Evaluate_Method_Accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "hundred-universal",
   "metadata": {},
   "source": [
    "Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "educated-sociology",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os\n",
    "from os.path import join, exists, expandvars\n",
    "import pandas as pd\n",
    "from IPython.display import display, Markdown\n",
    "import seaborn\n",
    "#import seaborn.xkcd_rgb as colors\n",
    "from tax_credit.plotting_functions import (pointplot_from_data_frame,\n",
    "                                           boxplot_from_data_frame,\n",
    "                                           heatmap_from_data_frame,\n",
    "                                           per_level_kruskal_wallis,\n",
    "                                           beta_diversity_pcoa,\n",
    "                                           average_distance_boxplots,\n",
    "                                           rank_optimized_method_performance_by_dataset)\n",
    "from tax_credit.eval_framework import (evaluate_results,\n",
    "                                       method_by_dataset_a1,\n",
    "                                       parameter_comparisons,\n",
    "                                       merge_expected_and_observed_tables,\n",
    "                                       filter_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "middle-harvey",
   "metadata": {},
   "source": [
    "Configure local environment-specific values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "modified-malta",
   "metadata": {},
   "outputs": [],
   "source": [
    "## project_dir should be the directory where you've downloaded (or cloned) the \n",
    "## tax-credit-data repository. \n",
    "project_dir = expandvars('$HOME/tax-credit-data/')\n",
    "\n",
    "## expected_results_dir contains expected composition data in the structure\n",
    "## expected_results_dir/<dataset name>/<reference name>/expected/\n",
    "expected_results_dir = join(project_dir, \"data/precomputed-results/\", \"mock-community\")\n",
    "\n",
    "## mock_results_fp designates the files to which summary results are written.\n",
    "## If this file exists, it can be read in to generate results plots, instead\n",
    "## of computing new scores.\n",
    "mock_results_fp = join(expected_results_dir, 'mock_results.tsv')\n",
    "\n",
    "## results_dirs should contain the directory or directories where\n",
    "## results can be found. By default, this is the same location as expected \n",
    "## results included with the project. If other results should be included, \n",
    "## absolute paths to those directories should be added to this list.\n",
    "results_dirs = [expected_results_dir]\n",
    "\n",
    "## directory containing mock community data, e.g., feature table without taxonomy\n",
    "mock_dir = join(project_dir, \"data\", \"mock-community\")\n",
    "\n",
    "## Minimum number of times an OTU must be observed for it to be included in analyses. Edit this\n",
    "## to analyze the effect of the minimum count on taxonomic results.\n",
    "min_count = 1\n",
    "\n",
    "## Define the range of taxonomic levels over which to compute accuracy scores.\n",
    "## The default given below will compute order (level 2) through species (level 6)\n",
    "taxonomy_level_range = range(2,7)\n",
    "\n",
    "\n",
    "# we can save plots in this directory\n",
    "outdir = join(project_dir, \"plots\")\n",
    "if not os.path.exists(outdir):\n",
    "    os.makedirs(outdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "broke-rochester",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/mosele/tax-credit-data/'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "amino-movie",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dataset_ids = ['mock-3']\n",
    "dataset_ids = ['mock-1', 'mock-2', 'mock-3', 'mock-4', 'mock-5','mock-6', 'mock-7', 'mock-8','mock-12','mock-13',\n",
    "               'mock-14', 'mock-15', 'mock-16','mock-18', 'mock-19', 'mock-20', 'mock-21', 'mock-22']\n",
    "method_ids = ['q2-NB', 'q2-SFM-RF', 'q2-SFM-SDG','q2-SFM-NB']\n",
    "ref_ids = ['gg_13_8_otus']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "periodic-improvement",
   "metadata": {},
   "source": [
    "Find mock community pre-computed tables, expected tables, and \"query\" tables\n",
    "\n",
    "Note: if you have added additional methods to add, set append=True. If you are attempting to recompute pre-computed results, set force=True.\n",
    "\n",
    "This cell will take a few minutes to run if new results are being added, so hold onto your hat. If you are attempting to re-compute everything, it may take an hour or so, so go take a nap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "psychological-congo",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_results = evaluate_results(results_dirs, \n",
    "                                expected_results_dir, \n",
    "                                mock_results_fp, \n",
    "                                mock_dir,\n",
    "                                taxonomy_level_range=range(2,7), \n",
    "                                min_count=min_count,\n",
    "                                taxa_to_keep=None, \n",
    "                                md_key='taxonomy', \n",
    "                                subsample=False,\n",
    "                                per_seq_precision=True,\n",
    "                                exclude=['other'],\n",
    "                                dataset_ids=dataset_ids,\n",
    "                                reference_ids=ref_ids,\n",
    "                                method_ids=method_ids,\n",
    "                                append=False,\n",
    "                                force=True, #force=True the first time \n",
    "                                backup=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extra-filing",
   "metadata": {},
   "source": [
    "Compute and summarize precision, recall, and F-measure for mock communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "contemporary-nutrition",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_palette={ \n",
    "    'q2-NB':'black',\n",
    "    'q2-SFM-RF':'darkgreen',\n",
    "    'q2-SFM-SDG':'red',\n",
    "    'q2-SFM-NB':'blue',\n",
    "    }\n",
    "\n",
    "y_vars = [\"Precision\", \"Recall\", \"F-measure\", \"Taxon Accuracy Rate\", \"Taxon Detection Rate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "wanted-boxing",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "point = pointplot_from_data_frame(mock_results, \"Level\", y_vars, \n",
    "                                  group_by=\"Reference\", color_by=\"Method\",\n",
    "                                  color_palette=color_palette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "structured-tunisia",
   "metadata": {},
   "outputs": [],
   "source": [
    "for k, v in point.items():\n",
    "    v.savefig(join(outdir, 'mock-{0}-lineplots.pdf'.format(k)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dietary-period",
   "metadata": {},
   "source": [
    "Heatmaps show the performance of individual method/parameter combinations at each taxonomic level, in each reference database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dressed-combination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x529.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7338f7aa90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"Precision\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "located-hudson",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x529.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7338e54908>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"Recall\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "female-blanket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mT07ZCyGEaFSDBw9mz549lJSUUFRUxL59+5Sy7OxsNmzYQFxcHM2bN1e2L1++HK1Wy4YNGwzamzFjBps3bzbZ3/Hjx+natavRdiqfzgKQkpKiXJxTlYuLC3l5eVy4cAG9Xo9Op1NmdWsTb05d4nU6HX5+fibLf/75Z2bNmsWKFSv4v//7v981fi8vL3Q6HXq9ngsXLpCXl0e/fv1q3B97e3uOHTsGVMzUduvWDahIaMPDww3qd+rUiS+//JLi4mLKy8s5fPgw3bt3r7EfIe4Fch9SIYRowvr27cvYsWPRaDQ4OTkxaNAgpWzp0qXo9Xplzaerq6vZ2yYB9OzZkz59+vD1118r2yrXkJaXl9OsWTOWLVtmNHb69OnMmTOHnTt30rFjR1avXg1UJFALFy5kw4YNWFtbs2jRIp5++mnKysoYN24cPXv2NBsPFUldUVERt2/fJjMzk82bN9OjRw9iYmIIDQ3FxcWlVv0DFBcXc+jQIYPPonL96MSJE/nXv/7FtWvXWLx4MVBxF4KkpKR6jb9nz574+voyduxYrKysWLRoEVZWVgDVxn+3pUuX8tprr1FaWoqtra0y3oKCAuVitapcXV1RqVQEBQVhbW1N7969f9fzyEXT0VhXxjcki3Jji1uEEEI0SWvWrMHOzo5p06Y19lBEHYWFhTFv3rwa18Ru27aNjh071nkGubbHRoxN2zq1a8q9egrW9UGbBmsraJ35W5HVldWEufWKW9CsTYOO47Xb1xq0vdq4V48XIYQQ4k+ldevWREdHK7eRMmXy5Ml1TkZjY2NJTU2ttnRDiHuJzJAKIYQQQiEzpLV3r8yQNtR3VulV/dUGba827tXjRQghhBBC/EnIRU1CCCGEUDTYidNGup9lTe40ZGM1PNnsf+V+mF2UhFQIIYQQogm7H66yvx+SaiGEEEII0YTJDKkQQgghRBN2P8wuSkIqhBBCCNGENdbz5xvS/ZBUCyGEEEKIJkxmSIUQQgghmrD7YXbxftgHIYQQRsTGxjJmzBjUajUzZ86ksLDQaL2oqCiGDh2KXq8H4MqVK3h5eQFw8eJF+vXrh0ajISAggNDQUM6dO2e0nWvXrjFlyhR8fHyYMmUK169fN1ovOzsblUqFt7c369evrzH+6tWrhIWFMWDAAIPnz9e1/3PnzqHRaJTXwIEDiY+PN9nmyZMn6d27Nx999FG9xw+wbt06vL29UalUHDhwwGhfYWFhqFQq5UlN33zzDRMmTECj0RAcHMzJkydNjrNSfHw8fn5++Pv7ExkZya1bt4CKY8HDw4NNmzbV2IZoeiwtGvbVKPvQON0KIYT4o3l4eJCenk5aWhrdunVj3bp1JutaWVmxc+dOo2Vdu3ZFq9WSmppKYGCgyXbWr1+Pu7s7GRkZuLu7V0vWKpWVlbFkyRI2btyITqcjPT2dM2fOmI23tbVl9uzZzJtn/qk4ten/oYceQqvVotVqSUpKonnz5nh7exttr6ysjFWrVuHp6fm7xn/mzBl0Oh06nY6NGzeyePFiysrKjPa5atUq5bGgK1euZObMmWi1WmbPns3KlSvN7n9+fj5bt24lMTGR9PR0ysrK0Ol0AMyfP5/Q0FCz8UI0JklIhRCiiYuLi0OlUhEREUFkZKQyC+bp6Ym1dcXKrP79+3Pp0iWTbYSHh7NlyxZKa7jRd1FREa1atTJalpWVRWBgIACBgYFkZmYa1Dl58iTOzs506dIFGxsb/Pz8lBlBU/F2dna4ublha2trdmy16b+qw4cP06VLF5ycnIyWv/vuu6hUKtq3b/+7xp+VlYWfnx82NjZ06dIFZ2fnWs12WlhYcPPmTQBu3LiBvb19jTFlZWWUlJRQWlpKSUlJrWJE02fZwK/GIAmpEEI0YadOnWLXrl2kpKSwdu1acnNzjdZLTExk2LBhJtvp2LEjAwcORKvVGpSdP38ejUbD6NGjiY+PZ8qUKUbbuHz5spIA2dvbc+XKFYM6+fn5ODo6Ku8dHBzIz8+vdfzdYmJilH2ua7xOp8Pf3195n5CQQEJCgjLOzMxMg1nF+ozfXEzV8d9twYIFrFixguHDhxMbG0tkZKTZ/XFwcGDq1KmMHDkST09PWrRoUW12V9y/LLFo0Ffj7IMQQogmKycnh9GjR9O8eXNatGihrP2sKi4uDisrKwICAsy2NWPGDDZt2mTw6MjKU/aZmZksWLCAl156qd7jNfZYSovfccuaV199FRcXlzrH6fV69u7dy5gxY5RtEydOZOLEiUq7f//737GysqoWV5/xm4sxN/6EhASio6P55JNPiI6OJiYmxmw/169fJysri6ysLA4cOEBxcbHRPzCEuBdJQiqEEE2cuYQoOTmZ/fv3s2rVKqVedHQ0Go2GZ555plpdZ2dnevfuze7du0225+XlRU5OjtF22rdvT0FBAQAFBQW0a9fOIN7R0bHa0oH8/HxlVrE28ebUJT47O5u+ffvSoUMHo+WnTp0iMjISLy8vPv74YxYvXkxmZma9xm8uxpzk5GR8fHwA8PX1rfE0/6FDh+jcuTPt2rWjWbNm+Pj4cOLEiRr7EU2fXNQkhBCiUQ0ePJg9e/ZQUlJCUVER+/btU8qys7PZsGEDcXFxNG/eXNm+fPlytFotGzZsMGhvxowZbN682WR/x48fp2vXrkbb8fLyIiUlBYCUlBTl4pyqXFxcyMvL48KFC+j1enQ6nTKrW5t4c+oSr9Pp8PPzM1m+d+9e5aVSqXj55ZcZPXp0vcbv5eWFTqdDr9dz4cIF8vLy6NevX437Y29vz7FjxwA4cuQI3bp1AyoS2vDwcIP6nTp14ssvv6S4uJjy8nIOHz5M9+7da+xHiHuB3IdUCCGasL59+zJ27Fg0Gg1OTk4MGjRIKVu6dCl6vV5Z8+nq6mr2tkkAPXv2pE+fPnz99dfKtso1pOXl5TRr1oxly5YZjZ0+fTpz5sxh586ddOzYkdWrVwMVCdTChQvZsGED1tbWLFq0iKeffpqysjLGjRtHz549zcZDRVJXVFTE7du3yczMZPPmzfTo0YOYmBhCQ0NxcXGpVf8AxcXFHDp0yOCzqFw/Wnna3pj6jL9nz574+voyduxYrKysWLRokbIUoOr477Z06VJee+01SktLsbW1VcZbUFCgXKxWlaurKyqViqCgIKytrenduzdPPPGEyX0R94/7YXbRotzY4hYhhBBN0po1a7Czs2PatGmNPRRRR2FhYcybN6/GNbHbtm2jY8eOdZ5Bru2xsaBZmzq1a4rVPfo4S5cHbRqsrXH/Mn+hWV1ZTZpfr7jVD7avuVIdzL55uUHbq437IakWQgghmrzWrVsTHR2t3EbKlMmTJ9c5GY2NjSU1NbXa0g0h7iUyQyqEEEIIhcyQ1t69MkO65kHjF+fV16ybvzZoe7Uha0iFEEIIIZqwxroyviFJQiqEEEIIxe+5L2xTcH/vXdMlCakQQgghRBN2P1wQJAmpEEIIIUQTdj+csr8fkmohhBBCCNGEyQypEEIIIUQTZnkfrIyVGVIhhBBCCNGoZIZUCCGEEKIJkzWkQggh7lmxsbGMGTMGtVrNzJkzKSwsNFovKiqKoUOHotfrAbhy5QpeXl4AXLx4kX79+qHRaAgICCA0NJRz584ZbefatWtMmTIFHx8fpkyZwvXr143Wy87ORqVS4e3tzfr162uMv3r1KmFhYQwYMMDg+fN17f/cuXNoNBrlNXDgQOLj4422d/ToUTQaDX5+fkyePLne4wdYt24d3t7eqFQqDhw4YLS/sLAwVCqV8qSmb775hgkTJqDRaAgODubkyZMm971SfHw8fn5++Pv7ExkZya1bt4CKY8HDw4NNmzbV2IZoeiwa+NUYJCEVQoj7lIeHB+np6aSlpdGtWzfWrVtnsq6VlRU7d+40Wta1a1e0Wi2pqakEBgaabGf9+vW4u7uTkZGBu7t7tWStUllZGUuWLGHjxo3odDrS09M5c+aM2XhbW1tmz57NvHnzzO5vbfp/6KGH0Gq1aLVakpKSaN68Od7e3gb1CgsLWbx4MXFxceh0OlavXl3v8Z85cwadTodOp2Pjxo0sXryYsrIyo/uwatUq5bGgK1euZObMmWi1WmbPns3KlSvN7n9+fj5bt24lMTGR9PR0ysrK0Ol0AMyfP5/Q0FCz8UI0JklIhRCiiYuLi0OlUhEREUFkZKQyC+bp6Ym1dcXKrP79+3Pp0iWTbYSHh7NlyxZKS0vN9lVUVESrVq2MlmVlZREYGAhAYGAgmZmZBnVOnjyJs7MzXbp0wcbGBj8/P2VG0FS8nZ0dbm5u2Nramh1bbfqv6vDhw3Tp0gUnJyeDsrS0NLy9venUqRMA7du3r/f4s7Ky8PPzw8bGhi5duuDs7Fyr2U4LCwtu3rwJwI0bN7C3t68xpqysjJKSEkpLSykpKalVjGj6LC0a9tUo+9A43QohhGgIp06dYteuXaSkpLB27Vpyc3ON1ktMTGTYsGEm2+nYsSMDBw5Eq9UalJ0/fx6NRsPo0aOJj49nypQpRtu4fPmykgDZ29tz5coVgzr5+fk4Ojoq7x0cHMjPz691/N1iYmKUfa5rvE6nw9/fX3mfkJBAQkICAHl5eRQWFhIWFkZwcDApKSn1Hr+5mKrjv9uCBQtYsWIFw4cPJzY2lshI889Nd3BwYOrUqYwcORJPT09atGiBp6en2Rhxf7DEokFfjUEuahJCiCYsJyeH0aNH07x5cwBl7WdVcXFxWFlZERAQYLatGTNm8NxzzzFixIhq2ytP2QPs2rWLl156qd5rEcvLyw22/Z5HVb766qv1itPr9ezdu5e5c+cq2yZOnKj8/7KyMr766ivi4+MpKSkhNDQUV1fXeo3fXIy58SckJBAdHY1KpWLXrl3ExMSYXO8KcP36dbKyssjKyqJly5bMnj0brVaLRqMxOz4h7gUyQyqEEE2cuYQoOTmZ/fv3s2rVKqVedHQ0Go2GZ555plpdZ2dnevfuze7du0225+XlRU5OjtF22rdvT0FBAQAFBQW0a9fOIN7R0bHa0oH8/HxlVrE28ebUJT47O5u+ffvSoUMHo+WOjo4MHToUOzs72rVrh5ubG6dPn67X+M3FmJOcnIyPjw8Avr6+NZ7mP3ToEJ07d6Zdu3Y0a9YMHx8fTpw4UWM/oumTU/ZCCCEa1eDBg9mzZw8lJSUUFRWxb98+pSw7O5sNGzYQFxenzKACLF++HK1Wy4YNGwzamzFjBps3bzbZ3/Hjx+natavRdry8vJRT2ykpKcrFOVW5uLiQl5fHhQsX0Ov16HQ6ZVa3NvHm1CVep9Ph5+dnsnzUqFHk5ORQWlpKcXExJ0+epHv37vUav5eXFzqdDr1ez4ULF8jLy6Nfv3417o+9vT3Hjh0D4MiRI3Tr1g2oSGjDw8MN6nfq1Ikvv/yS4uJiysvLOXz4MN27d6+xH9H0WTbwqzHIKXshhGjC+vbty9ixY9FoNDg5OTFo0CClbOnSpej1emXNp6urq9nbJgH07NmTPn368PXXXyvbKteQlpeX06xZM5YtW2Y0dvr06cyZM4edO3fSsWNH5cr0/Px8Fi5cyIYNG7C2tmbRokU8/fTTlJWVMW7cOHr27Gk2HiqSuqKiIm7fvk1mZiabN2+mR48exMTEEBoaiouLS636ByguLubQoUMGn0Xl+tGJEyfSvXt3hg4dSkBAAJaWlowfP56HH34YoM7j79mzJ76+vowdOxYrKysWLVqElZUVQLXx323p0qW89tprlJaWYmtrq4y3oKBAuVitKldXV1QqFUFBQVhbW9O7d2+eeOIJ01+2EPcQi3Jji1uEEEI0SWvWrMHOzo5p06Y19lBEHYWFhTFv3jyjyWlV27Zto2PHjnWeQa7tsRFj07ZO7Zpyr56C7fegTYO1Ffwv8xea1ZXVpPn1ikto07B3U5h4raBB26uNe/V4EUIIIf5UWrduTXR0tHIbKVMmT55c52Q0NjaW1NTUaks3xP3D0sKiQV+NQWZIhRBCCKGQGdLau1dmSD9s69Cg43jian6DtlcbsoZUCCGEEKIJuw8eZS8JqRBCCCH+vwY7cdpIp37/p27rG3sE9w1JSIUQQgghmrD7IfWXhFQIIYQQogm7HxLSe3XNsRBCCCGE+JOQGVIhhBBCiLBp7xgAACAASURBVCbM3OODmwpJSIUQQgghmrCmn47KKXshhBBCCNHIJCEVQoj7VGxsLGPGjEGtVjNz5kwKCwuN1ouKimLo0KHo9RW3sLly5QpeXl4AXLx4kX79+qHRaAgICCA0NJRz584ZbefatWtMmTIFHx8fpkyZwvXr143Wy87ORqVS4e3tzfr162uMv3r1KmFhYQwYMMDg+fN17f/cuXNoNBrlNXDgQOLj4w3q3bhxgxkzZhAQEICfnx+JiYn1Hj/AunXr8Pb2RqVSceDAAaPjDwsLQ6VSKU9q+uabb5gwYQIajYbg4GBOnjxpct8rxcfH4+fnh7+/P5GRkdy6dQuoOBY8PDzYtGlTjW2IpseygV+NQRJSIYS4T3l4eJCenk5aWhrdunVj3bp1JutaWVmxc+dOo2Vdu3ZFq9WSmppKYGCgyXbWr1+Pu7s7GRkZuLu7V0vWKpWVlbFkyRI2btyITqcjPT2dM2fOmI23tbVl9uzZzJs3z+z+1qb/hx56CK1Wi1arJSkpiebNm+Pt7W1Q77333qN79+6kpqby7rvvEhsbi16vr9f4z5w5g06nQ6fTsXHjRhYvXkxZWZnRfVi1apXyWNCVK1cyc+ZMtFots2fPZuXKlWb3Pz8/n61bt5KYmEh6ejplZWXodDoA5s+fT2hoqNl40XRZWDTsqzFIQiqEEE1cXFwcKpWKiIgIIiMjlVkwT09PrK0rLhXo378/ly5dMtlGeHg4W7ZsobS01GxfRUVFtGrVymhZVlYWgYGBAAQGBpKZmWlQ5+TJkzg7O9OlSxdsbGzw8/NTZgRNxdvZ2eHm5oatra3ZsdWm/6oOHz5Mly5dcHJyMiizsLDg5s2blJeXc/PmTVq3bo21tXW9xp+VlYWfnx82NjZ06dIFZ2fnWs12Vo4BKmZs7e3ta4wpKyujpKSE0tJSSkpKahUjxL1AElIhhGjCTp06xa5du0hJSWHt2rXk5uYarZeYmMiwYcNMttOxY0cGDhyIVqs1KDt//jwajYbRo0cTHx/PlClTjLZx+fJlJQGyt7fnypUrBnXy8/NxdHRU3js4OJCfn1/r+LvFxMQo+1zXeJ1Oh7+/v/I+ISGBhIQEAJ588knOnj3L0KFDCQgIICYmBktLy3qN31xM1fHfbcGCBaxYsYLhw4cTGxtLZKT556Y7ODgwdepURo4ciaenJy1atMDT09NsjLg/WDTw/xqDJKRCCNGE5eTkMHr0aJo3b06LFi2UtZ9VxcXFYWVlRUBAgNm2ZsyYwaZNmwweHVl5yj4zM5MFCxbw0ksv1Xu8xh5L+XtuWfPqq6/i4uJS5zi9Xs/evXsZM2aMsm3ixIlMnDgRgIMHD9K7d28OHDhASkoKS5YsoaioqF7jNxdjbvwJCQlER0fzySefEB0dTUxMjNl+rl+/TlZWFllZWRw4cIDi4mKjf2CI+49FA78agySkQgjRxJlLiJKTk9m/fz+rVq1S6kVHR6PRaHjmmWeq1XV2dqZ3797s3r3bZHteXl7k5OQYbad9+/YUFBQAUFBQQLt27QziHR0dqy0dyM/PV2YVaxNvTl3is7Oz6du3Lx06dDBanpSUhI+PDxYWFjg7O9O5c2fOnTtXr/GbizEnOTkZHx8fAHx9fWs8zX/o0CE6d+5Mu3btaNasGT4+Ppw4caLGfoS4F0hCKoQQTdjgwYPZs2cPJSUlFBUVsW/fPqUsOzubDRs2EBcXR/PmzZXty5cvR6vVsmHDBoP2ZsyYwebNm032d/z4cbp27Wq0HS8vL1JSUgBISUlRLs6pysXFhby8PC5cuIBer0en0ymzurWJN6cu8TqdDj8/P5PlHTt25PDhwwD8+uuv/PDDD3Tu3Lle4/fy8kKn06HX67lw4QJ5eXn069evxv2xt7fn2LFjABw5coRu3boBFQlteHi4Qf1OnTrx5ZdfUlxcTHl5OYcPH6Z79+419iOavvthhlRujC+EEE1Y3759GTt2LBqNBicnJwYNGqSULV26FL1er6z5dHV1NXvbJICePXvSp08fvv76a2Vb5RrS8vJymjVrxrJly4zGTp8+nTlz5rBz5046duzI6tWrgYoEauHChWzYsAFra2sWLVrE008/TVlZGePGjaNnz55m46EiqSsqKuL27dtkZmayefNmevToQUxMDKGhobi4uNSqf4Di4mIOHTpk8FlUrh+dOHEizz//PNHR0ajVasrLy/n73/+uzHjWdfw9e/bE19eXsWPHYmVlxaJFi7CysgKoNv67LV26lNdee43S0lJsbW2V8RYUFCgXq1Xl6uqKSqUiKCgIa2trevfuzRNPPGH6yxbiHmJRbmxxixBCiCZpzZo12NnZMW3atMYeiqijsLAw5s2bV+Oa2G3bttGxY8c6zyDX9thY0KxNndo1xeoefZxlvwdtGqyt4LdeaLC2AKzC67c+e1eHjg06jrG//tKg7dWGnLIXQggh7gGtW7cmOjpauY2UKZMnT65zMhobG0tqamq1pRvi/nE/XGUvM6RCCCGEUMgMae3dKzOkuzt0atBx+P76c4O2VxuyhlQIIYQQogm7N1P/upGEVAghhBCKaT3/0iDt/J77y/7RWjzYMOnP5X8l0GHrpgZp6/e4hz/qWpM1pEIIIYT402ioZBS4J5LRxpCdnY1KpcLb25v169cblN+4cYMZM2YQEBCAn58fiYmJNbYpM6RCCCGEEE3Y/3KCtKysjCVLlvDOO+/g4ODA+PHj8fLyokePHkqd9957j+7du/Of//yHK1euMGbMGNRqNTY2ptfvygypEEIIIUQTZolFg77MOXnyJM7OznTp0gUbGxv8/PwM7gxhYWHBzZs3KS8v5+bNm7Ru3drovXOr74MQQgghhBC1kJ+fj6Ojo/LewcGB/Pz8anWefPJJzp49y9ChQwkICCAmJgZLS/Mpp5yyF0IIIYRowhr6lP2HH37Ihx9+qLx/4oknlKd+Gbtb6N0XsB08eJDevXuzdetWzp8/z5QpU3Bzc6NFixYm+5SEVAghhBBCKKomoHdzdHTk0qVLyvv8/Hzs7e2r1UlKSmL69OlYWFjg7OxM586dOXfuHP369TPZp5yyF0IIIYRowiwsGvZljouLC3l5eVy4cAG9Xo9Op8PLy6tanY4dO3L48GEAfv31V3744Qc6d+5stl1JSJuw2NhY5cq1mTNnUlhYaLTeF198QUhICBqNBl9fX9asWQNU/AUzZMgQNBoNGo2GefPmARAVFYWrqytFRUVKG8uWLaNXr15cuXLFoP2kpCQeeeQRTp8+rWzz9/fn4sWLAHh5eaFWq9FoNKjVajIzM42Os7y8nGXLluHt7Y1arearr74yWu/ChQuEhITg4+PDnDlz0Ov1NcZHR0fj7u6Ov7+/yc+ztv1PmjRJ+cw8PT15/vnnDeocPXqUQYMG8cwzzyjbVqxYgZ+fH76+vixbtszoaY+qfv75Z8LCwggMDEStVvPJJ58AcP78eTQaDQMGDDAa16tXL/7xj38o70tLSxkyZAjPPvus2f4Apc2LFy+SlpambM/NzWXZsmU1xv8eWVlZRm8fUlVSUhJLliwBKp7L7erqyuXLl5Xyqp9J79690Wg0BAQEEBQUxOeff66UFRQU1Ph5XLx40ezxUts69ZGcnIyPjw8+Pj4kJycbraPX65kzZw7e3t6EhIQoP2/m4rdt24a3t7fBz/K+fft4++23TY4nMjIStVpNfHx8nffl6NGj1T77P8If9T38Hl5eXkZ/X9bk9u3bzJ8/H7Vaja+vL+vWrVPKTp06hVqtxtvbu9rvkPocC+L+YtHAL3Osra1ZtGgRTz/9NGPHjsXX15eePXuSkJBAQkICAM8//zwnTpxArVYTERHB3//+d9q1a2e2XUlImzAPDw/S09NJS0ujW7du1X5xVTV//nyWLl2KVqslPT0dX19fpWzs2LFotVq0Wi0rVqxQtnft2lW5au7OnTscPXoUBwcHk2NxdHTkP//5j8nyLVu2oNVqWb16Na+++qrROtnZ2eTl5ZGRkcHSpUt55ZVXjNZbtWoVERERZGRk0KpVK3bu3FljfHBwMBs3bjQ5vrr0//777yuf2YABA/Dx8TFaz83NjQ0bNgDw+eef8/nnn5Oamkp6ejq5ubkcO3bM7Hji4uLw9fUlJSWFN998k8WLFwMV341WqzUZZ2dnx/fff09JSQkAn376qdnvzpiffvqJ9PR05b2LiwsLFy6sUxt1NWrUKKZPn16nmLZt27J582ajZQ888ABarZbU1FQiIyN54403lLJ33nmHkJCQ3zXeP8q1a9dYu3Yt27dvZ8eOHaxdu5br168b1NuxYwetWrViz549REREsGrVqhrjBw4cyDvvvIOTk1O1tkaMGMHevXspLi426Oe///0vJ06cIC0tjYiIiDrvz7Fjxzhx4kSdYsrKyurcT2MoLS1t8DY/+ugj9Ho9aWlpJCUl8eGHHyoJ5iuvvMKSJUvIyMggLy+P7OxsoH7HghC/x/Dhw/n444/JzMzkueeeA2DixIlMnDgRqLjQafPmzaSlpZGeno5Go6mxTUlIm4C4uDhUKhURERFERkayaVPFjXg9PT2V2yj079+/2pqOqq5cucJf/lLx5A0rK6tq9wozxd/fn927dwMVMxwDBw40e8uGESNGcObMGc6dO2e23aKiIlq1amW0LCsri8DAQCwsLOjfvz+FhYUUFBRUq1NeXs6RI0dQqVQABAUFKYmzufjBgwfTunVrs2OrTf9378uRI0cYPXq02XahYsG3Xq/n9u3byn87dOhQY0zlLPWNGzcM1uiYM2zYMPbv3w+ATqfDz89PKVuzZo1yDEH12exKr7/+Ojk5OWg0GuLj4zl69Kgyo7hmzRqio6MJCwtj1KhRbN26VYl755138Pf3x9/fX5lNu3jxImPGjCEmJgZ/f3/mzp3LoUOHCA0NxcfHh5MnTwLVZz/37t1LSEgIgYGBRERE8Ouvvxrdz3HjxrF7926uXbtm9vO4+7jLyMhg2LBhyvgmTZpEUFCQwUxqpaSkJJ577jmmTZuGSqVi7dq1SllZWRkLFy7Ez8+PqVOnKn8IbN++nXHjxhEQEMCsWbOMJnvGHDx4EA8PD9q0aUPr1q3x8PDgwIEDBvX27t1LUFAQACqVisOHD1NeXm42vk+fPkZPm1lYWPDYY4+xb98+g7KpU6dy+fJlNBoNOTk5nD9/nmnTphEcHMykSZM4e/asMp67v7OLFy/ywQcfEB8fr8RHRUXx0UcfKe1XzmofPXqUsLAw5s6di1qtpqysjNjYWMaNG4dareaDDz6o1ed369YtoqOjUavVBAYGcuTIEQCKi4uZPXs2arWaOXPmEBISQm5ursl2BgwYwD//+U+CgoIIDw9XZjvDwsJ44403mDx5Mlu3buXw4cPKWYzo6GjljA3Apk2bGD9+POPHj+fHH3+s1fgtLCwoLi6mtLSUkpISmjVrRosWLSgoKKCoqIgBAwZgYWFBYGCg8ruvPseCuL9YNPD/GoMkpPe4U6dOsWvXLlJSUli7dq3JX6CJiYnKP7B3Cw8PZ8yYMcycOZMPPviAW7duKWW7du1STj9XfZKCs7MzV65c4fr16wYJjTGWlpY8/fTTJmdpw8PD8ff3JywsjDlz5hitc/etJBwdHQ1uJXH16lVatWqlJMdV69Qm/m5VTzHUNT4zMxN3d3flqsHc3FxiYmKM1h0wYAB//etf8fT0xNPTk6FDh9K9e3ezY3vhhRdIS0tj2LBhTJ8+3eQMZX5+frWlAVAx871r1y5u3brFt99+i6urq9m+7jZ37lzc3NzQarVGZ8V++OEHNm3axI4dO/jXv/7F7du3OXXqFElJSWzfvp0PP/yQHTt28PXXXwMVywyeeuopUlNT+eGHH0hLSyMhIYF58+YZnVkfNGgQ27dvJyUlBT8/P5Oz23Z2dgQHB1dLiiuVlJSg0WgYM2YMCxcuVJZWXLhwgdatWys3aG7fvj3vvPMOycnJvPnmmyaXJuTm5rJq1Sq0Wi0fffSR8rP4448/8uSTT6LT6WjZsiUff/wxAN7e3iQmJpKamspDDz2kzOSnpqYqP3NVX3/729+A2t1SpbJex44dgYpTaC1btuTq1au1jr/bo48+yvHjx4GKP85Wr14NVPxBXDkr7+bmxksvvcRLL71EUlIS8+fPV2bujX1nnTt3JjQ0lIiICCXenNzcXObMmcOuXbvYuXMnLVu2JDExkcTERLZv386FCxdq3I/33nsPgLS0NF5//XWioqK4desW77//Pq1btyYtLY3nn3/e5JKcSr/99ht9+vQhOTmZwYMHV/sjpLCwkG3btvHkk08SFRXFm2++SVpaGmVlZbz//vtKvRYtWrBz504mT57Ma6+9ZrKvqp+3SqWiefPmeHp6MnLkSKZOnUqbNm3M/n5q6GNBND2WFg37agxylf09Licnh9GjR9O8eXMAg4XDUPEPhpWVFQEBAUbbeOGFFwgICODgwYOkp6ej0+l49913gYrEZdGiRUbjvL290el0fPnll8rMlTn+/v7ExcUZ/Udjy5YttGvXjvPnzxMREcFjjz3Ggw8+WK1ObW4lYUxlnfrEV55eqE98enp6tdO+Li4uuLi4GK37448/cvbsWWUd6NSpU/nss88YPHiwyfZ1Oh1BQUFMnTqVEydOMG/ePNLT0w3u5ebg4KAsDaj0yCOPcPHiRdLT0xk+fLjJPupr+PDh2NjY0K5dO9q1a8fly5c5fvw4o0ePxs7ODqg4fnJycvDy8qJz58706tULgB49euDu7o6FhQW9evXip59+Mmj/0qVLvPjii/z3v/9Fr9ebXQz/1FNPERgYyNSpU6ttrzxlD3DixAnmz59Peno6//3vf2nbtq1Sr7S0lCVLlnD69GksLS3Jy8sz2s/jjz+uxHl7eyv727lzZ3r37g1A3759lf35/vvveeutt7hx4wY3b97E09MTgICAAJM/q1D749BUvfr+HLVv3145IzBq1ChGjRplUOfmzZucOHGC2bNnK9sqZwTr8p2Z4uLiQpcuXYCKpSbffvutkuDfuHGDH3/8USk35fjx40yePBmA7t2706lTJ3744QeOHz/OU089BcDDDz+sHI+mWFpaMnbsWAA0Gg0vvPCCUla5vfJCjf/7v/8DKs7YvPfee8ofcZXrWv38/Fi+fLnJvqp+3idPnsTS0pIDBw5QWFjIpEmTePzxx81+rw19LAjRGCQhbQLM/QJJTk5m//79xMfHK/Wio6P5+uuvsbe3VxKVrl27MmnSJCZMmIC7uztXr16tsV8/Pz/lNGbVJOi9995j+/btANUuQrG2tmbq1KkGyVFVXbt2pX379pw9e5bc3Nxq7dx9K4lLly4ZnKZu27YthYWFlJaWYm1tXa1ObeLNqUv81atXyc3N5V//+let2t6zZw+urq5KEj506FC++OILswnpzp07lZnBAQMGcOvWLa5evUr79u1r1aeXlxcrVqxg69at1U5pW1lZcefOHeV91Rnz2qr6+DcrKytKS0vNXqRVtb6lpaXy3sLCwuh6wWXLlhEREcGoUaM4evRotdmpu7Vq1Qp/f/9qM1N3GzBgAFevXuXKlSs88MAD1U6rxsfH06FDB7RaLXfu3DF5W5K7fw4r39/9WVR+nlFRUfz73//mkUceISkpSVkznJqaWm3JRCVnZ2fefvttHB0dq60vzs/P57HHHjOo7+joyC+//IKjoyOlpaXcuHGDNm3a1Dr+brdu3cLW1tZsnfLyclq1amV0DXNtv7Oqx195eTm3b99Wyir/mKksW7hwIUOHDq1x7HePsS7ba6vq9185QfB72zQmPT2doUOH0qxZM9q3b8/AgQPJzc3Fzc3N5O+nhj4WRNNzP/yZIafs73GDBw9mz549lJSUUFRUVG2NV3Z2Nhs2bCAuLk75BQmwfPlytFqtkhju379f+cX5448/YmlpaXIdZ1WdOnXixRdfZNKkSdW2P/nkk8pFPXdfLBMUFMThw4dNXl16+fJlLl68SKdOnQza8fLyIiUlhfLycr744gtatmxpkBBaWFjw17/+VZk1SU5OVmaNaxNvTl3iP/roI0aMGFHjP+CVOnXqxGeffUZpaSm3b9/ms88+U07Zv/766+zZs8cgpuptM86ePcutW7dqvEqxqvHjx/P8888bzAQ5OTkpp9K/+uorg/WjAA8++CA3b96sdV9QcaxmZmZSXFzMb7/9RmZmZo2naE25ceOGcmylpKTUWD8iIoIPPvjA5EUmZ8+epaysjDZt2tCtW7dqs7I3btzgL3/5C5aWlmi1WpMX1Hz66adcu3aNkpISMjMzGThwoNkx3bx5k7/85S/cvn272h0LAgIClOO+6qvyKndPT08OHjzI9evXuX79OgcPHlRmV6vy8vJSrpr++OOPGTJkCBYWFrWOv1teXh4PP/yw2TotWrSgc+fOyvry8vJy5e4apr6zu48lJycn5XR5VlZWtYS0Kk9PTxISEpTyH374gd9++63G/Rg8eLDyef/www/88ssvPPTQQwwaNEgZ95kzZ/juu+/MtnPnzh3l90xaWhqDBg0yqPPQQw/x008/KetDtVpttT8yK/vbtWuXybti3K1jx44cPXqU8vJyfvvtN7788kseeugh7O3tefDBB/niiy8oLy8nJSVFmVVt6GNBND3/y6vs/ygyQ3qP69u3L2PHjkWj0eDk5FTtl+LSpUvR6/VMmTIFAFdXV6On1rVaLcuXL+eBBx7AysqKVatWYWVlVav+Q0ND6zReGxsbwsLCDK6kDw8Px9LSktLSUubOnWv0gp7hw4fzySef4O3tTfPmzautuXrmmWdYtmwZDg4O/OMf/+DFF1/krbfeonfv3sppc3PxkZGRHDt2jKtXrzJs2DBmzZpFSEiIsn504sSJte4fKv6BuXvdZm5uLh988IHRuwioVCqOHDmCWq3GwsKCoUOHKon0d999Z3QpRlRUFAsXLlRmv//5z38anS3Pz89n4cKFBjPTjo6OhIeHGx2LVqtFo9Hg4uJCt27dDOr06tVLWQYSHBysnJI2p2/fvgQHByvfx/jx4+nTp4/RhLcmL7zwArNnz8bBwQFXV9ca22jXrh3e3t7VbktUuYYUKhKn2NhYrKyssLOzo0uXLvz44484OzszadIkZs2axUcffcRf//rXarN0VQ0aNIh58+bx448/olarcXFxMTuu2bNnExISgpOTEw8//HCtE/w2bdrw/PPPM378eABmzpxJmzZtAFi9ejWPPvooo0aNYvz48fzjH//A29ub1q1b8+abb9YYv3XrVjZu3Mivv/5KQEAAw4cPV47Xo0ePEhkZCVQkiqdOnap2ar7SypUreeWVV4iLi6O0tJSxY8fyyCOPmPzORo4cyd/+9jeysrJ46aWXmDBhgjI+d3d3k593SEgIP/30E8HBwZSXl9O2bVv+/e9/1/j5TZo0iZdffhm1Wo2VlRXLly/HxsaGSZMmERUVhVqtpk+fPvTq1YuWLVuabKfybhXBwcG0aNGCt956y6COra0ty5cvZ/bs2ZSVlfHoo49WWwak1+sJCQnhzp071e7ycLeqn/eTTz5JdHQ0/v7+lJeXExwczCOPPAJUXGUfHR1NSUkJw4YNU64bqM+xIMS9xqL8jzjnIP4wa9aswc7OjmnTpjX2UIQZR48eZfPmzSYv8qpq2rRpRk/h1mTAgAF1vp2OqLBnzx5OnTrFiy++WKv6SUlJnDp1yuR666bu119/Ze7cuWzZsqWxh/KHKSsro7S0FFtbW2Ut+0cffVRtyUVVf+afr7N9ejZIO/fqetUWDzbcXFyHrXX/3W2O5SPu9Yr71MH82uq68siv+QLChiYzpEL8AZo1a8b333/PM888Y3ZNLVDnZPT8+fPMmjWr1mtJhSFvb+8abxX1Z/Lzzz8TFRXV2MP4QxUXF/PUU08p651ffvllk8moEE3NPZr714nMkAohhGgSvv32W+WJcpVsbGzYsWNHvdsMCQmpdpEbVDxVraar8OsjLi6u2j1YAcaMGaPcWPxeITOktXevzJAecmzYGdLHL/3vZ0glIRVCCCGEQhLS2rtXEtIjDZyQDmmEhFRO2QshhBBC0T0np7GHIP6EJCEVQgghhGjC7s256LqRhFQIIYQQogm7V5dH1IXcGF8IIYQQQjQqmSEVQgghhGjCmv78qCSkQgghhBBN2v2QkMopeyGEEEII0agkIb1PxcbGMmbMGNRqNTNnzqSwsNBovS+++IKQkBA0Gg2+vr6sWbMGqHhU4pAhQ9BoNGg0GuVm1FFRUbi6ulJUVKS0sWzZMnr16sWVK1cM2k9