Plant_Dataset_Independent_Test_Set_Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "00039046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1490, 81) (1490,)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from Bio import SeqIO\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "os.chdir(\"/home/t326h379/\")\n",
    "\n",
    "\n",
    "aminoacids='ARNDCQEGHILKMFPSTWYV-'\n",
    "\n",
    "def binary_encode(Sequence_one_hot_encoding): \n",
    "    aa2v={x:y for x,y in zip(aminoacids,np.eye(21,21).tolist())}\n",
    "    aa2v['X']=np.zeros(21)\n",
    "    encoding=[]\n",
    "    for i in range(len(Sequence_one_hot_encoding)):\n",
    "        s=Sequence_one_hot_encoding[i]\n",
    "        encoding.append([aa2v[x] for x in s])\n",
    "    encoding=np.array(encoding)\n",
    "    return encoding\n",
    "\n",
    "aaindex=pd.read_table('aaindex31.txt',sep='\\s+',header=None)\n",
    "aaindex=aaindex.subtract(aaindex.min(axis=1),axis=0).divide((aaindex.max(axis=1)-aaindex.min(axis=1)),axis=0)\n",
    "aa=[x for x in 'ARNDCQEGHILKMFPSTWYV']\n",
    "aaindex=aaindex.to_numpy().T\n",
    "index={x:y for x,y in zip(aa,aaindex.tolist())}\n",
    "index['-']=np.zeros(31).tolist()\n",
    "index['X']=np.zeros(31).tolist()\n",
    "\n",
    "def index_encode(Sequence_one_hot_encoding):\n",
    "    encoding=[]\n",
    "    for i in range(len(Sequence_one_hot_encoding)):\n",
    "        s=Sequence_one_hot_encoding[i]\n",
    "        encoding.append([index[x] for x in (s)])\n",
    "    encoding=np.array(encoding)\n",
    "    return encoding\n",
    "\n",
    "alphabet = 'ARNDCQEGHILKMFPSTWYV-'\n",
    "\n",
    "# Independent Testing\n",
    "indep_test_x_81 = []\n",
    "indep_test_y_81 = []\n",
    "posit_1 = 1;\n",
    "negat_0 = 0;\n",
    "\n",
    "\n",
    "char_to_int = dict((c, i) for i, c in enumerate(alphabet))\n",
    "int_to_char = dict((i, c) for i, c in enumerate(alphabet))\n",
    "\n",
    "def inner1():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "\n",
    "    for char in data:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in data]\n",
    "    indep_test_x_81.append(integer_encoded)\n",
    "    indep_test_y_81.append(posit_1)\n",
    "for seq_record in SeqIO.parse(\"Positive_UbiCom_testing.fasta\", \"fasta\"):\n",
    "    inner1()\n",
    "\n",
    "def inner2():\n",
    "    #Input\n",
    "    data = seq_record.seq\n",
    "\n",
    "    for char in data:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in data]\n",
    "    indep_test_x_81.append(integer_encoded)\n",
    "    indep_test_y_81.append(negat_0)\n",
    "for seq_record in SeqIO.parse(\"Negative_UbiCom_testing.fasta\", \"fasta\"):\n",
    "    inner2()\n",
    "\n",
    "# Changing to array (matrix)    \n",
    "indep_test_x_81 = np.array(indep_test_x_81)\n",
    "indep_test_y_81 = np.array(indep_test_y_81)\n",
    "\n",
    "print(indep_test_x_81.shape,indep_test_y_81.shape)\n",
    "\n",
    "AAindex_Test_AAAAA_81_Sequence = []\n",
    "for i in range(len(indep_test_x_81)):\n",
    "    integer_of_string = str(indep_test_x_81[i])\n",
    "    integer_of_string = integer_of_string.strip(\"[\").strip(\"]\").strip(\" \")\n",
    "    keras_job = integer_of_string.split()\n",
    "    individual_AA_peptide = []\n",
    "    for i in range(len(keras_job)):\n",
    "        index_value = int(keras_job[i])\n",
    "        individual_AA_peptide.append(int_to_char.get(index_value))\n",
    "    individual_AA_peptide = \"\".join(individual_AA_peptide)\n",
    "    AAindex_Test_AAAAA_81_Sequence.append(individual_AA_peptide) \n",
    "\n",
    "yval = indep_test_y_81\n",
    "\n",
    "x_ind_49 = indep_test_x_81[:,[i for i in range(16,65)]]\n",
    "\n",
    "AAindex_indi_AA_Sequence = []\n",
    "for i in range(len(x_ind_49)):\n",
    "    integer_of_string = str(x_ind_49[i])\n",
    "    integer_of_string = integer_of_string.strip(\"[\").strip(\"]\").strip(\" \")\n",
    "    keras_job = integer_of_string.split()\n",
    "    individual_AA_peptide = []\n",
    "    for i in range(len(keras_job)):\n",
    "        index_value = int(keras_job[i])\n",
    "        individual_AA_peptide.append(int_to_char.get(index_value))\n",
    "    individual_AA_peptide = \"\".join(individual_AA_peptide)\n",
    "    AAindex_indi_AA_Sequence.append(individual_AA_peptide) \n",
    "\n",
    "binaryCoding_Independent_Test = binary_encode(AAindex_indi_AA_Sequence)\n",
    "\n",
    "AAindexCoding_Independent_Test = index_encode(AAindex_indi_AA_Sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "450c3a70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1490, 49)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_ind_49.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "138d1753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1490, 49, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binaryCoding_Independent_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8502221f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1490, 49, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AAindexCoding_Independent_Test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781d7bc5",
   "metadata": {},
   "source": [
    "# Load the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a1658cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "model = keras.models.load_model(\"UBICOMB_DeepUBI_AAindex_One_Hot_ProtT5_drop_out_Changed3257.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94a5e43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict([AAindexCoding_Independent_Test,binaryCoding_Independent_Test,x_ind_49])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b3ee973a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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teRBGgTo1x9CQnJx8/fr1YcOGaZbHmjZtmhBi4cKFysNz584pPY07fNQTiqEBANAU1JsANEBJSYnOTAflbwzNxjomJiZ37tzR7lBaWqoTpEErXxicUpfRWcOomShbCwUFBTX0xBdffPH27dvbt2/X3J302GOP1b+miXIPguaNUNbr1bnjQHmzXFxctBtv3Lghy7LysgBA/drAKFCn5hgaXn75Zfk/bdmyRQixePFi5eFjjz12t3NbcvioE0MDAKDpqDcBaICKioq0tDTNw99++y0vL0+tVmv+Serm5qasOqEoKCi4ePGiThArKyvNXyPdu3dfv359M2f9H3r16iWEuHz5cp1Hq6qqHn/88UaELS4uNjEx0Z54cuPGjfj4+ClTpnTr1q1Bod56661Tp07t2bPHwsKizg7x8fHa6+8KIWRZ3r59u/hzxpwQ4plnnvHw8Ni7d68ySUShzLabMGGC9rnK+6W8LABQvzYwCtSpmYYGfbTk8KE/hgYAQNNRbwLQAHZ2dq+99lpqamp5eXl6enpoaKi5uXlcXJymg7+/f15e3po1a8rKyrKzsyMiIjT/6a3Rr1+/rKysS5cupaam5uTkDB48WGlvmZ2J1Gq1s7NzZmZm7UMbNmxQqVQLFiyofSg0NFSSpPPnz9cTWZblGTNmnDt37vbt28ePHw8ICHBxcVm7dm2D4iQmJi5atOjYsWM2NjaSFs2u5Iqff/755ZdfPnfuXEVFxdmzZ6dNm3bixIm5c+f6+PgoHSwsLOLj40tKSqZMmfL777+XlpZu2bJl+fLlPj4+2tvYCSEyMjKEEP7+/vU8NQBQtIFRoE7NNzTooyWHDz0xNAAAmo56E/Bg2b17tyRJe/bsEUJYWlqGhoYePXpUu+WNN94QQkiS9O677woh+vbtO3bsWM3p1tbWq1evXrRokZub25AhQxwcHA4cODB06FBNhyVLloSFhS1btszZ2fn555+PiopydXUtKSmRJCk6Olrps3Llyj59+vTo0WPy5MlxcXE9evRQ2quqqlpgZyJJksLCwo4dO6azCK6od2uk/Px8a2vrTp063S3sQw899P3337dv337w4MEODg4vvPDCM888k5qa6ujo2KA4O3fuvOdTmDZt2o4dO/Lz8wMCAuzt7X18fHJzc7dt27Zq1SrtbgEBAT/++GNFRcWAAQNcXFyWLVsWFRV18OBBnfU4du3a5eHhMWbMmHteF0AbwChQp2YaGjReeuklnfWbAgIClEMtOXwIIVJSUpQ6VG5ubnV1tfJzfHy8TjeGBgCAAchtWlJSUpt/jniQCSGSkpJa7HJqtdrDw6PFLtdQ+v++l5aWenh4vPjii3pGvnbtmqWlZVhYWBOyM2QcA8rIyJAk6fPPP9ezf3BwcHBwcLOm1HR88+PBwSigTc/ffWURpY8//lin3VhDQ2u7llzv0GBqaqrsoFc/vocBALIsc39THb744gvlf3uUzUoeNOPHj5ckacmSJQ3tc+3atXXr1vn5+Tk6OlpaWnbt2jUkJKTOW9OFEF9//XW3bt20d2pvEGtra+17xU1MTBwcHNRq9ezZs0+cONG4mHhw2NnZJScn79y5U2e2Qp1kWQ4PD7e1tV28eHFTLmqoOAaUk5MTFBQUExMzZcoUY+eChqn/K7S6unrlypVeXl5WVlZ2dnZ+fn779u2r3a2ysnLFihXe3t42NjbOzs6jR49OTk6W/7yJY926ddJdjB49Wv9Udb6uY2NjG/F8m0lrzg0tzyhDgz5aePhgaAAAGAr1pjpMmTJFluURI0YYOxEj2Lx5s7KicCP6REVFzZ07NzAw8PTp0yUlJQkJCRkZGd7e3rt379bulp2dPX78+JiYmMLCwkbnWVZW9ssvvwghAgMDZVmurKw8c+bM22+/febMmf79+8+YMePf//53o4PjQdC3b9/09PRvvvnmxo0b9fcsLCzMycnZv3+/q6trU65oqDgG9MknnyxdunTp0qXGTsQ4ysrKunbtqj1R6L5wz6/Q6urqCRMmLFiwICws7NKlSxkZGZ6env7+/l988YV2t/Lycj8/v8TExBUrVhQVFaWnp1tbW48fP/7UqVP3zGHgwIH6J6zzdT1//nz9z21urTk3NLd//OMfkiRZW1trN7b80KCPFh4+6hwaoqOjlbJsdXV1C+QAAGgbqDe1NdbW1so+6I2Ql5cXGRmpLC7QuD4zZ86MiIhwdXW1srIaPHjwtm3bqqurdZbYXLhw4cCBA0+cOGFjY9O4PGszNTV1cXEJDAw8cODAggULEhMTp06dKhtjAYimaMp719xiY2MlScrMzMzNzZUkSVnd437n6emZkpJia2tbfzdXV9fDhw8/8cQTTbycoeIY0Lvvvvsg//e1LMs1NTU1NTXGSqBxv/L3/ArdunVrSkrKSy+9NGfOHCcnp86dO2/cuLF79+6zZ8/W3pY+Kirq119//e6774YMGWJpadmpU6fExESdPa2UKoy2rKwsCwuLWbNmNTRto2vNX7D3hbY0CoSGhmo+0mVlZTpHW3ho0EcLDx91Dg3vvPOO5kUzyoLuAID7EfUm/J9Zs2YFBwfXvxdJPX3i4+M/+eQT7Ra1Wm1paZmdna1d+tm4cWN0dHSjZ9Ld0zvvvOPj4/Pll1/q/Gc+mmL+/Pnaf3PWP90SuC/Y2NhkZ2d//fXXxk6kYe75Fbpr1y4hxLhx4zQtkiQFBgZeu3ZNs5xwYWHh+vXrQ0JCXFxcNN1UKlVFRYVm+/PHHntMs2uYxurVqydMmNB67tFDi2EUAAAADUW9Cf8rISHh1KlT9a9eoU8fbeXl5bdu3erVq5ckSZpGnb2xDE6SpDlz5gghPvroo2a9EAC0vHt+hSrz7HR2oHdzcxNCHD58WHn45ZdfVldX13+/z8iRI+fNm6fdcvPmzU2bNs2ePbsRaQMAAOBBQ73pf505c2bChAl2dnYqlWrw4MGaf5QrlM2DFWfPnp00aZKTk5PysLi4WAhRUlLy6quvdunSxdzc3MHBYfTo0QcPHlTOVW5BlySpQ4cOaWlpI0aMsLGxsbKyGj58+JEjR7SvUk+QJUuWKEE0fx7s3btXaXnooYe0L1ReXn7kyBHlkP73EF2+fHnevHkJCQn1zHHTp4+OHTt2CCFef/11PfsbivIqHT16tLKyss2/dwAaQfuboaKiQqflwoULkydPtre3d3JyGjt2bHZ2tnKWPt8JRv+VV66is7rTlStXhBAXLlxQHv78889CCAcHh3nz5nXs2NHc3PyRRx4JDw+/evVqPZE//fTTTp06DRkypOlJ3l+vdlVVVVJS0l/+8hdXV1dLS8vevXvHxcUpMzFLS0slLcpdP1VVVZqWiRMnKkGuXLkSHh7u6elpbm7+8MMPBwUFZWRk1H416hynAAAA7ksG2ueuldJzN9bff//d3t7ew8Pju+++u3nz5q+//urv7+/p6WlhYaHdLTAwUAgxdOjQgwcPlpeXHz161NTU9MqVK/n5+Z07d3ZxcUlOTr5+/frZs2eDgoIkSdqwYYPmXLVarVKpfH19f/rpp7KysrS0tD59+pibmx86dEjpoE8QlUr19NNPa6fk7e3t5OSk3VK7jz5GjRo1e/Zs5Wdln+DFixc3oo+2goICFxeXevbu9fDwMDU1rfPQ8OHDHR0dU1NT64mvvcirjlu3bikf77y8PKWlrb53omV3wm7l2H25BQQHBwcHBxs7i3vQ/5OgfDPcunVLpyUwMFD5ff/+++8tLS0HDBigfdY9vxPk5vy61rjbV+jq1auFEHPnztW5uhCif//+2k/T1dU1JCQkOzv72rVrmzZtUqlU3bp1Ky0trfNyNTU13bp1++ijj3Tam/J13Rpe7XqGEg1li4xly5ZdvXr1ypUrq1atMjEx0Z5fNmrUKBMTk3Pnzmmf5evr+9lnnyk/5+XlPfLIIy4uLl999dXNmzdPnjw5dOjQ9u3b//TTTzqvRu1xqp7EZEaB/8Qo0HrwXgAAZFlu4yOBnqNdcHCwEGLnzp2altzcXAsLizrrTV9//bXO6c8//7wQ4vPPP9e0VFRUuLu7W1paFhQUKC1qtVoI8csvv2j6/Prrr0IItVqtf5Bm+gNm/fr1jz76aFlZmfKwzlqSPn20FRcXe3l5TZ48uaqq6m596qk3DR061MHBQfsf4rXV80eCZnM6nXpT23vv+EtDG/+6bQEPSL0pOTlZ06Lcn6L9Z/89vxNko9abbt265e3tbWZmtmbNmuLi4j/++OPll19WVlwaPHiw0mfUqFFCiM6dO1dWVmpOVO7NWbhwYZ2X++qrr2xsbG7evKnT3pSv69bwautZbxo2bJh2S2hoqJmZ2fXr15WH3377rRBC818ysiwfPnzYw8Pjzp07ysPnnntOCKEpP8mynJ+fb2Fh4e3trfNq1B6n6scooI1RoPXgvQAAyLLMnB0hhNi7d68QQvn3t8Ld3b1bt25ZWVm1Oz/55JM6LcrirGPGjNG0WFhYjBgxYsuWLd9+++306dOVRpVK5eXlpenTu3dvd3f3zMzM/Px8Nzc3PYMY3MWLF6Oiovbs2aNSqZrSR1t5efmoUaN69uy5efNmU1PTRmR16NChRpylkZ+fL4QwMzPTzKdQtLH3TnH06FFJa3msB5myY44yixPN5PLlyx06dDB2Fs1uwIABmp87duwohMjLy9P+Pqn/O6ElU62tffv2Bw8efPvtt2NjY1955RUnJ6egoKAdO3YMHjxYs8638mU+cuRI7Zll48aNe+ONN7799tu33367dthVq1ZNnz5dZ/N40eSva3E/vNpjx44dO3asdotard66deupU6d8fX2FEP7+/r17905MTHz77bednJyEEO+///7cuXPNzMyU/rt37zYxMdEO4urq+sQTT5w4cULnd6r2OHVPjAIajAKtB3vYAfj/7N15XFPH/j/+CVsMIQSkZRFoXZGHVVGxV6lQFbxEa0XlimJBvVoo1w2oW8Fqv7boxboUaUVRkeJat8ejWmm1tWrtA0UFvVCrIopaZZd9qYTt/P6YX8/nNIFwgJBA8nr+ReZM5kzOIfPOmTNnBoAQgv4mIpfLq6ure/XqpfAz2trausX+JoU+F7lcXllZ2atXL4VZjeiiP4WFhWyKhYWFQlHW1tb5+fnFxcW9e/fmWYja0UfAJkyYoJC+fv369evXE0IePnz4448/tpln4MCBNLGxsdHPz8/e3v7AgQMd62zqPDr9lpubG/tbn9Kxc0fFxMTExMR09V56kNmzZ2u7CjqODgjVbVKplP3bxMSEEEIn62GpaBO03t9ECJFIJFu3bt26dSubQgfgjBo1ir7s27cvIYT2jLDoFON0picF2dnZP/300xdffNEVte3+R7uysnL79u3ffvttbm5uRUUFm86OpSWEhIeHv//++7t27Vq/fn12dvalS5e+/vpruonGGvL3T8p6+PAht7+J530dLkQBBYgCAAAA3QTmCydCoVAikdTV1dXU1HDTVU+byn27VCqtq6urrq7mptO5WrmLRpeWljIMw81TXFxMCLG2tuZZiIGBQX19PTcD94cv1d6bnEuXLlUY86bwrNzAgQP55GELDAkJkcvlJ06cYG+bDxw4UJO3uZqbm+Pi4uhHU52zp587Ck9SsDB6XwP0obOJDxVtAn3ZdV/5jqG98L6+vvQlnV2bDgVl0Y9A+8oVfPnll2+//faQIUO6vKIt0frRnjZtWlRUVHBwcHZ2dnNzM8MwtH+HW6uAgAAbG5udO3fK5fLt27cvWLDA0tKSbhIKhRYWFkZGRtynF1kTJ07scMUoRAEWokD3Qc8FAADoOfQ3EULIlClTyF9P1VElJSUPHjzg+faZM2cSQr7//ns2RS6XX7x4USQScZ/Rq6urS0tLY1/euXMnPz/fxcWF3p7lU4idnV1eXh6bobCw8NmzZwqVMTU1ZX92Dx48eO/evTw/hVps2LDh7t27Z86cEQqFmtwvV2Rk5M2bN2fOnMnnwhjnDgA6QHWbQLT6lS8pKTEwMMjPz2dTqqqqEhIS/P39nZycaMo777xjb29//vx5ujYfRWfFnjFjhkKBVVVVBw8ebLMHv+to62gbGRllZWU1NTVdvXrV1tY2NDT01Vdfpf1W7KoULKFQuGTJkuLi4u3btx85ciQsLIy71dfXt7GxUWFh088///y1115rbGxssyYAAAAAPRH6mwgh5L///W/v3r3Dw8MvXLhQU1Nz7969wMBA5VkqWhMdHd2vX7/w8PDk5OTq6urs7Oz33nuvoKAgNjaWe6NYKpWuXbs2NTW1trY2PT09MDDQxMQkNjaWfyHe3t75+fk7d+6sqanJyckJCwtj7+6yRo0alZ2d/fz589TU1MePH3t4eHT68PCVlJT06aef3rhxQyKRcNeHZhe35s/T09PKyor/qKjm5ubi4uIzZ854eXlt2bJl0aJFR44c4XM3G+cOADpAdZtAtP2VZxhm4cKFjx49ksvlN2/enDx5so2NDR34SQmFwoSEhNLSUn9//4cPH1ZUVBw6dCg6OnrMmDGhoaEKpSUmJpqZmdGOdWXtba47QLtH29DQcMKECYWFhVu3bi0pKXn58uXly5fj4+OVcy5ZskQkEq1bt27SpEncYb+EkOjo6AEDBixatOjcuXOVlZVlZWV79uyhc2xxp9ACAAAA0CnaHm/btfiPrH7w4MGMGTPMzc3pYszJycleXl70EL3//vupqamqj1tJSUl4eHi/fv2MjY2lUqlMJrt48SI3g4uLi729/b1792QymUQiEYlE48ePT0lJaVchFRUVQUFBdnZ2IpHI3d09LS2NLnFNCPnoo49onqysLA8PD7FY7OjoGBcX167DFRISovAxZTIZ/zzcCbMVcJfKprfQFezbt4+7Fw8PD9ULHinMcCEQCKRS6bBhwxYvXnzr1i1uTt0+dwRPUnDgSQoN0Jn16eg0/6yAgACFtuLjjz9m/v4M19SpU+l7+bQJXddc82lCL1y44OPjY2trKxKJhg4dGhUV9eeffyoXde3aNZlMJpVKTUxMnJ2dN2zYoJytubl54MCBn3zySWv1aW9zvXXrVkapZdbW0W5zsqT79+8zDPPixYuQkBBHR0djY2MbG5t///vfERERNAN3gTmGYYKDgwkhV65cUT4OpaWlK1as6N+/v7Gx8auvvurt7X3hwgW6qc04pQJBFOBAFOg+cC4AAIBhGAHz9194OubEiRNz5szpDp9xxIgRJSUlubm52q4ItFt3PncCgeD48eOYG5XqPt93HUb/2U6cOKHtiqjS1f8J3blN0D0962h//fXXcXFx6enpGtsjogAXokD3gXMBAAAEz9MBAAAAqEV8fPyKFSu0XQsAAACAbgH9TQCgp/744w8fH5+qqipCSEFBwaZNm0aPHm1ubm5razt+/HiF5634aGpq2rFjx4gRI0xNTaVSqaen588//9zeQsrLy+Pj4z09PXv37i0SiQYNGhQQEJCZmamQzd3dXaAkPDxcIVtDQ0NMTIyrq6tEIrG2tp4yZcrZs2fZG84RERFYQgigkxISEmbOnFlTUxMfH19eXo6hRt3f4cOH2WZTebJOtYcGLh8fH4FAsHHjRoV0TYYP6ocffnBycmpx+rAWQ0NERAR70MaOHdveugEAgH5Cf1OX27Ztm0AgyMzMzMvLEwgE69at03AFlC9KWRs2bNBwZXoWrZ876DoZGRmjR4/29vY2NzcnhAQHB+/YsePTTz8tKCi4fv26g4ODr68vO0ULH01NTTNmzFizZk1QUNDz588zMjL69u3r7e197NixdlVs9erVy5cvnz59+r1790pLSxMTEzMyMlxdXU+fPt2+T0hIbW2tp6dnUlJSTExMcXFxenq6mZmZj4/P3bt3aYbg4ODIyMj169e3t2S91dVtApprrh7UAp8+fdrS0nL37t3Hjh3D/N89xe7duxmGqamp4SaqPTRwHTx4sMX51zQcPnJycnx8fCIjI4uKilosp8XQsHnzZjoTh6GhYbtqBQAAek0700ZpCmYrBN1GNDhTrFgsHjduXHcun//3vbKy0sHBISQkhE2ZOnVqUlIS+1Iul9vb2xsaGhYVFfHce1JSEiFk+fLlbEpzc7Ozs7OlpWV5eTnPQhiGef/99z/44ANuSkZGBiFk0KBB3MRx48alpaWpLmrx4sXm5uaFhYVsSk1NjVAovHPnDrdwOv8Lz+rpzHzhALoBUYCL53f/0KFD5K/+Jq6uCA2svLw8S0vLefPmEUKioqK4mzQcPubOnRsdHd3Q0EA/S4tFqQgNhoaGY8aMabMyaIcBAIBhGIxvAgC9s2XLlsLCwk8++YRNSU5OXrBgAfvSxMRkyJAhTU1NDx484Fkmfchi2rRpbIpAIJg+fXp5efmpU6f41y0hIWHPnj3cFBcXF5FIlJOTw7Rn4tWioqK9e/cGBATY2NiwiWKxuK6ubujQodzCZ82atXLlysbGRv6FAwDonq4IDazg4GA/Pz9vb2/lTRoOH/v374+IiFA9EA+hAQAA1AL9TQCgXxiGSUhIGDNmTJ8+fVS3Zf2lAAAgAElEQVRke/z4MSGkd+/ePIulDyZYW1tzE+3s7AghKSkpHawrIYSQ2traly9fDh06VCAQ8H/Xd99919TU5O7u3mbOmTNn5ubmfv/9952oIwBAz9ZFoYFKTEy8e/futm3bWtyq4fAhEon4vBehAQAAOg/9TQC6r7S0dMWKFQMGDDAxMbG0tJwyZcrly5fppo0bN9LZYdiOifPnz9OUV155habQWVRqa2uvXr1KN9H7ojRdIBA4ODikpaV5eXlJJBJTU9OJEydevXq18+V3kczMzKKiIhcXFxV5Dhw4kJOT4+TkNGTIEJ7F0o+jMB3GixcvCCFPnz7tYF0JIYScPHmSEPLxxx8rpB86dGjEiBFisVgqlXp4eBw9epS79fbt24QQS0vLlStXOjo6mpiYvP7666GhoWVlZQrljBgxghDy448/dqaSANCdIQq0qYtCAyEkNzd35cqViYmJEomkxQyaDx98IDQAAEDnob8JQMcVFha++eabR48ejY2NLSkpuXHjhqmpqZeXV0JCAiFk3bp1DMOIxWI2/+TJkxmGcXV1ZVNWrVrF/H1mDTrAnqa7uLhUVFSEhYVt3LixsLDw119/LSsr8/T0vHLlSifLpzw9Pa2srK5fv66uA/L7778TQhwcHFrceu/evbCwsEWLFllaWtI1jHgWK5PJCCHJycncxPPnzxNCamtrO1zboqKiiIiIoKAg5UWvysvLExMTi4uLb9682a9fv4CAgNDQUHZrQUEBIWTRokVFRUVXrlwpLi6OiopKTEx0c3OrrKzklmNvb0/+OiwAoHsQBfjootBACAkKCnrvvfc8PT1by6D58MEHQgMAAHQe+psAdFxkZOSTJ0927Njx7rvvmpubOzk5HT161M7OLjQ0tLW1adqrtrZ2165dbm5uYrF49OjRhw8frq+vDwsLU0vhzc3N9PJDLaWRvzpipFJpi1uHDx9+8uTJZcuW/f7772+++Sb/YoOCglxdXePj4+Pi4kpLS589e7Zs2bK8vDzC++EFZaWlpZMnT54wYUJ8fLzCppSUlIMHD44aNUosFg8ePPjgwYP/+Mc/vvrqqxs3btAMdXV1dNdJSUn9+/e3sLCYP39+ZGRkdnb29u3buUWZm5sLBAJ6WABA9yAK8NFFoWHfvn0PHz7csmWLijwaDh88ITQAAEDnob8JQMfRiUinTp3KpgiFQi8vr5cvX6prnLxYLKYD76lhw4b16dMnMzNTLb9Tf/nll7KyMjc3t84XRdGOGGNj49YyXLp0KTY2VvUUHsp69ep1+fLlsLCwbdu22dnZ0eV76LMMtra2HahnbW2tTCYbMmTIkSNH+Kw/PWvWLEIIu9g2HU0wadIk7mMpdD5a5fNuZGT08uXLDlQSALo/RAE+uiI0PHv2bPXq1YmJidzhXcq0Hj5ag9AAAACdhP4mAF0ml8srKyt79eqlMG0EXbOssLBQLXuxsLBQSKHznhYXF6ulfPXq1as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bAMCMNTQ0BAYGPvvss1wXwqWCgoLQ0NCYmBhnZ2dCyOLFi7dv375x48aKior8/HxfX9+EhIQ33niD/YRr1qxJTk6Oj4+/du1adXX1wYMHCwoKQkJCTpw40dfaGhsbo6OjMzIy0tLS7t69e+HCBUdHx7i4uKtXrzIDFi9enJqaun79+r7ODADGhLAlBghblhQKRVxcXGpqalVVVbcD2IQ2whbAxCFmCWLWEqHfBABmjKbpzs7Ozs5OrgpwdHSMiori6tEJIXV1dTNmzHjuueeWLVumWrht27bp06cLhUJ/f/+PP/5YLBZv27bt7t277Kd98cUXly9f7uXl5eDgIJPJjh492tHRsXbtWo1hcrmcftz27dvVB6xZs+by5cvff//9U089JRAIBg8enJGRwefzVQMCAgKOHz++ZcuWzMxMnV4AADAGhK2BwpawCNL169c/+eSTFy9edHJy6mmSXkMbYQtg4hCziFmL1M3eYgAA5sLJyUmhUHBdBZfef//9ysrKt99+W7Xk1KlT6gN4PN7IkSPLysqKi4s9PDzYzJmenq6xRCqVCgQChUJB0zRFUSxrq6qq2rdv38svv+zp6alaKBQKm5ubNSZ//vnnV61alZCQ0O0+zADAOYStIcKWpQMHDggEAi0DWIY2whbAlCFmEbMWCfs3AQCYK5qm09PTw8PDfXx8tAxTKpWEEDc3N50fqLGxsampadSoUeybTYSQr776qqOjg813ZbNmzSotLf366691rhAAwHCMFrbd0v5fULd6Cm2ELQCYJsSspUK/CQDM1YkTJ1Sn/WN2mVFfcuvWrTlz5ri4uLi7uz/77LOqr4y2bdvGDPD19ZXL5VOmTHFycnJwcJg8eXJOTg4zZvPmzcwYVa/k9OnTzJKBAweqz9PY2JiTk8PcZPzvMQoLC6uqqqRSqZYxhw4dUigUEolk5MiROj9QVlYWIeTNN9/UWH7kyJHg4GChUCgSiZhdi9VvvXTpEiHE1dV11apVfn5+PB5vyJAhKSkpDx480JgnODiYEPLdd9/pXCEAGA7C1qBhqz1IddNTaCNsAUwTYhYxa7FoMBPM6e65rgLAUAghX3zxhQ53jI+PJ4Q0NTVpLImPj8/NzW1oaPjhhx8EAkFYWJj6vaRSqVAojIyMZMbI5fIxY8bweLyzZ8+qxgiFwgkTJqjfKyQkxN3dXX1J1zGMyZMnu7m55eXl6fCMaNaf9yNHjhBC3n333W5vvXr1akpKio2Njaur6/nz53WrhKbpyspKT0/PpKQkjeUTJkxYuHDhxYsXGxoaioqKFi5cSAhJTk5WDWDeCC8vr/nz5ysUipqamkOHDgmFQolEUltbqz7Vw4cPCSEymYxNPYmJiYmJiTo/Hc7nB+BQf7YlLDJsWX7eDRe2vQapOrFYbGtr2+ucPYU23cew1Xm93Cts04JlwzatOs63aU05Zq1hmxP7NwGAZUpKSoqMjBQKhVOnTp0+fbpcLr9//776gMbGxj179jBjQkNDP/nkk9bW1uXLl+vl0Ts7O5mQ1ctsPamoqCCEiESibm8dM2ZMVlbWsmXLfvvtt7CwMN0eorq6+umnn540adLevXs1bsrOzj58+PC4ceOEQuGIESMOHz48fvz4nTt3njt3jhnAfEEnEAgyMjKGDRvm4uKyaNGi1NTUkpKSDz74QH0qZ2dniqKYpwMA5gVh25+w7TVI+0pLaBOELYB5QswiZs0X+k0AYJnU10Z+fn6EkPLycvUBQqGQ2eWVMXr0aB8fn8LCQr2sIc6ePfvgwYPIyMj+T6UF09Cxt7fvacBPP/20Y8cO7UfCa9HY2BgbGzty5MhPP/3U1ta21/HPP/88IeTkyZPMr0KhkBAydepU9b2yZ8yYQbrbzdjOzq6pqUm3OgGAQwhb0u+wVacRpH3CJrQRtgBmBzFLELNmC/0mALBM6t+Q8Hg8QojGJWZdXFw07sJc6qKv11jl0IABAwghbW1thpi8vb09MTFRLBYfOnSITbOJEOLt7U3UXkB/f39CiLu7u/oY5kW+d+9e14fT4WSNAMA5hK1+aQQpeyxDG2ELYHYQs/qFmDUm9JsAwEpVV1dr7BvMrHhUF1i1sbFpbW1VH1BbW6sxSZ+u16Z3zPqSOVBc75YsWdLS0pKZmanaO2n48OH5+fla7sJ826Z6AZkzU2p8t8a8yJ6enuoL6+rqaJpmng4AWBiEbZ9oBCl7bEIbYQtgkRCzfYKYNSb0mwDASjU3N8vlctWvV65cKS8vl0qlqjWEt7d3WVmZakBlZeXt27c1JnFwcFCtv0eMGLFv3z4DV/2YUaNGEUJKS0u7vbW9vT0oKEi3mTds2HD16tUvv/ySz+d3OyA9PT0kJER9CU3TmZmZ5L9HzBFCnnnmGbFYfPr0aWYfaQaz9/LMmTPV78u8zszTAQALg7DtCZsgZanX0GYgbAEsEmK2J4hZzqHfBABWSiQSrVu