specific_metabolics_accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "_2KKqfz-5Ioi"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OktzdLyrDm34"
   },
   "source": [
    "# Dataset\n",
    "[Download DATASETS from here](https://github.com/utshabkg/LungCancer_Biomarker/tree/main/dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "id": "mbm_F-i_bhGw",
    "outputId": "b85d04af-5016-462f-b31f-9724615b3765"
   },
   "outputs": [],
   "source": [
    "plasma = pd.read_csv('plasma\\metabolic tests\\plasma(Specific Metabolics).csv')\n",
    "serum = pd.read_csv('serum\\metabolic tests\\serum(Specific Metabolics).csv')\n",
    "plasma.head()\n",
    "plasma = plasma.drop('Unnamed: 0', axis=1)\n",
    "serum = serum.drop('Unnamed: 0', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep labels as an numpy array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.reshape(plasma.Class.to_numpy(), (82,1))\n",
    "targets = plasma.iloc[:, -1].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hHIN4cpOUseh"
   },
   "source": [
    "# PCA, tSNE & Other Functions\n",
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "hPeZ-YigP1aJ"
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "def pca_analysis(df, n_components):\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca_result = pca.fit_transform(df.iloc[:, :-1].values)\n",
    "\n",
    "    pca_concatenate = np.concatenate([pca_result, labels], axis=1)\n",
    "\n",
    "    return pca_concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "WV9uX0vFQDyE"
   },
   "outputs": [],
   "source": [
    "def make_dataframe(array, n_features):\n",
    "    col = []\n",
    "    for i in range(1, n_features+1):\n",
    "        col.append(f'pc{i}')\n",
    "    col.append('Class')\n",
    "    return pd.DataFrame(array, columns = col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "1-8uMYFWTBSG"
   },
   "outputs": [],
   "source": [
    "def plotting(pca_df, n_components):\n",
    "    fig, axes = plt.subplots(nrows=n_components-1, ncols=4, figsize=(20, 8))\n",
    "    fig.suptitle(\"Principal Component Analysis\",fontsize=20)\n",
    "\n",
    "    for i in range(n_components):\n",
    "        for j in range(i+1, n_components):\n",
    "            if j==n_components:\n",
    "                break\n",
    "            axes[i, j-1].set_xlabel(f'PC{i+1}',fontsize=20)\n",
    "            axes[i, j-1].set_ylabel(f'PC{j+1}',fontsize=20)\n",
    "            for target, color in zip(targets,colors):\n",
    "                indicesToKeep = pca_df.Class == target\n",
    "                axes[i, j-1].scatter(pca_df.loc[indicesToKeep, f'pc{i+1}']\n",
    "                                , pca_df.loc[indicesToKeep, f'pc{j+1}'], c = color, s = 50)\n",
    "    fig.legend(targets,prop={'size': 15}, loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W4-0mzTTXRvT"
   },
   "source": [
    "## t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "d5FexA5TVUMe"
   },
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "def tsne_analysis(df, n_components):\n",
    "    tsne = TSNE(n_components=n_components)\n",
    "    tsne_result = tsne.fit_transform(df.iloc[:, :-1].values)\n",
    "\n",
    "    tsne_concatenate = np.concatenate([tsne_result, labels], axis=1)\n",
    "\n",
    "    return tsne_concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "I_R9XARdt-V0"
   },
   "outputs": [],
   "source": [
    "def tsne_dataframe(array, n_features):\n",
    "    col = []\n",
    "    for i in range(1, n_features+1):\n",
    "        col.append(f'tsne{i}')\n",
    "    col.append('Class')\n",
    "    return pd.DataFrame(array, columns = col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "RROhs0r4uigO"
   },
   "outputs": [],
   "source": [
    "def tsne_plotting(tsne_df, n_components):\n",
    "    fig, axes = plt.subplots(nrows=n_components-1, ncols=4, figsize=(20, 8))\n",
    "    fig.suptitle(\"t-SNE\",fontsize=20)\n",
    "\n",
    "    for i in range(n_components):\n",
    "        for j in range(i+1, n_components):\n",
    "            if j==n_components:\n",
    "                break\n",
    "            axes[i, j-1].set_xlabel(f'TSNE{i+1}',fontsize=20)\n",
    "            axes[i, j-1].set_ylabel(f'TSNE{j+1}',fontsize=20)\n",
    "            for target, color in zip(targets,colors):\n",
    "                indicesToKeep = tsne_df.Class == target\n",
    "                axes[i, j-1].scatter(tsne_df.loc[indicesToKeep, f'tsne{i+1}']\n",
    "                                , tsne_df.loc[indicesToKeep, f'tsne{j+1}'], c = color, s = 50)\n",
    "    fig.legend(targets,prop={'size': 15}, loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "## Plasma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8mJbTitszFRG",
    "outputId": "e2269dc1-ef0d-456b-c8ed-7e874a3380cd"
   },
   "outputs": [],
   "source": [
    "pca2_plasma = pca_analysis(plasma, 2)\n",
    "\n",
    "pca2_plasma_df = make_dataframe(pca2_plasma, 2)\n",
    "# print(pca2_plasma_df.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "--HrgVwLB0wH",
    "outputId": "d5522eca-81a4-47d1-c11b-38a85fb38205"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24cdbc141c8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.figure(figsize=(4, 4))\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('PC1',fontsize=20)\n",
    "plt.ylabel('PC2',fontsize=20)\n",
    "plt.title(\"Principal Component Analysis(PLASMA)\",fontsize=20)\n",
    "colors = ['r', 'g']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = pca2_plasma_df.Class == target\n",
    "    plt.scatter(pca2_plasma_df.loc[indicesToKeep, 'pc1']\n",
    "               , pca2_plasma_df.loc[indicesToKeep, 'pc2'], c = color, s = 50)\n",
    "\n",
    "plt.legend(targets,prop={'size': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "sXliCThzzpBO"
   },
   "outputs": [],
   "source": [
    "pca3_plasma = pca_analysis(plasma, 3)\n",
    "\n",
    "pca3_plasma_df = make_dataframe(pca3_plasma, 3)\n",
    "# print(pca3_df.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "Z0U0vdvJCmSj",
    "outputId": "4b1b710e-3bba-4d5c-f628-eb401315a9ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(pca3_plasma_df, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "EYPgDQ9A0gx8"
   },
   "outputs": [],
   "source": [
    "pca4_plasma = pca_analysis(plasma, 4)\n",
    "\n",
    "pca4_plasma_df = make_dataframe(pca4_plasma, 4)\n",
    "# print(pca4_plasma_df.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "k9JGmoWDH-iP",
    "outputId": "292d2af0-d150-417c-e3d5-24c86db26581"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABR8AAAImCAYAAADaE/03AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAD4eklEQVR4nOzdeXxcdb3/8dcna9skLF3YWkqRRpCiCFS4alQ2gUYWBXRAVNQqiqCW1styRcUV5CdgvYCKFQGpEC6gIgQKCKjxKheooC1bCmIpQZa2wDRts83n98eZaSeTM5OZZNbk/Xw88kgy58yZ70xmvjnn8/18P19zd0RERERERERERETyrarUDRAREREREREREZGxScFHERERERERERERKQgFH0VERERERERERKQgFHwUERERERERERGRglDwUURERERERERERApCwUcREREREREREREpiJpSN0BERERERERERARg+fLlR9bU1HzD3XdCSXPlLmZm/+7v7//m/vvvvyzdTubuxWyUiIiIiIiIiIjIEMuXLz+yvr7+8lmzZvVOnDhxc1VVlYJWZSwWi9mmTZsmPPfcc3U9PT1npgtAKoIsIiIiIiIiIiIlV1NT841Zs2b1NjQ0bFLgsfxVVVV5Q0PDplmzZvXW1NR8I+1+xWyUiIiIiIiIiIhIGHffaeLEiZtL3Q7JzcSJEzfHp8mHUvBRRERkBMxslpm5mV1T5Md9zsyeK+Zjxh/34PjzvaDYjy0yUmZ2Tfx9O6tIj1eSz6eIiMgYUqWMx8oT/5uljTEq+CgiIuNGPAiR/DVgZq+a2X1m9tFSt2+sMrP3m9lSM/unmW00s01mtsrMfmlm80rdPgnkK1BnZvfEj/O8mVXnqXkiIiIiUqG02rWIiIxH34x/rwX2Ao4DDjGzue6+MMtjvAC8BXi9AO3L5LAiP96ImVkTcB3wQWAzcB9wK9AH7A60Ah8zs0vc/Sulaqfkj5m9ieA96sAMYB5we0kbVVwV8/kUERERKRZlPoqIyLjj7hfEv77q7icARxIESxZkm/Xl7n3u/qS7v1jItoY87jPu/kwxH3MkzKwK+B+CwOP9wB7u/gF3X+Tu57p7BNgFWABMLFlDJd8+Cxjw/fjvp5WwLUVXKZ9PERERKb6HHnpogpkdcPvttzcBmNkB3/ve96aVul3FoOCjiIiMe+7+e+BJgqDJOwDM7IL41NGDzeyjZvagmW1I1HNLV/MxeeqqmX3OzP5hZpvN7CUzu8rMtg1rg5nNMLMfmVlnfFryOjP7PzP7Wsp+Q2rKmdkn44/5STP7gJn9r5l1m9l6M7vZzJpDHu/NZnaRmT1sZq+YWY+Z/SvexhkjfzW3OJkgqLsKOMbdu1J3cPced18MDMo2NbN6Mzs3/tptNLM3zOxPZvaRkOex5e9gZnvEn+9aM4ua2d1mtk98v2nx5/Zi/O/xkJkdEnK85L/7qWb2t/jf42Uzu9rMQgtpm1mzmV1nZi+YWa+ZdcV/D3vtkx/jxPjfeWP8b36jmU1P8xiTzexCM3si3qbXzez3ZnZEyL7J74lDzOyB+GvyhpndYWZvSdnfgVPjv/7TtpYmeC6sLWnaVwN8EngD+BbwCNCa4fk8EH+MGjP7r/h7v8eC6drfN7O6kPt80MyuN7On4+/xbjN7xMy+ZEHAe7g27hV/zPsz7PMPM+szs53jv1v8vfC/8c/K5ngbl5lZJOW+YZ/Punj7lsc/kxvj+/3WzA4frs0iIiIyNt17771PfvzjH19f6nYUg6Zdi4iIBCz+PbXA9SLg/cDvCDL4QoOHIS4mCL79DrgbOIQgK2w2cOigBzabCywDJgN/JJiaPAnYG7gA+HaWj3k8wTTXXwMPAG8HTiCYUv4ud38qZd/Px5/T/wK9wBzgM8AxFkxBfyHLxw2TyHj7gbt3Z9rR3XsSP8cDTsuA9xEEhK8geC1OBNrM7O3u/l8hh5kFPAg8AVwT//1DwANm9k7gLoKgWBvB63wScKeZvdndV4cc7yzgiPj+dwEtwKeAg83sIHd/JanN7wDuBZqA24DHCabzfww4zswOd/eHQh7jC8Cx8fv8ATgIiAD7xp9n8uuyG8HfdBbwp3ibGoCjgbvM7HPu/rOQxziaoKzAncBPCN5TrcA7zGxvd381vt83CbJU9wUWA6/Fb3+N7B0L7AT8zN03WRCY/2/g02R+D/8KeE+8jW/E23c2sAPBa57sIiBG8Ld+geDzeGi8ze8APp6pge7+ZDzweEj8b/908nYzexewD3BLUlbzd4HzgH8CNxGUWtg5/ngfJniPZHINQTB+BUEZgk0EWb8twFEE7x0REREZZw477LCM58hjiTIfRURk3ItnH+1JEHhMDRIdCrzT3T8cny58epaH/Q/gre7+MXdfBBxIEDQ6xMwOTHrsOoLpyZOBU9z9fe5+jrt/0d0PA3bL4akcA3zI3T/i7v/l7q0E05onA1em7PtLYEd3/5C7f8ndv+Lu8wiCITsA5+fwuIPEM+D+I/7r73O8+yKCwOOdBK/ff7r7GcBbgX8B58UDRKneB1zm7u+JT+0+AfgGMIUgUHUPcIC7L3D3TwDzgXqCIGOYecC73P2T7n6eu78H+CFBrcqLkp6rEQSUtgE+7u4fjL/2xxMEnLYBfpkmK+8o4B3xv9d/uvt7gRvYWoc02bUE74WT3f297n6Wu59GEEz8O/AjM9sx5DE+CBzl7sfGH+MD8fZPIwgKAkEpAuDR+K8/TCpN8MM0r0+YRMD5F/HvvyIIas8fJitxD2COu3/a3RcA+wHPAJ8IyTT9gLvv7e4fT/o8voXgb/AxMzsoi3YmPgthU8ITt/006bbPEQQ693H3M+J/30+5+z4EAfy0LMh0PokgC/Tt7v7leLs/4e5vAi7Mor0iIiIyEuvXV3HppVM5/fTpXHrpVNavL2oM7KKLLpq20047vW3ixIn7HXroobPXrFkzaFaHpUy7XrZsWeMBBxywZ2Nj436NjY377bXXXntfffXV2yff59JLL506e/bsOXV1dfvvsssubz3//PMHnf/de++9DYceeujsadOmvW3ixIn77bXXXnv/+Mc/npy8z6uvvlodiUR222GHHd5WX1+//8477/zWk046adA1x0MPPTTh4IMPnt3Q0LBfQ0PDfvPmzXvT6tWrR5zAqOCjiIiMO/FprxeY2XfN7GaCLDIjCLr8K2X3q9z9byN4mG8lZ9S5ez9bgzIHJu13DEE2223u/qvUg7j7mhwe8z53T13c43KCQM6h8ey5xHFfSM6sS7r9bmAlQdbmSE0GEidXubQfgoCYAwvjr1miXS+zNXvuMyH3e46koGDctfHv9cB/unssaduvgH6C7NAwvwz5u19AkPX2UTOrj9/2LoJg4V/cfWnyzu7eBnQQBLZbQh7jR+7+j5TbEtmLyQHqfQmCq7e4+40pj/EaQZB1AkGWa6ob42UFkl2V+hijFX9vvR94yt3/Em/bOoLM393I/H46J74v8ft1A0sJzlPnJu8YVk8x/nddHP81m/ftb4AXgU8m/R0xs+2AjxB8XlKzEfuAgZDHfjX1ttRdCPqWHoKMzdT7r82ivSIiIpKrZcsamTFjX84/f1d+8pOdOP/8XZkxY1+WLWssxsNff/3125133nkzDzvssNeuv/76Z/bee+9Np59++qx0+69bt67qxBNPnL3bbrv1/PKXv3zmuuuueyYSiaxdv359dWKfr33tazueffbZM+fNm/daW1vbqk9+8pOvXHzxxdOTA5jPPvts3Tvf+c4Nl19++b/a2tpWHXPMMeu/9KUvzfrpT3+6JQB5+umn7/p///d/jd/97nefv/XWW5/+2te+9kIwnh5YsWJF/aGHHrpXT09P1U9/+tN/XnHFFf98+umnJ7a2tjbHYkNOZ7KiadciIjIefSP+3Qmmlf4J+Lm7Xx+y7/+N8DEeDrnt+fj35BHMRIbgnSN8nGR/SL3B3QfMrIMgu2w/guzBRMbeKQQ1+vaNt6k66a69eWhPTixYHXs28IK7Pxmyy33x7/uFbHvU3VODQ4k6k0+7ezR5Q/x1eYlgReYwYa/l62b2KEEg8C0EmYL7p7QtrM0t8Tb/MWVbtu+Rd8a/b2tmF4TcJ3HC+ZaQbdk+xmh9hiBYeE3K7dcQBEU/S/r3eNZtNLMpwH8STM1+E8HU82Sh9SWTuXu/mf0M+Hq8bYmg/8cJFj+6yt2Tyy8sBb4IPG5mNxG8N/7i7sOudO/ub5jZ7wgGGR41s1sI+psH3X3jcPcXERGREVi/vorjj29m48atCXebNgU/H398M11dj7HttiOLomXp+9///s7vec973li6dOlqgBNOOOGNV199taatrW1q2P4rVqyYsGHDhuqf//znq7fffvtY0NTj30hsX7duXdUll1yyy5e//OUXL7nkkhcBPvShD72xcePGqksvvXSXs88++5WamhpOO+20LTUkY7EY8+bNi77wwgt1v/jFL6Z+7nOfWwfw6KOPNnzmM5955bOf/eyWfb/whS9sGQg+//zzd5kyZUr//fff3zlhwgQHOOCAAza9/e1v3+emm27a9qSTThr2HCiVMh9FRGTccXeLf1W5+2R3PyRN4BHg3yN8mNdCbktk8iUH+baLfx9NfcWEl9LcnngOyfUqLyWYer03QY3FSwjq/n2TIEA5ZLGPHKxja/By2GBQkkT70q0gnrh9u5BtQ06CkjIn050g9QO1abZl+1qOps2vpWkTDH6PTIl/fz9B4Dz16wvx7WEj+UMeI+l1qU7dNhJmVk2QsRojeE8lu4vgNTsmZAp1oj1D2kjI6xDPTHwIOIegbuJ1BPUYv8nWzMd6snMVQSbj55JuO43gffuLlH3Pin9tAM4lCKK+Gl8wZnYWjxWJt3Fi/Pt9wFoz+2WaqfIiIiIyGr/4xWQGjSMmcYerr54cvjE/+vr6eOKJJyYdffTRryXffvzxx6ddXOYtb3lLz6RJk2InnHDCm66//vrtXn311UHnaffff3/jpk2bqj760Y+u7+vrI/F1+OGHR9euXVvz7LPP1gG88sor1Z/85Cd33WWXXd5aV1d3QF1d3QE33HDD1H/+858TEsfae++9N15++eU7XnTRRdP+/ve/Dzl3+vOf/9zU2tq6vrq62hOPs9dee/XssssuPQ899NCkkbwmCj6KiIhklubMJW9ei3/PJUiXTrpARiLo8zqAme0AfIlgAYw943Upz0nU+SOYIjpi8eDWX+O/HpbDXRNBwtAgFcEiH8n7FVJWryXFaXPivl9OCpyHfaUuzlIsRxMsoFIFrElaKdsJpivvRDDb5tMZjpGNzxDU3Pymux/k7l9w9/Pj79nhFn0ZxIPFlG4D3htfATux0MyvPWkxofi+A+7+Q3ffl+B9cQLBok7HEiz2kzHg6e6b4p+tNwMzCRYi6oh/vzmXdouIiEgWOjvrt2Q6ptq0qYpVq7IdrByRF198sWZgYIAdd9yxL/n2nXfeuT/dfaZNmzZw2223Pd3X12ef/vSn37TTTjvte/DBB89+/PHH6wBefvnlGoC5c+fOSQQV6+rqDjjmmGPeDPDPf/6zDuCkk06a9bvf/W7ymWee+e9bb7316T/84Q9PfPjDH361t7d3y7zqn//856uPPPLI137wgx/ssu++++6z22677XPVVVdtmW2yfv36mh//+Mc7JT9OXV3dAWvWrKlPrVuZLU27FhERKa1EkG4ewWrEo/G+1BviWWmJeoOJGoZvIggU3Z06HdnMZsS3j9ZVwHuBr5jZ9ZmmmJpZvbv3uHvUzJ4B3mRmze7embLrIfHvy/PQvuG8jyCzLrmd2xLUiNxMsKo2bH1ND05znHy0OfEeeQ/wo1EcZziJaeu5ZkR+Nv79dsIzRqsJpvfPN7MLU6Y05yKRZXhLyLYh7/0sXEmwIvrn2Dq9+6fpd99Se/RW4FYz+z3BglT7ECwoMyx3fx5YamY3AE8BLWY2RbUfRURE8qi5uYeJE2OhAciJE2PMnj2qgfbh7Lzzzv3V1dW89NJLg2bYvPjiixljcIcddlj3YYcd1rlhwwa77bbbtjnvvPN2Pfnkk9/02GOPPTllypR+gBtuuGHVLrvs0pd637e+9a2bN27caA888MB2F1544eqzzz57y2Dqj370I0ved+rUqQPXXHPN88DzDz744MTvfe97O51++ulvOuCAA1YecMABm7fddtuBI488cv3nP//5IbWtd9xxx7QB1EyU+SgiIlJavyNYLOVYMzs5dWM8GJitQ83s6JTbziSo93h/0mI6z8W/t8SDk4nHaiRY8CQfg5M3EEznbgZ+a2Y7p+5gZnVmdgbBlO+EqwkW6Ph/KW2bCnwtaZ9C+7iZpdaWvIBgmvUNSYv1/JmtQaQTk3eO//4e4GmCTLcRcfeHCeoEHm9modmDZvbWeEbraCQCYDOzvYOZ7Uqwavd64MPu/pmQr08RPP83AYePon3Pxb8fnNKG/YDzRnC83xP8bU4lWGjmKXe/P+XY9Wb27tQ7mlktwcJKAJkC69PM7K0hmxoIpsn3U4L6qiIiImPapz61DrPwbWbw6U+vC9+YH7W1tey1114bb7/99u2Sb7/11luzqrfd2NjoH/3oR1//6Ec/+uozzzwzEeDQQw/tnjBhQuyFF16ofe9737sx9Wv77bePbdq0qSoWi1FfX7+lnuX69eur7r333u3SPdZBBx206Uc/+tGaWCzGihUrJgC8613veuOpp56a2NLSMuRx9txzzxGdtyjzUUREpITcvdfMPgzcDfzKzD5HkOk2gWABkcPI/v/174Bfm9mvgVUEWXrzCGowJuoC4u7/NrMbgZMIFsG4myCo9n6CrL5HSb8KdLbPKxZ/Xr8EjgOejWeKPUGQYTeLIGtsGvCDpLv+IN7m44DHzKwdmAR8GNgBuNjdRxzIy8GdwJ/jC4y8SJA92kIQADs3sZO7u5mdCtwDtJnZb4EnCVa4/iAQBT6RstL2SHyUoFbgz83sS8CDBFP2ZwBvI8i+eyfw8ige4/cEi7n8LL4wShR4zd0vz3Cf+QSZjde7++YM+y0heP1OI3itRuK6ePt+aGaHAJ0Ewe2jCbIRI7kcLP63+wlB/VPYugp4solAh5mtIshu/BfBZ/P9BJ/P29z9iZD7JUwH/mZm/wD+TrCQzjbxNu9EsOJ5NMP9RUREJFfbbx/j1ls7Of74ZtyDqdYTJ8Ywg1tv7Sz0YjMAZ5999ounnnrqHqeccsrME0444bX777+/6YEHHtg23f433njjtr/4xS+mHnvssetnzZrV+/zzz9ddd9110/7jP/7jDQiyFRctWtR1/vnn7/qvf/2r7uCDD94Qi8V48sknJ/zhD39ouueee56ZMmXKwD777LPx4osv3mXbbbcdqKqq4gc/+MFOjY2NA93d3VuSDw844IA9jz766Nfe9ra3bTIzfvazn02dOHFirKWlpRvgu9/9bte73/3utxxyyCGzP/nJT66dNm1a//PPP1977733bvOpT31q7dFHH53zuYuCjyIiIiXm7g+b2dsJglrzgHcRBH5WEazIm61bCQIoXwU+QFBv71bgPHd/OmXf+cCzBAGbM4BXCGrgfZ3waa05iwdVPmhmRxBMu30nQTDVCFaivhe4zt3vSrpPr5m9H1hIEHD7IkF22GPAAne/IR9ty8JlBHX9FhC8RhsIVm7+r/jU2y3c/UEzewdwPkFm3zHAqwTZn99296dG2xh3X2NmBxC8HicQrFReTbCYy+PAfwP/GOVjLDOzRQTTqBcQLDr0LyA0+GhmVWyt47hkmMP/D8GiMMeZ2Q6pr2GW7esys/cAFxEEMo8kCPR+geC9lFPwMe4agoB3L3BtyPZuggVuDiH4XH6Q4LP5DHA6w2fhPkewKNDB8WNMJRgMeIrg837jCNosIiIiwznyyA10dT3G1VdPZtWqembP7uHTn15XjMAjwCc+8YnX1qxZs3rx4sU733rrrVMOPPDA6JVXXvncCSec0By2/1ve8pbNZubf/va3Z6xbt65m++237z/ssMNeu+yyy7YsSvmd73znpV122aXviiuu2PGqq67aqa6uLjZr1qzNyQvZ3HDDDc9+9rOf3e3000/ffbvttuufP3/+yxs3bqz+xS9+MS2xz9y5c7tvuOGGKT/4wQ/qq6qqfO+999546623du6xxx59AG9729t6/vjHPz553nnn7bJgwYLdenp6qnbYYYfelpaW6Fve8pZMg81p2cjL7oiIiEg5MLNPEqzQ+yl3v6a0ralsZnYBQbDoEHd/oLStkUIzs4OB+wkyNz9e2taIiIjIY4899ty+++47pNaglL/HHnts6r777jsrbJtqPoqIiIjIeHV2/HumqeUiIiIiMgqadi0iIiIi40Z8AZijgQMIyhzc7u4PlrZVIiIiImOXgo8iIiIiMp4cAHwPeIOgFuUXMu8uIiIiIqOhadciIiIVzt2vcXdTvcfRc/cL4q/lA6VuixRG0udlW3f/iLurrpSIiIhIASn4KCIiIiIiIiIiIgWh4KOIiIiIiIiIiIgUhIKPIiIiIiIiIiIiUhAKPoqIiIiIiIiIiEhBKPgoIiIiIiIiIiIiBaHgo4iIiIiIiIiISIW59dZbt/nWt761Qz6PuXDhwl223377ffN5TAUfRUREREREREREKsxdd921zU9/+tMdS92O4Sj4KCIiIiIiIiIiMkb19/ezefNmK9XjK/goIiIiIiIiIiJjyvrN66su/culU0+//fTpl/7l0qnrN68vagzszjvvbDzooIPePGnSpP2amprefuCBB+755z//eSLA//7v/0585zvf+eaJEyfut80227z92GOP3f3555+vSdz3qaeeqjOzA5YsWbL9Rz/60d2amprevuOOO77trLPO2mVgYAAIpkf/7Gc/27Grq6vOzA4wswNOOOGEWQAnnHDCrH322ectv/zlL7ebPXv2nAkTJuz/wAMPNAB873vfm7bbbrvtU1dXt//MmTP3+eY3v5nXadthaobfRUREREREREREpDIsW7Ws8fibjm92dzb1b6qaWDMx9rX7v7brrR+5tfPI2UduKPTj33777U0f/OAHmw866KDolVde+c/GxsbYn/70p8bVq1fX7b777n1HHnnknnvsscfmq6666p/RaLTqm9/85ozDDjvszX//+9+fmDBhgieO841vfGNGa2vr+uuuu+7Ze+65p+mHP/zhznPmzNn0mc98Zv0ZZ5zxyqpVq+r/8pe/NN14443PAOy00079ifu+8MILdV/72tdmnH322V277LJLX3Nzc88ll1wy9atf/erMz3zmMy/Nmzfvjd///vdN3/zmN3ft6emp+t73vvfvQr0eCj6KiIiIiIiIiMiYsH7z+qrjbzq+eWPfxi2Zjpv6N1UBHH/T8c1dC7se23bCtrFCtuH888+fvtdee23605/+1FlVFTTjxBNPfAPgC1/4wnSA++677+nJkyfHAPbaa6+eww47bK9rr712+8997nPrEsc58MADoz/72c/WAHzoQx9647777tv2N7/5zfaf+cxn1u+xxx59O+20U19dXZ0fdthh3alteO2112ruuOOOp9/1rndtAhgYGOD73//+LieccMLaxDGPP/74N15//fXq//7v/97p/PPPf2nSpEmeepx80LRrEREREREREREZE37xt19Mdg+Pobk7V//t6smFfPw33nij6u9//3vDySefvDYReEy2fPnyhve85z1vJAKPAIceemj3Lrvs0tvR0dGYvO/73//+N5J/b25u3vTiiy/WZtOOHXbYoS8ReAR49tln61555ZXaD3/4w+uT9zv55JPXbdiwofqhhx6amOVTzJmCjyIiIiIiIiIiMiZ0ru2sT2Q6ptrUv6lq1bpV9YV8/FdeeaXa3dlll136wra//PLLtdOmTRuyberUqX2vvfZadfJt22+//UDy73V1dd7T05NVLG/q1KmDHuP555+vBYa0a5dddukHePXVVws2O1rBRxERERERERERGROapzT3TKyZGDqtemLNxNjsybN7Cvn406ZNG6iqqqKrqys0Q3GHHXboe/XVV4dse/XVV2u32267gbD7jITZ4MWtd9111z6AF198cVCQsaurqwZg6tSp/RSIgo8iIiIiIiIiIjImfGq/T61LDbwlmBmf3u/T60I35sk222wTe9vb3tZ9ww03TInFhsZADzjggO4//vGP26xfv3X17T/84Q+Turq66lpaWnJaDCeXTMg3velNvdOmTev7n//5n0HTzm+88cbJjY2NA+94xzs2pbvvaCn4KCIiIiIiIiIiY8L2E7aP3fqRWzsn1U6KJTIgJ9ZMjE2qnRS79SO3dhZ6sRmA733ve2ueeuqpie973/uar7322u1uueWWbRYuXLjLDTfcsO1Xv/rVlwAOPfTQN19//fXb/fjHP54ciURmNzc3bzr11FPXD3fsZHvttdfmtWvX1vzoRz+a8sc//nHSU089VZdu3+rqas4555yuW265ZcrnPve5Gbfeeus2X/ziF6cvXbp02he/+MV/F2qxGdBq1yIiIiIiIiIiMoYcOfvIDV0Lux67+m9XT161blX97Mmzez6936fXFSPwCDBv3rwNv/nNb57+xje+Mf3zn//87jU1Nb733ntvPPHEE9fvsssu/XfddddTixYt2vWzn/3s7jU1NX7wwQe/fuWVVz4/YcKEnAKAn/70p9c98MADTRdccMGM9evX1xx//PFrb7nllufS7b9o0aJXN2/eXPXTn/50h6uvvnqHHXfcse8b3/jG89/4xjdeHvWTzsDSrQAkIiIiIiIiIiJSLI899thz++6776ulbofk7rHHHpu67777zgrbpmnXIiIiIiIiIiIiUhAKPoqIiIiIiIiIiEhBKPgoIiIiIiIiIiIiBaHgo4iIiIiIiIiIiBSEgo8iIiIiIiIiIlIOYrFYzErdCMlN/G+WdiVxBR9FRERERERERKTkzOzfmzZtmlDqdkhuNm3aNMHM/p1uu4KPIiIiIiIiIiJScv39/d987rnn6rq7uycqA7L8xWIx6+7unvjcc8/V9ff3fzPdfubuxWyXiIiIiIiIiIhIqOXLlx9ZU1PzDXffCSXNlbuYmf27v7//m/vvv/+ydDsp+CgiIiIiIiIiIiIFoQiyiIiIiIiIiIiIFISCjyIiIiIiIiIiIlIQCj6KiIiIiIiIiIhIQSj4KCIiIiIiIiIiIgWh4KOIiIiIiIiIiIgUhIKPIiIiIiIiIiIiUhAKPoqIiIiIiIiIiEhBKPgoIiIiIiIiAJjZ1Wb2spmtSLPdzOxHZrbKzP5uZvsXu40iIlJZFHwUERERERGRhGuAozJsnwc0x79OA35chDaJiEgFU/BRREREREREAHD3PwLrMuxyHHCdB/4KbGdmOxendSIiUolqSt2AfJs6darPmjWr1M0QkVF65JFHXnX3aaVux0ipLxIZG9QXiUg5KLO+aDrwfNLva+K3vZi8k5mdRpAZSUNDwwF77bVX0RooIoVRZn2RVJAxF3ycNWsWDz/8cKmbISKjZGb/KnUbRkN9kcjYoL5IRMpBJfZF7n4VcBXA3LlzXX2RSOWrxL5IysOYCz5mI9oTpW1lG51rO2me0kxkToSm+qZSN0tEpKTUN4qIhFP/KDLIC8CuSb/PiN8mIiISatwFHztWd9C6tJWYx+ju66ahtoGFyxbSfko7LTNbSt08EZGSUN8oIhJO/aPIELcBZ5rZjcBBwOvu/uIw9xERkXFsXC04E+2J0rq0lWhvlO6+bgC6+7qJ9ga3b+jdUOIWiogUn/pGEZFw6h9lPDKzG4C/AHua2Rozm29mnzezz8d3aQeeBVYBPwO+UKKmiohIhSj74KOZnWVmK81shZndYGYTRnqstpVtxDwWui3mMdpWtI24nSIilSDaE2XJ8iWcc885LFm+ZMtUQvWNIiJDqX+U8cjdT3b3nd291t1nuPvP3f0n7v6T+HZ39zPcfQ93f6u7q5ijiIhkVNbTrs1sOvAlYG9332RmNwEnAdeM5Hidazu3jFqn6u7rZtW6VSNtqohI2Us3dfCYPY9R3ygiEkLnjiIiIiKjV/aZjwQB0olmVgNMArpGeqDmKc001DaEbmuobWD25NkjPbSIjANmVm1mfzOz20vdllxlmjp4y+O3qG8UEQmhc0cRERGR0Svr4KO7vwD8AFgNvEhQzPju1P3M7DQze9jMHn7llVfSHi8yJ0KVhT/lKqsisk8kPw0XkbHqy8ATpW7ESGSaOlht1Qz4QOg29Y0iMp7p3FFERERk9Mo6+Ghm2wPHAbsDuwANZvax1P3c/Sp3n+vuc6dNm5b2eE31TbSf0k5TXdOWUeyG2gaa6oLbG+saC/NERKTimdkM4APAklK3ZSQyTR3c2L+RE95ygvpGEZEUOncUERERGb2yrvkIHA78091fATCzW4F3AdeP9IAtM1voWtRF24o2Vq1bxezJs4nsE9HJo4gM54fA2UBTuh3M7DTgNICZM2cWp1VZSkwdDAtANtQ2cMisQ/jJ0T9R3yhSAcysGngYeMHdjy51e8Y6nTuKiIiIjE65Bx9XA/9hZpOATcBhBCfbo9JY18j8/eeP9jAiMk6Y2dHAy+7+iJkdnG4/d78KuApg7ty5XpzWZScyJ8LCZQtDtyWmDqpvFKkYiRIQ25S6IeOF+kcRERGRkSvradfu/iBwM7Ac+AdBe68qaaNEZDx6N3CsmT0H3AgcamYjzsAuBU0dFBkbKr0EhIiIiIiMP+We+Yi7fwP4RqnbISLjl7ufB5wHEM98/Iq7D6k/W+4qeupgNAptbdDZCc3NEIlAU9oZ8CJj2Q+p4BIQIiIiIjL+lH3wUURE8qcipw52dEBrK8Ri0N0NDQ2wcCG0t0NLS6lbJ1I0Y6EEhIiIiIiMPwo+iojkwN0fAB4ocTPGj2g0CDxGo1tv644vmtPaCl1d0FgBmZsi+ZEoAdEKTAC2MbPrKzETW0RERETGj7Ku+SgiIuUt2hNlyfIlnHPPOSxZvoRoT3T4O+WirS3IeAwTiwXbRcYJdz/P3We4+yzgJOA+BR5FREREpNwp81FEREakY3UHrUtbiXmM7r5uGmobWLhsIe2ntNMyM0/ToTs7t2Y6puruhlWr8vM4Mj6odqiIiIiISNEp+CgiIjmL9kRpXdpKtHdrpmN3XxAkbF3aSteirvwsZNPcHNR4DAtANjTA7NmjfwwZH8ZY7VCVgJBxRQMHIiIiFU3TrkVEJGdtK9uIefh06JjHaFuRp+nQkQhUpflXVVUVbBcZTnLt0EQgu7t76+0bNpS2fSKSXkcHTJ8OCxbAxRcH36dPD24XERGRiqDgo4iI5KxzbeeWTMdU3X3drFqXp+nQTU1BZlpTU5CpBsH3xO1abEayodqhIpVJAwciIiJjgqZdi4iMY9GeKG0r2+hc20nzlGYicyI01Q8/la15SjMNtQ2hAci66joe/fejLFm+JOvjZdTSEqxq3dYW1HicPTvIeFTgUbKl2qEilSmbgYP584vbJhEREcmZgo8iIuPUaBaMicyJsHDZwtBtvQO93PXMXfxp9Z/ytwBNY6MuMGXkVDtUpDJp4KAkzOwoYDFQDSxx94tSts8ErgW2i+9zrru3F7udIiJSOTTtWkRkHEpeMCaRvdjd1020N7h9Q2/mqWxN9U20n9JOU10TDbUNofvkcjyRglLtUJHKlBg4CKOBg4Iws2rgCmAesDdwspntnbLb+cBN7r4fcBJwZXFbKSIilUbBRxGRcSgfC8a0zGyha1EXi49azLw95lFfXT+q44kUjGqHilQmDRyUwoHAKnd/1t17gRuB41L2cWCb+M/bAl1FbJ+IiFQgTbsWERmH8rVgTGNdI/P3n8/Ta5/mzmfuHPXxRApGtUNFKk9igKC1Najx2N0dDBxUVWngoHCmA88n/b4GOChlnwuAu83si0ADcHhxmiYiIpVKwUcRkXEo04IxDbUNzJ6c21S2fB9PpCBUO1Sk8mjgoBydDFzj7peY2TuBX5rZPu6Dp1SY2WnAaQAzZ84sQTNFRKRclP20azPbzsxuNrMnzeyJ+D84EREZhcicCFUW/i+gyqqI7JPbVLZ8H09ERGSLxMDBhRcG3xV4LKQXgF2Tfp8Rvy3ZfOAmAHf/CzABmJp6IHe/yt3nuvvcadOmFai5IiJSCco++Eiw0tpd7r4XsC/wRInbIyJSUaI9UZYsX8I595zDkuVLiPZEQxeMaahtoKkuuL2xLrcLu3wfT0REREriIaDZzHY3szqCBWVuS9lnNXAYgJm9hSD4+EpRWykiIhWlrKddm9m2wHuBTwLEix73lrJNIiKVpGN1B61LW4l5jO6+bhpqG1i4bCHtp7RvWTCmbUUbq9atYvbk2UT2iYw4UJjv44mIyBgWjQZTqTs7g1WtI5GgxqOUlLv3m9mZwDKgGrja3Vea2beAh939NmAR8DMzO4tg8ZlPuruXrtUiIlLuyjr4COxOMIr2CzPbF3gE+LK7DyoqpnoiIiJDRXuitC5tJdob3XJboiZj69JWuhZ1bVkwJl/yfTwRERmDOjqGLiKzcGGwiExLS6lbN+65ezvQnnLb15N+fhx4d7HbJSIilavcp13XAPsDP3b3/YBu4NzUnVRPRERksGhPlDPbz2RT36bQ7TGP0baircitEhGRcS8aDQKP0WgQeITge+L2DRtK2z4RERHJu3IPPq4B1rj7g/HfbyYIRoqISBodqzuYful0blhxA/3eH7pPd183q9atKnLLRERk3GtrCzIew8RiwXYREREZU8p62rW7/9vMnjezPd39KYLCxo+Xul0iIuUqbKp1mIbaBmZPnl2kVoVQrS8RkfGps3NrxmOq7m5YpYExERGRsaasg49xXwSWxldbexb4VInbIyJSttpWthHzNBklSaqsisg+kSK0KIRqfYmIjF/NzUG/HxaAbGiA2SUcGBMREZGCKPdp17j7o/F6jm9z9w+6+/pSt0lEpFx1ru3csqhMmBqroamuifZT2kuzCrVqfYmIjG+RCFSluQSpqgq2i4iIyJhS9sFHERHJXvOUZhpqG0K31VbVcsrbTqFrURctM/OXYRjtibJk+RLOuecclixfQrQnw5Rv1foSERnfmpqCTPempiDTEYLvidsbSzAwJiIiIgVVCdOuRUQkS5E5ERYuWxi6bULNBC5vvTyvGY8dqztoXdpKzGN093XTUNvAwmULaT+lPTzAqVpfIiLS0gJdXcGA06pVwVTrSESBRxERkTFKwUcRkTGkqT6YUp0aEKyyqrxPtQ5b3CYx5bt1aStdi7qGPt6uu2Y+6IwZeWufiIiUscZGmD+/1K0QERGRIlDwUURkjGmZ2ULXoi7aVrSxat0qZk+eTWSfSN5rPGZa3CbmMdpWtDF/f11YipS7aE+UtpVtdK7tpHlKM5E5EZrqx9bq8+PhOYqIiIiUKwUfRUTGoMa6xoIH/jItbtPd182qdfEp1NFoMLWusxMeeyzzQdesyXMrRSSTnEsnVKDx8BxFREREypmCj3mmkXWRscfMdgWuA3YEHLjK3ReXtlWll1jcJiwA2VDbwOzJs6GjI1jFOhYLajrW1aU/YENDUPdLRNLKZ380otIJFWY8PEcRERGRcqfgYx5pZF1kzOoHFrn7cjNrAh4xs3vc/fFSN6yUMi1uU2VVRHZrhd33DDIfE3p70x+wvx9aWzWII5JZ3vqj8VA6oaKfY3LWeHNzsCBLk/pCERERqTwKPuaJRtZFxi53fxF4Mf5z1MyeAKYD4zr4OOziNr++I8h4TKe2Fvr6tv5uRsehe9B6ihGrMg3iiITIZ3+UdemEClaxzzE1a7yhARYuhPb2YKVoERERkQqi4GOeVPTIuohkzcxmAfsBD4ZsOw04DWDmzJnFbViJZFzcpvN3wUVzOimByWhsM60fgugAMBDcpkEckfTS9UfZ9kVZlU6ocBX5HKPRIPCYnDWe6EtbW6GrK1gpWiqXslpFRGScqSp1A8aKih1ZF5GsmVkjcAuwwN3fSN3u7le5+1x3nztt2rTiN7BEEovbXHj4hczff/7WAGFzc5CtE6auDmoGj3+17QPp8iQTgzgiEsjUH2XbF0XmRKiy8FPBKqsisk8kn00uiYp8jm1t6bPGY7Fgu1Sujg6YPh0WLICLLw6+T58e3C4iIjJGKfiYJ4mR9TBlO7IuIlkzs1qCC/2l7n5rqdtTESIRqErzb8YdenoG3dQ5Gbrrw3fXII7IVvnqjxKlE5rqmracwzTUNtBUF9xeqkzjaE+UJcuXcM4957Bk+RKiPdHh75RGuT7HjDo702eNd3fDKvWFFSs5qzXxN+7u3nr7hg2lbZ+IiEiBaNp1ngy78EI5jqyLSFbMzICfA0+4+6Wlbk/FaGoK6pOl1i2rqoLTT4crrhh0gd28Dhp6wgOQGsQRCeS7P8pYOqEEUhfvq6uu4wt3fIGz3nkW57/n/BEtPlVuz3FYiazxsABkQwPMVl9YsbLJap2vMk0iIjL2KPiYJ8MuvFCuJ7giko13Ax8H/mFmj8Zv+y93by9dkypES0tQn6ytLcjWmT07yIh0hx//eNCukRWw8Mjww2gQR2SLvPdHidIJpRa2eF/vQC8AF//5Yq586EruPOXOES0+VS7PMSuRSLC4TJiqqmC7VKYKyWo1s6OAxUA1sMTdLwrZ5yPABYADj7n7R4vaSBERqSgVEXw0s2rgYeAFdz+61O1Jp+JG1kXGIDObDAy4++v5Oqa7dwCWr+ONO42N4ZkcKVmRTbUNtP86NmS1aw3iSCUys0uBv7j7/+TzuJXWH0V7orStbKNzbSfNU5qJzImkzV7MtHgfwIbeDeNj8alMWePt7VpsppJVQFZr/LrrCuD9wBrgITO7zd0fT9qnGTgPeLe7rzezHUrTWhERqRQ5BR/N7EPAwUA/cJe735Nmv1OBU9390FG3MPBl4Algmzwdr2AqamRdpAKZ2XSCE95m4DHg++6+1szeDlwHzInv92fgNHd/slRtlWGEZEW2RCJ01aFBHBkLFgCNQF6Dj5UkdQp1Q20DC5ctpP2U9tDsxUyL9yUkFp/K+lyrUlcVTpc1rsBjZauMrNYDgVXu/iyAmd0IHAc8nrTPZ4Er3H09gLu/XPRWiohIRckq+BivL9QGnMDW0fYFZnYH8Al3fy3lLrOA9+WjgWY2A/gA8F0gzX9rERkP4lmNfwWmx296P3C4mR0J3AFMIQhI7gK0APea2T4hfZQUWrYX/CFZkY2gQRwpa2b26Sx33TN5X3e/ukBNKjthU6gTgcV02YuJxfsyBSDDFp9Km13Z0TE0e3DhwiB7sCX3qdtFly5rXCpXZWS1TgeeT/p9DXBQyj5vhi0DvdXABe5+V+qBzOw04DSAmTNnFqSxIiJSGbLNfPwUcCLBP6KfAH3AqcDRQIeZHVrAEa8fAmcDaYep9Y+tzFVq1oGUozMJToq/B9wEHAt8C7gGiALvdPfVAGb2XYIMyS8C3y5FY8etSr/gFxneEoI6Z5k4wSBIC8HArQPjJviYaQp1uuzFTIv3JTRQz+z7HoXnlkAkQsf6x8KzKz90My2tJwbnIAmJqa6trUFWYbEDPTofEhgrWa01BDNQDgZmAH80s7emDva6+1XAVQBz584drs8UEZExLJfg42vAOxJBRjO7DPg+QTbivfEA5Kv5bJyZHQ287O6PmNnB6fbTP7YypiCE5NcHgYfc/fz47383syOAo4DjE4HHuPOBCMEgybgKPuZSYy3/Dx4NPvPldMEvUhgbCAZkN4ZsM+DrwHLgd8VsVLnINIU6LHsRti7eN+/6eWzo2xB636qeHiKX3AW1fyJ6zlm0nhUjOrD1T7Alu/Km4+iqriK0tyn0qsJhQcbHHtP5kGxV3lmtLwC7Jv0+I35bsjXAg+7eB/zTzJ4mCEY+VJwmiohIpck2+PhW4Obk7EZ3HwC+YmarCbIT7zWzQxK1P/Lk3cCxZtYKTAC2MbPr3f1jeXwMyWQ0o/QKQkj+7QZcn3LbwwR9xf8m3+jubmZ/AI4vUtvKQq411vKurS24uA5T6At+keL5BPDfBP3Lp+KLwAxiZl8Hlrv7N4vduHKQaQp1XXUdj/77UZYsXzJkcKRlZgsvfuVFvn7f1/nR//0IgAEfYFIvVDu0L4XGXqC3m7Y9IbYJqBv6+LHYAG27b2b+30IaV8hVhcMGXc86K/h9Y1KcWudDUr4eAprNbHeCoONJQOpK1r8BTgZ+YWZTCaZhP1vMRoqISGWpynK/OuClsA3u/iPgS8DbgHvMbLv8NA3c/Tx3n+Huswj+8d2nwGMRdXTA9OmwYAFcfHHwffr04PZsZBOEEMnNRCD1SvZ1AHd/JWT/l4CGQjeqXCTXWEtc8Hf3dRPtDW7f0BueSZRXnZ3hq3hCYS/4RYrI3a8nOO/5F/CAmf3AzOpL3KyyEpkTocrCTzN7B3q565m7WHDXAqZfOp2O1YPPKx7996Ms+dsS6qvrGfABaqhiwODmNmhJym/vnAzdIYFHgG7rY9XkNI3Lx6rC0SgsWQLnnBN8j0YHD7om+sHubtiwYXDgMZnOh6TMuHs/QZmbZQQLft7k7ivN7Ftmdmx8t2XAWjN7HLgf+E93X1uaFouISCXINvj4ApC2mKK7X04w/Xp/gn9G246+aVJS6U6gE7dvyCKIoSCE5N+rwA4pt3UD6WrOTiEoGTEuZFNjreCam4ML+zD5uOAXKRPu/ry7Hw4sAr4A/M3MDixxs8pGYgp1U10TDbXhfULY4EjyIMrG/iBg10+Mnlo4MQIbkoKNzeugoSf88Rt6Yfa6NI0zG92qwukGZ7/znfSDrunofEjKkLu3u/ub3X0Pd/9u/Lavu/tt8Z/d3Re6+97u/lZ3v7G0LRYRkXKXbfDxH8AhmXZw9x8SLO7wDoLRsrxy9wfc/eh8H1fSyEfWooIQkn9PA3sn3+DuP3D3ndPsvztBXaJxYSQ11vIuEglW7QxTVTW6C/4wYdlHIkXk7ouBAwhqP/7ZzC40s9oSN6sstMxsoWtRF4uPWsy8PeZRXx2eHJoYHIn2RDmz/Uw2928O3w9om7P198iK9Cey/QYrp8GS/SGamh05Zw7ceOPWbMVc+pBMg7OXXZZ+0DUdnQ+JiIjIOJBt8LEd2MXMPpBpJ3f/PvANsq8lKeUqH1mLxQ5CyHjwCHCAmaWZaLeVmW1DsMpslnUCKl+ixlqYhtoGZmwzgyXLl3DOPeewZPkSoj0FCNQ1NQULKDQ1bR18aGjYens+65qNtjSESJ64+xPAQcCFwFeAvzH8atjjQmNdI/P3n89bd3wrPQPhaYrdfd3c/9z9TL90Or/6x6/oi/WF71fPoKnUTb3Q/uuJNNU2bun7JlRPAA9W/LnsXbDgSJi+CDqS5+88+GDQX+y0E+y4I5x5ZtCHnHkm7LJL5j4k0+CsGdTnOPte50MiIiIyDmQbJLwVqGZorbUh3P3b8UVoZo2iXVJqiazFsABktqP0iWBDauH1qqr8ByFKYTSL8ciIuPs5wDlZ7r4jQTb2vYVrUXmJzImwcNnC0G2Oc+695+J44ReiaWkJFlBoawsGKmbPDj4f+fzMa0GrkVPfVRDxhfi+bmZ3ANcRxL/GlWhPlLaVbXSu7aR5SvOgxWQyLUAzqWYStzxxS9qMx4SGXpjdXQf0bjmfaPlZO10Hvp22FW2sfHklP37kx2CwOZ572h2PBbaeAl2XxBergfDzm56e4OvII+Gll8L7kEyDs729UJdmbGziRKiuBvexdz4kIiIiMgxzH1sD83PnzvWHH3641M0orXxcWEajQQZR2PSjpqbcLuw3bChsEKIUwlazTFxEtBRhReFxwMwecfe5pW7HSJWqLwpb7drMGIgNsKl/05D9m+qa6FrURWNdhX0mlywJMpfSDZAsXqxVtcOo78rZSPoiM6sGGoEed88cUSuwYvVFYX1PlVVtGeCI9kSZful0or1Dzyvqq+upqapJWzYioam2ka5pF9H47JrQ84kly5ew4K4Focdp6IHFdxG++nWYyy+HM84Yevtwfc+ZZ8KVV4Z/xt7+9rF3PiRFo/MiESkHld4XSeloevRYE3ZhuXBh7heW+cxabGwcW0EAZVxJGUvUWGtb0caqdauYPXk2m/o3ce6954bun6i1Nn//CvuMakGr3KnvKpp4FuTrpW5HsSQvEpOQCAC2Lm2la1HXlgVowgKUx+x5DL/6x6/SHr+2qpYJNRNoP6WdxgyZ2hnr3qZM2R7WHXeEBx8jkeC8KkxVFZx/fvCVLsg4ls6HRERERLKUVfAxXl+tA4gCR7l7aDGe+H53Ag3Ae9LtJwWS7wvLYkydrETZLMaji4uCUF+UnUSNtYRz7jmnZAvRZJqGOSr5KA0x3qjvyhv1RYO1rWwj5uHvreQBjrDBkcg+EW5ccSO/efI3bOzbOOT+NVbDR9/6US5vvXzYDO1MU7vxHItwPvlkkOWYOnsk28FZfZZEREREtsg28/FjBCs5HpPpxNnde83s/xEsUHMKcM2oWyjZK8SF5VjLWswHZVyVkvqiEch0Qd5APbPvexSeC7nIzkaGMg9h0zDzVmdyuOwjLeAwlPqufFJflCRjxmHKAEfq4AjAzG1nhgYeASbWTswq8AhB3duzlp0VvtHgigPh/D8l1X3M5J//DKZXh80eKeTgrGqyioiIyBiUbfDxeOBZd28fbkd3v8vMOoEPM0ZPssuWLiyLQxlXpaS+aAQyLURT1dND5JK7oPZPuZdoSCnzEN1uEm2/OJPOz5zArnPeyXm/P48NvRu27J46DXNUdSbH+oJWhTCSvkuBkHTUFyXJOMBR28Dsyen/L0Z7opx404lpt9/8kZuz6yuiUZra2vjChr25uO7/Qndxg7Z9a5j/UP/wx4PMs0cKMTibr9I5IiIiImUm2+DjfgSj9tn6I9Cae3NkVBQUKw5lXJWS+qIRCK211gtVDu1L41lAvTmWaIhGiR43j7bmDXTG66hd+Y6NONC9+lfUvXAzvQPh6UV5qzOp0hC5ybXvUiAkE/VFSTIOcFgVkX0Gv7eSyzE8//rz9Az0hN53Us0knn/9+eEbkPxefWc3pHl7dtfBqhMPgUcfgL4cZsDnqyxBpmC+arKKiIjIGJZt8HEq8FIOx30JmJJ7c2RUFBQrDmVc5S5/2VPqi0ZoUK21+29m9s33EVneO3T6YZYX2R3XfYfWz20gRrCQAw7Y1u3pAo+Q5zqT+co+Gg8Zfrn0XQqEDEd9UZJMi8m0n9I+KHMxtRxDJhv7Nw7fV6S8V5vXBStbd9cP3bWhtoHZh0fgXV8f/Dmorw+CkelK1+Rj9shwwXzVZBUREZExLNvg4yYgl6uMRmBz7s2RUVFQrHiUcZW9/GZPqS8Kke2iLltqrbU9DX+9K/xgWVxkR3uitL7yQ6LJF/eWdvchhpuGWXTjKcMv275LgZDhqC9KkW4xmeTAY9cbXRx+3eFpMx3DzNhmRuYdUt6rkRWw8MjwXbdkYdY1Dv0cbNoE555bmNkj2QTzVTpHRERExrBsg4/PA3NzOO5cYHXuzRnDipVVo6BY8WgxnuHlP3tKfVGKES3qMsoSDW0r24hZDtHGFGHTMEtmPGb4ZdN3KRAyHPVFIcIWk0noWN2Rc+ARGH5gI+W92tQblJNoPYUtmdmhWZipn4NoFP7rv8IfY7SzR7IJ5qt0joiIiIxhVVnu9wDwTjMb9kTbzA4A3gXcP4p2jS0dHTB9erBq4sUXB9+nTw9uL4TECfWFFwbfx9qFs1SObC64cvMA6ou2iPZEaV3aSrQ3umX6YndfN9He4PbkxV4GiUSCi+kwWVxkd67tpJvsAwj11UGKZENtA011TUOmYZZU/t+jY0MiEBJGgRBQX5STaE+UI355RO6BR2DN62sy79DcTHS7SSzZH845HJbsD/v+G7ougcX313Fu4zwWH7WYrkVd6QdkYOvskaamre/9hoatt4/mXCqbYP4o+2URERGRcpZt5uPlwOnA/5hZq7s/EbaTme0F/A8wAFw52saZ2a7AdcCOBBXFrnL3xaM9blFlmVWT7bRJkYqS/+ypkvRF5aptZRsxDw+cZVzUZZgSDdFap235krT9UaaVbVM11jZy0fsvYs3ra0KnYZbccO/Rn/0M3MdmDchMVEN4OOqLcnDtY9eyqX/TiO47Y9vM0647WmbSevrGrVmOPcG06/alMH9lPdxzU/aBw0LNHskmq1Glc0RERGQMyyr46O5Pmdm3gAuAv5nZzcB9QGI4ejpwGHACUA983d2fykP7+oFF7r7czJqAR8zsHnd/PA/HLo4ssmo63r9n7tMmRSpBnqeRlbAvKkudazvTBgCHXdQlzUV2x7pHab10esb+KNPKtgnJ0xzLuh/L9B4FePBBWLFi7NaATEeBkIzUF+Xm9qdvH/mdPf2maE+U1l+fOKj+bGKhmdZToOuIm2nM9b1aiJIq2QbzVTpHRERExqhsMx9x92+ZWT/wDeCjwMkpuxjQB3zV3S/MR+Pc/UXgxfjPUTN7guCEvnKCj8Nk1URXraT15bOI9m7NjEwEE1qXttK1qKswWULjYWVXKb0CZE+Voi8qV5kyELNa1CXlIjt5GndCWH+UbmVbwzjjwDMwLMhy3K2Vxl/fAZ2/K99+JtN7NGGs14BMR4GQjErZF5nZUcBioBpY4u4X5fP4+bZ+0/oR33fNG+mnXWfM/m5soG2b5xlNGDFvs1JyCearnrSIiIiMQVkHHwHc/XtmthT4NPBuYOf4pheBDuAX7v6v/DYxYGazgP2AB0O2nQacBjBz5sxCPPzIDZP51bbzWgaiA6F3zThtcjTysbKrgpeSjQJlT5WyLyqVsIvgTBmII1nUJZdp3MOubNvRAbvvWf4rSIe9R9MZj6s8KxCSUSn6IjOrBq4A3k+QafmQmd1WrrNCoj1RHnvpsRHdd7hBlFFlfw9jRIt5ZaJgvoiIiIxjOQUfAeIn0d8oQFvSMrNG4BZggbu/EdKmq4CrAObOnZthgk4JDJP5df+Om9m4fmPo5tGeOIfKx8qu+QheyvhRoAuuUvRFpZLpIjgsA3HIqq5ZyvVCPu3KtpW2gnTye/RnPwumWofRKs8SogR90YHAKnd/FsDMbgSOo0xnhbStbKPaqtNur7ZqDKPf+4dsG24QJVP2d111HTO2SV8vMtoTpe2Ra+n8y+00r4XIzofTZBPg+eeJ7rErra+eR7Rv66JdeZmVomC+VIhss6vN7ATgZuAd7v5wEZsoIiIVJqfgo5nNBN5BUIHnIXd/viCtGvyYtQSBx6XufmuhHy/vMmR+RW+7mZv/dEzau06qmTT8tMlcZbOya6YT40oLKkh5yPMFV4n6opJMc8xmKnTGDMQhB0yftTzqadwJafqZaB20zemh86cfpvmQE4q/sFamjO3Ee9Q9qPGYpzqlMraVoi8iKD+T/DhrgINS2lU2M0I613aysT98kBXgpH1O4vN7f4LWm44jFhug2/poqJ1ElVUPO4iSKfu7d6CXc39/LvvutO+QTMWO1R20XncksU0b6a6DBmDhi8toXwotq6HtP+qIHdoLdUOPm5dZKZo9ImUs2+zqeD3+LxMyK01ERCRV1sFHM/sBsICghhGAm9ll7v6fhWhY/DEN+DnwhLtfWqjHKbg0mV/XPn4NvbHetHcb8IGcp00Oe0I72tWHRxu8FBmlEvVFJZvmmO1U6KwuhONZy9HqAdretJHOf9TQ/IsziXz3NpoOPiJ/07hD+pmOmcECEDF66d5wFw13/am4C2tlm7EdicBZZ4UfQ6s8S5JS9EXZKqcZIZkGNQBmvAEtB55IV3UVbbtvZtWONcx+fYDI935N4zB9Q6L+7Lzr57EhKUsxYUPvhiGZitGeKK3XzyM6sHFLcHHQIjWXQGdjL90hgUfIw6wUzR6R8pdtdvW3ge8DJe/zRESk/FVls5OZnQwsJDjBfhJ4Kv7zwvi2Qnk38HHgUDN7NP7VWsDHK5xEVs2FFwbfGxuHXf1xv532y2paT7QnypLlSzjnF6ew5KhpRP/zy3DxxbBgAUyfHpzoJiRqUIbJJqtntMFLkVEoYV+05UTc3XuBxIl4weWtplk8a7lj+yjTT9/IgiPh4oP6WfC+HqbffSQdT9695UK+qa6Jhtqgn2iobaCprim3adwp/Uy0Lrioj9Zvvcjv7usm2htkdW7oHRo0yKvkjO1E/9XdvfX2DUmP/9hjMBBSh3fSJK3yLFuUsC8CeAHYNen3GfHbSmrLucg957Bk+RKiPUG2dmROhGAsOdwVTy9lQ0+Uxtc2Mv9vcOFd/cz/Sw+Nx544+LOZRsvMFi48/ELqq+tDtycGaRLaVrYR6+sL3xdomwPN66ChJ/zxcsoCT5VLXyRSOmHZ1dOTdzCz/YFd3f2OTAcys9PM7GEze/iVV17Jf0tFRKRiZBV8BD4D9AOHu/scd98bOJLgPK1gaW7u3uHu5u5vc/e3x7/aC/V45Wb7idsPu0/H6g6mXzqdBXd9mYtX/yoIJJy+kY6ZhJ/QRiJB9k6YbLJ6Rhu8FBmdkvRFZHEiDoU5yU5kDYXJdSp0tHpgaBCwPvj98LYPsHDZQp589UmeOvMpFh+1mHPffS6Lj1pM12lP0XL3k3DOObBkyeCyC2FS+pm2fYI/UJjUwEBBZJOxDVv7y02bhu5XVQVvf3t+2hONwuWXw1FHBV+XXz78ayrlplR9EcBDQLOZ7W5mdcBJwG0FfsyMtp6LLODi/72YBXctYPql0+lY3UFTfRNfmPuFtPd1CwJ+QyR/NlOkBjqfWfcMPQPh0cLUQZrOtZ10k2bfelg1GSIr0p8gj2Qxry2y7YtEypiZVQGXAouG29fdr3L3ue4+d9q0aYVvnIiIlK1sp12/Dfitu9+fuMHd7zWz3wIHF6Jh48HRbz6aZc8sS7v9A80fyHj/0FpwKVOHGnsZPB16tKsPD7OAjqYkSoGVdV9UiKmO+ZwK3famjWmDgD3ez2V/vWzQYjbz958/slWrU/qZzsndW/qmVAVZWCtVthnbmQID7vkpK9HRAUceCRuTauAtWwZnnw133x3+mqo+XDkqWV/k7v1mdiawjKAG7dXuvrKQj5lOtCfKtY9dy8JlC+mLbc0mTK1Lm0l3XRDwG7ohfDZF2AJc/bF+JtRMYHP/5iH7pw7SNDfsSkOshu6qoQvcNPTA7HXQ1AvtS+OlIibU0+09o1rMa4tymz2ivkXCDZdd3QTsAzwQz2reCbjNzI7VojMiIpJOtsHH7QmmFaV6Evhg3lozzpy676mcfc/ZbOofmmUzsWYip7791Iz3z1gLjiCTYP7fGHpCO5rVh0cbvBQZnVL1RSWb5piYCj3qFa2bm+n8Rw3d9UMvuIEtVesGBQ1Oe4rGkS4wldTPNP/zFhrsPrp9aLZRA/XMnpR+Rdq8SGRsD7eITKEDA9EozJs3OPCYsGlTsO3FFwe/pqoPV65Kel4UnwVS0pkgiSBg70DvoMBjskRmc8bFrHqDgN/QDUNnU2RagCudQYM0HR1EjjuPhZ/rh5ABkSogEg/jtqyGrp820nb7RazqXjP8Yl7ZyLYvKgb1LZLeluxqgnOdk4CPJja6++vA1MTvZvYA8BUFHkVEJJNsp11XAWFnln1sLbQucenqHqVqqm/i7o/fTWNtI3XVQWXzuuo6Gmsbg9uHOcHNWAuuPimTIOyENqUGZbTWs2ozsDWosHgxnHtu8L2rSyerUgyl6otKOs2xZWYLXYu6Bk+FXtSV20ItkQjNr1enrWOWqnegl2t/+ZXRTRGM9zORr7VRVRu+ekNVTw+RY84dXJs237ItN1HoshJtbZCm1hwAvb2DX1PVhytn4/q8KDkImG66M2zNbI7MiVBl4Z/BKt8a8Bu8YehsikyDrhP6YEJV3ZYyFZNqJzGhZgLHvPkYblxxI9G1XdDaStO6DbQvhaaerXUdG3qC39uXxmeMNDRAUxONv72T+e88gwsPv5D5+88fXeARMvdFZsEgRLblLUZDfYtk4O79QCK7+gngJndfaWbfMrNjS9s6ERGpVFmvdg2UdLXEchTtidK2so3OtZ00T2kmMifCYy89NiRDKXVF19T7Pf3Fp2nvbGfVulU5jaxnzCToScokGGY6dNgUpmFXoU0EL8cqTUUqZ0Xviwo1zTGsD2mqD3+fNdY1ZreidTpNTUS+exsL7z4yq917Bno467Ub2XdKjJawMY4cMgG3ZG9eP49Y9wa664I+qor4xf66DcNnUo5GthnbhS4r0dkJPRmiv729g1/TbOrDjeV+uPyN2/OiTEHAZIkpzxkzuA+4iMbLz4Xa4WdTZBp03VwLCx+MsfdXv8/9L/6Fmx+/mWqr5lcrfsVvn/oNC3tOp32q0xKNZzVeEswQWTUZZr9eTWTP42k8+32wZk1uM0Jyka4visWCr3PPLU4WovoWGUZYdrW7fz3NvgcXo00iIlLZcgk+XmBmF4RtMLOQpUFxd8/l+BUlLGB31l1nESPGxr6tU+pS6x49+u9H0wb6cg0sZKwFB0T+OQmaqjNOh840hSnR5lGP9FcaTUUqdyXpi/I9zXFEQf9Rajr4CNp3WkbrTccRiw3QbRmy8IA+iw2uH5ssx0zAlpktdE29kLbfLWJVQy+z1wXZTluOW+gL3mzKTRS6rERzM9TXpw9A1tUNfk3LrT6cpBqX50XRnig3P37zsNOdYfCU50QGd9uKtqGDrV2fyKoUTPOUZhqoD10wpqEH9l5bxUdWwFmv3jYoI7O7byNUQetJW/uzxt54aRoABuDwPeCMM0b0muQktS+aMSMIOiZnG2Zb3mKk1LeIiIhIkeVyEpzrNKIxO+0o2hNl3vXz2NC39URxuJPwmMe49tFrOe/35+Ut0Jc2k6B/gHZOoPEHhww7cp+xbmS8VtOosq0qTfJUpIRCXwRIriq+L8p30D+XDMqWvY6g69xXaFvRxsqXV/LjR34cukhDQsyS6scmG0EmYOMzzzP/r6lRzLhiXPBmk7E9mpq4w4lE4KyzMgcfk1/TcqoPJ2Eqvi/KVXKdx0zqquuor64fUpc2bQZ3lrMpInMiLLztjNBXsgqILO/lxsPuIDYpi3rYyYr9eUp+vkuWBAtahSnUoIz6FhERESmyrIKP7p5tbchx4Tt//M6gwGM2uvu6uf3p2/Me6MuYSZCFjHUji7EKbbkZ6VQkTdMuirHSF+Uz6D+SDMrkAMDxex/PYdceRm8sPJjQXQerdqqFhrrCLjBVThe8hSor0dQEd945dLVrgIkTg23Jr2mhp4HLiI2VvigXYYMmYaqtmvfOfC/H7XUc++64b17b0FTfRPu0BbS+cDExgvrWg0o41DbQub3TvSGLetjJ+vth5cogEFjs/9/ZZCHm+xxDfYuIiIgUWcVP/ym2aE+UH/71hznfL1EAPVOgb+XLK1myfElW2UvJGusa+cicj9C2so2n1z7NjStuzPq+GetGxms1jSsjmYqkadqSo3wF/bPNoMyUGdkys4VLjryERXcvCs1maqhtYPbXvg+PT8gqEzDtY0WjcOWV6Z+M2fi44G1pgZdegmuvhTvuCG77wAfg1FOHvqaFngYukoPh6jzWWA393k9tdS33/vNe/rLmL5z3+/P4wtwvAOR0XpNJyyfOp2vWlbTttiGo15hcwqGpiuZ3foCGB/40fD1sgOpqGBgI+p/LLhvd/++RBgiHy0J0h+nT83uOob5FREREikzBxxy1rWzDLPeZU1VWxeFvOpz7nrsv9AK/vrqe/37ovzGMvlgfk2onZV3/bTS14zLWjUyq1TRu5DoVSdO0ZQTyFfTPJoNyz6l7Dts/nLrvqZx9z9lpjxM54FR45/Dv44x90W2PZl7p+YwzQj8ruUwprxiNjcHzzaa+XCGngYvkINOgCYDH199JlHFI7Hvx/14MMKTvia7tou368+h85Umap+1F5GMX0jRll+EbkliFOjVwVh8EziIH7MvCP/xX6F2rSFpZu7o6CDoCbI6Xnhjp/+/RDEJmykI0gyuuKEw9SPUtIiIiUkQKPmYQdtHbubZzUBHzbEyomcBFh1/EOfeek7ZOUuoxE4vWDFf/bbS14zKuQJlSq6kgym26cq5TkbRipIxAvoL+w2VQrnx5JWctOytt//DUmU9xR+cdrHh5BQMetj4GWQ+2ZOyLrjuSrgt7aezpT3+AkMcpxaI8ZalQ08BFcpBp0KS+uh7HGRgI70dgcN9z894XcOKDi4hZUNqh4eX/Y+El19H+ritoOfoLwzcmQ+CsCZLOawbo7ts4eGp24jSspib46g/pl3L5/z3aQchMWYinnx4EH8Pk4xxDfYuIiIgUiYKPaaS76D197ulpT77T+cx+n+Hce89lQ29udSJh+PpvbSvbiPWHZxPF+vuyqh032rqRI1aO05VznYqkFSNlBPIV9B8ug3LtprVpMyP7Yn3svnh3aqpqhu3P0vUjyQM0/97wbwZi4YGH2KaNtO0ZssjDlsY2BCu+LlmyZSAi+qHWvC7KIyKjk2nQxN3T1o1NNRDr57iHF7G5futt3XXB9/c/eAaf98eZM/3tROYEgzBpM58zBM4Gndf8/X5mX3UzkSeqaXxt49b/6cccA7/6VXgjc/n/nY9ByHTB1G9/W+cYIiIiMiYo+BgiUwbPlQ9fieWwYGVDbQNv9L6RNgBQYzXEPEaM8O1h9d+SL/gfe2E53bHwlWq7Y5tZ9dLjWbUz7QqUhVLO05VzmYqkFSNlhPIR9B8ug3LKpClpA4uJqZHDZXJv7NvI/c/dP6R/SB2gqa2qpS8WPhDSXZdmkYeEWAzOPTeobRYP+Lf94kxiR4X3tSNdoEtEshc2+yPdoMnpc0/nioeuyGpgdmP/JmrTnEZtroYfLr+Chn808OW7voy7U2VVI8p83nJes/98OPEnQ/+n33gj/Pa3o///na9ByLBg6mjOMcptZomIiIiMawo+hshUR83dOfPAM7nyoSu3nHxPqp20ZZp0qv5YPytfXpn2hLzfM0xDBGqragfVf0u94K+nBhzC4qENvTD78X/DC0tKc/KZ6cS33KcrZzsVSStGyiiMNug/XAblk68+mXOmdpibH7+Znxz9ky2B0bABmnSBRwhZ5CFZbW0w5TqlplnnJOhO00V093Wz6tafwXLXBbVIAWQqeRA2aOLu/PjhH2d17Bo3+qo9fGP8XCaszxpV5nPY//R8/f8u5CDkSNtYjjNLREREZFwr++CjmR0FLAaqgSXuflGhH3O4OmqGDTn53nXbXTnxphO3nKhPqJ7A5oHNmBmPvPhI2seqq64DJ+10peqq6i3138Iu+HvoDw08AlQ5RL5+E9hvi3/yOdyJ71iZrlyoFSOVsSBZypRBue+O+6bNjMxFtVUPyjQcbtXbVIMWeUhWVwcnnhhkH6VoXhcMoCSmYyZr6IXZdz4IT6/QBbVInmVTSzps0CR1ICSdaqumvrc/9LOdjbxlPufr/3chByFH0sZcZ5bofENERESKoKyDj2ZWDVwBvB9YAzxkZre5e3ZziUcom5VowzKWEgGAlS+v5MqHrwS2Tm1Mp66qDgx6e8ODj7896bdbRvczXvA71PVDby2DC6tv7Afi2ZWJk89DD4VLL4VTTy3MCWY2J75jabpyvleMVMaC5ChdBmVTfRMXHX4RZ7QPXVW5rqou6xptG/s3Dir/MNyqtzUD0F8dBAmrHNr/p47GsD6uvh523DG0H4isgIVHhh+/yuPBzN4sSzXo4loka5nONTIF/lIHQhzniv+7AscHZWXffPR1nHjDh0bcvrByNCOWj//fhRqEHGkbc5lZovMNERERKZKyDj4CBwKr3P1ZADO7ETgOKGjwcaQr0SYCAOfce86wddSSp0ZCkE0wEBtgY/9GaqyG6qpqbjv5No7Y44gt98l4wW9w2D9h35eC6Y2RlUkrOqbq64OvfAX+678Kc4KZzYnvWJuunK8VI8u5FqYURVidtS0LLIzgWOfee27otmwDj7B10CUh4wBNL5y4EnbekNQX1dRAY92gmo5bLsyffDJ0IKKpF9pvmUDrKRCrqQ6CF4lg5tKU/i0Wg2uvDYKZqQHG4S6uFZgUGWS42R+ZAn+pAyHnv/f80Kzs9n9dQev/nrFltet05WPCpPZHo5aP/9/5HoQcTRuznVmi8w0REREponIPPk4Hnk/6fQ1wUOpOZnYacBrAzJkzR/2go1mJNtoT5bK/XJbx+FVU8f3Dv8+pbz91y7GyWXhiuAv+E56tY/5fe4OLa/qADMGFnp7gqxAnmNmc+IZlCkyaBAMDwQqUN95YmCBAuQcayr0WphRUpjpryQssZBugbFvZxoCHr0A9oWYC7r5ltetMtWtTB10yDtA4XH5nSnCw1uCii2DChKEX5vvum3YgouXFWrrOfJq25+5g1a0/Y/adD4YPrHR3B8eorR0cYLz55mBad7qL68T2fGX9lHv/IpKFbGZ/ZCtdVnbL0V+g613H03b9uTz+yuNcUfO3oIxMFjINApdUvgYhRyvbmSU63xAREZEiKvfgY1bc/SrgKoC5c+emqWKem5GuRNu2sm3Y1bBrqmqYUDNh0LGyWXgi4wV/QyORT18EB68JTiw3bQpWj00XBEwoxAlmc3MQZNgcMuV8woStJ77JmQL33x8EAmIx+NWvguDjmWfCbbfBEUcMPc5IVML0orFSC1Nylk2dtca6xqwDlAD3P3d/2oDi5v7NLHznQvaeunfa2rXpBl1CB2i8lqrevqFZiRC8d9esgQsvHNqQYaYsNk7eifmT5weLyzy9YutU61S9vcFX4vEAjjsuOE6YgYFge3I/NZqsn0roX0SyMNLZH7lqnLwT8790DQAfivdrPQM99A6kHzitr64fdhB43Mt2ZslIzjc0wCIiIiIjVO7BxxeAXZN+nxG/rShGshJt59rOYacz9sZ6c69XFI3S1NZG+xvHMG/CLfRWO70DvdRV11FXVUf7x+6kMTnwEI0G06qHM9qAVtiJaGsrfPaz4ftv3gwf+MDW3xsb4SMfgS9+McjETIjFgt+PPBKWLRt9ALJSpheNpVqYkpNs6qx9ZM5HsgpQQhDMvOXxW9I+XkNtA3tP3Ttt7drhBl2GDNCs7CJywc00vhYS7BzuvZvNlMVMF9TpDAyED4IAbNwYZEqGyXVQplL6F5EsjGb2x0gl+pNrH72WhXcvDA1ATqiZwD+//E92atwp748/pmRbgzLX8w0NsIiIiMgolHvw8SGg2cx2Jwg6ngR8tLRNyqx5SjM1VkO/p58+VGM1zNhmBkuWLxl22mS0J0rbb75D53WX0bzOmPlyL5wEVgXUgPUPQFVfcCGdLPnks6dna0ZQqtEEtNKdiJ5+eubMxzvuGHxR//WvB5ma6Rx7LLz66ugWk6iU6UVjrRamZC2bOmu5LATRtrKNaqtO+3gDPhCawZTLoMugfQ+Kwtd+Hb5jNu/d4aYsprug7u0N6tiG6euDmhroD+mPa2rS3y/XQZly61+UnSSjNNLZH6PRWNfIGQeewb477Zs28KnAY5ZGO6CT2mdrgEVERERGqayDj+7eb2ZnAsuAauBqd19Z4mZlFJkT4cz2M+kfSB98NDPOvfdc+mJ99Az0UF9dz1nLzuLOU+4cNG2yY3UHrdfPI9a9ge4DYVIPbKxjUFH2HhugxweY98sjuXCfBTw/tW5rMDNx8nnttcEJZlgAcqQBrUwnopddlv6ifvPmwRf10Sj8939nfqz+/vQX79mOxFfKdOZCr5opZSubOmtPr30664UgOtd2srE/fMo1wAlvOYHGusb8LXBTjPdu2AV1phITiTqyYcHH6upggZqw+9XVwaOPwpIl2QXuyql/UXaS5MlIZn/kQykCn2PSSAd0wvrsbAdYNPAxZpjZUcBiguuvJe5+Ucr2hcBngH7gFeDT7v6vojdUREQqRlkHHwHcvR1oL3U7stVU38RtJ9/GkdcfmXafKqtiQ9+GLb/3DPTQM9DDkdcfyUtfeWlLQKD1+nlE+zZAXbDfxvr0j7uhFhY9/kN6axhaA+6MM4JFHVpbgwvxjRuDk8uqKrjuupEFBTKdiJoFF/XJ06gTUjMt29qGf6yBgfS1h7Idia+k6cyFXjVTylI2ddZuXHFj1gtBZApmTqqZxCGzDqFjdQfzls6jbyDzQEjWivHeTb2gzlRioroafv3roYvKVFVtXWwmTG8v3HUX/OlP2QXuyqV/UXaSjBGNdY18ZM5HaFvZxtNrn+bGFTeOfGBE0su2z85mgEUDH2OGmVUDVwDvJ1js8yEzu83dH0/a7W/AXHffaGanAxcDmp4jIiJppanEL6NxxB5HsOxjy5hQM4HaqqCmWG1VLRNqJvCFuV+gZyAkKAds7NvItY9dC0Dbb75DrHtD6H6hDHrjoeTuvm6ivcHiFRt6NxDtibJk0pOcc+5cluy1kWgdwclhfz986ENw5ZW5P8lMJ6K9veBp1v1JzbTs7AyCi5nU1YVfvGczEp8QiaRfeKIcpzMnAiwXXhh8V8BgzEvUWWuqa6KhtgEIAopNdU1b6qxF5kSosvD3cdiK1On2ra6q5r27vZfDrjuMDb0btvRJPQM9bOjdwJHXH8mG3hz6n2TFfu8msneamoKLXQi+J24/4ojg4nrx4iBDcvHi4Pcjjhh6v1Td3VsDehsyvB7l0r/k0ieKlLGO1R1Mv3Q6C+5awMX/ezEL7lrA9Eun07G6o9RNG3uy6bMTAyxhGhpgxoytAx+Jc8Ns+08pRwcCq9z9WXfvBW4Ejkvewd3vd/fE9Iq/EtTlFxERSUvBxwI5Yo8jePZLz3LyPidz0PSDOHmfk3n2S8/yzPpnMt7vjqfugGiUzusuo7tudG2IeYzv/OE7wQn8nV/m4r77WXAkTF8EHTOTdjzjDPj3v3M7+HAnomedlT4YkHxi29wcTI3MpK4u/OI9l6mOwwUoFNyTMpCYbrj4qMWc++5zWXzUYroWdW3JQswmQJmQvO+k2uAzlhgEueB9F/DWH7817aqyyQMhFSGRvZMaYExk27gHX7HY1p9T7zdvXpCxHWZgIHPgrlz6l3Ka/i0yQtGe6JaFtRKZ26mDqqVtYDQoyXDOOcH35EzjsWq4AZZE/xpGAx+VaDrwfNLva+K3pTMfuDNsg5mdZmYPm9nDr7zySh6bKCIilabsp11Xqo7VHYMKpq94eQW/fvLXvHnKmzPf0YC2NprXGQ090J1hqvVwuvu6uezBywYFGBLHaz0Fui6BxsSmc8+Fa67J/uDDFSo///zga7ipPMOtYDthAtx5Z+aR+GynOmo6s1SA4eqs5VIPrWVmCzd/5GaOveFYaqyGvlgfNVbDonsWDduOO566gzPeccaonktRpatvNtxUwMT9nn466GvCbNwI99+fuX5aOfQv5TL9WwrGzP4fcAzQCzwDfMrdXytpo/Isl4W1iq4UU4vLoY7icPUhf/c7DXyMU2b2MWAu8L6w7e5+FXAVwNy5c9NMixIRkfFAwccCSB61T0iM3j/y4iMZ7/uB5g/ALZ1Elvey8JDRtaOuug5LXp0mSQxomwPz/xa/4e67s19cAbIvVJ7uYj35ZPr004Op3/39g1fIrqkJvtIZycrQwxVgF6kA2S4EEe2JcuJNJw4q9bBpIMPK8slSuo68LUyz5YBFuKDOtS7spElBoDHMzTfDT36SOZhY6v5lJH2iVJp7gPPiC/J9HzgPOKfEbcqrzrWdWS+slVfD9UmlqKl6991w3HFB9nVfX9BHlaqOYqYBlief1MDH2PICsGvS7zPitw1iZocDXwXe5+7hNaVERETiFHwcoUwX4plG7TOpq67j1LefCn+/kabaBtqXdtN6ShAo7K6Hhh7orwKqjJ7q4QcP3Z3eWPi0yu56WDU56YYXX4QFC9Kf1IadlI800ycscwCGTtnp7w/qBKU7qdfK0CIZjbQvgvhASFxqJveQRa1yVazsoWxXaIWg7/riF9Mfq7p68P7lSH3imOfudyf9+lcgzapJlSvTYlmpC2vlTTZ9Ui79ST7cfTccmbJ4YWJwpFQLSKUbYNHAx1jzENBsZrsTBB1PAj6avIOZ7Qf8FDjK3V8ufhNFRKTSKPg4AsNdiGcatc9kS5Zi/CSuZXUwNbptThAonL0OWrsa2PNLRk9f+ppHDbUNGMa7Z76b+565lz6GLujS0BMcb5B0I/jDnZTncrKdKXMgnUwn9bkEQMth6pJIEY20L5pYMzEYCCFzJnfr0la6FnWFTvlOq5jZQ7nWhT3+ePjVr8L337ixMqYOlsP0bymWTwOhxfTM7DTgNICZM2eG7VK2InMiLFwWHshKXVgrL7Ltk4pZUzUahWOPTb+9EMHO0dDAx5gSz6w+E1gGVANXu/tKM/sW8LC73wb8P6AR+B8zA1jt7hnetCIiMt4p+JijbC7EM43aZ9Iz0MO1j10b1FmLn8Q1xmLM/9vgk7j2mQwJfhrGGQeegWG4O1c8fAV/+tefQgOPEKw0FFmZpiHJJ7X5DhRkyhxIZ7iT+mymOpaiTpNIiY2kL2qoaeCuj9+1JaCY9/prxcweyrUG4iGHwG9+Ez71upKmDpZ6+reMipndC+wUsumr7v7b+D5fBfqBpWHHqOQ6a4nFslLPc6qsasjCWnmRbZ9UzJqqbW3BVOt0yrGOogY+xhR3bwfaU277etLPhxe9USIiUtEUfMxRNhfimUbth7NlkYcMJ3EtkHbBiWhPlOmXTk+7GmRDL1Q5tC9NWmwmVXc30VUraVu+hM77b6Z5Tg+R5dCUuv9IAgWZMgfSGe1JfSnqNIkUSaYSEMP1RYnAZH11PY5z1kFncf77zh90cZ/3+mvFzB7KdSpgpU4dVFb3mDLcRb2ZfRI4GjjM3SsqsJitXBbWGrVs+6Ri9g+dnUHpmXRqa8tzMEQDHyIiIpKGgo85yuZCPGzUflLtJDb2pVnIIFnyIg8ZTuLSLTiRKTha0w8nroTL78wQeAQ63jyB1ok/JnZXdZBxcAgsPCQIWLasTn7CIwgUZMocSGe0J/XFrtMkY0o5ry47XAmITBlEN3/kZp5//flhL+zzXn+tmNlDuU4FrMSpg8rqHlfM7CjgbIIFHrI4qahc2S6sNWrZ9knF7B+GWwCrurp8B0NEREREQij4mKNsL8TDRu13rZ3KsTd9kJ5qhqwkm7D9hO2J9kRHvIpspuBofw3svAEaayZBXVVw8pxyYhutg9YTNhN1oC+4rbs++N56SlCDckvgciSBgkyZAxMnBifU7vk9qS9mplU6ykyqZGW5umy2tRhHm0GU9/prxc4uzHUqYCVNHVRW93h0OVAP3BOvs/ZXd/98aZtU4XLpk4rVP0QicNZZ6bf/9rf6bIuIiEhFUfAxLtPUxWS5XIiHjdr/8+XfsNsDH6QvzSv/26d+y++e/t2IV5FtntJMA/V00zNkW0MPzH69Gj78Ybj8cnj00SEj+G379hObaBDbPOT+MYLFb+b/LfGERxAoGC5z4O1vz/9JfTEzrcIoM6milevqsrnUYhxNBlHe66+VIrsw16mAlTJ1UFnd4467l+Fc2wqXa59UjP7hscfCP9u1tXD77XDEEYV9fBEREZE8U/CR4acuJhvthfjOhx3HfdOXMa/tWHq8jz4bfHI5qlVkiQdHbzsjNLOyCoj8fQAO3znILnzySfjMZ2DtWpgyBebMoXPnlXQ/dFnosbvrg1W3Rx0oGC5zIN8n9aWs46bMpLEm7eqyUNwVZvNeizGDvNdfq6TswnJWDlndImNBOfVJifOGsCnX9fXwrncVv00iIiIio1S2wcdi1VnLdupiYt9EduT3DvsehrHmjTU5X4i37HUEL573KmfccQY3rLiBvljfkH1GtIos8eDotAW0vnAxMYKAYUNPEHhsXwqNVg/33w8//GEQeNu4cVAwsXmSp59WbvXM/o9D4eQTRn9SXszMolLWcVNmUkXIx+qyUNwVZvNei3EYea+/VinZheWs1FndImNJufRJmc4b3HXeICIiIhWpbIOPFKnOWrZTF8OyIxPZjiOZHt1Y18hOjTuFBh5hdJlLLZ84n65ZV9K22wZWTYbZ6yCyMlGrsQcefDDlwbZm4kWee4qFlmZaeW0dkXNvgkKsNllopcpqUGZSRajE1WXzXosxW/muX6p6qCNXqatzi0h6Om8QERGRMahsg4/FqrOWzdTFXLIjc1GwzKWmJhp/eyfzkzP9shGL0fTr9vzWdysnpchqUGZSxSvX1WXzXosxG/muX6p6qKNTiatzi0hmY+m8QYNLIiIiEmdlksSTkZn9Dmhz9+vTbE+us3bAv/71r6yPvWT5EhbctSBtAHDxUYtxfNh9RjIdMdoTZfql0wcFNROa6pqGDWoOu0jOhg3BSd/NN8N990Fvb9pjbXHuuXDhhWzo3ZC/+m7jWTQK06cPrvmY0NSkmo8ZmNkj7j63DNqximB12bXxm7JaXXbu3Ln+8MMPF7RtQN4/q2n7lXy/l/XZyJ9EX1/qWnVjVLn0RSNVrL5I8mSs9I1hg0uJgRENLo2I+iIRKQeV3hdJ6ZQ087Ec6qxlM3Xx23/4dkEWdhhN5lJWi+QkMv2efhruumv4BiWNqOe9vtt4pcykilfuq8vm87OasV+5+8n81i9VPdT8KZdadSIyemPhvEGL7YmIiEiKkgYfy6HOWjYBwHxMj06XTTSSVWRzngaeaQpPMtUIK4xyWkVTJI1h+5XX5tOYzzpk5V7XTNMFRSrKsLNBKkmlnzdocElERERSlG3Nx2LWWRsuADjahR2Gy1LMNXMp20Vytsi0KAFU3oh6JVJmkpS5YfuVXdYxP591yMq5rplqUYpUlKxmg1SaSj5vKPfBJRERESm6qlI3IIPLgSbgHjN71Mx+UsgHSwQALzz8QubvP39Q5mAiO7KpromG2gYgyHhsqmsadnp0cjZRIouou6+baG9w+4beDTm3NZtFcgZJTOFpagouogEmTYL6ejjlFFi8OBhh10W1yLg1bL/SPDUYpAgzkqzpSCS/x8uX5OmCiYvn7u6tt2/Ivc8WkcIpxHmWjFJicClMqQeXREREpCTKNvOx3OqsjWR6NIwgSzELI5oGXulTeESkoIbtV3baO791yMq1rpmmC4pUlEKcZ8koZZpxoxI/IiIi41LZBh/L0UgWdsg5SzELI54GXslTeESkoLLqV+oa8zuIUY6DIpouKFJRCnGeJaNUroNLIiIiUjIKPhZYPharSTWaVbJFRMJk3a/kexCj3AZFyrkWpYgMUYjzLMmDchxcEhERkZKxAi0iXTJz5871hx9+uNTN2CLaE2X6pdMHrSCb0FTXNHRl6hxs6N2Q8zRwkUphZo+4+9xSt2Okyq0vyta471eiUZg+PfieqqkpuJjWxfO4or6ovBXyPEuknKgvEpFyUOl9kZSOMh8LrJBZiiOZBi4iksm471c0XVCkomg2iEj+mdlRwGKgGlji7helbK8HrgMOANYCEXd/rtjtFBGRyqHgYxGMdLEaEREpAU0XFKkoOs8SyR8zqwauAN4PrAEeMrPb3P3xpN3mA+vdfbaZnQR8H9BKQiIikpaCj0Uy7rOJREQqSbnVohSRjHSeJZI3BwKr3P1ZADO7ETgOSA4+HgdcEP/5ZuByMzMfa/W8REQkb8Zc8PGRRx551cz+Ff91KvBqKduTpFzaUi7tALUlHbUlsFuJHjcv1BdlpVzaUi7tALUlHfVFI6S+KCtqy1Dl0g5QWxKK1RdNB55P+n0NcFC6fdy938xeB6aQ8tqY2WnAafFfe8xsRUFaXDzl9F4ciUpvP1T+c6j09gPsWeoGSGUac8FHd5+W+NnMHi6XYqjl0pZyaQeoLemoLWOD+qLhlUtbyqUdoLakU05tqTTqi4antpRvO0BtqWTufhVwFYyN167Sn0Oltx8q/zlUevsheA6lboNUpqpSN0BERERERETKwgvArkm/z4jfFrqPmdUA2xIsPCMiIhJKwUcREREREREBeAhoNrPdzawOOAm4LWWf24BT4z+fCNyneo8iIpLJmJt2neKqUjcgSbm0pVzaAWpLOmrL2FNOr6PaMlS5tAPUlnTKqS2VrJxeR7UlXLm0pVzaAWpLUcVrOJ4JLAOqgavdfaWZfQt42N1vA34O/NLMVgHrCAKUwxkLr12lP4dKbz9U/nOo9PbD2HgOUgKmQSoREREREREREREpBE27FhERERERERERkYJQ8FFEREREREREREQKYkwFH83sAjN7wcwejX+1ptnvKDN7ysxWmdm5BWrL/zOzJ83s72b2azPbLs1+z5nZP+Ltzduy9cM9RzOrN7O2+PYHzWxWvh475XF2NbP7zexxM1tpZl8O2edgM3s96e/29UK0Jf5YGV9vC/wo/rr83cz2L1A79kx6vo+a2RtmtiBln4K9LmZ2tZm9bGYrkm6bbGb3mFln/Pv2ae57anyfTjM7NWyf8U590aDjqi8Kb4/6ItQXFZr6okHHVV8U3h71Ragvyqdy+ayNRhbPYWH8M/x3M/u9me1Winamk22fbmYnmJmb2dxiti8b2TwHM/tIUl/6q2K3MZMs3kMz4/8L/hZ/H4X+fy6VsD4xZXtR/jfIGOPuY+YLuAD4yjD7VAPPAG8C6oDHgL0L0JYjgJr4z98Hvp9mv+eAqXl+7GGfI/AF4Cfxn08C2gr0N9kZ2D/+cxPwdEhbDgZuL9J7JOPrDbQCdwIG/AfwYBHaVA38G9itWK8L8F5gf2BF0m0XA+fGfz437D0LTAaejX/fPv7z9sX421XSl/qi7J+j+qK029UXufqiPLy+6ouyfI7qi9JuV1/k6oty/NuVxWetwM/hEGBS/OfTy+k5ZNunx/ufPwJ/BeaWut0j+Bs0A39LfN6AHUrd7hzbfxVwevznvYHnSt3ulPYN6RNTthf9f4O+Kv9rTGU+ZulAYJW7P+vuvcCNwHH5fhB3v9vd++O//hWYke/HyCCb53gccG3855uBw8zM8t0Qd3/R3ZfHf44CTwDT8/04eXQccJ0H/gpsZ2Y7F/gxDwOecfd/FfhxtnD3PxKsTpgs+T1xLfDBkLseCdzj7uvcfT1wD3BUodo5xqkvCqgvCqe+KKC+qPDUFwXUF4VTXxRQX5SdsvmsjcKwz8Hd73f3jfFfi92fDSfbPv3bBANBm4vZuCxl8xw+C1wR/9zh7i8XuY2ZZNN+B7aJ/7wt0FXE9g0rTZ+YrBT/G6TCjcXg45nx1N+r00yPmA48n/T7Ggp/0vdpgpGBMA7cbWaPmNlpeXq8bJ7jln3iFwOvA1Py9Pih4tMq9gMeDNn8TjN7zMzuNLM5BWzGcK93Kd4fJwE3pNlWrNcFYEd3fzH+87+BHUP2KcXrU6nUF6kvykR9UXrqi/JLfZH6okzUF6Wnvih3ZflZy1Guf9P5pO/PSmHY9senyO7q7ncUs2E5yOZv8GbgzWb2ZzP7q5mVU9A/m/ZfAHzMzNYA7cAXi9O0vFHfJzmrKXUDcmVm9wI7hWz6KvBjglEcj3+/hOAEt+htcfffxvf5KtAPLE1zmBZ3f8HMdgDuMbMn4yMNY4qZNQK3AAvc/Y2UzcsJptZsiNe7+A1BKn0hlNXrbWZ1wLHAeSGbi/m6DOLubmZejMeqVOqLKpP6onDqiyqX+qLKpL4onPoiqSRm9jFgLvC+UrclW2ZWBVwKfLLETRmtGoLP/8EEmad/NLO3uvtrpWxUDk4GrnH3S8zsncAvzWwfd4+VumEihVJxwUd3Pzyb/czsZ8DtIZteAHZN+n1G/La8t8XMPgkcDRzm7qEnLO7+Qvz7y2b2a4I07dGe9GXzHBP7rDGzGoJ077WjfNxQZlZLcIK91N1vTd2efNLt7u1mdqWZTXX3V/Pdlixe77y9P7I0D1ju7i+FtLVor0vcS2a2s7u/GE+bD5u+8ALBP/mEGcADBWpPWVNflBX1RWmoL8pIfVEO1BdlRX1RGuqLMlJflLuy+qyNUFbveTM7nGCQ533u3lOktmVjuPY3AfsAD1gw230n4DYzO9bd87bI1yhl8zdYQ1BnsA/4p5k9TRCMfKg4Tcwom/bPJ16iwd3/YmYTgKmE9zPlqNj/G2QMGFPTrm1wnYEPAWGrMz0ENJvZ7vHR1ZOA2wrQlqOAs4Fjk2qCpO7TYGZNiZ8JirGHriiVo2ye423AqfGfTwTuS3chMBoW/Ff7OfCEu1+aZp+d4vthZgcSvC/zfhKS5et9G/AJC/wH8HrSlJtCOJk0U4uK9bokSX5PnAr8NmSfZcARZra9BdP3jojfJknUF22hvij8cdQXZaa+KE/UF22hvij8cdQXZaa+KHdl81kbhWGfg5ntB/yUoD8rt2BRxva7++vuPtXdZ7n7LIKaleUUeITs3ke/IR74N7OpBNOwny1iGzPJpv2rCerbYmZvASYArxS1laNT7P8NMhZ4Gax6k68v4JfAP4C/E3wgdo7fvgvQnrRfK8Hqgs8QTAUqRFtWEdRBeDT+9ZPUthCsgPVY/GtlPtsS9hyBbxH8c4Ggg/ufeDv/D3hTgV6HFoLpXn9Pei1agc8Dn4/vc2b8+T9G8A/wXQVqS+jrndIWA66Iv27/oICrvwENBCfN2ybdVpTXheDE/kWgj2DkcD5BvZ3fA53AvcDk+L5zgSVJ9/10/H2zCvhUoV6fSv5SXzTo8dUXDW2L+qKtj6O+qIBf6osGPb76oqFtUV+09XHUF+XvtSyLz1qBn8O9wEtJn+HbSt3mXNqfsu8DhfxcF/BvYATTxx+P900nlbrNObZ/b+DP8f7sUeCIUrc5pf1hfWJJ/jfoa+x8mXs5DTSJiIiIiIiIiIjIWDGmpl2LiIiIiIiIiIhI+VDwUURERERERERERApCwUcREREREREREREpCAUfRUREREREREREpCAUfBQREREREREREZGCKFnw0cyuNrOXzWxFmu1mZj8ys1Vm9ncz27/YbRQRERERERlPdJ0mIiL5VsrMx2uAozJsnwc0x79OA35chDZJgZmZp3wNmNmrZnafmX00w/32MrP/NrMVZva6mfWaWZeZ3WFm882sPmX/E+P7/8nM3og/1vWFf4YiUgnUF4lIOVBfJGXqGnSdJiIieVRTqgd29z+a2awMuxwHXOfuDvzVzLYzs53d/cXitFAK7Jvx77XAXgR/70PMbK67L0ze0cy+DnyDIFj+F+BaYAOwI3AwsAQ4HZibdLfzgX3j+62JP4aISCr1RSJSDtQXSdnQdZqIiOSbBf8zSvTgwT+12919n5BttwMXuXtH/PffA+e4+8Mh+55GMOpGQ0PDAXvtpfMpkUr3yCOPvOru00rdjpGaOnWqz5o1q9TNEJFRUl8kIuWg2H1RPq7TdI0mMvZU+nmRlE7JMh/zyd2vAq4CmDt3rj/88JD4pIhUGDP7V6nbMBqzZs1CfZFI5VNfJCLloBL7Il2jiYw9ldgXSXko5+DjC8CuSb/PiN8mIjLuRXuitK1so3NtJ81TmonMidBU31TqZonIOKe+SWRc0HWaiIjkpJyDj7cBZ5rZjcBBwOuqIyIiAh2rO2hd2krMY3T3ddNQ28DCZQtpP6WdlpktpW6eiIxT6ptExg1dp4mISE5KFnw0sxsIimJPNbM1BIWzawHc/SdAO9AKrAI2Ap8qTUtFRMpHtCdK69JWor3RLbd193UD0Lq0la5FXTTWNZaqeSIyTqlvEhk7dJ0mIiL5VsrVrk8eZrsDZxSpOSIiRTeS6YltK9uIeSx0W8xjtK1oY/7+8wvRXBGRtNQ3iYwduk4TEZF8K+dp1yIiY9ZIpyd2ru3ckk2Uqruvm1XrVhWqySIiaalvEhEREZF0qkrdABGRSmFm1Wb2NzO7fTTHSZ6emLhY7+7rJtob3L6hd0Pa+zZPaaahtiF0W0NtA7Mnzx5N00RERkR9k4iIiIiko+CjiEj2vgw8MdqDZDM9MZ3InAhVFt51V1kVkX0io22eiEjO1DeJiIiISDoKPoqIZMHMZgAfAJaM9lijmZ7YVN9E+yntNNU1bckyaqhtoKkuuF0LOohIKahvEhEREZF0VPNRRCQ7PwTOBjKvCJOFxPTEsABkNtMTW2a20LWoi7YVbaxat4rZk2cT2Seii3uRccLMqoGHgRfc/ehStydBfZOIiIiIhFHwUURkGGZ2NPCyuz9iZgdn2O804DSAmTNnpj1eZE6EhcsWhm7LdnpiY12jVo4VGb8SJSC2KXVDUqlvEhEREZFUmnYtIjK8dwPHmtlzwI3AoWZ2fepO7n6Vu89197nTpk1LezBNTxSRkcpnCQgRERERkWJQ5qOIyDDc/TzgPIB45uNX3P1jozmmpieOE9EotLVBZyc0N0MkAk2jnrkv49sPGaYERLZZ2CIiIiIixaDgo4hIiWh64hjX0QGtrRCLQXc3NDTAwoXQ3g4tLaVunVSgbEtAuPtVwFUAc+fO9eK0TkREREQknIKPIiI5cPcHgAdK3Awpd9FoEHiMRrfe1h1fYKi1Fbq6oFFZrpKzRAmIVmACsI2ZXT/aTGwRERERkUJSzUcREZF8a2sLMh7DxGLBdpEcuft57j7D3WcBJwH3KfAoIiIiIuVOmY8iIuNctCdK28o2Otd20jylmcicCE31qks4Kp2dWzMdU3V3w6pVxW2PiIiIiIhIiSj4KCIyjnWs7qB1aSsxj9Hd101DbQMLly2k/ZR2WmaOs7qE+Vwcprk5qPEYFoBsaIDZs0fXVhn3VAJCRERERCqFgo8iIuNUtCdK69JWor1b6xJ29wXBstalrXQt6ho/q2/ne3GYSCS4f5iqqmC7iOSXVpcXERERKUuq+SgiMk61rWwj5uF1CWMeo23FOKlLmLw4TCJTsbt76+0bNuR+zKamIHDZ1BQEMiH4nrhdi82I5FdHB0yfDgsWwMUXB9+nTw9uFxEREZGSUuajiMgYlqmeY+fazi2Zjqm6+7pZtW6c1CXMZnGY+fNzP25LS7CqdVtbUONx9uwgE0uBR5H80uryIiIiImVNwUcRkTFquHqOzVOaaahtCA1A1lgNXdEuoj3Rsb/4TCEXh2lsHFngUkSyV6gBBBERERHJi5JOuzazo8zsKTNbZWbnhmyfaWb3m9nfzOzvZtZainaKiFSa5HqOieBid1830d7g9g29G4jMiVBl4f8G+r2fW564hemXTqdj9RiftphYHCaMFocRKX9aXV4kr3SNJiIi+Vay4KOZVQNXAPOAvYGTzWzvlN3OB25y9/2Ak4Ari9tKEZHKlE09x6b6JtpPaaeprolJNZOG7JcarByzIpFgEZgwWhxGpPxpAEEkb3SNJiIihVDKzMcDgVXu/qy79wI3Asel7OPANvGftwW6itg+EZGKlW09x5aZLXQt6uLEvU+ktqo2dP8xv/iMFocRqWwaQBDJJ12jiYhI3pWy5uN04Pmk39cAB6XscwFwt5l9EWgADg87kJmdBpwGMHPmzLw3VESk0mSq59hQ28DsyVszgRrrGtmpcSf6Yn2hxxoXi89ocRiRypUYKGhtDWo8dncHAwhVVRpAEMmdrtFERCTvyn3BmZOBa9z9EjN7J/BLM9vHffBcQne/CrgKYO7cuV6CdoqIlJXInAgLly0M3VZlVUT2GZwJlEuwcszS4jAilUsDCCLFpGs0ERHJSSmnXb8A7Jr0+4z4bcnmAzcBuPtfgAnA1KK0TkSkgiXXc2yoDaYSN9Q20FQX3N5YN/iCPNPiM2HBShGRspMYQLjwwuC7Ao8iI6FrNBERybtSZj4+BDSb2e4E/9BOAj6ass9q4DDgGjN7C8E/tleK2koRkQqVqOfYtqKNVetWMXvybCL7RIYEHmFrsLJ1aSsxj9Hd101DbQNVVhUarIz2RGlb2Ubn2k6apzQTmROhqb6pWE9NRMpFNBpkG3Z2Bgu/RCLBNGgRqVS6RhMRkbwrWfDR3fvN7ExgGVANXO3uK83sW8DD7n4bsAj4mZmdRVDY+JPurpR9EZEsNdY1Mn//7KYSZxus7FjdMSRIuXDZQtpPaadlZkshnoaIlKOOjqF1FhcuDOostqgvEKlEukYTEZFCKGnNR3dvB9pTbvt60s+PA+8udrtERMar4YKV0Z4orUtbifZGt9yWqBPZurSVrkVdoZmVUljKRJWii0aDwGN0a19Ad7xmbGtrUH9R055FKpKu0UREJN9KWfNRREQqTNvKNmKD68lvEfMYbSvaitwi6VjdwfRLp7PgrgVc/L8Xs+CuBUy/dDodqztK3TQZy9ragozHMLFYsF1EREREhPJf7VpERAoth5ptnWs7Q1fEhiADctW6VYVsqaRQJqqUTGfn1kzHVN3dwYrTIiIiIiIo81FEZHzr6IDp02HBArj44uD79OnB7SGapzRvWT07VUNtA7Mnzy5cW2UIZaJKyTQ3BzUewzQ0wGz1BSIiIiISUPBRRGS8Sq7Zlshg6u7eevuGDUPuEpkTocrC/3VUWRWRfSKFbLGkUCaqlEwkAlVpTiOrqoLtIiIiIiIo+CgiUjaiPVGWLF/COfecw5LlS4j2RIe/02iMoGZbU30T7ae001TXtCUDsqG2gaa64HZN8S0uZaJKyTQ1BataNzVtzYBsaNh6uxabEREREZE41XwUESkDHas7aF3aSsxjdPd101DbwMJlC2k/pZ2WmS2FedAR1mxrmdlC16Iu2h6+llV/uZ3ZayEy6wM0br9vYdopaUXmRFi4bGHoNmWiSsG1tASrWre1Bf3F7NlBxqMCjyIiIiKSRMFHEZESK9miIYmabWEByGFqtjX+36PMbz0vyJDs7oaGP8FX/ivIeGopULBUhkhkoqYGrqusSpmoUhyNjTB/fqlbISIiIiJlTMFHEZESy2bRkPn7F+DiPhKBheFZcxlrtiXXikxIBDBbW4NMKGU+Fc2WTNQVbaxat4rZk2cT2SeiwKOUXLQnStvKNjrXdtI8pZnInAhN9U2lbpZIyemzISIi442CjyIiJVayRUMStdlaW5MyGBuCwGOmmm3Z1IpUJlRRNdY1FiZALTJCJSklIVIB9NkQEZHxSMFHEZFhmNmuwHXAjoADV7n74nwdP7FoSFgAMutFQ6LRIOjX2RlMp45EguDicEZSs22EtSJFZPQK3R/lQ8lKSYiUOX02RERkvFLwUURkeP3AIndfbmZNwCNmdo+7P56Pg2e9aEi6AGNHx9DsxYULs6+/mGvNtlHUihSRUStof5QPJSslIVLm9NkQEZHxSsFHEZFhuPuLwIvxn6Nm9gQwHcjLxX5Wi4akCzDefDOceGJx6y+OtFZkFlQHSySzQvdH+VCwUhIjzfAWKRMlK7MiIiJSYgo+iojkwMxmAfsBD4ZsOw04DWDmzJk5HTfjoiGZFng57rgg4BemUPUXR1orchiqgyWSm3T90Wj6onzISymJVKPN8K4kCrKOWQX5bIiIiFQAc/dStyGv5s6d6w8//HCpmyEio2Rmj7j73FK3I5mZNQJ/AL7r7rdm2jevfdGSJbBgQfg059pa6OtLf99zz4ULL8xPO1Jt2JBbrcgMoj1Rpl86fVAdrISmuibVwZKSKce+CLLvj0pxXpT3z3M0CtOnDx6A2XLApsJkeJdKWJA1MbAz1oKs49BoPhvl2hdlS9doImNDpfdFUjpp0mVERCSZmdUCtwBLhws85l2mBV76+qAmTRJ7oesvJmpFXnhh8H0UF//Z1MESkUBJ+6MsJEpJNNU10VDbAARZXU11TVtLSeSirS0IxoVJZHiPBclZ7ok+v7t76+0bNpS2fTJqef9siIiIVAhNuxYRGYaZGfBz4Al3v7ToDci0wMukSTAwAP39Q7eNsv5iMakOlkh2St4fZSljKYlcZRqA6e4Osq9HoJA1Zkd07GyCrPkuoyFFl9fPhoiISIUoafDRzI4CFgPVwBJ3vyhkn48AFwAOPObuHy1qI0VE4N3Ax4F/mNmj8dv+y93bi/LomRZ4qa6GX/86WHQmj/UXi011sESyVtr+KAeNdY35Wbk30wDMCDO8C1ljdsTHLlCQVcpP3j4bBaJrNBERybeSBR/NrBq4Ang/sAZ4yMxuc/fHk/ZpBs4D3u3u681sh9K0VkTGM3fvAKxkDRhugZeWlqDmWZ7qL5ZCZE6EhcvCA6xVVkVkn8rI4BQptJL3R6WQaQBmBBne0Z4orUtbB9XdSwx8tC5tHVWN2VEduwBB1mIrZDapFIeu0UREpBBKmfl4ILDK3Z8FMLMbgeOAx5P2+SxwhbuvB3D3l4veShGpKGY2GRhw99dL3Za8Gi7AmKi/WKESdbBSs4WqrEp1sKQimdmlwF/c/X9K3ZaKN9wATI4DLdnUmB1pVtqojp3nIGuxFTKbVIpK12giIpJ3pQw+TgeeT/p9DXBQyj5vBjCzPxOk/V/g7ncVp3kiUo7MbDrBaHsz8BjwfXdfa2ZvB64D5sT3+zNwmrs/Waq25l2FBxiHk3MdrGg0CMZ2dgYZQ5FIEKQQKQ8LgEZAwcd8yGOGd95rzCb1RZ07PDbyY+c5yFpMhcwmlaLTNZqIiORduS84U0MQYDgYmAH80cze6u6vJe9kZqcBpwHMnDmzyE0UkWKJZzX+leDEGIIpQYeb2ZHAHcAUgoDkLkALcK+Z7ZPaZ0j5yroOVkfH0Av0hQu3TkMXKSAz+3SWu+6ZvK+7X12gJo0Nww0oJAZgEvt9+9sZBx7STQHOa43ZlL6o+T/qaDgUuuuG7prVsSu0jEYhs0mlLOkaTUREclLK4OMLwK5Jv8+I35ZsDfCgu/cB/zSzpwn+0T2UvJO7XwVcBTB37lwvWItFpNTOJAg8fg+4CTgW+BZwDRAF3unuqwHM7LsEGZJfBL5disZKgUSjwcV+dGuGzZYaaa2twYV7mV+oS8VbQrDIQiZOMAjSQlCj0QEFH9PJdkAhy/0yTQHOW43ZkL4osryXhYeE7571sSswyz3v2aRSSrpGExGRvCtl8PEhoNnMdif4h3YSkLpK2m+Ak4FfmNlUghT/Z4vZSBEpKx8EHnL38+O//93MjgCOAo5PBB7jzgciwNEo+FhWRr0gQVtbEHgIE4sF2yvswl0q0gbgJ8DGkG0GfB1YDvyumI2qSNkMKLjDtdfCokXQ25t+v8bGjFOA5119KBdudyLH7H4Utz7zO6qsio19G0dWYzakL2rqhfal0HoKxCbU0+0946J+bV6zSaXUdI0mIiJ5V7Lgo7v3m9mZwDKCWiFXu/tKM/sW8LC73xbfdoSZPQ4MAP/p7mtL1WYRKbndgOtTbnsYeDfwv8k3urub2R+A44vUNslCXhYk6OwMXw0WgttXKcNGCu4TwH8T9C+fiq9APYiZfR1Y7u7fLHbjKs5wAwrf+Q5ceSX09AwOPKbuFx94uPaxa+kZ6AndbQN9fGXtDfS8DpOsnoGqAU556ykcMuuQzDVmw6Tpi1pWQ9cl0Pafh7Lq4H2Hr187BuQtm1RKTtdoIiJSCCWt+eju7UB7ym1fT/rZgYXxLxGRiUDqld7rAO7+Ssj+LwENhW6UZCdvCxI0NwdTLcMCkA0NQY00kQJy9+vjgxu/AB4wsx8CX3X38IiXBNLVdBxuQOGyy9IHHZP3W7WKjtUdLFq2iN5Ymv0NeuJnvxu9Bwbgtqdu4ydH/yT34GCGvqixtoH5u58Ah4+PLOym+ibaT2kfMrg01jM+xypdo4mISL5VlboBIiI5eBXYIeW2buDlNPtPAV4rZIMke9ksSJCVSCRY/TVMVVUw/XLJEjjnnOB78lROkTxx9+fd/XBgEfAF4G9mdmCJm1W+Ojpg+nRYsAAuvjj4Pn16cHsiiBemrg7Mhj9+QwPRN82gdWlr+sBjGjn1P8mG64si4yvbr2VmC12Lulh81GLOffe5LD5qMV2LurLPahcREZExq9xXuxYRSfY0sHfyDe7+A+AHafbfnaAouhRArrUb87YgQVNTsLhE6qITVVVw0UWw555aBVuKxt0Xm9ndwC+BP5vZDwjqPY5PYdmNAK2tQZ+xD3ROhuZ13URWQFNrKzz1FHzpS+HHGxgIvoZTVUXbHCf2QJrp2xmMZEGUaE+Uts42OhcfQ/OSW4g8Xk3Taxu39kXt7eNy4avGukatai0iIiJDKPgoIpXkEWCBmdW5e8bUFjPbhmCV2SVFadk4M5LajXldkKClJVhcoq0tqPE4e3YQjNxzT62CLUXn7k+Y2UHAN4DzgGMYfjXssSfdStSnn07Hzn20ngAxoLseGnpg4ZHQfuNGWr7ylfTHrK6GCRPST8uurw+yI9vb6ez+XdoBDiD4i4QkUYb1P5kGV4b0fx9oYOGRA7RvPoWWPQ4JAq7qa0RERES2UPBRRCqGu58DnJPl7jsSBAHuLVyLSihd7bRiPPQIazfmfUGCxsbBq1ovWaJVsKVk3H0A+LqZ3QFcR2iYawzLsGJ19MrLaP1iH9H6pE3xn1tPGqDrshtp3Jzms2uWPvOxthYuuQROPRUaG2le/mTaAY66fjCHntqhh0ntfzINruy7477p+7+m2+j6+AhqR4qIiIiMcar5KCJjkrt3uvtid19Z6rbkXabaaUUw0tqNiQUJmuqaaKgN6rs11DbQVNeUnwUJtAq2lAF3f5CgPMT2QJq5xGNQhhWr2/Z2YmlCsTGg7S0Zpkr39MBxxwWDK4m6kA0Nwe/33QdnnLElyzAyJ0KVhZ/a1g/Abb+up6kHGuJ58w1VE4b0P8mDK4mgYndfN9He4PZrH7s2P7VrRURERMYRZT6KiFSSDNlFianF0VrPqRZjrkZTuzGxIEHbijZWrVvF7MmziewTyU+mkFbBljIRz4J8vdTtKKoMwf/Obfrprgu/W3c9rJo8zLEnTBhaZiFkanPGFZcjN9Oyx/N0rXqctp3Wsqp5CjOmvAnH+d1Tv+PJV58kMicy7ODKHU/fkZ/atSIiIiLjiIKPIlIxzKwO6ACiwFHu3pdhvzuBBuA96farSBmyi4jF6LjuO7S+fmVOtRiHGGZK92hrNxZsQYJIJKgvF2YcrjwrhaO+KESG4H/zhjoaYjG6q/qHbGvogdnrtv4erSNpURqCRWmmTBlaZiGNjAMce0EjMJ/0U6uP2fOYjMFFw/JXu1ZERERknFDwUUQqyceAA4BjMl3Eu3uvmf0/oB04BbimOM0rggzZRdG+blpfvoyobV2LZ0stsmveT1f352lsnpO5PmS6BSOSVovOe+3GHGRcYTvTKtjjdOVZKRj1RakyBP8jT9Wy8JBuqB+6rQqIxItjdMyE1lNCFqV5dSUt55yTdX3b0AGOpEGV6B670vrqeUT7NmzZnOgrb3n8lozBxQ+8+QN0PB9e4qLQ/Z+IiIhIpVLwUUQqyfHAs+7ePtyO7n6XmXUCH2YsXfBnyC5q27+OWJWFrrEb69lM2z0/ZP4VQ4OJW2QxpZvGxvCpjdRT5c7NTfO58eFr6Ox+Pu9TvrNaYTtsFWytPCv5p74oVYbgf9NnT6f9lh/ResLmLYHFST0wUAXHPAk37gOtTweBx9BFaaYuo+uSZTTWhvdfGQclYMigStt/1BE7tBdCpoJXWzUDHr7ATZVVcerbT2XfnfYNn9qdj9q1IiIiImOQgo8iUkn2I8ggytYfgdYCtaU0MmQXdU52ur03dFt3XbyuWkgwcYthpnQnrxa9ZWrjb77DqmsuZfZ6Z9eXezkx8kNiLwaP11A7iYW3f5H2TcfTsscho1qRO6cVtrOcnikyCuqLwqQL/n/727Q8vZmuS6BtDtw/C27ZG2ocfrUv/PYt8MVWQgdO4P+3d/fxkdX13f9fn8kmWXZmvNkFBbOsSBNRdlsq3aqtuS6lIG5HhavlZrDLVayx/ErBumZtd735KbW2IF6A6wVIaWrFupXRpeheEllR8Fejgiw32l1gSUrtsgQFd5VrdpbN3Xx/f5yZZDI5c5fMzJmTvJ+PRx5JzpzM+ZzcfHPOZz7f7wfGInDB+XDe4xlvGnbB+DX3RYmicSeRmPOiynBsvOQalEcmj7Dx1zeyc9/OksnFhq5dKyIiIrIIKfkoImFyLPDzGvb/ObCqQbEEo0x1Uc8fX0b0P270ny5YtK5acTIRqLlbdGzM0ffemyA9QboDujYXVS1NHAEgYf/C6F9+jVipissqVNNhuyHrSIr401hUil/yP1exHctkuHAvfGADHG2febhUIjBvvB3uejV875W5adi3T9CbSpG++EKfFyWKxp0rrgCbabWd7oCfxWDZJEz6XAVH26OccdIZ3PyOm8smFxu2dq2IiIjIIhQJOgARkRq8gNcvoFox4GiDYglOvrpo2zbYutV7PzpK8n98lIj5D+uF66oBvsnE6Sndfvy6RRdUSqbWeeu0+ckCqVcdmZnWffhwiT29CseBhwbYcvcWBh4aID3mJRUW0mFbpAE0FtUimfTWXqX8WFFJptN7gSNx3lEOjzzKrT++lbGpMd99p8edsTE46n3rh9Z4L5LsONU/8Qje1OpET4Lb9tzGEwef4NdW/hoXrr1QVY0iIiIiC6DKRxEJk6eA9TXsvx7Y36BYguVTXRSHuWsxjkPEweB2iBXOyPZLJtbaLbqgUnJ45cz6bMUynbkp3+BfcZlTbk3HhXbYFqkzjUW1KKjYHn75C2Q653a9rkXW4JOrHuX6XTcwni2x1EThuINX8Vi8pmShFctW0BZp4+qzruaUG04pv7asiIiIiNRElY8iEibfBX7HzCre9JvZbwG/C9zb6KBaSX4tsm0btrH1t/vZdm8no9dCb3HaY2rKq0IslE8QxOMzFZDR6Mz24qYtBZWSPYe8qd1+Zk359qu4ZPaajvkEY2YiQ3rc2/72nreXrupUh1lpvu+isag2uYrtng1/RNS1V96/jEwHXJf5TsnEI8xdaqJcxWV7pJ0L1l7Aviv2sfXbW0uOQ4fHS1dti4iIiEhpSj6KSJjcgNeS4Ktm9tpSO5nZa4CvAlPATU2KrWXk1yK7KnEtfdd8m1hnHJYvn7vjKad4XWALlZjS7btOY8FUyuSe0v9QZk35jkZJn7x6ztTqSms6Dg4PMrhxkHhHnGi7l/CMtkeJd8TVYVaCoLFoPmIxkv/PDUQ6fcajAp1tnaw7bh2dVjpJ6axEd5qcWePO8uUMH9dWsjp7IjvBCbETuHP4zpLj0NjUGLc+cmvZY4qIiIiIP027FpHQcM7tM7NPAFcCD5vZDuAe4EBuly7gTOA8oBP4mHNuXxCxtozeXti3D171qtnbjx713vy6XlfbLbpgKmU8m2Vwe4bERm9KZKbDqzyKMHvK99CJWRLPbSV7l5s1pfGdr35nxTUd+07vU4dZaQlBj0VmtgHYBrQBA865q+v13I0W7/ReMPi9W3+PieyE7z5jU2Oc/Wtn89Pnf8rYuP8+k9nSU7fbJ4uWmmhvp2fLJ4l+7yNk3NwS7fzSDU8cfKLkODQ+NU7/t/o57fjTNP1aREREpEZKPopIqDjnPmFmk8DHgT8C3lW0iwETwEecc1c1O76WdOedsGyZ13ihWJk1GKuSr5RMpegdGWH0VatJrYORfT+k+5YdJB9rwx05wsAbO9hzbJa/f/0URydemP7y/I3+vz7+r6xYtoIjk0fmHKJwTceW6TCbTnvft+Fhb/p5MuklY2XJCGosMrM24EbgrXjJzgfMbKdz7tF6HaPRetf0ct3bruOD3/qgb8OYaHuUU487lT9f/+dc84Nranruzkm49t4OevePe8tCRCIwOEjyt0+j/76/hvG5x8sv3XDbnttKri0LXgIysT3B6OZRveghIiIiUoNAk4/VvnJvZucBO4Dfds7tbmKIItKCnHN/Z2bbgfcAbwJOyD30DDAE/JNz7r+Ciq/lFDSGmaPEGow1KaiUjAF9AL9zOZx/M0Nf/CSJZ68nGzEybrLkomsRizCVnSr5WEut6Tg05FWMZrPe9y8a9Rr1DA76T0+XRSugsej1wIhz7kkAM7sNOBdoXvKxDsn3S067hA9/58O+ycfJ7CSJngRPHHyi5tA6jolxySVXw3874DXVSiYhFvNvyNUeJWKR6aUbkmuT9O8q0XQrZyI7QWpPqjVeBBFpEN2jiYhIvQWWfKz2lXsziwPvB+5vfpQi0qpyN/Qfb9bxwjzNcboxjE8CMv2SFaROGGX47i30rOohuTZJvLM+FXzpdkfi+ZtI27i3Ol4ZRyaOsPHXN7Jz386SiYGWkE57icd0emZb/vvqN4W91HOoanLRaPZYhDel+6mCzw8AbyjcwcwuBS4FWLNmTc0HyK/DOnxwePa4kE7DJz8Jn/kMmHnV1PNMvuenXye2J5iYmuDo1NHC+DnlhlO4bP1lJSsRl7ctB4M2a5s7XpSYFp1vyFVq6YbpKeFf+D0mnP9076OTR3n0F6EpMhWpme7RRESkEYKsfKz2lfu/AT4F/GVzwxORVmVma4DfxktpPeCce6rClyz0eOGe5phMesmBIkNrILHxCNnDt5P5wcz6i4MbB+uyplm5JjLFou1RzjjpDG5+x82NX9NxIcm/VMqrePRTzRR2VU0uKs0ei6rlnLsFuAVg/fr1FVL/sw3tH5pTHdi/q5/BX7+a3ndtgcNFHZ9rSb4X/e31JpPsu2IfJ3/25Fm7HZ08ylGOctPumzDM96na29p54n1PcPujt/ONJ74BwNtf/XZOe/lpZc+v0tINvWt6OX/t+Xx5z5dL7nPwyMGyxwgNvRAi/nSPJiIidRdk8rGaV+5PB050zt1pZiX/sS30FX4RCQ8z+1/AJpi+I3Vmdr1zrpEXv8FPc1yIgsYw+aRX+iUrSGw8QroTyFUV5auL5qxpNs8b1OGDwyXXTiuWn1rd8DUdF5r8W8gU9npUTUrLCGgsAngaOLHg89W5bQuWHkuT2J4gPT7zOzo9LvzgckbHvaUVfFVKvpf427vzHy6jzdp8v8Q5xxWvv4KbHrjJtyJ65NAIH/rOh6Yf+97+7/Hh73x4wS+gvCz6srKPrzpm1byfu2XohRApTfdoIiJSdy3bcMbMIsB1wLsr7buQV/hFJDzM7F1AP16V0eN4N/2nAP1m9pBzrnSpysJUvBDPxdcaF9lFycL0HyRIrXic4dveS8/IQZLPrCJ1wkGyh2+fTjwWyrrszJpmC7hBPfHFJ5Z9HGju1Op6JP/KTGEnGvXWmCtloVWT0jICHIsAHgB6zOxVeEnHi/Aa3ixYuWrlrEFqLfQ9XOKLyyTf0wdHSW05i+E3jNFzCJJ7IJ77Gxr+4vVkXu8/xTkzkcEw36nSzjm6ruvyT5QusCnMupetY3nb8llTwfOWty3n1ONOndfztgy9ECILoHs0ERGZj0iAx670yn0cWAd818x+CrwR2Glm65sWoYi0mvcCk8BZzrm1zrlTgbfhtTEJPGvjnLvFObfeObf+uOOOCyaIoSHo6oJNm+Caaxj69PvouraLTXe+j2seuJ5Nh2+n60UD3Lt6smRVYmYiw8ihkdk3qPkb00zG+/z3fg9uuGH2zWuN3nryW9m2YRujm0e9KqV0GgYGYMsW7/0CnttXNcm/SpJJr3uun0jEe7yURjf+kWYKbCxyzk0CVwC7gMeArzjn9tbjuctVK2c6YMepsOUsGDgd0h1FO/gl39Nphm7cQtdn1rDpLWNc0wub3gZdm71lHwB6DhlR6/Q9ZrQ9yuoXrea2PbfxxMEn+LWVv8aFay8k1hErnygdHyd15QXzHkeSa5O0t7X7Ptbe1t5aDbDmox5joSxmukcTEZG6CzL5OP3KvZl14L1yvzP/oHPueefcsc65k5xzJwH3Aeeok5rIkvYbwNedc/fmNzjnvg18HfjNBh63YdMc66ooWZjugMR5R0l3QibrVfBkJjKkx9Pc/ujtRMf9nyY6Dt3R1eVvUCcm4IMf9BKdQ0O+uzz1fPnl737rhN+i7/Q+rzqpKGnKpk1ln3te6pH8y09hj8e9ZAt47/Pby1UL5asm/VSqmpRWE9RYlD/WoHPu1c65X3PO/W29nrdnVQ/R9hK/ow7uOQnfBCIwN/k+NET6pFeQePoa0sumyOSSlZlOSHdCYiMc7oDkQ+NEsv4FUQ7H1ru3sOn/XME1P7iGTf/nCrqufQVD+4fKJ0oZY+RHd817HMk3nol3xKe/H9H2KPGOeGs1wJovvRAi5ekeTURE6i6w5GOpV+7N7BNmdk5QcYlIS3sp3hTHYo8DL2ngccteiDdctRWBRcnC1DqvDMtP25RjqlQBn/OmRZa9QQWv020+4VnchILyiYxoe5TulblkW7kKyxLPPS/1Sv719nrTErdtg61bvfejo5XXSVtI1aS0mqDGooZKrk0SsRK/owbjuWLA4gQisdjs5Hvubzd10uGSY1AWbxp3vD3K4Ms+MCfRF2uPMTExzuHJDBnGvOMyRnriMIkvvo0TX3xi6fFlDLoPsaBxJN8Ze9uGbWx909bZVdphpxdCpAzdo4mISCMEuuajc24QGCza9rES+76lGTGJSEuLAH6Lg01AiZaodeCcmzSz/IV4G/D5ek1zrKiWNReLkoXDK70kgZ8jjLNxL+x8jZcEyHR6N+wRYHA7xP7oQPn1DQuVWK8wuTZJ/665XbZhpsEMULe1ENNjaVJ7UwwfHKZnVQ/JtUninQWNcUp0/fYCqjH5F4vVvj6jT+MfolHv2JWqJqUuKv6OVC+QsajR8hV/hd2uO9s6GZsa890/a5D64Ab6PvTV2b+/ub/pcmNQphNGVgJTU/T+wfsZXfXR6XUdHY7P/PB6Jtyk73cz+8IRbGysZKI0AiQLR+h5rqna8AZY9TCfhmD1HAtlUdI9moiI1FvLNpwRESkhkAXL/S7EG67WpgBFycKeQ15C0e/mP0onZzzjuPnOcVJrvSRA9yHvhj3Wnqt8ufDC0jeohUpM0/NLZPg2mKnDFMCh/UNzjtO/q39219tWSP7lqyZTKe+8uru9G30lHhuuqt+R2izK5gn5ir98IvCRnz3CXf9xl+++mQ4YedNvzv39zf1Nlx2D8tWJAKecQmxwkL7ePtJjabqu62IsO14yjZvpgAM/+s7c8aXwBZTCZSVy40gdk8+tYb4NwVphLBQREZElRclHEQmbK83sSr8HzGzKZ7NzzoVzrKu1IrComiW5B/rf5v/lESC5r53Y+Pjc7rWdkZmEWP4GdWwMxkstEll6ml5xIiPfqXbWmmkL6SCNV82W2J6oruttKyT/5lM1KQtS0+9I9RbtWFRY8Tfw0ADf2/893/UVZy2fUCj3N53ckyk/Bu0Fxo/C0aPTL6iknijdSGb6uGPQPVE0vty7g+677iH50PjsxCNANMrQiY7EdV31TD4Ha6Edq1thLBQREZElIxQXwSIiBWqd0hjaKZA1VwQWVbPEMxkGb19O4ryjZDs7yDBOdNxb03HwdiM25uCYY7xql1KVL/kb1Ftv9RKbfgnICtP0yk1dTI+lSZ16lOE3j9PzMy9hGi88RBVTAMt2vXVZUntSs4+v5N+SU/PvSHWWxFhU9fIJs77IeyEkPu5VISY2lljeofBvPfeCyvCa0o1kpo8LJF/5dqBgfOm5EP66y3eMSncaiV/dSHpiZt3H4uSzcy5cVZH1WK5CY6GIiIg0iZKPIhIazrnAmmQFYj4VgUXVLL3d3Yy+9c2kLlrHyIqCqdXjXvdrYjG4+mo4cKB05UssBpdfDqedVtdperOmwb5+gui4V6k5uB16D1b/3GW73k5kGDmkzq1LXb1/R5bSWFT18gmzvmjmhZDeg1lGr82QOr2Dkfgk3b/I5sagoq/JvaDS8zqvUZXvz8tB5yQM3nEMsQcumdmeX/fwne+E22+HtjY4cmR6jErddBnZ/7jR9/zGp8Y569az+PGzPyZiEY5MHKlvVeR81mSshjpWi4iISIgo+Sgi0qpqaAowZy2zi2eqdmIDA/T9ZBlkfJpGOAfLl8NVV1WOp9Q0PedgYID08B5Sr/glw90r6TlhbcnKofRYmlt/fCubd21mPDuTgch0eO8Tf9LBaNeniL3rkqqSmvmu2n7JihXLVvhPC5UlpdzvSMmpwzKtquUT5nzRzHgRGxmhr7sbfvUr+OAHS3/N6tVlKy2XT8F/DkQ5fsddM2OD37qHU1OwcSOccQYkkwz/8G/IPO6fqBubGuP+0ftnbavDlPzSsVWzJmM1FrhchYiIiEgzKfkoItKqqmwKULGRRj0rZIqn6eVurodOmPCmdz8LmV9BNLLct3IoH+vY1NisxGOhbEc7qd9aTl+V1ZTlkhVHJo9w4otPrPr0ZHGa19ThJaSaRizz6vxcPF7ccEPFL/GttLROIlnH4Akf4PiRj84kHsute7hzJ9x8M8RiZZPP5SxgSn71azLOtzJSHatFREQkRJR8FBFpZRWaAlTVSOPEE6Gjw3+9xjIVMhUTErmb6/RYmsR5kC7oaJvJHoXxo7Mqh/xi9ZOZyLDj0R1cuPbCqtZci3fG2XHhDt72Jf/OFud/5fyFVS9J6M1r6vAS0YAu4L7SY2lSo99g+CzoOeSzvit4yz9QQ6Vlleselks+l7OgZRuqie2UU+ZfGamO1SIiIhIiSj6KiLS6Mk0BKjbS+Non6fvQjaU7VZeokKkqIZG7uU6t85pJlIwhVzlULtZi9/z0Hrqu66o6AbL/+f0lK5uyLsutj9xK57LO8DSTkLqb19ThRa5BXcDnmB5POsfJ9HpNZ6bXd92f26nohZCqKi2rrOr2Sz5XY0FT8svElp7IkBpOMfyv36WnZ8JLxOZjhuq6VYM6VouIiEhoKPkoIhJiFRtp/NN1cHjC/4tjMd8KmaoTErmb6+GVXhfbkjHkKofKxVpsfGqc8anxqhMglb4PH9j1ATraOhpa2SWtb15ThxexhXQBz1dG73l2D7984ZesPGYla182d61X3/EkN14kNsLotbnmM6WmCpebllzDuoeFyecdj+7gnp/ew/hUiRdlchY0Jb9EbENrct2/2+8l8/pJ/0Rstd2qQR2rRUREJBSWTLdGEZHFKL+WmZ+oa6f75yUSj52dXpdrn6l91SQkvIN7N9c9h7xKJt8YCiqHysVayqzjlVHpuSeyE9PJycxEhvS4lxA5PH64pnhEFpP5dgEf2j9E13VdvG/wfWy7fxtf/MkX+cz9n+F933wfXdd1MbR/aHrfsuMJkDq9Y2YKcXHF3tAQdHXBpk1wzTXe+64ub3s6DUePwkSJMc4nmZlPPn/lgq/Q2VbiFRO8cSveEV/YlPxk0ouhQLrDSzymOyETmQS8RGy609t+ONd0S92qRUREZLFR8lFEJMSSa5NEzH8oj4xPkNzr/3VpN8bAM3ey5e4tDDw0QHpspiqp6oRE7uY6uaf0P5MIRnL3C7BlC8ndR4lg1Z7a3OOVUe77UEq1iU2RxarsixclphwXVjIenTo667Gjk0fnJPbLjiedMHL+md7U4eIXQgobtuSrBzMZ7/Ozz4ZXvAK2bp27pEQ0WjqZmZOfhh3viE+f/4plK+hs62Tjr29k24ZtjG4eXVhldD6GeNyLCS/Rmi0xBGaB1NqCc1C3ahEREVlENO1aRCTESjbSmJxiMDVJbHxyztdMT/vruIfMD745Zxpyuc6wsxISuZvreCLB4I5xEuePkTXIdHjdriMWYfCfp4g9tRUyGeLRKIMnZklsXEE2YtOxTrkpcMxJZMw53jy+DxNTEyW7ai+omYTIIjCfLuDVrN1aOGW74nhyxnn+ScJyDVteeMF/e3s7fOpTcMklFdc9bMoaoEVrMg4f+wiZw3f57prphJGVuU/UrVpEREQWGSUfRURaWHHH6UR3gsGRwVmNU3xvolN7iD35mbnPVzDtD+fNlS5ezzG5Nkn/XR/wjWdOQqK3F3bsoPeccxj9TBup10wx8rJldD/vSD7miD1fkCTIZOh9HEavj5H6xtWMZA7QvbKbRE+CU244xTf5OJmdJNGTqOp75fd9eGHyBbZ+e2vlRKrIEjSfLuDVrN1amNifT4LTO1CZZjKldHTA8uVVN1xpyhqgBWsy9jw0QPSu7/mPR2PQnemAeKe6VYuIiMiio+SjiEiLKu44vbxtOX869acsX7aco5NH51QszrqJfmjAt9lBVZ2pj5zC4D+Nk7jA2zfT6d0YR4DB37p6dkIinYbzz4exMWJA38MAk7k3f7ExR9+jy6Hvqult+QTIxNTErCSkmXHKDadU3RymOJmQHkvz4e982HffBTWTEFkkaq0ALFfJmFeY2J9PgtM7UJlmMqW0+FqJZROxHR0k33MdvKty1aaIiIhI2Cj5KCLSgvw6xOaTckcnvfe+Hajzkknon3uTW7Ez9c8eheQmeg+PM3qttwbZyEroPgTJvRD731vgmT+euTkuNzWyFJ8EQe+aXvZdsY+TP3vyrO1HJ49ylKNVd70uNu/Eh8gSUksFYLkEWl5xYn9eU5xLjGFl1WutxHIdtheg4ni0kDUmRURERFqYko8iIi2omnXV8grXV5uWb3aQSMDY2HRThnxnar8EZLQ9SvfwL6b3jY3nKxkL2IR3U56bRjivqZElEgR3Dt9Jm7VVf45VasrabiJLRGECrbhSefmy5bRH2n0T+zVPcS4cw7JZb5yJRsHM+/zIkblfU4+1EoeG5h6zv9+LpbgpzjxoPBIREZGlSMlHEZEWVM26anklG6fkmx1ccAHc5TU5SO6B/rf5P0/EIiRHV87tHltobGx21eJ8pkaaeQ0jtmyZVVVUdZfteWjK2m4iS0RhAu3R5x7l4AsHWbViFacee2p9E2lFDVvo7vbGi0cemZsgjEQWvlZiYYftvPzYlkh4sdRhSrTGIxEREVlqAk0+mtkGYBvQBgw4564uerwfeC/e4mHPAe9xzv1X0wMVEWmyatZVyyvbOCUWg/POg+99z+s4PQ6D23Pdrsmt52idRNo7vGqlbz1OOtZO6tUTDK/0KiWTeyCez0d2ds6uWiw3NfKYY6CtDZybSRA4B1NTsHXrnKqiqrtsi0jgmpZAK2jYMq1UUnKhicFyy0hks7OrvltNg6aKy9KkezQREam3wJKPZtYG3Ai8FTgAPGBmO51zjxbs9jCw3jl3xMwuA64B1B1ARBa9atZVy6vYOCWXIEx3eA1nhlfC330bDDhwbDvd/++1JNb9IXcO38nnYt/iXzdNEMnCkQ5vinb/27yEZe9+5k5rLDU1Ml+F9Ju/OZMgWL3aSzoePjzz9QVVRcmf7qPf5tEVV0SWHr+k5EKVW0aixmY26bE0qb0phg8O07Oqh+TaJPHOBiUDGzxVXJYW3aOJiEgjBFn5+HpgxDn3JICZ3QacC0z/Y3PO3Vuw/33AxU2NUEQkIH6NCZa3Lefo1NFZ3a6rapwSjzP0L1eT+MHlZA0yHRAdh4iDwd/9DHT9BqfccApT2SmOTB6Z9Z8hvzZkYiOMXguxqSnS/+8WUicdZrh7JT0nrCX520ni5aqQ8gmCgQGv8tFPNkv8jkE1hxGR4JRbRqKGZjZD+4fmjGP9u/oZ3DhIb72byjRpqrgsKbpHExGRugsy+dgFPFXw+QHgDWX27wO+6feAmV0KXAqwZs2aesUnIhIov8YEb3/127nziTtralSQHkuT+PetpAuazGQ6vPe//+9b4CdweOKw/xfnZPE6X59ycJzEMTeRfRYyv4JoZPnMTXWlKqQqqop6+/rUjEGkBDP7NPBOYBz4D+BPnHO/CjSoxaTcMhJVNrNJj6VJbE+QHp9JBuaXkkhsTzC6ebS+41m1U8U1LVuqp3s0ERGpu1A0nDGzi4H1wJv9HnfO3QLcArB+/foSZTUiIuHjt65areusleucPTE1gaPysJnphEePhQ9sYHYSM3sUxo9Wd1NdZVWRmjGIlHQ38CHn3KSZfQr4ELAl4JiaJ59A27sXDh2Cl74U1q2rXyKt0jISVVQQlhtvsy5Lak+qvuNbNVPFNS1bGkT3aCIiUq0gk49PAycWfL46t20WMzsL+AjwZufcWJNiExFZNMp1kR6bqm5YXTEOv4h6FZB+qrqprkNVkchS5pz7VsGn9wHnBxVL0+UTaBMTcPTozPbly+ubSFtgM5ty421mIsPIvTug58L6VR1WelFn9WpNy5Za6R5NRETqLhLgsR8AeszsVWbWAVwE7CzcwcxeB/w9cI5z7tkAYhQRCb18F2k/nZPQMVH5OY60wwttM2tAFstMZBg5VKEZQ76qKB73borBe5/frhtgkVq8hzJTHc1st5ntfu6555ocVgMUrmtYmHgE7/P844fLLx9RtXwzm6uu8t7XMDaVG2+jY9C94x7o6vKSqfWQTHov3viJRLx1ditNyxaZTfdoIiJSd4ElH51zk8AVwC7gMeArzrm9ZvYJMzsnt9ungRjwVTN7xMx2lng6EREpIbk2ScT8h/v2LHSUKmcsZLDzNV6jGj/R9ijdK6toxpCvKtq2zet8vW2b97mm/okAYGbfNrM9Pm/nFuzzEWAS2O73HM65W5xz651z64877rhmhd445dY1zGuRRFq58TYCJB8ar2+ytNKLOk89VbcO3rI06B5NREQaIdA1H51zg8Bg0baPFXx8VtODEhFZZPw6Z0fbo0TGJxj8kpdNTGz0plRnOgEH2NznacvCVKkCG4uQXFfltOl8VVHYqYGDNEClax8zezfwDuBM50q1j19kyq1rmNciibRZ4+34OBnGiI55icfB7RDLv4BT2AxmocpNFX/88bp08JalRfdoIiJSb6FoOCMiEpQwd5dNj6VJ7U0xfHCYnlU97LtiH4PDgzNdpHe/QOzgVshkGL3W62b9D6fD/Sf6P9+RTtj4Y68CMp+ojI5DJBpjcOPg0upIrQYOEgAz2wD8Fd4aa0eCjqdpyq1rmNdCibTeNb2Mbh4ldeUFjPzoLroPQXJvQeIR6p8sLfWijtbaFRERkRag5KOISHmh7C47tH9oTqVj/65+BjcOzjSF6UnDBz8MeDfFfQ+DM9jzMv+1HaPjcMaBZdx85ySp05Yxssro/pN+kv/jo0sr8Vi4/lyeGjhIc9wAdAJ3mxnAfc65Pws2pCYol0DLa7FEWqwjRt/J58Fnvxds1WEdOniLiIiILJSSjyIiZYSxu2x6LE1ie4L0+ExyLN99NbE9wejmUS9Z6HNTmnxyBf34F1RFojGSf3o1sScP0FdjB9hFpdz6c/WcSilSxDnXGqV9QbjsMrj+eq+ByuTkzPbly6G9vTUTaa1SdbjADt4iIiIiC6Xko4hI9d4DlOxoYGaXApcCrFmzplkxzZHamyLr/JNjWZcltSc1U/1YdFMa7+5m8E0nkrjj/NnrQ1rEm1q9RlOKy64/1yLrzoksGoVLHExMQGcnmMGZZ8Kpp3pvrZpIa6Wqw8Wy1q6IiIiEkpKPIrLkmdm3geN9HvqIc+7ruX3KdpcFr8MscAvA+vXrA2sEMXxweLrSsVhmIsPIoaLkWNFNaS9465XtSc2sD7kuubSmVpdTbv25Flp3TiT0/JY4GBvz3n//+/DVr7Zm0rGQqg5FRERElHwUEVls3WV7VvUQbY/6JiCj7VG6V1ZOjsU6YjPVkWHVqG7UrTKVUmSxWyxLHKjqUERERJY4JR9FRMoIY3fZ5Nok/bv8k2MRi5BcV0VyrFGJu2ZpZDfqVppKKbKYNXqJg7CPcyIiIiIhoeSjiEh5oesuG++MM7hxcE636+l1GytNn25k4q4ZmtGNWlMpRRqvkUschH2cExEREQkRJR9FRMoIa3fZ3jW981u3sQGJu/RYmtTeFMMHh+lZ1UNybZJ4ZwOri5o1VVNTKUUaq1FLHDTjBQoRERERmabko4jIIjWvdRvLJe5eeAEuvxxuuKHqqYlD+4fmVGD27+pncOMgvY3qnK1u1CKh5PtCRSOWOFgsa0mKiIiIhISSjyIiMqNc4m5yEr78ZbjjjqqmJqbH0iS2J0iPz1QX5ZvgJLYnGN082pgO2upGLRI6ZV+oqPcSB3qBQkRERKSpIkEHICIiLSSfuCtlYmJmyuLhw2WfKrU3Rdb5VxdlXZbUntRCIi0tmfQqo/yoG7VIyyl8oSL/AkVmIkN63Nt+uAOvEvGqq7z3C50SXW6c0wsUIiIiInWn5KOIiMwol7grlJ+aWMbwweHpREKxzESGkUMNqi7Kd6OOx2cSDNHozHat5SbSUpr+QoVeoBARERFpKk27FhGRGfkEXSLhrfE4Oem/XxVTE3tW9RBtj/omIKPtUbpXNrC6SN2oRUKj6S9UFI5z9VxLUkRERER8KfkoIiKz5RN3l1/urfE4MTF3nyqmJibXJunf5d+pNmIRkusaXF2kbtQioRDICxV6gUJERESkaTTtWkRE5orFvK7Wy5f7P17F1MR4Z5zBjYPEO+JE273pz9H2KPEOb3tDms2ISOgk1yaJmP8laUNfqMi/QFGvtSRFRERExFegyUcz22Bm+8xsxMy2+jzeaWap3OP3m9lJAYQpIrI01WHtxN41vYxuHmXbhm1sfdNWtm3YxujmUXrXlO+ULSJLh16oEGktukcTEZF6C2zatZm1ATcCbwUOAA+Y2U7n3KMFu/UBv3TOdZvZRcCnAK0CLiLSLHWYmhjriNF3uqY/i0hp+RcqUntSjBwaoXtlN8l1SSUeRZpM92giItIIQa75+HpgxDn3JICZ3QacCxT+YzsXuDL38Q7gBjMz55xrZqAiIkua1k4UkSbQCxUiLUH3aCIiUndBJh+7gKcKPj8AvKHUPs65STN7HlgF/KJwJzO7FLg09+mYme1pSMTNcSxF5xdCOofghT1+gFOCDmAhHnzwwV+Y2X814KnD8LNVjPWhGOtjoTG+sl6BBEFjkWKsA8VYH2EZi3SPVloYfs/KCXv8EP5zCHv8EPJ7NAnOouh27Zy7BbgFwMx2O+fWBxzSvIU9ftA5tIKwxw/eOQQdw0I4545rxPOG4WerGOtDMdZHGGJsJI1FinGhFGN9hCHGeltM92gQ/nMIe/wQ/nMIe/wQ/ns0CU6QDWeeBk4s+Hx1bpvvPma2DHgxcLAp0YmIiIiIiCwtukcTEZG6CzL5+ADQY2avMrMO4CJgZ9E+O4FLch+fD9yjtUREREREREQaQvdoIiJSd4FNu86tD3IFsAtoAz7vnNtrZp8AdjvndgL/CPyzmY0Ah/D++VVyS8OCbo6wxw86h1YQ9vhhcZxDI4Th+6IY60Mx1kcYYgyjMHxfFWN9KMb6CEOMukcrL+znEPb4IfznEPb4YXGcgwTA9CKViIiIiIiIiIiINEKQ065FRERERERERERkEVPyUURERERERERERBoitMlHM9tgZvvMbMTMtvo83mlmqdzj95vZSQGEWVIV8feb2aNm9hMz+46ZvTKIOMupdA4F+51nZs7M1jczvkqqid/MLsz9HPaa2b80O8ZKqvg9WmNm95rZw7nfpUQQcZZiZp83s2fNbE+Jx83MPps7v5+Y2enNjrEVmdmnzezx3PfkDjN7SdAxQfVjQlDM7MTc30P+b/r9QcdUipm15f5uvxF0LH7M7CVmtiP3e/iYmf1O0DEVM7MP5H7Oe8zsy2a2POiYFhuNRfOjsah+NBa1nrDfo0H479PCfo8G4b9P0z2aiA/nXOje8BY//g/gZKAD+DFwatE+fw7