KSuKRRx7h9OnTyjZ/f3/lOddeXl6o1Wo0Gg1qtZrMzEyj4ywvL2fZsmV4e3ujVqv56quvjNa7cOECISEh+Pj4MGfOHOWG1+bio6OjcXd3x9/f3+TnWdv+J02apHxmnp6ePP/88wZ1jh49yqBBg3jmmWeUbStWrMDPzw9fX1+WLVtGTbcH/vnnnwkLCyMwMBC1Ws0nn3wCVDzFSaPRMGDAAKNxvXr14h//+IfyvrS0lCFDhvDss8+a7Q9Q2rx48SJpaWnK9tzcXJYtW1Zj/O+RlZXF+vXrzdZJSkpiyZIlQMUjdl1dXbl8+bJSXvUz6d27NxqNhoCAAIKCgvj888+VsoKCgho/j4sXL5o9Xmpbpz6Sk5Px8fHBx8eH5ORko3X0ej1z5szB29ubkJAQ5ecNKh5V6+bmZrCPL774Inl5eUbbO3v2LBqNhsDAQM6fP1/nMcfHx1NcXFznuLpYs2ZNvR7B+0c5evRorX6ujDH3+3vdunV4e3ujUqk4cOCAsv3UqVOo1Wq8vb2r/Q4xdyyI+4eFhUWDvhqDJKT3KQ8PD9LT00lLS6Nbt24mn6k+f/58li5dilarJT09HV9fX6Vs7NixaLVatFotK1asULZ37dqVrKwsAO7cucPRo0dxcHAwORZHR0f+85//u1X6CgAAIABJREFUmCzfsmULWq2W1atX8+qrrxqtk52dTV5eHhkZGSxdupRXXnnFaL1Vq1YRERFBRkYGrVq1YufOnTXGBwcHs3HjRpPjq0v/77//vvKZDRgwAB8fH6P13NzclEeKfv7553z++eekpqaSnp5Obm4ux44dMzueuLg4fH19SUlJ4c0332Tx4sVAxXej1WpNxtnZ2fH9999TUlICwKeffmr2uzPmp59+Ij09XXnv4uLCwoUL69RGXY0aNYrp06fXKaZt27Zs3rzZaNkDDzyAVqslNTWVyMhI3njjDaXsnXfeISQk5HeN949y7do11q5dy/bt29mxYwdr167l+vXrBvV27NhBq1at2LNnDxEREaxatUope/rpp6v9PFeaOHGiyZ+DrKwsRo0aRUpKCl27dq3zuLdu3VrnhLS0tLTO/fyvlZeXc+fOnQZv19Tv7zNnzqDT6dDpdGzcuJHFixdTVlYGwCuvvMKSJUvIyMggLy+P7OxswPyxIO4flhYN+2qUfWicbkVDiYuLQ6VSERERQWRkpDJD4OnpibV1xRLh/v37c+nSJaPxV65c4S9/+QsAVlZW9OjRo8Y+/f392b17N1AxCzBw4EClL2NGjBjBmTNnOHfunNl2i4qKaNWqldGyrKwsAgMDsbCwoH///hQWFlJQUFCtTnl5OUeOHEGlUgEQFBSkJM7m4gcPHkzr1q3Njq02/d+9L0eOHGH06NFm24WKv2z1ej23b99W/tuhQ4caYypnqW/cuIG9vX2N/VQaNmwY+/fvB0Cn0+Hn56eU3T3LVHU2u9Lrr79OTk4OGo2G+Pj4ajNBa9asITo6mrCwMEaNGsXWrVuVuHfeeQd/f3/8/f2Jj48HKmYRx4wZQ0xMDP7+/sydO5dDhw4RGhqKj48PJ0+eBKrPfu7du5eQkBACAwOJiIjg119/Nbqf48aNY/fu3TU+s/7u4y4jI4Nhw4Yp45s0aRJBQUEGM6mVkpKSeO6555g2bRoqlYq1a9cqZWVlZSxcuBA/Pz+mTp2q/CGwfft2xo0bR0BAALNmzap1snbw4EE8PDxo06YNrVu3xsPDo9osWaW9e/cSFBQEgEql4vDhw8qMmbu7Ow8++KBBjJubG4cOHTJIBD/55BO2bNnCjh07CAsLA0Cr1TJ+/Hg0Gg2LFi1SkqKXX36Z4OBg/Pz8ePvtt4GKZLSgoIDw8HAlvups9UcffURUVBRQcQZm+fLlhIWFsWrVKs6fP8+0adMIDg5m0qRJnD17tlaf0zfffMOECROUGcbKpP3kyZOo1WqeeOKJ/8feuYdFVe2N/zODmaKgogIqiG9ZaB5EElQSsRccAWdggMCUXoSDXX6lHkWPiGHmhfJIeqy06KqozwkFBQYG8EZHzZOSKMZQxzIUBE/iDRVQRC6/P3hmH8a5MPB6Mn3353nmgdn7uy57r7XXfPf3+11rsXbtWpMWbGPtWlVVRUBAACtWrCAkJIRff/1VyCswMJC8vDwhj7q6OubMmcO0adNYvny52cqrsfG7oKAAuVxO9+7dcXR0xMnJiZKSEi5dukRdXR1ubm5IJBKCg4OFsc9UXxAR+T0hKqQPMaWlpeTl5ZGVlcWmTZvQaDQG5Xbv3i38wN5LVFQU/v7+zJkzhx07dnDnzh3hXF5enuB+3r17t3DcycmJa9eucePGDT2FxhBSqZSXX37ZqJU2KioKhUJBZGQkCxYsMChTXV2Nvb298N3e3p7q6modmZqaGqytrYWBvL2MOenvJTU1ldTU1C6lP3DgAJ6envTu3Rtoc2snJCQYlHVzc2P8+PF4eXnh5eXFpEmTePLJJ03Wbe7cueTk5ODt7c2rr75q1EJZXV2tExoAbZbvvLw87ty5w08//YSrq6vJsu5l0aJFuLu7o1KpiI6O1jt/7tw5vvzyS9LT0/noo4+4e/cupaWlZGRkkJaWxs6dO0lPT+fHH38E2sIMZs2aRXZ2NufOnSMnJ4fU1FTi4uIMWtbHjh1LWloaWVlZyOVyo1Y9S0tLQkNDdZRiLQ0NDSiVSvz9/Vm2bJkQWlFZWUmfPn3o3r07AP3792fLli1kZmayYcMGo6EJGo2GdevWoVKp2LNnj/AsVlRU8NJLL5Gbm4uVlRV79+4FQCaTsXv3brKzs3niiScES352drbwzLX//OlPfwL0+6GdnZ3BflhdXc2gQYMA6NatG1ZWVtTU1BisuxapVIqTk5MQXpOQkIBGo2Hy5MnMmDGD6Ohotm/fTllZGfn5+aSmpqJSqZBKpUIIR2xsLBkZGWRnZ3P8+HFOnz7NrFmzsLW1ZevWrWzfvt1kHQDKy8tJSUkhPj6et956i7feeouMjAyWLFkieAI6Ii4ujj//+c/k5OTw9NNPC8rkm2++ycqVK9m5cycWFhYd5mOsXc+dO0dwcDBZWVmUlpZy+vRpVCoVW7ZsISkpSXhZLSkpIT4+npycHCorK9m3b5/RsrT3+17aj9/G2t/U+NSVviDy8CGRSu7r50EgzrJ/iCkqKmLKlCn07NkTaIvHvJfk5GQsLCwICgoymMfcuXMJCgriyJEjqNVqcnNzhR8N7Vu9IWQyGbm5uXz//feC5coUCoWC5ORkKiv198fdunUrNjY2nD9/nujoaMaNG6dnwTH0Rm9OnItWpivpZ86c2eXy1Wq1jtvXxcUFFxcXg7IVFRWUlZUJcaAxMTEcP34cDw8Po/nn5uYSEhJCTEwMxcXFxMXFoVarkUp13zHt7OyE0AAtI0aMoKqqCrVazeTJk42W0VUmT55M9+7dsbGxwcbGhqtXr3LixAmmTJmCpaUl0NZ/ioqK8PHxwcHBAWdnZwCGDx+Op6cnEokEZ2dnLly4oJf/xYsXiY2N5fLlyzQ2NuLg4GC0LrNmzSI4OJiYmBid41qXPUBxcTFLlixBrVZz+fJl+vXrJ8g1NTWxatUqTp8+jVQqNRpj+dxzzwnpZDKZcL0ODg6MHDkSgFGjRgnXc+bMGd5//31qa2upr6/Hy8sLgKCgIKPPKpjfD7v6vNjY2AjKlLHwmaNHj1JaWkpYWBjQptz3798fgPz8fNLS0mhqauLy5cuUlZUxYsSIDsttj7+/PxYWFtTX11NcXMz8+fOFc9qYcFPU1tZSW1vLuHHjgDZPyfz587l58yb19fU8++yzQNuYpPUUGMNYuw4ePJgxY8YAcOLECeRyORYWFgwYMAAPDw80Gg29e/dm9OjRODq27TEul8s5ceIE/v7+BssydL/vHb+Ntaup9u5qXxAR+a0RFdKHHFMDS2ZmJgcPHiQlJUWQW7p0KT/++CO2traCojJ06FAiIiKYPn06np6eZr09y+VywY3ZXgn629/+RlpaGoDOJJRu3boRExOjpxy1Z+jQofTv35+ysjI0Go1OPvb29jphBxcvXtRzU/fr14+bN2/S1NREt27ddGTMSW+KzqSvqalBo9Hw0UcfmZX3/v37cXV1FZTwSZMmcerUKZMK6a5duwTLoJubG3fu3KGmpkZQDDrCx8eHpKQktm3bpuPStrCw0HErtreYm4vWuqjNr6mpyaSLsL28VCoVvkskEsEV3J7ExESio6Px9fWlsLBQx0V+L9bW1igUCr766iujMm5ubtTU1HDt2jV69Oiho/SkpKQwYMAAVCoVLS0tjB492mAe9z6H2u/33gvt/YyPj+fjjz9mxIgRZGRkCDHD2dnZBifmODk58eGHH2Jvb68TX1xdXS0oXu2xt7fn119/xd7enqamJmpra+nbt6/Re6ClsbGRHj16mJRpbW0lJCSERYsW6RyvrKxk8+bN7Nq1iz59+hAfH29W/7lXRvuC3drairW1tcmY6M7QFTe1sXbVvlh1lK+x9OZgaPy+dxyqrq7G1tbW5PjU1b4g8nDxKLxjiC77hxgPDw/2799PQ0MDdXV1/P3vfxfOHT58mM8//5zk5GRhgAdYs2YNKpVKUAwPHjwoDKgVFRVIpVKjcZztGTx4MLGxsUREROgcf+mll4RJPfdOlgkJCeHo0aMGZ+MDXL16laqqKgYPHqyXj4+PD1lZWbS2tnLq1CmsrKz0FEKJRML48eMFt2hmZqZgNTYnvSk6k37Pnj08//zzPP7442blPXjwYI4fP05TUxN3797l+PHjgst+/fr17N+/Xy/NoEGDOHr0KNA2A/rOnTvY2NiYfT1hYWG88cYbgmVSy5AhQwRX+g8//GBwRm6vXr2or683uyxo66sHDhzg9u3b3Lp1iwMHDuDu7t6pPLTU1tYKfSsrK6tD+ejoaHbs2GF0kkxZWRnNzc307duXYcOG6Vhla2trGThwIFKpFJVKZVBBhrbJYdevX6ehoYEDBw4IVjhj1NfXM3DgQO7evauzYkFQUJDQ79t/tPGYXl5eHDlyhBs3bnDjxg2OHDkiWFfb4+PjI8zA37t3LxMmTDBLGSovL+8wjtzT05O9e/cKKxhcv36dCxcuUF9fT8+ePbGysuLKlSvCpBrQ7zMDBgygrKyMlpYWoytr9O7dGwcHByFevbW1VWe1DmNYWVlhbW1NUVER0Bbvqo0T79WrF6dOnQLQifU0hjnt6uHhQX5+Ps3NzVy7do2ioiLhxaWkpITKykpaWlrIz89n7NixHZYJxsdvHx8fcnNzaWxspLKykvLyckaPHo2tra1wba2trWRlZeHr6yuk6UpfEHm4kEju7+dBIFpIH2JGjRrFtGnTUCqVDBkyRGewW716NY2Njfzxj38EwNXV1aBrXaVSsWbNGnr06IGFhQXr1q0zK7YKYMaMGZ2qb/fu3YmMjNRzTUVFRSGVSmlqamLRokUGJ/RMnjyZQ4cOIZPJ6NmzJ++++65w7pVXXiExMRE7OzsWL15MbGws77//PiNHjhTc5qbSL1y4kO+++46amhq8vb2ZN28e4eHhQvzozJkzzS4f2n7o7o3b1Gg07Nixw6Bbzs/Pj2PHjhEYGIhEImHSpEmCIv3zzz8bDMWIj49n2bJlgvXkL3/5i8EfmerqapYtW6Znmba3tycqKspgXVQqFUqlEhcXF4YNG6Yn4+zsLLgRQ0NDBZe0KUaNGkVoaKjQHmFhYTzzzDNdWoJm7ty5zJ8/Hzs7O1xdXTvMw8bGBplMJkykgn/HkEKborN27VosLCywtLTE0dGRiooKnJyciIiIYN68eezZs4fx48frWMbaM3bsWOLi4qioqCAwMBAXFxeT9Zo/fz7h4eEMGTKEp59+2mwFv2/fvrzxxhuCu3zOnDmCteuDDz7gD3/4A76+voSFhbF48WJkMhl9+vRhw4YNQh4RERGcPXuWW7du4e3tzTvvvMOkSZO4cuUKjz/+uPCilZCQwIwZM/RCTYYPH86CBQuIiYmhpaWFxx57jOXLlzNmzBieeeYZ5HI5jo6OOsrb9OnTeeWVVxg4cCDbt29n0aJFvPbaawwaNIinnnqKW7duGbze9957jxUrVpCcnExTUxPTpk0zKwRg7dq1vP3229y+fRtHR0fWrFkDtLnFly1bhqWlJePGjRNivI1hTrvKZDKKi4tRKpVIJBIWL17MwIEDOXv2LGPGjGH9+vX8/PPPuLu7I5PJjJbV/n4bG7+feuopAgICmDZtGhYWFixfvlwYr1esWMHSpUtpaGjA29tbiDs11RdERH5PSFrF6XaPDBs3bsTS0pLZs2c/6KqImKCwsJDNmzcbneTVntmzZ3dpbUU3NzeKi4u7Ur3/8+zfv5/S0lJiY2PNks/IyKC0tNRovPXDQkpKCr169frdLnl1P6ivrxdCYz777DMuXbpkdELgo9KuXeKW/lJiIr8RlqZXfDGGZth/3ddquJSfu6/5mYPoshcR+Y157LHHOHPmjJ4V1RCdVUa1C+ObG0sqoo9MJjM5UepRxcrKSlge6FHl0KFDKJVKFAoFRUVFBjeuEBF5GHkUXPaihVRERERE5KEhOTmZPXv26Bzz9/fn9ddf71J+33zzjd5i8Q4ODmZPSuws4eHheqsFJCUl6cVzP1BEC+mDo4sW0tL/ur8W0j+c++0tpKJCKiIiIiIiIvJvRIX0wdFFhfSHJ564r9UY1cFGNv8JxElNIiIiIiIiIgKtV/TXi+4av9PZ/Bb3T/WRPG54omOX6aJC+igsnCDGkIqIiIiIiIiIiDxQRAupiIiIiIiIiMhDjPQRMJGKCqmIiIiIiIiIyEPMI6CPii57ERERERERERGRB4toIRUREREREREReYh5FLaDFS2kIiIiIiIiIiIiDxRRIX1EWbt2Lf7+/gQGBjJnzhxu3rxpUO7UqVOEh4ejVCoJCAhg48aNQNu2eRMmTECpVKJUKomLiwPa9lB3dXWlrq5OyCMxMRFnZ2euXbuml39GRgYjRozg9OnTwjGFQiHsB+3j40NgYCBKpZLAwEAOHDhgsJ6tra0kJiYik8kIDAzkhx9+MChXWVlJeHg4U6dOZcGCBcIC1KbSL126FE9PTxQKhdH7aW75ERERwj3z8vIyuBNMYWEhY8eO1dmpKSkpCblcTkBAAImJiXS0PPC//vUvIiMjCQ4OJjAwkEOHDgH/3qnJzc3NYDpnZ2cWL14sfG9qamLChAm89tprJssDhDyrqqrIyckRjms0GhITEztM/7+hoKCAzz77zKRMRkYGq1atAtq20XV1deXq1avC+fb3ZOTIkSiVSoKCgggJCeHkyZPCuUuXLnV4P6qqqkz2F3NlukJmZiZTp05l6tSpZGZmGpRpbGxkwYIFyGQywsPDdfZfnz17Nu7u7nrXGBsbS3l5ucH8ysrKUCqVBAcHc/78+U7XOSUlhdu3b3c6XWfYuHFjl7bZ/U9RWFho1nNlii+//FJvbP3000+RyWT4+fnxzTffCMdLS0sJDAxEJpPpjCGm+oLIo4NEen8/DwJRIX1EmThxImq1mpycHIYNG2Z03/QlS5awevVqVCoVarWagIAA4dy0adNQqVSoVCqSkpKE40OHDqWgoACAlpYWCgsLsbOzM1oXe3t7PvnkE6Pnt27dikql4oMPPuCdd94xKHP48GHKy8vZt28fq1evZsWKFQbl1q1bR3R0NPv27cPa2ppdu3Z1mD40NJQvvvjCaP06U/5XX30l3DM3NzemTp1qUM7d3Z3PP/8cgJMnT3Ly5Emys7NRq9VoNBq+++47k/VJTk4mICCArKwsNmzYwMqVK4G2tlGpVEbTWVpacubMGRoaGgD4xz/+YbLtDHHhwgXUarXw3cXFxeh+4PcLX19fXn311U6l6devH5s3bzZ4rkePHqhUKrKzs1m4cCF//etfhXNbtmz53e7nfv36dTZt2kRaWhrp6els2rSJGzf0FzFPT0/H2tqa/fv3Ex0drbMT0csvv6zzPGuZOXOm0eegoKAAX19fsrKyGDp0aKfrvW3btk4rpE1NTZ0u57emtbWVlpaW/0jev/76K99++y2DBw8Wjv3yyy/k5uaSm5vLF198wcqVK2lubgZgxYoVrFq1in379lFeXs7hw4cB031B5NFBIpHc18+DQFRIH3KSk5Px8/MjOjqahQsXChYCLy8vunVrCxEeM2YMFy9eNJj+2rVrDBw4EAALCwuGDx/eYZkKhYL8/HygzQrw7LPPCmUZ4vnnn+eXX37hbAc7P9TV1WFtbW3wXEFBAcHBwUgkEsaMGcPNmze5dOmSjkxrayvHjh3Dz88PgJCQEEFxNpXew8ODPn1ML0ZsTvn3XsuxY8eYMmWKyXyhbSBpbGzk7t27wt8BAwZ0mEZrpa6trcXW1rbDcrR4e3tz8OBBAHJzc5HL5cK5e61M7a3ZWtavX09RURFKpZKUlBQdS9DGjRtZunQpkZGR+Pr6sm3bNiHdli1bUCgUKBQKUlJSgDYror+/PwkJCSgUChYtWsS3337LjBkzmDp1KiUlJYCu9fPrr78mPDyc4OBgoqOjuXLlisHrfOGFF8jPz+f69esm78e9/W7fvn14e3sL9YuIiCAkJETPkqolIyOD119/ndmzZ+Pn58emTZuEc83NzSxbtgy5XE5MTIzwIpCWlsYLL7xAUFAQ8+bNM1tZO3LkCBMnTqRv37706dOHiRMn6ljJtHz99dfCvvR+fn4cPXpUsJh5enrSq1cvvTTu7u58++23eorgoUOH2Lp1K+np6URGRgKgUqkICwtDqVSyfPlyQSl6++23CQ0NRS6X8+GHHwJtyuilS5eIiooS0re3Vu/Zs4f4+HigzQOzZs0aIiMjWbduHefPn2f27NmEhoYSERFBWVmZWffpn//8J9OnTxc8RFqlvaSkhMDAQF588UXWrl1r0oJtrF2rqqoICAhgxYoVhISE8Ouvvwp5BQYGkpeXJ+RRV1fHnDlzmDZtGsuXL++U8rpmzRoWL16soxwUFBQgl8vp3r07jo6OODk5UVJSwqVLl6irq8PNzQ2JREJwcLAw9pnqCyIivydEhfQhprS0lLy8PLKysti0aRMajcag3O7du4Uf2HuJiorC39+fOXPmsGPHDu7cuSOcy8vLE9zPu3fvFo47OTlx7do1bty4oafQGEIqlfLyyy8btdJGRUWhUCiIjIxkwYIFBmWqq6uxt7cXvtvb21NdXa0jU1NTg7W1taAct5cxJ/29pKamkpqa2qX0Bw4cwNPTk969ewNtbu2EhASDsm5ubowfPx4vLy+8vLyYNGkSTz75pMm6zZ07l5ycHLy9vXn11VeNWiirq6t1QgOgzfKdl5fHnTt3+Omnn3B1dTVZ1r0sWrQId3d3VCoV0dHReufPnTvHl19+SXp6Oh999BF3796ltLSUjIwM0tLS2LlzJ+np6fz4449AW5jBrFmzyM7O5ty5c+Tk5JCamkpcXJxBy/rYsWNJS0sjKysLuVxu1KpnaWlJaGiojlKspaGhAaVSib+/P8uWLRNCKyorK+nTpw/du3cHoH///mzZsoXMzEw2bNhgNDRBo9Gwbt06VCoVe/bsEZ7FiooKXnrpJXJzc7GysmLv3r0AyGQydu/eTXZ2Nk888YRgyc/OzhaeufafP/3pT4B+P7SzszPYD6urqxk0aBAA3bp1w8rKipqaGoN11yKVSnFychLCaxISEtBoNEyePJkZM2YQHR3N9u3bKSsrIz8/n9TUVFQqFVKpVAjhiI2NJSMjg+zsbI4fP87p06eZNWsWtra2bN26le3bt5usA0B5eTkpKSnEx8fz1ltv8dZbb5GRkcGSJUsET0BHxMXF8ec//5mcnByefvppQZl88803WblyJTt37sTCwqLDfIy167lz5wgODiYrK4vS0lJOnz6NSqViy5YtJCUlCS+rJSUlxMfHk5OTQ2VlJfv27TNalvZ+Q5viaWtry4gRI3RkjLW/qfGpK31B5OFDIrm/nweBOMv+IaaoqIgpU6bQs2dPoC0e816Sk5OxsLAgKCjIYB5z584lKCiII0eOoFaryc3NFX40tG/1hpDJZOTm5vL9998LlitTKBQKkpOTqazU35Ju69at2NjYcP78eaKjoxk3bpyeBcfQG705bgWtTFfSz5w5s8vlq9VqHbevi4sLLi4uBmUrKiooKysT4kBjYmI4fvw4Hh4eRvPPzc0lJCSEmJgYiouLiYuLQ61WI5XqvmPa2dkJoQFaRowYQVVVFWq1msmTJxsto6tMnjyZ7t27Y2Njg42NDVevXuXEiRNMmTIFS8u2bfZkMhlFRUX4+Pjg4OCAs7MzAMOHD8fT0xOJRIKzszMXLlzQy//ixYvExsZy+fJlGhsbcXBwMFqXWbNmERwcTExMjM5xrcseoLi4mCVLlqBWq7l8+TL9+vUT5Jqamli1ahWnT59GKpUajbF87rnnhHQymUy4XgcHB0aOHAnAqFGjhOs5c+YM77//PrW1tdTX1+Pl5QVAUFCQ0WcVzO+HXX1ebGxsBGXKWPjM0aNHKS0tJSwsDGhT7vv37w9Afn4+aWlpNDU1cfnyZcrKyvSUqo7w9/fHwsKC+vp6iouLmT9/vnBOGxNuitraWmpraxk3bhzQ5imZP38+N2/epL6+nmeffRZoG5O0ngJjGGvXwYMHM2bMGABOnDiBXC7HwsKCAQMG4OHhgUajoXfv3owePRpHR0cA5HI5J06cwN/f32BZ2vt9+/ZtPvnkE4PhJsba1VR7d7UviDxcPAptKiqkDzmmOmFmZiYHDx4kJSVFkFu6dCk//vgjtra2gqIydOhQIiIimD59Op6enma9PcvlcsGN2V4J+tvf/kZaWhqAziSUbt26ERMTo6cctWfo0KH079+fsrIyNBqNTj729vY6YQcXL17Uc1P369ePmzdv0tTURLdu3XRkzElvis6kr6mpQaPR8NFHH5mV9/79+3F1dRWU8EmTJnHq1CmTCumuXbsEy6Cbmxt37tyhpqZGUAw6wsfHh6SkJLZt26bj0rawsNBxK7a3mJuL1rqoza+pqcmki7C9vFQqFb5LJBLBFdyexMREoqOj8fX1pbCwUMdFfi/W1tYoFAq++uorozJubm7U1NRw7do1evTooaP0pKSkMGDAAFQqFS0tLYwePdpgHvc+h9rv994L7f2Mj4/n448/ZsSIEWRkZAgxw9nZ2QYn5jg5OfHhhx9ib2+vE19cXV0tKF7tsbe359dff8Xe3p6mpiZqa2vp27ev0XugpbGxkR49epiUaW1tJSQkhEWLFukcr6ysZPPmzezatYs+ffoQHx9vVv+5V0b7gt3a2oq1tbXJmOjO0BU3tbF21b5YdZSvsfSmOH/+PFVVVSiVSqBtrAkNDSU9PV1vHKqursbW1tbk+NTVviAi8lsjuuwfYjw8PNi/fz8NDQ3U1dXx97//XTh3+PBhPv/8c5KTk4UBHtriklQqlaAYHjx4UBhQKyoqkEqlRuM42zN48GBiY2OJiIjQOf7SSy8Jk3runSwTEhLC0aNHDc7GB7h69SpVVVUMHjxYLx8fHx+ysrJobW3l1KlTWFlZ6SmEEomE8ePHC27RzMxMwWpsTnpTdCb9nj17eP7553n88cfNynvw4MHx32XaAAAgAElEQVQcP36cpqYm7t69y/HjxwWX/fr169m/f79emkGDBnH06FGgbQb0nTt3sLGxMft6wsLCeOONNwTLpJYhQ4YIrvQffvjB4IzcXr16UV9fb3ZZ0NZXDxw4wO3bt7l16xYHDhzA3d29U3loqa2tFfpWVlZWh/LR0dHs2LHD6CSZsrIympub6du3L8OGDdOxytbW1jJw4ECkUikqlcqgggxtk8OuX79OQ0MDBw4cEKxwxqivr2fgwIHcvXtXZ8WCoKAgod+3/2jjMb28vDhy5Ag3btzgxo0bHDlyRLCutsfHx0eYgb93714mTJhgljJUXl7eYRy5p6cne/fuFVYwuH79OhcuXKC+vp6ePXtiZWXFlStXhEk1oN9nBgwYQFlZGS0tLUZX1ujduzcODg5CvHpra6vOah3GsLKywtramqKiIqAt3lUbJ96rVy9OnToFoBPraQxz2tXDw4P8/Hyam5u5du0aRUVFwotLSUkJlZWVtLS0kJ+fz9ixYzss09nZmaNHj/L111/z9ddfY29vT0ZGBgMHDsTHx4fc3FwaGxuprKykvLyc0aNHY2trK1xba2srWVlZ+Pr6Al3vCyIPF6LLXuSBMmrUKKZNm4ZSqWTIkCE6g93q1atpbGzkj3/8IwCurq4GXesqlYo1a9bQo0cPLCwsWLdunVmxVQAzZszoVH27d+9OZGSkniswKioKqVRKU1MTixYtMjihZ/LkyRw6dAiZTEbPnj159913hXOvvPIKiYmJ2NnZsXjxYmJjY3n//fcZOXKk4DY3lX7hwoV899131NTU4O3tzbx58wgPDxfiR2fOnGl2+dD2Q3dv3KZGo2HHjh0G3aB+fn4cO3aMwMBAJBIJkyZNEhTpn3/+2WAoRnx8PMuWLROs33/5y18M/shUV1ezbNkyPcu0vb09UVFRBuuiUqlQKpW4uLgwbNgwPRlnZ2chDCQ0NFRwSZti1KhRhIaGCu0RFhbGM88806UlaObOncv8+fOxs7PD1dW1wzxsbGyQyWTCRCr4dwwptCk6a9euxcLCAktLSxwdHamoqMDJyYmIiAjmzZvHnj17GD9+vI5lrD1jx44lLi6OiooKAgMDcXFxMVmv+fPnEx4ezpAhQ3j66afNVvD79u3LG2+8IbjL58yZI1i7PvjgA/7whz/g6+tLWFgYixcvRiaT0adPHzZs2CDkERERwdmzZ7l16xbe3t688847TJo0iStXrvD4448LL1oJCQnMmDFDL9Rk+PDhLFiwgJiYGFpaWnjsscdYvnw5Y8aM4ZlnnkEul+Po6KijvE2fPp1XXnmFgQMHsn37dhYtWsRrr73GoEGDeOqpp7h165bB633vvfdYsWIFycnJNDU1MW3aNLNCANauXcvbb7/N7du3cXR0ZM2aNUCbW3zZsmVYWloybtw4IcbbGOa0q0wmo7i4GKVSiUQiYfHixQwcOJCzZ88yZswY1q9fz88//4y7uzsymcxoWcbud3ueeuopAgICmDZtGhYWFixfvlwYr1esWMHSpUtpaGjA29tbmDdgqi+IPDo8CnvZS1rF6XaPDBs3bsTS0pLZs2c/6KqImKCwsJDNmzcbneTVntmzZ3dpbUU3NzeKi4u7Ur3/8+zfv5/S0lJiY2PNks/IyKC0tNRovPXDQkpKCr169frdLnl1P6ivrxdCYz777DMuXbpkdELgo9KuXaH1fOl9yul3qiRZ3D9bnORxwy+pXWaAY5eSVbg4dyzUCZw0P93X/MxBdNmLiPzGPPbYY5w5c0bPimqIziqj2oXxzY0lFdFHJpOZnCj1qGJlZSUsD/SocujQIZRKJQqFgqKiIoMbV4iIPIw8Ci570UIqIiIiIvLQkJyczJ49e3SO+fv78/rrr3cpv2+++UZvsXgHBwezJyV2lvDwcL3VApKSkvTiuR8kooXUfH4vFtJK186tZtERjt93HK99vxEVUhEREREREREBUSE1H1EhvX+Ik5pEREREREREBEq9A+9LPlLp/VNI76cb2dqqe8dCZjIkvePNHjqDpIsK6SMwp0mMIRUREREREREREXmwiBZSEREREREREZGHmEfBQioqpCIiIiIiIiIiDzGS+xge8aAQXfYiIiIiIiIiIiIPFNFCKiIiIiIiIiLyECO67EVERERERERERB4oj8LWoaLL/hFl7dq1+Pv7ExgYyJw5c7h586ZBuVOnThEeHo5SqSQgIICNGzcCbdvmTZgwAaVSiVKpJC4uDmjbQ93V1ZW6ujohj8TERJydnbl27Zpe/hkZGYwYMYLTp/+9pplCoRD2g/bx8SEwMBClUklgYCAHDhwwWM/W1lYSExORyWQEBgbyww8/GJSrrKwkPDycqVOnsmDBAmEBalPply5diqenJwqFwuj9NLf8iIgI4Z55eXkZ3AmmsLCQsWPH6uzUlJSUhFwuJyAggMTERDpaHvhf//oXkZGRBAcHExgYyKFDh4B/79Tk5uZmMJ2zszOLFy8Wvjc1NTFhwgRee+01k+UBQp5VVVXk5OQIxzUaDYmJiR2m/99QUFDAZ599ZlImIyODVatWAW3b6Lq6unL16lXhfPt7MnLkSJRKJUFBQYSEhHDy5Enh3KVLlzq8H1VVVSb7i7kyXSEzM5OpU6cydepUMjMzDco0NjayYMECZDIZ4eHhOvuvz549G3d3d71rjI2Npby83GB+ZWVlKJVKgoODOX/+fKfrnJKSwu3btzudrjNs3LixS9vs/qcoLCw067kyxvbt2/Hz80Mul5OUlCQc//TTT5HJZPj5+fHNN98Ix0tLSwkMDEQmk+mMIab6gojI7wlRIX1EmThxImq1mpycHIYNG2Z03/QlS5awevVqVCoVarWagIAA4dy0adNQqVSoVCqdAXHo0KEUFBQA0NLSQmFhIXZ2dkbrYm9vzyeffGL0/NatW1GpVHzwwQe88847BmUOHz5MeXk5+/btY/Xq1axYscKg3Lp164iOjmbfvn1YW1uza9euDtOHhobyxRdfGK1fZ8r/6quvhHvm5ubG1KlTDcq5u7vz+eefA3Dy5ElOnjxJdnY2arUajUbDd999Z7I+ycnJBAQEkJWVxYYNG1i5ciXQ1jYqlcpoOktLS86cOUNDQwMA//jHP0y2nSEuXLiAWq0Wvru4uBjdD/x+4evry6uvvtqpNP369WPz5s0Gz/Xo0QOVSkV2djYLFy7kr3/9q3Buy5Ytv9v93K9fv86mTZtIS0sjPT2dTZs2cePGDT259PR0rK2t2b9/P9HR0To7Eb388ss6z7OWmTNnGn0OCgoK8PX1JSsri6FDh3a63tu2beu0QtrU1NTpcn5rWltbaWlpue/5Hjt2jIKCAnJycsjNzWX27NkA/PLLL+Tm5pKbm8sXX3zBypUraW5uBmDFihWsWrWKffv2UV5ezuHDhwHTfUHk0eFR2DpUVEgfcpKTk/Hz8yM6OpqFCxcKFgIvLy+6dWuLyBgzZgwXL140mP7atWsMHDgQAAsLC4YPH95hmQqFgvz8fKDNCvDss88KZRni+eef55dffuHs2bMm862rq8Pa2trguYKCAoKDg5FIJIwZM4abN29y6dIlHZnW1laOHTuGn58fACEhIYLibCq9h4cHffr0MVk3c8q/91qOHTvGlClTTOYLIJFIaGxs5O7du8LfAQMGdJhGa6Wura3F1ta2w3K0eHt7c/DgQQByc3ORy+XCuXutTO2t2VrWr19PUVERSqWSlJQUHUvQxo0bWbp0KZGRkfj6+rJt2zYh3ZYtW1AoFCgUClJSUoA2K6K/vz8JCQkoFAoWLVrEt99+y4wZM5g6dSolJSWArvXz66+/Jjw8nODgYKKjo7ly5YrB63zhhRfIz8/n+vXrJu/Hvf1u3759eHt7C/WLiIggJCREz5KqJSMjg9dff53Zs2fj5+fHpk2bhHPNzc0sW7YMuVxOTEyM8CKQlpbGCy+8QFBQEPPmzTNbWTty5AgTJ06kb9++9OnTh4kTJ+pYybR8/fXXwr70fn5+HD16VLCYeXp60qtXL7007u7ufPvtt3qK4KFDh9i6dSvp6elERkYCoFKpCAsLQ6lUsnz5ckEpevvttwkNDUUul/Phhx8CbcropUuXiIqKEtK3t1bv2bOH+Ph4oM0Ds2bNGiIjI1m3bh3nz59n9uzZhIaGEhERQVlZmVn36Z///CfTp08XPERapb2kpITAwEBefPFF1q5da9KCbaxdq6qqCAgIYMWKFYSEhPDrr78KeQUGBpKXlyfkUVdXx5w5c5g2bRrLly83W3lNTU3l1VdfpXv3tgXc+/fvD7SNQ3K5nO7du+Po6IiTkxMlJSVcunSJuro63NzckEgkBAcHC2Ofqb4g8uggkUju6+dBICqkDzGlpaXk5eWRlZXFpk2b0Gg0BuV2794t/MDeS1RUFP7+/syZM4cdO3Zw584d4VxeXp7gft69e7dw3MnJiWvXrnHjxg09hcYQUqmUl19+2aiVNioqCoVCQWRkJAsWLDAoU11djb29vfDd3t6e6upqHZmamhqsra0F5bi9jDnp7yU1NZXU1NQupT9w4ACenp707t0baHNrJyQkGJR1c3Nj/PjxeHl54eXlxaRJk3jyySdN1m3u3Lnk5OTg7e3Nq6++atRCWV1drRMaAG2W77y8PO7cucNPP/2Eq6urybLuZdGiRbi7u6NSqYiOjtY7f+7cOb788kvS09P56KOPuHv3LqWlpWRkZJCWlsbOnTtJT0/nxx9/BNrCDGbNmkV2djbnzp0jJyeH1NRU4uLiDFrWx44dS1paGllZWcjlcqNWPUtLS0JDQ3WUYi0NDQ0olUr8/f1ZtmyZEFpRWVlJnz59dBSBLVu2kJmZyYYNG4yGJmg0GtatW4dKpWLPnj3Cs1hRUcFLL71Ebm4uVlZW7N27FwCZTMbu3bvJzs7miSeeECz52dnZwjPX/vOnP/0J0O+HdnZ2BvthdXU1gwYNAqBbt25YWVlRU1NjsO5apFIpTk5OQnhNQkICGo2GyZMnM2PGDKKjo9m+fTtlZWXk5+eTmpqKSqVCKpUKIRyxsbFkZGSQnZ3N8ePHOX36NLNmzcLW1patW7eyfXvHu9qUl5eTkpJCfHw8b731Fm+99RYZGRksWbJE8AR0RFxcHH/+85/Jycnh6aefFpTJN998k5UrV7Jz504sLCw6zMdYu547d47g4GCysrIoLS3l9OnTqFQqtmzZQlJSkvCyWlJSQnx8PDk5OVRWVrJv3z6jZWnvt/YeFBUVER4ezv/8z/8IL2bG2t/U+NSVviAi8iAQJzU9xBQVFTFlyhR69uwJtMVj3ktycjIWFhYEBQUZzGPu3LkEBQVx5MgR1Go1ubm5wo+G9q3eEDKZjNzcXL7//nvBcmUKhUJBcnIylZWVeue2bt2KjY0N58+fJzo6mnHjxulZcAy90ZvzFqeV6Ur6mTNndrl8tVqt4/Z1cXHBxcXFoGxFRQVlZWVCHGhMTAzHjx/Hw8PDaP65ubmEhIQQExNDcXExcXFxqNVqpFLdd0w7OzshNEDLiBEjqKqqQq1WM3nyZKNldJXJkyfTvXt3bGxssLGx4erVq5w4cYIpU6Zgadm277NMJqOoqAgfHx8cHBxwdnYGYPjw4Xh6eiKRSHB2dubChQt6+V+8eJHY2FguX75MY2MjDg4ORusya9YsgoODiYmJ0TmuddkDFBcXs2TJEtRqNZcvX6Zfv36CXFNTE6tWreL06dNIpVKjMZbPPfeckE4mkwnX6+DgwMiRIwEYNWqUcD1nzpzh/fffp7a2lvr6ery8vAAICgoy+qyC+f2wq8+LjY2NoEwZC585evQopaWlhIWFAW3KvdaCl5+fT1paGk1NTVy+fJmysjJGjOjcHtv+/v5YWFhQX19PcXEx8+fPF85pY8JNUVtbS21tLePGjQPaPCXz58/n5s2b1NfX8+yzzwJtY5LWU2AMY+06ePBgxowZA8CJEyeQy+VYWFgwYMAAPDw80Gg09O7dm9GjR+Po2LYVpFwu58SJE/j7+xssq/39bm5u5ubNm6SlpaHRaFiwYAEFBQVG29VUe3e1L4g8XDwKTSoqpA85pgaWzMxMDh48SEpKiiC3dOlSfvzxR2xtbQVFZejQoURERDB9+nQ8PT3NenuWy+WCG7O9EvS3v/2NtLQ0AJ1JKN26dSMmJkZPOWrP0KFD6d+/P2VlZWg0Gp187O3tdcIOLl68qOem7tevHzdv3qSpqYlu3brpyJiT3hSdSV9TU4NGo+Gjjz4yK+/9+/fj6uoqKOGTJk3i1KlTJhXSXbt2CZZBNzc37ty5Q01NjaAYdISPjw9JSUls27ZNx6VtYWGh41ZsbzE3F611UZtfU1OTSRdhe3mpVCp8l0gkgiu4PYmJiURHR+Pr60thYaGOi/xerK2tUSgUfPXVV0Zl3NzcqKmp4dq1a/To0UNH6UlJSWHAgAGoVCpaWloYPXq0wTzufQ613++9F9r7GR8fz8cff8yIESPIyMgQYoazs7MNTsxxcnLiww8/xN7eXie+uLq6WlC82mNvb8+vv/6Kvb09TU1N1NbW0rdvX6P3QEtjYyM9evQwKdPa2kpISAiLFi3SOV5ZWcnmzZvZtWsXffr0IT4+3qz+c6+M9gW7tbUVa2trkzHRnaErbmpj7ap9seooX2PpO8LOzg6ZTIZEImH06NFIpVJqamr0xqHq6mpsbW1Njk9d7QsiIr81osv+IcbDw4P9+/fT0NBAXV0df//734Vzhw8f5vPPPyc5OVkY4AHWrFmDSqUSFMODBw8KA2pFRQVSqdRoHGd7Bg8eTGxsLBERETrHX3rpJWFSz72TZUJCQjh69KjB2fgAV69epaqqisGDB+vl4+PjQ1ZWFq2trZw6dQorKys9hVAikTB+/HjBLZqZmSlYjc1Jb4rOpN+zZw/PP/88jz/+uFl5Dx48mOPHj9PU1MTdu3c5fvy44LJfv349+/fv10szaNAgjh49CrTNgL5z5w42NjZmX09YWBhvvPGGYJnUMmTIEMGV/sMPPxickdurVy/q6+vNLgva+uqBAwe4ffs2t27d4sCBA7i7u3cqDy21tbVC38rKyupQPjo6mh07dhidJFNWVkZzczN9+/Zl2LBhOlbZ2tpaBg4ciFQqRaVSGVSQoW1y2PXr12loaODAgQOCFc4Y9fX1DBw4kLt37+qsWBAUFCT0+/YfbTyml5cXR44c4caNG9y4cYMjR44I1tX2+Pj4CDPw9+7dy4QJE8xShsrLyzuMI/f09GTv3r3CCgbXr1/nwoUL1NfX07NnT6ysrLhy5YowqQb0+8yAAQMoKyujpaXF6MoavXv3xsHBQYhXb21t1VmtwxhWVlZYW1tTVFQEtMW7auPEe/XqxalTpwB0Yj2NYU67enh4kJ+fT3NzM9euXaOoqEh4cSkpKaGyspKWlhby8/MZO3Zsh2UCTJkyhWPHjgFt4QF3796lX79++Pj4kJubS2NjI5WVlZSXlzN69GhsbW2Fa2ttbSUrKwtfX1+g631B5OHiUYghFS2kDzGjRo1i2rRpKJVKhgwZojPYrV69msbGRv74xz8C4OrqatC1rlKpWLNmDT169MDCwoJ169aZFVsFMGPGjE7Vt3v37kRGRuq5AqOiopBKpTQ1NbFo0SKDE3omT57MoUOHkMlk9OzZk3fffVc498orr5CYmIidnR2LFy8mNjaW999/n5EjRwpuc1PpFy5cyHfffUdNTQ3e3t7MmzeP8PBwIX505syZZpcPbT9098ZtajQaduzYYdAN6ufnx7FjxwgMDEQikTBp0iRBkf75558NhmLEx8ezbNkywfr9l7/8xeAgUl1dzbJly/Qs0/b29kRFRRmsi0qlQqlU4uLiwrBhw/RknJ2dhTCQ0NBQwSVtilGjRhEaGiq0R1hYGM8880yXlqCZO3cu8+fPx87ODldX1w7zsLGxQSaTCROp4N8xpNCm6KxduxYLCwssLS1xdHSkoqICJycnIiIimDdvHnv27GH8+PE6lrH2jB07lri4OCoqKggMDMTFxcVkvebPn094eDhDhgzh6aefNlvB79u3L2+88YbgLp8zZ45g7frggw/4wx/+gK+vL2FhYSxevBiZTEafPn3YsGGDkEdERARnz57l1q1beHt788477zBp0iSuXLnC448/LrxoJSQkMGPGDL1Qk+HDh7NgwQJiYmJoaWnhscceY/ny5YwZM4ZnnnkGuVyOo6OjjvI2ffp0XnnlFQYOHMj27dtZtGgRr732GoMGDeKpp57i1q1bBq/3vffeY8WKFSQnJ9PU1MS0adPMCgFYu3Ytb7/9Nrdv38bR0ZE1a9YAbW7xZcuWYWlpybhx44QYb2OY064ymYzi4mKUSiUSiYTFixczcOBAzp49y5gxY1i/fj0///wz7u7uyGQyo2W1v98vvPACb775JgqFgscee0x4vp966ikCAgKYNm0aFhYWLF++XBivV6xYwdKlS2loaMDb21uYN2CqL4g8OjwK7xiSVnG63SPDxo0bsbS0FJYIEfl9UlhYyObNm41O8mrP7Nmzu7S2opubG8XFxV2p3v959u/fT2lpKbGxsWbJZ2RkUFpaajTe+mEhJSWFXr16/W6XvLof1NfXC6Exn332GZcuXTI6IfBRadeuoBn2X/clH+l93F/9fipc1lbdOxYykyHpHU/U6wySp4yHapni2nN/uK/1sPm29L7mZw6iy15E5Dfmscce48yZM3pWVEN0VhnVLoxvbiypiD4ymczkRKlHFSsrK2F5oEeVQ4cOoVQqUSgUFBUVGdy4QkTkYeRRcNmLFlIRERERkYeG5ORk9uzZo3PM39+f119/vUv5ffPNN3qLxTs4OJg9KbGzhIeH660WkJSUpBfP/SARLaTm83uxkF6fZHgVl67S9xvDy0j+JxEVUhEREREREREBUSE1H1EhvX+Ik5pEREREREREBG413J9tW++n6/ex+6jcNnQ3vFpGV2i9dfO+5QXQ1at8FFZOMCuG9Pz584KLobCwkG3btnHz5v1tBBERERERERERkS4gldzfz4O4BHOE5s2bh1QqpaKigoSEBKqqqvQWRRYRERERERERERHpCmYppFKplG7durF//36ioqJ48803uXz58n+6biIiIiIiIiIiIh0hkdzfzwPALIW0W7duqNVqsrKyeP755wGM7noiIiIiIiIiIiLy2/FbL/t0+PBh/Pz8kMlkOtuEt6ewsBClUolcLud//ud/OszTrElNa9asYceOHfy///f/cHR0pLKykqCgIHOSioiIiIiIiIiIPCI0NzezatUqtmzZgp2dHWFhYfj4+OhsO3zz5k1WrlzJF198weDBg4Wthk3RoULa3NzMJ598orNOm6OjI6+++moXL0VEREREREREROS+8RtORCopKcHJyQlHR0cA5HI5BQUFOgppTk4OMpmMwYMHA5i1WUuHLnsLCwtqamr0FvIV+X2zdu1a/P39CQwMZM6cOUZXRTh16hTh4eEolUoCAgLYuHEj0LZt3oQJE1AqlSiVSuLi4oC2PdRdXV2pq6sT8khMTMTZ2Zlr167p5Z+RkcGIESM4ffq0cEyhUAj7Qfv4+BAYGIhSqSQwMJADBw4YrGdrayuJiYnIZDICAwP54YcfDMpVVlYSHh7O1KlTWbBggdBvTaVfunQpnp6eKBQKo/fT3PIjIiKEe+bl5WVwJ5jCwkLGjh2rs1NTUlIScrmcgIAAEhMT6Wh54H/9619ERkYSHBxMYGAghw4dAv69U5Obm5vBdM7OzixevFj43tTUxIQJE3jttddMlgcIeVZVVZGTkyMc12g0JCYmdpj+f0NBQYFRt5CWjIwMVq1aBbRto+vq6qrzVt7+nowcORKlUklQUBAhISGcPHlSOHfp0qUO70dVVZXJ/mKuTFfIzMxk6tSpTJ06lczMTIMyjY2NLFiwAJlMRnh4uM7+67Nnz8bd3V3vGmNjYykvLzeYX1lZGUqlkuDgYM6fP9/pOqekpHD79u1Op+sMGzdu7NI2u/8pCgsLzXquDPHPf/6T6dOno1QqCQ0NpaSkRDj36aefIpPJ8PPz45tvvhGOl5aWEhgYiEwm0xlDTPUFEZGuUF1djb29vfDdzs6O6upqHZny8nJu3rxJZGQkoaGhZGVldZivWTGkQ4YMYebMmXz00Uds2bJF+Ij8fpk4cSJqtZqcnByGDRtmdN/0JUuWsHr1alQqFWq1moCAAOHctGnTUKlUqFQqkpKShONDhw6loKAAgJaWFgoLC7GzszNaF3t7ez755BOj57du3YpKpeKDDz7gnXfeMShz+PBhysvL2bdvH6tXr2bFihUG5datW0d0dDT79u3D2tqaXbt2dZg+NDSUL774wmj9OlP+V199JdwzNzc3pk6dalDO3d2dzz//HICTJ09y8uRJsrOzUavVaDQavvvuO5P1SU5OJiAggKysLDZs2MDKlSuBtrZRqVRG01laWnLmzBkaGhoA+Mc//mGy7Qxx4cIF1Gq18N3FxcXofuD3C19f3057Zfr168fmzZsNnuvRowcqlYrs7GwWLlzIX//6V+Hcli1bfrf7uV+/fp1NmzaRlpZGeno6mzZt4saNG3py6enpWFtbs3//fqKjo3U8XC+//LLO86xl5syZRp+DgoICfH19ycrKYujQoZ2u97Zt2zqtkD4M8xRaW1tpaWm57/m+9957zJkzB5VKxfz583nvvfcA+OWXX8jNzSU3N5cvvviClStX0tzctqbmihUrWLVqFfv27aO8vJzDhw8DpvuCyCPEfZ7UtHPnTkJDQ4XPzp07haIMGUzujTttbm7mhx9+4NNPP+WLL77g448/5ty5cyYvwSyF1NbWlv/+7/+mtbWV+vp64SPy4ElOTsbPz4/o6GgWLlwoWAi8vLzo1q0tImPMmDFcvHjRYPpr164xcOBAoM0a3t7kbgyFQkF+fj7QZgV49tlnhbIM8fzzz/PLL79w9uxZkxLKcWEAACAASURBVPnW1dVhbW1t8FxBQQHBwcFIJBLGjBnDzZs3uXTpko5Ma2srx44dw8/PD4CQkBBBcTaV3sPDgz59+pismznl33stx44dY8qUKSbzhbYHubGxkbt37wp/BwwY0GEarZW6trYWW1vbDsvR4u3tzcGDBwHIzc1FLpcL5+61MrW3ZmtZv349RUVFKJVKUlJSdCxBGzduZOnSpURGRuLr68u2bduEdFu2bEGhUKBQKEhJSQHarIj+/v4kJCSgUChYtGgR3377LTNmzGDq1KmCZai99fPrr78mPDyc4OBgoqOjuXLlisHrfOGFF8jPz+f69esm78e9/W7fvn14e3sL9YuIiCAkJETPkqolIyOD119/ndmzZ+Pn58emTZuEc83NzSxbtgy5XE5MTIzwIpCWlsYLL7xAUFAQ8+bNM1tZO3LkCBMnTqRv37706dOHiRMn6ljJtHz99dfCvvR+fn4cPXpU+BHx9PSkV69eemnc3d359ttv9RTBQ4cOsXXrVtLT04mMjARApVIRFhaGUqlk+fLlglL09ttvExoailwu58MPPwTalNFLly4RFRUlpG9vrd6zZw/x8fFAmwdmzZo1REZGsm7dOs6fP8/s2bMJDQ0lIiKCsrIys+6T1sKo9RBplfaSkhICAwN58cUXWbt2rUkLtrF2raqqIiAggBUrVhASEsKvv/4q5BUYGEheXp6QR11dHXPmzGHatGksX77cbOVVIpEIv7Htn++CggLkcjndu3fH0dERJycnSkpKuHTpEnV1dbi5uSGRSAgODhbGPlN9QeTRQSKV3NfPiy++SEZGhvB58cUXhbLs7e11dIrq6mq93yB7e3smTZqEpaUlNjY2uLu763hKDWGWQjp37lzmzp1LTEyM8P/cuXM7c69E/gOUlpaSl5dHVlYWmzZtQqMxvNXX7t27hR/Ye4mKisLf3585c+awY8cO7ty5I5zLy8sT3M+7d+8Wjjs5OXHt2jVu3Lihp9AYQiqV8vLLLxu10kZFRaFQKIiMjGTBggUGZe51Edjb2+u5CGpqarC2thaU4/Yy5qS/l9TUVFJTU7uU/sCBA3h6etK7d2+gza2dkJBgUNbNzY3x48fj5eWFl5cXkyZN4sknnzRZt7lz55KTk4O3tzevvvqqUQtldXW1TmgAtFm+8/LyuHPnDj/99BOurq4my7qXRYsW4e7ujkqlIjo6Wu/8uXPn+PLLL0lPT+ejjz7i7t27lJaWkpGRQVpaGjt37iQ9PZ0ff/wRaAszmDVrFtnZ2Zw7d46cnBxSU1OJi4szaFkfO3YsaWlpZGVlIZfLjVr1LC0tCQ0N1VGKtTQ0NKBUKvH392fZsmVCaEVlZSV9+vShe/e2rQX79+/Pli1byMzMZMOGDUZDEzQaDevWrUOlUrFnzx7hWayoqOCll14iNzcXKysr9u7dC4BMJmP37t1kZ2fzxBNPCJb87Oxs4Zlr//nTn/4EmOcq08oNGjQIaFslxcrKipqaGoN11yKVSnFychJ+NBISEtBoNEyePJkZM2YQHR3N9u3bKSsrIz8/n9TUVFQqFVKpVAjhiI2NJSMjg+zsbI4fP87p06eZNWsWtra2bN26le3bO95msby8nJSUFOLj43nrrbd46623yMjIYMmSJYInoCPi4uL485//TE5ODk8//bSgTL755pusXLmSnTt3YmFh0WE+xtr13LlzBAcHk5WVRWlpKadPn0alUrFlyxaSkpKEl9WSkhLi4+PJycmhsrKSffv2GS1Le7+19UxKSmLy5MmsXbuWhQsXAsbb39T41JW+ICJiChcXF8rLy6msrKSxsZHc3Fx8fHx0ZHx9fSkqKqKpqYnbt29TUlLS4e+aWbPsi4uLSUhI4NatWxw8eJDTp0+zY8cOo25Lkd+GoqIipkyZQs+ePQH0OgS0WVAtLCyMroowd+5cgoKCOHLkCGq1mtzcXOFHQ/tWbwiZTEZubi7ff/+9YLkyhUKhIDk5mcrKSr1zW7duxcbGhvPnzxMdHc24ceP0LDjmuAgMoZXpSvqZM2d2uXy1Wq3j9nVxccHFxfBewxUVFZSVlQlxoDExMRw/fhwPD+N7Gufm5hISEkJMTAzFxcXExcWhVquRSnXfMe3s7ITQAC0jRoygqqoKtVrN5MmTjZbRVSZPnkz37t2xsbHBxsaGq1evcuLECaZMmYKlpSXQ1n+Kiorw8fHBwcEBZ2dnAIYPH46npycSiQRnZ2cuXLigl//FixeJjY3l8uXLNDY24uDgYLQus2bNIjg4mJiYGJ3jWpc9tI1vS5YsQa1Wc/nyZfr16yfINTU1sWrVKk6fPo1UKjUaY/ncc88J6WQymXC9Dg4OjBw5EoBRo0YJ13PmzBnef/99amtrqa+vx8vLC4CgoCCTK5iY2w+7+rzY2NgIypSx8JmjR49SWlpKWFgY0Kbcaycs5Ofnk5aWRlNTE5cvX6asrIwRI0Z0WG57/P39sbCwoL6+nuLiYubPny+cM2cuQ21tLbW1tYwbNw5o85TMnz+fmzdvUl9fz7PPPgu0jUlaT4ExjLXr4MGDGTNmDAAnTpxALpdjYWHBgAED8PDwQKPR0Lt3b0aPHq0z8ePEiRP4+/sbLKv9/U5NTWXp0qX4+fmRl5dHQkICKSkpRtvVVHt3tS+IPGT8hm3arVs3li9fzssvv0xzczMvvPACTz31lGDAmTlzJk8++SSTJk0iKCgIqVRKWFgYTz/9tOl8zSn83Xff5csvv+T1118H2n7QioqK/peXJHI/MDWwZGZmcvDgQVJSUgS5pUuX8uOPP2JraysoKkOHDiUiIoLp06fj6elp1tuzXC4X3JjtlaC//e1vpKWlAehMQunWrRsxMTF6ylF7hg4dSv/+/SkrK0Oj0ejkc6+L4OL/Z+/dw6Kq9sf/1zBeMW+YgKJpfjQ1I0JF5Yho4HARhgGFHsMQpKN9Q8zb8UKSqaCkaaaopKcSraPmBRkuSoBpRilp4hHsePISCKZQgspFEITfH/xYh4GZYeBoXs5+Pc88MHuv26y99trv/b6sdeNGIxNB165duXPnDlVVVbRq1UojjSH59dGc/EVFRWRmZrJ582aDyk5JScHKykoI4WPGjOHs2bN6BdL9+/cLzaC1tTUVFRUUFRUZFMkItS8va9asYefOnRombblcrmFWrK8xN5Q67WJdeVVVVXpNhPXTGxkZie8ymUyYgusTHh5OQEAAjo6OpKena5jIG9KpUyfc3d3ZtWuXzjTW1tYUFRVRWFhIu3btNISe6Ohonn32WdRqNdXV1bz88stay2h4H9Z9b9gXdf25ePFitmzZwqBBg4iJiRE+w3FxcVoDc/r06cPGjRsxNzfX8C/Oz88Xgld9zM3NuX79Oubm5lRVVVFcXEyXLl109kEd9+7do127dnrT1NTU4OXl1Wi3vtzcXD7//HP2799P586dWbx4sUHjp2GauhfsmpoaOnXqpNcnujm0xEyt67rWvVg1Va6u/E1x8OBBYVFxdXUVFhBdplJ981NLx4LEk4XsT97uc+zYsY0UGvWVOFDrr/7Xv/7V4DINMtkDQuUvMhoZnFXiIWFjY0NKSgrl5eWUlJRw9OhRce748eP8/e9/JyoqSkzwULumrFqtFoLhsWPHxISak5ODkZGRTj/O+vTs2ZO5c+fi6+urcXzKlCkiqKdhsIyXlxcnTpzQGo0PcPPmTfLy8ujZs2ejchwcHIiNjaWmpoazZ8/SsWPHRgKhTCZj5MiRwix68OBBoTU2JL8+mpM/KSmJcePG0bZtW4PK7tmzJ6dOnaKqqorKykpOnTolTBvr1q0jJSWlUZ4ePXpw4sQJoDYCuqKiAhMTE4N/j7e3N0FBQUIzWYeFhYUwpZ8/f15rRG6HDh2a7UNuY2NDamoqd+/epaysjNTUVIYPH96sMuooLi4WY8uQyM2AgAD27NmjM0jm8uXL3L9/ny5dutC3b18NrWxxcTHdu3fHyMgItVqtVUCG2uCwW7duUV5eTmpqqtDC6aK0tJTu3btTWVmpsWKBh4eHGPf1P3X+mHZ2dqSlpXH79m1u375NWlqa0K7Wx8HBQUTgf/3114waNcogYSg7O7tJP3JbW1u+/vprsYLBrVu3uHbtGqWlpbRv356OHTvyxx9/iKAaaDxmnn32WS5fvkx1dbXOlTWeeeYZevXqJfzVa2pqmvRBA+jYsSOdOnUSShO1Wi38xDt06MDZs2cBNHw9dWHIdbWxseHw4cPcv3+fwsJCTp8+LV5czp07R25uLtXV1Rw+fJhhw4Y1WSfUxm3UvXicPHmSvn37ArXXNTExkXv37pGbm0t2djYvv/wypqam4rfV1NQQGxuLo6OjyNOSsSAh8WdjkIa0R48enDlzRgRffPHFF036Akg8fIYMGcKECRNQqVRYWFhoTHZhYWHcu3ePadOmAWBlZaXVtK5Wq4mIiKBdu3bI5XLWrl1rkG8VwOTJk5vV3jZt2uDn59fIFOjv74+RkRFVVVXMnz9fa0DP2LFj+fbbb1EoFLRv355Vq1aJc9OnTyc8PBwzMzMWLFjA3Llz+fjjjxk8eLAwm+vLP2/ePH788UeKioqwt7dn1qxZ+Pj4aJgfDK0fah90Df02MzMz2bNnj1YzqLOzMydPnkSpVCKTyRgzZowQpH/55RetrhiLFy8mNDRUaL8/+OADrQ+Z/Px8QkNDG2mmzc3N8ff319oWtVqNSqXC0tJSPAjrM3DgQOEGMnHiRGGS1seQIUOYOHGiuB7e3t68+OKLLVqCJjg4mNmzZ2NmZoaVlVWTZZiYmKBQKEQgFfzHhxRqBZ3Vq1cjl8sxNjamd+/e5OTk0KdPH3x9fZk1axZJSUmMHDlSQzNWn2HDhrFw4UJycnJQKpVYWlrqbdfs2bPx8fHBwsKCF154wWABv0uXLgQFBQlz+cyZM4W2a8OGDbz00ks4Ojri7e3NggULUCgUdO7cmfXr14syfH19uXLlCmVlZdjb27Ny5UrGjBnDH3/8Qdu2bcWL1pIlS5g8eXIjV5P+/fszZ84cAgMDqa6upnXr1ixdupRXXnmFF198ETc3N3r37q0hvL322mtMnz6d7t2788UXXzB//nzeeustevTowYABAygrK9P6ez/88EOWLVtGVFQUVVVVTJgwwSAXgNWrV/P+++9z9+5devfuTUREBFBrFg8NDcXY2JgRI0YIH29dGHJdFQoFGRkZqFQqZDIZCxYsoHv37ly5coVXXnmFdevW8csvvzB8+HAUCoXOuur3d1hYGKtWraKqqoq2bduKuXvAgAG4uroyYcIE5HI5S5cuFfP1smXLCAkJoby8HHt7exE3oG8sSDxFPAUvGbIaA+wYhYWFrFy5UkTnjR49mtDQUEnt/5gRGRmJsbExb7755qNuioQe0tPT+fzzz3UGedXnzTffbNHaitbW1mRkZLSkef/zpKSkkJWVxdy5cw1KHxMTQ1ZWlk5/6yeF6OhoOnTo8NguefUgKC0tFa4x27Zto6CgQGdA4NNyXVtCunnvB1LOg9TEtn6AJumOndo0nchA+u3RvaRhSzCycmxRvhLlqAfajmfiTz7Q8gzBIA3pr7/+yrp16zSO/fTTTwabHyQkJP5D69atuXjxItOnT9frUws0Wxi9evUqs2bNMtiXVKIxCoWiyaWinkY6duwotMZPK99++y1bt27l/v379OzZkw8++OBRN0lCQuL/xyANqZeXV6MdQbQdk5CQkJCQeJhERUWRlJSkcczFxUUE3TaX7777rtFi8b169TI4KLG5+Pj4NFotYM2aNY38uR8lkobUcB4XDWmph+0DbUeHuBMPtDxD0KshzcjIICMjg8LCQo2dmUpKSnQ690tISEhISDws3n777RYLn9oYM2YMY8aMeWDlNcW+ffv+tLok/of4k6PsHwZ6BdLKykrKysq4f/++htP9M888I6I+JSQkJCQkJJ4eRl7JetRNkPgfxCCT/bVr17CwsKCsrExnlKmEhISEhITEU0DZ7Ufdgv9djPVvY62LUq/RD7QZHQ5+/0DLMwSDFhMtKChgwoQJTJgwAYALFy5IuzRJSEhISEhISEg8EAwSSOt2aqpb5knaqUlCQkJCQkJC4vFAZvRgP48Cg5Z9AmmnJgkJCQkJCQmJx5KnYGF8aacmCQkJCQkJCQmJR4pBas5ly5bxj3/8g/z8fMaOHcu//vWv/8ndKyQkJCQkJCQkHjdkRrIH+nkUGCSQmpiYsG7dOn744QdOnDjB2rVr6dq168Num8RDYPXq1bi4uKBUKpk5cyZ37tzRmu7s2bP4+PigUqlwdXUlMjISqN1Ob9SoUahUKlQqFQsXLgRq91a3srKipKRElBEeHs7AgQMpLCxsVH5MTAyDBg3iwoUL4pi7u7vYJ9rBwQGlUolKpUKpVJKamqq1nTU1NYSHh6NQKFAqlZw/f15rutzcXHx8fHBycmLOnDliYWp9+UNCQrC1tcXd3V1nfxpav6+vr+gzOzs7goKCGqVJT09n2LBhTJ8+XRxbs2YNbm5uuLq6Eh4eTlOLYvz222/4+fnh6emJUqnk22+/BWp3cFKpVFhbW2vNN3DgQBYsWCC+V1VVMWrUKN566y299QGizLy8POLj48XxzMxMwsPDm8z/33DkyBG2bdumN01MTIzYCzwyMhIrKytu3rwpztfvk8GDB6NSqfDw8MDLy4szZ86IcwUFBU32R15ent7xYmialnDw4EGcnJxwcnLSuWnJqVOn8PLy4sUXX9RYXL6wsFDvlsM7d+7E1dWV+fPnN7tdDcfFw8LBwUHrXPOoWLx4caMF/A3h+vXr+Pn54erqipubGzt27BDnbt26xbRp03BycmLatGncvv2faPitW7eiUChwdnbmu+++E8ezsrJQKpUoFAqD5hCJJxSZ7MF+HgEGCaS5ublEREQQHBzM//t//098JJ48Ro8eTUJCAvHx8fTt21fnfuqLFi0iLCwMtVpNQkICrq6u4tyECRNQq9Wo1WrWrFkjjj/33HMcOXIEgOrqatLT0zEzM9PZFnNzcz75RPcuFzt27ECtVrNhwwZWrlypNc3x48fJzs4mOTmZsLAwnas/rF27loCAAJKTk+nUqRP79+9vMv/EiRP59NNPdbavOfXv2rVL9Jm1tTVOTk5a0w0fPlxsJ3rmzBnOnDlDXFwcCQkJZGZm8uOPP+ptT1RUFK6ursTGxrJ+/XqWL18O1F4btVqtM5+xsTEXL16kvLwcgO+//17vtdPGtWvXSEhIEN8tLS117hP+oHB0dGTGjBnNytO1a1c+//xzrefatWuHWq0mLi6OefPm8dFHH4lz27dvf2z3eb916xabNm1i79697Nu3j02bNmkIK3X06NGDiIiIRgKxiYkJpqam/PTTT1rL37VrF9u2bWu0hbQhNBwXhvIkbL5SVVX1wMuUy+UsXryYw4cP89VXX7Fr1y4uXboEwLZt27C1tSU5ORlbW1vxMnbp0iUSExNJTEzk008/Zfny5aL/li1bxooVK0hOTiY7O5vjx48/8DZLSDwIDBJIZ86ciYWFBW+88QaBgYHiI/H4EhUVhbOzMwEBAcybN0/siW5nZ0erVrWuw6+88go3btzQmr+wsJDu3bsDtRNk//79m6zT3d2dw4cPA7XavqFDh4q6tDFu3DguXbrElStX9JZbUlJCp06dtJ47cuQInp6eyGQyXnnlFe7cuUNBQYFGmpqaGk6ePImzszNQu+1tneCsL7+NjQ2dO+tfE86Q+hv+lpMnTzJ+/Hi95QLCZ7uyslL8ffbZZ5vMU6elLi4uxtTUtMl66rC3t+fYsWMAJCYm4ubmJs5FRkaKMQSa2uw61q1bx+nTp1GpVERHR5Oeni40ipGRkYSEhODn54ejoyM7d+4U+bZv3467uzvu7u5ER0cDtVo1FxcXlixZgru7O/Pnz+eHH35g8uTJODk5ce7cOUBT+/nNN9/g4+ODp6cnAQEB/PHHH1p/56RJkzh8+HCT+9U3HHfJycnY29uL9vn6+uLl5dVIk1pHTEwMb7/9Nm+++SbOzs5s2rRJnLt//z6hoaG4ubkRGBgoXgT27t3LpEmT8PDwYNasWdy9e1dvG+tIS0tj9OjRdOnShc6dOzN69GgNLVkdvXr1YtCgQVqDUh0dHbVqMpcuXUpeXh5BQUFER0dTVlZGSEgIkyZNwtPTU1gvdPVJw3FR/5oBvPXWW6SnpwO12uoNGzbg4+NDRkYGarUab29vVCoVS5cuNVhI1TamADZv3oyLiwvTpk3TmBe14efnx8qVK5k8eTLu7u5izEVGRvLee+8RGBjIokWLuHbtGv7+/iiVSvz9/fntt99EGT/88AO+vr44Oztz9OhRg9puamrKkCFDgNpNaPr160d+fj7wn/kG0Oj7I0eO4ObmRps2bejduzd9+vTh3LlzFBQUUFJSgrW1NTKZDE9PTzH3STxlGMke7OdR/ARDErVt25apU6cyatQoRowYIT4SjydZWVkcOnSI2NhYNm3aRGZmptZ0Bw4cEA/Yhvj7++Pi4sLMmTPZs2cPFRUV4tyhQ4eE+fnAgQPieJ8+fSgsLOT27duNBBptGBkZ8de//lWnltbf3x93d3f8/PyYM2eO1jT5+fmYm5uL7+bm5mLyrqOoqIhOnToJ4bh+GkPyN2T37t3s3r27RflTU1OxtbXlmWeeAWrN2kuWLNGa1trampEjR2JnZ4ednR1jxoxpMpgwODiY+Ph47O3tmTFjhk4NZX5+voZrANRqvg8dOkRFRQX//ve/sbKy0ltXQ+bPn8/w4cNRq9UEBAQ0Ov/rr7/y2WefsW/fPjZv3kxlZSVZWVnExMSwd+9evvrqK/bt28fPP/8M1LoZTJ06lbi4OH799Vfi4+PZvXs3Cxcu1KpZHzZsGHv37iU2NhY3Nzed2m1jY2MmTpyoIRTXUV5ejkqlwsXFhdDQUOFakZubS+fOnWnTpnYP7G7durF9+3YOHjzI+vXrdbomZGZmsnbtWtRqNUlJSeJezMnJYcqUKSQmJtKxY0e+/vprABQKBQcOHCAuLo5+/foJTX5cXJy45+p/3nnnHaDxODQzM2tyHDfE0tJSaEjrj48VK1ZgamrKjh07CAgI4JNPPmHUqFEcOHCAnTt38uGHH1JWVqazT5oaF/UpKytjwIAB7Nu3j65du3L48GF2796NWq3GyMjIINO/rjGVmZlJcnIysbGxREZGkpXV9G5Ed+/eZc+ePbz//vu8++674vj58+fZsmUL69atIywsDE9PT+Lj41EqlRpj4dq1a3z55Zds3bqV999/X2MerY+2+xFqhfx//etf4l68efOmeMk0NTUVLgq6rn9L5jeJJxOZTPZAP48Cg6Lsp06dyqZNmxg9erSYkAHxFifxeHH69GnGjx9P+/btgVrfqoZERUUhl8vx8PDQWkZwcDAeHh6kpaWRkJBAYmIiX3zxBVAruOgKalMoFCQmJvLPf/5TQwuiC3d3d6KiosjNzW10bseOHZiYmHD16lUCAgIYMWIEHTp00EijzR/KkJupLk1L8r/++ustrj8hIUHD7GtpaYmlpaXWtDk5OVy+fFn4gQYGBnLq1ClsbGx0lp+YmIiXlxeBgYFkZGSwcOFCEhISGmnEzMzMhGtAHYMGDSIvL4+EhATGjh2rs46WMnbsWNq0aYOJiQkmJibcvHmTn376ifHjx4sd4BQKBadPn8bBwYFevXoxcOBAAPr374+trS0ymYyBAwdy7dq1RuXfuHGDuXPn8vvvv3Pv3j169eqlsy1Tp07F09OzkaWnzmQPkJGRwaJFi0hISOD333/X8JuvqqpixYoVXLhwASMjI7Kzs7XW85e//EXkUygU4vf26tWLwYMHA7XzaN3vuXjxIh9//DHFxcWUlpZiZ2cHgIeHh857FVp+H9THxMREaPe1jY860tLS+Oabb4TbQ0VFBdevX8fU1NSgPtGHXC4XlowTJ06QlZWFt7c3UPuy0K1btybL0DWmqqurcXR0pF27dgC8+uqrTZZV91JtY2NDSUmJ8Ll3cHAQ5WRkZAgfe5VKxYcffijyu7q6YmRkRN++fenduzdXrlwR170+2vq7tLSUd955h3fffVe8wOpC1/V/EONCQuLPwiCB9JdffkGtVnPy5EkxmGUymVYNg8Tjgb5J5+DBgxw7dozo6GiRLiQkhJ9//hlTU1MxMT733HP4+vry2muvYWtrS1FRUZP1urm5CZNdfSHoH//4B3v37gXQCEJp1aoVgYGBOh9+de3o1q0bly9fJjMzU6Mcc3NzDbeDGzduNDJTd+3alTt37lBVVUWrVq000hiSXx/NyV9UVERmZiabN282qOyUlBSsrKyEED5mzBjOnj2rVyDdv3+/0AxaW1tTUVFBUVGRQQ9yqH3Qrlmzhp07d2qYtOVyOdXV1eK7Lk2PPuq/zMrlcqqqqvQGWNRPb2RkJL7LZDKtptvw8HACAgJwdHQkPT1dw0TekE6dOuHu7s6uXbt0prG2tqaoqIjCwkLatWsnAuEAoqOjefbZZ1Gr1VRXV/Pyyy9rLaPhfVj3vWFf1PXn4sWL2bJlC4MGDSImJkb4DMfFxWk1L/fp04eNGzdibm6u4V+cn5/fbCtWRUUFbdu2NSjtxo0b6devn8axyMhIg/pE31hq27YtcrkcqBWyvLy8mh1I9SCDdnRdv7qX/aby6MrfFJWVlbzzzjsolUoNf/Nu3bpRUFCAqakpBQUFmJiYAI3nofz8fExNTf/r+U3iCeIRmdkfJAaZ7FNSUkhNTeXLL7/kiy++4IsvvpCE0ccYGxsbUlJSKC8vp6SkRMN36fjx4/z9738nKipKY1KNiIhArVYLwfDYsWNiYs/JycHIyEinH2d9evbsydy5c/H19dU4PmXKFBHU0zBYxsvLixMnTuiMkL158yZ5eXn07NmzUTkODg7ExsZSU1PD2bNn6dixY6MJVyaTMXLkSGEWPXjwoNAaG5JfNTT76AAAIABJREFUH83Jn5SUxLhx4wx+6Pfs2ZNTp05RVVVFZWUlp06dEib7devWkZKS0ihPjx49OHHiBACXL1+moqJCPLQMwdvbm6CgIKGZrMPCwkKY0s+fP9/IfxSgQ4cOlJaWGlwX1I7V1NRU7t69S1lZGampqQwfPrxZZdRRXFwsxlZsbGyT6QMCAtizZ4/OwJTLly9z//59unTpQt++fTW0ssXFxXTv3h0jIyPUarVO38bvv/+eW7duUV5eTmpqKkOHDtXbptLSUrp3705lZaWGedrDw0OM+/qfjRs3ArW+4Wlpady+fZvbt2+TlpYmtKuGkp2dzYABA5pMZ2dnx5dffinmh7pxoatPGo4LCwsLLly4QHV1NdevXxe+mQ2xtbXl66+/Fisi3Lp1S6tmvCG6xtTQoUM5evQoFRUVlJaWCn9pfRw6dAiotTp17NiRjh07NkpjbW1NYmIiAPHx8QwbNkycS0pKorq6mqtXr5Kbm8vzzz/fZJ01NTUsWbKEfv36MW3aNI1zdfMN1I5xR0dHcTwxMZF79+6Rm5tLdnY2L7/8MqampnTo0IGzZ89SU1OjkUdC4nHDIA3poEGDKC4uNljLIvFoGTJkCBMmTEClUmFhYaExQYaFhXHv3j0x0VlZWWk1ravVaiIiImjXrh1yuZy1a9cKzUVTTJ48uVntbdOmjQggqI+/vz9GRkZUVVUxf/58rQE9Y8eO5dtvv0WhUNC+fXtWrVolzk2fPp3w8HDMzMxYsGABc+fO5eOPP2bw4MHCbK4v/7x58/jxxx8pKirC3t6eWbNm4ePjI/xHX3/9dYPrh9qHW0M/sczMTPbs2aN1FQFnZ2dOnjyJUqlEJpMxZswYIUj/8ssvWl0xFi9eTGhoqNB+f/DBB1q1Mvn5+YSGhjbSTJubm+Pv76+1LWq1GpVKhaWlJX379m2UZuDAgcINZOLEiVpNkw0ZMmQIEydOFNfD29ubF198UavA2xTBwcHMnj0bMzMzrKysmizDxMQEhUKhEfRS50MKtYLB6tWrkcvlGBsb07t3b3JycujTpw++vr7MmjWLpKQkRo4cKczDDRk2bBgLFy4kJycHpVKJpaWl3nbNnj0bHx8fLCwseOGFFwwW8Lt06UJQUJAwb8+cOVNs9bxhwwZeeuklHB0dOXfuHMHBwdy5c4ejR48SGRkphKn09HTGjRsH6B4fAEFBQaxatQoPDw9qamqwsLBg69atOvuk4bjw9/fHwsICpVLJgAEDdLp+9e/fnzlz5hAYGEh1dTWtW7dm6dKlWFhY6O0LXWMKagU3Dw8PLCwseOmll7QKmPXp3LkzkydPpqSkROPerk9oaCjvvvsun332GSYmJkRERIhzzz//PG+88QY3b95k+fLlOl9G6/f3Tz/9hFqt5oUXXhBjcd68eYwdO5YZM2YwZ84c9u/fT48ePdiwYQMAAwYMwNXVlQkTJiCXy1m6dKmYr5ctW0ZISAjl5eXY29vrjBuQeMJ5ClwxZDUG2Df8/Pz497//jaWlJa1btxbH9S3ZI/H4EBkZibGxsd51BiUePenp6Xz++ec6g7zq8+abb+qNENaFtbU1GRkZLWne/zwpKSlkZWUxd+5cg9LHxMSQlZX1xGwiMmXKFLZs2dLkyhJPMqWlpXTo0IG7d+8yZcoUwsLCdArEfn5+LFy4UKeP91NNWeMlwyT+JIxbdv9VTH2wmu+2O//81RgM0pDOmjXrYbdDQuJ/ntatW3Px4kWmT5+u16cWaLYwevXqVWbNmiVZOf4LFApFk0tFPakUFhYybdq0p1oYhdolrC5dukRFRQVeXl5SYK6ExGOEQRpSCQkJCQmJxwEfHx+NADOo3dGsod+zoSxfvrzROrJTp05l0qRJLW6jLr777jvWrl2rcaxXr14GBzr+aUga0kdHSzWkAU2vbd0c2kZr3x3xYWKQQHr27FnCwsK4cuUKlZWV3L9/n/bt22tdDFpCQkJCQkLiCUYSSB8dLRRI701TPNBmtNneOGj2YWOQyX7FihWsX7+e2bNnc+DAAWJjY8nJyXnYbZOQkJCQkJD4sym786hb8HCRGyT6GMgDDiZqoUD6NGDQsk9Qu97d/fv3kcvlTJo0qck9tSUkJCQkJCQkJP4EnoKtQw16TWjfvj337t1j8ODBrFmzBlNTU8rKyh522yQkJCQkJCQkJJriKVj2ySAN6Zo1a6ipqWHp0qUYGxtz/fp1sVWahISEhISEhISExH9DkxrS+/fvs379etauXUvbtm0JDg7+M9olISEhISEhISFhALKnYOvQJgVSuVxOUVER9+7d09h/WUJCQkJCQkJC4jHgKTDZG+RDamFhweuvv46Dg4PGFnkN99mVkJCQkJCQkJCQaC4G+ZCampry6quvUlNTQ2lpqfhIPHmsXr0aFxcXlEolM2fO5M4d7ct7nD17Fh8fH1QqFa6ursJnOCYmhlGjRqFSqVCpVCxcuBCo3UPdysqKkpISUUZ4eDgDBw6ksLCwUfkxMTEMGjSICxcuiGPu7u5in28HBweUSiUqlQqlUklqqvZFemtqaggPD0ehUKBUKjl//rzWdLm5ufj4+ODk5MScOXPEwtr68oeEhGBra4u7u7vO/jS0fl9fX9FndnZ2BAUFNUqTnp7OsGHDNPa7X7NmDW5ubri6uhIeHk5Tywb/9ttv+Pn54enpiVKp5NtvvwVqd2pSqVRYW1trzTdw4EAWLFggvldVVTFq1CjeeustvfUBosy8vDzi4+PF8czMTMLDw5vM/99w5MgRtm3bpjdNTEwMK1asAGq30bWysuLmzZvifP0+GTx4MCqVCg8PD7y8vDTWWi4oKGiyP/Ly8vSOF0PTtISDBw/i5OSEk5MTBw8e1Jrm1KlTeHl58eKLL5KUlCSOFxYW6t1aeOfOnbi6ujJ//vxmt6vhuHhYODg4aJ1rHhWLFy/W6OPmcv/+fTw9PTXG3K1bt5g2bRpOTk5MmzaN27f/s17o1q1bUSgUODs7891334njWVlZKJVKFAqFQXOIxBPKUxBlb5BAGhwcrPUj8eQxevRoEhISiI+Pp2/fvjr3TV+0aBFhYWGo1WoSEhJwdXUV5yZMmIBarUatVrNmzRpx/LnnnuPIkdr9b6urq0lPT8fMzExnW8zNzfnkk090nt+xYwdqtZoNGzawcuVKrWmOHz9OdnY2ycnJhIWFsWzZMq3p1q5dS0BAAMnJyXTq1In9+/c3mX/ixIl8+umnOtvXnPp37dol+sza2honJyet6YYPHy62DT1z5gxnzpwhLi6OhIQEMjMzm1xuLSoqCldXV2JjY1m/fj3Lly8Haq+NWq3Wmc/Y2JiLFy9SXl4OwPfff6/32mnj2rVrJCQkiO+WlpaEhoY2q4zm4ujoyIwZM5qVp2vXrnz++edaz7Vr1w61Wk1cXBzz5s3jo48+Eue2b9+Oj4/Pf9Xeh8WtW7fYtGkTe/fuZd++fWzatElDWKmjR48eRERENBKITUxMMDU15aefftJa/q5du9i2bRvr1q1rdtsajgtDuX//frPz/NlUVVU9tLJ37tzJ//3f/2kc27ZtG7a2tiQnJ2Nraytexi5dukRiYiKJiYl8+umnLF++XPTfsmXLWLFiBcnJyWRnZ3P8+PGH1mYJif8GgwTSwsJCVq9ezfTp05k6dar4SDy+REVF4ezsTEBAAPPmzRN7n9vZ2dGqVa2nxiuvvMKNGze05i8sLKR79+5ArR9x//79m6zT3d2dw4cPA7XavqFDh4q6tDFu3DguXbrElStX9JZbUlJCp06dtJ47cuQInp6eyGQyXnnlFe7cuUNBQYFGmpqaGk6ePImzszMAXl5eQnDWl9/GxqbJvb0Nqb/hbzl58iTjxze9zZtMJuPevXtUVlaKv88++2yTeeq01MXFxZiamjZZTx329vYcO3YMgMTERNzc3MS5yMhIMYZAU5tdx7p16zh9+jQqlYro6GjS09OFdicyMpKQkBD8/PxwdHRk586dIt/27dtxd3fH3d2d6OhooFar5uLiwpIlS3B3d2f+/Pn88MMPTJ48GScnJ86dOwdoaj+/+eYbfHx88PT0JCAggD/++EPr75w0aRKHDx9ucl/6huMuOTkZe3t70T5fX1+8vLwaaVLriImJ4e233+bNN9/E2dmZTZs2iXP3798nNDQUNzc3AgMDxYvA3r17mTRpEh4eHsyaNYu7d+/qbWMdaWlpjB49mi5dutC5c2dGjx6toSWro1evXgwaNAgjo8ZTv6Ojo1ZN5tKlS8nLyyMoKIjo6GjKysoICQlh0qRJeHp6CuuFrj5pOC7qXzOAt956i/T0dKBWW71hwwZ8fHzIyMhArVbj7e2NSqVi6dKlBgup2sYUwObNm3FxcWHatGka86I2/Pz8WLlyJZMnT8bd3V2MucjISN577z0CAwNZtGgR165dw9/fH6VSib+/P7/99pso44cffsDX1xdnZ2eOHj1qUNsBbty4wbFjx/D29tY4XjffABp9f+TIEdzc3GjTpg29e/emT58+nDt3joKCAkpKSrC2tkYmk+Hp6SnmPomnC5lM9kA/jwKDBNK//e1v9OvXj7y8PIKDg7GwsMDS0vJht02ihWRlZXHo0CFiY2PZtGkTmZmZWtMdOHBAPGAb4u/vj4uLCzNnzmTPnj1UVFSIc4cOHRLm5wMHDojjffr0obCwkNu3bzcSaLRhZGTEX//6V51aWn9/f9zd3fHz82POnDla0+Tn52Nubi6+m5ubk5+fr5GmqKiITp06CeG4fhpD8jdk9+7d7N69u0X5U1NTsbW15ZlnngFqzdpLlizRmtba2pqRI0diZ2eHnZ0dY8aMaaQxaUhwcDDx8fHY29szY8YMnRrK/Px8DdcAqNV8Hzp0iIqKCv79739jZWWlt66GzJ8/n+HDh6NWqwkICGh0/tdff+Wzzz5j3759bN68mcrKSrKysoiJiWHv3r189dVX7Nu3j59//hmodTOYOnUqcXFx/Prrr8THx7N7924WLlyoVbM+bNgw9u7dS2xsLG5ubjq128bGxkycOFFDKK6jvLwclUqFi4sLoaGhwrUiNzeXzp07i8DObt26sX37dg4ePMj69et1uiZkZmaydu1a1Go1SUlJ4l7MyclhypQpJCYm0rFjR77++msAFAoFBw4cIC4ujn79+glNflxcnLjn6n/eeecdoPE4NDMza3IcN8TS0lJoSOuPjxUrVmBqasqOHTsICAjgk08+YdSoURw4cICdO3fy4YcfUlZWprNPmhoX9SkrK2PAgAHs27ePrl27cvjwYXbv3o1arcbIyMgg07+uMZWZmUlycjKxsbFERkaSlZXVZFl3795lz549vP/++7z77rvi+Pnz59myZQvr1q0jLCwMT09P4uPjUSqVGmPh2rVrfPnll2zdupX3339fYx6tT8P7cdWqVSxYsKDRi8PNmzfFS6apqalwUdB1/Vsyv0k8oTwFJnuDgppu3bqFj48PO3fuZMSIEYwYMYI33njjYbdNooWcPn2a8ePH0759e6DWt6ohUVFRyOVyPDw8tJYRHByMh4cHaWlpJCQkkJiYyBdffAHUCi5Lly7Vmk+hUJCYmMg///lPDS2ILtzd3YmKiiI3N7fRuR07dmBiYsLVq1cJCAhgxIgRdOjQQSONNn8oQ97u6tK0JP/rr7/e4voTEhI0zL6WlpY6X+5ycnK4fPmy8AMNDAzk1KlT2NjY6Cw/MTERLy8vAgMDycjIYOHChSQkJDR6sJmZmQnXgDoGDRpEXl4eCQkJjB07VmcdLWXs2LG0adMGExMTTExMuHnzJj/99BPjx48XwZIKhYLTp0/j4OBAr169GDhwIAD9+/fH1tYWmUzGwIEDuXbtWqPyb9y4wdy5c/n999+5d+8evXr10tmWqVOn4unpSWBgoMbxOpM9QEZGBosWLSIhIYHff/+drl27inRVVVWsWLGCCxcuYGRkRHZ2ttZ6/vKXv4h8CoVC/N5evXoxePBgAIYMGSJ+z8WLF/n4448pLi6mtLQUOzs7ADw8PHTeq9Dy+6A+JiYmQruvbXzUkZaWxjfffCPcHioqKrh+/TqmpqYG9Yk+5HK5sGScOHGCrKwsoSUsLy+nW7duTZaha0xVV1fj6OhIu3btAHj11VebLKvupdrGxoaSkhLhc+/g4CDKycjIED72KpWKDz/8UOR3dXXFyMiIvn370rt3b65cuSKue33q9/fRo0cxMTHhpZdeEprjptB1/R/EuJCQ+LMwSCCt0yyZmppy7NgxTE1NdZp6JR4P9E06Bw8e5NixY0RHR4t0ISEh/Pzzz5iamoqJ8bnnnsPX15fXXnsNW1tbioqKmqzXzc1NmOzqC0H/+Mc/2Lt3L4BGEEqrVq0IDAzU+fCra0e3bt24fPkymZmZGuWYm5trjMUbN240MlN37dqVO3fuUFVVRatWrTTSGJJfH83JX1RURGZmJps3bzao7JSUFKysrIQQPmbMGM6ePatXIN2/f7/QDFpbW1NRUUFRUZFBD3KofdCuWbOGnTt3api05XI51dXV4rsuTY8+6i8bJ5fLqaqq0htgUT+9kZGR+C6TybSabsPDwwkICMDR0ZH09HQNE3lDOnXqhLu7O7t27dKZxtramqKiIgoLC2nXrp0IhAOIjo7m2WefRa1WU11dzcsvv6y1jIb3Yd33hn1R15+LFy9my5YtDBo0iJiYGOEzHBcXp9W83KdPHzZu3Ii5ubmGf3F+fj4jRozQ+du0UVFRQdu2bQ1Ku3HjRvr166dxLDIy0qA+0TeW2rZti1wuB2qFLC8vr2YHUj3IoB1d16/uZb+pPLry6+PMmTN88803HD9+nIqKCkpKSvjb3/7G2rVr6datGwUFBZiamlJQUICJiQnQeB7Kz8/H1NT0v57fJJ4gnoIXDYNM9m+//TbFxcUsWrSIzz77jNDQUEJCQh522yRaiI2NDSkpKZSXl1NSUqLhu3T8+HH+/ve/ExUVpTGpRkREoFarhWB47NgxMbHn5ORgZGSk04+zPj179mTu3Ln4+vpqHJ8yZYoI6mkYLOPl5cWJEyd0RsjevHmTvLw8evbs2agcBwcHYmNjqamp4ezZs3Ts2LHRhCuTyRg5cqQwix48eFBojQ3Jr4/m5E9KSmLcuHEGP/R79uzJqVOnqKqqorKyklOnTgmT/bp160hJSWmUp0ePHpw4cQKAy5cvU1FRIR5ahuDt7U1QUJDQTNZhYWEhTOnnz59v5D8K0KFDh2avvmFjY0Nqaip3796lrKyM1NRUhg8f3qwy6iguLhZjKzY2tsn0AQEB7NmzR2dgyuXLl7l//z5dunShb9++GlrZ4uJiunfvjpGREWq1Wqdv4/fff8+tW7coLy8nNTWVoUOH6m1TaWkp3bt3p7KyUsM87eHhIcZ9/c/GjRuBWt/wtLQ0bt++ze3bt0lLSxPaVUPJzs5mwIABTaazs7Pjyy+/FPND3bjQ1ScNx4WFhQUXLlygurqa69evC9/Mhtja2vL111+LFRFu3bqlVTPeEF1jaujQoRw9epSKigpKS0uFv7Q+Dh06BNRanTp27EjHjh0bpbG2tiYxMRGA+Ph4hg0bJs4lJSVRXV3N1atXyc3N5fnnn2+yzvnz53P8+HG++eYbPvroI0aNGsXatWuB/8w3UDvGHR0dxfHExETu3btHbm4u2dnZvPzyy5iamtKhQwfOnj1LTU2NRh6JpwyZ7MF+HgF6NaQVFRXs3r2bq1evkp+fj7e3tzDbSjy+DBkyhAkTJqBSqbCwsNCYIMPCwrh3755YQ9bKykqraV2tVhMREUG7du2Qy+WsXbtWaC6aYvLkyc1qb5s2bUQAQX38/f0xMjKiqqqK+fPnaw3oGTt2LN9++y0KhYL27duzatUqcW769OmEh4djZmbGggULmDt3Lh9//DGDBw8WZnN9+efNm8ePP/5IUVER9vb2zJo1Cx8fH+E/+vrrrxtcP9Q+3Br6bWZmZrJnzx6tqwg4Oztz8uRJlEolMpmMMWPGCEH6l19+0eqKsXjxYkJDQ4X2+4MPPtCqlcnPzyc0NLSRZtrc3Bx/f3+tbVGr1ahUKiwtLenbt2+jNAMHDhRuIBMnTtRqmmzIkCFDmDhxorge3t7evPjii1oF3qYIDg5m9uzZmJmZYWVl1WQZJiYmKBQKjaCXOh9SqNW0rV69GrlcjrGxMb179yYnJ4c+ffrg6+vLrFmzSEpKYuTIkRrrM9dn2LBhLFy4kJycHJRKJZaWlnrbNXv2bHx8fLCwsOCFF14wWMDv0qULQUFBwrw9c+ZMunTpAsCGDRt46aWXcHR05Ny5cwQHB3Pnzh2OHj1KZGSkEKbS09MZN24coHt8AAQFBbFq1So8PDyoqanBwsKCrVu36uyThuPC398fCwsLlEolAwYMYMiQIVp/U//+/ZkzZw6BgYFUV1fTunVrli5dioWFhd6+0DWmoFZw8/DwwMLCgpdeekmrgFmfzp07M3nyZEpKSjTu7fqEhoby7rvv8tlnn2FiYkJERIQ49/zzz/PGG29w8+ZNli9frvNlVF9/12fGjBnMmTOH/fv306NHDzZs2ADAgAEDcHV1ZcKECcjlcpYuXSrm62XLlhESEkJ5eTn29vY64wYkJB41sho99o05c+bQqlUrhg8fzvHjx+nZs+dDX8ZF4sETGRmJsbGx3nUGJR496enpfP755zqDvOrz5ptv6o0Q1oW1tTUZGRktad7/PCkpKWRlZTF37lyD0sfExJCVlaXT3/pxY8qUKWzZsqXJlSWeZEpLS+nQoQN3795lypQphIWF6RSI/fz8WLhw4f9mAO8fjX36nyrkBnkrGsgD1iZ2NW86jRaqZqseaDNabdC9TODDQu9VuXz5sjAbeXt7P7Zr8ElIPA20bt2aixcvMn369CY1Jc0VRq9evcqsWbMM9iWVaIxCoWhyqagnlcLCQqZNm/ZUC6NQu4TVpUuXqKiowMvLS6cwKiHxxKFlKbcnDb0aUi8vL40dPxp+l5CQkJCQ+DPx8fHRCDCD2h3NGvo9G8ry5csbrSM7depUJk2a1OI26uK7774T/qB19OrVy+BAxz8NSUPaDB4TDelcrwfajFbr/3xZT69AOnjwYBH4UlNTQ0VFBe3ataOmpgaZTKZ1MWgJCQkJCQmJJxhJIG0Gj4lAOm/iA21Gq49iHmh5BtWp7+S//vWvP6sdEhISEhISEo8BJVMfjHZY1sqwQFiDkD84k3TrLtqDEFtU1pIPHlhZALIWCqRPA82+wl999dXDaIeEhISEhISEhERLeAqWfWq2QLpnz56H0Q4JCQkJCQkJCYmW8L8okD7IXTAkJCQkJCQkJCQkmu3Z+8knnzyMdkhISEhISEhISLSEp2DZJ70C6fbt2/VmrtvtR0JCQkJCQkJC4hHxFOxlr1cgrdu27tdffyUzM1NsVXj06NEW7zctISEhISEhISEhUR+9Ot7g4GCCg4MpKioiJiaGxYsXs3jxYg4cOMCNGzf+rDZKPEBWr16Ni4sLSqWSmTNncufOHa3pzp49i4+PDyqVCldXVyIjI4Ha7RBHjRqFSqVCpVKxcOFCoHYPdSsrK0pKSkQZ4eHhDBw4kMLCwkblx8TEMGjQIC5cuCCOubu7i32+HRwcUCqVqFQqlEolqampWttZU1NDeHg4CoUCpVLJ+fPntabLzc3Fx8cHJycn5syZIxbW1pc/JCQEW1tb3N3ddfanofX7+vqKPrOzsyMoKKhRmvT0dIYNG6ax3/2aNWtwc3PD1dWV8PDwJn24f/vtN/z8/PD09ESpVPLtt98CtTs1qVQqrK2tteYbOHAgCxYsEN+rqqoYNWoUb731lt76AFFmXl6e2NkNIDMzk/Dw8Cbz/zccOXKEbdu26U0TExPDihUrgNptdK2srLh586Y4X79PBg8ejEqlwsPDAy8vL421lgsKCprsj7y8PL3jxdA0LeHgwYM4OTnh5OSkcwOTU6dO4eXlxYsvvkhSUpI4XlhYqHdr4Z07d+Lq6sr8+fOb3a6G4+Jh4eDgoHWueVQsXrxYo4+bw507d3jnnXdwcXHB1dVVbPd769Ytpk2bhpOTE9OmTeP27dsiz9atW1EoFDg7O/Pdd9+J41lZWSiVShQKhUFziMQTyv9KUNNvv/1GmzZtxPc2bdpw7dq1h9YoiYfH6NGjSUhIID4+nr59++rcN33RokWEhYWhVqtJSEjA1dVVnJswYQJqtRq1Ws2aNWvE8eeee44jR44AUF1dTXp6OmZmZjrbYm5urtcneceOHajVajZs2MDKlSu1pjl+/DjZ2dkkJycTFhbGsmXLtKZbu3YtAQEBJCcn06lTJ/bv399k/okTJ/Lpp5/qbF9z6t+1a5foM2tra5ycnLSmGz58uNg29MyZM5w5c4a4uDgSEhLIzMzkxx9/1NueqKgoXF1diY2NZf369SxfvhyovTZqte69iY2Njbl48SLl5eUAfP/993qvnTauXbtGQkKC+G5paUloaGizymgujo6OzJgxo1l5unbtyueff671XLt27VCr1cTFxTFv3jw++ugjcW779u2P7fbJt27dYtOmTezdu5d9+/axadMmDWGljh49ehAREdFIIDYxMcHU1JSffvpJa/m7du1i27ZtrFu3rtltazguDOX+/fvNzvNnU1VV9VDKXblyJWPGjCEpKQm1Ws3//d//AbBt2zZsbW1JTk7G1tZWvIxdunSJxMREEhMT+fTTT1m+fLnov2XLlrFixQqSk5PJzs7m+PHjD6XNEo+Y/xWBVKVS4e3tTWRkJJGRkfj4+ODl9WC3qZJ4sERFReHs7ExAQADz5s0Te5/b2dnRqlWtp8Yrr7yiU9NdWFhI9+7dAZDL5fTv37/JOt3d3Tl8+DBQq+0bOnSoqEsb48aN49KlS1y5ckVvuSUlJXTq1EnruSNHjuDp6YlMJuOVV17hzp07FBQUaKSpqanh5MmTODs7A7Vb4NYJzvry29gCpECdAAAgAElEQVTYNLm3tyH1N/wtJ0+eZPz48XrLBZDJZNy7d4/Kykrx99lnn20yT52Wuri4GFNT0ybrqcPe3p5jx44BkJiYiJubmzgXGRkpxhBoarPrWLduHadPn0alUhEdHU16errQKEZGRhISEoKfnx+Ojo7s3LlT5Nu+fTvu7u64u7sTHR0N1GrVXFxcWLJkCe7u7syfP58ffviByZMn4+TkxLlz5wBN7ec333yDj48Pnp6eBAQE8Mcff2j9nZMmTeLw4cNN7kvfcNwlJydjb28v2ufr64uXl1cjTWodMTExvP3227z55ps4OzuzadMmce7+/fuEhobi5uZGYGCgeBHYu3cvkyZNwsPDg1mzZnH37l29bawjLS2N0aNH06VLFzp37szo0aM1tGR19OrVi0GDBmGkJQDC0dFRqyZz6dKl5OXlERQURHR0NGVlZYSEhDBp0iQ8PT2F9UJXnzQcF/WvGcBbb71Feno6UKut3rBhAz4+PmRkZKBWq/H29kalUrF06VKDhVRtYwpg8+bNuLi4MG3aNI15URt+fn6sXLmSyZMn4+7uLsZcZGQk7733HoGBgSxatIhr167h7++PUqnE39+f3377TZTxww8/4Ovri7OzM0ePHjWo7SUlJZw6dQpvb2+gVgFUNw7r5htAo++PHDmCm5sbbdq0oXfv3vTp04dz585RUFBASUkJ1tbWyGQyPD09xdwnIfG4YZBA+vbbbxMREUGnTp3o3LkzERERBpnyJB4NWVlZHDp0iNjYWDZt2kRmZqbWdAcOHBAP2Ib4+/vj4uLCzJkz2bNnDxUVFeLcoUOHhPn5wIED4nifPn0oLCzk9u3bjQQabRgZGfHXv/5Vp5bW398fd3d3/Pz8mDNnjtY0+fn5mJv/Z2cLc3Nz8vPzNdIUFRXRqVMnIRzXT2NI/obs3r2b3bt3tyh/amoqtra2PPPMM0CtWXvJkiVa01pbWzNy5Ejs7Oyws7NjzJgxQlOii+DgYOLj47G3t2fGjBk6NZT5+fkargFQq/k+dOgQFRUV/Pvf/8bKykpvXQ2ZP38+w4cPR61WExAQ0Oj8r7/+ymeffca+ffvYvHkzlZWVZGVlERMTw969e/nqq6/Yt28fP//8M1DrZjB16lTi4uL49ddfiY+PZ/fu3SxcuFCrZn3YsGHs3buX2NhY3NzcdGq3jY2NmThxooZQXEd5eTkqlQoXFxdCQ0OFa0Vubi6dO3cWlqJu3bqxfft2Dh48yPr163W6JmRmZrJ27VrUajVJSUniXszJyWHKlCkkJibSsWNHvv76awAUCgUHDhwgLi6Ofv36CU1+XFycuOfqf9555x2g8Tg0MzNrchw3xNLSUmhI64+PFStWYGpqyo4dOwgICOCTTz5h1KhRHDhwgJ07d/Lhhx9SVlams0+aGhf1KSsrY8CAAezbt4+uXbty+PBhdu/ejVqtxsjIyCDTv64xlZmZSXJyMrGxsURGRpKVldVkWXfv3mXPnj28//77vPvuu+L4+fPn2bJlC+vWrSMsLAxPT0/i4+NRKpUaY+HatWt8+eWXbN26lffff19jHq1P/f7Ozc3FxMSEkJAQPD09WbJkCWVlZQDcvHlTvGSampoKFwVd178l85vEE4qR0YP9PAIMXvZJLpdjZGSETCbT+nYt8fhw+vRpxo8fT/v27QFEMFp9oqKikMvleHh4aC0jODgYDw8P0tLSSEhIIDExkS+++AKoFVyWLl2qNZ9CoSAxMZF//vOfGloQXbi7uxMVFUVubuO9k3fs2IGJiQlXr14lICCAESNG0KFDB4002vyhZAaYG+rStCT/66+/3uL6ExISNMy+lpaWWFpaak2bk5PD5cuXhR9oYGAgp06dwsbGRmf5iYmJeHl5ERgYSEZGBgsXLiQhIaHRPWtmZiZcA+oYNGgQeXl5JCQkMHbsWJ11tJSxY8fSpk0bTExMMDEx4ebNm/z000+MHz8eY+ParfwUCgWnT5/GwcGBXr16MXDgQAD69++Pra0tMpmMgQMHanUZunHjBnPnzuX333/n3r179OrVS2dbpk6diqenJ4GBgRrH60z2ABkZGSxatIiEhAR+//13unbtKtJVVVWxYsUKLly4gJGREdnZ2Vrr+ctf/iLyKRQK8Xt79erF4MGDARgyZIj4PRcvXuTjjz+muLiY0tJS7OzsAPDw8NB5r0LL74P6mJiYCO2+tvFRR1paGt98841we6ioqOD69euYmpoa1Cf6kMvlwpJx4sQJsrKyhKawvLycbt26NVmGrjFVXV2No6Mj7dq1A+DVV19tsqy6l2obGxtKSkqEz72Dg4MoJyMjQ/jYq1QqPvzwQ5Hf1dUVIyMj+vbtS+/evbly5Yq47vWp399VVVX8/PPPvPfee1hZWREeHs62bdt0vpSD7uv/IMaFxBPCU3BdDRJId+zYwb59+3BycqKmpoYFCxbw2muv4efn97DbJ9FC9E06Bw8e5NixY0RHR4t0ISEh/Pzzz5iamoqJ8bnnnsPX15fXXnsNW1tbioqKmqzXzc1NmOzqC0H/+Mc/2Lt3L4BGEEqrVq0IDAzU+fCra0e3bt24fPkymZmZGuWYm5truB3cuHGjkZm6a9eu3Llzh6qqKlq1aqWRxpD8+mhO/qKiIjIzM9m8ebNBZaekpGBlZSWE8DFjxnD27Fm9Aun+/fuFZtDa2pqKigqKiooMepBD7YN2zZo17Ny5U8OkLZfLqa6uFt91aXr0Ud8PXS6XU1VVpTfAon56IyMj8V0mk2k13YaHhxMQEICjoyPp6ekaJvKGdOrUCXd3d3bt2qUzjbW1NUVFRRQWFtKuXTsRCAcQHR3Ns88+i1qtprq6mpdffllrGQ3vw7rvDfuirj8XL17Mli1bGDRoEDExMcJnOC4uTqt5uU+fPmzcuBFzc3MN/+L8/HxGjBih87dpo6KigrZt2xqUduPGjfTr10/jWGRkpEF9om8stW3bFrm8dv/zmpoavLy8mh1I9SCDdnRdv7qX/aby6MqvD3Nzc8zNzYWFwsXFRcyZ3bp1o6CgAFNTUwoKCjAxMRF56s9D+fn5mJqa/tfzm4TEn4lBqs79+/ezd+9e3nnnHWbPns1XX30lhAKJxw8bGxtSUlIoLy+npKREw3fp+PHj/P3vfycqKkpjUo2IiECtVgvB8NixY2Jiz8nJwcjISKcfZ3169uzJ3Llz8fX11Tg+ZcoUEdTTMFjGy8uLEydO6IyQvXnzJnl5efTs2bNROQ4ODsTGxlJTU8PZs2fp2LFjowlXJpMxcuRIYRY9ePCg0Bobkl8fzcmflJTEuHHjDH7o9+zZk1OnTlFVVUVlZSWnTp0SJvt169aRkpLSKE+PHj04ceIEAJcvX6aiokI8tAzB29uboKAgoZmsw8LCQpjSz58/38h/FKBDhw5iqThDsbGxITU1lbt371JWVkZqamqLl5QrLi4WYys2NrbJ9AEBAezZs0dnYMrly5e5f/8+Xbp0oW/fvhpa2eLiYrp3746RkRFqtVqnb+P333/PrVu3KC8vJzU1laFDh+ptU2lpKd27d6eyslLDPO3h4SHGff3Pxo0bgVrf8LS0NG7fvs3t27dJS0sT2lVDyc7OZsCAAU2ms7Oz48svvxTzQ9240NUnDceFhYUFFy5coLq6muvXrwvfzIbY2try9ddfixURbt26ZVAwra4xNXToUI4ePUpFRQWlpaXCX1ofhw4dAmqtTh07dqRjx46N0lhbW5OYmAhAfHw8w4YNE+eSkpKorq7m6tWr5Obm8vzzzzdZZ/fu3TE3Nxe+9SdOnBD3fd18A7Vj3NHRURxPTEzk3r175Obmkp2dzcsvv4ypqSkdOnTg7Nmz1NTUaOSReMp4CoKammWy1/a/xOPHkCFDmDBhAiqVCgsLC40JMiwsjHv37olNDaysrLSa1tVqNREREbRr1w65XM7atWsNvu6TJ09uVnvbtGkjAgjq4+/vj5GREVVVVcyfP19rQM/YsWP59ttvUSgUtG/fnlWrVolz06dPJzw8HDMzMxYsWMDcuXP5+OOPGTx4sDCb68s/b948fvzxR4qKirC3t2fWrFn4+PgI/9HXX3/d4Pqh9uHW0G8zMzOTPXv2aF1FwNnZmZMnT6JUKpHJZIwZM0YI0r/88otWV4zFixcTGhoqtN8ffPCBVq1Mfn4+oaGhjTTT5ubm+Pv7a22LWq1GpVJhaWlJ3759G6UZOHCgcAOZOHGiVtNkQ4YMGcLEiRPF9fD29ubFF1/UKvA2RXBwMLNnz8bMzAwrK6smyzAxMUGhUGgEvdT5kEKtpm316tXI5XKMjY3p3bs3OTk59OnTB19fX2bNmkVSUhIjR44U5uGGDBs2jIULF5KTk4NSqcTS0lJvu2bPno2Pjw8WFha88MILBgv4Xbp0ISgoSJi3Z86cSZcuXQDYsGEDL730Eo6Ojpw7d47g4GDu3LnD0aNHiYyMFMJUeno648aNA3SPD4CgoCBWrVqFh4cHNTU1WFhYsHXrVp190nBc+Pv7Y2FhgVKpZMCAAQwZMkTrb+rfvz9z5swhMDCQ6upqWrduzdKlS7GwsNDbF7rGFNQKbh4eHlhYWPDSSy9pFTDr07lzZyZPnkxJSYnGvV2f0NBQ3n33XT777DNMTEyIiIgQ555//nneeOMNbt68yfLly3W+jDbs7/fee4+//e1vVFZW0rt3b1HmjBkzmDNnDvv376dHjx5s2LABgAEDBuDq6sqECROQy+UsXbpUzNfLli0jJCSE8vJy7O3tdcYNSEg8amQ1Btg36hzVFQoFUBuY4eXl1aSDusTjQWRkJMbGxnrXGZR49KSnp/P555/rDPKqz5tvvqk3QlgX1tbWYk1DieaRkpJCVlYWc+fONSh9TEwMWVlZOv2tHzemTJnCli1bmlxZ4kmmtLSUDh06cPfuXaZMmUJYWJhOgdjPz4+FCxfq9PF+mimZ0Dx3D13IWj1A5ZX8wcWutO6i/QWyRWUt+eCBlQUg6z+s6UT/H3tnHlZj+j/+VxshIaYiyZB9mso2GqGJ06LlCM2MJVuDz5CxJztJfSQMRZiFsS9pUQchQoPGrmaYsYYQM6GivX5/dJ3n16lz6vDB4Pu8rsul8zz3/tzP/bzv9/t937cSiuYNe63l0PavvOjzTaOWhnTkyJF06dKF8+fPU1paSlBQkDDjFBEReT3o6Ohw7do1Ro8eXaVPLfDSwuidO3eYMGGC2r6kIpWRSCTVbhX1vpKZmcnIkSM/aGEUyrawun79Ovn5+Xh4eKgURkVE3jc0PoDF5mppSKFs37y///5bwVeqSZMmb6xgIiIiIiIiFfH09FRYYAZlJ5pV9HtWl4ULF1baR3bYsGEMGDDglcuoihMnThASEqJwrWnTpmovdHxbiBrSl0jrHdGQFi8Y8VrLobVg42tNTx3UEkg3b95MWFgYjRo1Ulg5/TaOgxMRERERERF5e4gC6Uuk9a4IpAtHvtZyaM3f8FrTUwe1TPabNm3iwIEDCvvwiYiIiIiIiHx4/Hnx9Wyer/mObo2pV1fntaXVMrv67RBfhldusg9gH1K1phzGxsbVrkYUEREREREREREReRWq1JBu2FCmsjU1NcXLyws7OzuFDZ3lWweJiIiIiIiIiIj8S3wAGtIqBVL5HnhNmjShSZMmFBYWUlhY+FYKJiIiIiIiIiIiogYfwCr7KgVSHx8fAPbv34+zs7PCvf3797+5UomIiIiIiIiIiPyfQS2RuvzZ41VdExERERERERERect86EeHHjt2jOPHj5ORkUFAQIBwPScnRzw+VERERERERETkXeAD8CGtUkNqZGTEJ598Qs2aNenQoYPwz97e/pWOLRT591myZAlOTk64ubkxfvx4srKylIa7ePEinp6eSKVSnJ2dCQ0NBcqOQ+zWrRtSqRSpVIqvry9Qdoa6paUlOTk5QhoBAQG0adOGzMzMSulHRkbStm1brl69KlxzdXUVzvm2t7fHzc0NqVSKm5sbhw8fVlrO0tJSAgICkEgkuLm58fvvvysNd/fuXTw9PXFwcGDSpEnCxtpVxZ85cyY2Nja4urqqbE918x88eLDQZra2towbN65SmOTkZDp16qRw3n1wcDAuLi44OzsTEBBAddsG379/Hy8vL/r164ebmxvHjh0Dyk5qkkqlWFtbK43Xpk0bpk+fLvwuKiqiW7dujB07tsr8ACHNe/fuKexNnJKSojCRfRMkJCRUa62JjIzE398fKDtG19LSkn/++Ue4X75N2rVrh1Qqxd3dHQ8PD4UN0x89elRte9y7d6/K/qJumFchKioKBwcHHBwciIqKUhrmzJkzeHh40L59ew4cOCBcz8zMrPJo4U2bNuHs7MzUqVNfulwV+8Wbwt7eXulY82/h5+en0MYvw8aNG3FxccHV1ZUpU6aQn58PwNOnTxk5ciQODg6MHDmSZ8+eCXHWrVuHRCLB0dGREydOCNdTU1Nxc3NDIpGoNYaIiPxbVCmQtm3bFg8PDw4ePIizszOWlpZ4eHjg4ODwwR8x96HSvXt34uLiiI2NpXnz5irPTZ8xYwaLFi0iJiaGuLg4BR/ivn37EhMTQ0xMDMHBwcL1Zs2akZCQAEBJSQnJyckYGRmpLIuxsTFr165Vef+XX34hJiaGlStXsnjxYqVhjh8/zu3btzl48CCLFi1iwYIFSsOFhIQwYsQIDh48iL6+PhEREdXG79+/Pz/++KPK8r1M/tu2bRPazNraGgcHB6XhOnfuLBwbev78ec6fP8/evXuJi4sjJSWF3377rcryhIeH4+zsTHR0NCtWrGDhwoVA2bOJiYlRGa927dpcu3aNvLw8AH799dcqn50y0tPTiYuLE35bWFgwZ86cl0rjZenduzdjxox5qTgNGjTg559/VnpPV1eXmJgY9u7dy5QpU1i+fLlwb8OGDXh6ev5P5X1TPH36lLCwMHbt2sXu3bsJCwtTEFbkNG7cmKCgoEoCsYGBAYaGhpw7d05p+tu2bWP9+vUsW7bspctWsV+oS/lTAd9VioqKXnuaGRkZbNq0iT179hAXF0dxcTEymQwoc5WzsbHh4MGD2NjYCJOx69evI5PJkMlk/PjjjyxcuFBovwULFuDv78/Bgwe5ffs2x48ff+1lFnkHeMsm++PHj+Po6IhEIqlSKXD58mXatWun1uRMLR/SEydOIJVK+eabbwC4cuUK//nPf9SJKvIvER4ejqOjIyNGjGDKlCmCRtvW1hZt7TJPDSsrKx4+fKg0fmZmJh999BEAWlpamJubV5unq6ursNgtOTmZjh07Cnkpw87OjuvXr3Pz5s0q083JyUFfX1/pvYSEBPr164eGhgZWVlZkZWXx6NEjhTClpaWcPn0aR0dHADw8PATBuar4Xbp0qXbipU7+Fety+vRp+vTpU2W6ABoaGhQUFFBYWCj836hRo2rjyLXU2dnZGBoaVpuPnJ49e5KYmAiATCbDxcVFuBcaGqpgFSmvzZazbNkyzp49i1QqZePGjSQnJwsaxdDQUGbOnImXlxe9e/dm06ZNQrwNGzbg6uqKq6srGzduBMq0ak5OTsyePRtXV1emTp3KyZMn+frrr3FwcODy5cuAovbzyJEjeHp60q9fP0aMGMHff/+ttJ4DBgxg//791Z5LX7HfHTx4kJ49ewrlGzx4MB4eHpU0qXIiIyP59ttv8fb2xtHRkbCwMOFecXExc+bMwcXFhVGjRgkTgV27djFgwADc3d2ZMGECubm5VZZRTlJSEt27d6d+/frUq1eP7t27K2jJ5DRt2pS2bdsqnLgnp3fv3ko1mfPmzePevXuMGzeOjRs38uLFC2bOnMmAAQPo16+fYL1Q1SYV+0X5ZwYwduxYkpOTgTJt9cqVK/H09OTChQvExMQwcOBApFIp8+bNU1tIVdanAFavXo2TkxMjR45UGBeV4eXlxeLFi/n6669xdXUV+lxoaChz585l1KhRzJgxg/T0dIYPH46bmxvDhw/n/v37QhonT55k8ODBODo6cvToUbXKDmX9Iy8vj6KiIvLy8oT3WD7eAAptn5CQgIuLCzVq1MDU1BQzMzMuX77Mo0ePyMnJwdraGg0NDfr16yeMfSIir0pxcTH+/v78+OOPyGQy4uLiuH79utJwISEh2NraqpWuWgJpWFgYERERwuDcrl070tPTX6L4Im+T1NRU9u3bR3R0NGFhYaSkpCgNt2fPHuEDW5Hhw4fj5OTE+PHj2bFjh2AyAti3b59gft6zZ49w3czMjMzMTJ49e1ZJoFGGpqYm33zzjUot7fDhw3F1dcXLy4tJkyYpDZORkYGxsbHw29jYmIwMxVNGnjx5gr6+viAclw+jTvyKbN++ne3bt79S/MOHD2NjY4Oenh5QZtaePXu20rDW1tZ89tln2NraYmtrS48ePWjZsmWVZfPx8SE2NpaePXsyZswYlRrKjIwMBdcAKNN879u3j/z8fP78808sLS2rzKsiU6dOpXPnzsTExDBixIhK92/dusVPP/3E7t27Wb16NYWFhaSmphIZGcmuXbvYuXMnu3fv5o8//gDK3AyGDRvG3r17uXXrFrGxsWzfvh1fX1+lmvVOnTqxa9cuoqOjcXFxUandrl27Nv3791cQiuXk5eUhlUpxcnJizpw5gmvF3bt3qVevnrAPc8OGDdmwYQNRUVGsWLFCpWtCSkoKISEhxMTEcODAAeFdTEtLY8iQIchkMurWrUt8fDwAEomEPXv2sHfvXlq0aCFo8vfu3Su8c+X/fffdd0DlfmhkZFRtP66IhYWFoCEt3z/8/f0xNDTkl19+YcSIEaxdu5Zu3bqxZ88eNm3axNKlS3nx4oXKNqmuX5TnxYsXtGrVit27d9OgQQP279/P9u3biYmJQVNTUy3Tv6o+lZKSwsGDB4mOjiY0NJTU1NRq08rNzWXHjh3Mnz+fWbNmCdd///131qxZw7Jly1i0aBH9+vUjNjYWNzc3hb6Qnp7Oli1bWLduHfPnz1cYR8tTvr2NjIwYNWoUX3zxBba2tujp6Qkf9H/++UcQTg0NDQUXBVXP/1XGN5H3FE3N1/uvCi5fvoyZmRmmpqbUqFEDFxcXpROdzZs34+joSMOGDdWqglpHh2ppaYknNb1HnD17lj59+lCrVi2gzLeqIuHh4WhpaeHu7q40DR8fH9zd3UlKSiIuLg6ZTMbmzZuBMsFl3rx5SuNJJBJkMhmXLl1S0IKowtXVlfDwcO7evVvp3i+//IKBgQF37txhxIgRdO3alTp16iiEUeYPpaGGuUEe5lXiDxo06JXzj4uLUzD7WlhYYGFhoTRsWloaN27cEPxAR40axZkzZ+jSpYvK9GUyGR4eHowaNYoLFy7g6+tLXFxcJY2YkZGR4Bogp23btty7d4+4uDh69eqlMo9XpVevXtSoUQMDAwMMDAz4559/OHfuHH369KF27bKzpSUSCWfPnsXe3p6mTZvSpk0bAMzNzbGxsUFDQ4M2bdoonRA/fPiQyZMn8/jxYwoKCmjatKnKsgwbNox+/foxatQohetykz3AhQsXmDFjBnFxcTx+/Fjh6OSioiL8/f25evUqmpqa3L59W2k+n3/+uRBPIpEI9W3atCnt2rUDoEOHDkJ9rl27xvfff092djbPnz8XBBF3d3eV7yq8+ntQHgMDA0G7r6x/yElKSuLIkSOC20N+fj4PHjzA0NBQrTapCi0tLcGScerUKVJTUxk4cCBQNllQ58Omqk+VlJTQu3dvdHV1Afjiiy+qTUs+qe7SpQs5OTmCz729vb2QzoULFwQfe6lUytKlS4X4zs7OaGpq0rx5c0xNTbl586bw3MtTvr2fPXtGQkICCQkJ1K1bl4kTJxITE4NUKlVZTlXP/3X0C5H3hLf4XJVNgOQWhPJhDh8+zC+//KJSKVYRtQTSVq1aERsbS3FxMbdv32bz5s0qF0eIvBtUNehERUWRmJjIxo0bhXAzZ87kjz/+wNDQUBgYmzVrxuDBg/nyyy+xsbHhyZPqz+x1cXERTHblhaCtW7eya9cuQHHLMG1tbUaNGqXy4ycvR8OGDblx4wYpKSkK6RgbGyu4HTx8+LCSmbpBgwZkZWVRVFSEtra2Qhh14lfFy8R/8uQJKSkprF69Wq20Dx06hKWlpSCE9+jRg4sXL1YpkEZERAiaQWtra/Lz83ny5InaM1R7e3uCg4PZtGmTgklbS0uLkpIS4bcqTU9VlD/lTUtLi6KioioXWJQPr6mpKfzW0NBQaroNCAhgxIgR9O7dm+TkZAUTeUX09fVxdXVl27ZtKsNYW1vz5MkTMjMz0dXVFRbCQdmik0aNGhETE0NJSQmffvqp0jQqvofy3xXbQt6efn5+rFmzhrZt2xIZGSn4DO/du1epednMzIxVq1ZhbGys4F+ckZFB165dVdZNGfn5+dSsWVOtsKtWraJFixYK10JDQ9Vqk6r6Us2aNYUdXEpLS/Hw8HjphVSvc9GOqucnn+xXF0dV/Ko4efIkTZs2xcDAAAAHBwcuXLiAVCqlYcOGPHr0CENDQx49eiSEqTgOZWRkYGho+D+PbyL/d9m5cyc7d+4Ufn/11Vd89dVXgHoT4MWLFzNt2rSX2pFJLZP93LlzuX79OjVq1GDKlCno6empNDOK/Pt06dKFQ4cOkZeXR05OjoLv0vHjx/nhhx8IDw9XGFSDgoKIiYkRBMPExESh06WlpaGpqanSj7M8TZo0YfLkyQwePFjh+pAhQ4RFPRUXy3h4eHDq1CmVK2T/+ecf7t27R5MmTSqlY29vT3R0NKWlpVy8eJG6detWGnA1NDT47LPPBLNoVFSUoDVWJ35VvEz8AwcOYGdnp/ZHv0mTJpw5c4aioiIKCws5c+aMYLJftmwZhw4dqhSncePGnDp1CoAbN26Qn58vfLTUYeDAgYwbN07QTMoxMTERTOm///57Jf9RgDp16ginu6lLly5dOHz4MLm5ubx48YLDhw/TuXPnl0pDTmlwoqYAACAASURBVHZ2ttC3oqOjqw0/YsQIduzYoXJhyo0bNyguLqZ+/fo0b95cQSubnZ3NRx99hKamJjExMSp9G3/99VeePn1KXl4ehw8fpmPHjlWW6fnz53z00UcUFhYqmKfd3d2Ffl/+36pVq4Ay3/CkpCSePXvGs2fPSEpKUttvS87t27dp1apVteFsbW3ZsmWLMD7I+4WqNqnYL0xMTLh69SolJSU8ePCgkmZFjo2NDfHx8cKOCE+fPlXLVUxVn+rYsSNHjx4lPz+f58+fC/7SVbFv3z6gzOpUt25dpZZCa2trYdFRbGwsnTp1Eu4dOHCAkpIS7ty5w927d/n444+rzbNJkyZcunSJ3NxcSktLOXXqlPDey8cbKOvjvXv3Fq7LZDIKCgq4e/cut2/f5tNPP8XQ0JA6depw8eJFSktLFeKIfGC85kVNX331FZGRkcI/uTAKqidA5UlNTWXKlCnY29sTHx/PwoULVe6WI0ctDWmtWrWYPHkykydPfpnmEfmX6NChA3379kUqlWJiYqIwQC5atIiCggJGjhwJgKWlpVLTekxMDEFBQejq6qKlpUVISIjaM52vv/76pcpbo0YNYQFBeYYPH46mpiZFRUVMnTpV6YKeXr16cezYMSQSCbVq1SIwMFC4N3r0aAICAjAyMmL69OlMnjyZ77//nnbt2glm86riT5kyhd9++40nT57Qs2dPJkyYgKenp+A/OmjQILXzh7KPW0W/zZSUFHbs2KF0FwFHR0dOnz6Nm5sbGhoa9OjRQxCk//rrL6WuGH5+fsyZM0fQfv/3v/9VqpXJyMhgzpw5lTTTxsbGDB8+XGlZ5GZDCwsLmjdvXilMmzZtBDeQ/v37KzVNVqRDhw70799feB4DBw6kffv2SgXe6vDx8WHixIkYGRlhaWlZbRoGBgZIJBKFRS9yH1Io0wIsWbIELS0tateujampKWlpaZiZmTF48GAmTJjAgQMH+OyzzwTzcEU6deqEr68vaWlpuLm5YWFhUWW5Jk6ciKenJyYmJrRu3VptAb9+/fqMGzdOMG+PHz+e+vXrA7By5Uo++eQTevfuzeXLl/Hx8SErK4ujR48SGhoqCFPJycnY2dkBqvsHwLhx4wgMDMTd3Z3S0lJMTExYt26dyjap2C+GDx+OiYkJbm5utGrVig4dOiitk7m5OZMmTWLUqFGUlJSgo6PDvHnzMDExqbItVPUpKBPc3N3dMTEx4ZNPPqnWFa1evXp8/fXX5OTkKLzb5ZkzZw6zZs3ip59+wsDAgKCgIOHexx9/zNChQ/nnn39YuHChyslo+fa2tLTE0dERDw8PtLW1adeunSAMjBkzhkmTJhEREUHjxo1ZuXIlUGbFdHZ2pm/fvmhpaTFv3jxhvF6wYAEzZ84kLy+Pnj17qlw3IPKe8xaPDrWwsOD27dvcvXsXIyMjZDJZpR04jhw5Ivzt5+eHnZ1dtYt5NUqrsG9Ut5K+qi17RN4dQkNDqV27dpX7DIr8+yQnJ/Pzzz+rXORVHm9v71faC9ja2poLFy68SvH+z3Po0CFSU1PVnphHRkaSmpqq0t/6XWPIkCGsWbPmg97S7/nz59SpU4fc3FyGDBnCokWLVArEXl5e+Pr6qvTx/pA518TstaSj+Y66q+rV1XltabXc/nrlIE3r6ndgUUbx8u9eazm0pqyq8v6xY8cIDAykuLiYAQMG8O233yooa8ojF0idnJyqTLNKDenFixdp3LgxLi4uWFpaihvqioi8QXR0dLh27RqjR4+u0qcWeGlh9M6dO0yYMEFtX1KRykgkkmq3inpfyczMZOTIkR+0MAplW1hdv36d/Px8PDw8VAqjIiLvHW95sVqvXr0qLX6tKIjK+e9//6tWmlVqSIuLi/n111+RyWT8+eef9OrVC1dXV7X8jERERERERF43np6eCgvMoOxEs4p+z+qycOHCSvvIDhs2jAEDBrxyGVVx4sQJQkJCFK41bdpU7YWObwtRQ6o+74yG9HvlWyO+KlqTvn+t6alDlQJpeQoKCoiLiyM4OJjx48fj5eX1pssmIiIiIiIi8pYRBVL1EQXS10e1i5oKCgpITEwkLi6O9PR0vLy8VB57KCIiIiIiIvJ+k/Qs598uQiU0eX3SreEL9bciqo4Wt35/bWkB8IoC6ds22b8JqhRIZ8yYwbVr1+jRowc+Pj60bt36bZVLRERERERERETk/whVCqQxMTHUqlWLW7duCaf0QNl2KBoaGkrPbxYREREREREREXmLvMVtn94UVQqkV69efVvlEBEREREREREReRU+AJP9+y9Si4iIiIiIiIiIvNeodVKTiIiIiIiIiIjIO8oHoCEVBVIRERERERERkfcZjfff4P3Wa7BkyRKcnJxwc3Nj/PjxZGVlKQ138eJFPD09kUqlODs7ExoaCpQdx9etWzekUilSqRRfX1+g7GgqS0tLcnL+/3YVAQEBtGnThszMzErpl0/HyclJ4Tzr0NBQevToIeRRcSNjOVFRUTg4OODg4EBUVJTSMAUFBUyaNAmJRIKnp6fCOdaq4m/ZsgWJRKKy7C+Tf2BgoFAPR0dHOnfurDRcu3btkEqlZGRkABAXF4ebmxtubm54e3tXWQ6AwsJCZsyYgZubG87OzgrHX3p5eWFtbU1KSkqleF5eXjg6OuLu7s6AAQO4cuVKlfmoy71793B1dX0tafn5+WFvby+046ZNm15LuhXJyspi69atCtdu3brF6NGjkUgkODs7M3HiRP7++2+Sk5MZO3bsa8t79uzZXL9+HYD9+/fj7OyMl5cXKSkpBAQEvHR6jx49eq3lU8Xhw4eFckPZue0nT578n9NV9izeJHv37hXet6+//lrBf//48eM4OjoikUhYv369cH3JkiWcOnVKaXqZmZl4enrSr18/zp49+9LliYyMFMaCN0VCQoJCfd4GFfvLu8iDBw/w8vLC2dkZFxcXfvnlF+He06dPGTlyJA4ODowcOZJnz54J99atW4dEIsHR0ZETJ04I11NTU3Fzc0MikRAQECCeuCjyzvLWNaTdu3dn6tSpaGtrs3TpUtatW8f06dMrhZsxYwYrV66kbdu2FBcXc+vWLeFe3759lZ4P3axZMxISEpBKpZSUlJCcnIyRkZHKssjTefLkCU5OTjg6OtK4cWMARowYUeXZ70+fPiUsLIw9e/agoaFB//79sbe3r3T03u7du9HX1+fQoUPIZDJCQkL4/vvvq4zfsWNH7OzsGDZs2P+c/6xZs4S/N2/ezB9//KE0PV1dXWJiYgAoKipi8eLFyGQyDAwMCA4OZuvWrUyYMEFleQ4cOEBBQQGxsbHk5ubi4uKCi4sLTZs2ZfPmzVUepBASEoKFhQV79uwhODiYDRs2qAz7b+Hr61vtObwVKSoqQltb/VcsKyuL7du3M2TIEADy8/MZO3asIBADnD59utrJwauwePFi4e+IiAjmz59Pt27dAF7qLG95nTds2ICnp+drL2dFDh8+jJ2dHebm5gBMnDjxtaRb8VmoQ3FxMVpar7a/YdOmTdmyZQv16tXj2LFjzJ07l927d1NcXIy/vz8bNmzAyMiIgQMHYm9vj7m5OUOHDmXu3LnY2NhUSu/UqVO0aNGCJUuWvFJ5oqKiaNWqVZXjZ0Vetr/37t2b3r17v0rxXomioqJK/eVdREtLCz8/Pzp06EBOTg4DBgyge/fumJubs379emxsbBgzZgzr169n/fr1TJ8+nevXryOTyZDJZGRkZDBy5Eji4+PR0tJiwYIF+Pv7Y2VlxejRozl+/HilIx9FPgDe1VMIXoI3JpCGh4cTHR1N48aNMTAwoEOHDnh7e2NrayuEsbKy4sCBA0rjZ2Zm8tFHHwFlL6g6A4irqyv79+9HKpWSnJxMx44dFWaKqmjQoAFmZmY8fvxYEEirIykpie7du1O/fn2gTNA+ceJEJa3ckSNH8PHxAcDR0RF/f39KS0urjN++ffvXln95ZDJZlUKlnNLSUkpLS8nNzaW0tJScnBzMzKo+uUNDQ4Pc3FyKiorIy8tDR0cHPT29avMqj5WVlXBG+/Pnzxk3bhxZWVkUFRUxceJE+vTpw7179xg9ejSdOnXiwoULGBkZsWbNGnR1dUlNTWXWrFnUqlWLjh07Cunm5+ezYMECUlNThcG+W7duREZGcvjwYUpKSvjrr78YNWoUhYWFxMTEUKNGDdavXy+0b0WqSjMxMZGCggJevHjB2rVrWbRoEX/99RfFxcX4+PjQp08frl27xsyZMyksLKSkpITQ0FBWrlzJnTt3kEqlfP7557Rs2RIrKytBGAUEITE5OVm4dvnyZQIDA8nLy0NXV5fAwEBatGihNA9DQ0MmTZrEw4cPKSkpYdy4cfTt2xcvLy98fX05duwY58+fZ/78+djb22NnZ8fPP//MunXrePHihdK6VKzzpk2bOHjwIJMnTwbKtG1HjhwhNzeXu3fv0qdPH8GyoYykpCRCQ0MpKCjA1NSUoKAg6tSpQ0hICEeOHEFLSwtbW1skEglHjhzht99+Izw8nNDQUNasWYOdnR1OTk7Y29vj6upKcnIyhYWFLFq0iOXLl5OWloa3tzeDBg1S2c+WLVum8Cx8fX0JDg7mxIkTaGho8O2339K3b1+Sk5MJCwvD0NCQK1euEBERobR9q6N8f7WysuLhw4fCszUzM8PU1BQAFxcXEhISMDc3x8TEhKdPn/L48WNhrAS4cuUKS5cuJS8vD6lUys6dOzl79qzSNg0LC+Po0aPk5+djbW2Nv78/8fHxpKamMm3aNHR1ddm5cyd9+/YlIiICAwMDUlJSCA4OZvPmzYSGhvLo0SPS09Np0KABs2fPZv78+dy/fx8omxB36tRJaZ0jIyNJTU1l3rx5+Pn5UbNmTW7evMn9+/cJCgoiKiqKixcvYmlpKZyFbW1tzVdffUVycjL6+vqsWLECAwMDrly5wvz588nNzaVZs2YEBgZSr149wTpz/vx5unfvXqm/nD59mp07d1JYWIiZmRnBwcHUqlULPz8/9PT0SE1N5fHjx0yfPl2YkP7www/s3bsXDQ0NevbsybRp07hz5w4LFy7kyZMn6OrqsmjRIlq2bMn+/ftZvXo1mpqa1K1bVy2tu6GhIYaGhgDo6enRokULMjIyMDc3JyEhQdiCsV+/fnh5eTF9+nQSEhJwcXGhRo0amJqaYmZmxuXLlzExMSEnJwdra2shTkJCgiiQfoh8ACb7NyKQpqamsm/fPqKjoykuLsbDw4MOHTpUCrdnzx6cnZ2VpjF8+HCcnJzo2rUrPXr0wMPDg5o1awKwb98+zp07ByieOWxmZkZCQgLPnj1DJpPh7u6ulkB6//598vPzFc5C3rhxI3v37gVg2rRp9OjRQyFORkYGxsbGwm8jIyOlJq6MjAxByNXW1qZu3bo8efJE7fjlSUlJYceOHSxevPil46enp3Pv3j1BoAGQSqWCVrQ8Ojo6LFiwADc3N2rXro2ZmRnz58+vsmyOjo4kJCRga2tLXl4eM2fOVCnMjR49moCAgEralxMnTtCnT9kpFTVr1mT16tXo6emRmZnJV199JWhT0tLSWL58OQEBAUycOJH4+HikUikzZ85k7ty5dO3aVUEzJP8IxMbGcuPGDby9vYmPjwfg2rVrREVFUVBQgEQiYdq0aURHRxMYGEh0dDQjRowAys7KDg8PF/7+9ddfVaZ58eJF9u7dS/369Vm+fDndunUjKCiIrKwsPD09+fzzz9mxYwfDhg3D3d2dgoICSkpKmDp1KteuXROeSVBQkNL3piItWrRgy5YtaGtrc/LkSVasWEFoaKjSPI4dO4ahoaFgKs3OzlZIy8fHh+TkZHx9fbGwsFAQfNeuXau0LhXrfPfuXerVq0eNGjWEuFeuXCE6OpoaNWrg5OSEl5eX0slfZmYm4eHhbNiwgdq1a7N+/Xo2bNjA0KFDOXToEAcOHEBDQ4OsrCz09fUFoVmV9trY2JidO3cSGBiIn58f27dvp6CgABcXFwYNGqSyn1V8FvHx8Vy9epWYmBiePHnCwIEDBfeXlJQUYmNjMTU1JT4+Xmn7BgYGKrSlHBcXF8aMGaNwLSIigp49ewLKx5nLly8Lv9u3b8/58+dxdHRk5cqVfPLJJ/Tu3ZvvvvtOEPZUtamPjw9Dhw4VJszTp0/n6NGjODk5sXXrVqEPVMfvv//Otm3b0NXVZerUqQwfPpzOnTtz//59vL292b9/f7VpQJlWetOmTSQkJPCf//yH7du306pVKwYOHMiVK1do164dL168oH379vj5+REWFkZYWBjz5s3D19dXePdXrlxJWFgYs2fPFtLdsmULUDZ2lO8vdevW5csvvwRgxYoVRERECNacR48esW3bNm7evMm3336Lk5MTx44dIyEhgV27dlGrVi2ePn0KwNy5c1m4cCHNmzfn0qVLLFy4kE2bNrFmzRp++uknjIyMVLqnyZ/znDlz+OGHHxSu37t3jytXrmBpaQnAP//8IwirhoaGgrUkIyNDCAP//3ugra2t0H+MjY3fuCuGiMir8kYE0rNnz9KnTx9q1aoFoKDhkRMeHo6Wlhbu7u5K0/Dx8cHd3Z2kpCTi4uKQyWTCzFCVyR5AIpEgk8m4dOkS/v7+VZZz3759JCcnc+vWLRYtWiQIvFC9yV6ZH46GklVuqsKpG788FhYWwgfiZePLZDIcHR0VTIrKhFEo8wfdvn070dHRmJqasmjRItatW8e4ceNUpn/58mU0NTU5ceIEWVlZDB48mM8//1zQ7JSn4qA7bdo0cnNzKSkpITIyUqjf8uXLOXPmDJqammRkZPD3338DZebNdu3aAdChQwfS09PJzs4mOzubrl27AmXCtnwycu7cOYYOHQpAy5YtadKkieAC8tlnnwma3Lp16wp9tXXr1vz5559CGSua7FetWqUyzfKa66SkJI4cOcLPP/8MlGlWHzx4gJWVFWvXruXhw4c4ODjQvHlzlW1bHdnZ2cyYMYO0tDQ0NDQoLCwEUJpH69atWbJkCUuXLuWLL75Q6VOsDFV1qVjnx48f06BBA4W4NjY21K1bFyhrr/T0dKUC6aVLl7h+/TqDBg0CyvqilZUVenp61KxZk9mzZ2NnZ4ednZ1aZZZPYlq3bs2LFy+EZ12zZk2ysrKoVauWyn5WnnPnzuHi4oKWlhaNGjWiS5cupKSkoKenh4WFhdDPVbVvedeZqjh9+jQRERFs27YNqP49b9iwIY8ePQJUuyuoalMo07T/+OOP5OXl8fTpU1q1aqV0vK4Ke3t7dHV1ATh58qSCj2ZOTg45OTlqWUu++OILNDQ0aNOmDY0aNRIUBObm5qSnp9OuXTs0NTUFjbNUKsXHx6fSu+/h4aHQFlVpqK9du8b3339PdnY2z58/V7Dg9enTB01NTczNzYU+cerUKfr37y982+rXr8/z58+5cOGCQp4FBQVAmUbXz88PZ2dnJBKJynIYGRlVGhefP3/Od999x6xZs6ptv9f5nRF5T/kAnusbM9lX1emjoqJITExk48aNQriZM2fyxx9/YGhoKLyYzZo1Y/DgwXz55ZfY2Njw5MmTavN1cXHBw8MDDw8PNMudXLB161Z27doFIGgv5ILthQsXGDt2LD179lQwfZXn0qVLghD83XffYWxszG+//Sbcz8jIEAbE8hgbG/PgwQOMjY0pKioiOzub+vXrqx1fFS8bf9++fSqF+IrIFxY1a9YMAGdn52oXH8TFxdGjRw90dHRo2LAhHTt2JCUlRalAWpGQkBDatm3LsmXL8Pf3JywsjNjYWDIzM4mMjERHRwd7e3vy8/MBFDRvWlpa5OfnC6eHKaMqJ/7yaWlqaqKjoyP8XVxcrDJeVWnKP1ZyVq1aRYsWLRSutWzZEktLSxITE/H29iYgIKBSW5mbm3PmzBmV+chZuXIln332GatXr+bevXuC77Gbm1ulPGxsbIiMjOTYsWMsW7aM7t27CxoydVBWl0uXLinUWVdXV/ggy6n4zFS1bWlpKd27d2f58uWV7kVERHDq1ClkMhlbtmxRa3FZ+edZ8VkXFRVV2c8qlksVtWvXFv7++OOPlbavOhrSq1evCloyuUBvbGwsmO+h7D2Xa8igbFIgFwZVoapN8/PzWbhwIXv27KFx48aEhoYqrTuUPTN5G1QMU/7Zl5SUsHPnzmrLpAz589HQ0FD6rJShjnBV8X0sj5+fH2vWrKFt27ZERkYqjKnlyyBH2ThTWlqKvr6+0gm+v78/ly5dIjExkX79+hEdHV1psqaMwsJCvvvuO9zc3HBwcBCuyycghoaGPHr0CAMDA0B1P6l4/eHDhwr9R+QD4gM4qemN1KBLly4cOnSIvLw8cnJyOHr0qHDv+PHj/PDDD4SHhysMFEFBQcTExAjCaGJiojAApqWloampib6+frV5N2nShMmTJzN48GCF60OGDCEmJoaYmJhKpmJra2vc3d2r/MBZWloK8Xv37o2trS1JSUk8e/aMZ8+ekZSUpDC7lmNvby+sgI+Pj6dbt25oaGioHV8VLxP/5s2bZGVlCX5E1WFkZMSNGzcEc9Cvv/5Ky5YtATh06BDLli2rFKdx48YkJydTWlrKixcvuHTpUiXBpSp0dHSYNGkSFy9e5MaNG2RnZ9OwYUN0dHQ4ffo06enpVcbX19dHT09PWFEcGxsr3OvSpYvw+9atWzx48OClyqYMddO0tbVly5YtQl+WLyq7e/cupqamDBs2DHt7e/7880/q1KnD8+fPhbhubm5cuHCBxMRE4drx48cVNLdQpiGV9+nyuy0oyyMjI4NatWohlUrx9vZWuchNGarqUpHmzZtX+7xUYWVlxfnz50lLSwMgNzeXW7du8fz5c7Kzs+nVqxezZs0SVqFXbLOXRVU/q5huly5d2L9/P8XFxWRmZnL27Fk+/fTTSumpat9Zs2YJ40f5f3Jh9P79+0yYMIHg4GA+/vhjIT0LCwtu377N3bt3KSgoQCaTKWgwb9++TatWraqso6o2lQuWDRo04Pnz54LLibL6m5iYkJqaCsDBgwdV5iXvI3Je164ZckpKSoRyxsbG0qlTJ+rWrYu+vr7w7sfExNClSxel8SvW6/nz53z00UcUFhYqjBmq6N69O3v27CE3NxcoW1yqp6dH06ZNBdeE0tJSoX/euXMHS0tLJk6cSIMGDRSEQ1WUlpYye/ZsWrRowciRIxXu2dvbEx0dDUB0dLRgAbC3t0cmk1FQUMDdu3e5ffs2n376KYaGhtSpU4eLFy9SWlqqEEdE5F3jjWhIO3ToQN++fZFKpZiYmCg4tS9atIiCggLhRbO0tFRqWo+JiSEoKAhdXV20tLQICQlRewXr119//dJlHj16NP3791d7q5r69eszbtw4Bg4cCMD48eMFk2V5X66BAwcyffp0JBIJ9erVY8WKFdXG37RpEz/++CN///037u7u9OrVi8WLFyv4kKqbP5SZ6/v27VtpZq/Kh9TIyIjx48czZMgQtLW1MTExISgoCCgbYJWZj4YMGcLMmTNxdXWltLSU/v3707ZtW5VtrcyHVFdXl1GjRvHTTz8xbdo0vv32W/r370+7du3UEiCDgoKERU3lhfPBgwczf/583Nzc0NLSIigoSKn242VQN81x48YRGBiIu7s7paWlmJiYsG7dOvbt28fevXvR1tamUaNGwvPr2LEjrq6u9OjRgxkzZrB27VoCAwMJDAxEW1ubNm3aMHv2bMF3DeCbb77Bz8+PDRs2KPgIK8tDviBFU1MTbW1tFixYoHadVdWlIrVr18bU1JS0tLRqF8NVxMDAgKCgIKZMmSJoWSdNmkSdOnUYN26cIETNnDkTKLNyzJ07l82bN7Nq1aqXygvKhH5l/axBgwYKz8LX15cLFy4glUrR0NBg+vTpfPTRR9y8eVMhvb/++uuV2nf16tU8ffqUhQsXAmUaycjISLS1tZk3bx7ffPMNxcXFDBgwQBBACwsLSUtL45NPPgEqv/fVtenHH3+Mp6cnbm5umJiYKPiLenh4MH/+fGFRk4+PD7Nnz2bdunUKvooVmT17Nv7+/ri5uVFcXEznzp2rdZ16GWrXrs21a9fo378/enp6fP/990DZFljyRU3yRVvKqNhfJk6ciKenJyYmJrRu3brayU3Pnj25evUqAwYMQEdHh169ejFlyhSWLl3KggULCA8Pp6ioiL59+9K2bVuCg4NJS0ujtLSUbt26qRwTy/uQnjt3jpiYGFq3bo1UKgVgypQp9OrVizFjxjBp0iQiIiJo3LgxK1euBKBVq1Y4OzvTt29ftLS0mDdvnvC9XLBgATNnziQvL4+ePXsK/skiHxgfgMleo/QtbEoWGhpK7dq1q/TJFPl3sba25sKFC9WGmzZtGrNmzRJMReoiX8X9MlsIiby/HDp0iNTUVGGlvcjr59ChQ/z+++9MmjTp3y7KW0PdcUrkf2NlnYb/dhEqocnrE7gMa7za9mzKGPiTev7h6qLV/9W2riv+ST2XPLXL4f36JpLq8v47HYi8FurUqaOwMb4qQkJCXkkYvXv3ruDPJ/LhI5FIaNq06b9djA+aoqIiRo0a9W8XQ0RE5F1AQ/P1/vs3qvA2NKQiIiIiFfH09Ky0+Ck4OFhh+zWR9589e/ZU8s/v2LFjtVvJfYicOHGi0sl/TZs2ZfXq1f9SiZQjakjV553RkG5Y+HrLMfLtv5/iWfYiIiL/Crt37/63iyDyFhgwYICwV/T/dXr06FFpT2sREZEyRIFUREREREREROBRYclrSedd9Qls/Bo1pGi9I65oH8C2T6JAKiIiIiIiIiLyPvMBrLJ//0VqERERERERERGR9xpRQyoiIiIiIiIi8j7zL62Mf52IAqmIiIiIiIiIyPuMpmiyFxEREREREREREfmfEDWkIiIiIiIiIiLvMx+Ayf6t12DJkiU4OTnh5ubG+PHjycrKUhru4sWLeHp6IpVKcXZ2JjQ0FIDIyEi6deuGhw2DMgAAIABJREFUVCpFKpXi6+sLgJ+fH5aWluTk5AhpBAQE0KZNGzIzMyulXz4dJycnNm7cKNwLDQ2lR48eQh4VNzKWExUVhYODAw4ODkRFRSkNU1BQwKRJk5BIJHh6enLv3r1q42/ZsgWJRKKy7C+Tf2BgoFAPR0dHOnfurDRcu3btFE5qiouLw83NDTc3N7y9vassB5Sdqz1jxgzc3NxwdnZWOOPcy8sLa2trUlJSKsXz8vLC0dERd3d3BgwYwJUrV6rMR13u3buHq6vra0nLz88Pe3t7oR0rbvL9usjKymLr1q0K127dusXo0aORSCQ4OzszceJE/v77b5KTkxk7duxry3v27Nlcv34dgP379+Ps7IyXlxcpKSkEBAS8dHqPHj16reVTxeHDh4VyQ9l57idPnvyf01X2LN4ke/fuFd63r7/+mqtXrwr3jh8/jqOjIxKJhPXr1wvXlyxZwqlTp5Sml5mZiaenJ/369ePs2bMvXZ7IyMhqT237X0lISFCoz9ugYn95lykuLqZfv34K79HTp08ZOXIkDg4OjBw5kmfPngn31q1bh0QiwdHRkRMnTgjXU1NTcXNzQyKREBAQgHgWjsi7ylvXkHbv3p2pU6eira3N0qVLWbduHdOnT68UbsaMGaxcuZK2bdtSXFzMrVu3hHt9+/Zl3rzK57Y2a9aMhIQEpFIpJSUlJCcnY2RkpLIs8nSePHmCk5MTjo6ONG7cGIARI0bg7e2tMu7Tp08JCwtjz549aGho0L9/f+zt7alXr55CuN27d6Ovr8+hQ4eQyWSEhITw/fffVxm/Y8eO2NnZMWzYsP85/1mz/v8pEps3b+aPP/5Qmp6uri4xMTFA2ZGEixcvRiaTYWBgQHBwMFu3bmXChAkqy3PgwAEKCgqIjY0lNzcXFxcXXFxcaNq0KZs3b8bLy0tl3JCQECwsLNizZw/BwcFs2LBBZdh/C19fX5ycnF4qTlFREdra6r9iWVlZbN++nSFDhgCQn5/P2LFjBYEY4PTp09VODl6FxYsXC39HREQwf/58unXrBoCFhYXa6cjrvGHDBjw9PV97OSty+PBh7OzsMDc3B2DixFc75aQiFZ+FOhQXF6Ol9Wr7GzZt2pQtW7ZQr149jh07xty5c9m9ezfFxcX4+/uzYcMGjIyMGDhwIPb29pibmzN06FDmzp2LjY1NpfROnTpFixYtWLJkySuVJyoqilatWlU5flbkZft779696d2796sU75UoKiqq1F/eZTZt2kTLli0VlCzr16/HxsaGMWPGsH79etavX8/06dO5fv06MpkMmUxGRkYGI0eOJD4+Hi0tLRYsWIC/vz9WVlaMHj2a48eP06tXr3+xZiJvhA9g26c3JpCGh4cTHR1N48aNMTAwoEOHDnh7e2NrayuEsbKy4sCBA0rjZ2Zm8tFHHwGgpaWl1gDi6urK/v37kUqlJCcn07FjR4WZoioaNGiAmZkZjx8/FgTS6khKSqJ79+7Ur18fKBO0T5w4UUkrd+TIEXx8fABwdHTE39+f0tLSKuO3b9/+teVfHplMVqVQKae0tJTS0lJyc3MpLS0lJycHMzOzKuNoaGiQm5tLUVEReXl56OjooKenV21e5bGysuKnn34C4Pnz54wbN46srCyKioqYOHEiffr04d69e4wePZpOnTpx4cIFjIyMWLNmDbq6uqSmpjJr1ixq1apFx44dhXTz8/NZsGABqampaGlp4efnR7du3YiMjOTw4cOUlJTw119/MWrUKAoLC4mJiaFGjRqsX79eaN+KVJVmYmIiBQUFvHjxgrVr17Jo0SL++usviouL8fHxoU+fPly7do2ZM2dSWFhISUkJoaGhrFy5kjt37iCVSvn8889p2bIlVlZWgjAKCEJicnKycO3y5csEBgaSl5eHrq4ugYGBtGjRQmkehoaGTJo0iYcPH1JSUsK4cePo27cvXl5e+Pr6cuzYMc6fP8/8+fOxt7fHzs6On3/+mXXr1vHixQuldalY502bNnHw4EEmT54MlGnbjhw5Qm5uLnfv3qVPnz6CZUMZSUlJhIaGUlBQgKmpKUFBQdSpU4eQkBCOHDmClpYWtra2SCQSjhw5wm+//UZ4eDihoaGsWbMGOzs7nJycsLe3x9XVleTkZAoLC1m0aBHLly8nLS0Nb29vBg0apLKfLVu2TOFZ+Pr6EhwczIkTJ9DQ0ODbb7+lb9++JCcnExYWhqGhIVeuXCEiIkJp+1ZH+f5qZWXFw4cPhWdrZmaGqakpAC4uLiQkJGBubo6JiQlPnz7l8ePHwlgJcOXKFZYuXUpeXh5SqZSdO3dy9uxZpW0aFhbG0aNHyc/Px9raGn9/f+Lj40lNTWXatGno6uqyc+dO+vbtS0REBAYGBqSkpBAcHMzmzZsJDQ3l0aNHpKen06BBA2bPns38+fO5f/8+UDYh7tSpk9I6R0ZGkpqayrx58/Dz86NmzZrcvHmT+/fvExQURFRUFBcvXsTS0pL//ve/AFhbW/PVV1+RnJyMvr4+K1aswMDAgCtXrjB//nxyc3Np1qwZgYGB1KtXT7DOnD9/nu7du1fqL6dPn2bnzp0UFhZiZmZGcHAwtWrVws/PDz09PVJTU3n8+DHTp08XJqQ//PADe/fuRUNDg549ezJt2jTu3LnDwoULefLkCbq6uixatIiWLVuyf/9+Vq9ejaamJnXr1lVb6/7w4UMSExP5z3/+o2C9S0hIYPPmzQD069cPLy8vpk+fTkJCAi4uLtSoUQNTU1PMzMy4fPkyJiYm5OTkYG1tLcRJSEgQBdIPkQ/AZP9GBNLU1FT27dtHdHQ0xcXFeHh40KFDh0rh9uzZg7Ozs9I0hg8fjpOTE127dqVHjx54eHhQs2ZNAPbt28e5c+cAGDZsmHAsnZmZGQkJCTx79gyZTIa7u7taAun9+/fJz89XOEN748aN7N27F4Bp06ZVOu4tIyMDY2Nj4beRkZFSE1dGRoYg5Gpra1O3bl2ePHmidvzypKSksGPHDhYvXvzS8dPT07l3754g0ABIpVJBK1oeHR0dFixYgJubG7Vr18bMzKzac6cdHR1JSEjA1taWvLw8Zs6cqVKYGz16NAEBAZW0LydOnKBPnz4A1KxZk9WrV6Onp0dmZiZfffWVoE1JS0tj+fLlBAQEMHHiROLj45FKpcycOZO5c+fStWtXBc2Q/CMQGxvLjRs38Pb2Jj4+HoBr164RFRVFQUEBEomEadOmER0dTWBgINHR0YwYMQIoO2M9PDxc+PvXX39VmebFixfZu3cv9evXZ/ny5XTr1o2goCCysrLw9PTk888/Z8eOHQwbNgx3d3cKCgooKSlh6tSpXLt2TXgmQUFBSt+birRo0YItW7agra3NyZMnWbFiBaGhoUrzOHbsGIaGhoKpNDs7WyEtHx8fkpOT8fX1xcLCQkHwXbt2rdK6VKzz3bt3qVevHjVq1BDiXrlyhejoaGrUqIGTkxNeXl5KJ3+ZmZmEh4ezYcMGateuzfr169mwYQNDhw7l0KFDHDhwAA0NDbKystDX1xeEZlXaa2NjY3bu3ElgYCB+fn5s376dgoICXFxcGDRokMp+VvFZxMfHc/XqVWJiYnjy5AkDBw4U3F9SUlKIjY3F1NSU+Ph4pe0bGBio0JZyXFxcGDNmjMK1iIgIevbsCSgfZy5fviz8bt++PefPn8fR0ZGVK1fyySef0Lt3b7777jtB2FPVpj4+PgwdOlSYME+fPp2jR4/i5OTE1q1bhT5QHb///jvbtm1DV1eXqVOnMnz4cDp37sz9+/fx9vZm//791aYBZVrpTZs2kZCQwH/+8x+2b99Oq1atGDhwIFeuXKFdu3a8ePGC9u3b4+fnR1hYGGFhYcybNw9fX1/h3V+5ciVhYWHMnj1bSHfLli1A2dhRvr/UrVuXL7/8EoAVK1YQEREhWHMePXrEtm3buHnzJt9++y1OTk4cO3aMhIQEdu3aRa1atXj69CkAc+fOZeHChTRv3pxLly6xcOFCNm3axJo1a/jpp58wMjJS6Z4mf85z5szhhx9+AMr6y/Tp03n+/LlCuH/++QdDQ0MADA0NBWtJRkYGlpaWQjj590BbW1uh/xgbG79xVwwRkVfljQikZ8+epU+fPtSqVQtAQcMjJzw8HC0tLdzd3ZWm4ePjg7u7O0lJScTFxSGTyYSZoSqTPYBEIkEmk3Hp0iX8/f2rLOe+fftITk7m1q1bLFq0SBB4oXqTvTI/HA0lKnNV4dSNXx4LCwvhA/Gy8WUyGY6OjgomRWXCKJT5g27fvp3o6GhMTU1ZtGgR69atY9y4cSrTv3z5Mpqampw4cYKsrCwGDx7M559/Lmh2yiMfdOVMmzaN3NxcSkpKiIyMFOq3fPlyzpw5g6amJhkZGfz9999AmXmzXbt2AHTo0IH09HSys7PJzs6ma9euQJmwLZ+MnDt3jqFDhwLQsmVLmjRpIriAfPbZZ4Imt27dukJfbd26NX/++adQxoom+1WrVqlMs7zmOikpiSNHjvDzzz8DZZrVBw8eYGVlxdq1a3n48CEODg40b95cZdtWR3Z2NjNmzCAtLQ0NDQ0KCwsBlObRunVrlixZwtKlS/niiy9U+hQrQ1VdKtb58ePHNGjQQCGujY0NdevWBcraKz09XalAeunSJa5fv86gQYOAsr5oZWWFnp4eNWvWZPbs2djZ2WFnZ6dWmeWTmNatW/PixQvhWdesWZOsrCxq1aqlsp+V59y5c7i4uKClpUWjRo3o0qULKSkp6OnpYWFhIfRzVe1b3nWmKk6fPk1ERATbtm0Dqn/PGzZsyKNHjwDV7gqq2hTKNO0//vgjeXl5PH36lFatWikdr6vC3t4eXV1dAE6ePKngo5mTk0NOTo5a1pIvvvgCDQ0N2rRpQ6NGjQQFgbm5Oenp6bRr1w5NTU1B4yyVSvHx8an07nt4eCi0RVUa6mvXrvH999+TnZ3N8+fPFSx4ffr0QVNTE3Nzc6FPnDp1iv79+wvftvr16/P8+XMuXLigkGdBQQFQptH18/PD2dkZiUSishxGRkbCuHj06FEMDAz45JNPlE5ilPE6vzMi7ykfwLZPb8xkX1Wnj4qKIjExkY0bNwrhZs6cyR9//IGhoaHwYjZr1ozBgwfz5ZdfYmNjw5MnT6rN18XFBQ8PDzw8PNAsd7br1q1b2bVrF4CgvZALthcuXGDs2LH07NlTwfRVnkuXLglC8HfffYexsTG//fabcD8jI0MYEMtjbGzMgwcPMDY2pqioiOzsbOrXr692fFW8bPx9+/apFOIrIl9Y1KxZMwCcnZ2rXXwQFxdHjx490NHRoWHDhnTs2JGUlBSlAmlFQkJCaNu2LcuWLcPf35+wsDBiY2PJzMwkMjISHR0d7O3tyc/PB1DQvGlpaZGfn09paanKPleVE3/5tDQ1NdHR0RH+Li4uVhmvqjTlHys5q1atokWLFgrXWrZsiaWlJYmJiXh7exMQEFCprczNzTlz5ozKfOSsXLmSzz77jNWrV3Pv3j3B99jNza1SHjY2NkRGRnLs2DGWLVtG9+7dBQ2ZOiiry6VLlxTqrKurK3yQ5VR8ZqratrS0lO7du7N8+fJK9yIiIjh16hQymYwtW7aotbis/POs+KyLioqq7GcVy6WK2rVrC39//PHHSttXHQ3p1atXBS2ZXKA3NjYWzPdQ9p7LNWRQNimQC4OqUNWm+fn5LFy4kD179tC4cWNCQ0OV1h3Knpm8DSqGKf/sS0pK2LlzZ7VlUob8+WhoaCh9VspQR7iq+D6Wx8/PjzVr1tC2bVsiIyMVxtTyZZCjbJwpLS1FX19f6QTf39+fS5cukZiYSL9+/YiOjq40WavI+fPnOXLkCMePHyc/P5+cnBymTZtGSEiIMAExNDTk0aNHGBgYAKr7ScXrDx8+VOg/Ih8QH8BE4404HXTp0oVDhw6Rl5dHTk4OR48eFe4dP36cH374gfDwcIWBIigoiJiYGEEYTUxMFAbAtLQ0NDU10dfXrzbvJk2aMHnyZAYPHqxwfciQIcTExBATE1PJVGxtbY27u3uVHzhLS0shfu/evbG1tSUpKYlnz57x7NkzkpKSFGbXcuzt7YUV8PHx8XTr1g0NDQ2146viZeLfvHmTrKwswY+oOoyMjLhx44ZgDvr1119p2bIlAIcOHWLZsmWV4jRu3Jjk5GRKS0t58eIFly5dqiS4VIWOjg6TJk3i4sWL3Lhxg+zsbBo2bIiOjg6nT58mPT29yvj6+vro6ekJK4pjY2OFe126dBF+37p1iwcPHrxU2ZShbpq2trZs2bJF6MvyRWV3797F1NSUYcOGYW9vz59//kmdOnUUTHRubm5cuHCBxMRE4drx48cVNLdQpiGV9+nyuy0oyyMjI4NatWohlUrx9vZWuchNGarqUpHmzZtX+7xUYWVlxfnz50lLS/t/7J15XM3Z//ifbSRZaqhMkn2nxTK2MJH2rqKxZpDlI42aUSkNktTINoQIw2AYM0lJQpOlzJhmbNEMH8aSMMInlJS2+/ujx33/unVvi2nG8H0/H48ej+6957zO+j7v13md1zkHgIKCAm7fvk1+fj55eXkMGzaMhQsXCrvQK9dZXVHWzyrL7devH4mJiZSWlpKTk8O5c+fo3bt3FXnK6nfhwoXC+FHxT6aMPnjwgE8++YTw8HDatWsnyOvVqxd37twhKyuLoqIiEhIS5CyYd+7coVOnTtWWUVmdyhRLHR0d8vPzBZcTReU3NDQkIyMDgOPHjytNS9ZHZNTXqRkyysrKhHzGx8fTp08fmjRpQtOmTYVnPy4ujn79+imMX7lc+fn5tGzZkuLiYrkxQxmDBw/mwIEDFBQUAOWbS7W1