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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Bio.Seq import Seq\n",
+    "from Bio import SeqIO\n",
+    "from Bio.SeqUtils import GC\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 1: My own class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Employee:\n",
+    "    \n",
+    "    def __init__(self, name, age, work_experience):\n",
+    "        self.name = name\n",
+    "        self.age = age\n",
+    "        self.work_experience = work_experience\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return 'Information about employee: {}'.format(self.name)\n",
+    "    \n",
+    "    def proposed_position(self):\n",
+    "        '''\n",
+    "        Shows the position the candidate is applying for\n",
+    "        :return: str, position name\n",
+    "        '''\n",
+    "        \n",
+    "        if self.work_experience <= 1:\n",
+    "            position = 'Junior DS'\n",
+    "        elif 1 < self.work_experience <= 3:\n",
+    "            position = 'Middle DS'\n",
+    "        else:\n",
+    "            position = 'Senior DS'\n",
+    "        return position\n",
+    "     \n",
+    "    def proposed_salary(self, position):\n",
+    "        '''\n",
+    "        Shows salary the candidate wants to have\n",
+    "        :param position: str, name of the position\n",
+    "        :return: int, salary in dollars\n",
+    "        '''\n",
+    "        if position == 'Junior DS':\n",
+    "            salary = 1000\n",
+    "        elif position == 'Middle DS':\n",
+    "            salary = 5000\n",
+    "        else:\n",
+    "            salary = 10000\n",
+    "        return salary\n",
+    "    \n",
+    "    def view_info(self):\n",
+    "        '''\n",
+    "        Shows basic information about candidate\n",
+    "        :return: str with information\n",
+    "        '''\n",
+    "        position = self.proposed_position()\n",
+    "        salary = self.proposed_salary(position)\n",
+    "        \n",
+    "        return f\"Name: {self.name}\\nAge: {self.age} years\\nPosition: {position}\\nSalary {salary} $\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Information about employee: Anton\n",
+      "Name: Anton\n",
+      "Age: 23 years\n",
+      "Position: Junior DS\n",
+      "Salary 1000 $\n"
+     ]
+    }
+   ],
+   "source": [
+    "employee_1 = Employee('Anton', 23, 1)\n",
+    "print(employee_1)\n",
+    "print(employee_1.view_info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Information about employee: Alexander\n",
+      "Name: Alexander\n",
+      "Age: 40 years\n",
+      "Position: Senior DS\n",
+      "Salary 10000 $\n"
+     ]
+    }
+   ],
+   "source": [
+    "employee_2 = Employee('Alexander', 40, 7)\n",
+    "print(employee_2)\n",
+    "print(employee_2.view_info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2: RNA class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RNA:\n",
+    "    \n",
+    "    def __init__(self, rna):\n",
+    "        self.rna = Seq(rna)\n",
+    "        \n",
+    "    def translation(self):\n",
+    "        '''\n",
+    "        Translates the RNA sequence into protein\n",
+    "        :return: protein sequence\n",
+    "        '''\n",
+    "        return self.rna.translate()\n",
+    "    \n",
+    "    def back_transcription(self):\n",
+    "        '''\n",
+    "        Convert RNA sequence into DNA\n",
+    "        :return: DNA sequence\n",
+    "        '''\n",
+    "        return self.rna.back_transcribe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TFF\n",
+      "ACGTTCTTC\n"
+     ]
+    }
+   ],
+   "source": [
+    "rna_seq = RNA('ACGUUCTTC')\n",
+    "print(rna_seq.translation())\n",
+    "print(rna_seq.back_transcription())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 3: Class inherited from Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SetModified(set):\n",
+    "    \n",
+    "    def __init__(self, *args):\n",
+    "        self.positive_numbers = [arg for arg in args if isinstance(arg, (int, float)) and arg > 0]\n",
+    "        super().__init__(self.positive_numbers)\n",
+    "        \n",
+    "    def add(self, element):\n",
+    "        if isinstance(element, (int, float)) and element > 0:\n",
+    "            super().add(element)\n",
+    "    \n",
+    "    def update(self, *elements):\n",
+    "        for element in elements:\n",
+    "            self.add(element)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SetModified({2, 3})\n",
+      "SetModified({2, 3, 100})\n",
+      "SetModified({800, 2, 3, 900, 100})\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_set = SetModified(-1, 2, 3)\n",
+    "print(my_set)\n",
+    "my_set.add(100)\n",
+    "print(my_set)\n",
+    "my_set.update(800, 900, -5)\n",
+    "print(my_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 4: Class for working with Fasta files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FastaStats:\n",
+    "    \n",
+    "    def __init__(self, path):\n",
+    "        self.path = path\n",
+    "        \n",
+    "    def __str__(self):\n",
+    "        return \"Path to the file: {}\".format(self.path)\n",
+    "    \n",
+    "    def count_sequences(self):\n",
+    "        '''\n",
+    "        Counts number of sequences presented in the file\n",
+    "        :return: int, number of sequences\n",
+    "        '''\n",
+    "        seq_numb = 0 \n",
+    "        with open(self.path) as f:\n",
+    "            for record in SeqIO.parse(f, \"fasta\"):\n",
+    "                seq_numb += 1\n",
+    "        return seq_numb\n",
+    "    \n",
+    "    def calc_sequences_length(self):\n",
+    "        '''\n",
+    "        Calculates length of sequences presented in the file\n",
+    "        :return: list with lengths of sequences\n",
+    "        '''\n",
+    "        with open(self.path) as f:\n",
+    "            length_ls = [len(record.seq) for record in SeqIO.parse(f, \"fasta\")]\n",
+    "        return length_ls\n",
+    "        \n",
+    "    def calc_gc_content_per_seq(self):\n",
+    "        '''\n",
+    "        Calculates GC content of sequences presented in the file\n",
+    "        :return: list with GC content per each sequence\n",
+    "        '''\n",
+    "        with open(self.path) as f:\n",
+    "            gc_content_ls = [GC(record.seq) for record in SeqIO.parse(f, \"fasta\")]\n",
+    "        return gc_content_ls\n",
+    "    \n",
+    "    def calc_mean_gc_content(self):\n",
+    "        '''\n",
+    "        Calculates mean GC content over all sequences presented in the file\n",
+    "        :return: int, mean GC content (%)\n",
+    "        '''\n",
+    "        return np.mean(self.calc_gc_content_per_seq())\n",
+    "    \n",
+    "    def calc_k_mers(self, kmer_size):\n",
+    "        '''\n",
+    "        Counts k-mer over all sequences in the file\n",
+    "        :param kmer_size: int, size of k-mers\n",
+    "        :return: dictionary, where keys - kmers, values - their counts\n",
+    "        '''\n",
+    "        k_mer_counts = {}\n",
+    "        with open(self.path) as f:\n",
+    "            for record in SeqIO.parse(f, \"fasta\"):\n",
+    "                for i in range(len(record.seq)-kmer_size+1):\n",
+    "                    kmer = str(record.seq[i:i+kmer_size])\n",
+    "                    k_mer_counts[kmer] = k_mer_counts.get(kmer, 0) + 1\n",
+    "        k_mer_counts = dict(sorted(k_mer_counts.items(), key=lambda item: item[1], reverse=True))\n",
+    "        return k_mer_counts\n",
+    "    \n",
+    "    def plot_length_distribution(self):\n",
+    "        '''\n",
+    "        Plots length distribution for sequences presented in the file\n",
+    "        '''\n",
+    "        length_ls = self.calc_sequences_length()\n",
+    "        plt.figure(figsize=(10, 8))\n",
+    "        sns.distplot(length_ls, bins=30)\n",
+    "        plt.title('Length distribution', fontsize=15)\n",
+    "        plt.xlabel('length', fontsize=15)\n",
+    "        plt.ylabel('frequency', fontsize=15)\n",
+    "    \n",
+    "    def plot_gc_content_distribution(self):\n",
+    "        '''\n",
+    "        Plots GC content distribution for sequences presented in the file\n",
+    "        '''\n",
+    "        gc_content_ls = self.calc_gc_content_per_seq()\n",
+    "        mean_gc_content = self.calc_mean_gc_content()\n",
+    "        plt.figure(figsize=(10, 8))\n",
+    "        sns.distplot(gc_content_ls, bins=30)\n",
+    "        plt.title('GC content distribution', fontsize=15)\n",
+    "        plt.xlabel('GC content, %', fontsize=15)\n",
+    "        plt.ylabel('frequency', fontsize=15)\n",
+    "        plt.axvline(mean_gc_content, color='red', linestyle = '--')\n",
+    "        \n",
+    "    def plot_kmer_distribution(self, kmer_size):\n",
+    "        '''\n",
+    "        Plots frequency of k-mers over all sequences\n",
+    "        :param kmer_size: int, size of k-mers\n",
+    "        '''\n",
+    "        k_mer_counts = self.calc_k_mers(kmer_size=kmer_size)\n",
+    "        plt.figure(figsize=(12, 10))\n",
+    "        plt.bar(k_mer_counts.keys(), k_mer_counts.values(), width=1)\n",
+    "        plt.title('{}-mers distribution'.format(kmer_size), fontsize=15)\n",
+    "        plt.xlabel('{}-mers'.format(kmer_size), fontsize=15)\n",
+    "        plt.ylabel('counts', fontsize=15)\n",
+    "        plt.xticks(fontsize=3, rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path to the file: ../data/example.fa\n",
+      "Number of sequences:  91\n",
+      "Mean GC content: 40.72 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "fasta_stats = FastaStats('../data/example.fa')\n",
+    "print(fasta_stats)\n",
+    "print('Number of sequences: ', fasta_stats.count_sequences())\n",
+    "print('Mean GC content: {} %'.format(round(fasta_stats.calc_mean_gc_content(), 2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vNtY/RcSKiHg2e513oLEkSZLyoKQhMCIqgG8C7wYmApdFxMRm3a4ENqWUjgK+AtyQ7TsRmA5MAs4FbszGA/hB1taSr6SUTspe97diLEmSpC6v1FcCpwA1KaUlKaU6YAYwrVmfacCt2ft7gLMiIrL2GSml3SmlpUBNNh4ppceBjUXUsc+xJEmS8qDUIXAUsLzJ59qsrcU+KaV6YDMwuJX7tuTqiJifTRkPLKIOSZKkLqvUITBaaEut7NOafZv7FnAkcBKwCvivIuogIq6KiDkRMWfdunUHOJQkSVLnUeoQWAsc3uTzaGDlvvpERCXQn8JUb2v2/RMppTUppYaUUiPwXf53yrdVY6WUbkopTU4pTR46dOgBTk2SJKnzKHUInA1MiIhxEdGdwuKMmc36zASuyN5fAjySUkpZ+/Rs9fA4YALw9P4OFhEjmny8CNi7erjosSRJkrqSylIeLKVUHxFXAw8BFcAtKaUFEXEdMCelNBO4Gbg9ImooXAGcnu27ICLuBhYC9cCnU0oNABFxJ3AGMCQiaoEvpJRuBv4jIk6iMNX7CvCJA40lSZKUB1G4yKYDmTx5cpozZ065y1ALfjhrWVH9L586pp0qkSSpY4iIuSmlyfvr4xNDJEmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHSh4CI+LciFgUETURcU0L26sj4q5s+6yIGNtk27VZ+6KIOKdJ+y0RsTYinm821pci4sWImB8RP42IAVn72IjYGRHPZq9vt98ZS5IkdTwlDYERUQF8E3g3MBG4LCImNut2JbAppXQU8BXghmzficB0YBJwLnBjNh7AD7K25h4G3pBSOgFYDFzbZNvLKaWTstcn2+L8JEmSOotSXwmcAtSklJaklOqAGcC0Zn2mAbdm7+8BzoqIyNpnpJR2p5SWAjXZeKSUHgc2Nj9YSumXKaX67ONTwOi2PiFJkqTOqNQhcBSwvMnn2qytxT5ZgNsMDG7lvvvzUeCBJp/HRcQzEfFYRJxexDiSJEmdXmWJjxcttKVW9mnNvi0fNOLvgXrgjqxpFTAmpbQhIk4FfhYRk1JKW5rtdxVwFcCYMWNacyhJkqROodRXAmuBw5t8Hg2s3FefiKgE+lOY6m3Nvq8TEVcA7wH+LKWUALIp5Q3Z+7nAy8DRzfdNKd2UUpqcUpo8dOjQVp2gJElSZ1DqEDgbmBAR4yKiO4WFHjOb9ZkJXJG9vwR4JAtvM4Hp2erhccAE4On9HSwizgX+FnhvSmlHk/ahexeVRMT4bKwlh3x2kiRJnURJp4NTSvURcTXwEFAB3JJSWhAR1wFzUkozgZuB2yOihsIVwOnZvgsi4m5gIYWp3U+nlBoAIuJO4AxgSETUAl9IKd0MfAOoBh4urC3hqWwl8NuA6yKiHmgAPplSet3CEkmSpK4qshlSHcDkyZPTnDlzyl2GWvDDWcuK6n/5VL/fKUnq2iJibkpp8v76+MQQSZKkHDIESpIk5ZAhUJIkKYcMgZIkSTlkCJQkScohQ6AkSVIOGQIlSZJyyBAoSZKUQ4ZASZKkHDIESpIk5ZAhUJIkKYcMgZIkSTnU6hAYEVdHxKD2LEaSJEmlUcyVwH8HVkTE3RFxbkREexUlSZKk9lVMCBwOfAo4DLgPWB4R10fE0e1SmSRJktpNq0NgSml7Sun7KaW3A0cD3wf+DHghIp6IiD+PiD7tVagkSZLazkEtDEkpvZxS+kfgncCTwFuAm4GVEfHViOjfhjVKkiSpjRUdAiOiV0RcERGPAouAIcDngAnANcDFwF1tWaQkSZLaVmVrO0bE6cCfA5cAAdwNXJtS+n2TbjdGRA0ws02rlCRJUptqdQgEHgOeAv4vMCOltH0f/RYBdx5qYZIkSWo/xYTAN6SUFh6oU0rpVQpXDCVJktRBFfOdwK0RcUpLGyLilIg4vI1qkiRJUjsrJgR+C/jgPrZdDtx46OVIkiSpFIoJgacBj+xj22+y7ZIkSeoEigmBvYC0n+29D7EWSZIklUgxIfA54LJ9bLsMWHDo5UiSJKkUilkd/EXgxxFRDfwAWAWMAK4A3pe9JEmS1Am0OgSmlH4aEVcA/04h8CUKN41eAXwwpfSz9ilRkiRJba2YK4GklG6PiP8BjgEGAxuARSml/X1XUJIkSR1MUSEQIAt8L7ZDLZIkSSqRokJgRIwE3gOMBno025xSSn/bVoVJkiSp/bQ6BEbERRSeCVwBrAXqmnVJgCFQkiSpEyjmSuC/Ab8EPpJS2thO9UiSJKkEigmBhwN/aQCUJEnq/Iq5WfTvKKwKliRJUidXzJXAzwB3RMQ24GHgteYdUko72qowSZIktZ9iQuD87M/vs+9nCFccWjmSJEkqhWJC4EfZd/iTJElSJ1LMY+N+0I51SJIkqYSKfmJIREwETqWwWviWlNLqiDgKWJNS2trWBUqSJKntFXOz6D7ALcAlwJ5s3weB1RTuIbgM+Gw71ChJkqQ2VswtYr4MvBk4C+gLRJNt9wPntmFdkiRJakfFTAdfDPxVSuk3EdF8FfCrwBFtV5YkSZLaUzFXAnsCG/axrS/QcOjlSJIkqRSKCYGzgQ/vY9slFJ4oIkmSpE6gmBD4D8DFEfEr4GMU7hl4XkTcDrwf+EJrBomIcyNiUUTURMQ1LWyvjoi7su2zImJsk23XZu2LIuKcJu23RMTaiHi+2ViDIuLhiHgp+3Ng1h4R8bVsrPkRcUoRPwdJkqROr9UhMKX0WwqLQqqBb1BYGPLPwHjgnSml2QcaI/su4TeBdwMTgcuyW840dSWwKaV0FPAV4IZs34nAdGAShUUoNzb5buIPaHlhyjXAr1NKE4BfZ5/Jjj8he10FfOtAtUuSJHUlxVwJJKX0ZErpdKAfMBrom1J6S0rpyVYOMQWoSSktSSnVATOAac36TANuzd7fA5wVEZG1z0gp7U4pLQVqsvFIKT0ObGzheE3HuhW4sEn7bangKWBARIxo5TlIkiR1ekWFwL1SSjtTSitTSjuK3HUUsLzJ59qsrcU+KaV6YDMwuJX7NndYSmlVNtYqYFgRdUiSJHVZxdws+u4D9UkpfeBAw7S0Wyv7tGbf1mrVWBFxFYXpYsaMGXOQh5IkSep4irlP4NAW2gYBx1C4dcyiVoxRS+Fxc3uNBlbuo09tRFQC/SlM9bZm3+bWRMSIlNKqbLp3bRF1kFK6CbgJYPLkyQcbOCVJkjqcYhaGvKOF14kUFlesorCI40BmAxMiYlxEdKew0GNmsz4zgSuy95cAj6SUUtY+PVs9PC477tMHOF7Tsa4Aft6k/cPZKuHTgM17p40lSZLy4KC+E9hUSmk58O/Af7Sibz1wNfAQ8AJwd0ppQURcFxHvzbrdDAyOiBrgM2QrelNKC4C7gYUUnln86ZRSA0BE3An8HjgmImoj4spsrC8CZ0fES8DZ2WcoPOZuCYXFJd8FPnUIPwJJkqROp5jp4P1poDClekAppfsphLCmbZ9v8n4XhfsOtrTv9cD1LbRfto/+Gyjc1qZ5ewI+3Zp6JUmSuqJiFoY0v58fQHfgOOBfKEz1SpIkqRMo5krg87S8GjcoBMCPtUlFkiRJanfFhMB3tNC2C6hNKa1oo3okSZJUAq0OgSmlx9qzEEmSJJVOMd8JLOpuySmlZcWXI0mSpFIoZjr4FVr3hI7I+lUcTEGSJElqf8WEwA8CN1C4v99PKDx9YxjwPuBY4G+ALW1doCRJktpeMSHwXcAvUkp/0az92xHxbeC8lNKH2q40SZIktZdinhhyMYUrgC35MfDefWyTJElSB1NMCNwJvHUf206ncLsYSZIkdQLFTAd/C/jHiBgMzOR/vxM4DfgELTzOTZIkSR1TMfcJ/KeI2ERhAcinKKwADmA18NmU0n+3T4mSJElqa8VcCSSl9NWI+DowBjiMQgBcnlJqbI/iJEmS1D6KCoEAKaXGiHgVqAPWGgClg/fDWcXdU/3yqUXds12SpH0qZmEIEXFeRMyisAhkGXBC1n5TRHywHeqTJElSO2h1CIyID1NYEPIicFWzfV8Crmzb0iRJktReirkS+PfAl1JKVwD/02zbAmBim1UlSZKkdlVMCDwCeHgf23YB/Q69HEmSJJVCMSFwOXDyPrZNBmoOvRxJkiSVQjEh8GbgC9kCkJ5ZW0TEWRTuHfjdti5OkiRJ7aOYW8TcABwO3Ao0ZG2/AyqA76SUvtbGtUmSJKmdFPPEkAR8OiK+DJwFDAE2Ao+klBa3U32SJElqB60KgRHRA/g6cHNK6Sng5XatSpIkSe2qVd8JTCntAqYDPdq3HEmSJJVCMQtDHgHe0V6FSJIkqXSKWRjyTeB7EdEbuB9YA6SmHVJKC9uwNkmSJLWTYkLgg9mfn8leTQNgZJ8r2qguSZIktaP9hsCIuAX4l5TSUgpTwf2ALaUoTJIkSe3nQFcCrwC+DSyl8J3AN6WUnm73qiRJktSuDhQCVwFnRMRCClO+PSKi1746p5R2tGVxkiRJah8HWh18E/BFYDOF7/z9Bti6n5ckSZI6gf1eCUwpXRcR9wHHAbcB/4o3ipYkSer0Drg6OKU0F5gbEWcB388WiUiSJKkTK+bZwX/enoVIkiSpdIp5YogkSZK6CEOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScqjkITAizo2IRRFRExHXtLC9OiLuyrbPioixTbZdm7UviohzDjRmRDwREc9mr5UR8bOs/YyI2Nxk2+fb96wlSZI6lspSHiwiKoBvAmcDtcDsiJiZUlrYpNuVwKaU0lERMR24Abg0IiYC04FJwEjgVxFxdLZPi2OmlE5vcuwfAz9vcpwnUkrvaZ8zlSRJ6thKfSVwClCTUlqSUqoDZgDTmvWZBtyavb8HOCsiImufkVLanVJaCtRk4x1wzIjoC5wJ/KydzkuSJKlTKXUIHAUsb/K5NmtrsU9KqR7YDAzez76tGfMi4NcppS1N2t4UEfMi4oGImHRwpyNJktQ5lToERgttqZV9im1v6jLgziaf/wAckVI6Efg6+7hCGBFXRcSciJizbt26lrpIkiR1SqUOgbXA4U0+jwZW7qtPRFQC/YGN+9l3v2NGxGAKU8b37W1LKW1JKW3L3t8PVEXEkObFppRuSilNTilNHjp0aHFnKkmS1IGVOgTOBiZExLiI6E5hocfMZn1mAldk7y8BHkkppax9erZ6eBwwAXi6FWO+H/hFSmnX3oaIGJ59z5CImELh57Chjc9VkiSpwyrp6uCUUn1EXA08BFQAt6SUFkTEdcCclNJM4Gbg9oiooXAFcHq274KIuBtYCNQDn04pNQC0NGaTw04HvtislEuAv4iIemAnMD0LmpIkSblQ0hAIf5x+vb9Z2+ebvN9F4epdS/teD1zfmjGbbDujhbZvAN8opm5JkqSuxCeGSJIk5ZAhUJIkKYdKPh0sdSY76upZsWkntZt2sn7bbo4d3o+JI/tR0a2lOxNJktR5GAKlZnbU1fP9J1/htt+/wpotu1+3vW91JaeOHcibxg/m0jcezoBe3UtfpCRJh8gQKGV21zdw56xlfOM3L7N+227efvRQPvymQYwe2JPRA3sxsFcVz63YzNNLNzJr6Ub+/YEX+dZjL/OZs4/m8iljqKzw2xWSpM7DECgBz6/YzCdun8uK13Zy2vhBfOdDp3DqEYNe12/80D5MO6nwVMIXV2/hunsX8vmfL+B/nnqVf3zPRE6f4E3FJUmdg5culHtzXtnIZd99ipQS/3PlVEkoOhIAACAASURBVO78+GktBsDmjh3ejzs+NpXvfOhUdu1p5EM3P82/3/8CjY3eclKS1PF5JVC59vjidXzi9rmM6N+D2z82lVEDeha1f0RwzqThnHHMUP7lFwv5zuNLWL5pB1/+wEn0qKpop6olSTp0XglUbj34/Co+duscxg7pzV2feFPRAbCp6soK/mXaG/j7847jgedXc9l3n2LDttcvKpEkqaMwBCqXfvfyej51xx94w6h+zPj4aQztW33IY0YEH3/beG68/BQWrtzCRTf+juUbd7RBtZIktT1DoHJnw7bd/PWMZxk7pDe3XTmV/r2q2nT8dx8/gjuvOo3XdtRxxfefZuP2ujYdX5KktmAIVK40psT/+9E8Xtu5h29cdgp9qtvna7GnjBnI9654I7WbdvLRH8xmR119uxxHkqSDZQhUrvyuZj2PLlrHP5x/HBNH9mvXY00ZN4ivTT+Z+bWvcfUPn6G+obFdjydJUjFcHazcqN20g4cWrGHiiH5URPDDWctKctwLThzJz59dyaU3PcXFJ48iwkfOSZLKzxCoXNhd38CM2cvp06OSi08pbRCbOm4wW3bW85tFaxnWt9obSkuSOgSng5ULjy9ex8btdbx/8mh6dS/9f/u887hhTBzRj4cWrObVDdtLfnxJkpozBKrLe21HHU+8tJ4TRvdn/JA+ZakhInjfKaPp37OKGbOXs323C0UkSeVlCFSX98uFawA4Z9LwstbRs3sFl085gm276/nR3OU0Jh8vJ0kqH0OgurTlG3fw7PLXeOtRQxjYq3u5y2HUwJ6cf/wIFq/ZxuOL15W7HElSjhkC1WWllLj/uVX0rq7k7Ud3nMUYU8cN4oTR/Xl44Rq/HyhJKhtDoLqs51du4dWNO3jXcYdRXVVR7nL+KCK46KRR9O9VxT1za6mr9/6BkqTSMwSqS6pvaOTB51cxvF8PTh07sNzlvE51VQXvO2U0G7bX8fDC1eUuR5KUQ4ZAdUnPLHuNTTv2cO4bhtOtg96c+cihfTht/CB+9/IGlq53WliSVFqGQHU5jSnx+EvrGDWgJxOGleeWMK11zqThDOzdnR//wWlhSVJpGQLV5SxYuYUN2+t429FDO/wj2qorC9PCG7fX8eACp4UlSaVjCFSXklLi8cXrGNy7O5NG9it3Oa0ybkhv3nzkYJ5a4rSwJKl0DIHqUpas386K13Zy+oShHfa7gC1518ThDOxVxc+fXUF9o9PCkqT2ZwhUl/LY4nX0qa7k5DEDyl1KUbpXduOCE0eydutunnxpfbnLkSTlgCFQXcaK13ZSs3YbbzlyMFUVne+f9rHD+zFpZD8eWbSWjdvryl2OJKmL63y/KaV9eHzxOqoruzF1/OByl3LQzj9+BEFw77yVJJ8tLElqR4ZAdQkbt9fx/IrNTB03iB4d6OkgxRrQqzvvPG4Yi9ZsZcHKLeUuR5LUhRkC1SXMfmUjAG86ckiZKzl0bzpyCCP69+AX81eye09DucuRJHVRhkB1evWNjcx5dRPHjuhH/55V5S7nkFV0C6adOJItu+r5zaK15S5HktRFGQLV6S1cuYXtu+uZMnZQuUtpM2MG9+aUMQN4smYD67ftLnc5kqQuyBCoTu/ppRsZ2KuKCYd17EfEFetdk4ZTURHc/9yqcpciSeqCDIHq1GrWbmPJ+u28ceygTnVz6Nbo16OKM48Zxourt7J4zdZylyNJ6mIMgerU7nx6Gd0CTj1iYLlLaRdvPnIwg3t35xfzV/kkEUlSmzIEqtPataeBe+bWMmlkf/r26PwLQlpSWdGN808Ywfptu3nq5Q3lLkeS1IUYAtVp3f/cKjbv3MOUcV1nQUhLjh3ej6MP68OvX1zrIhFJUpsxBKrTumPWMsYP6c34Ib3LXUq7O//4kexpaOQrDy8udymSpC7CEKhOadHqrcx9dROXTRlDdLEFIS0Z2reaqeMHc+fTy1wkIklqE4ZAdUo/+UMtld2Ci04ZVe5SSuasY4bRp7qS6+97odylSJK6AEOgOp36hkZ++swKzjhmKEP6VJe7nJLpVV3J/zlrAo8tXsdji9eVuxxJUidnCFSn8+TLG1i7dTfvO2V0uUspuQ+/aSxHDO7F9fctpL7BW8ZIkg6eIVCdzo/n1tK/ZxVnHjes3KWUXPfKblz77mNZvGYbd8+pLXc5kqROrOQhMCLOjYhFEVETEde0sL06Iu7Kts+KiLFNtl2btS+KiHMONGZE/CAilkbEs9nrpKw9IuJrWf/5EXFK+5612srWXXt4aMFqLjhxBNWVFeUupyzOmTScKWMH8eWHF7F1155ylyNJ6qRKGgIjogL4JvBuYCJwWURMbNbtSmBTSuko4CvADdm+E4HpwCTgXODGiKhoxZifSymdlL2ezdreDUzIXlcB32r7s1V7eOC51eyub+TiHE4F7xUR/MN7jmP9tjq+9ejL5S5HktRJlfpK4BSgJqW0JKVUB8wApjXrMw24NXt/D3BWFO4BMg2YkVLanVJaCtRk47VmzOamAbelgqeAARExoi1OUO3rnj/UMn5Ib04+fEC5SymrE0YP4OKTR/G93y6ldtOOcpcjSeqESh0CRwHLm3yuzdpa7JNSqgc2A4