04_network.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "secure-survivor",
   "metadata": {},
   "source": [
    "---\n",
    "output-file: network.html\n",
    "title: network\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "specialized-integrity",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| include: false\n",
    "#| default_exp network\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from nbdev.showdoc import show_doc\n",
    "from IPython.display import HTML\n",
    "#%load_ext autoreload\n",
    "#%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greatest-adaptation",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "from glycowork.glycan_data.loader import glycomics_data_loader\n",
    "human_skin_O_PMC5871710_BCC = glycomics_data_loader.human_skin_O_PMC5871710_BCC\n",
    "from glycowork.network.biosynthesis import *\n",
    "from glycowork.network.evolution import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "preliminary-approval",
   "metadata": {},
   "source": [
    "`network` contains functions to arrange and analyze glycans in the context of networks. In such a network, each node represents a glycan and edges represent, for instance, their connection via a biosynthetic step. It should be noted, since `glycowork` treats glycans as molecular graphs, that these networks represent hierarchical graphs, with the network being one graph and each node within the network also a graph. `network` contains the following modules:\n",
    "\n",
    "- `biosynthesis` contains functions to construct and analyze biosynthetic glycan networks\n",
    "- `evolution` contains functions to compare (taxonomic) groups as to their glycan repertoires"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "random-islam",
   "metadata": {},
   "source": [
    "## biosynthesis\n",
    ">constructing and analyzing biosynthetic glycan networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "musical-jumping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### construct_network\n",
       "\n",
       ">      construct_network (glycans, allowed_ptms=frozenset({'OAc', '4Ac', '1P',\n",
       ">                         '9Ac', '3P', 'OS', 'OP', '3S', '6P', '6S'}),\n",
       ">                         edge_type='monolink', permitted_roots=None,\n",
       ">                         abundances=[])\n",
       "\n",
       "*Construct a glycan biosynthetic network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| glycans (list): list of glycans in IUPAC-condensed format\n",
       "| allowed_ptms (set): list of PTMs to consider\n",
       "| edge_type (string): indicates whether edges represent monosaccharides ('monosaccharide'), monosaccharide(linkage) ('monolink'), or enzyme catalyzing the reaction ('enzyme'); default:'monolink'\n",
       "| permitted_roots (set): which nodes should be considered as roots; default:will be inferred\n",
       "| abundances (list): optional list of abundances, in the same order as glycans; default:empty list\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a networkx object of the network*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### construct_network\n",
       "\n",
       ">      construct_network (glycans, allowed_ptms=frozenset({'OAc', '4Ac', '1P',\n",
       ">                         '9Ac', '3P', 'OS', 'OP', '3S', '6P', '6S'}),\n",
       ">                         edge_type='monolink', permitted_roots=None,\n",
       ">                         abundances=[])\n",
       "\n",
       "*Construct a glycan biosynthetic network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| glycans (list): list of glycans in IUPAC-condensed format\n",
       "| allowed_ptms (set): list of PTMs to consider\n",
       "| edge_type (string): indicates whether edges represent monosaccharides ('monosaccharide'), monosaccharide(linkage) ('monolink'), or enzyme catalyzing the reaction ('enzyme'); default:'monolink'\n",
       "| permitted_roots (set): which nodes should be considered as roots; default:will be inferred\n",
       "| abundances (list): optional list of abundances, in the same order as glycans; default:empty list\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a networkx object of the network*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(construct_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "supported-valuable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView(('Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[Gal(b1-3)GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'GlcNAc6S(b1-3)Gal(b1-4)Glc-ol', 'GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'Fuc(a1-2)Gal(b1-4)Glc-ol', 'Gal(b1-4)Glc-ol', 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-3)GlcNAc(b1-6)[Gal(b1-4)GlcNAc(b1-3)]Gal(b1-4)Glc-ol', 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'Gal(b1-4)GlcNAc(b1-3)[GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-3)GlcNAc(b1-6)[GlcNAc(b1-3)]Gal(b1-4)Glc-ol', 'GlcNAc(b1-6)[GlcNAc(b1-3)]Gal(b1-4)Glc-ol'))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glycans = [\"Gal(b1-4)Glc-ol\", \"GlcNAc(b1-3)Gal(b1-4)Glc-ol\",\n",
    "           \"GlcNAc6S(b1-3)Gal(b1-4)Glc-ol\",\n",
    "           \"Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol\", \"Fuc(a1-2)Gal(b1-4)Glc-ol\",\n",
    "          \"Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[Gal(b1-3)GlcNAc(b1-6)]Gal(b1-4)Glc-ol\"]\n",
    "network = construct_network(glycans)\n",
    "network.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "distant-valley",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### plot_network\n",
       "\n",
       ">      plot_network (network, plot_format='pydot2', edge_label_draw=True,\n",
       ">                    lfc_dict=None)\n",
       "\n",
       "*Visualizes biosynthetic network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| plot_format (string): how to layout network, either 'pydot2', 'kamada_kawai', or 'spring'; default:'pydot2'\n",
       "| edge_label_draw (bool): draws edge labels if True; default:True\n",
       "| lfc_dict (dict): dictionary of enzyme:log2-fold-change to scale edge width; default:None*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### plot_network\n",
       "\n",
       ">      plot_network (network, plot_format='pydot2', edge_label_draw=True,\n",
       ">                    lfc_dict=None)\n",
       "\n",
       "*Visualizes biosynthetic network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| plot_format (string): how to layout network, either 'pydot2', 'kamada_kawai', or 'spring'; default:'pydot2'\n",
       "| edge_label_draw (bool): draws edge labels if True; default:True\n",
       "| lfc_dict (dict): dictionary of enzyme:log2-fold-change to scale edge width; default:None*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(plot_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interim-contact",
   "metadata": {},
   "outputs": [
    {
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cWQMHDoytRpNygrhRo0YpLS1Nf/3rXyVJ2dnZ+utf/yq73a7OnTsrGAzqH//4hz755BONGzdOl156qQ4ePKgDBw5IktLT03XgwIFYMCdJiYmJatq0qX788Ud9/vnnWrVqlfbu3Sufz6eff/45T2AnSZUqVVIwGNThw4dVvnx5SVJCQsJpJ7yuW7dOjz32mEaPHq0OHTpIOrnldeXKlWrSpMkZ99E7nwzTlNtvxgY55A3kcv47VbtqmVSrKpayqWFuu2rq8VAuwc6qqpLGGzA0eopb4aipx69IU1oKYR3yIrADAAAAgGLC4XAoNTVVR44cOenxdevWKT09PfZYenq66tatqx07dkiSotGo3nrrLf39739X9+7dJUkXXHBBbHVeSkqKJMnj8eQ596xZs/TYY4+pYcOGuuqqq9SsWTPddttt2r17tyKRiOz247ed5cuXl91u1+7du2OB3elkZWVp0KBBcjgc2rNnj4YNG6YNGzZoz549+vzzz9W0aVNJUkZGhrp27aqyZcv+kS/ZSSK501VP07Ka6TEUiR4/3pEglXXZVDbVqjqV7Gpd98TVcTalp1gYIIA8AmFTY6a6leUz9OigNJV1FUzYjKKFwA4AAAAAigmr1ar09HTt2rVL8+bN06ZNm7Rp0yaVKlVKVapU0eHDh2PDIux2u9LT03X06NHY0IbMzEy1bNkyzzmj0aisVqvKlSun1NRUrV+/PjapVZL++c9/qlq1avr3v/8da0u12Wzau3evgsFgnsAuOTlZpUuX1tKlS5WYmKhoNKoGDRooISEhdkxuy+v8+fOVkpIir9er8ePHq2bNmrrssstUt25d1apVK1ZbZmamunfvrsTExHOaEBs4sV31FBNWs7x521XTki2x4Q3VyyYen6z66z5yKY4/NoEXJVM4aurN6W7ty4xq5MA0VS5DWIdTI7ADAAAAgGKkZs2a+vLLL/Xzzz+rWrVqqlSpkgYMGKB9+/Zp27ZtOnr0qMqVKycpZ2BDIBDQkSNH5HK5lJaWpr179+Y5n81mk2nmRFjt2rXT4sWLdcUVV6h06dKxY8LhcKwl9e9//7v27Nkjr9errKwsOZ1OrV69Wi+88IJmzZqlQ4cOxfbEGzJkiJ5//vlYPdLxltc+ffrEBlyczr59+/Tkk0/q4osvzvPa3zJMU898ka1MjyFfMG+7ailnTgBXoZRVDarbYyvjyvy6Si6RdlWcJ1HD1HszPdq8L6Lh/VyqWYFIBqfHnw4AAAAAKEZq1KihZs2aaezYsXkGRLz44otavny5Dh06FAvIkpOT5ff7tW/fPlWrVk116tTRhAkT1KlTp9jU1X379ikhIUHlypXT4MGDNX78eK1evVqdOnWSJN1333169tln1aVLF1mtVvXt21eDBg1SVlaWAoGAJCkpKUmXXHKJOnbsqBYtWqh69eqqUKHCOf1+DMOIrZw7cVCFJFWrVk3VqlU7h3NItSvZ1fqEQQ5lXDaVol0VBcQwTf1vtlertoV1X+9U1auacPYXoUQjsAMAAACAYqR69eoyDEOZmZmScla/JSQkqEaNGvL5fNq2bZsaNGggSdq4caMOHjyoQ4cOSZKeeOIJPfbYY7rjjjt0xx136MCBA1q1apWuuuoqdezYUddff72++eYbjRs3LhbYXXnllbrgggu0aNEiVaxYUV26dFGZMmXytMLWrVtXDz300Em1nksLa+602D/DbrPops7Osx8I5APTNPXlAp/mbwjp9m5ONa2ZWNgloQggsAMAAACAYiR3H7nc6a652rdvL6fTqX//+9+qVKmSVq9erZ9++knRaFRr165Vnz59dOWVV6py5cp6+eWXdeedd6pUqVIaMGBAbPCEy+XSXXfdpTFjxigjI0P9+vWTYRhq1aqVWrVqdca6cttqTwzo2PsNJcHU5QF991NQN3RMUdt6jsIuB0UEgR0AAAAAFCO5raw7d+6UdHyFWtWqVfX888/r6aef1qWXXqp27drpnnvukcfj0YUXXhh7ffv27fMMlfit7t27y+l0xlbl5Z7/VIHciQjnUBLNWh3QpCV+DWydrMsaJxV2OShCCOwAAAAAoBhJS0tT+fLlY0Fa7jAIKWdoxOTJk+VwnH2Vj2maeUK43MDNbrerY8eOJx1PIAfktWhjUJ/O9enypknqewlhHX4fi5n7DgwAAAAAKDEMw6A9FcgnP20P6c3pHrWrl6hbL3Py/YXfjRV2AAAAAFAMnW2gw/kY5gDgZBv3hPX2DI+a1UrQzV0I6/DH8A4NAAAAAMUQIQFQ8LYfjOi1aW7VrWzXHZenymbl+xB/DCvsAAAAACCOmaYpb9DUEbehI25DmW5DRzxRZboNDWydrCpluK0D4sG+zKjGZLhVtYxN9/V2KcFGWIc/jnd2AAAAAChEhmHqmNfQEY+RJ5TL9Bg64s4J5oKR48cn2KSyLqvKpFp1KMtQ5dJnbn0FirKztXbHi8PZUb08JVvpKVY92NelpIT4rxnxjaETAAAAAJCPQhEzZ1XcCSvjjsQCOUNHPYaME+7KnA5LTiDnsqps6q+/umwqk2pVWZdVrmRLkQgwgPPB7XbL5XLFPjcMI+72X8zyGXp+YrZMU3p0cJpKOeOrPhRNBHYAAAAA8AeZpinfie2qsVVy0djnbv/xWy6LpFLO3BDuxEAuN6CzKSmRMA6QpB07dmjQoEHq3bu36tWrp1tvvbWwSzqJL2joha/dcvsNPXZFmsqn2Qq7JBQTBHYAAAAAcBqGYeqYz1TmCQFcbjiX83lUwfDx4+02xVbClXXZYq2ruYFcaadVdva1As7JoUOHtHjxYk2aNEnr1q3ToUOH9OKLL6pNmzaqWLFiYZenYNjUK1Pc2n80qkcGu1SV/SRxHhHYAQAAACixwhFTRzy/DnJwR08K5I55DUWN48enOCwnBHK/BnGpttjHtKsC518gEJDP59OoUaP0/fff65prrtGQIUN08cUXF1pNkaip16d5tGV/WCMHpKlWRcI6nF8EdgAAAACKpVi7aiyQOz5hNffj7N+0q6Y7LSqbavtNm2ruxzYl064K5JvTDZiIRqOy2XJaTV9//XW9//77atu2rR599FHVqlWroMuUYZh6d6ZXP20L6cF+LjWollDgNaD4IwIGAAAAUCQZpqksr5mzX9yvodzx4Q6GMt1RBU5sV7UqFsRVLWNTkxqJefaSK51KuypQWE4M677++mvt3btXR44c0VNPPSWbzaZQKKTExETdf//9Kl26tP7yl7+oTp06GjlyZIEOojBNUx//6NPKrSHd0zOVsA75hhV2AAAAAOJSOGLGWlRzfj2+Mi7Tk/Pfie2qyYmW422qv5mwmtuuaqVdFYhrTz75pL744gtVqlRJW7Zs0UUXXaQ5c+ZIkiKRiOz2nHVHr732mh5++GEtXLhQLVu2LJDaTNPUVwv9mrEqoNu6OtW+vqNArouSicAOAAAAQKHwBX87WTW3bTVnL7ksX95blfQUS2yYQ5nfTlhNtSrFUTArbADkj1dffVV///vfNX36dNWpU0eLFy/WE088oUWLFsnhODkcu/vuu2Wapt566y1ZLJZ8X2U3bblfExf7dW2HFHVvmpSv1wJoiQUAAABw3hmmqWxfTrtq5q8tqkdO2Ecu02PIHzoeyNmtUulf94qrUsamRhckHA/mXDntqgm0qwLF1ooVK/T666/rvffeU6tWrSRJ1apVk2maGjFihHbt2qW77rpLPXr0iIV3PXv21NNPPy2fzyeXy5Wv9c1eE9DExX71b5lEWIcCQWAHAAAA4HcLR00d9ZwwyOGEfeSOuA0d9RiK/KZdNXe66kVV7CeskMtpV01LoV0VKMmaN2+uhx56SI0bN449du2118rhcCgpKUmlS5fWwIEDNXnyZPXr10+SdMUVVygjI0OrVq1Sx44d8622xZuDGjfHp26NHerfKjnfrgOciJZYAAAAACfxBY+3qmae2K7664TVbJ+pE28k0lMsv4Zwx1fFlT2hbZV2VQCnc6qhEa+99ppmzZqlDz74QKVLl5Yk9e3bVz6fT999951M05TdbtfGjRtVu3bt2N5259vP20N68xuPWtdN1JCuTv5hAQWGwA4AAAAoYXLbVXMHOWS6T95L7sR2VZtVKvOb/eJyBzmUdVlV2mlVgp2bWADnz4EDB+RyuZSSkiLTNBWJRHT33XfL5XJpzJgxBVLDpr1hjZ7i1sUXJOienqmyWXmfQ8GhJRYAAAAoZiJRMzZF9beDHHKDuUj0+PFJCYrtF1enkl2t6x5fIVcm1ab0FIus3KgCKAC5q+3Kly8fW3VnsVh05MgR/fTTT7rtttsKpI4dhyJ6fZpHtSvZddflhHUoeKywAwAAAIoYf8hUpjunNfXICW2rOcMdosry5m1XTUvOma56qpbVnOmqFllo8wJQiKLRqGw2m7Zt26ZatWrFHg8EAtq+fbtuvfVWlS9fXhkZGfley/6jUT3/dbbKuax6eECakhJ5f0TBI7ADAAAA4ohpmnL7zdjKuN8Oc8j0GPIF87arlk49vldc7qq44wEd7aoA4ltuWLdq1Sq1aNFC06dPV8+ePRWNRjV9+nQ9//zzSktLi4V1pmnm2z8yHHFH9fxEtxwJFj062KXUJPbfROEgsAMAAECxk+mOav+xnBGl5dKsqpBu+8PnCkdMzd8QVPMLE5We8udv3CJRU0e9vx3k8Gswd4p2Vcev7ap5ArkTJqyWol0VQJw71VCJ39qxY4cuueQS3XXXXXr22Wdjj2/fvl2rV69W//79z/lcf1S2z9DzX2crEpUeG5ym0qmEdSg8BHYAAAAoNg5nRzV7TVAb94YVCOW0hSYlWHRhRbs6X+xQ1bK/bwvnLfvC+uB7rw5lG7qibbJ6t0g+62sCYTPvIIcT2laPuE9uV3Xltqv+ZpBDzue0qwIo2k5cDTd48GBdeeWVuummm/I8L0nPPPOMQqGQ/vGPf5z2XPkZ1vmChl6a5FaWz9Cjg9P+1D/0AOcDgR0AAACKhYNZUY2b49Oh7KhSkyw5QZdy9nvL9psq5bTqmg4pqlH+7KFdMGzq68U+ffdzUBaLZJHUsaFDN3ZKibWr5pmwesJwh9+2q5Zy5g3gcv6zxaauJtKuCqAEuOGGG/TZZ5/J4XBo9OjRuvvuu/M87/P5lJKSUii1BcOmRme4tTczqkcGuVTtd/7jDpAf+FMIAACAIs80TU1f4dfh7KgqpFtlPWFFWorDouREU4eyDWUs8+vuHqmy204fkq3YGtInP3qV7Td/PbdkSlq4MagFG4IKn9iuapfK/LoqrlYFu1rWztuyWspppV0VQIn37bffas+ePcrIyNCqVat03333ye126+GHH5bVapVpmnnCuvzco+63IlFTb8/waOehiB4eQFiH+MGfRAAAABR5ezOj2nkoqrSUvGFdLovFolJOqw5mRbVlf0T1qyac8jxuv6G3vvGc8jmb1aIBrZLzTFh10q4KAGdVsWJFXX311WrdurX69Okjl8ul4cOHKzs7W0899ZQSEvK+JxfU+6phmPrge6827A7rgb4u1a506p8NQGEgsAMAAECRt+tIVKGIqfSU09/kJdotMgxp9+HoaQM7V7JVN3dO0cptIa3fHVHUyGmHNZWzCqNbEwcBHQD8Tk2bNlWdOnXkdDplmqYeeOABlSpVSkOHDlV2draee+45ORwOffHFF0pLS1OvXr3yvSbTNPXJHJ+W/RLSPT1S1bA6YR3iC4EdAAAAijzDkGQ5t1UZxlm2cO50cZI6XZwkf8jUyq0hLdwY1MY9EYWjUjgqJfI3aAA4q9y21txfnU5nnqERN998s9LS0nTdddfJ6/WqTp06euKJJzR//vwCqW/iYr/mrAvq1sucalE7sUCuCfwe/HUDAAAARV4pp1UW5ayCO93+dIaRE9Slp5zbhMHkRIva13eofX2HjnkNHcqOMiACAM5BOByOtblGIpHYx1arVZFIRHZ7ThQxcOBAffPNN7rsssskSWPHjlW7du3yfQ+7GSv9mr4ioKvbJ+vSBo58uw7wZxDYAQAAoMirW9muMqk2HfMaKus69U2eO2DKmWT5Q21PpZw5AyQAAKf3yy+/qHbt2rGA7umnn9bixYuVmpqq+vXr65lnnpHdbs8T2h07dkwWi0X//e9/dfPNN+d7WDdnXUDjF/rV95Ik9WiWnG/XAf4s/tYBAACAIi/BblHHhg5ZLNIxr5Gn7dU0Tbn9hsIRU20vcsiVzF+BAeB8e/bZZzVs2DDNmzdPkvTkk0/qjTfeUJMmTZScnKzPP/9cLVq0kN/vl91ul2EY2rt3r5566ik9/vjjuuWWWyTl78CJpVuC+ni2T5c1cmhga8I6xDdW2AEAAKBYaHFhgsIRUz+sCehQtiHrr/d8hiElOyzqdLFDnRrS+gQA+aF27dqaMmWKXn75Ze3evVubN2/WhAkT1LFjR5mmqeXLl+uuu+7SZZddph9++EHJyclKS0vT2LFj1bx5c0nK19V1a3aG9P53XrWum6jrOqYwQAhxz2KaZ9l1FwAAAChCjnkNrdkZ1p4jEZmmVKm0TRdfkKDyabbCLg0Aip3Zs2erS5cusY//+te/qlSpUtq6dasmTpyounXrSpIMw9CPP/6o+++/Xy+99FKBTILNtWVfWK9McatBtQTd0zP1tHudAvGEfgAAAAAUK6WcVl3awKFrL3Xquo5OdWmURFgHAPlg48aNeuGFF+T1eiVJXbp00QsvvCCPx6N169Zp4sSJsWOtVquaN28ur9erTZs2FViNuw5H9OpUj2pWsOuuHoR1KDoI7AAAAAAAwO9Wvnx5rV+/Xv/5z39ij7Vp00Zvv/22evXqpa+++krvvfde7DmLxaLExEQ5HAWzPcGBY1G9MsWtCulW3d/HxaRvFCm0xAIAAAAAgN8ld7+5sWPHasKECXr++edVr1692OPbtm3T8OHDtWHDBjVp0kQNGjTQqlWrtG/fPi1dujTf68v0GHpuQrYS7dKjg9MYOIQihz+xAAAAAADgd8kd2tCkSRP98ssvGj9+fOzxaDSqWrVq6d1331WbNm2UkZGh7777Tr17946FdYZh5Fttbr+hVyZnS5JGDCCsQ9HECjsAAAAAAHDODMOQ1Xo8BPvwww81dOhQvfPOO7rzzjvzHON2uzVkyBCVL19eb7zxhmw220mvP5/8IVMvTcpWpsfQY4PTVLEUe5iiaLIXdgEAAAAAAKBoiEQistvtys7O1urVq9W6dWsNGTJE+/fv1z333KPs7Gw99NBDsttz4gaXy6Vx48YpEonIZrPJNM18C+tCEVOvT3PrYJahRwa5COtQpBHYAQAAAACAs4pGo7GwrnPnzrr00ktlt9vVpk0bjRgxQk6nUyNHjtSKFSt0yy23qGfPnpIkh8Mhh8MR298uP0Sipt6Z4dH2gxGN6O9S9XLEHSjaaIkFAAAAAADnxDRNNW/eXLVr19brr7+uihUr5lkxt3LlSo0cOVJWq1U+n0/Dhw9XixYtVKdOnXyryTBNffCdV8t+Cen+PqlqdEFivl0LKChEzgAAAAAA4Jx8/vnnstlsevfdd1W2bFlJOSvvcttdmzdvrokTJ2rXrl365JNPtHTpUlWuXDnfAjvTNPXZXJ+WbA7pzh5OwjoUGwR2AAAAAADgnPh8PmVlZeVpbbXZcvaK27Rpk9LT01WpUiWlp6frX//6l0KhkBIT8y9Em7TErx/WBHVzlxS1quPIt+sABY3ZxgAAAAAA4LQMw4h97HA4dPDgQR05ckRSzuq6XN9++60yMjIUDodjr8nPsO7bVX5NXR7QVe2S1alhUr5dBygMBHYAAAAAAEBSToupJK1du1abN2+WpDx71N1www1q1aqVLr/8cm3dujW2uu7HH3/U3/72N5UrV04JCQn5Ngk217z1QX25wK/ezZPUs3lyvl4LKAy0xAIAAAAAAEmSxWLRkiVL1LZtWw0cOFCmaeq5556LtbpaLBa9/PLLGjVqlBo0aKCrrrpKfr9fCxYs0EMPPaRBgwble43Lfwnpo9ledb7YocFtCetQPDElFgAAAAAAxEybNk3XXnutJk2apP/973/auXOngsGgbrvtNvXp00eVK1eWJL366qtavHixypYtq1atWunmm2+WlLNK78Q97s6ndbvCem2qW80vTNQd3Z2yWvPnOkBhI7ADAAAAAAB53HTTTSpVqpRef/11zZ8/X7t27dJDDz2kqlWrqkmTJvrb3/6m6tWry27P27iXn2HdL/sjemVytupWSdCw3qmy2wjrUHyxhx0AAAAAACXUb9fwRCIRSdL111+vLVu2aNeuXerQoYOuu+46JSUlqUqVKtq8ebPatWunZs2aacmSJXnOkV9h3e4jEb061a0Lytt1T0/COhR/rLADAAAAAKAE83q9ikQiSk9Pjz0WDofVvn17XXLJJXr77bfVpk0bJSQkKCMjQ6VKldLHH3+sgwcP6uGHH873+g5mRfX8xGylp1g1cqBLKQ7WHqH4I7ADAAAAAKCEMgxDLVu21AUXXKB3331XFSpUiD23bNkyPfroo9q2bZtKlSqljIwMVa1a9ZTnyK+psMe8hp6bmC2bVXp0UJrSUgjrUDLwJx0AAAAAgBLKarXqhRde0Jw5c3Tvvfdqz549seeqVq0q0zQVCAQ0b968WFj323U/+RXWeQKGXp7slmFII/q7COtQovCnHQAAAACAEuK3YVs0GlW3bt00e/ZszZ07V0OGDNHWrVslSZUrV9Zf//pX+Xw+zZkzJ/aa/Nqn7kSBkKkxGW55AoZG9HeprMuW79cE4gmBHQAAAAAAJcCJE1wPHDggSbLZcoKwJk2aaMGCBdq4caNuvPFGrV+/XoZhqFWrVurTp4/Gjx8vj8dTIHWGI6bemO7WgWOGHurnUqXShHUoeQjsAAAAAAAoAXLDuqFDh6phw4Z67LHH9Oyzz2rHjh3av3+/6tSpoyVLligSiejqq6/W6tWrlZqaqrZt2yojI+Ok1Xn5IWqYenemR7/sj+j+PqmqUd6e79cE4hFDJwAAAAAAKCE2btyoq6++WmvWrFHHjh2VnJyspUuXqkKFCuratas6dOigdu3a6fLLL1elSpX03HPPqUOHDjp48KAqVKiQZ5Xe+WaYpj6c5dWSzSEN652qxjUS8+U6QFFAYAcAAAAAQAkRiUT0448/6tVXX9XatWs1d+5cHT58WAsWLNBnn32mvXv3KhqNqnTp0lq+fLmaN2+u2bNny+Vy5Wtdpmnq83k+zVod1O2XO9WmriNfrwfEOwI7AAAAAACKOcMwYtNcTdPU4sWL9fjjj2vXrl2aOnWq6tevL4/HI4fDoa+//lqHDx/Wxx9/rCeffFJ9+/bN9/omL/VrylK/buyUoi6NkvL9ekC8I7ADAAAAAKAYi0ajseESs2bNUoMGDVS5cmWtXLlSf/3rX7V06VJNmDBB7du3z/O63JAvP9tgJem7nwL6fL5Pg9skq88lyfl2HaAoYegEAAAAAADFlGmasbDunnvu0SOPPKKlS5cqGo2qefPm+te//qXLLrtM/fv318yZM2OvMQwjFtLlZ1i3YENQn8/3qWezJPVuwco6IBfjVgAAAAAAKKZyw7b/+7//09SpUzVx4kQ1bNgwFuI1atRITz/9tFwulwYPHqy3335bN910U76GdLlWbQtp7A9edWzg0JXtkgvkmkBRQWAHAAAAAEAxcuJ+dYZh6NixY/rmm2/0t7/9TS1btjzpuHr16umpp55SKBTSjh07CqTG9bvDemeGR80vTNRNnVMI64DfILADAAAAAKCYGD16tMqWLaurr75aSUlJslqtikaj2rFjhypUqCDp+J52VqtV2dnZ2rlzpxo1aqQxY8YoPT0932vcdiCiN6a5Va9qgu7o7pTVSlgH/BZ72AEAAAAAUEwsXLhQt912mz777DP5fD5JUvny5VWxYkV9+umnkiSbzaZoNCpJ+vnnn/Xuu+/qwIEDBRLW7cmMaEyGW9XK2XVvr1TZbYR1wKkQ2AEAAAAAUISZphn7+PPPP9eoUaN0xx136P3339fRo0clSffee6/Wrl2rUaNGScoJ7bKysvTEE09o//79qlixYr7XeSg7qtFT3CqdatUDfVLlSCCsA06HllgAAAAAAIqo3H3ootGoNm3aJKvVqn//+99yOp166KGHlJ2drVGjRun666/X/v37NWHCBF100UWqXbu29u7dq6SkJH3xxReScoK//NpL7pjX0CuT3Uq0WzSiv0vOJNYPAWdiMU+M4gEAAAAAQJFw4nCJW2+9VZmZmapZs6ZGjx4tm82mt99+W/fdd59GjRqlZ555RhaLRcuXL9fkyZPl9/t14YUX6v7775fVas1zrvPNGzD0wtdueYOGHhucpnJptny5DlCcsMIOAAAAAIAixjTNWMDWuXNnlS5dWn/7299Ut25d2Ww2rVmzRnfccYcqVaqkK664Qm63W//4xz/Url07tWvXLs+58jOsC4RNvTrVrSyfoUcHEdYB54rADgAAAACAIia3dfWmm26Sx+PRpEmTVKpUKUnStGnTNHToUPXq1Uuvv/66Zs2ape7duysQCOipp57ShRdemOdc+RXWhaOm3pru1t7MqEYOTFPlMoR1wLmiaRwAAAAAgCJo+fLlWrNmjV588cVYWDd79mxdeeWVat++vdasWaP7779fjRo10rJlyzR27Fg988wzsQmx+SlqmHpvpkeb9kV0fx+XalZgvRDwexDYAQAAAABQBO3fv19bt25Vo0aNYo/t3LlTf/3rXzVhwgSNHDlS69ev10033aSLL75YK1as0MiRI2Wz5e9KN9M09b/ZXq3aFtY9PVNVr2pCvl4PKI6IuAEAAAAAKILC4bB8Pp8yMzNVvnx5SdItt9yi3NmS119/vXbu3KmPPvpI+/btU7NmzSTl7551pmnqywV+zd8Q0u3dnGpaMzFfrgMUd6ywAwAAAACgCKpSpYocDoc+++wzud3u2OMWi0WGYUiSXC6XateuraSkpNjz+RXWSdK05QHN/CmgGzqmqG09R75dByjuCOwAAAAAACiCWrdurZtvvln/+te/NG7cOB09ejT2nNVq1dSpU/Xkk0/qxhtvVIUKFfK9nh9WB/T1Er8Gtk7WZY2Tzv4CAKdFSywAAAAAAEXUSy+9pKysLA0bNkw//fSTevfurbS0NC1YsEAvvviinnjiCV177bX5XseiTUGNm+tT96YO9b2EsA74syxmbnM7AAAAAAAocrxer1588UW99tprysrKksViUceOHXXVVVfp3nvvzffr/7Q9pDene9SuXqJuvcwpi8WS79cEijsCOwAAAAAAioGtW7cqEokoEomoWrVqSktLk5QzCCK/QrSNe8Iak+FW4xoJuqtHqmxWwjrgfCCwAwAAAACgmMnPkC7X9oMRvTQpW7Uq2vVAX5cSbIR1wPnC0AkAAAAAAIqZ/A7r9mVGNSbDrSplbLqvN2EdcL4R2AEAAAAAgHN2xB3Vy1OylZ5i1YN9XUpKIKwDzjcCOwAAAAAAcE6yfYZenuxWgs2i4f1dciYRKwD5ge8sAAAAAABwVr6goVemuBUMmxoxwKVSTiIFIL/w3QUAAAAAAM4oGDb16lSPjnoMjRjgUvk0W2GXBBRrBHYAAAAAAOC0IlFTb8/waPfhiB7q51LVMvbCLgko9gjsAAAAAADAKRmGqfe+82rD7rCG9XGpVkXCOqAgENgBAAAAAICTmKapj+f4tHJrSHf1SFWDagmFXRJQYhDYAQAAAACAk3y1yK+564K69TKnml+YWNjlACUKgR0AAAAAAMhj+gq/ZqwM6NoOKWpf31HY5QAlDoEdAAAAAACImb0moAmL/OrfMkndmyYVdjlAiURgBwAAAAAAJElLNgc1bo5P3Ro71L9VcmGXA5RYBHYAAAAAAECrd4T0wfdeta2XqGsuTZHFYinskoASi8AOAAAAAIASbtPesN76xqPGNRJ062VOWQnrgEJFYAcAAAAAQAm281BEr0/zqHYlu+66PFU2K2EdUNgI7AAAAAAAKKH2H41qdIZbFUtZNay3Swl2wjogHhDYAQAAAABQAh1xR/XKFLdSk6x6qJ9LSYmEdUC8ILADAAAAAKCEyfYZemWKWxaLNKK/S6lJxANAPOE7EgAAAACAEsQXNDQmw61AyNTDA1wqnUo0AMQbvisBAAAAACghgmFTr