py index 04a18e9..6294066 100644 --- a/tools/import_psp_fitting.py +++ b/tools/import_psp_fitting.py @@ -27,9 +27,9 @@ ui = PspFitUI() -skip_btn = pg.QtGui.QPushButton('skip') +skip_btn = pg.QtWidgets.QPushButton('skip') ui.widget.addWidget(skip_btn) -save_btn = pg.QtGui.QPushButton('save') +save_btn = pg.QtWidgets.QPushButton('save') ui.widget.addWidget(save_btn) diff --git a/tools/import_spike_detection.py b/tools/import_spike_detection.py index 7d4cfba..79a1eb8 100644 --- a/tools/import_spike_detection.py +++ b/tools/import_spike_detection.py @@ -15,7 +15,9 @@ from neuroanalysis.ui.spike_detection import SpikeDetectUI from neuroanalysis.data import TSeries, TSeriesList, PatchClampRecording from multipatch_analysis.database import default_db as db -from multipatch_analysis.data import Analyzer, PulseStimAnalyzer, MultiPatchProbe +from multipatch_analysis.data import MultiPatchProbe +from neuroanalysis.analyzers.analyzer import Analyzer +from neuroanalysis.analyzers.stim_pulse import PatchClampStimPulseAnalyzer import pyqtgraph as pg pg.dbg() # for inspecting exception stack @@ -26,9 +28,9 @@ ui = SpikeDetectUI() -skip_btn = pg.QtGui.QPushButton('skip') +skip_btn = pg.QtWidgets.QPushButton('skip') ui.widget.addWidget(skip_btn) -save_btn = pg.QtGui.QPushButton('save') +save_btn = pg.QtWidgets.QPushButton('save') ui.widget.addWidget(save_btn) @@ -54,7 +56,7 @@ def iter_pulses(): print("sweep: %d channel: %d" % (sweep.key, channel)) # Get chunks for each stim pulse - pulse_stim = PulseStimAnalyzer.get(pre_rec) + pulse_stim = PatchClampStimPulseAnalyzer.get(pre_rec) chunks = pulse_stim.pulse_chunks() for chunk in chunks: yield (expt_id, cell_id, sweep, channel, chunk) diff --git a/mies_nwb_viewer.py b/tools/mies_nwb_viewer.py similarity index 90% rename from mies_nwb_viewer.py rename to tools/mies_nwb_viewer.py index b795307..fe6552d 100644 --- a/mies_nwb_viewer.py +++ b/tools/mies_nwb_viewer.py @@ -1,9 +1,9 @@ import os, sys -from pyqtgraph.Qt import QtGui +from pyqtgraph.Qt import QtWidgets from neuroanalysis.ui.nwb_viewer import MiesNwbViewer from neuroanalysis.miesnwb import MiesNwb -app = QtGui.QApplication([]) +app = QtWidgets.QApplication([]) # create NWB viewer v = MiesNwbViewer() diff --git a/tools/model_test.py b/tools/model_test.py new file mode 100644 index 0000000..745ae9b --- /dev/null +++ b/tools/model_test.py @@ -0,0 +1,42 @@ +import os +import h5py +from neuroanalysis.data.loaders.mies_dataset_loader import MiesNwbLoader +from neuroanalysis.data.loaders.acq4_dataset_loader import Acq4DatasetLoader +from neuroanalysis.data.dataset import Dataset +from optoanalysis.analyzers import OptoBaselineAnalyzer +from aisynphys.analyzers import MPBaselineAnalyzer +from neuroanalysis.miesnwb import MiesNwb +import pyqtgraph as pg + +pg.dbg() + + +f = "/Users/meganbkratz/Code/ai/example_data/data/2019-06-13_000/slice_000/site_000/2019_06_13_exp1_TH-compressed.nwb" +f2 = "/Users/meganbkratz/Code/ai/example_data/2019_06_24_131623-compressed.nwb" +f3 = "/Users/meganbkratz/Documents/ManisLab/L4Mapping/ExcitationProfileData/2012.11.09_000/slice_000/cell_004" + +#hdf = h5py.File(f, 'r') + +mies_nwb = Dataset(loader=MiesNwbLoader(f2, baseline_analyzer_class=MPBaselineAnalyzer)) +mies_nwb_old = MiesNwb(f2) +opto_nwb = Dataset(loader=MiesNwbLoader(f, baseline_analyzer_class=OptoBaselineAnalyzer)) +acq4_dataset = Dataset(loader=Acq4DatasetLoader(f3)) + +#old = OptoNwb(f) + + +### for profiling lazy load stimulus vs stimulus +# prof = pg.debug.Profiler(disabled=False) + +# for srec in mies_nwb.contents: +# recs = srec.recordings + +# prof('made recordings') + +# for srec in mies_nwb.contents: +# for rec in srec.recordings: +# desc = rec.stimulus.description + +# prof('got stimulus descriptions') +# prof.finish() + diff --git a/sta.py b/tools/sta.py similarity index 95% rename from sta.py rename to tools/sta.py index 55e98fc..088e737 100644 --- a/sta.py +++ b/tools/sta.py @@ -26,4 +26,4 @@ if not sys.flags.interactive: - pg.QtGui.QApplication.exec_() + pg.QtWidgets.QApplication.exec_() diff --git a/vc_spike_test.py b/tools/vc_spike_test.py similarity index 95% rename from vc_spike_test.py rename to tools/vc_spike_test.py index 85ec25f..4552445 100644 --- a/vc_spike_test.py +++ b/tools/vc_spike_test.py @@ -2,7 +2,7 @@ import scipy.ndimage as ndi import pyqtgraph as pg from neuroanalysis.ui.plot_grid import PlotGrid -from neuroanalysis.spike_detection import detect_vc_evoked_spike +from neuroanalysis.spike_detection import detect_vc_evoked_spikes from neuroanalysis.data import TSeries @@ -60,7 +60,7 @@ for j in range(8): # loop over pulses pstart = on_times[j+1] - start pstop = off_times[j+1] - start - spike_info = detect_vc_evoked_spike(TSeries(chunk, dt=dt), pulse_edges=(pstart, pstop)) + spike_info = detect_vc_evoked_spikes(TSeries(chunk, dt=dt), pulse_edges=(pstart, pstop)) if spike_info is not None: peak_inds.append(spike_info['peak_index']) rise_inds.append(spike_info['rise_index'])