3Li8vr7Gx8cKFCwsWLODxeDt27FANiImJKS8v37VrV0NDg0KhWL58eddvQsaNG1dSUnLnzp28vDylUimTyZjl0dHR7u7u2vcG6j+pVOrh4VFYWNj1pv379wuFwrVr13a9acGCBRRF3bx5s6dpMzIyNm7ceO7cOScnJ0qN6vq7jEuXLi1duvTGjRvNzc3FxcXMtT+Sk5PDw8OZAXw+Pz09vbq6eu7cudevX6+trT1y5MjWrVvDw8NTUlLUpyooKCCExMTE6PAiAICJQ9hqmbnXIGWDZWgThC2AhULMapkZMcsxo14ND/oB144Fy0b6fu3Y48ePq6fZ/Pnz8/Ly1Je8+eab9ON7F0+fPp25r1QqFYvF165di42NdXJyEggEEydOzM7OVp+/trY2KSnJ29tbIBBERUXJ5XLVNySvv/46M6aoqEgmkwmFQj8/v927d6vuK5PJXF1dc3NzdXs12H/e161bZ2dnV1ZWprF87969AoFg9erVXe8SHR3t6OjY3t7e05zTp0/vaZWhuhpuc3NzVlbWrFmzAgIC+Hy+SCSaNGnS0aNHu86Wm5sbGxsrEol4PF5QUNCGDRsePXqkMYY5GL61tZXNUzb0tWOt4dq0YLV025aw4LBl/3k3RNiyDNJuT2q7f/9+1QA2oa16vuzDluh6TfdeYZsWLJsOnx0Ljllut2lNPGatYZsTWW82sG4Gy2a47dpuMetmoz1cX7H/vNfW1orF4iVLlrCcuaamRiAQJCUl9aM6PSsoKKAo6rPPPmM5Hv0mAJ0Zf1vCxMOW/efdCsMW/SYA3WCbVh22abWwhm1OHE9n4T7//HNmZz/mnP/WJi4ujqKozZs393VMTU3N3r17o6Oj3dzcBAJBYGDg/PnzNfbwpGk6Jydn6dKlEomEz+d7eHhERUV98skn9ONfPvTK0dFRfbdMGxsbV1dXqVT66quvXrx4sU9TgRUSiUQnT548duzY7t27ex1M03RKSoqzs/OmTZuMUBsbSqUyISEhNTV17ty5XNfSLwhbw4WtDo/VLYQt9AfClnOIWd1iVuWbb76RSCSq8xxr6Ojo2L59e3BwsIODg0gkio6O/vHHH/taJGIW+gMxa5HQb7Jwc+fOpWl6ypQpXBfCgcOHD3e7bySbMWvWrElOTo6Pj7927Vp1dfXBgwcLCgpCQkJOnDihGlNcXBwVFVVSUnLs2LGHDx/m5+cPHjx44cKFa9as6VOdDQ0Nv/76KyEkPj6epum2traioqJ33nmnqKgoNDT0hRdeePToUZ8mBGszduzYCxcufPvtt3V1ddpHVlVVKZXKM2fOeHl5Gae2Xn300UdbtmzZsmUL14X0F8JWtzFswravj9UThC30E8KWW4hZnccoFIq4uLjU1NSqqqpuB3R0dMycOXPt2rVJSUl37twpKCjw9/ePiYn5/PPP+1QnYhb6CTFredBvAtPl6OjIXE9dB+Xl5StWrFi4cKHOY1588cXly5d7eXk5ODjIZLKjR492dHRonKnOzs4uMzNzzJgxAwYMGDZsWEZGhru7+65du1paWnQrmxBia2vr6ekZHx//008/rV27NiMjY968eX3dZ4pz/XnvDG3btm0URRUWFpaVlVEU9dZbb3FdkR74+/ufOnXK2dlZ+zAvL6/s7OwnnnjCOFWx8d577+FbIM6Zftiyfyz2ELaGhrBF2IIKtzG7fv36J5988uLFi05OTt0O+OSTT06dOvWPf/xj2bJl7u7uQ4cOPXDgwIgRI1599dXa2lrdyiaIWcNDzCJmTR/6TWCZFi9enJiYqP3SAFrGpKenf/TRR+pLpFKpQCBQKBSq1WRQUFBbW5urq6tqDI/H8/Pza2lpUb/6e3/885//DA8P/+qrr/r6/RJooXG6QR2OygEAFSOEbZ8eSzcIW0NA2ALoRT9jlhBy4MCBN954o6cj6QghzMmq1S8PT1FUfHx8TU3NsWPHdC38MYhZQ0DMgulDvwks0MGDB69evbpt27Z+jlHX2NjY1NQ0atQoiqJ6GlNbW3v9+vWxY8eKRKK+VdwDiqKWLVtGCNmzZ49eJgQA0CNjhm1f5+kThC0AmCa9xKxAIND+KMxxdh4eHuoLvb29CSHZ2dl9KLdniFkA64R+kwUqKiqaOXOmSCQSCoUymUxjPXHixAnVafyKi4tnz57t7u7O/Hr//n1CSHV19WuvvRYQEMDj8VxdXadNm/bzzz8z92V22qQoytfXVy6XT5kyxcnJycHBYfLkyTk5OeqPomWSzZs3M5Oo9k09ffo0s2TgwIHqD9TY2JiTk8PcpOU7GQ2lpaWrVq06ePBgT/sMsxyjISsrixDy5ptvdntrXV1dTk5OXFycl5fX4cOHWc7JBvMq5efnt7W1Wfx7B2BeELZGC1sd5ukrhC2ACULMGiJmu2Kq1Ti707179wght27d0nlaDYhZAGtkiIvegSGwvJbk9evXXVxcxGLx999/X19ff/ny5ZiYGH9/fz6frz4sPj6eEDJx4sSff/65sbExPz/f1tb23r17FRUVQ4cO9fT0PHny5MOHD4uLixMSEiiK2r9/v+q+UqlUKBRGRkbm5uY2NDTI5fIxY8bweLyzZ88yA9hMIhQKJ0yYoF5SSEiIu7u7+pKuY9iIjY199dVXmZ+PHDlCCNm0aZMOY9RVVlZ6enr2dLlN1WURJk2adPnyZY1bJ0+e7ObmlpeXp2V+9XMramhqamImLy8vZ5ZY6ntHjHvtWBOHa0VrYehrx7KcH2FrzLBlMw/Clg1kiwZruBa1zgy3XsY2LUt6j1mxWGxra9t1+c6dOwkhycnJGs+CEBIaGqpagphlCdu06rDe0cIa1kF4780Gy89qYmIiIeTYsWOqJWVlZXw+v9t18zfffKNx97///e+EkM8++0y1pLm52cfHRyAQVFZWMkukUikh5Ndff1WNuXz5MiFEKpWyn8RA+b5v375hw4Y1NDQwv3a73mUzRt39+/eDg4PnzJnT3t7e05iWlpbff//9H//4h62t7TvvvKN+08SJE11dXXNzc7WUrWXdrLqQh8a62fLeO6yb1WHdrIWJ9JsQtkYLW5bzIGzZQLZosIZtfZ1x3m9CzOo9ZnvqNzU1NYWEhNjb2+/atev+/ft//PHH0qVLmct+yWQy1TDELEvYplWH9Y4W1rAOwj6Blub06dOEkNjYWNUSHx8fiURSUlLSdfD48eM1ljDnC5w+fbpqCZ/PnzJlypEjR7777rtFixYxC4VCYXBwsGrM6NGjfXx8CgsLKyoqvL29WU6id7dv316zZs2XX34pFAr7M0ZdY2NjbGzsyJEjDx8+bGtr29MwHo8XFBT04YcfVlVVvf3225GRkVOnTmVuOnv2bB+fx2MqKioIIfb29qqdexkW9t4x8vPzqZ5Pj2VV8vPzyX8PLAINpaWlvr6+XFeBsDVS2LKfB2HLHrJFpbS0lOAFMVWIWf3GrBYDBgz4+eef33nnnW3btq1cudLd3T0hISErK0smk6lfbB4xyx62aVWwTauFiWzTGhT6TRalpaWlvr5+wIABjo6O6ss9PDy6XTdrrJ9aWloePnw4YMAA8CpxRAAAIABJREFUjSPAPT09CSGVlZWqJS4uLhpTeXh4lJeX3717183NjeUkesfsLjtp0iSN5evXr1+/fj0h5Pr16999912vY4YPH84sbG9vT0xMFIvFhw4d0tJsUjdjxozjx4+fOnVK1W/qJ+ZUBZGRkfb29urLLey9Y6SlpaWlpRn6UczI7NmzuS7BRDFfenMIYWu0sGXzWKp5+sOqwhbZoiEvL4/rEkATYla/MdsrJyenf/3rX//6179US7777jtCyLhx43R8Dl1YVcxim1YD1js94Xyb1tBwvnCLwufznZycmpubGxoa1Jc/ePCA5d1FIlFzc3N9fb36cub0gerfb1RXV9OPX6n67t27hBAPDw+Wk9jY2LS2tqoPqK2t1ainr18LLF26VGP/PY39iocPH85mjGrCJUuWtLS0ZGZmqs4OOHz4cKZJ3xM+n09Yv+C96uzs3L17N/PUtI809/eOgX2PVbDvsRamsGJG2Gq8KYYL2z7NozNrC1u9fiLNmzUcy6AzHf609Agxq/F29DNmdcC0hxISEvoziYq1xSy2aVWwTauFKWzTGhr6TZZm2rRp5L97IDPu379fXFzM8u6zZs0ihHz99deqJS0tLWfOnBEIBOr7Mzc3N8vlctWvV65cKS8vl0qlzJVT2Uzi7e1dVlamGlBZWXn79m2NYhwcHFTrgBEjRuzbt4/ls9CLDRs2XL169csvv2RaSF2tXr16wYIFGgu//fZbQkhYWJheakhNTT1//vysWbPYhBHeOwBjQtjqS69hawQIWwAThJg1mvv379vY2JSXl6uW1NXVpaenz507VyKR6OUhELMAVorrph6wxbI3fOPGDTc3N9W1PK5evRobG8u0+dWHMefna2pq0ri7+qUc6urqVJdy2Ldvn2qMVCoViURTpkxhcz2IniZZtmwZIWTnzp319fU3btyYPXu2WCzWOD/f008/LRKJbt++nZuba2dnd+3atb6+aGwuh9TtmI8//rinj4zqqhyrVq2iKGrjxo03b95sbm6+efPm2rVrCSEhISGPHj1STdXXa3l0dHRUVVWdOHEiOjqaEPLiiy+qz0Zb7ntH8F2QGnwXpIWJnC8cYavOoGHL8rEQtmzeO2SLBuzfpIXh1svYpjVmzKrr6Xzh9+7dI4TExMRcv369ubn53LlzkZGRUqmU2YFIBTGLbVodYL2jhTWsg/Demw32n9Xi4uKZM2c6OzsLBIKwsLBTp05NmTKF2Xx/6aWXup6kQOPu9+/fX7FixdChQ+3t7UUiUWxs7JkzZ9QHSKVSsVh87dq12NhYJycngUAwceLE7OzsPk1SW1ublJTk7e0tEAiioqLkcjlz1VVCyOuvv86MKSoqkslkQqHQz89v9+7dfXq5lixZovE0Y2Nj2Y9RP7lgT/8CPXz4MD09PTY21t/fn8fjOTo6hoSEbN26VWNVKpPJtF/LQ+OodYqiRCLR6NGjX3nllYsXL6qPtOz3DutmdVg3a2Ei/SYaYUvTtFHCluVjIWzZvHfIFg3WsK2vM8Otl7FN26eXq58xS9P0yZMnu2bs/v371Wf44Ycf4uLivLy8BALBqFGjNm3apLFBSyNmsU2rE6x3tLCGdRBFc314NrCUmZk5Z84cU3i/goOD79+/z1zSBcyLKb93FEV98cUXOJsgw3Q+7yaI+SPJzMw00/nZM+UPLGhnsu8dskWD6XzeTZDh1sum83dosh9V6JUpv3fYplVnOp93E2QN6yCcvwkAAAAAAAAAAPQJ/SYAALP3xx9/xMXF1dXVEUIqKiq2bNkSGhrq7Ozs5eU1ceLE48eP92m2mpqavXv3RkdHu7m5CQSCwMDA+fPnFxYWagyLioqiulixYoXGsLa2trS0tJCQECcnJw8Pj2nTpp08eVL1Ndcbb7zB7GgNAGD69Bu2hHWQEkK++eYbiUSiuoajOjahjbAFALOAmLUw6DdBH2zbto2iqMLCwrKyMoqi3nrrLSMX0DUsVDZs2GDkYswL5+8dGE5BQUFoaGhMTIyzszMhZPHixdu3b9+4cWNFRUV+fr6vr29CQsIbb7zBfsI1a9YkJyfHx8dfu3aturr64MGDBQUFISEhJ06c6GttjY2N0dHRGRkZaWlpd+/evXDhgqOjY1xc3NWrV5kBixcvTk1NXb9+fV9ntmycf2ARtjrj/L0Dw9F72LKkUCji4uJSU1OZS7l3xSa0EbYaOP+oImZ1xvl7B4aDmLVAHJ47CvoE51oDy0aMeG5FoVA4YcIEU56f/ef94cOHvr6+S5YsUS2ZPn16RkaG6teWlhbmejRVVVUsH/2ll156+eWX1ZcUFBQQQgIDA9UXTpgwQS6Xa5/qlVdecXZ2rqysVC1paGjg8/lXrlxRn5yiKPbvvumcLxzA7Bh5W8L0w5b9590QYUuzC9J58+Zt3bq1ra2tp4uLsQztvoat4dbL2KYFy4ZtWnXcbtPSph2z1rDN2c3eYgAAYC7ef//9ysrKt99+W7Xk1KlT6gN4PN7IkSPLysqKi4s9PDzYzJmenq6xRCqVCgQChUJB0zRFUSxrq6qq2rdv38svv+zp6alaKBQKm5ubNSZ//vnnV61alZCQ0O0+zAAAnDNE2LJ04MABgUCgZQDL0EbYAoApQ8xaJBxPBwBgrmiaTk9PDw8P9/Hx0TJMqVQSQtzc3HR+oMbGxqamplGjRrFvNhFCvvrqq46OjqioqF5Hzpo1q7S09Ouvv9a5QgAAwzFa2HZL+39B3eoptBG2AGCaELOWCv0mADAn1dXVr732WkBAAI/Hc3V1nTZt2s8//8zctHnzZubEB6oGx+nTp5klAwcOZJYwx/w3Njbm5OQwNzFfPjDLKYry9fWVy+VTpkxxcnJycHCYPHlyTk5O/+c3kMLCwqqqKqlUqmXMoUOHFAqFRCIZOXKkzg+UlZVFCHnzzTc1lh85ciQ4OFgoFIpEIplMdvToUfVbL126RAhxdXVdtWqVn58fj8cbMmRISkrKgwcPNOYJDg4mhHz33Xc6VwgA+oWwVWfQsNUepLrpKbQRtgCmAzGrDjFrsbg8mA/6Ase6g2UjLI51r6ioGDp0qKen58mTJx8+fFhcXJyQkEBR1P79+1Vjuh5nHhIS4u7urr6kp2PRpVKpUCiMjIzMzc1taGiQy+Vjxozh8Xhnz57Vy/yTJ092c3PLy8vT/jRp1p/3I0eOEELefffdbm+9evVqSkqKjY2Nq6vr+fPne52tJ5WVlZ6enklJSRrLJ0yYsHDhwosXLzY0NBQVFS1cuJAQkpycrBoQHx9PCPHy8po/f75CoaipqTl06JBQKJRIJLW1tepTPXz4kBAik8nY1IPzNwHojGW2WE/Ysvy8Gy5sew1SdT2dWERDT6FN9zFs2ayXdYNtWrBs2KZVx/k2rSnHrDVsc2L/JgAwG6mpqTdv3ty+ffuzzz7r7OwskUiOHj3q7e2dkpLS0+Uk+qqxsXHPnj2RkZFCoTA0NPSTTz5pbW1dvny5Xibv7OxkklcvsxFCKioqCCEikajbW8eMGZOVlbVs2bLffvstLCxMt4eorq5++umnJ02atHfvXo2bsrOzDx8+PG7cOKFQOGLEiMOHD48fP37nzp3nzp1jBjDnaRIIBBkZGcOGDXNxcVm0aFFqampJSckHH3ygPpWzszNFUczTAQDOIWw1GC5sew3SvtIS2gRhC2AyELMaELOWCv0mADAbx48fJ4RMnz5dtYTP50+ZMqWpqUlfu60KhUJmP1jG6NGjfXx8CgsL9bLaOHv27IMHDyIjI/s/FYNp6Njb2/c04KefftqxY4f2I+G1aGxsjI2NHTly5Keffmpra9vr+Oeff54QcvLkSeZXoVBICJk6dar6DtgzZswg3e1mbGdn19TUpFudAKBfCFsNhg5bdRpB2idsQhthC2AKELMaELOWCv0mADAPLS0tDx8+HDBggJOTk/py5tpnlZWVenkUFxcXjSXM9S/u3r2rl/n1a8CAAYSQtrY2Q0ze3t6emJgoFosPHTrEptlECPH29iZqr5W/vz8hxN3dXX0M83reu3ev68PpcLJGANA7hG1XBg1bDRpByh7L0EbYAnAOMdsVYtZSod8EAOaBz+eLRKLm5ub6+nr15cxex15eXsyvNjY2ra2t6gNqa2s1ptJykbXq6mqNfYOZtZHqqqv9nF+/mPUlc6C43i1ZsqSlpSUzM1O1d9Lw4cPz8/O13KW8vJyovVbMSSg1vkZjXk9mi0qlrq6Opmnm6QAAtxC2XRk0bDVoBCl7bEIbYQtgChCzXSFmLRX6TQBgNmbNmkUIUb/CaEtLy5kzZwQCQWxsLLPE29u7rKxMNaCysvL27dsa8zg4OKjWryNGjNi3b5/qpubmZrlcrvr1ypUr5eXlUqlUtdro5/z6NWrUKEJIaWlpt7e2t7cHBQXpNvOGDRuuXr365Zdf8vn8bgekp6eHhISoL6FpOjMzk/z3iDlCyDPPPCMWi0+fPs3sI81g9l6eOXOm+n2Zl5R5OgDAOYStBgOFLZsgZanX0GYgbAFMBGJWA2LWUqHfBABmY+vWrUOHDl2xYsWpU6fq6+tLSkr++te/VlRU7NixQ7W/TExMTHl5+a5duxoaGhQKxfLly7t+fTFu3LiSkpI7d+7k5eUplUqZTKa6SSQSrVu3Li8vr7Gx8cKFCwsWLODxeDt27FAN6M/80dHR7u7u2ncR6hOpVOrh4VFYWNj1pv379wuFwrVr13a9acGCBRRF3bx5s6dpMzIyNm7ceO7cOScnJ0qNQqFQH3bp0qWlS5feuHGjubm5uLiYufZHcnJyeHg4M4DP56enp1dXV8+dO/f69eu1tbVHjhzZunVreHh4SkqK+lQFBQWEkJiYGB1eBADQO4StBgOFLWERpGywDG2CsAUwGYhZDYhZi2XUq+FBP+DasWDZCLvrLt+/f3/FihVDhw61t7cXiUSxsbFnzpxRH1BbW5uUlOTt7S0QCKKiouRyueprjddff50ZU1RUJJPJhEKhn5/f7t27VfeVSqVisfjatWuxsbFOTk4CgWDixInZ2dn6ml8mk7m6uubm5vb6NNl/3tetW2dnZ1dWVqaxfO/evQKBYPXq1V3vEh0d7ejo2N7e3tOc6mev1KC68G1zc3NWVtasWbMCAgKY3cInTZp09OjRrrPl5ubGxsaKRCIejxcUFLRhw4ZHjx5pjGEOhm9tbWXzlA197VhruDYtWC322WIlYcv+826IsGUZpN2e1Fb9oulsQlv1fNmHLcv1sg6wTQuWDdu06rjdpjXxmLWGbU5kvdnAuhksm+G2a9lj1s3c1sBg/3mvra0Vi8VLlixhOXNNTY1AIEhKSupHdXpWUFBAUdRnn33Gcjz6TQA6M5FtCdMJW/afdysMW/SbAHSDbVp12KbVwhq2OXE8HQCAGROJRCdPnjx27Nju3bt7HUzTdEpKirOz86ZNm4xQGxtKpTIhISE1NXXu3Llc1wIA0COELQCAQSFmLRL6TQAA5m3s2LEXLlz49ttv6+rqtI+sqqpSKpVnzpxRXfqEcx999NGWLVu2bNnCdSEAAL1A2AIAGBRi1vKg3wQAQLZt20ZRVGFhYVlZGUVRb731FtcV