cnPv4IiAVdNw1xn8GsCL38WWtFH+155DbLw78G3AfsD7ouGv8GfQADwMvzX3+sqDjnsc53AJclvv4VOCnQcddFN9/B04H9pR4PAF8EzDgjcD9QcfcCm/A2cCy3MefAj7VAjFVNSYEHOMJwOm5j+PAE60WY0Gs/cC/AN8IOpYS8d0KvDf3cQfwkqBjKoqvC/hP4Jjc518B3h10XIvtTWPRvGPUWFS/+DQWtdBbldemLXuPVsM5tOx9WrVjIC16j1bDz6Bl79OqjF/3aHpbcm9hrXx8PTDinHvSOTcO3AacW7TPuXgXJAA7gDPNzJoYYzkV43fO3eucO5L79D5gdZNjrKSanwHA3+DdkBxtZnBVqCb+PwVudM79EsA592yTY6ykmnNwwItyH78YGG1ifBU55/4Nr0tiKecCX3Se+4CXmNkJzYmudTnnvuWcm8x92irjQ7VjQmCcc8845x7KfZwGHsO7MWwpZrYaeDswEHQsfszsxXgXpf8I4Jwbd879KtCg/C0DjjGzZcAKWmz8Www0Fs2PxqL60FjUksJ+jwbhv08L+z0ahP8+TfdoIj7CmnzsAp4q+PwAcy/apvfJXRg/D6xqSnSVVRN/oT68VxZaScVzyJVfn+icu7OZgVWpmp/Bq4FXm9n3zew+M9vQtOiqU805XAlcbGYHgEHgfc0JrW5q/VtZit5Da4wPofpZ5aZ5vQ64P+BQ/HwG+CsgG3AcpbwKeA74p9x0oQEziwYdVCHn3NPA/wL2A88AzzvnvhVsVIuexqJ50Fi0IBqLWk/Y79Eg/PdpYb9Hg/Dfp+keTcRHWJOPS4aZXQysBz4ddCy1MLMIcB2wOehYFmAZXkn/W4B3Af/QKutZ1eBdwBecc6vxyuP/OfezkRZnZt/OrQ9V/HZuwT4fASaB7cFFGj5mFgNuBzY55/5v0PEUMrN3AM865x4MOpYyluFNxfmcc+51QAZoqXX1zOyleK/Kvwp4BRDN/T+VGmksahyNRQumsUgCFcb7tEVyjwbhv0/TPZosOcuCDmCengZOLPh8dW6b3z4HctMcXgwcbE54FVUTP2Z2FvAR4M3OubEmxVatSucQB9YB383NpDge2Glm5zjndjctytKq+RkcwFu/YgL4TzN7Au+f3APNCbGias6hD9gA4Jz7YW6R82OBVpqaUE5VfyuLkXPurHKPm9m7gXcAZzrnXFOCKi8UPysza8e72d/unPvXoOPx8SbgnNzC48uBF5nZl5xzrXSzegA44JzLV2rtoMVu+IGzgP90zj0HYGb/Cvwu8KVAowohjUWNobGoLjQWtZ6w36NB+O/Twn6PBuG/T9M9moiPsGbXHwB6zOxVZtaBt1jxzqJ9dgKX5D4+H7inRS6KoYr4zex1wN8D57TYGhZ5Zc/BOfe8c+5Y59xJzrmT8NZDaaV/atX8Dn0N79U0zOxYvPL+J5sYYyXVnMN+4EwAM3st3g3Ec02NcmF2An+c66j2RrzpSs8EHVTQclNL/grvb+pIpf2bpJrfx0Dl1pT6R+Ax59x1Qcfjxzn3Iefc6ty4eRHe/65WutnHOfcz4CkzOyW36Uzg0QBD8rMfeKOZrcj93M/EW1dP6khj0fxoLKoPjUUtKez3aBD++7Sw36NB+O/TdI8m4iOUlY/OuUkzuwLYhddN6vPOub1m9glgt3NuJ95F3T+b2QjeYqkXBRfxbFXG/2kgBnw196rUfufcOYEFXaTKc2hZVca/CzjbzB4FpoC/dM61zCuzVZ7DZrxpCB/AW9j43a10gWdmX8a7cDjWvDVPPg60AzjnbsZbAyUBjABHgD8JJtKWcwPQCdydGx/uc879WZABlfp9DDImH28C/ifw72b2SG7bh51zg8GFFFrvA7bnLqqfpMX+Np1z95vZDuAhvOnAD+N1lpT60lg0PxqL6kdjUQsJ+z0ahP8+Lez3aBD++zTdo4n4sxb6HRcREREREREREZFFJKzTrkVERERERERERKTFKfkoIiIiIiIiIiIiDaHko4iIiIiIiIiIiDSEko8iIiIiIiIiIiLSEEo+ioiIiIiIiIiISEMo+SgiIiIiIiIiIiINEVjy0cw+b2bPmtmeEo+bmX3WzEbM7CdmdnqzYxSRxU9j0dJkZq7obcrMfmFm95jZH5X5uteY2f82sz1m9ryZjZvZqJndaWZ9ZtZZsO8qM3uvmd2R+/15Ifc1Q7l99QKgyBKnsUhaka6NRESk3sw5F8yBzf47cBj4onNunc/jCeB9QAJ4A7DNOfeG5kYpIoudxqKlyczy//z+Ove+HXgNcC7QBlzvnOsv+pqPAR/He+Huh8BuvN+dlwNvAU4GHnTOrc/t/2fA54BngHuB/bl9/xB4MXA7cIEL6h+xiAROY5G0Il0biYhIvQWWfAQws5OAb5T4p/b3wHedc1/Ofb4PeItz7pnmRikii53GoqUnf8PvnLOi7WcCd+c+Pdk599Pc9g8Dfws8hXeTfr/Pc74D2OycOyP3+e8BUeBO51y2YL/jgR8BJwLnO+dur+/ZiUhYaCySVqVrIxERqadWTj5+A7jaOTeU+/w7wBbn3G6ffS8FLgWIRqO/9ZrXvKahcYtI4z344IO/cM4d14xjaSwSkVKaORY1wrHHHutOOumkoMMQkQVq9lhUj2sjXReJLD5hvy6S4CwLOoB6cM7dAtwCsH79erd795ycgIiEjJn9V9Ax1EpjkcjiE8axqNBJJ52ExiKR8AvjWKTrIpHFJ4xjkbSGVk4+Po03DSRvdW6biEgzaSwSEVlE0mNpUntTDB8cpmdVD8m1SeKd8aDDEgkTXRuJiEhNWjn5uBO4wsxuw1vI+HmtIyIiAdBYJCKySAztHyKxPUHWZclMZIi2R+nf1c/gxkF61/QGHZ5IWOjaSEREahJY8tHMvozXke9YMzuA17WvHcA5dzMwiNdBbQQ4AvxJMJGKyGKmsUhEZGlIj6VJbE+QHk9Pb8tMZABIbE8wunmUWEcsqPBEWoaujUREpN4CSz46595V4XEHXN6kcERkidJYJCKyNKT2psjONHueJeuypPak6Du9r8lRibQeXRuJiEi9RYIOQERERESk0YYPDk9XOhbLTGQYOTTS5IhERERElgYlH0VERERk0etZ1UO0Per7WLQ9SvfK7iZHJCIiIrI0KPkoIiIiIotecm2SiPlf+kYsQnJdsskRiYiIiCwNSj6KiIiIyKIX74wzuHGQeEd8ugIy2h4l3uFtV7MZERERkcYIrOGMiIiIiNTOzNqA3cDTzrl3BB1PmPSu6WV08yipPSlGDo3QvbKb5LqkEo8iIiIiDaTko4iIiEi4vB94DHhR0IGEUawjpq7WIiIiIk2kadciIiIiIWFmq4G3AwNBxyIiIiIiUg0lH0VERETC4zPAXwHZUjuY2aVmttvMdj/33HNNC0xERERExI+SjyIiIiIhYGbvAJ51zj1Ybj/n3C3OufXOufXHHXdck6ITEREREfGn5KOIiIhIOLwJOMfMfgrcBvyemX0p2JBERERERMpT8lFEREQkBJxzH3LOrXbOnQRcBNzjnLs44LBERERERMpS8lFEREREREREREQaYlnQAYiIiIhIbZxz3wW+G3AY0kjpNKRSMDwMPT2QTEI8HnRUIiIiIjVT8lFEREREpJUMDUEiAdksZDIQjUJ/PwwOQm9v0NGJiIiI1ETTrkVEREREWkU67SUe02kv8Qje+/z2w4eDjU9ERESkRko+ioiIiIi0ilTKq3j0k816j4uIiIiESKDJRzPbYGb7zGzEzLb6PL7GzO41s4fN7CdmlggiThFZ3DQWiYhIyxgenql4LJbJwMhIc+ORJUfXRSIiUm+BJR/NrA24Efh94FTgXWZ2atFuHwW+4px7HXARcFNzoxSRxU5jkYiItJSeHm+NRz/RKHR3NzceWVJ0XSQiIo0QZOXj64ER59yTzrlx4Dbg3KJ9HPCi3McvBkabGJ+ILA0ai0REpHUkkxApcYkeiXiPizSOrotERKTugkw+dgFPFXx+ILet0JXAxWZ2ABgE3uf3RGZ2qZntNrPdzz33XCNiFZHFS2ORiIi0jnjc62odj89UQEajM9tjsWDjk8VO10UiIlJ3rd5w5l3AF5xzq4EE8M9mNidm59wtzrn1zrn1xx13XNODFJFFT2ORiIg0T28vjI7Ctm2wdav3fnTU2y4SPF0XiYhITZYFeOyngRMLPl+d21aoD9gA4Jz7oZktB44Fnm1KhCKyFGgsEhGR1hOLQV9f0FHI0qPrIhERqbsgKx8fAHrM7FVm1oG3WPHOon32A2cCmNlrgeWAavZFpJ40FomIiIh4dF0kIiJ1F1jlo3Nu0syuAHYBbcDnnXN7zewTwG7n3E5gM/APZvYBvIWN3+2cc0HFLCKLj8YiEREJtXQaUikYHvY6ZSeT3vqQIvOg6yIREWmEIKdd45wbxFukuHDbxwo+fhR4U7PjEpGlRWORiIiE0tAQJBKQzUIm4zWm6e/3GtNofUiZJ10XiYhIvbV6wxkRERERESmWTnuJx3TaSzyC9z6//fDhYOMTERERyVHyUUREREQkbFIpr+LRTzbrPS4iIiLSApR8FBEREREJm+HhmYrHYpkMjIw0Nx4RERGREpR8FBEREREJm54eb41HP9EodHc3Nx4RERGREpR8FBEREREJm2QSIiUu5SMR73ERERGRFqDko4iIiIhI2MTjXlfreHymAjIandkeiwUbn4iIiEjOsqADEBERERGReejthdFRr7nMyIg31TqZVOJRREREWoqSjyIiIiIiYRWLQV9f0FGIiIiIlKTko4iIiIhIgfRYmtTeFMMHh+lZ1UNybZJ4ZzzosFo2LhEREZFylHwUEREREckZ2j9EYnuCrMuSmcgQbY/Sv6ufwY2D9K7pVVwiIiIiNVLDGREREZGQMLMTzexeM3vUzPaa2fuDjmkxSY+lSWxPkB5Pk5nIAJCZyJAe97YfHj+suERERERqpOSjiIiISHhMApudc6cCbwQuN7NTA45p0UjtTZF1Wd/Hsi5Lak9q7gPpNAwMwJYt3vt0ujXiEhEREWkRmnYtIiIiEhLOuWeAZ3Ifp83sMaALeDTQwBaJ4YPD05WFxTITGUYOjczeODQEiQRks5DJQDQK/f0wOOh1og4qLqlOOu11Ch8ehp4er1N4XGtoioiI1JuSjyIiIiIhZGYnAa8D7i/afilwKcCaNWuaH1iI9azqIdoe9U30RdujdK/sntmQTnuJx8JKx0zu6xIJGB31OlE3Oy6pTpMSxyIiIqJp1yIiIiKhY2Yx4HZgk3Pu/xY+5py7xTm33jm3/rjjjgsmwJBKrk0SMf/L44hFSK5LzmxIpbzElZ9s1ns8iLikssLEcT5hnMnMbD+sNTRFRETqSclHERERkRAxs3a8xON259y/Bh1PGKXH0gw8NMCWu7cw8NAA6TGvejHeGWdw4yDxjjjR9ijgVRbGO7ztsY6CSsbh4ZnEVbFMBkbqNxW6priksiYmjkVERETTrkVERERCw8wM+EfgMefcdUHHE0ZD+4dIbE+QdVkyExmi7VH6d/UzuHGQ3jW99K7pZXTzKKk9KUYOjdC9spvkuuTcBF9PjzdV1y8BGY1C99yp0OmxNKm9KYYPDtOzqofk2iTxzurWGKw6LqmsiYljERERCTj5aGYbgG1AGzDgnLvaZ58LgSsBB/zYOfdHTQ1SRBY9jUUiEiJvAv4n8O9m9khu24edc4PBhRQe6bE0ie0J0uMz6zTm11FMbE8wunmUWEeMWEeMvtP7yj9ZMumtEegnEvEeL1Ap6VmNquJaJBaSqK1oHonjpUTXRSIiUm+BJR/NrA24EXgrcAB4wMx2OuceLdinB/gQ8Cbn3C/N7GXBRCsii5XGIhFpBDO7Dvihc+6r9Xxe59wQYPV8zqUktTdF1vlPt826LKk9qeqTe/G415ykuGlJJOJtL2g2U23SUzz1SNSWVWPieCnRdZGIiDRCkGs+vh4Ycc496ZwbB24Dzi3a50+BG51zvwRwzj3b5BhFZPHTWCQijbAJ7+ZdWsjwwWHfjtHgJQNHDtU43ba31+tqvW0bbN3qvR8dndMtuZqkZ2DSaRgYgC1bvPeF3buDCKcgUZv/WWUmMqTHve2Hx+vQDCafOI7HvYQxeO/z2+vUpTykdF0kIiJ1F+S06y7gqYLPDwBvKNrn1QBm9n28sv8rnXN3FT+RmV0KXAqwZs2ahgQrIouWxiIRqYmZvafKXU8p3Nc59/kGhSRV6lnVQ7Q96puAjLZH6V45j+m2sRj0la+WnE/Ss6HTjvOGhuZWbvb3ewm43jpUGM5DXatTy8knjlMpb43H7m6v4nFpJx5B10UiItIArd5wZhnQA7wFWA38m5n9unPuV4U7OeduAW4BWL9+vWtyjCKy+GksEpFCA3jrnJXjgN7cm+U+V/KxWDrtJX+Gh711+JJJr/qsQZJrk/Tv8p9uG7EIyXVVTretMe5ak54Nn3acP4dEYnalY34NxETCS8wFkIire3VqOVUkjsWXrotERKQmQSYfnwZOLPh8dW5boQPA/c65CeA/zewJvH90DzQnRBFZAjQWich8HAZuBo74PGbAx4CHgP/TzKBCZaFVd/NIXMY74wxuHJyT2ItYhMGNg9WtuziPuGtJejZtfchUyjsHP9ms93gAibmGVKdKLXRdJCIidRfkmo8PAD1m9ioz6wAuAnYW7fM1vFfUMLNj8Ur8n2xijCKy+GksEpFa/TGQBf4Q+I5z7q+L3q7M7fdQ4fbAoq1BeizNwEMDbLl7CwMPDZAea9D6f4VVd/lqu0xmZvvhCuv6DQ1BVxds2gTXXAPvfz8cdxxs3Fhx3cLeNb3su2If5732PN7Q9QbOe+157LtiX3UVhfOMO5/0jHfEibZ7awxG26PEO+Jzkp5NWx9yeNi/2zN420fqWGFYg+TaJBHzv0WpqTpV5kvXRSIiUneBVT465ybN7ApgF95aIZ93zu01s08Au51zO3OPnW1mjwJTwF865w4GFbOILD4ai0SkVs65L5nZ/wf8E/BdM/sM8BHn3FiwkS1MU6b65i2k6s5vuvCRXAHqv/wLfO1r8L73wR/+IZxxxpyKyOLz3PPsHu54/I7qznMBcfeu6WV08yipPSlGDo3QvbKb5LrknCrGpk077unxqjb9EpDRqLcGYgDqUp0q86brIhERaYRA13x0zg0Cg0XbPlbwsQP6c28iIg2hsUhEauWcewo4y8zeD1wFJMzs3c65HwUc2rw0bapv3kKq7solAGFuIrJgSvSCz3OB1YKxjljFZilNm3acTHrfGz+RiPd4QKpN1Epj6LpIRETqLchp1yIiIiKh5pzbBvwW3tqP3zezq8ysPeCwata0qb55+ao7P5Wq7solAIsdOTJrSvSCzzMXd7oDBk6HLWd579MdVcRdpXLTjg2r37TjeNxLysbjMz+LaHRm+3ybzaTT3tT3LVsqToEv93WxMUff6X1cddZV9J3ep8SjiIhIiLV6t2sRERGRluace8zM3gB8HPgQ8E4qd8NuKU3tMJxOw9GjMDHh/3ilqrty04VLyU2JHl6zwPNMJhm69i9I/IG36GemE6Jj0P82GLwjS28dqgXz047P/uezeWHyhdmnQZZHfvZI/abB9/Z6Xa1TKa9qs7vb+97PN/E43yZCC20+JCIiIi1NyUcRERGRBXLOTQEfM7M7gS/idbwOjaZN9S1MMo2PFx0o6iUeK1XdlZsuXEpuSnTP68qd5wq6947C3VtKds9Od0Bio5GeKnjqTu99YqMx2gG1pu3SY2lSe1MMHxymZ1UPybVJTnv5abRF2ubse2TiSP2nwcdi9elq7bcWZz5BnEh4SU6/n+t8v05ERERCQ9OuRUREROrEOXc/cCrwUuAvAg6nak3pMOzXKTqvvR0+9Skv0VSp0q1wuvCKFdUdOzcluux5Hj5C8sodXvfsTZu8btpDQ7P2Se1NkY3455WzEat5evrQ/iG6ruti012buOYH17Dprk10XdfFJ7/3Sbxl9XyO04hp8PVQTTOeen6diIiIhIaSjyIiIiJ15Jybcs4975w7GnQs1cpP9Y13xIm2e+v/RdujxDvi9eswXC7J1NEBy5dXX+GWny782c/Cxo3e15ZLROamcsc74+y4YAfLly2nPeItzbli2QriYzC4HWK/yjWryWRmrRWZV8/p6YXNb/LPmZnIkB5Pc/0Pr2/eNPh6mW8zngU28REREZHWp2nXIiIiIjUwsw5gCEgDG5xzvosX5vb7JhAF/lup/VpFwzsMV0gypUf2knpoYNb043hn3H9/mJku3NcHN9/sJTfvvRd27IC2Nq/ZTDQKZnDZZfA3f8PQGjj/+ZuIEOFo9ijLbBlTk+Pc8fVOevePzT1GvvIuNy25ntPTyzW/MTM62zoZm5obU12nwddTubU4yzXjme/XiYiISGgo+SgiIiJSm4vxOly/s1xC0Tk3bmafBgaBjcAXmhPe/MU6YvSdXof1//yUSTINvXo5iWM+R/auNjITGaLtUfp39TO4cbC65ip+iciREXAObrwRbryR9ESGxGZId8582aSbZBI4/9xJRkcgVrQMZXHlXXJtkv5d/utN1jo9vVwV5fjUOB3MXfNxPsdpmnJrcZZrIjTfrxMREZHQ0LRrERERkdr8IfCkc26w0o7OubuAYeCChkfV6pJJL5lUJN0Bv3/eUdLu6Jzpx4ntCQ6PH57zNWXlE5Ef/jDcdJM3bTqTIbXO61DtJ2uQWuvzQFHlXT2np+erKP1Ex+AD9xnxMYhGlnvb6CTuOhh80WXExlqwmXrhWpzR3HlFozPbS02pn+/XiYiISGio8lFERESkNq/Dq2as1r8BiQbFEh75ZFK+23UmA9Eon3zzOIc7/QtIp7JTpPakSldjptNelePw8NwO1UVrTA6vnOlMXSzTASMrfR7wqbyr1/T0slWUwEfvmeSj90DqdVOMvLSd7l85kg+NE2u/ET70Oe97WdCcx69rdtlp642QX4szX3na3e19/yolEOf7dSIiIhIKSj6KiIiI1OZY4Oc17P9zYFWDYgmXoiRT+uTVfOZnHyhZknhk8gj3/vRe/+Tj0NCcRCb9/TNJuaI1JnsOeRWFfgnIaGQ53UccRJfNPFckUrLyrnB6enoszW17bqs56ZevokxsT5AdHyfDGNExL/E4uH1mCnjf/UWJ2fHcOSUS3vcyFmNo/5D3PC47v2nr9ZSvPG3W14mIiEjLU/JRREREpDYvALWUZMWA0HS+briCJNOtP7qB7DPlpxDf/tjt3Dx+8+zKwnwn6nR6ZlumKClXtMZkcg/0v83/GJFl7SS/8QTccWdNlXcLTfpNV1FeeQEjP7qL7kOQ3Ouz9qSfXDOc9MUXTnfNnv5W5KavJ7YnGN08Wr+mQSIiIiLzoDUfRURERGrzFLC+hv3XA/sbFEtoDe0fon9XP5Nusux+bdZGak9q9saiKdXpDhg4HbacBQNrx0h/+dZZa0ymOyC1Dt65D5ZPwIpccm/Weo0rj/eSoldd5b2vkHhMj6Wnk34LWasy1hGj7+TzuOq+KH0PV5l4hOlmOOW6Zmdddu73TkRERKTJVPkoIiIiUpvvAn9uZuudc7vL7WhmvwX8LvC/mxFYWOQTdxPZks3Cp2UmMowcGpm9sWBK9dAaSGz0Zm5nOiE6Nk7/0/0M/vI0egcH+dZlb+Wcc48yZTC5DI4Zh6nODjauu4AzTjpjXus1AlUl/aruHF6u43MpuWY4wwefKNk12/d7JyIiItJkqnwUERERqc0NgAO+amavLbWTmb0G+CowBdzUpNhCoVzirli0PUr3yu7ZG3NTqtMdXuIx3TmzlmOmE9I2TmJ7gq+vOsjbzj/KWLuXeAR4oQPG3Dg79+2cd+IRYPjgcP2SfqU6PsdisGKF/9fkmuGU7Zrt970TERERaTIlH0VERERq4JzbB3wCeCXwsJl9yczeY2Zn597+xMy+BDwMnAT8de5rJKdc4q5YxCIk183uOJ3+gwQDp01ywQUw3ub/dVNuivN3nF/yefOdtOer7km/fDOebdtg61bv/TPPwK5dc5OS+WRlLEZybZKI+V/S+33vRERERJpN065FREREauSc+4SZTQIfB/4IeFfRLgZMAB9xzl1Vz2Ob2QZgG9AGDDjnrq7n8zdDPnFXLgEZbY8SsYi3HmNBdeJ0k5cNRqZM8eSRiSMYVvrxySMLmpKcXJukf5f/VOl5J/38Oj4XdQgvboYzq2t2QeMbv+9dI6TH0qT2pmru9i0iIiJLh5KPIiIiIvPgnPs7M9sOvAd4E3BC7qFngCHgn5xz/1XPY5pZG3Aj8FbgAPCAme10zj1az+M0WrnEXWdbJ5e//nJOPfbUOdOiC5u8VNIeaS+7puQyW7agKclNTfr5JSULTHfN3pNi5NAI3Su7FzSlvFoL7fYtIiIiS0OgycdqX7k3s/OAHcBvV1rYXUSkVhqLRGS+csnFjzfxkK8HRpxzTwKY2W3AuUCoko+VEnelEle1rBXZFmljWWQZL0y+UPLxqqoT02mv6nB42FtrMpn0pj0zO+m399m9HHzhIKtWrOLxXzzOaS8/rakVgLGOWPUNburALxGcr2RNfOn3Gf3gM77JT1VKtj5dF4mISL0Flnys9pV7M4sD7wfub36UIrLYaSwSkfkyszXAb+M1n3nAOfdUEw7bBRQe5wDwhqK4LgUuBVizZk0TQpqf+VTrVbNWZD6JueOCHZz/1dJrPu58187KlYFDQ5BIQDbrddeORr2u1IOD3nRovKTfKceewgfu2kR2YoIMY0TppP+uDzB48TfrUwFYJgEalLLdvjOHSX3tk/RdODtntZBKSSUtm0PXRSIi0ghBVj5W+8r93wCfAv6yueGJyBKhsUhEamZm/wvYBNOLCjozu945F/gY4Zy7BbgFYP369S7gcMqqtVqv3FqRHW0dnHnSmZx36nnTSczi6sr2SDttkTa+ftHXOfvXzi5/sHTaSzymC6Z4Z3LHTSS8dRhjMa8C8ItvIz11ZGY3xmBijMQX38boX/18YdOfq0iABqFst+8OGPnCdZD46PTalGUrJbcnGN08WvL7pOndTaXrIhERqbsgu137vXLfVbiDmZ0OnOicu7PcE5nZpWa228x2P/fcc/WPVEQWM41FIlITM3sX0I+XeHwc2Jf7uD/3WCM9DZxY8Pnq3LaWkB5LM/DQAFvu3sLAQwOkxyqvzViLcp2dO9s6+cqFX6Hv9L7pJFa+unLbhm1sfdNWPvf2z/HcXz5XOfEIXqVhtsQU72zWexxIPXgr2ReO+O/2whFSD95a+VilFCZA84nPTGZm++HD83/uBepZ1UOUTt/HomPQ/Uub/h5BhUpJly3ZebwwaZlPVmYmMqTHve2Hx4P7HixSui4SEZG6CzL5WJaZRYDrgM2V9nXO3eKcW++cW3/cccc1PjgRWTI0FomIj/cCk8BZzrm1zrlTgbcBWaDRi+49APSY2avMrAO4CNjZ4GNWZWj/EF3XdbHprk1c84Nr2HTXJrqu62Jo/xBQn8Rkfq3IeEecaHsU8KZZxzviJZu85KsrrzrrqlmJyYqGh2cSfsUyGa/zNDD8w2+Q6SixWweM/KBsfqa8KhOgQUiuTRJx/oW1ESD50Pj09wgqVEpOZEp2Hp9v0lIaQ9dFIiIyH0FOu670yn0cWAd818wAjgd2mtk5WtBYROpIY5GI1Oo3gK875+7Nb3DOfdvMvg68pZEHds5NmtkVwC68ZhCfd87tbeQxq1FpSu2OC3dw/lfOr8u02dNefhp/d+bfcecTXlLvHa9+B5f85iXVJxWrXT+xp8eb4uyXgFyxwpt2vWULPc8cILoaMj5FgNEx6C7dcLuyKhOgQYh3xhk8bhOJp68hi3f+0TEv8Ti4HWLtUeie6SZebsp8tD1asvP4fJOWMm+6LhIRkboLMvk4/co93j+0i4A/yj/onHseODb/uZl9F/ig/qmJSJ1pLBKRWr0Ub7p1sceB/9HogzvnBoHBRh9nWhXJunLVaVNuinO+fA5jU2PT26pd66+Y39p/33/q+5x2/GnVJTFrWT8xmfQe83PkCNx+O2QyJOMd9F/hv1sESL7y7VWdm69yCdDo7OReEHr/+KOMnnQTqVceZmQldB+C5F6IjQPxiPc9zEmuTdK/y//7GbFIyc7j801ayrzpukhEROousGnXzrlJIP/K/WPAV5xze83sE2Z2TlBxicjSorFIROYhAvjVs00w04BmcRgagq4u2LQJrrnGe9/V5W0vUK467cjEEaayU76P1TJttuzaf1/6/cpr/9W6fmI87iUl43Ev0QdexWNe7jni6XEGt0N8zKv8A+99fAwG7ziG2Lsuqer8fCWTEClxuR6ZndwLRDxO7OvfpG8kzlX3Rel7OFfxmP/exWaSyvOZMg/l1/ksl7TMa/Q6pIuNrotERKQRgqx89H3l3jn3sRL7vqUZMYnI0qOxSETmoaW7SNdFld2eoXx1WnuknYms/9zjWqbNll37L3OY1Nc+Sd+FV5d5girWT+wrWrKzt9c7z1TKm+I8Ojpd8Thrt/0wei2kTmtj5EVTdGc6SD7RQezLt8Ntt1We4l1KPolXXK0ZicxJ7gWm+HvU3e2dp09s+QZAqT0pRg6N0L2ye7ozeSn5pGVxxWvEImWTlqAu2fOl6yIREam3QJOPIiIiIiF1pZld6feAmfmV+TnnXLiuu2pI1pWbUtsWaaM90s6RybkdoWuZNlt27b8OGPnCdZD4aOmE3HzXT4zFZpKSW7aUfI7YOPQdezacdhp0d5N+xSoG/vochl88Rc/PJ0nesoJ4qSne5dSQ3AtM4feo0q65BkC1mE/SstI6pLVM9xcREZGFCddFsIiIiEhrqHV6dfimY9eQrCtXnbbjgh2c/9XzfZ+mmmmzeT2reojSSYaxOY9Fx6D7l+ZfvTj9BHVYP7HSc5x3HvT1MfT4t0h88W1k3zzTiKWfIwxuh96iqtGq1JDcW6xqTVpW0yW71iSoiIiIzE9gaz6KiIiIhJFzLjKft6Djrlk+0ebHJ1mXr07btmEbW9+0lW0btjG6eZSzu8+e11p/xZJrk0Sc/2z3CJB8aLx89+cS6yemO2DgtEm2nLC38pqAVazBmB5Lk/jKuaQ7ZzpgZzoh3QmJjXA4MgmXX+5VUQ4MzJ7Wvtil0945N+Hc1SVbRESkdajyUURERETmKtftuUSzk1LVafOZNlss3hln8LhNJJ6+hiwzFYURYHB7rtFJuepFn/UTh169nMR5R8keY2QeuL7ymoBVrMGYemiAbHbKt9Y1C6ROfoG+L38ZJibKd9uerzLdydNjaVJ7UwwfHKZnVQ/JtUninTWsQbkQtXQarwN1yRYREWkd5kq8ghxW69evd7t37w46DBFZIDN70Dm3Pug45ktjkcjisOTHIr+EUT7R1oCEUUXpNIdPegWpVx5mZCV0H4LkXm+9ReLx6qYzHz4MqRTpkb10HfM50u7onF3iHfHyawLmnmPWGozOQSrFlp/+A9e0/6jk4bd+D676TvEBq4y9kjI/r6E1lGza0vDmK+m01yXdr9KxXudefMixNF3Xdc1a83H6kJV+vnWMoV7J3iU/FolISwj7WCTBUeWjiIiIiPhrtWYn8Tixr3+TvuIEW2cN3Z9z6yemHhoge1cb+DTirrgmYPEajAVJv55TMkQ3eE1wikXHvITp3AOW6LZdizLdydPn/j6JzZCeODzzUDObr8yn0/gCLaRLdj2o07aIiMgMJR9FREREpLRWa3ZSQ0K0XOVZ3dYELEr6JfdA/9v8d43gVWrOPWCZbtvVKpPgS716nOykf8+jpjRfmW+n8QXqXdPLviv2sfU7W9n3i32ccuwpXH3m1ZwQP6Ehx8tTp20REZHZlHwUERERkVBJj/1fUof/jeG2x+k5PEpy7PeJFyUfK1We1W1NwKKkX3zcW4MysRGy5lVARl07EecYvH0ZsfG507yr7rZdTpkE33BsnEyJlZaa0nylHp3G56H4d2DPs3u447E7Gl59qE7bIiIis4Wv86KIiIiILFlD37iJrmu72PTsF7mm/UdsevaLdF3bxdA3bprep7DyLJ9czExkSI972w+PH/a6Z5v/pXDEIiTXzW2o49ut2Sfp17sfRq+Fbd+ErZNvZNs5n2P0L/bT+0y7/0mVaOBTkzLdyXsOdxC1Tt/HmtJ8pYou4fVWze9Ao1Sqqt37rNdZfcvdWyp3WBcREVkElHwUERERkVBIHxwl8YPLSXfOrKmY6YB0JyR+cDmHD/0MqK7yLL8mYLwjTrTdS9pF26PEO+L+awIODXlNUzZtgmuu8d53dXmP+ST9YuPQ90SUq056L32n9xFbdYK3LmU8PrN/NDrTQXuh62iWSfAln+ggssw/8Vky0VpP+XNs1Ln7qOZ3oFHyVbV+lrct53MPfo5Nd23imh9cw6a7NtF1XRdD+4caFo+IiEjQNO1aREREREIh9aUPkfVfupCsQepLW+n7iy9UvZ5j75peRjePktqTYuTQCN0ru0muS85NPJZp5sJNN4GVCKq4qq+RDXzyiTyfbtfxrw8yWKbbdVPWH2xy86K6rek5D8m1Sfp39fs+dnRq9rR7rQUpIiJLgZKPIiIiIhIKw8897ttFGrwKyJHn9gHUtJ5jrCNWef29ct2anYMrrvCSkEVJP9+qvkY28CmT4OuF6hKtjdTE5kV1W9NzHkp12p7MTmJmHJ2cu+6n1oIUEZHFTMlHEREREQmFnuNeQ/TZH/kmIKPj0P3yU4DylWfzmmZcqVuzWVOr+soqk+CrKtG6SNT9d6BGflW1e5/by/X3Xe+7f1Ma/4iIiAREyUcRERERCYXkxVfRf+0XfR+LOEhefDVQuvJs3tOMe3pgxQo4cmTuY/luzU2s6pPK6v47MA/Fyd6BhwYCq8YUEREJkpKPIiIiIhIK8VWvYPB3byTxg8vJmjfVOjruJR4Hf/dGYiuPn9636vUcq7FmjX/iERrWrVkWrq6/A3UQdDWmiIhIUJR8FBEREZHQ6H3HnzP6u39I6ktbGXluH90vP4XkxVfPSjzm1WWacToN559f+vEdO4KZXi1VaaWp5q1QjSkiIhKEQJOPZrYB2Aa0AQPOuauLHu8H3gtMAs8B73HO/VfTAxWRRU1jkYhIHaXT3tqHw8PedOVk0uvEXEexlcfT9xdfqOtzllSu2cyKFfDUU82JQxaFVqvG9KPrIhERqbfAko9m1gbcCLwVOAA8YGY7nXOPFuz2MLDeOXfEzC4DrgE0H0FE6kZjkYhIHQ0NQSIxu+tzf7/X9bm3N+joapIeS5Pam2L4p/9AzykZknsgPl6005EjXoMZkRq0UjVmMV0XiYhII0QCPPbrgRHn3JPOuXHgNuDcwh2cc/c65/IL7NwHrG5yjCKy+GksEhGph3TaSzym0zOdoTOZme2HDwcbXw2G9g/RdV0Xm+7axDXtP2LTBujaDENrinbMN5sRWTx0XSQiInUXZPKxCyicp3Igt62UPuCbfg+Y2aVmttvMdj/33HN1DFFElgCNRSIi9VBuenI26z0eAumxNIntCdLj6emuxJkOSHdCYiMc7ijYWc1mFr90GgYGYMsW7306HXREjabrIhERqbtQNJwxs4uB9cCb/R53zt0C3AKwfv1618TQRGQJ0VgkIkEys08D7wTGgf8A/sQ596sFP3G91mgcHp6peCyWyYRmenJqb4qs80+iZg1Sa6HviaiXeBwcVLOZxWwRLSPQCLouEhGRagWZfHwaOLHg89W5bbOY2VnAR4A3O+fGmhSbiCwdGotEJCzuBj7knJs0s08BHwK2LOgZq0muVJuc7Onxvt4vAek3PbkJjWnmY/jg8HTFY7FMB4y8/Y1w+Xu9eJV4XLwKlxHIy/9uJxIwOrpYf/66LhIRkboLctr1A0CPmb3KzDqAi4CdhTuY2euAvwfOcc49G0CMIrL4aSwSkVBwzn3LOTeZ+3Th66xVs0bj0BB0dcGmTXDNNd77ri5ve7Fk0qsG9FM8PbmW522ynlU9RNujvo9F26N0/8F7oa9vsSaeJG+RLCMwD7ouEhGRugss+Zi7eL4C2AU8BnzFObfXzD5hZufkdvs0EAO+amaPmNnOEk8nIjIvGotEJKTew0LXWauUXLn11toayMTjXsVkPO5VOoL3Pr89n6xr8cY0ybVJIuZ/iRyxCMl1WuNxSVgkywjUStdFIiLSCIGu+eicGwQGi7Z9rODjs5oelIgsORqLRKRVmNm3geN9HvqIc+7ruX0+AkwC2/2eo+p11iolV77xjcqVX319s7f39nrTUVMpLznT3T13enI1FWXFz9tE8c44gxsHSWxPkHVZMhMZou1RIhZhcOMgsQ5VPC4JtS4jsIjoukhEROotFA1nRERERJaCSjf1ZvZu4B3Amc65hTVwqJRcgflVfsVi5ZOHIago613Ty+jmUVJ7UowcGqF7ZTfJdUklHpeSZNJb/9SPupyLiIjURMlHERERkRAwsw3AX+E1eDiy4CeslFx5+9vhe9+rf+VXSCrKYh0x+k4PrgJTApZfLqC4IZO6nIuIiNQsyIYzIiIiIlK9G4A4cHdunbWbF/RsldZovOSS6hvI1KJcYxozeOEF2LIFBgZmdxoWabb8MgLbtsHWrd770dGZTvAiIiJSFVU+ioiIiISAc67+JYGV1mhsROVXqYqybNZ727p1Zlt/v7evkj0SlErLCIiIiEhFSj6KiIiILGXlkivVNJCZj+LnXb3aSzoWdrrOT8tOJLx9Nc1VREREJJSUfBQRERGR0hpV+VX4vAMDUKp/Tgt0wBYRERGR+dOajyIiIiISrBB0wBYRERGR+VHyUURERESCle+A7ae93Zt2reYzIiIiIqGk5KOIiIiIBKtcB+yJCdixA7q6YGiouXGJiIiIyIIp+SgiIiIiwcp3wI7HYcWKuY8fOeJVPiYSs5vSiIiIiEjLU/JRRERERIKX74B9/vmwrERPxHzzGZE6So+lGXhogC13b2HgoQHSY5riLyIiUk/qdi0iIiIirSEWg+OPh8lJ/8fVfEbqbGj/EIntCbIuS2YiQ7Q9Sv+ufgY3DtK7pjfo8ERERBYFVT6KiIiISOso13wmGoXu7ubGI4tWeixNYnuC9HiazITXbT0zkSE97m0/PK4p/iIiIvWg5KOIiIiItI5yzWciEe9xkTpI7U2RdVnfx7IuS2qPpviLiIjUg5KPIiIiItI6CpvP5Csgo9GZ7bFYsPHJojF8cHi64rFYZiLDyCFN8RcREakHrfkoIiIiIq0l33wmlfLWeOzu9ioelXiUOupZ1UO0PeqbgIy2R+leqSn+IiIi9aDko4iIiIi0nlgM+vqCjkIWseTaJP27+n0fi1iE5DpN8RcREamHQKddm9kGM9tnZiNmttXn8U4zS+Uev9/MTgogTBFZ5DQWiYiILD3xzjiDGweJd8SJtntT/KPtUeId3vZYx9KstNV1kYiI1FtglY9m1gbcCLwVOAA8YGY7nXOPFuzWB/zSOddtZhcBnwL0EqSI1I3GIhERkaWrd00vo5tHSe1JMXJohO6V3STXJZdy4lHXRSIiUndBTrt+PTDinHsSwMxuA84FCv+xnQtcmft4B3CDmZlzzjUzUBFZ1DQWiYiILGGxjhh9p2uKf46ui0REpO6CTD52AU8VfH4AeEOpfZxzk2b2PLAK+EXhTmZ2KXBp7tMxM9vTkIib41iKzi+EdA7BC3v8AKc06Tgai0oL++9R2OOH8J9D2OOH5o1FDfHggw/+wsz+K6DDB/3z1/F1/KDHn3rG8Mo6PU8lui4qrRV+pxYi7PFD+M8h7PFDyK+LJDiLouGMc+4W4BYAM9vtnFsfcEjzFvb4QefQCsIeP3jnEHQMtVpMYxGE/xzCHj+E/xzCHj+Ecywq5Jw7LqhjB/3z1/F1/KDHn1aIIUi6LmotYY8fwn8OYY8fwn9dJMEJsuHM08CJBZ+vzm3z3cfMlgEvBg42JToRWSo0FomIiIh4dF0kIiJ1F2Ty8QGgx8xeZWYdwEXAzqJ9dgKX5D4+H7hHa4mISJ1pLBIRERHx6LpIRETqLrBp17n1Qa4AdgFtwOedc3vN7BPAbufcTuAfgX82sxHgEN4/v0puaVjQzRH2+EHn0ArCHj806Rw0FpUV9nMIe/wQ/nMIe/ywOM4hKEF/73R8HT9orRBDTXRdVFbYzyHs8UP4zyHs8cPiOAcJgOlFKhEREREREREREWmEIKddi4iIiIiIiIiIyCKm5KOIiIiIiIiIiIg0RGiTj2a2wcz2mdmImW31ebzTzFK5x+83s5MCCLOkKuLvN7NHzewnZvYdM3tlEHGWU+kcCvY7z8ycma1vZnyVVBO/mV2Y+znsNbN/aXaMlVTxe7TGzO41s4dzv0uJIOIsxcw+b2bPmtmeEo+bmX02d34/MbPTmx1jJRqLgqexKHgai6QUM/u0mT2e+77dYWYvafLxL8j93WSb+bdf7bjUoGOX/X1uwvFPzP2958es9zf5+MvN7Edm9uPc8f+6mccviKMtN+Z9I4jjByXs10UQ/mujsF8XQfivjXRdJOLDORe6N7zFj/8DOBnoAH4MnFq0z58DN+c+vghIBR13jfGfAazIfXxZK8Vf7Tnk9osD/wbcB6wPOu4afwY9wMPAS3OfvyzouOdxDrcAl+U+PhX4adBxF8X334HTgT0lHk8A3wQMeCNwf9Axz+NnoLEo4HPI7aexKNhz0Fi0RN+As4FluY8/BXyqycd/LXAK8N1m/e1XOy418Phlf5+bcPwTgNNzH8eBJ5p8/gbEch+3A/cDbwzg+9AP/AvwjSB+DgH97EN9XVTDObTstVHYr4tq+Bm07LWRrov0pjf/t7BWPr4eGHHOPemcGwduA84t2udc4NbcxzuAM83MmhhjORXjd87d65w7kvv0PmB1k2OspJqfAcDf4N1sHG1mcFWoJv4/BW50zv0SwDn3bJNjrKSac3DAi3IfvxgYbWJ8FTnn/g2vS2Ip5wJfdJ77gJeY2QnNia4qGouCp7EoeBqLpCTn3Lecc5O5T5s+hjjnHnPO7WvmMal+XGqIKn6fG338Z5xzD+U+TgOPAV1NPL5zzh3Ofdqee2tqh00zWw28HRho5nFbQNiviyD810Zhvy6C8F8b6bpIxEdYk49dwFMFnx9g7kXN9D65i97ngVVNia6yauIv1If3ykIrqXgOufLrE51zdzYzsCpV8zN4NfBqM/u+md1nZhuaFl11qjmHK4GLzewAMAi8rzmh1U2tfyvNprEoeBqLgqexSKr1HlpvDGkE/b7k5KbUvg6v+rCZx20zs0eAZ4G7nXNNPT7wGeCvgGyTjxu0sF8XQfivjcJ+XQThvzbSdZGIj2VBByDlmdnFwHrgzUHHUgsziwDXAe8OOJSFWIZX0v8WvFc0/83Mft0596sgg6rRu4AvOOeuNbPfAf7ZzNY555baxbAskMaiQGkskpZmZt8Gjvd56CPOua/n9vkIMAlsD+L40nxmFgNuBzY55/5vM4/tnJsCfjO3xugdufGmKWtgmtk7gGedcw+a2VuacUwJRhivjRbJdRGE/9pI10Wy5IQ1+fg0cGLB56tz2/z2OWBmy/DKmQ82J7yKqokfMzsL+AjwZufcWJNiq1alc4gD64Dv5mZSHA/sNLNznHO7mxZladX8DA7grV8xAfynmT2B90/ugeaEWFE159AHbABwzv3QzJYDx+JVAoRBVX8rAdJYFDyNRcHTWLTEOefOKve4mb0beAdwpnOu7tNfKx0/AEv+98XM2vESj9udc/8aVBzOuV+Z2b1440+zGvC8CTgn10BiOfAiM/uSc+7iJh0/SGG/LoLwXxuF/boIwn9tpOsiER9hnXb9ANBjZq8ysw68xYp3Fu2zE7gk9/H5wD2NuOCdp4rxm9nrgL8HzmmxNSzyyp6Dc+5559yxzrmTnHMn4a2H0kr/1Kr5Hfoa3qtpmNmxeOX9TzYxxkqqOYf9wJkAZvZavIvg55oa5cLsBP4411HtjcDzzrlngg6qgMai4GksCp7GIikpNxXur/D+7o5U2n+RqOZvYtHKrd/3j8BjzrnrAjj+cbmKR8zsGOCtwOPNOr5z7kPOudW5/zkX4f3fXwqJRwj/dRGE/9oo7NdFEP5rI10XifhxLdD1Zj5veB2WnsDrJPWR3LZP4A2e4P0BfxUYAX4EnBx0zDXG/23g58AjubedQcdc6zkU7ftdWq+TWqWfgeFNS3gU+HfgoqBjnsc5nAp8H6/L2iPA2UHHXBT/l4FngAm8VzD7gD8D/qzgZ3Bj7vz+vdV+h6r8GWgsCvgcivbVWBTMOWgsWqJvubHvqYIx5OYmH/8Pcj/TsdxYtqtJx53zN9HEc57z+9zk4/fiNVP4ScHPPdHE4/8GXhfcn+BVO36smedfFMtbWELdrnPnHOrroirPoaWvjSrFX7Tvd1vxf1oVP4OWvjaqIn5dF+ltyb2Zc630QpOIiIiIiIiIiIgsFmGddi0iIiIiIiIiIiItTslHERERERERERERaQglH0VERERERERERKQhlHwUERERERERERGRhlDyUURERERERERERBpCyUcRERERERERERFpCCUfRUREREQCYGau6G3KzH5hZveY2R+V+brXmNn/NrM9Zva8mY2b2aiZ3WlmfWbWWbT/p8zsO2b2lJm9YGaHzOxhM/u4ma1q/JmKiIjIUmbOuaBjEBERERFZcswsfyH+17n37cBrgHOBNuB651x/0dd8DPg4XhHBD4HdwGHg5cBbgJOBB51z6wu+Zhx4CHgUeBaIAm8E1gOjwBudc0/V/wxFRERElHwUEREREQlEPvnonLOi7WcCd+c+Pdk599Pc9g8Dfws8BVzgnLvf5znfAWx2zp1RsG25c+6oz75/C3wY+Jxz7s/rclIiIiIiRZR8FBEREREJQKnkY+6xR4HXAhc6575qZicBT+QePt05t6fM83Y658aqOP5pwCPAt51zb639DEREREQq05qPIiIiIiKtJ5+QzFcK/AnetOzbyyUeAapJPOa8M/f+J7WHJyIiIlKdZUEHICIiIiIiM8zsLOAUvMTjA7nNvbn331nA834QiAEvxlvvsRcv8Xj1vIMVERERqUDJRxERERGRAJnZlbkP2/GSjv8Dr/Lxeufcf+UeOyH3/sACDvVBvMY0eXcB73bOPbeA5xQREREpS8lHEREREZFgfTz33gG/Ar4H/KNz7kv1PIhz7ngAM3s58Lt4FY8Pm9k7nHMP1fNYIiIiInlKPoqIiIiIBMiv4YyPZ/Aa0HTV4Xg/B+4ws4fwmth8EVi30OcVERER8aOGMyIiIiIirW8o9/7Mej1hbkr3o8BaMzu2Xs8rIiIiUkjJRxERERGR1vdPwARwnpmdWm5HM+us4XlfkXs/Nd/ARERERMpR8lFEREREpMU5534KXAl0AHea2Xq//cxsA/DNgs9fbWYv9tkvYmZ/C7wM+IFz7peNiFtEREREaz6KiIiIiISAc+7vzGwZXoOaB8zsB8Bu4DBeF+v/DvTktuUlgKvMbAj4T+Bgbt83AycDPwP+tGknISIiIkuOOeeCjkFEREREZMkxMwdVN5wp/LrXAn8OnAGsAZbjJRUfAXYAX3LOjeX2XQf8GdALrAZeAmTwGs3cCXzWOXdo4WcjIiIi4k/JRxEREREREREREWkIrfkoIiIiIiIiIiIiDaHko4iIiIiIiIiIiDSEko8iIiIiIiIiIiLSEEo+ioiIiIiIiIiISEMo+SgiIiIiIiIiIiINoeSjiIiIiIiIiIiINISSjyIiIiIiIiIiItIQSj6KiIiIiIiIiIhIQyj5KCIiIiIiIiIiIg3x/wNO0CRhfy/wWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(pca4_plasma_df, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "vZ16An_m-3TG"
   },
   "outputs": [],
   "source": [
    "pca5_plasma = pca_analysis(plasma, 5)\n",
    "\n",
    "pca5_plasma_df = make_dataframe(pca5_plasma, 5)\n",
    "# print(pca5_df.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "7tAZAD4o_Lwv",
    "outputId": "77199232-ad1c-42db-bce4-39bc4c8e32c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(pca5_plasma_df, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "1nMBpd5JIy3s"
   },
   "outputs": [],
   "source": [
    "# now do PCA for 10, 15, 20\n",
    "pca10_plasma = pca_analysis(plasma, 10)\n",
    "pca10_plasma_df = make_dataframe(pca10_plasma, 10)\n",
    "\n",
    "pca15_plasma = pca_analysis(plasma, 15)\n",
    "pca15_plasma_df = make_dataframe(pca15_plasma, 15)\n",
    "\n",
    "pca20_plasma = pca_analysis(plasma, 20)\n",
    "pca20_plasma_df = make_dataframe(pca20_plasma, 20)\n",
    "# print(pca20_plasma.head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "OJ4K_kNKNqWP"
   },
   "outputs": [],
   "source": [
    "# pca15_plasma_df.to_csv(\"plasma/pca15_plasma_metabolics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "BaIC09d1YJyp",
    "outputId": "237ba941-6c89-4eea-d789-7f8d6349cb10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24cdd94bb88>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAENCAYAAAASUO4dAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAlcklEQVR4nO3dfXBV9bkv8O+TSGDY2b4gOL2G5oISxiInoAVbbdSiaCVFaRXdp3psKM6xV6W9iB2D1ZnLbbV40FGpnjstTRU70Gs0vnbEF3w7NW2tBx31AB4brgcRoxVjCztBk5A894+VDSHsl7X3Xi+/31rfz0xmy35dWTs++7ef3/N7fqKqICIie1WEfQBERFQeBnIiIssxkBMRWY6BnIjIcgzkRESWYyAnIrIcAzkRkeUYyMkKIjJJRFRE1pb4+ItF5GkR+VhE+kWkS0S2isg6EWnK8VoqIn/O85wqIjvzPDbfz6RSfg+ibA4L+wCI/CYiawD8M4DPADwJ4L8ACIATAJwP4OsA7s/x8FNE5B9V9YEiX3Y3gLvy3P73Ip+PKCcGcoo0EWmAE8R3AjhVVUeOoEfBCeTZ7ADw3wD8TEQeUdW+Il7676q6ovgjJioeUytkPBFZAWcUDQBNI1IUiwo8/LShy4dHBnEAUNV+Vd2Y47HvA/g/ACYD+EHxR04UDI7IyQYvATgSwP8E8CaAx4bd9kaBx3YNXdaV+No/AdAE4EYRuU9VPy3xeYh8w0BOxlPVl0RkO5xA/kaRKYun4eSrG0XkCQAPAPh3ANvURcc4Vf1URG4BcBuAmwAsc/m6Rw59k8jmI1X9hcvnISpI2P2QbDBU5fFfAO5X1UVFPnYOgF8BOH7Y1WkAfwSwDsD/VdWBLK/1B1VtEJHRAP4TwLEAvqSq7w7dTwF8oKoTszw2nzdVdWYxvwNRPhyRk9VE5EgAS7PcdJeq/h0AVPVFEZkK4GsAzgRw0tB/f2Pop0lE5qtqb7bXUNVeEfkxgN8CuBXAJS4O7T1VnVTUL0NUIgZyst2RAP5XluvXYliJn6oOAnh56AciIgDOgVN2OBfAVchfLvgAgGsBXCwiX1XVV8o+ciKPsGqFrKaq21VVsvxsL/A4VdVn4eS9AeCsQvcH8KOhf95e9oETeYiBnGyRyWFXevy86aFLKXRHVf09gMcBfE1ELvL4OIhKxkBOtvgbAAVQW8yDROQ8EblwaOHPyNuqcSC//nuXT9kMYB+cXDmREZgjJyuoavdQ35PTRWQ9gL/AGaU/oapv5XnoCQDuBPA3EXkZQAecQDwRwDfh5Nj/DOAel8fxztCS/6sL3DVf+SEArC2U/iFyi+WHZA0RmQInKJ8G4Cg46ZDvqeraPI8ZD+ACOBObM+Asua+GMxG6GcBDAFqGL78fWX6Y5TknANgG4HCUVn4IAHNU9SUX9yMqiIGciMhyzJETEVmOgZyIyHIM5ERElmMgJyKyXCjlh+PHj9dJkyaF8dJERNZ67bXXPlHVCSOvDyWQT5o0CZs2bQrjpYmIrCUi72W7nqkVIiLLMZATEVmOgZyIyHLG9Frp7+/Hzp078fnnn4d9KMYaM2YMJk6ciFGjDun/REQxZkwg37lzJ5LJJCZNmgSn5z8Np6ro6urCzp07MXny5LAPh4KWTgOtrUBHB1BXB6RSQDIZ9lGRITwJ5CJyHoDVcHpFt6hq0S0+P//8cwbxPEQERx99NHbt2hX2oVDQ2tuBxkZgcBDo6QESCWDZMmDDBqDhkJ5eFENl58hFpBLAvwKYB2AagO+IyLQSn6vcw4k0np8YSqedIJ5OO0EccC4z13d3h3t8ZAQvJjtPAbBNVd8dagX6AIAFHjwvEbW2OiPxbAYHndsp9rxIrdQAeH/Yv3cC+MrIO4nIlQCuBIDa2qI2eQnFihUrUF1djT179uCMM87A3Llzwz4kiqOOjgMj8ZF6eoBt24I9HjJSYJOdqroGwBoAmDVrVvlN0AOa/PnJT37i+XMSuVZX5+TEswXzRAKYMiX4YyLjeJFa+QDAF4f9e+LQdf5pbwdqaoClS4FVq5zLmhrn+jLccsstmDp1KhoaGvDOO+8AABYtWoS2tjYAwPLlyzFt2jTU19fjRz9yNlTftWsXLrroIsyePRuzZ8/GH/7wBwDAq6++ilNPPRUnnXQSTjvttP3Pt2XLFpxyyimYOXMm6uvr0dHRAQBYt27d/uu///3vY2BgYOThURylUkBFjv9NKyqc24lUtawfOKP6dwFMBlAF4E0AJ+Z7zJe//GUdaevWrYdcl9WeParJpCpw6E8yqZpOu3ueETZt2qTTp0/Xnp4e3b17tx5//PF62223aVNTkz700EP6ySef6NSpU3VwcFBVVf/2t7+pqup3vvMdffnll1VV9b333tMTTjhBVVV3796t/f39qqq6ceNGvfDCC1VVdcmSJbpu3TpVVe3t7dW9e/fq1q1bdf78+drX16eqqldddZXef//9WY/T9Xmi6Hj5ZedvO5Fw/s4TCeffQ393FB8ANmmWmFp2akVV94nIEgDPwCk/vFdVt5T7vDm5mfy54oqin/bll1/Gt7/9bYwdOxYAcMEFFxx0+xFHHIExY8bgiiuuwPz58zF//nwAwHPPPYetW7fuv9+ePXvQ3d2N3bt3o6mpCR0dHRAR9Pf3AwBOPfVU3HLLLdi5cycuvPBC1NXV4fnnn8drr72G2bNnAwA+++wzHHPMMUX/DhRRDQ1AZ6fzt71tm5NOSaWA6uqwj4wM4UmOXFU3ANjgxXMVFNLkz2GHHYZXX30Vzz//PNra2nDPPffghRdewODgIF555RWMGTPmoPsvWbIEc+bMwaOPPort27fj61//OgDg0ksvxVe+8hU8+eSTaGxsxC9/+UuoKpqamrBy5Upfjt1KbuZA4rRIprq6pAFKaOL03hjAvl4rmcmfbMqY/DnjjDPw2GOP4bPPPkM6ncbvfve7g27PjLIbGxtx55134s033wQAnHvuubj77rv33++NN94AAOzevRs1NTUAgLVr1+6//d1338Vxxx2HH/7wh1iwYAHeeustnH322Whra8PHH38MAPj000/x3ntZu1XGg5s5EJ/mScgDfG8CZ18g92ny5+STT0YqlcKMGTMwb968/WmOjHQ6jfnz56O+vh4NDQ244447AAA///nPsWnTJtTX12PatGn4xS9+AQC4/vrrccMNN+Ckk07Cvn379j/Pgw8+iOnTp2PmzJnYvHkzvvvd72LatGm4+eabce6556K+vh7nnHMOPvzww5J+D+u5WQDDRTLm4nsTjmyJc79/yprsVI315E/kJzt/9asD7+vIn0RCtaXF3X0oHHxvfAW/JjtDwcmf6HIzB5LpOZLvPlQcr3LaXMAUCjsDOWDf5A+542YBjCoXyXjJy6ZcXMCUm48TwOKM1oM1a9YsHbln59tvv40vfelLgR+LbSJ/ntJpZ2IsnT70tmTS+SamWvg+/HbmjpvzXcy59Pr5oiLbh2VFRdEfliLymqrOGnm9fZOdFG3JpPPHnUweqE5KJA5cX13t7j7kjtdNubx8b9JpoKUFaG52LrN9ONgggAlge1MrFF1u5kA4T+INP3LaXrw3UerB7tMixuEYyMlMbuZAOE9SPr9y2uW8N8NHsBmZ42tstC89E8AEMFMrHtq+fTt++9vfFv24tWvXYsmSJT4cEVEBJjbliloPdp8WMQ5nbSBP96bR8noLmjc2o+X1FqR7w8+f5QvkwxcFERnDxPmGqJUwBvBhaWVqpX1HOxrXN2JQB9HT34PEqASWPbMMGy7bgIba0vNnv/nNb3D77bdDRFBfX4+f/vSnWLx4MT755BNMmDAB9913H2pra7Fo0SIcfvjh2LRpEz766COsWrUKCxcuxPLly/H2229j5syZaGpqwlFHHYVHHnkE3d3dGBgYwKOPPorFixfj3XffxdixY7FmzRrU19d7eGaISmDafIOpJYyllg9mPhRzVa14cZ6zrRLy+6eclZ17Pt+jyZ8lFStwyE/yZ0lN95bWxnbz5s1aV1enu3btUlXVrq4unT9/vq5du1ZVVX/961/rggULVFW1qalJFy5cqAMDA7plyxY9/vjjVVX1xRdf1G9+85v7n/O+++7Tmpoa7erqUlWnhe2KFStUVfX555/XGTNm7L/fNddc4+o4I7+yk8inVtVl8WI1eTrtrGxdvty5LOH3QI6VndalVlq3tGJQs+fPBnUQrZtLy5+98MILuPjiizF+/HgAwLhx4/CnP/0Jl156KQDg8ssvR/uwpj/f+ta3UFFRgWnTpuGvf/1rzuc955xzMG7cOABAe3s7Lr/8cgDAWWedha6uLuzZs6ek4yWKLNPSPV6VD2YmgFeudC49/D2sS610dHWgpz97/qynvwfbPg0mfzZ69Oj9/615FlUlck1yEFFuJqV7AigfLJd1I/K6o+uQGJU9OCZGJTBlXGn5s7POOgsPPfQQurq6ADitZE877TQ88MADAID169fj9NNPz/scyWQS6TyLFk4//XSsX78eAPDSSy9h/PjxOPzww0s6XqLI83EEWxQLJl+tG5GnTkxh2TPLst5WIRVITS9tBvjEE0/EjTfeiDPPPBOVlZU46aSTcPfdd+N73/sebrvttv2TnfnU19ejsrISM2bMwKJFi3DUUUcddPuKFSuwePFi1NfXY+zYsbj//vtLOlYiCpCpk6/DWNlrJVvVSoVUlF21YoPI91ohu8RhJyCD+sfk6rVi3YgcABpqG9B5XSdaN7di26fbMGXcFKSmp1BdZdFqLyLbRWkZfT7ZygfHjgUGBoDzzwceeCD0DzArR+RxxvNEBwlrRJxOA8cem71iI6pdDru7nXP94ovAww8DlZVldTIshRXdD8P4ULEJzw8dJMy9MW++OXfZnY3L6N2orgYuuQR44gng88+N2srOmEA+ZswYdHV1MVjloKro6urCmDFjwj4UMkGYe2Om08Bdd+W+3ZBKDl8Y2gfGmBz5xIkTsXPnTuzatSvsQzHWmDFjMHHixLAPg0wQZm1zaysgkvv2qiojKjl8YWgpojGBfNSoUZg8eXLYh0FkhzADSkcH0Nub+3bVcLomBsHQUkRjUitEVIQAWqOW9NqAU7kStYnODBPb/oKBnMhOYQaUfK9dXQ3cdJN/rx020/rADDEmtUJEcF9OGERr1FzCfG0TmNQHZogxdeREsVfKTuuZ2uYwAkqYrx1TuerIGciJTGDQMnAylxULgohiy9D6ZLIDAzmRCQytTyY7MJATmSDMckKyHgM5kQlMq09Op4GWFqC52bnMs2EKha+s8kMRuQ3A+QD6APw/AN9T1b97cFxE8WJSSV9c2tNGSFlVKyJyLoAXVHWfiPwLAKhqc6HHsWqFKIewS/pYPWM0XzaWUNVnh/3zFQALy3k+otjL7FMZFgs2GqZDebmyczGAnDVSInIlgCsBoLa21sOXpUDEYUsvYvWMpQoGchF5DsAXstx0o6o+PnSfGwHsA7A+1/Oo6hoAawAntVLS0VI4mDONDzfd/bz8UOcAwRNlr+wUkUUAvg/gbFXd6+YxzJFbhDnTeCn0fre1AQsXFtdGIJdSWhJ4zbIPEl9WdorIeQCuB3CB2yBOluGKw3jJ190vE8S92JUozB2OMsLcKs9j5daR3wMgCWCjiLwhIr/w4JjIJHHKmbJ22pHp7rd6NbB8uXPZ2Qns2OHdh3rYAwQTPkg8VG7VCpebRZ2hO6J4jvMAB8tWPePlh3rYA4SIVedwZSflZ9qKQz9EbHTmGy/bCITdkiDsDxKPMZBTfobuiOKpsL/m28LLD/WwBwhhf5B4jIGcCsuVM41KyiFiozPfePmhHvYAIewPEo9xqzdyJ+wVh36KyzyAF7zc5izMLdNM6m3jAe4QRMRa+fgKu7dNkXzptUIUCREbnXnGssUyJYnIN00GciLAyJ3RQ8VyTKswtUJEB2OqyVjcfJmI3GE5pnUYyInoYCzHtA5z5ERRV+ykpR/lmHGYOA0Rc+REUVZKq1ivc+QmtKuNCObIieKm1B4yXq66ZB+bQDCQE0VVOZOWXrVl4MRpIJgjJ4qqcictvVgsw4nTQHBEThRVJnT4M+EYYoCTnRRL6d40Wre0oqOrA3VH1yF1YgrJ0RGrojBhYY8JxxAh7LVCNKR9Rzsa1zdiUAfR09+DxKgElj2zDBsu24CG2ghVUZjQQ8aEY4gBjsgpVtK9adTcUYN036EjxGRVEp3XdaK6KmLBxYQOfyYcQwRwRE4EoHVLKwY1exXFoA6idXMrrjjZ/m54BzGhw58JxxBhnOykWOno6kBPf/Yqip7+Hmz7lFUUZB+OyClW6o6uQ2JUImswT4xKYMq4CFRRcDl87DBHTrES+Rw5l8NHGpfoEwFIjk5iw2UbkKxKIjHKqW9OjEogWeVcb3UQ53L42GJqhWKnobYBndd1onVzK7Z9ug1Txk1BanrK7iAOuFsOzwnHSGIgp1iqrqqOXnUKl8PHFgM5UVT40Uc8iiI4GczJTqKo4HL4wiyfDOaCIKIoGjm6bGsDFi7kcvhshk8GZ2S+vTQ2Wv1Bx0BOZKtco8u2NuD997kcfqQITwYzkBPZKN/ocuFCq0eXvonwZDDryIlsxJ13ihfh3ugM5BSudBpoaQGam53LbBN1dKgIjy59k0o5qadsKiqc2y3lSWpFRK4DcDuACar6iRfPSTGQLce7bJm5FQQmla2x1LB4Ee6NXnb5oYh8EUALgBMAfNlNIGf5oTuR3sXGtlK5Z58FLrgAGBgA9u0Dxo4FKivD+9Cx7fyZxOLe6H6WH94J4HoAj3vwXDQkUrvYZBvJ2lRB8OyzwDe+cfB1e/c6l2GVrUV4dOm7CPZGLyuQi8gCAB+o6psiUui+VwK4EgBqa2vLednIS/em0bi+8aAOfZm2q43rG+3q0JcrfXL++XbkeNNpYMGC3LcPDIT3odPQ4HyIWDq6JO8UDOQi8hyAL2S56UYAPwZwrpsXUtU1ANYATmqliGOMncjsYpOvRO6RR5z0RGZkO5xJOd7WVidY57J3b7gfOhEcXVLxCgZyVZ2b7XoR+QcAkwFkRuMTAbwuIqeo6keeHmXMWL+LTSaV0tYG9PZmv09FRe4AaVIFQUcH0N+f+/bDDjPnQ4diq+TUiqr+B4BjMv8Wke0AZrFqpXxW72IzMpWSy969wGWXAU88YXaOt64u9zcHwJnwNOVDh2KLdeQGSp2YQoVkf2sqpAKp6YYGjmwbG+SSSABz5jg53tWrgeXLncvOTrNKD1MpJ1jn8sQT5nzoUGx5tkRfVSd59Vxxl9nFZmTVSoVUmL2LTb5KlJEy6RPTc7zZqkNGjXKC++OPA+e6miKKlUiXzRqKvVYMZeUuNvlWG2aYmD4phNUhrkWqbNYi7EdO3mlpAZYuzR7MR48GzjoLuOgiBsGIivzG1gbg5svkv3y9LKqqgAcfdNIoDOKR5KZslvzBQE7eyeSTk8kDXeYSiQPXM4BHmvVlsxZjjpy8xXxybFldNms55sgDwpl8ijrmyP3HPTtDxJl8igNry2YjgCNyn3GUQnHT3ddtV9msRTgiD0lkGmARuVRdVc2/6YCxasVnnMknIr8xkPssM5OfDWfyicgLDOQ+870BFjcvJoo95sh95utMvm2bFxORL1i1EhDPZ/K5+S5R7LBqJWSez+TbtHkxEfmKgdxW+VrGmrR5cYxxNS8FhYHcVnV1Tk48WzA3afPimOJqXgoSq1Zsla9lrEmbF8dQujeNxvWNSPel968h6OnvQbrPub67rzvkI6SoYSC3FVvGGot9uSloTK3YjC1jjWT1at502vl76uhw0neplDM4IKMxkNsuz+bFnGwrjlfny9q+3FyXYC3WkUdUtsm2zCIkTrYdysvzZWXHS65LsAL37IyRdG8a89bN822yLd2bRsvrLWje2IyW11uQ7rW7LYDXk5OZ1bzJquT+PjuJUQkkq5Lm9uV2sy6BjMXUSgTd/Pub0d2fPfiU2zo3imV1frQabqhtQOd1nfb05ea6BKsxkEdMujeNu165K+ft5Uy2DR+5Dn8+AGhc32hmysAFvyYnrerLzXUJVmNqJWJat7RCRHLeXlVZVfJkW9hldX6ldNhqGFyXYDkG8ojp6OpA70BvzttVteTWuWGW1bXvaEfNHTVY+vRSrPrjKix9eilq7qhB+472sp/b91bDNuC6BKsxkEdMvtElACw7dVnJ6Y+wRq5+r5S0cnLSD5l1CatXA8uXO5ednSw9tADLDyMmX+lbdVU1Przuw5IDU1hldS2vt2Dp00tz1mWvPm+1J7lobhpMpmMb2wCFuRDHz40sfN0kI4+gUjpWTU4SDcNA7rGR5XlVlVW4+smrce2p1+Km028KJKD7WfoWRlmd1yslueKVooapFQ/lSz0Azojvqcuesq7eOuzA52VKhyteyWZc2RmAfOV5gJODta2NqZ/VIm55NRnJ9rIUVUyteChfLjej3JWVQTJpAZAXKZ1SVnCG/W2EyI2yA7mI/ADANQAGADypqteXfVSWypfLzQi6jWk5gciPpevlKHcysthJ0yi2I6BoKiuQi8gcAAsAzFDVXhE5xpvDOpQNI6PUiSkse2ZZ3vsEsVIwc65e3P4iHt76MCqlEnv37S06EFndVzuLYiZNTfo2Yg32Mg9NuSPyqwDcqqq9AKCqH5d/SIeyZWSUyeXOWzcvZ9Mqv1cKZs7VgA5gb//eg24rNhBZ21c7h3wftCPfF9O+jYSuUJBmL/NQlTvZORXA6SLyZxH5NxGZneuOInKliGwSkU27du1y/QK2TVA11Dbgwx99iObTmlFVWYXRlaMBBLNScPi5GhnEh3PbFyVqS9eLmTQN5dtIOg20tADNzc5ltt7gYWhvd3qVL10KrFrlXNbUONcDznE2NjqXmaZbPT0Hru826//RKCo4IheR5wB8IctNNw49fhyArwKYDeBBETlOs9Q0quoaAGsAp/zQ7QHaODKqrqrGrefcipvOvCnQeutCVTMZbgNRWAuA/OR20jTwbyOmjmiHB+mMTLBubDyw1WChXuY5drEibxQM5Ko6N9dtInIVgEeGAverIjIIYDwA90PuAmzO0wa9UtBN1QxQXCCyrq+2C27el2LSMGVzEyzDalrlJkizl3noys2RPwZgDoAXRWQqgCoAn5R7UMP5PTKyYRLVLTdVM0DxgSiOS9cD/TZi8ojWTZBmL/PQlRvI7wVwr4hsBtAHoClbWqUcfo6MbJlEdatQ1czYw8aisqLS2rRI0AL7NmLyiNZNkL7kEicNlA17mQfCiiX6fiyrtnKDXBeynasBHcBFX7oIcybNsT4tEkktLc4EYq5guXp1eCNyt5syZ8vxV1SEn+OPmFxL9K0I5ID3LUaDao0ahkLnKsh0UpRSV74xfQd7t0G6u9tJA23b5ozUUyluSOEx6wO515o3NmPVH1flvH3515Zj5dyVAR5RMIJsGsUGVUUwfUTLIG0E9iMfIWqLXdwIcrUiV0YWKbM7j6nBsrqaJYQGi233w6gtdnEjyM2Tw96o2UqZYLlypXPpcRD3a/NqCl9sR+RRXOxSSJA1+TbX/0dR1Cq06GCxDeRANBe75BNkOsnT12IzprIwzRV9sZ3sjKMgSy49ey2/JgFj9OEQRIUWq5OCwclOCjSd5Mlr+bV03dS+Jj7xO83FtE34GMhjJsh0Utmv5cfSdZP7mvjEz5Qa0zZmYCAPQdhfQ4PsnVLWa/mxdN3kviY+8bPNhY3dSaOIgTxg/BpaBD+aMZnc18QnfqbUWJ1kBgbyAPFraJFSKe+bMcW0U59fKbU4LqwzUWwXBIWBi2SKlEw6E5DJpBNkAecyc311dfG76qRSzodANhHv1JdJc62cuxJXnHyFJ4OGOC6sMxFH5AguZ73l4y2R/hrqy3nMt3S9lOqTzIdArpLGiE10+i2OC+tMFPs68qAaO7XvaMfc38xF70Bv1ttt77gYeIOscjsGsgmUp7X0XncnpezY/TCLoBbI5HsdP14vaKH0dje5h7cNTO+2SFnlCuSxzpEHlbMutCnymMPGWP01NJTcfwyrTzzDXe8jJ9aBPKjSqUKbIl8962qrSw9DKUHLVJ9kE+HqE0+4qaUnq8Q6kGdKp7LxsnSq0OtMmzDNk9cJS1Dn8SAxrj4pG7/NRE6sA3lQpVNRL9EK5fdzU5pI2fHbTOTEOpBnSqeSVcn9I8rEqASSVUlPc9ZBvU5YQvv9MqWJq1cDy5c7l52dnKwrhN9mIifWVSsZQZVORb1EK+q/X6SwasVKLD8kooOxlt467EdORAfjhsqREescORFRFDCQExFZjqmVIoS9IYTpwjo/fF8o7jjZ6VLgTaEsE9b54ftSOn4A2odVK2UIpSmURcI6P3xfSscPQDuxaVYZuCFEfmGdH74vpRm+U1WmR05Pfw/Sfc713X1smmUbBnIXuC9hfmGdH1vfl3RvGi2vt6B5YzNaXm9BurfArkYe4wdg9HCy04Ww9yU0PZcZ1vkJ+30phQmbb9v6AUi5cUTuQlhNr9K9aTQ/14yjVx2NazZcg1V/XIWlTy9FzR01aN/R7strlqLY8+PViNS2ZmSmpDTqjq7D2FFjs95m6gcg5VdWIBeRmSLyioi8ISKbROQUrw7MJGE0hWrf0Y5j7zgWq/6wCv2D/egb6ANgZi6zmPPTvqMdNXfUYOnTS8v+YLKtGZkpKY3aI2qxt39v1ttM/ACkwspNrawC8L9V9SkRaRz699fLPioDNdQ2oPO6zkCaQmVGbvkCdeZ/fFP2+HRzfoaPSDMyI9PG9Y0lVZkE+b6Uy4SURro3jYUPLsx5e9slbUaeO8qv3ECuAA4f+u8jAHSW+XxGq66qDiRwFtoaDjAzl1no/LgZkZZyfoN6X8plQk4/33sw9rCxeH/3+74fA3mv3Bz5UgC3icj7AG4HcEOuO4rIlUPpl027du0q82WjrdDWcICduUwTRqRhMiGnn+892Ltvb+Tfg6gqGMhF5DkR2ZzlZwGAqwBcq6pfBHAtgF/neh5VXaOqs1R11oQJE7z7DSIo39ZpGTbmMkPZEs4gJuT04/4eRFVZKztFZDeAI1VVRUQA7FbVwws9zraVnUHLt2IRAKpHVeOpf3rKuhV4XInpCHMDjmLfA9NLX+PGlyX6IvI2gKtU9SURORvAKlX9cqHHMZAX1r6jHY3r5mGwvx896MVoHAatEFz71WW46cybrA14XBoePrfvAd8r8/gVyBsArIYzafo5gKtV9bVCj2Mgd6G9Hd0L5qF1ah+2JfowpacKqb9Uofrxp6zfiotbwoWv0HvAb09mYtMsm6TTQE2NczlSMulsMMwtuchHLa+3YOnTS3NW2Kw+b7UVlUJRw6ZZNmltdTbFzWZw0LmdyCfp3jTatrbFusLINuy1YqKODmdn82x6epzNcol8kMmLZ1YSZ8PqFvNwRG6iujogkaP8MJFwdjwn8tjwlbe9A70572dj6WvUMZCbKJUCKnK8NRUVzu1EHiu0oriqssrYPjZxx9SKiZJJYMMGoLHRyYn39Dgj8YoK53q/JjrTaSf/3tHhfCtIpZxjoVgotKL47Eln48FLHmQQNxADuakaGpzqlNZWJyc+ZYoTWP0K4u3th35wLFvmfHBYXu5I7hTqBXPRtIsYxA3F8kNiuSMBYO24DVh+SLmx3JFgRi8YKg1TK8RyR9rPpv7udAADOR0od8wWzFnuGDu29HenA5haIZY7ElmOgZwOlDsmkwcWIiUSB67nRCeR0ZhaIUfQ5Y5E5BkGcjqguhq4grlRItswkBNRQdwpyGwM5ESUV7adgpY9s4w7BRmEk51ElNPwjoiZpfs9/T1I9znXd/d1h3yEBDCQE1Ee+ToiDuogWjdz1a8JmFqhwDDPap98HRG5U5A5GMgDEvcgxjyrnQp1ROROQWZg98MAZAtiFVIRmyDGrnr24ntnFnY/DAkni5hntRk7ItqBqRWfuQliUW9QxDyr3dgR0XwM5D5jEGOeNQrYEdFsTK34LBPEsolLEEudmEKFZP9T447sROVjIPcZgxjzrER+Y2rFZ5kglqtqJS5BjHlWIv+w/DAg3X3dDGJEVJZc5YcckQeEk0VE5BfmyImILMdATkRkOQZyIiLLMZATEVkulKoVEdkF4L3AX7h04wF8EvZBGI7nKD+en8J4jgr776o6YeSVoQRy24jIpmwlP3QAz1F+PD+F8RyVjqkVIiLLMZATEVmOgdydNWEfgAV4jvLj+SmM56hEzJETEVmOI3IiIssxkBMRWY6BvAgi8gMR+U8R2SIiq8I+HhOJyHUioiIyPuxjMY2I3Db09/OWiDwqIkeGfUymEJHzROQdEdkmIsvDPh7bMJC7JCJzACwAMENVTwRwe8iHZBwR+SKAcwHsCPtYDLURwHRVrQfwFwA3hHw8RhCRSgD/CmAegGkAviMi08I9KrswkLt3FYBbVbUXAFT145CPx0R3ArgeAGfQs1DVZ1V139A/XwEwMczjMcgpALap6ruq2gfgATiDJnKJgdy9qQBOF5E/i8i/icjssA/IJCKyAMAHqvpm2MdiicUAngr7IAxRA+D9Yf/eOXQducSNJYYRkecAfCHLTTfCOVfjAHwVwGwAD4rIcRqj+s0C5+fHcNIqsZbvHKnq40P3uRHAPgDrgzw2ii4G8mFUdW6u20TkKgCPDAXuV0VkEE6Tn11BHV/Ycp0fEfkHAJMBvCkigJMyeF1ETlHVjwI8xNDl+xsCABFZBGA+gLPjNAgo4AMAXxz274lD15FLTK249xiAOQAgIlMBVIGd2gAAqvofqnqMqk5S1UlwvhqfHLcgXoiInAdnDuECVd0b9vEY5N8B1InIZBGpAvCPAJ4I+ZiswhG5e/cCuFdENgPoA9DEERUV6R4AowFsHPrm8oqq/o9wDyl8qrpPRJYAeAZAJYB7VXVLyIdlFS7RJyKyHFMrRESWYyAnIrIcAzkRkeUYyImILMdATkRkOQZyIiLLMZATEVnu/wO1kH3dI6J2yQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne2_plasma = tsne_analysis(plasma, 2)\n",