tWndurXgmiCVSoX+effuXUxMTPDy8kJHR0dOOVTG/PnzSUlJ4cSJE4LLj+ykF0tLS2JjYwGIjY0VVgAsLS1JSEigqKiIrKws7ty5Q+/evdHT06Nx48ZcunQJqVQqF0dE5N/G32Ih7dGjB3Z2dkgkEgwNDeWc2pctW0ZRURHTpk0DyhU9RUvrcXFxhIWFoampiZqaGqtWrar1Dtbx48fXOc8zZ87ExcWl1kfVNG/eHA8PD8aOHQvA3LlzhSXLir5cY8eOxdfXFysrK5o1a8batWtrjL9r1y62bdvGkydPcHJyYtiwYSxfvlzOh7S26UP5cr2dnV2Vmb0yH1J9fX3mzp3LpEmTUFdXx9DQkLCwMKB8gFW0/DZp0iQCAgJwcHBAKpXi4uJC165dlda1Ih9STU1Npk+fzvbt2/Hx8WHOnDm4uLjQrVu3WimQYWFhwqamisr5xIkTWbJkCY6OjqipqREWFqbQ+lEXaivTw8OD0NBQnJyckEqlGBoasmXLFo4cOcKhQ4dQV1enRYsWQvuZm5vj4OCAhYUFCxYsYPPmzYSGhhIaGoq6ujpdunQhMDBQ8F0DmDFjBv7+/uzYsUPOR1hRGrINKaqqqqirqxMUFFTrMisrS2W0tLQwMjIiMzOzxs1wldHV1SUsLIzPPvtMsLJ6e3vTuHFjPDw8BCUqICAAKF/lWLRoEbt372b9+vV1SgvKlX5F/UxHR0euLfz8/Lh48SISiQQVFRV8fX1p2bIlt27dkpN3/fr116rfjRs38uzZM5YuXQqUWyRjYmJQV1dn8eLFzJgxg9LSUsaMGSMooMXFxWRmZtKzZ0+g6nNfU522a9cOV1dXHB0dMTQ0lPMXdXZ2ZsmSJcKmJk9PTwIDA9myZYucr2JlAgMDCQ4OxtHRkdLSUvr27Vuj61Rd0NLS4saNG7i4uKCtrc2XX34JlB+BJdvUJNu0pYjK/cXLywtXV1cMDQ3p3LlzjZOboUOHcu3aNcaMGYOGhgbDhg3js88+Y+XKlQQFBREZGUlJSQl2dnZ07dqV8PBwMjMzkUqlDBgwQOmYWNmHVBmzZs3C29ub6OhoWrVqxbp16wDo1KkTtra22NnZoaamxuLFi4X3ZVBQEAEBARQWFjJ06FDBP1nkHeMd2NSkIv0HDiWLiIhAS0urWp9MkTeLmZkZFy9erDGcj48PCxcuFJaKaotsF3ddjhASeXtJSkoiIyND2GkvUv8kJSXx22+/4e3t/aaz8o9R23FK5K8R2KB6t4La8m9VkUwa/zWDREWcd1a/4beuqEmU79WojtKYdfWbD5f6OUKvLvxb+4vIP0zjxo3lDsZXxqpVq15LGc3KyhL8+UTefaysrGjduvWbzsY7TUlJCdOnT3/T2RARERGpF/4RC6mIiIhIZVxdXatsfgoPD5c7fk3k7efAgQNV/PPNzc1rPEruXSQ1NbXKzX+tW7dm48aNbyhHihEtpLXnX2MhPRhRv/lwrvnM8vpGVEhFREREREREBESFtPb8axTS2A31m4/RtT99pb74x68OFRERERERERF5U6jW5xFJdbguV6R6xJoUEREREREREXmbeQd22b/9JRAREREREREREXmrES2kIiIiIiIiIiJvM+LVoSIiIiIiIiIiIm8UccleRERERERERERE5K8hWkhFRERERERERN5m6vPkgDfEP24hXbFiBTY2Njg6OjJ37lxyc3MVhrt06RKurq5IJBJsbW2JiCg/9DUmJoYBAwYgkUiQSCT4+fkB4O/vj4mJCS9evBBkhISE0KVLF3JycqrIryjHxsaGnTt3Cr9FRERgYWEhpFH5IGMZBw8eZNSoUYwaNYqDBw8qDFNUVIS3tzdWVla4urpy7969GuPv2bMHKysrpXmvS/qhoaFCOaytrenbt6/CcN26dZO7qenw4cM4Ojri6OiIu7t7tfmA8nu1FyxYgKOjI7a2tnJ3nLu5uWFmZsaVK1eqxHNzc8Pa2honJyfGjBnD1atXq02ntty7dw8HB4d6keXv74+lpaVQj5UP+a4vcnNz+eabb+S+u337NjNnzsTKygpbW1u8vLx48uQJaWlpzJ49u97SDgwM5I8//gAgMTERW1tb3NzcuHLlCiEhIXWW9+jRo3rNnzJ++OEHId9Qfp/7Tz/99JflKmqLv5NDhw4Jz9v48eO5du2a8FtKSgrW1tZYWVkRFRUlfL9ixQrOnj2rUF5OTg6urq6MHj2ac+fO1Tk/MTExNd7a9ldJTk6WK88/QeX+8m8lNzeXefPmYWNjg62trXBd6rNnz5g2bRqjRo1i2rRpPH/+XIizZcsWrKyssLa2JjU1Vfg+IyMDR0dHrKysCAkJQTx6/B1FVbV+/94A/7iFdPDgwcyfPx91dXVWrlzJli1b8PX1rRJuwYIFrFu3jq5du1JaWsrt27eF3+zs7Fi8eHGVOG3atCE5ORmJREJZWRlpaWno6+srzYtMztOnT7GxscHa2ppWrVoBMHXqVNzd3ZXGffbsGRs2bODAgQOoqKjg4uKCpaUlzZo1kwv3/fff07RpU5KSkkhISGDVqlV8+eWX1cY3Nzdn+PDhTJky5S+nv3DhQuH/3bt38/vvvyuUp6mpSVxcHFB+JeHy5ctJSEhAV1eX8PBwvvnmGz75RPnNDUePHqWoqIj4+HgKCgqwt7fH3t6e1q1bs3v3btzc3JTGXbVqFb169eLAgQOEh4ezY8cOpWHfFH5+ftjY2NQpTklJCep1OKMuNzeXffv2MWnSJABevXrF7NmzBYUY4Oeff65xcvA6LF++XPg/OjqaJUuWMGDAAAB69epVazmyMu/YsQNXV9d6z2dlfvjhB4YPH07Hjh0B8PKqn/uXK7dFbSgtLUVNTe210mvdujV79uyhWbNmnD59mkWLFvH9999TWlpKcHAwO3bsQF9fn7Fjx2JpaUnHjh2ZPHkyixYtYuDAgVXknT17lvbt27NixYrXys/Bgwfp1KlTteNnZera30eMGMGIESNeJ3uvRUlJSZX+8m9l+fLlWFhYsH79eoqKiigsLAQgKiqKgQMHMmvWLKKiooiKisLX15c//viDhIQEEhISyM7OZtq0aRw7dgw1NTWCgoIIDg7G1NSUmTNnkpKSwrBhw95wCUVEqvK3KaSRkZHExsbSqlUrdHV16dGjB+7u7gwZMkQIY2pqytGjRxXGz8nJoWXLlgCoqanVagBxcHAgMTERiURCWloa5ubmcjNFZejo6GBsbMzjx48FhbQmzpw5w+DBg2nevDlQrminpqZWscqdOHECT8/yGw+sra0JDg5GKpVWG7979+71ln5FEhISqlUqZUilUqRSKQUFBUilUl68eIGxsXG1cVRUVCgoKKCkpITCwkI0NDTQ1tauMa2KmJqasn37dgDy8/Px8PAgNzeXkpISvLy8GDlyJPfu3WPmzJn06dOHixcvoq+vz6ZNm9DU1CQjI4OFCxfSqFEjzM3NBbmvXr0iKCiIjIwM1NTU8Pf3Z8CAAcTExPDDDz9QVlbG9evXmT59OsXFxcTFxdGgQQOioqKE+q1MdTJPnTpFUVERL1++ZPPmzSxbtozr169TWlqKp6cnI0eO5MaNGwQEBFBcXExZWRkRERGsW7eOu3fvIpFIGDRoEB06dMDU1FRQRgFBSUxLSxO+u3z5MqGhoRQWFqKpqUloaCjt27dXmIaenh7e3t48fPiQsrIyPDw8sLOzw83NDT8/P06fPs2FCxdYsmQJlpaWDB8+nK+++ootW7bw8uVLhWWpXOZdu3Zx/PhxPv30U6Dc2nbixAkKCgrIyspi5MiRwsqGIs6cOUNERARFRUUYGRkRFhZG48aNWbVqFSdOnEBNTY0hQ4ZgZWXFiRMn+OWXX4iMjCQiIoJNmzYxfPhwbGxssLS0xMHBgbS0NIqLi1m2bBlr1qwhMzMTd3d3JkyYoLSfrV69Wq4t/Pz8CA8PJzU1FRUVFebMmYOdnR1paWls2LABPT09rl69SnR0tML6rYmK/dXU1JSHDx8KbWtsbIyRkREA9vb2JCcn07FjRwwNDXn27BmPHz8WxkqAq1evsnLlSgoLC5FIJOzfv59z584prNMNGzZw8uRJXr16hZmZGcHBwRw7doxCfZwnAAAgAElEQVSMjAx8fHzQ1NRk//792NnZER0dja6uLleuXCE8PJzdu3cTERHBo0ePuH//Pjo6OgQGBrJkyRIePHgAlE+I+/Tpo7DMMTExZGRksHjxYvz9/WnYsCG3bt3iwYMHhIWFcfDgQS5duoSJiQlffPEFAGZmZowbN460tDSaNm3K2rVr0dXV5erVqyxZsoSCggLatGlDaGgozZo1E1ZnLly4wODBg6v0l59//pn9+/dTXFyMsbEx4eHhNGrUCH9/f7S1tcnIyODx48f4+voKE9KtW7dy6NAhVFRUGDp0KD4+Pty9e5elS5fy9OlTNDU1WbZsGR06dCAxMZGNGzeiqqpKkyZNamV1f/HiBb/++qtQ5gYNGtCgQfnNQsnJyezevRuA0aNH4+bmhq+vL8nJydjb29OgQQOMjIwwNjbm8uXLGBoa8uLFC8zMzIQ4ycnJokL6LvIOLNn/LQppRkYGR44cITY2ltLSUpydnenRo0eVcAcOHMDW1lahjI8//hgbGxv69++PhYUFzs7ONGzYEIAjR45w/vx5AKZMmcKYMWMAMDY2Jjk5mefPn5OQkICTk1OtFNIHDx7w6tUruTu0d+7cyaFDhwDw8fHBwsJCLk52djYGBgbCZ319fYVLXNnZ2YKSq66uTpMmTXj69Gmt41fkypUrfPvttyxfvrzO8e/fv8+9e/cEhQZAIpEIVtGKaGhoEBQUhKOjI1paWhgbG9d477S1tTXJyckMGTKEwsJCAgIClCpzM2fOJCQkpIr1JTU1lZEjRwLQsGFDNm7ciLa2Njk5OYwbN06wpmRmZrJmzRpCQkLw8vLi2LFjSCQSAgICWLRoEf3795ezDMleAvHx8dy8eRN3d3eOHTsGwI0bNzh48CBFRUVYWVnh4+NDbGwsoaGhxMbGMnXqVKD8jvXIyEjh/x9//FGpzEuXLnHo0CGaN2/OmjVrGDBgAGFhYeTm5uLq6sqgQYP49ttvmTJlCk5OThQVFVFWVsb8+fO5ceOG0CZhYWEKn5vKtG/fnj179qCurs5PP/3E2rVriYiIUJjG6dOn0dPTE5ZK8/Ly5GR5enqSlpaGn58fvXr1klN8N2/erLAslcuclZVFs2bNhJcolCtJsbGxNGjQABsbG9zc3BRO/nJycoiMjGTHjh1oaWkRFRXFjh07mDx5MklJSRw9ehQVFRVyc3Np2rSpoDQrs14bGBiwf/9+QkND8ff3Z9++fRQVFWFvb8+ECROU9rPKbXHs2DGuXbtGXFwcT58+ZezYsYL7y5UrV4iPj8fIyIhjx44prN/Q0FC5upRhb2/PrFmz5L6Ljo5m6NChgOJx5vLly8Ln7t27c+HCBaytrVm3bh09e/ZkxIgRzJs3T1D2lNWpp6cnkydPFibMvr6+nDx5EhsbG7755huhD9TEb7/9xt69e9HU1GT+/Pl8/PHH9O3blwcPHuDu7k5iYmKNMqDcKr1r1y6Sk5P5z3/+w759++jUqRNjx47l6tWrdOvWjZcvX9K9e3f8/f3ZsGEDGzZsYPHixfj5+QnP/rp169iwYQOBgYGC3D179gDlY0fF/tKkSRM++ugjANauXUt0dLSwmvPo0SP27t3LrVu3mDNnDjY2Npw+fZrk5GS+++47GjVqxLNnzwBYtGgRS5cupW3btqSnp7N06VJ27drFpk2b2L59O/r6+krd02Tt/Pnnn7N161aysrLQ1dUlICCAa9eu0aNHDwIDA9HS0uJ///sfenp6AOjp6QmrJdnZ2ZiYmAjyZO8DdXV1uf5jYGDwt7tiiLwh3oFd9n+LQnru3DlGjhxJo0aNAOQsPDIiIyNRU1PDyclJoQxPT0+cnJw4c+YMhw8fJiEhQZgZKluyB7CysiIhIYH09HSCg4OrzeeRI0dIS0vj9u3bLFu2TFB4oeYle0V+OCoKZijKwtU2fkV69eolvCDqGj8hIQFra2u5JUVFyiiU+4Pu27eP2NhYjIyMWLZsGVu2bMHDQ/kdu5cvX0ZVVZXU1FRyc3OZOHEigwYNEiw7Fdm6davcZx8fHwoKCigrKyMmJkYo35o1a/j1119RVVUlOzubJ0+eAOXLm926dQOgR48e3L9/n7y8PPLy8ujfvz9QrmzLJiPnz59n8uTJAHTo0IH3339fcAH54IMPBEtukyZNhL7auXNn/vvf/wp5rLxkv379eqUyK1quz5w5w4kTJ/jqq6+Acsvqn3/+iampKZs3b+bhw4eMGjWKtm3bKq3bmsjLy2PBggVkZmaioqJCcXExgMI0OnfuzIoVK1i5ciUffvihUp9iRSgrS+UyP378GB0d+buwBw4cSJMmTYDy+rp//75ChTQ9PZ0//viDCRMmAOV90dTUFG1tbRo2bEhgYCDDhw9n+PDhtcqzbBLTuXNnXr58KbR1w4YNyc3NpVGjRkr7WUXOnz+Pvb09ampqtGjRgn79+nHlyhW0tbXp1auX0M+V1W9F15nq+Pnnn4mOjmbv3r1Azc/5e++9x6NHjwDl7grK6hTKLe3btm2jsLCQZ8+e0alTJ4XjdXVYWlqiqakJwE8//STno/nixQtevHhRq9WSDz/8EBUVFbp06UKLFi0EA0HHjh25f/8+3bp1Q1VVVbA4SyQSPD09qzz7zs7OcnVRnYX6xo0bfPnll+Tl5ZGfny+3gjdy5EhUVVXp2LGj0CfOnj2Li4uL8G5r3rw5+fn5XLx4US7NoqIioNyi6+/vj62tLVZWVkrzoa+vL4yLJSUl/P777yxatAgTExNCQkKIiorC29tbafz6fM+IiLwp/rYl++o6/cGDBzl16hQ7d+4UwgUEBPD777+jp6cnPJht2rRh4sSJfPTRRwwcOJCnT5/WmK69vT3Ozs44OzujWsEx95tvvuG7774DEKwXMsX24sWLzJ49m6FDh8otfVUkPT1dUILnzZuHgYEBv/zyi/B7dna2MCBWxMDAgD///BMDAwNKSkrIy8ujefPmtY6vjLrGP3LkiFIlvjKyjUVt2rQBwNbWtsbNB4cPH8bCwgINDQ3ee+89zM3NuXLlikKFtDKrVq2ia9eurF69muDgYDZs2EB8fDw5OTnExMSgoaGBpaUlr169ApCzvKmpqfHq1SukUqnSPledE39FWaqqqmhoaAj/l5aWKo1XnUzZy0rG+vXrad++vdx3HTp0wMTEhFOnTuHu7k5ISEiVuurYsSO//vqr0nRkrFu3jg8++ICNGzdy7949wffY0dGxShoDBw4kJiaG06dPs3r1agYPHixYyGqDorKkp6fLlVlTU1N4Icuo3GbK6lYqlTJ48GDWrFlT5bfo6GjOnj1LQkICe/bsqdXmsortWbmtS0pKqu1nlfOlDC0tLeH/du3aKazf2lhIr127JljJZAq9gYGBsHwP5c+5zEIG5ZMCmTKoDGV1+urVK5YuXcqBAwdo1aoVERERCssO5W0mq4PKYSq2fVlZGfv3768xT4qQtY+KiorCtlJEbZSrys9jRfz9/dm0aRNdu3YlJiZGbkytmAcZisYZqVRK06ZNFU7wg4ODSU9P59SpU4wePZrY2Ngqk7XKGBgYYGBgIFg8bWxshPFXNgHR09Pj0aNH6OrqCnEU9ZPK3z98+FCu/4i8Q7wDE42/xcbbr18/kpKSKCws5MWLF5w8eVL4LSUlha1btxIZGSk3UISFhREXFycoo6dOnRIGwMzMTFRVVWnatGmNab///vt8+umnTJw4Ue77SZMmERcXR1xcXJWlYjMzM5ycnKp9wZmYmAjxR4wYwZAhQzhz5gzPnz/n+fPnnDlzRm52LcPS0lLYAX/s2DEGDBiAiopKreMroy7xb926RW5uruBHVBP6+vrcvHlTWA768ccf6dChAwBJSUmsXr26SpxWrVqRlpaGVCrl5cuXpKenV1FcqkNDQwNvb28uXbrEzZs3ycvL47333kNDQ4Off/6Z+/fvVxu/adOmaGtrCzuK4+Pjhd/69esnfL59+zZ//vlnnfKmiNrKHDJkCHv27BH6smxTWVZWFkZGRkyZMgVLS0v++9//0rhxY/Lz84W4jo6OXLx4kVOnTgnfpaSkyFluodxCKuvTFU9bUJRGdnY2jRo1QiKR4O7urnSTmyKUlaUybdu2rbG9lGFqasqFCxfIzMwEoKCggNu3b5Ofn09eXh7Dhg1j4cKFwi70ynVWV5T1s8py+/XrR2JiIqWlpeTk5HDu3Dl69+5dRZ6y+l24cKEwflT8kymjDx484JNPPiE8PJx27doJ8nr16sWdO3fIysqiqKiIhIQEOQvmnTt36NSpU7VlVFanMsVSR0eH/Px8weVEUfkNDQ3JyMgA4Pjx40rTkvURGfV1aoaMsrIyIZ/x8fH06dOHJk2a0LRpU+HZj4uLo1+/fgrjVy5Xfn4+LVu2pLi4WG7MUMbgwYM5cOAABQUFQPnmUm1tbVq3bi24JkilUqF/3r17FxMTE7y8vNDR0ZFTDpXRsmVLDAwMuHXrFlBulZWNv5aWlsTGxgIQGxsrrABYWlqSkJBAUVERWVlZ3Llzh969e6Onp0fjxo25dOkSUqlULo7IO4a4y14xPXr0wM7ODolEgqGhoZxT+7JlyygqKmLatGlAuaKnaGk9Li6OsLAwNDU1UVNTY9WqVbXewTp+/Pg653nmzJm4uLjU+qia5s2b4+HhwdixYwGYO3eusGRZ0Zdr7Nix+Pr6YmVlRbNmzVi7dm2N8Xft2sW2bdt48uQJTk5ODBs2jOXLl8v5kNY2fShfrrezs6sys1fmQ6qvr8/cuXOZNGkS6urqGBoaEhYWBpQPsIqW3yZNmkRAQAAODg5IpVJcXFzo2rWr0rpW5EOqqanJ9OnT2b59Oz4+PsyZMwcXFxe6detWKwUyLCxM2NRUUTmfOHEiS5YswdHRETU1NcLCwhRaP+pCbWV6eHgQGhqKk5MTUqkUQ0NDtmzZwpEjRzh06BDq6uq0aNFCaD9zc3McHBywsLBgwYIFbN68mdDQUEJDQ1FXV6dLly4EBgYKvmsAM2bMwN/fnx07dsj5CCtKQ7YhRVVVFXV1dYKCgmpdZmVlqYyWlhZGRkZkZmbWuBmuMrq6uoSFhfHZZ58JVlZvb28aN26Mh4eHoEQFBAQA5ascixYtYvfu3axfv75OaUG50q+on+no6Mi1hZ+fHxcvXkQikaCiooKvry8tW7YUlAYZ169ff6363bhxI8+ePWPp0qVAuUUyJiYGdXV1Fi9ezIwZMygtLWXMmDGCAlpcXExmZiY9e/YEqj73NdVpu3btcHV1xdHREUNDQzl/UWdnZ5YsWSJsavL09CQwMJAtW7bI+SpWJjAwkODgYBwdHSktLaVv3741uk7VBS0tLW7cuIGLiwva2tp8+eWXQPkRWLJNTbJNW4qo3F+8vLxwdXXF0NCQzp071zi5GTp0KNeuXWPMmDFoaGgwbNgwPvvsM1auXElQUBCRkZGUlJRgZ2dH165dCQ8PJzMzE6lUyoABA5SOiRV9SKHcJ9XHx4fi4mK58syaNQtvb2+io6Np1aoV69atA6BTp07Y2tpiZ2eHmpoaixcvFt6XQUFBBAQEUFhYyNChQwX/ZBGRfxsq0n/gULKIiAi0tLSq9ckUebOYmZkJZ91Vh4+PDwsXLhSWimqLbBd3XY4QEnl7SUpKIiMjQ9hpL1L/JCUl8dtvv1XrW/iuUdtxSuSvEdigereC2vJv3WZjpt2w5kC1RLK7/iZcAGr2s2oOpIDS5N31m48Ryo9q/Lv4t/YXkX+Yxo0byx2Mr4xVq1a9ljKalZUl+POJvPtYWVnRunXrN52Nd5qSkhKmT5/+prMhIiIiUi/8IxZSERERkcq4urpW2fwUHh4ud/yayNvPgQMHqvjnm5ub13iU3LtIampqlZv/WrduzcaNG99QjhQjWkhrz7/GQnqifm+WU7Os/aUg9YWokIqIiIiIiIgIiApp7fnXKKQn99ZvPj6cWHOgeuYfvzpURERERERERKQuqNbjqUb1qii/5nXBIlURFVIRERERERERkbcZ8aYmERERERERERGRN0p9mpDfEG+/Si0iIiIiIiIiIvJWI1pIRURERERERETeZsQlexERERERERERkTeKeJe9iIiIiIiIiIiIyF/jH1dIV6xYgY2NDY6OjsydO5fc3FyF4S5duoSrqysSiQRbW1siIiIAiImJYcCAAUgkEiQSCX5+fgD4+/tjYmLCixcvBBkhISF06dKFnJycKvIryrGxsWHnzp3CbxEREVhYWAhpVD7IWMbBgwcZNWoUo0aN4uDBgwrDFBUV4e3tjZWVFa6urty7d6/G+Hv27MHKykpp3uuSfmhoqFAOa2tr+vbtqzBct27d5G5qOnz4MI6Ojjg6OuLu7l5tPqD8Xu0FCxbg6OiIra2t3B3nbm5umJmZceXKlSrx3NzcsLa2xsnJiTFjxnD16tVq06kt9+7dw8HBoV5k+fv7Y2lpKdRj5UO+64vc3Fy++Ub+cOPbt28zc+ZMrKyssLW1xcvLiydPnpCWlsbs2bPrLe3AwED++OMPABITE7G1tcXNzY0rV64QEhJSZ3mPHj2q1/wp44cffhDyDeX3uf/0009/Wa6itvg7OXTokPC8jR8/nmvXrgm/paSkYG1tjZWVFVFRUcL3K1as4OzZswrl5eTk4OrqyujRozl37lyd8xMTE1PjrW1/leTkZLny/BNU7i//Vnbu3Im9vT0ODg589tlnvHr1CoBnz54xbdo0Ro0axbRp03j+/LkQZ8uWLVhZWWFtbU1qaqrwfUZGBo6OjlhZWRESEoJ49Pg7iopq/f69Af7xVAcPHszhw4eJj4+nbdu2copLRRYsWMCyZcuIi4vj8OHD2NraCr/Z2dkRFxdHXFwc4eHhwvdt2rQhOTkZgLKyMtLS0tDX11eaF5mcffv2ERkZyZ9//in8NnXqVCENHx+fKnGfPXvGhg0b+O677/j+++/ZsGGD3OAg4/vvv6dp06YkJSUxdepUQbmtLr65uTk7duzA0NBQad5rm/7ChQuFckyePBkrKyuF8jQ1NYmLi0NfX5+SkhKWL1/O119/TXx8PF26dKnx5Xz06FGKioqIj48nJiaG/fv3C8r37t276dmzp9K4q1at4tChQ0ycOFGuPf9N+Pn5CfU4ZcqUWsUpKSmpUxq5ubns27dP+Pzq1Stmz57NhAkTSEpKIjExkQkTJtQ4OXgdli9fTseOHQGIjo5myZIl7N69m169evH555/XWo6szDt27MDV1bXe81mZygqGl5cXgwYN+styK7dFbSgtLX3t9Fq3bs2ePXuIj49nzpw5LFq0SJAZHBzMtm3bSEhI4PDhw0J5J0+ezNatWxXKO3v2LO3btyc2NlbpJLQ6Dh48yKNHj+oUp679fcSIEcya9XqHgL8OJSUlb4VCmp2dza5duzhw4ACHDx+mtLSUhIQEAKKiohg4cCDHjx9n4MCBgkL/xx9/kJCQQEJCAtu2bWPp0qVCfwwKCiI4OJjjx49z584dUlJS3ljZRESq42/zIY2MjCQ2NpZWrVqhq6tLjx49cHd3Z8iQIUIYU1NTjh49qjB+Tk4OLVu2BEBNTU14WVaHg4MDiYmJSCQS0tLSMDc3l5spKkNHRwdjY2MeP35Mq1atalW+M2fOMHjwYJo3bw6UK9qpqalVrHInTpzA09MTAGtra4KDg5FKpdXG7969e72lX5GEhAQ++eSTGmVLpVKkUikFBQVIpVJevHiBsbFxtXFUVFQoKCigpKSEwsJCNDQ00NbWrjGtipiamrJ9+3YA8vPz8fDwIDc3l5KSEry8vBg5ciT37t1j5syZ9OnTh4sXL6Kvr8+mTZvQ1NQkIyODhQsX0qhRI8zNzQW5r169IigoiIyMDNTU1PD392fAgAHExMTwww8/UFZWxvXr15k+fTrFxcXExcXRoEEDoqKihPqtTHUyT506RVFRES9fvmTz5s0sW7aM69evU1paiqenJyNHjuTGjRsEBARQXFxMWVkZERERrFu3jrt37yKRSBg0aBAdOnTA1NQUS0tLId0BAwYAkJaWJnx3+fJlQkNDKSwsRFNTk9DQUNq3b68wDT09Pby9vXn48CFlZWV4eHhgZ2eHm5sbfn5+nD59mgsXLrBkyRIsLS0ZPnw4X331FVu2bOHly5cKy1K5zLt27eL48eN8+umnQLm17cSJExQUFJCVlcXIkSOFlQ1FnDlzhoiICIqKijAyMiIsLIzGjRuzatUqTpw4gZqaGkOGDMHKyooTJ07wyy+/EBkZSUREBJs2bWL48OHY2NhgaWmJg4MDaWlpFBcXs2zZMtasWUNmZibu7u5MmDBBaT9bvXq1XFv4+fkRHh5OamoqKioqzJkzBzs7O9LS0tiwYQN6enpcvXqV6OhohfVbExX7q6mpKQ8fPhTa1tjYGCMjIwDs7e1JTk6mY8eOGBoa8uzZMx4/fiyMlQBXr15l5cqVFBYWIpFI2L9/P+fOnVNYpxs2bODkyZO8evUKMzMzgoODOXbsGBkZGfj4+KCpqcn+/fuxs7MjOjoaXV1drly5Qnh4OLt37yYiIoJHjx5x//59dHR0CAwMZMmSJTx48AAonxD36dNHYZljYmLIyMhg8eLF+Pv707BhQ27dusWDBw8ICwvj4MGDXLp0CRMTE7744gsAzMzMGDduHGlpaTRt2pS1a9eiq6vL1atXWbJkCQUFBbRp04bQ0FCaNWsmrM5cuHCBwYMHV+kvP//8M/v376e4uBhjY2PCw8Np1KgR/v7+aGtrk5GRwePHj/H19cXGxgaArVu3cujQIVRUVBg6dCg+Pj7cvXuXpUuX8vTpUzQ1NVm2bBkdOnQgMTGRjRs3oqqqSpMmTWptdS8tLaWwsBB1dXUKCwvR09MDyq3Ku3fvBmD06NG4ubnh6+tLcnIy9vb2NGjQACMjI4yNjbl8+TKGhoa8ePECMzMzIU5ycjLDhg2rVT5E3h5U3gEf0r9FIc3IyODIkSPExsZSWlqKs7MzPXr0qBLuwIEDcpbPinz88cfY2NjQv39/LCwscHZ2pmHD8uu+jhw5wvnz5wGYMmUKY8aMAcDY2Jjk5GSeP39OQkICTk5OtVJIHzx4wKtXr+Tu0N65cyeHDh0CwMfHBwsLC7k42dnZGBgYCJ/19fUVLnFlZ2cLSq66ujpNmjTh6dOntY5fkStXrvDtt9+yfPnyOse/f/8+9+7dExQaAIlEQlxcXJWwGhoaBAUF4ejoiJaWFsbGxjXeO21tbU1ycjJDhgyhsLCQgIAApcrczJkzCQkJqWK9Tk1NZeTIkQA0bNiQjRs3oq2tTU5ODuPGjWPEiBEAZGZmsmbNGkJCQvDy8uLYsWNIJBICAgJYtGgR/fv3Z8WKFYJc2UsgPj6emzdv4u7uzrFjxwC4ceMGBw8epKioCCsrK3x8fIiNjSU0NJTY2FimTp0KlN+xHhkZKfz/448/KpV56dIlDh06RPPmzVmzZg0DBgwgLCyM3NxcXF1dGTRoEN9++y1TpkzBycmJoqIiysrKmD9/Pjdu3BDaJCwsTOFzU5n27duzZ88e1NXV+emnn1i7di0REREK0zh9+jR6enqCZSUvL09OlqenJ2lpafj5+dGrVy85xXfz5s0Ky1K5zFlZWTRr1owGDRoIca9evUpsbCwNGjTAxsYGNzc3hZO/nJwcIiMj2bFjB1paWkRFRbFjxw4mT55MUlISR48eRUVFhdzcXJo2bSoozTJloTIGBgbs37+f0NBQ/P392bdvH0VFRdjb2zNhwgSl/axyWxw7doxr164RFxfH06dPGTt2rGB5vHLlCvHx8RgZGXHs2DGF9RsaGipXlzLs7e2rWAmjo6MZOnQooHicuXz5svC5e/fuXLhwAWtra9atW0fPnj0ZMWIE8+bNE5Q9ZXXq6enJ5MmThQmzr68vJ0+exMbGhm+++UboAzXx22+/sXfvXjQ1NZk/fz4ff/wxffv25cGDB7i7u5OYmFijDCi3Su/atYvk5GT+85//sG/fPjp16sTYsWO5evUq3bp14+XLl3Tv3h1/f382bNjAhg0bWLx4MX5+fsKzv27dOjZs2EBgYKAgd8+ePUD52FGxvzRp0oSPPvoIgLVr1xIdHY2bmxtQ7nayd+9ebt26xZw5c7CxseH06dMkJyfz3Xff0ahRI549ewbAokWLWLp0KW3btiU9PZ2lS5eya9cuNm3axPbt29HX11fqniZr588//5ytW7eir6/P9OnT+fDDD2nYsCGDBw8WDDn/+9//BOVUT09PWC3Jzs7GxMREkCd7H6irq8v1HwMDg7/dFUPkDSHuslfMuXPnGDlyJI0aNQKQs/DIiIyMRE1NDScnJ4UyPD09cXJy4syZMxw+fJiEhARhZmhnZ8fixYsVxrOysiIhIYH09HSCg6u/Y/bIkSOkpaVx+/Ztli1bJii8UL5k7+7urjSuIj8cRTMUZeFqG78ivXr1El4QdY2fkJCAtbU1ahWuOVOkjEK5P+i+ffuIjY3FyMiIZcuWsWXLFjw8PJTKv3z5MqqqqqSmppKbm8vEiRMZNGiQYNmpSOVlRh8fHwoKCigrKyMmJkYo35o1a/j1119RVVUlOzubJ0+eAOXLm926dQOgR48e3L9/n7y8PPLy8ujfvz9QrmzLJiPnz59n8uTJAHTo0IH333+f27dvA/DBBx8IltwmTZoIfbVz587897//FfLo5+cnp/SsX79eqcyKluszZ85w4sQJvvrqK6Dcsvrnn39iamrK5s2befjwIaNGjaJt27ZK67Ym8vLyWLBgAZmZmaioqFBcXAygMI3OnTuzYsUKVq5cyYcfflin5VxlZalc5sePH6OjI38X9sCBA2nSpAlQXl/3799XqJCmp6fzxx9/MGHCBKC8L5qamqKtrU3Dhg0JDAxk+PDhDB8+vFZ5lk1iOnfuzMuXL4W2btiwIbm5uTRq1EhpP6vI+fPnsbe3R01NjRYtWtCvXz+uXLmCtrY2vfUdNsQAACAASURBVHr1Evq5svpduHBhrfL7888/Ex0dzd695fdS1/Scv/fee8LSupeXl0KZyuoUyi3t27Zto7CwkGfPntGpUyeF43V1WFpaoqmpCcBPP/0ktyT+4sULXrx4UavVkg8//BAVFRW6dOlCixYtBANBx44duX//Pt26dUNVVVWwOEskEjw9Pas8+87OznJ1UZ2F+saNG3z55Zfk5eWRn58vt4I3cuRIVFVV6dixo9Anzp49i4uLi/Bua968Ofn5+Vy8eFEuzaKiIqDcouvv74+tra1SdykoVyBl4+Lz589JTk4mOTmZJk2a4OXlRVxcHBKJRGn8+nzPiIi8Kf62JfvqOv3Bgwc5deoUO3fuFMIFBATw+++/o6enJzyYbdq0YeLEiXz00UcMHDiQp0+f1piuvb09zs7OODs7o6r6/2cM33zzDd999x2AYL2QKbYXL15k9uzZDB06VG7pqyLp6emCEjxv3jwMDAz45ZdfhN+zs7OFAbEiBgYG/PnnnxgYGFBSUkJeXh7NmzevdXxl1DX+kSNHlCrxlZFtLGrTpg0Atra2NW4+OHz4MBYWFmhoaPDee+9hbm7OlStXFCqklVm1ahVdu3Zl9erVBAcHs2HDBuLj48nJySEmJgYNDQ0sLS0Fx/6Kljc1NTVevXqFVCpV2ueqc+KvKEtVVRUNDQ3h/+p8AquTKXtZyVi/fj3t27eX+65Dhw6YmJhw6tQp3N3dCQkJqVJXHTt25Ndff1Wajox169bxwQcfsHHjRu7duyf4uDo6OlZJY+DAgcTExHD69GlWr17N4MGDBQtZbVBUlvT0dLkya2pqCi9kGZXbTFndSqVSBg8ezJo1a6r8Fh0dzdmzZ0lISGDPnj212lxWsT0rt3VJSUm1/axyvpShpaUl/N+uXTuF9VsbC+m1a9cEK5lMoTcwMBCW76H8OZdZyKB8UiBTBpWhrE5fvXrF0qVLOXDgAK1atSIiIkJh2aG8zWR1UDlMxbYvKytj//79NeZJEbL2UVFRUdhWiqiNclX5eayIv78/mzZtomvXrsTExMiNqRXzIEPROCOVSmnatKnCCX5wcDDp6emcOnWK0aNHExsbW2WyVpmffvqJ1q1bo6urC8CoUaO4ePEiEolEmIDo6enx6NEjIYyyflL5+4cPH8r1H5F3iH/YQpqSksLy5cspKyvD1dW1ykrPoUOHBF2ucePGBAUF0bVr12pl/i0l6NevH0lJSRQWFvLixQtOnjwp/JaSksLWrVuJjIyUGyjCwsKIi4sTCnDq1ClhAMzMzERVVZWmTZvWmPb777/Pp59+ysSJE+W+nzRpkrAppfJSsZmZGU5OTtW+4ExMTIT4I0aMYMiQIZw5c4bnz5/z/Plzzpw5Ize7lmFpaSnsgD927BgDBgxARUWl1vGVUZf4t27dIjc3V/Ajqgl9fX1u3rwpLAf9+OOPdOjQAYCkpCRWr15dJU6rVq1IS0tDKpXy8uVL0tPTqygu1aGhoYG3tzeXLl3i5s2b5OXl8d5776GhocHPP//M/fv3q43ftGlTtLW1hR3F8fHxwm/9+vUTPt++fZs///yzTnlTRG1lDhkyhD179gh9+ffffwcgKysLIyMjpkyZgqWlJf/9739p3Lgx+fn5QlxHR0cuXrzIqVOnhO9SUlLkLLdQbiGV9emKpy0oSiM7O5tGjRohkUhwd3cX8lMblJWlMm3btq2xvZRhamrKhQsXyMzMBKCgoIDbt2+Tn59PXl4ew4YNY+HChcIu9Mp1VleU9bPKcvv160diYiKlpaXk5ORw7tw5evfuXUWesvqtuLmw4p9sEH/w4AGffPIJ4eHhtGvXTpDXq1cv7ty5Q1ZWFkVFRSQkJMhZMO/cuUOnTp2qLaOyOpUpljo6OuTn5wsuJ4rKb2hoSEZGBgDHjx9Xmpasj8ior1MzZJSVlQn5jI+Pp0+fPjRp0oSmTZsKz35cXBz9+vVTGL9yufLz82nZsiXFxcVyY4YyBg8ezIEDBygoKADKN5dqa2vTunVrwTVBKpUK/fPu3buYmJjg5eWFjo6OnHKojPfff5/09HTBh//s2bPC+GtpaUlsbCwAsbGxwgqApaUlCQkJFBUVkZWVxZ07d+jduzd6eno0btyYS5cuIZVK5eKIvGOoqtTvXzVUt9lShrKNmtXxt1hIe/TogZ2dHRKJBENDQzmn9mXLllFUVMS0adOAckVP0dJ6XFwcYWFhaGpqoqamxqpVq+SWm6tj/Pjxdc7zzJkzcXFxqfVRNc2bN8fDw4OxY8cCMHfuXGHJsqIv19ixY/H19cXKyopmzZqxdu3aGuPv2rWLbdu28eTJE5ycnBg2bBjLly+X8yGtbfpQvlxvZ2dXZWavzIdUX1+fuXPnMmnSJNTV1TE0NCQsLAwoH2AVLb9NmjSJgIAAHBwckEqluLi4KJ0NKfMh1dTUZPr06Wzfvh0fHx/mzJmDi4sL3bp1q5UCGRYWJmxqqqicT5w4kSVLluDo6IiamhphYWEKrR91obYyPTw8CA0NxcnJCalUiqGhIVu2bOHIkSMcOnQIdXV1WrRoIbSfubk5Dg4OWFhYsGDBAjZv3kxoaCihoaGoq6vTpUsXAgMDBd81gBkzZuDv78+OHTvkfIQVpSHbkKKqqoq6ujpBQUG1LrOyslRGS0sLIyMjMjMza9wMVxldXV3CwsL47LPPBCurt7c3jRs3xsPDQ1CiAgICgPJVjkWLFrF7927Wr19fp7SgXOlX1M90dHTk2sLPz0+wUqmoqODr60vLli25deuWnLzr16+/Vv1u3LiRZ8+esXTpUqDcIhkTE4O6ujqLFy9mxowZlJaWMmbMGEEBLS4uJjMzUzjBovJzX1OdtmvXDldXVxwdHTE0NJTzF3V2dmbJkiXCpiZPT08CAwPZsmWLnK9iZQIDAwkODsbR0ZHS0lL69u1bo+tUXdDS0uLGjRu4uLigra3Nl19+CZQfgSXb1CTbtKWIyv3Fy8sLV1dXDA0N6dy5c42Tm6FDh3Lt2jXGjBmDhoYGw4YN47PPPmPlypUEBQURGRlJSUkJdnZ2dO3alfDwcDIzM5FKpQwYMEDpmFjRh9TExARra2ucnZ1RV1enW7dujBs3DoBZs2bh7e1NdHQ0rVq1Yt26dQB06tQJW1tb7OzsUFNTY/HixcL7MigoiICAAAoLCxk6dKjgnywi8rpUt9lShrKNmtWhIv0HDiWLiIhAS0urWp9MkTeLmZkZFy9erDGcj48PCxcuFJaKaotsF3dtNkmIvP0kJSWRkZEh7LQXqX+SkpL47bff8Pb2ftNZ+ceo7Tgl8tcIbFC9W0Ftqc8l2BqMdnXCrHHDmgPVEse9y+tNFoCazevpSWW/HqnXfKj2U+57ffToUVJTU1m+vLzssbGxXL58Walb4Pbt27l165YQXhni1aEiQPlSlkQiISoqqtqzW5VdElAdbm5uZGVlCf58Iu8+VlZWclZckfqnpKSE6dOnv+lsiIiI/Buo581q+/fvZ//+/cLncePGCZb6umyWq7xRszr+EYW0NmdfirxZzpw587fJlp2OIPJ/i5oOxnd1da2y+Sk8PFzu+DUR5Sg7Mu/fxoEDB6r455ubm9d4lJwi3nbraGpqapVJfevWrdm4ceMbypGIiGIqKqCVqWmzpQxFGzWrQ7SQioiIvBG+//77N50FkX+AMWPGCGdF/1/HwsKiypnWIiL1wj+4y77iZkt9fX0SEhKqbHZWtlGzOkSFVERERERERESgvWb9qAb/1hNPi+tz60xe1Su73wj/4PmyyjZbyq5bnjBhgtKNmtXxj2xqEhEREREREXk72N5U8XncdeXfqpA2Uas/a6JL1IJ6kwWg5vrZa8Uru6D8OLbXQdV8VL3Kqw2ihVRERERERERE5G3mHbg69O0vgYiIiIiIiIiIyFuNaCEVEREREREREXmbqc+DWt8QokIqIiIiIiIiIvI2Iy7Zi4iIiIiIiIiIiPw1RIX0/yArVqzAxsYGR0dH5s6dS25ursJwly5dwtXVFYlEgq2tLREREQDExMQwYMAAJBIJEokEPz8/APz9/TExMeHFixeCjJCQELp06UJOTk4V+U+ePGH27Nk4OTlhZ2fHzJkzAbh37x69e/dm9OjR2NraMnbsWA4ePCgXNyUlhbFjx2JjY4NEIsHb25sHDx4oLEdKSgrW1tZYWVkRFRWlMIxUKiUkJAQrKyscHR357bffaoyfmJiIvb09Xbt25cqVKwrl1jb9bdu2CfXp4OBAt27dFN50ZGlpiaOjo5De2bNncXZ2RiKRMGHCBDIzM5XmQ0Z4eDj29vbY2toSEhIi3Loxf/58+vfvz9GjR6vEUdYXZOVT1hb+/v5YWlri5OSEtbU1fn5+ZGdnC3Hz8/NZvHgxI0eOxN7enkmTJpGeng6UXxNZX+zbt4/Y2FgAbt68iUQiYfTo0dy9e5fx48fXWZ5UKmXKlClCX6/PvNZEWloa/v7+f4vc2bNnA5CcnCz01ZycHFxdXRk9ejTnzp1j5syZSscMRcTExFR7n72/v7/CPlcdlpaWCseU2lKxDk+ePMn69etfW1ZtqNznamL+/PlYW1vj4OBAQEAAxcXFwOuNU8+ePWPatGmMGjWKadOm8fz5v+SYIpH6RUWlfv/eAOKS/f9BBg8ezPz581FXV2flypVs2bIFX1/fKuEWLFjAunXr6Nq1K6Wlpdy+fVv4zc7OTuG9tW3atCE5ORmJREJZWRlpaWlKryJdv349gwYN4uOPPwbKb3WoKEemQGRlZeHp6UlZWRljxozh+vXrhISEEBkZSYcOHYDyF+j9+/d5//335dIoLS0lODiYHTt2oK+vz9ixY7G0tKRjx45y4VJSUrhz5w7Hjx8nPT2doKAgvv/++2rjd+7cmYiIiGpvnKlt+jNmzGDGjBkAnDhxgp07d9K8eXOFMr/++mt0dXUBCAoKYtOmTXTo0IFvvvmGyMhIvvjiC6X5uXDhAhcuXODQoUMATJw4kV9++YUPPviA1atXK1V0lPWF2rSFn58fNjY2SKVSvv76a6ZMmUJ8fDwNGjTg888/p3Xr1hw/fhxVVVWysrK4efOm0vy/LhMmTBD+T05OZsSIEcybNw+Ab7/9ttZypFIpUqmUlJQUunbtira2dr3n9d/AiBEjGDFiBFA+6Wnfvj0rVqwAoG/fvm8ya3+ZkpISuc/Dhw9n3bp1zJw5k0aNGv0taVbuczXh5OQk3Og0f/58vv/+eyZOnPha41RUVBQDBw5k1qxZREVFERUVpXC8F3nLeQeW7EWF9B0mMjKS2NhYWrVqha6uLj169MDd3Z0hQ4YIYUxNTZVaJ3JycmjZsvw8OjU1tSpKlCIcHBxITExEIpGQlpaGubk5qampCsM+evSIwYMHC5+7du2qMJyRkRH+/v6sWLGCMWPGsHXrVmbPni0oQIDw8qzM5cuXMTY2xsjICAB7e3uSk5OrlCU5OZnRo0ejoqKCqakpubm5PHr0iPv37yuNXzF9ZdQ2/YokJCTg4OBQo2wZMivdixcvFF7fVhEVFRWKioooLi5GKpVSXFxMixYtakxDWV+oS1uoqKgwdepUkpKSSElJoXPnzqSnp7Nq1SpUVcsHUyMjI6GuZOTn5+Ph4UFubi4lJSV4eXkxcuRIXr58ibe3Nw8fPqSsrAwPDw/s7OxYtWoVJ06cQE1NjSFDhrBgwQIiIiLQ0tKiY8eOfP3116iqqvLrr7+ye/duzMzMhCspt23bRmJiIkVFRVhZWTFv3jzu3bvHzJkz+eCDD7h06RIbN24kPj6ejz76qEoZpVIp4eHhpKamoqKiwpw5c7Czs+PRo0d8+umnvHjxgtLSUoKCgjAzMyMwMJCMjAxUVFQYM2YMU6dOrbEtNDQ0BEU4IiKCe/fu8fjxY+7cuYO/vz+XLl0iNTUVPT09Nm/ejIaGBmfPnmXFihWUlpbSs2dPli5dSoMGDUhJSSE0NBQdHR169OghpBETE0NGRgaurq6sXLmSwsJCJBIJ+/fvx87OjujoaHR1dYmLi2P37t0UFxdjYmLCkiVLUFNT48CBA0RFRdGyZUvatm1LgwYNqi3TuXPn2LlzJ48fP8bX1xcbGxvS0tL46quv2LJlCwDBwcH07NkTFxcXALZv305aWhoAq1evxtjYmJycHJYsWSJY6BcuXEifPn2IiIgQnmcdHR0mTZok1KGKigr9+/fn5MmT2NnZyeWrtLSUVatWCVcrf/TRR7i5uSmtz4yMDL744gtevnyJjo4OYWFhXL16tUqfq4lhw4YJ//fu3VtYVXidcSo5OVlIc/To0bi5uYkKqci/ElEhfUfJyMjgyJEjxMbGUlpairOzs9wLR8aBAweU3on98ccfY2NjQ//+/bGwsMDZ2ZmGDRsCcOTIEc6fPw/AlClThKsBjY2NSU5O5vnz5yQkJODk5KRUIZ00aRKffvope/bsYdCgQbi4uCi1pvbo0YNbt24B8Mcff+Du7l6resjOzsbAwED4rK+vz+XLl2sMZ2BgQHZ2dq3jV5Ylu7+3rvELCgpITU1l0aJFwnczZ84kJCREYd0sX76cWbNm0bBhQ7S1tfnuu++qzZuZmRkffPABQ4YMQSqVMnnyZKWKdWBgIOPHj6dXr15K+0Jd2kJG9+7duXXrFioqKnTr1g01NbVqwzds2JCNGzeira1NTk4O48aNY8SIEYLSJVuezMvL49mzZyQlJXH06FFUVFSqLC0PGzaM8ePHo6WlVSXfZ86cITMzk+joaKRSKXPmzOHXX3+lVatW3L59m7CwMIKCgoByS7PsBpKKHD9+nGvXrhEXF8fTp08ZO3Ysffv25fDhwwwZMoQ5c+ZQWlpKQUEBV69eJTs7m8OHDwMIed22bRvx8fFVZPfr14/PP/8cc3NzzM3Nhe/v3r3Lrl27uHnzJuPGjWP9+vX4+fkxd+5cTp8+jYWFBf7+/uzcuZN27drh5+fH3r17mTBhAosWLeLrr7/G2NgYb2/vKml269aNefPmkZGRUWVF5ObNmyQmJrJv3z40NDQICgoiPj6eQYMGERERQUxMDNra2kyZMoXu3btX28aPHj1i79693Lp1izlz5mBjY1NteABtbW2io6OJjY0lNDSULVu2sHz5cj7++GP69u3LgwcPcHd3JzExEYDffvuNvXv3oqmpCSBXhz179uT8+fPY2dmRnJxMRkYGXl5e7N+/n3v37nHw4EHU1dV59uwZr169UlifkyZNIiQkhE2bNqGrq8uRI0dYu3YtYWFhSvtcRRQ958XFxcTFxREYGAi83jj1v//9T5io6unp/SVXB5F/MW9omb0+ERXSd5Rz584xcuRIYQnK0tKySpjIyEjU1NRwcnJSKMPT0xMnJyfOnDnD4cOHSUhIEGbaypbsAaysrEhISCA9Pb1a3zELCwt++OEHUlNTSUlJwdnZWXg5V0bZhWJPnz5l6tSpFBYW8tFHH1UZ8BXFU1Hw4CoLV9v4FdHX12fr1q11Sl/GyZMnMf9/7L15WFRHFrD/NovEJQ6IghA3jBJwwwV0VBSDEBGEBlkCatxQDIlxGT8RlyhRFMEljzCRiDpBoyZOFGEUyYhEQQ0imqgB5XOCooARNC0RUbamf3/w6/sB3Q2NMRN17vs8PMql6lTdqrp1zz3nVNXQoY3c9UpZ6oiPjycuLg4bGxt27dpFREQE69ev15j+9u3b5Ofnk56eDsDs2bPJzs7Gzs5OJW1DOc2NBSUt9YWS1h4Op1Ao2Lp1K9nZ2ejo6FBSUsKDBw+wtLQkMjKSTZs28fbbb2Nra0ttbS0GBgasXLmScePGMW7cOK3LOXfuHOfOncPT0xOAJ0+eUFBQgJmZGebm5gwePFhIW1ZWptZdf+nSJdzc3NDV1aVz587Y2dnx008/MXDgQFasWEFtbS1OTk5YW1vTvXt3CgsLWbduHQ4ODoLnomH4hjaMHTsWfX19LC0tkcvljB07FgBLS0uKioq4desW3bp1E86T9vLyYv/+/YwYMYJu3brRq1cvoN5N3NIHTUMyMzPJycnBx8cHgMrKSoyNjbl69SrDhw8XwkpcXV0pKChoVpaTkxM6Ojr06dOHBw8eaFW+0ovg5uZGREQEAN9//z0///yzkObx48eCB8HR0VFQRptibGxMaWkpoBqu4O/vj55e/avS0NCQvLw8te05atQobty4waxZswCoq6sTvAraoO45/+STT7C1tRXCJJ7nPCXyiiG67EVeZJqbkI4cOcLp06eJj48X0i1fvpxr165hYmIiTI49evRgypQp+Pn5MXLkSB4+fNhiuW5ubnh5eeHl5SW4YgH2798vvPDi4uIwNTXF0NAQd3d33N3dmTdvHtnZ2WotudeuXRMseX369CE3NxcrKyuMjIxISkpi9+7dPHnyhF9++YX3338fAH9/f6ysrLh3754gp6SkRK1bu2vXro3S3bt3DxMTE2pqarTKr4mmclvKn5ycjJubm1ayZTIZeXl52NjYAPUv/pYUmdTUVGxsbGjfvj1Q/1Fw+fJltQppU9SNheb6QhPXr19n5MiR9O3bl7y8POrq6hqNk6YcPXoUmUxGQkIC+vr6ODo6UlVVhYWFBQkJCaSnp7NlyxZGjx7N/PnzOXToEJmZmSQnJ7Nv3z727t3b4r1B/cs+KChIZZFTUVER7dq1a3RNT09Pbb01Kdt2dnbs27eP9PR0QkJCCAwMxNPTk6SkJM6ePcuBAwdISUkhIiKiRQtpU5TucB0dHfT19YXnWUdHB7lc3uwHwO9RWhQKBV5eXixZsqTR9ZMnT7ZarjqXvq6uLnV1dcLvVVVVLcqpq6vj4MGDahXP5uJDq6qqBO9PQxQKhcq9aGpPhUJB3759OXjwYIv11Ia///3vyGQy/v73vwvXnmWeUirbJiYmlJaWCh8KIiIvGi+/Si2iFjs7O1JTU6msrOTx48ecOnVK+FtGRgY7d+4kNja20SQdERFBUlKSoIyePn1amHxv376Njo4OHTt2bLFsc3NzFi9ezJQpUxpdnzp1KklJSSQlJWFqakpmZiZPnz4F6i0Zd+7cwczMTEVeUVERUVFRTJs2Dai3IH3++eeNFr8o5ZiZmQllBAQEMHDgQAoKCigsLKS6uprk5GS11mJHR0cSExNRKBRcvnyZ119/HRMTE63za6I1+cvLy8nOztYYg9mUjh07Ul5eLiwwOnfunKC0X716Vdj9oCHm5uZkZ2dTW1tLTU0N2dnZWsXCahoLzfVFUxQKBXv37uX+/fuMGTOGHj16MGDAAKKjowXZBQUFnDx5slG+8vJyjI2N0dfX5/z58xQXFwP1L922bdsilUoJDAzk2rVrVFRUUF5ejoODAytWrGi0UK4l7O3tOXz4MBUVFYL8X3/9VW1aCwsLCgsLVa7b2dmRkpKCXC5HJpNx8eJFBg0aRHFxMcbGxvj5+eHt7U1ubi4ymQyFQsGECRNYuHAh165dA+rHt3IMN/xRp4xqQ+/evSkuLhZ2YEhKSsLOzo7evXtTVFQkrPpOTk5uldyRI0fy73//W2ijsrIyiouLGTRoEBcuXODhw4fU1NS0egW9kjfeeIP8/Hyqq6spLy8nMzOz0d+Vrvjjx48LuxzY29uzb98+Ic3169e1KqugoABLS0uV66NHj+brr78WFkKVlZVpbE8LCwtkMpkQj1xTU8N//vOfVt51Pd988w1nz55l69atjT56nmWeUuYBSExM1Hp+EXnJ0NF5vj9/AqKF9BWlf//+uLq6IpVKeeONNxg2bJjwt3Xr1lFdXS24lmxsbNS61pOSkoiIiOC1115DV1eXzZs3txjvp0SbrXRyc3NZt24durq6KBQKfH19GTRokPCS9PT0pKqqivbt2zNt2jQhTvWtt95i5cqVhISEUFFRgaGhIebm5nz00UcqZejp6bF69WrmzJmDXC7H29ubvn37AvVbAUH9CmwHBwfS09Nxdnambdu2bNiwocX8qamprFu3DplMxrx587C2tmb37t2NYki1LV8pb/To0SrWOE0xpHp6eoSHh7NgwQIkEgl/+ctfhHrfvXtXrZVowoQJnD9/Hnd3dyQSCWPGjNGoIDeMIdU0FrTpi6ioKLZv305lZSU2Njbs3btXsIitX7+ejRs3Cu1uaGiosuDC3d2d4OBgJk+ejLW1Nb179wbqV/hHRUWho6ODnp4eYWFhwgIopTVt+fLlau9NHfb29uTn5wtjt127dmzatEmt9dbBwYELFy7Qs2fPRtednZ358ccfkUqlSCQSli5dSpcuXThy5Ai7d+9GT0+Pdu3aERkZSWlpKcuXLxesgH/729+0rmtrMDAwICIigoULFwqLcAICAmjTpg1r164lKCgIIyMjhg0b1ioFqk+fPixatIjZs2dTV1eHvr4+q1evZvDgwcyfPx9/f3+6dOlCv379Glk6tcXMzEzYnq5Xr14qcajV1dX4+vpSV1fH1q1bgfoxu3btWtzd3ZHL5dja2jYbNqQkKytLaP+GMaS+vr4UFBTg4eGBnp4efn5+TJs2TWN7RkdHEx4eTnl5OXK5nBkzZgjPe0s0fM7XrFmDubk57777LlA/rubPn/9M81RQUBCLFi3i0KFDmJmZsW3bNu06QOSl4lUI0ZAoWhvQJfJSolxl3NoFKCIvFo6OjsIK5+aIjIxEKpVq3LlAE6GhoYwbN06rRSX/y5SWlrJs2TK++OKLP7sqIr+TBw8esGTJEvbs2fNnV+WFYXdH7WNfm+NFVZFe131+FsDJccuemywAXd9n+zBV/N/zz7Uekrf++lzlaYPoshcReYkwMjJi5syZzW7ED/X7hrZWGV2yZAkXLlxQG0sn0hgTExN8fX0bHQIh8nJy9+7dP+SgARGR/yoSnef782fcgmghFRERERH5o4mNjVWJJ3VxcSE4OPhPqtF/H+VOFE2Jj4/HyMjov18hDYgWUu15YSykNy4813pILIc/V3lalSkqpCIiIiIiIiJKRIVUoOKjuwAAIABJREFUe14YhfQ/2c+1HpK+Le+88rwRFzWJiIiIiIiICNysrG05kRa8qDGBNu2bPzmsVeg/R1m/h1dgH9KX/w5EREREREREREReakQLqYiIiIiIiIjIy8wrsO2TqJCKiIiIiIiIiLzM/Emb2T9PXv47EBEREREREREReakRLaQiIiIiIiIiIi8zosteRERERERERETkT0VcZS/yMhIZGSmcEf3hhx/y6NEjtekuX76Mr68vUqmUiRMnEhMTA0BCQgJ//etfkUqlSKVSQkJCgPpjJ21sbBqdXhMeHs5bb72FTCZTkf/gwQPmzZuHh4cHrq6uzJ07F4CioiIGDRqEp6cnEydOxMfHhyNHjjTKm5GRgY+PDy4uLkilUhYtWsTdu3fV3kdGRgYTJkzA2dmZuLg4tWkUCgXh4eE4Ozvj7u5Obm5ui/lTUlJwc3PDysqq2ZOTtCl/165dQntOmjQJa2trysrKVNI5Ojri7u4ulJeZmYmXlxdSqZSAgABu376tsR5KoqKicHNzY+LEiYSHh6PcinjJkiUMHz5cZfNy0DwWlPenqS9CQ0NxdHTEw8ODCRMmEBISQklJiZC3oqKC1atX4+TkhJubG1OnTuXKlSsADBkypMV70ZavvvqKxMREAPLz85FKpXh6enLnzh3h7PrWoFAomD59ujDWn2ddWyIrK+sPOVkoKyuLefPmAfXnuSvHqkwmw9fXF09PTy5evMjcuXM1zhnqSEhIaPY8+dDQULVjrjkcHR3Vzina0rANT506RXR09DPL0oamY05b1q1b12hsPcs8VVZWxqxZs3jnnXeYNWsWv/322/O5KRGR54xoIf0fZPTo0SxZsgQ9PT02bdrEjh07WLp0qUq6ZcuWsW3bNqysrJDL5dy6dUv4m6urK6tXr1bJ06NHD9LS0pBKpdTV1ZGVlYWpqanaekRHRzNq1ChmzJgBQF5eXiM5SgWisLCQ+fPnU1dXh7e3Nzdu3CA8PJzY2FjefPNNoP4FWlxcjLm5eaMy5HI5a9eu5YsvvsDU1BQfHx8cHR3p06dPo3QZGRkUFBRw4sQJrly5QlhYGN98802z+S0tLYmJiWHNmjUa21rb8ufMmcOcOXMA+O6774iPj8fQ0FCtzD179ghn2YeFhbF9+3befPNN9u/fT2xsLBs3btRYnx9++IEffviBf/3rXwBMmTKFCxcuMGLECLZs2aJR0dE0FrTpi5CQEFxcXFAoFOzZs4fp06dz9OhR2rRpw6pVq+jWrRsnTpxAR0eHwsJC8vPzNdb/WQkICBD+n5aWxvjx41mwYAEAX3/9tdZyFAoFCoWCjIwMrKys6NChw3Ov64vA+PHjGT9+PFD/0dO7d28iIyMBsLW1/TOr9ruprW28x+a4cePYtm0bc+fOpW3btn9ImU3HnDb89NNPKor/s8xTcXFxjBw5kqCgIOLi4oiLi1M734u85LwCLnvRQvoKExsby4QJE5g5cyZ/+9vf2L17NwD29vbo6dV/iwwePJh79+6pzS+TyejSpf7EDl1dXRUlSh2TJk0iJSUFqLdCDB06VCirKaWlpXTt2lX4XdPZ6927dyc0NJQvv/wSgJ07dzJv3jxBAYL6F6idnerJElevXqVnz550796dNm3a4ObmRlpamkq6tLQ0PD09kUgkDB48mEePHlFaWtps/jfffJPevXs32x7alt+Q5ORkJk2a1GyahiitdI8fP8bExKTZtBKJhOrqampqaoR/O3fu3GIZmsZCa/pCIpEwc+ZMOnfuTEZGBnfu3OHKlSssWrQInf9/hWj37t0ZN25co3wVFRXMmDEDLy8v3N3dOXnyJABPnjwhKCgIDw8PJk2axPHjxwHYvHkzrq6uuLu7C0pUTEwMu3fvJj09nT179vDNN9/w3nvvAY2tm7t27cLb2xt3d3fBalZUVMTEiRMJCwvDy8uLX375haNHjwoKW0MUCgWRkZFMmjQJd3d3oU6lpaVMnTpVsIBfvHgRuVxOaGiokDY+Pr7FfgDQ19cXFOGYmBiWLVvG7NmzcXR05MSJE0RFReHu7k5gYCA1NTVAvVLp6emJu7s7y5cvp7q6GqhXcFxcXAgICCA1NVUoQ2nVvH79Ops2bSI9PR2pVEplZWUj62RSUhI+Pj5IpVJWr16NXC4H4PDhw0yYMIFp06bxww8/tHhPFy9exN/fn/HjxwvW0oYWW4C1a9eSkJAg/L579258fHzw8fERPAMymYyPPvoIb29vvL29uXTpktBOH3/8MbNnz2bZsmWN2lAikTB8+HBOnTqlUi+5XE5kZCTu7u64u7sLc5Cm9szJyWHatGlMnjyZwMBASktL1Y65lpDL5URFRakojs8yTynzAHh6egrPj4jIi4ZoIX1FycnJ4fjx4yQmJiKXy/Hy8qJ///4q6Q4fPszEiRPVypgxYwYuLi4MHz6cMWPG4OXlhYGBAQDHjx8XJvvp06fj7e0NQM+ePUlLS+O3334jOTkZDw8Pzpw5o1b+1KlTWbx4Mfv27WPUqFFMnjxZozW1f//+3Lx5E4Cff/6ZwMBArdqhpKSkkdJramrK1atXW0zXtWtXSkpKtM7fVNaqVavYuXNnq/M/ffqUM2fO8PHHHwvX5s6dS3h4uNq2Wb9+PUFBQRgYGNChQwf++c9/Nlu3IUOGMGLECOzt7VEoFEybNq2RMtmQlStX4u/vz8CBAzWOhdb0hZJ+/fpx8+ZNJBIJ1tbW6OrqNpvewMCAzz77jA4dOiCTyXj33XcZP348Z86cwcTERHBPlpeXU1ZWRmpqKt9++y0SiUTFwuTg4IC/vz/t2rVTqffZs2e5ffs2hw4dQqFQEBwcTHZ2NmZmZty6dYuIiAjCwsKAekvzJ598olLXEydOkJeXR1JSEg8fPsTHxwdbW1uOHTuGvb09wcHByOVynj59yvXr1ykpKeHYsWMAQl137drF0aNHVWTb2dmxatUqhg4dytChQ4Xrd+7cYe/eveTn5/Puu+8SHR1NSEgIH374Ienp6YwZM4bQ0FDi4+OxsLAgJCSEAwcOEBAQwMcff8yePXvo2bMnixYtUinT2tqaBQsWkJOTo+IRyc/PJyUlha+++gp9fX3CwsI4evQoo0aNIiYmhoSEBDp06MD06dPp169fs31cWlrKgQMHuHnzJsHBwbi4uDSbHqBDhw4cOnSIxMRENmzYwI4dO1i/fj0zZszA1taWu3fvEhgYKHwg5+bmcuDAAV577TWARm04YMAALl26hKurK2lpaeTk5LBw4UIOHjxIUVERR44cQU9Pj7KyMqqqqtS259SpUwkPD2f79u106tSJ48eP8+mnnxIREaFxzDWk4XO+b98+xo8fr/KB+Szz1K+//irIMTEx+V2hDiIvMi+/hVRUSF9RLl68iJOTk+CCcnR0VEkTGxuLrq4uHh4eamXMnz8fDw8Pzp49y7Fjx0hOThYsBJpc9gDOzs4kJydz5cqVZmPHxowZw8mTJzlz5gwZGRl4eXkJL+emKOMcm/Lw4UNmzpxJZWUlfn5+KhO+unwSNa4NTem0zd8QU1NTdu7c2arylZw6dYqhQ4c2ctcrZakjPj6euLg4bGxs2LVrFxEREaxfv15j+tu3b5Ofn096ejoAs2fPJjs7W61Fs6Gc5saCkpb6QommvtSEQqFg69atZGdno6OjQ0lJCQ8ePMDS0pLIyEg2bdrE22+/ja2tLbW1tRgYGLBy5UrGjRunYm1tjnPnznHu3DnBmvTkyRMKCgowMzPD3NycwYMHC2nLysrUuusvXbqEm5sburq6dO7cGTs7O3766ScGDhzIihUrqK2txcnJCWtra7p3705hYSHr1q3DwcEBe3t7oHH4hjaMHTsWfX19LC0tkcvljB07FgBLS0uKioq4desW3bp1w8LCAgAvLy/279/PiBEj6NatG7169QLAw8OjxQ+ahmRmZpKTk4OPjw8AlZWVGBsbc/XqVYYPHy6Elbi6ulJQUNCsLCcnJ3R0dOjTpw8PHjzQqnylF8HNzY2IiAgAvv/+e37++WchzePHjwUPgqOjo6CMNsXY2JjS0lJANVzB399f8PIYGhqSl5entj1HjRrFjRs3mDVrFgB1dXWCV0EblM95SUkJ3377rcrzBc93nhJ5xXgF+ltUSF9hmpuQjhw5wunTp4mPjxfSLV++nGvXrmFiYiJMjj169GDKlCn4+fkxcuRIHj582GK5bm5ueHl54eXlJbhiAfbv3y+88OLi4jA1NcXQ0FBwh82bN4/s7Gy1ltxr164Jlrw+ffqQm5uLlZUVRkZGJCUlsXv3bp48ecIvv/zC+++/D4C/vz9WVlaNQhJKSkrUurW7du3aKN29e/cwMTGhpqZGq/yaaCq3pfzJycm4ublpJVsmk5GXl4eNjQ1Q/+JvSZFJTU3FxsaG9u3bA/UfBZcvX1arkDZF3Vhori80cf36dUaOHEnfvn3Jy8ujrq6u0ThpytGjR5HJZCQkJKCvr4+joyNVVVVYWFiQkJBAeno6W7ZsYfTo0cyfP59Dhw6RmZlJcnIy+/btY+/evS3eG9S/7IOCglQWORUVFdGuXbtG1/T09NTWW5OybWdnx759+0hPTyckJITAwEA8PT1JSkri7NmzHDhwgJSUFCIiIlq0kDalTZv6s7R1dHTQ19cXnmcdHR3kcnmzHwC/R2lRKBR4eXmxZMmSRtdPnjzZarnKe2iIrq4udXV1wu9VVVUtyqmrq+PgwYNqFc/m4kOrqqoE709DFAqFyr1oak+FQkHfvn05ePBgi/VsjuvXr3Pnzh3eeecdoN5r4uzsTGpq6jPNU0pl28TEhNLSUuFDQUTkRUOMIX1FsbOzIzU1lcrKSh4/ftwoPiojI4OdO3cSGxvbaJKOiIggKSlJUEZPnz4tTL63b99GR0eHjh07tli2ubk5ixcvZsqUKY2uT506laSkJJKSkjA1NSUzM5OnT58C9ZaMO3fuYGZmpiKvqKiIqKgopk2bBtRbkD7//PNGi1+UcszMzIQyAgICGDhwIAUFBRQWFlJdXU1ycrJaa7GjoyOJiYkoFAouX77M66+/jomJidb5NdGa/OXl5WRnZ6uNTVRHx44dKS8vFxYYnTt3TlDar169Kux+0BBzc3Oys7Opra2lpqaG7OxsjS77hmgaC831RVMUCgV79+7l/v37jBkzhh49ejBgwACio6MF2QUFBSoxbuXl5RgbG6Ovr8/58+cpLi4G6l+6bdu2RSqVEhgYyLVr16ioqKC8vBwHBwdWrFjRaKFcS9jb23P48GEqKioE+b/++qvatBYWFhQWFqpct7OzIyUlBblcjkwm4+LFiwwaNIji4mKMjY3x8/PD29ub3NxcZDIZCoWCCRMmsHDhQq5duwbUj2/lGG74o04Z1YbevXtTXFwsxFkmJSVhZ2dH7969KSoqElZ9Jycnt0ruyJEj+fe//y20UVlZGcXFxQwaNIgLFy7w8OFDampqWr2CXskbb7xBfn4+1dXVlJeXk5mZ2ejvSlf88ePHhThge3t79u3bJ6S5fv26VmUVFBRgaWmpcn306NF8/fXXwkKosrIyje1pYWGBTCbjxx9/BKCmpob//Oc/rbzr+kVW586d47vvvuO7776jbdu2Qnzvs8xTyjwAiYmJWs8vIi8ZEsnz/fkTEC2kryj9+/fH1dUVqVTKG2+8wbBhw4S/rVu3jurqasG1ZGNjo9a1npSUREREBK+99hq6urps3ry5xXg/JdpspZObm8u6devQ1dVFoVDg6+vLoEGDhJekp6cnVVVVtG/fnmnTpglxqm+99RYrV64kJCSEiooKDA0NMTc356OPPlIpQ09Pj9WrVzNnzhzkcjne3t707dsXqN8KCOpXYDs4OJCeno6zszNt27Zlw4YNLeZPTU1l3bp1yGQy5s2bh7W1Nbt3724UQ6pt+Up5o0ePVrHGaYoh1dPTIzw8nAULFiCRSPjLX/4i1Pvu3btqrUQTJkzg/PnzuLu7I5FIGDNmjEYFuWEMqaaxoE1fREVFsX37diorK7GxsWHv3r2CRWz9+vVs3LhRaHdDQ0OVhRzu7u4EBwczefJkrK2thYVkN27cICoqCh0dHfT09AgLC6OiooIPPvhAsKYtX75c7b2pw97envz8fGHstmvXjk2bNqm13jo4OHDhwgV69uzZ6LqzszM//vgjUqkUiUTC0qVL6dKlC0eOHGH37t3o6enRrl07IiMjKS0tZfny5YIV8G9/+5vWdW0NBgYGREREsHDhQuRyOQMGDCAgIIA2bdqwdu1agoKCMDIyYtiwYa1SoPr06cOiRYuYPXs2dXV16Ovrs3r1agYPHsz8+fPx9/enS5cu9OvXr5GlU1vMzMyE7el69eqlEodaXV2Nr68vdXV1bN26Fagfs2vXrsXd3R25XI6trW2zYUNKsrKyhPZvGEPq6+tLQUEBHh4e6Onp4efnx7Rp0zS2Z3R0NOHh4ZSXlyOXy5kxY4bwvLdEc7HiSp5lngoKCmLRokUcOnQIMzMztm3bplV9RF42Xn6XvUTR2oAukZeSmJiYFoPqRV58HB0dOXToUItut8jISKRSqcadCzQRGhrKuHHjtFpU8r9MaWkpy5Yt44svvvizqyLyO3nw4AFLlixhz549f3ZVXhhWtjF6LnJeVBesTXvVEJFnxeuLj1tO1Ap0Pec/Uz5FkfbeIG2QdGvdu+N58KKOFxERETUYGRkxc+bMZjfih/p9Q1urjC5ZsoQLFy6ojaUTaYyJiQm+vr6NDoEQeTm5e/fuH3LQgIjIf5VXwGUvWkhFRERERP5wYmNjVeJJXVxcCA4O/pNq9N9HuRNFU+Lj4zEyej5WyeeBaCHVnhfGQnr3/z7XekjM33qu8rQqU1RIRURERERERJSICqn2iArp80Nc1CQiIiIiIiIi8lLz8i9qEhVSEREREREREZGXmVdgY/wX1aIuIiIiIiIiIiLyP4JoIRUREREREREReZkRLaQiIiIiIiIiIiIivw/RQioiIiIiIiIi8lLz8ltIRYVURERERERERORlRnTZi7yMREZGCmdEf/jhhzx69EhtusuXL+Pr64tUKmXixInExMQAkJCQwF//+lekUilSqZSQkBCg/thJGxubRqfXhIeH89ZbbyGTyVTkP3jwgHnz5uHh4YGrqytz584FoKioiEGDBuHp6cnEiRPx8fHhyJEjjfJmZGTg4+ODi4sLUqmURYsWcffuXbX3kZGRwYQJE3B2diYuLk5tGoVCQXh4OM7Ozri7u5Obm9ti/pSUFNzc3LCysmr25CRtyt+1a5fQnpMmTcLa2pqysjKVdI6Ojri7uwvlZWZm4uXlhVQqJSAggNu3b2ush5KoqCjc3NyYOHEi4eHhKLciXrJkCcOHD1fZvBw0jwXl/Wnqi9DQUBwdHfHw8GDChAmEhIRQUlIi5K2oqGD16tU4OTnh5ubG1KlTuXLlCgBDhgxp8V605auvviIxMRGA/Px8pFIpnp6e3LlzRzi7vjUoFAqmT58ujPXnWdeWyMrK+kNOFsrKymLevHlA/XnuyrEqk8nw9fXF09OTixcvMnfuXI1zhjoSEhKaPU8+NDRU7ZhrDkdHR7VzirY0bMNTp04RHR39zLK0oemYawmFQsGnn37KhAkTmDhxInv37hWut3aeKisrY9asWbzzzjvMmjWL33777fnfoIjIc0C0kP4PMnr0aJYsWYKenh6bNm1ix44dLF26VCXdsmXL2LZtG1ZWVsjlcm7duiX8zdXVldWrV6vk6dGjB2lpaUilUurq6sjKysLU1FRtPaKjoxk1ahQzZswAIC8vr5EcpQJRWFjI/Pnzqaurw9vbmxs3bhAeHk5sbCxvvvkmUP8CLS4uxtzcvFEZcrmctWvX8sUXX2BqaoqPjw+Ojo706dOnUbqMjAwKCgo4ceIEV65cISwsjG+++abZ/JaWlsTExLBmzRqNba1t+XPmzGHOnDkAfPfdd8THx2NoaKhW5p49e4Sz7MPCwti+fTtvvvkm+/fvJzY2lo0bN2qszw8//MAPP/zAv/71LwCmTJnChQsXGDFiBFu2bNGo6GgaC9r0RUhICC4uLigUCvbs2cP06dM5evQobdq0YdWqVXTr1o0TJ06go6NDYWEh+fn5Guv/rAQEBAj/T0tLY/z48SxYsACAr7/+Wms5CoUChUJBRkYGVlZWdOjQ4bnX9UVg/PjxjB8/Hqj/6OnduzeRkZEA2Nra/plV+93U1tY2+n3cuHFs27aNuXPn0rZt2z+kzKZjriUSEhL45ZdfSElJQUdHh19//RV4tnkqLi6OkSNHEhQURFxcHHFxcWrne5GXnZffQioqpK8wsbGxJCYmYmZmRqdOnejfvz+BgYHY29sLaQYPHqzROiGTyejSpQsAurq6KkqUOiZNmkRKSgpSqZSsrCyGDh3KmTNn1KYtLS1l9OjRwu+azl7v3r07oaGhREZG4u3tzc6dO5k3b56gAAHCy7MpV69epWfPnnTv3h0ANzc30tLSVO4lLS0NT09PJBIJgwcP5tGjR5SWllJcXKwxf8PyNaFt+Q1JTk5m0qRJLcpWorTSPX78GBMTk2bTSiQSqqurqampQaFQUFNTQ+fOnVssQ9NYaE1fSCQSZs6cSWpqKhkZGVhaWnLlyhU2b96Mjk69s6Z79+5CWympqKjggw8+4NGjR9TW1rJw4UKcnJx48uQJixYt4t69e9TV1fHBBx/g6urK5s2b+e6779DV1cXe3p5ly5YRExNDu3bt6NOnD3v27EFHR4fs7Gy+/PJLhgwZwo8//gjUW6pTUlKorq7G2dmZBQsWUFRUxNy5cxkxYgSXL1/ms88+4+jRo/j5+anco0KhICoqijNnziCRSAgODsbV1ZXS0lIWL17M48ePkcvlhIWFMWTIEFauXElOTg4SiQRvb2+1x0o2RV9fX1CEY2JiKCoq4v79+xQUFBAaGsrly5c5c+YMJiYmfP755+jr65OZmUlkZCRyuZwBAwbwySef0KZNGzIyMtiwYQNGRkb0799fKCMhIYGcnBx8fX3ZtGkTlZWVSKVSDh48iKurK4cOHaJTp04kJSXx5ZdfUlNTg42NDWvWrEFXV5fDhw8TFxdHly5d6NWrF23aNH8yzsWLF4mPj+f+/fssXboUFxcXsrKy+Mc//sGOHTsAWLt2LQMGDGDy5MkA7N69m6ysLAC2bNlCz549kclkrFmzRrDQr1ixgmHDhhETEyM8z0ZGRkydOlVoQ4lEwvDhwzl16hSurq6N6iWXy9m8eTNnz54FwM/Pj/fee09je+bk5LBx40aePHmCkZERERERXL9+XWXMtcRXX33Fli1bhOfC2NgYeLZ5Ki0tTSjT09OT9957T1RIX0VeAZe9qJC+ouTk5HD8+HESExORy+V4eXk1euEoOXz4MBMnTlQrY8aMGbi4uDB8+HDGjBmDl5cXBgYGABw/fpxLly4BMH36dLy9vQHo2bMnaWlp/PbbbyQnJ+Ph4aFRIZ06dSqLFy9m3759jBo1ismTJ2u0pvbv35+bN28C8PPPPxMYGKhVO5SUlNC1a1fhd1NTU65evdpiuq5du1JSUqJ1/qayVq1axc6dO1ud/+nTp5w5c4aPP/5/x9HNnTuX8PBwtW2zfv16goKCMDAwoEOHDvzzn/9stm5DhgxhxIgR2Nvbo1AomDZtmkbFeuXKlfj7+zNw4ECNY6E1faGkX79+3Lx5E4lEgrW1Nbq6us2mNzAw4LPPPqNDhw7IZDLeffddxo8fLyhdSvdkeXk5ZWVlpKam8u233yKRSFRcyw4ODvj7+9OuXTuVep89e5bbt29z6NAhFAoFwcHBZGdnY2Zmxq1bt4iIiCAsLAyotzR/8sknKnU9ceIEeXl5JCUl8fDhQ3x8fLC1teXYsWPY29sTHByMXC7n6dOnXL9+nZKSEo4dOwYg1HXXrl0cPXpURbadnR2rVq1i6NChDB06VLh+584d9u7dS35+Pu+++y7R0dGEhITw4Ycfkp6ezpgxYwgNDSU+Ph4LCwtCQkI4cOAAAQEBfPzxx+zZs4eePXuyaNEilTKtra1ZsGABOTk5Kh6R/Px8UlJS+Oqrr9DX1ycsLIyjR48yatQoYmJiSEhIoEOHDkyfPp1+/fo128elpaUcOHCAmzdvEhwcjIuLS7PpATp06MChQ4dITExkw4YN7Nixg/Xr1zNjxgxsbW25e/cugYGBpKSkAJCbm8uBAwd47bXXABq14YABA7h06RKurq6kpaWRk5PDwoULOXjwIEVFRRw5cgQ9PT3KysqoqqpS255Tp04lPDyc7du306lTJ44fP86nn35KRESExjHXkIbPeWFhIcePHyc1NZVOnTqxatUqevXq9Uzz1K+//ip8qJqYmPyuUAcRkT8SUSF9Rbl48SJOTk6CC8rR0VElTWxsLLq6unh4eKiVMX/+fDw8PDh79izHjh0jOTlZ+NLW5LIHcHZ2Jjk5mStXrjQbOzZmzBhOnjzJmTNnyMjIwMvLS3g5N0UZ59iUhw8fMnPmTCorK/Hz81OZ8NXlk6j5ktSUTtv8DTE1NWXnzp2tKl/JqVOnGDp0aCN3vVKWOuLj44mLi8PGxoZdu3YRERHB+vXrNaa/ffs2+fn5pKenAzB79myys7Oxs7NTSdtQTnNjQUlLfaFEU19qQqFQsHXrVrKzs9HR0aGkpIQHDx5gaWlJZGQkmzZt4u2338bW1pba2loMDAxYuXIl48aNY9y4cVqXc+7cOc6dO4enpycAT548oaCgADMzM8zNzRk8eLCQtqysTK27/tKlS7i5uaGrq0vnzp2xs7Pjp59+YuDAgaxYsYLa2lqcnJywtrame/fuFBYWsm7dOhwcHATPRcPwDW0YO3Ys+vr6WFpaIpfLGTt2LACWlpYUFRVx69YtunXrhoWFBQBeXl7s37+fESNG0K1bN3r16gWAh4dHix80DcnMzCQnJwcfHx8AKisrMTY25urVqwwfPlyJIz9RAAAgAElEQVQIK3F1daWgoKBZWU5OTujo6NCnTx8ePHigVflKL4KbmxsREREAfP/99/z8889CmsePHwseBEdHR0EZbYqxsTGlpaWAariCv78/enr1r0pDQ0Py8vLUtueoUaO4ceMGs2bNAqCurk7wKmhDw+e8uroaAwMDEhISOHHiBCtWrODAgQPPdZ4SecV4BfpbVEhfYZqbkI4cOcLp06eJj48X0i1fvpxr165hYmIiTI49evRgypQp+Pn5MXLkSB4+fNhiuW5ubnh5eeHl5SW4nAD2798vvPDi4uIwNTXF0NAQd3d33N3dmTdvHtnZ2WotudeuXRMseX369CE3NxcrKyuMjIxISkpi9+7dPHnyhF9++YX3338fAH9/f6ysrLh3754gp6SkRK1bu2vXro3S3bt3DxMTE2pqarTKr4mmclvKn5ycjJubm1ayZTIZeXl52NjYAPUv/pYUmdTUVGxsbGjfvj1Q/1Fw+fJltQppU9SNheb6QhPXr19n5MiR9O3bl7y8POrq6hqNk6YcPXoUmUxGQkIC+vr6ODo6UlVVhYWFBQkJCaSnp7NlyxZGjx7N/PnzOXToEJmZmSQnJ7Nv3z5hQUhLKBQKgoKCVBY5FRUV0a5du0bX9PT01NZbk7JtZ2fHvn37SE9PJyQkhMDAQDw9PUlKSuLs2bMcOHCAlJQUIiIiWrSQNkXpDtfR0UFfX194nnV0dJDL5c1+APwepUWhUODl5cWSJUsaXT958mSr5apz6evq6lJXVyf8XlVV1aKcuro6Dh48qFbxbC4+tKqqSvD+NEShUKjci6b2VCgU9O3bl4MHD7ZYz5YwNTXlnXfeAeo/8JcvXw482zylVLZNTEwoLS0VPhREXjVefoVUXGX/imJnZ0dqaiqVlZU8fvyYU6dOCX/LyMhg586dxMbGNpqkIyIiSEpKEpTR06dPC5Pv7du30dHRoWPHji2WbW5uzuLFi5kyZUqj61OnTiUpKYmkpCRMTU3JzMzk6dOnQL0l486dO5iZmanIKyoqIioqimnTpgH1FqTPP/+80eIXpRwzMzOhjICAAAYOHEhBQQGFhYVUV1eTnJys1lrs6OhIYmIiCoWCy5cv8/rrr2NiYqJ1fk20Jn95eTnZ2dkaYzCb0rFjR8rLy4UFRufOnROU9qtXrwq7HzTE3Nyc7OxsamtrqampITs7W6tYWE1jobm+aIpCoWDv3r3cv3+fMWPG0KNHDwYMGEB0dLQgu6CggJMnTzbKV15ejrGxMfr6+pw/f57i4mKg/qXbtm1bpFIpgYGBXLt2jYqKCsrLy3FwcGDFihWNFsq1hL29PYcPH6aiokKQr1xM0hQLCwsKCwtVrtvZ2ZGSkoJcLkcmk3Hx4kUGDRpEcXExxsbG+Pn54e3tTW5uLjKZDIVCwYQJE1i4cCHXrl0D6se3cgw3/FGnjGpD7969KS4uFnZgSEpKws7Ojt69e1NUVCSs+k5OTm6V3JEjR/Lvf/9baKOysjKKi4sZNGgQFy5c4OHDh9TU1LR6Bb2SN954g/z8fKqrqykvLyczM7PR35Wu+OPHjwu7HNjb27Nv3z4hzfXr17Uqq6CgAEtLS5Xro0eP5uuvvxYWQpWVlWlsTwsLC2QymRCPXFNTw3/+859W3nU9Tk5OnD9/HoALFy4IVuxnmaeUeQASExO1nl9ERP7biBbSV5T+/fvj6uqKVCrljTfeYNiwYcLf1q1bR3V1teBasrGxUetaT0pKIiIigtdeew1dXV02b97cYryfEm220snNzWXdunXo6uqiUCjw9fVl0KBBwkvS09OTqqoq2rdvz7Rp04Q41bfeeouVK1cSEhJCRUUFhoaGmJub89FHH6mUoaenx+rVq5kzZw5yuRxvb2/69u0L1C8cgPoV2A4ODqSnp+Ps7Ezbtm3ZsGFDi/lTU1NZt24dMpmMefPmYW1tze7duxvFkGpbvlLe6NGjVaxxmmJI9fT0CA8PZ8GCBUgkEv7yl78I9b57965aK9GECRM4f/487u7uSCQSxowZo1FBbhhDqmksaNMXUVFRbN++ncrKSmxsbNi7d69gEVu/fj0bN24U2t3Q0FBlwYW7uzvBwcFMnjwZa2trevfuDdSv8I+KikJHRwc9PT3CwsKEBVBKa5rSsqQN9vb25OfnC2O3Xbt2bNq0Sa311sHBgQsXLtCzZ89G152dnfnxxx+RSqVIJBKWLl1Kly5dOHLkCLt370ZPT4927doRGRlJaWkpy5cvF6yAf/vb37Sua2swMDAgIiKChQsXCotwAgICaNOmDWvXriUoKAgjIyOGDRvWKgWqT58+LFq0iNmzZ1NXV4e+vj6rV69m8ODBzJ8/H39/f7p06UK/fv0aWTq1xczMTNierlevXipxqNXV1fj6+lJXV8fWrVuB+jG7du1a3N3dkcvl2NraNhs2pCQrK0to/4YxpL6+vhQUFODh4YGenh5+fn5MmzZNY3tGR0cTHh5OeXk5crmcGTNmCM97SzR8zoOCgvg//+f/sGfPHtq1ayeEzzzLPBUUFMSiRYs4dOgQZmZmbNu2TbsOEHmpeBVCNCSK1gZ0ibyUKFcZt3YBisiLhaOjo7DCuTkiIyORSqUady7QRGhoKOPGjdNqUcn/MqWlpSxbtowvvvjiz66KyO/kwYMHLFmyhD179vzZVXlhWNnG6LnIeVFdsDbtm9/1oTV4ffFxy4laga7n/GfL+EDVY/O76Ny95TTPmRd1vIiIiKjByMiImTNnNrsRP9TvG9paZXTJkiVcuHBBbSydSGNMTEzw9fVtdAiEyMvJ3bt3/5CDBkRE/qtIJM/358+4BdFCKiIiIiLyRxMbG6sST+ri4kJwcPCfVKP/PsqdKJoSHx+PkdHzsUo+D0QLqfa8MBbSX4ufaz0wfuP5ytMCUSEVERERERERERAVUu0RFdLnh7ioSURERERERETkZeYVWNQkKqQiIiIiIiIiIi8zr4BC+qJa1EVERERERERERP5HEC2kIiIiIiIiIiIvNS+/hVRUSEVEREREREREXmZEl72IiIiIiIiIiIjI70O0kIqIiIiIiIiIvMy8/AZS0UL6v0hkZKRwRvSHH37Io0eP1Ka7fPkyvr6+SKVSJk6cSExMDAAJCQn89a9/RSqVIpVKCQkJAeqPnbSxsWl0ek14eDhvvfUWMplMRf6DBw+YN28eHh4euLq6MnfuXACKiooYNGgQnp6eTJw4ER8fH44cOdIob0ZGBj4+Pri4uCCVSlm0aBF3795Vex8ZGRlMmDABZ2dn4uLi1KZRKBSEh4fj7OyMu7s7ubm5LeZPSUnBzc0NKyurZk9O0qb8Xbt2Ce05adIkrK2tKSsrU0nn6OiIu7u7UF5mZiZeXl5IpVICAgK4ffu2xnooiYqKws3NjYkTJxIeHo5yK+IlS5YwfPhwlc3LQfNYUN6fpr4IDQ3F0dERDw8PJkyYQEhICCUlJULeiooKVq9ejZOTE25ubkydOpUrV64AMGTIkBbvRVu++uorEhMTAcjPz0cqleLp6cmdO3eEs+tbg0KhYPr06cJYf551bYmsrKw/5GShrKws5s2bB9Sf564cqzKZDF9fXzw9Pbl48SJz587VOGeoIyEhodnz5ENDQ9WOueZwdHRUO6doS8M2PHXqFNHR0c8sSxuajrmW0PRcP8s8VVZWxqxZs3jnnXeYNWsWv/322/O/QZEXAMlz/vnvI1pI/wcZPXo0S5YsQU9Pj02bNrFjxw6WLl2qkm7ZsmVs27YNKysr5HI5t27dEv7m6urK6tWrVfL06NGDtLQ0pFIpdXV1ZGVlYWpqqrYe0dHRjBo1ihkzZgCQl5fXSI5SgSgsLGT+/PnU1dXh7e3NjRs3CA8PJzY2ljfffBOof4EWFxdjbm7eqAy5XM7atWv54osvMDU1xcfHB0dHR/r06dMoXUZGBgUFBZw4cYIrV64QFhbGN99802x+S0tLYmJiWLNmjca21rb8OXPmMGfOHAC+++474uPjMTQ0VCtzz549wln2YWFhbN++nTfffJP9+/cTGxvLxo0bNdbnhx9+4IcffuBf//oXAFOmTOHChQuMGDGCLVu2aFR0NI0FbfoiJCQEFxcXFAoFe/bsYfr06Rw9epQ2bdqwatUqunXrxokTJ9DR0aGwsJD8/HyN9X9WAgIChP+npaUxfvx4FixYAMDXX3+ttRyFQoFCoSAjIwMrKys6dOjw3Ov6IjB+/HjGjx8P1CtHvXv3JjIyEgBbW9s/s2q/m9ra2ka/jxs3jm3btjF37lzatm37h5TZdMy1hKbn+lnmqbi4OEaOHElQUBBxcXHExcWpne9FRP5sRIX0FSY2NpbExETMzMzo1KkT/fv3JzAwEHt7eyHN4MGDNVonZDIZXbp0AUBXV1dFiVLHpEmTSElJQSqVkpWVxdChQzlz5ozatKWlpYwePVr4XdPZ6927dyc0NJTIyEi8vb3ZuXMn8+bNExQgQHh5NuXq1av07NmT7t27A+Dm5kZaWprKvaSlpeHp6YlEImHw4ME8evSI0tJSiouLNeZvWL4mtC2/IcnJyUyaNKlF2UqUVrrHjx9jYmLSbFqJREJ1dTU1NTUoFApqamro3Llzi2VoGgut6QuJRMLMmTNJTU0lIyMDS0tLrly5wubNm9HRqXfWdO/eXWgrJRUVFXzwwQc8evSI2tpaFi5ciJOTE0+ePGHRokXcu3ePuro6PvjgA1xdXdm8eTPfffcdurq62Nvbs2zZMmJiYmjXrh19+vRhz5496OjokJ2dzZdffsmQIUP48ccfgXpLdUpKCtXV1Tg7O7NgwQKKioqYO3cuI0aM4PLly3z22WccPXoUPz8/lXtUKBRERUVx5swZJBIJwcHBuLq6UlpayuLFi3n8+DFyuZywsDCGDBnCypUrycnJQSKR4O3trfZYyabo6+sLinBMTAxFRUXcv3+fgoICQkNDuXz5MmfOnMHExITPP/8cfX19MjMziYyMRC6XM2DAAD755BPatGlDRkYGGzZswMjIiP79+wtlJCQkkJOTg6+vL5s2baKyshKpVMrBgwdxdXXl0KFDdOrUiaSkJL788ktqamqwsbFhzZo16OrqcvjwYeLi4ujSpQu9evWiTZvmT8a5ePEi8fHx3L9/n6VLl+Li4kJWVhb/+Mc/2LFjBwBr165lwIABTJ48GYDdu3eTlZUFwJYtW+jZsycymYw1a9YIFvoVK1YwbNgwYmJihOfZyMiIqVOnCm0okUgYPnw4p06dwtXVtVG95HI5mzdv5uzZswD4+fnx3nvvaWzPnJwcNm7cyJMnTzAyMiIiIoLr16+rjDltUPdcP8s8lZaWJpTp6enJe++9JyqkryKvwKImUSF9RcnJyeH48eMkJiYil8vx8vJq9MJRcvjwYSZOnKhWxowZM3BxcWH48OGMGTMGLy8vDAwMADh+/DiXLl0CYPr06Xh7ewPQs2dP0tLS+O2330hOTsbDw0OjQjp16lQWL17Mvn37GDVqFJMnT9ZoTe3fvz83b94E4OeffyYwMFCrdigpKaFr167C76amply9erXFdF27dqWkpETr/E1lrVq1ip07d7Y6/9OnTzlz5gwff/z/jqObO3cu4eHhattm/fr1BAUFYWBgQIcOHfjnP//ZbN2GDBnCiBEjsLe3R6FQMG3aNI2K9cqVK/H392fgwIEax0Jr+kJJv379uHnzJhKJBGtra3R1dZtNb2BgwGeffUaHDh2QyWS8++67jB8/XlC6lO7J8vJyysrKSE1N5dtvv0Uikai4lh0cHPD396ddu3Yq9T579iy3b9/m0KFDKBQKgoODyc7OxszMjFu3bhEREUFYWBhQb2n+5JNPVOp64sQJ8vLySEpK4uHDh/j4+GBra8uxY8ewt7cnODgYuVzO06dPuX79OiUlJRw7dgxAqOuuXbs4evSoimw7OztWrVrF0KFDGTp0qHD9zp077N27l/z8fN59912io6MJCQnhww8/JD09nTFjxhAaGkp8fDwWFhaEhIRw4MABAgIC+Pjjj9mzZw89e/Zk0aJFKmVaW1uzYMECcnJyVDwi+fn5pKSk8NVXX6Gvr09YWBhHjx5l1KhRxMTEkJCQQIcOHZg+fTr9+vVrto9LS0s5cOAAN2/eJDg4GBcXl2bTA3To0IFDhw6RmJjIhg0b2LFjB+vXr2fGjBnY2tpy9+5dAgMDSUlJASA3N5cDBw7w2muvATRqwwEDBnDp0iVcXV1JS0sjJyeHhQsXcvDgQYqKijhy5Ah6enqUlZVRVVWltj2nTp1KeHg427dvp1OnThw/fpxPP/2UiIgIjWOuIQ2fc03P9bPMU7/++qug0JqYmPyuUAcRkT8SUSF9Rbl48SJOTk6CC8rR0VElTWxsLLq6unh4eKiVMX/+fDw8PDh79izHjh0jOTlZ+NLW5LIHcHZ2Jjk5mStXrjQbOzZmzBhOnjzJmTNnyMjIwMvLS3g5N0UZ59iUhw8fMnPmTCorK/Hz81OZ8NXlk6j5ktSUTtv8DTE1NWXnzp2tKl/JqVOnGDp0aCN3vVKWOuLj44mLi8PGxoZdu3YRERHB+vXrNaa/ffs2+fn5pKenAzB79myys7Oxs7NTSdtQTnNjQUlLfaFEU19qQqFQsHXrVrKzs9HR0aGkpIQHDx5gaWlJZGQkmzZt4u2338bW1pba2loMDAxYuXIl48aNY9y4cVqXc+7cOc6dO4enpycAT548oaCgADMzM8zNzRk8eLCQtqysTK27/tKlS7i5uaGrq0vnzp2xs7Pjp59+YuDAgaxYsYLa2lqcnJywtrame/fuFBYWsm7dOhwcHATPRcPwDW0YO3Ys+vr6WFpaIpfLGTt2LACWlpYUFRVx69YtunXrhoWFBQBeXl7s37+fESNG0K1bN3r16gWAh4dHix80DcnMzCQnJwcfHx8AKisrMTY25urVqwwfPlwIK3F1daWgoKBZWU5OTujo6NCnTx8ePHigVflKL4KbmxsREREAfP/99/z8889CmsePHwuWRkdHR0EZbYqxsTGlpaWAariCv78/enr1r0pDQ0Py8vLUtueoUaO4ceMGs2bNAqCurk7wKmhDw+dc03P9POcpkVeMV6C/RYX0Faa5CenIkSOcPn2a+Ph4Id3y5cu5du0aJiYmwuTYo0cPpkyZgp+fHyNHjuThw4ctluvm5oaXlxdeXl6CKxZg//79wgsvLi4OU1NTDA0NcXd3x93dnXnz5pGdna3Wknvt2jXBktenTx9yc3OxsrLCyMiIpKQkdu/ezZMnT/jll194//33AfD398fKyop79+4JckpKStS6tbt27doo3b179zAxMaGmpkar/JpoKrel/MnJybi5uWklWyaTkZeXh42NDVD/4m9JkUlNTcXGxob27dsD9R8Fly9fVquQNkXdWGiuLzRx/fp1Ro4cSd++fcnLy6Ourq7ROGnK0aNHkclkJCQkoK+vj6OjI1VVVVhYWJCQkEB6ejpbtmxh9OjRzJ8/n0OHDpGZmUlycjL79u1j7969Ld4b1Cu+QUFBKoucioqKaNeuXaNrenp6auutSdm2s7Nj3759pKenExISQmBgIJ6eniQlJXH27FkOHDhASkoKERERLVpIm6J0h+vo6KCvry88zzo6Osjl8mY/AH6P0qJQKPDy8mLJkiWNrp88ebLVctW59HV1damrqxN+r6qqalFOXV0dBw8eVKt4NhcfWlVVJXh/GqJQKFTuRVN7KhQK+vbty8GDB1usZ3M091w/yzylVLZNTEwoLS0VPhREXjVefoVUXGX/imJnZ0dqaiqVlZU8fvyYU6dOCX/LyMhg586dxMbGNpqkIyIiSEpKEpTR06dPC5Pv7du30dHRoWPHji2WbW5uzuLFi5kyZUqj61OnTiUpKYmkpCRMTU3JzMzk6dOnQL0l486dO5iZmanIKyoqIioqimnTpgH1FqTPP/+80eIXpRwzMzOhjICAAAYOHEhBQQGFhYVUV1eTnJys1lrs6OhIYmIiCoWCy5cv8/rrr2NiYqJ1fk20Jn95eTnZ2dkaYzCb0rFjR8rLy4UFRufOnROU9qtXrwq7HzTE3Nyc7OxsamtrqampITs7W6tYWE1jobm+aIpCoWDv3r3cv3+fMWPG0KNHDwYMGEB0dLQgu6CggJMnTzbKV15ejrGxMfr6+pw/f57i4mKg/qXbtm1bpFIpgYGBXLt2jYqKCsrLy3FwcGDFihWNFsq1hL29PYcPH6aiokKQ/+uvv6pNa2FhQWFhocp1Ozs7UlJSkMvlyGQyLl68yKBBgyguLsbY2Bg/Pz+8vb3Jzc1FJpOh+P/au/eYqu/7j+Ovw+EOKqisWK2rohUqVTYsXYOzDZtVEIpiKbKY2GGjklg0NquXVcMUyiqbTZkRm8VSrWayikiqomvsCjHDmqqhumpVYreirZehlZui8P39YTw/KR7OOcDhKOf5SJpwvudzXnw4Hug7n+/nYhiaMmWKFi1apK+++krSnc/33c/wvf/drxi1x8iRI3X+/HnLSu2ysjI9/fTTGjlypGpray2rvvfs2eNQ7rPPPqv9+/db3qNr167p/PnzGjdunA4fPqyrV6/q1q1bDq+gv2vo0KGqqalRS0uL6uvrVVVV1e75u7fi9+7da9nlYOLEidq6daulzcmTJ+36Xt98842eeOKJDtdjY2O1fft2y0Koa9euWX0/R4wYobq6Ost85Fu3bunMmTMO/tSd/1535e/U3ddI0q5du+z++wL0NkZI+6ixY8cqISFBycnJGjp0qKKjoy3PrVmzRi0tLZZbS+PHj7/vrfWysjLl5eXJ19dXZrNZf/rTn2zO97vLnq10/v3vf2vNmjUym80yDEOpqakaN26c5X+S06dP182bNxUQEKDZs2db5qmOGTNGv//97/XGG2+osbFRQUFBevTRR/Xaa691+B6enp5atWqVXn31VbW2tmrmzJkaPXq0pDtbAUl3VmA/99xzqqio0OTJk+Xn56e33nrL5us/+eQTrVmzRnV1dZo/f74iIiK0adOmdnNI7f3+d/NiY2M7jMZZm0Pq6empnJwcZWVlyWQyacCAAZZ+X7hw4b6jRFOmTNGhQ4eUlJQkk8mkX/7yl1YL5HvnkFr7LNjzb7F27Vpt2LBBN27c0Pjx47VlyxbLiFhubq7++Mc/Wt73oKCgDgsukpKSlJmZqZSUFEVERGjkyJGS7qzwX7t2rTw8POTp6ans7GzLAqi7o2nLly+/7892PxMnTlRNTY3ls+vv76/8/Pz7jt4+99xzOnz4sH7605+2uz558mQdO3ZMycnJMplM+t3vfqeQkBCVlpZq06ZN8vT0lL+/v95++21dunRJy5cvt4wCLlmyxO6+OsLHx0d5eXlatGiRZRFOenq6vL29tXr1as2bN0/BwcGKjo52qIAaNWqUFi9erIyMDLW1tcnLy0urVq1SVFSUFi5cqFmzZikkJERPPvlku5FOew0ZMsSyPd3jjz/eYR5qS0uLUlNT1dbWpnXr1km685ldvXq1kpKS1NraqgkTJnQ6beiuzz//3PL+3zuHNDU1Vd98841efPFFeXp66uWXX9bs2bOtvp8FBQXKyclRfX29WltbNWfOHMvvuy33/p5b+73uyt+pefPmafHixdqxY4eGDBmid999175/ADxc+sAte5Ph6IQuPJTurjJ2dAEKHixxcXGWFc6defvtt5WcnGx15wJrli1bpueff96uRSXu7NKlS1q6dKmKiopc3RV005UrV/T6669r8+bNru7KA+P33sE9kvOg3oIdH9D5rg+OmFG00nYjB5inL+zaCxttT6dzSEDPfAYc8aB+XgDcR3BwsF555ZVON+KX7uwb6mgx+vrrr+vw4cP3nUuH9n7yk58oNTW13SEQeDhduHDBKQcNAH2ZrQNfOjvEwRpGSAEATldYWNhhPunUqVOVmZnpoh71vrs7UfzYBx98oODg3h+RsoYRUvs9OCOkHU/265aA+x/MIt3Zn3fKlCntDmJYt25du/21Kyoq9OGHH+qvf/2rqqurlZubq48++qjTb8kcUgCA02VmZrpV8Xk/d3eiAHpcL84htefAF2uHOHS2ywwFKQAAsMht6eH5iHA+/wE9GldcXNxuC7O0tDSlpaVJsu/AGWuHOFCQAgAAwC73FqA/Zs9BDF05rOFBneIBAACAB4w9B75YO8ShMxSkAAAAsIs9B75YO8ShM9yyBwAAgF2sHcRgz2EznWHbJwAAALgUt+wBAADgUhSkAAAAcCkKUgAAALgUBSkAAABcioIUAAB0qqamRlVVVWpsbGx3vbKy0uGsL7/80nKyz9mzZ1VUVKSKiooe6ecbb7zRIzlffPGFioqKdPDgQYdfW11drYaGBknSjRs3VFBQoAULFig/P1/19fU90r++iFX2AADAqi1btmjbtm0KCwvTqVOntGLFCv3617+WJM2YMUOlpaV2Z61fv16VlZW6ffu2YmNjVV1drZiYGFVVVWnixInKzMy0O2vBggUdrn3++ed65plnJEkbN260O+ull17Sjh07JEl///vftW3bNk2ePFkHDx5UXFyc5s2bZ3fWtGnTVFZWJk9PT61cuVK+vr6aMmWKDh06pFOnTmn9+vV2Z7kT9iEFAABWffTRR9q5c6cCAgJUW1urrKwsnT9/XnPmzLnvEZGd2b9/v3bt2qWWlhbFxsaqsrJSgYGBevXVV5WamupQQXrx4kWFhYUpNTVVJpNJhmHoxIkTysjIcPRH1O3bty1fFxcXq6ioSAMHDlRGRobS0tIcKkjb2trk6XmnvDpx4oSlYJ8wYYKSk5Md7pu74JY9AACwqrW1VQEBAZKkYcOG6cMPP1RlZaXy8vIcLkjNZrPMZrP8/Pw0fPhwBQYGSpJ8fX3l4eFYSVJSUqLIyEht3LhR/fr10zPPPCMfHx/FxMQoJibGoay2tjb98MMPunr1qgzD0MCBAyVJ/v7+MpvNDmWNHj1aJWZ0q54AAAzHSURBVCUlkqTw8HAdP35cknTu3DlLoYqOeGcAAIBVgwcP1smTJxURESFJCggI0HvvvacVK1bo9OnTDmV5eXmpublZfn5+2rlzp+V6fX29wwWph4eHXnnlFU2dOlVvvfWWBg8erNbWVocy7mpoaFBKSooMw5DJZNLly5cVEhKixsZGh4vu3Nxc5ebmqrCwUMHBwZo1a5ZCQ0M1ZMgQ5ebmdql/7oA5pAAAwKrvv/9eZrNZISEhHZ47cuSIoqOj7c5qaWmRt7d3h+t1dXW6fPmyxowZ0+V+fvbZZzp69KiWLFnS5Ywfa25u1pUrV/TYY485/NqGhgbV1tbq9u3bCg0N1eDBg3usX30Rt+wBAIBVoaGhHYrR4uJiSXKoGJV032K0uLhYAwcO7FYxKknPP/+8hg4d2q2MexUXF8vPz69LxagkBQYGKjw8XJGRkTpw4ECP9auvoiAFAAAO2b59O1kuyuqrKEgBAIBDenK2H1mQmEMKAAAc9P333ys0NJQsF2T1VYyQAgAAmzZv3qyGhgYZhqGCggLNmDGjSycZkYX7oSAFAAA2lZSUKDAwUAcPHlRdXZ3y8vL05z//maxeyHIHFKQAAMCmuzP8KioqNHPmTIWHh3d5biRZ+DEKUgAAYFNkZKQyMjJUWVmpiRMnqqGhweHN7Mmi7LKGRU0AAMCmtrY2nTx5Uo899pj69++vq1ev6uLFiwoPDyfLyVnugKNDAQCATUeOHJEkff3112T1cpY7oCAFAAA2bdq0yfL1zZs39eWXX2rs2LHasmULWU7OcgcUpAAAwKaNGze2e/zdd98pPz+frF7IcgfMrgUAAA4LDQ3VmTNnyHJBVl/ECCkAALBpzZo1MplMkv5/wc6YMWPI6oUsd8AqewAAYFNpaanla7PZrKFDhyo6OpqsXshyB4yQAgAAm65fv645c+a0u7Z58+YO18jq+Sx3wBxSAABg065duzpcu3cUkCznZbkDRkgBAIBVu3fv1u7du1VbW6sFCxZYrjc2NiooKIgsJ2a5EwpSAABg1c9+9jOFhITo6tWrysjIsFwPCAhweJEOWSxqsoZFTQAAwC5XrlzR8ePHJUnjxo3ToEGDyOqlrL7OnJ2dne3qTgAAgAdbeXm5Fi9erObmZp05c0bvvvuuHnnkEY0aNYosJ2e5BQMAAMCGpKQk48qVK5bH//vf/4ykpCSyeiHLHbDKHgAA2GQYRrtbzkFBQTK6OOuPLPwYi5oAAIBNEydO1Ny5czVt2jRJ0t69ezVp0iSyeiHLHbCoCQAA2OUf//iHjhw5IsMw9PTTT2vy5Mlk9VJWX0dBCgAAui0tLU3FxcVkuSCrL2AOKQAA6LabN2+S5aKsvoCCFAAAdJvJZCLLRVl9AQUpAAAAXIqCFAAAdFtPLkkhy/2wqAkAADikrq5OAwcObHft9OnTeuKJJ8hyclZfxQgpAACwqqKiQnFxcUpPT9dXX32ladOm6eWXX9akSZNUVVVlaWdPcUWWY1lupbeOhAIAAA+fF1980Th79qxx9OhRIyYmxjh27JhhGIZx9uxZY/r06WQ5McudcFITAACwysPDQ2FhYZIkX19fRUVFSZLCwsLU1tZGlhOz3AkFKQAAsKpfv37avn27Ghoa1L9/f33wwQeKj4/Xv/71L/n7+5PlxCx3wqImAABg1XfffafCwkKZTCYtXLhQe/bs0Y4dO/Too49q6dKlltFAsno+y51QkAIAgG577733NH/+fLJckNUXsMoeAAB02759+8hyUVZfQEEKAAC6rSdvuJLlfihIAQBAtz2o57y7Q1ZfQEEKAAC67UEdPXSHrL6AghQAAHTb1KlTyXJRVl/AKnsAAGCVYRgqLy+XyWTS1KlTdejQIR04cEAjRoxQenq6PDzsH9siy7Esd0JBCgAArMrOzlZdXZ1aWloUGBiolpYWxcXFqaKiQoMGDdKbb75JlpOy3EovHlMKAAAeMomJiYZhGEZLS4sRExNj3Lx50zAMw7h165blObKck+VOGDcGAABWmc1mSZKXl5ciIyPl7e0tSfL09HT49jNZlF3W8M4AAACrBg8erMbGRknSpk2bLNcvX74sLy8vspyY5U6YQwoAABzW1NSk5uZmDRo0iKxezuqLGCEFAAAO8/f317Vr18hyQVZfREEKAAC6ZO7cuWS5KKuv8XR1BwAAwIMrJyfnvtcNw9D169fJcmKWO6EgBQAAVpWUlGjZsmWW1eL32r17N1lOzHInFKQAAMCqp556SqNHj9bPf/7zDs/95S9/IcuJWe6EVfYAAMCqa9euycfHR35+fmT1cpY7YVETAACwKigoyK7i6rXXXiOrh7PcCQUpAADotm+//ZYsF2X1BRSkAACg20wmE1kuyuoLKEgBAADgUhSkAACg23pyjTRZ7secnZ2d7epOAACAh9uwYcM0fPhwslyQ1Rew7RMAALApKSmpw7V+/fopMjJSmZmZCg4OJstJWe6AghQAANi0du1amc1mJSYmSpL27t0rSQoICNDRo0e1ceNGspyU5RYMAAAAG9LS0qxeS0xMJMuJWe6ARU0AAMCmpqYmVVdXWx5XV1erqalJkmQ2m8lyYpY74JY9AACw6fjx41qxYoUaGxsl3bn1nJubq1GjRumzzz5TQkICWU7KcgcUpAAAwKaioiJJd7YrMgxDHh4eCgwMVGRkpCIiIshyYpY7YNsnAABg09atW/Xpp5/qkUceUWNjo/bs2aPbt2+rpKRE169fV3R0NFlOynILrpi4CgAAHi4ZGRlGQ0OD5XFDQ4ORkZFhNDc3G/Hx8WQ5McsdsKgJAADYdOHCBXl5eVkee3l56cKFC/L19ZW3tzdZTsxyB56u7gAAAHjwJSYmKi0tTb/61a8kSZ9++qmmTZumpqYmhYWFkeXELHfAoiYAAGCXEydO6MiRIzIMQ9HR0XrqqafI6qWsvo6CFAAAAC7FHFIAAAC4FAUpAAAAXIqCFAAA2C0iIkLJyclKTEzUggULdP369U7b19XVKTU1VdOnT9cXX3zRS710TG1trRITE52Wv2zZMu3bt89p+X0BBSkAALCbr6+vysrKtHv3bg0YMEDbtm3rtH1VVZVGjhypXbt2acKECXZ9j9bW1p7oKh4iFKQAAKBLoqKidPHiRUnSf//7X82dO1cpKSn6zW9+o5qaGp08eVL5+fmqqKhQcnKybty4oYMHDyotLU0zZsxQVlaW5az3uLg4rV+/Xunp6dq3b1+n7QoKCjRjxgwlJSWppqZGktTY2Kjly5crKSlJSUlJ2r9/vyRZzbHHiRMnNHv2bKWkpGju3Lm6dOmSampq9NJLL1na1NbWKikpyWp72IeCFAAAOKy1tVVVVVWKi4uTJK1cuVIrV67Uzp07tXTpUv3hD39QRESEsrKylJCQoLKyMjU1NamwsFBFRUUqLS1VZGSk5cx3SfLx8dHf/vY3Pfvss522Cw4OVmlpqWbNmqX3339fkrRhwwYFBgbq448/1scff6xf/OIXqqur6zSnM7du3VJOTo4KCgq0c+dOzZw5U++8847CwsJ069Ytffvtt5KkvXv3Kj4+3mp72IeN8QEAgN1u3Lih5ORknT9/XmPHjlVsbKwaGxt17NgxLVq0yNKupaWlw2urq6t19uxZpaenS7pT9EVFRVmeT0hIsKvdCy+8IEmKjIzUJ598IunO1IB169ZZ2gwYMED//Oc/O83pzLlz53T69Gn99re/lSS1tbUpJCREkhQfH6/y8nLNmzdP5eXleueddzptD9soSAEAgN3uziGtr6/X/PnztW3bNqWkpKh///4qKyvr9LWGYSg2NrZd4XgvPz8/u9rdPZLTw8PDMt/UMAyZTCa7vl91dbVWrVolScrKytKYMWPu29fRo0eruLi4w3MJCQlatGiRJk+eLJPJpMcff1xff/211fawjVv2AADAYf369dObb76p999/Xz4+Pho2bJjKy8sl3SnmTp061eE1UVFROnr0qP7zn/9Ikpqbm3Xu3Lkut7tXbGystm7dann8ww8/WM0ZP368ysrKVFZWZjna88dGjBihuro6HTt2TNKd0dUzZ85IkoYPHy4PDw9t2LBB8fHxNtvDNkZIAQBAlzz55JMKDw/Xnj17lJ+fr+zsbBUWFur27dtKSEhQeHh4u/YDBw5UXl6elixZYrmlv3jxYo0YMaJL7e6VmZmp1atXKzExUR4eHlq4cKFeeOEFu3POnTunSZMmWR4vX75cBQUFysnJUX19vVpbWzVnzhyNHj1a0p1R0rVr1+rAgQOSJG9v707bo3McHQoAAACX4pY9AAAAXIqCFAAAAC5FQQoAAACXoiAFAACAS1GQAgAAwKUoSAEAAOBSFKQAAABwKQpSAAAAuNT/AXnFkKvEU2BlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x529.