9Y2/v/+pU6ecnZ21D/Py8srOzn7iiSeMUxUb7733Hr4FArAeCFuuIGwBrARiliuI2W7ZcV0AAAD3Vq9evXr1aq6rAACwcAhbAACDQsyCScH+TQAAAAAAAAAAoE/oNwEAAAAAAAAAgD6h3wQAAAAAAAAAAPqEfhMAAAAAAAAAAOgTzhduZmbPns11CQCGkpaWduzYMa6rMAl37twh+Lz3IC8vLzIy0tAPgRcfLBKyRUNeXh7BC8IRvOxgwbBNq4L1jhZG2KblHEXTNNc1ACu3bt1KTU3t6OjguhCAxzQ0NNy5c+cvf/kL14WAFUlMTExMTDTQ5FlZWVlZWQaaHICN+vr6srKyoKAgrgsBq2Zra7t161Z/f3+9z4xtWjCyhoaG8vLygIAAW1tbrmsBeIxBt2lNAfpNANAv//d//zdp0qRbt24NGTKE61oAACxBZmbmnDlzsIUGAKAXH3744euvv15bW2tjg5PJABgVPnIA0C9hYWF2dnb5+flcFwIAAAAAoEkul4eGhqLZBGB8+NQBQL84ODiMHj363LlzXBcCAAAAAKBJLpeHhYVxXQWANUK/CQD6KyIiAvs3AQAAAICpefToUVFREfpNAJxAvwkA+is8PPzSpUstLS1cFwIAAAAA8KeLFy+2t7ej3wTACfSbAKC/IiMjW1pafv31V64LAQAAAAD4k1wuHzRoEC5rA8AJ9JsAoL8CAwMHDhyIQ+oAAAAAwKTg5E0AHEK/CQD6i6Ko8ePH45ThAAAAAGBS0G8C4BD6TQCgB+Hh4di/CQAAAABMx4MHD5RK5fjx47kuBMBKod8EAHoQERFx69at8vJyrgsBAAAAACCEkAsXLtA0HRoaynUhAFYK/SYA0IOIiAgbGxscUgcAAAAAJkIulw8ZMsTDw4PrQgCsFPpNAKAHzs7OQUFB6DcBAAAAgInAyZsAuIV+EwDoR2RkJE7hBAAAAAAmAv0mAG6h3wQA+hEeHi6Xy9vb27kuBAAAAACsXUVFRXl5OfpNABxCvwkA9CMiIuLRo0dXrlzhuhAAAAAAsHbnzp2jKGrs2LFcFwJgvdBvAgD9eOKJJ5ydnXFIHQAAAABwTi6XjxgxwsXFhetCAKwX+k0AoB82NjZhYWHoNwEAAAAA53DyJgDOod8EAHqDU4YDAAAAAOdomr506RL6TQDcQr8JAPQmPDz8+vXr9+/f57oQAAAAALBeCoWiuroa/SYAbqHfBAB6ExkZSQg5f/4814UAAAAAgPWSy+X29vZSqZTrQgCsGvpNAKA37u7uAQEB586d47oQAAAAALBecrl81KhRAoGA60IArBr6TQCgTxEREXl5eVxXAQAAAADWCycLBzAF6DcBgD5FREScO3eus7OT60IAAAAAwBp1dHT8+uuv6DcBcA79JgDQp4iIiLq6ut9//53rQgAAAADAGl27dq2xsXH8+PFcFwJg7dBvAgB9GjNmjIODQ35+PteFAAAAAIA1ksvlDg4OI0eO5LoQAGuHfhMA6JO9vX1ISAhOGQ4AAAAAnJDL5WPHjrWzs+O6EABrh34TAOgZThkOAAAAAFzBycIBTAT6TQCgZ+Hh4deuXXv48CHXhQAAAACAdWlpafntt9/QbwIwBeg3AYCeRUZGdnZ2yuVyrgsBAAAAAOtSWFjY0tKCfhOAKUC/CQD0zMfHZ/DgwThlOAAAAAAYmVwuF4lEw4cP57oQAEC/CQAMICIiAqcMBwAAAAAjY07eRFEU14UAAPpNAGAA4eHheXl5NE1zXQgAAAAAWJHz58/jYDoAE4F+EwDoX0RERHV1tUKh4LoQAAAAALAW9fX1xcXF6DcBmAj0mwBA/0JCQvh8fl5eHteFAAAAAIC1uHjxYmdnJ/pNACYC/SYA0D8+nx8cHKx+Cqfq6ur29nYOSwIAAAAAC9PZ2VlTU6P6VS6Xe3l5+fr6clgSAKjYcV0AAFim8ePH//jjj7t27crLy8vOzr59+/b//u//btiwgeu6AABM0aefflpaWsr8fPnyZULIe++9p7p1ypQpoaGh3FQGAGDCvvjii7/+9a+enp6RkZHh4eGnT58ODg7muigA+A8KJ/QFAH25d+/eL7/8kpeXl5OTc+nSpdbWVjs7O4qi2trabG1t33vvvVWrVnFdIwCAKRo4cGBtba2dnR0hhKZpmqZtbP6zE3pLS8uyZct27tzJaYEAAKbo1KlTM2bMIIRQFGVnZ9fW1kYIGTJkSFRUVFhY2FNPPTV27FiuawSwXti/CQD0JjY2tqCgwN7evrW1lVmiOoaus7NzyJAh3JUGAGDS5syZs3///paWlm5vfe6554xcDwCAWRg2bBjzA03TTLOJEPLHH3+UlpZ+/vnnPB6voaFB1b4HACPDZw8A9OaVV16hKErVbFJH0zT6TQAAPZk3b57qPyUNgwYNkslkRq4HAMAsDB06lKKorss7OjooinrttdfQbALgED5+AKA3L730UmhoKHM8SFf+/v7GLQcAwGxMmDDBx8en63Iej7do0SJbW1vjlwQAYPoEAoGbm1vX5TY2NoMGDUpNTTV+SQCggn4TAOiNjY3NgQMHuj0rHI/HGzhwoPFLAgAwCxRFLVy40N7eXmN5a2vrvHnzOCkJAMAsBAQEdF3Y2dm5Z88eoVBo/HoAQAX9JgDQp1GjRq1cubLrLk5isbjbvZ0BAIDR7SF1Q4YMCQkJ4aQeAACzEBQUpLETqL29/ZQpU2bOnMlVSQDAQL8JAPRs48aNnp6eGit+1dkcAQCgW1KpNDAwUH0Jj8d74YUXuKoHAMAsDB06VOObTpqmd+/ezVU9AKCCfhMA6JmDg8OuXbs6OjpUS+zt7bvd1RkAANQtWrRI/ZC61tbWuXPnclgPAIDpGzZsmPrOoXZ2dqmpqSNGjOCwJABgoN8EAPo3c+bM6dOnq/5rsrGxwcXpAAB6NW/evPb2duZniqLGjBmDf5kAALQbNmxYZ2cn8zNzmvDXX3+d25IAgIF+EwAYxJ49e1T7Nre1taHfBADQq4CAgODgYObq3XZ2dn/729+4rggAwNSpn7Shs7Pzww8/xGnCAUwE+k0AYBCDBw/esGED819TZ2cn+k0AAGwsWrSISc729vbZs2dzXQ4AgKnz9vbm8/nkv6cJj4+P57oiAPgPqtsrlwMA9F9bW5tUKi0uLu7s7Pzjjz8GDx7MdUUAAKauoqLC19eXpuknn3wyOzub63IAAMzA8OHDFQqFvb39tWvXhg8fznU5APAf2L8JAAzF3t7+448/pmna1tZWLBZzXQ4AgBnw9vaWyWQ0TS9atIjrWgAAzANzqrvU1FQ0mwBMCw3Amq+vL9d/sAAAj7Gzs/vll1+4TkeapulffvlF43rMAACcY3aXMwUrV67k+sUAANC0cuVKrtPRkmHLGPqgtLR05cqVkZGRXBcC5qS9vb2+vt7V1ZX9XfLy8tLS0jIzMw1XlXlJS0sjhGBLvVuzZ8+uqKjgugpCCKmoqGhvb8ffLfQfTdMPHjxwd3fvaQAyQcPs2bOxfdItZn3KdRX/UVpaGhER8dprr3FdCFiglpaWlpYWZ2dn5ldkgjpsV2vx73//u7S0lOsqLBn6TdA3ERERiYmJXFcBFo6maUII/tJUsrKyCF4QM4G3CYwAmdAVtk+6RZvYeVr9/PzwNoFxIBNUsF2tBbM+BcPB+ZsAAAAAAAAAAECf0G8CAAAAAAAAAAB9Qr8JAAAAAAAAAAD0Cf0mAAAAAAAAAADQJ/SbAAAszR9//BEXF1dXV0cIqaio2LJlS2hoqLOzs5eX18SJE48fP96n2Wpqavbu3RsdHe3m5iYQCAIDA+fPn19YWKgxLCoqiupixYoVGsPa2trS0tJCQkKcnJw8PDymTZt28uRJ1Rlt33jjjS+++ELX5w0A0Dv9JiRhnX6EkG+++UYikdjZdXO5HjZJi4QEACNASIIeod8EAJajoaEhMDDw2Wef5boQLhUUFISGhsbExDBXBV68ePH27ds3btxYUVGRn5/v6+ubkJDwxhtvsJ9wzZo1ycnJ8fHx165dq66uPnjwYEFBQUhIyIkTJ/paW2NjY3R0dEZGRlpa2t27dy9cuODo6BgXF3f16lVmwOLFi1NTU9evX9/XmQGADYSk3hOSJYVCERcXl5qaWlVV1e0ANkmLhAQwKCQkQUiCvqHfBACWg6bpzs7Ozs5OrgpwdHSMiori6tEJIXV1dTNmzHjuueeWLVumWrht27bp06cLhUJ/f/+PP/5YLBZv27bt7t277Kd98cUXly9f7uXl5eDgIJPJjh492tHRsXbtWo1hcrmcftz27dvVB6xZs+by5cvff//9U089JRAIBg8enJGRwefzVQMCAgKOHz++ZcuWzMxMnV4AANDGykPSQAlJWKTf+vXrn3zyyYsXLzo5OfU0Sa9Ji4QEMCgrT0iCkAQD6GZfNQAAM+Xk5KRQKLiugkvvv/9+ZWXl22+/rVpy6tQp9QE8Hm/kyJFlZWXFxcUeHh5s5kxPT9dYIpVKBQKBQqGgaZqiKJa1VVVV7du37+WXX/b09FQtFAqFzc3NGpM///zzq1atSkhI6HaHagDQmZWHpCESkqUDBw4IBAItA1gmLRISwHCsPCEJQhIMAPs3AQBYCJqm09PTw8PDfXx8tAxTKpWEEDc3N50fqLGxsampadSoUeybTYSQr776qqOjg80Xd7NmzSotLf366691rhAAQIPRErJb2v+P6lZPSYuEBABDQEiCIaDfBAAW4sSJE6pzEDK7zKgvuXXr1pw5c1xcXNzd3Z999lnV91fbtm1jBvj6+srl8ilTpjg5OTk4OEyePDknJ4cZs3nzZmaMqldy+vRpZsnAgQPV52lsbMzJyWFuMv6XKoWFhVVVVVKpVMuYQ4cOKRQKiUQycuRInR8oKyuLEPLmm29qLD9y5EhwcLBQKBSJRMx+zuq3Xrp0iRDi6uq6atUqPz8/Ho83ZMiQlJSUBw8eaMwTHBxMCPnuu+90rhAAurLykDRoQmpPP930lLRISABDsPKEJAhJMBAagDVCyBdffMF1FWD5mEtL6Hbf+Ph4QkhTU5PGkvj4+Nzc3IaGhh9++EEgEISFhanfSyqVCoXCyMhIZoxcLh8zZgyPxzt79qxqjFAonDBhgvq9QkJC3N3d1Zd0HcOYPHmym5tbXl6ebk8qMTExMTGx12FHjhwhhLz77rvd3nr16tWUlBQbGxtXV9fz58/rVglN05WVlZ6enklJSRrLJ0yYsHDhwosXLzY0NBQVFS1cuJAQkpycrBrAvBFeXl7z589XKBQ1NTWHDh0SCoUSiaS2tlZ9qocPHxJCZDIZm3pMJ5f683cL0CcsM6FbFhmSbHLAcAnZa/qpE4vFtra2vc4zgDr4AAAgAElEQVTZU9LSfUxIk8ql/vzdAvSJztsGFpmQLHPAOkMSuWRo2L8JAKxCUlJSZGSkUCicOnXq9OnT5XL5/fv31Qc0Njbu2bOHGRMaGvrJJ5+0trYuX75cL4/e2dnJZK5eZutJRUUFIUQkEnV765gxY7KyspYtW/bbb7+FhYXp9hDV1dVPP/30pEmT9u7dq3FTdnb24cOHx40bJxQKR4wYcfjw4fHjx+/cufPcuXPMAObbQoFAkJGRMWzYMBcXl0WLFqWmppaUlHzwwQfqUzk7O1MUxTwdADAOiw9JwyVkr+nXV1qSliAhAbhg8QlJEJJgGOg3AYBVUF81+vn5EULKy8vVBwiFQmb/W8bo0aN9fHwKCwv1sro6e/bsgwcPIiMj+z+VFkxDx97evqcBP/30044dO7Qflq9FY2NjbGzsyJEjP/30U1tb217HP//884SQkydPMr8KhUJCyNSpU9V3EZ8xYwbpbp9nOzu7pqYm3eoEAB1YfEgaOiHVaaRfn7BJWiQkgJFZfEIShCQYBvpNAGAV1L+u4fF4hBCN6926uLho3IW57kZfL/jKoQEDBhBC2traDDF5e3t7YmKiWCw+dOgQm2YTIcTb25uovYD+/v6EEHd3d/UxzIt87969rg+nw5kjAUBnFh+SBk1IDRrpxx7LpEVCAhiZxSckQUiCYaDfBABACCHV1dUaOyoza0HV1V5tbGxaW1vVB9TW1mpM0qfrtekds/JmjlrXuyVLlrS0tGRmZqr2Tho+fHh+fr6WuzBf/aleQOY0mRpf9DEvsqenp/rCuro6mqaZpwMAJsLcQ9KgCalBI/3YY5O0SEgAE2TuCUkQkmAY6DcBABBCSHNzs1wuV/165cqV8vJyqVSqWl15e3uXlZWpBlRWVt6+fVtjEgcHB9XGxIgRI/bt22fgqh8zatQoQkhpaWm3t7a3twcFBek284YNG65evfrll1/y+fxuB6Snp4eEhKgvoWk6MzOT/PeIOULIM888IxaLT58+zeywzWB2pZ45c6b6fZnXmXk6AGAizD0kDZSQbNKPpV6TloGEBDBB5p6QBCEJhoF+EwAAIYSIRKJ169bl5eU1NjZeuHBhwYIFPB5vx44dqgExMTHl5eW7du1qaGhQKBTLly/v+rXMuHHjSkpK7ty5k5eXp1QqZTIZszw6Otrd3V373kD9J5VKPTw8CgsLu960f/9+oVC4du3arjctWLCAoqibN2/2NG1GRsbGjRvPnTvn5OREqVFdDJhx6dKlpUuX3rhxo7m5ubi4mLkQSXJycnh4ODOAz+enp6dXV1fPnTv3+vXrtbW1R44c2bp1a3h4eEpKivpUBQUFhJCYmBgdXgQAMBBzD0kDJSRhkX5ssExagoQEMEnmnpAEIQkGYtSr4YGZIyZz3XGwbLpdv/n48ePq4TZ//vy8vDz1JW+++Sb9+K7O06dPZ+4rlUrFYvG1a9diY2OdnJwEAsHEiROzs7PV56+trU1KSvL29hYIBFFRUXK5XPV1zeuvv86MKSoqkslkQqHQz89v9+7dqvvKZDJXV9fc3FzdXhD212pdt26dnZ1dWVmZxvK9e/cKBILVq1d3vUt0dLSjo2N7e3tPc06fPr2nNYjq0rzNzc1ZWVmzZs0KCAjg8/kikWjSpElHjx7tOltubm5sbKxIJOLxeEFBQRs2bHj06FHX5ysWi1tbW9k8ZdPJJZO67jhYNt2u32zBIckyBwyRkCzTr9vT4u7fv181gE3SMvqUkCaVS7juOBiNDtsGFpyQ7HPACkMSuWRoprIGArOgQ3YD6MD428fMhoIxH7FP2K8La2trxWLxkiVLWM5cU1MjEAiSkpL6UZ2eFRQUUBT12WefsRxvOrlkUv/XgWUz/vaxiYckyxywwoQ0qVzC/3VgNEbeNjDxhGSfA1YYksglQ8PxdGBYn3/+ObOvI3PJA/PS1taWlpYWEhLi5OTk4eExbdq0kydP0v/9ZoOm6ZycnKVLl0okEj6f7+HhERUV9cknn9CPf/XR3t5+4MCB8ePHu7u7u7q6hoSE7Nq1S/18gXv37qV6MG3aNPbVOjo6qt/XxsbG1dVVKpW++uqrFy9e1MsLAqZPJBKdPHny2LFju3fv7nUwTdMpKSnOzs6bNm0yQm1sKJXKhISE1NTUuXPncl2LkSAkew1JQkhHR8f27duDg4MdHBxEIlF0dPSPP/7Y12oRkoCENEcISTYhqS4uLo6iqM2bN/e1WoQkICRB79BvAsOaO3cuTdNTpkzhupA+a2xsjI6OzsjISEtLu3v37oULFxwdHePi4q5evcoMKC4ujoqKKikpOXbs2MOHD/Pz8wcPHrxw4cI1a9aoz/PCCy8kJSVNnTr1999/v3Hjxpw5c5KTk5977jk2NTz55JPsC25oaPj1118JIfHx8TRNt7W1FRUVvfPOO0VFRaGhoS+88MKjR4/Yzwbma+zYsRcuXPj222/r6uq0j6yqqlIqlWfOnPHy8jJObb366KOPtmzZsmXLFq4LMR6EZK8h2dHRMXPmzLVr1yYlJd25c6egoMDf3z8mJubzzz/vU8EISSBISDOEkOzTluThw4e7PS6JDYQkEIQk6J2xd6gCc0Z03Td1ypQpfD5f7/UY1CuvvOLs7FxZWala0tDQwOfzr1y5wvz6+++/29nZPXjwQDWgpaXF3d2dz+c3NzczS5gz2I0dO1Z95v/5n/8hhJw/f5759cMPP2TW6+pKSkr4fH5FRUWfalbfSlDHnN4vLi6us7OzTxNyxZj7///rX/9Sj0Tm4HxTg319tdA5l/SuP3+3CEn1mTVCMiMjgxCSnJysGtDZ2RkUFOTq6lpTU9Onmi0mJI2ZCWYRkqaTA6bGYo6nQ0iqz6wRkiplZWWurq4LFy4khGzatEmHmi0mJI2WCWaRkCaVA6YG29iGhv2bALpRVVW1b9+++fPne3p6qhYKhcLm5mbVxTWDgoLa2tpcXV1VA3g8np+fX0tLi+py73fu3CGE/OUvf1GfnLmYqOoaqMOHD1ddfkJl586dM2fO1NfXBf/85z/Dw8O/+uqrvu4OYA00zn2ow/7nAFbImCHJnMNV/arJFEXFx8fX1NQcO3ZML08HIakFQhJAB8YMSZXFixcnJiYa4rJcCMmeICEBtEO/CaAbX331VUdHR1RUVJ/uVVtbe/369bFjx4pEImZJUFCQvb19UVGR+rCioiKKokaPHs38OnXq1FWrVqkPqK+vP3To0KuvvtqPZ/AYiqKWLVtGCNmzZ4++5gQAa2bMkKyqqiKEaFw32tvbmxCSnZ2t81NQh5AEAP0yZkgyDh48ePXq1W3btvWz8m4hJAFAN+g3gf4VFRXNnDlTJBIJhUKZTNbt/wP37t1LSUnx9/fn8XiDBg1KSEgoKChgbjpx4oTqVIW3bt2aM2eOi4uLu7v7s88+y+xUzGhpaXn77beDgoIcHBzc3NxmzJjBrNrZPESvLl26RAhxdXVdtWqVn58fj8cbMmRISkrKgwcPuh1fV1eXk5MTFxfn5eV1+PBh1XJPT89t27YVFhauW7fu3r17Dx48eP/993/88ce3335bIpH09Ogff/zx4MGDn3rqKZbVssFs8eTn57e1tTFLTPwtALBgCEnVcjYhOXDgQPLfrpN68YSQW7dusSy4VwhJANOBkFQtZ7klWVpaumrVqoMHDzo5ObGssK8QkgCgC2MctAeWgrA4Fvr69esuLi5isfj777+vr6+/fPlyTEyMv7+/+lH35eXlQ4YM8fT0/Prrr+vr63/77beJEycOGDAgNzdXNSY+Pp4QEh8fn