    "\n",
    "tsne2_plasma_df = tsne_dataframe(tsne2_plasma, 2)\n",
    "# print(tsne2_plasma_df.head)\n",
    "# plotting(tsne2_plasma_df, 1, 1)\n",
    "plt.title(\"t-SNE\",fontsize=20)\n",
    "\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = tsne2_plasma_df.Class == target\n",
    "    plt.scatter(tsne2_plasma_df.loc[indicesToKeep, 'tsne1']\n",
    "               , tsne2_plasma_df.loc[indicesToKeep, 'tsne2'], c = color, s = 50)\n",
    "plt.legend(targets,prop={'size': 10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "qKnrjJTzYh63",
    "outputId": "d8263a8f-454b-47dc-e94d-6a016fd1153e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne3_plasma = tsne_analysis(plasma, 3)\n",
    "\n",
    "tsne3_plasma_df = tsne_dataframe(tsne3_plasma, 3)\n",
    "# print(tsne2_plasma_df.head)\n",
    "tsne_plotting(tsne3_plasma_df, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "P-BRS2vwvm8x"
   },
   "outputs": [],
   "source": [
    "# tsne2_plasma_df.to_csv('plasma/tsne2_plasma_metabolics.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Serum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8mJbTitszFRG",
    "outputId": "e2269dc1-ef0d-456b-c8ed-7e874a3380cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0546194</td>\n",
       "      <td>-1.48065</td>\n",
       "      <td>disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.05728</td>\n",
       "      <td>-1.52758</td>\n",
       "      <td>disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.826877</td>\n",
       "      <td>-1.97051</td>\n",
       "      <td>disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.411985</td>\n",
       "      <td>-1.74226</td>\n",
       "      <td>disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.01118</td>\n",
       "      <td>2.21965</td>\n",
       "      <td>disease</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         pc1      pc2    Class\n",
       "0  0.0546194 -1.48065  disease\n",
       "1    1.05728 -1.52758  disease\n",
       "2   0.826877 -1.97051  disease\n",
       "3   0.411985 -1.74226  disease\n",
       "4   -2.01118  2.21965  disease"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca2_serum = pca_analysis(serum, 2)\n",
    "\n",
    "pca2_serum_df = make_dataframe(pca2_serum, 2)\n",
    "pca2_serum_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "--HrgVwLB0wH",
    "outputId": "d5522eca-81a4-47d1-c11b-38a85fb38205"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24cdd319088>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.figure(figsize=(4, 4))\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('PC1',fontsize=20)\n",
    "plt.ylabel('PC2',fontsize=20)\n",
    "plt.title(\"Principal Component Analysis=\",fontsize=12)\n",
    "targets = ['disease', 'control']\n",
    "colors = ['r', 'g']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = pca2_serum_df.Class == target\n",
    "    plt.scatter(pca2_serum_df.loc[indicesToKeep, 'pc1']\n",
    "               , pca2_serum_df.loc[indicesToKeep, 'pc2'], c = color, s = 50)\n",
    "\n",
    "plt.legend(targets,prop={'size': 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "sXliCThzzpBO"
   },
   "outputs": [],
   "source": [
    "pca3_serum = pca_analysis(serum, 3)\n",
    "\n",
    "pca3_serum_df = make_dataframe(pca3_serum, 3)\n",
    "# pca3_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "Z0U0vdvJCmSj",
    "outputId": "4b1b710e-3bba-4d5c-f628-eb401315a9ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(pca3_serum_df, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "EYPgDQ9A0gx8"
   },
   "outputs": [],
   "source": [
    "pca4_serum = pca_analysis(serum, 4)\n",
    "\n",
    "pca4_serum_df = make_dataframe(pca4_serum, 4)\n",
    "# pca4_serum_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "k9JGmoWDH-iP",
    "outputId": "292d2af0-d150-417c-e3d5-24c86db26581"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(pca4_serum_df, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "vZ16An_m-3TG"
   },
   "outputs": [],
   "source": [
    "pca5_serum = pca_analysis(serum, 5)\n",
    "\n",
    "pca5_serum_df = make_dataframe(pca5_serum, 5)\n",
    "# pca5_serum_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "7tAZAD4o_Lwv",
    "outputId": "77199232-ad1c-42db-bce4-39bc4c8e32c4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABRcAAAImCAYAAADJxL3DAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdeXycVdn/8c81WZuFpQsUSkvBRLBFy1JANPoAZWvYFApBUVCKKIKPpfiToii4AfIAig+LloqCFBotRbZAAQUxj4pABWzZWpA1ZSktME2adc7vj3smmUzumcxMZk2+79erryRz3zNzOsmcOfd1rnMdc84hIiIiIiIiIiIikqpAvhsgIiIiIiIiIiIixUnBRREREREREREREUmLgosiIiIiIiIiIiKSFgUXRUREREREREREJC0KLoqIiIiIiIiIiEhaFFwUERERERERERGRtJTmuwEiIiIiIiIiIjL6rVq16vDS0tILnXOTUcJbMQiZ2Zu9vb0/2HvvvVfGO8mcc7lslIiIiIiIiIiIjDGrVq06vKKi4urp06d3jxs3rjMQCCggVeBCoZBt2bKl8uWXXy7v6uo6O16AUVFiERERERERERHJqtLS0gunT5/eXV1dvUWBxeIQCARcdXX1lunTp3eXlpZeGPe8XDZKRERERERERETGHufc5HHjxnXmux2SunHjxnWGl7L7UnBRRETEh5lNNzNnZr/N8fO+bGYv5/I5w897YPj/e1Gun1skXWb22/Df7fQcPV9e3p8iIiKjREAZi8Up/HuLG0NUcFFEREaNcJAh+l+fmW0wsz+b2efz3b7RyswONbOlZvYfM+swsy1mts7Mfmdmc/PdPvFkKhBnZg+EH+c1MyvJUPNEREREpEhpt2gRERmNfhD+WgbsDhwLHGRms51zC5N8jDeAjwDvZ6F9iczJ8fOlzcxqgZuAzwCdwJ+BFUAPsAvQCHzBzK5wzn0rX+2UzDGzXfH+Rh2wEzAXuDuvjcqtonl/ioiIiOSKMhdFRGTUcc5dFP73Xefc8cDheMGQBclmbTnnepxzzznn1mezrT7P+6Jz7sVcPmc6zCwA/AEvsPgQ8CHn3JHOuXOdc4ucc03AjsACYFzeGiqZ9hXAgJ+Gfz4jj23JuWJ5f4qIiEhuPfbYY5Vmts/dd99dC2Bm+1x88cWT8t2uXFFwUURERj3n3J+A5/CCIvsCmNlF4aWdB5rZ583sUTPbHKmnFq/mYvTSUjP7qpn928w6zewtM1tsZlv7tcHMdjKzX5jZ2vCy4Y1m9k8z+17MeUNqupnZl8LP+SUzO9LM/mZm7Wa2ycyWm1m9z/N92MwuNbPHzewdM+sys1fCbdwp/Vez3+fwgrbrgKOdc22xJzjnupxzVwGDskXNrMLMFoVfuw4z+8DM/mpmJ/r8P/p/D2b2ofD/910zC5rZ/Wa2R/i8SeH/2/rw7+MxMzvI5/Gif++nmtm/wr+Pt83sBjPzLVRtZvVmdpOZvWFm3WbWFv7Z77WPfo554d9zR/h3vszMpsR5jvFmdomZPRtu0/tm9iczO8zn3Oi/iYPM7OHwa/KBmd1jZh+JOd8Bp4Z//I8NlA542a8tcdpXCnwJ+AD4IfAE0Jjg//Nw+DlKzew74b/9LvOWU//UzMp97vMZM7vZzF4I/423m9kTZvbf5gW0h2vj7uHnfCjBOf82sx4z2yH8s4X/Fv4Wfq90htu40syaYu7r9/4sD7dvVfg92RE+7w4zO2S4NouIiMjo8+CDDz73xS9+cVO+25ErWhYtIiJjhYW/xhaRPhc4FLgLLwPPNzjo4zK84NpdwP3AQXhZXXXAwYOe2Gw2sBIYDzyCt3S4CpgBXAT8KMnnPA5vGertwMPAnsDxeEu+P+Gcez7m3K+F/09/A7qBmcDpwNHmLRF/I8nn9RPJWLvcOdee6ETnXFfk+3BAaSXwX3gB32vwXot5QLOZ7emc+47Pw0wHHgWeBX4b/vmzwMNmdgBwH17QqxnvdT4JuNfMPuyce9Xn8c4BDguffx/QAHwZONDM9nfOvRPV5n2BB4Fa4E7gGbzl9l8AjjWzQ5xzj/k8x9eBY8L3+QuwP9AEzAr/P6Nfl53xfqfTgb+G21QNHAXcZ2Zfdc5d7/McR+Et+78X+CXe31QjsK+ZzXDObQif9wO8LNNZwFXAe+Hb3yN5xwCTgeudc1vMC7z/L3Aaif+GbwE+FW7jB+H2fRvYDu81j3YpEML7Xb+B9348ONzmfYEvJmqgc+65cGDxoPDv/oXo42b2CWAP4LaorOSfAOcD/wF+j1cKYYfw852A9zeSyG/xgu2r8coEbMHL2m0AjsD72xEREZExZM6cOQnHx6ONMhdFRGTUC2cP7YYXWIwNAh0MHOCcOyG8nPfMJB/248BHnXNfcM6dC+yHFxQ6yMz2i3rucrzlw+OBk51z/+WcO8859w3n3Bxg5xT+K0cDn3XOneic+45zrhFv2fF44NqYc38HbO+c+6xz7r+dc99yzs3FC3ZsB1yQwvMOEs5g+3j4xz+lePdz8QKL9+K9fv/POXcW8FHgFeD8cAAo1n8BP3POfSq89Pp44EJgAl4g6gFgH+fcAufcKcB8oAIviOhnLvAJ59yXnHPnO+c+Bfwcr1bkpVH/V8MLGG0FfNE595nwa38cXkBpK+B3cbLqjgD2Df++/p9z7tPArQzUAY12I97fwuecc592zp3jnDsDL1j4NPALM9ve5zk+AxzhnDsm/BxHhts/CS/oB3ilAoAnwz/+PKp0wM/jvD5+IgHl34S/3oIXtJ4/TFbhh4CZzrnTnHMLgL2AF4FTfDJFj3TOzXDOfTHq/fgRvN/BF8xs/yTaGXkv+C3Zjtz2q6jbvooXyNzDOXdW+Pf7ZefcHngB+rjMy1Q+CS+Lc0/n3DfD7T7FObcrcEkS7RUREZFUbdoU4MorJ3LmmVO48sqJbNqU0/jWpZdeOmny5MkfGzdu3F4HH3xw3euvvz5oRYbFLIteuXJlzT777LNbTU3NXjU1NXvtvvvuM2644YZto+9z5ZVXTqyrq5tZXl6+94477vjRCy64YNDY78EHH6w++OCD6yZNmvSxcePG7bX77rvPuO6668ZHn7Nhw4aSpqamnbfbbruPVVRU7L3DDjt89KSTThp0vfHYY49VHnjggXXV1dV7VVdX7zV37txdX3311RElHyq4KCIio054WepFZvYTM1uOlwVmeEGVV2JOX+yc+1caT/PD6Iw451wvA0GX/aLOOxovG+1O59wtsQ/inHs9hef8s3MudvOMq/ECNQeHs98ij/tGdGZc1O33A2vwsi7TNR6IDKBSaT94AS8HLAy/ZpF2vc1A9tvpPvd7maigX9iN4a8VwP9zzoWijt0C9OJld/r5nc/v/SK8rLXPm1lF+LZP4AUD/+6cWxp9snOuGWjFC1w3+DzHL5xz/465LZJ9GB2AnoUXPL3NObcs5jnewwuiVuJlqcZaFl72H21x7HOMVPhv61Dgeefc38Nt24iXubszif+ezgufS/h+7cBSvHHo7OgT/eoZhn+vV4V/TObv9o/AeuBLUb9HzGwb4ES890tsNmEP0Ofz3Btib4s9Ba9v6cLLuIy9/7tJtFdERERSsXJlDTvtNIsLLpjKL385mQsumMpOO81i5cqaXDz9zTffvM35558/bc6cOe/dfPPNL86YMWPLmWeeOT3e+Rs3bgzMmzevbuedd+763e9+9+JNN930YlNT07ubNm0qiZzzve99b/tvf/vb0+bOnftec3Pzui996UvvXHbZZVOiA5QvvfRS+QEHHLD56quvfqW5uXnd0Ucfvem///u/p//qV7/qDzCeeeaZU//5z3/W/OQnP3ltxYoVL3zve997w5sr96xevbri4IMP3r2rqyvwq1/96j/XXHPNf1544YVxjY2N9aHQkKFM0rQsWkRERqMLw18d3rLPvwK/ds7d7HPuP9N8jsd9bnst/DV6FjKS4Xdvms8T7S+xNzjn+sysFS87bC+87L9Ixt3JeDXyZoXbVBJ11+4MtCcl5u0uXQe84Zx7zueUP4e/7uVz7EnnXGzwJ1Ln8QXnXDD6QPh1eQtvR2M/fq/l+2b2JF6g7yN4mX57x7TNr80N4TY/EnMs2b+RA8Jftzazi3zuExlUfsTnWLLPMVKn4wUDfxtz+2/xgp5fIf7feNJtNLMJwP/DWzq9K97S8Gi+9R2jOed6zex64PvhtkWC+l/E21xosXMuujzCUuAbwDNm9nu8v42/O+eG3SneOfeBmd2FN4nwpJndhtffPOqc6xju/iIiIpKiTZsCHHdcPR0dA8lyW7Z43x93XD1tbU+x9dbpR8mS8NOf/nSHT33qUx8sXbr0VYDjjz/+gw0bNpQ2NzdP9Dt/9erVlZs3by759a9//eq2224b8pp63AeR4xs3bgxcccUVO37zm99cf8UVV6wH+OxnP/tBR0dH4Morr9zx29/+9julpaWcccYZ/TUcQ6EQc+fODb7xxhvlv/nNbyZ+9atf3Qjw5JNPVp9++unvfOUrX+k/9+tf/3r/JO8FF1yw44QJE3ofeuihtZWVlQ5gn3322bLnnnvu8fvf/37rk046adjxjx9lLoqIyKjjnLPwv4Bzbrxz7qA4gUWAN9N8mvd8botk4kUH8bYJfx1JfcOIt+LcHvk/RNeLvBJvafQMvBqHV+DV3fsBXgByyGYaKdjIQHBy2GBPlEj74u3AHbl9G59jQwY6UZmP8QZBvUBZnGPJvpYjafN7cdoEg/9GJoS/HooXGI/99/Xwcb/Z+CHPEfW6lMQeS4eZleBlnIbw/qai3Yf3mh3ts8Q50p4hbcTndQhnFj4GnIdXt/AmvHqIP2Agc7GC5CzGy0T8atRtZ+D93f4m5txzwv82A4vwgqQbwhuy1CXxXE3hNo4Lf/0z8K6Z/S7OUnYRERFJ129+M55Bc4RRnIMbbhjvfzAzenp6ePbZZ6uOOuqo96JvP+644+Ju3vKRj3ykq6qqKnT88cfvevPNN2+zYcOGQWO0hx56qGbLli2Bz3/+85t6enqI/DvkkEOC7777bulLL71UDvDOO++UfOlLX5q64447frS8vHyf8vLyfW699daJ//nPfyojjzVjxoyOq6++evtLL7100tNPPz1k3PR///d/tY2NjZtKSkpc5Hl23333rh133LHrscceq0r3dVFwUURExro4o5OMeS/8NZUgXDzxAhWRoM77AGa2HfDfeBtM7BauC3lepM4e3hLOtIWDV/8I/zgnhbtGgoC+QSi8TTSiz8umpF5LctPmyH2/GRUY9/sXu/lJrhyFt0FJAHg9aqdph7eceDLeapjTEjxGMk7Hq3n5A+fc/s65rzvnLgj/zQ63qcogztus6E7g0+EdpCMbudzuojbrCZ/b55z7uXNuFt7fxfF4myYdg7eZTsKApnNuS/i99WFgGt5GP63hr8tTabeIiIgMY+3aiv5MxVhbtgRYty7Zici0rF+/vrSvr4/tt9++J/r2HXbYoTfefSZNmtR35513vtDT02OnnXbarpMnT5514IEH1j3zzDPlAG+//XYpwOzZs2dGgobl5eX7HH300R8G+M9//lMOcNJJJ02/6667xp999tlvrlix4oW//OUvz55wwgkburu7+9c9//rXv3718MMPf+/yyy/fcdasWXvsvPPOeyxevLh/pcimTZtKr7vuusnRz1NeXr7P66+/XhFbNzIVWhYtIiKSXZEg3Fy83XxH4r9ibwhnlUXq/UVqCO6KFwi6P3a5sJntFD4+UouBTwPfMrObEy0BNbMK51yXcy5oZi8Cu5pZvXNubcypB4W/rspA+4bzX3iZcdHt3BqvRmMn3q7UMPCaHhjncTLR5sjfyKeAX4zgcYYTWVaeakbjV8Jf78Y/47MEb/n9fDO7JGbJcSoiWYK3+Rwb8refhGvxdhT/KgPLr38V//T+2p8rgBVm9ie8DZ/2wNuwZVjOudeApWZ2K/A80GBmE1R7UUREJEPq67sYNy7kG2AcNy5EXd2IJtGHs8MOO/SWlJTw1ltvDVods379+oTxtTlz5rTPmTNn7ebNm+3OO+/c6vzzz5/6uc99btennnrquQkTJvQC3Hrrret23HHHntj7fvSjH+3s6Oiwhx9+eJtLLrnk1W9/+9v9E6W/+MUvLPrciRMn9v32t799DXjt0UcfHXfxxRdPPvPMM3fdZ5991uyzzz6dW2+9dd/hhx++6Wtf+9qQutLbb7993ADpcJS5KCIikl134W1GcoyZfS72YDjYl6yDzeyomNvOxqu3+FDUZjUvh782hIOPkeeqwdtQJBOTi7fiLbeuB+4wsx1iTzCzcjM7C29JdsQNeBtg/E9M2yYC34s6J9u+aGaxtR0vwlsGfWvUZjj/x0CQaF70yeGfPwW8gJeplhbn3ON4dfqOMzPf7D8z+2g4I3UkIgGuacnewcym4u16vQk4wTl3us+/L+P9/3cFDhlB+14Ofz0wpg17Aeen8Xh/wvvdnIq3kcvzzrmHYh67wsw+GXtHMyvD27gIIFHgfJKZfdTnUDXeMvZe8lDfVEREZNT68pc3YuZ/zAxOO22j/8HMKCsrY/fdd++4++67t4m+fcWKFUnVuq6pqXGf//zn3//85z+/4cUXXxwHcPDBB7dXVlaG3njjjbJPf/rTHbH/tt1229CWLVsCoVCIioqK/nqSmzZtCjz44IPbxHuu/ffff8svfvGL10OhEKtXr64E+MQnPvHB888/P66hoWHI8+y2225pj1mUuSgiIpJFzrluMzsBuB+4xcy+ipepVom3Qccckv88vgu43cxuB9bhZdnNxauBGKnLh3PuTTNbBpyEt8nE/XhBs0PxsvKeJP4uysn+v0Lh/9fvgGOBl8KZXs/iZchNx8v6mgRcHnXXy8NtPhZ4ysxagCrgBGA74DLnXNqBuhTcC/xfeAOP9XjZnw14Aa5FkZOcc87MTgUeAJrN7A7gObwdoj8DBIFTYnaqTsfn8Wr1/drM/ht4FG9J/U7Ax/Cy5w4A3h7Bc/wJb7OU68MbjwSB95xzVye4z3y8zMSbnXOdCc5bgvf6nYH3WqXjpnD7fm5mBwFr8YLXR+FlEzal8mDh390v8eqPwsAu2tHGAa1mtg4vO/EVvPfmoXjvzzudc8/63C9iCvAvM/s38DTeRjVbhds8GW/H8GCC+4uIiEgqtt02xIoVaznuuHqc85ZCjxsXwgxWrFib7c1cAL797W+vP/XUUz908sknTzv++OPfe+ihh2offvjhreOdv2zZsq1/85vfTDzmmGM2TZ8+vfu1114rv+mmmyZ9/OMf/wC8bMNzzz237YILLpj6yiuvlB944IGbQ6EQzz33XOVf/vKX2gceeODFCRMm9O2xxx4dl1122Y5bb711XyAQ4PLLL59cU1PT197e3p84uM8+++x21FFHvfexj31si5lx/fXXTxw3blyooaGhHeAnP/lJ2yc/+cmPHHTQQXVf+tKX3p00aVLva6+9Vvbggw9u9eUvf/ndo446Kq1xi4KLIiIiWeace9zM9sQLWs0FPoEX2FmHt6NtslbgBUi+CxyJV+9uBXC+c+6FmHPnAy/hBWTOAt7Bq0H3ffyXnaYsHDT5jJkdhrcs9gC8YKnh7eT8IHCTc+6+qPt0m9mhwEK8gNo38LK7ngIWOOduzUTbkvAzvLp6C/Beo814Ox9/J7w0tp9z7lEz2xe4AC8z72hgA1725o+cc8+PtDHOudfNbB+81+N4vJ2+S/A2S3kG+F/g3yN8jpVmdi7eMucFeJv6vAL4BhfNLMBAHcUlwzz8H/A2XTnWzLaLfQ2TbF+bmX0KuBQvUHk4XiD363h/SykFF8N+ixfQ7gZu9DnejreBzEF478vP4L03XwTOZPgs2pfxNt05MPwYE/GC/c/jvd+XpdFmERERSeTwwzfT1vYUN9wwnnXrKqir6+K00zbmIrAIcMopp7z3+uuvv3rVVVftsGLFign77bdf8Nprr335+OOPr/c7/yMf+Uinmbkf/ehHO23cuLF022237Z0zZ857P/vZz/o3fPzxj3/81o477thzzTXXbL948eLJ5eXloenTp3dGbxRz6623vvSVr3xl5zPPPHOXbbbZpnf+/Plvd3R0lPzmN7+ZFDln9uzZ7bfeeuuEyy+/vCIQCLgZM2Z0rFixYu2HPvShHoCPfexjXY888shz559//o4LFizYuaurK7Dddtt1NzQ0BD/ykY8kmkhOyNIviyMiIiK5YGZfwtvh9svOud/mtzXFzcwuwgsGHeScezi/rZFsM7MDgYfwMi+/mN/WiIiIjG1PPfXUy7NmzRpS60+Kw1NPPTVx1qxZ0/2OqeaiiIiIiIxW3w5/TbT0W0RERERGQMuiRURERGTUCG+wchSwD14Zgrudc4/mt1UiIiIio5eCiyIiIiIymuwDXAx8gFcL8uuJTxcRERGRkdCyaBERkQLnnPutc85Ub3HknHMXhV/Lh/PdFsmOqPfL1s65E51zqu0kIiIikkUKLoqIiIiIiIiIiEhaFFwUERERERERERGRtCi4KCIiIiIiIiIiImlRcFFERERERERERETSouCiiIiIiIiIiIiIpEXBRRERERERERERkQKyYsWKrX74wx9ul8nHXLhw4Y7bbrvtrEw+Jii4KCIiIiIiIiIiUlDuu+++rX71q19tn+92JEPBRRERERERERERkSLU29tLZ2en5bMNCi6KiIiIiIiIiEjR2NS5KXDl36+ceObdZ0658u9XTtzUuSmn8a177723Zv/99/9wVVXVXrW1tXvut99+u/3f//3fOIC//e1v4w444IAPjxs3bq+tttpqz2OOOWaX1157rTRy3+eff77czPZZsmTJtp///Od3rq2t3XP77bf/2DnnnLNjX18f4C1fvv7667dva2srN7N9zGyf448/fjrA8ccfP32PPfb4yO9+97tt6urqZlZWVu798MMPVwNcfPHFk3beeec9ysvL9542bdoeP/jBDzK6rDqe0uFPERERERERERERyb+V61bWHPf74+qdc2zp3RIYVzou9L2Hvjd1xYkr1h5ed/jmbD//3XffXfuZz3ymfv/99w9ee+21/6mpqQn99a9/rXn11VfLd9lll57DDz98tw996EOdixcv/k8wGAz84Ac/2GnOnDkffvrpp5+trKx0kce58MILd2psbNx00003vfTAAw/U/vznP99h5syZW04//fRNZ5111jvr1q2r+Pvf/167bNmyFwEmT57cG7nvG2+8Uf69731vp29/+9ttO+64Y099fX3XFVdcMfG73/3utNNPP/2tuXPnfvCnP/2p9gc/+MHUrq6uwMUXX/xmNl8TBRdFRERERERERKTgbercFDju98fVd/R09GcqbundEgA47vfH1bctbHtq68qtQ9lswwUXXDBl99133/LXv/51bSDgNWPevHkfAHz961+fAvDnP//5hfHjx4cAdt999645c+bsfuONN2771a9+dWPkcfbbb7/g9ddf/zrAZz/72Q/+/Oc/b/3HP/5x29NPP33Thz70oZ7Jkyf3lJeXuzlz5rTHtuG9994rveeee174xCc+sQWgr6+Pn/70pzsef/zx70Ye87jjjvvg/fffL/nf//3fyRdccMFbVVVVLvZxMkXLokVEREREREREpOD95l+/Ge+cf4zMOccN/7phfDaf/4MPPgg8/fTT1Z/73OfejQQWo61atar6U5/61AeRwCLAwQcf3L7jjjt2t7a21kSfe+ihh34Q/XN9ff2W9evXlyXTju22264nElgEeOmll8rfeeedshNOOGFT9Hmf+9znNm7evLnkscceG5fkfzEtCi6KiIiIiIiIiEjBW/vu2opIpmKsLb1bAus2rqvI5vO/8847Jc45dtxxxx6/42+//XbZpEmThhybOHFiz3vvvVcSfdu2227bF/1zeXm56+rqSipON3HixEHP8dprr5UBQ9q144479gJs2LAhqyuXFVwUEREREREREZGCVz+hvmtc6TjfZc/jSseF6sbXdWXz+SdNmtQXCARoa2vzzTDcbrvtejZs2DDk2IYNG8q22WabPr/7pMNs8ObQU6dO7QFYv379oCBiW1tbKcDEiRN7ySIFF0VEREREREREpOB9ea8vb4wNrEWYGaftddpG34MZstVWW4U+9rGPtd96660TQqGhMc599tmn/ZFHHtlq06aB3av/8pe/VLW1tZU3NDSktNlMKpmMu+66a/ekSZN6/vCHPwxaFr5s2bLxNTU1ffvuu++WePfNBAUXRURERERERESk4G1buW1oxYkr1laVVYUiGYzjSseFqsqqQitOXLE225u5AFx88cWvP//88+P+67/+q/7GG2/c5rbbbttq4cKFO956661bf/e7330L4OCDD/7wzTffvM111103vqmpqa6+vn7Lqaeeumm4x462++67d7777rulv/jFLyY88sgjVc8//3x5vHNLSko477zz2m677bYJX/3qV3dasWLFVt/4xjemLF26dNI3vvGNN7O5mQtot2gRERERERERESkSh9cdvrltYdtTN/zrhvHrNq6rqBtf13XaXqdtzEVgEWDu3Lmb//jHP75w4YUXTvna1762S2lpqZsxY0bHvHnzNu24446999133/Pnnnvu1K985Su7lJaWugMPPPD9a6+99rXKysqUAnynnXbaxocffrj2oosu2mnTpk2lxx133Lu33Xbby/HOP/fcczd0dnYGfvWrX213ww03bLf99tv3XHjhha9deOGFb4/4Pz0Mi7fLjoiIiIiIiIiISCY89dRTL8+aNWtDvtsh6Xnqqacmzpo1a7rfMS2LFhERERERERERkbQouCgiIiIiIiIiIiJpUXBRRERERERERERE0qLgooiIiIiIiIiIiKRFwUUREREREREREcm2UCgUsnw3QlIX/r3F3Y1bwUUREREREREREckqM3tzy5Ytlfluh6Ruy5YtlWb2ZrzjCi6KiIiIiIiIiEhW9fb2/uDll18ub29vH6cMxuIQCoWsvb193Msvv1ze29v7g3jnmXMul+0SEREREREREZExaNWqVYeXlpZe6JybjBLeikHIzN7s7e39wd57770y3kkKLoqIiIiIiIiIiEhaFCUWERERERERERGRtCi4KCIiIiIiIiIiImlRcFFERERERERERETSouCiiIiIiIiIiIiIpEXBRREREREREREREUmLgosiIiIiIiIiIiKSFgUXRUREREREREREJC0KLoqIiIiIiIiIiEhaFFwUERERERGRQcysxMz+ZWZ357stIiJS2BRcFBERERERkVjfBJ7NdyNERKTwlea7AamaOHGimz59elLnhlyIjVs20tnbSWVpJePHjSdgiqeK5NsTTzyxwTk3Kd/tGIlU+qKcCIVg40bo7ITKShg/HgLq70QSGat9kcZHIoWlEPsiM9sJOBL4CbBwuPMLblwkI6ex5ZhUiP2RFIeiCy5Onz6dxx9/fNjzWl9tpXFpIyEXor2nneqyajbaRlpObqFhWkMOWioi8ZjZK/luw0gl2xflRGsrNDZ6g8D2dqiu9gaDLS3QoP5OJJ6x2BdpfCRSeAq0L/o58G2gNt4JZnYGcAbAtGnTCmdcJCOnseWYVaD9kRSBUTn1EOwK0ri0kWB3kPaedgDae9oJdnu3b+7enOcWiohkSDDoDf6CQW/wB97XyO2b1d+JiEfjIxFJhpkdBbztnHsi0XnOucXOudnOudmTJinRadTQ2FJE0jAqg4vNa5oJuZDvsa6+Lm588sYct0hEJEuam71ZZT+hkHdcRITE46OQC9G8Wv2FiADwSeAYM3sZWAYcbGY357dJkjMaW4pIGkZlcHHtu2v7Z+Rjdfd1s/D+hbS+2prjVomIZMHatQOzyrHa22Hduty2R0QKVqLxUXtPO+s2qr8QEXDOne+c28k5Nx04Cfizc+4LeW6W5IrGliKShlEZXKyfUE91WXXc49193Vr+IyKjQ329VwfHT3U11NXltj0iUrASjY+qy6qpG6/+QkRkzNPYUkTSMCqDi00zm4bd9VDLf0RkVGhqir9zXyDgHRcRIfH4KGABmvZQfyEigznnHnbOHZXvdkgOaWwpImkYlcHF2opaWk5uoSxQFvccLf8RkVGhttbbua+2dmCWubp64Paamvy2T0T6mdnLZvZvM3vSzIZsq2qeX5jZOjN72sz2zuTzR8ZHteW1/RmM1WXV1JZ7t9eUq78QERnzNLYUkTSU5rsB2dIwrYErD7+Sb93/Lbr6uoYc1/IfERk1Ghqgrc0rsL1unbdcpalJgz+RwnSQc25DnGNzgfrwv/2B68JfM6ZhWgNt57bRvLqZdRvXUTe+jqY9mhRYFBGRARpbikiKRm1wEeDUWafynT99xze4qOU/IjKq1NTA/Pn5boWIjMyxwE3OOQf8w8y2MbMdnHPrM/kkNeU1zN9b/YWIiCSgsaWIpGBULouO0PIfERERKSAOuN/MnjCzM3yOTwFei/r59fBtg5jZGWb2uJk9/s4772SpqSIiIiIiyRnVmYug5T8iIiJSMBqcc2+Y2XbAA2b2nHPukVQfxDm3GFgMMHv2bJfpRoqIiIiIpGLUBxdBy39ERHIqGPRq9KxdC/X1Xo2e2tp8t0ok75xzb4S/vm1mtwP7AdHBxTeAqVE/7xS+TURERESkYI2J4KKIiORIays0NkIoBO3t3u6CCxd6uws2NOS7dSJ5Y2bVQMA5Fwx/fxjww5jT7gTONrNleBu5vJ/peosiIiIiIpmm4KKIiGRGMOgFFoPBgdva272vjY3eroPaZVDGru2B280MvPHXLc65+8zsawDOuV8CLUAjsA7oAL6cp7aKiIiIiCRNwUUREcDMXgaCQB/Q65ybnd8WFaHmZi9j0U8o5B3XroMyRjnnXgJm+dz+y6jvHXBWLtslIiIiIjJSCi6KiAw4yDm3Id+NKFpr1w5kKsZqb4d163LbHhEREREREck6BRdFRCQz6uu9Got+Acbqaqiry32bRERERPJJG92JyBig4KKIiMcB95uZA37lnFsce4KZnQGcATBt2rQcN68INDV5m7f4CQS84yIiIqOZAkmFoVB+D9roTkTGiLwHF1XnTEQKRINz7g0z2w54wMyec849En1COOC4GGD27NkuH40saLW13mA5dhAdCHi3azMXEREZzRRIKgyF8nvQRnciMobkPbgYpjpnIpJXzrk3wl/fNrPbgf2ARxLfS4ZoaPAGy83NXo3FujovW0CDZxERGc0USEpZsCtI85pm1r67lvoJ9TTNbKK2YoTZhYX0e9BGdyIyhhRKcFFEJG/MrBoIOOeC4e8PA36Y52YVr5oaDZZFRGRsUSApJa2vttK4tJGQC9He0051WTULVy6k5eQWGqaNILswmd/DiSeObMl0skuutdGdiIwhhRBcVJ0zEcm37YHbzQy8fvEW59x9+W2SiIiIFA0FkpIW7ArSuLSRYPdAdmF7j/faNS5tpO3cNmrK08wuHO738NBDcM456S+ZTmXJtTa6E5ExJJDvBuDVOdsbmAucZWafjj3BObfYOTfbOTd70qRJuW+hiIxqzrmXnHOzwv9mOud+ku82iYjICAWDsGQJnHee9zV6maRIpkUCSX6KLJBkZlPN7CEze8bM1pjZNzP5+M1rmgk5/+zCkAvRvLo5/QdP9HuoqoLbbvP6gkjAr719YCn15s3+94v0JeecA4cckvz9m5q8utN+tNGdiIwyeQ8uRtc5AyJ1zkRERERE0tPaClOmwIIFcNll3tcpU7zbRbJhdAWSeoFznXMzgI/jJYDMyNSDr313bX+mYqz2nnbWbRxBlmei30NfH5SU+B+LLJmOFd2X/Pzn0NWV/P0jG93V1g4EPKurB25XDU4RGUXyGlw0s2ozq418j1fnbHU+2yQiIiIiRSx6Q4dUspNERmIUBZKcc+udc6vC3weBZ4EpmXr8+gn1VJf5ZxdWl1VTN34EWZ6Jfg/HH5/a0nW/viSeeEvfIxvdXXUVLFrkfW1r0+7hIjLq5LvmouqciYiMZckWRRcRSZY21pB8iQSSmpu9QFNdnfe5VkSBxVhmNh3YC3jU51hadfGbZjaxcOVC32MBC9C0xwizPOP9HpYtgz/+ETo6ht7Hb+l6or4kmftHaKM7ERkD8hpcdM69BMzKZxvyShfVIjKWpVIUXUQkWdpYQ/JpFAWSzKwGuA1Y4Jz7IPZ4eCPOxQCzZ892yT5ubUUtLSe3DNktOmABWk5uSX8zl2gxv4dgV5DmijWs/UQH9RuhaTXUdg+cHqwwmj+yhbUPnEf9hHqaZjZRm6gviTXc0ne/6z7QtaCIjBr5zlwcu3RRLSJjWfRSo4jIAL6x0cs4KOJMD5HRKNgVpHlNM2vfXTtw8V1RgBfC2qFVZMTMrAwvsLjUObci04/fMK2BtnPbaF7dzLqN66gbX0fTHk2ZCSzGaH21lcab5xJq30x7A1R3wcLDoWUpNLwKrdOg8fQ+Qg8v6g90Lly5kJZpZ9JQVeWf6RhRXe0FFhMtffe77vvmN8E57766FhSRUUDBxXzQRbWIjHVatihSVFpfbR2SZbRw5UJaTm6hYVqBXQg3NRE87xyad4O14xmcpVR8G2uI5Jx5Nat+DTzrnLsyW89TU17D/L1T/6xPZaIj2BWkcWkjwZ7NUO7d1l7hfW08GZ7/X+9rMLQFwsOSyGYzjRv/l7beLfhelVVWwte/DjNmJF76nui6L1rktrlz4ZJL4LXXlM0oIkVFwcV80EW1iIx1WrYoUjT6L867By6O+y++lzbSdm5bVrKN0tW66SkazwkR2gLt5VFZSrePo+H64tpYQyRPPgl8Efi3mT0Zvu07zrmWvLUovKy49cWHaBy3glBpgPaejmEnOprXNBNy/tddIWDRnP6Y4tDjnVtongnz/+Vz8I474LDDhm93KnUbwdtw6txzobtb2YwiUlTyulv0mKWLahEZ6yLLFv1o2aJIQUl4ce5CNK9uznGL4usPhPZ10B6VpRSsgMYvlrB5vz3z2j6RYuCca3XOmXPuY865PcP/8hdYbG2FKVMI/r9v0mi3EHSdtPd4S5Xbe9oJdnvv+83dQ3eCX/vu2v7JkFjtFfDMpIFMxiHHy2HdeJ8DVVVeZmEyUqnbGNEdLgapXe5FpIgouJgPuqgWyRozG29mW+e7HTKMpiZveaIfLVuUPDKzK83shHy3o5AkvDjvaWfdxsKZFE2cpeQKKhAqIkmIWlbcvGtH/CzD8ERHsCvIklVLOO+B81iyaglTt55KdVmc6y4HT+4AlT3+h6u7oG6jz4GOjuSTQRJd9yUrsrJNRKSApbQs2sw+CxwI9AL3OeceiHPeqcCpzrmDR9zC0aipyUtx96OLapGEzGwKcD5QDzwF/NQ5966Z7QncBMwMn/d/wBnOuefy1dZ0Fc2mCSNRW+st84ktcD5cUXSR7FsA1AB/yHM7Ckb9hHqqy6p9A4zVZdXUjS+cSdFiCoSKpMvMPgpcBHwaqAZeAn4HXOGc681j0zKvuRn6+gCvhmrcLMOedh56+SHOWXnOoNqwZhZ3wgGD3hLvn58A0LTG50AqySBNTXDOOcmdG082Vrb57V4dr7ZjKueKyJiVVHAxXNS3GTgesPDNC8zsHuAU59x7MXeZDvxXhto4+uiiWiQtZjYe+AcwJXzTocAhZnY4cA8wAS/guCPQADxoZnv49FEFq6g2TRiphgZvA6vmZm/QXFeXuCi6yAjdcMMNsTdNMLPTfE7dLfp259yQO6bKzKbiTYBsDzhgsXPuqphzDgTuAP4TvmmFc+6HI33ukWqa2cTClf6TogEL0LRH4UyKFlMgVGQ4ZtYGXB69qYqZfRpoAaqiTp0BXIxXK/GYnDYy2x56qH+35vqNXjahX4CxqrSK2569jc7ezv7bIv1ARUmciGRYZWklzjlKA6X9Y68ARsuyPmq6twy9QyrJIE891R8cHaSiwnucyG7RFRXQ1eX/GJle2ea3e3W82o6pnCsiY5o554Y/yRtgLwFeA34J9ACn4mUIPQMc7Jx7O+r8C4HvO+fizAOlb/bs2e7xxx/P9MPmx+bNuqiWMcnMnnDOzU7jft/Hm6m/GPg93gD6h8C9wIeAw5xzr4bP/QlehuOFzrkfZajp/bLRFwW7gky5csqgTRMiastrC27TBJFiEwgE8OZLPSGvyH6iSvsGuEyMZ8xsB2AH59wqM6sFngA+45x7JuqcA4FvOeeOSvZxczUu8pv4CFig4CY+1I9KMYo3LjKzEHBRZJLBzALAWrxEjivxrsveAQ4ArgZ2xUv8WJqjpvfLSl8UDMJ220GnFzAMlsOUc70aqrEqSir6g4PpWHjAQmZMnMG6jeuoG19H0x5N1PzzyfjJIMkE1oJBmDJl8E7RETU1XibgPfd414I77QSLFvnXVqyt9SZjM3GdmKhNsc+TyrkyaqR7nSaS7LLoLwPvAftGgohm9jPgp8BCvOygg51zG7LSytGqpka7Qouk5jPAY865C8I/P21mhwFHAMdFAothFwBNwFFAxoOL2ZDMpgnz91afITISNTU1fO1rX6OqqoqLLrqoDVgcddiA7wOrgLsy+bzOufXA+vD3QTN7Fi8L+5mEdywQDdMaaDu3jebVzYMvvocJ1OW6zENtRS0tJ7fEDYQqsChF7tPALsAvnXP/L+r2lWZ2KF5/cgqQ8+BixgWDcPbZg7L+aruhZSk0nuzNCrVX0P/+Pnq3o7nl37ek9VTVZdXMmDhj6BhrpCssEu0U7ZwXWIy+Fpw1K/sr2xK1KVLbMdKmVM4VkTEv2eDiR4Hl0dmJzrk+4Ftm9irwc7wA40HOuU2Zb+YYoFoWIsnYGbg55rbH8ZYB/S36RuecM7O/AMflqG0jlnKtMPUbIim56aab+MY3vsGKFSv4zW9+w0UXXbTeOfeD6HPCGdKrYm/PJDObDuwFPOpz+AAzewpow8tiHFLxy8zOAM4AmDZtWraaOURNeU1KExz5KvOQbiBUpAh8DK+swtWxB5xzL4dLVn06563KtMhS3C1boHdwCcmGV6HtCmieCes+NYO6UxfStEcTy1Yv447n7kgrczFheYeRJIMk2inar45iLsrFpNKmVNsvImNassHFcuAtvwPOuV+EU/Z/ATxgZodkqnFZVygX5qplIZKscUDsKOd9AOfcOz7nv4VX6LwopFQrTP2GSMq+8IUv8F//9V98+ctf5sADDwTYycwqnHNxCl1lnpnVALcBC5xzH8QcXgXs7JzbbGaNwB/xNq8axDm3mHDG5ezZs4evb5MHwa4gjUsbBy1PjvRtjUsbs748OdVAqEiRiIxpXopz/EWKveZi1O7Q8dR0w/wXquGshRB+nyeqDYtjYNeAmNsrKMleVnNkp2i/AF28OorZXtmWSpvSab+IjFmBJM97A4g7Ne6cuxpvefTewEpg65E3LctaW70aEgsWwGWXeV+nTPFuz6XoD9BIx93ePnC7X90NkbFrA7BdzG3twNs+54K3wct72WxQJjXNbCJg/t3yoFn1Yug3gkFYsgTOO8/7muAiQSSXpk6dyoMPPsgVV1wBMAn4l5ntl4vnNrMyvMDiUufcitjjzrkPnHObw9+3AGVmNjEXbcu0ZMo8iEhSoicQIuVf4mVD1DJ0Era4JFqKGy28qUqwK8iSVUv48SM/5szZZ1JTXkN1mReDrS6rpsYqGNfj/xCVffDyDpfFz6Qe6VimqclrZ4L251wqbSrE9otIwUo2c/HfwEGJTnDO/dzMKoBL8Jb6FC6/GbHIBXpjY26L06qWhUgqXsDbEbGfc+5y4PI45+8CvJ7tRmVK0rXCUuk38pGhraxKKQLf/OY3WbBgwbNAJ/B/ZnY5Xr3FrDBvJ5lfA89G7/wac85k4K1wWYf98CaB381Wm7Ip5TIPIhLPOWb25fD3ka1MZgIP+5y7M/BmLhqVNT5LcYPl0LwHrB0P9e+X0PTiOGrvaKF145NDxkyGcfZ+Z2OYVxJh50aebPgQjZ/dMlCnscvrXFtuH8fkx87wb0cmxjK1td752a6jmIpU2lSI7ReRgpVscLEF+IyZHemcuyfeSc65n5pZOfADBs+yFZZCCuiploVIKp4AFphZuXOuO9GJZrYV0IC3033RSKpWWLL9Rj6CfIU0eSMyvE5gf+BCvN3ljyZ745dPAl8E/m1mT4Zv+w7hlSHOuV8C84AzzawX2AKc5Jwr3PFUAimVeRCReF5l8KLe7vBtnyImuGhmVXj1FjO6GVXOxSzFbZ0Ws4FLyFhYAcsndzBv6Tzf0gvXtv6Mth2voGb/E6G2lobr76ft2Lk0f7ibddXd1LWX0/RCOTV33Os/JsnkWCYXdRRTlUqbCrH9IlKQkg0urgBKSCLN3jn3o/AmL9NH0K7sykVAL9lsofp6qKqCjo6hx1TLQmQQ59x5wHlJnr49XrDgwey1KDuGrRWWTA2cfAX5CmnyRiQJ4Q3qvh/eCOEm/CtzZeJ5Wod77HCZmSEbNWRbNnZ0TlT/LOHmCSLSzzk3PYXTpwFXAA9lpzU50tTkTYTiZSw2ngzBioHD7YFe6NnMsc3HEohT4SvU3U3zb85l/re+0z+hWvPKeuYnCJAN6gefeZOmkl7/tefpjGWyXUcxHam0qRDbLyIFJ6ngonNuI/CrZB/UOXdj2i3KhUQX5lVV3kX3eeelv4QwlWyhadP8A4ugWhYiI+CcWwtcle92JC2V5ctRA+8hIv3GsmUQCg1eSrQRmlZDbTaDfMrGliLlnHvUzGYANUDONnjJt2zt6Jx0mYdsK5TN+0SyzDn3HN7qseIWXoobPHYuZx+8hc7SPt/T+kJ9dIY6fY+1V8CarbpZUt/N2ksOpv67V9K0z6nUxhn3DOkHQyUsPLOPlqXe7tSDH3zwWCYbkzMiIsUo2czF0SXRhXlHByxf7n1NZwlhKtlCwSDMmxf/sZYvV8q5yCgSdwCa6vLlZGrgrF1L64T2wUuJumDh4dCytJ2GbAX5tLOgFLFwFuP7+W5HrmR7R+ekyjxkk+q/ihSl1mnQeC5s6XH0xjmnJ9RDqZXS64aeUdkN1+0LJSFor+ih+sFvsfAv3/GdNPHtBwN9UOFlTbZd4a1L75+o3VxO0647UUv2JmdERIpRUsHFcB3FViAIHOGc891zK3zevUA18Kl45+VdzIV5sKed5r3LWVvTHc7s6fDS4NNZQpjKksBE51ZVwWuvpfo/ExnVirkvijsA/exyGhrnpb58eZgaOMEPTR26lCj8fePJ0LbLTvQ/arzMnnQyfhob4Rvf8D+mbGzJs+7ubhoaGqitreW+++6Le14h9iHZkGhH575QH2fdcxaTayb3T4ZE7pNKhs6wZR6yRfVfZRQo5nFPuvqDfT2bE55XVVZFX6iP3r6hwcXOMgYVoWh3XdDd5TtpknBne+DHn4Jr94uaqO3uZuE7i1j+7ynMu/1zBN1A9mTKkzPKrBaRUSTZzMUvAPsARyf6sHLOdZvZ/+BtAHMy8NsRtzBbwhfmrTf9mMa3f+Zd8AeiM3ui0uBTWUKYypLAROd2dGj5oMhQRdkXJcwO+v2xtJUE8B1+Dtf3JKiB07wHhF7xv1vIvOPzIX5mz6WXwqJFqWX8RB4rVmUllJVpZ0HJu5tvvpknnniCu+66i7KysrjnFVofki2JdnTu6O3g1tW30hPqobqsmm/e902ccwQsUBwZOqr/KqNDUY57RiJRsG/IufOaaVreRF9fD72EqOqCvoAXV+z06eJDLkTz6uZBEx4Jd7avgCsPgJ6oK+b2cqBnM8c0f5ZSB5T7PU/fkOcZolAzqxXwFJE0JRtcPA54yTnXMtyJzrn7zGwtcAIF/sEWLHM0vn8tQevun90alNlzBdR0k1qdsFSWBGr5oEiqirIvSjgrHuqjeZdO5v/L5+AIahSubX/NGwD7PWw5rGt/PXFmz1lnDW0LxM/48XusCOfghRdg8uS0/i8imbJixQp23XVXGv2C4DEKqQ/JlkQ7OoO37BDwPZ7J5dNZofqvMjoU5bhnJBIF+6L1hfr4woovUBoopauvi7I+CAXg2Ofg9x/1v097TzvPvPMMS1Yt6c/Anrr1VKqpoN2n1G5FrzeE8X1+g644c1TtPR2se/ohqD8x/sqQXGRWpxooLNSAp4gUBf8ttobaC3g4hcd9BNgz1cbk2nBp8M0zwz+kEuhravKW/vmJXRKYyrkiAkXaFyWcFbce1m0fZ55nBJMMkaCB78OWVVM3vi5xZk88kYyfWIkeq7QU7rkntecRyYJ//etfHHjggancpSD6kGxpmtlEwJIdCvqLZAIVnMgErh9N4ErxyNu4x8yOMLPnzWydmS3KxGMmI9H4JVpXXxebezb3j696Srxsxds/glck0UdlaSXXPHYNC+5bwGV/u4wF9y3g/D+dT8j5bxrjgO44AcTeUijzvxvVXVD3yz/AjjvCggVw2WXe1ylTvABeMpnVI9Xa6j2f3/P7iQ54RgKd7e0Dt29OvExdRCTZEeVE4K0UHvctYELqzcmt4dLg140P/xAIEPxsI0tWLeG8B85jyaolBLt8snNgoJ5jbe3AoLa6euD26FmoVM4tRsEgLFni7by9ZIl/RpNIaoqyL0oc6Kui7v0S/zv29MCWLSm/d4JdQTp7O+nu6/Y9HrAATXs0Jc7siSdexo+yhKQIbNiwge233z6VuxREH5ItkR2da8tr+/uoUkttr7/2nnbWbSzA97cmcGV0yMu4x8xKgGuAucAM4HNmNmOkjzuc4cYvyegpZVC9xWidvZ109XX1X/+197SzuXszFghQ0+UFBcH7WtsFC/4xcFusqi5vwxg/AaDpX91eQM4vUPfMM9kdM6UTKMxWwFPXgyJjRrIjyC3gXxIsjhqgc9iz8izRcqDqLtips5wlnzAe+uLHue3aXSixEjp6O4avMzTMRgtpn1tMlFZfWEZP/ZSi7IuaZjaxcKX/DvUBK6Hp4tvhmHkD75eI7m6v7uF3vuO9d2bNGvb3GL1xTGRJY0R1WTUBC9Bycou3hLG+nuA2VTTv2uHtgLgRmlZDbaIxfbyMH5V5kCIwbtw4NqeWfVEQfUg2DdrR+emHaLunmds+TNyyCrH6M6EzJVOfVzGb9/WPRQKBwpzAHT2f05JZ+Rr37Aesc869BGBmy4BjgWcy8NiAF0iM3iBq2lbTmPeHeb7jl0wpC5T5PraVlnLpX43K9i7WTIJ3x8GELTD1/bhxSkqA22+FeU1Rm710eYHFlqXh0lp+QiF4993sjpnSqTmbjUliXQ+KjCnm4hWSiD7J7N/A+865pHoBM2sFtnLOfWyE7Rti9uzZ7vHHH8/IYwW7gky5csqgTRYiqlwpASshVOoFFP3UltcWZp2hfAsGvbR7v5mp2lrt0Jhrfh/skYurPH2wm9kTzrnZadyvaPsiv92iI4G+hmkN3izyjTfCOed4GYuxqqq835tzcX+Pifq08pJyrjzsSk7d89T+Pqv1uftpvOlw30Fx/4ZWseK9h/W+lyLw0Y9+lK233prW8LKw4fqibPYhmZKxcVH4PRzsCjLl3ME7zSeS0bFQNj6vNm8u/AncAvycHvUKLJgbry/K17jHzObh7U59evjnLwL7O+fOjjnvDOAMgGnTpu3zyitxdpKLETsmqiqtinu9lSuLdj6ZI3+6gsbPbonaGRpcZQUuEIjazKqKwOaO/rHS5nKvlNa68VC3EZrWJAgsRpxzTvwsvkyMmc47z1sKHfc/uwguuWTwbUuWeEun4wU8r7oqtU2wNC5MToH1RZD+dZpIspmLDwNfN7PZzrmEI1gz2wf4BPC/I2xb1kTPlJ2575lc+89rcbj+C34zoy/Ux+beLdAb/3H8dhwTtENjIclVwejceZgi7YsGZQdtXEfd+Dqa9mgauCCvqYGKCigv9w8udsQMun1+j4nqyJYFyqgsrex/vmBXkMbb5w0KIPhuaBWtpobgnctpfmFZf6ZB08wmaitqiy9LSMakAw88kGuvvZbHH3+c2bMTj5sLrQ/JtuCtN9I8s4u1NXDmY3Dtvl69sfYKb/lfb4m3gUFJyKtBNiQTesQNyNLnVU1NYY85Rt/ndOErrmyqhyngcY9zbjGwGLyJjmTuE+wK0ri0cdBEaKLAYmVpJc45SgOltPe0U1FS0T/WSSXDsbykHMPo6hu6zrm6rJqdZhxA4xfvIBj1kO3lQKiLmtIaLp1zKa9/8Lo3fvtgKjVXz4OqPmo6Ovw35YunuhpmzszumCmd1SRNTd77wE86pSR0PTi84uqLRIaVbHDxauBM4A9m1uice9bvJDPbHfgD0Adcm5kmZpZf9pCZcfbsszEz6sbXsaV3C4seHL5ucS7rDMUuHei/oC9Eqr1WOEbfB3tR90U15TWJJyPSqYEY9XtMWEc2pr9KZkOrQYPligpaf/QVGv8+b1D/OahExGgt8yCjxtlnn811113HCSecQEtL/M1XC7UPyZbWV1tpfP0cQgf19mcxG3D2P+H1reC2GVAWgu5yKHVQ3gtn73sGFxzywxEHFvvHNw8tp35mF02rfEozFOfnVXJG3+d0YSu+YG6+xj1vAFOjft4pfNuIJRp/+Ons7WTfHfdlwrgJPPjSgzgcPaEeKksrva8llXT2dXrXdBghQnT0DA1WlgfKwfANLgYsgMMRirMTjHOOytJKLjkkKtsvMt556CFYvtxbWdLd7U0Sl5V5798tW4Y+WCRQV1MTf8w00my2dAKFmZ4k1vVgYsXXF4kMK6ngonPueTP7IXAR8C8zWw78GXg9fMoUYA5wPFABfN8593zmmzsyfjNlkQvxax+/tn9Zz3kPnBf3Aj1a3DpDaX4gxAsg+gVEE9Z8zDfVXisco+yDfbT0RRFD3vMfmkptvPdOPFG/x4R1ZGP6q6Q3tIq01XXR+N41BG3gqj9y/8aljQPLIgs9S0hGryQ+e3fbbTe+//3vc9FFF7HXXnsB7GJmp1GkfUgmBLuCNN48l2BJr1dEjIEs5mv28752Ru2W2hUeOV77+HVccMgPU3qe2DHOU289NXh8cxAsPMinNEM+P6+yvWRtlH1OF7wiC+bmcdzzGFBvZrvgBRVPAj6fgcdNOP6I25i2xwZ+CO/Q3NkbLi3Z18vCntnM2GYGTV+4lCfbX4xbhgaIe+yu5+9KeoIWGBjv7LYb3HHHwKoTM+/f5Zd7y48TBer8xkyZyGZLN1CYyUliXQ8mVmR9kUgykt4S0Dn3QzPrBS7E+3D5XMwpBvQA33XOXRJ7/0KQaKasL9THWfecxeSayby5+c2kan8Y5u24Gi3ND4R4AcTlJy5n3u/n+QZEB13QF5JMp9VL+nLxwZ7jWiGjoS+COO95jJapIRqe879PsBya92Dw5itlA7/HxBvHBAb1V8NtaFW3cfBtzXuXEwoYfpP6KhEheZfCZ+/3v/99SktL+cEPfgAwHrg+5tGKog9Jh1+Ar3lNMyG/UgxAdwmE4uxm0Bfq46yWs5hcPXnYFRWtr7Yy9+a5dIe66e7rpryknAX3LSDkQmzpHcjsiS7N8Pz/wj0fDvd3m8tp2nUnamM/bxobvd9xtj5/crFkTRfguVWEwdx8jHucc71mdjawEm/a4Qbn3JpMPHai8Uc6Srp7mXHv48x/4Vn43u00tLQkLEMT79hzG55LeoK2n9/uy11d3r9Fi+CFF+Cee5IP1GUymy3dQGGmJol1PZhYEfZFIsNJakOXQXcw2xk4DfgksEP45vVAK/Ab51xylXzTNJLC5ec9cB6X/S1+cdvIDmJVZVW+6fRDHu+T53HpIZcO3JBC4droAf7Urady/oPns7ln6A6WFSUV/TVGYlWXVXPVEVcxf+/5hbdserji5AVYvHZUynYx5TSL0GeiUHAx90WJNl6pLami7WcBarqiNm4xo3VyN40ndA/dfOX2cTQ89nb/73EgaNlHe08H1a6MQKCElhPvoGH3w5JrQ9fQmovnNZZx2X7xaxst+uSiwcuFRHIlzX7ulVdeYfr06evxdj/NeR+SKcn2RfE2lTp6t6O55d+3pPXcpVZKr+vtX4749f2+Do7+cQjAjU/dyIL7FtDn+pJ+3MpuIFzfMbKpQqCsjJZlpTS8HvD6xspK6Owc+JrpTVBytRmBNj3IrUxvWpEhyYyL8j3uGU6yfVGi8QeQVuBx0V/hkj+Ff0jzfZNwXBRv46pM/z0V6N9n2rRZVXwF/LvWhi6SrqQzFyPCH1wXZqEtGRMv0DbcTFmkKHAygUXwMhcHSTK9OXaAX1FS4Vv/A7zMgHjHIin6GV02namgX6LZMhWvzZ1sbrKR51ohxdAXxZOw3mHAaL77UuY/U9n/3gl+tpHGa+sI9kUtSQ5n+Mz9PFzyzG957f3X+vu7tgOW0/ydY1i3dSl1b/XQ9J8yai6fN+g9VltRS8vJLYN3ayyrItTXy9Ev9rFs7xKaVnV7mZGBAPWnnEn1i9ekNqMvkgtpLi3aeeedAdqcc4dktX0FIFFZmNueuY1qymlnuO1Nh+p1vYMe67LWy8CguqyK/773vzEzekO9KQUWATrLB//cXg7QQ+NxPd7EB3gBxeivmf78ydWSNW2GlVtFnE1VzOOeaH7jj8hkx/ITl/Pa+69x/arrefSNR5N6vPIeeHJ7WLJ3eEXHMO/PeNeJtRW1XHrIpZzVctaQ+1x6yKX+K8UynX022rLZVIs7viLui0TiSSm4aGbTgH3xFsY95px7LSutGoFEgbZESwb9lJeU0xfq8x0U+15MJ/GB0PZBG4f+7tCBOiH4FxaO6HW9lFkpPW7ottXVgUp22mqnuBcMKS+bznTQzy+tXsVrcy9bH+x5rBVSDH1RIsNuvNL+OswfyAJsXrXEW5Lsc32+ObSFb93/Lbr6urz+7r5zaPldH/Ofi+5XvAmT4LFzab7rEta2RwUiw8uDHnr5IZY/s5ySENwyo487djcWHl5Gy3Zn03DKBTSVORZeeZ1vmwN+JSJEciWNi7FXX32Vxx57DGAbM5tabH1IqhJNaJRYCX0lfb79S8rCc67tSU7Spsp3s6khJ2Xo8ydbF/l+k7i6AM+dIg3mFvu4J1bDtIa4y5ODXUEeeeURnmh7on8CI5HuUrjvw/DXnWHh4dCytJ2GOO/PRNeJs7afFXdDz0UPLuKUWacMvaZKtazBcEkco7FMgmpx+yvSvkgkkaSDi2Z2ObCA/qEjzsx+5pz7f9loWDoSzcxHAm3xZqT8dPd1UxYo8w0uxtYvA4b9QGid6jjkF7smDCbGqqKMUE8PPT6/qcCWTlxXZ/wMqFTqoOUq6KfitfmRjQ/2PM2uFkNfFJFOFrXfxMVwxc8jfUrknLnHwfrLvWXNkTqND+0Mt83YTMkD59JB96AB9YkzT+Sclv8e1De1B7wBfePG/6Wt/AJqy8OZBjcdTmhLB+3lUUuzl/VR8+knYdYslTuQ3EvxYuxb3/oWP//5zwmXhfkQ8J9C7UMyZc3ba+L2IR29HTTNaOK2Z5fTG+ojdlFGIfHbbGroST6fP+msysjGRf5wk7ga/+RGkQVzi2nck4qa8poh1yiR4F+f6xs+sOjwXpHIpEZUzda2XXYi9rc53HXixXMuTv2aKpXss2SSOJTNNrYUWV8kMpykgotm9jlgIV43/hxeN74bsNDMVjnnbs1eE5OXcKmhC3Hjkzdy/p/OT/rxqsuqOXu/s7n2sWt9dxUbMnsV9YEQu/FC4xvQ+N41KQUWAUq6eri9GeY1MbTW2m2V3DXhwdR2NosnV0G/XGYCKKiRXXmYXS2WvgjSz6L2m7hItfj55jL48afgqLXeILvPoKN/maG39LE/ELl0LkfvcgSd3R39O8VGC23poPmJG5l/wFk0bDuLtp8FaN7Zu8Cv2whNa6CmewvMmQNl4S1lVe5AcimFi7Fbb72VK6+8EjNj991355lnnukEyslRH2JmRwBX4b3bljjnLo05XgHcBOwDvAs0OedeHslztr7aynWP+2cdA1SWVvLH5/+Ii1yoF4I4bfHbbGroSTGfP+muysj0RX46k7ga22RPkWRTFdO4Z6T8gn/RqkqrCAQCnDX7LJ5uW8Wf1z3Qv3t9tJB512Cxv90bn7qR7j7/8g8hF+KeF+5J/Zoq2eyzZN//ymYbe4qkLxJJRrKZi6cDvcDhzrmHAMzsEOBevL67ID7YhltqePcLd8cNPvoJWIALPn0BF3z6grg7jg0S/kBo/cphNH52y0AwsBu+UdkDoUDy/xkHFb3QshQaXvU2V2ieGXtB38lzm4zqqhR3NvOTqyy0fGQCSHbkZ3a1KPqiZLKo49Ub8pu4SLWkAwb/8wm48gB8s56jbe7eTPPztxHyCSyCV+ts3V/vgDUVsHw5NZu7/Zckdnd7//rvmELm82i6gB5N/5dikcLF2JIlSygtLWXlypUcdNBBmNkaYBE56EPMrAS4BjgUeB14zMzudM49E3XafGCTc67OzE4Cfgqk3ZlG+qJEE5vRZVoKXQBv/JP4pKjPn5Gsysj0RX6qk7ga24inKMY9mZAoSaQsUMYJM0/g6sarqSmv4Zz7zuHelx/wPbe9HK+8TJTWV1tZuHJhf339Iffpacew1HeLhuSyz1J5/yubTUSKVLLBxY8Bd0Q+1ACccw+a2R3AgdloWDqGW2oIJMz8iewW7XeRn9TSYiC47ywav1hCMOqzq70cCKVYKN3grH96gUXwljcOuaCvrqZp5yNZuKHV9yF8l27Hk6sstEQBqb4+byCdCtVwzJ/8zK5mrS8aLqMoFcNlUUeW1sSrNxTLr/h5eUl53Bl48OYyQklmIYVwcY9Vd0Hd3X+Gfz8CXallXnsPPkzm82i6gB5N/5dik+TF2NNPP82xxx7LQQcd1H9bDscz+wHrnHMvAZjZMuBYvN2qI44FLgp/vxy42szMhddwpypRXwRQGiil1Erp7Mt9gDGy07QvY3D2ooNxPd6Ea/8u9ol2i4783hNd0Hd3wwknwPHHx58EyORFfiqTuBrbyICiuAbLhERJIj2hHnao2YGa8pphs7FjA4GRSZZ4gcXIfY788JG0vpbmNdVw2WepJnEom01EilCywcVt8VLxYz0HfCZjrRmh4ZYaHvnhI/nrq3/1/eCqKq3ihJknsEPNDomzE4fRvKY54YV6sqq7YMaGYU4KBKj93Km0bJyVdAZUv9jsmsbG3GShRQekenoGdnmM2G231C7EVcMxv3I/u5qVvijJjKKkDbthS3hpjV+9IT/BriDPbXiO+XvNZ2PnRiaOm8iu2+7KOSvPiT9YztDyxgDQ9HQfdKe520OizOdcXUDnIptQwYD8S+JibNOmTey+++5+h3IxnpkCRG/C8Dqwf7xznHO9ZvY+MAEYNCIwszOAMwCmTZsW9wmHq9c6pXYKr7z/SvL/g2GU9UFPCZT2QW+AuP1QqZVy4swTueP5O+K3zwZ/X+JgzzeBI46APff0Pm+OPBLuuSf+50+iC/quLrjvPvjrXxNPAmTqIj+VSVyNbWRAUVyDZUIy9aiTycaODQQON8kC3sZ0p+55KrMmD72mMjPOnH0mP/rLjwbVz07tPzcKN2oREYmRbHAxAPhdwfYwwkvYTGYL+WX3RAfaZm0/i+/86Tu+9y0JlPSn2o/EcAP5ZPku/amo8AbDMbPzDTXxd1zzFS+75tJLYdGi7GehNTTA88/DLrsMvr2z0/t3yCHwn//ADjsM/1h52lREouR2djVbfVEyGUVJS3XDFl/hgFjriw/ROG4FodIA7T0dg/q0cz5+Dpf97bJ0mjissj6o7I3JFEpCbL3ZppeqqI03aM7FBXSusgkVDCgKoVCIskht0MFGPJ7JJefcYmAxwOzZs+POaA5Xr/WND96I+xwBAgQskNRurQBVXfDJ1+DPu3h1XhO9mr2ul8k1kwlY8uViHOGdovfcEy65ZOBAovdVogv6iFxNAqRSSkRjGxmQtWuwQtM0s4lzVp7jeywSMPztk79NGFisLK0cklwx3LVZSQiOfmoLy27/EU2fuWDQNZXDcc0/r+Gax64ZUj+7YVoKYwht1CIiY0DSu0VDBtLxYmQ6WwigYVriQFvCOmddDm5aklJ2S+xusFO3nhp3IF9ZWolzjtJAaf9zh1wIM8Mw77ZuCLg4F/Snnw4vvuh9f+SR3s6sYclmQCXMrlm0CF54IXEWQKbccw+Ulvovs+zqgl13hQceGP7iXzOBY1HG+yKSyyhKOlso1Q1bhggHxIIlfTSe2UHQ0X9pEV278fmzn+fax65lc8/mxI/nozxQTneccg2lfTD7Ddj/dXhuIsx6E2qTCDC2TvM2kInefGohHbR8ciq+7+RsX0DnMptw9WoFA4qEWd6ux98Apkb9vFP4Nr9zXjezUmBrvI1d0jJcvdZEgcOSQEnCZYSxQgYPfIikwh0VJRXMmDSjf0zW09cz7NLs9gpYt31pap/riS7oY2V7EiCVUiKZGNuo/utoko1xT8F56q2n6AsNXSVRVVZFy8ktPPnmkwnrJgJ8ffbXhwT96ifUU1VaRUdvx9A7OC+4eMuMPu54+jIWvngtLV+4l/l7zyfYFWTKlVMGjbH6x2A3zKFtxyuo+dypyb2vxuJGLeqDRMYcS6aMj5mFSP2DzTnnEgYvzewA4CLn3OHhn88P3/GSePeZPXu2e/zxx1NsymCbuzcPDT7+88m4HX5w31mDAohNOzdSe3vLkIyiqtIq+lwfvaFe+tzQD8fa8lpe+MYL3PPCPYOeGxhoz/oumk7/WfxMoaoq6OgY/IGUSvbNkiWwYEH8AetVV+Umu+a88+CyYTKuamuT2whiypTBwYNU7i95YWZPOOdmp3G/bPVF84AjnHOnh3/+IrC/c+7sePcZri/y2y06MpExZLY7PAALrl1N86S3Wfvn5dS/1cN75fDdOdDtk2hVXVbNVUdcxW4Td6NxaSN9oT7/gXOU8pJyAhbg+I8czwE7HcCiBxf5ByYdVHVDR/Tu9EsHasD6CZbDlHMhWDH0WG15LW3ntnkTONEDzc5Ob1IjW/1Rrvq71lY49NChZR6y8VwyIoFAYFBwMeRlmw6309uwfUgywsHCF4A5eEHEx4DPO+fWRJ1zFvBR59zXwhu6HOecOzHR46baFw1Xr7W8pJyKkgrO3PdMrvnnNf4ZP5Fe2Aa+L+2D3iRfpYqSCv7zzf+wQ+0OrA+uZ9df7DrsxjLVXXDVwxXMv39Dap/rsdnLiSxaNDgrMhs2bx6+lMhIxzZ+GdvpjBklZ+KNi7I17smGkVyjRQJ5fjtF15TV8MI3XqDuf+vo6Ik/zomMi2KTLe5fdz+HLz086bZExizLVi9jwX0L/FehdMFVD5Uzf01Fau+rZN7/8RRTsE59UFFL9zpNJJUPnlSn+pM5P6PZQskakuWXILul9SuH0fjFEkI4L0AQqGThlq+w/I4K5h3bNSijaLgL+0sPuZTJNZN9Mwz7b1