0/z6LDb0PB+LlVItxV2SQBOgcAOAAAAAIASIBI19c4Mj3YciujBvqmqVs5e2CUBOA0COwAAAAAAijnDMPXB916t2x3WsN4u1a6UUNglATgDAjsAAAAAAIox0zQ1bq5Py34J6c7LU9WwOmEdEO8I7AAAAAAAKMYmLvbrx7VB3dLFqUtqJxZ2OQDOAYEdAAAAAADF1IyVfk1fEdDV7ZN1aQNHYZcD4BwR2AEAAAAAUAzNXRfQ+IV+9b0kST2aJRd2OQB+BwI7AAAAAACKmWVbQvrfbJ8ua+TQwNaEdUBRQ2AHAAAAAEAxsmZnSO9951Hruom6rmOKLBZLYZcE4HcisAMAAAAAoJjYsi+st77x6OLqCRrS1SkrYR1QJBHYAQAAAABQDOw6HNGrUz2qWcGuu3umym4jrAOKKgI7AAAAAACKuAPHonplilsV0q26v49LiXbCOqAoI7ADAAAAAKAIy/QYemWKW06HRQ/1cyk5kbAOKOoI7AAAAAAAKKLcfkOjp2TLNKUR/V1yJXObDxQHfCcDAAAAAFAE+UOmxmS45QmYeniAS2VctsIuCcB5QmAHAAAAAEARE4qYen2aWwezDA3v71LFUoR1QHFCYAcAAAAAQBESiZp691uPth+M6MG+qbqgnL2wSwJwnhHYAQAAAABQRBimqbE/eLVmZ1j39kpVncoJhV0SgHxAYAcAAAAAQBFgmqY+m+vT4k0h3d7dqUYXJBZ2SQDyCYEdAAAAAABFwOSlfv2wJqibuqSoVR1HYZcDIB8R2AEAAAAAEOdm/hRQxrKArmyXrE4Nkwq7HAD5jMAOAAAAAIA4Nm99UF/M96l38yT1ap5c2OUAKAAEdgAAAAAAxKkVv4T00WyvOjV0aHBbwjqgpCCwAwAAAAAgDq3bFdZ/ZnrUsnaibuyUIovFUtglASggBHYAAAAAAMSZX/ZH9OZ0t+pXS9DQbk5ZrYR1QElCYAcAAAAAQBzZfSSiV6e6Vb28Xff0TJXdRlgHlDQEdgAAAAAAxImDWVGNnuJWWZdVD/RJlSOBsA4oiQjsAAAAAACIA8e8hl6Z4lZSokXD+7mU4uCWHSip+O4HAAAAAKCQeQKGXp7sVjQqjejvUloKt+tAScY7AAAAAAAAhSgQMvVqhltuv6ERA1wq67IVdkkAChmBHQAAAAAAhSQcMfXGdLf2HzM0vL9LlUsT1gEgsAMAAAAAoFBEDVPvzvTol/0R3d8nVTXK2wu7JABxgsAOAAAAAIACZpimxv7g1eodYd3TM1UXVUko7JIAxBECOwAAAAAACpBpmvpivk+LNoZ0WzenmtRMLOySAMQZAjsAAAAAAArQlGUBff9zUDd0SlGbuo7CLgdAHCKwAwAAAACggHz3U0BTlvo1uE2yujRKKuxyAMQpAjsAAAAAAArAgg1BfT7fpx7NktS7BWEdgNMjsAMAAAAAIJ+t2hbS2B+86tjAoavaJctisRR2SQDiGIEdAAAAAAD5aP3usN6Z4VHzCxN1U+cUwjoAZ0VgBwAAAABAPtl2IKI3prl1UVW7bu/ulNVKWAfg7AjsAAAAAADIB3syIxqT4Va1cnbd18ulBBthHYBzQ2AHAAAAAMB5dig7qtFT3CqdatUDfVLlSCCsA3DuCOwAAAAAADiPjnkNvTLZrUS7RcP7u+RM4tYbwO/DuwYAAAAAAOeJN2Bo9BS3wlFTI/q7lJ7CbTeA3493DgAAAAAAzoNA2NSrU93K8hl6uH+ayqXZCrskAEUUgR0AAAAAAH9SOGrqrelu7cmM6qF+LlUuQ1gH4I8jsAMAAAAA4E+IGqbem+nRpn0RPdDHpZoV7IVdEoAijsAOAAAAAIA/yDRN/W+2V6u2hXVPz1TVq5pQ2CUBKAYI7AAAAAAA+ANM09SXC/yavyGk27o61bRmYmGXBKCYILADAAAAAOAPmLY8oJk/BXR9xxS1reco7HIAFCMEdgAAAAAA/E4/rA7o6yV+DWydrK6Nkwq7HADFDIEdAAAAAAC/w6JNQY2b61P3pg71vYSwDsD5R2AHAAAAAMA5+ml7SP/93qv29RN1dfsUWSyWwi4JQDFEYAcAAAAAwDnYuCesd2Z41KxWgm7p4pSVsA5APiGwAwAAAADgLLYfjOj1aW7VqWzXHZenymYlrAOQfwjsAAAAAAA4g32ZUY3JcKtyGZvu6+1Sgo2wDkD+IrADAAAAAOA0jrijenlKttJTrHqor0tJCYR1APIfgR0AAAAAAKeQ7TP08mS3EmwWDe/vkjOJW2gABYN3GwAAAAAAfsMXNPTKFLeCYVMj+rtUysntM4CCwzsOAAAAAAAnCIZNvTrVo0yPoRH9XSqfbivskgCUMAR2AAAAAAD8KhI19fYMj3Yfjuihfi5VLWsv7JIAlEAEdgAAAAAASDIMU+9959WG3WEN6+PShRUJ6wAUDgI7AAAAAECJZ5qmPp7j04qtId3ZI1UNqiUUdkkASjACOwAAAABAiffVIr/mrgtqyGVOtbgwsbDLAVDCEdgBAAAAAEq06Sv8mrEyoGs7pKh9fUdhlwMABHYAAAAAgJJr9pqAJizyq1/LJHVvmlTY5QCAJAI7AAAAAEAJtWRzUOPm+NS1sUMDWiUXdjkAEENgBwAAAAAo9nxBQ69Nc2vT3rAkafWOkD743qs2FyXq2ktTZLFYCrlCADiOGdUAAAAAgGLvp+1h/bw9rLU7wxrUOlmTl/rV6IIE3XqZU1bCOgBxhhV2AAAAAIBi7+ftIVktUtTImQhbLs2qu3ukym4jrAMQfwjsAAAAAADFWiRqavXOsAzz+GP7jhqavyFYeEUBwBkQ2AEAAAAAirVf9kcUDJ/8+CdzfNpzJFLwBQHAWbCHHQAAAACgWFu2JRT72GqRDFOqUsamTg0dqlzGVoiVAcCpEdgBAAAAAIq1RZtyWl+TEy3q0CBR7es5VL0ct8MA4hfvUAAAAACAYu3ypklKT7GqQwMHQyYAFAkW0zTNsx8GAAAAAAAAoCAwdAIAAAAAAACIIwR2AAAAAAAAQBxhDzsAAAAAQJESipjauCes9bvD8gRMOR0W1auaoAbVEuRIYI86AEUfe9gBAAAAAIqMg1lRfbXQp31HozJNyWa1yDBNmZIqpNt0ZdsUVSljK+wyAeBPIbADAAAAABQJbr+hsT94tf9oVGVd1jwTXyNRU5keQ2VdNg3p6lQpJztAASi6eAcDAAAAABQJP+8I68CxqMql5Q3rJMlus6icy6rD2VGt3BoqpAoB4PwgsAMAAAAAxD3TNLVya0g2q0U266n3qbNaLUpMsGjVtpAiUZrJABRdBHYAAAAAgLgXiuS0xCYlnPk4h90iX8iUL0hgB6DoIrADAAAAAMQ9m1WyWCwyzpLDmaYpq0WyM3cCQBFGYAcAAAAAiHt2m0V1KtnlD505sfMGTVUra1Ny4qnbZgGgKCCwAwAAAAAUCc1qJSjBbpEnYJzyeV/QkM1q0SW1HbJYCOwAFF0EdgAAAACAIqF2Jbva10tUMCwdzo4qGDZlGKZCEVNH3Ia8QVOt6iaqfjV7YZcKAH+KxTRNduIEAAAAABQJOdNiw1q8OaiDWYaihimbVSrnsqll3US1qp0o62mmyAJAUUFgBwAAAAAocqKGqb2ZUflDphwJFlUtY5PdRlAHoHggsAMAAAAAAADiCHvYAQAAAADizqY9IT320TGt2Rkq7FIAoMCxEycAAAAAIK4s2xLUuzO9Mk0pEqUpDEDJwwo7AAAAAEDc+O5nv975Niesu7lziprVcmhPZkTfrPQXdmkAUGAI7AAAAAAAhc40TY1f4NPn8/yySLqze4o6XZykqGHq/e+8+mlbuLBLBIACQ2AHAAAAAChUkWhOKDdjVUBWi3Rf71S1vihJkjR7TVC7D0d1TYeUQq4SAAoOe9gBAAAAAApNIGzqzelurd8dkc0qPdDHpYsvSJAkHfMa+nqxT50udqhWRW5fAZQcvOMBAAAAAApFts/Q6Ay3dh+JKsEmDe/v0kVVEmLPf7nApwS7RYPbJBdilQBQ8AjsAAAAAAAF7mBWVK9Mduuo15DDLo0YkKYLT1hFt353WEs2hzSkq1POJHZzAlCyENgBAAAAAArUtgMRvTrVrUDIlMMujRyUpgvKHb89jURNjZvjVZ3KdrWrl1iIlQJA4SCwAwAAAAAUmNU7QnprhkcypWSH9MjAdFUuY8tzzLerAjqYZejunqmyWiyFVCkAFB4COwAAAABAgZi/IaiPfvDKbpOcSRaNGpSmCul5w7rD2VFNXe5X9yZJqlaWW1YAJRPvfgAAAACAfGWapqatCOjrxX4l2qX0FItGDkxTWZftpGM/n+9TisOi/q0YNAGg5CKwAwAAAADkG8Mw9ek8n2avCSrRLpVJtWrkwDSVcp48SOKn7SGt2hbW3T1SlZRIKyyAkovADgAAAACQL0IRU+9/59HKrWEl2qWKpWwa0d8lV/LJYV0wbOrTuT41rG7XJbUTCqFaAIgfBHYAAAAAgPPOGzD0+nSPth+MKMEuVStr00P9XEpxnBzWSdK0FX5leQ2N6O+ShUETAEo4AjsAAAAAwHmV6Y5qdIZHR72GJKlWBbvu7+M6bZvr/qNRfbsyoF4tklSx1Mn72gFASUNgBwAAAAA4b3YfiWhMhltRQwpHTNWvmqB7e6XKkXDqsM40TY2b61WpVKt6t2DQBABI0qnXIgMAAAAA8Dtt3BPW8xPdslkt8gZMNa6RoGF9Th/WSdKyLSGt3x3RDR1TlGinFRYAJFbYAQAAAADOg2VbQnr/O4/Kp1u176ihVnUSNbSbU3bb6UM4f8jU5/N9al4rQY1rJBZgtQAQ3wjsAAAAAAB/yvc/B/T5PJ9qVLBp+8GoOtRP1C1dnLJaz7xibvJSn/whU9demlJAlQJA0UBgBwAAAAD4QwzT1MRFfn2zMqB6VezauDeiro0duvbSFFnPMul11+GIZv0c1KA2ySrrYtAEAJyIwA4AAAAA8LtFoqbG/uDVok0hNbogQWt2htWreZKuaJssy1nCOsM09ckcnyqWsurypkkFVDEAFB0MnQAAAAAA/C6BkKnXprm1bEtIzWrmhHUDWyefU1gnSQs2hPTL/ohu7HTmPe4AoKRihR0AAAAA4Jxl+wyNmerWwayoGtdM0MqtYV3dPlk9miWf0+s9AUNfLfSp7UWJqlc1IZ+rBYCiicAOAAAAAHBODhyLanSGW+GIqQbVcsK6GzulqEujc29rnbjIr6ghXdWeQRMAcDq0xAIAAABAATMMo7BL+N22HYjo3xOyZbNKtSrYtWpbWLd1df6usO6X/RHNXZczaCI9hdtRADgd3iEBAAAAoIBkZ2dLkqzWnFuxohLcrd4R0ouTslUh3aqK6Vat3hnWnZc71b6+45zPETVMjZvjVfVyNnW5+NxfBwAlEYEdAAAAABSAaDSqu+66S5deeqmefPJJ7dmzJxbcxbP564N6fZpH9asmKDnBonW7Irq3V6pa1fl9odvsNUHtOhzVjZ2dsloZNAEAZxL/Px0AAAAAoBiw2Wx6//33dccdd2jLli1q3LixPvjgA+3Zs6ewSzsl0zSVscyvD3/wql29RAXDhjbvj+iBvi41rZn4u86V5TM0aYlfHRs6dGFFtlIHgLOxmKZpFnYRAAAAAFAcGYZx2lV0f/nLXzR16lR16NBBjzzyiGrWrFmwxZ2BYZj6dK5Ps9cG1adFkjbsCWtvZlQP9HXpoiq/f7LrezM9WrsrrH/ckC5nEutGAOBseKcEAAAAgHyQG9YdPXpU//d//xfbvy53zcQ///lP3Xnnnfr555/1z3/+U5mZmYVZbkwoYuqtGR7NWRfUtZcma+2usPYfM/TwgLQ/FNZt2BPW4s0hXdUuhbAOAM4R75YAAAAAcJ6Zpimr1aqNGzeqcePGWr58udatWydJslgsikQikqRhw4bpyiuv1Nq1azVhwgQVdgOUN2DolclurdsV1pCuTs1bF1Kmx9CogS7V+gOtrJGoqU9+9KpOJbva1f99bbQAUJLREgsAAAAA+WDPnj3q1q2bunbtqjfffPOMx15//fU6ePCgZs6cWWiDKI64oxqT4Zbbb+rWy5wav9CnYNjUw/3TVLmM7Q+dc/oKv75e7NdTV6epWjn2rgOAc8UKOwAAAADIB9u2bVPFihVjYd3jjz+uO++8UzfccIOWLFmiQCAQO3bMmDFatmyZ3n///UKpdffhiP49IVuhiHTX5U59Ns+nSFR6ZNAfD+uOuKPKWOZX1yYOwjoA+J0I7AAAAAAgH+zatUt+v1/Hjh1Tp06dtGDBAiUlJWnt2rW688479dFHH8kwDJmmqQoVKujJJ5+Mtc0WZCPUxj1hPf+1W2nJVg3t5tQHs7yyWaVHBrlUIf2PhXWS9Nk8n1IcFg1olXIeqwWAkoHADgAAAADyQVJSkg4dOqQFCxaoYsWK+vrrr/Xaa6/pp59+UuPGjfXhhx/qyJEjslgskqTy5ctr3rx5ikQiscfy29ItQY2e4latCjbd0DFFb8/wKMVh1aOD01TW9cfDup+3h7RqW1jXdEhRcmLB/F4AoDghsAMAAACAfDB48GClp6fryiuv1P79+5WcnBx77rXXXtPSpUu1YMGC2GNDhw7VPffcExtIkd+++ymg/3zr1SW1E9W/VbJeneZR6VSrRg10KT3lj98qhiKmPp3nU8NqdrWszaAJAPgjCOwAAAAA4DyLRqOSpH/961+qWrWqsrKydPjw4djzwWBQzZo1U5UqVfK87tZbb1VSUlK+1maYpsYv8Onz+T71aJakThcnakyGW5VL2TRygEuu5D93mzh9hV/HPIau7+QssJWCAFDcENgBAAAAwHkUjUZls+W0k/bq1UtPPvmkduzYoQcffFDz58/Xnj179Oabb+rYsWOqUKFCntfa7fk7nCESNfXBd159uyqgazukqGH1BI3J8KhGebuGD3ApxfHnbhH3H4vqmxUB9WyepEql/nhLLQCUdBazIHczBQAAAIAizjTN064cOzGse+SRR3TZZZepT58+mjVrlm6//XaFQiGlp6fL7/fr66+/VtOmTQus7kDI1JvfuLV5b0RDuzuVaLfo7Rke1a+aoHt7pSrR/udWw5mmqVemuHUo29D/XZf+p88HACUZs7UBAAAA4BzlhnX79u2T0+lUWlpanudzw7o77rhDEyZMUP/+/SVJXbt21eLFi7V79255PB41bdpU6enpMgxDVmv+Nz5l+Qy9mpETpg3v75Lbb+qtbzxqUiNBd/ZIVYLtz4dry34Jaf3uiO7v8+fDPwAo6WiJBQAAAIBzZLFY9O2336pq1ap67LHHFAqFTjrmhRde0LJlyzR37lx16tRJUk7QV6FCBbVo0UKdOnUq0LBu/7Go/j0hW9l+Q48OcumI29C7Mz26pHai7u55fsK6QMjUF/N9alYrQU1rMmgCAP4sWmIBAAAA4BwtWbJEt912m2rVqqUffvhBo0aN0tNPP52nRXbDhg0qX768ypYtW4iV5th6IKLXprrlSrbooX4urdkZ1sc/+tSxgUM3dU6R1Xp+VsJ9Md+nH9cG9Pfr01XWxd51APBn0RILAAAAAOcgEolo8+bNuvTSS/WXv/xFs2fP1pAhQ1ShQgUNGzYsdlz9+vULscrjftoe0rvfenRBObvu75OqBRtD+mK+T10bO3TtpSmynqcJrrsPR/T9zwENapNMWAcA5wmBHQAAAACcA7vdrs6dO6tJkya64IILdMstt2jfvn0aPny4KlSooKuvvvqk15xpQEV+mrsuqI9/9KpJzQTd0d2pmT8FNWmJX72bJ2lw2+TzVpNhmvpkjk8V0q26vGnSeTknAIDADgAAAADOKjd4q1atmqpVqxZ77LHHHtPBgwd12223qWzZsuratask6aefflKDBg2UmFiw+7mZpqmpywOatMSvLhc7dN2lyZq0JKDpKwMa2DpZfS9JOq8B4sKNIW3ZH9HIgS7Zz8NeeACAHOxhBwAAAACnYBiGTNOMTX49UTQazfP4zTffrG+//VZTpkzRZ599pjlz5mjixImqXr16gdUbNUyNm+PTnHVBDWqdrF4tHPpyvl/frw7qmg4p530FnDdg6P+Ny1LD6gm68/LU83puACjpWGEHAAAAACdYsWKF6tevr5SUFEnS0aNH9cknnyg1NVX16tVTu3btZLPZ8oR2H3zwgfr166e2bdsqOTlZ33zzTYGGdcGwqf/M9Gj1jrBuvcyp9vUS9fGPPs1dH9SNnVLUpdH5b1edsMivqCFd3T7lvJ8bAEo6AjsAAAAA+NXUqVP18MMP695779Xw4cO1e/duNWnSRBdeeKF27typ0qVLq2vXrnrrrbdks9lkGIasVquys7MVCoVUp04dzZw5UzVq1DhpFd6Jcltsz8ced56AodenerTrSET390lVw+oJev97r5ZuCem2rk61r+/4U+c/lW0HIpq7LqhrL01RKaf1vJ8fAEo6AjsAAAAA+FXXrl3VsmVLffXVV0pLS9O6det066236qWXXtKuXbs0e/Zs3X///UpLS9Nzzz0nq9WqQCCgp59+Wps3b9batWuVnp4uwzBOG9ZFo1H5fD7ZbDalpKT8qdDuiDuq0VPc8gRMjRqYpmrlbHpnhkc/7wjrrstT1bLO+d9DzzBMfTzHq+rlbOrS6PyHgQAAiX8KAQAAAFDiPfvss1q/fr2Sk5M1ZswYlS1bVhMnTtScOXPUrVs3Wa1W1ahRQ9dee61eeeUVvffee/r+++8lSYmJibrlllu0e/dupaenKxqNymo9/a2W1WrVDTfcoCFDhkjSHw7rdh2O6F9fZStiSI9fkaaqZW16c7pHq3eGdW+v/AnrJOnHtUHtPBTVDZ2cslkZNAEA+YHADgAAAECJtmXLFk2aNEmlS5eWJJUrV04vvviiQqGQli1bpu3bt8eOTUpKUu/evVW9enVt3rxZUk4A16pVK0k648q63Hl/FotF99xzj/bu3asFCxb8oZrX7w7rha/dSnda9cQVaUp3WvXqVLc27Q3rgT4uNa2ZP2Fdls/QxMV+dWzoUO1KNGwBQH4hsAMAAABQotWpU0dly5bVM888k+exl156Se3atdP48eM1Z86c2HNVq1ZVQkKCjh07dtK5TreyzjRNHT58WOFwWJLUsmVLWSwWrVix4nfXu2RzUGMy3KpV0aZHBqbJbpNGT3Zrx8GIhvd3qWH1hN99znM1foFPNqt0RdvkfLsGAIDADgAAAEAJlrvq7corr9Tu3btjq+ZM01SjRo3097//XT6fT//85z/1/fffa8eOHfr444+1du1aNWvW7Jyv8eWXX2rFihWy2+0yDEMVK1bUrbfeqq+//lput/uc6535U0D/melVqzqJeqCPS+GoqZcmu7XvWFQPD0hT3cr5F9Zt3BPWok0hXdkuRalJ3EoCQH7iXRYAAABAiXTisIeOHTtq+fLl+uqrr2LPSVK3bt302GOPaevWrerfv7+GDh2q//73v3rzzTfVq1evc7rOq6++qqFDhyohIUEWi0WGYUiSunTpoltuuUVOp/Os5zBMU1/O9+mL+T71ap6kod2c8gZNvTjJraMeQ48MdKlWxfxrUY1ETX0yx6faFe1qXz9/2m0BAMdZzNyfRAAAAABQQkSjUdlsNkWjUYXDYSUlJemdd97RfffdpxkzZqh79+4yDCPW4vrxxx9r5MiRuu2223T//ferWrVqZzx/bhj4+OOPq1y5ctqyZYs++ugjTZs2TV26dIkdFwqFlJh45gAsEjX131leLd0c0rWXpqhbkyRluqN6abJboYiphwekqXLpU++bd758s9KvCYv8eurqNFUvx951AJDfeKcFAAAAUKLkDobYuXOn/vGPf6hjx4667rrrdPfdd2v58uW68sorNWPGDLVt2zb2mptuukmHDx/WVVddddawzjAMGYahe++9Vz169NDVV1+tjRs36tChQ9q1a1eeY88W1vlDpt76xq3NeyO6q0fO5NeDWVG9PDmnjfbRQWkqn56/Yd0Rd1RTlvrVrbGDsA4ACggr7AAAAACUOCtWrFD37t3Vv39/3X777Wrfvr3sdruOHTumYcOGadq0aRo9erS6d++uqlWr5nntiSvvfss0TRmGoSFDhujaa69Vv379Ys9lZWUpPT39nGs85jX06lS3DmcbGtY7VfWqJmjf0ahenpwtR4JFD/d3qYwrf8M6SXpzultbD0T0zA2llJxoyffrAQAI7AAAAACUMIcPH9YVV1yhtm3b6vnnnz/lMY8++qiWLl2qSCSip556Sk2bNlXFihXPeN5IJKJgMCin06mjR4+qdOnSf7jG/UejGp3hVtQw9VA/l6qVtWvX4YhemeJWWrJVIwa4lJ6S/1uSr94R0qtTPbrrcqda1XXk+/UAADkYOgEAAACgRAkGg9q5c6c6d+4ce+y36xief/55vfjii+ratav++te/aty4ccrKyjrtOSORiDIzM2W357SM/pmw7pf9ET03MVuJdoueuCJN1crate1ARC9Ocqt0qlWjBhVMWBeKmPp0rk8NqtnVsg6DJgCgILEBAQAAAIBi5cTpr7lObGPds2ePsrOzVblyZUl5Bz9s27ZNR44cUcuWLXXJJZfokksu0V133aVSpUqddpqraZrat2+fqlatetpW2XP10/aQ3v3WowvK23V/71Q5k6zatDes16a6VbWsXQ/2TVWKo2DWXUxf4Vemx9CDfV0nfT0BAPmLllgAAAAAxdKLL76omjVrqlu3bieteGvTpo2SkpL0448/5nn87bff1v79+zVq1CilpKScNYA7VTj4R81dF9D/fvSpea0E3d49VYl2i9btCuuN6W5dWNGuYX1cSkoomODswLGonv4sSz2aJ2lwm5QCuSYA4DhaYgEAAAAUO19//bUeffRRZWRkqF27dpo3b5727t0be/4f//iHNm7cqIEDB2rbtm3asGGD3nnnHY0cOVKNGjVSamrqOa2WOx/rH0zT1OSlfn0026fOFzt0d4+csO6n7SG9NtWtelUS9EDfggvrTDOnFTbdaVWfFskFck0AQF6ssAMAAABQ7KxevVodOnTQzJkzNWvWLP3444/yeDx66qmndNlllykxMVFz587VrbfeqkAgoJSUFIVCIY0ePVpXXHHFGc+du6ru8OHDKleunKLRaCzc+72r7aKGqU/m+DR3XVCD2iSrT4skWSwWLd0S1PvfedW0ZoLuvDxVdlvBtaQu/yWkt2d4dH+fVDWtyd51AFAYCOwAAAAAFCu5gdqDDz4oh8OhF154QcuWLdPSpUv1xBNPqHfv3rrmmms0ePBgeb1erVmzRl6vVxdeeKFq1qx51jZX0zT1+uuv66WXXtKPP/6oGjVq6LHHHtOgQYPUrl27c64zGDb1n5kerd4R1i2XOdWhfs4U1gUbgvrwB69a10nUbd2cslkLLqwLhEw99ekx1Shv1/19XAV2XQBAXrTEAgAAACiyctcf+P3+k567+OKLtWLFCpmmqZYtW+qqq66S2+3W/v37de2112rIkCEaN26c2rRpo65du541rMu9lsViUYcOHVSvXj316NFDffr00QsvvKAaNWqcc91uv6GXJ2drw+6w7u+TGgvrZq8J6L+zvLq0vkNDCzisk6QpS/3yBU1ddyn71gFAYSKwAwAAAFBkWSwWbdq0Sc2aNdPChQtjj0nSLbfcorVr1+qrr77S4sWLVbNmTd1999369ttv9f3332vr1q2aPn26wuFwnjDuTNeKRCKSpBYtWmjUqFHavHmztm/frr1796pKlSoyDOOsNR/Ojur5idk6lG1o5MA0Na6R03b67Sq/PpnjU7cmDt3cJUXWAg7r9hyJ6LufA+p7SbLKpdkK9NoAgLxoiQUAAABQ5DVt2lQWi0WfffaZ6tevH1sp9+KLL+r999/X1q1b9eijj+r//u//JElWq1UHDhxQxYoVz3ruSCSiQCCg1NRUSZJhGPJ6vRowYIB27typlStXKi0tTdFoVDbbmYOunYcjejXDrUS7RcP7u1Qh3SbTNDV1eUCTlvjVp0WSBrVJPm+TZ8+VaZp6/mu3PH5Df7s2vUD3zAMAnIwVdgAAAACKjN+uNwiFQpKkBQsWKBgM6s4779TOnTtjgVejRo20ceNGvfzyy3rmmWckKTYgIjesO9MaBtM0dejQIV155ZX63//+F3v93LlzlZaWpjVr1pxzWLd+d1gvTMxWKadVj1+RFgvrJizya9ISvwa1TtbgtikFHtZJ0sKNIW3ZF9ENnZyEdQAQBwjsAAAAABQZuWHWqlWrJEmJiTntpE6nU9999502b96sBx98UAcOHJAk9erVSz169ND8+fMlHQ/rTnXO013P5/MpLS1N06dPj4V7ffr00aRJk5ScnHxOYd3izUGNyXCrdqUEjRqUprQUqwzT1GfzfPpmZUDXdkhR35bJv++LcZ54A4bGL/Spdd1ENaiWUCg1AADyIrADAAAAUKQ8+OCDatGihQYPHqx///vf2rVrl3w+n6pWrapvvvlGs2bN0hNPPKHMzExJ0sCBA7Vp0yZt2rTpnK9x4qq72rVrq2/fvpowYYL27t170nFnC+u+XeXXezO9al03Uff3SVVSgkWGYeqjH7z6YXVQN3VOUfemSb/jK3B+TVzsVzhi6ur2DJoAgHhBYAcAAACgyDh8+LACgYDsdrvWr1+v5cuXq1GjRrruuus0btw41apVS3PnztX//vc/jRkzRqZpqnPnzlq3bp2CweA5X2fevHmxVXqSVKFCBdWrV++k9tkzrc4zTFNfzPfpywV+9W6RpNu65rSbRqKm3v/eqwUbQ7qtm1OdLy68sG7bgYjmrA1qYOsUlXJyewgA8YKhEwAAAACKlA0bNmjMmDGaOHGi5s2bp23btmnChAn68ssvVapUKV1zzTXatGmTvvnmGz3zzDMaMWKEDh06pPLly5/xvLmDKhYvXqx27dppyJAh6tWrl8qVK6dhw4apcePG+uKLL86pxnDU1IezvFq6OaTrOqaoa+Ok2OPvfuvR6h1h3Xl5qi6pnfinvx5/lGGYevarbEUN6f9dnSZbAU+lBQCcHoEdAAAAgLhnGEae/efWrl2rESNGaM+ePZo3b55Kly6tn376Sb/88otefPFFSdKiRYvUsGFDLVq0KDbh9UxyAztJeuuttzRs2DBZrVbdfvvtcjqdevnll0867lR8QUNvfePRlv0R3dH9eCgXDJt6e4ZHG/aEdW/PVDWpWXhhnST9sCagcXN8evwKl2pXYu86AIgnBHYAAAAA4lbu7YrFYtGqVas0ffp0XXnllbrooou0ZMkSDR8+XKFQSEuWLIkFeh6PR263W59++qluueUWlStX7ozXiEQislqtslqtCoVCsUEWb775pu6//34tWrRIrVu3jtVzprDumNfQmAy3Mj2GhvVO1UVVcoKwQMjU69Pc2nYwovv7uAp9uEO2z9D/G5ellrUTdctlzkKtBQBwMjYpAAAAABC3LBaLLBaLJk2apM6dO2vHjh3Kzs6WJLVq1Ur//Oc/FQwG1adPn9hrEhMTVblyZT388MMqV66cotHoac9vGIY8Ho9uu+027dq1S4mJibHj77vvPk2fPj0W1uXWczr7jkb17wnZ8gZMPTrYFQvrfEFDr0zJ1o5DEQ3vX/hhnSSNX+iTzSpd0a5wJtMCAM6MwA4AAABAXFuxYoUeeOABvfDCC3r77bfVsmVLSTnhWceOHfXss89qy5YtuummmyQptkIu15mmuFqtVq1YsUL79+9X69atNWfOHHk8ntjzPXv2lKSThk381i/7w3puQrYcCRY9foVLVcvYJUluv6GXJrl14JihkQPTVLdy4Yd1G/eEtXBjSFe0TVZqEreEABCPeHcGAAAAEBcOHz6sO+64Qxs3bszz+I4dO1S6dGkNHDhQhmFIUuxXu92unj176umnn9a4ceOUkZFxTteKRqPav3+/JKlr16567733NGjQIN1yyy0aMWKE/vvf/yoSicSOP9PKulXbQnppkltVytj06CCXyrhyAsJjXkMvfO3WUa+hUYNcqlnBfu5fjHwSiZr6ZI5PF1a0qUMDR2GXAwA4DQI7AAAAAHEhFArpgw8+0NNPP629e/fGHl+zZo0OHz6sihUrymq1KhqNxvar27p1q0KhkAYPHqwVK1aoX79+Z72OaZrasmWLmjZtqv/85z+SpOrVq+utt97SO++8o6ZNm2rMmDGaMmWKwuHwGc81Z21Ab37jUeMaCRrR3yXnryvWjrijeuHrbPlDhh4dlKZqZQs/rJOk738OaP+xqG7s7JT1DCEkAKBwEdgBAAAAKDS5rabRaFRVqlTRihUrNGHCBP3jH//QwYMHJUm9e/eWaZp65ZVXJOW0uJqmKb/fr7Fjx2rhwoVyOp1q1qxZnnOejsViUUpKioYOHaqRI0fqs88+iz3Xs2dPPfTQQ/rhhx80YMAAJSScuoXVNE1NWuLT/370qUsjh+7ukaoEe04AdjArqucnuhU1pEcHp6lS6dO35BakTHdUU5b61bWxQxeUi48AEQBwarxLAwAAACgUhmHIarXKMAwtWrRI4XBYXbp00aRJk9SnTx+VK1dOjz32mOrUqaOBAwfq888/l9Pp1F133aWjR4/q7bff1ltvvaWBAwfmOe+p2ldzp7t+/PHHuv7661W9enXdc889ikQiuvHGG2W323XVVVfFji9duvRp644apj750ae564O6om2yejVPil1zb2ZUL0/OVlKiRQ8PSFOZ1PhZI/H5fJ+SEi0a0IpBEwAQ7wjsAAAAABS43LDu4MGDuvHGG5WcnKzk5GRddNFF6tWrl9577z3dcccdKl26tEaMGKGRI0fqjTfe0KhRo/Svf/1LpUqV0r59+zRlyhS1aNHinK75xBNP6LnnntN7772n2bNnq0aNGrrnnnvkdrvlcJzbfm7hiKm3Z3i0dldYt3V1qn3946/beTiiVya7lZ5i1cMDXEpLiZ+wbvWOkFZsDeuOy51KccRPXQCAU7OYZ1svDgAAAADnUe5qt3Xr1qlr164aOHCgnnzySaWnp6tUqVI6cOCAKlasqFdffVXDhw/X2LFjdfPNNysQCGjjxo2aP3++0tLS1L17d1WqVEnRaPSMk2BN09Q999yjYDCoG264QVdccYUGDRqkjz/+WJLk8/mUkpJy1rp9QUOjM9zaeySqe3qlqtEFx6fRbj0Q0ZgMt8qnWTW8vyuupq+GI6b+9lmWyrpygsQzDdAAAMQHVtgBAAAAKFAWi0W7du3SkCFDdNVVV+m1116LhUizZs3Svffeq969e2v06NHavXu37r77bpUpU0bdu3dX06ZN1bRp09i5DMM4bViXuzbh0Ucfje135/V6Va9ePY0bN05er1cTJ048p7DuqDeqlya55QuaGjUoLc/E1017w3p1qlvVy9r1QN/UuFvBNvPngDI9hh7oS1gHAEVFfP0kAQAAAFAibNy4UVlZWbrzzjtjj40dO1b9+vVTpUqVNGfOHD333HN6/vnndc0112jo0KGaM2fOSefJnRb7W7lhndfr1fTp03XdddfJYrHIZrOpRYsW+vzzz/XUU0+dU637j0b0zy+zZRjS41fkDevW7gxrTIZbtSrYNby/K+7COtM01bGBQ0O7OVU5ToZfAADOLr5+mgAAAAAoEaZPn65QKKSmTZvGVn35fD698847+vHHH9W1a1eNHz9er732mj788EOVLVtWCxcuPKdzG4ahzZs369NPP5UkhUIhjRs3Trt27dLf/vY3LViwQB07dlSLFi1kGMYZz/XL/rD+OT5bpZxWPX5FmiqkHw+9Vm0L6fVpbtWrkqAH+rrkSCiY1Wtz587VihUr5PV6z3qsxWKR02FR67rntkcfACA+sIcdAAAAgAL3/PPP66WXXtLcuXN10UUXnfKYrl27KhQKad68ebF9787GMAwtXrxYzz33nDIzM3XnnXcqOTlZDzzwgBITExWNRpWRkaFmzZqd9ZyrtoX09gyPGlRL0N09U5V0QiC3dHNQ733nVbNaCbrz8lTZbQUT1s2bN0/33XefBg8erMcff1zJyUx8BYDiiBV2AAAAAApcvXr1dOjQIc2YMUN+v1+SYqvdotGoJKlJkyZq3bq1wuFwrMX1bOsNli5dqlq1aun9999XpUqVNHbsWHk8Hq1YsUL/+9//tGLFCjVr1kyGYZw2rDNNU3PWBvTGdI/aXJSoYb3zhnXzNwT1n++8al03UXf1KLiwTpJuu+02DR48OBZESjlft6ysLIXD4QKrAwCQvxg6AQAAAKDADRw4UNdff72efvppVa5cWT179pTL5ZJpmrLZbHruuec0fvx4ffrpp0pISIi97myr7Nq0aRP7+F//+pe6du2q7du3q3LlyqpcubKknIDrTHvfTVnq15RlAfW5JEmDWifnueYPawIaN8enTg0durFziqwFOMThpZdektPp1F/+8hclJuZMqN28ebP+/ve/a/PmzbLZbHryySfVt2/fAqsJAJA/COwAAAAAFIrnn39e+/fv19ChQ3XTTTfppptu0saNG7Vw4UKNGzdOkydPVseOHc94jtzwLbe99cQwLi0tTQ0bNlTdunUlKXbMmcK6j3/0ae66oG7olKLLGiXleX7GSr/GL/SrexOHrumQUqATV8PhsJYsWaL+/fvLbs+5jZs5c6bGjBmjzZs3a+DAgdq8ebOeffZZtWjRIhZOAgCKJlpiAQAAABSKqlWratq0abryyis1bdo0XXrppXrppZe0b98+LVu2TF27dj3jUAjTNGUYhp5++mmtWrVKkUhEVqs11iJ6++23y+Px6IorrpB05tV5UcPUG9M8WrAxqHt6puYJ60zT1OSlOWFdn0uSCjysk6SEhASVLl1aa9askdVq1Z49e3T//ferUqVKmjBhgp5//nkNHTpUmzZt0q5duwq0NgDA+cfQCQAAAACFzu12a+fOnapataqSk5PlcDjO2Lqaa9myZerbt68qVKiga665Rn369NHSpUv1xRdf6MCBA1q2bJmSk5PPOGAiEjX14qRs7TtqaFjvVF1U5XgLrmma+mqhXzNWBTSoTbL6XlJ4Qx4mTpyoG264QVWrVlUwGNRFF12k999/XzVr1pQk7d69W/3799crr7yiLl26FFqdAIA/j8AOAAAAQNw516mwkrRu3Trdeeed2rhxozIzM3XJJZeocePGeu+992S1WhWNRmWz2U752mDY1D++zFIwLD3UP1VVyxzfNcgwTX0216cf1gR1bYcUdW+adMpz5De/3x8bMLF8+XJ99tlnqlevnm6++WY5HI7YcU8//bQmTJign3/+uVDqBACcPwR2AAAAAIq8QCAgj8ejffv26YILLlB6eroknTGs8wYM/e2zLKUmWfVgP5fKpB5fzWcYpj6a7dWCDSHd1CVFnRoWTlj3xhtvyDAMXXPNNapYsWLs8RMDTZ/Pp4ULF+raa6/Vl19+qcsuu6xQagUAnD8EdgAAAACKpTOt0jvmNfTUuGO6oLxdw3qnKsVxPKyLRE198L1Xy34JaWhXp9rWc5zyHPlty5YtatKkid555x1df/31stvtCgaDikQicjqdseOefvppTZs2TV27dtW///3vQqkVAHB+EdgBAAAAKFEOHIvqb59lqVmtBN3eLVUJ9uOhXjhq6t1vPVq9I6w7L0/VJbUTC63O/v37q2zZsvrwww+1c+dOffTRR5o5c6aSk5PVpUsXPf7445KkRYsWad++ferbt68SEwuvXgDA+WM/+yEAAAAAUDxsPxjRs+OzdVljh67tkCKr9XhYFwybeusbjzbuDWtY71Q1rlF44dexY8cUCoU0YMAASdI111yjKlWqqHbt2ipdurQ++OADrVy5Uv/5z3/Utm3bQqsTAJA/COwAAAAAlAjrdoX0yhSPrmyXrJ7NkvK0ywZCpl6b5taOgxE92NelBtUSznCm/FeqVCkFAgFt27ZNS5Yskd/v11tvvaWKFSvK7XarQ4cOGjlypBYuXKiePXsWaq0AgPPvzDPSAQAAAKAYWLolqFenejS0m1O9mifnCeu8AUMvT8nWrsNRDe9f+GFdruuvv17jx49XRkaG6tevr3LlykmSXC6XBg0apIsuukhr164t5CoBAPmBwA4AAABAsTZ7TUBjZ3n1QB+X2v1mgITbb+ilyW4dPGZo5ACX6lSOj7BOkm644QZVqFBBb731lqZMmaJZs2bFnrNarQqFQgoGg4VYIQAgvzB0AgAAAECxZJqmpizza/aaoB7q61KNCnl3BDrmNfTyZLe8AUMjBrhUrWz87RhkGIbeffdd3Xfffapfv76uvvpqXXjhhfr55581duxY7d27l0ETAFAMEdgBAAAAKHY8AUNvTXfrmM/U8H4ulU+35Xn+iDuqlya5FY6aGjkgTZVK205zpviQmZmpUaNGac2aNVq/fr0GDRqkm266if3rAKCYIrADAAAAUKys2xXWm9+4Vbm0TQ/2dcmVnHcnoINZOWGd1So9PMCl8mnxHdadyO12KxwOy+VyKSEhftp3AQDnF4EdAAAAgGJj0cagPvzBq4bVEnR3z1Q5Eix5nt+bGdXLk7OVnGjRwwPSVDqVbb0BAPEn/jZpAAAAAIDfyTRNzVgV0FcL/epQP1E3dXbKbssb1u08FNErU9xKT7Hq4QEupaUQ1gEA4hOBHQAAAIAizTBNfTHPp+9XB9X3kiQNbJ0siyVvWLf1QERjMtwqn2bViP4uOZMI6wAA8YvADgAAAECRFY6Yev97r1ZsDenGTinq0ijppGM27gnrtWluVS9r1wN9U5XiKJphnWmaJwWRAIDiqWj+pAIAAABQ4vmChkZnuPXz9pDu7Zl6yrBuzc6QXp3q1oUV7Rre31Vkw7qDWVF9uyoggy3IAaBEYIUdAAAAgCLnqMfQmAy3jnoNPTzApTqVT56YumpbSO/M8KhB9QTd2zNVCfaiuTrNNE19OtenvZlRXdYoSYkMhwWAYo/ADgAAAECRsjczqjEZbknSY4PTVKWM7aRjlmwO6v3vvGp+YaLu6H7yAIqiZOW2sNbsDGtY71QlJhTd3wcA4NwR2AEAAAAoMjbvC+v1aR6Vdlr1UD+XSqee3OI6f31QY3/wqs1FiRrS1SmbteiGXIGwqc/n+dS4RoKa1mRpHQCUFAR2AAAAAIqEFVtDem+mRxdWtOu+3qceHvHD6oDGzfWpU0OHbuycImsRH9KQscwvt9/Q9Ze6GDgBACUIgR0AAACAuPfDmoA+