P3s++Bxrw+m/L9SkTsXUramjrUwSzfuIOnl27k4lNG5eLegAfy2XOOIYD/eHBRuUuRJHVCpQ6BLf3mTq3sU2w7wLXAscAbgUHA3xZRBxFxVUTMiYg569Z5S45y+8kfVgBwUY6ngpsaOaAnV71tPDPnreSZZZvKXY4kqZMpdQisBQ5v8nk0sHJffSKiEugPbNzPvvscM6W0Kpvy3Q18n8LUcWvrIKV0U0ppckpp8tChQ4s4TbW1lBI/eaaWN40fzKgBPctdTofxybcfydC+1fzrfS+Q0uv+O0aSpH0qdQicDUyIiHER0Z3CQo+ZzfrMBK7I3l8CPJIKv91mAtOz1cPjKCzqeHp/Y+79nl/2ncILgeebHOPD2Srh04DNKaVV7XPKagt/WPYar27YwcU5ekJIa/SuruSz7zqaua9u4v7nVpe7HElSJ1LSEJh9x+9q4CHgBeDulNKCiLguIt6bdbsZGBwRNcBngGuyfRcAdwMLgQeBT6eUGvY1ZjbWHRHxHPAcMAT416z9fmAJhcUl3wU+1Y6nrTZw77yVdK/sxrlvGF7uUjqcS049nGOH9+WLD77Arj0N5S5HktRJVJb6gCml+ymEsKZtn2/yfhfw/n3sez1wfWvGzNrP3Mc4Cfh0UYWrbBoaE/c9t4ozjxlG3x5V5S6nw6noFvzD+RP54M2zuPV3r/CJtx9Z7pIkSZ2ATwxRhzdryQbWbd3NBSeOLHcpHdZbJwzhzGOH8Y1HatiwbXe5y5EkdQKGQHV4985fSe/uFZx5bP4eE1eMvzvvWHbsaeCrv36p3KVIkjoBQ6A6tLr6Rh54fjVnTzyMnt3z+Zi41jpqWF/+bOoY7pi1jJq1W8tdjiSpgzMEqkP7bc06Xtuxx6ngVvqrsybQq6qCf7v/xXKXIknq4AyB6tDunbeK/j2rOH2C92lsjcF9qrn6zKN45MW1PPGSNziXJO2bIVAd1q49DfxywWre/YbhdK/0n2prXfHmsRw+qCfX3/cCDY3eQFqS1DJ/s6rDeuTFtWyva3AquEg9qiq45tzjeHH1Vn40Z/mBd5Ak5ZIhUB3WvfNWMqRPNaeNH1zuUjqd844fzqlHDOS/Hl7Mtt315S5HktQBGQLVIW3dtYdHXlzLe04YQUW3KHc5nU5E8A/nH8e6rbv5zmMvl7scSVIHZAhUh/TwwjXsrm/kghNHlLuUTuvkMQN574kjuenxJax8bWe5y5EkdTCGQHVI985byagBPTllzMByl9Kp/c25x5CALz20qNylSJI6GEOgOpxN2+t44qX1vOfEEUQ4FXwoRg/sxcfeOo6fPrOCectfK3c5kqQOxBCoDueB51dT35i44ARXBbeFvzjjSIb06c71971ASt4yRpJUUFnuAqTm7p23kvFDezNpZL9yl9Lp/XDWMgDectQQfv7sSv7up89z/Kj+Lfa9fOqYUpYmSSozrwSqQ1m7ZRdPLd3ABSeMdCq4Db1x7CBG9O/B/c+toq6+sdzlSJI6AEOgOpRfzF9FSniD6DbWLYILThjJ5p17eHTx2nKXI0nqAAyB6lDunb+SiSP6cdSwPuUupcsZO6Q3Jx0+gCdeWs+GbbvLXY4kqcwMgeowlm/cwTPLXvMqYDs6d9JwKroF9z23qtylSJLKzBCoDuPe+SsBeM8J3iC6vfTrWcWZxwzjxdVbWbR6a7nLkSSVkSFQHca981ZxypgBHD6oV7lL6dLefNRghvSp5hfzV1Lf4CIRScorQ6A6hJq1W3lh1Rangkugsls3LjhhBBu21/H4S+vLXY4kqUwMgeoQZs5bRbeA8493KrgUJhzWl+NH9efRRWtdJCJJOWUIVNmllPjFvJWcNn4ww/r1KHc5uXH+CSOo6Bb8fN5KnyQiSTlkCFTZLVi5hSXrtzsVXGL9elTxrknDqVm7jfm1m8tdjiSpxAyBKrt7562ksltw7qTh5S4ld6aOG8SoAT2577lVbN65p9zlSJJKyBCosmpsTPxi/iredvRQBvbuXu5ycqdbBBeePIrtu+v50kMvlrscSVIJGQJVVs8s38SK13ZywYkuCCmXUQN68uYjB3PHrGXMfXVjucuRJJWIIVBlNfPZlVRXduPsiU4Fl9M7Jx7GyP49+dw989m1p6Hc5UiSSsAQqLKpb2jkvudWcdZxw+hTXVnucnKturKCG953AkvWbecrv1pc7nIkSSVgCFTZzFq6kfXb6rjgBFcFdwRvnTCEy6YczncfX8Kzy18rdzmSpHZmCFTZzHx2JX2qK3nHscPKXYoy1553HIf168HnfjSP3fVOC0tSV+YcnMqirr6RB55fxbsmHkaPqopyl9Np/HDWsnYdv1+PKv7t4uP58+/P5uu/ruGz5xzTZmMXW/vlU8e02bE7Mn8uksrFK4Eqi8cWr2PLrnpvEN0BveOYYbzvlNF867GXmV/rtLAkdVWGQJXFz55ZweDe3Tl9wpByl6IWfP49EzmsbzV/PeNZdtTVl7scSVI7MASq5Lbs2sPDL6zhghNHUlnhP8GOqH+vKv7rAyexdMN2/uUXL5S7HElSO/A3sEruwedWU1ffyEUnjyp3KdqPNx05mKveNp47n17GLxesLnc5kqQ2ZghUyf3kmVrGD+nNCaP7l7sUHcD/O/sYJo3sxzU/eY61W3aVuxxJUhsyBKqkVry2k6eWbOTCk0cREeUuRwfQvbIbX51+Ejvq6vnsPfNpbEzlLkmS1EYMgSqpmc+uBODCk5wK7iyOGtaXvz9/Io8vXsd3n1hS7nIkSW3EEKiSSSnx02dqOfWIgYwZ3Kvc5agIH5w6hvOPH8F/PLSIWUs2lLscSVIbMASqZF5YtZXFa7ZxoQtCOp2I4IvvO54jBvXi6jufYe1Wvx8oSZ2dIVAl87NnV1DZLXjP8SPKXYoOQt8eVdz4wVPYumsP/+fOZ6hvaCx3SZKkQ2AIVEk0NCZ+/uwKzjhmGAN7dy93OTpIxw7vx/UXHs9TSzby5YcXl7scSdIhMASqJH5bs541W3Z7b8Au4H2njuayKYdz46Mv8+Dzq8pdjiTpIBkCVRJ3z1nOwF5VvHPisHKXojbwhQsmcfKYAfzfu+bx/IrN5S5HknQQSh4CI+LciFgUETURcU0L26sj4q5s+6yIGNtk27VZ+6KIOOdAY0bEHVn78xFxS0RUZe1nRMTmiHg2e32+fc863zZtr+PhBWu48ORRVFdWlLsctYEeVRV850OnMrBXFR+7dY43kpakTqikITAiKoBvAu8GJgKXRcTEZt2uBDallI4CvgLckO07EZgOTALOBW6MiIoDjHkHcCxwPNAT+FiT4zyRUjope13X9mervX76zArqGhq59I2Hl7sUtaFhfXvwvSveyJZde/j4bXPYtaeh3CVJkopQ6iuBU4CalNKSlFIdMAOY1qzPNODW7P09wFlReLTENGBGSml3SmkpUJONt88xU0r3pwzwNDC6nc9PzaSUuHvOck4Y3Z9jh/crdzlqYxNH9uMrl57E/BWb+eyP5lH4v5okqTModQgcBSxv8rk2a2uxT0qpHtgMDN7PvgccM5sG/hDwYJPmN0XEvIh4ICImHewJaf/m127mxdVb+cBkrwJ2VedMGs7fnHMsv5i/in9/4MVylyNJaqXKEh+vpYfFNr90sK8++2pvKcg2H/NG4PGU0hPZ5z8AR6SUtkXEecDPgAmvKzbiKuAqgDFjxrRwGB3IXXOW06OqG+89aWS5S1E7+uTbx7Nq805uenwJg3p355NvP7LcJZXVD2ctK3cJknRApb4SWAs0vSQ0Gli5rz4RUQn0BzbuZ9/9jhkRXwCGAp/Z25ZS2pJS2pa9vx+oioghzYtNKd2UUpqcUpo8dOjQ4s5U7Kxr4N5nV3LeG0bQr0dVuctRO4oI/umCSbznhBF88YEXuXv28gPvJEkqq1KHwNnAhIgYFxHdKSz0mNmsz0zgiuz9JcAj2Xf6ZgLTs9XD4yhcuXt6f2NGxMeAc4DLUkp/fLxBRAzPvmdIREyh8HPwgaht7P7nVrF1dz0fcEFILnTrFnz5Aydx+oQhXPOT+Ty0YHW5S5Ik7UdJQ2D2Hb+rgYeAF4C7U0oLIuK6iHhv1u1mYHBE1FC4endNtu8C4G5gIYXv9n06pdSwrzGzsb4NHAb8vtmtYC4Bno+IecDXgOnJb7S3ubvmLGfs4F5MHTeo3KWoRLpXduPbHzyV40cP4C/vfIbHFq8rd0mSpH0o9XcC906/3t+s7fNN3u8C3r+Pfa8Hrm/NmFl7i+eXUvoG8I2iCldRXl63jaeXbuRz5xxDdtFVOdG7upIffOSNXP69WXz8tjl850On8o5jvEm4JHU0PjFE7eL2379K94purgrOqYG9u/PDj01lwrA+fOK2ufz6hTXlLkmS1IwhUG1u6649/GjOcs4/YQRD+1aXuxyVSSEInsaxI/ryyf+Zy8KVW8pdkiSpCUOg2tyP59ayva6Bj7x5bLlLUZn171XF7VdOZdLI/vzw6Vf5w7JN5S5JkpQxBKpNNTYmbvv9q5x0+ABOPHxAuctRB9C/ZxW3XzmFsUN6c8/cWh55ca1PFpGkDsAQqDb1RM16lqzf7lVA/Ym+Par4yJvHctLhA/jVC2v46TMraGg0CEpSOZV8dbC6th88uZQhfao57/gR5S5FHUxlt268/9TRDOhZxaOL17Fl1x6mv3EMPaoqyl2aJOWSVwLVZl5Zv51HF6/jz6aOoXul/7T0ehHBuyYN58KTRlGzdhvf/E0NqzbvLHdZkpRL/qZWm7nt969SEcGfTfU5y9q/KeMGceVbx1PX0Mi3H3uZZ1wwIkklZwhUm9i2u54fzVnOecePYFi/HuUuR53AuCG9ufodRzF6YC9+NLeWnz27gj0NjQfeUZLUJgyBahN3PPUqW3fXc+Vbx5W7FHUifXtU8dG3jOP0CUN4eulGvv7IS7y6YXu5y5KkXDAE6pDtrGvgu08s4fQJQ7wtjIpW0S149xtG8NG3jKO+MXHT40u4b/5KdtY1lLs0SerSDIE6ZDNmL2P9tjr+8swJ5S5FndhRw/rwV2dOYMq4QTz58gbe/dXHeXjhGu8pKEntxBCoQ7K7voHvPLaEKeMGMWXcoHKXo06uuqqCaSeN4sq3jqNbt+Djt83h0pueYt7y18pdmiR1Od4nUIfkx3NXsHrLLr70/hPKXYoO0Q9nLSt3CX905NA+XPPuY5kxezn//fBipn3zSS44cSR/8fYjmTiyX8nr6Ug/m46kvX8ul3unAaldGQJ10PY0NHLjozWcePgA3nrUkHKXoy6mqqIbHzrtCC48aSTfeWwJN/92KffOW8lp4wfx0beM46zjDqOiW5S7TEnqtAyBOmgzn11J7aad/PN7JxHhL2O1j749qvjsOcfw8dPHM2P2Mm793StcdftcDh/Uk/OPH8m73zCcE0b3L9u/wZQSu/Y0sr2unsaUoPA/AujZvYKeVRVUVvjNG0kdjyFQB6WhMfHNR2s4bkQ/zjx2WLnLUQ7071XFJ95+JFe+dRwPLVjDjNnL+N4TS/j2Yy8zsn8Pzp54GKccMZATRw/giMG92iQUbt9dz5otu1iybhtbdtWzddcetuzc87/vsz/3NOx/8Ur3im70rq5gSJ9qhvSpZmjfaob1rWb0wF4+XUdS2RgCdVB+NGc5S9Zt51t/dopXAVVSlRXdOP+EEZx/wghe21HHr19Yy4MLVnPXnOXc+vtXAejfs4pJI/sxckBPDutXzWH9ejC4dzWVFUFFBBXdgkRi6676Pwa5zTv2sGbLLtZs2c2arbtYu2U323bXv+74VRVBvx5V9OtZxeiBPenXox/9elTSu7qSbhFEFB6P19iY2LmnofCqa2DLrj1s2FbH3GWbqKsv3BS7W8DIAT05YlAvxg3pzZHD+lBd6bOUJZWGIVBF27a7nv/85WImHzGQc98wvNzlKMcG9OrO+04dzftOHU19QyOL12xjfu1rzKt9jRdWbeXJmvWs3bqbhsYD32ame0U3hmWB8bjh/Xj70YX3h/Wr5vkVW+jbo5J+Paqorux2SP/hk1Jiy656Vm/eyasbdvDKhh3MWrqRJ1/eQEW3YPyQ3hw3oh/HDu/LgF7dD/o4knQghkAV7cbf1LB+226+d8VkrwKqw6is6MbEkf2YOLIf06f876rSxsbEhu11bNxeR31jI42N0JDde7BPdSX9eh443O2sa7tVsBFB/55V9O9ZxTHDCyud6xsaWbZxBy+u3soLq7Ywc95KZs6DsYN7c/KYARw/qj89qrxCKKltGQJVlOUbd/C93y7lwpNGcpJPB1En0K1bMLRv4Xt4HVVlRTfGD+3D+KF9OO/4EazbupvnVmzm2eWb+OkzK7h33kqOHdGPkw8fwJ6GRqpcaCKpDRgCVZQbHnyRbgF/c+6x5S5F6rKG9q3mzGOH8Y5jhlK7aSfPLH+N+bWv8fyKzdz33CouOGEEF58yuqyroiV1fv+/vTuPr6us8zj++d0t96a52dOmSVvaphu0QEsZFlGoUFkVEFwQdxHQGcVBXqKo46jDaxSYQZkZBlccUaEsgq0glLKJqJRCKaWleyltmjRd0mzNdm/yzB/nNNyGJE3aJDft/b5fr/O65zzn3HOe88vNub/7nOVREij99spbtTy6sprrzp5CWX4s3dUROeqZGeMLsxlfmM1Fx49lfU0jtfvauW+ZdxNMRckoLjtpHJfOKadc/5MiMkBKAqVfOjsd3390DaPjWVx7VkW6qyOScYIB49ixuVx56gTqWxL86fVqHlm+ndsWr+M/nlzHaZOKuOykci44fiw5WTq0i8jB6Ugh/XLP37fw2rY6/vPDJzJKXzAiaZUXC/OxUybwsVMmsK22mUde3c7Dyyv52kMr+ZeFqzhvZimXzinnjIpiPYdQRHqlb3M5qDd37+OHT6xl3vQSLjupPN3VEZEU4wuzue6cqXz57Cks31rHw8sreXRlNQtXVBGPhjh7xmjOm1nKWdNK9ANORA6gI4L0qaPTccMDK8gKBbnl8hN0EbrICGVmzD2mgLnHFPCdDxzHX9bvZvHqHTy1poaFK6qIBAPMPaaAd08t5t1TiplVnqe+l0UynJJA6dPP/7KZ5VvruOOK2YzJjaa7OiLSD1mhIPOPG8P848aQ7Ohk2Za9PLO2hhc27uG2xeu4bfE64tEQJ47LZ/Z4bzhhfB4lOVn6oSeSQZQESq/W7Wjk9ifXc8GsUi4+sSzd1RGRQxAKBji9oojTK4oA2N3Uxt827eHFzXtYsbWOu/68qatHlYLsMFPHxJk2JoeKkhzGFWRTnh+jvCBGXiyczt0QkSGgJFB61Jbs4IYHVxCPhrj50llqHRA5ShTnZHHxiWVdP+ya25Os2t7A69vr2VDTyPqaRha+WkVjt36T41khygtiXUlheX6Mzbv3ke/3fhKPhnV6WeQIoyRQ3sE5xzcfXsWq7Q387JNzKcoZuT0tiMjhyY6EOGVSIadMKuwqc86xu6md7XUtbN/bwva6Zv+1he11rSzbUktD64FJYsAgHg13dYlXkB2hOCdCcU4WxfEsRkWC+jEpMsIoCZR3+Onzm/n98kqunz+Nc2eWprs6Igd179KB9e175akTDr5QhuopljlZYaaXvt3XMUBrooP6loQ3NCeoa2mnviVBXUuCqroW3qhu6DrNDBALBxmbH6UsL0ZZfpSy/JiuQRyh9P+UOZQEygEWr97BLU+s5QMnlnHdOVPSXR0RGaGi4SDRcLDXG8Y6naOuOcGuxjZ2N7VR09BKdX0rL27eQ9JPDrMjQSYUZjOxaBQTi7IpL8jWKWWRYaQkULqsrqrn+vtXcEJ5Hrd9SI+DEZFDFzCjcFSEwlERphPvKu/odOxqaqOytpm3apt5a88+1u5oBCAaDlBRksO00XGmjslJV9VFMoaSQAFgW20zV//6ZfJiYX7+qZOJhoPprpKIHIWCAaM0N0ppbpSTJ3rXITa1Jdm8q4kNO5vYUNPI6qoGABa/UcOFs0q5YNZYJhRlp7PaIkclJYHChppGPvHLpbQmOrn36lMZrecBisgwyskKccK4fE4Yl49zjp2NbaytbqC6oZUfPL6WHzy+lplluVx4/FgumFXK5BK1EooMBiWBGW7V9no+dfdLBMy4/9rTmJFy4beIyHAzM8bkRhmTG+XKU72+kZ9YtYM/raruetD1jNI4F8way8Wzy5hUPCrdVRY5YikJzGDLttTyuV8tIzcW5nefP5WJOpiKyAgzvjCbq8+czNVnTqa6vsVLCF+v5sdPr+dHT63nxHF5XDK7nPefMFZnMUQGSElgBnLOcf+ybXz3j6spy4vx28+fSll+LN3VEhHp09i8GJ89YxKfPWMS1fUt/PG1KhauqOL7j77BzY+9vqqRYgAAD4VJREFUwbsqirl4dhnnzyolN6oeTkQORklghqlvSfDNh1/nsderOWNKEXdcMYdiPQxaRI4wY/NiXHNmBdecWcHGnY0sWlHFwtequPGhlXz7D6s4e/poLp1Txrzpo3Wjm0gvlARmkFfequW6+1ZQ09DK18+fwbVnTiagZ3KJyBFuyug4Xz13Ote/bxorttWxcEUVj66s5onVO4hnhTh/VikXzy7jtMlFhIOBdFdXZMRQEpgBqutbuP3J9Ty0vJJxBTEe/MLpzJlQkO5qiYgMKjNjzoQC5kwo4NsXHcvfN+9h4YoqHl+1gwdfqSQ/O8z8Y8dwwaxS3j21mKyQWgglsykJPIo1tCa467lN3P3CmzgHV79nMl8+ewpxXSsjIke5UDDAe6aW8J6pJdx86Sz+vH4XT6zaweLVO3jolUqyI0HOmFLM2TNG897poynN000lknmUBB6FNtQ0ct9L2/j98krqWxJ8cE45X33fNMYX6mGrIpJ5ouEg580s5byZpbQnO/nbpt08taaGZ9fuYskbNQDMKI1zekURp00u4rRJReRl68eyHP2UBB4lave18/SaGh54eRvLtuwlHDTOnVnKF8+qYFZ5XrqrJyIyIkRCAeZNH8286aNxzrFhZxPPrN3JCxt2c99LW/nVX7dgBjNKc5k9Pp854/OZPSGfipKcjOvX2DlHp4NkRyfJTucN/ninc5gZBmzc2UTAvK4CzX8NBwPEwkFikSCRkK7DHKmGPQk0s/OBO4Ag8Avn3A+7zc8C7gHmAnuAjzrntvjzbgKuAjqA65xzi/tap5lNAhYAhcBy4JPOufa+tnGkaE10sLqqgb9s2MWz63axsrIO52BiUTY3XTCDy+eO012/IiJ9MDOmjYkzbUycL5xVQVuyg9e21fP3TXt4+a1aHl1ZxX0vbQUgFg4ybUwO08bEmV4ap2J0DscUZlNeEBux1xZ2dDoaWxM0tCSpb0lQ35KgodV/9ae9smRXWUNLgpqGVto7Okl2OFw/tnPH0xv6nB8KGLFIkFg4SHYkSCwSIp4VIjcWJj87TH4sTJ4/npcd8cb3T8fCxKPhjEvAh8uwJoFmFgTuBN4HVALLzGyRc+6NlMWuAvY656aY2RXALcBHzew44ApgJlAGPGVm0/z39LbOW4AfOecWmNlP/HXf1ds2hnbvD01rooPKvS1srd3Hlt3NrKlu4PXt9WzY2URHp8MMZo/P5yvnTGXe9NGcUJ6nO35FRA5BVijIKZMKOWWS16dxZ6fjzT37WLG1jlVV9ayvaeTZdbt48JXKrveYwdjcKOUFMUbHo5TEsyiJZ1GcEyE3GiY3FiY3GmZUVpBwMEAwYISCRjgQ8F6DAUIBw8xoT3bS3tFJe7KTRMprW7KTtmQHDa1JmlqTNLUlaWxN0NSapLHNK3s7uUt2JXuNrck+9zcYMPL8BCw36iVl5QUxinOyiIQChINGMLD/1atzMGiEAkbADIfXWviuKcV+q6GjsxM6nSPR4WhuT9Ka6KC53Rta2jtoTnivja0JKvc2s7rKq3dze0ev9TSD3GhKohh7e9g/nR+LdCWVqeWxcBAzfSf2ZrhbAk8BNjrnNgOY2QLgEiA1CbwE+K4//hDwP+b9BS8BFjjn2oA3zWyjvz56WqeZrQHOBq70l/m1v967etuGc64/P3qGRGNrgjue2sCefe3sbmpjT5P3urOx7YDlikZFmFWex/xjxzCrPI9TJxVSMCqSplqLiBy9AgGjoiSHipIcLp87rqt8T1Mbm3btY1ttM1trm9lW20xlXQtrdjTw/Po2Gtv6Tr4Gi5nX73I8K0RONEReLExZfpQZY+NdSVNuSsKUGw2Rlx3umpcd6TlBunfp1gHV4+ITyw57X9qTnX7LZDt1zV5iWNecoK4lQX1ze1erZZ1fvn1vizevJUFHZ+9f3ZFggLzsMPGsELHI2y2RsXCA7IhfltJCme23WGaFA4QCftIeMIJ+4r4/iQ8F7O35Qe+0eKfzEuBO53Bd416LbLIrmfcS+qJRWZxeUXTYcTtcw50ElgPbUqYrgVN7W8Y5lzSzeqDIL3+x23vL/fGe1lkE1Dnnkj0s39s2dh/ynh2mUCDA75ZupTgeoWhUFmX5UY4vz6O8IMaEwmzGF2YzoTCb4pyIftWIiKRRUU4WRTlZXS2G3bW0d7BnXxuNrUnvFGtrkub2JIkOLxlI7L+2rsOR6PRPuzrvesVIKEAkaF3j4WCASDBANBwkHg0Rj4bIyQqTEw2RHQ4eNWd+IqFAVyvqQDjnaGpLdiWNXcni/kSypZ365gSNbUla/RbJ+pYEO+qTXa2TLX5r5XA6a1pJRiaBPX1au6fwvS3TW3lPV5z2tXx/64GZXQNc4082mdm6Ht7Xl2LSmFhmCMV46KUlxh8/Qtd9iPod4xFY9yEzyPuqY8UQSfk7Kcb9dA9wz1WH9NaBxPiYgy0w3ElgJTA+ZXocUNXLMpVmFgLygNqDvLen8t1AvpmF/NbA1OV728YBnHM/A342wH3sYmYvO+dOPtT3y8EpxkNPMR56ivHQU4yHnmI89AY7xsN93/YyYKqZTTKzCN6NHou6LbMI+LQ//iHgGf9avUXAFWaW5d/1OxV4qbd1+u951l8H/joXHmQbIiIiIhlhWFsC/evvvgQsxnucy93OudVm9n3gZefcIuCXwG/8Gz9q8ZI6/OUewLuJJAn8k3OuA6Cndfqb/DqwwMxuBl71101v2xARERHJFKYGsKFjZtf4p5RliCjGQ08xHnqK8dBTjIeeYjz0BjvGSgJFREREMpD6chERERHJQEoCD5GZfdjMVptZp5md3G3eTWa20czWmdl5KeXn+2UbzewbKeWTzGypmW0ws/v9G1ykD73FUvrHzO42s51mtiqlrNDMlvifwyVmVuCXm5n9lx/rlWZ2Usp7Pu0vv8HMPt3TtjKRmY03s2fNbI1/nPiKX64YDxIzi5rZS2b2mh/j7/nlPR5P/ZsK7/djvNTMJqasq8djtnjMLGhmr5rZo/60YjzIzGyLmb1uZivM7GW/bOiPF845DYcwAMcC04HngJNTyo8DXgOygEnAJrwbVoL++GQg4i9znP+eB4Ar/PGfAF9M9/6N5KGvWGrodwzPBE4CVqWU3Qp8wx//BnCLP34h8Dje8zVPA5b65YXAZv+1wB8vSPe+jYQBGAuc5I/HgfX+sUExHrwYG5Djj4eBpX7sejyeAv8I/MQfvwK43x/v8Zid7v0bSQPwVeBe4FF/WjEe/BhvAYq7lQ358UItgYfIObfGOdfTw6O7urdzzr0J7O/erqvLPOdcO7C/ezvD697uIf/9vwYuHfo9OKL1GMs01+mI4px7nnc+G/MSvM8fHPg5vAS4x3lexHv+5ljgPGCJc67WObcXWAKcP/S1H/mcc9XOueX+eCOwBq+nIsV4kPixavInw/7g6P14mhr7h4Bz/ONvb8dsAcxsHHAR8At/uq/vLMV4cA358UJJ4ODrqWu88j7K++reTnrWWyzl8IxxzlWDl8QAo/3ygX6mJYV/SmwOXkuVYjyI/NOUK4CdeF94m+hnd6FAapekinHvfgzcCHT60/3ukhXFeCAc8KSZvWJeb2UwDMeL4e4x5IhiZk8BpT3M+pZzbmEP5TC43dtJzxSz4TXQrhzFZ2Y5wO+Bf3bONVjv/X4rxofAec+KnW1m+cAjeJfpvGMx/1UxHiAzez+w0zn3ipnN21/cw6KK8eE7wzlXZWajgSVmtraPZQctzkoC++Ccm38IbxvM7u2kZ/3pflAGrsbMxjrnqv1TCzv98t7iXQnM61b+3DDU84hgZmG8BPB3zrmH/WLFeAg45+rM7Dm866MG2l2ojie9OwO42MwuBKJALl7LoGI8yJxzVf7rTjN7BO90+ZAfL3Q6ePANZvd20rP+dD8oA5fanWL3bhY/5d+RdhpQ75+aWAyca2YF/l1r5/plGc+/DuqXwBrn3O0psxTjQWJmJX4LIGYWA+bjXXs50O5CeztmZzzn3E3OuXHOuYl4x9lnnHMfRzEeVGY2yszi+8fx/s9XMRzHi3TfEXOkDsAH8bLuNqAGWJwy71t416asAy5IKb8Q7y7BTXinlPeXT8b7h9gIPAhkpXv/RvrQWyw19Dt+9wHVQML/HF+Fd+3O08AG/7XQX9aAO/1Yv86Bd8N/zv/cbgQ+m+79GikD8G680zArgRX+cKFiPKgxPgGvO9CV/hfmd/zyHo+neC1ZD/rlLwGTU9bV4zFbwwHxnsfbdwcrxoMb28l4d0+/Bqze/502HMcL9RgiIiIikoF0OlhEREQkAykJFBEREclASgJFREREMpCSQBEREZEMpCRQREREJAMpCRQR6Qcz+z8zezmN278xpdeG1HJnZl9KQ5VE5AinJFBE5MhwIwf2BiAicliUBIqIiIhkICWBIiKHwMwmmNkCM6s1s2YzW2xm01PmT/RP1X7EzH5qZvVmVmlm3zOzQLd1fdjMNphZi5k9a2Zz/Pd+xp+/Ba/3gH/1y123U8NBM/t3M9tlZjvN7E4zyxr6KIjIkUxJoIjIAJlZIfACMB34AvARYBTwlN+PbapbgSa8vlR/C3yHt/tdxcxOBhYAy/G6o1wE3N9tHR8E6vH6Iz7dH5anzL8BKAM+AdwGXAt85TB3U0SOcqF0V0BE5Ah0PV7SN9s5VwtgZn8FtuD13XlnyrLPO+du8MeXmNn5wGXAA37Z14E1wBXO68fzCTMLA7fsX4Fz7lUzSwKVzrkXe6jPFufcZ/zxxWZ2hr+NWw97T0XkqKWWQBGRgZsPLAEazCxkZiGgEXgFOLnbsk92m34DGJcy/Q/AH92BHbkvGmB9DrYNEZF3UBIoIjJwxcBHgUS34b3A+G7L1nWbbgeiKdOlwK5uy3SfPpiDbUNE5B10OlhEZOBq8Vrr/q2HeY0DXNcOoKRbWfdpEZFBpyRQRGTgnsa7GWS1c67lMNe1DPiAmX0z5ZTwxT0sp9Y9ERlUSgJFRAbudrw7cZ8xs/8GtgNjgLOAF5xz9w1gXbcAS4EFZvYr4Fjgan9eZ8pya4GLzOwJvLuN1znnBtrqKCLSRdcEiogMkHNuN3AaXmL2I7wbM24F8oCVA1zXy8DHgLnAH4DLgS/6sxtSFv0asA94DK/1cO6h74GICNiBN6SJiEi6mdkngN8Ak51zb6a7PiJydNLpYBGRNDOzu/AeObMXOAn4NvCYEkARGUpKAkVE0q8I+F//dQ9ejyE3prVGInLU0+lgERERkQykG0NEREREMpCSQBEREZEMpCRQREREJAMpCRQRERHJQEoCRURERDKQkkARERGRDPT/Lw5DU2Vj7RoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fasta_stats.plot_length_distribution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e7iAzu8HMlpvZ8t27d0cdUERERMRvEj2tRxkwIuL9cKCih+eeCpxpZjcDA4AMMzvgnPvIwAXn3APAAwAzZszoaTEoIhJIK29Sp4RIECS6YFsGjDezMUA58Fng2p6c6Jz7XMfnZnY9MKNzsSYiIh9VM22G1xFEJAYS2iXqnGsFvga8DKwHnnTOrTWzO83sMgAzO8nMyoCrgPvNTBMIiYgcpSGrljNk1XKvY4hILyV8pQPn3DxgXqdtt0V8voxQV+nhrvEI8Egc4omIBMpx9/0cgFfve8LjJCLSG1rpQERERMTnVLCJiIiI+JwKNhERERGfU8EmIiIi4nMJH3QgIiKJ8+4ttx35IBHxPRVsIiIBVjdhstcRRCQG1CUqIhJghUsXUbh0kdcxRKSX1MImIhJgUx7+HwCqZp7hcRIR6Q21sImIiIj4nAo2EREREZ9TwSYiIiLicyrYRERERHxOgw5ERAJs6a0/9TqCiMSACjYRkQDbP2qc1xFEJAbUJSoiEmAlb75CyZuveB1DRHpJLWwiIgE28bEHASg/8xyPk4hIb6iFTURERMTnVLCJiIiI+JwKNhERERGfU8EmIiIi4nMadCAiEmCL77jH6wgiEgMq2EREAuxQYbHXEUQkBtQlKiISYCPnv8DI+S94HUNEekktbCIiATb+mT8CsOPcSz1OIiK9oRY2EREREZ9TwSYiIiLicyrYRERERHxOBZuIiIiIz2nQgYhIgC266z6vI4hIDKhgExEJsKb8QV5HEJEYUJeoiEiAjXnxKca8+JTXMUSkl1SwiYgE2Ni5TzN27tNexxCRXlLBJiIiIuJzKthEREREfE4Fm4iIiIjPqWATERER8TlN6yEiEmAL73nE6wgiEgMq2EREAqwtK9vrCCISA+oSFREJsPFPP8r4px/1OoaI9JIKNhGRABv56lxGvjrX6xgi0ksq2ERERER8TgWbiIiIiM+pYBMRERHxuYQXbGZ2gZltNLPNZnZrF/vPMrMVZtZqZldGbJ9uZovNbK2ZrTKzzyQ2uYiIiIg3Ejqth5mlAvcC5wJlwDIze945ty7isB3A9cC3O51+CPiic+4DMysG3jWzl51zdQmILiKSlF697wmvI4hIDCR6HraZwGbn3FYAM3scmAP8o2Bzzm0P72uPPNE5tyni8wozqwYKABVsIiIiEmiJ7hItAXZGvC8Lb4uKmc0EMoAtMcolIhJIE/90PxP/dL/XMUSklxJdsFkX21xUFzArAv4AfMk5197F/hvMbLmZLd+9e/dRxhQRCYaSRQsoWbTA6xgi0kuJLtjKgBER74cDFT092cxygbnAD51z73R1jHPuAefcDOfcjIKCgl6FFREREfGDRBdsy4DxZjbGzDKAzwLP9+TE8PHPAo86556KY0YRERERX0loweacawW+BrwMrAeedM6tNbM7zewyADM7yczKgKuA+81sbfj0q4GzgOvN7P3wa3oi84uIiIh4IdGjRHHOzQPmddp2W8Tnywh1lXY+74/AH+MeUEQkQNoyM72OICIxkPCCTUREEmfhLx71OoKIxIAKNhGRPqDdOSrqGtjf2MrBplYONLXS7hzHDc9n8AC1won4nQo2EZEAm/LQL9nX0Mqtk+dQtrfhY/tfXV/NxKJcTh83mDFD+mPW1exLIuI1FWwiIgHV2NJG2sLXSGtoYe+Yi7h8egnF+Vn0z0xjQGYaDc1tLNm2hyXballfuY/i/Cw+N3MUA/tneB1dRDpRwSYiEkA7ag/xp3c+5MGGFnKz0/nmORPIzkj9yDHp2SmcWzqMWccO5f2ddby0Zhf3v7GFL58xhqE5WR4lF5GuJHoeNhERibPqfY38/u3tpKelUJyfzeD+mR8r1iKlp6Zw0uhBfPXMsbQ7ePCNrVTUfbz7VES8o4JNRCRA6htaePjt7aSmGF8+fQyZad0Xap0Ny8vihjPHkpaawm8XbeXDPQfjmFREoqGCTUQkIBqa23jk7W00trRx/WmjGdQ/g6a8fJry8nt8jSE5mdx41lj6Z6Tx8Fvbqd7XGMfEItJTKthERAKgpa2dP7yznZr9zXz+lFEU52cDsOju+1l09/1RXSu/XwZfPXMsaanGk8t30treHo/IIhIFFWwiIgEwf10V2/cc4qoZwxlXMKDX18vNTueK40uoqG/k1fXVMUgoIr2hgk1EJMmV723grc01nDR6ENOGf7T787hf/4zjfv2zo7puaXEeM0YN5I1Nu1mydU8soorIUVLBJiKSxNraHc++V8aArDQumDzsY/uHrF7BkNUrjvr6F08rYlD/DL755ErqG1p6E1VEekEFm4hIEntrcw0V9Y1cOq34sFN3HK3MtFSunjGCXfsaue2va2J+fRHpGRVsIiJJqvZgM69uqGLSsBwmF+fG7T4jBvXj/5x9DH99v4K3NtfE7T4i0j0VbCIiScg5x3Pvl5NixmXTS+K+Bug/f2IcJfnZ3P23DbS3u7jeS0Q+TgWbiEgSWl1ez+bqA5w3eRh52endHndo6DAODf34s23RykpP5VvnTWB1eT1zV1f2+noiEh2tJSoikmTanePV9dUMzcnk5DGDDnvs4h/9Mmb3nTO9hAfe2Mp//n0j508eRkaa/uYXSRT93yYikmRWldWz+0ATsycVkhLnrtBIqSnG9y6cyId7DvH4sh0Ju6+IqGATEUkqbe2OBRuqGJab1aOBBifc8yNOuOdHMbv/rAkFnDJ2EP/96gccaGqN2XVF5PBUsImIJJFVZXXUHGjm7IlDe9S6NnDTOgZuWhez+5sZ379wEjUHmnnwja0xu66IHJ6eYRMRSRKh1rVqivKyKI3jNB5deWzJR7tAp5Tkcd/CLQzITKN/5sd/lVx78sheXf9Ior2+SLJTC5uISJJYubOOPQebmd3D1rV4mj1xKM1t7SzZpiWrRBJBBZuISBJoa3cs2FhNcV4Wk4oS27rWlcLcLCYUDmDx1lpa2tq9jiMSeCrYRESSwKqyOmoPNjN7UmFUk+TuHzmG/SPHxCXTGccUcLCplZU76+JyfRH5X3qGTUTE55xzvL1lDwU5mUwclhPVuUu/f3ecUsG4gv4U5WWxaHMNJ44aGPfVFkT6MrWwiYj43M7aQ5TXNXDauMG+KorMjDOOGUL1/iY2VR3wOo5IoKlgExHxube27CErPYXjRwyM+tyZd93KzLtujUOqkKnD88jNSmPR5t1xu4eIqGATEfG1+oYW1lbUM2PUoKNaCipnxzZydmyLQ7KQtJQUTh03hC27D1JR1xC3+4j0dSrYRER8bMm2PTgHp4wd7HWUbs0cPYiM1BTe2lzjdRSRwFLBJiLiUy1t7SzdVsvEolwG9c/wOk63sjNSOXH0QFaW1bGvscXrOCKBpIJNRMSnVpXVc6i5jdPG+bd1rcOpYwbT7mDFh3u9jiISSCrYRER8yDnH4i01DM3JZOyQ/kd9nb0TStk7oTSGybo2JCeTMUP6s/zDvbQ7F/f7ifQ1modNRMSHdtQeoqK+kcunl/RqKo8Vt9wew1SHN2PUQJ56t4xtNQcTdk+RvkItbCIiPrRsey2ZaSlMH5HvdZQem1KSR1Z6Csu213odRSRwVLCJiPjM/sYWVpfXM2143lFN5RHp1Nu/zqm3fz1GyQ4vPTU0V9zain3UHmxOyD1F+goVbCIiPvPiqkpa2hwzRg3q9bX6Ve+iX/WuGKTqmRmjB9LW7nhmRVnC7inSF6hgExHxmSeW7WRoTibDB2Z7HSVqRXnZDB+YzRPLduI0+EAkZlSwiYj4yKaq/by/s44ZSbyY+kmjB/FB9QFW7NAUHyKxooJNRMRHnly2k7QUY/rI6NcN9Ytpw/Pon5HKn5fu9DqKSGCoYBMR8Ynm1naeea+ccyYVMiAzNrMu1Uw9gZqpJ8TkWj2VmZbKZdOLeXFVBfu18oFITKhgExHxiQUbqqg92MxnThoRs2uuvPl7rLz5ezG7Xk9deeIIGlvaeWlN4gY8iASZCjYREZ94YtlOhuVmcdaEAq+j9NoJI/MZOagfz71f7nUUkUBQwSYi4gO76ht5fdNuPn1iCakpsRtscMatN3LGrTfG7Ho9ZWZcfnwJb2/ZQ9W+xoTfXyRoVLCJiPjAc++X0+7gqhNj1x0KkFlfR2Z9XUyv2VOXTy/GOXj+/QpP7i8SJAkv2MzsAjPbaGabzezWLvafZWYrzKzVzK7stO86M/sg/LoucalFROLruffKOX5kPqN7sdC734wtGMBxw/N49j11i4r0VkILNjNLBe4FLgRKgWvMrLTTYTuA64HHOp07CLgdOBmYCdxuZsk77l1EJGx95T427NrPp44v8TpKzF1+fAnrKvexqWq/11FEklqiW9hmApudc1udc83A48CcyAOcc9udc6uA9k7nng/Md87VOuf2AvOBCxIRWkQknp57v5zUFOPiqUVeR4m5S6YVk5piPKdWNpFeSXTBVgJEzqRYFt4Ws3PN7AYzW25my3fv3n3UQUVEEqG93fH8+xV8YkIBgwdkxvz6VSedRtVJp8X8uj1VkJPJmeOH8Nf3K2hv11JVIkcr0QVbV0Ofevp/cI/Odc494Jyb4ZybUVCQ/EPjRSTYlmyrpbK+kTnTi+Ny/TVf/jprvvz1uFy7py6fXkJ5XQPLttd6mkMkmSW6YCsDIodADQd6OnyoN+eKiPjSX98vp39GKueVDvM6StycN7mQfhmpmpNNpBcSXbAtA8ab2RgzywA+Czzfw3NfBs4zs4HhwQbnhbeJiCSlxpY25q6u5PzJw8jOSI3LPWZ944vM+sYX43LtnuqXkcb5k4cxd1UlTa1tnmYRSVYJLdicc63A1wgVWuuBJ51za83sTjO7DMDMTjKzMuAq4H4zWxs+txb4MaGibxlwZ3ibiEhSWrixmv2NrVwex9GhqU1NpDY1xe36PXXZ9GL2Nbby5qYar6OIJKXYrC4cBefcPGBep223RXy+jFB3Z1fnPgQ8FNeAIiIJ8ux75QwZkMlp4wZ7HSXuTh83hLzsdOauruSc0kKv44gkHa10ICLigfpDLby2YTeXHVdMWmrwfxRnpKVw/uRC5q+rorFF3aIi0Qr+TwkRER96aW0lzW3tXH58fEaH+tHF04o50NTKG5s05ZJItBLeJSoiIvDCykpGDe7H1JK8uN6n/Iyz43r9aJw2bjD5/dKZt7qS8yYHd1SsSDyoYBMRSbCaA028vaWGm2aNw6yrKSZjZ8Pnbozr9aORnprCBZOH8cLKChpb2shKj8/IWJEg6nGXqJl9Lbyep4iI9MLf1uyi3cGlx/Wd7tAOF08r4mBzG6+rW1QkKtE8w3YXUG5mT5rZBRbvPwtFRALqhZUVHDN0AMcW5sT9XrNv+gyzb/pM3O/TU6eOHczAfunMXVXpdRSRpBJNwTYMuBkoBOYCO83s381sQlySiYgE0K76RpZtr+XSacVx7w71o7TUFC6YUsQr6zVaVCQaPS7YnHMHnXMPO+c+AUwAHgY+B6w3szfN7EtmNiBeQUVEgmDu6kqcg0uOK/I6imcumVbEoeY2Fm6s9jqKSNI4qkEHzrktwL+Z2e8JTWR7BnA68Eszexi4zTlXH7uYIiL+8diSHVEdf+3JI//x+YurKphUlMu4gr779+3JYwYxuH8GL66q5IIpfbdwFYlG1POwmVk/M7vOzBYCG4EhwHeA8cCtwBXAE7EMKSISBDtrD/Hejjou7cOta9DRLTqMV9dX09CsblGRnuhxC5uZnQl8CbgSMOBJ4PvOucURh/3azDbT8wXdRUT6jLmrQw/aXzI1caNDd8y+OGH3isZFU4v405IdvPHBbs7XnGwiRxRNl+jrwDvALcDjzrmD3Ry3Efhzb4OJiATNi6sqOG54HiMH90vYPT+48osJu1c0Zo4ZRF52Oi+v3aWCTaQHoinYpjjn1h3pIOfch4Ra4kREJGxbzUHWlO/jhxdPSuh9UxsbAGjLyk7ofY8kPTWF2ZOG8ur6alra2r2OI+J70TzDtt/MTuhqh5mdYGYjYpRJRCRw5oW7Qy+amtjn12bdcj2zbrk+offsqfMnD6O+oYWl22q9jiLie9EUbPcBn+9m37XAr3sfR0QkmF5cVckJI/MpzvdXS5eXzhpfQFZ6Ci+v3eV1FBHfi6ZgOwVY0M2+18L7Rabaa8wAACAASURBVESkk201B1lfuS/hrWt+l52RyicmFPD3tVW0O+d1HBFfi6Zg6wcc7v+o/r3MIiISSF51hyaD8ycPY9e+Rsr3NngdRcTXoinYVgPXdLPvGmBt7+OIiATP3FWVHK/u0C7NnlhIWoqxrnKf11FEfC2aUaJ3A38xs0zgEaASKAKuAz4dfomISIQ9B5pYV5n40aEdtl58pSf37am8fumcMnYwayv2aXoPkcPoccHmnHvWzK4D7iJUnDlCE+iWA593zj0Xn4giIslrdXlolb4LPeoO3XbJVZ7cNxrnTy5k0eYaqvc1MjQ3y+s4Ir4U1dJUzrk/ACOAUuCs8MeRzjlNlCsi0oU15fUcPzKfEo+6QzPrasms8/e0GeeWhlrW1qpbVKRbUa8l6kI2OOfeCn/U0B4RkS7sOdBERX0jF3s42OCM79/EGd+/ybP798SwvCxGDMxmXYUKNpHuRPMMG2ZWDFwCDAc6t1s759z3YhVMRCTZed0dmkxKi/N4ee0u6g41k98vw+s4Ir4TzeLvnyK0RmgqUA00dzrEASrYRETC1pTXM2JgtmfdoclkclEuL6/dxbrKfZw2bojXcUR8J5ou0Z8CfwcKnXMlzrkxnV5j45RRRCTpdHSHTinJ8zpKUhiSk8nQnEzWqltUpEvRFGwjgP92zvn76VURER9YE+4OVcHWc6XFuWyvOcjBplavo4j4TjQF29vAsfEKIiISJKsrQt2hAz1+HuuDKz7PB1d0twy0v0wuysMBG3aplU2ks2gGHXwT+JOZHQDmA3WdD3DOHYpVMBGRZLXnQBMVdY1cOMX7iWB3nHup1xF6rDg/i/zsdNZW7OPEUYO8jiPiK9EUbKvCHx+m+zVFU3sXR0Qk+fmpO7RfVQUAhwqLPU5yZGbGpOJclm2rpam1jcw0/UoR6RBNwfZlDr/4u4iIEOoOHe6D7lCAU++4BYBX73vC4yQ9M7kol8Vb9rCp6gBTfVDwivhFNEtTPRLHHCIigVB7sNk33aHJaNTg/vTLSGVdRb0KNpEIUU2cC2BmpcCJhEaNPuSc22VmxwBVzrn9sQ4oIpJMOibLnVKsYuNopKYYk4pyWVtRT2t7O2kpUS/IIxJIPf4/wcwGmNmTwBrgt8CPgY6HIn4K3B77eCIiyWVNebg7tL/33aHJanJRLo0t7WzdfdDrKCK+Ec2fLv8FnAbMBnIAi9g3D7gghrlERJJO7cFmyusa1JXXS+OGDiAjLUVri4pEiKZL9Arg686518ys89CdD4FRsYslIpJ81viwO3TDtV/1OkLU0lNTmFCYw/rKfVw2vZgUsyOfJBJw0RRs2cCebvblAG29jyMikrxW+7A7tPzMc7yOcFRKi3JZU15PWe0hRg7u73UcEc9F0yW6DPhiN/uuJLQSgohIn9TRHeqn1jWAnA+3kPPhFq9jRO3YwhxSDNZVaiybCERXsP0QuMLMXgH+idCcbBeZ2R+Aq9CgAxHpw1b7aLLcSDPv/gEz7/6B1zGilp2RytghA1hfqefYRCCKgs05t4jQgINM4FeEBh38CBgLnOOcWxaXhCIiSWB1eR3DB2YzyEfdocluUlEOuw80UbO/yesoIp6LaoIb59xbzrkzgVxgOJDjnDvdOfdWXNKJiCSBjrVDp/msdS3ZTSrKBWCdWtlEoivYOjjnGpxzFVrsXUQEVvm0OzTZ5ffLoDgvS92iIkQxSjQ8ae5hOeeu7l0cEZHks7qsnlGD+pHvg7VDg2ZSUS4LNlRzoKmVAZlRL84jEhjR/Ndf0MW2QcCxhKb72BiTRCIiSaR6XyO79jVyybQir6N0ac2X/o/XEXplUlEur26oZkPlPmaMHuR1HBHPRLP4+ye72m5mI4BngXt6ch0zuwD4JZAK/NY5d3en/ZnAo4TWK90DfMY5t93M0gktiXVCOPejzrm7eppfRCQeVpfXY/hrstxIVTPP8DpCrxTlZZHfL511Ktikj+v1qrrOuZ3AXcB/HOnY8AoJ9wIXAqXANeHF5CN9BdjrnDuGUBH4s/D2q4BM59xUQsXcjWY2urf5RUSOlnOOVeX1jB7Sn9zsdK/jdCl/01ryN631OsZRMwstBr+5+gDNre1exxHxTK8LtrA2QqNGj2QmsNk5t9U51ww8DszpdMwc4Pfhz58GZpuZEZr3rb+ZpRFadaEZ0JOoIuKZqn1N7N7f5Ou1Q0+8505OvOdOr2P0SmlRLq3tjs3VmkRX+q5oBh10bgkDyAAmAT8mtBLCkZQAOyPelwEnd3eMc67VzOqBwYSKtzlAJdAPuMU5V9vT/CIisba6vA4DJhfneh0l0EYP7k9WegrrKvdT6tOuZ5F4i2bQwRpCrVydGaFi7Z96cI2uVvDtfM3ujplJqCWvGBgIvGlmrzjntn7kZLMbgBsARo4c2YNIIiLRc86xqqyesQX9ycnyZ3doUKSmGBOH5bJh1z7a2h2pKVoMXvqeaAq2rgYdNAJlzrnyHl6jDBgR8X44UNHNMWXh7s88oBa4FnjJOdcCVJvZW8AM4CMFm3PuAeABgBkzZnRVYIqI9FplfSN7DjZz1viuBtBLrE0qyuX9nXXsqD3EmCFaDF76nmhGib4eg/stA8ab2RigHPgsoUIs0vPAdcBiQovKL3DOOTPbAZxtZn8k1CV6CvCLGGQSEYna6vJ6UgxK1R2aEBOGDiA1xVhfuU8Fm/RJ0TzDFlX/onNuRxfbWs3sa8DLhKb1eMg5t9bM7gSWO+eeB34H/MHMNhNqWfts+PR7gYcJdc0a8LBzblU0mUTEHx5b8rEfD4d17cn+erwh1B1ax7iCAfT3+WSuK2/6jtcRYiIzPZVxBf1ZV7mPC6cM8zqOSMJF85NmO10/w9ZZx4jO1K52OufmAfM6bbst4vNGQlN4dD7vQFfbRUQSrbyugb2HWjh74lCvoxxRzbQZXkeImUlFufz1/QqqtRi89EHRFGyfJzQn2nrgGaAaGAp8GpgIfBdNsyEifcCqsnpSzSgt8v+IxSGrlgPBKNwmDcvlr1RobVHpk6Ip2M4DXnTO3dRp+2/M7DfARc65L8QumoiI/zjnWF1ezzFDB5Cd0WVHgq8cd9/PAXj1vic8TtJ7udnpDB+YzToVbNIHRTNx7hWEWta68hfgst7HERHxtxU76qhvaGHacP+3rgVRaVEuZXsbqNrX6HUUkYSKpmBrALpblO5MQlN8iIgE2ourKkhLCS2XJInX8X2fv67K4yQiiRVNl+h9wL+Z2WBCU290PMM2B7gR+PfYxxMR8Y/2dse81ZWML8whK93/3aFBNDQnk8H9M5i/rorPnzLK6zgiCRPNPGx3mNleQoMLbiY0EtSAXcC3nXOaE01EAm35h3up2tfErAn+Hx0aVB2LwS/esocDTa0M8Pm0KiKxEtV/6c65X5rZ/wAjgUJCxdpO51x7PMKJiPjJi6sqyEpPYWJRjtdReuzdW2478kFJZlJRLos21/D6xt1cPK3I6zgiCRHNM2wAhIuzDwkt0F6uYk1E+oK2dse81bs4e+JQMtOSpzu0bsJk6iZM9jpGTI0a3I9B/TOYv26X11FEEiaqgs3MLjKzJYQGGOwApoW3P2Bmn49DPhERX1iydQ81B5q4eGqx11GiUrh0EYVLF3kdI6ZSzDh74lAWbKimpU1tBtI39LhgM7MvEhpssAG4odO5HwBfiW00ERH/+Ov7FfTPSE2K1Q0iTXn4f5jy8P94HSPmzi0tZF9jK0u31XodRSQhomlh+7/Az51z1wF/7LRvLVAas1QiIj7S1NrGvDWVnD9lWFJMltsXnDl+CJlpKZreQ/qMaAq2UcD8bvY1ApqUSEQCaeHG3exvbGXO9BKvo0hYv4w0zhxfwPx1VTjXk2WuRZJbNAXbTuD4bvbNADb3Po6IiP/89f1yhgzI4PRxg72OIhHOKy2kvK5BS1VJnxBNwfY74Pbw4ILs8DYzs9mE5mZ7MNbhRES8tr+xhVfWV3PJtGLSUqMeWC9xdPakoZhp1QPpG6KZh+1nwAjg90BbeNvbQCpwv3Puv2OcTUTEcy+vraK5tZ3LpifX6NAOS2/9qdcR4mbIgExOHDmQ+euq+MY5E7yOIxJX0ax04IB/MbP/AmYDQ4BaYIFzblOc8omIeOqv75czclA/jh+R73WUo7J/1DivI8TVuaWF3PW3DZTXNVCSn33kE0SSVI/a980sy8weNLNTnHNbnHMPOOd+6pz7jYo1EQmq6v2NvLW5hjnTizEzr+MclZI3X6HkzVe8jhE355YWAjB/rSbRlWDrUcHmnGsEPgtkxTeOiIh/zF1VSbuDOUnaHQow8bEHmfhYcB8xHlswgHEF/Zm/Xs+xSbBF8wTtAuCT8QoiIuI3z71fQWlRLscMTZ61Q/ui8yYPY8nWWuobWryOIhI30RRs9wJfMrP/NLOzzWyymZVGvuIVUkQk0bbXHGTlzjouPz55W9f6inNLC2ltdyzcWO11FJG4iWaU6Evhj98MvyJnKrTwe00BLiKB8Ox75ZjBpcepYPO76cPzKcjJ5O/rqjS5sQTWYQs2M3sI+LFzbhuh7tBcQDMUikigOed45r0yTh83hKI8jTz0u5QU45xJQ3lhZSVNrW1kpqntQILnSC1s1wG/AbYReobtVOfc0rinEhHx0PIP97KztoFbAjC31+I77vE6QkKcW1rIn5fuZPGWPcw6dqjXcURi7kjPsFUCs8xsAKFuzywz69fdK/5xRUTi75kVZfTLSOX8ycO8jtJrhwqLOVQY/G7d08YNoV9GqlY9kMA6UsH2AHA3UE/oGbXXgP2HeYmIJLXGljZeXFXJBVOG0T8zmsd8/Wnk/BcYOf8Fr2PEXVZ6KmeNL+CV9VW0t2sxeAmew/40cs7daWZzgUnAo8BPgC2JCCYi4oVX11ezv7GVK44f7nWUmBj/zB8B2HHupR4nib/zJhfy0tpdrC6v57gkXZlCpDtH/PPROfcu8G54kfeHwwMQREQC6ZkVZQzLzeLUcYO9jiJROnviUFJTjPnrqlSwSeD0eB4259yXVKyJSJDVHGhi4abdXH58CakpybkUVV+W3y+Dk0YP1HNsEkjRTJwrIhJoz79fQVu744oTNJdXsjq3dBgbq/bz4Z6DXkcRiSkVbCIiYc+8V8bUkjwmFGopqmR1Xsdi8Gplk4BJ/iFQIiIxsHHXftaU7+P2S4O1yt6iu+7zOkJCjRjUj4nDcvj7uir+6cyxXscRiRm1sImIAE8t30lainFZwJaiasofRFP+IK9jJNR5pYUs315L7cFmr6OIxIwKNhHp85pb23n2vXLOmVTI4AGZXseJqTEvPsWYF5/yOkZCnVs6jHYHCzZoMXgJDhVsItLnLdhQzZ6DzVx9UjDmXos0du7TjJ37tNcxEmpKSS5FeVnMX7fL6ygiMaOCTUT6vKeW76QwN5Ozxhd4HUViwMw4Z1Ihb2yqobGlzes4IjGhgk1E+rSqfY28trGaT58wnLRU/UgMivMnD6OhpY03Nu32OopITOink4j0ac+sKKfdwVUzRngdRWLo5LGDyMtO56U16haVYFDBJiJ9lnOOp5bvZOboQYwZ0t/rOBJD6akpnDOpkFfWV9Hc2u51HJFeU8EmIn3Wux/uZWvNQa6aEbzBBh0W3vMIC+95xOsYnrhwyjD2NbbyztY9XkcR6TVNnCsigfPYkh09Ou4vK8rISEvhQFNrj8+JZ554aMvK9uS+8f6ae3L9lrZ2MlJT+J8Fmynb2xDV9a89eeTRRhOJC7WwiUif1NTSxuqyeqaV5JGZlup1nLgZ//SjjH/6Ua9jeCI9NYVjh+WwrnIf7c55HUekV1SwiUiftKq8nua2dmaMGuh1lLga+epcRr461+sYnplcnMvBplY+3HPI6ygivaKCTUT6pKXbainMzWTEoH5eR5E4OrYwh7QUY11FvddRRHpFBZuI9DnldQ2U1zUwc/QgzMzrOBJHmempHDN0AGsr9uHULSpJLOEFm5ldYGYbzWyzmd3axf5MM3sivH+JmY2O2DfNzBab2VozW21mWYnMLiLBsHRbLempxvQRwe4OlZDJxXnUNbRQXhfdwAMRP0lowWZmqcC9wIVAKXCNmZV2OuwrwF7n3DHAPcDPwuemAX8E/tk5NxmYBbQkKLqIBERjSxsrd9YxrSSf7IzgDjaQ/zWpKIcUg7UV+7yOInLUEj2tx0xgs3NuK4CZPQ7MAdZFHDMHuCP8+dPAryzUZ3EesMo5txLAOaeJdUQkaivL6mhua2fmmEFeR0mIV+97wusInuuXkcbYIQNYW1HPeaWF6gaXpJToLtESYGfE+7Lwti6Pcc61AvXAYGAC4MzsZTNbYWbf7eoGZnaDmS03s+W7d2sNORH5X845lm6rpSgvi+EDvZmfTLxRWpxLzYFmqvY1eR1F5KgkumDr6s+azk+BdndMGnAG8Lnwx0+Z2eyPHejcA865Gc65GQUFBb3NKyIBUra3gcr6Rk7qQ4MNJv7pfib+6X6vY3hucnEuBqwu12hRSU6JLtjKgMgVlocDFd0dE35uLQ+oDW9/3TlX45w7BMwDToh7YhEJjKXba8lITWH6iHyvoyRMyaIFlCxa4HUMz+VkpTNmSH/WlNdrtKgkpUQXbMuA8WY2xswygM8Cz3c65nnguvDnVwILXOj/rpeBaWbWL1zIfYKPPvsmItKtxpY2VpXVMW14HlnpGmzQF00pyWP3gSZ1i0pSSmjBFn4m7WuEiq/1wJPOubVmdqeZXRY+7HfAYDPbDHwTuDV87l7gvwgVfe8DK5xzfXf6bhGJyoode2lpc31msIF8nLpFJZklfPF359w8Qt2Zkdtui/i8Ebiqm3P/SGhqDxGRHmt3jne27mHEwGyGD9TKBn1VZLfoOZOG9pnnGCUYtNKBiATe5uoD1Bxo5tRxg72OknBtmZm0ZWZ6HcM31C0qySrhLWwiIon2ztY99M9MY0pxntdREm7hLx71OoKvTC7O5YWVFawur2dYnhbLkeShFjYRCbTag81s3LWfmaMHkZaqH3l9nUaLSrLSTy8RCbR3tu7BjD472GDKQ79kykO/9DqGr6hbVJKRCjYRCazm1naWf1jL5OI88rLTvY7jicJlb1O47G2vY/iKRotKMlLBJiKBtXJnHY0t7Zwytu8NNpDuqVtUkpEKNhEJJOcci7fuoSgvi9GDNZWHfNTU4aFu0V37Gr2OItIjKthEJJC27TnIrn2NnDJ2sObbko+ZUpxHisHKneoWleSggk1EAumtD2rol5HKccP7zrqhXWnKy6cpr29/D7rSPzONY4YOYFV5He3qFpUkoHnYRCRwavY3sWHXfmYdO5SMtL79d+miu+/3OoJvHTc8n6feLWNn7SFGDe7vdRyRw+rbP8lEJJDe2lJDSopxyti+OZWH9ExpUS5pKcbKsjqvo4gckQo2EQmUvQebWbFjL9NH5JOT1Ten8oh03K9/xnG//pnXMXwpMz2ViUW5rC6rp61d3aLibyrYRCRQHlu6g5Y2xxnHDPE6ii8MWb2CIatXeB3Dt6YPz+Ngcxtbdh/wOorIYalgE5HAaGpt45G3tzN+6AAKc7VOpBzZhMIcstJTWLlT3aLibyrYRCQwXlhZye79TWpdkx5LS01hclEe6yr30dLW7nUckW6pYBORQHDO8ds3t3JsYQ7HDB3gdRxJIseNyKeptZ2Nu/Z7HUWkWyrYRCQQFm2uYcOu/XzlzDGaKDfCoaHDODR0mNcxfG1sQX8GZKZptKj4muZhE5FA+PVrWyjMzWTO9GL+8m6513F8Y/GPful1BN9LMWNqSR7LttfS0NxGdkaq15FEPkYtbCKS9N7bsZfFW/fwT2eMJTNNv2wlesePzKe13bG6XEtViT+pYBORpPfrhVvIy07nmpNHeh3Fd06450eccM+PvI7heyX52RTkZLJix16vo4h0SQWbiCS1D6r2M39dFdedNpoBmXrKo7OBm9YxcNM6r2P4nplxwoh8dtQeYs+BJq/jiHyMCjYRSWr3vb6F7PRUrj9ttNdRJMlNHzkQA1bs0OAD8R8VbCKStMr2HuL59yv47MwRDOqf4XUcSXJ52emMGzqA93bupV1LVYnPqGATkaT14BtbMYOvnjnW6ygSECeMzKfuUAtLttV6HUXkI1SwiUhSqjnQxOPLdnL59BKK87O9juNb+0eOYf/IMV7HSBqlRXlkpqXwlxVlXkcR+Qg9oSsiSenBN7fS3NbOjZ8Y53UUX1v6/bu9jpBUMtJSmFKSx99WV3LnnMn0y9CvSfEHtbCJSNKpPdjMHxZ/yKXTirUMlcTcCSMHcrC5jZfX7vI6isg/qGATkaTz4JtbaWhp419nH+N1FN+bedetzLzrVq9jJJVRg/sxfGC2VswQX1Fbr4gklb0Hm3n07e1cPLWIY4bmeB3H93J2bPM6QtJJMePTJwznvxd8QNneQwwf2M/rSFF7bMmOqI6/VpNO+55a2EQkqfx20VYOtbTxr7PHex1FAuyqGcMBeHLZTo+TiISoYBORpFF3qJnfv/0hF00tYkKhWtckfoYP7McnJhTwxPKdtLa1ex1HRAWbiCSP3y3axoGmVv71bLWuSfxdM3MkVfuaeG3jbq+jiKhgE5HkUH+ohUfe2s5FU4dx7DC1rvXU3gml7J1Q6nWMpHT2xKEMzcnkz0ujex5MJB406EBEksIDb25hf1Ornl2L0opbbvc6QtJKT03h6hkj+PXCzVTUNWiCZvGUWthExPd272/ioUXbuey4YiYOy/U6jvQhnzlpBA54crkGH4i3VLCJiO/d+9pmmtvaueXcCV5HSTqn3v51Tr39617HSFojBvXjzPEFPLFsJ21aEF48pIJNRHyt7lAzjy3ZwVUnDmfMkP5ex0k6/ap30a9aM/b3xrUzR1BZ38jrm6q9jiJ9mAo2EfG1BRtCvyT/j55dE4/MnlTIkAGZPLZE3aLiHRVsIuJbNfubWLFjL587ZSQleuBbPBIafDCcBRuqKK9r8DqO9FEq2ETEt17ZUEVqinHzLK0ZKt76/CmjMDMeXbzd6yjSR6lgExFfqqhrYFVZPaePG0JBTqbXcZJWzdQTqJl6gtcxkl5xfjYXTB7G40t3cqi51es40gepYBMRX3p57S6y01M5c3yB11GS2sqbv8fKm7/ndYxA+NLpo6lvaOHZ98q9jiJ9kAo2EfGdD6r380H1AT45cSjZGalexxEB4MRRA5laksfDb23HOU3xIYmV8ILNzC4ws41mttnMbu1if6aZPRHev8TMRnfaP9LMDpjZtxOVWUQSp905Xlqzi4H90jllzCCv4yS9M269kTNuvdHrGIFgZnzp9NFsrj7Amx/UeB1H+piEFmxmlgrcC1wIlALXmFnnRe6+Aux1zh0D3AP8rNP+e4C/xTuriHhj5c46KusbObd0GGmp6gTorcz6OjLr67yOERgXTytiyIBMHn5rm9dRpI9J9E/DmcBm59xW51wz8Dgwp9Mxc4Dfhz9/GphtZgZgZpcDW4G1CcorIgnU0tbO/HVVFOdnMW14ntdxRD4mMy2Vz58yktc27mbr7gNex5E+JNEFWwkQOfNgWXhbl8c451qBemCwmfUHvgf8KAE5RcQD72zdQ11DCxdOKSIl9HeaiO987uRRZKSm8Pu3t3sdRfqQRBdsXf0E7vzkZnfH/Ai4xzl32D9pzOwGM1tuZst37959lDFFJNEONbeycONuJhQOYFzBAK/jiHSrICeTS44r4ul3y6g/1OJ1HOkjEl2wlQEjIt4PByq6O8bM0oA8oBY4GfgPM9sOfAP4gZl9rfMNnHMPOOdmOOdmFBRoOgCRZLFgQzWNLW1cMLnI6yiBUnXSaVSddJrXMQLnq2eO5WBzG79fvN3rKNJHpCX4fsuA8WY2BigHPgtc2+mY54HrgMXAlcACFxo/fWbHAWZ2B3DAOferRIQWkfjavb+Jd7buYcboQQzLy/I6TqCs+fLXvY4QSJOKcjln0lAeemsbXzljDP0zE/3rVPqahLawhZ9J+xrwMrAeeNI5t9bM7jSzy8KH/Y7QM2ubgW8CH5v6Q0SC5W9rKklPTeHc0kKvo4j02M2fPIa6Qy38eekOr6NIH5DwPwmcc/OAeZ223RbxeSNw1RGucUdcwolIwn1QvZ8Nu/ZzweRhDFArRczN+sYXAVj4i0c9ThI8J4wcyGnjBvPAG1v5wqmjyEzTJM8SP5rkSEQ809bumLe6kkH9Mzht3GCv4wRSalMTqU1NXscIrH/55DFU72/i6XfLvI4iAaeCTUQ8s/zDWqr2NXHBZE2SK8nptHGDmT4in9+8voXWtnav40iA6SekiHiiobmN+euqGDOkP5OLc72OI3JUzIx/+eQx7Kxt4MVVlV7HkQBTwSYinnh1QxUNzW1cPLUI0yS5ksRmTxzKsYU53PvaZtrbtSi8xIcKNhFJuF37Gnln6x5OGj2I4vxsr+MEWvkZZ1N+xtlexwi0lBTja2cfwwfVB3h+ZeepRUViQwWbiCSUc44XV1aQmZbKeZrGI+42fO5GNnzuRq9jBN7FU4soLcrl/83fSHOrnmWT2FPBJiIJNW/1LrbWHOTc0kL6aRoPCYiUFOO7FxzLztoGHl+medkk9lSwiUjCHGpu5d/nrqMoL4uZYwZ5HadPmH3TZ5h902e8jtEnfGJCASePGcR/v7qZg02tXseRgFHBJiIJc9/CLVTUN3LJtGJSNNBAAsbM+O4FE6k50MTDb23zOo4EjAo2EUmIHXsOcf8bW5kzvZgxQ/p7HUckLk4cNZBzSwu5//Wt7D3Y7HUcCRAVbCISd845bn9+DekpxvcvnOR1HJG4+s75x3KguZX7Xt/idRQJEBVsIhJ3L6+t4rWNu7nl3AkMy8vyOo5IXE0ozOGK44fzyNvb2bHnkNdxJCBUsInI/2/vzsOrqu79j7+/mecJEggkkDDJpKAgg2KLVusszlOtE9VWrba21dbe3mqtvW1/HbC3g730Os8jXhXHVwdNeAAAIABJREFUSi2VMg/KIPMMISQEMs9Zvz/2BmMkSCDJPif5vJ7nPOecfdY5+5PFzuGbvfbeq0NV1jZw/+srGdo7metPygs6Trez9SvnsvUr5wYdo9v5wZlDiIowfj5zVdBRpItQwSYiHeq/Z61jZ2kND1w4UvOFBmDdpdey7tJrg47R7WSnxnP7aYN5b1Uh/1izO+g40gXo21NEOszawnIe/tcmLh+bw9g8XcYjCJE11UTWVAcdo1u6cVIeA3omcv/rq6htaAw6joQ5FWwi0iGcc/zk1RUkxUXxI51oEJjJd17P5DuvDzpGtxQbFcm9F4xgU3ElD3+oy3zI0VHBJiId4qXF21mwqYQfnjWUjMSYoOOIBOLLQzL56vBe/PH99RSUak+nHDnNCyMin/PM/LZNrXP1+H6feV5UXssDMz/hxLx0rhib257RRELSoX5nRuWkMWv1bm5+YjFXjevXartDafk7Jt2P9rCJSLu7/41VVNc18suLjyMiQjMaSPeWnhjD5GMyWb6jlE8KyoKOI2FKBZuItKtZqwt5/aOd3HbqIAZlJQUdRyQkfGlIJr1T4nh12Q6q63QCgrSdCjYRaTcVtQ38ZMYKhvRK4pbJA4OOI8DGcy9l47mXBh2j24uKiOCSE3KorG1g5vKCoONIGNIxbCLSbn77zhoKymp46eqTiInS34OhYNN5lwUdQXx90+P50pBMPlhTxLF9Uzimd0rQkSSM6BtVRNrF0q17eXzuZr4+oT9j+qcHHUd8sftKiN1XEnQM8Z12TBZZybHMWKqhUWkbFWwictTqGpq455Xl9E6J464zjwk6jjQz6Z5bmHTPLUHHEF9UZASXjsmhoraBNzU0Km2ggk1Ejtr02RtYvaucn08ZSXJcdNBxREJaTnoCpwzOZPHWvazYURp0HAkTKthE5KgUl9fy37PWc+6x2Zw+vFfQcUTCwleGZZGTHs8rS7dTUlkXdBwJAyrYROSINTnHjGU7iIuK4N4LhgcdRyRsREVEcOWJ/XAOnl+4lcYmF3QkCXEq2ETkiC3espdNxZX8x7nDyEqOCzqOSFjJSIzh4hNy2La3mvdW7Qo6joQ4XdZDRI5IWU09b60oIL9nIpdr+qmQte7ia4KOIIdwbN9UNuRlMHtdMQMykxjSKznoSBKitIdNRNrMOceMJTtobHJcdHxfzDT9VKjaesb5bD3j/KBjyCGce1w2vVJieWHRNvZV6Xg2OTgVbCLSZou37GVNYTlnjuhNz6TYoOPIISQU7iShcGfQMeQQoiMjuGpcPxqbHE/N30J9Y1PQkSQEqWATkTbZW1XHzOXeUOiEAT2CjiNfYOJ9dzLxvjuDjiFfICs5jsvH5lKwr4YZS3fgnE5CkM9SwSYih63JOV5esh0HXHpCDhEaChVpN8OyU/jKsF4s27aPOeuLg44jIUYFm4gctvmbSthYVMm5I7NJT4wJOo5IlzP5mExG9EnhrRW7WLe7POg4EkJUsInIYdlTUcvbKwoY0iuJsXmaK1SkI0SYcemYHLJSYnluwTaKymuDjiQhQgWbiHyhJud4afF2IiOMi47P0VmhIh0oNiqSr0/II8Lg8bmbqahtCDqShAAVbCLyheasL2ZLSRXnH9eH1HjNFRpOVl99E6uvvinoGNJGGYkxfH1iHmXV9Tw5dzM19Y1BR5KAqWATkUMqLKvhvVWFDM9OYXRuWtBxpI12nHI6O045PegYcgT6ZSRw+dhctu+t5nsvLKNJ01d1ayrYRKRVjU3eUGhMVARTRvfRUGgYSt6ygeQtG4KOIUdoZN9UzhrZmzeX7+JXb68OOo4ESFNTiUir/rm2iB37qrlqXD+S4zQUGo7G/erHALz/0PMBJ5EjNWlQT3omxTJ99kaykmP5xikDgo4kAVDBJiIHtWNfNbNWF3JcTirH9k0NOo5It2Vm3HfBCIrKa3lg5icHJo2X7kVDoiLyOXUNTTy/cBtJsVFcMKpP0HFEur3ICOPBK0czcUAP7nrpY2atLgw6knQyFWwi8jkzlxewp6KWy8bmkhCjHfEioSAuOpLp145hWHYytz69hEWbS4KOJJ1IBZuIfMbbK3axcHMJpwzOZGBmUtBxRKSZ5LhoHrthHNmp8dz42ELW7NJsCN1FpxdsZnaWma0xs/Vm9qODvB5rZs/7r883szx/+RlmttjMlvv3p3V2dpGubldpDT965WP6psVz+vCsoONIO1hxw+2suOH2oGNIO+qZFMsTN44jPiaSax+Zz7aSqqAjSSfo1ILNzCKBPwNnA8OBq8xseItmU4G9zrlBwDTg1/7yYuB859yxwHXAk52TWqR7aGpyfP/FZdTWN3H52FyiIrQDvisoHDeJwnGTgo4h7Sw3I4EnbhxPdV0j1z6ygOIKTWHV1XX2N/I4YL1zbqNzrg54DpjSos0U4HH/8UvAV8zMnHNLnXM7/eUrgTgzi+2U1CLdwEP/3MCc9Xu49/zhZCbrV6urSFu7krS1K4OOIR3gmN7JPHrDiRSUVnP9owsor6kPOpJ0oM4u2PoC25o93+4vO2gb51wDUAr0aNHmEmCpc+5zf1KY2c1mtsjMFhUVFbVbcJGubOHmEn7/3lrOOy6bK07MDTqOtKMx0+5nzLT7g44hHWRM/wwe+toYPiko55tPLtYUVl1YZxdsB7tMesu5Ng7ZxsxG4A2TfvNgK3DOTXfOjXXOjc3MzDzioCLdxd7KOu54dik56fH88uJjNZuBSJg5dWgWv73sOP69YQ93PLuUhsamoCNJB+jsgm070PzP9xxgZ2ttzCwKSAVK/Oc5wAzgWuec5loROUrOOX7w4kfsqajjT1edoNkMRMLURcfn8LMLRvDuqkLufvljmpzmHe1qOrtgWwgMNrN8M4sBrgRea9HmNbyTCgAuBWY555yZpQEzgXucc3M6LbFIF/bwh5t4f/VufnzOUI7N0WwGIuHsupPy+N4ZQ3hlyQ5mLi/AqWjrUjq1YPOPSfs28A7wCfCCc26lmd1vZhf4zR4GepjZeuB7wP5Lf3wbGAT8p5kt82+67oDIEVqydS+/fns1Xx3ei+tOygs6joi0g9tPG8TUSfnM3bCH91fvDjqOtKNOv4S5c+5N4M0Wy37a7HENcNlB3vcA8ECHBxTpBoorarn1qSX0To3jN5eO0nFrXdhHt9wVdATpRGbGT84dxrKt+5i1ejfx0ZGcPKhn0LGkHWjOGZFupqGxiTueXcreqjpevuUkUhN03FpXVnzc2KAjSCczMy48vi81DY3MXF5AXHQkY/qnBx1LjpKujCnSzfzuvbX8e8Mefn7hSEb21XFrXV3PjxfR8+NFQceQThYZYVwxNpdBWUm8smQ7q3aWBh1JjpIKNpFu5N2Vu3jogw1cNS6Xy8fqemvdwaiHfsOoh34TdAwJQFRkBF8b34+c9HieXbiN9bsrgo4kR0EFm0g3sbGogu+/8BHH9k3l3vNHBB1HRDpBbFQk15+UT2ZSLE/N26J5R8OYCjaRbqCspp5vPLGI6KgIHrrmBOKiI4OOJCKdJD4mkhtOziMpLorH/r2ZXWU1QUeSI6CTDqRTPDN/a5vaXz2+Xwcl6Rra0p9NzvHE3M1sLq5k6qQBzF5bHGgekXDQ1bbp5Lhopp6cz//M3sCjczbxzS8NJCMxJuhY0gbawybSxb27chdrCys4f1Qf8nsmBh1HRAKSnhjDDSfn09jkePjDjZRVa7L4cKKCTaQLW7ZtL7PXFTMuP4Px+T2CjiMBWHznT1l850+/uKF0C71S4rj+pDwq6xp5ZM4mqmobgo4kh0kFm0gXta2kileW7CCvRyLnHZcddBwJyL4hI9g3RCeZyKdy0hO4dkJ/SirreGzuZmrrG4OOJIdBBZtIF1RSWccTczeTEh/N1eP7ERWhX/XuqteCD+m14MOgY0iIGZCZxFXj+rFzXzVPzt9CjYq2kKdvcZEuprqukcf/vZkmB9dNzCMpVucWdWcjH/0jIx/9Y9AxJAQNy07hkhNy2FhUyR3PLqWhsSnoSHIIKthEupCGxiaemr+Fkqo6rpnQn8zk2KAjiUgIO75fOucfl827qwq5++WPaWpyQUeSVuhPb5EuwjnHjKU72FRcyeVjc3VGqIgclokDezK4VzK/f28tKXHR3Hv+cMws6FjSggo2kS7inZW7WLptH6cP68Xo3LSg44hIGLn9tEGUVtfz8IebSI2P5s4zhgQdSVpQwSbSBcxeW8TsdcWMz8/g1GMyg44jImHGzPjJucMoq67nD++vIzU+mhsn5QcdS5pRwSYS5hZtLuHtlbs4LieV80f10VCGfMaCH/1X0BEkTJgZv7z4WMprGrj/jVUkx0Vx2djcoGOJTycdiISxlTtLmbF0B4Ozkrh0TA4RKtakhfL+AynvPzDoGBImoiIj+MNVozllcE9++PLHvLNyV9CRxKeCTSRMrS0s57mF28hJj+dr4/vrWmtyUH3/9Xf6/uvvQceQMBIbFclfrxnDqNw0bn9mKXPWt//8w9J2+oYXCUPrCst5at4WspJjue6kPGKi9KssBzf0mb8x9Jm/BR1DwkxibBSPXT+OAZmJ3PTEIpZu3Rt0pG5P3/IiYebDdcU8OW8LmcmxTD05n4QYHYoqIu0vNSGaJ6aOIzM5lusfXciaXeVBR+rWVLCJhJE564uZ+vhCeibFcuPJ+SRoFgMR6UBZyXE8NXU8cdERXPPwfNbvrgg6Urelgk0kTHywZjdTH19IXo9EbpyUT6KKNRHpBLkZCTz9jfE4B1dOn8f63drTFgQVbCJh4PWPdnLTE4sY0DOJp28ar/lBRaRTDcpK5rmbJ2AGV06fz7pCFW2dTQWbSIh7at4W7nhuKcfnpvPcNyfQM0nzg8rhm3vfNObeNy3oGNIFDMpK4tmbvKLtqr/NY62Ktk6lgk0kRDnn+NOsdfzk1RWcdkwWT0wdR0pcdNCxJMxU9epDVa8+QceQLmJQVhLP3TyBCDOunD6PFTtKg47UbahgEwlB9Y1N/HjGcn777louHN2Hv359DHHRkUHHkjDU773X6ffe60HHkC5kYGYSz39zIvHRkVw5fR7zNu4JOlK3oIJNJMTsq6rjukcW8OyCbdw6eSC/v3w00ZH6VZUjM/iVpxj8ylNBx5AuJr9nIi/dMpFeKbFc98gC/r6qMOhIXZ7+FxAJIRuLKrjoL/9m4eYSfnfZKO4+aygREZpuSkRCT3ZqPC9+6ySO6Z3MN59azCtLtgcdqUtTwSYSImatLuSiv/yb0up6nrlpApeMyQk6kojIIWUkxvDMTRMYn5/B9174iAf/vhbnXNCxuiQVbCIBq29s4pdvfcKNjy2iT1o8r956MifmZQQdS0TksCTFRvHoDSdyyQk5PPj3dXznuWXU1DcGHavL0cWcRAJUUFrN7c8sZdGWvVw9vh8/PW+4Ti4QkbATGxXJby87jgGZifzmnTVs31vF9GvH6jJE7UgFm0hAZn5cwE9eXU5dQxN/uHI0U0b3DTqSdEEf/vKhoCNIN2Fm3HbqIPJ7JnLn88uY8qc5/PHq4zmhX3rQ0boEDYmKdLKi8lpueWoxtz2zhNyMBF67fZKKNekwtWkZ1KZpiF06zznHZvPityYCcPlf5/K//9qo49ragQo2kU7inOP/lu3gq9P+yfurd/PDs4byyi0nMTAzKeho0oXlv/Ei+W+8GHQM6WaOy0njzTtO4dShWTww8xNufnIxpVX1QccKayrYRDrB8u2lXDF9Ht95bhl5PRN5845J3DJ5IFG6vpp0sAEzX2LAzJeCjiHdUGpCNNO/PoafnDuMf6zezdl/mM0/1xYFHSts6Rg2kQ5UWFbDb95Zw8tLtpOREMMvLhrJlSf2I1LXVhORbsDM+MYpAxibl8H3X1jGdY8s4JITcvjP84aRlhATdLywooJNpAMUlFbzt9mbeHbBVhqamrj5lAHcdtogzQUqIt3S6Nw0Zt5xCn+ctY6//nMjs9cV8bMLRnD2yN6Y6Q/Yw6GCTTpMfWMT1fWNOAc19Y2YgWFER1qX/QXdWFTBX/+5gRlLd9DkYMqoPnzn9MH075EYdDQRkUDFRUdy15lDOXtkNne/9DG3Pr2EE/PSueecYTqT9DCoYJM2cc6xu7yWrSVVbNlTxbaSKnaX11BUXktReS3FFXWU19RTXd9IfePBzwoyIDY6grjoSOKjI0mKjSI5LpqUuCiS46PJSIhhy55K+qbFh8UxXlV1Dby1fBcvLt7GvI0lxEZFcPW4fnzjlAHkZiQEHU9EJKSM7JvKa98+mRcWbef3763l4r/8m3OO7c1dZw4lv6f+uG2NCjZpVU19I2sLy1m5s4xVO8tYVVDG6oIyKus+vYK1GfRIjCUzOZaeSTEMzEwiOS6K+JgoEmIiSYiJxMxYvLkEBzQ5qGtopLq+idr6RqrrG6mobWB3eS3lNfU0+TXe43M3Ex1p5KYnkN8zkbyeieT7t4GZSfRKiQ10L11FbQNz1hfz91WFvLViFxW1DfTvkcAPvjqEK8f108UiJWR8MO2xoCOIfE5UZARXj+/HlNF9mD57I9Nnb+TtFbs4e2Q2N31pAKNz04KOGHJUsAkAeyvrWFXgFWYrd5ayqqCMDUWVNPoVVFJsFMOzU7hsbC4DMxPJzUigX0YCfdPjiY364ivzxx/G1fubnKOytoGSyjoGZiWxubiSTf5tzoZiauqbDrRNjIlkQGYSAzO9Am5gVhIDM5Po3yOhQ2YKKKupZ+WOMj7avo/Za4tYuLmE+kZHUmwUZ43szeVjczkxL73LDvVK+GqMiw86gkirEmOjuPOMIXxtQj8e+XAzT8/fwszlBYzLy+CGk/M4bVjWYf0f0x2oYOtmnHNsK6lmVUHpgb1mK3eWUVBac6BNdmocw7NTOHNEb0b0SWF4dio56fFEdPCZjRFmJMdFkxwXzeVjcz/zWlOTo7C8hk1FlWwormTD7go2FleycPNeXl2280A7M+idEkdOejx90+Lpmx5PZlIsGUmx9EiMISMxhvjoSKKjIoiJ9G51jU3U1DdSVddIVV0DhWW17NxXTUFpNTv2VbNqZxmb91QdWMfQ3sncOCmfyUOyGNM/nZio0B+2le5r8EtPALDu0msDTiLSuqzkOH509lC+fdognl+4jUc+3MQtTy8hLSGac4/N5uIT+nJCv+79R7EKti6sqq6BjUWVB/acrSoo45OdZZTXNgAQYTAwM4lx+RkHCrNh2cn0CMHhvIgIIzs1nuzUeE4a1PMzr1XVNbCpuJINRV4ht31vNdv3VrFoy15e/7jgwF7CtoqNiqBvWjzD/D2LI/umMrJPSkj2j0hr+r0/E1DBJuEhKTaKqZPyuW5if/61vpgZS3bw8pLtPD1/K33T4pl8TCZfHpLJSYN6khTbvUqYTv9pzews4A9AJPC/zrlftXg9FngCGAPsAa5wzm32X7sHmAo0Anc4597pxOgha09FLRuKKlm/u8K7FVWwYXcFO/ZVH2gTHx3JsOxkphzfh+HZqYzok8IxvZO7xETjCTFRjOiTyog+qZ97rbHJsa+qjpLKOvZU1rG3so6ahkbqGpq8W6MjJiqCeP8EiPiYCLKS4+iTFk96QnS3/mtORCQoUZERnHpMFqcek0V5TT1vr9jFu6sKeXXpDp6ev5XoSGN0bhrH90tnVE4ao/ul0Sc1rkt/Z3dqwWZmkcCfgTOA7cBCM3vNObeqWbOpwF7n3CAzuxL4NXCFmQ0HrgRGAH2Av5vZEOdcI12Uc47q+kYKy2opKK2mYF8NBaXV7CytYVdpjT9sV0Np9afTfcRFRzAwM4kx/dO54sRcBmYmMTQ7mbweid3yYq2REUaPpFh6JMUyOOgwIiLSZslx0Vw2NpfLxuZS19DE4i17+WDtbuZvLOGxOZupa/SOb+6RGHPgeOZBWUnk90wgOzWePqnxpMRHhX0x19l72MYB651zGwHM7DlgCtC8YJsC3Oc/fgn4k3m9PAV4zjlXC2wys/X+583tpOwHta2kioYmR2OTwzlHo/MeNzV5B9E3Okddg3eMlHdr9thfXl7TwL6qekqr6ymtrjvweF91PXUNTZ9bZ3pCNNmp8eSkxzM2L528HokM8jfSvmkdf6yZiIhIEGKiIpg4sAcTB/YAoK6hiU8Kyli2bR+rdpaxoaiCt1YUsK/FvKUJMZFkJceSGh9NSnz0Z+733xJiIomNiiA6MoKYZvcx/n18dGSgl2rq7IKtL7Ct2fPtwPjW2jjnGsysFOjhL5/X4r19Oy7q4TnnD/86cEzYkUqMiSQtIYaU+GjS4qMZmJlEWkI0qQneRpSVHEef1Diy0+LpnRJHfEz4D2OKiIgcrZioCEblpjGqxWVA9lTUsqWk6tORqX01FFXUUlbt7RDZsbfa30lST8NhHuc8MDOR978/uQN+isPT2QXbwXb9tOyp1tocznsxs5uBm/2nFWa2pk0JQ1dPoDjoEJ3la523qm7Vr52s3fq2E7eHcHBk/Tqhf/sn6XpC9vugo38HOvjzQ7Zf22ILYD/o8NW0+ova2QXbdqD59RpygJ2ttNluZlFAKlBymO/FOTcdmN6OmUOCmS1yzo0NOkdXo37tOOrbjqF+7Tjq246hfm0fnX0BqYXAYDPLN7MYvJMIXmvR5jXgOv/xpcAs55zzl19pZrFmlg8MBhZ0Um4RERGRwHTqHjb/mLRvA+/gXdbjEefcSjO7H1jknHsNeBh40j+poASvqMNv9wLeCQoNwG1d+QxRERERkf06/Tpszrk3gTdbLPtps8c1wGWtvPcXwC86NGDo6nLDvCFC/dpx1LcdQ/3acdS3HUP92g7MG20UERERkVClSRBFREREQpwKthBjZrlm9g8z+8TMVprZd/zl95nZDjNb5t/OCTpruDGzODNbYGYf+X37M395vpnNN7N1Zva8f0KMHKZD9OtjZrap2TY7Ouis4crMIs1sqZm94T/XNtsODtKv2mbbgZltNrPlfh8u8pdlmNl7/jb7npmlB50z3KhgCz0NwPedc8OACcBt/rRcANOcc6P925utf4S0ohY4zTk3ChgNnGVmE/CmP5vmnBsM7MWbHk0OX2v9CnBXs212WXARw953gE+aPdc22z5a9itom20vp/p9uP9yHj8C3ve32ff959IGKthCjHOuwDm3xH9cjvdlEviMDl2B81T4T6P9mwNOw5sGDeBx4MIA4oWtQ/SrtAMzywHOBf7Xf25omz1qLftVOtwUvG0VtM0eERVsIczM8oDjgfn+om+b2cdm9oh2Jx8ZfwhkGbAbeA/YAOxzzu2fXywkpjwLNy371Tm3f5v9hb/NTjOz2AAjhrMHgbuB/RML90DbbHto2a/7aZs9eg5418wW+7MPAfRyzhWAt2MCyAosXZhSwRaizCwJeBn4rnOuDHgIGIg35FQA/C7AeGHLOdfonBuNN1PGOGDYwZp1bqrw17JfzWwkcA8wFDgRyAB+GGDEsGRm5wG7nXOLmy8+SFNts23QSr+Cttn2crJz7gTgbLzDer4UdKCuQAVbCDKzaLxi7Wnn3CsAzrlC/z/FJuBveMWGHCHn3D7gA7zjBNP8adCglSnP5PA069ez/OF955yrBR5F2+yROBm4wMw2A8/hDYU+iLbZo/W5fjWzp7TNtg/n3E7/fjcwA68fC80sG8C/3x1cwvCkgi3E+MenPAx84pz7fbPl2c2aXQSs6Oxs4c7MMs0szX8cD5yOd4zgP/CmQQNvWrT/CyZheGqlX1c3+3I2vONVtM22kXPuHudcjnMuD2/Wl1nOua+hbfaotNKv12ibPXpmlmhmyfsfA1/F68fm005qmz0CnT7TgXyhk4GvA8v9Y4IAfgxc5Z9i7oDNwDeDiRfWsoHHzSwS74+VF5xzb5jZKuA5M3sAWIpXMMvha61fZ5lZJt4Q3jLgW0GG7GJ+iLbZjvC0ttmj1guY4dW8RAHPOOfeNrOFwAtmNhXYSiszGknrNNOBiIiISIjTkKiIiIhIiFPBJiIiIhLiVLCJiIiIhDgVbCIiIiIhTgWbiIiISIhTwSYincLMLjSzd81sj5nVmdkOM3vOzE4+SNsUM7vfzFaZWbWZlZvZbDO73MxC4nvLz3J9B6/jbjObfITvzTaz98yszMzeMbPeLV4fZGZ7/Tk1RSTEhcQXn4h0bWY2DW/2jh3AN/AurvsjIBn40MwGNmubBczz2z0CnIN3zaZ5eBN1n9+p4Vt3OXB9B6/jbmDyEb53mn9/Kd71sH7f4vXfAdOcc9uP8PNFpBPpwrki0qHMbArwXeAG59xjLV5+0szOB6qbLXsISAfGOud2NFv+tpn9CUjtyLxdyOnA2c65hWZWCryx/wUzOx1vXuIrgwonIm2jPWwi0tG+Cyw8SLEGgHPu9f1zD5pZf7yp1/6rRbG2v+1W59zyQ63MzC4yswX+UOoeM3vT/9z9r59mZvPNrMbMCs3sL2aW1Oz1yWbm/PsXzazCzDaa2a3N2jwGXAJ82W/rzOy+Zq9PMbNF/jp2mdn/8+cI3v/6fWZWbGbHm9k8M6sys6VmdkqzNpuBHsC9zdYx+VA/ewsxfFoIVwGx/udG4s1HerdzrrqV94pIiFHBJiIdxp+gfCLw7mG+5Ut40wK9fYTr+zrwCrABb8jyBmAtkOm/Ptz/7GK8gute4GrgpYN83N+Aj/AKyA+AP5vZ/snAf443n+dSvJ9vIt5wLWZ2uZ9hAXAB8DPgZuCXLT4/AXgc+B8/Sy3elD4J/usXAaV4007tX8eSNnTHYuBWM0sHbgMW+su/Bexzzj3fhs8SkYBpSFREOlIPvD0725ov9CfXjmy2qNF58+T19Z9vbeuK/JMRfgXMcM5d1eyl15o9/imwBbjAOdfov68EeN7MJjrn5jZr+6xz7gG/zQd4x85dDCxwzm3w3xfhnJvX4uf6DfCEc675HrlavILvl865Pf7ieOC7zrlZfpsCvALwS8DbzrmlZtYAbG++jjb4AfAmcAtQCJztF29/cPdVAAADdklEQVT3AmcfweeJSIC0h01EOpL59y0nLf4+UN/sdluL149kkuNjgD7Ao4doMw6voGtstuxloAGY1KLtgb2Czrl6YB3wRWdUDgH64U1yHbX/BswC4oCRzdrW4+2522+Vf98uZ2065xb7WYYC/ZxzS/H29r3unFtsZheb2Tp/yPbn7bFOEek42sMmIh2pGG+or2UR8iSfFisLmy3ff9xaP2B9G9fVw78vOESbbLy9TQc45xrNbA+Q0aLtvhbP6/CKrkPp6d+/2crruc0elznnmprlqPN20H3hOg6bc64WWANgZsOAa4DhZtYLeAxvT9smYI6ZzXfOvdHaZ4lIsFSwiUiHcc41mNlc4Kt4w5H7lxfiF05+kbLfbLy9a2fS9oJt/1Bj9iHaFABZzRf4B+H3AErauL6D2f8ZN+MNb7a0qR3WcaSmAb92zu3yz9xd65ybA2BmLwOn0uxMUhEJLRoSFZGO9iAw3j8h4JCcc1uAGcCPzexzhZeZ5ZrZsa28fQ3eHrrrDrGK+cBFfpG238V4f7x++EX5WjjYHrf9GfKcc4sOctvz+Y9p8zrazMzOAwbh/Vvsl9DscSKfDl+LSAjSHjYR6VDOuf8zsweBx8zsVOB1vKHSHsAZfrOKZm+5BfgnsMjMfo93tmMs8GW8Y92uBT53aQ/nXJOZ3Q08bWZPA8/i7a07De8EgkXAA3h7vl41s4fwhmp/DbzT4oSDw7EamGJmFwLbgZ3OuZ1m9n2868ulAG/hFV0DgAuBS51zVW1cx7lm9jZeH61xzpX7lxWZ7JzL+6IP8C8n8jvgB/4QKXiFa76Z3QVsBq7C61cRCVEq2ESkwznn7jSz2cCteJepSAaKgLnAOc65t5q13W1mE/DOcrwJ+AXeAfpLgTs5xLCdc+4ZM6sB/gPvUh2VeDMkFPmvrzSzs4H/wrv0RhleYXf3EfxYfwGOx5uNIR3vgP77nHPPm1kZ8GPgRqAR2OjnrmvjOu4C/gzMxNsjdiresX8JwO7D/Izb8c40fXX/An9Y9Hq8YjUZeMg591or7xeREGDemfQiIhIuzGwL8FPn3ONBZxGRzqFj2EREwoiZ9QGi8fYMikg3oT1sIiIiIiFOe9hEREREQpwKNhEREZEQp4JNREREJMSpYBMREREJcSrYREREREKcCjYRERGREKeCTURERCTE/X8v3wymJWG1BAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fasta_stats.plot_gc_content_distribution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fasta_stats.plot_kmer_distribution(kmer_size=4)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/hw8/code/classes.ipynb b/hw8/code/classes.ipynb
new file mode 100644
index 0000000..f83ff9c
--- /dev/null
+++ b/hw8/code/classes.ipynb
@@ -0,0 +1,457 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Bio.Seq import Seq\n",
+    "from Bio import SeqIO\n",
+    "from Bio.SeqUtils import GC\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 1: My own class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Employee:\n",
+    "    \n",
+    "    def __init__(self, name, age, work_experience):\n",
+    "        self.name = name\n",
+    "        self.age = age\n",
+    "        self.work_experience = work_experience\n",
+    "    \n",
+    "    def __str__(self):\n",
+    "        return 'Information about employee: {}'.format(self.name)\n",
+    "    \n",
+    "    def proposed_position(self):\n",
+    "        '''\n",
+    "        Shows the position the candidate is applying for\n",
+    "        :return: str, position name\n",
+    "        '''\n",
+    "        \n",
+    "        if self.work_experience <= 1:\n",
+    "            position = 'Junior DS'\n",
+    "        elif 1 < self.work_experience <= 3:\n",
+    "            position = 'Middle DS'\n",
+    "        else:\n",
+    "            position = 'Senior DS'\n",
+    "        return position\n",
+    "     \n",
+    "    def proposed_salary(self, position):\n",
+    "        '''\n",
+    "        Shows salary the candidate wants to have\n",
+    "        :param position: str, name of the position\n",
+    "        :return: int, salary in dollars\n",
+    "        '''\n",
+    "        if position == 'Junior DS':\n",
+    "            salary = 1000\n",
+    "        elif position == 'Middle DS':\n",
+    "            salary = 5000\n",
+    "        else:\n",
+    "            salary = 10000\n",
+    "        return salary\n",
+    "    \n",
+    "    def view_info(self):\n",
+    "        '''\n",
+    "        Shows basic information about candidate\n",
+    "        :return: str with information\n",
+    "        '''\n",
+    "        position = self.proposed_position()\n",
+    "        salary = self.proposed_salary(position)\n",
+    "        \n",
+    "        return f\"Name: {self.name}\\nAge: {self.age} years\\nPosition: {position}\\nSalary {salary} $\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Information about employee: Anton\n",
+      "Name: Anton\n",
+      "Age: 23 years\n",
+      "Position: Junior DS\n",
+      "Salary 1000 $\n"
+     ]
+    }
+   ],
+   "source": [
+    "employee_1 = Employee('Anton', 23, 1)\n",
+    "print(employee_1)\n",
+    "print(employee_1.view_info())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Information about employee: Alexander\n",
+      "Name: Alexander\n",
+      "Age: 40 years\n",
+      "Position: Senior DS\n",
+      "Salary 10000 $\n"
+     ]
+    }
+   ],
+   "source": [
+    "employee_2 = Employee('Alexander', 40, 7)\n",
+    "print(employee_2)\n",
+    "print(employee_2.view_info())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 2: RNA class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RNA:\n",
+    "    \n",
+    "    def __init__(self, rna):\n",
+    "        self.rna = Seq(rna)\n",
+    "        \n",
+    "    def translation(self):\n",
+    "        '''\n",
+    "        Translates the RNA sequence into protein\n",
+    "        :return: protein sequence\n",
+    "        '''\n",
+    "        return self.rna.translate()\n",
+    "    \n",
+    "    def back_transcription(self):\n",
+    "        '''\n",
+    "        Convert RNA sequence into DNA\n",
+    "        :return: DNA sequence\n",
+    "        '''\n",
+    "        return self.rna.back_transcribe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TFF\n",
+      "ACGTTCTTC\n"
+     ]
+    }
+   ],
+   "source": [
+    "rna_seq = RNA('ACGUUCTTC')\n",
+    "print(rna_seq.translation())\n",
+    "print(rna_seq.back_transcription())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 3: Class inherited from Set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SetModified(set):\n",
+    "    \n",
+    "    def __init__(self, *args):\n",
+    "        self.positive_numbers = [arg for arg in args if isinstance(arg, (int, float)) and arg > 0]\n",
+    "        super().__init__(self.positive_numbers)\n",
+    "        \n",
+    "    def add(self, element):\n",
+    "        if isinstance(element, (int, float)) and element > 0:\n",
+    "            super().add(element)\n",
+    "    \n",
+    "    def update(self, *elements):\n",
+    "        for element in elements:\n",
+    "            self.add(element)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SetModified({2, 3})\n",
+      "SetModified({2, 3, 100})\n",
+      "SetModified({800, 2, 3, 900, 100})\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_set = SetModified(-1, 2, 3)\n",
+    "print(my_set)\n",
+    "my_set.add(100)\n",
+    "print(my_set)\n",
+    "my_set.update(800, 900, -5)\n",
+    "print(my_set)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Task 4: Class for working with Fasta files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class FastaStats:\n",
+    "    \n",
+    "    def __init__(self, path):\n",
+    "        self.path = path\n",
+    "        \n",
+    "    def __str__(self):\n",
+    "        return \"Path to the file: {}\".format(self.path)\n",
+    "    \n",
+    "    def count_sequences(self):\n",
+    "        '''\n",
+    "        Counts number of sequences presented in the file\n",
+    "        :return: int, number of sequences\n",
+    "        '''\n",
+    "        seq_numb = 0 \n",
+    "        with open(self.path) as f:\n",
+    "            for record in SeqIO.parse(f, \"fasta\"):\n",
+    "                seq_numb += 1\n",
+    "        return seq_numb\n",
+    "    \n",
+    "    def calc_sequences_length(self):\n",
+    "        '''\n",
+    "        Calculates length of sequences presented in the file\n",
+    "        :return: list with lengths of sequences\n",
+    "        '''\n",
+    "        with open(self.path) as f:\n",
+    "            length_ls = [len(record.seq) for record in SeqIO.parse(f, \"fasta\")]\n",
+    "        return length_ls\n",
+    "        \n",
+    "    def calc_gc_content_per_seq(self):\n",
+    "        '''\n",
+    "        Calculates GC content of sequences presented in the file\n",
+    "        :return: list with GC content per each sequence\n",
+    "        '''\n",
+    "        with open(self.path) as f:\n",
+    "            gc_content_ls = [GC(record.seq) for record in SeqIO.parse(f, \"fasta\")]\n",
+    "        return gc_content_ls\n",
+    "    \n",
+    "    def calc_mean_gc_content(self):\n",
+    "        '''\n",
+    "        Calculates mean GC content over all sequences presented in the file\n",
+    "        :return: int, mean GC content (%)\n",
+    "        '''\n",
+    "        return np.mean(self.calc_gc_content_per_seq())\n",
+    "    \n",
+    "    def calc_k_mers(self, kmer_size):\n",
+    "        '''\n",
+    "        Counts k-mer over all sequences in the file\n",
+    "        :param kmer_size: int, size of k-mers\n",
+    "        :return: dictionary, where keys - kmers, values - their counts\n",
+    "        '''\n",
+    "        k_mer_counts = {}\n",
+    "        with open(self.path) as f:\n",
+    "            for record in SeqIO.parse(f, \"fasta\"):\n",
+    "                for i in range(len(record.seq)-kmer_size+1):\n",
+    "                    kmer = str(record.seq[i:i+kmer_size])\n",
+    "                    k_mer_counts[kmer] = k_mer_counts.get(kmer, 0) + 1\n",
+    "        k_mer_counts = dict(sorted(k_mer_counts.items(), key=lambda item: item[1], reverse=True))\n",
+    "        return k_mer_counts\n",
+    "    \n",
+    "    def plot_length_distribution(self):\n",
+    "        '''\n",
+    "        Plots length distribution for sequences presented in the file\n",
+    "        '''\n",
+    "        length_ls = self.calc_sequences_length()\n",
+    "        plt.figure(figsize=(10, 8))\n",
+    "        sns.distplot(length_ls, bins=30)\n",
+    "        plt.title('Length distribution', fontsize=15)\n",
+    "        plt.xlabel('length', fontsize=15)\n",
+    "        plt.ylabel('frequency', fontsize=15)\n",
+    "    \n",
+    "    def plot_gc_content_distribution(self):\n",
+    "        '''\n",
+    "        Plots GC content distribution for sequences presented in the file\n",
+    "        '''\n",
+    "        gc_content_ls = self.calc_gc_content_per_seq()\n",
+    "        mean_gc_content = self.calc_mean_gc_content()\n",
+    "        plt.figure(figsize=(10, 8))\n",
+    "        sns.distplot(gc_content_ls, bins=30)\n",
+    "        plt.title('GC content distribution', fontsize=15)\n",
+    "        plt.xlabel('GC content, %', fontsize=15)\n",
+    "        plt.ylabel('frequency', fontsize=15)\n",
+    "        plt.axvline(mean_gc_content, color='red', linestyle = '--')\n",
+    "        \n",
+    "    def plot_kmer_distribution(self, kmer_size):\n",
+    "        '''\n",
+    "        Plots frequency of k-mers over all sequences\n",
+    "        :param kmer_size: int, size of k-mers\n",
+    "        '''\n",
+    "        k_mer_counts = self.calc_k_mers(kmer_size=kmer_size)\n",
+    "        plt.figure(figsize=(12, 10))\n",
+    "        plt.bar(k_mer_counts.keys(), k_mer_counts.values(), width=1)\n",
+    "        plt.title('{}-mers distribution'.format(kmer_size), fontsize=15)\n",
+    "        plt.xlabel('{}-mers'.format(kmer_size), fontsize=15)\n",
+    "        plt.ylabel('counts', fontsize=15)\n",
+    "        plt.xticks(fontsize=3, rotation=90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Path to the file: ../data/example.fa\n",
+      "Number of sequences:  91\n",
+      "Mean GC content: 40.72 %\n"
+     ]
+    }
+   ],
+   "source": [
+    "fasta_stats = FastaStats('../data/example.fa')\n",
+    "print(fasta_stats)\n",
+    "print('Number of sequences: ', fasta_stats.count_sequences())\n",