uyBMqqoTvOALijY1D7+rNvnEvuA6dQltoks1Qp2SyCM8+En/0MzLwC7aN9JlCy0Rclk1GUkuGyqPuFB2CtO/Qw9/hOut+H7v2htBd6S+K3PlK7cf7e81l+4nKOXXZs/6ZUfsoCZVx5+JWcOuvU/jbE1haq6oaOMu85O8JBwvbw18aTvV3rB018lJdDWRm0t9O8dzkh8w9YhFyI5j/+mPmnXzt4oGnm9V1+zGDLFm8iIt1BdDr9XaqD98jnR7zAImjJU4HxmVQdro/ISKpjuIbi2cBKvFIwNzjn1pjZD4HHnXN3Ar8Gfmdm64CNwEkjfd7YvujJN5/kvhfvi3v+nOlz+P2Jv8c5x3WPxdk0wYZ+3xtnx3n/uxu7Xb0bLSe38NyG5yix4e8cAJouvjP1z/Xo2sDLl8Of/zx4Z/uIXK12SKaUyEiynFT/dTTKxrinoCSqi+hwfOv+byUMLIL/ypBgV5B5f5jnf4foTaOiRDbeS1g/uwLWVXdDsDu191W6pYSKacM49UEiY1ZSwUXnXPJFcbIg2dpCqRi0nPmZN2kq6SP28jFYDo2f3TJ45+dQJ1TAsZ/tIjBcrkOMRQ8u4pRZpwwJLgxqy0tP0dTTPqQtcYVC8OMfw7XXxv/Aib5YfvPNgexHP+lsSJnOTFoyS5WGC3ZGf9D29Hg1KcvL4eyz4YIL9ME1CmWxL3oMqDezXfCCiicBnx/pgw5briA8AAt2BTlsHmwpHzg0XAZQdHHzeb+fFzfrp6q0ipJAiW/G5KCgw4rraXvmUZZ/ZCCwGC1EuN5ZZNf6cePgpZf6yyisnfgk7Zv9AxbtPe2s++2VDO5M2wcep6bG63si/Zdz3u7xkazGdAfRqS4tTGfwnqjWInj9kiY6CkYo5neV69l551wL0BJz2/ejvu8ETsj080b3RUtWLYm7uV11WTXHzzi+f5yy/ITlyWf8pBDO6OzrpLOvk8aljZw+85SE9dBKnTEuUEHLKXdQM+UAbxI21cydyAX9iSd6GYF+wcVCmwRId8M01X8dVfJ9DZYrw22E9/fX/57w/gH8N7JMuJnLMJO3U7ee6n8CgIOd3g9/H/u+irkuCn62keZXWgZWwKW6IUyxBevUB4mMWTlPmY+R8WyhZAxZrujKWHhmz5Blf817xF8r1ReATt+a8PGFXIjmx29k/jMV/R84rQ3TaLx93kBbKipYeO7wSxD7tbd7WXvRA+XoD5zly2HevIGL5USBRYBrrkktMJfuTFpkVv6QQ/zrLkLiLAK/D9rI41x7rfd/EElSvIyirD9xeAB24yzYkmJ/EpmhX7Z6WdyBc1mgjBNmnsDVn7qEmtvvgbV3Dbkg7w86rHKct+oJOir8a7C1V8C68XgB/PJyuPdemDy5f4BYv2oJ1ffFCVhQQd2mOBMXgYC3mVRlpXcBvdNOXlBxc9Ry7WQH0bETHY2NwxdQj9xn9Wr41a8GZyAm87yJsiMBzjqr8LIKZExLpSbsqx+8Gr9WWQaEent493e/ovrD0F4+9HhZoIzPf/Tz3oZ7/3wS9puS3ERqvMBjsdU9SyfLSfVfpQgNtxFeVVlVwvvP2XWO7wYr6Wy0GZm83dK7JfGJkeBk9Psq5rqo9cOVNL70FULjKmkPdaa3IUyxBesKZZWciORcvmfD+rOFzKwcL1vozmw+YbArSOPSRoLdwf4Pm3brIVgBc06Bq/f1MhYB1kwcWBIYq6fEW7aYivaedtZcvIAlvz6L81ZdxtW/PYu5Nx0+uC10EazwliBu9hloD1Fe7i0f9NPXB8cc4w24I518osAiDCyvTkZ0gC/y+O3tA7dHBwf8NDR4u0JXVvofT5RFkMwHrUgKnHMtzrkPO+c+5Jz7SU6eNDwAu/vDpJT1YxhHf/holq1expq318QdOPeEetjh8eeo2WFnL8h12WVeDcIpU7wBcLSmJurfL6E6Xqyfcuo+PtcL3q9fPyRg1jSzKe7Or4HeHppWxanx1t4Or7/uDYwvucTL9IuXQZ3ovd3a6v2/FiwY+H/uvrsXuKyt9QII4H2NBBiefHLgPlddFX9pc6LnjWRH+qmuhhkz/I+J5EltRS0tJ7dQW15LdZn3t1tdVk1tea3vLqvZCiyCtxpkwge9cQvKlZWUeYHFLpd4vHH//UPf/379HAxkBF51lTeRcdVV3s+jYRKgtdWbJIlHG91JgUo0huhzfUyqmpTw/sfudqzv7ZGgpa94c57hSZbX3n/N/wQAg9e3Cn8feV/FXBcFy6Hx+E6CFeGVb3jXgsFu71p0c3eSm/EVW7BuuHGR+iCRUSuvmYv5yBZKlB7fXQrnHgbnHwLHPO8tAYxXj6OqG/oMUokvlvfCdXv2UhLygpYVvd10xSkzFDJo3ruc+f/oHn4Zs9/yHvDuE0gxfpzKh1QmZtJ22MHbFTrVLIJi+6AV8dM/AEttVt0wbll9C3c8fwe9oV4qSyt9l0VXd0PdvY/214UF4mfj1dbS9JM7WXi//xLIQHkFTef/HmJrRoZFAhaDssK7IeCgZWko/iZVsQPNdOskxlsytGgRvPBC//Lt/qWFzsXfMCHZ54XEJR4KbZmlSFiyNWETZRPFqiqrIkCAkAvR0dtBqZVSEijhzH3P5PonFtPuUy+tugs+tCn+bhX99TFvvDH+WCcykRq9CmK4rON0654VMtV/lSLmN4boH9v09vHwKw/Hve+40nGcuuepvscSZWpXBcoJhBwuEKDddQ3aeK+mvCZxNmUX1G0M/xB5Xy1bNui6KNEKuEhdx4SlcyIysXt8LmlcJDJm5XtZtG/9oWwaLj2+uwy6gVs+yrCZROaIG3z0feyYzRm6Erz67eWwbt4cOHCW96Exderg5c2RANyZZ3pLmeNdjCeqBeYnlQ+pTAX40qkrVGwftCJ+wgOwo16AlXUk3ZeEwsPV4S74ew3WTIIle0PTaqiNvjb3mQCoPfAwWiavpPH3xxIK9dFuPUMG24n0Bywev5F1Pz6Hujd7aFpD/MAiDB1opvPeHm6i4557hgYSlixJvn9M1KcU2zLLfCum3S5HuUQ1YSO1oFe/vZo+15fwcaL7iD0n7zkkYOmc49eP/tL3vgG8YVQgQQZR8x9/zPyFV3q1laPbWO5dvK8d30H9ewGano7p46Awlwxmi+q/SoaY2f8AR+NdEr0IfNk59162nzd60uOZd57hmkevBqCTHt/zy0vKKQ+Uc+8X7o07PvGd+Bymz4o8VsISEkDTf6qgtmTgfRVzXbR2fPwVcJG6jkkptmCdxkUiY1beg4u5lvQsfLyLfAcVvd5guDOZZctpqi6rpu6g4yF64O8XgHMOrouzm2M6UvmQymSAL9UsgmL7oBXxEx6AnXrsXM7r3ezt1JyGypJKMCixEm+2nzI6XQ8G/OwTXp91ViMs+Adc8Ej4AjzOBEDD7ofRtuid4Xe5jqOmvMarK/tIObT7XxAA8Qea6by305noGK5WYjLPG5HuxgtjTTHtdjmGxdalrizxSpdEsoiqy6oxjLP2OwvDhvQRfgHLlg+OprH8D4TwLraru7yL85alcNeHh7kA/+3QwGLrNK98zMDjhVh4qE+96rG0kkH1XyVzHgDOD68w+ylwPnBeLp44Mumx5G9XU9rVTZfPtVZ5L8ypO5TjP9qU1PhkuEzteJMscQOTvX20cDw1lx80+LM+5rqofqPX1/n1b5G6jkkpxmCdxkUiY9KYCy4mmoVKisGnX4a/7ZzefZMVW1QdiB+Ai3zgdHfH3xwlVmRThugdWlP9kMpngK8YP2hFfAT3nUXzXZfwmdab+UPHY/RYitnGeDuvLvz4QmZMmsGat9dw3T9+ATaw6VQkS/qyT8K1+8K9S6FhQ5U38DvvvPibvKRruIvcHXaAQw/16iHusMPgY+m8t9OZ6Eh0n+j7JtunjMZllplUbLtdjlHRdakjOvu8ZbbOORYesJAZE2ekNOFAaysNV99Jm/PKzawbDzt94E3S3vVheLMGqrr8d6n32wwqWO4FFoNR50cu3htPhrYrorKlU5noDAa95dd33+39fNRRcOqpxZNZO1w/qPqvkiTn3P1RP/4DmJfrNqz9+92+mzyBV8Zq1obSlMYp6Y5rki0hAQy5LmpaDQv9K834X+clbEgRBus0LhIZc8ZccDF6Fqq7r5uuviSDcWHVXVDi/Hc1HFa8JdTOK1reE0ptCWK/yAfOCSfAffcld5/SUi8AEFuHLJUPqXwH+Irxg1bGpMgSw7XvrqV+Qj1NM5uoragdkiGUygREtPKScmZMmuHN9q9aAhYAfJYyGmyuiFyAd1CzfLlXmzXTGWTDBe7Wr4fbboPbb/d/zlTf2+lMdCS6T0WFl+UzY4b6lEwptt0ux6hEdalLA6XMmDgjtQv0SFC5q4saYP6/hmYdVnUTN2s74NyQzaAS1jHDC2DO/1f4ht5eWLPGK4OQaAl+ayscdhhsidodduVKb/Jl5crU+8V8LP/Xig7JjtOAuLskmtkZwBkA06ZNy9iT1r8L1cTJ+uuCugQLIzIt6cBkzHVRbXs7LbdV0nh856DdolO+zutviIJ1IlLYxlxwEQZmoW588kbOWXkOPaHkP6ECwO4flHFfnPofCcULHJg3g3XyuP056L1taZp+JDXbzkr+cSODWOe8i+JkshfN/OuQpSrfAT590EqBiw0gVpdVs3DlQpafuJx5v583KEMoXc65/hnwNW+viVufKGLgAjy8wUJUBlnw5edpfvmeIYHQlCS6yI3I5IYL6Ux0DHcfLR/MLG3CVRQS1aVOqUZYRExQ2S/rsCMyWeugysrpoJvqsioCvSGWr5nJsj2fYu1WvdRv9DKBEtYxq/AyIykt9QKLZvCznyWeQAkGYe7cwYHF/sZ1eMfWr09+XJOv5f/5nvCVomJmDwKTfQ591zl3R/ic7+LtXbk03uM45xYDiwFmz54db2+mlDVNP4qFL6/0PRYAmnY+MlNPlVkx10UNdXW0ffZIml++J61SMyIixWRMBhfBm4U6a7+zmLVV/aDNCxLex5WxfMezOL5myZD6PyPV1dfFne89yi+vgJqyv8K3vpPcQDR2EJusTF7MKcAn4stviWHkwv2YW4+hNJCZLnjhAQv7B6obt2wc5mzvAvyZid5GL2vH03/R/tQOPTRevSuh0pJBgdCWk1tomJbCRbHfRW48mcpaS2eiI9+TI2OJNuEqCgl3R02lRlhETFA5UdZhdXk182bMY4f3eqlbvJyp78O8Y58gVDdQp3Hh4XDmYwnqmEV2cI3sMh3ZOTnRZEZzc/ydqMEb7yXbR+V7+b/6NEmSc+6QRMfN7EvAUcAc179te+7Ufu5UWvb7No2f3TK0Vuvt46h5zH936IIQc11UA8wfr+skERn9xmxwEfDqADXOo60kQPMunazbvhRnxjWfKMUFArT3tFNeUt5fZ+iCT1/AstXLcPel8Bmbwm7SA9lE4YHo3LlwySXw2mv+y2r8BrHJ0sWcSNYlWmLYF+pLuSyDn5ryGi749AX9P287btth71PZA9fsB6WhgQH7OYdDn3WyxUEk8TESYGhc2kjbuW2pzbRHX+Refz08+qj/efme6NDkSG5oyWZRSLg7aqo1wsAbu1RVeRmADL976g7lE7jkv68n2NXFlHP96ypeu2/8YVUAaHqhDEoD0OdTGsJvMmPt2sTBxa6u5PuoQlj+rz5NRsjMjgC+DfyXc64j28/nWzqmtpaG6++n7di5NH+4m3XV3ezUWYYLBLjr25/luReWpbeyQkREsmbsBhejAnOROkDB8l6a94CvPArvnnICE2q3Z+Z2MwelrydaMgRQ2gu9pd7Fek+JV3Q4Wf3LeSI2b4Zvfcsb2Potq0k0iB2OLuZEsi5Rf9Hrekf02JUllZSVlA2p27PHdntQWVLZvwmDn85SwCAS2uy/2I8zbxJyIZpXNw/UHEq2nljkItc5WL1aWWtjmZZsFoW4u6OmWyNs2rT+wCIksXvq2g0QCiXMcHSVFZz9sdO59p/X+O4+XdMN9MSZuPGbzKiv9za4ixdgrKhIvo/S8n8ZHa4GKoAHzAzgH865r2XjieKVjmk5uYWGhgZqXlnP/OZmWl98iMbK27yVFa/eQvX6O9JbWSEiIlkzdoOLMYG51mkw92ToDkB3WQ/lz/ye8rJK7v3CvYMG0wmXDHXDvDWww2ZvWc6WUlh0SPxZ+iH3jyzniRapnxi9rOb55716iddfP/xS6MpK78K+tFQXcyI5lqi/SFeplXLQLgfRNLPJt25PJPMoXnCxoqQC6+zq30l6kDjpQINqraVTT6wYstbysQHDWKMlm0Uhpd1REwkGYd7gTWaH3T21bTy0tyfOcHRdWE0tbYetpPk7x7Bu6z7q3uql6T9V1PSVwDlnwjXXJD+Z0dQE55wTP7hYVpZ8H6Xl/zIKOOdy8oeaqHRM/4qJmhqCXziRxivPIdjdmZmVFSIikhVjN7gYnl0OlsONs2DB4dAX9Wp000t3z2YOvvFgXlnwCjvU7gAMs2TIwdX3hmfN8QqXfydhRZOY+wNNa4Y5qacHdtllIFg4nM5O76J+xgxdzInkWKL+Il3jysaxomlF3IG0X+ZRdHmHrt4ufv7oz1N6zv5aa+nWEyv0rLV8bcAwFmnJZlFIenfURHxWV9R2e9mF0btFV3dDoKLSy4y8/zmorqZ+Y3viDMfxddTsfRjz798wNFjtHFx3nX+b/CYzamvh3nuH7hYN3pLue+9Nvo8qhokUkQKRqHRM9IqJZM8TEZH8GrvBxfp6Wj9cSePxnXSVDA4sRusJ9bDLVbvw4CkP0jCtIf6SIYyWZX3UdA8MTOMNokPjxmEBwzDv/t1eYLJ/OU8ikeLkyewIDd5F8owZupgTyYN4/UV3X3dKu9RH1JTVJLU0MVHm0dX/vDrl5+2vtXbTsvTriRVq1lq+N2AQGa3iLBFueBXarvBqTK8b763YaDr869RMa4CmWbBw4fAZjpHaj/GC1cuXw7HHenUXe3q8IGFJSfzJjIYGePttuPFGb2UIwJFHwqmnpvb+L/SJFJECkuzu9BnfxV5ERLJizAYXg59tpPGlrwwqFh5PV1/XoLT7uBfun37SG1B2dfUvrxkyiG4vp+m0/4FTTx24/0ZH05nXUNPlgPbEtX9SpZlykbzy6y+29G5h0YOLUlouXVFSwaWHXpp0baF0M48qSiooDZT611obaT2xQsxaK4QNGERGowRLhGu6vVrXgHfOWTO878PBudrGRlpu66Hx+E5CBu3lUB2oJFA6tM7sEK2t3nLsQMCbkC0t9YKMt9+eOBO5pgbOOsv7NxKZmEhRmQYZA5LdnT7ju9iLiEhWjNngYvMrLYTGVUIo/qYH0WLT7n0v3CMDyhNOgPvu67950CCabjjwdYi9f+MFAwPRnXaCRYu8DV1SEQh4tYEiG8BoplykIMT2F8GuIN/503dSeoyuvi5ef//1EbfltfdfS3j8rH3PYsakGf611kZjPTFtwCAyhO/uranuyppoiXC02EnQ8FiqobmZtnXP0Dz5XdbVT6Bu+xnD1370y0Tu7fX+zZuXu0zkkUykqEyDjBHJ7k6f8V3sRUQkK0Z9cDHeAHntu2tpTzKwCCmk3dfUwPHHw1//mtoFeOxAdNasoYPLvj7vWKdPu6ur4ac/9TZwKaQlhyIyRLzl0n2uDxy+m7FkanZ+uAyAGZNmxM94HI31xEZjwFRkBBLu3prKrqx+S4SjJZoEDY+JaoCUQnTFnomsMg0yhiS7O33Gd7EXEZGsGNXBxUQD5FR3cU3pwj4TF+B+y2oaG2G33fyDi4GAVxvIOe8+L7wAy5ZpKY1IgfCb6IhdLt1Y38huV+/mG1zM1Oz8iDIARmM9sdEYMJWCY2b/AxwNdAMvAl92zr3nc97LQBDoA3qdc7Nz2Mzkdm9N5UI+diyz007e7a+/np1J0GQzkQt12XGxB0dFhpHMWMgvQzlju9iLjDWF+nkno9KoDS4ON0B+/uznU9rF1eGSv7DP1AW437KaRI/75JNaSiNSgBJNdMRmCWZ7dn7EGQCFujFLukZjwFQK0QPA+c65XjP7KXA+cF6ccw9yzm3IXdMGZGVX1lzWWk0mE7mQlx2rTIOMYqmMhfxkZBd7kbGkkD/vZFQatcHF4QbILWtbhlxgjysZx5a+Lb73cc6l1oBsXYDHe1znYMoULaURKTCpZgLlYnZ+xM9RiBuzjMRoC5hKwXHO3R/14z+AeflqSyJFvyvrcJnIkRUghTpWUpkGGaUynhUtIompzIbkwagNLiYzQJ6/9/xBF9htwTZue/Y23/sFLJD6jH22LsD9HnfJEi2lESlA6WQC5WJ2XhkAMUZbwFQK2WlAc5xjDrjfzBzwK+fc4tw1axTsyjpcJvI99xT2WEllGmSUykpWtIjEpzIbkgejNriY7AA5+gL7vAfOK94Zey2lESlIRZ8JJCJJMbMHgck+h77rnLsjfM53gV5gaZyHaXDOvWFm2wEPmNlzzrlHfJ7rDOAMgGnTpmWk/TBKdmVNlIl8112FPVZSmQYZpTQWEskxxQYkD0ZtcDGdAXJRz9hrKY1IQSrqfkVEkuacOyTRcTP7EnAUMMfFqbXinHsj/PVtM7sd2A8YElwMZzQuBpg9e3aKdVviGzW7ssbLRC6GsZLKNMgopLGQSI4Vw+edjDqBfDcgWyID5NryWqrLqgHvw6u2vDbuALlpZhMB839JCn7GvqnJm9n2o6U0InlT1P2KiGSEmR0BfBs4xjnXEeecajOrjXwPHAaszl0rPZGarFcdcRWLPrmIq464irZz22iYNgqKvxfLWCkSHL3kEu+rAotS5DQWEsmxYvm8k1Fl1GYuQuqbFhT1jL2W0ogUpKLuV0QkU64GKvCWOgP8wzn3NTPbEVjinGsEtgduDx8vBW5xzt2Xj8aO2pqsGiuJ5IXGQiI5ps87yQNLeRfkPDOzd4BXMvBQE4EN/k9CgCrGU0oFvXTRwUYccSqiZlT8NiWpBAITYHwlVHRC17uwsY8RtX3EbcqCQmtTobUHCr9NOzvnJuWzMSOVcl+UuF8ptN9XobUH1KZkFFp7oPDbNPb6osQK7feVlfaMcKxUaK8RqE3JKLT2wFjsi5K/xiq031ehtQfUpmQVWpty2p4kP+9i21T0/ZHkR9EFFzPFzB53zs3OdzuiqU3JKbQ2FVp7QG0qNoX22hRae0BtSkahtQfUpmJTaK9NobUH1KZkFVqbCq09UJhtKhSF9toUWntAbUpWobWp0NoDhdkmKU6jtuaiiIiIiIiIiIiIZJeCiyIiIiIiIiIiIpKWsRxcXJzvBvhQm5JTaG0qtPaA2lRsCu21KbT2gNqUjEJrD6hNxabQXptCaw+oTckqtDYVWnugMNtUKArttSm09oDalKxCa1OhtQcKs01ShMZszUUREREREREREREZmbGcuSgiIiIiIiIiIiIjoOCiiIiIiIiIiIiIpGXMBBfN7H/M7Dkze9rMbjezbeKc97KZ/dvMnjSzx7PUliPM7HkzW2dmi3yOV5hZc/j4o2Y2PRvtiHq+qWb2kJk9Y2ZrzOybPuccaGbvh1+XJ83s+1luU8Lfg3l+EX6NnjazvbPcnt2i/u9PmtkHZrYg5pysv0ZmdoOZvW1mq6NuG29mD5jZ2vDXbePc99TwOWvN7NQst6lg3m+FppBeG/VFSberYPoj9UUpt6lg3m+FqFBeH/VFSbdLfdHQdqgvGgUK5fVRX5R0u9QXDW2H+iIR59yY+AccBpSGv/8p8NM4570MTMxiO0qAF4FdgXLgKWBGzDlfB34Z/v4koDnLr80OwN7h72uBF3zadCBwdw5/Xwl/D0AjcC9gwMeBR3PYthLgTWDnXL9GwKeBvYHVUbddBiwKf7/I728bGA+8FP66bfj7bbPYpoJ4vxXiv0J5bdQXpdSuguyP1Bcl1aaCeL8V6r9CeH3UF6XULvVFQ59bfdEo+FcIr4/6opTapb5o6HOrL9K/Mf9vzGQuOufud871hn/8B7BTnpqyH7DOOfeSc64bWAYcG3POscCN4e+XA3PMzLLVIOfceufcqvD3QeBZYEq2ni9DjgVucp5/ANuY2Q45eu45wIvOuVdy9Hz9nHOPABtjbo7+e7kR+IzPXQ8HHnDObXTObQIeAI7IVpsK6P1WcArotVFflDn56o/UFw3TpgJ6vxWkAnl91Bdljvoij/qiIlMgr4/6osxRX+RRXyRjypgJLsY4DW82xY8D7jezJ8zsjCw89xTgtaifX2foh0T/OeE3//vAhCy0ZYhwev9ewKM+hw8ws6fM7F4zm5nlpgz3e0jmdcyWk4Bb4xzL5WsUsb1zbn34+zeB7X3Oyefrlc/3W6FTXxRHAfVFULj9kfqi1KgvSixfr4/6ouSpL0qO+qLipr7Ih/qipKgvSo36Ismo0nw3IJPM7EFgss+h7zrn7gif812gF1ga52EanHNvmNl2wANm9lw46j/qmVkNcBuwwDn3QczhVXgp5pvNrBH4I1CfxeYU5O/BzMqBY4DzfQ7n+jUawjnnzMzl8jkTGavvN/VFI1NgfREU4O9CfVFqxvL7Tf1R+tQXDU99UWrG8ntNfVH61BcNT31RavRek2wYVZmLzrlDnHN7+PyLfGB9CTgKONk55/vmds69Ef76NnA7Xop8Jr0BTI36eafwbb7nmFkpsDXwbobbMYiZleF9aC11zq2IPe6c+8A5tzn8fQtQZmYTs9WeJH4PybyO2TAXWOWceyv2QK5foyhvRZYahL++7XNOzl+vAnm/5YX6ovQVWl8Ufp5C7I/UFyWpQN5veVME/ZH6oiSpL0qa+qICpL4oPeqLkqa+KEkF8F6TUWpUBRcTMbMjgG8DxzjnOuKcU21mtZHv8QqervY7dwQeA+rNbJfwDMtJwJ0x59wJnBr+fh7w53hv/EwwMwN+DTzrnLsyzjmTw+dhZvvh/e1k5cM0yd/DncAp5vk48L4bSDvPps8RJ90+l69RjOi/l1OBO3zOWQkcZmbbmrdT2WHh27KigN5vBaeAXhv1Rcm1qVD7I/VFSSig91tBKpDXR31Rcm1SX5Q89UVFpkBeH/VFybVJfVHy1BfJ2OIKYFeZXPwD1uHVM3gy/C+y09eOQEv4+13xdgZ7CliDl6afjbY04u329WLkOYAf4r3JASqBP4Tb/E9g1yy/Ng14dRWejnp9GoGvAV8Ln3N2+DV5Cq/46yey2B7f30NMewy4Jvwa/huYnYO/oWq8D6Kto27L6WuE96G5HujBq8kxH6/Wy5+AtcCDwPjwubOBJVH3PS38N7UO+HKW21Qw77dC+1dIr436oqTaVHD9kfqilNpUMO+3QvxXKK+P+qKk2qS+yL8N6otGwb9CeX3UFyXVJvVF/m1QX6R/Y/6fOTeyyRYzuwEvrfZt59wePscNuAqvI+wAvuTCu16Z2anABeFTf+ycuzH2/iIiIiIiIiIiIlKYMrEs+rck3i59Ll7B1HrgDOA6ADMbD1wI7I+3hv/CcCqwiIiIiIiI5JGZlZjZv8zs7ny3RURECtuIg4vO2zVoY4JTjgVucp5/ANuYV9D0cOAB59xG59wm4AESBylFREREREQkN74JPJvvRoiISOErzcFzTMFb1x/xevi2eLcPYWZn4GU9Ul1dvc/uu++enZaKSE488cQTG5xzk/LdjpGYOHGimz59er6bISIjoL5IRApBIfZFZrYTcCTwE2DhcOerLxIZHQqxP5LikIvg4og55xYDiwFmz57tHn/88Ty3SERGwsxeyXcbRmr69OmMmb4oGITmZli7FurroakJamvz3SqRERtrfVGwK0jzmmbWvruW+gn1NM1sorZC72WRfCvQvujneLvKJtVJjKlxUSo0hpIiU6D9kRSBXAQX3wCmRv28U/i2N4ADY25/OAftERGRZLW2QmMjhELQ3g7V1bBwIbS0QENDvlsnIklqfbWVxqWNhFyI9p52qsuqWbhyIS0nt9AwTe9lERlgZpHNOp8wswMTnNe/umzatGm5aVwx0RhKRMaQTGzoMpw7gVPM83HgfefcemAlcJiZbRveyOWw8G0iIlIIgkFvUBwMeoNi8L5Gbt+8Ob/tE5GkBLuCNC5tJNgdpL3Hey+397QT7PZu39yt97KIDPJJ4BgzexlYBhxsZjfHnuScW+ycm+2cmz1pklZRDqIxlIiMMSMOLprZrcDfgd3M7HUzm29mXzOzr4VPaQFeAtYB1wNfB3DObQR+BDwW/vfD8G0iIlIImpu92XY/oZB3XEQKXvOaZkLO/70cciGaV+u9LCIDnHPnO+d2cs5NB04C/uyc+0Kem1VcNIYSkTFmxMuinXOfG+a4A86Kc+wG4IaRtkFERLJg7dqB2fZY7e2wbl1u2yMiaVn77tr+jMVY7T3trNuo97KISEZpDCUiY0wulkWLiEgxqq/36gP5qa6GurrctkdE0lI/oZ7qMv/3cnVZNXXj9V4WEX/OuYedc0flux1FR2MoERljFFwUERF/TU0QiPMxEQh4x0Wk4DXNbCJg/u/lgAVo2kPvZRGRjNIYSkTGGAUXRUTEX22tt6Nhbe3A7Ht19cDtNTX5bZ9IkTGzl83s32b2pJk97nPczOwXZrbOzJ42s70z8by1FbW0nNxCbXltfwZjdVk1teXe7TXlei+LiGSUxlAiMsaMuOaiiIiMYg0N0NbmFR5ft85bxtPUpEGxSPoOcs5tiHNsLlAf/rc/cF3464g1TGug7dw2mlc3s27jOurG19G0R5MCiyIi2aIxlIiMIQouiohIYjU1MH9+vlshMhYcC9wU3gzvH2a2jZnt4Jxbn4kHrymvYf7eei+LiOSMxlAiMkZoWbSIiIhIbjjgfjN7wszO8Dk+BXgt6ufXw7cNYmZnmNnjZvb4O++8k6WmioiIiIgkR5mLIiLFJhj0ltisXevtRtjU5NXwEZFC1+Cce8PMtgMeMLPnnHOPpPogzrnFwGKA2bNnu0w3UkREREQkFQouiogUk9ZWaGyEUAja273i4AsXesXBGxry3ToRScA590b469tmdjuwHxAdXHwDmBr1807h20RERERECpaWRYuIFItg0AssBoNeYBG8r5HbN2/Ob/tEJC4zqzaz2sj3wGHA6pjT7gROCe8a/XHg/UzVWxQRERERyRZlLoqIAGb2MhAE+oBe59zs/LbIR3Ozl7HoJxTyjqtouEih2h643czAG3/d4py7z8y+BuCc+yXQAjQC64AO4Mt5aquIiIiISNIUXBQRGXCQc25DvhsR19q1AxmLsdrbYd263LZHRJLmnHsJmOVz+y+jvnfAWblsl4iIiIjISCm4KCJSLOrrvRqLfgHG6mqoq8t9m0RERETyTZvdiYjklYKLIiIeB9xvZg74VXg31kHM7AzgDIBp06bluHl4A+WFC/2PBQLecREREZGxJNeb3SmQKSIyhIKLIiKeBufcG2a2HfCAmT3nnIvexZVwwHExwOzZs13OW1hb6w2UYwfQgYB3e01NzpskIjKqKYggUtiiN7uLiKzwaGyEtrbMjo9yHcgUESkSCi6KiADOuTfCX982s9uB/YBHEt8rDxoavIFyc7NXY7GuzrvYVWBRRCSzFEQQyYpgV5DmNc2sfXct9RPqaZrZRG1FmkH7XG52l+tApohIERlxcNHMjgCuAkqAJc65S2OO/ww4KPxjFbCdc26b8LE+4N/hY686544ZaXtERFJlZtVAwDkXDH9/GPDDPDcrvpoa7QotIpJNCiKIZEXrq600Lm0k5EK097RTXVbNwpULaTm5hYZpaQTtc7nZXS4DmSIiRWZEwUUzKwGuAQ4FXgceM7M7nXPPRM5xzp0Tdf43gL2iHmKLc27PkbRBRCQDtgduNzPw+sVbnHP35bdJIiKSNwoiiGRcsCtI49JGgt0DQfv2Hi8w2Li0kbZz26gpTzFon8vN7oYLZK5ZA0uWjO4yCioVISJxjDRzcT9gnXPuJQAzWwYcCzwT5/zPAReO8DlFRDIq3IfNync7RESKymi+yMxlNpRIgTGzqcBNeJOvDljsnLtqpI/bvKaZkPMP2odciObVzczfO8WgfS43u0sUyKyshOuug5KS0VtGIZ1SEaP5c0JEBhlpcHEK8FrUz68D+/udaGY7A7sAf466udLMHgd6gUudc3+Mc9/87tAqIiIiIgNGez3CXGZDiRSeXuBc59wqM6sFnjCzB6JXp6Vj7btr+zMVY7X3tLNuYxpB+1xudpcokNnZOfjn0VZGIZ1SEaP9c0JEBgnk8LlOApY75/qibtvZOTcb+DzwczP7kN8dnXOLnXOznXOzJ02alIu2ioiIiIif6IvMyMVle/vA7Zs357d9mdDU5AUn/GQ6G0qkwDjn1jvnVoW/DwLP4iWVjEj9hHqqy6p9j1WXVVM3Ps2gfWSzu6uugkWLvK9tbZkPYEUCmbW1XqAMvK8VFV7mop9IGYVil0ypiGhj4XNCRAYZaebiG8DUqJ93Ct/m5yTgrOgbonZnfcnMHsarx/jiCNskIiK5pCUvImPLWKhHmMtsKJECZmbT8a7RHvU5ltLqsqaZTSxc6Z/5F7AATXuMIGifq83uIoHM5mavPEJdnVdr8Wc/8z8/G2UU8jHuSrVUxFj4nBCRQUYaXHwMqDezXfCCiifhZSEOYma7A9sCf4+6bVugwznXZWYTgU8Cl42wPSIikkta8iIy9oyVeoR+QYSmJgUWZcwwsxrgNmCBc+6D2OPOucXAYoDZs2e74R6vtqKWlpNbhuwWHbAALSe3pL6ZS7qGCc4Fu4I0r2lm7btrqZ9QT9PMJmorooJ3sYHMJUtyV0YhX+OuVEtFjJXPCRHpN6LgonOu18zOBlYCJcANzrk1ZvZD4HHn3J3hU08Cljnnoj90PgL8ysxCeMuzLx1pHQ8REcmhdOrviEjxG0v1CHOVDSVSYMysDC+wuNQ5tyJTj9swrYG2M56n+eZFrHvneeq22Y2mL1xKzYQdMvUUiYOHwwTnWl9tHRL8XLhyIctPWM6rH7zqH3DM1aYy+Rx3pfp/HEufEyICgA2O9xW+2bNnu8cffzzfzRCRETCzJ8L1VouW+iK8mfoFC+IPHK+6ShflUtDUF6UpGIQpUwZf4EbU1hb9xMKwWUs5egwZOwqtLzIzA24ENjrnFiRzn6T7Ir/gXqTcQCYy7xI9/qxZCfuu4MvPM+VXuxHs9jkOVJVW0dHbMSjbsmFaQ27+X5D/cVcq/8dR/jkxmhVafyTFY6TLokVEZKzSkheRsWkU1yOMl7U0KIiQg8cQybNPAl8E/m1mT4Zv+45zrmVEj5rtzLvhHv/iixPWAWy+eREhF+c40NHb4T1keMfrxqWNtJ3b5i3nji6j8MwzsGEDjB8Pzz3nBTUzURMxjXFXxiY6gkHv/zJ/PmzcCBMnwowZ8UtFjOLPCRHxp+CiiIikR0teRMauUViPMNgVpHFp46CsJd8gQpYfQyTfnHOtgGX8gbO9ycdwj3/PPQmDc2vfeZ720jjH/R7ShWhe3cz8vcNtrqmB3XaDc87JTk3EFMddGZvoSJSxmKjPH4WfEyISXyDfDRARkSLV1OQNLv1kssaQiBSmSD3CSy7xvhb5BWPzmua4WUuRIEIuHkNk1Mr2iofhHt/MC4z5qa6mftJuVJfFOe73kD3trNsY1ebozMlIO9rbB27fvDnpx/aVwrgreqIjMsHR3tNOsNu7fXN3km0Z6f9plH1OiEh8Ci6KyKhiZuPNbOt8t2Mkgl1BlqxawnkPnMeSVUsIdvnX/sm7yJKX2tqBwXp19cDtGkBKETKzK83shHy3Q3Jv7btr+y/CYw0JImTxMURGrUjmnZ9MrHjwefxgOSzZG86bW8qSOdsSrIiTkBkI0PSFSxMui45VXVZF3fg6L9C2ZAmccAJ0dfmfHMnMHIkUxl0JJzq6Omm+7iz/eoixksk2FRFBy6JFpMiY2RTgfKAeeAr4qXPuXTPbE7gJmBk+7/+AM5xzz+WrrekoulpdWvIio88CoAb4QyYf1Mym4vVR2wMOWOycuyrmnAOBO4D/hG9a4Zz7YSbbIfHVT6inuqzaNzhYXVbtBRFy8BgiwzGzjwIXAZ8GqoGXgN8BVzjnevPYtMSyvatyzOO3ToPGkyEEtFf0Ut11BwvPcbQsHUfDawFobye4TRXNM/pYe/rRTF13W0pPF9jcQdPf34cDpwwsGY4nU7Wokxx3JZzosB7W3XcLfO/24Zdrq762iCRJwUURKRpmNh74BzAlfNOhwCFmdjhwDzABL+C4I9AAPGhmezjn3stDc1NWtLW6IkteRArcDTfcEHvTBDM7zefU3aJvd84NuWMaeoFznXOrzKwWeMLMHnDOPRNz3l+dc0dl4PkkRU0zm1i40j/wEbAATXsMH/jIxGOIRJhZG3C5c+7KqNs+DbQAVVGnzgAuxtuI5ZicNjIV2d7kI/L4c+cSpIvGk3sIVgwc7h9TfbGGtkmX8uR//k5j5W2ESktpf/UWKt64ja6+OJmHDsp7obsMqru85X8tS6Hm1XOTa1tlZeZqUScx7ko40dEFdW/1DixtTrSRjupri0iSFFwUkWJyNl5g8WLg93gD6B8CvwWCwAHOuVcBzOwneBmO3wB+lI/GpiqZWl39RcNFJGWnn346ZoOWxE0Hro85zeFNTjTgbWjggBEHF51z64H14e+DZvYsXn8WG1yUKBnb6TQJtRW1tJzcMiR7PGABWk5uSWpyJxOPIRJlMl4mNQBmFgB+A4wDrgB+CbwDHABcDRxpZic755bmoa3JydGKh+YZjngLnEM4bpwF52+4k2B3J/R4t8cNLAIYzPkPzHoL6jZC0xqo6U6hQZ2dcOSRKdxhZBJOdOC1Hxh+I51sZ5uKyKih4KKIFJPPAI855y4I//y0mR0GHAEcFwkshl0ANAFHUSTBRdXqEsm+mpoavva1r1FVVcVFF13UBiyOOmzA94FVwF3ZaoOZTQf2Ah71OXyAmT0FtAHfcs6tiT3BzM4AzgCYNm1atpqZd/koE9EwrYG2c9toXt3Muo3rqBtfR9MeTSkFBTPxGCJxfBrYBfilc+7/Rd2+0swOxZusOAUo3OAiZG/FQ9QmI2u3gvYK/9Pae9q5+4W7U6uv2AXHPwvz/5Vm2yorvd2qc7TSY9BER1cn7dYzOOMyEhgdbmnzSLJNg0EvcLl2rZcB2dTkPZ6IjEoKLopIMdkZuDnmtsfxlgH9LfpG55wzs78Ax+WobSNW0LW6NECUUeCmm27iG9/4BitWrOA3v/kNF1100Xrn3A+izzGz7wOrYm/PFDOrAW4DFjjnPog5vArY2Tm32cwagT/i1ZcdxDm3mHBQdPbs2S4b7cy3fJaJqCmvGXGWeCYeQ8THx/Cyqa+OPeCce9nM7sELQI5NUZuP1G/0AoJ+AcbIjtDxJnT9DMr2S0dnZ87rE/ZPdFx3Fuvuu4W6t3qHZlwms7Q5nWzT1tahAcmFC4ev8SgiRUu7RYtIMRkHxI4E3wdwzr3jc/5beIXOi0LTzCYC5t8t57VWV2srTJkCCxbAZZd5X6dM8W4XKSJf+MIXePrpp9l555058MADAXYyszi5LZlnZmV4gcWlzrkVscedcx845zaHv28BysxsYq7aV0iSKRMhMgZFxjQvxTn+IrBNbppSgKI2H2laHf9CN2ABjvzwkf1BRj/lJeWAF4isLauh5fZxqS2DjpWn+oQ15TXMP+1qvvPPSpzBjz7t7Z4dLA+fkOzS5poaOPFE+NCH4IUXYNmy+LtNRzJIg8GBWo3t7YMyS0Vk9FFwUUSKyQZgu5jb2oG345w/AXgvmw0aVjAIS5bAeed5X+MNxBhYwlJbXts/4K0uq6a2vDZ/tbo0QJRRZurUqTz44INcccUVAJOAf5nZftl+XvOKPf4aeDZ6c4aYcyaHzyPcpgDwbrbbVohUJkKkX3R2cqT8S7ylA7UMnYQdOyKbjwC13d7y39ouL4MRoNoq+sdUp846Ne6Ebk15DVcediWLPrmIq464irZvrafh+vu9FRvhx6e62vv5mmsG3x5PHusTtm56iinnhFhwBFzWAAsOhynnQuvu45LfSCeVieaoDNIhIjUeRWTU0bJoESkmL+DtiNjPOXc5cHmc83cBXs92o+JKY0lIwdXqSmaAOJp3itZy8FHrm9/8JgsWLHgW6AT+z8wux6u3mC2fBL4I/NvMngzf9h1gGoBz7pfAPOBMM+sFtgAnOedG5bLn4RR0mQiR3DrHzL4c/j6SaT0TeNjn3J2BN3PRqIIUs/lIw6vQdgU0z4R1k8uo+94VNO1zav+YKtHmS0PquiZaGnzKKQO3O+cFHJ3L+G7Y6Wxw1V9ioq8DwtmKkaXijV8soW2/PRm2VdETzRGRCWe/3aajMkiHGK7Go4gULQUXRaSYPAEsMLNy51zCxSlmthXebq9LctKyWKkOxKIUVK2usTxAVL2gsaAT2B+4EG93+aMZnCWUMc65VrwNYxKdczU+tdTGooQ7neazTESMXO5mLWPSq3h9UqTv6A7f9iligotmVoVXbzFrm1EVPJ/NR2rKqpm/LgC/aIEDBn92pzyhG70RTTDoLQ32m3y84IKUd8Meri9Jd4OrhCUmcDSvbh5+zJnqRHMkg9Rv/Jin5eEikn0KLopI0XDOnQecl+Tp2+MFCx7MXosSGC0Zf2N1gDiC4LAUF+dcH/D98EYINzFMALBoFVkW7qCdTn2yigph5+V87GYtY4tzbnoKp08DrgAeyk5rikSKm4+kNaEbM/kY3KaK5t+czdrTj6d+1kFeYDCFMd5wfUmiDa4O/d2hfG2frzFzu5m+kxsZKTGR6kRzTAbpINlcHl5kn3Mio42CiyIyKjnn1gJX5a0B2c74y9UAKgMDxKLM7BktweFRJNt/R865R81sBlADdGXsgQtBkWbhFlyZiCj53M1axI9z7jkgK7vc51NafX90hmHGGzR48rF1GjSe3EEIaH/1FqrX35HSJEMyfUmi7MPO3k5+/ujP405uZKTERKoTzT4ZpJlcHu6rSD/nREaTEQcXzewIvAv4EmCJc+7SmONfAv4HeCN809XOuSXhY6cCF4Rv/7Fz7saRtkdEpCBkM+MvlwOoEQ4Qh87GV7Hw7m/QsuU4Gj50UOHOKo/l5eAFKFcZYuEsxvcz9oCFoEiycOMFEAqqTESUZHazLsR2ixQTv77/nJXn8PXZXwfIzYRlMAg33gh33+39PH489PV5h8qh8WQIVgycnuokQzJ9SaLsw+GeNyMlJtKZaE4xg3REiuRzTmS0G1Fw0cxKgGuAQ/E2TXjMzO50zj0Tc2qzc+7smPuOx6txNBuvlsgT4ftuGkmbRGT0MrNyoBUIAkc453oSnHcvUA18Kt55WZWtJSG5HEBFZ0defDGYweuvp1Q/aOhsfIfXVLuFtv/3R2oKdVa50JeDj6GlP5nMEOvu7qahoYHa2lruu+++uOcVRB+SKUWQhVvoy4v9Ap/azVpyoajGPRmWqO+/7G+XAWR/wrK1FQ4/HDo6fA837wFxetekJxmG7UseWk79u47qigrak0iqj33ejJSYSHeiOZsZpNGK4HNOZCwYaebifsA659xLAGa2DDgWiA0u+jkceMA5tzF83weAI4BbR9gmERm9vgDsAxydaODsnOs2s/8BWoCTgd/mpnlRsrUkJFcDKL/syEjbkwwEJi4iDs27dDD/XxTmrHK+6gUlY4wt/clkhtjNN9/ME088wV133UVZWVnc8wqiD8mUAs/CLfTlxfECn2fOPlO7WUsuFM+4J8MS9f0RmZywHDKJsHMjtXPnxg0sAqwdP7Dz8tC2JTfJkHDZcjfULf8zJ67qZuG5DOwVnoDf82akxEQuMxFTVeCfcyJjxUiDi1OA16J+fh1v18VYx5vZp4EXgHOcc6/Fue8UvycxszOAMwCmTZs2wiaLSBE7DnjJOdcy3InOufvMbC1wAvkaZGdjIJaLAVSGsiMTzsZXwLrx4R8KcVY5H/WCkjEGl/5kMkNsxYoV7LrrrjQ2Ng57bkH0IZlQ4Fm4hby8OFHg89rHr8Xi7P1TSLtZS9HL27hnuNJX2ZbMUuCIQROWc+fCJZfAa68lndnvO4nQ+w1atu+jYfMwTx69l3eUZCcZEi5bdtC0qpuabmhZCnNOge4krt4dbshtGSkxkatMxFQV+OecyFgRyMFz3AVMd859DHgASLmuonNusXNutnNu9qRJkzLeQBEpGnsBD6dw/iPAnllpSbIiA7FLLvG+jjTwExlA+cnUAComOzJYDkv2hvMOgSUzuwiefQacdx4sWTI4yBXb1PBsvG9Tu6BuY/iHQp1VjgSHr7oKFi3yvra15Tc7MJnM1VEm4d9Rihli//rXvzjwwANTefr89yEj1dTkBcX9ZCILNxj0+oIk+gQ/hby8OFHg0znHWfudRW15bf/fZ3VZNbXltQWzm7WMCnkZ90SVvpoLzAA+F97wKmcS9f2xBk1Ybt4M554Ll10GCxbAlClexn8c0ZMIkb6ovaedoOuk8cQeNpfHuV85XLsvvoFFAMOSmmSILFse1JdQQW2XF1Cs6fbOa3gVrlgJ5b3DPiQ//8fPWR9cP/yJo0W2P+dEJCkjzVx8A5ga9fNODGzcAoBz7t2oH5cAl0Xd98CY+z48wvaIyOg2EXgrhfPfAiZkqS35kYvlulHZkd4uiF5WQHsFVHd1s5BbaVkKDe8mXo6bcDYeaFoT/qGQZ5VTnaXPdi3EMbj0JyPF6MM2bNjA9ttvn8rTF38fks0s3Aws0c/ITqZZMlzg07CC3c1aRo18jXtGUvoqIxL1/bEGTVgCdIcjcklk9ifMnjZonomXERl7vz3wyQ8Mc3BWZUPiviBqvNBQX0/bGc/T/EqL15f8+UmarrivP7AYcepT8J1Dhs9e7OrrYtdf7MoDX3ygIOrWZl2hrjYpAGntti6SppEGFx8D6s1sF7xg4UnA56NPMLMdnHORqZNjgGfD368ELjazbcM/HwacP8L2iMjotgVIZYRQA3RmqS35kWAAFbxzOc0vLEtvABEdFHvzTaiqItjbMXQXxPD3jSdD2xXt3sA3zqB9cBHxPtp7Oqju8gKL0bPxo2ZWORe1EMfg0p+MFKMPGzduHJs3D7fGbZDR0Ydko0RDhpbopx08zmYgP/zY9S89FXcThUjgs1B3s86ZMbS5VJ7ka9yTVOmrbJau8uv74xk0YeknQfmVhJMI5VEZkbH3S1BvEQPb9F789viMF2oWLmR+SwscMh9eXgJlfyVIO817eM9VvxGaVkPLbZU0ngyd1kdPKP6+PZ29nTTePJe2iZdQ82LyS8SLViHXhMyTQt8sTUafEQUXnXO9ZnY2XqCwBLjBObfGzH4IPO6cuxP4bzM7BugFNgJfCt93o5n9CC9ACfDDyOYuIiJxvIa3w3yyZgOvZqkt+eMzgGr95FQab5+X3gAidpBbVQUdHTTvnWAXRKJm8xMM2gcVEX/6IeoWL6fp2RJq3usYXbPKuaqFWMgbzWRRRorRA1OnTuXxxx9P5S6jog8JdgVpfqGZtdPWUr9XPU0zT6S2YoR/jxnaXCpe8Ngwztz3TH70lx8NnSzJZiA/6rGbetrjbqKQs8BnIQfvxtjmUnlS0OMe59xiYDHA7Nmz4ybypSu273c4rvnnNThC8Scs/STI7E+YPR0qoW5jn//9NnobrrT/f/buPT6uus7/+OuTa+lkvBQQsKVcbCzboghUvBB3Qa4OArtLYcCiqHX5ycW1pO5S0EVWdwVxQbrLRTGioBVGiyxVIgVE180qSEHUtlwSUQsEpLSI0wnJJJnv748zk0wmZ26Za5L38/HoI8mZM2e+0yTfnPM5n+/n47NsOhCHRXst9h9LIecL4TA9V/8job9LXzkCnSdA951G/4VPc8HPLuY7v/kOIy77OulEbBeRH6xm5YPx2fH7Wa81IWug3pulycxUauYiyQLD3RnbLkv7/BKyZCQ6524Gbi51DCIya/wUON/MljnnckYIzOxw4N3Af1VjYFWXdgIVHYoSumb+1E4g/E5yk50Rc3ZBTK9vlGc57lhmz2ErYflXpnxXua6XdlSri/csXvpTjgyxo446ihtuuIFNmzaxbFnu6/WZModULHOhjEv0JwUQnOP6Tddz/S+vnzzm1x9S1kD+hHklsC/hUy8hGPWyW4N4QYvQCm95ZKyF/Fmz5Qy4TfVY1QhIzsLmUjXyU2pz3pO39FW1ZM79n/nrz/jfsGxthaHJWcZAzsz+8NIwF228yPexhsZmwk+34CWQZjzv6d3odJO3Q7IRy9lZ+t8UcL4QPfsMQiuMaFpcc3zliNHf1sZ177uOOx+/c8K5X6ZYC/QFCl8iLjNHPTdLk5mr5OCiiEgVXQecB3zPzELOucf9djKzg4DvAaPADVUcX02UdAKR4yS3fVcLgUSCWMPku+IT6hsVsxw3465ydChK5NGuyQHDjIvjno6FU8/MrIZq1kLU0p8pu/DCC7nxxhs5/fTT6e7O3nx1pswhFc1cKPMS/VQAIToUZf4189kVH1++PmHMu3+BtikG8jNvUCx87UKWfzdtXqGVzv835NWUTeZ+dWyD/qshclgLfcuPYdHRp2XPmi1nwG2qx6pWNmG1bqhIrc578pa+qpWsNywXLPCar/mVvsiR2f/rP/2a0cTk7MS5zXPp/vs7afuP5b7PC8ZG6I6fTojvjd98iHuBxe53X0/bvL3930AB5wuRLRESDeZ9NzMkGmzsvK57RTfH3nosQ6P+QdVJtSih6Hmyrm7kSsHquVmazFwKLorItOGcezJZduFy4Fdmth54AK8WEHg1go4BTsNbyHaZc+7JWoy1mko6gchxkht+NE7nCf5tEifUN5rictysGVVvuZKOD6wZuziOvm4uofMGJtZ+rLelHdWuhailP1OyePFiLrvsMi6//HIOPfRQgAPM7KPM0DmknJkLkzL8Xh0kOJyl3lcJS/TzjvkPd7NyCoH8zPlmbvNcBoYHJj6dIWiFYz8Iv18L+yRjFG1xvGWFRx3iBTSyDn5iwC3awni9tF1DhG+7heA/XJD9+TmONUG24EA1swlnYXOpQpUzOFOr855spa9KPW7ZZf4tPOSQojL7UzdgXh2ZnIHY4Iy3/ewpOPlk+P73veMMpM0Zw8N0fKWb/pYAkfPeQ9/In1m012LCZ1+ZPbAIBZ0v9O54qqDzuo6FHfz+k7/nwP88kMGRyaU2fWtRFjFP1t2NXClYPTdLk5lLwUURmVacc58zsxHgs3h30c/K2MWAYeDTzrkrqj2+WijpBCLHSW6wOUD3Gy4k9MoN4xfkcUg0wMlPwu2HtRB+qoXgXcUvx82ZUfXzC+gfGq+fFDlwIHvtx3pZ2jFLayFOR5dddhlNTU3867/+K8A84GsZu8yYOaSUGw/pARIMbvjlDTicd8EZh04H3XuPZ/gBEy7ko83OPyu5xDHf8ZpneOp9Tez7kpdR/cxrxxsdBJv9A/l+801mYDHdUBMc+Em471tp72/OnPw3CdICbj0Lk8upSdVLi9P5XCfd2w4p7CJ9KsG7amYTzsLmUoWoRHCmVuc9fqWv6l6Rmf25bma4gRiRbyTrFc6dCyMj0NTkfUyJxWiLwcr/+r/Cg/cFnC+0/+abues5BhaMfb1PcB/u++B9E3/uaKVhaMi/FmWW30/V6JtZptwsTaQECi6KyLTjnPuCma0DPgocCeyTfOh5oAf4hnPuj7UaX1VFo4Q3DdIZH/YuLzIYlvsEwuckdyzbZu847W9bwJNveZLu3m5+8oefsH7rehpHHd95a5y7DjE6T4buhVDsJVPO7CRLaxZDntqP9bK0YxbXQpyOLr30UlasWMH+++//PLCVGTqHTPXGQ2aAJFPqgtfrGp+8eG1uhi9+Ec45h56djxG6Zv6UAiy5xgzwwHAvP3rHCKRaR1h6o4MEHT6B/FzzjS+DweaM9zc4CCedlPt5yYBbdDhGaAUTs61bAeKFX6RPJXhXzWxC3VCZpJLBGZ33FCGjJnVky+1Zb3Lk7RSdqlc4kP1mBFBc8D4YJLphPZFPn0Lva0dp/9MI4afnEhxtHDtfCG/2buD4aXDezRTeNb5tUtOzuQsIn7yGtp2FLxFXjb6ZJVuztJw1g0VKpOCiiExLyZPoz9Z6HDWVrK0VTCTo3j3O8WfDq81MCDImSPDYC49lv6DPCIr17B5La14wTOCna+j8n0tZf8Z6Njy5YUJdn5gbguGhKV005T2hnzf+dftOL3jgF2Cs6NKOYpsiqBbitLLffvsB9Dvnjq31WCplKpkLfgGSbCZ0jW9pgTlziDa7kgIsucYMMJRIXuynzXMTGh20QObRc803uUx4f01NcPfduYMHyYBb5GCyZ1vH40Q23cLKd+dZHj2V4F2ugCSAK2MzX91QmaTSwRmd9xSnkCzSnDdgkvUKJ5Q3SGVJp2UDRlsgsjhG7x++RvujLm+Wds+2HkK/WE7ipCZiw0MEXDOdDQm6z7iTjoO8cQV/9wzdt2dmP6d1xv7As5OOO6np2V3FLRFXjb6ZZ1LQed6i7DWDRcpAwUURmXbMbCHwdrzclYedc8/UeEjVl1Fb60DzAoKZ2YsDwwP5L+iTQbHobbcQeq6TqI2fNadONE+57RSaGvz/ZEzloinXCf3cjALk4c1eVpKfii3tmGpTBNVCnBa2bdvGww8/DPA6M9t3ps4hU8lciGyJMDyapZZiBr+u8UUFWNIC+NE37etdwMee4by3nzdhGXZLYwvx0cy1fT7HT2t0kC5fNmRB729kJH/mXzLg1nvFe4m1+v8fxhii798ugksPyT2XTCV4Fw7DRf5dbwG4/nr4zGfKF/jTDZUJKhmc0XlPcQrNIs15AwbY9xWYv3pigK/zBMaaPo2VPzCINT1E4J7NObO0fcdlw+CGCd25fPxcrb2djh0B+q+OEVnqzUOLdnr1E9uylH+YpMjfT9Xom5kmBZ1FKkjBRRGZVszsP4BVjIfRnJl92Tn3T7UbVQ1EIpBsptCz0GtAMJRlRvcL/vkVnI8c3kpiezMMT76IH02MZu1GOJWLplwn9AMt3gl9SjDunciHVkCiLVD5pR3VbIogVfepT32Ka6+9Fudlcb0J+H215hAzOxFYi9cgocs5d2XG463ArcDhwA4g7Jz7QymvWWzmwuYXNzM4OrkxgB+/rvGFNiKI/vTesWWBDI9wQyO4PyY7rjYHMDMuXHYhZsZjLzzGPb+7J+94YsMx+n6yHiJPTcg2zpcNmc2c4cnvLyVr046ODto/fQ2B+z/lZXdnCAzBoheGC5tLig3eBYNw/vlw1VX+jztX/i7OuqEyplLBGZ33FK/QmxzB1iDdb7mS0M8vmNTxeX0ETgvDrknlDbzzkSf/i8nlD/JkaRd88yWZudwWHy8RM6a1iLIDRfx+qkafiJRKwUURmTbM7CygE+/O/RN4J9qLgU4ze9Q5d1stx1dW+ZbkbtkCg4NEW7yT26Hm7IfKDP75LRW6aONFLNljSdagwIgb8d0OgIMFm7fBO6K5lw2nCbYGWX/Gek74tk9KosGpZ8HT18I+MaClhY6XW+k/fj2R1zxT+aUd1WyKIFV12223cc0112BmHHTQQWzdunUQaKEKc4iZNQLXA8fhdXp92Mw2OOe2pu22EnjZObfIzM4EvgiUfEVXTObCy6++XPBxJ3WND4Vo/+7PCLhmLxsnQyrA0vPEvYTuPYHE3yQv1h0Tlzkn56FrH7qWf3/vv+NGRmgdyX4DZez4cVi0/gF48J4J2cbBjo6sGZxfff9X+cD3P+B7vMEm2CM1JaYtRc633DJ8+Dl0/s+lEJ8cXBz7P2sucC4pZ/BulndxrrRKBGdm1XlPGRWcRRqN0vGBNfQPMZYhuOAv3n/2v7wXdvk0VAEvU3HNMckVI36PZ1nRUfC4alB2QDX6RKRUCi6KyHTyMWAEOME59xMAMzsW+BHeRfmUT7LzZRRVVSFLcnd66TS5anulpGdM5Foq9Mv+X2Y9RrNrYNhyvNL678FlP/BfNpwlULrtlW1ZszwGm+CAVXD/t6Bj6THw3e/S1tZGVUJ61WyKIFXV1dVFU1MTGzdu5Oijj8bMtgBrKMMcUoAjgD7n3NMAZnY7cCpeQ5mUU4HLk5+vB64zM3OunMXycpu327y8+6Qye7rXJZfotTbAlVfC4sWEG0fpPG8YfGqkNlgDofYQi7984IRsH79mVABDo0N86r5PeV805h97g4Pwo8nM64xs42wZnLdvvp3Wxlb/zGyDM8+A7Te00XaXd0FfyHLLsYv0m48hEY9PrpcWB+IVmkvUxblmKhScqdh5z0xWcBZp8mZiKkMwtcx5FBjI0kgOvAzHJw/Zh1jD8/6PZ1nRUVR2aw3KDqhGn4iUQsFFEZlO3grclTrBBnDO3W9mdwFHTfWgBWYUVUeuJbnvex9ccQU88ww89xyQu5NySnrGRNFdU1NGE9n/Yhg8O2cYoj5L/XIESntfzdFkwbxszNAK6D/gJNqmcjJdbEOWFF2cz1i/+c1vOPXUUzn66KPHtpVjDinQfCC9TtqzwDuy7eOcGzGzV4DdgZcqPLYxS9+wlDlNcxgc8V8a3WiNXHjkP/KZ597kNRVYtMj7HV+8GKJRgqSVMSBZpywtwHJ3790kEqNZA4pZjS0ITX6e1i26aQQak8sY2zKrOqRlG/tlcPbu6M1a8gFgtKmRyA+vZOW7vJsmhS5r7FjYQf8brybyjdX0BeLj9dJS46vUXKIuzhWXdUk8FQnOVOS8Z6YrOIs07WZiaiVINM85FUBrYyuL33ocmx+/o6hl8IWMa9LP19m5G8SUm2r0ichUKbgoItPJ6/GWBWV6AvjbEo5bSEZRdeRakrtrF3zqUzA0BK3e2W+uTsoAc5rmTMiYKLZrasswtCbgvIfh+rdn6dicXnctfdlwntqF7fd8IW+ThYR52ZlFn+ZOtSEL6OJ8Bnv55Zc56KCD/B4qdQ6pKjM7FzgXYOHChWU9duridxD/4OKoG+WGX3fxmdX9kAqYdHVNmLcOeQG+cD/c/WagoYH3H3Yq55z/Vdpa2vjBkz/wXTJdqJYROOb3cNBLcMPbIdEAw03QGofl4fFGC2PyZBu3795OkzVlLf0wzCh9sfHOrMU07Wg76xxWfupSiPo0o6nUXKIuzhVVSAfiMgdnKnXeM61MCLgF9vU6Nv/umaw3DgvOIk27mVjISpAUh+PKY6/kzifu9H082zL4fON67IXH8v58iYjUKwUXRWQ6aQD8rkqHKT4PJl0hGUUVvaAfk2tJLniBxbSPuToptza28vtP/p692/Ye21Zs19Rjfg/fXe8lCd34dv99JtRdS7+Qz1O7MLzF6LSGnK8fa2HChX1BSm3IEgzC+vVw6qkwOuo1zpk7FxobdXE+zSUSCZqbfQuUljqHFOI5YN+0rxckt/nt86yZNQGvxWvsMoFz7ibgJoBly5aVdcl06uL3uG8dlzV7cVI9sbR5a6x7Kqnuqgn+78/rOeSF8+hY2JGcg+YSGx6Y0vjizV5g8WuHT6w1G0vWRgutgP6rs2QI+mQzh5eGubD7QkZG/YOLmRlIRS1rrFWgT12cK6LQDsRlVqnznmljUkA3Dp0Oum+Hjh3ZbxwestchfOGYL3D3U3cD8P43v59z3nbOxO9R2s3EQlaCpFy029G0/dsXOe8N7+bLww9gDUZ8NF7QMvhs2a3OOeZfM7/aP18iImWT+6pORKT+VK322KQXdu4m59wy59yyPffcM+/+0aEoXY92cfF9F9P1aBfRoWje54zdRS9QqpNycMjLIATvY3AI7l++YUJgEbyspIY8Ab2UwBCc9rh3kZ7rdcZqiMHEC/k8tQuDTz9L94puWhtzn83/8ZU/Fvd/WEhDllx6emD5ci8AMDwMTU1ekHH9+uxZj9Gol7118cXex2gB45SaMKvZ9fjDQLuZHWBmLcCZwIaMfTYA5yQ/Xw48UM16iykdCzv4+OEfz/r4pHpiyXkrfVlh6iI91gpRixNaF2JXfFdyDspSQLGAdxoYgq175Gi0gNeYYUwqQ7CnB+bPh1WrvG7Kq1bB/PkEH/41G87K/DakPT0jAynXHOqbrZQK9K1dC2vWeB/7+/NnUJcq1Qjmiiu8jwoslqyQJfEVUrPznnIr9rwoPaCbCrTFWrw5JrQCdg3Hxm8o7to19ryebT3Mv2Y+a+5fwz2/u4f/3fa/XPLjS3jshccmvkDqBkAwSPuulrHzm1xaR+Cdt/6E+Y1ruX7nRobdMJZwtDS2cOHbL6R/dX/eLMNUdusVx17ByvYzaLv1diL/ejrDcf8bOsOJ4Ur+fImIlIWCiyIy3VxuZqPp/4DLADK3J//laHM8ppCMoqKkTmxX3bOKq35+FavuWcX8a+bTs60n5/Oifxei65ARLj4Wug7zagDl07HNy9RZew+s+V/vY/+Nc+n4v2cm7ZvKSprTNCfvcSdkJKa9zhfvh/f1Ge/5o7f08ZAX0p+UttQvV6A0GYTsWNjBv73333KO446tdxT1f1hSQ5b0rMeBZGbVyIiXKbp8+YSLlzFZghb05Bmn1MTll19OY2MjjY2NAIeXaQ7Jyzk3AlwIbAQeB77rnNtiZp8zs1OSu30d2N3M+vA6xK4px2tPxdI3LCXQ7P/7OylDLxyGhoacywpTwZfUHLRb026Tdyog7mvAA4ss676xVq/jK4HAeODAufHf69TcEBsPShy/17vZePZG5jTNobmheew9BluCkzKQUuMPtgTH/n+y7TtGgb4ZoZgl8WVWifOeqpvKeVHOgC5pNxLSbhz6BiSHY0Tj0bGbHBMkbwCEP3oNDS0FnHQl4O/+Lu7dREnuPuSGiY/GuWHTDfmfny7t/GHLIxsZ9E1ShcGRQbY+ENGNSxGpayUHF83sRDN70sz6zGzSSbCZdZrZVjP7jZn92Mz2S3ts1MweS/7LfttYRGScFfmvkHmukIyighV9YpvUs62H+TcdxKoTjas6YNWJMH819ByYv4JFqtPhFT/2Prb9eSBrEC1fVhIAWZojPLY3XHIs/OxNTdzzZlhzXHKMb2qGlhY47zzvQh7GAg6+0oKQ22Pbcw4lnvAGUcj/IVBQUDNlUhbFbbcUl/WYHoz0CVr4BiOlppxzY/+SyjGHFPra3c65Nzvn3uSc+/fktsuccxuSnw865053zi1yzh2RqgNbC0Vl6CWDeL17N2ddVpgefDlkr0MYTYwWNpDktymVKX3+r5tpaPJd2g5AC00seuf7JmYIFpDNfPybjmf7P23nxpNuZM2Ra1h74tqsGUipZY1rT1ybd1+ZOVJL4v1ka+BRJpU476mqqZ4X5Qzopm4kwIQbh1PKMG1rI/gPF9D90R9PuHGwW+PkmyBDLWS9uVFUBmvG+cPOuWTPUXWw48EHdONSROpaSX980jqsvg9YApxlZksydvsVsMw591ZgPXBV2mOvOufelvx3CiIiOTjnGqbyr4Dj+mYUTXWcUzmxnXDinfCWxYwt/fmAY1dbAXfT0+XpRLr0DUtzLkduHoFnXpsxxvQlj8mGDGNjPGOYXcTh+uvHT37TlhuNBfvSs4mS2TvtOyHg0+8gm7wn7wUGNX2zKPpX07N7EVmPpS7BlqpKJBIT/gGPlGMOmYmKztDr6KD909cQMP95JT34UkzX+pZReN9TsPaBZvr/qwXetIihRPYJw+EIX/T1iRmCBWYzp5YqXvqeS3E4Pv8/n8+6dHPCssbDVla+FppKL9Rc0Uviy6BS5z3VNtUl5TkDuunN5NLOeUrJMM28cXD60tOzvv5Ujj9BxvnD618le/a2wZbXj9LVHiV66vt041JE6lKpf3zGOqw65+JAqsPqGOfcT5xzqardD+ItNxQRqSt+GUVTNZUT25wn3qOjRJYkH5uTXM4cCHgXzrv5LC2E3J1Io1HCmwZxI9k7tg43p2UEpMaYa8kjyeVJmVl7+eqNRaOEz7+BhiIqSuU9eS8gqJk1i8LiXh0nv1iuX8C2lCXYInWuY2EH/ec+ydrAaawZfgdrA6fRf+6TWTP0woefQ0Oz/42Q9OBL747erN2ZM8Wb4JDtxsqHhmkbaaD9/x7PfjPCQeeDRtsBiydm9xSRzTzVkhYVpdILdWFKS+IFmHrAL2dAl7TSLWnnPKVmmKbfONi7be+CG+AVevwxGecPB2+HOTnmtk0LYNUJMP//7aLn1tzlZEREaqHU4KJfh9X5OfZfCfwo7es5ZrbJzB40s7/N9iQzOze536bt23MvnxMRqbWpnNjmXfoTTLsQ7+z0gnTPPw/33ps3M3CC5EVqsHMNq/4vkXUJzoSMgNQYc3RSnLA8CSZm7eWqNxaJEBxMTGoW0zpC9rHlOXmPDkXpmvsEF9/+Mbq+cBrRNRdNCmrmDOZaRkOIFL+AbRFBC5Fpp6eHtgMWs/LTd3DFvz/Eyk/fMTlwl6bQ4Ev77u3MbZpb0BACQ7DopeRkMDhIeDNZb0a0xeEzD4xMLktQYDbzVJduVpRKL9QVLYmfmqkG/IKtQbr/bn3uZnJtbRPOecqVYRodivLCrhfG6rAWoqgM1ozzh/BmaM52ozWZ0RhrTa4WefHLtZmPRERyyF/Iq0zM7GxgGfA3aZv3c849Z2YHAg+Y2W+dc7/LfK5z7ibgJoBly5bNmI5pIjIzhZeG6dzY6ftYthPP1Im3X4BxQqCvsRGWLPGCdDCeGRiJeFlyixZ5F8p+gcX0i1TgMz+DG94Ou3wChpnNXADa/9JEID4yVsA86xih8Ky93l4YGBhrFhNZ6gUpF/wF1hzrP7bR0WE2v7iZrke7CC8NE2wNeu8pEuHe393LKXPuZNRgxI0wt2kuna9ppPu4bjrS/k9yBnNboG/vZgi0eO8jEPACEH4B23DYC/b6yZU9KlLvMuYLYDy4FQp5847PPJMKvkQ2R+jb2ceieYsIHxyekNWVa47MZEyci1Kd60MrvBsBsRZv/mkgo3N96gbHypXjN1xCIW97LAZz53qfn3wy3H47hMNEevMv3Vx52EqiQ1EiWyL07uilfff28XmoEgopvZD6eyBVkcpsk8JN5bwopaNnG/03ziVywAB987xzjfCW5O96SwtceaV3LpQ8Dwj29tK98DxCr9xAAkdsOEagOUCDNRScYdqzrYfQuhCjiVGGE1lWeTgIDCfnoIY5NDQ1F5fBmnH+4De34fBdKp1osLH5SESkXpQaXCyow6qZHQt8Gvgb59xQartz7rnkx6fN7KfAocCk4KKIyHSSyt4JrQuRcImCTmxznniTdnHtF7RLZQbmE4kQbRwlcpiXhdi+E+6IwGlhiDd6yw9bhqElkXGRDtDQQPjsK+mMXg7Dk++WTwpGFpq1194OTU0wMjLWlCblkD8lT7Lx7tbPGYbBJmAozrUPXUugOUDnxk6633IlHR9Yw737xjnh9OSfmORtqIERrypHaF2I/tX9EzKnsgZzmwMs+pcvwtY5+QO2fkGLXMFIkemihKBWvuBL+hw5mhhlYGSARhoZZXTixbSDUfMaSXVsG39+5s2ICcGGlMy5Mv1GzE9+AuvXezdrvvMduOsu6Oykd+3JuZduvrCVnusvJrT9WhJmxBgan4dWdFcme02lF6TKzOxLwMlAHO+67CPOuT+XcsypnBeN6e2l7c8DE84PxsTj8OyzXjZ12t/hjkCA/lYjcuOF9M0z35sc2aRnMPsJNM2lYTTB+qFTeGbv3ehr351Fey0p+PhjfM4fOnYE6P+qEbn2H/haz7U89Eb/nJqYG6pkd3IRkSkpNbg41mEVL6h4JvCB9B3M7FDgq8CJzrkX07a/Hhhwzg2Z2R7AkUxs9iIiMm0Vkr2TbsKJdzzuXbT6ZeNMZalt8m5+z/prCJ03MBasCwyBs4nX8pb8ZKAJutKCkOElywl+YjXd294x8eIg7i1R9AtGFpS1Fw7DhRfCyOT6a4e8AF+4H+5uh9EG+J/9vIEOJlcopYIAoZ9fwJMOTv07shZDH02MTrjLnzeL4vBz4F0FXiQUkz0qMl1UOKiVOUcueM0CLr7vYmIjaa9p8GqykVT/1RPnmLY4rPxNo/fFqE/n6dZWWJBR5rutDc44Ay66CIaGxrcn32d71x0ETspy06FhDguuuI7Qe+JE0zKqx+ahjBsYZZNaOun3vVDpBamM+4BLnHMjZvZF4BLg4lIPWux50Zh8vwMLFvhmWbfFYOXHbsiaZZ1NrrIpTdbE8qWnc13ouvL8rvucP7SFw6xsa8O1NLP5N1f5rxapbHdyEZEpKSm4mPyjk+qw2gjc7JzbYmafAzY55zYAXwLagO+Zd9W6LdkZ+q+Ar5pZAu/6+Urn3NZSxiMiUk+KXTo1duK96Rb6/u0iFr0wPDkbp9iltsm7+dHGUULnDUy8KPZZcjzU5P074YNex+jhZq+Tc2frBrq39Uy+ONjpCJ93PW1DDphC1l4wCBs2wAknTBz2wolZi60jXnaln4TBmmO8AGQ2AyMDE+7yl5RF4afQ7FGR6WKKQa1ilgynz5Fdj3ZlvTmQahg1KXPJL6iYMjTkNZE65JDxBlKQMyMzvLWRzhP8j9nw6iBuKEdTq7Rl02Wl0gtSZc65e9O+fBBYXq5jT2lJeb7fAefKWjogV9mUETfCPm37lPcmQpbzh/DffobO393gv1qkQt3JRURKUXLNRedcN9Cdse2ytM+PzfK8nwNvKfX1RURmEuccbk4ridNPx3XdgZvbCPGBqS21TauZFjks+0WxL/MCi5Cq+zM4ITNnwsVB6DOlZe0dfzxs3Ainngqjo0RtmNAKJgRCh3L8tYq1wJN7wHCW4CN42QaZd/mnnEUhdamqdfBmgykEtVJ1ytID9oUuGc7b1Gqe70O57do1uT5kjozM4J8H6B5cQSi4YeJNh5FR1t/luObQoexNrfJ1sZ+qYNBbwp2cHxke9upFNjaq9IJUw0eBSLYHzexc4FyAhQsXVmYE+cqP/OAHZc2yzls2pUoZg8HWIN1n/6h8N0FFRCqsag1dREQkt0kX5icF6DxhlO7BFXS86ejig3ZpGTq5Oj0XKmtmTglZe2MBIeul/cdfIrzFiPzxbhLNP8Yr95RfIA6LX4Lf7gUDPsuHABobGn3v8qsw/8xQSlBLsiiynqhfnbJilgwX3NSqWJmZS3kyMjvedDT9H/zKhJsO+35vI8tP/R5DObKjKxZ06OmB5cu9//fBQa9G7ego3HnnxIxMkSKY2f3A3j4Pfdo5d1dyn08DI8C6bMepWtPNXOVHnniirKUDSmk+U266CSoi04mCiyIidSDnhXlwA/0f/ErxJ5NpGTrtO2FuPHvwrRDlzszxDQhZAycfdjKx3xYWWASv5uOVP4Y7l2TfZ8NZG3QyPkOVGtSSHFIX9LfcAnff7S0/POkkb6lxhlx1ygpZMlxwU6tiZWYuFZCRmX7TIToUZf4PLySaJ2xSkaCDX8fukRHv3/LlRdeSE0nJtrIsxcw+DLwfOMY5V7mgYTGy3cgsc+mAspdNKZFugko2WrEh9SbHPVgREamWQi7Mi5bK0AHCm73ahKUoZ2ZOekAoFQiKDceIxqPcsfUOAs2BrM9taWwZ+9jc0Mx5b/4AbS1tdN8xh+CQl8kI0GxNzGmaw8azN3L8m44vy7il/lTkd0fGPfYYXHIJ/OxncM89Xh3D+fO9jLo0OZc1F3BjInVBH2wJEsBLsw4MQXDIp2FUMTIzl1IZmcHg2PxIIDC+PSNYF9kSIdGU/XS5dQSCzW2VCToU0rFbpMzM7ETgn4FTnHMDtR5PSnQoStejXVx838V0PdpFdCgZdC/yd7oQqYzBtSeuZc2Ra1h74lr6V/crE17qRs+2HuZfM59V96ziqp9fxap7VjH/mvn0bOvJ/2SRClHmoohIHch3Yf61R7+GwxV3VzLtbn4wDn//OHznrVMfY4M1EN4vBF1dXlZke7v3GsHi75Le8utbiI/6RwsarZFR599Uoa2ljX849B+4ftP1GEY8Eef6/ru4cbXR/bpP0v/MMJG9d9DXvjuL9lqi5UOzQKlBLcnBL3MutfQwo5Zh+04vsF9KZ9OxJYCXn07fL+9h0U4mN7Uqll/mUhEd3r2fr+zxlfe+4R189//dX5l5psIdu0WyuA5oBe5LNuN80Dn38VoOKG/piyJ+pwuljEGpV1qxIfVKwUURkTqQq94YwEPPPcTmFzcXV0cuo2ba0X+I8d8HTWFptIO25gDdb7mStgMWT6y/1tnpvUaq9lc06p3c5wg+9mzroXNjJ8OJYd+XGxgZYMVbVrDhyQ2TliStP309y7+3fEJgcuyE6pUb6P/XflbqhGpWqZfi+zNSIZlzK1dCNEr4/Bvo/H/+uzZgBS8ZbmtpY+WBp8F//m/2wFqmQMBbsu2cF0zMUx/Se6HCasXm+/k67ch/qNxF3BQ7douUwjlXVz9YBQdSSqj/LDKdlFqGRKRStCxaRKQOhJeGabDcU3Jq2XBoXYhd8V2FHTh1N3/tWsLHX0Rj65yCx9Q6Ai2jcPH+K3j+4710fGCNFzxMXejGYuOZTbt2ecsk58+HVavgqqu8jxnLJ1MXCdkCi+BdsB+9/9G+S5K2/WWblsDKBLl+d6pdfH/GKTRzLhIhOOToXuctYw4MeZvHljW/7oLiAnDhsBcYzOeii7xl2mvXwp/+BC++6H2e2tbfX3LTk5r+fOX6f5hCLTmR6UilL0Qm0ooNqVfKXBQRqQN+BcSzKfquZPJufhDo3vb3hNaFGBodyrosuZUm3ju6kNP2fA/hs6+kbd7e3lLoXBlMt9zi1WXLs3wy10VCSuqC3W9Jkk6oJFO9Fd+fUQrNnEsGITti0H81RJZC3zzGlzV3FlnwNT3renjY65Kc0tTk/bvrLjjep5ZqKnMpGoXbby+5hENNf76K7NgtMhPp777IRFqxIfVKwUURkToxVm9sc4SvPfo1HnruId/9SjmZTr3GLY/dQue9nb4BxpaW3fju6l9PvGjOl8H0wx8WtHwy10UCeE1acl2w64RK/KT/7vTt7GPRvEWqt1kOU+jC2haHlb9K2zDV5bvpNdS2boUdO2D33