nePTJbUTNbS7UwmnaHGdsdKv8Qv96t7UoWvapxT5gGtPZkTf/RRQv5bJKp9+ctsvAKD4IrADAAAAELdM09TXi/2atiKgbk0cuqbDyavmTNPUlKV+TVkWUN9LkjSwdXKRD+tM09S4OT6VdVnVs/nJ028BAMUbgR0AAACAuBSJmvr4R6/mbwjpqnbJ6tEs6aQgzjRNjV/o17erArqibbJ6t0gupGrPr8WbQtq0N6IR/V2nXE0IACjeCOwAAAAAxJ1g2NTbMzxavzus27s71fYix0nHGKapT+f6NHtNUNddmqJuTYrHSjRf0NAXC3xqWTtRDaszaAIASiICOwAAAABxJdtn6LVpbu07GtWDfV2nDK0Mw9TY2V4t3BDSLV1S1LFh8QjrJOnrxX6FIqau6ZBS2KUAAAoJgR0AAACAuHEwK6oxGW4FQqYeHZSmC8qffMsSiZr64Huvlv8S0tDTrL4rqnYcjGj2mqCu7pCi0qknT8EFAJQMBHYAAAAA4sKOgxGNmepWcqJFj1+ZpvJpJ09GDUdMvfOtR2t2hnV3j1S1qJ1YCJXmD8Mw9fEcr6qWtalr4+ITQgIAfj8COwAAAACFbs3OkN7+xqMqZW16oI9LruSTV5cFw6benO7W5n0RDeudqsY1ik9YJ0lz1we1/WBUjw12yWZl0AQAlGQEdgAAAAAK1YINQX0026uG1RN0d49UORJODqsCIVOvTXNrx8GIHuznUv2qxWsYQ7bP0IRFfnWon6g6lYvX7w0A8PsR2AEAAAAoFKZp6puVAU1Y5NelDRy6qXPKKVeWeQOGxmS4tf+YoeH9XcUy0PpqkU+SdGU7Bk0AAAjsAAAAABQCwzD12XyfflgdVL+WSRrQKlkWy8lhndtv6JUpbmW6DY0c4FKNCsXvFmbzvrAWbAjp5s4pp2wFBgCUPMXvpx0AAACAuBaOmHr/e49WbA3rps4p6nxx0imPO+Y19PLkbHkDph4Z5FLVssXv9iUSNfXJjz7VqmDTpQ0ZNAEAyFH8fuIBAAAAiFvegKE3v/Fo24GI7uuVqma1Tj044og7qpcmuRWJSo8MTlOlUidPjC0OZq0OaO/RqP5yZZqsp1hhCAAomQjsAAAAABSITE/OXnRZXkMPDzj9XnQHjkX18mS3bFbp0cEulUsrnmFdpsfQ5KV+dWnkKJatvgCAP46fCgAAAADy3Z7MiMZM8chikR67Ik2VS586hNuTGdErk91KTrTo4QFpKp1afPd0+2K+Tw67RYNaJxd2KQCAOENgBwAAACBfbdob1hvTPSqTatVD/Vwq5Tx1CLfzUESvTHGrlNOqEf1dSkspvmHd2p1hLf8lpNu7OZXiKL6/TwDAH0NgBwAAACDfLP8lpPe+86h2Jbvu65V62nDql/0Rjclwq2Ipq4b3c8mZVHxDrHDE1Li5Xl1Uxa42F516Dz8AQMlGYAcAAAAgX8xaHdBnc31qWSdRt3VzKsF26qEKG/eE9do0t6qXs+vBvi4lJxbv4QszVgV0xG1oWG+XLAyaAACcAoEdAAAAgPPKNE1NXOzX9BUBdW/q0NXtU047AXXNzpDenO5Rncp2DevtkiOheAdYh7Kimrbcr8ubJqlKmeI5TAMA8OcR2AEAAAA4byJRUx/N9mrhxpCubp+sHs1OP1Bh5daQ3vnWo0YXJOjuHqlKsBfvsM40TX061ydXslX9WjJoAgBwegR2AAAAAM6LQNjUOzM8Wr87rDu6O9XmIsdpj128OagPvvOq+YWJuqO7U/bTtMsWJ6u2hbV6Z1j39kot9isJAQB/DoEdAAAAgD8t22fo1aluHTgW1UP9XGpQLeG0x85bH9RHP3jVtl6ibr3MKZu1+IdXwbCpz+b51PiCBDWvdfqvDQAAEoEdAAAAgD/pYFZUo6e4FYqYemRwmi4od/rbjFmrA/p0rk+dL3bohk6n39uuuMlY5pfbb+j6jgyaAACcHYEdAAAAgD9s+8GIXp3qltNh0eNXpKlc2ukHKUxf4deERTkDF65un1xigqu9mVHN/Cmgfi2TVT6dQRMAgLMjsAMAAADwh6zZGdLb33hUtaxN9/dxyZVsPeVxpmlq8lK/MpYF1K9lkga0KjlhnWmaGjfHq7Iuq3o2SyrscgAARQSBHQAAAIDfbcGGoD6a7dXF1RN0V4/TD1EwTVPjF/j17U8BXdE2Wb1blKzpqIs3h7Rxb0TD+7mK/RRcAMD5Q2AHAAAA4JyZpqlpKwL6erFfHRs6dGOnlNMOjTBMU5/O8Wn22qCuuzRF3ZqUrBVmvqChL+f7dEntRF18AYMmAADnjsAOAAAAwDkxDFOfzvNp9pqg+rdKVv+WSadtbTUMU2Nne7VwQ0i3dHGqY0NHAVdb+L5e4lcwYuraDimFXQoAoIghsAMAAABwVuGIqfe+82jltrBu7pKiTg1Pv1ouEjX1/nderdga0u3dnWpzUckL63Ycimj2mqCuapes0qmn3tsPAIDTIbADAAAAcEbegKE3pnu041BEw3qnqmnNxNMeG46Yeudbj9bsDOvunqlqceHpjy2uDNPUJz96VaW0TV0bl6w2YADA+UFgBwAAAOC0Mt1Rjc7wyO039PAAl2pXOv1ebMGwqTenu7V5X0T390lVowtKXlgnSfPWBbXtYFSPDnbJbmPQBADg9yOwAwAAAHBKe45ENDrDLZvVoscGp6lSadtpj/WHTL021a2dhyJ6qJ9L9aqWzCELbr+hrxb51b5+oupWLplfAwDAn0dgBwAAAOAkG/eE9cZ0j8q6rHqon0ulnKffh80bMDQmw639xwyNOMsqvOLuq4U+SdJV7Rg0AQD44wjsAAAAAOSx/JeQ3pvpUd0qdt3by6XkxNO3dWb7DL0yxa1jXkMjB7pUo3zJvcXYsi+s+RtCurFTilzJDJoAAPxxJfenKQAAAICTfP9zQJ/P86lV3UTd1tV5xj3YjnoMvTw5W/6QqVEDXapatuTeXkQNUx//6FPNCjZ1aljypuICAM6vkvsTFQAAAECMaZqasMivb1YG1KNpkq5snyyr5fRh3eHsqF6e7FbUkB4ZlKaKpU6/v11JMGt1UHszo/rLVWmyWhk0AQD4cwjsAAAAgBIuEjU19gevFm0K6ZoOKbq8adIZjz9wLCess1mlRwe7VNZVssO6ox5Dk5b41KWRQzUqcIsFAPjz+GkCAAAAlGCBkKm3Zri1cU9Ed17uVOu6Z27n3JMZ0SuT3UpOtOjhAWkqncpebV8u8CnRbtGgNsmFXQoAoJggsAMAAABKqGyfoVenunXgWFQP9XOpQbUzT3fdcSii0VPcKuW0akR/l9JSCOvW7Qpr6ZaQhnZzKsXB1wMA8P/Zu88wKaq0jeP/ThO7Z4acM0jOSJacM5gVAyZMKKKyru7uu6sbjIiACmsWRV0RJOcgIEEyIiA55zgdpmPV+2GWEVYmMoFw/66LS+iuOnV6/NA1dz3nPLlDgZ2IiIiIyHXo2NkI70x3EwybDO+fQLmiGf9qsOtoiHemeyiRZGVoLxfxMQqnQhGTL5d4uaG0neY3RBX0dERE5BqiwE5ERERE5Dqz51iYUTPcOGMs/PHmhEz3oNt2KMSYGW7KF7MzpKeL2Cg1VQCYs97PKbfB492dWDJo0CEiIpJdCuxERERERK4jP+8LMnaOh3JF7DzZ04kzk0q5zfuDvDfLQ7VSdh7v7iLaoWAK4ERyhJlrU+hUL4YyhfVrlYiI5C59s4iIiIiIXCd+3Brg88Ve6lZw8HBnZ6bh27rdQf4910Od8g4Gd3HisCusAzBNk6+W+nDGWOl1oxpNiIhI7lNgJyIiIiJyjTNNkxlr/Uz5KYU2taK5q00cNmvG4duq7QE+XuClUeUoHuwUj92msO68jXtD/LwvxGNdncSo4lBERPKAAjsRERERkWuYYaRWgy3+JUDfprH0bByT6X5rS7cEGL/YS/PqUdzfPh5rJuHe9SQQSv151invoGHljLvqioiI5JQCOxERERGRa1QwbPLhPA8b94a4t10cN9WKyfScBZv8fL3MR7va0dzZJg6rmilcZMbaFJJTDJ69yaVGEyIikmcU2ImIiIiIXIO8foMxMz3sPxnm8e5O6leMyvScWetSmLQyhS4NYrilRawCqf9x5HSEuRv89GgcS/HEjDvrioiIXA4FdiIiIiIi15hT7gjvTHfjTjF5tm8ClUtkfNtvmiZTV6cwfY2f3k1i6H2jwrr/ZZomXy71UthppXvDzCsVRURELocCOxERERGRa8jBU2Heme7GZrXwhwEJlEzKuBLMNE2+XZ7CvI1+bm4RS7eG6np6KT/tCPLroTBP91K3XBERyXsK7ERERERErhG/Hgrx7iwPxRKsPNXLRWKcNcPjDdNkwhIfP/wS4M6b4uhQV5Vjl+ILGHy73EfjKg7qlM98abGIiMjlUmAnIiIiInINWL0zwMfzvVQrbeexbi5iozKuAosYJp8t8rLy1yD3tY+ndc3ofJrp1WfqTyn4gya3tYov6KmIiMh1QoGdiIiIiMhVbv5GP//50ceN1aIY1CEeuy3jsC4cMflwvpf1u4M82DmeZtUU1qVn/4kwCzcHuLl5LIWdGVcsioiI5BYFdiIiIiIiVynDNJm0IoU5G/x0bRDDgBaxWDNpFhEKm4yd42HLgRCPdnXSsLKWeKbHME2+WOKlVCEbHetpubCIiOQfBXYiIiIiIlehcMTk04VeftoR5PZWcXSqn3mgFAiZvDvLzc4jYZ7o4dR+bJlYtjXAnmMRnu/nyrRqUUREJDcpsBMRERERucr4gybvz3az/XCYh7vEc2PVzJe0pgRNRs1wc+BEmKd7uahexpEPM716uVMMJq1IoUX1KG4orZ+ViIjkLwV2IiIiIiJXkXM+g1HT3ZxINni6t4saWQjevH6DkdPdHDtr8EyfBKqU1K8BmZm0MgUTuKVFXEFPRURErkP6phYRERERuUocPRvhneluwhGT4f1clC2a+e18ss/g7WluznoNnuvronwx/QqQmZ1HQizbGuDuNnEkxKnRhIiI5D99W4uIiIiIXAV2HwszeoYbV6yF5/omUMRly/ScMx6DEVOTSQmaPN8vgdKFMz/nehcxTL5c4qNCMRttaql7roiIFAwFdiIiIiIiV7hNe4OMm+uhXFE7Q3o4iY/JvOrrZHKEEVPdRAx4vl8CJZIU1mXFop8DHDoV4cVbErBa1WhCREQKhgI7EREREZEr2NItAb74wUu9ig4e7uwkyp55iHT0bIQRU9zYbTC8vytL1XgCZ70GU37y0bZ2NBWL61clEREpOPoWEhERERG5ApmmyYy1fqb8lELb2tHcdVNcliq+Dp0KM2Kqm/gYC8P6JJAUrz3Ysuo/P/pw2C30axZb0FMREZHrnAI7EREREZErjPHffdSWbAnQr2ksPRrHYLFkHtbtOx5m5HQ3SfFWhvVx4YpVWJdVWw+GWL0zyKAO8VlaciwiIpKXFNiJiIiIiFxBgmGTD+Z52LQ3xH3t42ldM2uND3YeCTFqhoeShaw83dOl0CkbQhGTCUu8VCtlp0X1qIKejoiIiAI7EREREZErhcdvMGamhwMnwzzR3Um9ilkLj7YeDPHuTDcVitsZ0sNFTJSaJWTH3A1+jp8zeLSrM0uVjCIiInlNj91ERERERArAyeQIk1b4CIZNAE65I7w2KZljZyM81zchy2Hdz/uCjJ7hpkopO0/1VFiXXSeSI8xYk0Kn+jGUKaJ6BhERuTLoG0lEREREpADMWJvCsq1BDp+J0LtJDKNnenDYLbwwIIESSVnr6rpuV5B/z/NQp7yDwV2dOGwK67Lrm2U+4mMs9L5RjSZEROTKocBORERERCSf+YMmq7YHAdi4N8Tm/SFKF7YytFcCCXGXXgRjmuZFyzVXbg/wyQIvjatE8UDHeOwK67Jtw54gG/eGeLSrkxiHfn4iInLl0JJYEREREZF89tOOAKHIb/+OGFC/QlS6Yd3+E2GGfnyW9btTQ74lW/x8PN9L8+pRPNRJYV1OBEImXy/zUae8g0aVHQU9HRERkYuowk5EREREJJ8t/iXwu9emr/VTsrCNZtV+3xV26ZYAvoDJ2DkeWteMZsmWAO3qRHPnTXFY1SQhR2auTeGcz2BYH5caTYiIyBVHFXYiIiIiIvlo7/EQB07+Vl5ntfz239Nu43fHhyMmq3akVtYZJizZEuDGKg7uUliXY0fORJizwU/3hjEUT8zafoEiIiL5SRV2IiIiIiL5aOLylLS/F3VZqVvRQe1yDqqXdlyyw+svB0KkBM2LXtuwN8TuYxGqlNTtfHaZpsmEJV4KO610a6RGEyIicmXSN7yIiIiISD7qWC+aSiXstK0dTdGEzKu7Vv4awAJcGNmFIvDvuR5euzcpr6Z5zVq9M8i2Q2Ge6ukkyq4KRRERuTIpsBMRERERyUcNK0fTsHLWjvUFDNbuDmECFguYJsRGWahf0UGrmr/f604y5gsY/OdHH40qO6hbIaqgpyMiIpIuBXYiIiIiIleon/eFME2Ii7bQsnoUDSpFUbWUHZtVlWE5MXV1Cv6gye2t4gp6KiIiIhlSYCciIiIicjnch2DvbDi7C6x2KFILKnSB2MKXPXTTalFUKm6jWKJNnUwv0/6TYRb+HODm5rEUdqnRhIiIXNkspmmamR8mIiIiIiIXMQ3YOA5++QwCZ+HCnebiS0CjZ6Bav4Kbn6QxTJPXJ7nxBU3+clsCdpvCTxERubKpwk5EREREJCc2fQAb3gV7LCRVAYs19XUjDJ7DsPIVsMdApW4FO0/hx61Bdh0L81xfl8I6ERG5KiiwExERERHJLt9x2PJ5aiAXX/Li96x2SCgP53bDxvehfEewOQAwTZODpyL8eiiML2AQ7bBQpaSdyiXsWLUvXZ5wpxh8t8JH8xuiqF7GUdDTERERyRIFdiIiIiIi2bVvHqScgsQM2r3Gl4Kzu+HICijbBo/fYOpPKew8GiYY+u/SWQus+DVA2SJ2+jWLpWiC9lbLbZNXpmCYcGtLNZoQEZGrh7WgJyAiIiIictVxH0zdss6aQcBmjwUzAu6DBMMmE5f72HIgRIzDQvFEKyWSbJRItOGMsbL3RJhvlvlI9hn59hGuB7uOhli6NUD/5rEkxOlXHxERuXroW0tEREREJLss1rT+EukyzdTGFBYbWw6E2H0sTGGnldgoy0UdX6MdFoq6rBw5G2Hd7mDezvsa5wsYrN8dJGKYRAyTL5f4qFDMRtta0QU9NRERkWzRklgRERERkewqWjc1tIsEwJZOGBRMBkc8FK3Duo2pQZzDful96mxWC1E2C+t3B2lVMxqHGiPkyIpfg3y9zEeZwjZqlrVz8GSEP96coP0BRUTkqqMKOxERERGR7CrXDhIrpi6NNS9RamdGwHcMSjTGKFSTE+cMYhwZh0axURa8ARN3Smale5KeZJ+BxQKHz0SYvylAiSQrxRL1K4+IiFx99O0lIiIiIpJd9hho+iJEJ8HZnRB0/3cJrAn+M6mvJVSEG5/HYrVgsVw617uQyX+3xVMxWI65/SYWfvtZHztn8OIX5/h5n5Yai4jI1UWBnYiIiIhITpRtDe1HQqlmEDiXGtKd3Zm6TLZCV+j4LhSqhsVioWJxG/5QxomdN2BQJMFKQqwSu5xypxgYF/6YTUgJmuw7ES6wOYmIiOSE9rATEREREcmpUk2h5I1w8mc4tzd1X7vCNaBQ1YsOa1Apiq0HQ/gCBnHRv39mHgiZmCY0qhyl/dYuw1nPb112LRZIjLUwsF089StGFeCsREREsk+BnYiIiIjI5bBYoFi91D/pqFrKTqPKUfy0M0gwbOCKtWCzWjAME4/fJCVkUrOsg4aVFCxdjuPnfgvsOtWLpk/TuEz3DhQREbkSKbATEREREcnAoVNh3p3loWHlKG5tGZejMawWCz0ax5IYZ2X1ziCnPUbaPmvOGCutq0TRvm5Mul1kJWtCERNXrIWhvVyUL6ZfdURE5OplMc3Mtr8VEREREbk+/bI/xHuz3YTCUL+igyd6uC57TH/IZPfRMN5AaufYyiXsxMdoa+nc4AsYxERZsFoUfIqIyNVNgZ2IiIiIyCUs2eLnix98YEKNsnae6O4iWssrRUREJB/oUZ6IiIiIyAUM0+S7FT7GL/ZhmtCgkp0hPbMe1u0/Gead6W7O+YzMDxYRERG5BFXYiYiIiIj8VzBs8vECD2t3hQBofoOD+zs4sWWxc+uWA6lLaOOjrUTZTf52Z5KWZ4qIiEi2aSdWEREREREg2WcwZpab/SciALSrE82dN8VlOXBbvi3A54u9lC5k48CpCB3rRiusExERkRxRYCciIiIi173DpyOMmuHG4zeIGNCtYQwDmsdiyULgZpomM9f5+X5VCrXL2dl6MEzzG6K4rXXOOsrKpfmDJr8eDuFOMbHboGIxOyWSrFn6fyQiInK1UWAnIiIiIte1rQdDvD/bg80KgRD0axZLz8axWTo3Yph8tdTHD78EaFUjip92BKlbwcF97eNVXZdLDMPkx20BVm0Pci7FwAKYJkT/t8Nu90axFHJqa24REbm2aA87EREREbluLdsaYPwPXhJjLZzxmtzROo6O9WKydG4gZPLBPA8/7wvRs3EM8zcFKFfUxtO9XETZFdblBtM0WbDJz5ItARw2C65YCzarBdM0SQmauFNMShW2MbBtPIlxCu1EROTaoW81EREREbnuGKbJ5JU+PlvkpYjTyhmvyf3t47Mc1rlTDN6aksy2gyEGto3jh18CFEu08mQPhXW56fCZCCu3B4lxWEiKt6Y1/7BYLMRFWymaYOXw6QjLtwUKeKYiIiK5S4GdiIiIiFxXQmGTD+d5mbnOT+nCVk65DQZ3cdKqZnSWzj9+LsKrk5JTz+vqZMZaP3HRFob2chEbpbAuN23aG8IfMnHGXPrnarNaiI2y8PO+EF6/kc+zExERyTvaw05ERERErhvuFIN3Z3nYfyJM2cJWjp0zeLKHk7oVorJ0/p5jYUbPdBMXbWFITycfL/BimvBMbxeuWD0Lz20HT4Zx2CwZNpaIj7ZwzmdwMtkgPkb/D0RE5NqgwE5ERERErgtHz6R2gk0JmpRItHHCHeHpXi6ql3Fk6fyNe4P8e66HckXsPNQ5nrFzPHj8JsP7J1DYZcvj2V+fslozZ/73j4iIyLVCj6BERERE5Jr366EQ/5qUjNUKCXEWTnsNhvVJyHJYt+QXP+/O8lC7nIMhPZ18usjL8XMGQ3u5KJmksC6vlClsIxQxyahPXkrQJDbKQhGXfrUREZFrh77VREREROSatuLXAG9Pc1O6sA1McKeYPN/XReUSmS82MU2T71f5GP+Dj3Z1onm4czyfLvKy+2iYJ3s4KV9MC1byUr0KUUTZLfiClw7sDMPEGzCpXc6hJckiInJN0R2GiIiIiFyTTNNk6uoUpq/x06SKg73Hw0QMC8P7JVCyUOZVceGIyeeLvaz4NcjNLWLpXD+azxb5+HlfiMe7ObmhdNaq8yTnyhW10bCSg592BIkYBs4YC9b/7mcXCJmc9RkUS7DRskbWGoaIiIhcLSxmRvXlIiIiIiJXoVDE5LOFXlbtCNKpfjSrdwSIslsZ1sdF0YTMwzp/0OT92W5+PRxmUId4mlaL4psffSzcFODBzvE0q6aAKL+EIyYLN/lZuzuIL5D6q4sJOGwWShe20efGWEpoWbKIiFxjFNiJiIiIyDXF4zd4b5aHPcfD9Gkay7wNflyxVp7p7SIpPvNlk2e9BqNmuDmZbPBYNyc1yzqYviaFKT+lcHebONrVicmHTyH/66zXYNuhEMk+A4fNQsXidioUt6VV3ImIiFxLFNiJiIiIyDXj2NnUTrC+gEn/ZrF8tzKFYglWnu7lytIeZ0dOR3hnhpuIYfJ0Lxdli9hZsMnP18t89GsaS88msfnwKUREROR6pz3sREREROSasP1wiPdmeXDFWritVRxf/uClfDE7Q3q6iI3KvAprx5EQY2Z6SIq38nRPF4VdNlb+GuDrZT4614+hR2NV1omIiEj+UGAnIiIiIle9VdsDfLrQS5WSdm6qHc2nC73cUNrO491cRDsyD+vW7gry4XwPVUrYeby7k7hoKxv3BvlkoZdWNaK4tWUsFi29FBERkXyiwE5ERERErlqmaTJjrZ8pP6XQonoUtcra+WSBl3oVHDzcxYnDlnnINn+jn//86KNJ1SgGdYzHYbPw66EQY+d4aFDJwT3t4hXWiYiISL5SYCciIiIiV6VwxOTzxV5W/Bqkb9NYEmItfLzAR7Mbori/Qzw2a8Yhm2GafLc8hbkb/XRtEMOAFrFYLRb2HQ8zZqabaqXsPNTZmek4IiIiIrlNgZ2IiIiIXHW8foP3Z3vYdTTMQ53icaeYjP/BR9va0dzVJi7TzqGhiMknC7ys2RnkjtZxdKyXuj/dkTMRRk53U6qwjce7u7JUoSciIiKS2xTYiYiIiMhV5cS51E6wHr/JM31cbD8cZspPKXRtEMPNLTLfa87rN3hvtoc9x8I82tVJoypRAJxyR3h7qpuEOCtP9XQRk4W970RERETygsU0TbOgJyEiIiIikhU7j4R4d5aHuGgLQ3o4WbY1yJwNfvo2jaVn45hMw7rT7gjvzPBwzmvwZA8nVUs5AEj2Gbw2ORnDhD/0TyAp3pofH0dERETkklRhJyIiIiJXhdU7Any80Eul4nYe7RbP1J/8/PBLgNtbxdGpfkym5x88GeadGW5sVgt/GJBAqUI2AHwBg5HT3QRCJsMV1omIiMgVQIGdiIiIiOSJMWPGEAqFKFKkCPfeey+maWKxWDAMA6s166GYaZrMWudn8qoUmlWLYmC7OL78wceq7UHubRfPTbWiMx1j68EQ78/2UCzBylO9XCTGpV4/EDIZM9PDKbfB8/1cFE+05fjzioiIiOQWPT4UERERkUzlZBeV6Ohojhw5wt/+9jdat27NG2+8wYEDB7BarRiGkaUxwhGTzxZ5mbwqhd5NYri3fRwfz/eyemeQhztnLaxbuT3AO9PdVC5h5/l+CWlhXThiMm6Oh30nwjzV00nZInqWLSIiIlcG7WEnIiIiItmS3Qq5lJQU/vznP7Nx40aSk5MZM2YMN954Y1rFXXpM02TboRCjpnu4t308jSpH8d5sN9sPpzaLqF8xKsPrmqbJ7PV+Jq1MoVWNKAa2jcf+366vhmny0Xwva3cFGdLDRe3yjix/HhEREZG8psBORERERDI0d+5c/v73vzNs2DCaNm1K6dKls3zuhaHc/Pnz+fe//82yZcv48ssvad++fZbGOHw6TFK8ldEzPRw4EeaJHi5qls04YDMMk6+W+Vi8OUCvJjH0ufG37rGmaTJhqY8ffgkwuIuTxlUyDv5ERERE8psCOxERERHJ0IkTJ3j99dfZs2cPq1evZsyYMbRs2ZIiRYqke45hGITDYWw2Gzbbb/vC/fLLL7zxxhts3bqVcePG0aBBgwyvbZompgn//C6ZE8kGT/V0UaVkxktXAyGTD+d72LQ3xN1t42hT6+KGFJNX+Zi51s+97eK4qVbmzSpERERE8pv2sBMRERGRDBUrVow33niDDz/8kNtvv50nn3ySf/3rX+zatSvdc6xWKzabDY/HA5C2Z13t2rV5+OGHKVSoEFOmTAEgEomkO47FYiFswGmPwbN9Mw/r3CkGI6Yms+VAiCe6O38X1s3dkMLMtX5uaRGrsE5ERESuWKqwExEREZE0Fy5hXbNmDeXKlaNEiRIXHfPuu+8yYcIEqlWrxquvvkrJkiWzNN6F3nvvPf7617+ydu1aypUrl+H5Z30m/qBJqUIZd3A9cS7CyOluUoImQ3q4qFTi4nBv2dYAny3y0r1RDAOax2U4loiIiEhBUoWdiIiIiKQ5H669/fbb9OvXj6+//jqtAu58ldwTTzzBwIED2bFjBx9++CGhUCjT8davX39RRd7jjz9Ohw4d+Oijj/677DX9Z8gOG5mGdXuPh/nXpGQAXhiQ8Luwbt2uIJ8v9tKmVjT9m8VmOJaIiIhIQVNgJyIiIiIX+de//sXf/vY33n//fQYPHpy2B53VaiUcDgPw2GOP0bRpU2bOnMmhQ4cyHM80TapXr84rr7zCyZMn08K5Vq1acebMGSwWyyWr8CKGScQAZ0zGt6w/7wvy5vfJFE2w8sKABIonXhzubTkQ4oN5HppUieLuNnEZdqYVERERuRIosBMRERGRNB6Ph+XLl/Pvf/+b3r17s2fPHr777jvGjh3LiRMnLqqE++c//8mBAwcYN25chmNaLBZiY2MpVKgQe/fuTQvMhgwZQu/evS95TsQwsQB2W8bh2tItAcbM9FCjrINn+yTgir349nb3sTDvzXJTo6yDBzrGY7UqrBMREZErnwI7EREREUlz+vRpVq1aRc2aNZk7dy5dunTh7bff5g9/+ANt27blo48+SlsCGxsbyzvvvMOyZcs4ceJEpmMXKVLkd8trO3Xq9LvjDMPEaiHDcM00Tab+5Etb5vp4NyfRjouPP3QqzDvT3ZQraufRrs5Mwz8RERGRK4UCOxERERFJY7FYKFmyJCdOnGDs2LG88MILTJ8+nXPnznHjjTfyySefsGrVqrTjK1asyNmzZ/H7/ZmOe+rUKQoVKgSkLq+9FMMwsVjIcNlqOGLy2SIv09b4GdA8lrvaxP0u3DtxLsLb09wUdloZ0vP3YZ6IiIjIlUyBnYiIiIikKVeuHKVKleLBBx9k3759tGnThqSkJAA+/vhjTp8+zZw5c9KOb9SoEd26dePAgQPpjmmaJps2baJChQrccMMN6R5nmJmHdf6QybuzPKzcHuTBjvF0bxT7u+PPeg3enuYm2mHhmd4u4qJ1yysiIiJXF929iIiIiAjw2zLVf/zjH7hcLrZu3Zq2/NUwDGw2G40bN8bn8wGkLW997rnnaNGixSXHjEQinD17lnnz5jF06ND0r22m7lmXUVh3zmfwxvfJ7DwS4uleLppXj/7dMV6/wchpbkIRk2d6u0iI0+2uiIiIXH10ByMiIiIiwG+BXe3atXn++eeJiori5Zdf5sCBA5imyalTp9i2bRvVqlUDSOseW6JEiXSDNp/Px9y5c3n22WcBLmpacZ6ZhbDu6JkI//oumWSfwfD+CdQs6/jdMYGQyagZbs75DJ7pnUDRBNslRhIRERG58lnMS901iYiIiMh1xTRNLBYLbreb5cuX07VrVyZPnsz9999P2bJlKV68OIcOHaJ69epMmzYty+Nl9lpW7DwSYsxMDwlxVp7u5aSI6/dBXChiMmaGm13HwjzbJ4FKJezZvo6IiIjIlUJ3MiIiIiLXCL/fT0xMTI7OtVgsmKaJ3W5n/vz5dO3alf79+9OgQQOmTZuG2+2mePHiPPzww0BqNV56jSPOj5eV1zKzbneQD+d5qFTCzuPdnMTH/P6ahmHy0XwP24+EebqXS2GdiIiIXPVUYSciIiJyDVi0aBHjx4+nb9++dOzYEafTmaNxDMPAYrFgsVjSDeVyWimXXQt/9vP1Uh+Nq0TxQKd4HLbfX9M0TT5f7GP5tgCPdXPSoFJUns9LREREJK9pDzsRERGRa0StWrUYOnQoDz74IF9//XWOxrBarWlhXHoVdJmFdZf7PNgwTSau8PHVUh+d6sfwcJf0w7qJK1JYtjXAfe3jFdaJiIjINUMVdiIiIiJXsUgkktb8AWDlypVMmzaNESNG8Morr/DUU08RFZU/QdbOnTupWrXqZY0Riph8utDL6h1BbmsVR6f66S/xnbUuhUkrU7g9k+NERERErjba4ENERETkKnPhktQLwzqA5s2b06RJEypWrMjgwYMxTZMhQ4Zccm+73Fjaen6MsWPHMnbsWFasWEFsbGyOxvIFDN6b7WHX0TCPdHHSpGr6QeMPv/iZtDKF3k1iFNaJiIjINUeBnYiIiMhV5MJ95T766CO2bNlCfHw8nTp1ok2bNgDY7XYefvhhfD4fzz33HPXq1aNr166/25PufKOJnIZ24XAYm83Ghx9+yDPPPMPGjRuJjY3N0ZinPQajprs54zV4preLG0o70j129Y4AX/7go0PdaHrfmLNwUERERORKpiWxIiIiIleJC4OwW2+9lXXr1tGsWTN2796N1Wrlyy+/pFKlShedc9999/Hjjz+yc+fOLI+d1bkcOHCATz/9lFdeeYUff/yRpk2bZto99lIOnQrzznQPFgsM7eWiVGFbusdu3h9kzEwPN1aNYlDHeKz50PxCREREJL+p6YSIiIjIVcJisRCJRBgwYAAHDx5k8eLFTJgwgZdffpmTJ0/icPxWlRYKhQD4v//7PxISEhgxYkSmY2dHSkoKDRo04K9//Stz5syhadOmRCKRbId12w6FeG2ym/gYC3+8OSHDsG7nkRDvz/ZQp7yD+9orrBMREZFrlwI7ERERkavI9u3bcTqdjBw5knLlygFQrVo1SpQowbfffsvnn39OcnIyDocD0zQpVaoUN910E3v27AFSl9RejvOLM5KTk2ndujXz5s2jQ4cOGIbxu/30MrNqR4B3prmpVNzG8P4JJMWnf2u6/2SYUTM8VCxu55EuTuyX6BorIiIicq3QklgRERGRq0gwGGT//v1UqFABh8PBjh07uOmmmyhdujROp5Pk5GQ8Hs9FS2AXLlzI3XffzcaNGylevHi6Y2dlWezkyZPp1KkTLpeLU6dOUaRIkRwtp527wc/EFSm0qB7Fve3iMwzgjp2N8NrkZAo7rTzbN4HYKIV1IiIicm1ThZ2IiIjIVSQqKoqqVavicDiIRCK88sor3HHHHSxdupR58+YxceJEfD4fI0eOBFLDscaNG3PfffcRCAQyHDu90O38890vv/ySm2++mVq1auH3+ylUqFCG512KYZh8vczHxBUp9Ggcw6AOGYd1pz0Gb09zEx9t4eleLoV1IiIicl1Ql1gRERGRq9D5Jajvv/8+8fHxaa9HR0cTExNDYmIikBqmJSYmcscdd1C2bNlLjmWaJoFAgJiYmEu+d97evXt57bXXqFKlyiWPzUwwbPLRfA/r94QY2DaOtrUzHsOdYjByWjKmCc/0duGK1bNmERERuT7orkdERETkKnJ+D7rzzR2io6Mvev/QoUMULVqU6tWrX3R8gwYN0q2E8/l8GYZ1FouFbdu2MWHCBMqVK8eAAQMuej8rPH6DEVPdbN4f4onuzkzDOn/Q5J3pbjx+k2f6uCjsyt7+eCIiIiJXMwV2IiIiIleICxtC/G9ziPPhmNVqZeXKlWlLXu321AUTbrebOXPm0K9fP2666SZatmyZdnxmLqzQ+1933303JUuWpEOHDjRo0IA77rgj7b2sLoU9kRzhtUnJHDsb4bm+CdSvGJXh8aGwyZhZbo6fMxjay0XJJIV1IiIicn3RklgRERGRK4BhGFitVk6cOIHdbqdQoUKEw+G0QO58ODZ+/Hgef/xx/vnPf6ad+9NPPzFlyhQ+/PBDnnnmGV544QUga00kMhIKhfjzn/9MixYtKFasWFpYl51x950IM2q6m2iHhT/enEDxxIzDt4hh8u95HnYfDTO0t4vyxXS7KiIiItcfdYkVERERuUKsXr2aVq1aUaVKFX755ResViuRSASbLTXk+vrrr3nooYcYO3YsAwcOTDvv8OHDzJ8/n8qVK9O6dWsg81Atvfdzet6lbN4fZOxsD6UL23iyh4uEuIyr/QzT5NOFXn7aEeSJ7k7qVsi4Ek9ERETkWqXATkREROQKsHHjRgYPHkyRIkXYv38/FStWZNq0acBv1Xdnz55lz549NGzY8HfnXxjsXY6zZ88ybNgwnn/+ecqVK4fT6czROD9uDfD5Yi91yjt4pIuTaEfGIZ9pmnyzzMfCnwM82DmeZtWiMzxeRERE5FqmPexERERErgC7d++mevXq/O1vf+Pvf/8769ev54knngBS96ELhUIkJSVdMqwDciWsMwyDXbt2sXnzZh555BEGDBjAN998w7Fjx7I8hmmaTFudwqeLvLSuGc3j3TMP6wCmr/Gz4OcAd7WJU1gnIiIi1z1V2ImIiIhcITZs2ECDBg0IBAKMHz+e4cOH88ILLzB8+PA8ve6llrkuWrSIjRs3snfvXoYNG0b58uUzHSdimHy5xMfSLQH6NY2lR+OYLC2fXbDJz9fLfPRrFkvPxrE5/hwiIiIi1woFdiIiIiIF7FKB2ZkzZ3j//fd59dVX+fDDD7ntttswTZNjx45RtGjRtGYUuXntrVu3UrhwYUqUKJH2figUwuFwZDqOP2Ty7zkethwMcW+7eFrWyFqV3MpfA3y0wEuX+jHc0jL2sppkiIiIiFwrtCRWREREpACEw+G0v18YUhmGAUChQoW4//77GTRoEIMHD2blypV8+OGHdO7cmSNHjuTaPCwWC6Zp4vV6+eSTT9iwYQMXPs/NSliX7DN48/tkth8JMaSHK8th3ca9QT5Z6KVVjSiFdSIiIiIXyJ1HsyIiIiKSqW+//Zb27dunVcht2bKF//znP4TDYW699VYqVqxIYmIi4XAYu91O6dKleeqppzh27BgtW7bEbrfz3nvvUa5cuQyvk9VOridPnqRo0aKEw2EmTJjAxx9/zH333Zet4Ozo2QijprsJhk2G90+gfNGs3V7+eijE2DkeGlRycE+7eIV1IiIiIhdQhZ2IiIhIPli2bBm33347n3zyCQBLly6lSZMmLF26lC+++ILbb7+doUOHkpycjN1uT6vAK1KkCIcPH6Z48eKsXLmShx56iIx2NDEMI0vh14wZM6hduzY9e/akZ8+ePP/883zyySfUrl07rcovM7uOhnltUjI2K/zx5qyHdXuPhxkz0021UnYe6uzEZlVYJyIiInIh7WEnIiIikodSUlJwOBzY7XbeeustXnzxRT7//HOWL19O2bJlef755wEYPXo03377LbVr1+b9998HwOv1MmzYMGbPns3y5cspU6YMhmFgtV76mWtG7/2vgwcPMnPmTBYsWECdOnVo06YNbdu2zfIYG/YE+WCeh/LF7DzZ3Ul8TNaue+R0hNe/T6ZYopVhfRKIyUIHWREREZHrjQI7ERERkTxy4MAB/u///o8333yTwoULAzB48GAmTJhA6dKlGT16NF26dAFSg73Ro0fzzTff8N5779GsWTMA5syZQ5cuXbBYLEQiEWw22yWvdX4ZbGbLYUePHs2OHTt49NFHqVWrVo4+1+LNfiYs9dGosoMHOzpx2LMWup1yR3htkpu4aAvP93NlOeQTERERud7oLklEREQkjxQqVIhFixaxZs2atNfGjRtHr1692LFjB/v37097PTY2loceeohdu3axffv2tNe7du2KxWLBMIx0wzog07DONE0ef/xxFixYwP79+6lTpw5du3Zlx44dWf48pmkyaYWPL5f46FA3mke6ZD2sS/YZjJjqxmaDob0V1omIiIhkRE0nRERERPJAMBgkLi6O7t27s379ejp37kwoFCIqKoqvvvqKbdu2MWbMGJo2bUq9evUAcDqdVKlS5aIOsuelt0z1fEiX3lLW84spHn30USwWCxMnTsRut/Of//yHs2fPUq1atSx9nnDE5LNFXlZuD3Jry1i6NIjN6o8CX8Bg5HQ3gVBqY4qkeIV1IiIiIhnR3ZKIiIhILpo5cyYAUVFRWK1Wypcvz7hx49LCOr/fD8CCBQs4fPgwTz75JN9++y3btm3j9ddfZ+vWrWkBXlacr6hLL6yLRCLce++9nDt3jjfeeAO7PfV57W233cYjjzySpWv4AgajZrhZszPII53jsxXWBUImY2Z6OOU2GNrbRfHE9KsERURERCSV9rATERERySXff/89AwYM4KOPPmLQoEFpr1evXp3WrVvz0UcfARAIBIiOjubnn3+mWbNm+P1+br/9dvbv389rr71G69atM7xOZvvUXXjcgw8+yNKlS9m2bVvaktqsng9wxpMa1p32GDzezUn1Mo4snQepVXnvzfLw6+EQw/q4qFIy6+eKiIiIXM9UYSciIiKSS5o1a8bw4cN59tlnWbhwIQDhcJiXXnqJhQsXMnr0aACio6MJhULUrVuXadOmAdCtWzfmzp1L69atMQwj3WtEIpEM37+QxWKhS5cuFC9enE2bNgHZC+sOnQ7z6qRkfAGT4f1d2QrrDMPk4wVethwM8UR3hXUiIiIi2aHATkRERCSXlCpVikcffZQ+ffpw9913s2PHDux2O+3bt+e2227j7bff5osvvgBIW5rasWNHJk6cyMCBA4mPj8c0zQz3qzvffCK9RRKmaeJ2uzlw4AAAdevW5fjx43z33XcAWQ7rfj0U4vXJqR1dXxiQQJnCWd/62DRNvlrqY82uIA93dlKrnMI6ERERkexQYCciIiKSC85XvVWsWJFnn32W+vXr06dPHzweD+XKlWPQoEHcfvvtPPDAA7z33nscO3Ys7dwBAwZgs9mIRCKZBmoOhyPd40zTxO/38+ijj3LjjTeyb98+ateuzcCBA1mwYEGWP8vqnQFGTnNToZiN4f0TKOTM3i3j9z+lsPiXAPe0jadxlahsnSsiIiIi2sNORERE5LJEIpG0veEutGzZMoYOHUp8fDw//PADAD6fjwkTJvDqq6/SuHFjWrduzRNPPJFuRd15Fy5jTW9J64WvJycn06FDB2rUqMHnn3/OsWPHKFy4MNHR0ZkuiZ27IYVvl6fQ/IYo7msfj92WtYq8/z3/lhaxdG2Y9eYUIiIiIvIbBXYiIiIiOWQYBlarlQMHDvD9999TqFAhKleuTMuWLQmFQsybN48hQ4bQokWLtKWwAOvWrWPz5s2MGTOGl156iR49euBwZL5s9Pz1/tf527kLg7ibb74Zq9XKt99+e9Fx6YV1pmkycbmPuRsDdG8YQ//msVlePnvesq0BPlvkpXujGAY0j8vWuSIiIiLyGwV2IiIiIpdh3rx53HPPPVSuXJlwOMyBAwf44IMP6NWrFz6fj8mTJzNs2DAGDx7Myy+/fNG5Z8+exeVyXbJCLxKJEAqFiImJyXQO4XA4bU+886Hc/fffT4kSJfjXv/6FxWLJMHwzTJOP53v4aWeIO2+Ko32dzK/5v9buCjJuroebakYzsG1ctsM+EREREfmN9rATERERyUR6zzcXL17MQw89xLPPPsvy5cuZPn06drud2267jWXLlhEXF0ePHj148cUX+fvf/87EiRMvOj8pKemSYZ1hGJw+fZr27dszfvx4vF5vhvM6H9adP3fMmDFMnTqVgQMHYrVaMwzPIobJqBlu1u8J8Xg3Z47