5ub29DQ8MMPPwgEgrCwMNWApKQkkUj0/fffP3r0qLKycvXq1YSQn3/+mf1DaME8upeX1/z58xUKRU1NzaFDh4RCoUQiqa2t1RisuijDpEmTLl++3HW2zMxMX19fZszAgQMPHDig5aE7OzslEsmePXs0lk+ePNnNzS0vL0/LfXs66p6m6aamJqaA8vJy2uTfAhxnrgHHlmvBJpeMg+XfLUKy62zaQ3Lnzp3k8fM30TT9/9m787goy/3/49fNPqwiyiJoLrnkhgoeRcUFDfSIG4VamXU8mueU4Yph28k9O5RSVqYej1udA/r4aaGp5R4EphWYmssX3NgVBBRln98f9/fc3zkDDgMMzACv519y3RfX/RkYPzO85158fHyEEL6+vspIi2qS9AQtptMHTI1JvZ7q+bylSVZdrcZ3kkFBQa+++qr87127donqrt/UopokPUGTSfUBU8PraUPjmYda0Kd3h4aGCiH27t2rjKSnp1tbW2u+S3jppZeEEF9++aUykpmZaW1t7ePjo4zIL1HyXWNlzz77rBDizp078pedOnUaMmSI5q67deumvETpswsdgoKChBCdOnUqKytTBuVTst95552q80tKSn7//fe//OUv5ubmK1asUMYrKyvnzJljaWn50UcfZWVl3blz54svvlCpVNOmTdNcWdPBgwcdHBzu37+vNT5ixAhnZ2fdL7E63iUotxSR3yWY+K+A10UtvBbqYDrvKfV83tIka9skHz165OPjY2lpuXHjxrt37968efO1116TL2/n7++vLNWimiQ9QYvp9AFTY1Kvp3o+b2mStW2Smzdv7ty584MHD+QvH5c3tagmSU/QZFJ9wNTwetrQLARgUIcPHxZCyK+ysnbt2nXr1u3q1avKyP79+83MzIKDg5URd3f3Xr16/fzzz2lpacoHOEKIgQMHKv9u3769ECIjI0M+t2Ls2LGff/75K6+8MmvWrIEDB5qbm1+5cqUOu6iWnZ2dEGLMmDEWFv/3f2TChAlvv/32kSNHVqxYoTXfysqqR48en3/+eXZ29rvvvuvn5zdmzBghxK5du7Zs2fL6668vXLhQnvnKK69kZWX97W9/Gzx48IIFC6ru+uOPP545c6a9vb3W+MmTJ3XXrFtmZqYQwtLSUv7pmf6vQAixZ8+e+jzk5iQtLU3wA2kuaJK1bZI2NjYnTpxYsWJFZGTkwoULXVxcQkJC9uzZ4+/vr3lThZbWJNPS0ugJmhITEyVJMnYVJicxMdHYJdQaTbJWTfLWrVvh4eFff/21vEcdWlqTpCco5D7AS0a19PyrBHVn7MALTYmo6bMC+W4aNjY2WuOad7FV7rhRrePHj8vT5I9EHj16pCzyxhtvCCF+/fVX+cvKysqdO3eOHj3a1tbW1tY2KCjo//2//1erXeiwaNEiIURERITmoPxC26lTJx3fuG3bNiHE/Pnz5S+fe+45IcTXX3+tOUe+DEFgYGDVb79y5YokSRcvXqyxwmrp+FRqx44dQojhw4erm8KvQP4cBtCTiXyGqc/nhzTJejZJhfwX6dq1a2ssVVOzaZLyASCAnmr136Th6HMcAU2ytk1y48aNun/7165dq7FaRbNpkrp/JoAmjm9qUFwvHIZkbW3t4OBQXFz84MEDzXHNiyNaW1u3atXKwsKi2hPKRo0apee+JEl68cUXjx49mp+fv3//frVaHRIS8tFHHxlkF/I1EeW3BYqcnBwhhOZ9bav9CWg+3qKiosfN1PoRyT7++OPhw4f37NmzxgprpbKy8tNPPxVCvPbaa6KJ/AqEybw/NgUc66tDXf9bGAdNsp5NUiH/uRUSElJjqfpoik2SnqBJmEzubGqa3Oc3NMnaNsnXXntNqzyt8+mefPLJGqutUVNskvQEBefT6cDnNw2NvAkGNm7cOPGfY6Fld+/e1Tw4VggREhJSXl4eHx+vObhu3boOHTqUl5fruaNWrVrJd4e1tLR8+umn5RthHDx40CC7+OMf/+jp6Xn48GHNT1diY2OFEJMnT5a/XLJkyYwZM7S+8dChQ0LjyOFBgwYJIY4dO6Y55/jx40KIwYMHa31vYWHhzp075Rdyw1q2bNlPP/00ZcoUpaWa/q8AaK5okqKWTfLu3btmZmYZGRnKhMLCwq1bt06fPl3HjT5rhSYJmA6apKjrO8mGQ5MEUEfGjhTRlAg9Piv4n//5n9atWyt3Fbl48WJQUJCrq6vmVR6zs7O7dOnSuXPnb7/9Nj8/Pzc3d9OmTba2tpqL13gIrpOT04gRI5KTk4uLi7Ozs9977z0hxKpVq/TfhW6HDh2ysLCYNGnS1atX7927t3PnTjs7u0GDBj18+FCesHjxYkmSli9ffv369eLi4uvXry9dulQI4ePjo8y5d+9e165dLS0to6KisrOz7969u3XrVltbW09PT/lqi5rWr1/v4eHxuOuI1/auIhUVFdnZ2fv37w8ICBBCzJo1S6lKz5+PEX8FfA6jheObdNCnLzUOPZ+3NMnaNsk7d+4IIQIDA69du1ZcXHzmzBk/Pz9vb+/c3FzNelpUk6QnaDGdPmBqTOr1VM/nLU2yzu8kZYa6P12TbpL0BE0m1QdMDa+nDY1nHmpBz9595cqVyZMnOzo6yjc9PXDgwOjRo+V8889//rM8Jzc3d9GiRZ07d7a0tGzbtm1gYOD3338vb0pISNCMRN966y31f58yM378eLVanZSUNHfu3KeeesrW1rZ169aDBw/esmVLZWWlUoaOXejpxx9/DAoKcnJyki/i+N5772m+0BYUFGzdujUoKKhjx45WVlb29vY+Pj5r167VnKNWq/Py8sLDw3v06GFtbW1lZdWlS5d58+ZlZWVp7auysvLJJ5989913H1eMv7+/7ruKaF0nUpIkJyenPn36/PWvf/3555+rzjflXwGvi1p4LdTBdN5T6v+8pUlqLqJPk/z+++8nTpzo7u6uUql69+69cuVKrUXULaxJ0hO0mE4fMDUm9Xqq//OWJqm5iJ7vJNVq9dy5c8V/CwoKUra2qCZJT9BkUn3A1PB62tAkdVO7+AWMSJKk6OjoqVOnGrsQNHMxMTHTpk2jOynk/3QxMTHGLsQUmU5f4nmLRkNP0GI6fcDUmFRf4nmLRkNP0GRSfcDU0JcaGtdvAgAAAAAAgCGRNwFAS3Tz5s2JEycWFhYKITIzM1evXu3r6+vo6Oju7j5ixIh9+/bVarV79+5t2rQpICCgdevWKpWqa9euL7zwQnJysuaciIiIJnenJAAtEx0SAHSgSUJP5E1oiaTHky9VCDRvSUlJvr6+gYGBjo6OQog5c+Zs2LBh+fLlmZmZiYmJXl5eISEhERER+i8YHh7++uuvT5o06dKlS7m5udu2bUtKSvLx8dm/f78yZ86cOcuWLXvnnXcM/3hgaDRJtGR0SNSIJomWjCaJWjDq1aPQxAiuvYdG0cjXNbSzsxs6dKgpr2/YaxkWFBR4eXnNnTtXGRk/fvz27duVL0tKSjw9Pc3NzbOzs/Vc889//vMrr7yiOZKUlCSE6Nq1q9agfEmFepSvzXT6EtfjRKNp5Oubmn6TNGAfaGYd0qT6EtflRaNpzPcGpt8hDdsHmlmTpC81NAsjxVwAAOP44IMPsrKy3n33XWXkwIEDmhOsrKx69uyZnp5+5coVV1dXfdbcunWr1oi3t7dKpUpJSVGr1ZIkKYPPPvvs4sWLQ0JCLCx4AQJgcuiQAKADTRK1wvl0ANCCqNXqrVu3Dho0qF27djqmpaamCiFat25d5x0VFRU9evSod+/eyrsE2ZQpU9LS0g4ePFjnlQGggdAhAUAHmiRqi7wJQBOWm5u7aNGiLl26WFlZOTs7jxs37sSJE/KmVatWyVdSGDZsmDxy+PBheaRNmzbySGRkpCRJRUVF8fHx8ib50xJ5XJIkLy+vs2fPjh492sHBwdbWdtSoUfHx8fVf34iSk5Ozs7O9vb11zNmxY0dKSkq3bt169uxZ5x3t2bNHCPHWW29pjffr108IceTIkTqvDEB/NMlaoUMCLQodsrZokqg1Y57Mh6ZGmMx1UtC86XmeeWZmZqdOndzc3GJjYwsKCq5cuRISEiJJ0pYtW5Q5VU969/HxcXFx0Rx53Inx3t7ednZ2fn5+P/7444MHD86ePdu3b18rK6uTJ08aZP1Ro0a1bt06ISGhxkdqwHPLd+3aJYRYs2ZNtVsvXrwYFhZmZmbm7Oz8008/1XkvWVlZbm5us2fPrrqpoKBACOHv71/nxbWYTl8yqeukoHnTsye0nCZpqD7Q/DqkSfUlrpOCRqNPT2g5HdKAfaD5NUn6UkPj+CYATdWyZcuuX7++YcOG4OBgR0fHbt26ffXVVx4eHmFhYdnZ2QbZRVFR0Weffebn52dnZ+fr67t79+7S0tL58+cbZPHKykq5ERtkNT1lZmYKIZycnKrd2rdv3z179sybN+/ChQsDBw6s2y5yc3PHjh07cuTITZs2Vd3q6OgoSZJcBoAGRZOsLTok0HLQIeuAJonaIm8C0FTt27dPCDF+/HhlxNraevTo0Y8ePTLUcbZ2dnbygbuyPn36tGvXLjk52SCvcydPnszLy/Pz86v/UvorLi4WQlhaWj5uwvHjx6OionSflq9DUVFRUFBQz549v/zyS3Nz82rnWFhYPHr0qG7rA9AfTbK26JBAy0GHrAOaJGqLvAlAk1RSUlJQUGBjY+Pg4KA57ubmJoTIysoyyF5atWqlNSLfaCMnJ8cg6zc+GxsbIURZWVlDLF5eXh4aGurp6bljx47HvUuQp6lUqoYoAICCJlkHdEighaBD1g1NErVF3gSgSbK2tnZyciouLr5//77muHwItLu7u/ylmZlZaWmp5oT8/HytpbTufKEpNzdX60Bl+S2CcnvXeq7f+Dw8PIQQ8tnvBjd37tySkpKYmBjlepZPPvlkYmKi5pzCwkK1Wi2XAaDh0CTrgA4JtBB0yLqhSaK2yJsANFVTpkwRQmjeErWkpOTYsWMqlSooKEge8fDwSE9PVyZkZWXdunVLax1bW1vlxb579+6bN29WNhUXF589e1b58rfffsvIyPD29lZe5+q5fuPr3bu3ECItLa3areXl5T169Kjbyu+9997Fixe//vpra2trHdPkH5dcBoAGRZOsLTok0HLQIeuAJonaIm8C0FStXbu2U6dOCxYsOHDgwP37969evfr8889nZmZGRUXJh0MLIQIDAzMyMjZu3PjgwYOUlJT58+crnykpBgwYcPXq1du3byckJKSmpvr7+yubnJyc3nzzzYSEhKKionPnzs2YMcPKyioqKkqZUJ/1AwICXFxctD63aWje3t6urq7JyclVN23ZssXOzm7p0qVVN82YMUOSpOvXrz9u2e3bty9fvvzMmTMODg6ShpSUFK2ZSUlJQojAwMD6PQ4ANaNJ1hYdEmg56JB1QJNErTX2DfHQlAmTue84mjf979t69+7dBQsWdOrUydLS0snJKSgo6NixY5oT8vPzZ8+e7eHhoVKphg0bdvbsWR8fH7n7vfHGG/Kcy5cv+/v729nZtW/f/tNPP1W+19vb29PT89KlS0FBQQ4ODiqVasSIEXFxcYZa39/f39nZ+ccff6zxYRr2Xq1vvvmmhYVFenq61vimTZtUKtWSJUuqfktAQIC9vX15efnj1tS83KYWrTv1ymfml5aWGuSxqE2pL5nUfcfRvOnfE1pIkzRgH2hmHdKk+hL3HUej0bMntJAOadg+0MyaJH2poZnKKxCaBNP5uw7Nm4m8P5bfKBi7CrXa0K+F+fn5np6ec+fO1XP+vXv3VCrV7Nmz67/rpKQkSZL+9a9/1X8phen0JRN53qIlMJH3x6bTJA3YB5pZhzSpvmQiz1u0BKbw3sB0OqRh+0Aza5L0pYbG+XQA0LI4OTnFxsbu3bv3008/rXGyWq0OCwtzdHRcuXJlPfebmpoaEhKybNmy6dOn13MpAGggdEgA0IEmiVohbwKAFqd///7nzp07dOhQYWGh7pnZ2dmpqanHjh1T7tVSZ1988cXq1atXr15dz3UAoEHRIQFAB5ok9EfeBADaIiMjJUlKTk5OT0+XJOntt982dkWG17FjxwMHDjg6Ouqe5u7uHhcX16tXr/rvcd26dXwkBTQPzb5J0iEB1Fmz75CCJgm9WRi7AAAwOUuWLFmyZImxqwAAE0WTBIDHoUMCCo5vAgAAAAAAgCGRNwEAAAAAAMCQyJsAAAAAAABgSORNAAAAAAAAMCSuF47aWb9+/d69e41dBZq527dvCyGmTp1q7EJMRUJCgqjlD6SsrOz27dtt27Z1cHBosLpQDZ63aAR16AnNHu9PqiW/npqOhISE5v28rayszMvLe/jwYYcOHYxdS0tHT1DwvlqHhIQEPz8/Y1fRnElqtdrYNaDJWLRoUVpamrGrAFCz3Nzc06dPl5eX29jYtP2PGm9b2xSZm5uvXbu2Y8eOxi5E3LhxY9myZRUVFcYuBE3e/fv309PTe/ToYexC0Bx4eXl99NFHxq5CCCH27NmzZ88eY1dheHLGlJOTc+fOndzc3IqKCkdHx6CgIGPXBUAvoaGhoaGhxq6i2SJvAoDmqaKiIikpKS4uLj4+/vvvv8/Pz3d1df3DH/4wbNiwMWPGDBgwQJIkY9cIoBoxMTHTpk3jHRpgssrLy5OTk48ePXr06NH4+PhHjx65u7v7+/uPGTNm6NChvXr1MnaBAGASyJsAoPkjewKaEPImwARVzZg8PDzkl1EyJgCoFnkTALQsZE+AiSNvAkwEGRMA1Ad5EwC0XJrZ09GjR+/du9e2bdtBgwbJb6b79+9vZsZtTIHGRt4EGJFmxhQXF1dcXEzGBAB1Q94EABCC7AkwGeRNQCMrKys7f/48GRMAGBZ5EwBAW0VFxeXLl+XgiewJaGTkTUAjePTo0c8//yy/0mllTGPGjOncubOxCwSAJo+8CQCgi2b2dOzYsby8PEdHxz/84Q/yO3KyJ8DgyJuABqIjY3r66ac7depk7AIBoFkhbwIA6Ktq9uTg4DBo0CCyJ8CAyJsAA3r48OEvv/xCxgQAjY+8CQBQF2RPQAMhbwLqSTNj+uGHH0pKSsiYAKDxkTcBAOqrsrLy999/l9/ZHz9+PDc3l+wJqDPyJqAOdGRMgYGBHTt2NHaBANDikNi319oAACAASURBVDcBAAyJ7AmoJ/ImQE9VM6bOnTsPHTp02LBhZEwAYHTkTQCABpSamirf5E4rexo6dOigQYMsLS2NXSBgcsibAB0ePnz4448/xsXFxcfHa2VMQUFBTzzxhLELBAD8L/ImAEAj0cqe7O3tBw8eTPYEaCFvArQUFRUlJCSQMQFA00LeBAAwAiV7OnHixN27d8meAAV5EyD+O2M6ffp0aWkpGRMANC3kTQAAIyN7AjSRN6HF0pExjR07tkOHDsYuEABQC+RNAAAToiN7+sMf/mBlZWXsAoEGR96EFoWMCQCaK/ImAICJkrOnuLi4EydOpKWl2dnZ+fn5kT2h2SNvQrNXbcYkt/dRo0a1b9/e2AUCAAyAvAkA0ARUmz3JH4APHz6c7AnNCXkTmqUHDx4kJibKnfzs2bNkTADQ7JE3AQCaGLInNG/kTWg2NDOmn376qaysTMmYAgICvLy8jF0gAKABkTcBAJowJXs6efLk7du3yZ7QDJA3oUkjYwIAyMibAADNhI7syd/f39ra2tgFAnohb0KTQ8YEAKiKvAkA0AylpqbKV6I9fPjwrVu3bG1thwwZQvaEJoG8CU3C4zKmMWPGjBo1qk2bNsYuEABgZORNAIBmrmr21L9//2HDho0ZM4bsCSaIvAkm6/79+2fOnDl69OjRo0d//fXXyspKMiYAwOOQNwEAWhCyJ5g+8iaYFDImAEDdkDcBAFooJXs6cuTIzZs3yZ5gIsibYHRkTACA+iNvAgBAV/Y0bNgwGxsbYxeIFoS8CUahI2MKCAhwcXExdoEAgCaGvAkAgP+iZE/ffffdjRs3VCrVgAEDyJ7QaMib0GgKCwt/+uknJWMSQvTo0UNud2RMAIB6Im8CAOCxMjIy4uPjjx49SvaERkPehAZ1586dxMREubORMQEAGg55EwAAelGyp++///769euWlpZ9+/aVTzYhe4IBkTfB4LQyJkmSunfvLmdMo0ePbt26tbELBAA0Q+RNAADUmlb2ZGFh4e3tTfYEgyBvgkGQMQEAjIu8CQCAetGRPQ0dOlSlUhm7QDQx5E2os5ycnDNnzsgd6ZdffjEzMyNjAgAYC3kTAAAGo2RPR48eTU1NJXtCHZA3oVZ0ZExjxoxxdnY2doEAgBaKvAkAgAahZE9xcXGXLl0ie4KeyJtQo6oZU79+/YYOHSrHTGRMAABTQN4EAECDy8zMjIuLq5o9DR06dMSIEY6OjsYuECaEvAnVysnJOXXqVFxcXHx8PBkTAMD0kTcBANCoyJ6gG3kTFNnZ2adPn642Y3r66adbtWpl7AIBAHgs8iYAAIyG7AmyL7/8Mi0tTf73+fPnv/rqq/fff1/ZOnr0aF9fXyOVhsZGxgQAaB7ImwAAMAlZWVk//PBDtdnT8OHDnZycjF0gGlCbNm3y8/MtLCyEEGq1Wq1Wm5mZyZtKSkrmzZv3ySefGLVANCwyJgBA80PeBACAydHMnn7//Xdzc3Oyp+bttdde27JlS1lZWbVbT5w4MXLkyMatCA1O/m9ebcYUGBjIf3MAQFNH3gQAgEmr9o9SsqdmJi4uzt/fv9pNbdu2zczMNDc3b+SS0BC0/jsTJQMAmjHyJgAAmgwdB0Rw0k2Tplarvby8MjIytMatrKxef/31yMhIo1QFg+BUWQBAy0TeBABAk8QFX5qZiIiIjz76qOopdefOnfPx8TFKSagzHbcCIGMCALQQ5E0AADR5hsqeHjx4MGXKlDlz5kydOrVBC0ZVycnJ/fr10xp84oknbty4YYxyWrSHDx++9957kiStW7dO/+/idpMAAGgibwIAoFnRkT2NGTPG2dlZx/d+++2348ePF0JMnDjxiy++cHd3b6yqIYQQ3bp1u3btmvKllZXVm2+++be//c2IJbVAp06deumll27evGlra1tYWKj7yllkTAAAPA55EwAAzVZOTs6pU6f0z57eeOONDRs2lJaWWlpaWltbf/jhh6+88opRKm+ZVq1atWLFCs1T6