WHJkvy11Hp6JgfkMks4FDOUKvx8Ze36WYFaciLTif7ui0wUXhqmc6P/32at2JBaUnBRRKSOpLL1HI7NL26uyMl0W0sbFxxxAYfsfUjh2Tj5MpigoOWTuS4SWhpbuOb4a3LWk9QJlWSj4vsVUEjmXDQKN9yQ/RhmU1++O5UaakU0oSlqKBX8+crbrEK15GQW0999kYm0YkPqlWouiojUoWrU+Upl42TWNfQN7uWr/XXSSeNBxkxpmUu53ldrYyvnvO2cnGNOnVAFW4IEmr3XCzQHCLYEdUIlUglpdVt9axlGIl4jlWwuuKC6WXaFNKGpI+nNKlI3XaZUX1dkhtLf/aRo1CtRc/HF3sf0Gygy6xR1Di9SJcpcFBGpoKxL3fKo1l3JgrNx8mUwHXIIXHqp/3PTlk+W431pCaxMN2b2JeBkIA78DviIc+7PPvv9AYgCo8CIc25ZFYeZXa7MuVwlE8DLXEwpoJt8yQptQlMn1PVTJL/Z9nd/0rnjXxYSPGV52Uo9yMygFRtSbxRcFBGpkLxL3fKou5PpfLW/Cmw8UI73pRMqmWbuAy5xzo2Y2ReBS4CLs+x7tHPupeoNrUSFNn0pcx3EksdTJ9SsQqQws+Xv/uRzx7l07hqg+/XQsS25UxlKPYiIlJuCiyIiFZC+1C0ldQEZWheif3V/QcG0ujuZzpXBVETjgbp7XyIV5Jy7N+3LB4HltRpL2RXS9KVCdRCnPJ46omYVIpLif+44AK0QWgH9V3tNs8akNcwTEak11VwUEamAQpa6zUip4OMVV3gfdTddJNNHgR9lecwB95rZI2Z2brYDmNm5ZrbJzDZt3769IoMsWKpkQjA4Xnc1EBjf3tZW3TqIhYynjlSjvq6ITA85zx2ByNKMjXVY6kFEZi9lLoqIVICWuonMLmZ2P7C3z0Ofds7dldzn08AIsC7LYTqcc8+Z2RuA+8zsCefczzJ3cs7dBNwEsGzZshzdVKokX9ZytesgFpFFXWvq+ikiKTnPHVuhb17Gxjos9SAis1fJwUUzOxFYCzQCXc65KzMebwVuBQ4HdgBh59wfko9dAqzEK1z+j865jaWOR0SkHmipm8js4pw7NtfjZvZh4P3AMc75t1d2zj2X/Piimd0JHAFMCi7WpVwlE2pRBzHXeOpM3dXXFZGayHnuOASLdmZsrMNSDyIye5W0LNrMGoHrgfcBS4CzzGxJxm4rgZedc4uALwNfTD53CXAmsBQ4EbgheTwRkWlPS91EJCV5I/afgVOccwNZ9gmYWTD1OXA8sLl6o6ygcNi7CPaji2NgvA7tFcdewcrDViqwKDIL5Tx3BMK/n+t9UcelHkRk9iq15uIRQJ9z7mnnXBy4HTg1Y59TgVuSn68HjjEzS26/3Tk35Jz7PdCXPJ6IyLSXWuoWbAkSaPbqfgWaAwRbglrqJjL7XAcE8ZY6P2ZmXwEwszeaWXdyn72AHjP7NfBL4G7n3D21GW6ZTbM6iCIitZDz3PFDG2n7j/+ENWtg7Vqv9ENHR41HLCIyzrKszCnsyWbLgROdcx9Lfv1B4B3OuQvT9tmc3OfZ5Ne/A94BXA486Jz7dnL714EfOefW+7zOuUCqsPnBTO87+XsAL9V6ECWa7u9huo8fpv97WOycC9Z6EKUws+3AH/PvSANzmUcTrYwwxAA7cWTpbDDJdP0+a9zVpXFP3X7OuT1rPIaSFDwXlaYs36tGaNgd5s2B1kEY2gE7Ryl4PpyKevgZmwqNu7rqYdyaiwpTD9+rqShu3KWdO5bT7Pj/rh/1Mu5pPx9JbUyLhi7phcvNbJNzblmNhzRl0338MP3fw3QfP0z/92Bmm2o9hlJV44/udP0+a9zVpXHPbpqLstO4q0vjnt00F2WncVeXxi1SG6Uui34O2Dft6wXJbb77mFkT8Fq8xi6FPFdERERERERERETqVKnBxYeBdjM7wMxa8Bq0bMjYZwNwTvLz5cADyS6JG4AzzazVzA4A2vFqDImIiIiIiIiIiMg0UNKyaOfciJldCGwEGoGbnXNbzOxzwCbn3Abg68C3zKwP2IkXgCS533eBrcAIcIFzbrSAl72plDHXgek+fpj+72G6jx+m/3uY7uOvlun6/6RxV5fGLZU2Xb9XGnd1adxSadP1e6VxV5fGLVIDJTV0ERERERERERERkdmr1GXRIiIiIiIiIiIiMkspuCgiIiIiIiIiIiJTUpfBRTM70cyeNLM+M1vj83irmUWSjz9kZvvXYJg5FfAeOs1sq5n9xsx+bGb71WKc2eQbf9p+p5mZM7Nl1RxfIQp5D2Z2RvL7sMXMvlPtMeZSwM/QQjP7iZn9KvlzFKrFOLMxs5vN7EUz25zlcTOz/0y+v9+Y2WHVHuN0YGZfMrMnkv9Hd5rZ62o9pmwKnTfqiZntm/w9Ss0Dn6z1mIphZo3JOeCHtR5LoczsdWa2Pvlz/biZvavWY5L8ptNcBJqPqk1zkVSL5qLK01xUfZqPZCaou+CimTUC1wPvA5YAZ5nZkozdVgIvO+cWAV8GvljdUeZW4Hv4FbDMOfdWYD1wVXVHmV2B48fMgsAngYeqO8L8CnkPZtYOXAIc6ZxbCqyq9jizKfB78Bngu865Q/EaJd1Q3VHm9U3gxByPvw+vS3w7cC5wYxXGNB3dBxycnCuewvuZrTuFzht1aARY7ZxbArwTuGCajDvlk8DjtR5EkdYC9zjnDgIOYfqNf7aaFnMRaD6qEc1FUi2aiypPc1H1aT6Saa/ugovAEUCfc+5p51wcuB04NWOfU4Fbkp+vB44xM6viGPPJ+x6ccz9xzg0kv3wQWFDlMeZSyPcA4PN4gd3Bag6uQIW8h38ArnfOvQzgnHuxymPMpZDxO+A1yc9fC/RXcXx5Oed+htchPptTgVud50HgdWa2T3VGN3045+51zo0kv6y3uSJdofNGXXHOPe+cezT5eRTvZG5+bUdVGDNbAJwEdNV6LIUys9cCfw18HcA5F3fO/bmmg5KCTKO5CDQfVZXmIqkmzUWVp7moujQfyUxRj8HF+cAzaV8/y+TJbGyf5B+XV4DdqzK6whTyHtKtBH5U0REVJ+/4k0tY93XO3V3NgRWhkO/Bm4E3m9n/mdmDZpYry67aChn/5cDZZvYs0A18ojpDK5tif08EPkp9zRXppv3307wSG4dSh9nYWVwL/DOQqPE4inEAsB34RnLZUpeZBWo9KClaPc9FoPmo2q5Fc5HUhuaiCtNcVBWaj2RGqMfg4qxiZmcDy4Av1XoshTKzBuAaYHWtx1KiJrwluUcBZwFfq/e6LRnOAr7pnFsAhIBvJb83Ms2Y2f1mttnn36lp+3wab5nKutqNdOYyszbgDmCVc+4vtR5PPmb2fuBF59wjtR5LkZqAw4AbkyUdYsC0qEE1G2guqg/TaT7SXCSVoLmoPmguqhrNRzIjNNV6AD6eA/ZN+3pBcpvfPs+aWRPektAd1RleQQp5D5jZscCngb9xzg1VaWyFyDf+IHAw8NPkavS9gQ1mdopzblPVRplbId+DZ4GHnHPDwO/N7Cm8YOPD1RliToWMfyXJmobOuV+Y2RxgD6CelnfnUtDvyWzgnDs21+Nm9mHg/cAxzjlXlUEVb9p+P82sGe/keZ1z7vu1Hk+BjgROMa+R0xzgNWb2befc2TUeVz7PAs8651IZEOvRCXTdmCFzEWg+qibNRVJ2motqT3NRVWk+khmhHrOcHgbazewAM2vBa1SxIWOfDcA5yc+XAw/U2R+WvO/BzA4FvgqcUme1/iDP+J1zrzjn9nDO7e+c2x+v3kk9BRahsJ+j/8bLWsTM9sBbJv10FceYSyHj3wYcA2Bmf4X3h3R7VUdZmg3Ah8zzTuAV59zztR5UvUku1/9nvN+xgXz711AhP7N1J1mv9+vA4865a2o9nkI55y5xzi1IzsFn4v0drPsTaOfcC8AzZrY4uekYYGsNhyQFmkZzEWg+qhrNRVJtmosqT3NRdWk+kpmi7jIXnXMjZnYhsBFoBG52zm0xs88Bm5xzG/Amu2+ZWR9ew4gzazfiyQp8D18C2oDvJbP/tjnnTqnZoNMUOP66VuB72Agcb2ZbgVHgn5xzdZEBW+D4V+Mt5b4Ir7nLh+spyG5mt+EFb/dI1oX8LNAM4Jz7Cl6dyBDQBwwAH6nNSOvedUArcF9yrnjQOffx2g5psmw/szUeViGOBD4I/NbMHktuu9Q51127Ic14nwDWJS+0nka/+9PFtJiLQPORFExz0fSkuajyNBdVn+Yjmfas1FiEmd2Ml5b+onPuYJ/HDa+1eggvgPDhVPcpMzsH+Exy139zzt2S+XwRkUJoLhIRERERERGpvnIsi/4mybpvWbwPr45dO3AucCOAmc3Dy2R6B3AE8Fkze30ZxiMis9M30VwkIiIiUhZm1mhe99of1nosIiJS30oOLjrnfoa3NDmbU4FbnedB4HVmtg9wAnCfc26nc+5l4D5yBwZERLLSXCQiIiJSVp8EHq/1IEREpP5Vo+bifOCZtK+fTW7Ltn0SMzsXL9OIQCBw+EEHHVSZkYpIVTzyyCMvOef2rPLLai4SkQlqNBeV1R577OH233//nPskXIKdr+5kcGSQOU1zmLfbPBqsHnv6icxO9TgXmdkC4CTg34HOfPsXMheVJJGAnTthcBDmzIF586BB85hIudXjfCTTQ901dPHjnLsJuAlg2bJlbtOmempKLCLFMrM/1noMU6G5SGRmma5zUbr999+fXHNRz7YeQutCJFyC2HCMQHOAnbaT7hXddCzsqOJIRSSbOp2LrsXryhzMtkP6TdeFCxfmnItK0tMDoZAXYIzFIBDwAo3d3dCheUyknOp0PpJpoBq3e54D9k37ekFyW7btIiKVoLlIRGaV6FCU0LoQ0XiU2HAMgNhwjGjc274rvqvGIxSRemRmqQZ5j+Tazzl3k3NumXNu2Z57VijRKRr1AovRqBdYBO9javsuzWMiIvWgGsHFDcCHzPNO4BXn3PPARuB4M3t9snnC8cltIiKVoLlIRGaVyJYICZfwfSzhEkQ2R6o8IhGZJo4ETjGzPwC3A+81s2/XZCSRiJex6CeR8B4XEZGaK3lZtJndBhwF7GFmz+J1XW0GcM59BegGQkAfMAB8JPnYTjP7PPBw8lCfc87lasYgIpKV5iIRkYl6d/SOZSxmig3H6NvZV+URich04Jy7BLgEwMyOAj7lnDu7JoPp7R3PWMwUi0Gf5jERkXpQcnDROXdWnscdcEGWx24Gbi51DCIimotERCZq372dQHPAN8AYaA6waN6iGoxKRKQI7e1ejUW/AGMgAIs0j4mI1AO12BIRERGZgcJLw1m7QjdYA+GDw1UekYhMN865nzrn3l+zAYTD2btCNzR4j4uISM0puCgiIiJSBWb2BzP7rZk9ZmaT2qoma8L+p5n1mdlvzOywUl4v2Bqke0U3wZYggeYA4GUsBlu87W0tbaUcXkSk8oJBryt0MOhlKoL3MbW9TfOYiEg9KHlZtIiIiIgU7Gjn3EtZHnsf0J789w7gxuTHKetY2EH/6n4imyP07exj0bxFhA8OK7AoItNHRwf093vNW/r6vKXQ4bACiyIidUTBRREREZH6cCpwa7JG7INm9joz2yfZ2X7K2lraWHnYyvKMUESkFtraYKXmMRGReqVl0SIiIiLV4YB7zewRMzvX5/H5wDNpXz+b3DaBmZ1rZpvMbNP27dsrNFQRERERkcIouCgiIiJSHR3OucPwlj9fYGZ/PZWDOOducs4tc84t23PPPcs7QhERERGRIim4KCIiIlIFzrnnkh9fBO4EjsjY5Tlg37SvFyS3iYiIiIjULQUXRURERCrMzAJmFkx9DhwPbM7YbQPwoWTX6HcCr5Rab1FEREREpNLU0EVERESk8vYC7jQz8M6/vuOcu8fMPg7gnPsK0A2EgD5gAPhIjcYqIiIiIlIwBRdFREREKsw59zRwiM/2r6R97oALqjkuEREREZFSaVm0iIiIiIiIiIiITIkyF0VEREREREREKiEahUgEenuhvR3CYQgGaz0qkbJScFFEREREpBC6QBQRkWL09EAoBIkExGIQCEBnJ3R3Q0dHZV9bf7OkihRcFBERERHJp5YXiCIzQHQoSmRLhN4dvbTv3k54aZhgqwIdMoNFo97fjWh0fFss5n0MhaC/H9raKvPa+pslVVZycNHMTgTWAo1Al3PuyozHvwwcnfxyLvAG59zrko+NAr9NPrbNOXdKqeMRkdlJc5GIiFRMLS8QRWaAnm09hNaFSLgEseEYgeYAnRs76V7RTcdCBTpkhopEvOCen0TCe3zlyvK/rv5mSQ2UFFw0s0bgeuA44FngYTPb4JzbmtrHOXdR2v6fAA5NO8Srzrm3lTIGERHNRSIiUlG1ukAUmQGiQ1FC60JE4+OBjtiwF+gIrQvRv7qftpZZEuiYictUZ+J7Kpfe3vGgXqZYDPr6KvO6+pslNVBq5uIRQJ9z7mkAM7sdOBXYmmX/s4DPlviaIiKZNBeJiEjl1OoCUWQGiGyJkHD+gY6ESxDZHGHlYbMg0DETl6nOxPdUTu3t3v+J39+PQAAWLarM6+pvltRAQ4nPnw88k/b1s8ltk5jZfsABwANpm+eY2SYze9DM/jbbi5jZucn9Nm3fvr3EIYvIDKS5SEREKid1geinkheIUppoFLq64OKLvY/pSwQlKzPb18x+YmZbzWyLmX2ylOP17ugdy1TMFBuO0bdzFgQ60peppoI+sdj49l27Kve6lfodqNV7mk7CYWjIEnJpaPAerwT9zZIaKDW4WIwzgfXOudG0bfs555YBHwCuNbM3+T3ROXeTc26Zc27ZnnvuWY2xisjMpblIRESKU6sLRJm6nh6YPx9WrYKrrvI+zp/vbZd8RoDVzrklwDuBC8xsyVQP1r57O4Fm/0BHoDnAonmzINBRyDLVcqv070At3tN0Ewx6WZzB4HiwLxAY316puof6myU1UGpw8Tlg37SvFyS3+TkTuC19g3PuueTHp4GfMrEGmohIoTQXiYhI5dTqAlGmRhlVJXHOPe+cezT5eRR4nCwrQgoRXhqmwfwvOxusgfDBsyDQUe1lqtX4HdDS28J0dHgNVNauhTVrvI/9/ZVdNq6/WVIDpdZcfBhoN7MD8C7kz8TL/JnAzA4CXg/8Im3b64EB59yQme0BHAlcVeJ4RGR20lwkIiKVlbpAjES8i+ZFi7zsD12k1R81MygbM9sf76brQz6PnQucC7Bw4cKsxwi2Bule0T2pW3SDNdC9orsszVyiQ1EiWyL07uilffd2wkvDBFvTmooU2nSkUs1Jql17rxq/A7WqJzgdtbVVf87R3yypspKCi865ETO7ENgINAI3O+e2mNnngE3OuQ3JXc8EbnfOubSn/xXwVTNL4GVQXpne2VVEpFCai0REpCpqcYEoxVNGVVmYWRtwB7DKOfeXzMedczcBNwEsW7bMZT6ermNhB/2r+4lsjtC3s49F8xYRPjhclsBiz7aeSYHLzo2ddK/opmNhx1jTkWjjKJEDB+j9bRPt37iQ8L9vIHjU8WkHqmBzknDYO5afSixTrcbvQK73NDrq/V9KbelvllRRqZmLOOe6ge6MbZdlfH25z/N+Dryl1NcXEQHNRSIi00neLKMaH0+mOWVUlczMmvECi+ucc98vxzHbWtrK3hU6OhQltC5END7eqCTVPCa0LkT/uU/SFgrR8/oooRWQAGKtIwSGRui89wS6995Ix0HHT1xGPHag5M9PKORlgBWa8ZUt+7G7e3LwsqGhMstUq/E7kP6ehodhcHDi44sXq2u0yCxScnBRRERERKRQebOManw8mQGqnSU2w5iZAV8HHnfOXVPr8eQS2RIh4fyX/yZcgsi313BG4yihFRBtHX8slvz8fd89hStO/A+e+d8f0r50iPCjEIxnHqiIZcT5sh+rtUy1Wr8DHR3w5JNwwAETtw8Oev+KDcyWS6WWt4tIVgouioiIiEhV5M0yWt1f1DLJch9PZohqZ4nNPEcCHwR+a2aPJbddmlwlUld6d/SO/c5nig3H6Nv+JJEDB8hSfZBdiSFW37ua+GicwNHQeTR0r4OObekHKnAZcX8/HHssDA1NfC5MDLJVY5lqNX8H7r4bmpomvu+UWtQ4reTydhHJqtRu0SIiIiIiBcmbZbQ5UtPjyQxSiw6tM4Rzrsc5Z865tzrn3pb8V3eBRYD23dsJNAd8Hws0B1i052J692oay1ScxCA+6qUqxlq97MbQCtjVkn6gApYR9/TAm97kH2CD8SBbNVXrd6CeapyqU7xIzShzUURERCTJzK4BfuGc+16txzIT5c0y2lncRWi5jyczjJoZzHjhpWE6N/ov/22wBsJnX8nt340QGBrJHmDMkAAiS2Hlr1IHyrOMOBW4yqw5mK7IIFvZ6sgW+zswleXE9VTjVJ3iRWpGwUURERGRcauANkDBxQpIZRn5BQQDzQEWzSvuIrTcxxOZDszsLcDlwF8DAeBp4FvA1c65kRoOreqCrUG6V3RPqrvaYA10r+imbfd9CP/7BjrvPaHgY8ZaoW8ek5cRZwu85QpopbS0wIIFBb1+RerIFhI0nOpy4nqqcVpPWZQis4yCiyIiIjIr3HzzzZmbdjezj/rsujh9u3Nu0hOLZWb7ArcCewEOuMk5tzZjn6OAu4DfJzd93zn3uVJfu57kzTI6uLiL0HIfT6SemFk/8B/pTVXM7K+BbmBu2q5LgC/g1Uo8paqDrAMdCzvoX91PZHOEvp19LJq3iPDB4bF6q8Gjjqd7740cGzmJITcClvt4AWtl0TvfC2edNt5wJVfgLVdAKyUe95YmH3JIzkBdzjqy3zyO/guepm33fQr7j0kpJGhYSrfseqpxWk9ZlCKzjIKLIiIiMit87GMfw2uCOmZ/4GsZuzmgI/nPkl+XHFwERoDVzrlHzSwIPGJm9znntmbs97/OufeX4fXqUt4soyKbr5T7eCJ1Zm+8TGoAzKwB+AawG3A18BVgO/Au4DrgJDNb4ZxbV4Ox1lRbSxsrD8u+3LXjoOM5/52f4MsPfjnvsRqaWwiv+S6k5o98gbcvfCF7QCvdrl15A3U568gODRI5+QBWXnV/4XUTCw0alrqcuJqdsHOppyxKkVlGwUURERGZNdra2vj4xz/O3Llzufzyy/uBm9IeNuAy4FHgB+V8Xefc88Dzyc+jZvY4MB/IDC7OOH61w3JlGRUrX9aSyAzy18ABwFecc/+Utn2jmR2HN598CJh1wcVCLNlzCS2NLWMNXPy0NrZOvjGRK/AWj8Ndd8FIgavR8wTqctaRbYG+uUP5MwnTFRo0LMdy4nqocVpPWZQis4yCiyIiIjIr3HrrrXziE5/g+9//Pt/4xje4/PLLn3fO/Wv6PmZ2GfBo5vZyMrP9gUOBh3wefpeZ/RroBz7lnNvi8/xzgXMBFi5cWKlhlkWu2mG5soyKlS9rSWSGeCteNvV1mQ845/5gZnfjBSDFR3hpmPPvPj/nPhccccHkuoa5Am9DQ3D//TBnjvf1nDklNXbJWUd2CBbtpLjGJIUGDWfScuJ6yaIUmWUaaj0AERERkWo4++yz+c1vfsN+++3HUUcdBbDAzArsH1oeZtYG3AGscs79JePhR4H9nHOHAP8F/LffMZxzNznnljnnlu25554VHW8p0muHpS6UY8MxonFv+674rhqPUGTaCSQ/Pp3l8d8Br6vOUKafYGuQi951UdbHA80BluyxZPIDqcBbLukBxeOOg9Ysf1ryBOrCS8M0mP8legMQ3kJxjUlyjT19LOGwl93n+8LTcDlxKovyiiu8jwosilScgosiIiIya+y7777cf//9XH311QB7Ar8ysyOq8dpm1owXWFznnPt+5uPOub8453YlP+8Gms1sj2qMrRJy1g5zCSKbI1Uekci05NI+35b8GPTbMbk9T+G/2e0z7/lM1pIJDdZAqD1E16NdXHzfxXQ92kV0KJo78JapsRFOPdXrDu37IjkCddEowW9F6P7LyQQTTQSSq7cDQxAcgu510BanuEzCQoOGqeXEweB4MDIQGN+u4JyI5KFl0SIiIjLrfPKTn2TVqlWPA4PA/5nZf+DVW6wI8zrJfB14PL3za8Y+ewN/cs65ZMCzAdhRqTFVWs7aYcMx+nYWmHkjMrtdZGYfSX6eSodbCvzUZ9/9gBeqMajp7Pxl53Ptg9diZgyNDo01gbry2CtZfN1i3zIOHZl1/LKJxeDZZ4uv+5fW0bkjFqP/tbsROXCEvnneUujwlmRgEYrLJCymBqGWE4tICRRcFBERkdlqEHgH8FngEuBkJmYJldORwAeB35rZY8ltlwILAZxzXwGWA+eZ2QjwKnCmc65S46m4nLXDmgMsmjeNaniJ1MY2vDkp1eY+ntz2HjKCi2Y2F6/eYlmbUc0k6TVg44k4LY0tNDc0c+ERF/LJd3ySxdctJhof76qcmrtC60L0r+6nLRV4W78eHnjAa+aSKZVVWEygzqejc9srr7LyV2nHjJfQmKSYsZS7KUs06r1ub6+3RDsc9gKeIjLjKLgoIiIis5ZzbhS4LNkI4VbGL+LL/To9+Y7tnLsOn0YN9cav+3OwdfLFYnhpmM6Nnb7HaLAGwgdPsxpeIlXmnNu/iN0XAlcDP6nMaOpDofOP3/NSNWBTUl2jb3j4Bha8ZkHeMg4rD1vpBd7OOAPmz/cPLqZnFRYaqMvV0XnuXFi+HPbZp7RMwkLGUu5AYFo25ljGZGenFxzt6Mj/fBGZVhRcFBERkVnPOfeQmS0B2oChWo+nXuXq/pzZYTXYGqR7Rfek/Rusge4V3VnrnkmRlBkkgHPuCaBiXe7rQTHzT2YQcnBkMGfw8O6n7i68jEMwSHTDeiKfPoXe147S/qcRwk/PJTjaOLX6hLk6Og8MeIHFK67IeYipBl3HlDsQ6JONOfYeQyEvk1LLrUVmlJKDi2Z2IrAWaAS6nHNXZjz+YeBLwHPJTdc557qSj50DfCa5/d+cc7eUOh4RmZ00F4lIqZJZjK/Uehz1yi/zZ9KywYyAYcfCDvpX9xPZHKFvZx+L5i0ifHBYgcVyUWaQzBLFzD9+Qcj4SJxhN+x77NRxCi3j0LOth9DPTyNxvCPGCHNHG/hEY5y/P+jvOXruE4SHDikusJfq6OwXYCygeUsxQVdflQgE5srGTCS8x8u5/FpEaq6kbtFm1ghcD7wPWAKclbzrnyninHtb8l/qYn4eXo2jdwBHAJ81s9eXMh4RmZ00F4lIIeLxOEcccQTHHHMMw8P+F5kAZtZiZj82sweTHZ6FqXd/bmtp44ylZ/CmeW/iqR1Pcfvm270OrBUQHYpO7vQ6U6UHBFKBgFhsfPuuXbUdn5QsORf9MjkfZZ2LZsOcFdkSYXjUf94eHh0em3/Sg5CpQGFsOOYFFrNUsA00B3j/m99Pg/lfGqeXcYgORQndegLR4V3E8JZFDzQmGGSE7zzxXT75o08y/5r59GzrGT9ANApdXXDxxd7HaMa8VGhHZx/Z3m807m3fFS9gHigkEFisXNmYsZhX+1FEZpRSMxePAPqcc08DmNntwKnA1gKeewJwn3NuZ/K59wEnAreVOCYRmX00F4lIXt/+9rd55JFH+MEPfkBzc/brb+dc3My+BHQDK4BvVmmIdW2q3Z9LzqopULVep24oM2g2OBs4HDjZuSxpd8yOOWvzi5sZHB30fWxwdJCt271Tvlw3QbIZHh3G4Vh/xnqWf3f5xDIOGN2vOY+2f/k8tLcTOfDPJF4dgBb/Yw2MDABp2ZS/fCx/dnEJy6wLuemz8rA880AlAoElZmOKyPRTanBxPvBM2tfP4mX/ZDrNzP4aeAq4yDn3TJbnzvd7ETM7FzgXYOHChSUOWURmIM1FIpLX97//fQ488EBCoVDefZ1z95hZL3A6M/BCfSqm0v15Kkupp6Jar1NXlBk0G/w98LRzrjvfjuWes/KVm6m2l199OfuDDnb80Qsu5roJgkHLCDSPQqyVsT7c8UScNfevocEaWH/Gep555RmvjMPzQ4QvuIG2wS/D8DDMnUvve4aIvSv/eBMuQWTTLawMXZJ3ufG9v/1vTv2f0xk9LsGwJZjrmuhsSNB9xp10HJT7xshUb/pMUIlAYDjsBVH95MnGFJHpqaRl0QX6AbC/c+6twH1A0bXMnHM3OeeWOeeW7bnnnmUfoIjMCpqLRGa5X/3qVxx11FHFPOVnwNsqMphpKLw0XNCywXRTXUpdrGq9Tl1JBQT8KDNopjgU+GkR+5dlziqi3EzVzNttXvYHDXbf8GPYtWvsJoifwBBccw988X5oHvGel5JaSrz8u8sJHxzmisTRrPzQl2mLDnmBRYCBAdq3jxIooOVXbDhG3y9+mDe7+N67ruGEO/6OQUYYNm/fARsh6gYJ3bk877LmnO83y02fSUpYlp1VMOhlXQaD4/NUIDC+Xc1cRGacUoOLzwH7pn29gPFmCQA453Y451JTcBdean9BzxURKZDmIhHJ66WXXmKvvfYq5il/Anav0HCmnVT352BLcOxiNtAcINgSzNr9uSxZNQWo1utUVQXrtMm0sQfePFSocs1ZY+VmnHNxIFVupmaWvmEpc/AvZ9E4Ci/tliB62y25b4IA5/wGWkehZdT/dVIZh5xyiu/j4c2FXUAHmgMs2kHO7OLor3/JKQ+vnhDknDCWxGjeGyNTuekzSaUCgR0dXnbm2rWwZo33sb9fzaZEZqhSl0U/DLSb2QF4F+NnAh9I38HM9nHOPZ/88hTg8eTnG4EvpDVOOB64pMTxiMjspLlIRPLabbfd2FVck4s2wL/I1yxVbPfnqSylBrxAWiTiLf1tb/cCZcHs3Ven/DqVVsj78Nvn178uqE4b3d2T92toUGbQzPEq3jxUqHLNWYWWm6ma8NIwnRsuYNAnEDfaCHe8eZQ7n+uk+0+H0L2im9C6EKOJUQZGBmgahcYErI9AWxx65yWXRfsYyzgc9Y8+BuPQvQ5CK2DUYCBL7cUGayC8/0kQ+F/fAGO0BS589mvE35L9PcdGBvLeGEnd9MmsN9tgDVlv+vhKBQIjEa+kwqJF3lxU6jzS1qbaryKzREnBRefciJldiHdx3gjc7JzbYmafAzY55zYA/2hmpwAjwE7gw8nn7jSzz+MFBQA+l2qoICJSDM1FIlKIfffdl02bNhXzlGXAtgoNZ9pqa2nL3yAgKbw0TOdG/7pbDSOjhCOb4dGuiUG3np6cgbXoUJTIlgi9O3pp372d8H4hwpsG6YwP+2YAFZy9U2553kfWfS66yAtsvPrq+LF86rQBlQsISL14Bm8eKlRV56xq1qIOtgbp3nMVoeeuYhQYyAgOxloA4mM1VtefsZ5Tbz+V5oZmhhmmdRSWh73AYPuuFgLxePI5EwWa53oZhyMjWcfS0d9E/381EDkYfrJPnPVLoNF5gcYJgb3XHwKfunTS83sWesHJVxsdLkcaZLNrKOjGSLE3fbJSIFBESmDOuVqPoSjLli1zRV4YiEidMbNHnHPFnCzXHc1FItPPJz7xCW644QYeeughli1blnMuMrPD8W46/Jdz7pPVHWnh6nkuSgUBf/L7n/D9J75PgzUwMDxAoGEODa8O0n3HHDqeGpyYbXfIITB//uQlwADBID2/XE/o+6eRGB4mxhAB10xDfJjuO+bA4CChFZAwL9CQfpFf9W7R0WjO90F/PziXfZ9sAgFvaaECADNKtrnIzP4LOB94h3Mu5y96OecsM3sXcLlz7oTk15cAOOeuyPacqsxF0Si79n8jF7xnF7e9FYYbJ+8SaA7wxWO/yCU/vmRCg6eUoGvhyX2uYPGOzxIdnpzJHhyC/tFVtF13EwwM+I9jzhz4/e/h7ruhr49dBy4gcjD0xZ6dHNjr6YFjj4UhrzJPtAXmr4ZolszJCS9DE9sveXnmNaOSujYTrtOkNkpdFi0iIiIyLVx44YXceOONnH766XR3Z2++amYHAd8DRoEbqjW+ejYpW3BpmGBr9mXKPdt6JizTm9s0l9HEKCsOCnP02v8m/Ai0xZOrN9Oz8r7whawNEKKNo4RuP5moxce2xWwYWiF02iD9V0P/1RBZCn17N7PoX75I+PBzanNhHonkbeSAc9n3yUZdoGeb64DzgO+ZWcg597jfThWYs/KWm6mJYJC2u37E3le8l+HGYd9dYsMxfvjUDxlO+D8+3NxA99tfS/fcOwjdegIJvCXSgSGvlmL3Omjb2ZW1nmm0BSI3fZze336Z9kPbCZ99KcHWIFnD/R0dcP758OUvAxA5GAr6rXdw12v+H21DDrIsvRYRqScKLoqIiMissHjxYi677DIuv/xyDj30UIADzOyjePXEwKszdgxwGtAKXOace7I2o60fmYHCQHOAzo2dWTMCo0NRQutCE7KGBka8DKANT97FVx5vpC0+6WleoO3uu7M2QIgcOEAijvedyXwqXlBx5a+8f8xthj1/CXtvK6hmY9n19uZs5EBf3/hS6CyiLV4goncetO/0GkkEm9UFejZxzj2ZLPFyOfArM1sPPECF56xs5WZKPW5ZdHTQ/ulrCNz/KWJuctvmQHOAkcQIgyP+pScHRwbZ+tJWVm5z9N84l8gBA/TNg0U7IbzFq8lIwMGFF8INN3glCgYGoKmJnv2M0DlNJJ75GrGn88+FY5rHG9HkqvcIYAmv2cyG2+D4P30TLr91YimFYhRZu1ZEpBQKLoqIiMiscdlll9HU1MS//uu/AswDvpaxiwHDwKdzLQGcLfwChammKanaZm0tbRMyG5/5yzMMjU6+6Idk99MDBr0AYPrrtEBkcYzefZ+k/Z0thB+NE8wIQOZswtAKffPSNgwMwHe+49VN86t1SEY2ZmBfL3j3u2fKcxHe3u69rl/wMJAMEDqXdZ9UTbb0rKrOE6D7zgQd6gI9qzjnPmdmI8Bn8bIHz8rYpSJzlnOuG8ie4l1D4cPPofN/LoX45HmmwRrYc+6eOZ+/Y2AH9PbS9ueBSXMR4P1Omk2oZxo9cAGh7Wu8pdTJ1EO/uXCSaNQLUia17/R+n/3msqZRWPEbuO5HySAnWWqtFqKQmq8iImWk4KKIiIjMKpdeeikrVqxg//33fx7YCuyTfOh5oAf4hnPujzUbYB2JbIkwPJpleeHoMJHNERbvsXhCZmMuMRumb68mvN5anrFAmkGs5fcE9oTOo73liR1prSlyXZQHhrzMowlSDRl8mqFMysaMQ6eD9eth2xta6L3zfNo/dBHhv/1MzuXfWYVCXuYTPhmIfzCC4bAXXOyc3Owm2uL9f6TXZEu959AKo7+luPbBMv05575gZuuAjwJHMsvnrHwdktdvXZ/z+bvvtnthNwDSGpxEHu0icY9/r4KESxDZHPFvdJUqgZAU3uzdKPCz20h6YDH9BZKlFAqttRqNQijk3UAZm3ti3g2UKQQqiy2LISKzk4KLIiIiMuvst99+AP3OuWNrPZZ6tvnFzQyOZlleODrIYy88xkUbL/JtnOAn0DyXRa+Mkgou+gbSkvXFQiu8GoptcW+/wUaI+zRwAK9WWjjfos3kBXr07DMmZ2MmX/OED0Jg2OsiG/jNVXT+7ga6z/5RcQ1hUhlDZpMzEOPQuVuC7p2Pecfs7p6UXRQ5ZITEbgaJyf/viQbLHsRIp+WQM04yePjZWo+jXuTqkPzES08wp3GO79w1p3EOS/ZcAu8+wze4D3j1FjMyhHt39Ga9eRIbjtG3M0st1IwSCcG4d+NkwrzQHPAaU62L+5eMiMWI9m0h8mhXYQG+SISefYYJneaT/XzHMB1FBCqLLYshIrOXgosiIiIyq2zbto2HH34Y4HVmtq9z7plaj6levfzqyzkf/+Vzv8y6BNpPgzUS/sKdcMpySCSILI6RMP99E02NRJaOsnjH+IX4cBOQSgKyjCYMcbzaZsP+mZapWoeRLRESLktLBRsPNMZagOFduZc8ZkpmDBGNZg+cjg6MH7OjY8LSSxYtonefLcQe/rL/W8gVxEjRcsjaqkBg18wWAm/H++l/WHOWp62lzTfQHl4apnNjp29w0eEItYe874lPcJ+GBi+F+fbbJ3wP23dvJ9Ac8A0wBpoDLJqXpRaqT4Zkx7Zk86nDWuhbfgyLjj6N8KZXaduxhrGl0Gl63jyH0G43krinsbC6t72bCZ026J/9fNog/X1bC8p+LrQshogIKLgoIiIis8inPvUprr32Wpy3TO1NwO/N7MvOuX+q8dDq0rzd5uV8fNPzm7IH6jK0NLbQvaIbt9chdN3zBXp/cTe/fuVJYo2/990/1jjKln0auejE0QkXySSDkS0j8MWfNXPO1hbazr3Qq5H2q1/Bxo3+A0gudezd8VTe5dvp0pc85l0emNYlOldX2AnLKNOWXgK0P9o1tSAGTAhujvFZFi4VUoHArpn9B7CKsZ98nOas3NKXTQ+PDk8IMpoZi69b7AXmfIL77LsvLF8+6XsY3rCeTvPvIN1gDYQPzlILNRz2zZBsi8PKLa1w33e938n2KHzq0kn7RVu8gGDU4VXWJH+AL/LGl0m86D+chEFk7x3Zu1unHyfHjZicS8FFZFZScFFERERmhdtuu41rrrkGM+Oggw5i69atg0AL0Glmjzrnbqvk65vZicBavO6rXc65KzMebwVuBQ4HdgBh59wfKjmmfJa+YSlzmuZk7bxaaGCx0Rq55vhrGBgeYM8v7cloYpQRN0JLc0vWCFygYQ475o5kDdA1NzYz54wP0PbR67yL8/5+OPBA332jLRB56wi9+2zhhV07cr6nTKlswYKWB6YtgczZgCZHBmIq68pPziAGTAhuTlJs3TYpTgUCu2Z2FtCJl7H4BF6AcTFVmrOms46FHTx54ZMc+J8T54TBkUEGGRwPzKUH96NRmD/f93sYPGU53b9cT+jO5b51HrNm8GVkSEaHY0QOa6F3nqP9Q+cRbnYEffYrqExClgBf76J5xP7sP5xYC/S1717A/2AJS8FFZFZScFFERERmha6uLpqamti4cSNHH300ZrYFWAP8CFgJVOxC3cwageuB44BngYfNbINzbmvabiuBl51zi8zsTOCLQE1bA48tL6SwQFw2zY3NzH/NfE749sROBvGEX4ExT8Org+x+6F8TG/6Z7+MxG6ZvyT5ewKanB449FoYmL9Eeq3vYkiD28JfZrWm3ggOLAHOa5rDgNQsKWx7Y3g5z58LAQO4GNDkyEPM1q8i5DDGjvtsEyWXhftSwoQwqE9j9GF6B0hOccz8BMLNjqcKcNRPc3Xs3jeZfqNU3MJfne9jxf89krfOYadLv1B+e5Ne3ryX04pdJNBgxFyfwu+vpvObG8RsUZSqT0L7PUgINc4j5BCQDDXNYtNcS//eYeZypLgUXkVlJwUURERGZFX7zm99w6qmncvTRR49tc87db2Z3AUdV+OWPAPqcc08DmNntwKl43apTTgUuT36+HrjOzMw559+itAqyBbrio3GGE1lqG/q47bTbOOuOs7Lv4LxlzvHmtDqK61t54vU/J3BMlgBd01zv4jaVMeYTWJxY99Ab76sjrxY8bkhmOo0MFrY8cOFCGBgAcneFzZeBmKtZRU6FdMDNoIYNZTLFwG4ebwXuSgUWoapz1rRXdOZdAd/DbHUe0/n9Tl3ERSRIMGDxsbqxvjcoylAmYSz7OT45uNjQ1Jw7+9nvOD7yZlGLyKzjXzhCREREZIZ5+eWXOeigg/weegJ4XYVffj6Q3oTh2eQ2332ccyPAK8Ck9Wtmdq6ZbTKzTdu3b6/QcMelAl1rT1zLmiPXsPbEtVxzwjUEmgM5n9dkTbQ2trLx7I28NPASo4nR7DsbHPN7WPO/sPYer9lBR+8Q4S3ZT1ZHBwcIvbxHzmyjXHUPCzWncQ73P31//iBFNOrVaktKdYUNDnkBU/CCAcGWYN4MxOhQlNs3385TO57iTfPexBlLzyiscUI47DWk8OPTATe9YUPq/cWGY0Tj3vZd8V35X1M8qcCunyyB3QK8Hm9+ylSNOWtaig5F6Xq0i4vvu5gXdr3A3Oa5vvv5BuZyfQ+bmqC/n+iO/rHjdz3aRXQoOmG3bL9Tu4Z3MTA84Hvo1A0KP+GlYRqKrPWYuikUbAmOzdOFzj2VOI6IzA7KXBQREZFZIZFI0Nzc7PfQMOPNEuqec+4m4CaAZcuWVSWrMTNbJzoU5dIfT24+AN4y4vOXnc+SPZeMZdtdfN/FOTMdm0bgtMdh5a/SNzYRjI3Qvc7LPhxugMFmvKwfAxKw+Md/S3f8dDqyZBvlqntYqFQziLzZQ8kgZ7TFC2r2zoP2nfDkf0L3wS30nZ7sCpsnA7GkTMJcHXC7uyfV/FPDhjLK0rgD8A3sFqiBsTYeE0yrOataMn935jbNZWDEP6DnG5jL9T0cGaHnwe8SuvpWErt5S479fjdzdqPPInWDIlt5Ar/scTPjvGXn8fn/+bxvKQO/7OdQe4i7e+/mB0/+oODyB1POohaRWUfBRREREZk1zGp2Pf4csG/a1wuS2/z2edbMmoDX4jV2qTv56gJmBsHad29nbvPcrJk7jQ7CWzI3NkJrKx3bYjz5n3DAquT25LdwsAUGgVDznfS/bi5tf5587PadEIh7TQymqqWxhZHECCOJEd/Hx4IUkc/Ts3vMq++IF9QMDHlLo7vXxVn50iGQJ1CXnvWUkq8z7CR+HXDDYd9mImrYUEZFBnaLULOyCNOJ3+9OemAxdXMgZ/3S9O/h6OhYiQNI69rcylhzldTvzjG3HMPVf/MFznmild5t3yLWXHg3+tTYnHPMv2Z+1psK6QE+h+P6X17P9Q9fn/MGRPpNoZ5tPSy+bnFhNy2iUW/+6O2F9nbawmHdZBCRvBRcFBERkVnj8ssv5/LLL099ebiZja3VTf88jXPOleN86WGg3cwOwAsingl8IGOfDcA5wC+A5cADtay3mE+2jBbnHF2Pdk3IvgkvDXPRxouyHmvDXXNoa26EeFpAZv36sWXGdy+GpgRMrqoICTMifzXKyl9Mfiz8ZBOdJ/gHBQsVH41z/+/vZ07THMBbJj04OjgpSBF9075p9R09qazJ0AroP2AB+cJLZcskzKjblo0aNpRZEYHdIlxuZpf7PVDhOWtayfW7M7dpLsuXLGeftn3yZ96lvocXXAC33QbDXuJorhIL8USc1fd9iktH4byHIXBEcTc0DOP6TddPKEMwIXB5wtWcc8g5rDxsJdGhKPOvmc+u4cn7ZrsBUdRNi56eyQHyzk4v6NqhGqwikt2s+8MjIiIis5dPrC5fKmNZUh2dcyNmdiGwEWgEbnbObTGzzwGbnHMbgK8D3zKzPmAnXgCyrmUul862pPfKY6/0rbnY0tDCDz7wA45f/W7/gEx3Nxx3HL3zBrMub47ZMH3nroDNGyZeEJsRTCToXjcyIZtwbFl1hjlNc3DO0dTQ5BtsG+swbdD5zs4Jy74hGXz4o/8YE+Y9ni/cV+1MQjVsqIACA7tFKHYOmpXLpXP97gyMDLBP2z5ccewVhR2srQ323nsssAj5SyzEm7x/N7wdLMstod2adqOxoRHn3IQsyvPefh7X//J6/+Mm4qy+dzWX/vhSuld088RLTxR9A6Lgmxap5ljRtDqSqZIToZAXdC0tUC4iM1jJwUUzOxFYi3ei3OWcuzLj8U7gY8AIsB34qHPuj8nHRoHfJnfd5pw7pdTxiMjspLlIRPJJZDT9MLNHnHPLqvX6zrluoDtj22Vpnw8Cp1drPOWWKzvmgu4LfJ/T0tjCu/d9N7RkCch0dMDTT9P+twsJDI34d41uDrDorUdD/1cmBihffRXWrKFjm9cgJrIUvnYYPLTv5GOAFzzsfFcnS/ZYwh1b7+CBPzzA0OjkXMlGa2TJnksmXcD3xp7Jmq0Ua4G+2LP+D6apdiZhvuXtqqtWW845Nd8sUNl/dzI6r7fv9Moc5KvhmgD++o/w4wO9KG+8iQm/U2/b+21ENt1C3y9+yKIdEN7/JD4/9LusgVHwsqfjo3FC60KsPHRl0TcgCr5pkaM5FomE93h5A+ciMoOUFFw0s0bgeuA4vK6HD5vZBufc1rTdfgUsc84NmNl5wFVA6jboq865t5UyBhERzUUiIrWXs5FBlmxBh8u/1HeffQh/8W467z3B9+GGkVHCkc3Q7ryMx2CyQcHFF48FBtricMYW+Nl+8OgbYbhx8nECzQGW7OEFDZ/a8RQ/+t2PfF8v2wV8OYIbRWUSZtRFm/Dei6CGDTITlD0LN6O5S3izVz81n4FW+PGbvDmmdQRaXAMXvv1CPvM3n/F+p3p6WBm6JC3L+n9pP2SEwIlek5hcEi7BzsGdRc8zBc9Nvb3jmYqZYjHvxo2ISBal3g07Auhzzj3tnIsDtwOnpu/gnPuJcy5VDfdBvALmIiLlpLlIRKTGcmXHZFuoWehS3+BRx9P9oY0EbQ4B53X8DjTMITgE3eug7aprYdUqmD/fqxkGXsBt7lwAehbC/NWw/q/8A4swMQCRuhj3k+0CPrw0TIP5n1oXGtxIZRIGW4Jjrx9oDhBsCU7MJOzp8d7rqlVw1VWT33uRUsvbrzj2ClYetlKBRZl2Cv7dKfiAyeYuwSA0NxOMe3NNcMgLGuaSmmOGmiBuCW7YdIO3IX3ZcSqIF4sR3jREw6u5A4vgzZd77LZH0fNMwXNTKlvTTyDgZYSLiGRRanBxPvBM2tfPJrdlsxJIvw08x8w2mdmDZva32Z5kZucm99u0ffv2kgYsIjOS5iIRkRrLFZDL1u+2mOWKHQcdT/+a7aw95UbWvL2TtT9y9F8NHU8lL8pjsfGL9127vMyj0VGvy2uy0cqA37LqprmTAhBTCRSWHNyIRqGri47rf0D/7l9g7VFfZM2Ra1h74lr6V/ePd3TNEqCY8N5FZqFUFu7aE9f6/+4UfcBkc5ezzoKmprESC1dvhCa/VjpZpOoaZlt2HIxD9x1zCNocWhuzr7sONAdYsueSoueZguemcNhrpuVnZAS2bIGurok1GUVEkqrW0MXMzgaWAX+Ttnk/59xzZnYg8ICZ/dY597vM5zrnbgJuAli2bFnddk0UkfqnuUhEpDJyLUvMptjlim0tbZyx9Awi/3shT71ulNsP9pYrBuNpO6XXBjvtNCJPfCdrl9emUVg+921c98mNEy7KC61FGB2KEtkSmdAZe0pLjDM6tLYFAqxsaPAypw7LCIyoLppUkJl9CTgZiAO/Az7inPtzTQdVhMwmU6UfsA2uuw7uvBOiUdri8KFfwz8dByNZsqAzjWVo9yayLjvueGqQ/lc7ueXvDuSijRcxnBietE9qvmxraSt6nimo/EEqWzO9W/ScOTA4CGbw5S+re7SIZFVqcPE5IL0s9oLktgnM7Fjg08DfOOfGKmM7555LfnzazH4KHIr3R0xEpBiai0REKi1Pjb+xgNw3jyMxNEisxWuA0ABceT+sOdbrmhxrYXKgrsD6gWPdqIcGib1jhMCQVwetex10bEvulF4b7Oij6X3pNmKt/veDRhphn78kfC/K812MZ+uM3b2iu7jgRrEdWlUXTSrrPuCSZIf7LwKXABfXeEy1lRF0iyyOFbX8byxDu91NaBIzcacAbYuWcMERKzlk70Py3tiYShC1oOeksjUjES9T8cYbve2DaRnioO7RIjJJqcHFh4F2MzsA70L+TOAD6TuY2aHAV4ETnXMvpm1/PTDgnBsysz2AI/EaLIiIFEtzkYhIJWVk1mXLXulY2EH/BU8TOfkA+uYOsWgnhLcwlu0TObyVvn+5gEV7LRkP1BV47AndqJM1HFOdW0MrvOWKbXEm1gYLhWj/qhEYcr5dXufGof91xsX3XTyWeRhsHQ9qZrsYz9UZO7QuRP/q/sJrvBWbiZjRxXaCWtdFK1OTGakd59y9aV8+CCyv1VjqSlrQrfcPXyPW9FDBTx3L0G53E5rETNypwft9oQ6aLLW1eXNOVxc0ZknPVJa0iGQoKbiYvKN1IbARaARuds5tMbPPAZuccxuALwFtwPfMDGCbc+4U4K+Ar5pZguRN7YzOriIiBdFcJCJSQUVm1rXtvg8rr7p/PGAY9wKGbQ0NrLwqYyldrmMfdxw8/TTssw+Quxt1AogshZW/YvwiPRm0DNNE53Fx3+cNNMMd8V8T+/kvJmQe5qvRlnMsyfpqBWcVFZuJmNHFdoK0AEXVFRgklmnlo0Ak24Nmdi5wLsDChQurNabaSQbd2h91BO7ZnLWBVWtjK0OjQ5MzDluYvOw4EPB+b7u7J86j5V7ePRXKkhaRIpRcc9E51w10Z2y7LO3zY7M87+fAW0p9fRER0FwkIlIxU6nxl760rq/Py6YLhycvoct17MFBOOAAuP9+6OjI2Y061gpb9mmEvrneRbpzY0HLIN6y6dAKLwgZa/UyFgeaAYPYyIB3jFTm4bffR/+nns+ZJZRzLAV2wC6IXyaiX120LAGKqil2abfUlJndD+zt89CnnXN3Jff5NDACrMt2nNlaizpXfdm25jauPO5Knn3lWf+Mw0LnxnpQz1nSIlJ3qtbQRURERESmoalmr6SW1k312ABDQ2PBqVQ3at+gnoMbjzD+/ur1dBzU4S3nSwtaprq8RpZC3x5G/1sP5I65z48FFtMlYruI/Pe/sfKMK7MOK9dYiumATTQKN9yQ/XEz/0zEegtQqMnMtJLthmuKmX0YeD9wjHNu1gQNC5Wv4VPe7tSFzI31IBwmevFFRBZD7zxo35nWQKuWWdIiUpcUXBQRERGR7CqZvZLr2ClDQ3DLLYQ/9qHs3agNBhkhdOdyr96hT9CyLZ5cNo3j4nfsQWzEv29XrAX6vnENvLgAnnnGt3ZgrsylojpgRyJelmU2F1yQPWBYTwEKLZ+cMczsROCf8ZrfTY6+C1AHdRGroOflXxO6KEHiVcYadHWeAN137kbH1yqTJR0dihLZEqF3R69vHVwRqV8KLoqIiIhIdpWs8Zfr2CnxOHR2EjzkELpXdHPsrccyNDrku+tYvcM8AdH2PRcTePkxYkw+TmAIFv1pGD71KS+w6VM7MF/mUsEBhnyZm2aFHafWtHxyJrkOaAXuS9aoftA59/HaDqk+1UVdxAoZa1o1OuDViiStgdYHG+k/4m2UO7TYs61n0pxaaB1cEam9hloPQERERETqWKrGXzDoBYrA+5jaXkr2SuoYc+bk3i8eh1CIjnlv4/y3n591t7F6h+GwF/j009BA+OwraciSMdiA1+GaoWTgMRYbrym4a9fYfqnMpbUnrmXNkWtYe+Ja+lf3F3cRnArK+ZlOQbk8/99aPjl9OOcWOef2dc69LflPgcVZKHcDLUdkc9Y+P1MyFsyMR8fKTcSGY0Tj3vZd8V15jiAitabgooiIiIjklqrxt3YtrFnjfezvL08X4I4Oryt0a2vu/ZK1+5bsuYRAs39AbqzeYZ6AaHDePnTvuYrgkJepCN7H4JDXdMNZvgAAQV9JREFU/KXNr7l0qnZgmlTm0hXHXsHKw1YWvyRypgTlKhmAFpGqq1rTqqScwcxkRrqI1DctixYRERGR/CpZ42+ffbyu0O99LwwP+++TrN0XPvvSwuod5ml60vF3n6T/oLVEFg3RNw8W7fQyFn0Di2mvX1b12Pl5quqtyYyITFnZmlYVqNrBTBEpPwUXRURERMRfNOoFi3p7fRublFVHB1xzzXitw0zJZcJF1TvMFhDt6YFQiLZhSzZ5SWpqAkb8x1epZcozKShXT01mRGTKyta0qkDVDmaKSPkpuCgiIiIikyUDcBMy6jIam5TdOefApZdOCi5GWyByyAi9+2yh/dEuwkvDU+/UmqqfGI1OfqyhwQuQ7fKp71XJZcoKyolIHSlb06oCVTuYKSLlp+CiiIiIiEzkF4BLdQIOhbxMu0pk1vksE+558xxCpw2S2M2IPfzlCR1Ep9SpNRLxju2nuRkuuABuuGH6L1MWESlBqmnVlG7iFKnawUwRKT8FF0VEREQqyMy+BJwMxIHfAR9xzv3ZZ78/AFFgFBhxzi2r4jAnyhWASzU2KSTTbirLqtOWCUf7thDa7UaiDkgMAowtmwutC9G/ur/4i87e3vFAaaZYDMxmzjJlEZkWokNRIlsi9O7opX33dsJLwwRbc8yVVSpZkWpaVQ3VDGaKSPkpuCgiIiJSWfcBlzjnRszsi8AlwMVZ9j3aOfdS9YaWRb4AXCGNTUpZVp1cJhx5tIvEPY3g0+Ml1UG06Avf9nZvLH7vL1VXUcuURaRKerb1TMrYS2Vndyz0mStrUbKiSqoZzBSR8mqo9QBEREREZjLn3L3OuVSXkAeBBbUcT0FSATg/hTQ2SV9WnQrixWLj2/1qGvqoSAfRcNhb5uynknUVRUQyRIeihNaFiMajY3NdbDhGNO5t3xXPmCvLNLeKiJSbgosiIiIi1fNR4EdZHnPAvWb2iJmdm+0AZnaumW0ys03bt2+vyCBLDsAVsqy6AKkOon6m3EE0VdcxGBwPoAYC49u1/FlEqiSyJULC+c+VqezsiU8oz9wqIlJuWhYtIiIiUiIzux/Y2+ehTzvn7kru82lgBFiX5TAdzrnnzOwNwH1m9oRz7meZOznnbgJuAli2bJkryxvI5NNYpajGJuVYVk0FO4im1XVUXUURqZWis7PLNLeKiJRbycFFMzsRWAs0Al3OuSszHm8FbgUOB3YAYefcH5KPXQKsxCtc/o/OuY2ljkdEZifNRSJSS865Y3M9bmYfBt4PHOOc8w0IOueeS3580czuBI4AJgUXq6aUAFwhdQ0LUNEOoqqrKCI1lsrO9gsw+mZnl2luFREpt5KCi2bWCFwPHAc8CzxsZhucc1vTdlsJvOycW2RmZwJfBMJmtgQ4E1gKvBG438ze7JwbLWVMIjL7aC4SkXqWvPnxz8DfOOcGsuwTABqcc9Hk58cDn6viMP1NNQAXDnsNBvwUWddQHURFZKYqOju7jHOriEg5lVpz8Qigzzn3tHMuDtwOnJqxz6nALcnP1wPHmJklt9/unBtyzv0e6EseT0SkWJqLRKSeXQcE8ZY6P2ZmXwEwszeaWXdyn72AHjP7NfBL4G7n3D21GW4ZlLmuYaqD6BXHXsHKw1YqsCgiM0IqOzvYEhyrLxtoDhBsCfpnZ6tmrIjUqVKXRc8Hnkn7+lngHdn2cc6NmNkrwO7J7Q9mPHe+34ski5qnCpsPmdnmEsddS3sAL9V6ECWa7u9huo8fpv97WFzm42kuKt50/xmC6f8epvv4Yfq/h3LPRb6cc77r1Jxz/UAo+fnTwCHFHvuRRx55ycz+WNoIi1LU97wRGnaHeXOgdTAWG9oBO0ff854s3QiqYrr/zE738cP0fw+VGP9+ZT5e1dX7XFSHJo7faGAu82iiNTYSG2KAne+5NPtcWQdz63T//4fp/x4qNf5pPx9JbUyLhi7phcvNbJNzblmNhzRl0338MP3fw3QfP0z/92Bmm2o9hqnQXFRfpvt7mO7jh+n/HqbrXJTOObdnNV9vJnzPNf7amu7vYbqPv1I0FxVH46+96f4epvv4ZeYpdVn0c8C+aV8vSG7z3cfMmoDX4jVTKOS5IiKF0FwkIiIiIiIiUgOlBhcfBtrN7AAza8FrirAhY58NwDnJz5cDDyS7JG4AzjSzVjM7AGjHqzEkIlIszUUiIiIiIiIiNVDSsuhk3bILgY1AI3Czc26LmX0O2OSc2wB8HfiWmfUBO/Eu+knu911gKzACXFBgd9abShlzHZju44fp/x6m+/hh+r+Hso5fc9GUTPfxw/R/D9N9/DD938N0H38tTPf/M42/9qb7e5ju458ppvv3QeOvven+Hqb7+GWGMS9xR0RERERERERERKQ4pS6LFhERERERERERkVlKwUURERERERERERGZkroMLprZiWb2pJn1mdkan8dbzSySfPwhM9u/BsPMqYD30GlmW83sN2b2YzPbrxbjzCbf+NP2O83MnJktq+b4ClHIezCzM5Lfhy1m9p1qjzGXAn6GFprZT8zsV8mfo1AtxpmNmd1sZi+a2eYsj5uZ/Wfy/f3GzA6r9hjz0VxUe5qL6oPmI0lnZl8ysyeS/1d3mtnraj2mQhQ6n9QjM9s3+TuWmic+WesxTYWZNSbniR/WeizFMrPXmdn65M/+42b2rlqPabbTXFR9motqT3OR1C3nXF39w2vG8DvgQKAF+DWwJGOf84GvJD8/E4jUetxTeA9HA3OTn59XT++hkPEn9wsCPwMeBJbVetxT+B60A78CXp/8+g21HneR478JOC/5+RLgD7Ued8b4/ho4DNic5fEQ8CPAgHcCD9V6zFP4HmguqvH4k/tpLqr9e9B8NIv+AccDTcnPvwh8sdZjKmDMBc0n9foP2Ac4LPl5EHhqOo0/7X10At8BfljrsUxh7LcAH0t+3gK8rtZjmu3/NBfVZPyai2o/ds1F+leX/+oxc/EIoM8597RzLg7cDpyasc+peL9UAOuBY8zMqjjGfPK+B+fcT5xzA8kvHwQWVHmMuRTyPQD4PN4f8sFqDq5AhbyHfwCud869DOCce7HKY8ylkPE74DXJz18L9FdxfHk5536G15U5m1OBW53nQeB1ZrZPdUZXEM1Ftae5qD5oPpIJnHP3OudGkl/W27yRTaHzSV1yzj3vnHs0+XkUeByYX9tRFcfMFgAnAV21HkuxzOy1eDcpvg7gnIs75/5c00GJ5qIa0FxUW5qLpJ7VY3BxPvBM2tfPMnnCGtsn+QflFWD3qoyuMIW8h3Qr8TIm6kXe8SeXjO3rnLu7mgMrQiHfgzcDbzaz/zOzB83sxKqNLr9Cxn85cLaZPQt0A5+oztDKptjfk2rTXFR7movqg+YjyeWj1Ne8kc2M+R4nS3AcCjxU46EU61rgn4FEjccxFQcA24FvJJdSdplZoNaDkgk0F1WZ5qKa0Fwkdaseg4uzipmdDSwDvlTrsRTKzBqAa4DVtR5LiZrwliMeBZwFfG261GpJOgv4pnNuAd6Svm8lvzciRdNcVFPTfS4CzUczjpndb2abff6dmrbPp4ERYF3tRjq7mFkbcAewyjn3l1qPp1Bm9n7gRefcI7UeyxQ14ZVWuNE5dygQA6ZVrbzpSnNRfdJcVDOai6RuNdV6AD6eA/ZN+3pBcpvfPs+aWRPeEqwd1RleQQp5D5jZscCngb9xzg1VaWyFyDf+IHAw8NPkCtC9gQ1mdopzblPVRplbId+DZ/Hqag0Dvzezp/Au8B+uzhBzKmT8K4ETAZxzvzCzOcAeQL0tqcymoN+TGtJcVHuai+qD5qNZyDl3bK7HzezDwPuBY5xzriqDKs20/x6bWTPexfw659z3az2eIh0JnJJs9jQHeI2Zfds5d3aNx1WoZ4FnnXOpDK316IK+KjQX1R/NRTWluUjqVj1mFTwMtJvZAWbWgtckYUPGPhuAc5KfLwceqLM/Jnnfg5kdCnwVOKUO62vlHL9z7hXn3B7Ouf2dc/vj1Tipp4t5KOzn6L/xMoUwsz3wliY+XcUx5lLI+LcBxwCY2V/h/YHcXtVRlmYD8CHzvBN4xTn3fK0HlUZzUe1pLqoPmo9kguTS/X/G+30byLd/nSjk57huJev5fh143Dl3Ta3HUyzn3CXOuQXJufpMvL+X0+ViHufcC8AzZrY4uekYYGsNhyRoLqoFzUW1pblI6lndZS4650bM7EJgI143rZudc1vM7HPAJufcBrwJ7Vtm1odXoP3M2o14sgLfw5eANuB7yYybbc65U2o26DQFjr+uFfgeNgLHm9lWYBT4J+dcXWSdFTj+1XjLJy/Ca6bw4XoKbJnZbXgBkz3Mq8P2WaAZwDn3Fby6bCGgDxgAPlKbkfrTXFR7movqg+Yj8XEd0Arcl5w3HnTOfby2Q8ot289xjYdVjCOBDwK/NbPHktsudc51125Is84ngHXJgNDTaJ6oB5qLqk9zUe1pLpK6ZKWe+5vZzXip6C865w72edyAtXgn7QN4FxyPJh87B/hMctd/c87dkvl8EZFCaC4SkXqguUhEREREZptyLIv+Jsk6S1m8D692VDtwLnAjgJnNw8sceAdwBPBZM3t9GcYjIrPTN9FcJCK19000F4nIDGBmjeZ1pP1hrcciIiL1reTgonPuZ3jLAbM5FbjVeR4EXmdm+wAnAPc553Y6514G7iP3ybiISFaai0SkHmguEpEZ5JPA47UehIiI1L9q1FycDzyT9vWzyW3Ztk9iZufi3d0nEAgcftBBB1VmpCJSFY888shLzrk9q/yymotEZALNRSJSD2o0F+VkZguAk4B/Bzrz7b/HHnu4/fffv9LDEpEKq8f5SKaHumvo4sc5dxNwE8CyZcvcpk311AhURIplZn+s9RimQnORyMyiuUhE6kGdzkXX4nViDmbbIf1Gx8KFCynLXBSNQiQCvb3Q3g7hMASzDkFEyqxO5yOZBspRczGf54B9075ekNyWbbuISCVoLhKReqC5SETqmpmlmlI9kms/59xNzrllzrlle+5ZhkSnnh6YPx9WrYKrrvI+zp/vbRcRkbpWjeDiBuBD5nkn8Ipz7nlgI3C8mb0+WbD8+OQ2EZFK0FwkIvVAc5GI1LsjgVPM7A/A7cB7zezbFX3FaBRCIe9jLOZti8XGt+/aVdGXFxGR0pS8LNrMbgOOAvYws2fxOh02AzjnvgJ0AyGgDxgAPpJ8bKeZfR54OHmozznnchVAFxHJSnORiNQDzUUiMt055y4BLgEws6OATznnzq7oi0YikEj4P5ZIeI+vXFnRIYiIyNSVHFx0zp2V53EHXJDlsZuBm0sdg4iI5iIRqQeai0REpqC3dzxjMVMsBn191R2PiIgUpRrLokVERERERGSacc791Dn3/oq/UHs7BAL+jwUCsGhRxYcgIiJTp+CiiIiIiIiI1E44DA1ZLk0bGrzHRUSkbim4KCIiIiIiIrUTDEJ3t/cxlcEYCIxvb2ur7fhERCSnkmsuioiIiIiIiJSkowP6+73mLX193lLocFiBRRGRaUDBRREREREREam9tjZ1hRYRmYa0LFpERERERERERESmRMFFERERERERERERmRIFF0VERERERERERGRKFFwUERERERERERGRKVFwUURERERERERERKZEwUURERERERERERGZkqZaD0BEREREREREpOKiUYhEoLcX2tshHIZgsNajEpn2FFwUERERERERqRUFvKqjpwdCIUgkIBaDQAA6O6G7Gzo6aj06kWlNwUURERERERGRWlDAqzqiUe//ORod3xaLeR9DIejvh7a22oxNZAZQzUURERERERGRaksPeKUCXbHY+PZdu2o7vpkkEvECuH4SCe/xGSY6FKXr0S4uvu9iuh7tIjoUzf8kkSkqOXPRzE4E1gKNQJdz7sqMx78MHJ38ci7wBufc65KPjQK/TT62zTl3SqnjEZHZSXORiNQDzUUiIlKwQgJeK1dWd0wzVW/veAA3UywGfX3VHU+F9WzrIbQuRMIliA3HCDQH6NzYSfeKbjoWKiNWyq+k4KKZNQLXA8cBzwIPm9kG59zW1D7OuYvS9v8EcGjaIV51zr2tlDGIiGguEpF6oLlIRGSG6e+HSy6BJ56Agw6CK66AN76xfMefZQGvmmpv95ac+/1/BwKwaFH1x1Qh0aEooXUhovHxTMXYsPe+Q+tC9K/up61FS8ClvEpdFn0E0Oece9o5FwduB07Nsf9ZwG0lvqaISCbNRSJSDzQXici0Z2b7mtlPzGyrmW0xs0/Wekw1ccMNMH8+3Hor/PKX3sf5873t5ZIKePkJBGDBAujqgosv9j5Gtax1ysJhaMgS/mho8B6fISJbIiScf0ZswiWIbJ55S8Cl9koNLs4Hnkn7+tnktknMbD/gAOCBtM1zzGyTmT1oZn+b7UXM7Nzkfpu2b99e4pBFZAbSXCQi9UBzkYjMBCPAaufcEuCdwAVmtqTGY6qu/n644AL/xy64AF54oTyvkyvglUjAmjWwahVcdZX3cf58rwGMFC8Y9JrkBIPjAd1AYHz7DGrm0rujdyxTMVNsOEbfTmXESvlVs6HLmcB659xo2rb9nHPLgA8A15rZm/ye6Jy7yTm3zDm3bM8996zGWEVk5tJcJCL1QHORiNQl59zzzrlHk59HgcfJcqNkxrrkktyPr1lTntfJFvBqawMzr6GLGr2UT0eHFzheu9b7Hq5d6309w7pyt+/eTqDZPyM20Bxg0byZswRc6kepDV2eA/ZN+3pBcpufM4EJt3+cc88lPz5tZj/Fqzv0uxLHJCKzj+YiEakHmotEZEYxs/3x5qKHfB47FzgXYOHChdUdWCmiUa9RSm+vtyw5HPaCe+meeCL3MZ58snzjSQW8IhGvxuKiRfDqq9kDmGr0Upq2thn/fxdeGqZzY6fvYw3WQPjgmbMEXOpHqZmLDwPtZnaAmbXgnShvyNzJzA4CXg/8Im3b682sNfn5HsCRwNbM54qIFEBzkYjUA81FIjJjmFkbcAewyjn3l8zHi86ijkZrXz+wp8dbWpxvqfFBB+U+zuLFZR1WtNnRdajj4vcm6DrUEX2mT41eZMqCrUG6V3QTbAmOZTAGmgMEW7ztauYilVBS5qJzbsTMLgQ2Ao3Azc65LWb2OWCTcy51Qn0mcLtzzqU9/a+Ar5pZAi/IeWV6N0URkUJpLhKReqC5SERmCjNrxgssrnPOfb/kA/b0eMt5EwkvOBYIQGentyS4WktSU0uK04OaqQBeKORlD6bq7l1xhdfAJZsrryx9LMnsyZ6FEHrlBhI4YsMxAs0BOgOjdL95Dh1PDU5+bi06GxeS7VnIYYaiRLZE6N3RS/vu7YSXhgm2Fn8cya9jYQf9q/uJbI7Qt7OPRfMWET44rMCiVIxNPK+tf8uWLXObNm2q9TBEpARm9kiyrti0pblIZPrTXCQi9aDe5iIzM+AWYKdzblUhz8k5F0WjXnagX6ZiMDgxqFdJXV1epqJfRmAg4NXfS18ue8MN/k1drr8ezj9/6uNIC7RGh2PMXw3R1sm7BYeg/2poi2c+UMX/M/APDDc0FB0Y7tnWQ2hdiIRLjAVRG6yB7hXddCycWTUPp7N6m49k+qhmQxcRERERERGpb0cCHwTea2aPJf+Fpny0SMQLTPlJ1Q+shi1biltqfP758PzzcM458M53eh+ff760wGJ69mQsRuRgyPI/Q2K3OUQOby2us3G5l55njBeYUmOZ6FCU0LoQ0Xh0rItxbDhGNO5t3xWfWoOa6FCUrke7uPi+i+l6tIvoUA2W2osIUHpDFxEREREREZkhnHM9gJXtgL29ta8f2NMDN96Y/fFsS4333hu++c3yjSMj0No7D2I+WYsAscQgff/SCf1Lxhu9hMPZA4uVWHpeSGC4gOYokS0REs7/OAmXILI5wsrDimuy4pcJ2bmxU5mQIjWi4KKIiIiIiIhURnu7F+jKthy50vUDU1l2Q0PZ92lo8AJ3xRx2KvUDMwKt7TshMOQfYAw0B1i01xJ4XwFBt2LqSRajTIHh3h29YxmLkw4zHKNvZ3EB5vRMyPTjAITWhehf3a/agiJVpmXRIiIiIiIiUhnhsBe88zOFoF7BUkuETz89d2BxzpzcS4199GzrYf4181l1zyqu+vlVrLpnFfOvmU/Ptp7cT0wFWpPCm7NfkDeMjBLeL1TY0t9cGYavvurVjpzKEumM8U5QRGC4fff2sa7Fkw7THGDRvOICzIVkQopIdSm4KCIiIiIiMk2Y2VvM7A4z225mA2a22cwuNrP6XJWWqhMYDBZXP7AUPT1eE5lVq2DjRohndkVJc/75RS0bLql+YFqgNdoCkYPh5CegdRjmJocYiHvNXLrXwWMdb2L+l/bOH8TMlWE4MgK33eb9f/TkCX7mGO8kRQSGw0vDNJj/cRqsgfDBxQWYy50JKSKlq88/QCIiIiIiIrOYmfUD/+GcuyZt218D3cDctF2XAF/Aa8RySlUHWaiODm9pbiRSWP3AQkSj3vF6e70Mu3DYC1j6LRHOJhCAJUuKetmS6gcmA6o9HzmG0OlxEnhLogNxGDVY8Ws4+g8Q3gKOQa+T9Cgw6j0969LfXEvPAYaHvX/FLpFOBYCzdYsu8DjB1iDdK7qzdosudglzKhPSL8A4lUxIESmdgosiIiIiIiL1Z29gLOpiZg3AN4DdgKuBrwDbgXcB1wEnmdkK59y6Gow1v7a2gpp/FCRX85Innsi+RDjTFJZll5o1F/2rAwmdHieaVmcx1uJ93HAQfOVuaItD12E5OklnBjFDIbjwwvyDL6IJy5gyBYY7FnbQv7qfyOYIfTv7WDRvEeGDw1OqjRheGqZzY6fvY1PJhBSR0im4KCIiIiIiUv/+GjgA+Ipz7p/Stm80s+OArcCHgPoMLpZLvuYlH/tY9gy+lClk36WUmjUX+fYlJLL04k4AkaWw8ld5OkmnBzFTgVYroMH3VLtzlykw3NbSVnRXaD/lzoQUkdIpuCgiIiIiIlL/3go4vCzFCZxzfzCzu/ECkDNbruYliQTs2JF9iXBrK7z3vXDaaVNell1q1lzv9ifGMhUzxVqhb573ed5O0vMWFbcEHKrTnbtKypkJKSKlU3BRRERERESk/qXa7T6d5fHfUa81F8spV/OSWAxe8xoYHfV/vKUFvvvdkmo9lpo1177nQQRe/KVvgDEwBIt2ep+HN0PnCf7HGHWjhNpDuQOtfirZnbsGypUJKSKlU3BRRERERESkPrm0z7clPwaBQZ99g0Ce9cAzQK7mJXPmwNe+NnmJ8Jw50Nxctu7UpWTNhc++gs6rb/V9rAEIP70b8CrB5gDddyYIrTCGLcHgSNq33MHi6xbT/ZeT6ci1BLy52WvkUuAy8OhQlMiWCL07emnfvZ3w0jDB1mDe9yQiouCiiIiIiIhIfbrIzD6S/Dy1QHYp8FOfffcDXqjGoGoqHPaat/gZ9Iu5As7BU0/B3nuXbRhTzZoL7v5Gut99PaGfX0DCvGYugTg0OOg+7Grarn7tWOOUjnCYJ12UA9YeMOEYg6ODDI4OEtrt+/S/bi5tfx6Y/EJz58Lpp8M++xTUhKVnW8+kbMzOjZ10r+imY2FH0e9TRGYXBRdFRERERETqzza8zMVUGl48ue09ZAQXzWwuXr3FH1RxfLURDHoZeJndolNLof0CjE1NcPfd5etWXaKO959P/7v/nsi319C3/UkW7bWY8NlX0jYvGfyMRr0lz5//PHe/8QWaGhoZ8lnpnWhq4JaDR2gd8BrAtO/0llMH43iZisuWwTPPeMFV5yYfICk6FCW0LkQ0Pl67MdWwJrQuRP/qftUyFJGcFFwUERERERGpM865/YvYfSFwNfCTyoymznR0QH+/F4BLZvmxeTNce63//lm6JFdyGXC+Y7fN25uV//jNyU9MdX9OBk5739dE7B0j/m9reIDO45poHkpmQA55dRq7v9dCR38C1qwZD752dnpB2Y7JWYiRLRESzr92Y8IliGyOqLahiORUcnDRzE4E1gKNQJdz7sqMxz8MfAl4LrnpOudcV/Kxc4DPJLf/m3PullLHIyKzk+YiEakHmotEpBacc08A/1rrcVRVW9vETMSurrFajNEWiBycls339FyCGV2SC14GnMoi7O316j2Gw172ZA5TXmLs0/1535dGaBmGeLP/U+JuhHiyOUyqs3To9Dj9V8dpiyd3StVlDIW8oGzG8ujeHb1jmYqZYsMx+nZODsyKiKQrKbhoZo3A9cBxwLPAw2a2wTm3NWPXiHPuwoznzgM+CyzDS/d/JPncl0sZk4jMPpqLRKQeaC4SEamhZC3GnoUQWgEJvGBbYAg6GaD7yH1JhfUKXgackUWYLwMw77G/eRz9FzxN2+77+L+HjO7PPQvhkmMhXuRVe8IgshRW/irzgYT3GhnLw9t3byfQHPANMAaaAyyat2jSdhGRdA0lPv8IoM8597RzLg7cDpxa4HNPAO5zzu1MnjjfB5xY4nhEZHbSXCQi9UBzkYiUjZm1mNkvzezHZpYlb21svx+b2YO59pvxgkGiG9YTWgHR1vEsvlir93XozuXsiu8CClsGPCGLMJX5F4uNb9+1y/f5OY89NEjk5AO8oCVeILLr0S4uvu9iuh7tItq7eey1oi1ekHRXK+NVN9NlL6FIrAX65vk94L88PLw0TIP5hwYarIHwweHsLyYiQunBxfnAM2lfP5vcluk0M/uNma03s32LfK6ISD6ai0SkHmguEpFyOhs4HLjaOTecbafkzYwv4d3gWFGlsdWlyGu2kWgL+D6WcAkim26Bri56v/+1/MuAM7IIJx4smQGYIToUZf3W9dmP3QJ9c4cgFKLniXuZf818Vt2ziqt+fhWr7lnF/MBX6XnzHO+9HOxlX2blF3BMCsRh0U6fB+bM8epTZgi2Bule0U2wJUig2fv/CzQHCLZ429XMRUTyqUZDlx8Atznnhszs/wG3AO8t5gBmdi5wLsDChQvLP0IRmQ00F4lIPdBcJCKF+nvgaedcd74dnXP3mFkvcDrwzVJfOF/92IqaQo3DlLy1A//tIvhZC+2LYwRO9IJ9mcaWAd/31HjG4qSDTc4ATNVZHBodyjq+wJAX9Is2jhL67qlE3Xhn67Gl06dB/9VevchU9mWxGhyEt/g8MDgIJ53k+5yOhR30r+4nsjlC384+Fs1bRPjgsAKLIlKQUjMXnwP2Tft6AeMFygFwzu1wzqVm2C68u28FPTftGDc555Y555btueeeJQ5ZRGYgzUUiUg80F4lIOR0K/LSI/X8GvK3UF02rH/s+YAlwlpktKfW4BenpgfnzYdUquOoq7+P8+WPLiPNJ1Q70ExiCRS8MQyxGeLMXgPMztgy4vd2rseh7sMCEDMD0Oovx0bj/c/AuvsNbIHLgAInEqO8+id3mEDm8lfZdLczNHqecoMl5aYyB5gBBm0P391rGm7mkmzMH7r4763HaWtpYedhKrjj2ClYetnJSYHHSMu6haJYjichsU2pw8WGg3cwOMLMW4ExgQ/oOZpZerfYU4PHk5xuB483s9Wb2euD45DYRkWJpLhKReqC5SETKaQ/gT0Xs/ydg9zK8bin1Y6duijUO04WXhmnIsl44FdgDCMahex0Eh7wlxOCzDDgchoYsl8sNDd7jSZFHbiERzx5UbB3xXqt7HbTFoXevJmLmv9I9lhik718uIPzRaxhtacz7ngNxWBF4F2uOXMPaE9fSH/s4HX1ZxjI46FtzsRA923omL+O+Zj492woL/IrIzFbSsmjn3IiZXYh38tsI3Oyc22JmnwM2Oec2AP9oZqcAI8BO4MPJ5+40s8/jnYgDfM4551cZQkQkJ81FIlIPNBeJSJm9ChSzJrUNGMy7V35+NWDfkblT2Us0FFLjMKPLcaZga5Du155P6LmrJnSLbmA8sJfSsc1bfhxZCn0nvZNFJ39s4jLgZIOYyKdPofe1o7T/aYTw03MJjjZ63aLbkvv19NB7RSexI7KWxeS9T8N314+/fvsrjQSaW7N3Z95rCcH3reS07/+c7/z2Oznfc4OD61beQdu8vb0NP78OWlrAL9iZkXFZqIK7a4vIrFVyzcVkDZDujG2XpX1+CXBJlufeDNxc6hhERDQXiUg90FwkImX0DLCsiP2XAdsqNJZJnHM3ATcBLFu2LEfv4gL19hZV4zCbjm3Qf20yaDjPq3EY3oLvMuG2OKx8KgAXfAwOmxi47NnWQ+gXy0mc1ERseIiAa6azIUH3GXfScVCHt1Myq7K9fZjAkH+NxEAcTnu6hbZ43AvuNTQQ/vf1dP5iue/407szH73/0fz3E//NwPCA/5t1sP4dV48HFnt64JJL/AOLMCnjslCFdNdeeVjuwK+IzGzVaOgiIiIiIiIixfkpcL6ZLXPObcq1o5kdDrwb+K8yvG7BNWDLKlXj0C/AWEzGXXs7bc0BVv4qS6Ayk0/AzTdTz4bBDRO6czn9q/txzhG5+UJ6O15l35eyN29uCLQR/uiVcNSz3nsIhwm2tdF9YDehdSESLkFsOEagOUCDNUzozhxeGuYTP/rE/2/v7qMjL6sEj39vupO0nZQv4I64vIhOBxWYdeGgzh4zowgLvcERdwYN2u6M2uoZRndHw4wguMo6L6KOKDvi+NJ61heUCOrYR+Iyoni0d8ShVVQahfT4Ahj3qKBskaaT7s7dP36V7nS6KqlUqlNV6e/nnDpV9atf/eo+ne6bzs3zPLdm6OvXPoJ7j39UJehFlo/39x8843IJFm2U80BjS60lrR4WFyVJkiSp/bwXuAi4PiKGMvMH1U6KiKcA1wP7gPc14XP37x9LUVS8EHhJE65bVXmqzOiOUcYfv4OBp+1leHuxJ+JBljLjbngYRkZqv75+PezatX8WYbWC22Iz9f76a3/N+257HzNTu5l85l76pmAmYP00RM5Zjt3Tw9hLv0j/CYOHXKee7syl3hJ/+JQ/5JN3VF8avWvfwwcKewstK+/thSuvhMFD46jHbKOcmsu4j1r6UmtJq4vFRUmSJElqM5l5V2XP1iuA70TEDcBXKPZAhGJvxLOAPwJ6gTdn5l1N+Nyq+8cu97rVbLtn28Gz9zauY+TZMPaZdQzevXvBAmBNpVJx/tAQ7NlTNDGZ1dsL+/bBpk1w5plFIbLKdRebqXfVN65iz8ye/dMVZ5dD90/BlTfDfY+EDZM9DL/iqgOFxXK5KACOjxezNIeH6S+VFl1OfOYTz+Qfd3yGXXlo6+i+rnUHCnsLLSufmoL77qv+Wh2GTxlm5KbqBdu5y7glHbksLkqSJElSG8rMt0bEXuAtFLMHXzzvlAD2AJdn5tua+LmH7B/bbFWXHs/shl4Y2gQTD4/Qv+HkmgXABQ0Owl13wZOedPDxqUqBbutWeP/7a153oZl6vWt6SapvMTl79Ld/DXcfk1x3SjI8VaZ023eLYufMTFEA7OsrZleOjS06m3D4CUOM7J4qysfzdD28m+ETz6sE3aRl5VWUeosu2ost45Z05OpqdQCSJEmSpOoy82+Bk4C/Am4Bfli53QK8FTipmYXFlbLg0uO1axh94clFd+gG9ggE4MYbYc2a6q/Ndp+uYfiUYbqi+o/Kmcn0vuoNUyZ7YWQjvG4jvOMZe3jdVy/l2Hf9W7a96pxi5uJs4W9ycvE9EitKnxtj7DPrKE0VS60B1k9B7x74g51ruO4Tl1CeKhdF2K4aP9432Mhlrtll3FdvvJpLn3UpV2+8momLJxissuRb0pHHmYuSJEmS1MYy86cUsxdXjcPeJGQZ3acXmql30RkXcc1t11SPPWF6TXGbHQfA0H+GiXdV6Vg9MwMf/WixXHvOcmlKpYPGMXj3bibeVXTAvuVE+MzJsDbhk6fs4/MPfIqRqz7HDS+6gXs+dBHjH3s3Aw8Ew9+eptTdwLLyBfT39NsVWlJVFhclSZIkqU1FxAnA0ylW3d6Wmfe2OKSmOOxNQpa5TLhWw5XM5B+2/0P1N9VoGT1DURjc/J15L0xOFsuju7trL5eujKN/cpIX7YDXb4Td3XMuEXtgeg/nfuLc4s/zGXvoi15Gzu1h7Ldey+Afv6kphUVJWojLoiVJkiSpDUXE3wE/Aj5N0RH6xxHxztZG1RwLLT1uSpOQJiwTnp2p97az38bm0zfT39O/f1ZjqafE+rXr6wplshd2HlU8LvfAltPhkrOL+zLTCy+XnjOO0VOLQmXNz6kUaidzinJMM/Tg+3iop64QJWlZLC5KkiRJUpuJiBcDIxTz4X4I3FV5PFJ5raPNLdL1dfcBxYzFUk+pOU1CZrtGl0rFjEAo7mePL2M23+ysxgtOvoDuru5Fz++bhg0PwLYT4NiL4XXnwjsGi/tjLy6OH2TunpBzxjH+uLX7O1PXYyZnGL2j9t6S1ZSnymz59hYu+dIlbPn2lmI/R0lahMuiJUmSJKn9vBLYC5ybmbcARMTZwBeBzcCnWhhbU9Raety07sODgzAxURTqdu4slkI30n26iv6efo7pP4Y9M3sWPbfrEesZmgie/IpJynOKg7OFwqFN8/ZknL8nZGUcAx95DX0PfKpYCl2Hpe5due2ebYfsMzly0whjm8Zs3CJpQRYXJUmSJKn9/Dvg87OFRYDMvDkiPg88p2VRNdlhbxLS3190nT4MFto3EqBnTQ+9a3oZ2zTGjc+9nZmb/wJy6pDzDtmTscqekOXuZPfvPp09N1238NroOZayd2V5qszQtUOUpw/MVNzfkObaISYunmhe0VfSquOyaEmSJElqP4+hWA493w+BR69sKG2qXIYtW+CSS4r78qFLeA/nMt+F9o3sWdPDVedcxcTFEwyeMMj45L1MViksQpU9GZ+2l0sev2N/vNvu2caxVx3LpTdfyvTMwS2nF9r3cSl7V47uGGUmq1ctG1leLenI4sxFSZIkSWo/XUC19a97qNmX+AiybVvR+GRmpman5WUt8y2Xi+XU4+NFx+bh4WL/wzlm942c/xld0XXIZyw0y3H9NEyUYNML1/DZJ++jqyfYddu76evu43X/+3VM75uuuvy6u6ubd5zzDgaOGuCCT19QNYZ6ZxuO3z9ecwbmUpdXSzryWFyUJEmSpPaUrQ6gLc12VJ47U3G24/LQEExMUO7O+pb5Visifve7ixYuZ9W7b+TwKcOM3DRSdTi7euD6p63h4dhXHJjZfVC8tfSs6WHdmnWc89vnMHHxBB+9/aPcePeNJMl5J53H0x73tEX/KGctVPxcyvJqSUcmi4uSJEmS1J6uiIgrqr0QMVuJOkhm5ur/GW90tCj8VVPptDx6Wi66zHfzricfWkR8/euL57t2HXjDvMLl/IYw9ewbWeotceXZV/KasddUff3hql/OhU3umWTnZz8E305uHzyBN375jftnL379nq9z2Zcvq7sZy0LFz6Usr5Z0ZFr2nosRsTEi7oqInRFxaZXXRyLizoj4XkR8OSKeMOe1fRFxe+W2dbmxSDpymYsktQNzkaQmiyXejow99cfHDxT85qt0Wl50me//vfPA7MfZa01OwkMPHVxYnKtSuGxEearMpTcf8m1hWfqmYcPYNyn/5Z8z9LFzKU+X9495cs8k5emiSctD0w8teq3ZJd6lnhJ93X3F9bv7KPWUlrS8eqUczr00JS3dsn6rFRFrgGuA/wjcB9wWEVsz8845p30HOCMzd0XERcA7gNlfezycmf9+OTFIkrlIUjswF0lqpsw8MgqFjRgYKGYZViswVjotDxydCy/zHf9V7dmPtVQKl41YqGFKo7oShnfAdafuqtlAev8szTo6cte7xLvVlrWXpqTDYrnfsJ4B7MzMH2XmNHAdcP7cEzLzlsyc/dXPrcBxy/xMSZrPXCSpHZiLJGklDA9DV40fZbu6YHh4wU7OXdHF8MRRtWc/1rJuHWxobO/BhWZSLllC7x4Yuxb6p2H8qKLjdDVLbcYyu8T7bWe/jc2nb267wmJ5qrx/L81GZ2lKar7lFhePBe6d8/y+yrFaNgNfnPN8XURsj4hbI+IFtd4UEa+unLf9l7/85bIClrQqmYsktQNzkSSthFKpaK5SKhUzFaG4nz3e37/4Mt+BUyg/ej1bTodLzoYtp0O5Z5HP3b0bzjuvoZBnG6Y0w7q98JP3wOA9lWs/AH1T1c9dbc1YFpoBOjtLU9LKW7HNfiPipcAZwLPnHH5CZv4sIp4EfCUivp+Z/zr/vZn5QeCDAGeccYYd0yQ1zFwkqR2YiyRpmQYHi+Yqo6PFUuUNG4oZjXOarSy0zHfb4C6GLiqWE0/2FsW5kXOL2YCzRbtDrFsHN94ImxdfYjzfQg1TANavXc+uvbtYv3Y9+3IfF5x8AceVjuOa7deQmfuX/3bt3cfY9XDM5O4D176jiL2a/c1YqnXFLpWWPI5WW3QvzSXM0pTUPMstLv4MOH7O8+Mqxw4SEWcDlwPPzsz9v1PJzJ9V7n8UEV8FTgMO+U+0JC3CXCSpHZiLJHW0iHgn8AfANEX+eXlm/qalQS2kv3/RQl+1Ts7lqTJDn7uA8pylxLPLioc2wcS7iuXGh9i9u+E9F2dnUs7fK7ArurjhRTdw74P3Vt3n8E3PftPBxdEnDNH/d08GDhQXS9NFUXRoE8z09x107bFNY/T/y+2HdsUeGSlmeQ521h6FszNAa+6luYpmaUqdZLnFxduAgYh4IsV/ni8EXjL3hIg4DfgAsDEzfzHn+GOAXZk5FRGPBZ5Fsam5JC2VuUhSOzAXSep0XwLemJl7I+LtwBuBS1ocU9MtuLS2t4fR02HzrVWqi5VmMY1qpGFKteIoY2OHFAsHf93FxDk3MPrIeUXKqYRTjy1mLs6a3WtyaKiY/dnfXvsqLmShGaD7Z2lKWnHLKi5Wvum8FrgJWAN8JDN3RMRbge2ZuRV4J9APXB8RAPdk5vOBpwIfiIgZir0fr5zXTVGS6mIuktQOzEWSOl1m/tOcp7cCF7QqlsNpwaW1TLPzMd3V31hpFrMcVYuFS1VjSXh/fz+HXPljW2p3xZ6ZKa7RwDLvVlloBujYprG2a0AjHSmWvediZo4BY/OOvXnO47NrvO+fgd9Z7udLEpiLJLUHc5GkVeQVQM3uGBHxauDVACeccMJKxdQUiy6tfdlrYdv7Dl5G3NW1v1lMW6hjSThQ7LFYqyv25GTDy7xbqZEZoJIOrxVr6CJJkiRJaq2IuBk4pspLl2fm5yvnXA7sBa6tdZ1Obi616NLaF7wJht60YLOYjjEwUBRHqxUYl7nMu5WaMgNUUtNYXJQkSZKkI0StGdSzIuJlwPOAszKzo4qG9apraW0Py1ouXJ4qM7pjlPH7xxk4eoChDUOM7Rzb/3z4lGFKvSvQrXl4uGjeUk0TlnlLElhclCRJkiQBEbEReANFN/tdrY7ncDqcS2u33bPtoMLlurXreNXeV7FuzTp279tNX3cfIzeNMLZpjMETDnO35lKpagOYtlvmLamjWVyUJEmSJAG8F+gFvlRpOnVrZv5pa0M6fA7H0tryVJmha4coTx/ozrx77+7ifl9xP7vX49C1Q0xcPHH49wqs0QDGwqKkZrG4KEmSJEkiMztzA742MrpjlJms0Z15npmcYfSO0ZXZO7DeBjCS1ICuVgcgSZIkSdJqMH7/eNUu1NVM7plk5wOd161ZkuazuChJkiRJUhMMHD1AX3dfXef2dfex4Sgni0rqfBYXJUmSJElqguFThumK+n7M7oouhk+1W7OkzmdxUZIkSZKkJij1lhjbNEapp7R/BuO6teuK+zXFfV93H6We4rzD3sxFklaADV0kSZIkSWqSwRMGmbh4gtE7Rtn5wE42HLWB8046jxvvvnH/8+FThy0sdoJyueiyPT4OAwNFl+1SqdVRSW3H4qIkSZIkSU3U39N/SBfo+c/LU2VGd4wyfv84A0cPMHzKMKVeC1dtY9s2GBqCmRmYnIS+PhgZgbExGBxsdXRSW7G4KEmSJEnSCtp2zzaGrh1iJmeY3DNJX3cfIzeNMLZpjMETLFy1XLlcFBbL5QPHJitdwIeGYGIC+p15Ks1yz0VJkiRJklZIearM0LVDlKfLTO4pClaTeyYpTxfHH5p+qMURitHRYsZiNTMzxeuS9rO4KEmSJEnSChndMcpMVi9czeQMo3dYuGq58fEDMxXnm5yEnTtXNh6pzVlclCRJkiRphYzfP75/xuJ8k3sm2fmAhauWGxgo9lispq8PNmxY2XikNrfs4mJEbIyIuyJiZ0RcWuX13ogYrbz+zYg4cc5rb6wcvysizl1uLJKOXOYiSe3AXCRJWszA0QP0dVcvXPV197HhKAtXLTc8DF01yiVdXcXrkvZbVnExItYA1wD/CTgZeHFEnDzvtM3ArzNzA/Bu4O2V954MXAicAmwE3le5niQtiblIUjswF0mS6jF8yjBdUf1H8a7oYvhUC1ctVyoVXaFLpQMzGPv6Dhy3mYt0kOXOXHwGsDMzf5SZ08B1wPnzzjkf+Gjl8Q3AWRERlePXZeZUZv4Y2Fm5niQtlblIUjswF0mSFlXqLTG2aYxST2n/DMa+7j5KPcXx/h4LV21hcLDoCn311XDppcX9xERxXNJB1i7z/ccC9855fh/wzFrnZObeiHgQOLpy/NZ57z222odExKuBV1eeTkXEHcuMu5UeC/yq1UEsU6ePodPjh84fw5ObfD1z0dJ1+t8h6PwxdHr80PljMBe1Xqf/HYLOH0Onxw+dP4Zm56IV961vfetXEfHTFfioTv9aHyzoYj1HsY9HT66Z/A27eOD3Lvu9Gi2KO9Lq+noBvPKVs49W39gKT2h1AOpMyy0urojM/CDwQYCI2J6ZZ7Q4pIZ1evzQ+WPo9Pih88cQEdtbHUMjzEXtpdPH0OnxQ+ePwVzUep0eP3T+GDo9fuj8MXRqLporM//NSnxOp3+ta3FcnWc1j01qxHKXRf8MOH7O8+Mqx6qeExFrgUcB99f5Xkmqh7lIUjswF0mSJOmIs9zi4m3AQEQ8MSJ6KDYi3zrvnK3An1QeXwB8JTOzcvzCStfEJwIDwL8sMx5JRyZzkaR2YC6SJEnSEWdZy6IrewW9FrgJWAN8JDN3RMRbge2ZuRX4MPDxiNgJPEDxH20q530auBPYC7wmM/fV8bEfXE7MbaDT44fOH0Onxw+dP4amxm8uakinxw+dP4ZOjx86fwzmotbr9Pih88fQ6fFD54+h0+NfSav1z8pxdZ7VPDZpyaL4ZbkkSZIkSZIkLc1yl0VLkiRJkiRJOkJZXJQkSZIkSZLUkLYsLkbExoi4KyJ2RsSlVV7vjYjRyuvfjIgTWxDmguoYw0hE3BkR34uIL0fEE1oRZy2LxT/nvD+KiIyIM1YyvnrUM4aIeFHl67AjIj650jEupI6/QydExC0R8Z3K36OhVsRZS0R8JCJ+ERF31Hg9IuJ/Vsb3vYg4faVjXIy5qPXMRe3BfNR6nZ6POj0XQefnI3NR662GXNQOIuKdEfHDyp/R5yLi0a2OaTnqzS2dJCKOr/xbnM0nf97qmJopItZU8swXWh2L1DYys61uFBug/yvwJKAH+C5w8rxz/gx4f+XxhcBoq+NuYAxnAusrjy9qpzHUE3/lvBLwNeBW4IxWx93A12AA+A7wmMrz32p13EuM/4PARZXHJwM/aXXc8+L7feB04I4arw8BXwQC+F3gm62OuYGvgbmoxfFXzjMXtX4M5qPWfw3aNh91ei6qdwyV89oyH5mL2uPW6bmoXW7AOcDayuO3A29vdUzLGEtduaXTbsDjgdMrj0vA3athXHPGNwJ8EvhCq2Px5q1dbu04c/EZwM7M/FFmTgPXAefPO+d84KOVxzcAZ0VErGCMi1l0DJl5S2buqjy9FThuhWNcSD1fA4C/oviGvnslg6tTPWN4FXBNZv4aIDN/scIxLqSe+BN4ZOXxo4CJFYxvUZn5NYpOqLWcD3wsC7cCj46Ix69MdHUxF7Weuag9mI9ar9PzUafnIuj8fGQuagOrIBe1hcz8p8zcW3najvliKerNLR0lM3+emd+uPC4DPwCObW1UzRERxwHnAVtaHYvUTtqxuHgscO+c5/dxaCLaf07lG8uDwNErEl196hnDXJspfkvZLhaNv7JM4/jMvHElA1uCer4GJwEnRcT/iYhbI2LjikW3uHrivwJ4aUTcB4wB/3VlQmuapf47WWnmotYzF7UH81HrdXo+6vRcBJ2fj8xFnaHdc1E7egXtly+WYtV/zSvbdJwGfLPFoTTLe4A3ADMtjkNqK2tbHcCRLiJeCpwBPLvVsdQrIrqAq4CXtTiU5VpLsQToORS/8fxaRPxOZv6mlUEtwYuB/5WZ74qI/wB8PCJOzUy/0WnJzEUt1em5CMxHapJOzEWwavKRuUhtIyJuBo6p8tLlmfn5yjmXA3uBa1cyNtUvIvqBzwCvy8z/1+p4lisingf8IjO/FRHPaXE4Ultpx+Liz4Dj5zw/rnKs2jn3RcRaimUP969MeHWpZwxExNnA5cCzM3NqhWKrx2Lxl4BTga9WVlwdA2yNiOdn5vYVi3Jh9XwN7qPYy2YP8OOIuJviP9W3rUyIC6on/s3ARoDM/EZErAMeC7TbMqZa6vp30kLmotYzF7UH81HrdXo+6vRcBJ2fj8xFnaHdc9GKycyzF3o9Il4GPA84KzNzRYI6PFbt1zwiuikKi9dm5mdbHU+TPAt4fqVZ1DrgkRHxicx8aYvjklquHZdF3wYMRMQTI6KHYlPyrfPO2Qr8SeXxBcBX2uybyqJjiIjTgA8Az2/DPW0WjD8zH8zMx2bmiZl5IsVeJ+3yn+dZ9fw9+keK384TEY+lWA70oxWMcSH1xH8PcBZARDyV4hvcL1c0yuXZCvxxpTPi7wIPZubPWx3UHOai1jMXtQfzUet1ej7q9FwEnZ+PzEWdod1zUVuoLNl/A8W/sV2Lnd/m6vl73XGi+C3Lh4EfZOZVrY6nWTLzjZl5XCXPX0jxvdbCokQbzlzMzL0R8VrgJoruWR/JzB0R8VZge2ZupUhUH4+InRSbIl/YuogPVecY3gn0A9dXfsN9T2Y+v2VBz1Fn/G2tzjHcBJwTEXcC+4C/zMy2mOVRZ/wXAx+KiNdTbGD+sjb6QZKI+BTFDymPjWLvo7cA3QCZ+X6KvZCGgJ3ALuDlrYm0OnNR65mL2oP5qPU6PR91ei6Czs9H5qL20Om5qI28F+gFvlTJF7dm5p+2NqTG1Pp73eKwmuFZwH8Bvh8Rt1eOXZaZY60LSdLhFG30/VaSJEmSJElSB2nHZdGSJEmSJEmSOoDFRUmSJEmSJEkNsbgoSZIkSZIkqSEWFyVJkiRJkiQ1xOKiJEmSJEmSpIZYXJQkSZIkSZLUEIuLkiRJkiStUhGR8277IuJXEfGViHjJAu97SkT8fUTcEREPRsR0RExExI0RsTkiehf53JfO+cxXNn9kktpFZGarY5AkSZIkSYdBRMz+0P8/KvfdwFOA84E1wLszc2Tee94MvIViQtI3gO3AQ8DjgOcATwK+lZln1PjM44HvV67fD7wqM7c0b1SS2onFRUmSJEmSVqnZ4mJmxrzjZwFfqjx9Umb+pHL8MuBvgHuBF2bmN6tc83nAxZl5ZpXXonLdJwKfBf4Ci4vSquayaEmSJEmSjjCZ+WXgh0AATweIiBOBK4A9wFC1wmLlvV8ANta49H8Dngu8HJhsatCS2pLFRUmSJEmSjkyzsxlnlzS+nGLZ9Gcy846F3piZU4dcLOKpwJXA1Zn5tWYGKql9WVyUJEmSJOkIExFnA0+mKCzeVjk8WLn/cgPXWwt8HLgHuKwZMUrqDGtbHYAkSZIkSTq8IuKKysNuiqLiCyhmLr47M39aee3xlfv7GviINwOnAYOZ+XDjkUrqNBYXJUmSJEla/d5SuU/gN8DXgQ9n5ieWe+GIeCbFbMV3ZeY3lns9SZ3F4qIkSZIkSavc/G7RNfwceCpwbL3XrSyH/hhwN/DfG4tOUidzz0VJkiRJkgSwrXJ/1hLe0w+cRFGU3B0ROXvjwGzJD1WOvad5oUpqF5GZi58lSZIkSZI6TqXIV9fMxYg4kWIGYgKnZeadC5zbm5lTEfEI4O9rnHY6xT6M24C7gC9l5ujSRiCp3VlclCRJkiRplVpKcbFy/mXA3wA/AV6YmdurnLMReENmPneRa11BMXvxVZm5ZWmRS+oU7rkoSZIkSZIAyMy/reyj+Bbgtoj4Z2A78BDwOOD3gYHKMUmyuChJkiRJkg7IzLdGxPXAnwFnAi8H1gH3A7cDbweW3WVa0urgsmhJkiRJkiRJDbFbtCRJkiRJkqSGWFyUJEmSJEmS1BCLi5IkSZIkSZIaYnFRkiRJkiRJUkMsLkqSJEmSJElqiMVFSZIkSZIkSQ2xuChJkiRJkiSpIRYXJUmSJEmSJDXE4qIkSZIkSZKkhvx/6zPqfKY/xxQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting(pca5_serum_df, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "1nMBpd5JIy3s"
   },
   "outputs": [],
   "source": [
    "# now do PCA for 10, 15, 20\n",
    "pca10_serum = pca_analysis(serum, 10)\n",
    "pca10_serum_df = make_dataframe(pca10_serum, 10)\n",
    "\n",
    "pca15_serum = pca_analysis(serum, 15)\n",
    "pca15_serum_df = make_dataframe(pca15_serum, 15)\n",
    "\n",
    "pca20_serum = pca_analysis(serum, 20)\n",
    "pca20_serum_df = make_dataframe(pca20_serum, 20)\n",
    "# pca20_serum.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "OJ4K_kNKNqWP"
   },
   "outputs": [],
   "source": [
    "# pca15_serum_df.to_csv(\"serum/pca15_serum_metabolics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "id": "BaIC09d1YJyp",
    "outputId": "237ba941-6c89-4eea-d789-7f8d6349cb10"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne2_serum = tsne_analysis(serum, 2)\n",
    "\n",
    "tsne2_serum_df = tsne_dataframe(tsne2_serum, 2)\n",
    "# print(tsne2_serum_df.head)\n",
    "# plotting(tsne2_serum_df, 1, 1)\n",
    "\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = tsne2_serum_df.Class == target\n",
    "    plt.scatter(tsne2_serum_df.loc[indicesToKeep, 'tsne1']\n",
    "               , tsne2_serum_df.loc[indicesToKeep, 'tsne2'], c = color, s = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "id": "qKnrjJTzYh63",
    "outputId": "d8263a8f-454b-47dc-e94d-6a016fd1153e"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x576 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne3_serum = tsne_analysis(serum, 3)\n",
    "\n",
    "tsne3_serum_df = tsne_dataframe(tsne3_serum, 3)\n",
    "# print(tsne3_serum_df.head)\n",
    "tsne_plotting(tsne3_serum_df, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "P-BRS2vwvm8x"
   },
   "outputs": [],
   "source": [
    "# tsne2_serum_df.to_csv('plasma/tsne2_serum_metabolics.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O5YQ-9mOzz0x"
   },
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "sLVgPz17wu7w"
   },
   "outputs": [],
   "source": [
    "# !pip install lazypredict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "wofvYf1N0sEL"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\UTSHAB\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\utils\\deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import lazypredict\n",
    "from lazypredict.Supervised import LazyClassifier\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.model_selection import ShuffleSplit, train_test_split, StratifiedKFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "mFGBwSwYBpsx"
   },
   "outputs": [],
   "source": [
    "def get_xy(df):\n",
    "  X = df.drop('Class', axis=1)\n",
    "  X = X.apply(pd.to_numeric)\n",
    "  y = df['Class'].copy()\n",
    "  y = y.map({\"control\": 0, \"disease\": 1})\n",
    "  return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "eKoAzi2-XCzq"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pc1      object\n",
       "pc2      object\n",
       "pc3      object\n",
       "pc4      object\n",
       "pc5      object\n",
       "pc6      object\n",
       "pc7      object\n",
       "pc8      object\n",
       "pc9      object\n",
       "pc10     object\n",
       "pc11     object\n",
       "pc12     object\n",
       "pc13     object\n",
       "pc14     object\n",
       "pc15     object\n",
       "Class    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca15_serum_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # make the independent variables float type\n",
    "# pca2_plasma_df['Class'].map({\"control\": 0, \"disease\": 1})\n",
    "# plasma = pca2_plasma_df.apply(pd.to_numeric)\n",
    "# plasma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "or5h3YFslXGj",
    "outputId": "47d00867-deaf-48ef-fd2b-98d91a421e76"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [00:01<00:00, 23.67it/s]\n",
      "100%|██████████| 29/29 [00:01<00:00, 25.31it/s]\n",
      " 97%|█████████▋| 28/29 [00:00<00:00, 34.69it/s]"
     ]
    }
   ],
   "source": [
    "accuracy = []\n",
    "for k in [0, 2, 3, 4, 5, 10, 15, 20, 22, 23]:\n",
    "    if k==0:\n",
    "        name = ['Plasma(Specific Metabolics)', 'Serum(Specific Metabolics)']\n",
    "        dfs = [plasma, serum]\n",
    "    elif k==22 or k==23:\n",
    "        name = [f'tsne{k-20}_plasma(Specific Metabolics)', f'tsne{k-20}_serum(Specific Metabolics)']\n",
    "        dfs = [vars()[f'tsne{k-20}_plasma_df'], vars()[f'tsne{k-20}_serum_df']]\n",
    "    else:\n",
    "        name = [f'pca{k}_plasma(Specific Metabolics)', f'pca{k}_serum(Specific Metabolics)']\n",
    "        dfs = [vars()[f'pca{k}_plasma_df'], vars()[f'pca{k}_serum_df']]\n",
    "    \n",
    "    random_state = 42\n",
    "\n",
    "    for idx, data in enumerate(dfs):\n",
    "      result = []\n",
    "      X, y = get_xy(data)\n",
    "      rs = ShuffleSplit(n_splits=10, test_size=.2, random_state=random_state)\n",
    "      raw_x = X\n",
    "      for train_index, test_index in rs.split(X):\n",
    "        clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=matthews_corrcoef, predictions=True)\n",
    "        X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    #     print(X_train, X_test, y_train, y_test)\n",
    "\n",
    "        models, predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
    "        result.append(models)\n",
    "    #     print(result)\n",
    "\n",
    "      acc = []\n",
    "      for i in range(10):\n",
    "    #     print(result[i].iloc[0]['Balanced Accuracy'])\n",
    "        acc.append(result[i].iloc[0]['Balanced Accuracy'])\n",
    "\n",
    "      print(f'\\n{name[idx]}: {sum(acc)/len(acc)}')\n",
    "      accuracy.append([f'{name[idx]}', f'{sum(acc)/len(acc)}'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_df = pd.DataFrame(accuracy, columns=['Processed Dataset', 'Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Processed Dataset</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Plasma(Specific Metabolics)</td>\n",
       "      <td>0.8820318570318569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Serum(Specific Metabolics)</td>\n",
       "      <td>0.9286761849261849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pca2_plasma(Specific Metabolics)</td>\n",
       "      <td>0.8960372960372961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pca2_serum(Specific Metabolics)</td>\n",
       "      <td>0.8873300310800311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pca3_plasma(Specific Metabolics)</td>\n",
       "      <td>0.8938956876456878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>pca3_serum(Specific Metabolics)</td>\n",
       "      <td>0.8795600233100233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pca4_plasma(Specific Metabolics)</td>\n",
       "      <td>0.8977466977466978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>pca4_serum(Specific Metabolics)</td>\n",
       "      <td>0.8761509324009324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pca5_plasma(Specific Metabolics)</td>\n",
       "      <td>0.9033022533022533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>pca5_serum(Specific Metabolics)</td>\n",
       "      <td>0.8973679098679099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pca10_plasma(Specific Metabolics)</td>\n",
       "      <td>0.9001456876456878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pca10_serum(Specific Metabolics)</td>\n",
       "      <td>0.9043657731157732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>pca15_plasma(Specific Metabolics)</td>\n",
       "      <td>0.8973630536130536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>pca15_serum(Specific Metabolics)</td>\n",
       "      <td>0.8959158896658896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>pca20_plasma(Specific Metabolics)</td>\n",
       "      <td>0.8834790209790212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>pca20_serum(Specific Metabolics)</td>\n",
       "      <td>0.8966637529137531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>tsne2_plasma(Specific Metabolics)</td>\n",
       "      <td>0.8936383061383062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>tsne2_serum(Specific Metabolics)</td>\n",
       "      <td>0.9033935508935509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>tsne3_plasma(Specific Metabolics)</td>\n",
       "      <td>0.724814491064491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>tsne3_serum(Specific Metabolics)</td>\n",
       "      <td>0.7389413364413365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Processed Dataset            Accuracy\n",
       "0         Plasma(Specific Metabolics)  0.8820318570318569\n",
       "1          Serum(Specific Metabolics)  0.9286761849261849\n",
       "2    pca2_plasma(Specific Metabolics)  0.8960372960372961\n",
       "3     pca2_serum(Specific Metabolics)  0.8873300310800311\n",
       "4    pca3_plasma(Specific Metabolics)  0.8938956876456878\n",
       "5     pca3_serum(Specific Metabolics)  0.8795600233100233\n",
       "6    pca4_plasma(Specific Metabolics)  0.8977466977466978\n",
       "7     pca4_serum(Specific Metabolics)  0.8761509324009324\n",
       "8    pca5_plasma(Specific Metabolics)  0.9033022533022533\n",
       "9     pca5_serum(Specific Metabolics)  0.8973679098679099\n",
       "10  pca10_plasma(Specific Metabolics)  0.9001456876456878\n",
       "11   pca10_serum(Specific Metabolics)  0.9043657731157732\n",
       "12  pca15_plasma(Specific Metabolics)  0.8973630536130536\n",
       "13   pca15_serum(Specific Metabolics)  0.8959158896658896\n",
       "14  pca20_plasma(Specific Metabolics)  0.8834790209790212\n",
       "15   pca20_serum(Specific Metabolics)  0.8966637529137531\n",
       "16  tsne2_plasma(Specific Metabolics)  0.8936383061383062\n",
       "17   tsne2_serum(Specific Metabolics)  0.9033935508935509\n",
       "18  tsne3_plasma(Specific Metabolics)   0.724814491064491\n",
       "19   tsne3_serum(Specific Metabolics)  0.7389413364413365"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy_df.to_csv('accuracy/accuracy_Specific Metabolics.csv')\n",
    "accuracy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29/29 [00:00<00:00, 43.54it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 42.77it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 37.28it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 40.79it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 40.79it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 42.65it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 40.17it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 39.46it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 48.58it/s]\n",
      "100%|██████████| 29/29 [00:00<00:00, 48.41it/s]\n"
     ]
    }
   ],
   "source": [
    "name = ['plasma']\n",
    "dfs = [plasma]\n",
    "\n",
    "random_state = 42\n",
    "\n",
    "for idx, data in enumerate(dfs):\n",
    "  result = []\n",
    "  X, y = get_xy(data)\n",
    "  rs = ShuffleSplit(n_splits=10, test_size=.2, random_state=random_state)\n",
    "  raw_x = X\n",
    "  for train_index, test_index in rs.split(X):\n",
    "    clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=matthews_corrcoef, predictions=True)\n",
    "    X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "#     print(X_train, X_test, y_train, y_test)\n",
    "\n",
    "    models, predictions = clf.fit(X_train, X_test, y_train, y_test)\n",
    "    result.append(models)\n",
    "#     print(result)\n",
    "\n",
    "  acc = []\n",
    "  for i in range(10):\n",
    "#     print(result[i].iloc[0]['Balanced Accuracy'])\n",
    "    acc.append(result[i].iloc[0]['Balanced Accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " RidgeClassifierCV                  0.88               0.91     0.91      0.88   \n",
       " SVC                                0.88               0.91     0.91      0.88   \n",
       " NuSVC                              0.88               0.91     0.91      0.88   \n",
       " LogisticRegression                 0.88               0.91     0.91      0.88   \n",
       " RandomForestClassifier             0.88               0.87     0.87      0.88   \n",
       " BaggingClassifier                  0.88               0.87     0.87      0.88   \n",
       " LinearDiscriminantAnalysis         0.88               0.87     0.87      0.88   \n",
       " CalibratedClassifierCV             0.82               0.86     0.86      0.83   \n",
       " BernoulliNB                        0.82               0.83     0.83      0.83   \n",
       " RidgeClassifier                    0.82               0.83     0.83      0.83   \n",
       " LGBMClassifier                     0.82               0.79     0.79      0.82   \n",
       " XGBClassifier                      0.82               0.79     0.79      0.82   \n",
       " ExtraTreesClassifier               0.76               0.78     0.78      0.77   \n",
       " LabelPropagation                   0.71               0.77     0.77      0.71   \n",
       " KNeighborsClassifier               0.71               0.77     0.77      0.71   \n",
       " NearestCentroid                    0.71               0.77     0.77      0.71   \n",
       " LabelSpreading                     0.71               0.77     0.77      0.71   \n",
       " PassiveAggressiveClassifier        0.76               0.74     0.74      0.76   \n",
       " Perceptron                         0.76               0.74     0.74      0.76   \n",
       " GaussianNB                         0.71               0.73     0.73      0.71   \n",
       " ExtraTreeClassifier                0.65               0.69     0.69      0.65   \n",
       " DecisionTreeClassifier             0.71               0.66     0.66      0.70   \n",
       " QuadraticDiscriminantAnalysis      0.53               0.64     0.64      0.49   \n",
       " AdaBoostClassifier                 0.65               0.61     0.61      0.65   \n",
       " SGDClassifier                      0.65               0.61     0.61      0.65   \n",
       " LinearSVC                          0.65               0.61     0.61      0.65   \n",
       " DummyClassifier                    0.59               0.61     0.61      0.60   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " RidgeClassifierCV                           0.78        0.01  \n",
       " SVC                                         0.78        0.01  \n",
       " NuSVC                                       0.78        0.01  \n",
       " LogisticRegression                          0.78        0.01  \n",
       " RandomForestClassifier                      0.74        0.11  \n",
       " BaggingClassifier                           0.74        0.04  \n",
       " LinearDiscriminantAnalysis                  0.74        0.01  \n",
       " CalibratedClassifierCV                      0.70        0.02  \n",
       " BernoulliNB                                 0.63        0.02  \n",
       " RidgeClassifier                             0.63        0.01  \n",
       " LGBMClassifier                              0.60        0.02  \n",
       " XGBClassifier                               0.60        0.02  \n",
       " ExtraTreesClassifier                        0.54        0.09  \n",
       " LabelPropagation                            0.55        0.01  \n",
       " KNeighborsClassifier                        0.55        0.01  \n",
       " NearestCentroid                             0.55        0.01  \n",
       " LabelSpreading                              0.55        0.01  \n",
       " PassiveAggressiveClassifier                 0.48        0.01  \n",
       " Perceptron                                  0.48        0.01  \n",
       " GaussianNB                                  0.45        0.01  \n",
       " ExtraTreeClassifier                         0.37        0.01  \n",
       " DecisionTreeClassifier                      0.33        0.01  \n",
       " QuadraticDiscriminantAnalysis               0.34        0.01  \n",
       " AdaBoostClassifier                          0.23        0.13  \n",
       " SGDClassifier                               0.23        0.01  \n",
       " LinearSVC                                   0.23        0.01  \n",
       " DummyClassifier                             0.20        0.01  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " NearestCentroid                    0.94               0.94     0.94      0.94   \n",
       " LogisticRegression                 0.88               0.88     0.88      0.88   \n",
       " GaussianNB                         0.88               0.88     0.88      0.88   \n",
       " BernoulliNB                        0.82               0.83     0.83      0.82   \n",
       " SVC                                0.82               0.83     0.83      0.82   \n",
       " RandomForestClassifier             0.82               0.83     0.83      0.82   \n",
       " ExtraTreesClassifier               0.82               0.83     0.83      0.82   \n",
       " NuSVC                              0.82               0.83     0.83      0.82   \n",
       " SGDClassifier                      0.82               0.83     0.83      0.82   \n",
       " PassiveAggressiveClassifier        0.82               0.83     0.83      0.82   \n",
       " LinearSVC                          0.82               0.82     0.82      0.82   \n",
       " CalibratedClassifierCV             0.82               0.82     0.82      0.82   \n",
       " KNeighborsClassifier               0.82               0.82     0.82      0.82   \n",
       " Perceptron                         0.82               0.82     0.82      0.82   \n",
       " LGBMClassifier                     0.76               0.77     0.77      0.76   \n",
       " XGBClassifier                      0.76               0.76     0.76      0.76   \n",
       " RidgeClassifier                    0.76               0.76     0.76      0.76   \n",
       " RidgeClassifierCV                  0.76               0.76     0.76      0.76   \n",
       " BaggingClassifier                  0.71               0.71     0.71      0.71   \n",
       " AdaBoostClassifier                 0.71               0.70     0.70      0.70   \n",
       " DecisionTreeClassifier             0.65               0.65     0.65      0.64   \n",
       " ExtraTreeClassifier                0.65               0.65     0.65      0.65   \n",
       " LinearDiscriminantAnalysis         0.65               0.63     0.63      0.62   \n",
       " LabelSpreading                     0.65               0.62     0.62      0.59   \n",
       " LabelPropagation                   0.65               0.62     0.62      0.59   \n",
       " QuadraticDiscriminantAnalysis      0.53               0.56     0.56      0.42   \n",
       " DummyClassifier                    0.47               0.47     0.47      0.47   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " NearestCentroid                             0.89        0.01  \n",
       " LogisticRegression                          0.76        0.01  \n",
       " GaussianNB                                  0.76        0.01  \n",
       " BernoulliNB                                 0.70        0.01  \n",
       " SVC                                         0.70        0.01  \n",
       " RandomForestClassifier                      0.70        0.11  \n",
       " ExtraTreesClassifier                        0.70        0.14  \n",
       " NuSVC                                       0.70        0.01  \n",
       " SGDClassifier                               0.65        0.01  \n",
       " PassiveAggressiveClassifier                 0.65        0.01  \n",
       " LinearSVC                                   0.65        0.01  \n",
       " CalibratedClassifierCV                      0.65        0.02  \n",
       " KNeighborsClassifier                        0.65        0.01  \n",
       " Perceptron                                  0.65        0.01  \n",
       " LGBMClassifier                              0.55        0.02  \n",
       " XGBClassifier                               0.53        0.02  \n",
       " RidgeClassifier                             0.54        0.01  \n",
       " RidgeClassifierCV                           0.54        0.01  \n",
       " BaggingClassifier                           0.42        0.02  \n",
       " AdaBoostClassifier                          0.41        0.10  \n",
       " DecisionTreeClassifier                      0.31        0.01  \n",
       " ExtraTreeClassifier                         0.29        0.01  \n",
       " LinearDiscriminantAnalysis                  0.31        0.01  \n",
       " LabelSpreading                              0.39        0.01  \n",
       " LabelPropagation                            0.39        0.01  \n",
       " QuadraticDiscriminantAnalysis               0.24        0.01  \n",
       " DummyClassifier                            -0.06        0.01  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " KNeighborsClassifier               0.71               0.81     0.81      0.73   \n",
       " NearestCentroid                    0.71               0.81     0.81      0.73   \n",
       " LabelPropagation                   0.59               0.73     0.73      0.61   \n",
       " LabelSpreading                     0.59               0.73     0.73      0.61   \n",
       " NuSVC                              0.71               0.72     0.72      0.73   \n",
       " SVC                                0.71               0.72     0.72      0.73   \n",
       " ExtraTreeClassifier                0.65               0.68     0.68      0.67   \n",
       " ExtraTreesClassifier               0.65               0.68     0.68      0.67   \n",
       " RandomForestClassifier             0.65               0.68     0.68      0.67   \n",
       " RidgeClassifierCV                  0.59               0.64     0.64      0.62   \n",
       " RidgeClassifier                    0.59               0.64     0.64      0.62   \n",
       " XGBClassifier                      0.59               0.64     0.64      0.62   \n",
       " DummyClassifier                    0.59               0.64     0.64      0.62   \n",
       " DecisionTreeClassifier             0.59               0.64     0.64      0.62   \n",
       " CalibratedClassifierCV             0.59               0.64     0.64      0.62   \n",
       " LinearDiscriminantAnalysis         0.53               0.61     0.61      0.56   \n",
       " LogisticRegression                 0.53               0.61     0.61      0.56   \n",
       " BernoulliNB                        0.53               0.61     0.61      0.56   \n",
       " AdaBoostClassifier                 0.53               0.61     0.61      0.56   \n",
       " LGBMClassifier                     0.47               0.57     0.57      0.50   \n",
       " GaussianNB                         0.59               0.56     0.56      0.62   \n",
       " PassiveAggressiveClassifier        0.53               0.52     0.52      0.57   \n",
       " QuadraticDiscriminantAnalysis      0.24               0.50     0.50      0.09   \n",
       " BaggingClassifier                  0.47               0.48     0.48      0.51   \n",
       " LinearSVC                          0.47               0.48     0.48      0.51   \n",
       " SGDClassifier                      0.53               0.43     0.43      0.56   \n",
       " Perceptron                         0.47               0.39     0.39      0.51   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " KNeighborsClassifier                        0.52        0.01  \n",
       " NearestCentroid                             0.52        0.01  \n",
       " LabelPropagation                            0.41        0.01  \n",
       " LabelSpreading                              0.41        0.01  \n",