Cui0HQnw4z0OTKlHc3UZhnYiIiMjlUoWdiIiISAYuXIa6YsUKAoEApUuX5oYbbmDcuHEcOXKEv/71r5w5c4bOnTsTGxtLYmIiq1atYu3atZQvX56DBw8yb948Bg4cmKWlr+cdO3aMhIQEYmN/vxecYRjs2bOHmJgYypQpA8DGjRsZO3Ys33zzDV999RVdu3ZNdxktpIZ1b05J5ugZgyd7uKhSMuudYM/bdTTM21OTqVbawRPdndne805EREREfk+BnYiIiEg6Lgy7Bg4cyPHjx7HZbPzpT3+iVatWnDx5kuPHj1OyZEl69epFxYoV+eCDD/j5559p2bIlDRs2ZMmSJcTHx19yzMuxb98+GjduzJ133smzzz5LxYoV2bZtG19//TWtWrWic+fOmYZ1r05KxuM3ebqXi5JJv6/0y8yhU2Fe/95N6UI2hvZ2Ee1QWCciIiKSG7L/GFVERETkOmG1Wjl79ixt2rShRIkSvPfee8TFxaVVtKWkpFCrVi0WLFhAKBTir3/9K/Hx8djtdpo2bcru3buZNGkS99xzz0Vj/q/zz0+XLVvGTTfdlKW5VahQgffee48777yTcDjM0KFDqVGjBi+99FJaFV9GYd2/vjsHWPjjgAQS4rIfIJ44F+HtaW4KO60M6elUWCciIiKSixTYiYiIiKTD7XYzaNAgqlatyqeffkpCQgKQulT1jjvuoFixYrz22mskJyezdu1aSpQoAcDChQupW7cuM2fOpHDhwplex2Kx4PP5shTWhUKhtEDutttuY9asWYwbN47Dhw/zySefZHq9iGHy+uRkEmJtDO6as6DtrNfg7Wluoh0WnuntIi5a2yKLiIiI5CYFdiIiIiLpOHXqFJs2bWLEiBG4XC4A1q9fT7t27ahWrRrnzp1j9OjRDBw4kHbt2lG+fHkaNmzImjVrmD17dlp4dr5z6/8KhUJ4vV6SkpKIi4vLdD5vvfUWAA8++CBJSUkAVKxYkT/84Q+UKVMmw7DONE1ME96e6qZMYTt3t43DZs1+WOf1G4yc5iYUMflD/5xV54mIiIhIxhTYiYiIiKRj6dKl7Nu3jz59+qQFbmfOnOHpp5/m5Zdf5o033mDy5MnExsYyevRopkyZgs/n45NPPqFSpUppQd2lwrpIJILX62Xjxo20bds2S/OJRCK88MILuN1uunbtSsmSJfnmm28YN25chtV555fcvjfbQ/UyDno1iclRJ1d/KLWj7DmfwfP9EiiakP1970REREQkc2o6ISIiIpKOqVOncvvttzNt2jQ6dep0yWPuueceVq9ezbx58yhXrlza6xk1fDBNk+XLl1OqVCkqV66c7vXPB36vvvoqN998M9WqVePjjz/mL3/5CxaLhVAoRPv27fnqq68y/SyfLPRwQykHrWpGZ3rspYQiJmNmuNl1LMyzfRKoVELPfUVERETyiu60RERERNJRs2ZNAL7//nsaNWp00ZLTcDiM3W6nUaNGBINBEhMTLzo3o06w48ePz3JYt379el588UWqV69OtWrVeOCBB2jYsCHHjx8nHA7Ts2fPi46/lInLvdxYNYo65aOy/NkvZBgmH87zsP1ImKd7uRTWiYiIiOQx3W2JiIiIpKNatWr8+c9/5q9//StVq1blnnvuoUiRIgDY7Xa+++47RowYwd/+9re0hhTpOR+o7dixg7Zt21KhQoV0q/DOH7t//366dOnCK6+8Qv/+/fF4PFitVho2bHjJ4y9l7voUbqwWTYViObvtM02T8T/42LAnxGPdnNQo48jROCIiIiKSdVoSKyIiIpIBt9vN0KFD+eSTT7jzzju5//77OX78OJs3b2bEiBGMHDmSxx57LMMxIpEIpmlit/8WmqUXshmGgWEYpKSkUL16de666y7efPNNAMaNG8eePXt4+eWXiYrKuFrONE1Wbg9StZSdYjnca840TSauSGHuBj+DOsTTskbOltOKiIiISPaowk5EREQkAy6Xi48++ogSJUowadIkvvrqK6pWrUr58uWZNWsWHTp0ANIP4EzTxOv1snjxYvr06ZNhI4rzx7/00kt8/vnn9OzZkzfffBPDMIhEIixZsoTChQtfFPylN8YvB0LUreDAGZPzLq6z1/uZu8HP7a3iFNaJiIiI5CMFdiIiIiJZ8M9//pM//elP7Nu3j1KlSmGz2XC5XGnLWtML644cOcJjjz3GZ599BpBuUBcOh9PG2rhxI6Zp0qVLFwCOHTvGP/7xD5YtW8a6deuwWq0ZVOiZ7D8Z4YbSDqLs2e8Ee97izX4mrUyhd5MYOtWPyfE4IiIiIpJ9WhIrIiIikkMZ7R1nmiabNm1i0KBBLFy4kKSkpAz3rAsEArRq1YqBAwfyyCOP0Lt3b44cOcLp06e54YYbOHbsGLNmzaJKlSrpjhMxTE65DYq6rFitOQ/rftoR4MN5XtrXjeaO1nHpfkYRERERyRuqsBMRERHJoYyCrKlTpzJlyhRWrFhBdHQ0kUgEm+3Se8lZLBZiYmIYNGgQw4YN48yZMyxcuJDly5ezdOlSbrzxRqpXr06ZMmUyDOu8fpNiCZeu9suqn/cF+XiBl2Y3RHG7wjoRERGRAqEKOxEREZFccr7i7ujRo8TGxpKYmAiQblgXDoc5efIk48ePp06dOlSpUoV169bx8MMP8+CDDzJy5MhLjv+/IoZJKGwSE5Xz/eoAdhwJMXKam5plHTza1YndprBOREREpCAosBMRERHJQ+mGbJEIgUCAd999l2XLljFv3jxq1qxJKBSiSJEi/PDDD9x9992MHz8+w6W3EcPEMMFxmeHa/pNh3vzeTfmiNp7u5cJxGfvfiYiIiMjl0ZJYERERkTx06cYQBnv37uXLL7/kpZde4vnnn+fXX3/l8OHDfP311xw5cgSAdu3apTsGpIZ1cPlh3bGzEUZOc1M80coTPRTWiYiIiBQ0VdiJiIiI5DPTNPn00095//33adCgAX/7298oVarURe+fPn2aIkWKpDuGYZhYLBnvo5cVpz0Gr09OxmGD4f0TcMVe3rJaEREREbl8CuxERERECsiECRN49913qVKlCq+88goVKlTgwluz9MI4wzSxZPB+VrlTDN74PplACP7Q30Vh16WbYoiIiIhI/lJgJyIiIpIP0tuHburUqYwYMYLixYvzl7/8hTp16mQ6Dlx+WJcSNHlrSjKnPQbD+ydQMklhnYiIiMiVQmseRERERPJYRk0j+vTpw//93/+xdetWVqxYkaXxLjesC4VN3p3p5vg5g6G9XArrRERERK4wqrATERERyWXnb69ME6zWrIVre/bsoVKlSnk5LSC1UcX7sz1sPRhiaG8X1Uo58vyaIiIiIpI9CuxEREREcolpmoQN+HlfkOKJNsoUtmW7Gi6jarzLZZgmny708tOOIE90d1K3QlSeXEdERERELo+WxIqIiIjkAtM02Xs8wsvfnCPZZ1K2iD3D4C29Z6Z5FdaZpsl/lvlY+WuQQR3jFdaJiIiIXMHsBT0BERERkatdss/gk4Uedh4JM7irkzrlMw7DvH6D/SfD1CjjyLOA7n9NW+Nnwc8B7m4TR7Nq0flyTRERERHJGQV2IiIiIpfhwMkwo2a4sWDhD/0TKFs049urZJ/ByOluzngM/nZHIglxeR/YLdjkZ9rqFPo1i6VdnZg8v56IiIiIXB4FdiIiIiI59PO+IOPmeiiRZGNIDxdJ8RnvNnLaY/D21GR8AZPn+rpIiPvt+Lzau27FrwG+XuajS/0YejRSWCciIiJyNVBgJyIiIpIDizf7mbDUR70KDh7u7CTakXHYduJchLemujFNGN4/gRJJNiA1qNu0L0SFYnaS4nM3sNuwJ8inC720rhnNLS1j8235rYiIiIhcHgV2IiIiItlgGCYTV6Qwb6OfTvWiubVlHFZrxkHY4dMRRkxNJtphYVgfF0VctrSxvlrqY/EvAfrcGEOvJrkXqv16KMS4uR4aVHJwT9s4hXUiIiIiVxEFdiIiIiJZFAiZfDjfw8a9Ie68KY4OdTNfYrrvRJiR09wkxlkZ1ue3ZbDnx9q0N8S97eK4qVbuLVfdezzMmJluqpWy81BnZ6aBooiIiIhcWRTYiYiIiGTBWa/BmJlujp6J8GR3J/UqZtwJFmDnkRCjZngomWTl6V4u4mNSwzp3SupYB09FeLKHk7oVMh8rq46cjvDOdDelCtt4vLsLh01hnYiIiMjVxmKaplnQkxARERG5kh08FWb0DA+GaTKkp4vymXSCBdhyIMS7s9xUKm7nyR4uYqJSg7Pj51IDtZSgyVM9XVQsnnvPT0+5I7w2yU1ctIXn+/0WEIqIiIjI1UUVdiIiIiIZ2Lw/yLg5Hool2niyRwKFnZmHYBv2pJ5To6yDx7o5ibKnhnV7joUZPdNNbJSFPw5IoFiiLdfmmewzGDHVjd0GQ3srrBMRERG5mimwExEREUnHkl/8fLnER+3yDh7p7EyrksvIqu0BPl7gpWFlBw91cmL/75LUn/cFGTvHQ9kiNp7s4cIVm3uBmi9gMHK6m0DI5A/9E0iKV1gnIiIicjVTYCciIiLyPwzTZNKKFOZs8NO+TjS3t47DloXGDUt+8fPFDz5a1Iji3nbxaecs3ZL6er2KqSFetCP39pULhExGz/Rwym0wvJ8rV6v2RERERKRgKLATERERuUAwbPLRfA/rd4e4vVUcHetFY7FkHrDN3ZDCt8tTaF83mjtax2G1WDBNk6mrU5i+xk+7OtHc2TouVzu2hiMmY+d42H8izLA+CZQpols7ERERkWuB7upERERE/ivZZzBmlptDpyI83t1Jg0qZd281TZNpq1OYtsZP90Yx9G8Wi8ViIRwx+eIHLz9uCzKgeSzdGsZkKfjLKsMw+XiBl60HQzzV00WVkrqtExEREblW6M5OREREBDh8OsKoGW7CEZPh/RKokIXuraZp8u3yFOZt9NO/WSw9GscC4A+ZjJvjYevBEA92jKd59ehcnatpmkxY6mPNriCDuzipVc6Rq+OLiIiISMFSYCciIiLXva0HQ7w/20Nhp5Uh/VwUcWW+D5xhmHzxg4+lWwPcdVMc7evGAHDOZzBqhpsT5wye7uWiZtncD9O+X5XCD78EuLddPI2rZF4FKCIiIiJXFwV2IiIicl1btjXAFz94qVHGweCuTmKz0Ak2HDH5ZKGX1TuDDOoQT8saqRV0R89EGDndTcQwGd7PRdmiuX+rNWd9CjPX+bmlRSw31crdyj0RERERuTIosBMREZHrkmGafL8qhVnr/LSpFc1dbbLWCTYUNhk318Pm/SEGd3GmVbjtPBJizEwPCXFWnu6VtSq97Fq6JcDEFSn0aBRD14axuT6+iIiIiFwZFNiJiIjIdScYNvlkgZe1u4Lc2jKWzvWz1hDCHzJ5b5abnUfCPNHdSd0KqWHdul1BPpzvoVIJO493cxIfY831Oa/dFWT8D17a1o6mXzOFdSIiIiLXMgV2IiIicl1xpxi8O9PDgVNhHu3mpFHlrO0B5wsYjJrh4eCpME/3clG9TOredAs2+flmmY8mVaMY1DEehy33OsGet+VAiA/neWhSJYq7borL1W6zIiIiInLlUWAnIiIi140jZyKMnuEmEDJ5rm8ClUpk7VbInWIwcpqbk26DZ/uknmeYJpNWpDBng58u9WO4uWUs1jwI0nYdDfPeLDc1yjp4oGM81iws2xURERGRq5sCOxEREbku/HooxHuzPSTGWRnWx0XRhKztMXfGY/D2tGS8fpPn+7koW8ROKGLy6UIvq3cEub1VHJ3qx+TJnA+eCjNqhptyRe082tWJPQ+q90RERETkyqPATkRERK55y7cF+HyxlxtKpwZfcdFZ22PuRHKEEVPdGAY83z+Bkkk2fAGD92Z72HU0zOCuvzWdyG0nzkUYOc1NYaeVIT2dRDsU1omIiIhcLxTYiYiIyDXLNE2mrk5h+ho/rWtGc3ebuCxXqR05HWHEtGSi7Bae65/a9fW0O8KoGR7OeA2e6e3ihtKOPJn3Wa/BiGluoh0WnuntynLAKCIiIiLXBgV2IiIick06v2z1px1BBjSPpVvDrHWCBdh/Iszb09wkxll5po+LxDgrB0+FeWe6G5vVwgv9EyhVOGtLarPL60/dLy8cMXlhQAIJcQrrRERERK43CuxERETkmuNOSV22uvd4mMFdnDSpmvVlqzuPhBg1w0OJJCtDe7mIj7Gy7VCI92Z5KJpg5ameLpLi8yZE84dMRs1wc85nMLxfAkVceRMKioiIiMiVTYGdiIiIXFOOnY0waoablKDJc31dVCmZ9WWrWw6EeHeWm4rF7TzZw0VslIVV2wN8stBL9dJ2Hu2W+lpeCEVM3p/l5tDpCM/1zbsKPhERERG58imwExERkWvG9sOplXCuWAt/HJBAscSsh14b9gQZN8dDjbIOHu3qJMoOs9en8N2KFFpUj+LedvF51qU1Yph8OM/D9iNhnu7lomJx3aKJiIiIXM90NygiIiLXhJXbA3y20EuVUnYe6+okPibry1ZX7Qjw8XwvDSo5eKizE5sFvlrmY9HPAXo2jqFv09gs73+XXaZpMn6xlw17QjzWzUmNMnnTyEJERERErh4K7EREROSqZpom09f4mbo6hZY1orinbfYq4ZZu8TN+sY/m1aO4r308EQPGzvWwYW+IgW3jaFs7Jk/nPnF5Cj9uCzKoQzwNKmV9rz0RERERuXYpsBMREZGrVihi8vkiLyu3B+nXLJYejbLeCRZg3kY///nRR7s60dx5Uxy+gMmYmR4OnAzzRHcn9SvmbYA2a52fuRv93NE6jpY1ovP0WiIiIiJy9VBgJyIiIlclrz+1E+zuY2Ee7hxP02pZD7wurMrr1jCGAc1jOek2eGe6G1/A5Lm+CVQqkbe3SYs3+5m8KoXeN8bSsV7eVfGJiIiIyNVHgZ2IiIhcdY6fS+0E6/WbPNvHRdVSWd/3zTRNJq5IYe4GP/2axdKzcSz7jocZNcNNTJSFFwYkUDwbzSpy4qcdASYs8dGxbjS9myisExEREZGLKbATERGRq8rOIyHeneUhLtrCH2/OXrhmGCZfLvGxZEuAO1rH0bFeDD/vS+0OW7qwjSE9Xbhis96sIid+3hfk4wVemt0QxW2t4/KsmYWIiIiIXL0U2ImIiMhVY/WOAB8v9FK5hJ3HujlxZqMTbDhi8slCL6t3Brm/fTytakazbGuA8Yu91Cnv4JEuTqIdeRuebT8c4v3ZHuqUd3Bf+3isCutERERE5BIU2ImIiMgVzzRNZq7z8/2qFJrfEMW97eNxZKMTbChi8u+5Hn7eF+KRzk4aV3EwdXUK01an0KZWNHe1icNmzdvwbP+JMGNmeqhcws7gLs5sdbIVERERkeuLAjsRERG5ooUjJl/84OXHbUF63xhL7ybZ6wQbCJm8N8vNjiOpnV9rlXMwfrGPpVsDOeosmxNHz0YYOd1N8UQrT/Rw4bArrBMRERGR9CmwExERkSuWL2Dw/mwPO46EebBjPM2rZ70T7PnzR8/wcOBUmKd7uahQ3M67Mz1sORhiUId4WtbI3ng5cdpj8PZUN/HRFp7u5SI2SmGdiIiIiGRMgZ2IiIhckU4kRxg9w02yz2RYHxc3lM56J1gAd4rByOluTiYbDOuTQFGXlTe/T+bo2QhDerioXT574+WEO8Xg7anJADzTJyHPG1qIiIiIyLXBYpqmWdCTEBEREbnQrqNh3p3lJjbKwpCeLkomZb0TLMBZr8GIqW48foNhvV3Y7Rbeme4mFDZ5qpeL8kXz/pllStDkrSnJnPYYDO+fkO3PICIiIiLXL1XYiYiIyBVl7a4gH833UKGYnce7O7NdlXYyOcKIqW7CERjeLwFvwOCtqW5csRae65tAEVfeB2fBsMmYmW6OnzN4rl/2A0cRERERub4psBMREZErgmmazNng57sVKTStFsX97eOz3ZzhyJkII6Ym47BZGN7fxcFTEf4910PF4nae6O4kPibvl6SG/9uRdu/xMEN75081n4iIiIhcW3QHKSIiIgUuHDGZsNTH0i0BejWJoc+Nsdnu3Lr/ZJiR09y4Yq0809vF+j1Bvlrqo1FlBw92dOZLZ1bDNPlskZfN+0M80d1JtVJ5v0+eiIiIiFx7FNiJiIhIgfIFDMbN8fDr4TD3d4inVQ46t+46GmLUDA/FEqw81dPJvI1+Zq/307FeNLe1isOazfAvJ0zT5JtlPlZtD/JQ53jqVojK82uKiIiIyLVJgZ2IiIgUmFPuCKNmeDjrNXi6l4uaZbNfkbb1YIh3Z7opX8zOY92cqaHZjiC3tYqjc/2YPJj1pU1b42fhzwHubhNH02rZDx1FRERERM5TYCciIiIFYs+xMGNmuolyWHihfwKlCme/McPGvUHGzvFwQ2k7gzrE88E8DzsOh3mkSzw3Vs2/0Gz+Rj/TVqfQv1ks7erkX0goIiIiItcmBXYiIiKS79btTu0EW7aIjSe6u0iIy34ziNU7Any0wEu9Cg5ubRnHO9M9nPYYDO3tonqZ/Ns7bvm2AN/86KNLgxi6N1JYJyIiIiKXT4GdiIiI5BvTNJm30c/E5Sk0qhLFAx3jicpBM4ilWwKMX+yl2Q1RdG4QzRvfu7FYYHh/F2UK59/tzYY9QT5b5KV1zWhuaZH9RhkiIiIiIpeiwE5ERETyRcQw+Xqpj8W/BOjeKIZ+zWJz1Axi/kY/3/zoo23taJpUcfDm9x6KuKw81dNFIWf2K/VyatuhEOPmemhQycE9beMU1omIiIhIrrGYpmkW9CRERETk2pYSNPn3XA9bD4a4u008N9XK/v5ypmkyY62fKT+l0LVBDOWKWvl0oY9qpe081s1FbFT+BWZ7j4d5c0oyVUrYebKnC4dNYZ2IiIiI5B4FdiIiIpKnTrsjjJ7p4ZTb4NGuTmqVy/7+cqZp8t3KFOas99O3aQwOG0xc4af5DVHc1z4eez4GZkdOR3j9+2SKJ1p5pk8CMQ6FdSIiIiKSu7QkVkRERPLMvhNhRs9wY7dZ+MOAnO0vZ5gmE5b4+OGXALe2jOW0x2DBpgDdG8bQv3n+7ht3MjnCiGnJJMalLsFVWCciIiIieUGBnYiIiOSJjXuD/HuuhzKFbTzZI2edYCOGyacLvazaHuTuNnFsPRhi/Z4Qd7WJo32d/O3ImuwzeHuaG4fNwtDeLuJj8m+/PBERERG5viiwExERkVy3YJOfb5b5aFjZwQMdnUTnoBItFDH5YK6HTftC3NMujuXbguw/Gebxbk4aVIrKg1mnzxdIDesCIZM/9E8gKV5hnYiIiIjkHQV2IiIikmsMw+SbH30s/DlAlwYx3NwiZ51gAyGT92a72X44zN1t4pi7wY/HbzKsTwJVSubv7UsgZDJ6hofTHoPh/VwUS7Tl6/VFRERE5PqjwE5ERERyhT+UWhG3eX+Iu9vE0S6HS1Z9AYPRMz0cOBHmjtZxTPkpBYfdwh8GJFAyKX/DsnDEZOwcD/tPhhnWJ4EyRXTrJCIiIiJ5T3edIiIictnOeAzGzHRz/FyEIT2d1CmfsyWrHr/ByGluTiQb9GsWx7fLfZRMsvFUz5ztgXc5DMPk4wVeth0MMaSnK98r+0RERETk+mUxTdMs6EmIiIjI1evAyTCjZrixWiwM6emkbA6r0M56U/eJc6cYtKsTzcy1fmqVdTC4a872wLscpmny5RIfS7YEeLSLk0ZV8nfPPBERERG5vulRsYiIiOTYz/uCjJvroWRSaifYnDZjOOWO8NYUN6GIyY1Vo5i22s9NNaO5u20cNmv+hnUAk1el8MMvAe5rH6+wTkRERETynQI7ERERyZFFm/18tdRH/YoOHuqU8yq4o2cjjJjixmaDaqUcLPw5QJ8bY+nVJAZLDhpWXK4561OYtc7PrS1jaV0zOt+vLyIiIiKiwE5ERESyxTBMvl3hY/7GAJ3qR3NrizisOayCO3AyzNvT3MRHWygUb2XtriD3t4+nVQEFZUu3+Jm4IoUejWLo0iC2QOYgIiIiIqLATkRERLIsEDL5cL6HjXtD3HVTHO3r5qwTLMCuo6l73xV2WrFYTHYfC19Ww4rLtXZXkPE/+GhbO5p+zRTWiYiIiEjBUWAnIiIiWXLWm9oJ9ujZCEN6OKlbIefB2rZDIcbMcFOykA2P3yAUhuf7J1ChWMHcmmw5EOKDeR6aVInirpviCmQproiIiIjIeQrsREREJFMHT4UZPcODYZoM759A+aI5v4XYtDfI2DkeyhSxceJcBFeslWf7uiiWYMvFGWfdrqMh3p3lplZZBw90jM/x8l4RERERkdyiwE5EREQytHl/kHFzPBRLtDGkRwKFnDnrBAuwZmeQD+d7KF/MxsGTEcoXs/NkDyfOmJyPeTkOngozaoaH8sXsDO7qxG5TWCciIiIiBU+BnYiIiKTrh1/8TFjio3Z5B490dhITlfNA68etAT5b7KVCMRt7jkVoWMnBQ52dRNkLJiQ7fi7CyGluirisDOmR8y63IiIiIiK5TYGdiIiI/I5hmkxakcKcDX7a143m9lZx2C5jqeiCTX6+XuajXFEbe49H6PDfMQtq+elZr8Hb09xEOywM7eUiLrpgKvxERERERC5FgZ2IiIhcJBAy+XiBh/W7Q9zROo6O9XLeCRZgxtoUvl+VQqlCVg6cjHBLi1i6NIgpsMYOXn9qWBeOmLwwIIGEOIV1IiIiInJlUWAnIiIiaZJ9qZ1gD52O8Hh3Jw0q5bwTrGmaTF6Zwqz1foomWDl21uChzvE0qxadizPOHn/I5J0ZbpJ9BsP7JVDEVTCNLkREREREMmIxTdMs6EmIiIhIwTt8OsKoGamVZ0N6uKhQPOfP9QzT5KulPhZvDpAYZyEYhse7O6lRxpGLM86eUMRk9Aw3e46FebZvAhUv4/OJiIiIiOQl3amKiIgIWw6EGDvHQ2Gnlaf6uSh8GZVnEcPks0VeVv4aJC7agsUCw/u7KFuk4G47IobJh/M87DgSZmgvl8I6EREREbmi6W5VRETkOrd0S4Avl3ipWdbBI12cxF5GJ9hQJDUY27AnhMMOheKtPNXLRWFnwe0TZ5om4xd72bAnxGPdnFQvwCo/EREREZGsUGAnIiJynYgYJhYLWP/b7MEwTb7/7x5zbWtHc+dNl9cJNhAyGTvHw9aDIQAqFbfzeHdngXZgNU2Tb5en8OO2IA90jL+sPflERERERPKLAjsREZHrxLi5Hk4lGzzfPwGrBT5e4GXdriC3toylc/3L69qaEjQZM9PNrqNhIgY0rRbF/R3icdgKphPseTPX+Zm30c8dreNoUb3gml2IiIiIiGSHAjsREZHrwPFzEdbvTq18e2+mG3/I4NBpg0e7OWlU+fKqzjx+g3emuzl4MkLEgK4NYxjQPDatkq+gLN7s5/tVKfS+MZaO9WIKdC4iIiIiItmhwE5EROQ6sHizH6sFDBO2HgrjsMHz/RKoVOLybgXO+QxGTEnm+DmDsAF33hRHh7oFH46t2hFgwhIfHetG07tJwc9HRERERCQ7Cm5TGREREckXgZDJ0i0BDPO310IR2H0snK1x3CkGO4+E0v59yh3h1UnJHDtnAPBYV+cVEdZt2hvkkwVemleP4rbWcZe11FdEREREpCCowk5EROQa99OOIP7Q71//ZpmPWmUdlCpsy9I4E5Z4WbMrRPMboujVJJY3vk/G4zeJssNTPV1ULVXw3Ve3Hw4xdo6HOuUd3Nc+vsCX5YqIiIiI5IQCOxERkWuYaZpMXuUDwAKcL7IrW8RG7XIOCrmyVmzvD5qs++8eeCu3B1mzK4hhQEKchWf7JFCyUNZCv7y0/0SYMTM9VC5pZ3AX52V1vBURERERKUgK7ERERK5hEcPE6zdJiLVwY7UoapRxUK2UnfiY7O2KsW538KIlteEIJMTCn25NJDGu4HfYOHo2wsjpboonWnmyuwuHXWGdiIiIiFy9FNiJiIhcw+w2K+MeK3zZ4yz82f+715JTYMvBIC1uKNh96067I7w91U18tIWne7mIiVJYJyIiIiJXt4J/JC4iIiJXtNMeg30nIpd8b8IPPkzTvOR7+cGdYvD2NDcAz/RJwBWrWxsRERERufqpwk5ERORqk3IKjq+HsB/iikGJxmDNu6/0ySu9l3y9WIKVVjWi8uy6mUkJmrwz3Y03YPKH/gkUdiqsExEREZFrgwI7ERGRq0XgHGx4D3bPhJSTgAm2KEiqCrXvhyq9IQ+6orpTUivo7FZoWNlBw0pR1CjrKNBqtmDYZMxMN8fPGTzXz0WJpIJveiEiIiIiklssZkGuYxEREZGsCbph4VNweDlEJ0JMkdSqunAKeI+CxQ5NnoHa9+XJ5c96DRLjLFjyIBDMijU7g8zZkMLgLk6S4q2MneNh68EQQ3u7qFbKUSBzEhERERHJKwrsRERErgYb3od174CrHNgv0eTBewQsNujxBRSqlv/zy2NvTUlm26EwCbEWKha388uBEE90d1K3QsEtyRURERERySva7EVERORKF/LBzslgj710WAcQVxICZ2H3jKyNaZqQchp8xyESzLWp5gV/0GT7kTAAySkmm/aF6N0kRmGdiIiIiFyztIediIjIle7sTvAeS20wkR6LBWwxcHglNM5gLCMMe+fA9u/g1BYwDYhJgqr9oVp/iC+Z27O/bFsOhjCM3/5tAWas81OllIMaZbQcVkRERESuPaqwExERudIZYcAESyZf2xYrmKH034+EYPnfYOkLcGQlWG2pFXu+E7BuJMx5EM7syM2Z54qf9wa5cOc8EwiF4fNFl+5eKyIiIiJytVOFnYiIyJXOWQbscamNJ2Kj0z8u7E/tGJueLeNhx3cQWzS1ccV50YmpoeDZXbDkBej5ZfpLb/OZaZqs3hXkwg1346IstKgeRZvaV8YcRURERERymwI7ERGRK118CSjfHrZPgpjCl660C3rAZofKvS49RtgP2/8DtuiLw7rzrHZwlYcz2+DgUqjYOXc/Qw7tPR4m8N+iwXoV7LSuGUPdCg7stoLpVisiIiIikh8U2ImIiFwNat0Hh1fAud2pwZrtvw0XTBOCyeA7BhW7QukWlz7/2DpwHwJnqfSvYY8GIwIHFl0xgV25Yjb6No3lpppRJMbbCno6IiIiIiL5QoGdiIjI1aBIDWj3Fvz4Zzi3N7VZhMUKZgQc8VCpB7T8W2ql3KWEPGCEwJpJZ1WrPbXb7BXCbrXSq0lsQU9DRERERCRfKbATERG5WpRoDL0nplbAHVkFIV9qV9eKXaBo3dROsemJTgSbAyJ+sGcQgJnh1D3uRERERESkwCiwExERuZo44qByz9Q/2VG8ISRUhOT9kFD+0seEU8DigPIdLnuaIiIiIiKScwrsRERErge2KKhxF6z6O/hPpzavuFAkCO4DqVV8pVvl7VxObYPd0+HQUogEILESVO6dGhReId1pRUREREQKksU0TbOgJyEiIiIX84dMfj0Uol4FB5aMlrpmh2nA6jdh2wQIByA6ASw2CLkhEkpdVttuBCSUy53r/e76JmwZDxvGQOAc2OPAaktd2gtQsim0fR3iiufN9UVERERErhIK7ERERK4w4YjJu7M87D4WZsSgJGzWXArsIDU0O7QMdk6FIysAA+JLQ7X+UKknxCTl3rX+1+6ZsOzF1MYWcSUv3nMvnJJa4Ve6FXT5d/rNM0RERERErgO6GxYREbmCmKbJhKU+th4M8XQvV+6GdZAakpW9KfWPEU79Y4vOuGFFbjAN+OWz1Eo+1yUq+Oyx4CwDx9bA4ZVQtnXezkdERERE5ApmLegJiIiIyG9mr/ezdEuAe9vFU7OsI28vZrWn7hmX12EdwImf4cyvEJ/BcldHfOpeevvm5v18RERERESuYArsRERErhCrdgSYtDKF3k1iaFkj+rLGMk2T6Wt8bNwbzKXZXaaUk6n75tnjMj7O5gDvkfyZk4iIiIjIFUqBnYiIyBVg++EQny7w0qJ6FL1vjL2ssUzT5LNFXqb85Gfxz/5cmuFlssemNpgwQhkfFwlDlCt/5iQiIiIicoVSYCciIlLAjp6J8O4sD1VL2bm3Xfxld4X9epmPH7cFSYyz8FBnZy7N8jIVrw/xpVIr7dJjhMAClLkp36YlIiIiInIlUmAnIiJSgJJ9Bu/McJMYZ+Wxbk7stssL6yat9LHw5wBx0Rb+dGsi8TFXyFe9Ix6qDYCQD0Ke379vGpC8H1wVoEKn/J+fiIiIiMgVRF1iRURECkggZDJmpptQ2OS5vi7ioi8vXJu+JoVZ6/xE2+GlWxJIir9Cwrrz6gyC09tg3zywnoKYIqnLZIMe8J8BZylo/YqWxIqIiIjIdc9imqZZ0JMQERG53hiGydg5Hn45EGJ4vwQqFL+8Z2hzN6Tw7fIU7Fb4480uyhfL4w6zORUOwPb/wPbvwL0/tbLOEQ/lO0HNu6Bw9YKeoYiIiIhIgVNgJyIiUgC+WeZlwc8BnujupH7FqMsaa/FmP18u8WG1wLN9ndxQ+vLGyxdGGM7tSf1vXHGILVLQMxIRERERuWJoSayIiEg+W7DJz/xNAe5qE3fZYd2PW1PDOgvwRPf4KyqsM00z/QYaVjsUqpa/ExIRERERuUpcYZvbiIiIXNs27AnyzTIfXRrE0L5OzGWN9dOOAJ8u8gEwqGMc9SpG58YUL1s4YvL5Yi9vTnEX9FRERERERK5KCuxERETyyZ5jYT6Y56FRFQc3t4i9rLHW7Qry4TwvALe3iqVF9csL/3KLP2Ty7iwPy7cFaFP7yggQRURERESuNloSKyIikg9OJEcYPdNNuSJ2HujoxJreUtEs2Lg3yLi5HkygR+NoOtW/vPAvtyT7DEbNcHP0bISnerqoVe4KbXwhIiIiInKFU2AnIiKSx7x+g1HT3cRGWXiih5Moe87Dul/2h3hvlgfDhDa1o+jXNC4XZ5pzx89FGDnNTTBsMrxfAuWL6RZDRERERCSndDctIiKSh0IRk/dme/D4TV4YkIArNue7Ufx6KMTomW5MExpXcXB3m/j0mzrkoz3Hwoye6SYu2sIfBiRQLMFW0FMSEREREbmqKbATERHJI6Zp8ulCL7uPhXm2j4sSSTkPsnYeCfHOdDeGCTXL2Xmo0+Utq80tP+8LMnaOh3JF7DzRw3lZgaSIiIiIiKRSYCciIpJHvv8phZ92BBncxUnVUjnfz23PsTAjp7mJGFCxmI3Hu7mw2wo+rPtxa4DPF3upW8HBw52dRDsKfk4iIiIiItcCBXYiIiJ5YOkWPzPX+rmlRSxNqkbleJz9J8KMmJZM2IASSVae7uUq8GDMNE1mrPUz5acU2tSK5q42cdisCutERERERHKLAjsREZFc9sv+EF/84KNd7Wi6NIjJ8TgHT4UZMdVNJAIJcRaG9UkgPqZgl5wahsmEpT5++CVA36ax9Gwcc0XsoyciIiIici1RYCciIpKLDpwMM3aOm9rlHdxxU1yOw6wjZyKMmOImGDGJdsBzfRNIii/YsC4QMvlwvodNe0Pc3z6eVjWjC3Q+IiIiIiLXKgV2IiIiueSMx2DUDDfFk2w80sWZ42Wix89FeGtKMoGwic0Cz/RJoHhiwXZe9fgNxszwcOBUmCd7OKlbIefLfEVEREREJGMK7ERERHJBStBk1Aw3VouFIT1cxORwn7mTyRHenJKMP2RiGPB0Hxflixbs1/XJ5AjvTHfjDZg81zeBSiV0+yAiIiIikpd0xy0iInKZwhGTcXM8nHIb/KG/K8dLV097DN6a4iYlYBIMwRM9nNxQOufdZXPD/pNhRk13E2W38MKAgq/0ExERERG5HiiwExERuQymaTJhiY9th0I83ctFmSI5+2o96zV4a0oynoCBPwQPdIynfsWCXXa69WCI92a5KZFk46meLhLiCnYPPRERERGR64UCOxERua6YppmrXU1nrfOzdGuAQR3iqVk2Z9VwyT6DEVOTcacY+INwe6s4WlQv2IYOK7cH+HShl5plHQzu6szxEl8REREREck+BXYiInLNC4fD2O2pX3nnw7rcCO5WbQ8weVUKvZvE0LJGzgI2j9/g7WluznhN/EHo2TiGTvVjLmtel8M0TeZu8DNxRQqtakQxsG08dpvCOhERERGR/KTATkRErmler5du3bpRr149KlSowODBg4mLi8PhcGAYBlZrzpZ5bj8c4tOFXlpUj6L3jbE5GsMXMBg5zc3J5Aj+ELStHU3fpjkbKzcYpsl/fvSxYFOAno1j6Ns0NlerEUVEREREJGsspmmaBT0JERGRvDRhwgRCoRAvvfQS1apVo3Hjxjz77LOUKlUqR6HdkTMRXp2UTPmiNp7u5cpRBZo/aPL2tGQOn44QikDDSlE83Dkeq7VgArJQ2OSjBV7W7Q5y101xtKtTcFV+IiIiIiLXOwV2IiJyzfrfZa8+n4+3336bRYsWcerUKT777DPq1auXreWxbp/Bkq0B4qMttKgeRbQj+xV6gZDJO9Pd7D8RxjShamk7Q3rkLPjLDV6/wXuzPew5FuaRLk4aVCrYZhciIiIiItc7BXYiInLdWbx4MaNGjWLt2rV8++23NG3aNEvnhSOpX5lWK1ggR8tFg2GT0TPc7D4WxmaFkoVsDOuTUGBNHU67I7wzw8M5r8GTPZxULZWzxhkiIiIiIpJ7FNiJiMg15Xy13KpVq9i0aRMnT56kV69e1KlTB/gtZNu4cSP/+Mc/8Pl8jB49mkqVKuX53EIRk/dmefj1UIiYKAvOGCvD+7twxuRsH73LdehUmJHT3disFp7u5aJUIVuBzENERERERC5WML8hiIiI5AHDMLBYLEybNo2+ffsyceJEXnvtNR566CHOnTuHxWLBMAwA6tevz7333svJkydZunRp2vl5JRwxGTfHw7ZDIZwxFqLsFp7pXXBh3a+HQrw22Y0zxsoLAxIU1omIiIiIXEEU2ImIyDXDarWyevVq7rnnHl555RVmzZrFvn372Lp1K9u2bUs75rxevXrRpUsXXnrpJc6dO5du84nLLUaPGCYfzveyeX+Iwk4rYQOe6e2ikLNgvobX7AwycpqbisVtDO+fQFK8bgdERERERK4kWhIrIiLXjOTkZB588EEqV67Ma6+9lvZ6q1ataNiwIXv27KFVq1bccccdVK5cGUitquvVqxdPPvkkPXr0yPU5GYbJxwu8rN4VpHQhK6fcJs/1dVG+mD3Xr5UVCzb5+WaZj6bVori/Q3yBNboQEREREZH06ZG6iIhcMywWC7169eLuu+9Oe+3+++9n27ZtFC5cmOLFizNp0iTGjBlDKBTCNE2sVit169Zl//79uT4fwzT5fLGXn3YGqVDMxtGzBk90dxZIWGeYJhOX+/h6mY8uDWN4oJPCOhERERGRK5Uq7ERE5JoSDAaJiooCYNOmTdx5552MHz+eRo0aAfDiiy/y6aefsmnTJooWLQpAJBLB7XaTlJSUpWsYhpHu8tnzTNNkwhIfP/wS4IbSdnYcCfNYNycNKkXl/MPlUDhi8ulCLz/tCHJ76zg61ovJ9zmIiIiIiEjWFcx6HBERkTxyPqwDqFu3Lj/++CNJSUmEw2Hsdjt169alRo0aREdHA6nBms1my3JYB7/tg5dRcGexWHDGWqhd3s7m/WEGdYgvkLAuJWjy/mw3Ow6HeaSLkyZV838OIiIiIiKSPVoSKyIi15QLC8ctFgsJCQkA2O2pz6gmT55MyZIlcblcacdk1dChQ2nevDmvvvoqhw4dyrTKrm/TOJLirdzaMpaWNaKz+1Eu21mvweuTk9l7PMLQ3i6FdSIiIiIiVwktiRURkWvG+Sq6cDjMjh07qFmzZtp7x48f589//jOzZ8/m559/JiEhAdM0sxXYnTt3ji+//JLFixezcOFCRo4cSYcOHShduvQljz//FZuda+SWI2civDPdjWHA072clCmionoRERERkauFAjsREblqpBewmaZJJBJJq6Jr1KgRd955J8899xwWi4WlS5fy6quvcvDgQaZNm0b58uWztA9dRse8+OKLTJ06lfbt2zNs2DAqVaqUrTnnpZ1HQoyZ6SEx3srTPZ0Udtny9foiIiIiInJ5FNiJiMg1pUWLFjgcDqZNm0ZiYmLa61OnTqVRo0aULVs2S2FdJBLBZksNukaNGsU999xDoUKFLgrg3n33Xb766iuqV6/O66+/TpEiRfLug2XRhj1B/j3XQ6USdp7o7iQuWrtfiIiIiIhcbXQXLyIiV5TLeY50+vRpSpcuzX/+85+0sM4wDAD69OmT5bDufCMKn89HkyZN+Oqrr1i9ejWQurw1EokA8MQTT3D77bezdetWvvrqq7RrFZQffvHz3mwP9SpGMbSXS2GdiIiIiMhVShV2IiJyRbiwou287C4nNU0TwzB+N05OeDweunbtSmJiIpMnT07rKnspgwYNYteuXcyZM4fY2NjLvnZ2mabJlJ9SmLHWT8e60dzWOg5rAeybJyIiIiIiuUOP3kVE5IrwyiuvcNttt7F48WI2bdoEZL9Zg8ViyZWwDmD//v1YrVbGjx9PdHQ0o0aN4sUXX2Tw4MFs2rQJv9+fduzbb7/N5s2bGTt2bK5cOzvCEZPPFnmZsdbPLS1iuV1hnYiIiIjIVU+BnYiIFLhTp05Ro0YNChUqxMsvv8zdd9/Nc889h8fjKbA5HTx4kKNHj+JwOLjnnnsYN24cBw8eZOHChdx11118+umnaUtjk5KSeOWVV9i0aRPhcPiylvVmhz9k8u4sDyu3B3mwUzxdG8YWSEdaERERERHJXfaCnoCIiFzfTNOkSJEi3HHHHdxxxx1s2bKFdevWMXz4cBYtWsTo0aNp3rx5pvvO5baoqCjC4TCrV6/m7NmzTJ06lSpVqgBwzz338Omnn9KtWzcqVqwIQIkSJVizZg2hUCitW21eSvYZjJrh5tjZCE/1dFGrnCPPrykiIiIiIvlDe9iJiEiB+N896wKBwEX7xJ0+fZr+/fuzfft2Jk+eTPPmzbPUMCI3NW3alB07dlCpUiUWLFhAoUKFAPD7/RQtWpR3332X++67L+34kSNHcu+991K4cOE8ndfxcxFGTnMTDJs81ctF+aJ6/iYiIiIici3RklgREcl357uwHjt2jCeffJJwOEx0dDThcDjt/cKFC/PDDz/QvHlzBgwYwJEjR/ItrDu/1PVf//oXJUqUIDk5mbNnz170fr169ShWrFjafAEeffTRPA/r9hwL8+qkZGxWeGFAgsI6EREREZFrkAI7ERHJd+f3Wbv//vt57733aN26NYcPH8ZutxMOh7FYLGmh2SeffEL9+vV59tln82VPO9M004LBtm3b8oc//IETJ04wbNgwNmzYwOnTp/nuu+/Yv38/JUuWvOjzxMTE5Oncft4X5M0pyRRPsPGHAQkUTcidBhsiIiIiInJl0WN5EREpME2aNKFw4cL4fD6aN2/OxIkTadq0KZC6RDYuLo6kpCQeeeQR3nzzTY4fP47T6bzkWKZpZqnhQkbHXbjkdty4cZQsWZJBgwZRunRpHnjgAXr37k1SUhJHjx5l7NixNGrUKIefPPuWbQ0wfrGXuhUcPNzZSbRDzSVERERERK5VqrATEZECU6tWLc6dO8ef//xnGjRoQPfu3Vm8eDHPPvsss2fPJhQKAdC/f3+KFSvGyy+/nG4HVtM08fv9aX9P7xiLxYLH4yE5Ofl3758P6/7xj38wZMgQ3G43AF27dmXNmjV89tln/Otf/2LFihXcfPPN+dIN1jRNpq9J4bNFXlrXjOaxbgrrRERERESudaqwExGRAlOzZk3cbjf16tVjxIgRjBs3jm7duhEKhfjrX/+Kw+HA7/cTExPDSy+9xOrVqy9ZIWeaJuvWreOzzz5j9OjR6VbQnX990KBBJCYm8u67717U6ALg008/5YMPPmDGjBl07tw5bfxSpUpRqlSptOPyowFGxDCZsMTHki0B+jaNpWfjmCxVEYqIiIiIyNVNFXYiIlJgGjRogMPhYPPmzVStWhWHw4HFYiE+Pp7XXnsN+G1fuAYNGnDLLbf8LiQ7H+CdOnWK77//nokTJ2Z4zVAoxK233spXX311yYq9pk2bsmjRorSwDrhkSJbXYV0gZPL+bA/Ltga4v308vZrEKqwTEREREblOqMJOREQKxPmgLSoqig0bNnDo0CHeeOMNxo0bRygU4qmnnuLYsWN88MEHGIaBw+GgePHivxvnfIh144030r17d1avXs0tt9yS7l51FouFjh078u9//5t7772XEiVK8NRTT6W9X6tWrbz70Fnk8RuMmeHh4KkwT/ZwUrdCVEFPSURERERE8pECOxERKRAXLk8dNWoUa9eu5Y033uCBBx7A7XbjcrnSwrNLVbMZhoFpmthsqZ1SCxcuzIABA7jvvvt45JFHqFKlyiWva7fbKVKkCHfffTfHjh1j2LBhFCtWjDvvvPN3x2a1kUVuOpkc4Z3pbrwBk2f7JlCphL6qRURERESuNxYzP3bMFhERucD5/d9OnTqFx+OhcePGPPLII/z9739PC+fOH5NRaHbHHXfQvn17Bg8eDKQGbC+++CJ33HEH9evXz9Ichg8fzpgxY5gyZUraMtidO3dSrly53+1vl9f2nwgzaoabKLuFob1dFE+05ev1RURERETkyqDATkRE8lUkEsFms7Fw4UJeeOEFVqxYwblz53A6nURFZW/pZ+/evYmPj+frr79Oe23NmjU0aNAAuz39yrTzczjvvvvuY+bMmcyePZulS5fy+eef88UXX+Tr8tgtB0K8P9tNiSQbT/V0kRCnbWZFRERERK5XWmcjIiK56n8r4sLhcFp4dn4J66+//sott9zCH//4R2w2G4ULF87RtRITEylXrhzwW8VckyZNfnecYRj4fD52795NvXr1sNlsF4V2H374If369aNp06aYpsknn3ySr2Hdyu0BPl3opWZZB4O7OolxqLmEiIiIiMj1TI/vRUQkV50P6+bMmQOk7hkXiUTS3otEItx7773cfffdPP/88zm+zrp161i5ciUNGjQA0u/aGg6HOXr0KM2aNeOOO+7gscceA8Bms2EYBpBacZeUlERiYiLLly/nvvvu+1332Lxgmiaz16fw0XwvzW+I4onuCutEREREREQVdiIikgfeeustnn/+eW6++Wa+/fbbiyrabDYbX375JZUrV87R2GfOnGHNmjUMGjSI22+/ndtvvz3dYyORCMnJySQlJTFlyhSWLVvG008/TWxsLCNGjMBqtRIKhXj11Vf57rvvWL9+PTVr1kyr1stLhmHyzY8+Fv4coFeTGPrcGJvvDS5EREREROTKpD3sREQkV61cuZL777+f1q1bs2LFChITE5k5cyZJSUmEw2GsVmu2w7Dzy2y9Xi8TJkxg/PjxNGvWjDfeeOOi9y8UDodJTk5m6dKl9O3bF4BAIMBXX33F0KFDmTBhAj169MA0TZYvX06dOnVITEzMl7AuFDb5aIGHdbtD3N0mjra1Y/L0eiIiIiIicnXRklgREck1hmGwbt06mjdvzrBhwxg9ejSGYdC0aVO2bduWYSOI9FwYxsXHx9OhQwdef/31DMM60zTxer20bNmSbdu2pb0eHR1Nz549qV27Nps3bwZSl+m2atUq38I6r9/g7WluNu0N8Xg3p8I6ERERERH5HQV2IiKSa6xWK/fddx+DBg2iVq1adOjQgXfeeYfq1avTtm1b5s+fnxaIrV+/nmPHjmU43qXCuCpVqtC8efN034fUEO7kyZMULVqUqVOnsnjx4rT3ihUrht1u59SpU5ecf1467Y7w2mQ3h09HeLaviwaVstcVV0RERERErg8K7EREJNcYhkF8fDxt27ZNe61Zs2a8+uqr9OjRg5tvvpnx48fz008/0bhxY3799dcMx8tsT7dLVdad/2+VKlX4xz/+QTgc5rXXXmPRokUcP36cefPmsX79eurVq5fDT5kzB0+F+dekZIJhkxcGJFClpCNfry8iIiIiIlcP7WEnIiKX7X8r3c7/+3yjCYB9+/Yxbtw4Ro0ahc/n44knnmD06NG5NodIJEIkEiEqKuqipa2TJ0/mhRde4ODBgzRr1gzTNOnYsSN/+tOfcu3amfn1UIh3Z3kommDlqZ4ukuL1vExERERERNKn3xhEROSyhEIhLBYLPp+PrVu3snPnTs6cOQOAzWYjFAoBUKFCBfr3749hGDz88MNpYV16z41M0yQUCqX7/qWOb9++PbNmzcJqtWIYBgD9+/fnr3/9Ky6Xixo1avD++++nhXX58cxq9c4AI6e5qVjcxvP9EhTWiYiIiIhIpvRbg4iI5Mi2bdsIhUI4HA4ikQjt2rWjX79+1KhRgzvvvJORI0cCpL3v8/n4v//7P2rVqsW4ceOAS+9BF4lEADh9+jSDBw9m586dGc7DMAxM08Rut1O/fn1uu+02li9fftF+dHfeeSfDhg3j8ccfp0aNGmmvZ7bk9nLN3+jng7leGleJ4qmeLmKj8vZ6IiIiIiJybVBgJyIi2TZkyBCaNWvGsmXLALjvvvtwuVx8+eWXzJgxgwoVKvDFF1/w0ksvYZomNpsNt9tNo0aNWLlyJZAatP1vYGYYBvv376dbt2507NiRVatWUa1atXTncb6KLhgMAvDPf/6Tfv360bNnTz7//HMOHz6cduzw4cOpU6dOvlTVGabJt8t9fPOjjy4NY3igUzx2m8I6ERERERHJGu1hJyIiOdK5c2c2bNjA2LFjmTVrFoMHD+bGG28E4PDhw4wdO5YZM2bw0Ucf0aBBA+C3iroL95i7kGmaTJkyhQEDBtCpUyfmzp0LcMnjzwd+l6qSGz58OCtWrCAqKoqXXnqJevXqUbRo0Vz+CVxaOGLyyUIvq3cEub11HB3rxeTLdUVERERE5NqhwE5ERLJszZo1lC9fnuLFiwOplXVffPEFVquVr776iltuuSXt2JSUFKpWrco999zDq6++munYpmni9Xrp0KEDkUiEtWvXAlkL6y61tHbFihXMmDGD6dOnM3DgQAYNGkSRIkUu6/NnJiVo8v5sNzsOh3mwk5MmVaPy9HoiIiIiInJt0pJYERHJlGma7Nu3jwceeICjR4+mvf7ZZ5/x5ptvEolEmDhxIvv37097LzY2lnbt2hGJRLK0DNVisTBz5kyKFCnCTz/9BFw6rDs/1rZt24DU5bDnw7rDhw+nBX0tWrTg73//O5MnT+ahhx7K87DurNfg9cnJ7D0eYWhvl8I6ERERERHJMVXYiYhIlg0ZMgSPx8Po0aOJjY3FZrMB8MUXX3Dvvfdy11138eSTT1K1alV2795N+/btGT16NA888ECG414YzJ3/eyQSSRv/f/39739n5syZLF++/KLXJ0yYwNatW3nuuedISEjI86YS5x05E+Gd6W4MA57u5aRMEXu+XFdERERERK5NqrATEZFMnW/q0LVrV3755Rd27NiBzWYjEAgAMHDgQBYsWMD3339Px44d6datG3//+98ZMmRIhmGdaZqMHj2aU6dOpb1mtVrTGlWkp1WrVuzdu5c+ffpw6NAhdu/ezcSJE3n00UcpW7YsiYmJ+RbW7TwS4rVJycQ4LLxwc4LCOhERERERuWyqsBMRkXSdD+Sio6PTXrvtttv46aefWL16NcWKFSMcDmOxWLDZbOzevZvevXuzdetW1q9fT/369YFL7zEHcPr0aVq2bMmMGTOoUqVKpvPZunUr1apVw263s2TJEu69915CoRCxsbGkpKTwl7/8hcGDB+fSp8/c+t1BPpjnoVIJO090dxIXredgIiIiIiJy+RTYiYhIuh5++GGmTJnC6tWrqVChAgDHjx/njjvuIDk5mfHjx1OzZk0MwyASieBwOPB4PAwdOpQPP/ww3XFN0yQcDvPYY4+xfPly1q5dS2xsbIZzWbZsGX369KF79+7cdttt9O3bF4/Hw9q1a/H5fFSoUIFatWqljZ/XFXaLN/uZsNRHo8pRPNgxHoc9fyr6RERERETk2qfATkRELsk0TRYtWsTrr7/O5s2b+c9//kPLli0xDIOFCxfy9ttvs3fvXl5//XV69ux5yTEu1TQiFApx9OhR/vnPf7Ju3TomTZpEmTJlMg3ZAoEASUlJNGzYkLVr1zJw4EDatGnDfffdd9Gc8zqoM02T71elMHOdn471ormtVRzWfFp+KyIiIiIi1wet3RERkUuyWCx06NCB119/nTZt2tCzZ0++/vprrFYrnTp1Yvjw4dx0003079+fxx57jEWLFpGcnHzRGP8b1gGEw2E6depEmzZtmD9/PmXKlCESiaQbtG3atIlff/2VkiVL0r9/f+bNm8f8+fPZtm0bX3zxBR6PB8Mw0uacl8IRk08Wepm5zs8tLWK5XWGdiIiIiIjkAVXYiYjIJV1YrbZz505Gjx7NJ598wp/+9CeGDx8OwLlz51i/fj1/+ctfSEhIYNOmTYwYMYJ27dpRtGjRdMd79tlnefrppylfvnyGVXFr165l0KBB/PrrrwwePJi33noLu92OxWLhyJEjFC9ePMPmFLnJHzIZO9vDtkMhBnWIp9kN0ZmfJCIiIiIikgMK7EREJEv27dvHF198wZtvvsnAgQMZPXp02nvBYJBz586xZMkSKlasSOPGjbM05qWWzF5o/vz5aR1nX3jhhUsenx/LYJN9BqNmuDl2NsLj3V3ULOvI0+uJiIiIiMj1TYGdiIhcJBKJYLPZOH78OMnJyZw6dYpmzZoBqQ0nJk+ezN/+9jdatWrFt99+m+VxV61aRUxMTFrn2PSYpklKSgpxcXEA9OnTh6ioKCZOnJjzD3UZjp2N8M50N8GwyVO9XJQvai+QeYiIiIiIyPVDe9iJiEia82Hd3r17GTBgAF26dOGBBx6gc+fOuN1uihcvzp133smoUaPYvHkzN9xwA36/n4ye/ZimycqVKxk9ejTt2rXj/fff5/Dhw+ke++WXX/L4449z6tQpAPr27cvevXvZsmVLnnzmjOw5FubVScnYrPDCgASFdSIiIiIiki/0m4eIiACpYZnNZsPv99OxY0e6dOnCBx98wL59++jRoweNGzdm0qRJ1KlTh169ehEdHc2KFSuIiYnJcFyLxULz5s1p1qwZ69evJyoqitKlS1/y+uFwmAYNGvDggw9StmxZXn75Zdq0acNjjz2G2+3Oq49+SZv2Bhk310O5onae7OHEGaNnXCIiIiIikj+0JFZERC7ypz/9iV9++YXJkycD0Lx5c4oUKYLf72fdunVMnTr1/9u77yip6sP//68pO1tmZmnSu6h0kCYgICggdSl2jcaC2DGiYDT5fszHmHySXyJVLLFHMWoQpSwdpElRQASUonRp0tk7vdz7+2NkYd1dQNrO7j4f5+QE5t65c0fPzp55+i7q1KlTnvXkClpHLhaLKRKJ5E5tPdVac6ZpKjs7W88884wGDhyobdu2afLkyXrhhRf05JNPau/evapateoFfNd5LV4f1viFfjWrk6LB3T1yOdkJFgAAAMDFw3ABACilTNOUJB0+fFg7duzQ7t27ZVmWatSooVtuuUWSdM8998jlcunDDz/U888/r2PHjqlz58768MMP82z+8MsQZ1mWjh07pquuuko5OTkyTbPQWGdZlux2u2rVqqU///nPmjdvnnbs2KFgMKhx48bpyJEjFy3WWZalqSuCem+BX50aperhHsQ6AAAAABcfI+wAoBQ6Pjpu5cqVGjlypA4fPqynnnpK3bt3lySFw2H9+OOPuuWWW/SPf/xD3bp10/bt2zVs2DA1b95ct912my6//PJTvsb333+vYDB4yk0m4vG4vvzyS7Vp00YpKYmdVw3D0LFjx/TBBx/o9ttvV61atc7fGz+FuGnpP4sCWrQ+rAFXpat3q7QLvvssAAAAABSEYAcApczxWLdt2zZ16dJFDzzwgDp27KjOnTtLkhYuXKgOHTpo69atatasmZYtW6YWLVros88+0z/+8Q9NnjxZlSpVyjMl9uRrS8r3eEEsy9LRo0fVo0cPlSlTRnPmzJEkRSIRuVyu3POOb4RxIYWjlt6Y49O6HVH99lq3OjRIvaCvBwAAAACnwpRYAChl7Ha7wuGwevbsqV69eumPf/xjbqwbPHiw7rnnHo0dO1YVKlRQVlaWOnXqpNtuu02333677r77blWqVCn3OgVde9CgQVq5cuVp78Nms8nr9eq5557T9u3b9Zvf/EaS8sQ6SRc81hlBUyOn5Gjjrqge6+0h1gEAAAAocoywA4BSaMyYMZo4caI++eST3AA3ZMgQTZo0SR06dNDmzZvVu3dvderUSStWrNDWrVvVoUMH3Xvvvae87rx589SkSRNVrlz5lOedvAFFNBrVhAkTdOedd2rChAm68cYbz8+bPAMHcuIak20oELY0pLdXdSuzeToAAACAosc3EwAohbZt2yan05k7tdVms+nIkSOaNGmSWrVqpeeff14TJ07UgQMH9OqrryoajeauMVfQbq+WZemjjz5SLBZT165dT/naJz9/+/btqlChggYMGKAVK1aoVatWF+YNF2DngZjGTjPkctr0zA2ZqlTmwo7kAwAAAIAzxZRYACiFdu/eLb/fLykxjdVms2n8+PG5wexPf/qT6tevn3vO8Vgn5d8RNhaL6euvv9aBAwd01113Ffqax9e3s9lssixL4XBY48eP19KlS5WRkZH72hdj4Pf6H6P6x6QclXXbiXUAAAAAkg7BDgBKoXbt2mnjxo2aM2dObkg7/v/xeFzxeFypqamqUaPGKa9zfLRc9erV9fjjj+c+9kvxeFw//vij3n33XUnS0aNH9fLLL2v06NHKzMzMc25BO7Oez4i3bFNYY6cZuqJqioYNyFRmBr8KAQAAACQX1rADgFJoz549atu2rSpVqqRx48apZcuWSk1NbLZgmqb+9Kc/6a233tLq1atVuXLlAqfBxmIxWZaVZ/RdYaLRqP7yl79o9OjRuuSSS1S2bFnt3LlTEyZMUJcuXS7EW8zHsizNXB3Sp8uD6tDApbu6uOWw54+DAAAAAFDUCHYAUEqtWLFCd9xxh8LhsIYMGaJu3bpp165dWrhwoV5//XXNmTNHbdu2lWma+XaEtSxLkUhEe/bsUd26dQt9jWg0qnA4LI/Ho1AopI0bN2rRokUqU6aMrrvuOtWsWbPA6x9/jYJG250N07T00ZKA5q8Lq2/rNPVrk37erg0AAAAA5xvBDgBKsR9++EFDhw7V559/rlAoJK/Xq06dOumJJ55Qt27dFI/H5XDkX9/NNE199NFHuuOOO055/ZycHLVp00YjRoxQ9+7dc0fxnXydwmKdVPD02F8rGrP05lyfVm+L6s5rMnRN47RzviYAAAAAXEgEOwCAVq1aJcuyVLZsWVWpUkUej+eUI9w++OAD/eY3vyn0erFYTOFwWG63W4MGDdLUqVM1fvx4de/e/bQRzjQt2WznJ9b5Q6ZenuHTjgMxDe7u0ZV1Xed8TQAAAAC40JxFfQMAgKJ3fIdW6dSj2yzL0vLly3NjXWFRLxwO6+6771anTp301ltvqUmTJlq8eLGuv/76U95H3LRkP0+x7pAR15hsn4ygqSf7eVWvyunX2gMAAACAZMAIOwDAGbEsS7FYLHeTidOtMde9e3f5fD4tW7bsjK4fNxO/js7HRhC7DsU0JtuQw27TE329qlIu/7ReAAAAAEhWjLADAJwRm82WZ0fYX8a64wHv+Lp3zZo1UywWUyQSkdPplN1uLzTymaYl05RSnOce6zbtjurlGT5dkmnX4328KuvOv0YeAAAAACQzgh0A4JwdD3GWZcnhcGjEiBH6+OOPNX78eLlcJ9aNKzDWWZZCUUsZqece1lb8ENbb8/y6vJpTD/f0Kt3FTrAAAAAAih+CHQDgnMTjccXjcW3YsEGrV6/W8uXL9f777+uzzz5Tly5dTvv8nICpsu5zn7I6d01IHy8JqN0VLt19rVtOB7EOAAAAQPFEsAMAnDXLsmSapu6++259/PHHatiwoerWrasVK1aoUaNGMk1TdnvhI+f2HI6pWvlz+1VkWpYmLg1q9pqQerZI08B26bKfh00rAAAAAKCosOkEAOCcGYahnTt3qkaNGkpNTVVaWlqhse74r53Ne2O6vNq57dwajVt693O/VvwQ0a0dM9S1Wdo5XQ8AAAAAkgHBDgBw3hW2uYRlWYqb0jfbImp9Weo5vUYgbOrVmT5t3hfT/d08alXPdfonAQAAAEAxQLADAPwqhcW4M3leKCot3RDSdc3Szuoaxx31mxqTbeiwz9SjvTy64hxH6gEAAABAMiHYAQB+lVjcksNe8I6vhbEsSzkBS3PWhHRD+3NbY27v4bhGZxuyLOl3WR5VP8c18AAAAAAg2fAtBwBwWr6QqX9OylHclP5wY6YyUgvfSOKXLMvST0dNTf86qN92cZ9TrNu8N6qXpvtUzm3X4329Ku858/sAAAAAgOKCYAcAOKVozNLLM3zKCVh69ixi3dZ9MU1dFdTDPbxyOs4+1n29NaI35/hUt7JTj/by/Kr7AAAAAIDihGAHACiUaVl653O/duyP6an+XlUq4zjj51qWpbU7opq6Iqgn+3mVmnL2sW7+tyF9uCiglvVcGtTVrRTn2V8LAAAAAJIdwQ4AUKhJy4NauTmiB3t4VK9K4Rs7xE1LDnveiLZkY1gzVoX09A2/blTeySzL0mdfBjXj65C6NkvVLR0yzmlKLQAAAAAUBwQ7AECBFn0X0ozVId18dbpa1XMVes7XW6Ma1M0tb3oipJmmpc+WB7Xs+7B+PzBTZTLOLtbF4pbeW+DXsk0R3Xx1uro3P7edZQEAAACguCDYAQDyWbcjog8WBXRt01R1b55W4DlLN4Y1fmFAXZqkypOWCGmRmKUx2YZ2HYpreH+vKv6KKbQnC0UsvTbLp427o7q/u1ttL0896/cCAAAAAMUNwQ4AkMfOgzH9a7ZPTWun6LYOGQWOavvqh7Dene9Xx4apuq1T4py4aen12T5t3x/T0Cyvalxydr9icgKmxk4z9NPRuH7X16uGNQqfigsAAAAAJRHBDgCQ67AR10vTDFUp69Dg7h7Z7flj3ddbInprrl9tL3fpzs6JNeVMy9K/5/v17c6oHu3l0WVVzy6y/XQ0rtHZhqIxS08PzFTNs4x+AAAAAFCc8U0IACBJCoRNjZ3mk8Nu05A+Be/qumZ7RK/P8anlpS7dc51bdrtNlmXpk6VBLd8U0aBubjWtXfB6d6ez7aeYxk4z5EmzadiNmargPbvptAAAAABQ3BHsAACKxS39a5ZPh32mnrmh4I0ivtsZ1WszE1NlB3Vz5+4KO3N1SHPWhHR7pwy1veLs1ppbuz2if832qeYlTg3p7ZE77ew2qgAAAACAkoBgBwClnGVZGr/Qr017Ynoiy6tq5fOPbNu0O6qXZxhqWCNFD1zvkdORiHWL1of06fKgstqk67qmBW9OcTqL14c0fmFAzeqkaHB3j1xOdoIFAAAAULoR7ACglJu2KqQlGyO6r6tbDarnX3tu896oXppm6LKqTj3c06OUn2Pdqi0RjV+Y2Ek2q/Wvj3WWZSl7ZUhTVgTVpXGqbu+UUeCaeQAAAABQ2hDsAKAUW74prMlfBdX/qnS1r59/Ouu2n2Iak22odiWnHu3lVcrPo9/W/xjVm3N8anOZS7d1LHgn2VOJm5Y+WBTQ4vVhDWibrt4t0371NQAAAACgpCLYAUAptWl3VO/O96tDA5f6tMo/Qm7ngZhGZxuqXt6pIb1PbEKx7aeYXplhqEGNFN17nVv2XxnawlFLb8zxad2OqO65zq0ODc5u3TsAAAAAKKkIdgBQCu09HNcrM326oppTd3Z25xvdtvtQTKOmGqqYadfjfT1Kc9lynzcm21CNS5x6qMeJtezOlBE09dJ0Q3sOxTWkj0dNap3djrIAAAAAUJIR7ACglDkWMDVmmqFybnuB0W3vkbhGTDFU1m3X0CyvMlITO7YeMuIaOTVHZd12DentyR1xd6YO5MQ1eqqhYMTSsAGZqlOJX0EAAAAAUBC+LQFAKRKOWnppmqFY3NLjA07EuOP2H4trxOQcedPterKfV+60xHEjaGrUVENOh01PZJ14/EztOBDT2GxDaS6bnrkhU5XK5N+JFgAAAACQQLADgFLCNBNrx+07GtfTAzJV3ps3mh3MiWvE5ERUe7KfV970RJQLRiyNyTYUDFv6/Q2ZKuv+dbHuu51RvTrLUNVyDg3p7VVmxq97PgAAAACUNgQ7ACgFLMvSx0sCWrcjqsd6e1SrYt6P/8M+UyOmGHLYpaf6ZarMz1EtGrP08gxD+4+ZGj7A+6tHxi3dGNZ7C/xqVCNFD/b49dNoAQAAAKA0ItgBQCkwd21Yn68L687OGWpaO+9GD0f9pkZMzpFpSk8P9KqcJxHr4qal1+f4tHVfTEP7eVXzkjP/lWFZlmauDunT5UF1aODSXV3cctiJdQAAAABwJgh2AFDCfb0loglLAurZIk2dG6flOWYETY2cYigSszR8QKYq/DxN1rIsvb/Ar7Xbo3q0l0eXV00549czTUsffRHQ/G/D6ts6Tf3apOfbhRYAAAAAUDiCHQCUYFv2xfTmXJ9aX+bSwHbpeY75Q4lY5w+ZGj4g70YQE5cFtWRjRIO6utWsjuuXly1UJGbprbk+rd4W1V2dM3TNLwIhAAAAAOD0CHYAUELtPxbXuOmGaldy6t7r3LKfNMotEE7s+nrUb2rYAK+qlDsR62auDmrWNyHd1jFD7eqnnvHr+UOmxs3waeeBmB7p6dGVdc889AEAAAAATiDYAUAJ5AuZGjvNkDvNpkd7eZTiPBHrQj/v+nogx9RT/b2qXv7Er4LF60OauCyovq3T1LXZmY+OO2TENSbbkBG09GQ/r+pVOfMptAAAAACAvAh2AFDCJHZ29ckfsvTsjZnypNlzj4WjlsZOM7T3iKmhWV7VOmkjia+3RPT+woC6NElVvzbpBV26QLsOxjRmmiGH3abfD8zMM1oPAAAAAPDrEewAoAQxLUvvfO7Xjv0xPdU/77p0kZill2cY2nkgpieyvKpb+cSvgA27onpjjk+t67l0e6eMM94kYuPuqF6Z4VPFTLse7+tVmQz76Z8EAAAAADglgh0AlCCTlge1cnNED/XwqF6VEx/x0bilV2f6tGVfTI/38eqyk3Z93fZTTC/PMFS/eoru65p3rbtTWfFDWG/P8+uKak493NOrNBc7wQIAAADA+UCwA4ASYtF3Ic1YHdItHTLUst6JDR9icUuvz/Zp4+6ohvT2qn71E7Fu75G4xk4zVL28Qw/39MjpOLPoNmdNSP9dElC7K1y6+1r3GT8PAAAAAHB6BDsAKAHW7Yjog0UBXds0Vd2andjZNW5aenOuX+t2RPVoL48a1TwR6w4ZcY2aYigzw67H+3iVmnL66GZalj5ZGtScNSH1apGmge3Sz3j6LAAAAADgzBDsAKCY23kgpn/N8qlp7RTd1uHE+nOmaemdeX6t3pqYItu09olRd0bQ1Oiphux26Ym+XrnTTr/2XDSeuN7KzRHd3ilD1zU9811kAQAAAABnjmAHAMXYYSOul6YbqlLOocHdPbLbf451lqX3Fvj11eaIBndzq8WlJ2JdKGJpTLYhf9jS7wdmqpzn9LEuEDb1ys9r4D3Yw6NWJ025BQAAAACcXwQ7ACimAmFTY6f55LDbNOSkKa2WZenDRQEt3RjRvV3danP5iSmy0Xhip9j9x0wN6+9V5bKOwi6f64jP1Nhphg77TA3N8uqKaimnfQ4AAAAA4OwR7ACgGIrFLf1rlk9H/KaeGZipMhmJUXKWZem/SwJa8F1Yv+2Sofb1T8Q607T05pzEKLknsryqVfH0vwL2HI5rTLYhy5KeHuhV9fL82gAAAACAC41vXgBQzFiWpfEL/dq0JxHeqpZ35D7+2fKg5q4N645OGerUKC3Pc95fGNA326J6pJfnjEbJ/bA3qnHTfSrntuvxvl6VP4OpswAAAACAc0ewA4BiZtqqkJZsjGhQV7caVD8R3qauDGnG6pBuvjpd1/5iQ4hPlwf1xYaw7r3OreZ1Tr/+3NdbInpjrk/1Kjv1SC+PMlKJdQAAAABwsRDsAKAYWb4prMlfBdX/qnS1O2m664yvg5q6IqiBbdN1/ZXpeZ4za3VQM1eHdGuHDF3dIPWXl8xn/rqQPlwcUKt6Lt3Xza0Uh+28vw8AAAAAQOEIdgBQTGzcHdW78/3q0MClPq1OjKCbsyakT5cH1bd1IEDY0wAAR59JREFUmnq3yhvrvtgQ1ifLgurdKk3dmqf98pJ5WJalz74MasbXIXVrlqqbO2TIbiPWAQAAAMDFRrADgGJgz+G4XpnhU/1qTt3Z2S3bzyFt/rch/XdJQD1bpKlfm7yxbvXWiN5b4Nc1jVI14Kr0gi6bKxa39N4Cv5Ztiujmq/OP0gMAAAAAXDwEOwBIcscCpsZOM1TeY9eDPTxy/jxFdfH6sP6zKKCuzVJ1Q7v03IgnJUbjvT7Hp5aXuvSbazLyHPulUMTSq7MMbdod0/3d3Wp7+emnzQIAAAAALhyCHQAksXDU0kvTDMVNS4/38eZu/rB8U1jvL/Crc+NU3dohb5DbsT+ml6cbuqKqU4O6uWW3Fx7rjsfA/Ufj+l1frxrWOP3usQAAAACAC4tgBwBJyjQtvTHHp31H43p6YKbKex2SpJWbI3r7c7+ubuDSHb8YPbfvSFyjsw1VLe/Qw728p9wwYt/RuMZkG4rGLD09MFM1L+FXAgAAAAAkA76dAUASsixLHy0JaN2OqB7r7VGtn2PaN9sienOuT1dd5tJvu7jzbApx2Ihr1FRD3nS7Hu/jVVpK4bFu608xvTTNkCfNpmE3ZqrCzzEQAAAAAFD0CHYAkITmrg1r/rqw7uqcoaa1XZKkdTsiem2WT1fWTdG9XfNOdTWCpkZNNWSzSUOzvPKk2Qu99prtEb0+26dalzj1WG+P3Kc4FwAAAABw8RHsACDJrNoS0YQlAfVqkaZrGqdJkjbsiuqVmT41qZWi+7t55Dgp1oUilsZOM+QPJ6a2lvMUHuAWrw/p/YUBXVknRfd398jlLHwUHgAAAACgaBDsACCJbNkX1VtzfWp9mUsD2qVLkr7fE9W46YYaVE/Js0usJEXjll6ZaWjfkbiGDchUlbIFT221LEtTV4Y0dUVQXZqk6vaOGafcjAIAAAAAUHQIdgCQJPYfi2vcdJ9qV3Lq3usS69Nt2RfV2GmGLq3s1MM9PXk2kTBNS2/N9emHvTE90der2hUL/kiPm5Y+WBjQ4g1hDWybrl4t0/JsVAEAAAAASC4EOwBIAr6QqTHZhtxpNj3ay6MUp03b98c0Jvv4WnPePNNXLcvSB4sC+nprVI/09Kh+9ZQCrxuOWnp9tk/f/RjVvde5dXWD1Iv1lgAAAAAAZ4lgBwBFLBqz9PJ0n4IRS8/ckClPml0/Hoxp1FRDVcsldnxN/cWOr5O+DGrR+rDuuc6tK+u6CryuETT10jRDew7H9Vhvj5rUKvg8AAAAAEByIdgBQBEyLUvvfO7XjgMxPdU/U5XKOLTncFwjpxi6JNOu3/X1Ks2VN9bN/iao6V+HdPPV6epQyIi5A8fiGp1tKBixNHxApmpX4uMeAAAAAIoLvsEBQBH6bHlQKzdH9FAPj+pVcWrf0bhGTslRWbddQ7O8ykjNu+Prko1hTVgaVK+Wabr+yvQCr7ljf0xjphlKd9n07A2Zqlim4I0oAAAAAADJiWAHAEVk4XchzVwd0i0dMtSynksHjsU1YnKOMlJtGprllSctb6z7ZltE7833q1OjVA1sW3Cs+3ZnRK/N9KlaeYeG9PHKm24v8DwAAAAAQPIi2AFAEVi3I6IPFgV0XdNUdWuWqkNGXCOmGHI5bXqyX6YyM/KGtk27o/rXbJ+urJuiO6/JKHCX16Ubw3pvgV+Naqbowes9+da9AwAAAAAUDwQ7ALjIdh6I6V+zfGpWO0W3dsjQUb+lEZMN2WzSU/0zVdZtz3f+uOmGLq/q1P3dPbLb84Y4y7I04+uQPvsyqI4NU3Vn5ww57MQ6AAAAACiuCHYAcBEdMuIaO81QlXIODe7ukS9kaeSUHMVNafgAr8p78sa6fUcTm0dUKefQIz29SnHkDXGmaenDLwJa8G1YWa3TlNUmvcDRdwAAAACA4oNgBwAXSSBs6qVpPjkdNj3ex6tIzNLIKYmdXJ8emKlLMvNuDnHEZ2r0VEOetMT5v9wtNhKz9OYcn77ZHtVdXTJ0TaO0i/l2AAAAAAAXCMEOAC6CWNzSa7N8OuI39czATDns0ogphnKCpoYPyFSlX+zk6guZGjXVkGVJT2Rl5ts8wh8yNW66TzsPxvRoL4+a13FdzLcDAAAAALiACHYAcIFZlqXxC/36fk9MQ7O8Kuuxa+SUHB3xmXqqv1dVy+WNdaGopbHTDBlBU78fmJlvmuwhI64x2YaMoKUn+2WqXhU+ygEAAACgJOFbHgBcYNNWhbRkY0SDurlVu5JTo6ca2n/M1JP9vKpRIe/HcDRu6dUZhvYcjmt4/0xV+UXM23UwpjHTDDnsNv1+YP7jAAAAAIDij2AHABfQsk1hTf4qqP5XpatFXZdemmZo96GYhvbLVO2KeT+CTdPS23MTI/F+l+VV7Up5j2/YFdWrM32qmGnX4329KpORd+QdAAAAAKBkINgBwAWycXdU/57vV8eGqbq+eapenmFo+/5EjLu0ct6PX8uy9MGigFZtjeihHh41qJ6S5/hXP4T19jy/6ldz6uGe+TegAAAAAACUHAQ7ALgA9hyO65UZPtWv5tRtHdL12my/ftgb0+N9vbq8akq+8yd/FdSi9WHdfa1bLS/Nu4HE7G+CmrA0qHZXuHT3tW45HcQ6AAAAACjJCHYAcJ4dC5gaO81QeY9d93f36K15fm34MapHe+cfOSdJc9eENG1VSDe1T1fHhqm5j5uWpQlLA5q7JqxeLdI0sF26bDZiHQAAAACUdAQ7ADiPwlFLL00zFDctDent1YeLA1q7I6qHe3rUpJYr3/lLN4b18ZKAerRIU48W6bmPR+OJ9exWbYno9k4Zuq5p2sV8GwAAAACAIkSwA4DzxDQtvTHHp31H4xo+wKvJK4JatSWiB673qHmd/LFuzfZI7hp3N7Y7EesCYVOvzPBpy08xPdTDo5b18j8XAAAAAFByEewA4DywLEsffRHQuh2Jqa8Lv4to+fcRDermVqsCgtv3e6L61yyfrqybojs7Z+ROdT3iMzUm29ARv6kn+xW83h0AAAAAoGQj2AHAeTBnTUjzvw3rrs4Z+nZnVIvXh3XvdW61vTw137k7D8Y0brpP9ao4dX83jxz2RKzbfTimsdk+SdLvB2aqWnnHRX0PAAAAAIDkQLADgHO0aktEnywNqleLNP101NT8dYlwd3WD/LHup6NxjZ5qqFIZux7t5VWKMxHrvt8T1cszfCrntut3fb0q57Ff7LcBAAAAAEgSBDsAOAdb9kX11lyfWl/mkmyWZq8J67aOGbqmcf5NIo76TY2aasidatPv+nqV5krEulVbInpzrk/1Kjv1SC+PMlKJdQAAAABQmhHsAOAs7T8W17jpPtWp5FTlsnZlrwzppvbp6tosf6zzh0yNnmrINKUnBnjlTU9Euc/XhfTR4oBaX+bSvV3dSnHYLvbbAAAAAAAkGYIdAJwFI5jYHMKTZlPD6k5NWRlS/6vS1aNFer5zw1FLL03z6VjA1NMDMlXB65BlWfpseVAzVofUrXmqbr46Q3YbsQ4AAAAAQLADgF8tGrP0ygyfghFLnRunasrKkPq0SlPf1vljXSxu6dWZPu06HNNT/TJVtbxDsbilf8/3a/n3Ed18dbquvzL/8wAAAAAApRfBDgB+BdOy9PY8v3YciKlrs1Rlrwzp+ivT1P+q/NHNNBPnbtod1eN9vapb2alQxNKrswxt2h3T4O5uXVXALrIAAAAAgNKNYAcAv8Kny4NatSWiLk1TNXN1WNc1TdVN7dNl+8V0Vsuy9OEXAa3cEtFD13vUsEaKjgVMjc02dCDH1O/6etWwRkoRvQsAAAAAQDIj2AHAGVrwbUizVofU7gqXFqwL65pGqbqtY0a+WCdJU1YEteDbsH7bJUMt67m072hco6caisUtDR/gVc1L+PgFAAAAABSMb4wAcAbW7YjoP4sDalIrRV/9EFG7+i79pnPBsW7e2pCyV4Z0Q7t0dWqUpi37Yho33ZA33abhP286AQAAAABAYQh2AHAaOw7E9K9ZPtWp5ND6H6NqVc+le651F7ir6/Lvw/roi4CuvzJNPVukac32iF6f7VOtS5x6rLdH7jR7EbwDAAAAAEBxQrADgFM4ZMT10jRDZd127dwf15V1U3RfV7fs9vyxbu32iN6Z51eHBi7d1D5dizeENX5hQFfWSdH93T1yOfM/BwAAAACAXyLYAcBJNu6Oqnp5h7zpdgXCpl6a5pMl6ZBhqnGtFA3u7pHTkT+8fb8nqtdm+dSsToru7JyhKSuCyl4ZUpcmqbq9Y0aBgQ8AAAAAgIIQ7ADgZzkBUyMmG0pxSHdck6GvfojooBGXaUpXVHfqoR4Fx7ofD8Y0brpPl1Z2alBXtz5YFNQXG8Ia2DZdvVqmFbjOHQAAAAAAhSHYAcDPNu6OSpKicenf8wOySUpxSJdWceqRnl6lOG0yLUs2KTfC7T+W2P21Yhm77u/u0euz/Vq/K6p7r3Pr6gapRfdmAAAAAADFFsEOAH62cVc0z98tSRmpNj3W26vUlESg+2x5UKu2RDQ0KxHwRk01lJ5q06Cubr08w9Dew3E91tujJrVcRfAOAAAAAAAlAcEOAH727Y/RfI8dDViauiKom6/OkGVZWrYprGMBS//vP8fkTbfJZpPuudajl2f4FIxYGj4gU7Ur8dEKAAAAADh79qK+AQBIBoeMuI74rAKPzf4mpMNGXHsOx3UskDjHtKRjAUtNa6boX7N9kqRnbyDWAQAAAADOHcEOACR9+X2kwMcrZdp0Y/t0lfXYtW5nVL/cP2LxxohcDumZGzJVsYzjItwpAAAAAKCkYygIAEg65Ivn/tmTJnVsmKa2l7tUvYIjd4OJb7ZFZBUwCO9owFLcvFh3CgAAAAAo6WyWVdDXTwAofSZ/GVCdSk41rZMi+y+G0vlDpp54+2ihz61X2aFnbixzge8QAAAAAFAaMMIOAH7Wv21GocdWbyt4yqwkVcy0q1kddoUFAAAAAJwfBDsAOAPtrnBp066olv8QVYpDal4nRY1rudSohlPlvaxdBwAAAAA4f5gSC6Dki0ckMyo5M5Rv14hfIWaa2rE/rrqVnfmmzAIAAAAAcL4Q7ACUTJYl7V4ibZ4s7V0mWXEpo5J02UCpXpaUXqGo7xAAAAAAgAIR7ACUPJYlrRolrX9PioWk1DKS3SlFfFI8LJVvIHUZKZW99MT5linZmdoKAAAAACh6BDsAJc+mCdKy5yVXZv6RdGZUOrZNuqSZdOUj0vaZ0t7lkhmXytSVLhsg1e0ppbiL5NYBAAAAACDYAShZzJg05Sbp2BYps07B50SD0qF1UopHsqckwp7NLkUMSaZUuY3U5cXEFFoAAAAAAC4ye1HfAACcVwfWJEbQpZ8itoUOSaGjiemy5S6X3JWljIqJKbKemtLeL6XFf0hMkwUAAAAA4CIj2AEoWcI5khmRnGkFH7dMybc7MaKuoDXrnKmSp5q0b2XifwAAAAAAXGQEOwAli+vnaa7xcMHHw0ekWPDnYJdS+DXiQWnn5xfuPgEAAAAAKATBDkDJUrG5lFlbChwo+Hg8lhhlZ7Pn35DiZDaHFDx0Ye4RAAAAAIBTINgBKFkcLqnBbZIVk0JH8h+3KbExhTNdSruk8OtYcSk184LdJgAAAAAAhXEW9Q0AwHlX/7bExhOb/pvYYCK1XGK9uohPivoSU17TKxa8hp2UmDJrT5Gqd7y49w0AAAAAgCSbZVlWUd8EAJx3lintmCv9MFE6sDbx99Qy0mUDE1NdN4yX3FUT8e5kZiwR+yo2lXp/kBixBwAAAADARUSwA1CyWZYUyZHi0cQUV4dLioWkxc9K22dLNpuUVi6xZl0kR4r6pbKXS9eOkspdXtR3DwAAAAAohQh2AEqneETaMkX6fqJ0dIskMzF1tl4/6YqbJE/Vor5DAAAAAEApRbADUOyt/zGqCl67KpctZE26U7FMyf9TYpOJ9EskZ9r5v0EAAAAAAH4FdokFUKwt3RjWqKmGNuyKnt0FbPbEaDpvDWIdAAAAACApEOwAFFsrNof17ny/OjVM1TWNUws8JxA29emygIygeZHvDgAAAACAs0OwA1AsfbMtorfm+nXVZS7d2TlDdpst3zmBsKnRUw0tXB9WnF4HAAAAACgmnEV9AwDwa323M6p/zfKpeZ0U3dvVLbs9f6zzh0yNmmroYI6pp/p5VdbNf58AAAAAABQPBDsAxcqm3VG9MtNQw5opGtzdI0chsW7kVEOHDVNP9veq1iV81AEAAAAAig++xQIoNrbsi+ml6YbqVXHq4R