i5fvty9e3cjltSiPHz4cMWKFR988IGZmVlFRYUQ4uzZs76+vlrTMjIy4uPjyZgAANCNvAkAgBYhJyfnzJkz8t/JcvbUvXt3OXhSsqcBAwb8+uuvyrdIkjRmzJitW7d26NDBeIW3ICkpKV27dpXfm0mS1KdPn+TkZGMX1VJ89913s2bNys7OLi8vl0csLS3XrFmzZMkS8fiMSY6ZVCqVUWsHAMAUkTcBANDiZGdnnzp16tSpUydPnvz999/NzMz69+8/ZMiQjRs3VlZWas60tLSU/+p+/fXXzczMjFVwyzFgwIDk5OTKykpLS8v3339/0aJFxq6o+SsoKFiyZMk//vEPSZI0n//y8YC9e/c+derUzZs3bWxsBg0aNHLkyJEjRw4ePNjGxsaINQMAYPrImwAAaNHkc+5OnTp14MCBmzdvVjtHkqTBgwfv2LGja9eujVxeS7Nhw4bw8PDy8nJJkm7duuXl5WXsipq5b7/9dtasWXl5eVXvDCiEsLS0HDJkyKhRo0aOHDlo0CAyJgAA9EfeBAAAhNC4eFO1Wy0tLSVJWrFixZIlS3RfQRn1kZmZ6eXlpVarhwwZEhcXZ+xymrN79+4tXbp069atZmZmWof1aTp37pyPj09jFgYAQPPAgfEAAEAIIY4ePfq4sEkIUVZWVlpa+uabbw4dOvTKlSuNWViL4uHh4e/vr1arZ86caexamrOYmJgnn3xyx44dQggdYZOlpeXJkycbrywAAJoRjm8CgBatffv2aWlpxq4CAABTt3Dhwo8++sjYVQBAk2Fh7AIAAMaUlpa2cOFCPz8/YxcCIzt//vyqVavMzc3VarVyuIckSfb29o6Ojm3atGn1H05OTp6enp06dTJuwc2YWq3Oy8tzcXHRMSchIWH9+vUxMTGNVpWJW79+vRBi4cKF+kwuLy+/cOFCfn7+vXv3CgoKCgoK7ty5I/+juLhYmSb/d7Cystq5c2dD1Y2m46OPPuLjGQCoFfImAGjpBg8eHBoaauwqYGRDhgwpLy9v3bq1m5ubm5tbu3bt2rZt6+rqyj3pTJN8fDr/cxV79uwRhviBFBcX37lzJz09PScnJysrKysry9bWlp8zxH+eYwAA/ZE3AQAA4enpuXbtWmNXARiZjY1N+/bt27dvb+xCAABo8vjQEgAAAAAAAIZE3gQAAAAAAABDIm8CAAAAAACAIZE3AQAAoEXYvXu39B/29vZaW2/evDlx4sTCwkIhRGZm5urVq319fR0dHd3d3UeMGLFv375a7evevXubNm0KCAho3bq1SqXq2rXrCy+8kJycrDknIiIiOjq6Po+Imutcc0REhPJkGDx4cH0eEQCgWuRNAAAALcWDBw+6du0aHBxs7EKM6fPPP1er1Q8ePNAcTEpK8vX1DQwMdHR0FELMmTNnw4YNy5cvz8zMTExM9PLyCgkJiYiI0H8v4eHhr7/++qRJky5dupSbm7tt27akpCQfH5/9+/crc+bMmbNs2bJ33nmnbg+EmutT8/vvv69Wq9Vqtbm5ed0eCwCgBmoAQAsmhIiOjjZ2FQBqRz5Yow7fWFhY2Llz53Hjxhm8JD3Z2dkNHTrU4MuGhoaGhobWOG3Xrl3iP3mTpoKCAi8vr7lz5yoj48eP3759u/JlSUmJp6enubl5dna2niX9+c9/fuWVVzRHkpKShBBdu3bVGpQkqQ59mJoNVbO5ufmgQYNq3JGezzEAgMLCmFkXAAAAGpGDg0NKSoqxqzA5H3zwQVZW1rvvvquMHDhwQHOClZVVz54909PTr1y54urqqs+aW7du1Rrx9vZWqVQpKSlqtVqSJGXw2WefXbx4cUhIiIVFLd6ZU3Pj1AwAqDPOpwMAAEDLpVart27dOmjQoHbt2umYlpqaKoRo3bp1nXdUVFT06NGj3r17KyGIbMqUKWlpaQcPHtR/KWrWwYA1AwDqg7wJAACgRdi/f79ygeTi4mKtkRs3bkybNq1Vq1YuLi7BwcHKYVCRkZHyBC8vr7Nnz44ePdrBwcHW1nbUqFHx8fHynFWrVslzhg0bJo8cPnxYHmnTpo3mOkVFRfHx8fImEznMJDk5OTs729vbW8ecHTt2pKSkdOvWrWfPnnXe0Z49e4QQb731ltZ4v379hBBHjhzRfylq1sGANQMA6oO8CQAAoEWYPHmyWq2eNGlStSMLFixYsGBBenp6dHT08ePHn3vuOXnOkiVL1Gq1t7d3fn7+/PnzV61alZWVdfr06by8vICAgFOnTgkh3n77bbVabWdnp6w8duxYtVrt4+OjjMjraF6/qby8XNkaEBDg4uKSmJjY0D+Eqi5cuCCE8PLyqnbrpUuX5s+fP2vWLGdnZ/n2dnXbS3Z2dkRExOzZs6dOnaq1ydPTUymDmk2qZgBAfZA3AQAAQMyePdvPz8/Ozm7MmDHjx48/e/bs3bt3NScUFRV99tln8hxfX9/du3eXlpbOnz/fIHuvrKyUQyiDrFYrmZmZQggnJ6dqt/bt23fPnj3z5s27cOHCwIED67aL3NzcsWPHjhw5ctOmTVW3Ojo6SpIkl0HNJlUzAKA+TOIwZgAAABiX5h/57du3F0JkZGQoZ8MJIezs7OQzkmR9+vRp165dcnJyZmamh4dHPfd+8uTJeq5QZ/KphZaWlo+bcPz48R49etR5/aKioqCgoJ49e+7cudPc3LzaORYWFo8ePdJ/TWquqiFqBgDUB8c3AQAA4L8OPLGyshJCVFZWak5o1aqV1rfIdxDLyclp+OoakI2NjRCirKysIRYvLy8PDQ319PTcsWPH40IQeZpKpdJ/WWquWkxD1AwAqA/yJgAAANQsNzdX63w3OWlS7ltvZmZWWlqqOSE/P19rkTpfl6fhyAdnFRQUNMTic+fOLSkpiYmJUS6O/uSTT2pdpqqwsFCtVtfqGDFq1tJANQMA6oO8CQAAADUrLi4+e/as8uVvv/2WkZHh7e2t/AHv4eGRnp6uTMjKyrp165bWIra2tkom1b17982bNzdw1TXr3bu3ECItLa3areXl5XU+yeu99967ePHi119/bW1trWOa/EOTy9ATNWtquJoBAPVB3gQAAICaOTk5vfnmmwkJCUVFRefOnZsxY4aVlVVUVJQyITAwMCMjY+PGjQ8ePEhJSZk/f75y6JNiwIABV69evX37dkJCQmpqqr+/vzxuxPvTeXt7u7q6JicnV920ZcsWOzu7pUuXVt00Y8YMSZKuX7/+uGW3b9++fPnyM2fOODg4SBpSUlK0ZiYlJQkhAgMD9V+cmg1SMwCgQZE3AQAAtAj79++XJOnrr78WQqhUqhkzZiQmJmqOvP3220IISZLWrVsnhOjfv39wcLDy7fb29p988sny5cs9PDyGDx/u7Ox8/PjxESNGKBNWrVo1e/bsNWvWuLq6vvzyy+Hh4e7u7rm5uZIkRUREyHM2bNjQt2/fp556atq0aVFRUU899ZQ8Xl5ebqz700mSNHv27DNnzmRkZGht0nHXvMzMTHt7+w4dOjxu2b179+pZwL59+zw9PcePH6//4tRskJoBAA1LDQBowYQQ0dHRxq4CQO1ER0c38rs4b29vT0/PxtxjrYSGhoaGhtY4bdeuXUKIzz//XGs8Pz/f09Nz7ty5eu7u3r17KpVq9uzZtS60iqSkJEmS/vWvf9V2cWqulao1K8zNzQcNGlTjCno+xwAACo5vAgCgyZg4caIkSatWrdIar6io2LBhQ79+/WxtbZ2cnAICAo4ePVrtCt9++223bt2US+rW2dmzZ19++eVOnTqpVKrWrVv37t37mWee+fzzz6uewwKYPicnp9jY2L1793766ac1Tlar1WFhYY6OjitXrqznflNTU0NCQpYtWzZ9+vTaLk7N9akZANAIyJsAAGgadu7cGRsbW3W8oqJi8uTJS5cunT179u3bt5OSkjp27BgYGPjvf/9bc1pKSsrEiROXLVuWnZ1dnzIqKyvDw8OHDBni6up66NCh/Pz833//ff369YWFha+++uqTTz5ZXl5en/WBhvbXv/5VkiR7e3vNwf79+587d+7QoUOFhYW6vz07Ozs1NfXYsWPu7u71rOSLL75YvXr16tWr67Y4Nde5ZiFERESEfKWnioqKeq4PAKiWpDbGefIAABMhSVJ0dPTUqVONXYhh2Nvb9+vXLy4urvntPSMjo3fv3sHBwbt27Vq5cqV8nR3Zjh07Xn755ddff/3jjz+WR9Rqdc+ePeW/1lq1aiUPPv/883379l2yZEnHjh2zsrLqnAq99dZba9as2bx585w5czTHKyoqJkyYcOjQobKysvofP2VYze+JERMTM23atMZ5FxcZGRkeHq58+dZbb1U9ws7o5CYWExNj7ELQbPEcA4Da4vgmAACagDlz5oSGhlZ7Z6V9+/YJISZMmKCMSJI0adKke/fuaV5J9x//+EdEREQ9k6DLly+///77Pj4+WmGTEMLc3Pydd96pz+IwTUuWLNG8FoMJhk0AAMAEkTcBAGDqtm3bdvHixcjIyGq3yufHad143sPDQwiheUyNSqWqfyWbN2+urKwMDQ2tdqufn59arTa1g5sAAADQ+MibAAA1y83NXbRoUZcuXaytrb28vMaMGbN9+/ZHjx5VnWBlZeXs7Dxu3LgTJ07Im+RbsMtu3Lgxbdq0Vq1aubi4BAcHa11bWvdeysvLo6Ojn376aXd3d5VK1adPn6ioqMrKSnlrZGSkJElFRUXx8fHyvjRTjzt37oSFhXXs2NHKyqpt27YhISFJSUm1Kq+B9q6PtLS0xYsXb9u2zcHBodoJbdq0Ef9JnTR3KoS4ceOG/jvSx+nTp4UQffv21XM+T4yGe2IAAACYtEa+Hx4AwKQIIaKjo3XPyczM7NSpk7u7e2xsbGFhYVZWlny3oPXr12tOcHNzi42NLSgouHLlSkhIiCRJW7ZsURaZNGmSEGLSpEk//vjjgwcPvv/+e5VKNXDgQP33Il8qe82aNXl5eXfu3Pn444/NzMy0zvSxs7MbOnSoVv0ZGRlPPPGEm5vbwYMH79+/f+HChREjRtjY2Pz444/6l9ege9ctKCjo1Vdflf8t38195cqVmhM++eQTIcTrr7+uOejj4yOE8PX1rbqgp6enubl5tfsaNWpU69atExISHleMfNjUmTNn9KmcJ0Y9965DdHQ07+I0ca96NDSeYwBQW7xTAYAWTZ+86eWXX646bezYscof/PKEf/3rX8rW4uLidu3aqVSqrKwseUT+uz02NlaZ8+yzzwoh7ty5o+deYmNjR44cqbl1xowZlpaWBQUFyki1f9i/9NJLQogvv/xSGcnMzLS2tvbx8VFGaiyvQfeuw+bNmzt37vzgwQP5y2rzpkePHvn4+FhaWm7cuPHu3bs3b9587bXX5Ds6+fv7V11TR940YsQIZ2dnHZGHnDf99NNP+hTPE6Oee9eBvEkLWQAaGs8xAKgtrrAAAKiBfDnqcePGaQ4eOnRIa8L48eOVEWtr69GjR+/atevIkSMzZ85UxgcOHKj8u3379kKIjIwM+XSwGvcSHBwcHBysudXb23v37t0XL1708/PTUf/+/fvNzMw0v9fd3b1Xr14///xzWlqal5eXPuU1wt6runXrVnh4+Ndff21nZ6djmo2NzYkTJ1asWBEZGblw4UIXF5eQkJA9e/b4+/vX9j7iJ0+e1D2hXbt2mZmZd+/e1Wc1nhgG2bsOe/bs0WdaS5CWlib4gaAh6f8fEwAgI28CAOhSUlJSUFBgY2PzuIsHPW6Cm5ubECIrK0tz0MnJSfm3lZWVEEK+0k2NexFCFBQUfPjhh/v27UtLS8vPz1fGHz58WGP9WrtWXLt2TfPvh8eV1zh7r0o+DW3kyJFa4++88458J7hr1649+eSTQggHB4e///3vf//735U5R44cEUIMGDBAx/p1MGLEiJ9//vn8+fNaAVBVPDEMtXcd5Bu0Q5GQkGDsEtCcPe5WCQCAanG9cACALtbW1k5OTsXFxffv36/VBPny1XoeX1PjXoQQEyZMWLly5Zw5c65evVpZWalWq9evXy+EUKvVyhxJkqqu3KpVKwsLi7KysqpH+Y4aNUqf8oy199dee03rW7TOp5PDpmrJd6YLCQnR8wHqae7cuRYWFnv37q1269KlS83MzC5fvix4YjTK3vU+nr3541wnNDTCJgCoLfImAEANpkyZIoT49ttvNQf79++/cOFCzQkHDx5UtpaUlBw7dkylUgUFBRlkLxUVFfHx8e7u7mFhYW3btpX/gNe8QZ7M1ta2tLRU/nf37t03b94shAgJCSkvL4+Pj9ecuW7dug4dOpSXl+tTm3H3XqO7d++amZllZGQoI4WFhVu3bp0+fXq3bt0MsgtFt27d/va3v507d27btm1am65cufLFF19MnTq1R48e8ghPjAbdOwAAgEkz9kcFAABjEnrfn87Dw+PAgQOFhYW3b9/+61//6ubmdvPmTc0J8m3ICgsLlduQbd68WVlEvu7yo0ePlJE33nhDCPHrr7/quZeAgAAhxAcffHDnzp2HDx8eP368Q4cOQojvv/9eWXPs2LFOTk63bt368ccfLSwsLl26pFars7Ozu3Tp0rlz52+//TY/Pz83N3fTpk22traaD7zG8hp07/qr9nrhd+7cEUIEBgZeu3atuLj4zJkzfn5+3t7eubm51S5Sn/vTySIiIiwtLd94440rV66UlJSkpaVt3brVw8Nj2LBhyqXN1Twx6r13HbheuBaOb0JD4zkGALXFOxUAaNH0yZvUavXdu3cXLFjQqVMnS0tLDw+P6dOnX7169XETnJycgoKCjh07Jm/SuqLKW2+9pdY41UgIMX78eH32cufOnblz57Zv397S0tLNze3ll1+OiIiQV1Bu6XX58mV/f387O7v27dt/+umnyvfm5uYuWrSoc+fOlpaWbdu2DQwMVOIAPctroL3rb+7cuVqfGAUFBSlbv//++4kTJ7q7u6tUqt69e69cufLhw4daK8TGxlb92GnLli2ac/z9/XXfn07x008/vfjii/IPxMHBYfDgwVFRUSUlJVrTeGLUbe81Im/SQhaAhsZzDABqS1L/95snAECLIklSdHQ0Vx0GmpaYmJhp06bxLk4hN7GYmBhjF4Jmi+cYANQW128CAAAAAACAIZE3AQAAAE3PzZs3J06cWFhYKITIzMxcvXq1r6+vo6Oju7v7iBEj9u3bV4c1y8rK1q9f7+Pj4+Dg4OrqOm7cuNjYWOVIuoiICPlcTgAAakTeBACA0UiP99577xm7OgCmKykpydfXNzAw0NHRUQgxZ86cDRs2LF++PDMzMzEx0cvLKyQkRLmamJ6KiooCAgK2b9++fv36nJycc+fO2dvbT5w48eLFi/KEOXPmLFu27J133jH84wEANDvkTQAAGI2OKyySN8F02NvbDxs2rOmu3/wUFhZOmDDhmWeemTdvnjIYGRk5fvx4Ozu7jh07/vOf//T09IyMjMzJydF/2fDw8PPnz3/33XfDhw9XqVQdOnTYvn27tbW1MqFLly779u1bvXo1lzECANSIvAkAAABoSj744IOsrKx3331XGTlw4MBLL72kfGllZdWzZ8+KioorV67ouWZ2dvbmzZtfeOEFNzc3ZdDOzq64uLh3797KiLe397PPPrt48eLy8vJ6Pw4AQHNG3gQAAAA0GWq1euvWrYMGDWrXrp2OaampqUKI1q1b67nsN998U1FRoc+BZlOmTElLSzt48KCeKwMAWibyJgAAgGYrNzd30aJFXbp0sbKycnZ2Hjdu3IkTJ+RNq1atki8WpkQMhw8flkfatGkjj0RGRkqSVFRUFB8fL2+ysLBQxiVJ8vLyOnv27OjRox0cHGxtbUeNGhUfH1//9aFDcnJydna2t7e3jjk7duxISUnp1q1bz5499Vz2l19+EUI4OzsvXry4ffv2VlZWTzzxRFhYWF5entbMfv36CSGOHDlSp/IBAC0FeRMAAEDzlJWVNXDgwK+++ioqKuru3btnzpyxtbUdPXr01q1bhRBvv/22Wq22s7NT5o8dO1atVvv4+CgjS5YskecMHTpUvrKYfBaVPO7t7Z2fnz9//vxVq1ZlZWWdPn06Ly8vICDg1KlT9VxfFhAQ4OLikpiY2JA/pKbnwoULQggvL69qt166dGn+/PmzZs1ydnbevXu3JEl6LpuZmSmEmDVrVnZ29qlTp3JyclauXLlt2zY/P7+CggLNmZ6enkoZAAA8DnkTAABA87Rs2bLr169v2LAhODjY0dGxW7duX331lYeHR1hYWHZ2tkF2UVRU9Nlnn/n5+dnZ2fn6+u7evbu0tHT+/PkGWbyyslIOoQyyWrMhB0NOTk7Vbu3bt++ePXvmzZt34cKFgQMH6r9scXGxEEKlUm3fvr1z586tWrWaOXPmsmXLrl69+uGHH2rOdHR0lCRJLgMAgMchbwIAAGie9u3bJ4QYP368MmJtbT169OhHjx4Z6mQoOzs7+ewqWZ8+fdq1a5ecnGyQMOLkyZN5eXl+fn71X6o5kYMhS0vLx004fvx4VFSU7qs7VSUfiTZmzBjNUxonTJggqjt1zsLC4tGjR7VaHwDQ0pA3AQAANEMlJSUFBQU2NjYODg6a4/Ldx7Kysgyyl1atWmmNuLq6CiFycnIMsj6qsrGxEUKUlZUZdtmOHTsKIVxcXDQH5d/mnTt3tCaXl5erVCrDFgAAaGbImwAAAJoha2trJyen4uLi+/fva47LZ9K5u7vLX5qZmZWWlmpOyM/P11pKxzWAcnNztc53k5MmOaeo//qoysPDQwihdU2l+pMv6651YJr825QzSkVhYaFarZbLAADgccibAAAAmqcpU6YIITTvW19SUnLs2DGVShUUFCSPeHh4pKenKxOysrJu3bqltY6tra2SGXXv3n3z5s3KpuLi4rNnzypf/vbbbxkZGd7e3koYUc/1UVXv3r2FEGlpadVuLS8v79GjSF9l8AAAIABJREFURx2W/eMf/+jp6Xn48GH5fD1ZbGysEGLy5MmaM+VfqFwGAACPQ94EAADQPK1du7ZTp04LFiw4cODA/fv3r169+vzzz2dmZkZFRSlHrAQGBmZkZGzcuPHBgwcpKSnz589XDk1SDBgw4OrVq7dv305ISEhNTfX391c2OTk5vfnmmwkJCUVFRefOnZsxY4aVlVVUVJQyoT7rc3