Human_Dataset_Independent_Test_Set_Assessment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "079c4dc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from Bio import SeqIO\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "aminoacids='ARNDCQEGHILKMFPSTWYV-'\n",
    "\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",
    "\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",
    "\n",
    "seed = 321\n",
    "cut_off = int(16)\n",
    "r_test_x = []\n",
    "r_test_y = []\n",
    "alphabet = 'ARNDCQEGHILKMFPSTWYV-'\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",
    "#for positive sequence\n",
    "def innertest1():\n",
    "    #Input\n",
    "    sequence = str(seq_record.seq)\n",
    "    sequence = sequence[cut_off:-cut_off]\n",
    "    for char in sequence:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in sequence]\n",
    "    r_test_x.append(integer_encoded)\n",
    "    r_test_y.append(1)\n",
    "for seq_record in SeqIO.parse(\"Ubiquitylation_81_window_Testing_positive.fasta\", \"fasta\"):\n",
    "    innertest1()\n",
    "#for negative sequence\n",
    "def innertest2():\n",
    "    #Input\n",
    "    sequence = str(seq_record.seq)\n",
    "    sequence = sequence[cut_off:-cut_off]\n",
    "    for char in sequence:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    integer_encoded = [char_to_int[char] for char in sequence]\n",
    "    r_test_x.append(integer_encoded)\n",
    "    r_test_y.append(0)\n",
    "for seq_record in SeqIO.parse(\"Ubiquitylation_81_window_Testing_negative.fasta\", \"fasta\"):\n",
    "    innertest2()\n",
    "\n",
    "\n",
    "# Changing to array (matrix)    \n",
    "r_test_x_embedding = np.array(r_test_x)\n",
    "r_test_y_embedding = np.array(r_test_y)\n",
    "\n",
    "\n",
    "r_test_x = []\n",
    "r_test_y = []\n",
    "#for positive sequence\n",
    "def innertest1():\n",
    "    #Input\n",
    "    sequence = str(seq_record.seq)\n",
    "    sequence = sequence[cut_off:-cut_off]\n",
    "    for char in sequence:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    r_test_x.append(sequence)\n",
    "    r_test_y.append(1)\n",
    "for seq_record in SeqIO.parse(\"Ubiquitylation_81_window_Testing_positive.fasta\", \"fasta\"):\n",
    "    innertest1()\n",
    "#for negative sequence\n",
    "def innertest2():\n",
    "    #Input\n",
    "    sequence = str(seq_record.seq)\n",
    "    sequence = sequence[cut_off:-cut_off]\n",
    "    for char in sequence:\n",
    "        if char not in alphabet:\n",
    "            return\n",
    "    r_test_x.append(sequence)\n",
    "    r_test_y.append(0)\n",
    "for seq_record in SeqIO.parse(\"Ubiquitylation_81_window_Testing_negative.fasta\", \"fasta\"):\n",
    "    innertest2()\n",
    "\n",
    "binaryCoding_Independent_Test = binary_encode(r_test_x)  \n",
    "\n",
    "AA_Independent_Test = index_encode(r_test_x)\n",
    "\n",
    "y_independent = np.array(r_test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "78b1daf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6805, 49, 21)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binaryCoding_Independent_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b7fe172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6805, 49, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AA_Independent_Test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "694f5fa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6805, 49)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_test_x_embedding.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92938d90",
   "metadata": {},
   "source": [
    "# Load the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8439afb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "model = keras.models.load_model(\"DeepUBImodel_HubiPred_AAindex_One_Hot_Emb_drop_out_changed73887.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "04ba25d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = model.predict([AA_Independent_Test,binaryCoding_Independent_Test,r_test_x_embedding])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a1fbc2f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X17x5854/f97+cEF/DB069ObNm2fPnq2srFQ+s7i4ODc399KlSw4ODtqJTS2+/PLLzZs3b968me9AQBF9ardMJmMPVldX9+3bd8qUKZrYY05OTnBwcHR0dHFxMZf57StJXPbS5pzGxsbQ0NC1a9dGRUU9fvw4PT3dzc0tICDg6NGjHXlGzVlYWFAURZcDQkhrx4IlJSVxcXGEkNmzZ1MU9d5777VvdwoUKhFFUWVlZXv37k1NTR09enRr/TWV6ODLDPVXF+hm7dNyndXx+tjiP6/u4HLwzzGTt6YjB/arV69ubebcuXMXL14cGhqamZlZVVX1yy+/DB06dNmyZV5eXsycEydOMGdb0BdSnT17lhmJiIggfJcPaBEyG9H5zNYm9JugZSYmJsePHx88eHCXLl169eqVkJBga2u7a9euuro69rS0tDSFU+a2b9/OnsAcczOys7PNzMwWLFig0hxVGRsb29vbh4SE/PTTT2vXrk1ISJg1a5bCRzRA/vtpW0ZGRn5+vkAgMIyq6ebmdurUKUtLS+XTHBwcrl69+tJLL2knKnXZunWrnn6+0TlRFNXU1NTU1KSJjb///vujRo367bffJBKJJrbPfS9tzjl8+PCpU6f++c9/LlmyxNbWtmfPngcOHOjXr9+iRYvY92BW4zMSiUQ9evQ4e/bszZs3mz8aGxvr6urawV1wIZVKp02b9tlnnzU0NLDP/1UvnXqZ6Uv9Nbzyp4O1T8t11iDro4WFhY+Pj3b21ebBP8dMrokD+9akpaV9++238+fPX7t2rYuLS5cuXXr37r158+a33nqr3dvUkfLRDoaX1ggym/5nNvSboAUeHh4NDQ3W1tbMiFAodHV1raurY9+Gtk19+vRh7mXA2LlzZ2hoKNMP5jKngz755JMRI0b88MMPHD9+6VQUFtvDWcEA6iWRSHJycs6cOaOJjR84cGDdunXtu+5MvXtpcw699OnUqVOZEYFAEBISUlZWlpiYqNK+ODIyMlq3bh0hpHlaKy8v37NnzzvvvNPxvXA0btw4QkhmZib7LZka6ezLTJfrL8ofABuXg38umVwLB/ZsmZmZhJB+/fopjM+YMYP5Oj09ffr06Uo2cvToUXZrRqfKh0qQ1kAHod8EnJSXl9+7d2/o0KFSqZT7T02YMGHVqlXskaqqqoMHDy5atEilOR0kEAiWLFlCCPniiy/UtU0AAN6JRCId2Uubc+hLsRRut+zo6EgIuXr1qkr74m7evHnOzs4//PDDn3/+yR7//PPPJ0+e3Lt3bzXuSznm7B6BQKC1napLR/4oqL8A+qv5wT+XTK6FA3s2e3t7Qsign2vAAAAgAElEQVSFCxcUxv38/J49e9buzepO+QDQd+g3qV9WVlZoaKhUKhWLxb6+vuwjacbTp0+XLVtG3xyxW7duYWFh6enp9EMnT55klqB7+PBhRESElZWVra3tlClTcnJymC3U1dVt2LDBw8PD3NzcxsZm6tSpP/zwQ2NjI5ddqKSysjIlJYW+JeTXX3+t8OihQ4eGDBkiFoulUqmvr++RI0eUb+2rr77q3r37mDFjOjhHVfSpyNevX29oaKBH9OhPAKBH2P8+f//9d0REhEQisbW1nTt3bllZ2cOHD6dOnSqRSBwdHRcsWFBVVcX8oFwuP3bs2CuvvELffXbQoEE7duxgrg9i351gzpw5hJAJEyYwI22eM8JepTUtLW38+PESicTc3HzcuHEpKSnsmSUlJW+//Xbv3r2FQqG1tfWkSZN+/vlnlSa09tugPx/mmF4Iq5SYm5u//PLLp06dYp5yVFQUpz/G/25HSUnSgq5du5L/vldhPH36lBDCLOmqdmZmZmvWrKEoir3qQXV19c6dO9evX998vuZehJcvXyaEvPTSS/TbNsN7mSnRvP4CAEPJwSRduWpqalJSUuj/Svo0w3bXWe5aO/hvXybXxIE9w9fX18HB4fz585MmTbp8+bK6LitWtXwAQKso4IxwuLPgvXv3rKysnJ2df/zxx6qqqj///DMgIMDNzc3MzIyZU1BQ0KNHD3t7+9OnT1dVVf31119+fn5dunS5du0aMyckJIQQEhIScu3aterq6gsXLohEouHDhzMToqKipFLpjz/++Pz586KiInoVvZ9//pn7LrhglsobO3bsn3/+qfDo6NGj586d+9tvv1VXV2dlZc2dO5cQsnTp0ta21tTU5O7u/sUXXyjZY2tzxo0bZ2Njk5qaquRnFVZpZXvx4gX9RAoKCiid/xPgrroKtH+HbAPDJXdxwf2VSf/7hIWF3bx5s7q6mj5anTRpUkhIyB9//FFVVUWvtbly5UrmR5KSkgghH3/8cWlp6dOnTz///HMjIyP2meHp6elisVgmk1VXV1MUVVtbO2LECO63hqUoSiaTicVib29v+p86LS1t8ODBQqHw8uXL9ITCwsKePXva29snJSVVVFTcvXs3LCxMIBDs27eP4wSqpfsQ07+NFy9eKIwoSS8KpeSvv/6aMGFCt27d2KWEzdnZ2djYuPk4l5LEXWt74TJn586dzQuEp6cnIcTLy0ulfXEsB2KxmKKo58+f29vbGxkZ3b59m37ok08+mTFjBkVRv/zyC/nvgq80tbwIFSpRRUXF//3f/9nZ2Zmaml64cIEyxJdZ82fNplB/lUO2V6Cu7A2tUdcRF/ftKPzzKj+YpChKLBaPHj26+XbaUWc5UnLwr2ompzR2YM/2yy+/MAsq2dnZzZ49+8iRIzU1Na3Nb75euMJO21E+lMBRvQLkeS3QqdqBV78KuPzlwsPDCSGJiYnMSH5+vpmZGfvo7fXXXyeEfPPNN8xIYWGhmZmZp6cnM0JXkaSkJGaEvvD46dOn9Lc9e/YcNWoUe9fu7u5MfeKyC47q6uru3Lnzz3/+09jY+KOPPlI++eWXXyaEXL9+vcVHT58+LZFIqqqqlGyhtTl+fn7W1tbKmzVKyhJzcxz6eFfH/wSoTApQmTqIr37T6dOnmRF6VcUrV64wIz179uzXrx/zbVJS0tixY9kbmTNnjqmpaUVFBTNy/Phx+vC6qanp9ddfX79+vUrx03cF+uOPP5gR+jx5mUxGf/uPf/yDEMJuH9TW1jo5OYlEoqKiIi4TKFUaAUrSS/NS8uTJE3Nzc1UbAVxKEncd6Te9ePHC09PT1NR0165dz549+/vvvxcvXkyv5eHr66vSvjiWA/oNA0VRW7duJYTMmTOHoqiamhp7e/uMjAyqlX5Tx1+EzL2NaAKBwNbWNjg4+MaNG/QEw3uZUarUX+WQ7RXo1HsGg8R7v0n5wSTVVr9JpTrLXWsH/6pmckpjB/YKamtrDx48GBISwtzWwNbWtrVPpDj2myhVyocSOKpXgDyvBTpVOzS7yGgndO7cOUJIYGAgM+Lk5OTu7p6dnc2MnDx50sjIiH3fYgcHh5deeum3337Ly8tzcXFhxocPH858TXfuCwoK6HNZJ06cuGfPnjfffPONN94YPny4sbHx3bt327GLNgmFQg8Pjz179hQXF2/YsMHb23vChAmtTZ4+ffqNGzeSkpJGjBjR/NHPP/88MjLSwsJCye5am0NfjNBuhYWFhBBTU1P6t6cXf4ITJ0505Ckbkry8PIJfiB5i34rYyckpMzOTPeLs7JyRkcF8O2XKFIW7uctkssOHD2dmZnp7e9Mj4eHh77777ubNm318fGxsbOLj41UNSSwWDxkyhPl20KBBTk5OGRkZhYWFjo6O9EqoQUFBzAQzM7Px48cfOnTo/PnzkZGRbU5QKRgl6aV5KenWrZuHhwe9MCp3XEqSdnTp0uXnn3/+6KOPYmJiVq5caWtrGxYWduLECfpSCJU2pWo5WLRo0bZt27799tt//etfSUlJI0eOHDx4cIsz1fgiDAkJOXnyZIsPGd7LTDmF+tumvLw8ZHu269ev6+OyX/ri+vXr/Aag/GCyTSrVWe5aO/hvRybX0IG9AjMzs8jIyMjISLlcnpycvG/fvqNHj86ZM6dfv35Dhw5t92a5l482Ia0xcFTf2aDfpE51dXVVVVVdunRRyKp2dnbMwX1dXV1FRQUhpMWFt+/du8fuRLDnCIVCQghzWfLu3bu9vb0PHjw4fvx4Qoivr+/ChQunTZum6i64mzp16nfffUcv7tDaHHrJwCdPnjR/KDs7+8cff/zss8+U7ILLnPahlyzx9vY2NTXVlz8B+84aQAhJTU3lOwRQDfvmtUZGRsbGxubm5syIsbExe52FioqKf//73999911eXh57KRzm5Ajaxo0bL168eO3atYMHDxoZqbwEoZWVlcKInZ1dQUHBkydPbGxsKioqunTponDTd3ot0qKiIvr/WskEVYNpLb20VkrYtw3igktJ0iaJRPLpp59++umnzMj58+cJIcOGDdPofi0sLFasWPH+++//61//unz58vfff9/aTC28CNt8Fendy6xN7PrLZX5qaiqyPVtsbGxsbCzfUYCmKDmY5EKlOtsOzQ/+Vcrkmjuwb42JiYm/v7+/v3+PHj22bt2amJjYkX4T9/LRJhzVK0Ce7zywXrg6mZmZSSSS2tra6upq9nhpaSl7jpWVlYmJSUNDQ/PzzehbJnMhEAjmzp178eLF8vLykydPUhQVFhZGJ3R17aL5s1N4Ls0VFBSQZvetoH3++edjxowZMGCAkh/nMqcdmpqadu/eTQhZvHgx0Z8/QUdPXjQgOPO2g9r7r6NVU6dO3bhx44IFC7Kzs5uamiiKot9iKcR/+fLlioqKQYMGLVq0qB0f25aUlChskO6P29nZmZmZSaXS2tpahdVV6YVRHRwc2pygajCtaa2UtNjKb8d2lKdxbaI7EWFhYZre0dKlS6VS6ZEjR2QyGfvDfwVaeBEa3stMOYX6ywWyPRvRpWsiDBJ9rROPlBxMMhN4DI/Lwb+STK6hA3u2lJQUuh2vgD7YLisr6+D2OZaPNvH9StchOKrXgg6+7NUL/SY1mzRpEvnvOeq0Z8+eKZwZGxYWJpfLFW6KtHXr1u7du8vlco47srKyysrKIoSYmpq+8sor9L0qTp8+rZZdrF69mr75DtvZs2cJ68z8/fv30wsEMqj/LmwxdepUhZ+trKz8+uuvlR9ucpnTPtHR0Tdu3Jg2bRq9VgXRhz8BQKfS2NiYkpLi4OCwbNmybt260YfXzDLDjAcPHsyfP/8///nPDz/8IBKJQkJC6NvicFdbW5uWlsZ8e+vWrYKCAplMRp+bSX+kzPwXE0Lq6uouXbokEonoa47anKAuzUtJUVFRO05K4lKStOPZs2dGRkb0ZxK0ysrK/fv3z5w5093dXdN7l0qlb7/9tlQqfe+991qbo7UXoeG9zJRoXn8BgE35wSQhxNzcvL6+nv66X79+e/fu1VAkXA7+VcrkmjuwJ4SYmJjQvzeKop48edL8usibN28SQjpychONS/kAACXQb1Kzjz/+2MbGZsWKFRcuXKiurr59+/acOXMUTlbfsmVL796933jjjbNnz1ZUVJSWln755Zf0tdD0jU45+uc///nnn3/W1dU9efJk27ZtFEX5+/uraxdHjhz56KOPHj58WFdX9/Dhw3feeefw4cOenp7sGyT//vvvixcvvn//fm1t7d27d+l71S1durT54k3x8fEWFhbKTw9WPsff39/W1pb7ZfZNTU1Pnjz5/vvvx48fv23btjfeeOObb75hPiPSiz8BQOdhbGw8duzYoqKiTz/99NmzZy9evPj555/pe+swqqurQ0NDt2/fPmDAADc3t8TExIKCgunTp6t0k3WpVLp+/frU1NSampqbN2/OmTNHKBTu2LGDfnTLli09e/ZcsWLFqVOnqqqqsrOzX3vttcLCwh07dtAfn7Y5QV0USslff/01b968dpzbwqUkaQ1FUfPmzbt//35dXd2NGzcmTpxob29Pn/yiElXLAW3Dhg3l5eWjRo1qbYLWXoSG9zJToLz+AoACJQeThJBhw4ZlZ2c/fvw4NTU1NzfX19dXc5FwOfjnnsnVe2Cv3IwZM44cOVJQUEBHHhMT89FHH3l6eqq64F2L2iwfAKAMjyd66R3C7azmu3fvhoaGWlpa0jcePnXqFH1JNiFk/vz59JySkpK33367V69epqam3bp1CwgIoG+TTFGUwuWs7777LvW/J8UFBQVRFJWenr5w4cL+/fubm5vb2NiMHDly37599Pn/be6iTRUVFfv37w8MDHRzcxMKhRYWFp6enlu2bHn+/Dkzp7a29sSJE9OmTevduzd9/v/YsWOPHDnSfGtNTU19+vTZsGGDkj22OcfX11f5bSzEYjH7tyQQCKRS6aBBg956663ffvut+Xxd/hPgThYKcOZtB3HMXW3i8sps/u/DPp+IELJlyxb6xi6Mf/3rXxRFPX36dOHCha6urqampvb29v/4xz/WrVtHT/D09GR/QHrr1i2FM0o2btzIJX76rkC3b98ODAyUSCQikcjPz+/q1avsOc+ePVuxYkXPnj1NTU2lUmlgYOClS5c4TmCvZ0E/d3rhZ8bs2bM5pheKVUrMzc1HjRp15cqVsWPHmpubs4NJSkoizezbt489h0tJUo7LXrjMuXDhQnBwsIODg0gkGjhw4MaNG9k1hft2VCoHgYGBLc5R2MXOnTspdbwIFSpRa3eGMrCXmar1VwlkewUE19NpmDbvT9f8n5ficDCZlZXl6+srFotdXV13795NdaDOKsfl4J/ilskpDRzYN3fnzh2KohobG69evbp69eoRI0Y4OTmZmJhIJBIvL6+PP/64pqZGYZtfffWVwkYUbpzX7vKhBI7qFSDPa4FO1Q4BpWMX+OkygUBw7NgxrPcGmnb8+PGIiAj8bzLofzr6ak1oB3XlLn1/ZQ4ZMuTZs2f0jVH0kYeHx4sXL/7++2++AwFDxu/LDNleAY48NU1ddU3f6yNoDl4bCpDntUCnageupwMAANAtRUVFNjY27Ku0Hj58mJOTw77IAqCD8DIDAAAAjUK/CQAM399//x0cHFxZWUkIKSws3Lx5s5eXl6WlpYODg5+fn8LVKG2Ki4sTtIJef5fR0NAQGxvr6ekpkUjs7OwmTZqUlJTE/oxLLpcfOHDg5ZdftrW1tba29vT03LVrF7MyKCFk3bp1vN89B3hRVla2cOHCx48fP3/+/MaNGxEREZaWlu+//z7fcYFBwcvMkKi30pWVlcXFxfn7+9vY2IhEor59+86ePVv5DRmDg4MFAsGmTZva/RTOnDnj7u6uZJlL5VUVFRPA8Kg3syloLWs1NjZu3759yJAh5ubmUqnU39//4sWL7d5LJ89s6Dd1Rq29VRYIBB988AHf0QGoWXp6upeXV0BAgKWlJSFkwYIF27dv//DDDwsLC69fv+7i4hIWFsas0tJB7OUka2pq/P39ExISYmNjnzx5cvPmTQsLi+Dg4MzMTGbOvHnzoqKiJkyYcOfOnfv370dERCxduvTVV19lJixYsCA6Ohpv/9qkPK3FxMQIBIKMjIz8/HyBQKD7d5lxcHCgb489ZswYa2vr4ODgvn373rhxo1evXurdEcpBZ6a1lxlogdor3Zo1a5YuXRoSEnL79u2SkpL4+Pj09HRPT8+TJ0+2OP/rr79ucakvjnJycoKDg6Ojo4uLi1ub02ZVRcVUAtke9JFGj+Fby1qNjY2hoaFr166Niop6/Phxenq6m5tbQEDA0aNHVd0FMhshWL1MFUSXVt4CA6bllQXFYvHo0aN1efsdWVmwoqLCxcVl4cKFzEhQUFBCQgLzbV1dnbOzs7GxcXFxMcdt7tmzJyQkRGEwOzvbzMyssLCQGXnrrbcsLS2LioqYkerqajMzs1u3btHf5uTkEEKGDh3K3s4rr7xCCLlx4wYzkp6eTl+GzTG85tSVu7DmJQBolJbXkdX98scxe2ui0s2fP//NN99kj6SnpxNC+vbt23xyfn6+tbX13LlzCecbOCiYNWvWli1bGhoa6DhbnNNmVaVUr5jaXC8cOicc1Svgnuc1kdkYSrJWQkICIWTp0qXMSFNTk4eHh7W1dVlZmUp74Suz6VTXAuc3AYAh27ZtW1FR0YYNG5iRU6dOvf7668y3QqFwwIABjY2Nd+/e5bjNPn36NL8h8c6dO0NDQ5lbiRcXF+/du3f27Nns+5eLxeLa2tqBAwfS3z5+/JgQ0r9/f/Z2PDw8CCGPHj1iRmQy2fTp01etWiWXyzlGCAAAnYcmKt3+/fu//PJL9ohMJhOJRDk5OVSzlY8XLFgQHh4eEBDQ3mdADhw4sG7dOiXXm3CpqgQVE8CAaCKzMZRkLfoavalTpzIjAoEgJCSkrKwsMTFRpb0gsxFcTwcABoyiqP3799O3yFUyLTc3lxBiY2PDcbMTJkxYtWoVe6SqqurgwYOLFi1iRn744YfGxkYfHx8l2/Hw8DA1Nc3KymIPZmVlCQSCQYMGsQenTZuWl5d3+vRpjhECAEAnoaFK11xNTc2LFy8GDhwoEAjY4/Hx8ZmZmTExMe3eMiFEJBIpn8ClqtJQMQEMgEYzm/KsRV/7Zmdnxx50dHQkhFy9elWlHSGzEfSbAPRaSUnJ22+/3bt3b6FQaG1tPWnSpJ9//pl+aNOmTfRl+UwKO3fuHD3StWtXeoRe1KampiYlJYV+iG7A0+MCgcDFxSUtLW38+PESicTc3HzcuHEpKSkd377WZGRkFBcXy2QyJXMOHjyYk5Pj7u4+YMCAdu/oq6++6t69+5gxY5iR33//nRBibW29atUqV1dXoVDYo0ePZcuWlZaWMnPs7e1jYmIyMjLWr1//9OnT0tLSbdu2Xbx4ccOGDe7u7uztDxkyhBBy/vz5dkcIAGBIUP4YWqt0J06cIIS8++677MG8vLxVq1bFx8dLJJJ2b5kLLlWVhooJegppjU1zma3NrEU/ZYUVl54+fUoIefjwIfcdcdEpMhu/l/PpF6JLV0KCAeN4pXdhYWHPnj3t7e2TkpIqKiru3r0bFhYmEAj27dvHzGl+lbWnp6etrS17pLUrsWUymVgs9vb2vnbtWnV1dVpa2uDBg4VC4eXLl9Wy/XHjxtnY2KSmprb5TNu9osehQ4cIIR9//HGLj2ZmZi5btszIyMja2pq9XpKqmpqa3N3dv/jiC/ZgSEgIIcTBwWH27Nk5OTllZWUHDx4Ui8Xu7u7l5eXsmcePH3dxcaETcteuXQ8cONB8FxUVFYQQX1/f9kWortyF9SkAQKM4ZvvOU/64ZG/tVLqioiJ7e/uoqCiF8cDAwEWLFrEjad/6TYzWVjnhXlVVqphYvwk0DUf1Cjjmec1ltjaz1s6dO8n/rt9EUZSnpychxMvLS6V9MbSc2XSqa4HzmwD0VXR09IMHD7Zv3z5lyhRLS0t3d/cjR444OjouW7ZMyU0QVFJTU/PFF194e3uLxWIvL6/Dhw/X19cvX75cLRtvamqi05BattaiwsJCQohUKm3x0cGDB584cWLJkiV//fXX8OHD272Xs2fPFhYW0isOMmprawkhIpEoISGhV69eVlZWkZGR0dHR2dnZ//73v+k5FEW9+eabs2fPfvvtt4uKip4+fbp58+YlS5bMnDlT4fJsS0tLgUBAPx0AgE4O5Y9NC5WupKRk4sSJY8eOjYuLY4/v27fv3r1727Zta99mVcKlqtJQMUEfIa0p0FBm45K1oqKiPD094+Lidu/eXVJS8ujRoyVLluTn5xMO18epqjNkNvSbAPQVvZpdUFAQM2JmZjZ+/PgXL16o62RLsVhMn71JGzRokJOTU0ZGhlqS3eXLl0tLS729vTu+qdbQSdzU1LS1CT/99NOOHTuUXxneps8//zwyMtLCwoI9KBaLCSETJkxgn2xMLz3I/HUOHTq0b9++f/7znytXrrS3t+/ateubb765bt26Y8eO7dq1S2EvJiYmL1686EicAACGAeWPTdOVrqamJjAwcMCAAd98842xsTEz/ujRozVr1sTHx9P1TtO4VFUGKiboHaQ1BZrIbByzVpcuXX7++efly5fHxMQ4OjqOGDGCoij6gmLmvkDq0hkyG/pNAHqprq6uoqKiS5cuCtce03c3KCoqUsterKysFEboxfOePHmilu1rWpcuXQghDQ0NmttFdnb2jz/+yF4pnObm5kYIsbW1ZQ/Svz36CnBCyLlz5wghEyZMYM8ZP348IeTs2bMKG5TL5Wr/UAUAQO+g/CnQaKWTy+Xh4eHOzs4HDx5kN5sIIfRVP2PHjhX8F32e7/vvv09/e//+fTVGwqWqssNGxQQ9grTWnCYyG/esJZFIPv300wcPHtTX1xcWFu7evbumpoYQMmzYMDXGQzpHZkO/CUAvmZmZSaXS2traqqoq9jh9zi3TfTcyMqqvr2dPKC8vV9iUwo1m2EpKShTOjKVrEnPLhg5uX9Poe0nQFzxryOeffz5mzJjm6xTSCy4qfGRE//aYO57SpatF1dXV7G8rKyspiqKfDgBAZ4byp0CjlW7hwoV1dXXHjx9nPnvv06fP9evXCSGLFy9WWKRDYSWUPn36qDESLlWVhooJegdprTlNZLaOZC36znRhYWFqjId0jsyGfhOAvpo2bRohhH1fzLq6ukuXLolEosDAQHrE0dGRvt6YVlRU9OjRI4XtmJubM9WlX79+e/fuZR6qra1NS0tjvr1161ZBQYFMJmOSXQe3r2kDBw4khOTl5bX4qFwu9/Dw6Mj2Kysrv/7668WLFzd/aPLkyc7OzufOnaPPB6YlJSURQkJDQ+lvR4wYQQi5dOkS+wd/+uknQsjIkSPZg/QvmX46AACdHMofm+Yq3QcffJCZmfn999+bmZm1Pz414VJVaaiYoI+Q1hRo+hheiWfPnhkZGRUUFDAjlZWV+/fvnzlzpsL9ozuuM2Q29JsA9NWWLVt69uy5YsWKU6dOVVVVZWdnv/baa4WFhTt27GA64gEBAQUFBbt27aqurs7JyVm+fDnzIQZj2LBh2dnZjx8/Tk1Nzc3N9fX1ZR6SSqXr169PTU2tqam5efPmnDlzhELhjh07mAkd2b6/v7+trS39MamGyGQyOzu7jIyM5g/t27dPLBavXbu2+UNz5swRCAQPHjxoc/vx8fEWFhb0IYICMzOz/fv3l5SUzJw58969e+Xl5YcOHdqyZcuIESOWLVtGz1m0aFHfvn337Nnz+eefP3nypKSk5MCBA5988omzs/Pq1avZW0tPTyeEBAQEcHnWAACGDeWPTUOVLiEh4cMPP/z1118lEomAJScnR9UIuVdVJbhUVRoqJugjpDUFmj6GV46iqHnz5t2/f7+uru7GjRsTJ060t7ffvXu32vfVKTKbGu91Z/CILt1ZEAwY97vqPnv2bMWKFT179jQ1NZVKpYGBgZcuXWJPKC8vj4qKcnR0FIlEPj4+aWlp9O08CSHvvPMOPScrK8vX11csFru6uu7evZv5WZlM5uzsfPv27cDAQIlEIhKJ/Pz8rl69qq7t+/r6WltbX7t2rc2nyfHOqS1av369iYlJfn6+wnhcXJxIJFq9enXzH/H397ewsJDL5cq33NTU1KdPnw0bNiiZc+3atcDAQKlUKhQKPTw8Pvjgg+fPn7MnlJaWrlmzxsPDw8zMTCgU9u7de8mSJUVFRQrboZfPqK+vVx5Sa9SVu3C/ZwDQKO7ZvpOUP47ZWxOVjr1usYLmtzxfuHChwpzAwEDu+6Ioiv48XwH7TvC0NqsqpWLFVFddQ32E1uCoXgH3PK+5Y3iqrax14cKF4OBgBwcHkUg0cODAjRs3Nk81upzZdKprgcyoAp36y4EB05GjFroy8R0FRXWs31ReXu7s7Lxw4UKO88vKykQiUVRUVPt2pwnp6ekCgeDbb79t9xbQbwIAvdCRbK9GulP+OGZvXa50Wq6qqlZM9JtA03TktaE7aY17nkdmY6ia2XSqa4Hr6QDAkEml0qSkpMTERIWTYFtEUdSyZcssLS03btyohdi4yM3NDQsLi46OnjlzJt+xAACALtLZSqflqoqKCWBIkNlo+p7Z0G8CAAM3dOjQmzdvnj17trKyUvnM4uLi3NzcS5cuMbcC4d2XX365efPmzZs38x0IAADoLt2sdFquqqiYAAYGmY3of2Yz4TsAANA5MTExa9asob8WCATvvvvupk2b+A2pg9zc3E6dOtXmNAcHB/p2p7pj69atfIcAANBZ6HX508FKp+WqiooJ0JxepzWCzKb/mQ39JgBQtHr1aoX7owEAABg8lD8AMDBIa8AvXE8HAAAAAAAAAADqhH4TAAAAAAAAAACoE/pNAAAAAAAAAACgTug3AQAAAAAAAACAOmG9cNXExsYmJibyHQUYuMePH/D1hV8AACAASURBVBNCZsyYwXcguiI1NZXgF6Iz8IcAAA1Btm8OR54aRR9xqQteutAcjuoVIM93NgKKoviOQW+8/fbbeXl5fEcBnUtDQ8P9+/d79uzZpUsXvmMBfWVsbLxlyxY3N7cObufhw4fR0dGNjY3qCAoAWlVVVZWfn+/h4cF3IACGz8XF5bPPPuvgRlAfQSUURd25c8fNzc3c3JzvWMAAqevIXy3QbwLQaS9evOjatevOnTvfeOMNvmMBAABtOH78eEREBI7QAAAMUlpa2ssvv5yZmTlgwAC+YwHQLKzfBKDTRCKRn5/f6dOn+Q4EAAAAAAA6Kjk5uWvXrv379+c7EACNQ78JQNdNnjz5xx9/rKur4zsQAAAAAADokOTk5DFjxggEAr4DAdA49JsAdF1QUFB1dfXVq1f5DgQAAAAAANqvqakpJSVlzJgxfAcCoA3oNwHoup49e/bv3//MmTN8BwIAAAAAAO1369atkpISPz8/vgMB0Ab0mwD0QFBQEJZwAgAAAADQa8nJyVKpdNCgQXwHAqAN6DcB6IGgoKC7d+/ev3+f70AAAAAAAKCdkpOTfX19jY2N+Q4EQBvQbwLQAz4+PlZWVrikDgAAAABAT1EU9csvv2DxJug80G8C0AMmJiavvPIKLqkDAAAAANBTd+/eLS4uRr8JOg/0mwD0Q1BQ0JUrV6qqqvgOBAAAAAAAVHblyhULC4thw4bxHQiAlqDfBKAfJk+e3NDQ8NNPP/EdCAAAAAAAqCw5OXnUqFGmpqZ8BwKgJeg3AeiHbt26eXl54ZI6AAAAAAB9hMWboLNBvwlAbwQFBZ06dYqiKL4DAQAAAAAAFeTm5j5+/Bj9JuhU0G8C0BtBQUGFhYUZGRl8BwIAAAAAACq4cuVKly5dhg8fzncgANqDfhOA3hg2bJiTkxMuqQMAAAAA0C/JyckjR47s0qUL34EAaA/6TQB6QyAQTJw4Ef0mAAAAAAD9kpycjIvpoLNBvwlAnwQFBf36669PnjzhOxAAAAAAAOAkPz8/NzcX/SbobNBvAtAnAQEBpqamP/74I9+BAAAAAAAAJ1euXDE1NR05ciTfgQBoFfpNAPrEwsJi9OjRuKQOAAAAAEBfJCcne3l5icVivgMB0Cr0mwD0TFBQ0Pnz5+VyOd+BAAAAAABA265cueLn58d3FADahn4TgJ4JCgoqKyu7fv0634EAAAAAAEAbnj59evfuXSzeBJ0Q+k0AeqZfv359+vTBJXUAAAAAALrvypUrRkZGo0aN4jsQAG1DvwlA/0yePBn9JgAAAAAA3ZecnDxkyBCpVMp3IADahn4TgP4JCgq6devW33//zXcgAAAAAACgDBZvgk4L/SYA/ePn5yeRSM6ePct3IAAAAAAA0KqysrK//voLizdB54R+E4D+MTMz8/f3xyV1AAAAAAC67JdffqEoysfHh+9AAHiAfhOAXgoKCrp06dLz58/5DgQAAAAAAFqWnJw8cOBAW1tbvgMB4AH6TQB6KSgoqLa29sqVK3wHAgAAAAAALUtOTsbFdNBpod8EoJecnJxkMhkuqQMAAAAA0E3V1dV//PEH+k3QaaHfBKCvgoKCkpKS+I4CAAAAAABacPXqVblcjsWboNNCvwlAXwUFBT169Oj27dt8BwIAAAAAAIqSk5P79evn5OTEdyAA/EC/CUBfjRgxws7ODpfUAQAAAADoICzeBJ0c+k0A+srIyCggIAD9JgAAAAAAXfPixYubN2+i3wSdGfpNAHps8uTJKSkp5eXlfAcCAAAAAAD/v9TU1Lq6OvSboDNDvwlAj02cOJEQcuHCBWbkzp07dXV1/EUEAAAAANAZ1dXVFRYWMt8mJyf37Nmze/fuPIYEwC8TvgMAgPaztrYeOXLk999/b2Zmdvr06e+//764uDguLm7hwoV8hwYAACr45ptv8vLy6K///PNPQsjWrVuZR8ePH+/l5cVPZAAAwE1sbGx0dLSTk9P48eP9/PzOnz+Pk5ugkxNQFMV3DADQHrm5uWfOnImLi8vKympqajI1Na2vrzcyMtq7d+/8+fP5jg4AAFTQtWvX8vJyExMTQghFURRFGRn9v5PQ6+rqlixZsnPnTl4DBACANuzcuXPlypWNjY3GxsaEkMbGRisrq4CAgLFjx44ZM2bAgAECgYDvGAG0Cuc3AeifPXv2fPbZZ/fv3zcxMWlqampqaiKE1NfXE0IoipJIJHwHCAAAqomIiNi3b19rF0S/+uqrWo4HAABU5ejoSB+WNzY20iPl5eX/+c9//u///k8ul48ZM+bKlSu8BgigbVi/CUD/JCcn379/nxAil8vpqsagKMrS0pKnuAAAoJ1mzZrV0NDQ4kPdunXz9fXVcjwAAKAqR0fH5hcPNTY2yuVygUAQGBjIS1QAPEK/CUD/fPHFF46OjvSZus2h3wQAoHdGjx7t5OTUfFwoFEZGRraW8AEAQHc4Ojq2OG5iYuLh4bFmzRotxwPAO/SbAPSPtbX10aNHW1t8DdfTAQDoHYFAMHfuXFNTU4Xx+vr6WbNm8RISAACopLV+U1NT06FDh5pneACDh34TgF4aM2bMmjVrWvzEG/0mAAB91OIldT169PD09OQlHgAAUIlIJLKwsFAYNDExWbt2LTI5dE7oNwHoq02bNslksuYfleB6OgAAfSSTyfr27cseEQqF8+bN4yseAABQlZ2dHftbY2NjV1fXDRs28BUPAL/QbwLQVyYmJsePH6dvns3W/HMVAADQC5GRkexPEerr62fOnMljPAAAoBJnZ2f2t01NTQcPHhSJRHzFA8Av9JsA9Fjv3r137twpEAiYEaFQKBQKeQwJAADabdasWXK5nP5aIBAMHjy4X79+/IYEAADcde/e3cjo/73FNjU1feutt3CDUejM0G8C0G/z588PDw9nPg83NzfnNx4AAGi33r17DxkyhH6vYmJi8vrrr/MdEQAAqMDR0ZG++MDIyKhr166ffPIJ3xEB8An9JgC99+WXX3br1o1eO1wsFvMdDgAAtF9kZCTdb5LL5TNmzOA7HAAAUAFzi7qmpqa4uDjcxgc6OfSbAPSelZXVkSNHKIoiuDkdAICei4iIaGpqEggEo0aNcnFx4TscAABQgaOjY0NDg6mp6WuvvRYcHMx3OAA8Q78JwBD4+fmtW7eOECKVSvmOBQAA2s/R0dHX15eiqMjISL5jAQAA1Tg6OlIUJRaLt2/fzncsADqAAg1LTk5ufgcxAIBOaOXKlXyn5DbgdBIAADbdzNvI1QAAbLqZqymKQh9E4woLC+Vy+fHjx/kOBAzfixcvGhsbLSwstLnTGTNmrFy50tvbW5s71VmpqamxsbH4f2/RZ599lpeXx3cUbcjLy8PrGXhHUVRpaamtra3COPItG/KtFuhs3kauBl2GXM2GXK0FOpurCSHoN2lJeHg43yEAaMrIkSPxCqdRFEXw/96KEydO8B0CJ3g9gy7D65OBfKsFupy38b8AugyvTwZytRbocq7G+k0AAAAAAAAAAKBO6DcBAAAAAAAAAIA6od8EAAAAAAAAAADqhH4TAAAAAAAAAACoE/pNAAC8+fvvv4ODgysrKwkhhYWFmzdv9vLysrS0dHBw8PPz++6771TaWlxcnKAVkyZNYs9saGiIjY319