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73385d80b8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"F-measure\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "expected-enhancement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x529.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73411005f8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"Taxon Accuracy Rate\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "spread-termination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x529.2 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f73384ed9e8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"Taxon Detection Rate\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interested-crack",
   "metadata": {},
   "source": [
    "now we will focus on results at species level (for genus level, change to level 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "under-distribution",
   "metadata": {},
   "source": [
    "Method optimization\n",
    "\n",
    "Which method/parameter configuration performed \"best\" for a given score? We can rank the top-performing configuration by dataset, method, and taxonomic level.\n",
    "\n",
    "First, the top-performing method/configuration combination by dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "identified-buffalo",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## mock-20"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.431430</td>\n",
       "      <td>0.322209</td>\n",
       "      <td>0.368905</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000</td>\n",
       "      <td>0.425668</td>\n",
       "      <td>0.314717</td>\n",
       "      <td>0.361879</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.358282</td>\n",
       "      <td>0.237079</td>\n",
       "      <td>0.285344</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.119167</td>\n",
       "      <td>0.212956</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Method  \\\n",
       "4         q2-NB   \n",
       "39    q2-SFM-RF   \n",
       "49   q2-SFM-SDG   \n",
       "104   q2-SFM-NB   \n",
       "\n",
       "                                                                             Parameters  \\\n",
       "4                                                                 0.001:0.7:8000:[8, 8]   \n",
       "39   0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000   \n",
       "49                     0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "104                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000   \n",
       "\n",
       "     Precision    Recall  F-measure  Taxon Accuracy Rate  Taxon Detection Rate  \n",
       "4     0.431430  0.322209   0.368905             0.291667              0.368421  \n",
       "39    0.425668  0.314717   0.361879             0.240000              0.315789  \n",
       "49    0.358282  0.237079   0.285344             0.200000              0.263158  \n",
       "104   1.000000  0.119167   0.212956             0.500000              0.105263  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-3"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:6000:[8, 8]</td>\n",
       "      <td>0.453090</td>\n",
       "      <td>0.157622</td>\n",
       "      <td>0.233881</td>\n",
       "      <td>0.384615</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000</td>\n",
       "      <td>0.437113</td>\n",
       "      <td>0.131325</td>\n",
       "      <td>0.201971</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.436794</td>\n",
       "      <td>0.131325</td>\n",
       "      <td>0.201937</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000</td>\n",
       "      <td>0.496680</td>\n",
       "      <td>0.081928</td>\n",
       "      <td>0.140654</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Method  \\\n",
       "183       q2-NB   \n",
       "204   q2-SFM-RF   \n",
       "284  q2-SFM-SDG   \n",
       "384   q2-SFM-NB   \n",
       "\n",
       "                                                                             Parameters  \\\n",
       "183                                                               0.001:0.7:6000:[8, 8]   \n",
       "204  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000   \n",
       "284                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "384                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000   \n",
       "\n",
       "     Precision    Recall  F-measure  Taxon Accuracy Rate  Taxon Detection Rate  \n",
       "183   0.453090  0.157622   0.233881             0.384615                  0.25  \n",
       "204   0.437113  0.131325   0.201971             0.214286                  0.15  \n",
       "284   0.436794  0.131325   0.201937             0.214286                  0.15  \n",
       "384   0.496680  0.081928   0.140654             0.166667                  0.10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "## mock-2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</td>\n",
       "      <td>0.783545</td>\n",
       "      <td>0.594806</td>\n",
       "      <td>0.676253</td>\n",
       "      <td>0.348837</td>\n",
       "      <td>0.405405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:6000</td>\n",
       "      <td>0.728003</td>\n",
       "      <td>0.594806</td>\n",
       "      <td>0.654698</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>0.405405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.652199</td>\n",
       "      <td>0.594806</td>\n",
       "      <td>0.622181</td>\n",
       "      <td>0.365854</td>\n",
       "      <td>0.405405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000</td>\n",
       "      <td>0.682014</td>\n",
       "      <td>0.352422</td>\n",
       "      <td>0.464711</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.324324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Method  \\\n",
       "494   q2-SFM-RF   \n",
       "514  q2-SFM-SDG   \n",
       "469       q2-NB   \n",
       "534   q2-SFM-NB   \n",
       "\n",
       "                                                                             Parameters  \\\n",
       "494  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000   \n",
       "514                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:6000   \n",
       "469                                                               0.001:0.7:8000:[8, 8]   \n",
       "534                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000   \n",
       "\n",
       "     Precision    Recall  F-measure  Taxon Accuracy Rate  Taxon Detection Rate  \n",
       "494   0.783545  0.594806   0.676253             0.348837              0.405405  \n",
       "514   0.728003  0.594806   0.654698             0.365854              0.405405  \n",
       "469   0.652199  0.594806   0.622181             0.365854              0.405405  \n",
       "534   0.682014  0.352422   0.464711             0.352941              0.324324  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-21"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.635348</td>\n",
       "      <td>0.366473</td>\n",
       "      <td>0.464829</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000</td>\n",
       "      <td>0.633236</td>\n",
       "      <td>0.363151</td>\n",
       "      <td>0.461588</td>\n",
       "      <td>0.227273</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.609409</td>\n",
       "      <td>0.328168</td>\n",
       "      <td>0.426607</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003817</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.052632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "         Method  \\\n",
       "559       q2-NB   \n",
       "594   q2-SFM-RF   \n",
       "604  q2-SFM-SDG   \n",
       "659   q2-SFM-NB   \n",
       "\n",
       "                                                                             Parameters  \\\n",
       "559                                                               0.001:0.7:8000:[8, 8]   \n",
       "594  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000   \n",
       "604                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "659                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000   \n",
       "\n",
       "     Precision    Recall  F-measure  Taxon Accuracy Rate  Taxon Detection Rate  \n",
       "559   0.635348  0.366473   0.464829             0.272727              0.315789  \n",
       "594   0.633236  0.363151   0.461588             0.227273              0.263158  \n",
       "604   0.609409  0.328168   0.426607             0.181818              0.210526  \n",
       "659   1.000000  0.003817   0.007605             0.333333              0.052632  "
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/markdown": [
       "## mock-18"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>Taxon Accuracy Rate</th>\n",
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       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000</td>\n",
       "      <td>0.594351</td>\n",
       "      <td>0.435689</td>\n",
       "      <td>0.502801</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.557428</td>\n",
       "      <td>0.435689</td>\n",
       "      <td>0.489097</td>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>704</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000</td>\n",
       "      <td>0.509354</td>\n",
       "      <td>0.435689</td>\n",
       "      <td>0.469651</td>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:8000</td>\n",
       "      <td>0.557550</td>\n",
       "      <td>0.248758</td>\n",
       "      <td>0.344025</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.266667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         Method  \\\n",
       "684   q2-SFM-RF   \n",
       "664       q2-NB   \n",
       "704  q2-SFM-SDG   \n",
       "739   q2-SFM-NB   \n",
       "\n",
       "                                                                             Parameters  \\\n",
       "684  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000   \n",
       "664                                                               0.001:0.7:8000:[8, 8]   \n",
       "704                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000   \n",
       "739                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:8000   \n",
       "\n",
       "     Precision    Recall  F-measure  Taxon Accuracy Rate  Taxon Detection Rate  \n",
       "684   0.594351  0.435689   0.502801             0.294118              0.333333  \n",
       "664   0.557428  0.435689   0.489097             0.294118              0.333333  \n",
       "704   0.509354  0.435689   0.469651             0.312500              0.333333  \n",
       "739   0.557550  0.248758   0.344025             0.363636              0.266667  "
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/markdown": [
       "## mock-4"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:2000:[8, 8]</td>\n",
       "      <td>0.488018</td>\n",
       "      <td>0.156813</td>\n",
       "      <td>0.237356</td>\n",
       "      <td>0.269231</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000</td>\n",
       "      <td>0.456503</td>\n",
       "      <td>0.137983</td>\n",
       "      <td>0.211913</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.456148</td>\n",
       "      <td>0.137983</td>\n",
       "      <td>0.211874</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1059</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:8000</td>\n",
       "      <td>0.469844</td>\n",
       "      <td>0.083801</td>\n",
       "      <td>0.142233</td>\n",
       "      <td>0.190476</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "819        q2-NB   \n",
       "859    q2-SFM-RF   \n",
       "939   q2-SFM-SDG   \n",
       "1059   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "819                                                                0.001:0.7:2000:[8, 8]   \n",
       "859   0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000   \n",
       "939                     0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "1059                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:8000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "819    0.488018  0.156813   0.237356             0.269231   \n",
       "859    0.456503  0.137983   0.211913             0.240000   \n",
       "939    0.456148  0.137983   0.211874             0.222222   \n",
       "1059   0.469844  0.083801   0.142233             0.190476   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "819                   0.35  \n",
       "859                   0.30  \n",
       "939                   0.30  \n",
       "1059                  0.20  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-19"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1144</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000</td>\n",
       "      <td>0.369150</td>\n",
       "      <td>0.264054</td>\n",
       "      <td>0.307880</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:2000</td>\n",
       "      <td>0.369137</td>\n",
       "      <td>0.264054</td>\n",
       "      <td>0.307876</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
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       "    <tr>\n",
       "      <th>1124</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.356575</td>\n",
       "      <td>0.264054</td>\n",
       "      <td>0.303418</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1184</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:6000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "1144   q2-SFM-RF   \n",
       "1179  q2-SFM-SDG   \n",
       "1124       q2-NB   \n",
       "1184   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "1144  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000   \n",
       "1179                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:2000   \n",
       "1124                                                               0.001:0.7:8000:[8, 8]   \n",
       "1184                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:6000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "1144   0.369150  0.264054   0.307880             0.238095   \n",
       "1179   0.369137  0.264054   0.307876             0.250000   \n",
       "1124   0.356575  0.264054   0.303418             0.250000   \n",
       "1184   0.000000  0.000000   0.000000             0.000000   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "1144              0.333333  \n",
       "1179              0.333333  \n",
       "1124              0.333333  \n",
       "1184              0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-15"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1229</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.334856</td>\n",
       "      <td>0.272225</td>\n",
       "      <td>0.300310</td>\n",
       "      <td>0.074468</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1264</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000</td>\n",
       "      <td>0.299311</td>\n",
       "      <td>0.222645</td>\n",
       "      <td>0.255348</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.179935</td>\n",
       "      <td>0.118642</td>\n",
       "      <td>0.142997</td>\n",
       "      <td>0.054348</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.055743</td>\n",
       "      <td>0.105600</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "1229       q2-NB   \n",
       "1264   q2-SFM-RF   \n",
       "1274  q2-SFM-SDG   \n",
       "1324   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "1229                                                               0.001:0.7:8000:[8, 8]   \n",
       "1264  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000   \n",
       "1274                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "1324                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "1229   0.334856  0.272225   0.300310             0.074468   \n",
       "1264   0.299311  0.222645   0.255348             0.066667   \n",
       "1274   0.179935  0.118642   0.142997             0.054348   \n",
       "1324   1.000000  0.055743   0.105600             0.181818   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "1229                  0.35  \n",
       "1264                  0.30  \n",
       "1274                  0.25  \n",
       "1324                  0.10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-5"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1364</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:4000:[8, 8]</td>\n",
       "      <td>0.586677</td>\n",
       "      <td>0.132006</td>\n",
       "      <td>0.215519</td>\n",
       "      <td>0.296296</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1484</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000</td>\n",
       "      <td>0.417459</td>\n",
       "      <td>0.122120</td>\n",
       "      <td>0.188962</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.35</td>\n",
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       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</td>\n",
       "      <td>0.417345</td>\n",
       "      <td>0.122120</td>\n",
       "      <td>0.188951</td>\n",
       "      <td>0.259259</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1624</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:8000</td>\n",
       "      <td>0.384039</td>\n",
       "      <td>0.068890</td>\n",
       "      <td>0.116824</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "1364       q2-NB   \n",
       "1484  q2-SFM-SDG   \n",
       "1444   q2-SFM-RF   \n",
       "1624   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "1364                                                               0.001:0.7:4000:[8, 8]   \n",
       "1484                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000   \n",
       "1444  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000   \n",
       "1624                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:8000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "1364   0.586677  0.132006   0.215519             0.296296   \n",
       "1484   0.417459  0.122120   0.188962             0.259259   \n",
       "1444   0.417345  0.122120   0.188951             0.259259   \n",
       "1624   0.384039  0.068890   0.116824             0.200000   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "1364                  0.40  \n",
       "1484                  0.35  \n",
       "1444                  0.35  \n",
       "1624                  0.20  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-16"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>Taxon Detection Rate</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:2000:[8, 8]</td>\n",
       "      <td>0.270075</td>\n",
       "      <td>0.222134</td>\n",
       "      <td>0.243770</td>\n",
       "      <td>0.168831</td>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1774</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.001):feature_log_prob_:8000</td>\n",
       "      <td>0.405749</td>\n",
       "      <td>0.157921</td>\n",
       "      <td>0.227354</td>\n",
       "      <td>0.196429</td>\n",
       "      <td>0.22</td>\n",
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       "    <tr>\n",
       "      <th>1719</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</td>\n",
       "      <td>0.270595</td>\n",
       "      <td>0.171518</td>\n",
       "      <td>0.209955</td>\n",
       "      <td>0.139241</td>\n",
       "      <td>0.22</td>\n",
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       "    <tr>\n",
       "      <th>1739</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:6000</td>\n",
       "      <td>0.233652</td>\n",
       "      <td>0.189173</td>\n",
       "      <td>0.209073</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          Method  \\\n",
       "1699       q2-NB   \n",
       "1774   q2-SFM-NB   \n",
       "1719   q2-SFM-RF   \n",
       "1739  q2-SFM-SDG   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "1699                                                               0.001:0.7:2000:[8, 8]   \n",
       "1774                  0.001:0.7:[8, 8]:MultinomialNB(alpha=0.001):feature_log_prob_:8000   \n",
       "1719  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000   \n",
       "1739                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:6000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "1699   0.270075  0.222134   0.243770             0.168831   \n",