4eOR35Y50vZGrUFEOHfYmRdTWIdQAAAACAYoZvsgCKhR0HYhqTbajmJU492surFGf+WGcETY2cYuhYwNRT/b2qUYGPOAAAAABA8cO3WQBJb/ehmEZPNVS5rF2P9/EqNaXgWDdisqGcYCLWVS/PxxsAAAAAoHjiGy2ApLbvaFwjpxgq67brib5epbvyx7qcQGJknRE0Nax/pqqVdxTBnQIAAAAAcH4Q7AAkrQM5cY2cbMidZtPQLK/cafl3es0JmHpxsqFA2NSwAZmqWo5YBwAAAAAo3gh2AJLSYV9i1JzTIT3ZL1OZGflj3bGAqRGTcxQIWxrWP1NViHUAAAAAgBIg/zdgAChiiSmuOTJN6an+XpV15/+oOuo39eKkHAUjloYNINYBAAAAAEoOgh2ApOILJUbWhSKWnurvVQVv/hB31G/qxck5Ckel4QMyVaUssQ4AAAAAUHIwJRZA0giETY2amtjpdXj/TFUqkz/EHfElYl0sLg0b4C3wHAAAAAAAijNG2AFICqGIpbHZPh3MMTU0y6uqBez0evjkWNefWAcAAAAAKJkYYQegyIWjlsZNN7T7cExP9stUzUvyfzQdMuIaMdmQaUnDB3h1SSaxDgAAAABQMjHCDkCRisYtvTrTp237Y3q8r1d1Kxcc616cbMgSsQ4AAAAAUPIR7AAUmVjc0uuzfdq0J6rHent1edWUfOcczInrn5MM2ZSYBlvQJhQAAAAAAJQkBDsARcI0Lb09z691O6J6pKdHDWvkj3UHfo51dhuxDgAAAABQerCGHYCLzrQs/XuBX6u2RPTg9R41re3Kd87+Y4k165wOaVj/TJXz8N8XAAAAAAClA9+AAVxUlmXpw0UBLdsY0X1d3WpZr+BY9+IkQylOafgAYh0AAAAAoHThWzCAi8ayLE1YGtSC78L67bVutb0iNd85Px2N65+TcpSakhhZV9bNxxQAAAAAoHRhSixQwliWJZvNVtS3UaApK4Kasyak2ztlqGPD/LFu35G4Xpyco4xUm57qn6kyGcQ6AAAAAEDpQ7ADirnx48drzpw5ql27th5++GFVrFhRTmfy/WhPXxVU9sqQbmqfruuapuU7vvdIXCMm5ygj1a5h/b3KJNYBAAAAAEopvhEDxVy7du3UpEkTrV27Vq1atdKzzz6rhQsX5h63LKsI7y5h7pqQPvsyqKw26erRIj3f8T2H43pxUo48acQ6AAAAAABsVjJ8mwdwVkzTlN1+Im69/vrrmjVrljZu3Kjhw4frnnvuKbqb+9nC70IavzCgHi3SdGO79HzTdXcfjmnEZENlMux6sp9X3nRiHQAAAACgdCPYAcXMLyPdL61du1bvvfee3n33Xf31r3/Vgw8+eBHvLq+lG8N693O/rm2aqts6ZuSLdbsOxTRyCrEOAAAAAICTJd9CVwAKdTzWWZalTz/9VFu3blXHjh3VuHFjZWZmSpKaNWumoUOHKjU1VePGjVPt2rXVs2fPi36vKzdH9O58vzo2TNWtBcW6gzGNmGKovMeuof288qQR6wAAAAAAkBhhBxQbJ4+su+aaaxQMBnXgwAEFAgG98cYb6t+/f55zNmzYoOeee04VK1bUmDFj5HA4Tjky73xasz2iV2f61LqeS/d1dctuzxvrdh6MadQUQ+W9dj2Z5ZWbWAcAAAAAQC6+JQPFxPHY1q1bN6WlpWnKlCnavn272rdvr4kTJ+Y5R5IaNmyou+66Sx9++KG++eabixbr1v8Y1WszfWpeJ0X3FhDrdhyIaeRkQxWIdQAAAAAAFIhvykAx8uqrr8o0Tb311luqUqWKJKl9+/ZyOp164IEH9PLLL2v58uW55/fr10933XWX/v3vfysWi13wHWO/3xPVyzMMNayRosHdPXL8MtbtT6xZV7FMYs06Yh0AAAAAAPnxbRkoRho0aKChQ4eqatWqstlsWrJkif73f/9Xhw8f1uHDhzV+/Hj9+c9/1o4dO3Kf06ZNGwWDQTmdznzryJ1PW/bFNHaaoXpVnHq4p0dOR97X2vZTTCOnGqpc1q6hWV5lpPLxAwAAAABAQVjDDihmwuGwUlNTFQwG1b9/f7Vs2VLPP/+8UlNTNWfOHN10002aOHGiunXrlvucmTNnqkePHhcs2O04ENOIyYaqV3Doib5epabkj3WjphqqVs6h32V5le66cOEQAAAAAIDijl1igWImNTVVkpSenq4PPvhAFStWlGmakhLr1tWoUUMpKSmSpHg8LofDcUF3id19OKbRP4+ce7xP/li3ZV9MY7INVS/v0O/6epVGrAMAAAAA4JSYkwYUI8fD3HEVKlSQdGKzicWLF8tms6ly5cqSJIfDcUHvZ9/RuEZONlTWbdcTffOPnNuyL6rRU3NUvQKxDgAAAACAM0WwA4qJeDyeG+bWrVunWCyW+3e/368ZM2bowQcf1BNPPKEGDRpc8Ps5mJOIde40m4YWsNvr5r1RjZpqqFZFJ7EOAAAAAIBfgSmxQJKwLKvQNeZisZiczsSP63333SfDMPTvf/9bTqdTu3bt0gsvvKB58+bpf//3f3X//fdf8Hs94jM1Yoohp0N6sl+mMjPyxrrv90Q1NttQnUpODSlgmiwAAAAAACgcm04ASeDkWDd//nx9//33Sk9PV/PmzdW8efPc8x566CFNnz5dU6dOzfP4tGnTZJqmsrKyLvi95gRM/XNSjiIx6emBXlXw5p12ezzW1a3s1GO9iXUAAAAAAPxajLADksDxWPe3v/1Nr732mqpUqaJ9+/apYcOGev7559W2bVvt3btXR48e1YQJE3Jj3fHQ16dPn4tyn76QqZFTDAUjlp4emJkv1m3aHdXYaYbqVXHq0V7EOgAAAAAAzgZr2AFJ4r333tOoUaP01ltvadmyZfrkk0908OBBLV++XJJUuXJlvfPOO2rbtm3ucwqbQnshBMKmRk81lBM09VS/TFUqkzfWbdiViHWXVWFkHQAAAAAA54JgBySBffv2aeLEiXrqqafUrVs32e12tWnTRl27dlV2dnbuhhPp6elFcn+hqKWx2T4dyDE1NMurquXzxrr1P0b10jRDl1dN0aO9vXI5iXUAAAAAAJwtgh2QJOrVq6eOHTtKSkx1laSaNWvq4MGDBY6ku1jLT0ZilsZNM7T7cExP9PWq5iV5Z9J/tzOqcdMNNaieokd7eYh1AAAAAACcI4IdkASqVKmiJ554Qh06dJB0IsZdfvnlcrvduecdPXpU7733nqSLMx02Grf06kyftu2P6fG+XtWtnDfWfbszonEzDDWokaKHe3mUQqwDAAAAAOCcEeyAIhaPxyVJtWrVkpSIdXZ74kfTNE0dO3ZMdrtdx44dU6dOnTRz5syLcl+xuKXXZ/u0cXdUj/X26vKqKXmOr9sR0cvTfWpcM0UP9/QoxUGsAwAAAADgfCDYAUXg5Oms0Wg0N9pJiUh3XCAQUCgU0sGDB9WjRw9VqFBB//nPfy74/Zmmpbfn+bVuR1SP9PSoYY28sW7N9ohemeFTk9opeqgHsQ4AAAAAgPPJefpTAJxPlmXlTmf9z3/+o48//ljhcFg1a9bUG2+8IYfDoXg8LofDoapVq8rlcqlLly5yu91asGCBpETUOz4K73wzLUv/XuDXqi0RPXC9R01ru/Ic/2ZbRK/N8qlZ7RQ9cL1HTmIdAAAAAADnFSPsgIvseKx788039eijj6pJkybq06eP5s6dq86dO+vw4cNyOE7swrphwwZVrFhRX375paQLG+ssy9KHiwNatjGie7u61ape3li3emsi1jWvQ6wDAAAAAOBCIdgBF4Hf78/z9xUrVugf//iHXnvtNf31r39V27ZtdfToUa1du1ZXX3219u3bJymxGcUf/vAHzZ8/X1LBse587RZrWZY+WRrUgm/DuquLW+2uSM1z/OstEf1rtk9X1nVpcHdiHQAAAAAAFwrBDrjAPvvsM3344YeSTmwwcfDgQfXv31+33nqrvv32Ww0YMECPP/64Pv/8cx0+fFh9+/bV1q1bdemll+ovf/lL7nN/Gevi8bhCoZDWr19/zvc5ZUVQs9eEdFvHDHVqlDfWrfo51rW81KXB3d3EOgAAAAAALiCbdb6G5wAo0IsvvqgPP/xQy5cvV0rKic0bvvvuOzVq1EgDBgxQ5cqV9frrrysYDKpnz55avHixunTpos8//zzPmne/ZFmWHnroIS1btkzz5s1TxYoVT3l+YaavCuqzL4O6sX26erZIz3Nsxeaw3pzjV+vLXLqvq1sOO7EOAAAAAIALiRF2wAV22223qVKlSrkbRkSjUUlS48aNdezYMe3cuVPdu3eXJNntdtWpU0cLFy7U9OnTJemUsc5ms2n06NEKBoN6+OGHT3l+YeauCemzL4PKap2WL9Z99UMi1l11uUuDiHUAAAAAAFwUBDvgAqtWrZoyMjL0t7/9TZKUkpKSOzU2MzNTLpdLI0eO1OLFizV48GB99dVXatasmdLS0gpcn86yrNxYF4lElJ6erjfeeEPr16/XhAkTftW9LfoupI+XBNTjyjRltckb6778Pqw35/rV9gqX7r3OLTuxDgAAAACAi4JgB1xAlmXJbrfr1Vdf1fr163NHwTkcDkWjUdntdr344osKh8O66667tG7dOs2YMUNlypQpcGrr8YB34MABSZLLldjFtVmzZmrTpo1Wr16d57xTWbYprPELA7q2aapubJ+e57WWbwrrrXl+ta/v0j3XEusAAAAAALiYWMMOuADi8bgcDodCoZAsy1J6erree+89vfDCC+rfv79efPHFPOeHQiHt3LlTVapUUWZmZoG7wR43dOhQTZs2TVdffbXefffd3MdnzZql++67T8uWLVOtWrVOeX8rN0f0+hyfOjRI1V1dMmQ/KdYt3RjWu5/71aFh/mMAAAAAAODCY4QdcJ4dj3Xbt2/X/fffr7lz5yoSiSgrK0sPPfSQsrOzddtttykWi+U+Jy0tTVdccYUyMzNzR+UVZOXKlbrpppv04osvasKECXrllVckJUbUdezYUQ8//HCejS0KsnZHRG/O9anNZS7d1TlvkFuyIRHrOjYi1gEAAAAAUFQIdsB5ZFmWHA6H9uzZo/bt28tut6tq1apyuVwqV66cBg0apL/+9a9au3atmjZtqrffflvfffddnmsUtmnEtm3b1Lp1a3Xo0EHXXXed6tevr7179+Y+x+1265ZbblHVqlULvb9tP8X06gyfmtdJ0X1d8051Xbw+rH/P96tTo1Td2ZlYBwAAAABAUWFKLHCeRaNR3XLLLcrIyNAHH3yQ+/jxkXeSFIvF9PTTT2vv3r2aNWuW/u///k9ZWVmqXr36Gb2Gz+fTDTfcoDvuuEP33HPPKafQHvfjwZj+/mmO6ldL0SO9PHI6TgS5RetDen9BQF0ap+r2a4h1AAAAAAAUJWdR3wBQ0jgcDv3000+66667JEmmacpms+XGuqNHj6ps2bIaOXKkYrGYVq9eLZfLpWrVqhV6zV8GuRdeeEHbtm1T165dJem0sW734Zj+v89ydGllpx7qmTfWLfwulLv5xO0dMwod4QcAAAAAAC4OpsQCZ+Hkgam//HNOTo62bNkin88nKe8U1x9//FGff/65jh49KikR99q0aaPmzZsXGMomTZqk3bt35wa5r776SoMHD9bbb7+tSZMmqWbNmjJN85T3uu9ITP/4zFDNCk492tsrl/PE68z/NhHrujYj1gEAAAAAkCwIdsCvZFlWnrB1/M/Hw13ZsmV177336o033tCsWbNks9lyz5kxY4ZGjBihQCCQ57kFvcY777yjL7/8Mnea7N69e7Vo0SIdOnRIS5YsUePGjU87FfZATkz/32eGKmbaNaSPR2kpJ17v83Uh/WdRQN2ap+rWDsQ6AAAAAACSBWvYAWfpvvvu07Zt25SVlaUuXbqoZcuWuceWLFmiF198Ubt27dLgwYPVqFEjrV27VsOHD9err76q3/72t4Ve17Isffrpp1qxYoX+/ve/5zl28OBBpaWlyePxnDbWHTLi+tvEHHnT7RrW3yt32olz564J6eMlAV3fPE03XZ1OrAMAAAAAIImwhh3wK1mWJZ/Pp/nz56tMmTLy+Xzq0aOH7rvvPrVq1Uq33HKLOnToII/Ho3feeUe///3vlZ6erkqVKmnMmDGnjHVSYtTdZZddphtvvDHPa9psNl1yySW5j50q1h31xfWPzwylu2wampU31s1ZE9J/lwTUo0WabmxHrAMAAAAAINkwwg74lY7Hs48//lj//e9/9fLLL2vTpk168803tWvXLgUCAQ0dOlTXXHONqlWrpmPHjskwDNnt9lNuLCHl31zibOQETP390xxJ0tMDM1XWfeJ6s78JasLSoHq1SNNAYh0AAAAAAEmJNeyAX+l45Kpfv752796tb775Rp07d9b777+vxx57TCtWrNDYsWPVrl07Pffcc1q1apVq1KihqlWrFnrNeDyuSCQiu92ueDx+1vfmC5r656QcxU3pqf7ePLFu5upErOvdilgHAAAAAEAyI9gBZ+nKK69U165d9eSTT0qSNmzYoHvuuUd//OMf9cYbb2j48OEaN26cdu3aJenUG0x8++236tChg44cOSKHwyHDMPTDDz+cdgfYkwXCpl6cbCgYsfRUf68qeB25x2Z8HdTEZUH1bZ2mAVcR6wAAAAAASGZMiQXOwMcffyy3262+fftKOjEtduvWrXrqqafUvn17/fWvf9Vdd92lcePG5T7vwIEDqlix4imvbVmWli1bpmHDhikQCCg7O1tPPPGEDMPQ1KlT5XK5Tnt/wYipEZMNHTJMDR+QqWrlT8S6aSuDmvRVUFlt0tWvTfpZ/hMAAAAAAAAXCyPsgNN46aWXdPvtt+eZ0np8hNqll16qzMxMPfPMMxo8eLDGjRun4w3csqzcTSIK6+LHw1/79u31j3/8Q+np6apVq5a++OILvf7662cU68JRS2OyfTqQY2poP2+eWJf9c6zrfxWxDgAAAACA4oJgBxTgeGB75513NGzYME2cOFGtWrXKnaJ68lTVv//972rdurVq164t6UTMs9lsef58suPPt9lsisfjstlsatKkiSzLUr169bRmzRrVrl37tOvZRWOWXppuaPehmJ7o61WtS05s/DxlRVCTvwpqwFXp6tuaWAcAAAAAQHFBsAMKYLPZNHXqVA0aNEjjxo3TwIEDtWnTJv35z3/WbbfdpnvvvVc//PCDJMnr9apJkyaaN2/eGV07Fotpz549+vTTTyVJDodD4XBYf/7zn7V161YtXrxYlStXVjwel8PhKPQ60bilV2b6tO2nmIb08apu5USssyxLk78KaOqKoG5ol64+xDoAAAAAAIoVgh1QiCVLlig1NVVlypTRkiVL1KtXL61bt06BQEDbtm3TlVdeqdmzZ8vj8eiRRx7RlClTNHv27NNe1zRNjRs3Ti+++KJWrlwpSUpNTVXLli21YcMGValS5bSxLha39MZsnzbujurRXl5dUS1FUiLWTfoqqOyVId3YPl29WhLrAAAAAAAobth0AjiF4cOH64MPPpAk3XHHHXr++efldrsVDof1yCOPaNasWfrqq69UuXJlffTRR/rNb35zRtddtWqVfvOb3+jZZ5/V3XffneeYaZqy2wtv6aZp6a15fq3aEtEjPT1qViexzp1lWfpseVAzVod089Xpuv5KYh0AAAAAAMURI+yAk/yyX//zn//U4MGD1axZMw0ZMkQZGRmSEiPibr31VgWDQR06dEgOhyM31p28vt1x8Xg8z+OtWrVS9erVNX/+/HznnjLWWZbeW+DXis0RDe6eN9ZN/DnW3dIhg1gHAAAAAEAx5jz9KUDpcXxziK+//lput1v169fX888/r7Vr1+ZuKnF8BJzD4dCll14qt9ud5xq/DG6maSonJ0fTpk1T7969Vb58eQWDQfl8PtWsWfOM782yLH20OKClGyO6r6tbreqdiHWfLA1q9pqQbu2QoW7N087lHwEAAAAAAChiTIkFTmJZlvbt26c6dero9ttv1x/+8AddccUV+c6LxWLq16+f0tLScjePOJVXXnlFjz32mHr27KnmzZtr69atWrVqlb7++mtlZmae0X19siyo2d+EdFeXDF3TKC338f8uDWjumrBu75Sh65oS6wAAAAAAKO4YYQecxGazqWrVqvrkk080aNAgSdKzzz6r+vXrS5Ki0ahmzZqlV155RTt27NA333wjKRHOjo/OO9nxxx955BEdPnxYzz33nI4dO6a7775bb7/9ttxu92k3mJCkKSsSse62jnlj3cdLApq3Nqw7rsnQtU2IdQAAAAAAlAQEO5R6x6e4nhzOsrKy9PHHH+vmm29WOBzWc889p4YNG2r//v1avXq17Ha7VqxYoZSUlAKDm2mastlsstlsCofDSk1N1f/7f/9Phw4d0ldffaX7779fdrtdpmmeNtbN+Dqx6+sN7dLVtdmJWPfhFwHNXxfWnZ0z1LkxsQ4AAAAAgJKCKbGApGXLlmn27Nl67LHHVKFChTyP9+jRQ127dtULL7ygJk2ayDAMpaamyuVyFRjrLMtSPB7X//zP/+jZZ5/NN+U1Go0qJSWl0FF5J5u3NqSPvgiob+s09b8qseGFaVn6cFFAC74L55keCwAAAAAASgZ2iQUkbdy4Uc8//7xGjRqlgwcPSkqMkmvfvr3efPNNTZs2TcOGDdP69evl9XrlcrlkWVaho+NWrlypRYsWqXHjxpoxY4Z2796de+xMY92i9YlY1+PKNPVrk9j11bQsfbAwoIXfhfXbLm5iHQAAAAAAJRBTYlEq/TKY3XvvvXI4HLrnnnvk9/v1zDPPqHLlypKk1NRUde7cWQcPHsx9TFK+4HZ8sKrNZlObNm2UnZ2tP/7xj3rhhRdkWZZuv/123XTTTapWrdppY93yTWGNXxDQtU1SdWP7dNlsNpmWpfELA/pifVh3X+tWh4ap5+sfBwAAAAAASCIEO5Q6x6exHjx4UDt27NCxY8fUrl07/fa3v5Xb7dbNN9+sSCSiRx55RI0bN9aqVavUs2dPDRkyRC6XK3fNu1+y2Wx6+OGHlZWVpd69e6tcuXJ65ZVXtH79eq1fv14rV65ULBY77f2t2hLR25/7dXUDl27rlJEb695fENCSDWHdc51bVzcg1gEAAAAAUFKxhh1KleOxbtu2bbrpppsUCoXkdDp14MABTZgwQR06dNCMGTN0zz33qFy5ckpPT9f27du1YMECNW/e/JTX3r9/v/74xz9q4sSJ+vjjj9W9e/cCX/tU1m6P6JWZPrWq59Kgrm7Z7TaZpqV/L/Br2aaI7rvOrXb1iXUAAAAAAJRkjLBDiXby1NfjO7IePnxY1157rfr06aP/+7//k8/nU82aNTVhwgS1bdtWvXr10uzZs7VgwQKFQiFlZWWpUaNGhY6sO65SpUoaPny4bDabevTooRUrVqhVq1a5x08X69b/GNWrs3xqVjtF9153Ita9O9+v5d9HNKirW22vINYBAAAAAFDSMcIOpYppmpo7d65GjRqlGTNmSJKuueYapaen65NPPpHX61VOTk6+nV1PFev+53/+R5s3b9aHH34oSfruu+80bdq03Hh3Jr7fE9WYbENXVEvRI708SnEkYt07n/v11Q8RDerm1lWXE+sAAAAAACgN2CUWJd7dd9+twYMHS5Lsdrs2b96sHTt2SJJ69Ogh0zT10Ucfyev1avHixXr22WeVk5OT5xqFxbq1a9eqWbNm+vjjjzVkyBBJUuPGjfX000/LZrMpHo+f9v62/hTT2GmG6lZ26uGeiVgXNy29NS8R6+7vTqwDAAAAAKA0IdihRItEImrTpo3Gjx+vxx57TJI0YMAA1ahRQy1atNDBgwc1c+ZMlStXTpK0evVqbd26VX6//7TX/uabb9SsWTM1btxYlSpV0ssvv6y//vWvec453TTYnQdjGpNtqEYFpx7r7ZXL+XOsm+vXqi0RPXC9R20uI9YBAAAAAFCasIYdSjSXy6VBgwapXLlyeuCBBxQIBPT222+rbt26Wrp0qf72t7/J4/EoFApp8uTJ+uMf/6j3339fVatWLfSalmVp/PjxuvbaayVJKSkp6tOnjx588EFdddVVZ3xvew7HNWqKoYqZdj3ex6O0FJticUtvzvXrm20RPdDdo5b1XOf8zwAAAAAAABQvrGGHEuPkDSYMw5DX68095vP5NGPGDN199926++679eqrr+rmm2/Wt99+q0gkonr16mndunV64YUXdP/995/yNYLBoKpXr667775bt956q4YNG6Zq1appwoQJkk693t1xPx2N65+TcuRNt2tYf6/caXbF4pbemOPTmu1RPXi9Ry0uJdYBAAAAAFAaEexQ4vzlL3/R0qVLdf3116tt27Zq1qyZ3G63JOmzzz7Tfffdp/79++vdd9/VwoULNXv2bDVo0ECXXnqpOnToUOh1TdPU4sWLdfDgQfn9fg0bNkxly5ZV7dq1NWfOHEl5o2FhDhlx/eMzQy6nNHxApjIzErHu9dk+rd0R1UM9PLqyLrEOAAAAAIDSimCHEmXDhg266qqr5Pf7lZaWphYtWmjLli26+eab1bp1a3Xq1Enr16/XQw89pM6dO+uDDz4442sbhqE777xTgUBAQ4YMUfv27bVv3z41bdpU0pmNrDviM/XPSYkNLYYPyFQ5TyLW/Wu2T9/uiOqhnh41r0OsAwAAAACgNGMNO5QoDRs21Lvvvqu33npLmZmZuu+++7Rz504tWrRIQ4YM0eWXXy6/36+WLVvqww8/1MGDBzVr1qxTjow7fszr9eqZZ57RDTfcoCNHjqhixYqqWLFi7jmni3U5AVMjp+QobkrDB3hVzmNXNG7pX7N8+m5nVI/08qhpbWIdAAAAAAClHcEOJc6NN94ou92uMWPG6P3339fYsWN1//33a+/evdq0aZMmTZqkzZs3S5KaNWsmSaedxnpcuXLl1KBBA9WoUSPP46d7vj9kauQUQ8GIpeEDMnVJpkPRuKVXZ/q0YVdUj/b2qEktYh0AAAAAAGBKLEqwWbNm6YUXXlD16tX1pz/9SY0aNco9FovFdPjwYVWqVKnQ55886s6yLO3du1f9+/dXvXr19NFHH53xfQTCiVh3yDA1fECmqpV3KBqz9MpMnzbtjurRXl41rpVy9m8UAAAAAACUKAQ7lGjz58/X888/rwoVKujPf/6zGjduLCnvenMFTYeNxWLavn275syZozZ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      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el4274029481340590889469520618\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el4274029481340590889469520618\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.5818523907940966, 0.6095693927517838], \"ylim\": [-1.205397451694321, 1.1615636583069018], \"xdomain\": [-0.5818523907940966, 0.6095693927517838], \"ydomain\": [-1.205397451694321, 1.1615636583069018], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 8, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el427402948137351792\", \"lines\": [], 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.10.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el4274029481340590889469520618\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.5818523907940966, 0.6095693927517838], \"ylim\": [-1.205397451694321, 1.1615636583069018], \"xdomain\": [-0.5818523907940966, 0.6095693927517838], \"ydomain\": [-1.205397451694321, 1.1615636583069018], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 8, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el427402948137351792\", \"lines\": [], 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.10.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el4274029481340590889469520618\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.5818523907940966, 0.6095693927517838], \"ylim\": [-1.205397451694321, 1.1615636583069018], \"xdomain\": [-0.5818523907940966, 0.6095693927517838], \"ydomain\": [-1.205397451694321, 1.1615636583069018], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 8, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el427402948137351792\", \"lines\": [], 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       "            })\n",
       "         });\n",
       "}\n",
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      ],
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_network(network, plot_format = 'kamada_kawai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sustained-competition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### infer_network\n",
       "\n",
       ">      infer_network (network, network_species, species_list, network_dic)\n",
       "\n",
       "*Replaces virtual nodes if they are observed in other species\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network that should be inferred\n",
       "| network_species (string): species from which the network stems\n",
       "| species_list (list): list of species to compare network to\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network)\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns network with filled in virtual nodes*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### infer_network\n",
       "\n",
       ">      infer_network (network, network_species, species_list, network_dic)\n",
       "\n",
       "*Replaces virtual nodes if they are observed in other species\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network that should be inferred\n",
       "| network_species (string): species from which the network stems\n",
       "| species_list (list): list of species to compare network to\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network)\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns network with filled in virtual nodes*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(infer_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "piano-madness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### retrieve_inferred_nodes\n",
       "\n",
       ">      retrieve_inferred_nodes (network, species=None)\n",
       "\n",
       "*Returns the inferred virtual nodes of a network that has been used with infer_network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network with inferred virtual nodes\n",
       "| species (string): species from which the network stems (only relevant if multiple species in network); default:None\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns inferred nodes as list or dictionary (if species argument is used)*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### retrieve_inferred_nodes\n",
       "\n",
       ">      retrieve_inferred_nodes (network, species=None)\n",
       "\n",
       "*Returns the inferred virtual nodes of a network that has been used with infer_network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network with inferred virtual nodes\n",
       "| species (string): species from which the network stems (only relevant if multiple species in network); default:None\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns inferred nodes as list or dictionary (if species argument is used)*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(retrieve_inferred_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "modular-absolute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### update_network\n",
       "\n",
       ">      update_network (network_in, edge_list, edge_labels=None,\n",
       ">                      node_labels=None)\n",
       "\n",
       "*Updates a network with new edges and their labels\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): network that should be modified\n",
       "| edge_list (list): list of edges as node tuples\n",
       "| edge_labels (list): list of edge labels as strings\n",
       "| node_labels (dict): dictionary of form node:0 or 1 depending on whether the node is observed or virtual\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns network with added edges*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### update_network\n",
       "\n",
       ">      update_network (network_in, edge_list, edge_labels=None,\n",
       ">                      node_labels=None)\n",
       "\n",
       "*Updates a network with new edges and their labels\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): network that should be modified\n",
       "| edge_list (list): list of edges as node tuples\n",
       "| edge_labels (list): list of edge labels as strings\n",
       "| node_labels (dict): dictionary of form node:0 or 1 depending on whether the node is observed or virtual\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns network with added edges*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(update_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minute-tiffany",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### trace_diamonds\n",
       "\n",
       ">      trace_diamonds (network, species_list, network_dic, threshold=0.0,\n",
       ">                      nb_intermediates=2, mode='presence')\n",
       "\n",
       "*Extracts diamond-shape motifs from biosynthetic networks (A->B,A->C,B->D,C->D) and uses evolutionary information to determine which path is taken from A to D\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| species_list (list): list of species to compare network to\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network)\n",
       "| threshold (float): everything below or equal to that threshold will be cut; default:0.\n",
       "| nb_intermediates (int): number of intermediate nodes expected in a network motif to extract; has to be a multiple of 2 (2: diamond, 4: hexagon,...)\n",
       "| mode (string): whether to analyze for \"presence\" or \"abundance\" of intermediates; default:\"presence\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns dataframe of each intermediary glycan and its proportion (0-1) of how often it has been experimentally observed in this path (or average abundance if mode = abundance)*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### trace_diamonds\n",
       "\n",
       ">      trace_diamonds (network, species_list, network_dic, threshold=0.0,\n",
       ">                      nb_intermediates=2, mode='presence')\n",
       "\n",