+    "print('Mean GC content: {} %'.format(round(fasta_stats.calc_mean_gc_content(), 2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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vNtY/RcSKiHg2e513oLEkSZLyoKQhMCIqgG8C7wYmApdFxMRm3a4ENqWUjgK+AtyQ7TsRmA5MAs4FbszGA/hB1taSr6SUTspe97diLEmSpC6v1FcCpwA1KaUlKaU6YAYwrVmfacCt2ft7gLMiIrL2GSml3SmlpUBNNh4ppceBjUXUsc+xJEmS8qDUIXAUsLzJ59qsrcU+KaV6YDMwuJX7tuTqiJifTRkPLKIOSZKkLqvUITBaaEut7NOafZv7FnAkcBKwCvivIuogIq6KiDkRMWfdunUHOJQkSVLnUeoQWAsc3uTzaGDlvvpERCXQn8JUb2v2/RMppTUppYaUUiPwXf53yrdVY6WUbkopTU4pTR46dOgBTk2SJKnzKHUInA1MiIhxEdGdwuKMmc36zASuyN5fAjySUkpZ+/Rs9fA4YALw9P4OFhEjmny8CNi7erjosSRJkrqSylIeLKVUHxFXAw8BFcAtKaUFEXEdMCelNBO4Gbg9ImooXAGcnu27ICLuBhYC9cCnU0oNABFxJ3AGMCQiaoEvpJRuBv4jIk6iMNX7CvCJA40lSZKUB1G4yKYDmTx5cpozZ065y1ALfjhrWVH9L586pp0qkSSpY4iIuSmlyfvr4xNDJEmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHSh4CI+LciFgUETURcU0L26sj4q5s+6yIGNtk27VZ+6KIOKdJ+y0RsTYinm821pci4sWImB8RP42IAVn72IjYGRHPZq9vt98ZS5IkdTwlDYERUQF8E3g3MBG4LCImNut2JbAppXQU8BXghmzficB0YBJwLnBjNh7AD7K25h4G3pBSOgFYDFzbZNvLKaWTstcn2+L8JEmSOotSXwmcAtSklJaklOqAGcC0Zn2mAbdm7+8BzoqIyNpnpJR2p5SWAjXZeKSUHgc2Nj9YSumXKaX67ONTwOi2PiFJkqTOqNQhcBSwvMnn2qytxT5ZgNsMDG7lvvvzUeCBJp/HRcQzEfFYRJxexDiSJEmdXmWJjxcttKVW9mnNvi0fNOLvgXrgjqxpFTAmpbQhIk4FfhYRk1JKW5rtdxVwFcCYMWNacyhJkqROodRXAmuBw5t8Hg2s3FefiKgE+lOY6m3Nvq8TEVcA7wH+LKWUALIp5Q3Z+7nAy8DRzfdNKd2UUpqcUpo8dOjQVp2gJElSZ1DqEDgbmBAR4yKiO4WFHjOb9ZkJXJG9vwR4JAtvM4Hp2erhccAE4On9HSwizgX+FnhvSmlHk/ahexeVRMT4bKwlh3x2kiRJnURJp4NTSvURcTXwEFAB3JJSWhAR1wFzUkozgZuB2yOihsIVwOnZvgsi4m5gIYWp3U+nlBoAIuJO4AxgSETUAl9IKd0MfAOoBh4urC3hqWwl8NuA6yKiHmgAPplSet3CEkmSpK4qshlSHcDkyZPTnDlzyl2GWvDDWcuK6n/5VL/fKUnq2iJibkpp8v76+MQQSZKkHDIESpIk5ZAhUJIkKYcMgZIkSTlkCJQkScohQ6AkSVIOGQIlSZJyyBAoSZKUQ4ZASZKkHDIESpIk5ZAhUJIkKYcMgZIkSTnU6hAYEVdHxKD2LEaSJEmlUcyVwH8HVkTE3RFxbkREexUlSZKk9lVMCBwOfAo4DLgPWB4R10fE0e1SmSRJktpNq0NgSml7Sun7KaW3A0cD3wf+DHghIp6IiD+PiD7tVagkSZLazkEtDEkpvZxS+kfgncCTwFuAm4GVEfHViOjfhjVKkiSpjRUdAiOiV0RcERGPAouAIcDngAnANcDFwF1tWaQkSZLaVmVrO0bE6cCfA5cAAdwNXJtS+n2TbjdGRA0ws02rlCRJUptqdQgEHgOeAv4vMCOltH0f/RYBdx5qYZIkSWo/xYTAN6SUFh6oU0rpVQpXDCVJktRBFfOdwK0RcUpLGyLilIg4vI1qkiRJUjsrJgR+C/jgPrZdDtx46OVIkiSpFIoJgacBj+xj22+y7ZIkSeoEigmBvYC0n+29D7EWSZIklUgxIfA54LJ9bLsMWHDo5UiSJKkUilkd/EXgxxFRDfwAWAWMAK4A3pe9JEmS1Am0OgSmlH4aEVcA/04h8CUKN41eAXwwpfSz9ilRkiRJba2YK4GklG6PiP8BjgEGAxuARSml/X1XUJIkSR1MUSEQIAt8L7ZDLZIkSSqRokJgRIwE3gOMBno025xSSn/bVoVJkiSp/bQ6BEbERRSeCVwBrAXqmnVJgCFQkiSpEyjmSuC/Ab8EPpJS2thO9UiSJKkEigmBhwN/aQCUJEnq/Iq5WfTvKKwKliRJUidXzJXAzwB3RMQ24GHgteYdUko72qowSZIktZ9iQuD87M/vs+9nCFccWjmSJEkqhWJC4EfZd/iTJElSJ1LMY+N+0I51SJIkqYSKfmJIREwETqWwWviWlNLqiDgKWJNS2trWBUqSJKntFXOz6D7ALcAlwJ5s3weB1RTuIbgM+Gw71ChJkqQ2VswtYr4MvBk4C+gLRJNt9wPntmFdkiRJakfFTAdfDPxVSuk3EdF8FfCrwBFtV5YkSZLaUzFXAnsCG/axrS/QcOjlSJIkqRSKCYGzgQ/vY9slFJ4oIkmSpE6gmBD4D8DFEfEr4GMU7hl4XkTcDrwf+EJrBomIcyNiUUTURMQ1LWyvjoi7su2zImJsk23XZu2LIuKcJu23RMTaiHi+2ViDIuLhiHgp+3Ng1h4R8bVsrPkRcUoRPwdJkqROr9UhMKX0WwqLQqqBb1BYGPLPwHjgnSml2QcaI/su4TeBdwMTgcuyW840dSWwKaV0FPAV4IZs34nAdGAShUUoNzb5buIPaHlhyjXAr1NKE4BfZ5/Jjj8he10FfOtAtUuSJHUlxVwJJKX0ZErpdKAfMBrom1J6S0rpyVYOMQWoSSktSSnVATOAac36TANuzd7fA5wVEZG1z0gp7U4pLQVqsvFIKT0ObGzheE3HuhW4sEn7bangKWBARIxo5TlIkiR1ekWFwL1SSjtTSitTSjuK3HUUsLzJ59qsrcU+KaV6YDMwuJX7NndYSmlVNtYqYFgRdUiSJHVZxdws+u4D9UkpfeBAw7S0Wyv7tGbf1mrVWBFxFYXpYsaMGXOQh5IkSep4irlP4NAW2gYBx1C4dcyiVoxRS+Fxc3uNBlbuo09tRFQC/SlM9bZm3+bWRMSIlNKqbLp3bRF1kFK6CbgJYPLkyQcbOCVJkjqcYhaGvKOF14kUFlesorCI40BmAxMiYlxEdKew0GNmsz4zgSuy95cAj6SUUtY+PVs9PC477tMHOF7Tsa4Aft6k/cPZKuHTgM17p40lSZLy4KC+E9hUSmk58O/Af7Sibz1wNfAQ8AJwd0ppQURcFxHvzbrdDAyOiBrgM2QrelNKC4C7gYUUnln86ZRSA0BE3An8HjgmImoj4spsrC8CZ0fES8DZ2WcoPOZuCYXFJd8FPnUIPwJJkqROp5jp4P1poDClekAppfsphLCmbZ9v8n4XhfsOtrTv9cD1LbRfto/+Gyjc1qZ5ewI+3Zp6JUmSuqJiFoY0v58fQHfgOOBfKEz1SpIkqRMo5krg87S8GjcoBMCPtUlFkiRJanfFhMB3tNC2C6hNKa1oo3okSZJUAq0OgSmlx9qzEEmSJJVOMd8JLOpuySmlZcWXI0mSpFIoZjr4FVr3hI7I+lUcTEGSJElqf8WEwA8CN1C4v99PKDx9YxjwPuBY4G+ALW1doCRJktpeMSHwXcAvUkp/0az92xHxbeC8lNKH2q40SZIktZdinhhyMYUrgC35MfDefWyTJElSB1NMCNwJvHUf206ncLsYSZIkdQLFTAd/C/jHiBgMzOR/vxM4DfgELTzOTZIkSR1TMfcJ/KeI2ERhAcinKKwADmA18NmU0n+3T4mSJElqa8VcCSSl9NWI+DowBjiMQgBcnlJqbI/iJEmS1D6KCoEAKaXGiHgVqAPWGgClg/fDWcXdU/3yqUXds12SpH0qZmEIEXFeRMyisAhkGXBC1n5TRHywHeqTJElSO2h1CIyID1NYEPIicFWzfV8Crmzb0iRJktReirkS+PfAl1JKVwD/02zbAmBim1UlSZKkdlVMCDwCeHgf23YB/Q69HEmSJJVCMSFwOXDyPrZNBmoOvRxJkiSVQjEh8GbgC9kCkJ5ZW0TEWRTuHfjdti5OkiRJ7aOYW8TcABwO3Ao0ZG2/AyqA76SUvtbGtUmSJKmdFPPEkAR8OiK+DJwFDAE2Ao+klBa3U32SJElqB60KgRHRA/g6cHNK6Sng5XatSpIkSe2qVd8JTCntAqYDPdq3HEmSJJVCMQtDHgHe0V6FSJIkqXSKWRjyTeB7EdEbuB9YA6SmHVJKC9uwNkmSJLWTYkLgg9mfn8leTQNgZJ8r2qguSZIktaP9hsCIuAX4l5TSUgpTwf2ALaUoTJIkSe3nQFcCrwC+DSyl8J3AN6WUnm73qiRJktSuDhQCVwFnRMRCClO+PSKi1746p5R2tGVxkiRJah8HWh18E/BFYDOF7/z9Bti6n5ckSZI6gf1eCUwpXRcR9wHHAbcB/4o3ipYkSer0Drg6OKU0F5gbEWcB388WiUiSJKkTK+bZwX/enoVIkiSpdIp5YogkSZK6CEOgJElSDhkCJUmScsgQKEmSlEOGQEmSpBwyBEqSJOWQIVCSJCmHDIGSJEk5ZAiUJEnKIUOgJElSDhkCJUmScqjkITAizo2IRRFRExHXtLC9OiLuyrbPioixTbZdm7UviohzDjRmRDwREc9mr5UR8bOs/YyI2Nxk2+fb96wlSZI6lspSHiwiKoBvAmcDtcDsiJiZUlrYpNuVwKaU0lERMR24Abg0IiYC04FJwEjgVxFxdLZPi2OmlE5vcuwfAz9vcpwnUkrvaZ8zlSRJ6thKfSVwClCTUlqSUqoDZgDTmvWZBtyavb8HOCsiImufkVLanVJaCtRk4x1wzIjoC5wJ/KydzkuSJKlTKXUIHAUsb/K5NmtrsU9KqR7YDAzez76tGfMi4NcppS1N2t4UEfMi4oGImHRwpyNJktQ5lToERgttqZV9im1v6jLgziaf/wAckVI6Efg6+7hCGBFXRcSciJizbt26lrpIkiR1SqUOgbXA4U0+jwZW7qtPRFQC/YGN+9l3v2NGxGAKU8b37W1LKW1JKW3L3t8PVEXEkObFppRuSilNTilNHjp0aHFnKkmS1IGVOgTOBiZExLiI6E5hocfMZn1mAldk7y8BHkkppax9erZ6eBwwAXi6FWO+H/hFSmnX3oaIGJ59z5CImELh57Chjc9VkiSpwyrp6uCUUn1EXA08BFQAt6SUFkTEdcCclNJM4Gbg9oiooXAFcHq274KIuBtYCNQDn04pNQC0NGaTw04HvtislEuAv4iIemAnMD0LmpIkSblQ0hAIf5x+vb9Z2+ebvN9F4epdS/teD1zfmjGbbDujhbZvAN8opm5JkqSuxCeGSJIk5ZAhUJIkKYdKPh0sdSY76upZsWkntZt2sn7bbo4d3o+JI/tR0a2lOxNJktR5GAKlZnbU1fP9J1/htt+/wpotu1+3vW91JaeOHcibxg/m0jcezoBe3UtfpCRJh8gQKGV21zdw56xlfOM3L7N+227efvRQPvymQYwe2JPRA3sxsFcVz63YzNNLNzJr6Ub+/YEX+dZjL/OZs4/m8iljqKzw2xWSpM7DECgBz6/YzCdun8uK13Zy2vhBfOdDp3DqEYNe12/80D5MO6nwVMIXV2/hunsX8vmfL+B/nnqVf3zPRE6f4E3FJUmdg5culHtzXtnIZd99ipQS/3PlVEkoOhIAACAASURBVO78+GktBsDmjh3ejzs+NpXvfOhUdu1p5EM3P82/3/8CjY3eclKS1PF5JVC59vjidXzi9rmM6N+D2z82lVEDeha1f0RwzqThnHHMUP7lFwv5zuNLWL5pB1/+wEn0qKpop6olSTp0XglUbj34/Co+duscxg7pzV2feFPRAbCp6soK/mXaG/j7847jgedXc9l3n2LDttcvKpEkqaMwBCqXfvfyej51xx94w6h+zPj4aQztW33IY0YEH3/beG68/BQWrtzCRTf+juUbd7RBtZIktT1DoHJnw7bd/PWMZxk7pDe3XTmV/r2q2nT8dx8/gjuvOo3XdtRxxfefZuP2ujYdX5KktmAIVK40psT/+9E8Xtu5h29cdgp9qtvna7GnjBnI9654I7WbdvLRH8xmR119uxxHkqSDZQhUrvyuZj2PLlrHP5x/HBNH9mvXY00ZN4ivTT+Z+bWvcfUPn6G+obFdjydJUjFcHazcqN20g4cWrGHiiH5URPDDWctKctwLThzJz59dyaU3PcXFJ48iwkfOSZLKzxCoXNhd38CM2cvp06OSi08pbRCbOm4wW3bW85tFaxnWt9obSkuSOgSng5ULjy9ex8btdbx/8mh6dS/9f/u887hhTBzRj4cWrObVDdtLfnxJkpozBKrLe21HHU+8tJ4TRvdn/JA+ZakhInjfKaPp37OKGbOXs323C0UkSeVlCFSX98uFawA4Z9LwstbRs3sFl085gm276/nR3OU0Jh8vJ0kqH0OgurTlG3fw7PLXeOtRQxjYq3u5y2HUwJ6cf/wIFq/ZxuOL15W7HElSjhkC1WWllLj/uVX0rq7k7Ud3nMUYU8cN4oTR/Xl44Rq/HyhJKhtDoLqs51du4dWNO3jXcYdRXVVR7nL+KCK46KRR9O9VxT1za6mr9/6BkqTSMwSqS6pvaOTB51cxvF8PTh07sNzlvE51VQXvO2U0G7bX8fDC1eUuR5KUQ4ZAdUnPLHuNTTv2cO4bhtOtg96c+cihfTht/CB+9/IGlq53WliSVFqGQHU5jSnx+EvrGDWgJxOGleeWMK11zqThDOzdnR//wWlhSVJpGQLV5SxYuYUN2+t429FDO/wj2qorC9PCG7fX8eACp4UlSaVjCFSXklLi8cXrGNy7O5NG9it3Oa0ybkhv3nzkYJ5a4rSwJKl0DIHqUpas386K13Zy+oShHfa7gC1518ThDOxVxc+fXUF9o9PCkqT2ZwhUl/LY4nX0qa7k5DEDyl1KUbpXduOCE0eydutunnxpfbnLkSTlgCFQXcaK13ZSs3YbbzlyMFUVne+f9rHD+zFpZD8eWbSWjdvryl2OJKmL63y/KaV9eHzxOqoruzF1/OByl3LQzj9+BEFw77yVJJ8tLElqR4ZAdQkbt9fx/IrNTB03iB4d6OkgxRrQqzvvPG4Yi9ZsZcHKLeUuR5LUhRkC1SXMfmUjAG86ckiZKzl0bzpyCCP69+AX81eye09DucuRJHVRhkB1evWNjcx5dRPHjuhH/55V5S7nkFV0C6adOJItu+r5zaK15S5HktRFGQLV6S1cuYXtu+uZMnZQuUtpM2MG9+aUMQN4smYD67ftLnc5kqQuyBCoTu/ppRsZ2KuKCYd17EfEFetdk4ZTURHc/9yqcpciSeqCDIHq1GrWbmPJ+u28ceygTnVz6Nbo16OKM48Zxourt7J4zdZylyNJ6mIMgerU7nx6Gd0CTj1iYLlLaRdvPnIwg3t35xfzV/kkEUlSmzIEqtPataeBe+bWMmlkf/r26PwLQlpSWdGN808Ywfptu3nq5Q3lLkeS1IUYAtVp3f/cKjbv3MOUcV1nQUhLjh3ej6MP68OvX1zrIhFJUpsxBKrTumPWMsYP6c34Ib3LXUq7O//4kexpaOQrDy8udymSpC7CEKhOadHqrcx9dROXTRlDdLEFIS0Z2reaqeMHc+fTy1wkIklqE4ZAdUo/+UMtld2Ci04ZVe5SSuasY4bRp7qS6+97odylSJK6AEOgOp36hkZ++swKzjhmKEP6VJe7nJLpVV3J/zlrAo8tXsdji9eVuxxJUidnCFSn8+TLG1i7dTfvO2V0uUspuQ+/aSxHDO7F9fctpL7BW8ZIkg6eIVCdzo/n1tK/ZxVnHjes3KWUXPfKblz77mNZvGYbd8+pLXc5kqROrOQhMCLOjYhFEVETEde0sL06Iu7Kts+KiLFNtl2btS+KiHMONGZE/CAilkbEs9nrpKw9IuJrWf/5EXFK+5612srWXXt4aMFqLjhxBNWVFeUupyzOmTScKWMH8eWHF7F1155ylyNJ6qRKGgIjogL4JvBuYCJwWURMbNbtSmBTSuko4CvADdm+E4HpwCTgXODGiKhoxZifSymdlL2ezdreDUzIXlcB32r7s1V7eOC51eyub+TiHE4F7xUR/MN7jmP9tjq+9ejL5S5HktRJlfpK4BSgJqW0JKVUB8wApjXrMw24NXt/D3BWFO4BMg2YkVLanVJaCtRk47VmzOamAbelgqeAARExoi1OUO3rnj/UMn5Ib04+fEC5SymrE0YP4OKTR/G93y6ldtOOcpcjSeqESh0CRwHLm3yuzdpa7JNSqgc2A4P3s++Bxrw+m/L9SkTsXUramjrUwSzfuIOnl27k4lNG5eLegAfy2XOOIYD/eHBRuUuRJHVCpQ6BLf3mTq3sU2w7wLXAscAbgUHA3xZRBxFxVUTMiYg569Z5S45y+8kfVgBwUY6ngpsaOaAnV71tPDPnreSZZZvKXY4kqZMpdQisBQ5v8nk0sHJffSKiEugPbNzPvvscM6W0Kpvy3Q18n8LUcWvrIKV0U0ppckpp8tChQ4s4TbW1lBI/eaaWN40fzKgBPctdTofxybcfydC+1fzrfS+Q0uv+O0aSpH0qdQicDUyIiHER0Z3CQo+ZzfrMBK7I3l8CPJIKv91mAtOz1cPjKCzqeHp/Y+79nl/2ncILgeebHOPD2Srh04DNKaVV7XPKagt/WPYar27YwcU5ekJIa/SuruSz7zqaua9u4v7nVpe7HElSJ1LSEJh9x+9q4CHgBeDulNKCiLguIt6bdbsZGBwRNcBngGuyfRcAdwMLgQeBT6eUGvY1ZjbWHRHxHPAcMAT416z9fmAJhcUl3wU+1Y6nrTZw77yVdK/sxrlvGF7uUjqcS049nGOH9+WLD77Arj0N5S5HktRJVJb6gCml+ymEsKZtn2/yfhfw/n3sez1wfWvGzNrP3Mc4Cfh0UYWrbBoaE/c9t4ozjxlG3x5V5S6nw6noFvzD+RP54M2zuPV3r/CJtx9Z7pIkSZ2ATwxRhzdryQbWbd3NBSeOLHcpHdZbJwzhzGOH8Y1HatiwbXe5y5EkdQKGQHV4985fSe/uFZx5bP4eE1eMvzvvWHbsaeCrv36p3KVIkjoBQ6A6tLr6Rh54fjVnTzyMnt3z+Zi41jpqWF/+bOoY7pi1jJq1W8tdjiSpgzMEqkP7bc06Xtuxx6ngVvqrsybQq6qCf7v/xXKXIknq4AyB6tDunbeK/j2rOH2C92lsjcF9qrn6zKN45MW1PPGSNziXJO2bIVAd1q49DfxywWre/YbhdK/0n2prXfHmsRw+qCfX3/cCDY3eQFqS1DJ/s6rDeuTFtWyva3AquEg9qiq45tzjeHH1Vn40Z/mBd5Ak5ZIhUB3WvfNWMqRPNaeNH1zuUjqd844fzqlHDOS/Hl7Mtt315S5HktQBGQLVIW3dtYdHXlzLe04YQUW3KHc5nU5E8A/nH8e6rbv5zmMvl7scSVIHZAhUh/TwwjXsrm/kghNHlLuUTuvkMQN574kjuenxJax8bWe5y5EkdTCGQHVI985byagBPTllzMByl9Kp/c25x5CALz20qNylSJI6GEOgOpxN2+t44qX1vOfEEUQ4FXwoRg/sxcfeOo6fPrOCectfK3c5kqQOxBCoDueB51dT35i44ARXBbeFvzjjSIb06c71971ASt4yRpJUUFnuAqTm7p23kvFDezNpZL9yl9Lp/XDWMgDectQQfv7sSv7up89z/Kj+Lfa9fOqYUpYmSSozrwSqQ1m7ZRdPLd3ABSeMdCq4Db1x7CBG9O/B/c+toq6+sdzlSJI6AEOgOpRfzF9FSniD6DbWLYILThjJ5p17eHTx2nKXI0nqAAyB6lDunb+SiSP6cdSwPuUupcsZO6Q3Jx0+gCdeWs+GbbvLXY4kqcwMgeowlm/cwTPLXvMqYDs6d9JwKroF9z23qtylSJLKzBCoDuPe+SsBeM8J3iC6vfTrWcWZxwzjxdVbWbR6a7nLkSSVkSFQHca981ZxypgBHD6oV7lL6dLefNRghvSp5hfzV1Lf4CIRScorQ6A6hJq1W3lh1Rangkugsls3LjhhBBu21/H4S+vLXY4kqUwMgeoQZs5bRbeA8493KrgUJhzWl+NH9efRRWtdJCJJOWUIVNmllPjFvJWcNn4ww/r1KHc5uXH+CSOo6Bb8fN5KnyQiSTlkCFTZLVi5hSXrtzsVXGL9elTxrknDqVm7jfm1m8tdjiSpxAyBKrt7562ksltw7qTh5S4ld6aOG8SoAT2577lVbN65p9zlSJJKyBCosmpsTPxi/iredvRQBvbuXu5ycqdbBBeePIrtu+v50kMvlrscSVIJGQJVVs8s38SK13ZywYkuCCmXUQN68uYjB3PHrGXMfXVjucuRJJWIIVBlNfPZlVRXduPsiU4Fl9M7Jx7GyP49+dw989m1p6Hc5UiSSsAQqLKpb2jkvudWcdZxw+hTXVnucnKturKCG953AkvWbecrv1pc7nIkSSVgCFTZzFq6kfXb6rjgBFcFdwRvnTCEy6YczncfX8Kzy18rdzmSpHZmCFTZzHx2JX2qK3nHscPKXYoy1553HIf168HnfjSP3fVOC0tSV+YcnMqirr6RB55fxbsmHkaPqopyl9Np/HDWsnYdv1+PKv7t4uP58+/P5uu/ruGz5xzTZmMXW/vlU8e02bE7Mn8uksrFK4Eqi8cWr2PLrnpvEN0BveOYYbzvlNF867GXmV/rtLAkdVWGQJXFz55ZweDe3Tl9wpByl6IWfP49EzmsbzV/PeNZdtTVl7scSVI7MASq5Lbs2sPDL6zhghNHUlnhP8GOqH+vKv7rAyexdMN2/uUXL5S7HElSO/A3sEruwedWU1ffyEUnjyp3KdqPNx05mKveNp47n17GLxesLnc5kqQ2ZghUyf3kmVrGD+nNCaP7l7sUHcD/O/sYJo3sxzU/eY61W3aVuxxJUhsyBKqkVry2k6eWbOTCk0cREeUuRwfQvbIbX51+Ejvq6vnsPfNpbEzlLkmS1EYMgSqpmc+uBODCk5wK7iyOGtaXvz9/Io8vXsd3n1hS7nIkSW3EEKiSSSnx02dqOfWIgYwZ3Kvc5agIH5w6hvOPH8F/PLSIWUs2lLscSVIbMASqZF5YtZXFa7ZxoQtCOp2I4IvvO54jBvXi6jufYe1Wvx8oSZ2dIVAl87NnV1DZLXjP8SPKXYoOQt8eVdz4wVPYumsP/+fOZ6hvaCx3SZKkQ2AIVEk0NCZ+/uwKzjhmGAN7dy93OTpIxw7vx/UXHs9TSzby5YcXl7scSdIhMASqJH5bs541W3Z7b8Au4H2njuayKYdz46Mv8+Dzq8pdjiTpIBkCVRJ3z1nOwF5VvHPisHKXojbwhQsmcfKYAfzfu+bx/IrN5S5HknQQSh4CI+LciFgUETURcU0L26sj4q5s+6yIGNtk27VZ+6KIOOdAY0bEHVn78xFxS0RUZe1nRMTmiHg2e32+fc863zZtr+PhBWu48ORRVFdWlLsctYEeVRV850OnMrBXFR+7dY43kpakTqikITAiKoBvAu8GJgKXRcTEZt2uBDallI4CvgLckO07EZgOTALOBW6MiIoDjHkHcCxwPNAT+FiT4zyRUjope13X9mervX76zArqGhq59I2Hl7sUtaFhfXvwvSveyJZde/j4bXPYtaeh3CVJkopQ6iuBU4CalNKSlFIdMAOY1qzPNODW7P09wFlReLTENGBGSml3SmkpUJONt88xU0r3pwzwNDC6nc9PzaSUuHvOck4Y3Z9jh/crdzlqYxNH9uMrl57E/BWb+eyP5lH4v5okqTModQgcBSxv8rk2a2uxT0qpHtgMDN7PvgccM5sG/hDwYJPmN0XEvIh4ICImHewJaf/m127mxdVb+cBkrwJ2VedMGs7fnHMsv5i/in9/4MVylyNJaqXKEh+vpYfFNr90sK8++2pvKcg2H/NG4PGU0hPZ5z8AR6SUtkXEecDPgAmvKzbiKuAqgDFjxrRwGB3IXXOW06OqG+89aWS5S1E7+uTbx7Nq805uenwJg3p355NvP7LcJZXVD2ctK3cJknRApb4SWAs0vSQ0Gli5rz4RUQn0BzbuZ9/9jhkRXwCGAp/Z25ZS2pJS2pa9vx+oioghzYtNKd2UUpqcUpo8dOjQ4s5U7Kxr4N5nV3LeG0bQr0dVuctRO4oI/umCSbznhBF88YEXuXv28gPvJEkqq1KHwNnAhIgYFxHdKSz0mNmsz0zgiuz9JcAj2Xf6ZgLTs9XD4yhcuXt6f2NGxMeAc4DLUkp/fLxBRAzPvmdIREyh8HPwgaht7P7nVrF1dz0fcEFILnTrFnz5Aydx+oQhXPOT+Ty0YHW5S5Ik7UdJQ2D2Hb+rgYeAF4C7U0oLIuK6iHhv1u1mYHBE1FC4endNtu8C4G5gIYXv9n06pdSwrzGzsb4NHAb8vtmtYC4Bno+IecDXgOnJb7S3ubvmLGfs4F5MHTeo3KWoRLpXduPbHzyV40cP4C/vfIbHFq8rd0mSpH0o9XcC906/3t+s7fNN3u8C3r+Pfa8Hrm/NmFl7i+eXUvoG8I2iCldRXl63jaeXbuRz5xxDdtFVOdG7upIffOSNXP69WXz8tjl850On8o5jvEm4JHU0PjFE7eL2379K94purgrOqYG9u/PDj01lwrA+fOK2ufz6hTXlLkmS1IwhUG1u6649/GjOcs4/YQRD+1aXuxyVSSEInsaxI/ryyf+Zy8KVW8pdkiSpCUOg2tyP59ayva6Bj7x5bLlLUZn171XF7VdOZdLI/vzw6Vf5w7JN5S5JkpQxBKpNNTYmbvv9q5x0+ABOPHxAuctRB9C/ZxW3XzmFsUN6c8/cWh55ca1PFpGkDsAQqDb1RM16lqzf7lVA/Ym+Par4yJvHctLhA/jVC2v46TMraGg0CEpSOZV8dbC6th88uZQhfao57/gR5S5FHUxlt268/9TRDOhZxaOL17Fl1x6mv3EMPaoqyl2aJOWSVwLVZl5Zv51HF6/jz6aOoXul/7T0ehHBuyYN58KTRlGzdhvf/E0NqzbvLHdZkpRL/qZWm7nt969SEcGfTfU5y9q/KeMGceVbx1PX0Mi3H3uZZ1wwIkklZwhUm9i2u54fzVnOecePYFi/HuUuR53AuCG9ufodRzF6YC9+NLeWnz27gj0NjQfeUZLUJgyBahN3PPUqW3fXc+Vbx5W7FHUifXtU8dG3jOP0CUN4eulGvv7IS7y6YXu5y5KkXDAE6pDtrGvgu08s4fQJQ7wtjIpW0S149xtG8NG3jKO+MXHT40u4b/5KdtY1lLs0SerSDIE6ZDNmL2P9tjr+8swJ5S5FndhRw/rwV2dOYMq4QTz58gbe/dXHeXjhGu8pKEntxBCoQ7K7voHvPLaEKeMGMWXcoHKXo06uuqqCaSeN4sq3jqNbt+Djt83h0pueYt7y18pdmiR1Od4nUIfkx3NXsHrLLr70/hPKXYoO0Q9nLSt3CX905NA+XPPuY5kxezn//fBipn3zSS44cSR/8fYjmTiyX8nr6Ug/m46kvX8ul3unAaldGQJ10PY0NHLjozWcePgA3nrUkHKXoy6mqqIbHzrtCC48aSTfeWwJN/92KffOW8lp4wfx0beM46zjDqOiW5S7TEnqtAyBOmgzn11J7aad/PN7JxHhL2O1j749qvjsOcfw8dPHM2P2Mm793StcdftcDh/Uk/OPH8m73zCcE0b3L9u/wZQSu/Y0sr2unsaUoPA/AujZvYKeVRVUVvjNG0kdjyFQB6WhMfHNR2s4bkQ/zjx2WLnLUQ7071XFJ95+JFe+dRwPLVjDjNnL+N4TS/j2Yy8zsn8Pzp54GKccMZATRw/giMG92iQUbt9dz5otu1iybhtbdtWzddcetuzc87/vsz/3NOx/8Ur3im70rq5gSJ9qhvSpZmjfaob1rWb0wF4+XUdS2RgCdVB+NGc5S9Zt51t/dopXAVVSlRXdOP+EEZx/wghe21HHr19Yy4MLVnPXnOXc+vtXAejfs4pJI/sxckBPDutXzWH9ejC4dzWVFUFFBBXdgkRi6676Pwa5zTv2sGbLLtZs2c2arbtYu2U323bXv+74VRVBvx5V9OtZxeiBPenXox/9elTSu7qSbhFEFB6P19iY2LmnofCqa2DLrj1s2FbH3GWbqKsv3BS7W8DIAT05YlAvxg3pzZHD+lBd6bOUJZWGIVBF27a7nv/85WImHzGQc98wvNzlKMcG9OrO+04dzftOHU19QyOL12xjfu1rzKt9jRdWbeXJmvWs3bqbhsYD32ame0U3hmWB8bjh/Xj70YX3h/Wr5vkVW+jbo5J+Paqorux2SP/hk1Jiy656Vm/eyasbdvDKhh3MWrqRJ1/eQEW3YPyQ3hw3oh/HDu/LgF7dD/o4knQghkAV7cbf1LB+226+d8VkrwKqw6is6MbEkf2YOLIf06f876rSxsbEhu11bNxeR31jI42N0JDde7BPdSX9eh443O2sa7tVsBFB/55V9O9ZxTHDCyud6xsaWbZxBy+u3soLq7Ywc95KZs6DsYN7c/KYARw/qj89qrxCKKltGQJVlOUbd/C93y7lwpNGcpJPB1En0K1bMLRv4Xt4HVVlRTfGD+3D+KF9OO/4EazbupvnVmzm2eWb+OkzK7h33kqOHdGPkw8fwJ6GRqpcaCKpDRgCVZQbHnyRbgF/c+6x5S5F6rKG9q3mzGOH8Y5jhlK7aSfPLH+N+bWv8fyKzdz33CouOGEEF58yuqyroiV1fv+/vTuPr6us8zj++d0t96a52dOmSVvaphu0QEsZFlGoUFkVEFwQdxHQGcVBXqKo46jDaxSYQZkZBlccUaEsgq0glLKJqJRCKaWleyltmjRd0mzNdm/yzB/nNNyGJE3aJDft/b5fr/O65zzn3HOe88vNub/7nOVREij99spbtTy6sprrzp5CWX4s3dUROeqZGeMLsxlfmM1Fx49lfU0jtfvauW+ZdxNMRckoLjtpHJfOKadc/5MiMkBKAqVfOjsd3390DaPjWVx7VkW6qyOScYIB49ixuVx56gTqWxL86fVqHlm+ndsWr+M/nlzHaZOKuOykci44fiw5WTq0i8jB6Ugh/XLP37fw2rY6/vPDJzJKXzAiaZUXC/OxUybwsVMmsK22mUde3c7Dyyv52kMr+ZeFqzhvZimXzinnjIpiPYdQRHqlb3M5qDd37+OHT6xl3vQSLjupPN3VEZEU4wuzue6cqXz57Cks31rHw8sreXRlNQtXVBGPhjh7xmjOm1nKWdNK9ANORA6gI4L0qaPTccMDK8gKBbnl8hN0EbrICGVmzD2mgLnHFPCdDxzHX9bvZvHqHTy1poaFK6qIBAPMPaaAd08t5t1TiplVnqe+l0UynJJA6dPP/7KZ5VvruOOK2YzJjaa7OiLSD1mhIPOPG8P848aQ7Ohk2Za9PLO2hhc27uG2xeu4bfE64tEQJ47LZ/Z4bzhhfB4lOVn6oSeSQZQESq/W7Wjk9ifXc8GsUi4+sSzd1RGRQxAKBji9oojTK4oA2N3Uxt827eHFzXtYsbWOu/68qatHlYLsMFPHxJk2JoeKkhzGFWRTnh+jvCBGXiyczt0QkSGgJFB61Jbs4IYHVxCPhrj50llqHRA5ShTnZHHxiWVdP+ya25Os2t7A69vr2VDTyPqaRha+WkVjt36T41khygtiXUlheX6Mzbv3ke/3fhKPhnV6WeQIoyRQ3sE5xzcfXsWq7Q387JNzKcoZuT0tiMjhyY6EOGVSIadMKuwqc86xu6md7XUtbN/bwva6Zv+1he11rSzbUktD64FJYsAgHg13dYlXkB2hOCdCcU4WxfEsRkWC+jEpMsIoCZR3+Onzm/n98kqunz+Nc2eWprs6Igd179KB9e175akTDr5QhuopljlZYaaXvt3XMUBrooP6loQ3NCeoa2mnviVBXUuCqroW3qhu6DrNDBALBxmbH6UsL0ZZfpSy/JiuQRyh9P+UOZQEygEWr97BLU+s5QMnlnHdOVPSXR0RGaGi4SDRcLDXG8Y6naOuOcGuxjZ2N7VR09BKdX0rL27eQ9JPDrMjQSYUZjOxaBQTi7IpL8jWKWWRYaQkULqsrqrn+vtXcEJ5Hrd9SI+DEZFDFzCjcFSEwlERphPvKu/odOxqaqOytpm3apt5a88+1u5oBCAaDlBRksO00XGmjslJV9VFMoaSQAFgW20zV//6ZfJiYX7+qZOJhoPprpKIHIWCAaM0N0ppbpSTJ3rXITa1Jdm8q4kNO5vYUNPI6qoGABa/UcOFs0q5YNZYJhRlp7PaIkclJYHChppGPvHLpbQmOrn36lMZrecBisgwyskKccK4fE4Yl49zjp2NbaytbqC6oZUfPL6WHzy+lplluVx4/FgumFXK5BK1EooMBiWBGW7V9no+dfdLBMy4/9rTmJFy4beIyHAzM8bkRhmTG+XKU72+kZ9YtYM/raruetD1jNI4F8way8Wzy5hUPCrdVRY5YikJzGDLttTyuV8tIzcW5nefP5WJOpiKyAgzvjCbq8+czNVnTqa6vsVLCF+v5sdPr+dHT63nxHF5XDK7nPefMFZnMUQGSElgBnLOcf+ybXz3j6spy4vx28+fSll+LN3VEhHp09i8GJ89YxKfPWMS1fUt/PG1KhauqOL7j77BzY+9vqqRYgAAD4VJREFUwbsqirl4dhnnzyolN6oeTkQORklghqlvSfDNh1/nsderOWNKEXdcMYdiPQxaRI4wY/NiXHNmBdecWcHGnY0sWlHFwtequPGhlXz7D6s4e/poLp1Txrzpo3Wjm0gvlARmkFfequW6+1ZQ09DK18+fwbVnTiagZ3KJyBFuyug4Xz13Ote/bxorttWxcEUVj66s5onVO4hnhTh/VikXzy7jtMlFhIOBdFdXZMRQEpgBqutbuP3J9Ty0vJJxBTEe/MLpzJlQkO5qiYgMKjNjzoQC5kwo4NsXHcvfN+9h4YoqHl+1gwdfqSQ/O8z8Y8dwwaxS3j21mKyQWgglsykJPIo1tCa467lN3P3CmzgHV79nMl8+ewpxXSsjIke5UDDAe6aW8J6pJdx86Sz+vH4XT6zaweLVO3jolUqyI0HOmFLM2TNG897poynN000lknmUBB6FNtQ0ct9L2/j98krqWxJ8cE45X33fNMYX6mGrIpJ5ouEg580s5byZpbQnO/nbpt08taaGZ9fuYskbNQDMKI1zekURp00u4rRJReRl68eyHP2UBB4lave18/SaGh54eRvLtuwlHDTOnVnKF8+qYFZ5XrqrJyIyIkRCAeZNH8286aNxzrFhZxPPrN3JCxt2c99LW/nVX7dgBjNKc5k9Pp854/OZPSGfipKcjOvX2DlHp4NkRyfJTucN/ninc5gZBmzc2UTAvK4CzX8NBwPEwkFikSCRkK7DHKmGPQk0s/OBO4Ag8Avn3A+7zc8C7gHmAnuAjzrntvjzbgKuAjqA65xzi/tap5lNAhYAhcBy4JPOufa+tnGkaE10sLqqgb9s2MWz63axsrIO52BiUTY3XTCDy+eO012/IiJ9MDOmjYkzbUycL5xVQVuyg9e21fP3TXt4+a1aHl1ZxX0vbQUgFg4ybUwO08bEmV4ap2J0DscUZlNeEBux1xZ2dDoaWxM0tCSpb0lQ35KgodV/9ae9smRXWUNLgpqGVto7Okl2OFw/tnPH0xv6nB8KGLFIkFg4SHYkSCwSIp4VIjcWJj87TH4sTJ4/npcd8cb3T8fCxKPhjEvAh8uwJoFmFgTuBN4HVALLzGyRc+6NlMWuAvY656aY2RXALcBHzew44ApgJlAGPGVm0/z39LbOW4AfOecWmNlP/HXf1ds2hnbvD01rooPKvS1srd3Hlt3NrKlu4PXt9WzY2URHp8MMZo/P5yvnTGXe9NGcUJ6nO35FRA5BVijIKZMKOWWS16dxZ6fjzT37WLG1jlVV9ayvaeTZdbt48JXKrveYwdjcKOUFMUbHo5TEsyiJZ1GcEyE3GiY3FiY3GmZUVpBwMEAwYISCRjgQ8F6DAUIBw8xoT3bS3tFJe7KTRMprW7KTtmQHDa1JmlqTNLUlaWxN0NSapLHNK3s7uUt2JXuNrck+9zcYMPL8BCw36iVl5QUxinOyiIQChINGMLD/1atzMGiEAkbADIfXWviuKcV+q6GjsxM6nSPR4WhuT9Ka6KC53Rta2jtoTnivja0JKvc2s7rKq3dze0ev9TSD3GhKohh7e9g/nR+LdCWVqeWxcBAzfSf2ZrhbAk8BNjrnNgOY2QLgEiA1CbwE+K4//hDwP+b9BS8BFjjn2oA3zWyjvz56WqeZrQHOBq70l/m1v967etuGc64/P3qGRGNrgjue2sCefe3sbmpjT5P3urOx7YDlikZFmFWex/xjxzCrPI9TJxVSMCqSplqLiBy9AgGjoiSHipIcLp87rqt8T1Mbm3btY1ttM1trm9lW20xlXQtrdjTw/Po2Gtv6Tr4Gi5nX73I8K0RONEReLExZfpQZY+NdSVNuSsKUGw2Rlx3umpcd6TlBunfp1gHV4+ITyw57X9qTnX7LZDt1zV5iWNecoK4lQX1ze1erZZ1fvn1vizevJUFHZ+9f3ZFggLzsMPGsELHI2y2RsXCA7IhfltJCme23WGaFA4QCftIeMIJ+4r4/iQ8F7O35Qe+0eKfzEuBO53Bd416LbLIrmfcS+qJRWZxeUXTYcTtcw50ElgPbUqYrgVN7W8Y5lzSzeqDIL3+x23vL/fGe1lkE1Dnnkj0s39s2dh/ynh2mUCDA75ZupTgeoWhUFmX5UY4vz6O8IMaEwmzGF2YzoTCb4pyIftWIiKRRUU4WRTlZXS2G3bW0d7BnXxuNrUnvFGtrkub2JIkOLxlI7L+2rsOR6PRPuzrvesVIKEAkaF3j4WCASDBANBwkHg0Rj4bIyQqTEw2RHQ4eNWd+IqFAVyvqQDjnaGpLdiWNXcni/kSypZ365gSNbUla/RbJ+pYEO+qTXa2TLX5r5XA6a1pJRiaBPX1au6fwvS3TW3lPV5z2tXx/64GZXQNc4082mdm6Ht7Xl2LSmFhmCMV46KUlxh8/Qtd9iPod4xFY9yEzyPuqY8UQSfk7Kcb9dA9wz1WH9NaBxPiYgy0w3ElgJTA+ZXocUNXLMpVmFgLygNqDvLen8t1AvpmF/NbA1OV728YBnHM/A342wH3sYmYvO+dOPtT3y8EpxkNPMR56ivHQU4yHnmI89AY7xsN93/YyYKqZTTKzCN6NHou6LbMI+LQ//iHgGf9avUXAFWaW5d/1OxV4qbd1+u951l8H/joXHmQbIiIiIhlhWFsC/evvvgQsxnucy93OudVm9n3gZefcIuCXwG/8Gz9q8ZI6/OUewLuJJAn8k3OuA6Cndfqb/DqwwMxuBl71101v2xARERHJFKYGsKFjZtf4p5RliCjGQ08xHnqK8dBTjIeeYjz0BjvGSgJFREREMpD6chERERHJQEoCD5GZfdjMVptZp5md3G3eTWa20czWmdl5KeXn+2UbzewbKeWTzGypmW0ws/v9G1ykD73FUvrHzO42s51mtiqlrNDMlvifwyVmVuCXm5n9lx/rlWZ2Usp7Pu0vv8HMPt3TtjKRmY03s2fNbI1/nPiKX64YDxIzi5rZS2b2mh/j7/nlPR5P/ZsK7/djvNTMJqasq8djtnjMLGhmr5rZo/60YjzIzGyLmb1uZivM7GW/bOiPF845DYcwAMcC04HngJNTyo8DXgOygEnAJrwbVoL++GQg4i9znP+eB4Ar/PGfAF9M9/6N5KGvWGrodwzPBE4CVqWU3Qp8wx//BnCLP34h8Dje8zVPA5b65YXAZv+1wB8vSPe+jYQBGAuc5I/HgfX+sUExHrwYG5Djj4eBpX7sejyeAv8I/MQfvwK43x/v8Zid7v0bSQPwVeBe4FF/WjEe/BhvAYq7lQ358UItgYfIObfGOdfTw6O7urdzzr0J7O/erqvLPOdcO7C/ezvD697uIf/9vwYuHfo9OKL1GMs01+mI4px7nnc+G/MSvM8fHPg5vAS4x3lexHv+5ljgPGCJc67WObcXWAKcP/S1H/mcc9XOueX+eCOwBq+nIsV4kPixavInw/7g6P14mhr7h4Bz/ONvb8dsAcxsHHAR8At/uq/vLMV4cA358UJJ4ODrqWu88j7K++reTnrWWyzl8IxxzlWDl8QAo/3ygX6mJYV/SmwOXkuVYjyI/NOUK4CdeF94m+hnd6FAapekinHvfgzcCHT60/3ukhXFeCAc8KSZvWJeb2UwDMeL4e4x5IhiZk8BpT3M+pZzbmEP5TC43dtJzxSz4TXQrhzFZ2Y5wO+Bf3bONVjv/X4rxofAec+KnW1m+cAjeJfpvGMx/1UxHiAzez+w0zn3ipnN21/cw6KK8eE7wzlXZWajgSVmtraPZQctzkoC++Ccm38IbxvM7u2kZ/3pflAGrsbMxjrnqv1TCzv98t7iXQnM61b+3DDU84hgZmG8BPB3zrmH/WLFeAg45+rM7Dm866MG2l2ojie9OwO42MwuBKJALl7LoGI8yJxzVf7rTjN7BO90+ZAfL3Q6ePANZvd20rP+dD8oA5fanWL3bhY/5d+RdhpQ75+aWAyca2YF/l1r5/plGc+/DuqXwBrn3O0psxTjQWJmJX4LIGYWA+bjXXs50O5CeztmZzzn3E3OuXHOuYl4x9lnnHMfRzEeVGY2yszi+8fx/s9XMRzHi3TfEXOkDsAH8bLuNqAGWJwy71t416asAy5IKb8Q7y7BTXinlPeXT8b7h9gIPAhkpXv/RvrQWyw19Dt+9wHVQML/HF+Fd+3O08AG/7XQX9aAO/1Yv86Bd8N/zv/cbgQ+m+79GikD8G680zArgRX+cKFiPKgxPgGvO9CV/hfmd/zyHo+neC1ZD/rlLwGTU9bV4zFbwwHxnsfbdwcrxoMb28l4d0+/Bqze/502HMcL9RgiIiIikoF0OlhEREQkAykJFBEREclASgJFREREMpCSQBEREZEMpCRQREREJAMpCRQR6Qcz+z8zezmN278xpdeG1HJnZl9KQ5VE5AinJFBE5MhwIwf2BiAicliUBIqIiIhkICWBIiKHwMwmmNkCM6s1s2YzW2xm01PmT/RP1X7EzH5qZvVmVmlm3zOzQLd1fdjMNphZi5k9a2Zz/Pd+xp+/Ba/3gH/1y123U8NBM/t3M9tlZjvN7E4zyxr6KIjIkUxJoIjIAJlZIfACMB34AvARYBTwlN+PbapbgSa8vlR/C3yHt/tdxcxOBhYAy/G6o1wE3N9tHR8E6vH6Iz7dH5anzL8BKAM+AdwGXAt85TB3U0SOcqF0V0BE5Ah0PV7SN9s5VwtgZn8FtuD13XlnyrLPO+du8MeXmNn5wGXAA37Z14E1wBXO68fzCTMLA7fsX4Fz7lUzSwKVzrkXe6jPFufcZ/zxxWZ2hr+NWw97T0XkqKWWQBGRgZsPLAEazCxkZiGgEXgFOLnbsk92m34DGJcy/Q/AH92BHbkvGmB9DrYNEZF3UBIoIjJwxcBHgUS34b3A+G7L1nWbbgeiKdOlwK5uy3SfPpiDbUNE5B10OlhEZOBq8Vrr/q2HeY0DXNcOoKRbWfdpEZFBpyRQRGTgnsa7GWS1c67lMNe1DPiAmX0z5ZTwxT0sp9Y9ERlUSgJFRAbudrw7cZ8xs/8GtgNjgLOAF5xz9w1gXbcAS4EFZvYr4Fjgan9eZ8pya4GLzOwJvLuN1znnBtrqKCLSRdcEiogMkHNuN3AaXmL2I7wbM24F8oCVA1zXy8DHgLnAH4DLgS/6sxtSFv0asA94DK/1cO6h74GICNiBN6SJiEi6mdkngN8Ak51zb6a7PiJydNLpYBGRNDOzu/AeObMXOAn4NvCYEkARGUpKAkVE0q8I+F//dQ9ejyE3prVGInLU0+lgERERkQykG0NEREREMpCSQBEREZEMpCRQREREJAMpCRQRERHJQEoCRURERDKQkkARERGRDPT/Lw5DU2Vj7RoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fasta_stats.plot_length_distribution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fasta_stats.plot_gc_content_distribution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fasta_stats.plot_kmer_distribution(kmer_size=4)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/hw8/code/classes.py b/hw8/code/classes.py