       "1774   0.405749  0.157921   0.227354             0.196429   \n",
       "1719   0.270595  0.171518   0.209955             0.139241   \n",
       "1739   0.233652  0.189173   0.209073             0.157895   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "1699                  0.26  \n",
       "1774                  0.22  \n",
       "1719                  0.22  \n",
       "1739                  0.24  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "## mock-14"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1789</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.348550</td>\n",
       "      <td>0.268876</td>\n",
       "      <td>0.303572</td>\n",
       "      <td>0.069307</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1824</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000</td>\n",
       "      <td>0.311010</td>\n",
       "      <td>0.217942</td>\n",
       "      <td>0.256288</td>\n",
       "      <td>0.072917</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1834</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.164462</td>\n",
       "      <td>0.098879</td>\n",
       "      <td>0.123504</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032564</td>\n",
       "      <td>0.063074</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "1789       q2-NB   \n",
       "1824   q2-SFM-RF   \n",
       "1834  q2-SFM-SDG   \n",
       "1884   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "1789                                                               0.001:0.7:8000:[8, 8]   \n",
       "1824  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000   \n",
       "1834                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "1884                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "1789   0.348550  0.268876   0.303572             0.069307   \n",
       "1824   0.311010  0.217942   0.256288             0.072917   \n",
       "1834   0.164462  0.098879   0.123504             0.050505   \n",
       "1884   1.000000  0.032564   0.063074             0.181818   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "1789                  0.35  \n",
       "1824                  0.35  \n",
       "1834                  0.25  \n",
       "1884                  0.10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-12"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1954</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.514549</td>\n",
       "      <td>0.679475</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000</td>\n",
       "      <td>0.570209</td>\n",
       "      <td>0.519668</td>\n",
       "      <td>0.543767</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:6000</td>\n",
       "      <td>0.570200</td>\n",
       "      <td>0.519668</td>\n",
       "      <td>0.543763</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:2000:[8, 8]</td>\n",
       "      <td>0.569907</td>\n",
       "      <td>0.519668</td>\n",
       "      <td>0.543630</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "1954   q2-SFM-NB   \n",
       "1914   q2-SFM-RF   \n",
       "1939  q2-SFM-SDG   \n",
       "1899       q2-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "1954                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:4000   \n",
       "1914  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000   \n",
       "1939                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:6000   \n",
       "1899                                                               0.001:0.7:2000:[8, 8]   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "1954   1.000000  0.514549   0.679475             0.454545   \n",
       "1914   0.570209  0.519668   0.543767             0.263158   \n",
       "1939   0.570200  0.519668   0.543763             0.263158   \n",
       "1899   0.569907  0.519668   0.543630             0.263158   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "1954                  0.25  \n",
       "1914                  0.50  \n",
       "1939                  0.50  \n",
       "1899                  0.50  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2058</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000</td>\n",
       "      <td>0.452438</td>\n",
       "      <td>0.136638</td>\n",
       "      <td>0.209889</td>\n",
       "      <td>0.088889</td>\n",
       "      <td>0.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000</td>\n",
       "      <td>0.450786</td>\n",
       "      <td>0.135730</td>\n",
       "      <td>0.208639</td>\n",
       "      <td>0.065217</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:4000:[8, 8]</td>\n",
       "      <td>0.408641</td>\n",
       "      <td>0.136638</td>\n",
       "      <td>0.204798</td>\n",
       "      <td>0.104167</td>\n",
       "      <td>0.092593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:6000</td>\n",
       "      <td>0.991078</td>\n",
       "      <td>0.023969</td>\n",
       "      <td>0.046807</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.018519</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "2058   q2-SFM-RF   \n",
       "2103  q2-SFM-SDG   \n",
       "2013       q2-NB   \n",
       "2223   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "2058  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000   \n",
       "2103                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000   \n",
       "2013                                                               0.001:0.7:4000:[8, 8]   \n",
       "2223                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:6000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "2058   0.452438  0.136638   0.209889             0.088889   \n",
       "2103   0.450786  0.135730   0.208639             0.065217   \n",
       "2013   0.408641  0.136638   0.204798             0.104167   \n",
       "2223   0.991078  0.023969   0.046807             0.125000   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "2058              0.074074  \n",
       "2103              0.055556  \n",
       "2013              0.092593  \n",
       "2223              0.018519  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-8"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>Taxon Detection Rate</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.820433</td>\n",
       "      <td>0.675368</td>\n",
       "      <td>0.740866</td>\n",
       "      <td>0.574468</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2372</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000</td>\n",
       "      <td>0.780101</td>\n",
       "      <td>0.482051</td>\n",
       "      <td>0.595885</td>\n",
       "      <td>0.512195</td>\n",
       "      <td>0.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2344</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</td>\n",
       "      <td>0.800572</td>\n",
       "      <td>0.448633</td>\n",
       "      <td>0.575026</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000</td>\n",
       "      <td>0.734974</td>\n",
       "      <td>0.369469</td>\n",
       "      <td>0.491741</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.296296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "2267       q2-NB   \n",
       "2372  q2-SFM-SDG   \n",
       "2344   q2-SFM-RF   \n",
       "2464   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "2267                                                               0.001:0.7:8000:[8, 8]   \n",
       "2372                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000   \n",
       "2344  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000   \n",
       "2464                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "2267   0.820433  0.675368   0.740866             0.574468   \n",
       "2372   0.780101  0.482051   0.595885             0.512195   \n",
       "2344   0.800572  0.448633   0.575026             0.454545   \n",
       "2464   0.734974  0.369469   0.491741             0.500000   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "2267              0.500000  \n",
       "2372              0.388889  \n",
       "2344              0.370370  \n",
       "2464              0.296296  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-13"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.346142</td>\n",
       "      <td>0.272617</td>\n",
       "      <td>0.305011</td>\n",
       "      <td>0.084337</td>\n",
       "      <td>0.35</td>\n",
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       "    <tr>\n",
       "      <th>2564</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000</td>\n",
       "      <td>0.311306</td>\n",
       "      <td>0.222999</td>\n",
       "      <td>0.259855</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.35</td>\n",
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       "      <th>2574</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.174528</td>\n",
       "      <td>0.108850</td>\n",
       "      <td>0.134078</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2624</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.045271</td>\n",
       "      <td>0.086620</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "2529       q2-NB   \n",
       "2564   q2-SFM-RF   \n",
       "2574  q2-SFM-SDG   \n",
       "2624   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "2529                                                               0.001:0.7:8000:[8, 8]   \n",
       "2564  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000   \n",
       "2574                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "2624                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:4000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "2529   0.346142  0.272617   0.305011             0.084337   \n",
       "2564   0.311306  0.222999   0.259855             0.090909   \n",
       "2574   0.174528  0.108850   0.134078             0.062500   \n",
       "2624   1.000000  0.045271   0.086620             0.222222   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "2529                  0.35  \n",
       "2564                  0.35  \n",
       "2574                  0.25  \n",
       "2624                  0.10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-1"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2654</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</td>\n",
       "      <td>0.653438</td>\n",
       "      <td>0.330199</td>\n",
       "      <td>0.438708</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2634</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:4000:[8, 8]</td>\n",
       "      <td>0.499961</td>\n",
       "      <td>0.380347</td>\n",
       "      <td>0.432027</td>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.378378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2669</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000</td>\n",
       "      <td>0.617660</td>\n",
       "      <td>0.330199</td>\n",
       "      <td>0.430340</td>\n",
       "      <td>0.263158</td>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2679</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:6000</td>\n",
       "      <td>0.369925</td>\n",
       "      <td>0.186773</td>\n",
       "      <td>0.248220</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.270270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "2654   q2-SFM-RF   \n",
       "2634       q2-NB   \n",
       "2669  q2-SFM-SDG   \n",
       "2679   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "2654  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000   \n",
       "2634                                                               0.001:0.7:4000:[8, 8]   \n",
       "2669                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:8000   \n",
       "2679                    0.001:0.7:[8, 8]:MultinomialNB(alpha=0.1):feature_log_prob_:6000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "2654   0.653438  0.330199   0.438708             0.263158   \n",
       "2634   0.499961  0.380347   0.432027             0.318182   \n",
       "2669   0.617660  0.330199   0.430340             0.263158   \n",
       "2679   0.369925  0.186773   0.248220             0.454545   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "2654              0.270270  \n",
       "2634              0.378378  \n",
       "2669              0.270270  \n",
       "2679              0.270270  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-22"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2719</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:8000:[8, 8]</td>\n",
       "      <td>0.450976</td>\n",
       "      <td>0.338973</td>\n",
       "      <td>0.387035</td>\n",
       "      <td>0.189189</td>\n",
       "      <td>0.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000</td>\n",
       "      <td>0.376573</td>\n",
       "      <td>0.294809</td>\n",
       "      <td>0.330712</td>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000</td>\n",
       "      <td>0.363814</td>\n",
       "      <td>0.235989</td>\n",
       "      <td>0.286281</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2789</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:4000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.094711</td>\n",
       "      <td>0.173034</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "2719       q2-NB   \n",
       "2744   q2-SFM-RF   \n",
       "2759  q2-SFM-SDG   \n",
       "2789   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "2719                                                               0.001:0.7:8000:[8, 8]   \n",
       "2744  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000   \n",
       "2759                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000   \n",
       "2789                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:4000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "2719   0.450976  0.338973   0.387035             0.189189   \n",
       "2744   0.376573  0.294809   0.330712             0.176471   \n",
       "2759   0.363814  0.235989   0.286281             0.142857   \n",
       "2789   1.000000  0.094711   0.173034             0.333333   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "2719              0.368421  \n",
       "2744              0.315789  \n",
       "2759              0.263158  \n",
       "2789              0.105263  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## mock-7"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>Method</th>\n",
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       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>q2-NB</td>\n",
       "      <td>0.001:0.7:6000:[8, 8]</td>\n",
       "      <td>0.715887</td>\n",
       "      <td>0.497503</td>\n",
       "      <td>0.587043</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.481481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>q2-SFM-SDG</td>\n",
       "      <td>0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000</td>\n",
       "      <td>0.713512</td>\n",
       "      <td>0.441253</td>\n",
       "      <td>0.545287</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.388889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>q2-SFM-RF</td>\n",
       "      <td>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</td>\n",
       "      <td>0.741289</td>\n",
       "      <td>0.410166</td>\n",
       "      <td>0.528117</td>\n",
       "      <td>0.463415</td>\n",
       "      <td>0.351852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>q2-SFM-NB</td>\n",
       "      <td>0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000</td>\n",
       "      <td>0.676910</td>\n",
       "      <td>0.333210</td>\n",
       "      <td>0.446587</td>\n",
       "      <td>0.535714</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Method  \\\n",
       "2877       q2-NB   \n",
       "2937  q2-SFM-SDG   \n",
       "2909   q2-SFM-RF   \n",
       "3029   q2-SFM-NB   \n",
       "\n",
       "                                                                              Parameters  \\\n",
       "2877                                                               0.001:0.7:6000:[8, 8]   \n",
       "2937                    0.001:0.7:[8, 8]:SGDClassifier(loss='modified_huber'):coef_:4000   \n",
       "2909  0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000   \n",
       "3029                   0.001:0.7:[8, 8]:MultinomialNB(alpha=0.01):feature_log_prob_:8000   \n",
       "\n",
       "      Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "2877   0.715887  0.497503   0.587043             0.520000   \n",
       "2937   0.713512  0.441253   0.545287             0.500000   \n",
       "2909   0.741289  0.410166   0.528117             0.463415   \n",
       "3029   0.676910  0.333210   0.446587             0.535714   \n",
       "\n",
       "      Taxon Detection Rate  \n",
       "2877              0.481481  \n",
       "2937              0.388889  \n",
       "2909              0.351852  \n",
       "3029              0.277778  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mock_results_6 = mock_results[mock_results['Level'] == 6]\n",
    "\n",
    "pd.set_option('display.max_colwidth', None)\n",
    "for dataset in mock_results_6['Dataset'].unique():\n",
    "    display(Markdown('## {0}'.format(dataset)))\n",
    "    best = method_by_dataset_a1(mock_results_6, dataset)\n",
    "    display(best)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adopted-donor",
   "metadata": {},
   "source": [
    "Now we can determine which parameter configuration performed best for each method. Count best values in each column indicate how many samples a given method achieved within one mean absolute deviation of the best result (which is why they may sum to more than the total number of samples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "focal-accommodation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## q2-NB"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.001:0.7:8000:[8, 8]</th>\n",
       "      <td>20</td>\n",
       "      <td>26</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:0.7:4000:[8, 8]</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:0.7:6000:[8, 8]</th>\n",
       "      <td>13</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:0.7:2000:[8, 8]</th>\n",
       "      <td>21</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Taxon Accuracy Rate  Taxon Detection Rate  Precision  \\\n",
       "0.001:0.7:8000:[8, 8]                   20                    26         24   \n",
       "0.001:0.7:4000:[8, 8]                   14                    17         20   \n",
       "0.001:0.7:6000:[8, 8]                   13                    19         15   \n",
       "0.001:0.7:2000:[8, 8]                   21                    19         18   \n",
       "\n",
       "                       Recall  F-measure  \n",
       "0.001:0.7:8000:[8, 8]      28         24  \n",
       "0.001:0.7:4000:[8, 8]      25         21  \n",
       "0.001:0.7:6000:[8, 8]      26         18  \n",
       "0.001:0.7:2000:[8, 8]      19         15  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## q2-SFM-RF"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "    <tr>\n",
       "      <th>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                    Taxon Accuracy Rate  \\\n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000                   13   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000                   20   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000                   18   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000                    2   \n",
       "\n",
       "                                                                                    Taxon Detection Rate  \\\n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000                    16   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000                    23   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000                    18   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000                     2   \n",
       "\n",
       "                                                                                    Precision  \\\n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000         15   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000         12   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000         10   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000          3   \n",
       "\n",
       "                                                                                    Recall  \\\n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000      16   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000      27   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000      18   \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000       2   \n",
       "\n",
       "                                                                                    F-measure  \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:8000         15  \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:4000         14  \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:6000          8  \n",
       "0.001:0.7:[8, 8]:RandomForestClassifier(n_estimators=20):feature_importances_:2000          3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for method in mock_results_6['Method'].unique():\n",
    "    top_params = parameter_comparisons(\n",
    "        mock_results_6, method, \n",
    "        metrics=['Taxon Accuracy Rate', 'Taxon Detection Rate', 'Precision', 'Recall', 'F-measure'])\n",
    "    display(Markdown('## {0}'.format(method)))\n",
    "    display(top_params[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prospective-provincial",
   "metadata": {},
   "source": [
    "Optimized method performance\n",
    "\n",
    "And, finally, which method performed best at each individual taxonomic level for each reference dataset (i.e., for across all fungal and bacterial mock communities combined)?\n",
    "\n",
    "For this analysis, we rank the top-performing method/parameter combination for each method at family through species levels. Methods are ranked by top F-measure, and the average value for each metric is shown (rather than count best as above). F-measure distributions are plotted for each method, and compared using paired t-tests with FDR-corrected P-values. This cell does not need to be altered, unless if you wish to change the metric used for sorting best methods and for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "helpful-distance",
   "metadata": {},
   "outputs": [],
   "source": [
    "boxes = rank_optimized_method_performance_by_dataset(mock_results,\n",
    "                                                     dataset=\"Reference\",\n",
    "                                                     metric=\"F-measure\",\n",
    "                                                     level_range=range(4,7),\n",
    "                                                     display_fields=[\"Method\",\n",
    "                                                                     \"Parameters\",\n",
    "                                                                     \"Taxon Accuracy Rate\",\n",
    "                                                                     \"Taxon Detection Rate\",\n",
    "                                                                     \"Precision\",\n",
    "                                                                     \"Recall\",\n",
    "                                                                     \"F-measure\"],\n",
    "                                                     paired=True,\n",
    "                                                     parametric=True,\n",
    "                                                     color=None,\n",
    "                                                     color_palette=color_palette)"
   ]
  }
 ],
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