PSUSCR2dnaTJk1KSkqiF3SkyeXyAwcOvPzyy7a2ttbW1p6enrt27aqvr2cmrFu37tixYx179gAA2qPefFtWVhYXF+fv729jYyMSifr27Tt79uyMjAwlPxIcHCwQCDZt2tTup3DmzBl3d3cTk1bv9qM8tyNvA4DuU2+uVtBaHm5sbNy+ffuQIUPMzc2lUqm/v//FixfbvRfkajb0mwCAB9XV1X379p0yZQrfgfApPT3dy8srICDA0tKSELJgwYLt27d/+OGHhYWF169fd3FxCQsLW7dunVr2NWrUKObrmpoaf3//hISE2NjYJ0+e3Lx508LCIjg4ODMzk5kzb968qKioCRMm3Llz5/79+xEREUuXLn311VeZCQsWLIiOjn7//ffVEh4AaA7yLdFAvl2zZs3SpUtDQkJu375dUlISHx+fnp7u6el58uTJFud//fXXSUlJ7Y4/JycnODg4Ojq6uLi4tTlt5nbkbQBdhlxNNHxs3FoebmxsDA0NXbt2bVRU1OPHj9PT093c3AICAo4eParqLpCrW0CBhtHtSb6jANAUQsixY8dU/anKyspevXpNmjRJEyFxIRaLR48erfbNcv9/r6iocHFxWbhwITMSFBSUkJDAfFtXV+fs7GxsbFxcXMxx73v27AkJCVEYzM7ONjMzKywsZEbeeustS0vLoqIiZqS6utrMzOzWrVv0tzk5OYSQoUOHsrfzyiuvEEJu3LjBjKSnpwsEAu5//fDw8PDwcI6T+dK+1zOAdiDfsvGbb+fPn//mm2+yR9LT0wkhffv2bT45Pz/f2tp67ty5hJCNGzdy3AXbrFmztmzZ0tDQQMfZ4pw2cztlQHkbuRp0GXI1G7+5mqEkDyckJBBCli5dyow0NTV5eHhYW1uXlZWptBfk6uZaPcsLAEBzJBIJ3dTotLZt21ZUVLRhwwZm5NSpU+wJQqFwwIAB+fn5d+/etbOz47LNPn36+Pr6Kgzu3LkzNDTUwcGB/ra4uHjv3r1vvvmmvb09M0csFtfW1jLfPn78mBDSv39/9nY8PDwuXLjw6NGj4cOH0yMymWz69OmrVq0KCwtTcs4wAPAL+VYT+Xb//v0KIzKZTCQS5eTkUBQlEAjYDy1YsCA8PNzX1/fQoUPtewoHDhwQiURKJnDJ7QR5G0CHIVdrIlczlORh+hq9qVOnMiMCgSAkJGTr1q2JiYlRUVHc94Jc3RyupwMA0DaKovbv3z9ixAgnJycl03JzcwkhNjY2HDc7YcKEVatWsUeqqqoOHjy4aNEiZuSHH35obGz08fFRsh0PDw9TU9OsrCz2YFZWlkAgGDRoEHtw2rRpeXl5p0+f5hghAICWaSjfNldTU/PixYuBAwcqNJvi4+MzMzNjYmLavWVCiPI3MIRbbqchbwOADtJorlaeh+lr3xQaWI6OjoSQq1evqrQj5Orm0G8CAG07efIks4413c5njzx8+DAiIsLKysrW1nbKlCnMRz0xMTH0BBcXl7S0tPHjx0skEnNz83HjxqWkpNBzNm3aRM9h8vi5c+foka5du7K3U1NTk5KSQj+k/c8NMjIyiouLZTKZkjkHDx7Myclxd3cfMGBAu3f01Vdfde/efcyYMczI77//TgixtrZetWqVq6urUCjs0aPHsmXLSktLmTn29vYxMTEZGRnr169/+vRpaWnptm3bLl68uGHDBnd3d/b2hwwZQgg5f/58uyMEAI1CvtVavj1x4gQh5N1332UP5uXlrVq1Kj4+XiKRtHvLXHDJ7TTkbQAdhFytuVzdZh6mfw8KKy49ffqUEPLw4UPuO+KiM+Zqfi/n6wywfhMYNtLeNRRCQkIIIS9evFAYCQkJuXbtWnV19YULF0Qi0fDhw9k/JZPJxGKxt7c3PSctLW3w4MFCofDy5cvMnObXn3t6etra2rJHWrtGfdy4cTY2Nv8fe3ce1tSx8A98gpAYAwSIZRFwl1Kviho3KrgAQqmKSmu1F7WvLWqriHsV21LrUltrL9W6VdDrVtuqz6MtWm1d3+cV4Yq2oLUiiFoFwmIiIAiBmPP7Y973/E4DhEMIWcj385eZTOZMEvLNcTIzJz093YBnxPD+vNOpvJ9++mmj9966dSs+Pt7Ozs7V1ZW7X1JLabVaPz+/HTt2cAvpi+zp6RkTE5Ofn//kyZP9+/dLJBI/P7/y8nJuzSNHjvj4+NBvis6dO+/Zs6fhISoqKgghwcHBfPpjyWvLWQb/PQOYAPKWy6Lytri42MPDIzY2Vqc8IiJi/vz53J4Ytn8Tq6k9Qfhne/vIbWQ1WDJkNZfZs7rZHP7666/J3/dvYhhGLpcTQoYMGdKiY7GQ1SzMbwIAyxIbGxsYGCiRSMLCwsaPH5+Zmfn48WNuherq6h07dtA6Q4YMOXToUF1d3aJFi4xydK1WS8PRKK01RaFQEEKkUmmj9w4YMODo0aNxcXF//PEHu1mSAU6fPq1QKOjOiCz6o5lYLN63b1/Pnj1dXFxmzZqVkJCQm5v75Zdf0joMw8ydOzcmJmbp0qXFxcVlZWUbNmyIi4ubPn26RqPhtubs7CwQCOjTAQCrg7w1St4qlcpXXnllzJgxu3bt4pYnJyfn5eVt2rTJsGZbhE+2U8htAKuDrDY4q/nkcGxsrFwu37Vr1/bt25VK5cOHD+Pi4goLCwmP9XEtZYNZjfEmALAs3G8RX19fQkhRURG3gkQioVNMqf79+3fp0iU7O9soiXzp0iWVShUYGNj6pvSgXzYODg5NVbhw4cKWLVv0r2Bv1tatW2fNmuXo6MgtlEgkhJCwsDDuTGm6RSI7ZffgwYPJycnvvvvukiVLPDw8OnfuPHfu3FWrVv3www/btm3TOYq9vX1NTU1r+gkA5oK8Ja3O2+rq6oiIiL59+3777bcdOnRgyx8+fLhixYq9e/fS1G1rfLKdhdwGsC7IamJQVvPM4Y4dO168eHHRokWbN2/28vIaPnw4wzB0iTR7vR1jscGsxngTAFgW7i8bQqGQEKLVarkVXFxcdB5Cd/grLS1t+94ZR8eOHQkh9fX1bXeI3NzcX3/9lbtTONW9e3dCiEwm4xbSF5CuVCeEnDlzhhASFhbGrRMaGkoIOX36tE6DGo3G6D/+AIBpIG9bSaPRTJ061dvbe//+/dzBJkJIampqRUXFmDFj2B1Y6GzTjz76iN68e/euEXvCJ9u53UZuA1gRZLVh+Oewk5PTF198cf/+/bq6OoVCsX379urqakLI4MGDjdgfYpNZjfEmALAySqVSZ04v/TZlrythZ2dXV1fHrVBeXq7TiM71g0yMXvOCLsxuI1u3bh01alTD/RTpbpE6v3fRF5C9Miv9im1UVVUV92ZlZSXDMPTpAED7g7zVb968eWq1+siRI+wv1b17987IyCCELFiwQGcPC519Q3r37m3EnvDJdgq5DdD+IKsb1Zocplemi46ONmJ/iE1mNcabAMDK1NbWZmZmsjdv3rxZVFQUEBDAJrKXlxdddE0VFxc/fPhQp5FOnTqx37svvvji7t2727jXf9OvXz9CSEFBQaP3ajQaf3//1rRfWVl54MCBBQsWNLzr1Vdf9fb2PnPmDJ23TKWmphJCJk+eTG8OHz6cEHL+/HnuAy9cuEAIGTFiBLeQvs706QBA+4O81WPNmjW3bt368ccfRSKR4f0zEj7ZTiG3AdofZHVrPH782M7OjrtEsbKyMiUlZfr06TrXZW49G8xqjDcBgJWRSqWrV69OT0+vrq6+du3ajBkzhELhli1b2Arh4eFFRUXbtm2rqqrKz89ftGgR+/MOa/Dgwbm5uY8ePUpPT793715wcDAtDwkJkclk9NfpthMQEODu7p6dnd3wruTkZIlE8v777ze8a8aMGQKB4P79+822v3fvXkdHxylTpjS8SyQSpaSkKJXK6dOn5+XllZeXHzx4cOPGjcOHD4+Pj6d15s+f36dPn507d27durW0tFSpVO7Zs+ezzz7z9vZevnw5t7WsrCxCSHh4OJ9nDQBWB3nbVLP79u375JNP/vOf/zg5OQk42OuU88c/2/Xgk+0Uchug/UFWt/LoDMPMnj377t27arX66tWrr7zyioeHx/bt241+LFvMaiNe6w4axfMakABWirT8mq/Hjx/nplBMTEx6ejq35IMPPmD+Pit4/Pjx9LEBAQHe3t5//vlnRESEk5OTWCwePXr05cuXue2Xl5fHxsZ6eXmJxeKgoKDMzEx6TVNCyMqVK2mdnJyc4OBgiUTi6+u7fft29rHBwcGurq5Xrlwx7NXg/3lfvXq1vb19YWGhTvmuXbvEYvHy5csbPiQkJMTR0VGj0ehvWavV9u7dOzExUU+dK1euRERESKVSoVDo7++/Zs2aZ8+ecSuoVKoVK1b4+/uLRCKhUNirV6+4uLji4mKddujGJXV1dfq7xFa22Gu1sgz4ewYwGeQtl3nzdvz48U2dWje8avi8efN06kRERPA/FsMw9NdvHcnJyTrVms12pr3kNrIaLBmymssSzo2Z5nL47NmzUVFRnp6eYrG4X79+69ataxieyGrDYBykzWG8Cdo3E5/z0e9Ukx2upfh/3svLy729vefNm8ez5SdPnojF4tjY2Fb0zsiysrIEAsF3333Hs74lfxey8H8YsGTIW672kbcmzvZ2k9vIarBkyGouZLUB2k1WMwyD9XQW6vvvv6cTs+le/e3ekydPdu3aFRIS4ubmJhaL+/TpExMTozOjkmGYtLS0BQsW+Pn5iUQid3f3oKCgQ4cOMX8f7NdoNHv27Bk2bJhMJnN1dZXL5du2bdPZIY8QUl9fn5SUJJfLnZyc3N3dIyMjU1NTdZrSz9HRkTuF3s7OztXVNSAgYP78+devXzf4pQAbIZVKU1NTjx07pjNZt1EMw8THxzs7O69bt84EfePj3r170dHRCQkJ06dPN3dfzOOvv/6aPXt2165dhUIhmwPr1683d7+M4+eff/bz8+NerLeldZ4/f/7VV18NHDiwU6dOUqk0JCTk3Llz3Ap8Yp8PnShuKCUlZfPmzfTfPj4+LW0f2gGLzVsTZ7tt5jayupVZ3aJj6YGshmYhq6l2ltUYb7JQ06dPZxiGXoDcFqxYsWLhwoWTJk36888/lUrl3r17s7Ky5HL5iRMn2Dp37twJCgrKzc09duxYRUVFRkZG165dZ86cuWLFCm5Ts2fPjo2NDQsLu3379t27d6dNm7Zw4cLXXnuNW6e6ujokJGTfvn1JSUmlpaXXrl1zdHSMioq6desW/z5XVVX9/vvvhJBJkyYxDFNfX5+Tk7N27dqcnJwhQ4bMnj372bNnrXtVoJ0bNGjQtWvXTp8+XVlZqb9mSUnJvXv3zp8/7+npaZq+Neubb77ZsGHDhg0bzN0R8ygrKxsxYsRvv/125MiR8vJyhmF05r1br/z8/KioqISEhJKSEoPrPH/+fPLkye+//35sbOyjR4+ysrK6d+8eHh7+/fffs3X4xD4fOlGsY/To0YQQOgk/ICCgRS1De2KZeWvibLfB3EZWtz6reR6rWchq4ANZTdpfVrfFpCngas16utDQUJFIZNz+mIBEIhk5cmSLHvLOO+/MnTuXW0L3SOvTpw9bcvv2bXt7e5VKxZao1WqZTCYSiWpra2kJ3adz0KBB3KbGjRtHCLl69Spb8t577zk7O3M3o6mqqhKJRDdv3mxRt5v64qQb2kVFRWm12hY1aHYGvHfEVHOGv/jiC2520XXslgbrZ/Ww5Lm+LJ5/z59++ikh5Pz582wJ/T8Me4Vdnpr6xBnwSTSWN998c+PGjfX19d7e3h06dDCszr59+wghCxcuZEu0Wq2/v7+rq+uTJ09oCZ/Y50n//2HYfRMMWHFgaW8Q8pYLeWsCFpvbyGqTZTXPY/GBrDY6ZDVQFpvVDMMYOCUSwLhSUlJ0SgICAsRicX5+PsMwAoGAEOLv719fX8+tIxQKfX19s7Kyamtr6dWIHz16RAh56aWXuNX8/f3Pnj378OHDoUOHEkJKSkp27949d+5cDw8Pto5EIuFelrKVPvvss//+7//+6aefvv/++zfffNNYzdq45cuX61wcDcBcbt68SQjp37+/uTtifHv27BGLxa2sQ/c9nThxIlsiEAgmTZr0+eefHzt2LDY2lvCL/da7dOmSUdqxNchbaB+Q1a3Pap7HaiVktWGQ1WD5sJ4OLFR1dXVNTU2/fv30/K+jvLw8Ly9v0KBBUqmUlvj7+zs4OOTk5HCr5eTkCAQC9mzjp59+ev78eVBQUNt1XiAQxMXFEUJ27NjRdkcBAHOhq2WdnJzM3RHj4/Ofimbr0DUXOtda9vLyIoRcvny5qUfxiX3+4uLiFi9e3Pp2AMB6Iav1V+CZ1W062ISsBmjfMN5kQXJyciZPniyVSiUSSXBwsM5J+YkTJ9gd9e7cufPGG2/IZDJ68/Hjx4QQpVK5dOnSXr16CYVCV1fXyMjIixcv0sdyt9/LzMwMDQ11cnLq1KnT2LFj09LSuEfR08j69etpI+xIzZkzZ2hJ586duQeqrq5OS0ujdxm8reDRo0cJIR988EGj91ZWVqalpdHrVh44cIAt9/Dw2Lx5c3Z29urVq8vKylQq1aZNm86dO5eYmOjn50fr/Pbbb4QQV1fXZcuW+fr6CoXCbt26xcfHq1Qqw7raKPoqZWRk1NfX29p7B9CO0Y/zjz/+SAgRi8Xcj5UOjUbzww8/jBs3jl5et3///lu2bNFqtfTepj5x+j+JZWVl8fHx3bt3FwqFL7zwQnR0NF2DRv7+HfHgwYNp06a5uLjIZLIJEybQhcamRINFZ6ePsrIyQsiDBw+aepT+2Dc6W36DANo9ZDUfhmW1idnyGwTQHph7QV/7x3PNal5enouLi7e396+//vr06dMbN26Eh4d3795dZ/+mSZMmEUJGjx598eLF6urqjIyMDh06lJWVKRSKHj16eHh4pKamVlRU3LlzJzo6WiAQsGuhGYYJCAiQSCSBgYFXrlypqqrKzMwcMGCAUCi8dOkSrcCnkYYLoeVyuUwm45a0frF0cXGxh4dHU5ecZC8NMGbMmBs3bjSscOTIEfbCFp07d96zZw/3Xvoaenp6xsTE5OfnP3nyZP/+/RKJxM/Pj+4lSY0dO9bNzS09PV1PP/UsRK+pqaEdKCoq4h63/b13BNck5sAadT0seW05i+ffM/0419TUsCUN9wRJTU0lhHz66acqlaqsrGzr1q12dnZ0M1RWi7acKCoq6tatm4eHx6lTp54+ffrHH3+MHj26Y8eOV65c0enYpEmTaFacPXtWLBYPHTqU59Pn4rNPR1N1vv76a/L3PUEYhpHL5YSQqZ1hCwAAIABJREFUIUOGNNpUU7HPP4obWrRoEbeazp4g1vgGIW+5kLcmYLG5jaxmmTKr9RwLWc2FrOZCVpuAxWY1wzB479scz8/Y1KlTCSHHjh1jSwoLC0UiUaPjTT///LPOw//rv/6LEPLdd9+xJbW1tV26dBGLxeyu2PRyD7///jtb58aNG4SQgIAA/o2YYLzp8ePHAwcOnDZtmkajaaqOWq2+ffv2u+++26FDh7Vr17LlWq12zpw5Dg4O//rXv4qLi8vKyr755huxWDxt2rT6+npaJyIighDSo0cPtoRhGHpl3I8++ogtGT16tKurK/e7pyE9403sxel0xpva33uH71QufKfqYcnfhSzj/h9mzJgx3EfNmDHDwcGhoqKCLWnRKfJbb71FCPn222/ZEoVCIRKJ5HK5TsdSU1PZktdff50QUlZW1uyT0tGa/8PU1NTI5XIHB4dt27Y9fvz4r7/+WrBgAb2qS3BwcMP6emLf4ChesGBBs/+Hsbo3CHnLhbw1AYvNbWQ1y5RZredYyGouZDUXstoELDarGewXbjnOnDlDCKFDIVSXLl38/Pxyc3MbVh42bJhOCd3wb/z48WyJSCQKDQ09ePDgL7/8MmvWLFookUgGDhzI1unfv3+XLl2ys7MVCoWXlxfPRtpUdXV1RERE3759Dxw40KFDh6aqCYVCf3//nTt3lpSUJCYmBgYGhoWFEUIOHjyYnJy8cOHCJUuW0Jpz584tLi7++OOPR4wYQdeHSyQSQkhYWBh3Yu3EiRM//PDDX375Ze3atbSklTsXKhQKQoiDgwO7YI1ql+9dRkaGwEib+1q7jIwM8n/LgkBHQUEBO/HQFkyYMGHChAnckoCAgEOHDt26dSswMNCABk+cOGFnZ8dt09PT8x//+Mf169d1Xlt6bQTK19eXEFJUVKSTRW2qY8eOFy9eXLt27ebNm5csWSKTyaKjo48ePRocHNzwWsL6Y7/tNpG10jcIectC3pqALeS2lUaBUbQoq/VDVutAVrOQ1SZgyVmN8SaLoFarnz592rFjR0dHR265u7t7o+NNdMSE+/CKioqOHTvqbIhIr79WXFzMlri4uOg05e7uXlRUVFpa6ubmxrORtqPRaKZOnert7b1//349g01cEydOPH78+MmTJ+l4Ex22o/9mhYaGfvzxx6dPn6bjTd27dyeEyGQybh26VyJdsm4UdPutwMBABwcHbnm7fO+SkpKSkpLa+ihW5I033jB3FywUnchpIyoqKr788svjx48XFBSUl5ez5ezkxxahWUEIYS+PwJWXl8c9z+DWEQqFhBB2qwuTcXJy+uKLL7iXav7ll18IIYMHD+ZWMyD2edq2bZv+Clb6BiFvdSBv21q7z20rjQJj4ZnVbQdZbSOQ1W3NYrMa+4VbBJFI5OTkVFtbW1VVxS3nuYO1SCSSSqW1tbVPnz7lltP9/7g/UCiVSoZhuHVKS0sJIe7u7jwbsbOzq6ur41bgRj9l8HD+vHnz1Gr1kSNH2JlHvXv3poPiTRGJRITzQlVXVzdVk31t6YaRdP4Ri74OdICm9bRa7fbt2wkhCxYs0F+zfbx3mDPMwpxhPSz2i7CNTJw4cd26dXPmzMnNzdVqtQzD0FNPhvNBbuoT17BcJBK5uLjY29tzFwKzxo4d23ZPxFjoKHx0dDS30IDYNxYrfYOQtyzkrQnYQm5baRS0nUaz2oys9A1CVrOQ1SZgyVmN8SZLERkZSf5veg71+PHjO3fu8Hz4lClTCCGnTp1iS9Rq9fnz58ViMXeNXm1tbWZmJnvz5s2bRUVFAQEB9NKnfBrx8vIqLCxkKxQXFz98+FCnM506dWLHNV588cXdu3fzeQpr1qy5devWjz/+SIeQGlq+fPmMGTN0Ck+fPk04812HDx9OCDl//jy3zoULFwghI0aMoDdfffVVb2/vM2fO1NbWsnXoZoSTJ0/m09VmJSQkXL16dcqUKXw+/O3gvQMAHc+fP09LS/P09IyPj3/hhRfoKS97GQFWU5+4Rsujo6M1Go3OhSk///zzrl27ajSaNn06LfX48WM7O7uioiK2pLKyMiUlZfr06eylQgmP2G87Nv4GAQBl41HAM6vNyMbfIIB2AONNluLTTz91c3NbvHjx2bNnq6qq/vzzzxkzZugsr9Nj48aNPXr0WLx48cmTJ58+fZqbm/vPf/5ToVBs2bKFO2dHKpWuXr06PT29urr62rVrM2bMEAqFW7Zs4d9IeHh4UVHRtm3bqqqq8vPzFy1aRFeicQ0ePDg3N/fRo0fp6en37t0LDg5utv/79u375JNP/vOf/zg5OQk4dK48evjw4bVr1z548ECtVj948GDlypWHDh2Sy+WxsbG0wvz58/v06bNz586tW7eWlpYqlco9e/Z89tln3t7ey5cvp3VEIlFKSopSqZw+fXpeXl55efnBgwc3btw4fPjw+Ph49lghISEymYz/z+xarba0tPTHH38MDQ3dtGnT22+//e233/KZLmTt7x0ANNShQ4cxY8YUFxd/8cUXjx8/rqmpuXjx4q5du3SqNfWJa7R848aNvXr1evvtt0+fPl1RUaFSqb755hu67wZ3NzoLwTDM7Nmz7969q1arr169+sorr3h4eNCJnxTP2G9pFPOENwgACKKAR1bzhKwGgMaZe/JX+8d/DuGdO3cmT57s7OxMr7h58uTJ0NBQ+ja988479IIaet67x48fL168uEePHg4ODlKpNCIi4vz589wK9HIPf/75Z0REhJOTk1gsHj169OXLl1vUSHl5eWxsrJeXl1gsDgoKyszMpJdNJYSsXLmS1snJyQkODpZIJL6+vtu3b+fz3LkbXetgL61aUVGRkpISERHRvXt3oVDo6Ogol8s3btz47NkzblMqlWrFihX+/v4ikUgoFPbq1SsuLo69RhvrypUrERERUqmUbj2+Zs0anXaCg4P1X2hDZycmgUAglUr79+//3nvvXb9+nVuzfb93BHOGOTBnWA9LvnYGq9m/Z7o3PysmJoZhmF69enELHz16xDBMWVnZvHnzfH19HRwcPDw8/uu//mvVqlW0AnsRnKY+cU2VK5XKpUuX9uzZ08HB4YUXXggPDz979iy9SydnPvjgA+bvK3DHjx/P5xWgkz11JCcnt7TO2bNno6KiPD09xWJxv3791q1bp5OxfGKfaXkUe3h4NKzD3ZqEfXGs8Q0iyFsO5K0JWGxuN/tZQFbzrNNsVvNsB1nNRZDVHMhqE7DYrGYYRsD8/SMERnfkyJFp06ZZwus8cODAx48fFxQUmLsj0GKW/N4JBIIffvgBuwBSlvN5t0D0j+TIkSPm7og++HsGS4a/Ty7krQlYbG7jswCWDH+fXMhqE7DYrCZYTwcAAAAAAAAAAMaF8SYAALP566+/oqKiKisrCSEKhWLDhg1Dhgxxdnb29PQcPXq0znKAZu3atUvQBHpFAlZ9fX1SUpJcLndycnJ3d4+MjExNTeX+9KTRaPbs2TNs2DCZTObq6iqXy7dt28a9vuGqVavoBGkAgHbDuJnMMExaWtqCBQv8/PxEIpG7u3tQUNChQ4d0fudvNm91REVFCQSC9evXcwuRyQBgO8xy/oysNgzGm2zC5s2bBQJBdnZ2YWGhQCD48MMPTdyBpj7DAoFgzZo1Ju6MdTH7ewdtJysra8iQIeHh4c7OzoSQOXPmfPXVV5988olCocjIyPDx8YmOjmY3KWill19+mf13dXV1SEjIvn37kpKSSktLr1275ujoGBUVdevWLbbO7NmzY2Njw8LCbt++fffu3WnTpi1cuPC1115jK8yZMychIeGjjz4ySvfANBDFAHoYPZPv3LkTFBSUm5t77NixioqKjIyMrl27zpw5c8WKFdxqzeYt14EDBxrdTAeZ3J4gqwH0MNf5M7LaQObbOspWYI80aN+ICfdElEgkI0eOtOT2+X/eKyoqfHx85s2bx5aMHz9+37597E21Wu3t7d2hQ4eSkhKeR9+5c+ekSZN0CnNzc0UikUKhYEvee+89Z2dn7ib6VVVVIpHo5s2b9Ca9QNigQYO47YwbN44QcvXqVbYkKyuL7lDAs3uWvJchy5R/zwAthbzlMu75VVtk8u3bt+3t7VUqFbcRmUwmEolqa2tpCc+8pQoLC11dXWfOnEkIWbdunc69Lc1kPiw2t5HVYMmQ1VyWn9V8zp+R1QbD/CYAADPYtGlTcXFxYmIiW3Ly5Mm33nqLvSkUCvv27fv8+fM7d+7wbLN3797sNYBZX3/99eTJkz09PenNkpKS3bt3x8TEeHh4sHUkEkltbW2/fv3ozUePHhFCXnrpJW47/v7+hJCHDx+yJQEBAa+//vqyZcs0Gg3PHgIAWKa2yGR/f//6+npXV1duI76+vmq1ura2lpbwzFtqzpw5U6dODQ8Pb/RwyGQAaPfMdf6MrDYYxpsAAEyNYZiUlJThw4d36dJFT7V79+4RQtzc3Hg2GxYWtmzZMm7J06dP9+/fP3/+fLbkp59+ev78eVBQkJ52/P39HRwccnJyuIU5OTkCgaB///7cwilTphQUFJw6dYpnDwEALFAbZXJD5eXleXl5gwYNkkqltIR/3u7du/fWrVubN2/W0z4yGQDaMTOePyOrDYbxJgAwBaVSuXTp0l69egmFQldX18jIyIsXL9K71q9fT3clYAdBzpw5Q0s6d+5MS+g+VtXV1WlpafQue3t7tlwgEPj4+GRmZoaGhjo5OXXq1Gns2LFpaWmtb7+NZGdnl5SUBAQE6Kmzf//+/Px8Pz+/vn37Gnygf//73127dh01ahRb8ttvvxFCXF1dly1b5uvrKxQKu3XrFh8fr1Kp2DoeHh6bN2/Ozs5evXp1WVmZSqXatGnTuXPnEhMT/fz8uO0PHDiQEPLLL78Y3EMAMDrkbUuZIJMrKyvT0tKioqI8PT0PHDjAlvPM24KCgmXLlu3du9fJyUnPUZDJAFYEWd1SZjx/RlYbzszr+WwA9m+C9o3wWKOuUCh69Ojh4eGRmppaUVFx586d6OhogUCQnJzM1mm4Plwul8tkMm5JU2vIAwICJBJJYGDglStXqqqqMjMzBwwYIBQKL126ZJT2x44d6+bmlp6erv9pMrw/7wcPHiSEfPrpp43ee+vWrfj4eDs7O1dX14ZrwvnTarV+fn47duzgFk6aNIkQ4unpGRMTk5+f/+TJk/3790skEj8/v/Lycm7NI0eO+Pj40G+Kzp0779mzp+EhKioqCCHBwcF8+mPJa8tZfP6eAcwFectlxPOrts7kdevW0SwdM2bMjRs3GlZoNm8jIiLmz5/P7W3DPUGYFmYyHxab28hqsGTIai4rymqq0fNnClltAMxvAoA2l5CQcP/+/a+++mrChAnOzs5+fn6HDx/28vKKj48vKSkxyiGqq6t37NgRGBgokUiGDBly6NChurq6RYsWGaVxrVZLE9MorRFCFAoFIYRdT6FjwIABR48ejYuL++OPP4YOHWrwUU6fPq1QKOhuhSy6aYhYLN63b1/Pnj1dXFxmzZqVkJCQm5v75Zdf0joMw8ydOzcmJmbp0qXFxcVlZWUbNmyIi4ubPn26zlJzZ2dngUBAnw4AWALkrQHaOpM//PBDtVp9+/Ztf3//QYMGscNPhF/eJicn5+Xlbdq0qdkDIZMBrAWy2gBmPH9GVhsM400A0OaOHz9OCBk/fjxbIhKJQkNDa2pqjDWVVCKR0LmpVP/+/bt06ZKdnW2UKL906ZJKpQoMDGx9UxQd9HFwcGiqwoULF7Zs2aJ/dXqztm7dOmvWLEdHR26hRCIhhISFhXEnRU+cOJFwpvUePHgwOTn53XffXbJkiYeHR+fOnefOnbtq1aoffvhh27ZtOkext7evqalpTT8BwIiQtwYwQSYLhUJ/f/+dO3dGRUUlJiaeO3eOljebtw8fPlyxYsXevXtpejcLmQxgFZDVBjDj+TOy2mAYbwKAtqVWqysqKjp27KizkpleH624uNgoR3FxcdEpcXd3J4SUlpYapX3j6tixIyGkvr6+7Q6Rm5v766+/cnc6pLp3704Ikclk3EL6WpWVldGbZ86cIYSEhYVx64SGhhJCTp8+rdOgRqMRi8VG7DkAGAx5axgTZDKLju+fPHmS3mw2b+lamzFjxgj+D/3V/aOPPqI37969y30sMhnA8iGrDWPG82dktcEw3gQAbUskEkml0tra2qdPn3LL6Wxh9jqjdnZ2dXV13Arl5eU6TQkEgqaOolQqdeb00m9T+s3a+vaNy8vLixBCF2+3ka1bt44aNarhXol0Y0idn7boa0XPcggh1dXVTTVbVVXFvVlZWckwDH06AGB2yFvDmCCTWSKRiBDCXqKh2bxdsGCBzl4YOnuC9O7dm30IMhnAKiCrDWPG82dktcEw3gQAbW7KlCmEEO5VP9Vq9fnz58VicUREBC3x8vIqLCxkKxQXFz98+FCnnU6dOrHfiy+++OLu3bvZu2prazMzM9mbN2/eLCoqCggIYKO8le0bV79+/QghBQUFjd6r0Wj8/f1b035lZeWBAwcWLFjQ8K5XX33V29v7zJkzdE4ylZqaSgiZPHkyvTl8+HBCyPnz57kPvHDhAiFkxIgR3EL6ktKnAwCWAHlrgDbK5OXLl8+YMUOnkP4Szu4twj9v+UAmA1gLZLUBzHj+jKw2GMabAKDNbdy4sUePHosXLz558uTTp09zc3P/+c9/KhSKLVu2sHNqwsPDi4qKtm3bVlVVlZ+fv2jRIvbnF9bgwYNzc3MfPXqUnp5+79694OBg9i6pVLp69er09PTq6upr167NmDFDKBRu2bKFrdC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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='Human_Ubiquitination.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "888ad783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specificity =  0.7139076742134667  Sensitivity =  0.8040540540540541\n",