+6anl7e7u6uiYnJ1fdtGXLFjs7u6VLl1bdNGPGDEmSrl+//rhlra2tt27dmpubO3369GvXruXn5+/atWvt2rWDBg0KCwvTnJmUlCSECAwMrPdDAQA0Z+RNAAAAzZO7u/vZs2efe+65sLAwFxeXP/zhD0VFRUePHp0zZ44yZ9WqVbNnz16zZo2rq+vLL78cHh7u7u6em5srSVJERIQ8Z8OGDX379n3qqaemTZsWFRX11FNPKd9ub2//ySefLF++3MPDY/jw4c7OzsePHx8xYoRB1i8vL+f+dFVJkjR79uwzZ85kZGRobdJxR7/MzEx7e/sOHTroWHns2LGnT58uLi4eOHCgm5vbmjVrwsPDT5w4oXWppn379nl6empehx4AgKokXsIBoCWTJCk6Onrq1KnGLgRALcTExEybNs3o7+L69et39+7dx53Y1ZjkJhYTE2PsQhpJQUFBr169goODN23apM/8/Pz8du3avfDCC1u2bKnnrpOTk/v37//VV19Nnz69nks1LS3tOQYA9cfxTQAAAEBT4uTkFBsbu3fv3k8//bTGyWq1OiwszNHRceXKlfXcb2pqakhIyLJly1pa2AQAqAPyJgAAAKCJ6d+//7lz5w4dOlRYWKh7ZnZ2dmpq6rFjx5SbEtbZF198sXr16tWrV9dzHQBAS0DeBAAAgNqJjIyUJCk5OTk9PV2SpLffftvYFbVEHTt2PHDggKOjo+5p7u7ucXFxvXr1qv8e161bx5FNAAA9WRi7AAAAADQxS5YsWbJkibGrAAAApovjmwAAAAAAAGBI5E0AAAAAAAAwJPImAAAAAAAAGBJ5EwAAAAAAAAyJ64UDQEu3fv36vXv3GrsKALVw+/ZtIcTUqVONXYipSEhIEPxA0JASEhL8/PyMXQUANCWSWq02dg0AAKNZtGhRWlqasasA8H/u37+fnp7eo0cPYxcC4L+EhoaGhoYauwoAaDLImwAAAExITEzMtGnTeIcGAACaNK7fBAAAAAAAAEMibwIAAAAAAIAhkTcBAAAAAADAkMibAAAAAAAAYEjkTQAAAAAAADAk8iYAAAAAAAAYEnkTAAAAAAAADIm8CQAAAAAAAIZE3gQAAAAAAABDIm8CAAAAAACAIZE3AQAAAAAAwJDImwAAAAAAAGBI5E0AAAAAAAAwJPImAAAAAAAAGBJ5EwAAAAAAAAyJvAkAAAAAAACGRN4EAAAAAAAAQyJvAgAAAAAAgCGRNwEAAAAAAMCQyJsAAAAAAABgSORNAAAAAAAAMCTyJgAAAAAAABgSeRMAAAAAAAAMibwJAAAAAAAAhkTeBAAAAAAAAEMibwIAAAAAAIAhkTcBAAAAAADAkMibAAAAAAAAYEjkTQAAAAAAADAk8iYAAAAAAAAYEnkTAAAAAAAADIm8CQAAAAAAAIZE3gQAAAAAAABDIm8CAAAAAACAIVkYuwAAAICW7ssvv0xLS5P/ff78eSHEunXrlK2jR4/29fU1TmUAAAB1IqnVamPXAAAA0KK1adMmPz/fwsJCCKFWq9VqtZnZ/x6EXlJSMm/evE8++cSoBQIAANQO59MBAAAY2bRp08zMzEpKSkpKSkpLS8vKykr+QwjxzDPPGLtAAACA2uH4JgAAACOLi4vz9/evdlPbtm0zMzPNzc0buSQAAID64PgmAAAAIxs6dGi7du2qjltZWc2cOZOwCQAANDnkTQAAAEYmSdKLL75oaWmpNV5aWvrcc88ZpSQAAID64Hw6AAAA40tOTu7Xr5/W4BNPPHHjxg1jlAMAAFAvHN8EAABgfN7e3l27dtUcsbKy+tOf/mSsegAAAOqDvAkAAMAkzJw5U/OUutLS0unTpxuxHgAAgDrjfDoAAACTkJKS0rVrV/m9mSRJffr0SU5ONnZRAAAAdcHxTQAAACahS5cu/fr1MzMzE0JYWFi89NJLxq4IAACgjsibAAAATMXMmTPlvKm8vHzq1KnGLgcAAKCOOJ8OAADAVGRmZnp5eanV6iFDhsTFxRm7HAAAgDri+CYAAABT4eHh4e/vr1arZ86caexaAAAA6o7jmwAAaCQ//PBDQEBAeXm5sQsB0Bx4eXndvn3b2FUAAFA9C2MXAABAS5GZmVleXh4TE2PsQmDS1Gp1Xl6ei4tLfRZZv369EGLhwoUGKqrJmzp16sKFC/38/IxdiMEkJCTIv2UAAEwTeRMAAI0qNDTU2CWg+duzZ4/gyfbfBg8e3Jx+IJyjAAAwcVy/CQAAAAAAAIZE3gQAAAAAAABDIm8CAAAAAACAIZE3AQAAAAAAwJDImwAAAID/c/PmzYkTJxYWFgohMjMzV69e7evr6+jo6O7uPmLEiH379tVhzbKysvXr1/v4+Dg4OLi6uo4bNy42Nla55ndERER0dLQhHwMAAMZG3gQAAID/9eDBg65duwYHBxu7EKNJSkry9fUNDAx0dHQUQsyZM2fDhg3Lly/PzMxMTEz08vIKCQmJiIio1ZpFRUUBAQHbt29fv359Tk7OuXPn7O3tJ06cePHiRXnCnDlzli1b9s477xj+8QAAYCTkTQAAAPhfarW6srKysrLSWAXY29sPGzbMWHsvLCycMGHCM888M2/ePGUwMjJy/PjxdnZ2HTt2/Oc//+np6RkZGZmTk6P/suHh4efPn//uu++GDx+uUqk6dOiwfft2a2trZUKXLl327du3evXqmJgYQz4eAACMx8LYBQAAAMBUODg4pKSkGLsKo/nggw+ysrLeffddZeTAgQOaE6ysrHr27Jmenn7lyhVXV1d91szOzt68efMrr7zi5uamDNrZ2RUXF2tO8/b2fvbZZxcvXhwSEmJhwVt0AECTx/FNAAAAgFCr1Vu3bh00aFC7du10TEtNTRVCtG7dWs9lv/nmm4qKCn0O2poyZUpaWtrBgwf1XBkAAFNG3gQAAAAhhNi/f7/0H/LRN5ojN27cmDZtWqtWrVxcXIKDg5XDoCIjI+UJXl5eZ8+eHT16tIODg62t7ahRo+Lj4+U5q1atkucoscvhw4flkTZt2miuU1RUFB8fL29q5MN8kpOTs7Ozvb29dczZsWNHSkpKt27devbsqeeyv/zyixDC2dl58eLF7du3t7KyeuKJJ8LCwvLy8rRm9uvXTwhx5MiROpUPAIBpIW8CAACAEEJMnjxZrVZPmjSp2pEFCxYsWLAgPT09Ojr6+PHjzz33nDxnyZIlarXa29s7Pz9//vz5q1atysrKOn36dF5eXkBAwKlTp4QQb7/9tlqttrOzU1YeO3asWq328fFRRuR17Ozshg4dqlar1Wp1eXm5sjUgIMDFxSUxMbHhHv6FCxeEEF5eXtVuvXTp0vz582fNmuXs7Lx7925JkvRcNjMzUwgxa9as7OzsU6dO5eTkrFy5ctu2bX5+fgUFBZozPT09lTIAAGjqyJsAAABQs9mzZ/v5+dnZ2Y0ZM2b8+PFnz569e/eu5oSioqLPPvtMnuPr67t79+7S0tL58+cbZO+VlZVyCGWQ1aolB0NOTk7Vbu3bt++ePXvmzZt34cKFgQMH6r+sfKSYSqXavn17586dW7VqNXPmzGXLll29evXDDz/UnOno6ChJklwGAABNHXkTAAAAaqYZsrRv314IkZGRoTnBzs5OPiNM1qdPn3bt2iUnJxskQDl58mReXp6fn1/9l3ocORiytLR83ITjx49HRUXpvrpTVfJRXWPGjNE8PXDChAmiulPnLCwsHj16VKv1AQAwTeRNAAAAqJnmgT9WVlZCiMrKSs0JrVq10voW+Q5uOTk5DV+dAdjY2AghysrKDLtsx44dhRAuLi6ag/JP5s6dO1qTy8vLVSqVYQsAAMAoyJsAAABgALm5uVrnu8lJk5ytCCHMzMxKS0s1J+Tn52stov91kQzOw8NDCKF1TaX6ky+RrnWQl/yTcXNz0xwsLCxUq9VyGQAANHXkTQAAADCA4uLis2fPKl/+9ttvGRkZ3t7eSoDi4eGRnp6uTMjKyrp165bWIra2tkom1b17982bNzdw1f+nd+/eQoi0tLRqt5aXl/fo0aMOy/7xj3/09PQ8fPiwfL6eLDY2VggxefJkzZnyD0cuAwCApo68CQAAAAbg5OT05ptvJiQkFBUVnTt3bsaMGVZWVlFRUcqEwMDAjIyMjRs3PnjwICUlZf78+cqhT4oBAwZcvXr19u3bCQkJqamp/v7+8ngj3J/O29vb1dU1OTm56qYtW7bY2dktXbq06qYZM2ZIknT9+vXHLWttbb1169bc3Nzp06dfu3YtPz9/165da9euHTRoUFhYmObMpKQkIURgYGC9HwoAAMZH3gQAAAAhhNi/f78kSV9//bUQQqVSzZgxIzExUXPk7bffFkJIkrRu3TohRP/+/YODg5Vvt7e3/+STT5YvX+7h4TF8+HBnZ+fjx4+PGDFCmbBq1arZs2evWbPG1dX15ZdfDg8Pd3d3z83NlSQpIiJCnrNhw4a+ffs+9dRT06ZNi4qKeuqpp+Tx8vLyhr4/nSRJs2fPPnPmjNZ10IXOu+NlZmba29t36NBBx8pjx449ffp0cXHxwIED3dzc1qxZEx4efuLECa1LNe3bt8/T03P8+PH1fywAABid1KAv2wAAQBETEzNt2jReedEIpk6dKoSIiYlptD3269fv7t27jzsZzegkSYqOjpZ/LDoUFBT06tUrODh406ZN+iybn5/frl27F154YcuWLfWsMDk5uX///l999dX06dP1mU8/AQCYOI5vAgDApP373/+WJEmSJPnmWU1LWVnZ+vXrfXx8HBwcXF1dx40bFxsbq/yFrFar4+PjX3vttW7dullbW7u6ug4bNmz37t1af0JXVFRs2LChX79+tra2Tk5OAQEBR48e1bHTiRMnSpK0atWq2lZrb28vaTAzM3N2dvb29n711Vd//vnn2q6GpsjJySk2Nnbv3r2ffvppjZPVanVYWJijo+PKlSvrud/U1NSQkJBly5bpGTYBAGD6yJsAADBp06dPV6vVo0ePNnYhtVZUVBQQELB9+/b169fn5OScO3fO3t5+4sSJFy9elCdcuXJl2LBhV69e3bt3b0FBQWJiYocOHV588cXw8HBlkYqKismTJy9dunT27Nm3b99OSkrq2LFjYGDgv//972p3unPnTvlKzHXw4MGDX3/9VQgxadIktVpdVlZ2+fLlFStWXL582dfX909/+tPDhw/rtjKakP79+587d+7QoUOFhYW6Z2ZnZ6emph47dszd3b2eO/3iiy9Wr169evXqeq4DAIDpIG8CAAANIjw8/Pz58999993w4cNVKlWHDh22b99ubW2tOcfCwiImJqZv3742NjadO3fevn27i4vLxo0bS0pK5Am7d+8+cODAX/7yl3nz5rm4uHTq1Okf//hH9+7dX3311fz8fK09ZmRkLFiw4MUXXzRI/ebm5m5ubpMmTTp+/PjSpUu3b9/+3HPPcfpSVZGRkZIkJScnp6enS5IkX+OpSevYseOBAwccHR11T3N3d4+Li+vVq1f997hu3TqObAIANDPkTQAAwPCys7M3b978wgsvuLm5KYN2dnbFxcXK7d579OhRVlbm7OysTLCysmrfvn1JSYly5/h9+/YJISZMmKDMkSRp0qRJ9+7d27t3r9ZO58yZExoa2hC393r//fcHDRr0zTffPO64qpZsyZIlag11OJMRAAA0P+RNAADA8L755puKiophw4bV6rvy8/OvXbvWv39/JycneSQ7O1sI4erqqjnNw8NDCBEXF6c5uG3btosXL0ZGRtar7seQJGnevHlCiM8++6wh1gcAAGhmyJsAADA5ly9fnjx5spOTk52dnb+/v1awIrtz505YWFjHjh2trKzatm0bEhKSlJQkb5Lvai+7cePGtGnTWrVq5eLiEhwcnJKSoqxQUlLy7rvv9ujRw9bWtnXr1hMmTJBDIn12UaNffvlFCOHs7Lx48eL27dtbWVk98cQTYWFheXl51c4vLCyMj4+fOHGiu7v7zp07lfE2bdqI/6ROmoUJIW7cuKGMpKWlLV68eNu2bQ4ODnpWWFtydpaYmFhWVqaUYcq/AgAAACMibwIAwLT8z//8j5+f37lz5/bu3Zudnf3ZZ5+tXLlSM6QQQmRmZg4cODAmJuazzz7Ly8s7efJkXl6en59fQkKCEGLy5MlqtXrSpElCiAULFixYsCA9PT06Ovr48ePPPfecssi8efM+/vjjTz75JDc39/fff+/Ro8ekSZN++OEHfXZRo8zMTCHErFmzsrOzT506lZOTs3Llym3btvn5+RUUFGhNXrVqlZOT07Bhw8zNzfft26eccCeECAoKEkIcOHBAc/7hw4eFEEVFRcrI7Nmzn3/++YCAAB0lBQQEuLi4JCYm6lN/VfI1ocvLy+/evSuawq8AAADAmNQAAKBRREdH6/PKGxoaKoTYu3evMvL/2bvzuKrq/PHjn4MswmURMWTTwUxzBwUzSlLB0EbccEETNR0oJ0kol9FSJxVyVCZlXEohLDUn0McXHTB1XGs0cGl+UGpukAtwAYEAJUGR8/vjTGfuoCLLlQv6ev7l+Zz3fX/e994ZoPfj8/mc7OxsMzMzMzMzdWTq1KlCiC+//FId0Wq1ZmZmHh4e6ojS7EhKSlJHxo4dK4S4ceOGctmhQ4eXXnpJd+rOnTsfOXKk9lPUQOkTdejQ4e7du+qgcrLPokWL7o+vqKj46aefZsyY0aJFi6VLl6rjt2/f9vDwMDExWbduXUFBwdWrV2fOnKm0fry9vZWYTZs2Pfvss7du3VIut27dKoRYtmxZtSkGDBhga2v73Xff1VC27vPpqlEfTpeTkyM3+a9g3Lhx48aNe2TY00MIER8fb+gq9KmWP08AADAU40ZtbgEAgEdRFu8o/RqFk5NT586dL168qI7s2rXLyMjI399fHXFwcOjevfv333+flZXl4uKijvft21f9d7t27YQQOTk5yia1oUOHfvLJJ2+++eb06dP79u3bokWLCxcu1GOKB9JoNEKIwYMHGxv/94+N4cOHL1y4cP/+/UuXLq0Wb2pq2qVLl08++SQvL2/x4sVeXl6DBw8WQrRs2fLIkSNLly6Niop699137ezsAgICduzY4e3trXSdrl27Nnfu3N27dysz1uDo0aM1B9RMWbFlYmKifHpN/yvIysrasWNHQ97yEyY1NVWSJENXoTf1XqkHAEDjoN8EAEATUlFRcfPmzZYtW1paWuqO29vbq/2miooKZUuaeqi2rkuXLul2InRjTE1NhRBVVVXK5fr16728vL744gtfX18hhLe391tvvTV69Oi6TvFArq6uQgg7O7tq70L8dvrSwwwfPjwxMTE5OVnpNwkhrKysVq1atWrVKjVm//79Qog+ffoIIZKSkkpKSgYOHFgtz6JFixYtWqRU+9xzz9VcbW0op2h5eXmZmJg0i68gJSWFnXe6Vq9evXr1akNXAQDA04LzmwAAaELMzMysrKzKy8tv3bqlO657zLaZmVmrVq2MjY11t6qpBg0aVMu5JEmaPHnywYMHi4uLd+3aJctyQEDAxx9/rJcplNO1lTVBqvz8fCFE27Zta/4Eqr3f+ymtn4CAACHEzJkzq5VXbT+dXppNVVVV69evV6YTzeQrYD+dLvGE7qcDAKDJot8EAEDT8tprr4nfdtUpCgoKdLdZCSECAgIqKyuPHz+uO7hixYr27dtXVlbWcqJWrVqdP39eCGFiYvLqq68qj1Tbs2ePXqb4/e9/7+zsvG/fvvLycnUwKSlJCDFq1Cjlcs6cOUFBQdVeuHfvXqGzB62goMDIyCgnJ0cNKC0tjY2NnTBhQufOnWv5ThtuwYIFJ0+eHD16tHK6lmgOXwEAAIAB0W8CAKBp+eijj1q3bh0eHn7gwIFbt26dO3cuKCio2va65cuXd+zYcfr06Xv37i0pKSkqKtq4caNyyJHueUmPNGPGjB9++KGioiI/P3/lypWyLKuPeGvgFGZmZrGxsYWFhRMmTLh06VJxcfHWrVuXL1/er1+/WbNmqWHbt29funTplStXKioqrly58qc//Wnbtm0eHh7BwcFqjCzL06ZNu3z5ckVFxcmTJ4cOHdq2bVtltVGd1PX5dFVVVfn5+bt37/b19V25cuX06dO//PJL9QCgpv8VAAAAGJKBlwIDAPDUqP3zpC5cuDBq1Chra2tzc/O+ffsmJycr5/sIIf7whz8oMYWFhe+9996zzz5rYmLyzDPP+Pn5HThwQLlV7dSeDz74QJZl3ZFhw4bJspyWlvbWW2917drVwsKidevWL774YkxMTFVVlVpGDVPU0nfffTdkyBAbGxvlOPAPP/zw119/Ve+WlJTExsYOGTLE1dXV1NTU0tLSw8Nj+fLlujGyLB84cGDEiBEODg7m5uY9evRYtmxZtQDVW2+9Ve3vnCFDhqh3vb29a34+XbUTxyVJsrGx6dmz5x//+Mfvv//+/vim/BXwfLpqxBO6n87QVQAA8FCS/L9//QAAgMckISEhMDCQ37xoBOPHjxdCJCQkGLqQpkKSpPj4eOVjeTLw8wQA0MSxnw4AAAAAAAD6RL8JAAAA+K+rV6+OGDGitLRUCKHVaiMjIz09Pa2trR0cHAYMGJCYmNiQ5CNGjJAkKSIiQndw/vz5PG8OAPCEod8EAADqTHq4Dz/80NDVAfWXlpbm6enp5+dnbW0thAgJCVmzZs2SJUu0Wm1qaqqLi0tAQMD8+fPrl3zLli3KUxqrCQkJWbBgwaJFixpUOgAATQn9JgAAUGc1nA1Jv+kpZGlp2b9//+abX1VaWjp8+PAxY8aEhoaqg1FRUcOGDdNoNK6urps3b3Z2do6KisrPz69r8pycnPDw8MmTJ99/q2PHjomJiZGRkRy5BQB4YtBvAgAAAIQQYuXKlbm5uYsXL1ZHkpOTp06dql6ampp269bt3r17Fy5cqGvykJCQcePG+fn5PfCum5vb2LFjZ8+eXVlZWY/KAQBoaug3AQAAAEKW5djY2H79+jk5OdUQlpmZKYRo3bp1nZLHxcWdPXs2KiqqhpjRo0dnZWXt2bOnTpkBAGia6DcBAAA8vQoLC997772OHTuampra2tq+9tprR44cUW5FREQoZ3Kpe9n27dunjLRp00YZiYqKkiSprKzs+PHjyi1jY2N1XJIkFxeXU6dO+fr6WllZWVhYDBo