new file mode 100644
index 0000000..2369b94
--- /dev/null
+++ b/hw8/code/classes.py
@@ -0,0 +1,185 @@
+from Bio.Seq import Seq
+from Bio import SeqIO
+from Bio.SeqUtils import GC
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+class Employee:
+
+    def __init__(self, name, age, work_experience):
+        self.name = name
+        self.age = age
+        self.work_experience = work_experience
+
+    def __str__(self):
+        return 'Information about employee: {}'.format(self.name)
+
+    def proposed_position(self):
+        '''
+        Shows the position the candidate is applying for
+        :return: str, position name
+        '''
+        if self.work_experience <= 1:
+            position = 'Junior DS'
+        elif 1 < self.work_experience <= 3:
+            position = 'Middle DS'
+        else:
+            position = 'Senior DS'
+        return position
+
+    def proposed_salary(self, position):
+        '''
+        Shows salary the candidate wants to have
+        :param position: str, name of the position
+        :return: int, salary in dollars
+        '''
+        if position == 'Junior DS':
+            salary = 1000
+        elif position == 'Middle DS':
+            salary = 5000
+        else:
+            salary = 10000
+        return salary
+
+    def view_info(self):
+        '''
+        Shows basic information about candidate
+        :return: str with information
+        '''
+        position = self.proposed_position()
+        salary = self.proposed_salary(position)
+        return f"Name: {self.name}\nAge: {self.age} years\nPosition: {position}\nSalary {salary} $"
+
+
+class SetModified(set):
+
+    def __init__(self, *args):
+        self.positive_numbers = [arg for arg in args if isinstance(arg, (int, float)) and arg > 0]
+        super().__init__(self.positive_numbers)
+
+    def add(self, element):
+        if isinstance(element, (int, float)) and element > 0:
+            super().add(element)
+
+    def update(self, *elements):
+        for element in elements:
+            self.add(element)
+
+
+class RNA:
+
+    def __init__(self, rna):
+        self.rna = Seq(rna)
+
+    def translation(self):
+        '''
+        Translates the RNA sequence into protein
+        :return: protein sequence
+        '''
+        return self.rna.translate()
+
+    def back_transcription(self):
+        '''
+        Convert RNA sequence into DNA
+        :return: DNA sequence
+        '''
+        return self.rna.back_transcribe()
+
+
+class FastaStats:
+
+    def __init__(self, path):
+        self.path = path
+
+    def __str__(self):
+        return "Path to the file: {}".format(self.path)
+
+    def count_sequences(self):
+        '''
+        Counts number of sequences presented in the file
+        :return: int, number of sequences
+        '''
+        seq_numb = 0
+        with open(self.path) as f:
+            for record in SeqIO.parse(f, "fasta"):
+                seq_numb += 1
+        return seq_numb
+
+    def calc_sequences_length(self):
+        '''
+        Calculates length of sequences presented in the file
+        :return: list with lengths of sequences
+        '''
+        with open(self.path) as f:
+            length_ls = [len(record.seq) for record in SeqIO.parse(f, "fasta")]
+        return length_ls
+
+    def calc_gc_content_per_seq(self):
+        '''
+        Calculates GC content of sequences presented in the file
+        :return: list with GC content per each sequence
+        '''
+        with open(self.path) as f:
+            gc_content_ls = [GC(record.seq) for record in SeqIO.parse(f, "fasta")]
+        return gc_content_ls
+
+    def calc_mean_gc_content(self):
+        '''
+        Calculates mean GC content over all sequences presented in the file
+        :return: int, mean GC content (%)
+        '''
+        return np.mean(self.calc_gc_content_per_seq())
+
+    def calc_k_mers(self, kmer_size):
+        '''
+        Counts k-mer over all sequences in the file
+        :param kmer_size: int, size of k-mers
+        :return: dictionary, where keys - kmers, values - their counts
+        '''
+        k_mer_counts = {}
+        with open(self.path) as f:
+            for record in SeqIO.parse(f, "fasta"):
+                for i in range(len(record.seq)-kmer_size+1):
+                    kmer = str(record.seq[i:i+kmer_size])
+                    k_mer_counts[kmer] = k_mer_counts.get(kmer, 0) + 1
+        k_mer_counts = dict(sorted(k_mer_counts.items(), key=lambda item: item[1], reverse=True))
+        return k_mer_counts
+
+    def plot_length_distribution(self):
+        '''
+        Plots length distribution for sequences presented in the file
+        '''
+        length_ls = self.calc_sequences_length()
+        plt.figure(figsize=(10, 8))
+        sns.distplot(length_ls, bins=30)
+        plt.title('Length distribution', fontsize=15)
+        plt.xlabel('length', fontsize=15)
+        plt.ylabel('frequency', fontsize=15)
+
+    def plot_gc_content_distribution(self):
+        '''
+        Plots GC content distribution for sequences presented in the file
+        '''
+        gc_content_ls = self.calc_gc_content_per_seq()
+        mean_gc_content = self.calc_mean_gc_content()
+        plt.figure(figsize=(10, 8))
+        sns.distplot(gc_content_ls, bins=30)
+        plt.title('GC content distribution', fontsize=15)
+        plt.xlabel('GC content, %', fontsize=15)
+        plt.ylabel('frequency', fontsize=15)
+        plt.axvline(mean_gc_content, color='red', linestyle='--')
+
+    def plot_kmer_distribution(self, kmer_size):
+        '''
+        Plots frequency of k-mers over all sequences
+        :param kmer_size: int, size of k-mers
+        '''
+        k_mer_counts = self.calc_k_mers(kmer_size=kmer_size)
+        plt.figure(figsize=(12, 10))
+        plt.bar(k_mer_counts.keys(), k_mer_counts.values(), width=1)
+        plt.title('{}-mers distribution'.format(kmer_size), fontsize=15)
+        plt.xlabel('{}-mers'.format(kmer_size), fontsize=15)
+        plt.ylabel('counts', fontsize=15)
+        plt.xticks(fontsize=3, rotation=90)
diff --git a/hw8/code/requirements.txt b/hw8/code/requirements.txt
new file mode 100644
index 0000000..9a198d2
--- /dev/null
+++ b/hw8/code/requirements.txt
@@ -0,0 +1,4 @@
+biopython==1.79
+matplotlib==3.1.3
+seaborn==0.10.0
+numpy==1.20.0
diff --git a/hw8/data/example.fa b/hw8/data/example.fa
new file mode 100644
index 0000000..212eb3d
--- /dev/null
+++ b/hw8/data/example.fa
@@ -0,0 +1,182 @@
+>chr4:586253-586327
+tggaggactgaagtggagaagggtttcgtgtgaacagtggttggtcacgagttagtcaagttcaaggcgaaaac
+>chrX:23196751-23196806
+aaaatagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacga
+>chrX:23200761-23201056
+ttgttccccggatagtttagttagttagtttttaatttagttcttgcaaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcgattaacatccaattcagaactggcacggacttggggaatccgactgtttaattaaaacaaagcattgtgatggccctagcgggtgttgacataatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagttagcgcgggtcaacggcgggagtaactatgactctcttaagg
+>chrX:23205821-23206612
+ttatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggaggaaaagaaactaacaaggattttcttagtagcggcgagcaaacagaaaacagttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccgtataacgttaatgattactagatgatgtttccaaagagtcatgttgcttgatagtgcagcacttagtgggtggtaaactccatctaaaactaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaattgtaatattaaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatatcccaaatttatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgataaagtgctgatagatttatatgattacagtgcgttaatttttcggaattatataatgtaacaactcatgcagataccatgaaaggagttggttgcttaaatcatgatcatgtaacaatgtgtaacaactctactgccgaagcaa
+>chrX:23211191-23211370
+atatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggatatctgtggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtgc
+>chrX:23211397-23213166
+ggctgaaaccaagcgatcgcaagatcgttatattggttgaactctagataacatgcagatcgtatggtcttgtaccgacgacagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccacggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagacgcggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctcggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtattttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgtgggccggtactattactttgaacaaattagagtgctaaaagcaggctttaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattgcttttcagatcaagaggtaatggttaatagaagcagtttgggggcattagtattacgacgcgagaggtgaaattcttggaccgtcgtaagactaacttaagacttatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccaactagcaattgggtgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggcgcatggccgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaaagaaagtatcaacgagtatttactagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttggaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtacacaccgcccgtcgctactaccgattgaattatttagtgaggtctccggacgtgatcactgtgacgccttgcgtgttacggttgtttcgcaaaagttgaccgaacttgattatttagaggaagtaaaagtcgtaacaaggtttccgtaggtggacctgcggaaggatcatta
+>chrX:23214452-23217141
+tatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggaggaaaagaaactaacgaggattttcttagtagcggagagcgaaaagaaaacggttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccatataacgttaatgattactagatgatgtttccaaagagccgtgttgcttgatagtgcagcacttagtgggtggtaaactccatctaaaactaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaaattgtaatatttaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatatatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgatgaagtgctgatagatttatatgattacagtgcgttattttttcggaattacataatggcataattatcattgatttttgtgtttgtatgattaacaatgcgaaagattcaggataccttcgggacccgtcttgaaacacgtaccaaggagtctaacatatgtgcaagttattgggatatgaacctaatagcgtaattaacttgactaataatgggattagttttttaccgatttatagctaattaacacaatcccggggcgttctatatagttatgtaaaatgtatatttatattatttacgcctctaactggaacgtaccttgagcatatatgctgtgacccgaaagatggtgaactatacttgatcaggttgaagtcaggcgaaaccctgatggaagaccgaaacagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaaccatctaggagctggttccttccgaagtttccctcaggatagctggtgcattttaatattatataaaataatcttatctggtaaagcgaatgattagaggccttagggtcgaaacgatcttaacctattctcaaactttaaatgggtaagaaccttaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgggccacttttggtaagcagaactggcgctgtgggatgaaccaaacgtaatgttacggtgcccaaattaacaactcatgcagataccatgaaaggcgttggttgcttaaaacagcaggacggtgatcatggaagtcgaaatccgctaaggagtgtgtaacaactcacctgccgaagcaactagcccttaaaatggatggcgcttaagttgtatacctatacattaccgataaagtagatgatttatattacttgtgatataaaatttgaaactttattgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctgcatggaactgccattggtacagatcttggtggtagtagcaaataatcgaatgagaccttggaggactgaagtggagaagggtttcgtgtgaacagtggttgatcacgagttagtcggtcctaagttcaaggcgaaagccgaaaattttcaagtaaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaataattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatccgtttgttattaaatatgggcctcgtgttcatcctggcaacaggaacgaccataaagaagccgtcgagagatatcggaagagttttcttttctgttttatagccgtactatcatggaagtctttcgcagagagatatggtagatgggctagaagagcatgacatatactgttgtgtcgatatttttatcctcggaccttgaaaatttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctctttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttctttctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctcttaaggtagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacga
+>chrX:23223698-23223753
+aaaatagccaaatgcctcgtcatctaatttgtgacgcgcatgaatggattaacga
+>chrX:23239715-23240975
+attgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgaagaataagtgggagatattagacctcggtttgctatcgtcaatgaaataccactactcttattgtttccttacttacttgattaaatggaacgtgtatcatttcctagctattatacggatatatttattatatcttatggtattgggttttgatgcaagcttcttgatcaaagtatcacgagtttgttatataatcgcaaacaaattctttaataaaacgatgcatttatgtatttttgatttgaaaatttggtataactccaattactcaggtatgatccaattcaaggacattgccaggtagggagtttgactggggcggtaagtctctcaaataataacggaggtgtcccaaggccagctcagtgcggacagaaaccacacatagagcaaacgggcaaatgctgacttgatctcggtgttcagtacacacagggacagcaaaagctcggcctatcgatccttttggtttaaagagtttttaacaagaggtgtcagaaaagttaccatagggataactggcttgtggcggccaagcgttcatagcgacgtcgctttttgatccttcgatgtcggctcttcctatcattgtgaagcaaaattcaccaagcgttggaatgttcacccatgcaagggaacgtgagctgggtttagaccgtcgtgagacaggttagttttaccctactaatgacaaaacgttgttgcgacagcattcctgcgtagtacgagaggaaccgcaggtacgaaccaatggcacaatacttgttcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtctccgtgctggactgcaatgataaataaggggcaatttgcattgtatgccttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgggaggatcttcgatcacctgatgccgcgctagttacatataaaagcattatttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagctgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrX:23261771-23262666
+tcgaatcatcaagcaaaggataagcttcagtggatcgcagtatggcagctgctcaaccacttacaacaccttgcctgttacaaaagtcgtttacaattgattctaggctttgtcattgtattaaataatgcttttatatgtaactagcgcggcatcaggtgatcgaagatcctcccaatttactatgttacaaattacattggcatcacatccattgtcgtttataaagtaaattataaactttaaatggtttagaaggcatacaatgcaaattgccccttatttatcattgcagtccagcacggagacgaccttagaggcgttcaggcataatccaacggacgtagcgtcataccactgttcgctcgaacaagtattgtgccattggttcgtacctgcggttcctctcgtactacgcaggaatgctgtcgcaacaacgttttgtcattagtagggtaaaactaacctgtctcacgacggtctaaacccagctcacgttcccttgcatgggtgaacattccaacgcttggtgaattttacaatgataggaagagccgacatcgaaggatcaaaaagcgacgtcgctatgaacgcttggccgccacaagccagttatccctatggtaacttttctgacacctcttgttaaaaactctttaaaccaaaaggatcgataggccgagcttttgctgtccctgtgtgtactgaacactgagatcaagtcagcatttgcccttttgctctatgtgtggtttctgtccgcactgagctggccttgggacacctccgttattatttgagagatgtaccgccccagtcaaactccctacctggcaatgtccttgaattggatcatacctgagtaattggagttataccaaattttcaaatcaaa
+>chrX:23270802-23271160
+tacataaatgcatcgttttattaaagaatttgtttgcgattatataaaaaactcgtgatactttgatcaagaagcttgcatcaaaacccaataccataagatataataaatatatccgtataatggctaggaaatgatacacgttccacttaatcaagtaagtaagaaaacaataagagtagtggtatttcattgacgataccaaaccgaggtctaatatctcccacttattctacacctcttatgtctccttacactgccagattagagtcaagctcaaaagggtcttctttccccgctaattattccaagcccgttcccttggctgtggtttcgctagatagtagatagggacaat
+>chrX:23274813-23274864
+tcgttaatccattcatgcgcgtcactaattagatgtcgaggcatttggcta
+>chrX:23278006-23280010
+cttccatggtagtacggctataaaacagaaaagaaaactcttccgatatctctcgacggcttctttatggtcgttcctgttgccaggatgagcacgaggcccatatttaataacaaacggatactcaacaggttacggaattggaactgtattccctttcgttcaaaattattcaagtgtattataattcgctttgtttatatagttaggcatttttgttttacttgaaaattttcggctttcgccttgaacttaggaccgactaactcgtgatcaaccactgttcacacgaatcccttctccacttcagtcctccaaggtctcattcgattatttgctactaccaccaagatctgtaccaatggcagctccatgtaggcttacgccaaacacttctacgcataccattgtaccttcctactcactaaagtttcaaaatttatattacaagtaatataaatcatctactttagcggtaatgtataggtatacaacttaagcgccatccattttaatggctagttgcttcggcaggtgagttgttcccatgatcaccgtcctgctgtcttaagcaaccaacgcctttcatggtatctgcatgaattgttaatttgggcaccgtaacattacgtttggttcatcccacagcgccagttctgcttaccaaaagtggcccactgggcacattatatcataaccttgaacttcatatcaagaaagttaaggttcttacccatttaaagtttgagaataggttaagatcgtttcgaccctaaggcctctaatcattcgctttaccagataagattattttatataatattaaaatacaccagctatcctgagggaaacttcggaaggaaccagctcctagatggttcgattggtctttcgcccctatactcaattctgacaatcgatttgcacgtcagaactatttcggtcttccatcagggtttcccctgacttcaacctgatcaagtatagttcaccatctttcgggtcacagcatatatgctcaaggtacgttccagttagaggcgtaaataatataaatatacattatacataactatatagaacgccccgggattgtgttaattagctataaatcggtaaaaaactaatcccattattagtcaagttaattacgctattaggttcatatcccaataacttgcacatatgttagactccttgctccgtgtttcaagacgggtcccgaaggtatcctgaatctttcgcattgttaatcatacaagtgcataaaataaacacaaaaatcaatgataattatgccattatataattccgaaaaattaacgtactgtaatcatataaatctatcagcactttatcaaatgaataacatttattctgtgttaaaatgcaagcaatttaattggaataaactataagttatattttatgataaatttggtatacgctaatagattacaatgtccttatatggaaaaaatgcacactattctcataatgttatttaaatattacaattttaatgatgaattgtccataacggatattcaggttcatcgggcttaacctctaagcagtttcacgtactgtttaactctctattcagagttcttttcaactttccctcacggtacttgtttactatcggtctcatggttatatttagttttagatggagtttaccacccacttagtgctgcactatcaagcaacacgactctttggaaacatcatctagtaatcattaacgttatatgggcctggcaccctctatgggtaaatggcctcatttaagaaggacttaaatcgttaatttctcatactagaatattgacgctccatacactgcatctcacatttgccacatagacaaagtgacttagtgctgaactgttttcttttcgctcgccgcttttaaaaaaagcctttttattttcttttcctcctctaattaatatgcttaaattcagggggtagtcccatatgagttgaggttgtatataa
+>chrX:23280997-23282931