      "Model: \"functional_795\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1194 (InputLayer)         [(None, 49)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding_397 (Embedding)       (None, 49, 21)       483         input_1194[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1192 (Conv1D)            (None, 49, 256)      5632        embedding_397[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1192 (MaxPooling1 (None, 24, 256)      0           conv1d_1192[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2384 (Dropout)          (None, 24, 256)      0           max_pooling1d_1192[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1193 (Conv1D)            (None, 18, 256)      459008      dropout_2384[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1193 (MaxPooling1 (None, 9, 256)       0           conv1d_1193[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "input_1193 (InputLayer)         [(None, 49, 21)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2385 (Dropout)          (None, 9, 256)       0           max_pooling1d_1193[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_1191 (Conv1D)            (None, 49, 16)       1024        input_1193[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "input_1192 (InputLayer)         [(None, 49, 31)]     0                                            \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1193 (Flatten)          (None, 2304)         0           dropout_2385[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling1d_1191 (MaxPooling1 (None, 24, 16)       0           conv1d_1191[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "lstm_397 (LSTM)                 (None, 49, 16)       3072        input_1192[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_3577 (Dense)              (None, 768)          1770240     flatten_1193[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1192 (Flatten)          (None, 384)          0           max_pooling1d_1191[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1191 (Flatten)          (None, 784)          0           lstm_397[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2386 (Dropout)          (None, 768)          0           dense_3577[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_3575 (Dense)              (None, 512)          197120      flatten_1192[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_3573 (Dense)              (None, 512)          401920      flatten_1191[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_3578 (Dense)              (None, 256)          196864      dropout_2386[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2383 (Dropout)          (None, 512)          0           dense_3575[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2382 (Dropout)          (None, 512)          0           dense_3573[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2387 (Dropout)          (None, 256)          0           dense_3578[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_3576 (Dense)              (None, 2)            1026        dropout_2383[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_3574 (Dense)              (None, 2)            1026        dropout_2382[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "dense_3579 (Dense)              (None, 2)            514         dropout_2387[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_397 (Concatenate)   (None, 6)            0           dense_3576[0][0]                 \n",
      "                                                                 dense_3574[0][0]                 \n",
      "                                                                 dense_3579[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_3580 (Dense)              (None, 6)            42          concatenate_397[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "activation_794 (Activation)     (None, 6)            0           dense_3580[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_3581 (Dense)              (None, 2)            14          activation_794[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_795 (Activation)     (None, 2)            0           dense_3581[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 3,037,985\n",
      "Trainable params: 3,037,985\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "None\n",
      "testx (6805,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import matthews_corrcoef\n",
    "from sklearn.metrics import confusion_matrix\n",
    "yval = y_independent\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": "2f0a61a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matthews Correlation :  0.5200899736544208\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      " [[2428  973]\n",
      " [ 667 2737]]\n",
      "\n",
      "\n",
      "Accuracy on test set:    0.7590007347538574\n",
      "\n",
      "\n",
      "Sensitivity:    0.8040540540540541 \t Specificity:    0.7139076742134667\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.71      0.75      3401\n",
      "           1       0.74      0.80      0.77      3404\n",
      "\n",
      "    accuracy                           0.76      6805\n",
      "   macro avg       0.76      0.76      0.76      6805\n",
      "weighted avg       0.76      0.76      0.76      6805\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": "d08599c8",
   "metadata": {},
   "source": [
    "# Thank You"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc6d15a4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce3b2005",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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