       "*Extracts diamond-shape motifs from biosynthetic networks (A->B,A->C,B->D,C->D) and uses evolutionary information to determine which path is taken from A to D\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| species_list (list): list of species to compare network to\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network)\n",
       "| threshold (float): everything below or equal to that threshold will be cut; default:0.\n",
       "| nb_intermediates (int): number of intermediate nodes expected in a network motif to extract; has to be a multiple of 2 (2: diamond, 4: hexagon,...)\n",
       "| mode (string): whether to analyze for \"presence\" or \"abundance\" of intermediates; default:\"presence\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns dataframe of each intermediary glycan and its proportion (0-1) of how often it has been experimentally observed in this path (or average abundance if mode = abundance)*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(trace_diamonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "subtle-grace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### evoprune_network\n",
       "\n",
       ">      evoprune_network (network, network_dic=None, species_list=None,\n",
       ">                        node_attr='abundance', threshold=0.01,\n",
       ">                        nb_intermediates=2, mode='presence')\n",
       "\n",
       "*Given a biosynthetic network, this function uses evolutionary relationships to prune impossible paths\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network); default:pre-computed milk networks\n",
       "| species_list (list): list of species to compare network to; default:species from pre-computed milk networks\n",
       "| node_attr (string): which (numerical) node attribute to use for pruning; default:'abundance'\n",
       "| threshold (float): everything below or equal to that threshold will be cut; default:0.01\n",
       "| nb_intermediates (int): number of intermediate nodes expected in a network motif to extract; has to be a multiple of 2 (2: diamond, 4: hexagon,...)\n",
       "| mode (string): whether to analyze for \"presence\" or \"abundance\" of intermediates; default:\"presence\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns pruned network (with virtual node probability as a new node attribute)*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### evoprune_network\n",
       "\n",
       ">      evoprune_network (network, network_dic=None, species_list=None,\n",
       ">                        node_attr='abundance', threshold=0.01,\n",
       ">                        nb_intermediates=2, mode='presence')\n",
       "\n",
       "*Given a biosynthetic network, this function uses evolutionary relationships to prune impossible paths\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network); default:pre-computed milk networks\n",
       "| species_list (list): list of species to compare network to; default:species from pre-computed milk networks\n",
       "| node_attr (string): which (numerical) node attribute to use for pruning; default:'abundance'\n",
       "| threshold (float): everything below or equal to that threshold will be cut; default:0.01\n",
       "| nb_intermediates (int): number of intermediate nodes expected in a network motif to extract; has to be a multiple of 2 (2: diamond, 4: hexagon,...)\n",
       "| mode (string): whether to analyze for \"presence\" or \"abundance\" of intermediates; default:\"presence\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns pruned network (with virtual node probability as a new node attribute)*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(evoprune_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "under-meditation",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el4274029481367475202174014851\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el4274029481367475202174014851\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.5818523907940966, 0.6095693927517838], \"ylim\": [-1.205397451694321, 1.1615636583069018], \"xdomain\": [-0.5818523907940966, 0.6095693927517838], \"ydomain\": [-1.205397451694321, 1.1615636583069018], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 8, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el427402948139489936\", \"lines\": [], 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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.10.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el4274029481367475202174014851\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.5818523907940966, 0.6095693927517838], \"ylim\": [-1.205397451694321, 1.1615636583069018], \"xdomain\": [-0.5818523907940966, 0.6095693927517838], \"ydomain\": [-1.205397451694321, 1.1615636583069018], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 8, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el427402948139489936\", \"lines\": [], 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\"#6495ED\", \"edgecolor\": \"#6495ED\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data11\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"S\", \"M\", \"L\", \"L\", \"Z\"], \"id\": \"el427402948138437424\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#6495ED\", \"edgecolor\": \"#6495ED\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data12\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"S\", \"M\", \"L\", \"L\", \"Z\"], \"id\": \"el427402948139488496\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#6495ED\", \"edgecolor\": \"#6495ED\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data13\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"S\", \"M\", \"L\", \"L\", \"Z\"], \"id\": \"el427402948139770384\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#6495ED\", \"edgecolor\": \"#6495ED\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data14\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"S\", \"M\", \"L\", \"L\", \"Z\"], \"id\": \"el427402948138211072\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#6495ED\", \"edgecolor\": \"#6495ED\", \"edgewidth\": 1.0, \"zorder\": 1}, {\"data\": \"data15\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"pathcodes\": [\"M\", \"S\", \"M\", \"L\", \"L\", \"Z\"], \"id\": \"el427402948136639360\", \"dasharray\": \"none\", \"alpha\": 1, \"facecolor\": \"#6495ED\", \"edgecolor\": \"#6495ED\", \"edgewidth\": 1.0, \"zorder\": 1}], \"markers\": [], \"texts\": [{\"text\": \"Neu5Ac(a2-3)\", \"position\": [0.02577097067717493, -0.02818868541928987], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -47.06814923883934, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1, \"id\": \"el427402948136759328\"}, {\"text\": \"GlcNAc(b1-6)\", \"position\": 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\"middle\", \"v_baseline\": \"auto\", \"rotation\": -41.8086630299654, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1, \"id\": \"el427402948139877056\"}, {\"text\": \"GlcNAc(b1-3)\", \"position\": [0.41352811421812385, 0.757468761703425], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -47.00329329337029, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1, \"id\": \"el427402948139879072\"}, {\"text\": \"Fuc(a1-2)\", \"position\": [0.29319566246948947, 0.9049799427123237], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -6.853468894573609, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1, \"id\": \"el427402948139880464\"}, {\"text\": \"Gal(b1-3)\", \"position\": [-0.37971314414464447, -0.8044991582299533], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -44.71429931500981, \"fontsize\": 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.10.js\", function(){\n",
       "                 \n",
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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_network(evoprune_network(network), plot_format = 'kamada_kawai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "magnetic-feature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### highlight_network\n",
       "\n",
       ">      highlight_network (network, highlight, motif=None, abundance_df=None,\n",
       ">                         glycan_col='glycan', intensity_col='rel_intensity',\n",
       ">                         conservation_df=None, network_dic=None, species=None)\n",
       "\n",
       "*Highlights a certain attribute in the network that will be visible when using plot_network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| highlight (string): which attribute to highlight (choices are 'motif' for glycan motifs, 'abundance' for glycan abundances, 'conservation' for glycan conservation, 'species' for highlighting 1 species in multi-network)\n",
       "| motif (string): highlight=motif; which motif to highlight (absence/presence, in violet/green); default:None\n",
       "| abundance_df (dataframe): highlight=abundance; dataframe containing glycans and their relative intensity\n",
       "| glycan_col (string): highlight=abundance; column name of the glycans in abundance_df\n",
       "| intensity_col (string): highlight=abundance; column name of the relative intensities in abundance_df\n",
       "| conservation_df (dataframe): highlight=conservation; dataframe containing glycans from different species\n",
       "| network_dic (dict): highlight=conservation/species; dictionary of form species name : biosynthetic network (gained from construct_network); default:pre-computed milk networks\n",
       "| species (string): highlight=species; which species to highlight in a multi-species network\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a network with the additional 'origin' (motif/species) or 'abundance' (abundance/conservation) node attribute storing the highlight*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### highlight_network\n",
       "\n",
       ">      highlight_network (network, highlight, motif=None, abundance_df=None,\n",
       ">                         glycan_col='glycan', intensity_col='rel_intensity',\n",
       ">                         conservation_df=None, network_dic=None, species=None)\n",
       "\n",
       "*Highlights a certain attribute in the network that will be visible when using plot_network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| highlight (string): which attribute to highlight (choices are 'motif' for glycan motifs, 'abundance' for glycan abundances, 'conservation' for glycan conservation, 'species' for highlighting 1 species in multi-network)\n",
       "| motif (string): highlight=motif; which motif to highlight (absence/presence, in violet/green); default:None\n",
       "| abundance_df (dataframe): highlight=abundance; dataframe containing glycans and their relative intensity\n",
       "| glycan_col (string): highlight=abundance; column name of the glycans in abundance_df\n",
       "| intensity_col (string): highlight=abundance; column name of the relative intensities in abundance_df\n",
       "| conservation_df (dataframe): highlight=conservation; dataframe containing glycans from different species\n",
       "| network_dic (dict): highlight=conservation/species; dictionary of form species name : biosynthetic network (gained from construct_network); default:pre-computed milk networks\n",
       "| species (string): highlight=species; which species to highlight in a multi-species network\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a network with the additional 'origin' (motif/species) or 'abundance' (abundance/conservation) node attribute storing the highlight*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(highlight_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moral-planet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### export_network\n",
       "\n",
       ">      export_network (network, filepath, other_node_attributes=None)\n",
       "\n",
       "*Converts NetworkX network into files usable, e.g., by Cytoscape or Gephi\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| filepath (string): should describe a valid path + file name prefix, will be appended by file description and type\n",
       "| other_node_attributes (list): string names of node attributes that should also be extracted; default:[]\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| (1) saves a .csv dataframe containing the edge list and edge labels\n",
       "| (2) saves a .csv dataframe containing node IDs and labels*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### export_network\n",
       "\n",
       ">      export_network (network, filepath, other_node_attributes=None)\n",
       "\n",
       "*Converts NetworkX network into files usable, e.g., by Cytoscape or Gephi\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| filepath (string): should describe a valid path + file name prefix, will be appended by file description and type\n",
       "| other_node_attributes (list): string names of node attributes that should also be extracted; default:[]\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| (1) saves a .csv dataframe containing the edge list and edge labels\n",
       "| (2) saves a .csv dataframe containing node IDs and labels*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(export_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bded7c37-ff97-497f-b9df-6b19412d36c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_maximum_flow\n",
       "\n",
       ">      get_maximum_flow (network, source='Gal(b1-4)Glc-ol', sinks=None)\n",
       "\n",
       "*Estimate maximum flow and flow paths between source and sinks\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| source (string): usually the root node of network; default:\"Gal(b1-4)Glc-ol\"\n",
       "| sinks (list of strings): specified sinks to estimate flow for; default:all terminal nodes\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a dictionary of type sink : {maximum flow value, flow path dictionary}*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_maximum_flow\n",
       "\n",
       ">      get_maximum_flow (network, source='Gal(b1-4)Glc-ol', sinks=None)\n",
       "\n",
       "*Estimate maximum flow and flow paths between source and sinks\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| source (string): usually the root node of network; default:\"Gal(b1-4)Glc-ol\"\n",
       "| sinks (list of strings): specified sinks to estimate flow for; default:all terminal nodes\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a dictionary of type sink : {maximum flow value, flow path dictionary}*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_maximum_flow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d66873-a764-4572-b63a-43084fbd11aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_max_flow_path\n",
       "\n",
       ">      get_max_flow_path (network, flow_dict, sink, source='Gal(b1-4)Glc-ol')\n",
       "\n",
       "*Get the actual path between source and sink that gave rise to the maximum flow value\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| flow_dict (dict): dictionary of type source : {sink : flow} as returned by get_maximum_flow\n",
       "| sink (string): specified sink to retrieve maximum flow path\n",
       "| source (string): usually the root node of network; default:\"Gal(b1-4)Glc-ol\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a list of (source, sink) tuples describing the maximum flow path*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_max_flow_path\n",
       "\n",
       ">      get_max_flow_path (network, flow_dict, sink, source='Gal(b1-4)Glc-ol')\n",
       "\n",
       "*Get the actual path between source and sink that gave rise to the maximum flow value\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| flow_dict (dict): dictionary of type source : {sink : flow} as returned by get_maximum_flow\n",
       "| sink (string): specified sink to retrieve maximum flow path\n",
       "| source (string): usually the root node of network; default:\"Gal(b1-4)Glc-ol\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a list of (source, sink) tuples describing the maximum flow path*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_max_flow_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d3acc42-cfc2-4473-a51e-5dabbf6cb907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_reaction_flow\n",
       "\n",
       ">      get_reaction_flow (network, res, aggregate=None)\n",
       "\n",
       "*Get the aggregated flows for a type of reaction across entire network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| res (dict): dictionary of type sink : {maximum flow value, flow path dictionary} as returned by get_maximum_flow\n",
       "| aggregate (string): if reaction flow values should be aggregated, options are \"sum\" and \"mean\"; default:None\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a dictionary of form reaction : flow(s)*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_reaction_flow\n",
       "\n",
       ">      get_reaction_flow (network, res, aggregate=None)\n",
       "\n",
       "*Get the aggregated flows for a type of reaction across entire network\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx object): biosynthetic network, returned from construct_network\n",
       "| res (dict): dictionary of type sink : {maximum flow value, flow path dictionary} as returned by get_maximum_flow\n",
       "| aggregate (string): if reaction flow values should be aggregated, options are \"sum\" and \"mean\"; default:None\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a dictionary of form reaction : flow(s)*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_reaction_flow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69572140-718f-4bb1-9814-f6afc6412bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_differential_biosynthesis\n",
       "\n",
       ">      get_differential_biosynthesis (df, group1, group2=None,\n",
       ">                                     analysis='reaction', paired=False,\n",
       ">                                     longitudinal=False, id_column='ID')\n",
       "\n",
       "*Compares biosynthetic patterns between glycomes of two conditions or across multiple time points\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| df (dataframe): dataframe containing glycan sequences and relative abundances [or filepath to .csv]\n",
       "| group1 (list): list of column indices/names for first group of samples (or time points in longitudinal analysis)\n",
       "| group2 (list): list of column indices/names for second group of samples (ignored in longitudinal analysis)\n",
       "| analysis (string): type of analysis to perform on networks, \"reaction\" or \"flow\"; default: \"reaction\"\n",
       "| paired (bool): whether samples are paired or not; default: False\n",
       "| longitudinal (bool): whether to perform longitudinal analysis; default: False\n",
       "| id_column (str): name of the column containing sample IDs for longitudinal analysis in the ID-style of participant_time_replicate; default: \"ID\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| For binary comparison: A dataframe with differential flow features and statistics\n",