+taatgatccttccgcaggttcacctacggaaaccttgttacgacttttacttcctctaaataatcaagttcggtcaacttttgcgaaacaaccgtaacacgcaaggcgtcacagtgatcacgtccggagacctcactaaataattcaatcggtagtagcgacgggcggtgtgtacaaagggcagggacgtaatcaatgcgagttaatgactcacacttactgggaattccaagttcatgttcacaatcccaagcatgaaagtggttcagcggtttacccggacctctcggactagtaaatacacgttgatactttcattgtagcgcgcgtgcagcccaggacatctaagggcatcacagacctgttattgctaaatctcattgttgttagacgcaatttgtctgtttaagaagctagtgtccttataatgggacaaaccaacaggtacggctccacttacataaacacattcaaacacaataagaattttactgccaacatgaatgaaggctacataagcttcagcaccataatcctgaagatatctatttaatatatttgagtctcgttcgttatcggaattaaccagacaaatcactccacgaactaagaacggccatgcgccaccacccatagattcgagaaagagctatcaatctgtcttacacacggacctggtaagttttcccgtgttgagtcaaattaagccgcaggctccactcctggtggtgcccttccgtcaattcctttaagtttcagctttgcaaccatacttcccccggagcccaaaagctttggtttcccgggaagcgactgagagagccataaaagtagctacacccaattgctagctggcatcgtttctggttagaacaagggcggtatctgatcgccgtcgaacctctaactttcgttcttgattaatgaaaacatctttggcaaatgctttcgcttaagttagtcttaagccctcaaactgcttctattaataattacctcttgatctgaaaaccaatgaaagcagaacagaggtcttatttcattatcccatgcacagaatattcaggcatttgaagcctgctttaagcactctaatttgttcaaagtaatagtaccggcccacaataacactcgtttaagagcactaatgcaggtttttaaataggaggaacatatgaaaaaatacaagtatttaatcacatataagaactccaccggtaatacgcttacatacataaaggtacagtactaaccacaattgtaagttgtactacccgtatgaagcacaagttcaactacgaacgttttaaccgcaacaactttaatatacgctattggagctggaattaccgcggctgctggcaccagacttgccctccaattggtccttgttaaaggatttaaagtgtactcattccaattacagggcctcggatatgagtcctgtattgttatttttcgtcactacctccccgagctgggagtgggtaatttacgcgcctgctgccttccttagatgtggtatccgtttctcaggctccctctccggaatcgaaccctgattccccgttacccgttgcaaccagggtagtcctagatactaccattaaaagttgatagggcagacatttgaaagatctgtcgtcggtacaagaccatacgatctgcatgttatctagagttcaaccaatataacgatcttgcgatcgcttggttttagcctaataaaagcacatgtcccataaggttcatgttttaattgcatgtattagctctagaattaccacagttatccaagtaactgttaacgatctaaagaaccataactgatataatgagctttttgcggtttcacttttaattcgtgtgtacttagacatgcatggcttaatctttgagacaagcatataactactggcaggatcaaccagaat
+>chrX:23290787-23290882
+acggttgtttcgcaaaagttgaacgaacttgattatttagaggaagtaaaagtcgtaacagggtttctgtaggtgaacctgcttaaggatcatta
+>chrX:23431983-23433808
+attctggttgatcctgccagtagttatatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggataactgtggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtgcttttattaggctaaaaccaagcgatcgcaagatcgttatattggttgaactctagataacatgcagatcgtatggtcttgtaccgacgacagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccatggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagacgcggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctcggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtattttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgggggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaatgattaatagaagcagtttgggggcattagtattacgacgcgggaggtgaaattcttggaccgtcgtaagactaacttaagcgaaagcatttgccaaagatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccatttagcaattgggcgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggaggctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatggccgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaacagacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgagctgcacgcgcgctacaatgaaagtatcaacgtgtatttactagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttgaaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtaca
+>chrX:23438705-23438879
+tacacaccgcccgtcgctactaccgattgaattatttagtgaggtctccggacgtgatcactgtgacgccttgcgtgttacggttgtttcgcaaaagttgaccgaacttgattatttagaggaagtaaaagtcgtaacaaggtttccgtaggtgaacctgcggaaggatcatta
+>chrX:23440161-23440305
+tatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattagaggaggaaaagaaaataaaaaggctttttttaaaagcggcgagcgaaaagaaaacagttcagcactaagtcactttgtctatgtggca
+>chrX:23440310-23442171
+aaattcatcattaaaattgtaatatttaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatttatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgatgaagtgctgatagatttatatgattacagtgcgttattttttcggaattacataatggcataattatcattgatttttgtgtttgtatgattaacaatgcgaaagattcaggataccttcgggacccgtcttgaaacacgtaccaaggagtctaacatatgtgcaagttattgggatatgaacctaatagcgtaattaacttgactaataatgggattagttttttaccgatttatagctaattaacacaatcccggggcgttctatatagttatgtataatgtatatttatattatttacgcctctaactggaacgtaccttgagcatatatgctgtgacccgaaagatggtgaactatacttgatcaggttgaagtcaggggaaaccctgatgaaagaccgaaacagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaaccatctaggagctggttccttccgaagtttccctcaggatagctggtgcattttaatattatataaaataatcttatctggtaaagcgaatgattagaggccttagggtcgaaacgatcttaacctcttctcaaactttaaatgggtaagaaccttaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgggcctattttggtaagcagaactggcgctgtgggatgaaccaaacgtaatgttacggtgcccaaattaacaactcatgcagataccatgaaaggcgttggttgcttaaaacagcaggacggtgatcatgggagtcgaaatccgctaaggagtgtgtaacaactcacctgccgaagcatttagcccttaaaatggatggcgcttaagttgtatacctatacattaccgctaaagtagatgatttatattacttgtgttataaattttgaaactttagtgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctacatggagctgccattggtacagatcttggtggtggtagcaaataatcgaatgagatcttggaggactgaagtagagaagggtgtcgtgtgaacagtggttgataacgagttagtcagttcaaggcgaaagccgaaaatcttcaagtgaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaataattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatccgtttgttattaaatatgggcctcgtgctcatcctggcaacaggaaccaccataaagaagccgtcgagagatatcggaagagttttcttttctgttttatagacgtactaccatggaagtctttcgcagagagatatggtagatgggctagaagagcatgacatatactgttgtgtcgatatattctcctcggaccttgaaaacttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctaaatagaagttgctcgggtaaatagagtttcta
+>chrX:23454472-23456917
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+>chrX:23472157-23473476
+tcgaatcatcaagcaaaggataagcttcagtggatcgcagtatggcagctgctcaaccacttataacaccttgcctgttacaaaagtcgtttacaattgattctaggcttggtcattgtattaaaaaatgattttatatgtaactagcgcgtcatcatgtgatcgaaaatcctctcaatttactatgttacaaattacattggcatcacatccattgtcgtttataaagtaaattataaactttaaatggtttagaagccatacaatgcaaattgccccttacttatcattgcagtccagcacggatacgaccttagaggcgttcaggcataatccaacggacgtagcgtcataccactgttcgctcgaacaagtattgtgccattggtccgtacctgcggttcctctcgtactacgcaggaatactgtcgcaacaaggttttgtcattagtagggtaaaactaatctgtctcacgacggtctaaacctagctcacgttccctgggtgaacatccaacgcttggtgaattttgctccacatggataggaagagccgacatcgaaggatcaaaaagcgacgtcgctatgaacgcttggccgccacaagccagttatccctatggtaacttttctgacacctcttgttaaaaactctttaaaccaaaaggatcgataggccgagcttttgctgtccctgtgtgtactgaacaccgagatcaagtcagcatttgcccgtttgctctatgtgtggtttctgtccgcactgagctggccttgggacacctccgttattatttgagagatttaccgccccagtcaaactccctacctggcaatgtccttgaattggatcatacctgagtaattggagttataccaaattttcaaatcaaaaatacataaatgcatccttttattaaagaatttgtttgcgatatataatataacaaactcgtgatactttgatcaagaagcttgcatcaaaacccaataccataagatataataaatatatccgtataatggctaggaaattatacacgttccatttaatcaagtaagtaaggaaacaataagattagtggtatttcattgacgataccaaaccgaggtctaatatctcccacttattctacacctcttatgtctccttacactgccagattagagtcaagctcaaaagggtcttctttccccgctaattattccaaggcagttcccttggctgtggtttcgctagatagtagatagggacagtaggaatctcgttaatccattcatgcgcgtcactaattagatgacgaggcatttggctattt
+>chrX:23531371-23531426
+tcgttaatccattcatgcgcgtcactaattagatgacgaggcatttggctatttt
+>chrX:23535025-23537636
+ccttaagacagtcatagttactcccgccgttgacccgcgcttacttgaatttcttcactttgacattcagagcactgggcagaaatcacattgtgtcaacacccgctagggccatcacaatgctttgttttaattagacagtcggatttcccaagtccgtgccagttctgaattgattgttaattgataatcgttataattaataagaagtaattggtttaacccaaatagtattcttaaaaattttagcaagaaagttccacaattggctacgtaactaaactatccggggaacaagtaactaacataaatgctagaaactctatttacccagaacgagcacataaaccatgttattgtttcccaatcaagcccgactatctcaatcttcagagccaatccttatcccgaagttacggatctaatttgccgacttcccttacctacattattctatcgactagagactcttcaccttggagaccagctgcggatattggtacggcctgttgagaagtttgcgtgtccccaccataaattttcaaggtccgaggagaaaatatcgacacaacatctaccatatctctctgcgaaagacttccatgatagtacggctataaaacagaaaagaaaactcttccgatatctctcgacggcttctttatggtggttcctgttgccaggatgagcacgaggcccatatttaataacaaacggatactcaacaggttacggaattggaaccgtattccctttcgttcaaaattattcaagtgtattataattcgctttgtttatatagttaggcatttttgttttacttgaaaattttcggctttctccttgaacttaggaccgactaactcgtgatcaaccactgtttacacgaaacccttctccacttcagtcctccaaggtctcattccattagttgctactaccaccaagatctgtaccaatggcagctccaggcaggcttacgccaaacacttctacgcataccattgtaccttcctacttactaaagtttcaaattttatatcacaagtaatataaatcatctactttaccggtaatgtataggtatacaacttaagcgccatccattttaagggctagttgcttcggcaggtgagttgttacacactccttagcggatttcgacttccatgatcaccgtcctgctgttttaagcaaccaacgcctttcatggtatctgcatgagttgttaatttgggcaccgtaacattacgtttggttcatcccacagcgccagttctgcttaccaaaagtggcccactgggcacattatcataaccttgaacttcatatcaagaaagttaaggttcttacccatttaaagtttgagaataggttaagatcgtttcgaccctaaggcctctaatcattcgctttaccagataagattattttatataatattaaaatgcaccagctatcctgagggaaacttcggaaggaaccagctcctagatggttcgattggtctttcgcccctatactcaattctgacaatcgatttgcacgtcagaactgtttcggtcttccatcagggtttcgcctgacttcaacctgatcaagtatagttcaccatctttcgggtcacagcatatatgctcaaggtacgttccagttagaggcgtaaataatataaatatacattttacataactatatagaacgccccgggattgtgttaattagctataaatcggtaaaaaactaatcccattattagtcaagttaattacgctattaggttcatatcccaataacttgcacatatgttagactccttggtacgtgtttcaagacgggtcccgaaggtatcctgaatctttcgcattgttaatcatacaaacacaaaaatcgatgataattatgccattatgtaattccgaaaaaataacgcactgtaatcatataaatctatcagcacttcattaaattaataacatttattctatgttaaaatgcaagcaatttaattggaataaactataagttatattttatgataaatttggtatatgctaatagattacaatgtccttatatggaaaaaatgcacactattctcataatattatttaaatattacaattttaatgatgaattttccagaacggatattcaggttcatcgggcttaacctctaagcagtttcacgtactgtttaactctctattcagagttcttttcaactttccctcacggtacttgtttactatcggtctcatggttatatttagttttagatggagtttaccacccactaagtgctgcactatcaagcaacacggctctttggaaacatcatctagtaatcattaacgttatatgggcctggcaccctctatgggtaaatggcctcatttaagaaggacttaaatcgttaatttctcatactagaatattgacgctccatacactgcatctcacatttgccatatagacaaagtgacttagtgctgaaccgttttcttttcgctctccgctactaagaaaatcctcgttagtttcttttcctcccctaattaatatgcttaaattcagggggtagtcccatatgagttgaggttgtatataa
+>chr2R:1150691-1150719
+tcagtgacgcgcatgaatggattaacga
+>chr2R:4868897-4868922
+tcgttaatccattcatgcgcgtcac
+>chr3L:25135046-25135071
+tcgttaatccattcatgcgcgtcac
+>chrUn_CP007120v1:3735-3783
+tcgaatcatcaagcaaaggataaccttcagtggagccaattttgtagc
+>chrUn_CP007120v1:4492-5312
+gtaaaactaacctgtctcacgagggtcttaacccagctcacgttcccttgcatgggtgaacaatccaacgcttggtaaattttgcttcacaatattaggaagagccgacatcgaaggatcaaaaagcgacgtcgctatgaacgcttggccgccacaagccagttatccccatggtaacttttcagacacctcttgttaaaaactctttaaaccaaaaggatcgataggccgagcttttgctgtccctgtgtttactgaacaccgagatcaagtcagcatttgcccttttgctctatgtgtggtttctgtccgcactgagttggccttgggacacctgcgttattatttgagagatgtaccgagtcaatttccttgaattggatcatacctgagcaattggagttataccaaattttcaaatcaaaaatacataaatgcatcgttttattaaagaatttgtttgcgattatataacaaactcgtgatactttgatcaagaagcttgcatcaaaacccaataccataagatataataaatatatccgtataatggctaggaaatgatacacgttccatttaatcaagtaagtaaggaaacaataagagtagtggtatttcattgacgacaccaaaccgaggtctaatatctcccacttattctacacctcttatgtctccttacactgccagattagagtcaagctcaaaagggtcttctttccctaatacttattccaagcccgttcccttggctgtggtttcgctaaatagtagatagggacagtaggaatcccgttaatccattcatgcgcgtcactaa
+>chrUn_CP007120v1:16936-19585
+tatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattagaggaggaaaagaaactaaaaaggattttcttaaaagcggcgagcgaaaagaaaacagttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccgtataacgttaatgattactagatgatgtttccaaagagtcgtgttgcttgatagtgcagcactaagtgggtggtaaattccatctaaagctaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaattgtaatattaaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatttatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgatagagtgctgatagatttatatgattacagtgcgtaaatttttcggaattatataatggcataattatcattgatttttgtgtttattatatgcacttgtatgattaacaatgcgaaagattcaggataccttcgggacccgtcttgaaacacggaccaaggagtctaacatatgtgcaagttatcgggatataaacctaatagcgtaattaacttgactaataatgggattagttttttaccgatttatagctaattaacacaatcccggggcgttctatatagttatgtaaaatgtatatttatattatttacgcctctaactggaacgtaccttgagcatatatgctgtgacccgaaagatggtgaactatacttgatcaggttgaagtcaggcgaaaccctgatggaagaccgaaacagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaaccatctaggagctggttccttccgaagtttccctcaggatagctggtgcattttaatattatataaaataatcttatctggtaaagcgaatgattagaggccttagggtcgaaacgatcttaacctattctcaaactttaaatgggtaagaaccttaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgggccacttttggtaagcagaactggcgctgtgggatgaaccaaacgtaatgttacggtgcccaaattaacaactcatgcagataccatgaaaggcgttggttgcttaaaacagcaggacggtgatcatggaagtcgaaatccgctaaggagtgtgtaacaactcacctgccgaagcaactagcccttaaaatggatggcgcttaagttgtatacctatacattaccgataaagtagatgatttatattacttgtgatataaaatttgaaactttattgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctgcatggaactgccattggtacagatcttggtggtagtagcaaataatcgaatgagaccttggaggactgaagtggagaagggtttcgtgtgaacagtggttgatcacgagttagtcggtcctaagttcaaggcgaaagccgaaaattttcaagtaaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaataattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatccgtttgttattaaatatgggcctcgtgctcatcctggcaacaggaaccaccataaagaagccgtcgagagatatcggaagagttttcttttctgttttatagccgtactaccatggaagtctttcgcagagagatatggtagatgggctagaagagcatgacatatactgttgtctcgatatattctcttcggaccttgaaaacttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcgacaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattacttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttgggaaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactgtcttaagg
+>chrUn_CP007120v1:22812-22940
+aaatagccaaatgcctcttcatctaattagtgacgcgcatgaatggattaacgagattcctactgtccctatctactatctagcgaaaccacagccaagggaactgccttggaataattagcggggaa
+>chrUn_CP007120v1:35940-36403
+ataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctcttaaggtagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacgagattcc
+>chrUn_CP007120v1:37480-38740
+attgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgtagaataagtgggagatattagacctcggtttggtatcgtcaatgaaataccactactcttattgtttccttacttacttgattaaatggaacgtgtatcatttcctagccattatacggatatatttattatatcttatggtattgggttttgatgcaagcttcttgatcaaagtatcacgagtttgttatataatcgcaaacaaattctttaataaaacgatgcatttatgtatttttgatttgaaaatttggtataactccaattactcaggtatgatccaattcaaggacattgccaggtagggagtttgactggggcggtacatctctcaaataataacggaggtgtcccaaggccagctcagtgcggacagaaaccacacatagagcaaaagggcaaatgctgacttgatctcggtgttcagtacacacagggacagcaaaagctcggcctatcgatccttttggtttaaagagtttttaacaagaggtgtcagaaaagttaccatagggataactggcttgtggcggccaagcgttcatagcgacgtcgctttttgatccttcgatgtcggctcttcctatcattgtgaagcaaaattcaccaagcgttggattgttcacccatgcaagggaacgtgagctgggtttagaccgtcgtgagacaggttagttttaccctactaatgacaaaacgttgttgcgacagcattcctgcgtagtacgagaggaaccgcaggtacggaccaatggcacaatacttgttcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtatccgtgctggactgcaatgataaataaggggcaatttgcattgtatggcttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgggaggatcttcgatcacctgatgccgcgctagttacatataaaagcattatttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagctgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrUn_CP007120v1:43483-45478
+attctggttgatcctgccagtagttatatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggataactgtggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtgcttttattaggctaaaaccaagcgatcgcaagatcgttatattggttgaactctagataacatgcagatcgtatggtcttgtaccgacgacagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccatggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagaaacggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctcggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtatcttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgtgggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaatgattaatagaagcagtttgggggcattagtattacgacgcgagaggtgaaattcttggaccgtcgtaagactaacttaagcgaaagcatttgccaaagatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccagctagcaattgggtgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatggccgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaatgaaagtatcaacgtgtatttcctagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttggaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtacacaccgcccgtcgctactaccgattgaattatttagtgaggtctccggacgtgatcactgtgacgccttgcgtgttacggttgtttcgcaaaagttgaccgaacttgattatttagaggaagtaaaagtcgtaacaaggtttccgtaggtgaacctgcggaaggatcatta
+>chrUn_CP007120v1:46770-49489
+ttatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggaggaaaagaaactaacaaggattttcttagtagcggcgagcgaaaagaaaacagttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccgtataacgttaatgattactagatgatgtttccaaagagtcgtgttgcttgatagtgcagcactaagtgggtggtaaactccatctaaaactaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaaattgtaatatttaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatttatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgataaagtgctgatagatttatatgattacagtgcgttaatttttcggaattatataatggcataattatcattgatttttgtgtttattatatgcacttgtatgattaacaatgcgaaagattcaggataccttcgggacccgtcttgaaacacggaccaaggagtctaacatatgtgcaagttattgggatataaacctaatagcgtaattaacttgactaataatgggattagttttttagctatttatagctgctaattaacacaatcccggggcgttctatatagttatgtataatgtatatttatattatttatgcctctaactggaacgtaccttgagcatatatgctgtgacccgaaagatggtgaactatacttgatcaggttgaagtcaggggaaaccctgatggaagaccgaaacagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaaccatctagtagctggttccttccgaagtttccctcaggatagctggtgcattttaatattatataaaataatcttatctggtaaagcgaatgattagaggccttagggtcgaaacgatcttaacctattctcaaactttaaatgggtaagaaccttaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgggccacttttggtaagcagaactggcgctgtgggatgaaccaaacgtaatgttacggtgcccaaattaacaactcatgcagataccatgaaaggcgttggttgcttaaaacagcaggacggtgatcatggaagtcgaaatccgctaaggagtgtgtaacaactcacctgccgaagcaactagcccttaaaatggatggcgcttaagttgtatacctatacattaccgctaaagtagatgatttatattacttgtgatataaattttgaaactttagtgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctgcatggagctgccattggtacagatcttggtggtagtagcaaataatcgaatgagaccttggaggactgaagtggagaagggtttcgtgtgaacagtggttgatcacgagttagtcggtcctaagttcaaggcgaaagccgaaaattttcaagtaaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaataattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatccgtttgttattaaatatgggcctcgtgctcatcctggcaacaggaacgaccataaagaagccgtcgagagatatcggaagagttttcttttctgttttatagccgtactaccatggaagtctttcgcagagagatatggtagatgggctagaagagcatgacatatactgttgtgtcgatattttctcctcggaccttgaaaatttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctcttaaggtagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacgagattcctactccct
+>chrUn_CP007120v1:55964-57959
+attctggttgatcctgccagtagttatatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggataactgtggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtgcttttattaggctaaaaccaagcgatcgcaagatcgttatattggttgaactctagataacatgcagatcgtatggtcttgtaccgacgacagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccatggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagaaacggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctcggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtattttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgtgggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaatgattaatagaagcagtttgggggcattagtattacgacgcgagaggtgaaattcttggaccgtcgtaagactaacttaagcgaaagcatttgccaaagatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccagctagcaattgggtgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatggccgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaatgaaagtatcaacgtgtatttcctagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttggaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtacacaccgcccgtcgctactaccgattgaattatttagtgaggtctccggacgtgatcactgtgacgccttgcgtgttacggttgtttcgcaaaagttgaccgaacttgattatttagaggaagtaaaagtcgtaacaaggtttccgtaggtgaacctgcggaaggatcatta