       " NuSVC                                       0.38        0.02  \n",
       " SVC                                         0.38        0.01  \n",
       " ExtraTreeClassifier                         0.31        0.02  \n",
       " ExtraTreesClassifier                        0.31        0.09  \n",
       " RandomForestClassifier                      0.31        0.19  \n",
       " RidgeClassifierCV                           0.25        0.01  \n",
       " RidgeClassifier                             0.25        0.01  \n",
       " XGBClassifier                               0.25        0.02  \n",
       " DummyClassifier                             0.25        0.01  \n",
       " DecisionTreeClassifier                      0.25        0.02  \n",
       " CalibratedClassifierCV                      0.25        0.03  \n",
       " LinearDiscriminantAnalysis                  0.18        0.01  \n",
       " LogisticRegression                          0.18        0.02  \n",
       " BernoulliNB                                 0.18        0.02  \n",
       " AdaBoostClassifier                          0.18        0.10  \n",
       " LGBMClassifier                              0.12        0.02  \n",
       " GaussianNB                                  0.10        0.01  \n",
       " PassiveAggressiveClassifier                 0.03        0.02  \n",
       " QuadraticDiscriminantAnalysis               0.00        0.01  \n",
       " BaggingClassifier                          -0.03        0.03  \n",
       " LinearSVC                                  -0.03        0.01  \n",
       " SGDClassifier                              -0.12        0.01  \n",
       " Perceptron                                 -0.18        0.02  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " ExtraTreesClassifier               0.88               0.89     0.89      0.88   \n",
       " LinearSVC                          0.88               0.88     0.88      0.88   \n",
       " LogisticRegression                 0.88               0.88     0.88      0.88   \n",
       " CalibratedClassifierCV             0.88               0.88     0.88      0.88   \n",
       " SGDClassifier                      0.88               0.88     0.88      0.88   \n",
       " RidgeClassifierCV                  0.88               0.88     0.88      0.88   \n",
       " Perceptron                         0.88               0.88     0.88      0.88   \n",
       " PassiveAggressiveClassifier        0.88               0.88     0.88      0.88   \n",
       " BernoulliNB                        0.88               0.88     0.88      0.88   \n",
       " NearestCentroid                    0.88               0.88     0.88      0.88   \n",
       " LinearDiscriminantAnalysis         0.82               0.83     0.83      0.82   \n",
       " XGBClassifier                      0.82               0.83     0.83      0.82   \n",
       " RandomForestClassifier             0.82               0.83     0.83      0.82   \n",
       " BaggingClassifier                  0.82               0.83     0.83      0.82   \n",
       " AdaBoostClassifier                 0.82               0.82     0.82      0.82   \n",
       " LabelSpreading                     0.82               0.82     0.82      0.82   \n",
       " LabelPropagation                   0.82               0.82     0.82      0.82   \n",
       " RidgeClassifier                    0.76               0.77     0.77      0.76   \n",
       " SVC                                0.76               0.77     0.77      0.76   \n",
       " DecisionTreeClassifier             0.76               0.77     0.77      0.76   \n",
       " LGBMClassifier                     0.76               0.76     0.76      0.76   \n",
       " GaussianNB                         0.76               0.76     0.76      0.76   \n",
       " KNeighborsClassifier               0.76               0.75     0.75      0.75   \n",
       " NuSVC                              0.71               0.72     0.72      0.70   \n",
       " QuadraticDiscriminantAnalysis      0.71               0.72     0.72      0.70   \n",
       " ExtraTreeClassifier                0.71               0.71     0.71      0.71   \n",
       " DummyClassifier                    0.47               0.47     0.47      0.47   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " ExtraTreesClassifier                        0.79        0.10  \n",
       " LinearSVC                                   0.76        0.02  \n",
       " LogisticRegression                          0.76        0.02  \n",
       " CalibratedClassifierCV                      0.76        0.02  \n",
       " SGDClassifier                               0.76        0.01  \n",
       " RidgeClassifierCV                           0.76        0.01  \n",
       " Perceptron                                  0.76        0.02  \n",
       " PassiveAggressiveClassifier                 0.76        0.01  \n",
       " BernoulliNB                                 0.78        0.01  \n",
       " NearestCentroid                             0.78        0.02  \n",
       " LinearDiscriminantAnalysis                  0.70        0.02  \n",
       " XGBClassifier                               0.65        0.03  \n",
       " RandomForestClassifier                      0.65        0.12  \n",
       " BaggingClassifier                           0.65        0.02  \n",
       " AdaBoostClassifier                          0.65        0.09  \n",
       " LabelSpreading                              0.65        0.02  \n",
       " LabelPropagation                            0.65        0.02  \n",
       " RidgeClassifier                             0.55        0.01  \n",
       " SVC                                         0.55        0.01  \n",
       " DecisionTreeClassifier                      0.55        0.01  \n",
       " LGBMClassifier                              0.53        0.02  \n",
       " GaussianNB                                  0.54        0.02  \n",
       " KNeighborsClassifier                        0.59        0.02  \n",
       " NuSVC                                       0.45        0.02  \n",
       " QuadraticDiscriminantAnalysis               0.45        0.01  \n",
       " ExtraTreeClassifier                         0.42        0.01  \n",
       " DummyClassifier                            -0.06        0.01  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " BernoulliNB                        0.82               0.83     0.83      0.83   \n",
       " SVC                                0.82               0.83     0.83      0.83   \n",
       " ExtraTreesClassifier               0.82               0.83     0.83      0.83   \n",
       " NuSVC                              0.82               0.83     0.83      0.83   \n",
       " KNeighborsClassifier               0.82               0.79     0.79      0.82   \n",
       " NearestCentroid                    0.82               0.79     0.79      0.82   \n",
       " AdaBoostClassifier                 0.71               0.73     0.73      0.71   \n",
       " XGBClassifier                      0.71               0.73     0.73      0.71   \n",
       " RandomForestClassifier             0.71               0.73     0.73      0.71   \n",
       " BaggingClassifier                  0.71               0.73     0.73      0.71   \n",
       " QuadraticDiscriminantAnalysis      0.65               0.73     0.73      0.64   \n",
       " LabelPropagation                   0.76               0.70     0.70      0.75   \n",
       " LabelSpreading                     0.76               0.70     0.70      0.75   \n",
       " LogisticRegression                 0.71               0.70     0.70      0.71   \n",
       " LGBMClassifier                     0.65               0.69     0.69      0.65   \n",
       " ExtraTreeClassifier                0.76               0.67     0.67      0.72   \n",
       " RidgeClassifier                    0.71               0.66     0.66      0.70   \n",
       " GaussianNB                         0.65               0.65     0.65      0.65   \n",
       " PassiveAggressiveClassifier        0.65               0.65     0.65      0.65   \n",
       " Perceptron                         0.65               0.65     0.65      0.65   \n",
       " CalibratedClassifierCV             0.65               0.65     0.65      0.65   \n",
       " LinearSVC                          0.65               0.65     0.65      0.65   \n",
       " LinearDiscriminantAnalysis         0.65               0.61     0.61      0.65   \n",
       " RidgeClassifierCV                  0.65               0.61     0.61      0.65   \n",
       " SGDClassifier                      0.65               0.61     0.61      0.65   \n",
       " DecisionTreeClassifier             0.53               0.60     0.60      0.52   \n",
       " DummyClassifier                    0.24               0.22     0.22      0.25   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " BernoulliNB                                 0.63        0.02  \n",
       " SVC                                         0.63        0.01  \n",
       " ExtraTreesClassifier                        0.63        0.10  \n",
       " NuSVC                                       0.63        0.01  \n",
       " KNeighborsClassifier                        0.60        0.01  \n",
       " NearestCentroid                             0.60        0.01  \n",
       " AdaBoostClassifier                          0.45        0.11  \n",
       " XGBClassifier                               0.45        0.02  \n",
       " RandomForestClassifier                      0.45        0.11  \n",
       " BaggingClassifier                           0.45        0.04  \n",
       " QuadraticDiscriminantAnalysis               0.48        0.01  \n",
       " LabelPropagation                            0.46        0.01  \n",
       " LabelSpreading                              0.46        0.01  \n",
       " LogisticRegression                          0.38        0.01  \n",
       " LGBMClassifier                              0.37        0.02  \n",
       " ExtraTreeClassifier                         0.49        0.02  \n",
       " RidgeClassifier                             0.33        0.01  \n",
       " GaussianNB                                  0.29        0.01  \n",
       " PassiveAggressiveClassifier                 0.29        0.01  \n",
       " Perceptron                                  0.29        0.01  \n",
       " CalibratedClassifierCV                      0.29        0.03  \n",
       " LinearSVC                                   0.29        0.01  \n",
       " LinearDiscriminantAnalysis                  0.23        0.01  \n",
       " RidgeClassifierCV                           0.23        0.01  \n",
       " SGDClassifier                               0.23        0.01  \n",
       " DecisionTreeClassifier                      0.21        0.02  \n",
       " DummyClassifier                            -0.54        0.02  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " LabelPropagation                   0.82               0.83     0.83      0.82   \n",
       " LabelSpreading                     0.82               0.83     0.83      0.82   \n",
       " LGBMClassifier                     0.82               0.83     0.83      0.82   \n",
       " RidgeClassifier                    0.82               0.83     0.83      0.82   \n",
       " PassiveAggressiveClassifier        0.82               0.82     0.82      0.82   \n",
       " LogisticRegression                 0.82               0.82     0.82      0.82   \n",
       " AdaBoostClassifier                 0.82               0.82     0.82      0.82   \n",
       " CalibratedClassifierCV             0.82               0.82     0.82      0.82   \n",
       " LinearSVC                          0.82               0.82     0.82      0.82   \n",
       " KNeighborsClassifier               0.76               0.78     0.78      0.75   \n",
       " NearestCentroid                    0.76               0.78     0.78      0.75   \n",
       " BaggingClassifier                  0.76               0.78     0.78      0.75   \n",
       " XGBClassifier                      0.76               0.77     0.77      0.76   \n",
       " RidgeClassifierCV                  0.76               0.77     0.77      0.76   \n",
       " ExtraTreesClassifier               0.76               0.77     0.77      0.76   \n",
       " NuSVC                              0.76               0.76     0.76      0.76   \n",
       " BernoulliNB                        0.76               0.76     0.76      0.76   \n",
       " LinearDiscriminantAnalysis         0.76               0.76     0.76      0.76   \n",
       " SVC                                0.76               0.76     0.76      0.76   \n",
       " SGDClassifier                      0.76               0.76     0.76      0.76   \n",
       " Perceptron                         0.76               0.76     0.76      0.76   \n",
       " QuadraticDiscriminantAnalysis      0.76               0.75     0.75      0.75   \n",
       " DecisionTreeClassifier             0.71               0.72     0.72      0.70   \n",
       " RandomForestClassifier             0.71               0.71     0.71      0.71   \n",
       " ExtraTreeClassifier                0.71               0.69     0.69      0.69   \n",
       " GaussianNB                         0.65               0.66     0.66      0.63   \n",
       " DummyClassifier                    0.59               0.59     0.59      0.59   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " LabelPropagation                            0.70        0.01  \n",
       " LabelSpreading                              0.70        0.01  \n",
       " LGBMClassifier                              0.65        0.03  \n",
       " RidgeClassifier                             0.65        0.01  \n",
       " PassiveAggressiveClassifier                 0.65        0.01  \n",
       " LogisticRegression                          0.65        0.01  \n",
       " AdaBoostClassifier                          0.65        0.11  \n",
       " CalibratedClassifierCV                      0.65        0.04  \n",
       " LinearSVC                                   0.65        0.01  \n",
       " KNeighborsClassifier                        0.61        0.01  \n",
       " NearestCentroid                             0.61        0.01  \n",
       " BaggingClassifier                           0.61        0.04  \n",
       " XGBClassifier                               0.55        0.02  \n",
       " RidgeClassifierCV                           0.55        0.01  \n",
       " ExtraTreesClassifier                        0.55        0.09  \n",
       " NuSVC                                       0.53        0.01  \n",
       " BernoulliNB                                 0.53        0.02  \n",
       " LinearDiscriminantAnalysis                  0.53        0.01  \n",
       " SVC                                         0.53        0.01  \n",
       " SGDClassifier                               0.54        0.01  \n",
       " Perceptron                                  0.54        0.01  \n",
       " QuadraticDiscriminantAnalysis               0.59        0.01  \n",
       " DecisionTreeClassifier                      0.45        0.02  \n",
       " RandomForestClassifier                      0.42        0.11  \n",
       " ExtraTreeClassifier                         0.43        0.01  \n",
       " GaussianNB                                  0.35        0.01  \n",
       " DummyClassifier                             0.18        0.01  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " NearestCentroid                    0.88               0.89     0.89      0.88   \n",
       " BernoulliNB                        0.88               0.89     0.89      0.88   \n",
       " ExtraTreeClassifier                0.88               0.89     0.89      0.88   \n",
       " GaussianNB                         0.88               0.89     0.89      0.88   \n",
       " KNeighborsClassifier               0.88               0.89     0.89      0.88   \n",
       " LabelPropagation                   0.88               0.89     0.89      0.88   \n",
       " LabelSpreading                     0.88               0.89     0.89      0.88   \n",
       " SVC                                0.82               0.83     0.83      0.82   \n",
       " RandomForestClassifier             0.82               0.83     0.83      0.82   \n",
       " ExtraTreesClassifier               0.82               0.83     0.83      0.82   \n",
       " NuSVC                              0.82               0.83     0.83      0.82   \n",
       " BaggingClassifier                  0.82               0.83     0.83      0.82   \n",
       " AdaBoostClassifier                 0.82               0.82     0.82      0.82   \n",
       " RidgeClassifierCV                  0.76               0.77     0.77      0.76   \n",
       " LGBMClassifier                     0.76               0.77     0.77      0.76   \n",
       " XGBClassifier                      0.76               0.76     0.76      0.76   \n",
       " DecisionTreeClassifier             0.76               0.76     0.76      0.76   \n",
       " LogisticRegression                 0.71               0.71     0.71      0.71   \n",
       " CalibratedClassifierCV             0.71               0.71     0.71      0.71   \n",
       " LinearSVC                          0.71               0.71     0.71      0.71   \n",
       " LinearDiscriminantAnalysis         0.65               0.65     0.65      0.64   \n",
       " RidgeClassifier                    0.65               0.65     0.65      0.64   \n",
       " PassiveAggressiveClassifier        0.65               0.65     0.65      0.65   \n",
       " Perceptron                         0.65               0.65     0.65      0.65   \n",
       " SGDClassifier                      0.65               0.65     0.65      0.65   \n",
       " DummyClassifier                    0.59               0.59     0.59      0.59   \n",
       " QuadraticDiscriminantAnalysis      0.59               0.58     0.58      0.57   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " NearestCentroid                             0.79        0.01  \n",
       " BernoulliNB                                 0.79        0.01  \n",
       " ExtraTreeClassifier                         0.79        0.01  \n",
       " GaussianNB                                  0.79        0.01  \n",
       " KNeighborsClassifier                        0.79        0.01  \n",
       " LabelPropagation                            0.79        0.01  \n",
       " LabelSpreading                              0.79        0.01  \n",
       " SVC                                         0.65        0.02  \n",
       " RandomForestClassifier                      0.65        0.11  \n",
       " ExtraTreesClassifier                        0.65        0.09  \n",
       " NuSVC                                       0.65        0.01  \n",
       " BaggingClassifier                           0.65        0.04  \n",
       " AdaBoostClassifier                          0.65        0.14  \n",
       " RidgeClassifierCV                           0.55        0.01  \n",
       " LGBMClassifier                              0.55        0.04  \n",
       " XGBClassifier                               0.53        0.04  \n",
       " DecisionTreeClassifier                      0.54        0.01  \n",
       " LogisticRegression                          0.42        0.01  \n",
       " CalibratedClassifierCV                      0.42        0.02  \n",
       " LinearSVC                                   0.42        0.01  \n",
       " LinearDiscriminantAnalysis                  0.31        0.01  \n",
       " RidgeClassifier                             0.31        0.01  \n",
       " PassiveAggressiveClassifier                 0.29        0.01  \n",
       " Perceptron                                  0.29        0.01  \n",
       " SGDClassifier                               0.29        0.02  \n",
       " DummyClassifier                             0.18        0.01  \n",
       " QuadraticDiscriminantAnalysis               0.17        0.01  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " GaussianNB                         0.94               0.96     0.96      0.94   \n",
       " KNeighborsClassifier               0.94               0.96     0.96      0.94   \n",
       " NearestCentroid                    0.94               0.96     0.96      0.94   \n",
       " RandomForestClassifier             0.94               0.96     0.96      0.94   \n",
       " LogisticRegression                 0.88               0.92     0.92      0.89   \n",
       " BaggingClassifier                  0.88               0.92     0.92      0.89   \n",
       " Perceptron                         0.88               0.92     0.92      0.89   \n",
       " NuSVC                              0.88               0.92     0.92      0.89   \n",
       " ExtraTreesClassifier               0.88               0.92     0.92      0.89   \n",
       " RidgeClassifierCV                  0.88               0.92     0.92      0.89   \n",
       " SVC                                0.88               0.92     0.92      0.89   \n",
       " BernoulliNB                        0.88               0.92     0.92      0.89   \n",
       " LinearDiscriminantAnalysis         0.88               0.86     0.86      0.88   \n",
       " PassiveAggressiveClassifier        0.76               0.83     0.83      0.77   \n",
       " SGDClassifier                      0.76               0.83     0.83      0.77   \n",
       " XGBClassifier                      0.76               0.83     0.83      0.77   \n",
       " AdaBoostClassifier                 0.76               0.83     0.83      0.77   \n",
       " LinearSVC                          0.76               0.83     0.83      0.77   \n",
       " LabelSpreading                     0.76               0.83     0.83      0.77   \n",
       " LabelPropagation                   0.76               0.83     0.83      0.77   \n",
       " RidgeClassifier                    0.82               0.82     0.82      0.83   \n",
       " CalibratedClassifierCV             0.71               0.79     0.79      0.72   \n",
       " DecisionTreeClassifier             0.76               0.78     0.78      0.77   \n",
       " LGBMClassifier                     0.76               0.78     0.78      0.77   \n",
       " ExtraTreeClassifier                0.71               0.73     0.73      0.72   \n",
       " DummyClassifier                    0.53               0.55     0.55      0.55   \n",
       " QuadraticDiscriminantAnalysis      0.29               0.50     0.50      0.13   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " GaussianNB                                  0.87        0.01  \n",
       " KNeighborsClassifier                        0.87        0.01  \n",
       " NearestCentroid                             0.87        0.01  \n",
       " RandomForestClassifier                      0.87        0.14  \n",
       " LogisticRegression                          0.77        0.01  \n",
       " BaggingClassifier                           0.77        0.04  \n",
       " Perceptron                                  0.77        0.01  \n",
       " NuSVC                                       0.77        0.01  \n",
       " ExtraTreesClassifier                        0.77        0.09  \n",
       " RidgeClassifierCV                           0.77        0.01  \n",
       " SVC                                         0.77        0.01  \n",
       " BernoulliNB                                 0.77        0.01  \n",
       " LinearDiscriminantAnalysis                  0.72        0.01  \n",
       " PassiveAggressiveClassifier                 0.61        0.01  \n",
       " SGDClassifier                               0.61        0.01  \n",
       " XGBClassifier                               0.61        0.02  \n",
       " AdaBoostClassifier                          0.61        0.12  \n",
       " LinearSVC                                   0.61        0.01  \n",
       " LabelSpreading                              0.61        0.01  \n",
       " LabelPropagation                            0.61        0.01  \n",
       " RidgeClassifier                             0.60        0.01  \n",
       " CalibratedClassifierCV                      0.54        0.03  \n",
       " DecisionTreeClassifier                      0.51        0.01  \n",
       " LGBMClassifier                              0.51        0.02  \n",
       " ExtraTreeClassifier                         0.43        0.02  \n",
       " DummyClassifier                             0.09        0.02  \n",
       " QuadraticDiscriminantAnalysis               0.00        0.02  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " LabelPropagation                   1.00               1.00     1.00      1.00   \n",
       " LabelSpreading                     1.00               1.00     1.00      1.00   \n",
       " BernoulliNB                        0.94               0.92     0.92      0.94   \n",
       " NearestCentroid                    0.94               0.92     0.92      0.94   \n",
       " RandomForestClassifier             0.88               0.87     0.87      0.88   \n",
       " ExtraTreesClassifier               0.88               0.87     0.87      0.88   \n",
       " KNeighborsClassifier               0.88               0.87     0.87      0.88   \n",
       " LinearDiscriminantAnalysis         0.88               0.83     0.83      0.88   \n",
       " BaggingClassifier                  0.88               0.83     0.83      0.88   \n",
       " XGBClassifier                      0.82               0.83     0.83      0.83   \n",
       " DecisionTreeClassifier             0.82               0.83     0.83      0.83   \n",
       " GaussianNB                         0.82               0.79     0.79      0.82   \n",
       " LGBMClassifier                     0.76               0.78     0.78      0.77   \n",
       " NuSVC                              0.76               0.78     0.78      0.77   \n",
       " RidgeClassifier                    0.82               0.75     0.75      0.80   \n",
       " Perceptron                         0.76               0.74     0.74      0.76   \n",
       " SGDClassifier                      0.76               0.74     0.74      0.76   \n",
       " LinearSVC                          0.76               0.74     0.74      0.76   \n",
       " PassiveAggressiveClassifier        0.76               0.74     0.74      0.76   \n",
       " LogisticRegression                 0.76               0.74     0.74      0.76   \n",
       " CalibratedClassifierCV             0.76               0.74     0.74      0.76   \n",
       " SVC                                0.71               0.73     0.73      0.71   \n",
       " RidgeClassifierCV                  0.76               0.70     0.70      0.75   \n",
       " AdaBoostClassifier                 0.71               0.70     0.70      0.71   \n",
       " ExtraTreeClassifier                0.65               0.69     0.69      0.65   \n",
       " QuadraticDiscriminantAnalysis      0.35               0.50     0.50      0.18   \n",
       " DummyClassifier                    0.35               0.35     0.35      0.37   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " LabelPropagation                            1.00        0.01  \n",
       " LabelSpreading                              1.00        0.01  \n",
       " BernoulliNB                                 0.87        0.01  \n",
       " NearestCentroid                             0.87        0.01  \n",
       " RandomForestClassifier                      0.74        0.11  \n",
       " ExtraTreesClassifier                        0.74        0.09  \n",
       " KNeighborsClassifier                        0.74        0.01  \n",
       " LinearDiscriminantAnalysis                  0.75        0.01  \n",
       " BaggingClassifier                           0.75        0.02  \n",
       " XGBClassifier                               0.63        0.02  \n",
       " DecisionTreeClassifier                      0.63        0.01  \n",
       " GaussianNB                                  0.60        0.01  \n",
       " LGBMClassifier                              0.54        0.02  \n",
       " NuSVC                                       0.54        0.01  \n",
       " RidgeClassifier                             0.63        0.01  \n",
       " Perceptron                                  0.48        0.01  \n",
       " SGDClassifier                               0.48        0.01  \n",
       " LinearSVC                                   0.48        0.01  \n",
       " PassiveAggressiveClassifier                 0.48        0.01  \n",
       " LogisticRegression                          0.48        0.01  \n",
       " CalibratedClassifierCV                      0.48        0.02  \n",
       " SVC                                         0.45        0.01  \n",
       " RidgeClassifierCV                           0.46        0.01  \n",
       " AdaBoostClassifier                          0.38        0.10  \n",
       " ExtraTreeClassifier                         0.37        0.01  \n",
       " QuadraticDiscriminantAnalysis               0.00        0.01  \n",
       " DummyClassifier                            -0.29        0.01  ,\n",
       "                                Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
       " Model                                                                           \n",
       " AdaBoostClassifier                 0.76               0.76     0.76      0.76   \n",
       " BernoulliNB                        0.76               0.76     0.76      0.76   \n",
       " RandomForestClassifier             0.76               0.76     0.76      0.76   \n",
       " SGDClassifier                      0.71               0.71     0.71      0.71   \n",
       " LabelPropagation                   0.71               0.70     0.70      0.70   \n",
       " XGBClassifier                      0.71               0.70     0.70      0.70   \n",
       " PassiveAggressiveClassifier        0.71               0.70     0.70      0.70   \n",
       " BaggingClassifier                  0.71               0.70     0.70      0.70   \n",
       " LabelSpreading                     0.71               0.70     0.70      0.70   \n",
       " LGBMClassifier                     0.71               0.70     0.70      0.70   \n",
       " CalibratedClassifierCV             0.71               0.70     0.70      0.70   \n",
       " KNeighborsClassifier               0.71               0.69     0.69      0.69   \n",
       " ExtraTreesClassifier               0.71               0.69     0.69      0.69   \n",
       " NearestCentroid                    0.71               0.69     0.69      0.69   \n",
       " ExtraTreeClassifier                0.65               0.65     0.65      0.65   \n",
       " DecisionTreeClassifier             0.65               0.65     0.65      0.65   \n",
       " LinearSVC                          0.65               0.65     0.65      0.65   \n",
       " GaussianNB                         0.65               0.64     0.64      0.64   \n",
       " RidgeClassifierCV                  0.65               0.64     0.64      0.64   \n",
       " QuadraticDiscriminantAnalysis      0.59               0.60     0.60      0.56   \n",
       " NuSVC                              0.59               0.59     0.59      0.59   \n",
       " SVC                                0.59               0.59     0.59      0.59   \n",
       " RidgeClassifier                    0.59               0.58     0.58      0.59   \n",
       " LinearDiscriminantAnalysis         0.59               0.58     0.58      0.59   \n",
       " LogisticRegression                 0.53               0.53     0.53      0.53   \n",
       " Perceptron                         0.53               0.53     0.53      0.53   \n",
       " DummyClassifier                    0.47               0.47     0.47      0.47   \n",
       " \n",
       "                                matthews_corrcoef  Time Taken  \n",
       " Model                                                         \n",
       " AdaBoostClassifier                          0.53        0.09  \n",
       " BernoulliNB                                 0.54        0.01  \n",
       " RandomForestClassifier                      0.54        0.11  \n",
       " SGDClassifier                               0.42        0.01  \n",
       " LabelPropagation                            0.41        0.01  \n",
       " XGBClassifier                               0.41        0.03  \n",
       " PassiveAggressiveClassifier                 0.41        0.01  \n",
       " BaggingClassifier                           0.41        0.02  \n",
       " LabelSpreading                              0.41        0.01  \n",
       " LGBMClassifier                              0.41        0.02  \n",
       " CalibratedClassifierCV                      0.41        0.02  \n",
       " KNeighborsClassifier                        0.43        0.01  \n",
       " ExtraTreesClassifier                        0.43        0.09  \n",
       " NearestCentroid                             0.43        0.01  \n",
       " ExtraTreeClassifier                         0.29        0.01  \n",
       " DecisionTreeClassifier                      0.29        0.01  \n",
       " LinearSVC                                   0.29        0.01  \n",
       " GaussianNB                                  0.29        0.01  \n",
       " RidgeClassifierCV                           0.29        0.01  \n",
       " QuadraticDiscriminantAnalysis               0.25        0.01  \n",
       " NuSVC                                       0.18        0.01  \n",
       " SVC                                         0.18        0.01  \n",
       " RidgeClassifier                             0.17        0.01  \n",
       " LinearDiscriminantAnalysis                  0.17        0.01  \n",
       " LogisticRegression                          0.06        0.01  \n",
       " Perceptron                                  0.06        0.01  \n",
       " DummyClassifier                            -0.06        0.01  ]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "serum-plasma-utshab.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}