       "| For longitudinal analysis: A dataframe with reaction changes over time*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_differential_biosynthesis\n",
       "\n",
       ">      get_differential_biosynthesis (df, group1, group2=None,\n",
       ">                                     analysis='reaction', paired=False,\n",
       ">                                     longitudinal=False, id_column='ID')\n",
       "\n",
       "*Compares biosynthetic patterns between glycomes of two conditions or across multiple time points\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| df (dataframe): dataframe containing glycan sequences and relative abundances [or filepath to .csv]\n",
       "| group1 (list): list of column indices/names for first group of samples (or time points in longitudinal analysis)\n",
       "| group2 (list): list of column indices/names for second group of samples (ignored in longitudinal analysis)\n",
       "| analysis (string): type of analysis to perform on networks, \"reaction\" or \"flow\"; default: \"reaction\"\n",
       "| paired (bool): whether samples are paired or not; default: False\n",
       "| longitudinal (bool): whether to perform longitudinal analysis; default: False\n",
       "| id_column (str): name of the column containing sample IDs for longitudinal analysis in the ID-style of participant_time_replicate; default: \"ID\"\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| For binary comparison: A dataframe with differential flow features and statistics\n",
       "| For longitudinal analysis: A dataframe with reaction changes over time*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_differential_biosynthesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69ed83c6-422e-4334-953d-cd5e2e52da73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You're working with an alpha of 0.044390023979542614 that has been adjusted for your sample size of 40.\n"
     ]
    },
    {
     "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>Mean abundance</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>p-val</th>\n",
       "      <th>corr p-val</th>\n",
       "      <th>significant</th>\n",
       "      <th>Effect size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gal(b1-3)</th>\n",
       "      <td>9.367467</td>\n",
       "      <td>-0.452220</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.935558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-?)</th>\n",
       "      <td>4.814383</td>\n",
       "      <td>-0.406728</td>\n",
       "      <td>0.000866</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.882400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gal(b1-?)</th>\n",
       "      <td>7.090566</td>\n",
       "      <td>-0.448621</td>\n",
       "      <td>0.000972</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.871148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fuc(a1-2)</th>\n",
       "      <td>2.173742</td>\n",
       "      <td>-0.665850</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>0.006869</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.778493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-6)</th>\n",
       "      <td>4.553800</td>\n",
       "      <td>-0.475070</td>\n",
       "      <td>0.003775</td>\n",
       "      <td>0.007150</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.737642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gal(b1-4)</th>\n",
       "      <td>4.813666</td>\n",
       "      <td>-0.422501</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.007150</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.719071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GlcNAc(b1-6)</th>\n",
       "      <td>4.813666</td>\n",
       "      <td>-0.422501</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>0.007150</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.719071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-3)</th>\n",
       "      <td>7.584599</td>\n",
       "      <td>-0.346187</td>\n",
       "      <td>0.005372</td>\n",
       "      <td>0.007387</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.702486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-8)</th>\n",
       "      <td>2.304750</td>\n",
       "      <td>-0.422735</td>\n",
       "      <td>0.008033</td>\n",
       "      <td>0.009818</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.662001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <td>2.249050</td>\n",
       "      <td>-0.521844</td>\n",
       "      <td>0.019236</td>\n",
       "      <td>0.021160</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.571950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6S</th>\n",
       "      <td>2.639924</td>\n",
       "      <td>-0.273368</td>\n",
       "      <td>0.031858</td>\n",
       "      <td>0.031858</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.517978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean abundance    Log2FC     p-val  corr p-val  significant  \\\n",
       "Feature                                                                     \n",
       "Gal(b1-3)           9.367467 -0.452220  0.000503    0.003564         True   \n",
       "Neu5Ac(a2-?)        4.814383 -0.406728  0.000866    0.003564         True   \n",
       "Gal(b1-?)           7.090566 -0.448621  0.000972    0.003564         True   \n",
       "Fuc(a1-2)           2.173742 -0.665850  0.002498    0.006869         True   \n",
       "Neu5Ac(a2-6)        4.553800 -0.475070  0.003775    0.007150         True   \n",
       "Gal(b1-4)           4.813666 -0.422501  0.004550    0.007150         True   \n",
       "GlcNAc(b1-6)        4.813666 -0.422501  0.004550    0.007150         True   \n",
       "Neu5Ac(a2-3)        7.584599 -0.346187  0.005372    0.007387         True   \n",
       "Neu5Ac(a2-8)        2.304750 -0.422735  0.008033    0.009818         True   \n",
       "OS                  2.249050 -0.521844  0.019236    0.021160         True   \n",
       "6S                  2.639924 -0.273368  0.031858    0.031858         True   \n",
       "\n",
       "              Effect size  \n",
       "Feature                    \n",
       "Gal(b1-3)       -0.935558  \n",
       "Neu5Ac(a2-?)    -0.882400  \n",
       "Gal(b1-?)       -0.871148  \n",
       "Fuc(a1-2)       -0.778493  \n",
       "Neu5Ac(a2-6)    -0.737642  \n",
       "Gal(b1-4)       -0.719071  \n",
       "GlcNAc(b1-6)    -0.719071  \n",
       "Neu5Ac(a2-3)    -0.702486  \n",
       "Neu5Ac(a2-8)    -0.662001  \n",
       "OS              -0.571950  \n",
       "6S              -0.517978  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_differential_biosynthesis(human_skin_O_PMC5871710_BCC, [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39],\n",
    "                              [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40], paired = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a6eb5b-3b53-446a-8c34-5b81d7910876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### extend_network\n",
       "\n",
       ">      extend_network (network, steps=1, to_extend='all', strict_context=False)\n",
       "\n",
       "*Given a biosynthetic network, tries to extend it in a physiological manner\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx): glycan biosynthetic network as returned by construct_network\n",
       "| steps (int): how many biosynthetic steps to extend the network\n",
       "| to_extend (string/dict/list): which leaves to extend (default is \"all\"), a glycan as a string indicates a specific leaf node to extend, a dict indicates a target composition to be reached from the best leaf\n",
       "| strict_context (bool): whether to infer permitted sequence contexts for extension from database (False) or only from network (True); default:False\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns updated network and a list of added glycans*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### extend_network\n",
       "\n",
       ">      extend_network (network, steps=1, to_extend='all', strict_context=False)\n",
       "\n",
       "*Given a biosynthetic network, tries to extend it in a physiological manner\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network (networkx): glycan biosynthetic network as returned by construct_network\n",
       "| steps (int): how many biosynthetic steps to extend the network\n",
       "| to_extend (string/dict/list): which leaves to extend (default is \"all\"), a glycan as a string indicates a specific leaf node to extend, a dict indicates a target composition to be reached from the best leaf\n",
       "| strict_context (bool): whether to infer permitted sequence contexts for extension from database (False) or only from network (True); default:False\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns updated network and a list of added glycans*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(extend_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a8e8226-58b5-4134-9638-8308bd8c770d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_network, new_glycans = extend_network(network, strict_context = True)\n",
    "len(new_glycans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distant-graphics",
   "metadata": {},
   "source": [
    "## evolution\n",
    ">investigating evolutionary relationships of glycans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "conceptual-checklist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### distance_from_embeddings\n",
       "\n",
       ">      distance_from_embeddings (df, embeddings, cut_off=10, rank='Species',\n",
       ">                                averaging='median')\n",
       "\n",
       "*calculates a cosine distance matrix from learned embeddings\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| df (dataframe): dataframe with glycans as rows and taxonomic information as columns\n",
       "| embeddings (dataframe): dataframe with glycans as rows and learned embeddings as columns (e.g., from glycans_to_emb)\n",
       "| cut_off (int): how many glycans a rank (e.g., species) needs to have at least to be included; default:10\n",
       "| rank (string): which taxonomic rank to use for grouping organisms; default:'Species'\n",
       "| averaging (string): how to average embeddings, by 'median' or 'mean'; default:'median'\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a rank x rank distance matrix*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### distance_from_embeddings\n",
       "\n",
       ">      distance_from_embeddings (df, embeddings, cut_off=10, rank='Species',\n",
       ">                                averaging='median')\n",
       "\n",
       "*calculates a cosine distance matrix from learned embeddings\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| df (dataframe): dataframe with glycans as rows and taxonomic information as columns\n",
       "| embeddings (dataframe): dataframe with glycans as rows and learned embeddings as columns (e.g., from glycans_to_emb)\n",
       "| cut_off (int): how many glycans a rank (e.g., species) needs to have at least to be included; default:10\n",
       "| rank (string): which taxonomic rank to use for grouping organisms; default:'Species'\n",
       "| averaging (string): how to average embeddings, by 'median' or 'mean'; default:'median'\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a rank x rank distance matrix*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(distance_from_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numerous-newport",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### distance_from_metric\n",
       "\n",
       ">      distance_from_metric (df, networks, metric='Jaccard', cut_off=10,\n",
       ">                            rank='Species')\n",
       "\n",
       "*calculates a distance matrix of generated networks based on provided metric\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| df (dataframe): dataframe with glycans as rows and taxonomic information as columns\n",
       "| networks (list): list of networks in networkx format\n",
       "| metric (string): which metric to use, available: 'Jaccard'; default:'Jaccard'\n",
       "| cut_off (int): how many glycans a rank (e.g., species) needs to have at least to be included; default:10\n",
       "| rank (string): which taxonomic rank to use for grouping organisms; default:'Species'\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a rank x rank distance matrix*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### distance_from_metric\n",
       "\n",
       ">      distance_from_metric (df, networks, metric='Jaccard', cut_off=10,\n",
       ">                            rank='Species')\n",
       "\n",
       "*calculates a distance matrix of generated networks based on provided metric\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| df (dataframe): dataframe with glycans as rows and taxonomic information as columns\n",
       "| networks (list): list of networks in networkx format\n",
       "| metric (string): which metric to use, available: 'Jaccard'; default:'Jaccard'\n",
       "| cut_off (int): how many glycans a rank (e.g., species) needs to have at least to be included; default:10\n",
       "| rank (string): which taxonomic rank to use for grouping organisms; default:'Species'\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a rank x rank distance matrix*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(distance_from_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "honest-minnesota",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### dendrogram_from_distance\n",
       "\n",
       ">      dendrogram_from_distance (dm, ylabel='Mammalia', filepath='')\n",
       "\n",
       "*plots a dendrogram from distance matrix\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| dm (dataframe): a rank x rank distance matrix (e.g., from distance_from_embeddings)\n",
       "| ylabel (string): how to label the y-axis of the dendrogram; default:'Mammalia'\n",
       "| filepath (string): absolute path including full filename allows for saving the plot*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### dendrogram_from_distance\n",
       "\n",
       ">      dendrogram_from_distance (dm, ylabel='Mammalia', filepath='')\n",
       "\n",
       "*plots a dendrogram from distance matrix\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| dm (dataframe): a rank x rank distance matrix (e.g., from distance_from_embeddings)\n",
       "| ylabel (string): how to label the y-axis of the dendrogram; default:'Mammalia'\n",
       "| filepath (string): absolute path including full filename allows for saving the plot*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(dendrogram_from_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "exciting-lloyd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### check_conservation\n",
       "\n",
       ">      check_conservation (glycan, df, network_dic=None, rank='Order',\n",
       ">                          threshold=5, motif=False)\n",
       "\n",
       "*estimates evolutionary conservation of glycans and glycan motifs via biosynthetic networks\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| glycan (string): full glycan or glycan motif in IUPAC-condensed nomenclature\n",
       "| df (dataframe): dataframe in the style of df_species, each row one glycan and columns are the taxonomic levels\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network); default:pre-computed milk networks\n",
       "| rank (string): at which taxonomic level to assess conservation; default:Order\n",
       "| threshold (int): threshold of how many glycans a species needs to have to consider the species;default:5\n",
       "| motif (bool): whether glycan is a motif (True) or a full sequence (False); default:False\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a dictionary of taxonomic group : degree of conservation*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### check_conservation\n",
       "\n",
       ">      check_conservation (glycan, df, network_dic=None, rank='Order',\n",
       ">                          threshold=5, motif=False)\n",
       "\n",
       "*estimates evolutionary conservation of glycans and glycan motifs via biosynthetic networks\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| glycan (string): full glycan or glycan motif in IUPAC-condensed nomenclature\n",
       "| df (dataframe): dataframe in the style of df_species, each row one glycan and columns are the taxonomic levels\n",
       "| network_dic (dict): dictionary of form species name : biosynthetic network (gained from construct_network); default:pre-computed milk networks\n",
       "| rank (string): at which taxonomic level to assess conservation; default:Order\n",
       "| threshold (int): threshold of how many glycans a species needs to have to consider the species;default:5\n",
       "| motif (bool): whether glycan is a motif (True) or a full sequence (False); default:False\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a dictionary of taxonomic group : degree of conservation*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(check_conservation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "canadian-consultancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_communities\n",
       "\n",
       ">      get_communities (network_list, label_list=None)\n",
       "\n",
       "*Find communities for each graph in a list of graphs\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network_list (list): list of undirected biosynthetic networks, in the form of networkx objects\n",
       "| label_list (list): labels to create the community names, which are running_number + _ + label[k]  for graph_list[k]; default:range(len(graph_list))\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a merged dictionary of community : glycans in that community*"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_communities\n",
       "\n",
       ">      get_communities (network_list, label_list=None)\n",
       "\n",
       "*Find communities for each graph in a list of graphs\n",
       "\n",
       "| Arguments:\n",
       "| :-\n",
       "| network_list (list): list of undirected biosynthetic networks, in the form of networkx objects\n",
       "| label_list (list): labels to create the community names, which are running_number + _ + label[k]  for graph_list[k]; default:range(len(graph_list))\n",
       "\n",
       "| Returns:\n",
       "| :-\n",
       "| Returns a merged dictionary of community : glycans in that community*"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_communities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sweet-agriculture",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "import nbdev; nbdev.nbdev_export()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tribal-thread",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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