06Pjx4w3P/zikp6fn5eW5ubnVEPPFF19kZGR07ty5W7dutc+clZU1e/bsuLg4KyurGsLc3d2FEPv37699ZgAAmiz6TQAAAE+p3Nzcvn37bt++PTo6uqCg4MSJExYWFr6+vrGxsUKIhQsXyrKs0WjU+KFDh8qy7OHhoY7MmTNHiXn55ZeVA7yU7WDKuJubW3FxcVhYWERERG5u7rfffltUVOTj4/PNN980ML/Cx8fHzs4uNTVVL5/GmTNnhBAuLi4PvHvu3LmwsLDp06fb2tpu27ZNkqTaZw4ODn799dd9fHxqDnN2dlbLAACguaPfBAAA8JRasGDBzz//vGbNGn9/f2tr686dO2/fvt3R0XHWrFl5eXl6maKsrGzDhg1eXl4ajcbT03Pbtm137twJCwvTS/KqqiqlCaWXbFqtVghhY2PzwLu9evXasWNHaGjomTNn+vbtW/u0MTExly5dWrly5SMjra2tJUlSygAAoLmj3wQAAPCUSkxMFEIMGzZMHTEzM/P19b19+7a+dnVpNBplm5iiZ8+eTk5O6enpeumqHD16tKioyMvLq+GphBDl5eVCCBMTk4cFHD58ODo6uubTnaq5du3a3Llz4+LidJdx1cDY2Pj27du1zw8AQJNFvwkAAOBpVFFRUVJS0rJly2qHCrVt21YIkZubq5dZWrVqVW3E3t5eCJGfn6+X/HrUsmVLIcTdu3f1mDMpKamkpGTgwIHSbyZPniyEWLRokXJ5+fJl3fjKykpzc3M9FgAAgKHQbwIAAHgamZmZ2djYlJeX37x5U3dc2Unn4OCgXBoZGd25c0c3oLi4uFqqGg4zKiwsrLbfTek0KV2nhufXI0dHRyFESUmJHnPOnDlT/l9bt24VQixbtky5fO6559Tg0tJSWZaVMgAAaO7oNwEAADylRo8eLYTYs2ePOlJRUXHo0CFzc/MhQ4YoI46OjtnZ2WpAbm7utWvXquWxsLBQe0bPP//8pk2b1Fvl5eWnTp1SL3/88cecnBw3Nze1q9LA/HrUo0cPIURWVtYD71ZWVnbp0uVxzKtSPgelDAAAmjv6TQAAAE+p5cuXd+jQITw8PDk5+ebNmxcvXnz99de1Wm10dLSyq04I4efnl5OTs27dulu3bmVkZISFhalLk1R9+vS5ePHi9evXU1JSMjMzvb291Vs2Njbvv/9+SkpKWVnZ6dOng4KCTE1No6Oj1YCG5Nfv8+nc3Nzs7e3T09PvvxUTE6PRaObNm3f/raCgIEmSfv7554YXkJaWJoTw8/NreCoAAAyOfhMAAMBTysHB4dSpUxMnTpw1a5adnd0LL7xQVlZ28ODBkJAQNSYiIiI4OPijjz6yt7d/44035s6d6+DgUFhYKEnS/PnzlZg1a9b06tWra9eugYGB0dHRXbt2VV9uaWm5du3aJUuWODo6vvLKK7a2tocPHx4wYIBe8ldWVurx+XSSJAUHB584cSInJ6farRoehKfVai0tLdu3b//I/DNmzKh2ftPQoUN1AxITE52dnXWPbwcAoPmS9PUbGgAA1CwhISEwMJDfvGgE48ePF0IkJCQYtgx3d/fpo0IcAAAgAElEQVSCgoKH7VBrTJIkxcfHKx9LDUpKSrp37+7v7//pp5/WJm1xcbGTk9OkSZNiYmIaWGF6enrv3r23b98+YcKE2sTz8wQA0MSxvgkAAAAQQggbG5ukpKSdO3euX7/+kcGyLM+aNcva2nrZsmUNnDczMzMgIGDBggW1bDYBAND00W8CAAAA/qN3796nT5/eu3dvaWlpzZF5eXmZmZmHDh1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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.utils import plot_model\n",
    "plot_model(model,show_shapes = True, to_file='Plant_Ubiquitination.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "551f4c3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity =  0.8340026773761714  Sensitivity =  0.857335127860027\n",
      "Model: \"functional_983\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1966 (InputLayer)         [(None, 49)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_491 (Embedding)       (None, 49, 21)       483         input_1966[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1473 (Conv1D)            (None, 49, 256)      5632        embedding_491[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1473 (MaxPooling1 (None, 24, 256)      0           conv1d_1473[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4420 (Dropout)          (None, 24, 256)      0           max_pooling1d_1473[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1474 (Conv1D)            (None, 18, 256)      459008      dropout_4420[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1474 (MaxPooling1 (None, 9, 256)       0           conv1d_1474[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "input_1967 (InputLayer)         [(None, 49, 21)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4421 (Dropout)          (None, 9, 256)       0           max_pooling1d_1474[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1475 (Conv1D)            (None, 49, 16)       1024        input_1967[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "input_1965 (InputLayer)         [(None, 49, 31)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1474 (Flatten)          (None, 2304)         0           dropout_4421[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1475 (MaxPooling1 (None, 24, 16)       0           conv1d_1475[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "lstm_491 (LSTM)                 (None, 49, 16)       3072        input_1965[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_6385 (Dense)              (None, 768)          1770240     flatten_1474[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1475 (Flatten)          (None, 384)          0           max_pooling1d_1475[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1473 (Flatten)          (None, 784)          0           lstm_491[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4422 (Dropout)          (None, 768)          0           dense_6385[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_6388 (Dense)              (None, 512)          197120      flatten_1475[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_6383 (Dense)              (None, 512)          401920      flatten_1473[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_6386 (Dense)              (None, 256)          196864      dropout_4422[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4424 (Dropout)          (None, 512)          0           dense_6388[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4419 (Dropout)          (None, 512)          0           dense_6383[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4423 (Dropout)          (None, 256)          0           dense_6386[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_6389 (Dense)              (None, 2)            1026        dropout_4424[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_6384 (Dense)              (None, 2)            1026        dropout_4419[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_6387 (Dense)              (None, 2)            514         dropout_4423[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_491 (Concatenate)   (None, 6)            0           dense_6389[0][0]                 \n",
      "                                                                 dense_6384[0][0]                 \n",
      "                                                                 dense_6387[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_6394 (Dense)              (None, 4)            28          concatenate_491[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_982 (Activation)     (None, 4)            0           dense_6394[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_6395 (Dense)              (None, 2)            10          activation_982[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_983 (Activation)     (None, 2)            0           dense_6395[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 3,037,967\n",
      "Trainable params: 3,037,967\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "testx (1490,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "t_pred2 = Y_pred[:,1]\n",
    "\n",
    "Y_pred = (Y_pred > 0.5)\n",
    "y_pred1 = [np.argmax(y, axis=None, out=None) for y in Y_pred]\n",
    "y_pred1 = np.array(y_pred1)\n",
    "\n",
    "\n",
    "mcc_test = matthews_corrcoef(yval, y_pred1)\n",
    "# For sensitivity and specificity\n",
    "sp_1, sn_1 = confusion_matrix(yval, y_pred1)\n",
    "sp_2_test = sp_1[0]/(sp_1[0]+sp_1[1])\n",
    "sn_2_test = sn_1[1]/(sn_1[0]+sn_1[1])\n",
    "# ROC\n",
    "\n",
    "\n",
    "print(\"Specificity = \",sp_2_test, \" Sensitivity = \",sn_2_test)\n",
    "print(model.summary())\n",
    "print(\"testx\",yval.shape)\n",
    "\n",
    "y_pred = y_pred1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "75546643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matthews Correlation :  0.6914948051291077\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[623 124]\n",
      " [106 637]]\n",
      "\n",
      "\n",
      "Accuracy on test set:    0.8456375838926175\n",
      "\n",
      "\n",
      "Sensitivity:    0.857335127860027 \t Specificity:    0.8340026773761714\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.83      0.84       747\n",
      "           1       0.84      0.86      0.85       743\n",
      "\n",
      "    accuracy                           0.85      1490\n",
      "   macro avg       0.85      0.85      0.85      1490\n",
      "weighted avg       0.85      0.85      0.85      1490\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import roc_curve, auc, classification_report\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score,matthews_corrcoef\n",
    "Accuracyyyyy = accuracy_score(yval, y_pred)\n",
    "cm = confusion_matrix(yval, y_pred)\n",
    "mcc = matthews_corrcoef(yval, y_pred)\n",
    "\n",
    "print(\"Matthews Correlation : \",mcc)\n",
    "print(\"\\n\")\n",
    "print(\"Confusion Matrix : \\n\",cm)\n",
    "print(\"\\n\")\n",
    "print(\"Accuracy on test set:   \",Accuracyyyyy)\n",
    "print(\"\\n\")\n",
    "\n",
    "TP = cm[1][1]\n",
    "TN = cm[0][0]\n",
    "FP = cm[0][1]\n",
    "FN = cm[1][0]\n",
    "\n",
    "Sensitivity = TP/(TP+FN)\n",
    "\n",
    "Specificity = TN/(TN+FP)\n",
    "\n",
    "\n",
    "print(\"Sensitivity:   \",Sensitivity,\"\\t\",\"Specificity:   \",Specificity)\n",
    "\n",
    "print(classification_report(yval, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f7e801",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b79d338",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd47c26",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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