+>chrUn_CP007120v1:59219-59294
+ttatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggaggaaaagaaactaa
+>chrUn_CP007120v1:62794-63163
+tcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtatccgtgctggactgcaatgataaataaggggcaatttgcattgtatggcttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgggaggatcttcgatcacctgatgccgcgctagttacatataaaagcattatttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagctgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrUn_CP007120v1:67667-69662
+attctggttgatcctgccagtagttatatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggataactgtggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtgcttttattaggctaaaaccaagcgatcgcaagatcgttatattggttgaactctagataacatgcagatcgtatggtcttgtaccgacgacagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccatggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagaaacggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctgggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtattttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgtgggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaatgattaatagaagcagtttgggggcattagtattacgacgcgagaggtgaaattcttggaccgtcgtaagactaacttaagcgaaagcatttgccaaagatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccagctagcaattgggtgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatggccgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaatgaaagtatcaacgtgtatttcctagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttggaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtacacaccgcccgtcgctactaccgattgaattatttagtgaggtctccggacgtgatcactgtgacgccttgcgtgttacggttgtttcgcaaaagttgaccgaacttgattatttagaggaagtaaaagtcgtaacaaggtttccgtaggtgaacctgcggaaggatcatta
+>chrUn_CP007120v1:70954-74924
+ttatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggaggaaaagaaactaacaaggattttcttagtagcggcgagcgaaaagaaaacagttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccgtataacgttaatgattactagatgatgtttccaaagagtcgtgttgcttgatagtgcagcactaagtgggtggtaaactccatctaaaactaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaaattgtaatatttaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatttatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgataaagtgctgatagatttatatgattacagtgcgttaatttttcggaattatataatggcataattatcattgatttttgtgtttattatatgcacttgtatgattaacaatgcgaaagattcaggataccttcgggacccgtcttgaaacacggaccaaggagtctaacatatgtgcaagttattgggatataaacctaatagcgtaattaacttgactaataatgggattagttttttagctatttatagctaattaacacaatcccggggcgttctatatagttatgtataatgtatatttatattatttatgcctctaactggaacgtaccttgagcatatatgctgtgacccgaaagatggtgaactatacttgatcaggttgaagtcaggggaaaccctgatggaagaccgaaacagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaaccatctagtagctggttccttccgaagtttccctcaggatagctggtgcattttaatattatataaaataatcttatctggtaaagcgaatgattagaggccttagggtcgaaacgatcttaacctattctcaaactttaaatgggtaagaaccttaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgggccacttttggtaagcagaactggcgctgtgggatgaaccaaacgtaatgttacggtgcccaaattaacaactcatgcagataccatgaaaggcgttggttgcttaaaacagcaggacggtgatcatggaagtcgaaatccgctaaggagtgtgtaacaactcacctgccgaagcaactagcccttaaaatggatggcgcttaagttgtatacctatacattaccgctaaagtagatgatttatattacttgtgatataaattttgaaactttagtgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctgcatggagctgccattggtacagatcttggtggtagtagcaaataatcgaatgagaccttggaggactgaagtggagaagggtttcgtgtgaacagtggttgatcacgagttagtcggtcctaagttcaaggcgaaagccgaaaattttcaagtaaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaataattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatccgtttgttattaaatatgggcctcgtgctcatcctggcaacaggaacgaccataaagaagccgtcgagagatatcggaagagttttcttttctgttttatagccgtactaccatggaagtctttcgcagagagatatggtagatgggctagaagagcatgacatatactgttgtgtcgatattttctcctcggaccttgaaaatttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctcttaaggtagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacgagattcctacttgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgtagaataagtgggagatattagacctcggtttggtatcgtcaatgaaataccactactcttattgtttccttacttacttgattaaatggaacgtgtatcatttcctagccattatacggatatatttattatatcttatggtattgggttttgatgcaagcttcttgatcaaagtatcacgagtttgttatataatcgcaaacaaattctttaataaaacgatgcatttatgtatttttgatttgaaaatttggtataactccaattactcaggtatgatccaattcaaggacattgccaggtagggagtttgactggggcggtacatctctcaaataataacggaggtgtcccaaggccagctcagtgcggacagaaaccacacatagagcaaaagggcaaatgctgacttgatctcggtgttcagtacacacagggacagcaaaagctcggcctatcgatccttttggtttaaagagtttttaacaagaggtgtcagaaaagttaccatagggataactggcttgtggcggccaagcgttcatagcgacgtcgctttttgatccttcgatgtcggctcttcctatcattgtgaagcaaaattcaccaagcgttggattgttcacccatgcaagggaacgtgagctgggtttagaccgtcgtgagacaggttagttttaccctactaatgacaaaacgttgttgcgacagcattcctgcgtagtacgagaggaaccgcaggtacggaccaatggcacaatacttgttcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtatccgtgctggactgcaatgataaataaggggcaatttgcattgtatggcttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgggaggatcttcgatcacctgatgccgcgctagttacatataaaagcattatttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagctgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrUn_DS483999v1:0-1261
+gcccaaattaccaactcatgcagatcccatgaaaggcgttggttgcttaaaacagcaggacggtgatcatgggagtcgaaatccgctaaggagtgtgtaacaactcacctgccgaagcatttagcccttaaaatggatggcgcttaagttgtatacctatacattaccgctaaagtagatgatttatattacttgtgatataaattttgaaactttagtgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctgcctggagctgccattggtacagatcttggtggtagtagcaaataatcgaatgagaccttggaggactgaagtggagaagggtttcgtgtgaacagttgatcacgagttagtcggtcctcagttcaaggcgaaagccgaaaattttcaagtaaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaattattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatccgtttgttattaaatatgggcctagtgctcatcctggcaacaggaacgaccataaagaagccgtcgagagatatcggaaaagttttcttttctgttttatagccgtactaccatggaagtctttcgcagagagatatggtagatgggctagaagagcatgatatatactgttgtgtcgatatattctcctcggaccttgaaaacttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaagtagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgcttgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctacaatttttaagaatactatttgggttaaaccaattacttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaaaaaattcaagtaagcgcgggtcaacggcgcgagtaactatgactctcttaagg
+>chrUn_DS484096v1:2501-2916
+taatgatccttccgcaggtccacctacggaaaccttgttacgacttttacttcctctaaataatcaagttcggtcaacttttgcgaaacaaccgtaacacgcaatgcgtcacagtgatcacgtccggagacctcactaaataattcaatcggtagtagcgacgggcggtgtgtacaaagggcagggacgtaatcaatgcgagttaatgactcacacttactgggaattccaagttcatgtgaacagtttcagttcacaatcccaagcatgaaagtggttcagcggtttacccggacctctcggtctagtaaatacacgttgatactttcattgtaacgcgcgtgcagcccaggacatctaagggcatcacagacctgttattgctcaatctcattattgctagacgcaatttgtc
+>chrUn_DS484257v1:751-955
+attctggttgatcctgccagtagttatatgcttgtctcaaagattaatccatgaatgtctaaatatacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggatatctgtggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtgc
+>chrUn_DS484257v1:982-2511
+ggctgaaaccaagcgatcgcaagatcgttatattggttgaactctagataacatgcagatcgtatggtcttgtaccgacgacagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccacggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagacgcggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctcggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatccctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtattttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgtgggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaatgattaatagaagcagtttgggggcattagtattacgacgcgggaggtgaaattcttggaccgtcgtaagactaacttaagcgaaagcatttgccaaagatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccagctagcaattgggcgtagctacttttatggctctctcagtcgcttcccggaaaccaaagcttttgggctccgtgggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatggccgttcttagttcgtggagtgatttgtctggttaattccgattacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgttacaatgaaagtatcaacgtgtatttactagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtaa
+>chrUn_DS484776v1:124-137
+tattgtccctatc
+>chrUn_DS484852v1:0-828
+actagcccttaaaatggatggcgcttaagttgtatacctatacattaccgctaaagtatatgatttatattacttgtgatataaattttgaaactttagtgagtaggaaggtacaatggtatgcgtagaagtgtttggcgtaagcctgcatggagctgccattggtacagatcttggtggtagtagcaaataatcgaatgagaccttggaggactgaagtggagaagggtttcgtgtgaacagttgatcacgagttagtcggtcctcagttcaaggcgaaagccgaaaatcttcaagtgaaacaaaaatgcctaactatataaacaaagcgaattataatacacttgaataattttgaacgaaagggaatacggttccactttcgtaacctgttgagtatccgtttgttattaaatatgggcctcgtgctcatcctggcaacaggaaccaccataaagaagccgtcgagagatatcggaagagttttcttttctgttttatagccgtactaccatggaagtctttcgcagagagatatggtagatgggctagaagagcatgatatatactgttgtgtcgatatattctcctcggaccttgaaaacttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatgtaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttcta
+>chrUn_DS484916v1:600-874
+ttgcatcaaaacccaataccataagatgtaataaatatatccatataattgataggaaacgatacacgttccatttaatcaactaagtaaggaaacaataagagtagtggtatttcattgacgatagcaaaccgaggtctaatattatctttccccgctaattattccaagcccgttcccttggctgtggtttcgctagatagtagatagggacagtaggaatctcgttaatccattcatgcgcgtcactaattagatgactaggctttttgct
+>chrUn_DS484960v1:2-397
+aggtcggaccatggcacaatacttgttcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtctccgtgctggactgcaatgataaataaggggcaatttgcattgtatgccttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgagaggattttcgatcacatgatgacgcgctagttacatataaaatcattttttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagccgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrUn_DS484962v1:249-1310
+ttatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggaggaaaagaaactaacaaggattttcttagtagcggcgagcgaaaagaaaacagttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccgtataacgttaatgattactagatgatgtttccaaagagtcgtgttgcttgatagtgcagcactaagtaggtggtaaactccatctaaaactaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaaattgtaatatttaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatttatcataaaatataacttatagtttattccaattaaattgctggcattttaacacagaataaatgttattaatttgataaagtgctgatagagatagagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaatgaaagtatcaacgtgtatttcctagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttggaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtacacaccgcccgtcggctactaccggattgaattatttagtgaggtctccggacggtgatcactgtgacgccttgcgtgttacggttgtttcgcaaaagttgaccgaacttgattatttagaggaagtaaaagtcgtaacaaggtttccgtaggtgaacctgcggaaggatcatta
+>chrUn_DS485019v1:664-725
+atatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaac
+>chrUn_DS485031v1:607-1275
+tcttgcctttatacccgcgcttacttgaatttcttcactttgacattcagagcactgggcagaaatcacattgtgtcaacacccgctagggccatcacaatgctttgttttaattagacagtcggattccccaagtccgtgccagttctgaattgattgttaattgataatcgttataattaataagaactaattggtttaacccaaatagtattcttaaaaattttagcaagaaagttccacaattggctacgtaactaaactatccggggaacaagtaactaacataaatgctagaaactctatttacccagaacgagcacataaaccatgttattgtttcccaatcaagcccgactatctcaatcttcagagccaatccttatcccgaagttacggatctaatttgccgacttcccttacctacattattctatcgactagagactcttcaccttggagaccagctgcggatattggtacggcctgttgagaagtttgcgtgtccccaccataaattttcaaggtccgaggagaaaatatcgacacaacagtatatgtcatgctcttctagcccatctaccatatctctctgcgaaagacttccatggtagtacggctataaaacagaaaagaaaactcttccgatatctctcgacggcttcttt
+>chrUn_DS485228v1:1037-1198
+taatgatccttccgcaggttcacctacggaaaccttgttacgacttttacttcctctaaataatcaagttcggtcaacttttgcgaaacaaccgtaacacgcaaggcgtcacagtgatcacgtccggagacctcactaaataattcaatcggtagtagcga
+>chrUn_DS485280v1:555-1184
+atataattagtgacgcgcatgaatggattaacgagattcctactgtccctatctactatctagcaaaaccacagccaatggaacgggcatggaataattaggcgtgaaagaagacccttttgagcttggatctaatctggcagtgtaaggagacataagaggtgtagaataagtgggagatattagacctcggtttggtatcgtcaatgaaatactcctattgtttccttacttacttgattaaatggaacgtgtatcgtttcctagccattatacgaatatatttattacatcttatggtattgggtattgatgcaagcttcttgatcaaagtatcaccagtttgttatataatcgcaaacaaattctttaataaaacgatgcatttatgtattttttaattttttatttgaaaatttcgaattcaaggaattttgagaatttcgaattcaaggacattgccaggtagggagtttgactggggcggtacatctctcaaataataacggaggtgttccaaggacagctcagtgcggacagaagccacacatagagcaaaagggcaaatactgacttgatctcggtgttcagtacacacagggacagaaaaaggtcggcctatcgatcct
+>chrUn_DS485397v1:179-327
+taatgatccttccgcaggttcacctacggaaaccttgttacgacttttacttcctctaaataatcaagttcggtcaacttttgctcagtgatcacgtccggagacctcactaaataattcaatcggtagtagcgacgggcggtgtgta
+>chrUn_DS485453v1:0-506
+ctagataacatgcagatcgtatggtcttgtaccgacgccagatctttcaaatgtctgccctatcaacttttgatggtagtatctaggactaccatggttgcaacgggtaacggggaatcagggttcgattccggagagggagcctgagaaacggctaccacatctaaggaaggcagcaggcgcgtaaattacccactcccagctcggggaggtagtgacgaaaaataacaatacaggactcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtctggtgccagcagccgcggtaattccagctccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtatcttttcata
+>chrUn_DS485462v1:0-1134
+gagggcaagtctggtgccagcagccgcggtaattccagctccaatagtgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtatcttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgtgggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaatgattaatagaagcagtttgggggcattagtattacgacgcgagaggtgaaattcttggacccgtcgtaagactaacttaagcgaaagcattgccaaagatgttttcattaatcgaaggcgatcagataccgccctagttctaaccataaacgatgccagctagcaattgggtgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatggccgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaatgaaagtatcaacgtgtatttcctagaccgagaggtccgggtaaac
+>chrUn_DS485498v1:0-541
+cagagagatatggtagatgggctagaagagcatgacatatactgttgtgtcgatattttctcctcggaccttgaaaatttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcggggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgccca
+>chrUn_DS485509v1:383-1123
+ctcccttaagagagtcatagttactcccgccgttgacccgcgcttacttgaatttcttcactttgacattcagagcactgggcagaaatcacattgtgtcaacacccgctagggccatcacaatgctttgttttaattagacagtcggattcccgaagtccggtgccagttctgaattgattgttaattgataatcgttataattaataagaactaattggtttaacccaaatagtattcttaaaaattttagcaagaaagttccacaattggctacagtaactaaactatccggggaacaagtaactaacataaatgctagaaactctatttacccagaacgagcacatattgtttcccaatcaagcccgactatctcaatcttcagagccaatccttatcccgaagttacggatctaatttgccgacttcccttacctacattattctatcgactagagactcttcaccttggagaccagctgcggtctgcggaagtttgcgtgtccccaccataaattttcaaggtccgaggagaaaatatcgacacaacagtatatgtcatgctcttctagcccatctaccatatctctctgcgaaagacttccatggtagtacggctataaaacagaaaagaaaactcttccgatatctctcgacggcttctttatggtcgttcctgttgccaggatgagcacgaggcccatatttaataacaaacggatactcaaggggttacg
+>chrUn_DS485668v1:0-564
+tctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtatgcgcgggtcaacggcgggagtaactatgactctcttaaggcctcgtcatctaattagtgacgcgcatgaatggattaacgagattcctactgtccctatctactatctagcgaaaccacagccaagggaacgggctt
+>chrUn_DS485681v1:960-1076
+cccagctcggggaggtagtgacgaaaaataacaatacaggattcatatccgaggccctgtaattggaatgagtacactttaaatcctttaacaaggaccaattggagggcaagtct
+>chrUn_DS485751v1:0-610
+cagagagatatggtagatgggctagaagagcatgacatatactgttgtgtcgatattttctcctcggaccttgaaaatttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctct
+>chrUn_DS485792v1:0-1050
+cctagtcatctaattagtgacgcgcatgaatggattaacgagattcctactgtccctatctactatctagcgaaaccacagccaagggcttggaataattagcggggaaagaagacccttttgagcttggctctaatctggcagtgtaaggagacaaaagaggagtagaataagtgggagatattagacctcggtttgctatcgtcaatgaaatgccactactcttattgtttccttacttagttgattaaatggaacgtgtatcgtttcctatcaattatatggatatattattctcaaatttaaatgggtaagaacctaaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgtgccacttttggtaagcagaactggagctgtgggatgaaccaaacgtaatgttagggtgcccaaattaacaactcaagcagatacaatgaaagtcgttagttgcttaaaacagcaggacgataatcatggaaggcgaaatccgataaggagtgtgtaacaactcatctgccgacgcaactagcccttaaaatgaatggcgctaatgttgtatacttatacatacataccgctaaagtagatgatttatattacttgtgatataaattttgaaactttagatagtaggaaggtacaatggtaagcgcagaagtgtttggcgtaagcctgcatggagctgccattggtacggatcttggtggtagtatcaaataatcgagtgagaccttggaggactgaggtggagaagatttcgtgtgaacagtggttgatcacgagttagtctgtcctaagttcaaggcgaaagccaaaaatgttcaagtaaaacaaaaatgcctaactatattaggcatacacttgaataattttgaacgaaagggaatacggttccaattccgtaacctgttgagtatacgtttgttattaaatatgggcctcgtgctcatcctggcaacataaacgaccataaaaaagccgttgatatcagaagagttttcttttctgttttatagcccgactaccatg
+>chrUn_DS485808v1:487-1047
+tcgaatcatcaagcaaaggataagcttcagtggatcgcagtatggcagctgctcaaccacttacaacaccttgcctgttacaaaagtcgtttacaattgattttaggctttgtcattgtattaaataatgcttttatatgtaactagcgcggcatcaggtgatcgaagatcctcccaatttactatgttacaaattacattggcatcacatccattgtcgtttataaagtaaattataaactttaaatggtttagaagccatacaatgcaaattgccccttatttatcattgcagtccagcacggatacgaccttagaggcgttcaggcataatccaacggacgtagcgtcataccactgttcgctcgaacaagtattgtgccattggtccgtacctgcggttcctctcgtactacgcaggaatgctgtcgcaacaacgttttgtcattagtagggtaaaactaacctgtctcacgacggtctaaacccagctcacgttcccttgcatgggtgaacaatccaacgcttggtgaattttgcttcacaatgataggaagagc
+>chrUn_DS485820v1:0-533
+cataaataatataaatatacattatacataactatatagaacgccccgggattgtgttaattagctataaatagctaaaaaactaatcccattattagtcaagttaattacgctattaggtttatatcccaataacttgcacatatgttagactccttggtccgtgtttcaagacgggtcccgaaggtatcctgaatctttcgcattgttaatcatacaagtgcatataataaacacaaaaatcaatgataattatgccattatataattccgaaaaattaacgcactgtaatcatataaatctatcagcactttatcaaattaataacatttattctgtgttaaaatgcaagcaatttaattggaataaactataagttatattttatgataaatttggtatatgctaatagattacaatgtccttatatggaaaaaatgcacactattctcataatattatttaaatattacaattttaatgatgaattttccataacggatattcaggttcatcgggcttaacctctaag
+>chrUn_DS485859v1:833-1036
+attctggttgatcctgccagtagttatatgcttgtctcaaagattaagccatgcatgtctaagtacacacgaattaaaagtgaaaccgcaaaaggctcattatatcagttatggttccttagatcgttaacagttacttggataactggggtaattctagagctaatacatgcaattaaaacatgaaccttatgggacatgtg
+>chrUn_DS485964v1:0-463
+atattcaggttcatcgggcttaacctctaagcagtttcacgtactgtttaactctctattcagagttcttttcaactttccctcacggtacttgtttactatcggtctcatggttatatttagtgtttagatggagtttaccacccacttagtgctgcactatcaagcaacactgactctttggaaacatcatctagtaatcattaacgttatacgggcctggcaccctctatgggtaaatggcctcatttaagaaggacttaaatcgctaatttctcatactagaatattgacgctccatacactgcatctcacatttgccatatagacaaagtgacttagtgctgaactgtcttctttacggtcgccgctactaagaaaatccttggtagttacttttcctcccctaattaatatgcttaaattcagggggtagtcccatatgagttgaggttgtatataa
+>chrUn_DS485991v1:504-1005
+attatacacaatcccggggcgttctatatagttatgtataatgtatatttatattatttatgcctctaactggaacgtaccttgagcatatatgctgtgacccgaaagatggtgaactatacttgatcaggttgaagtcaggggaaaccctgatggaagaccgaaacagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaaccatctagtagctggttccttccgaagtttccctcaggatagctggtgcattttaatattatataaaataatcttatctggtaaagcgaatgattagaggccttagggtcgaaacgatcttaacctattctcaaactttaaatgggtaagaaccttaactttcttgatatgaagttcaaggttatgatataatgtgcccagtgggccacttttggtaagcagaactggcgctgtgggatgaaccaaacgtaatgttacggtgcccaaataacaact
+>chrX_CP007104v1_random:17087-17522
+attgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgaagaataagtgggagatattagacctcggtttgctatcgtcaatgaaataccactactcttattgtttccttacttacttgattaaatggaacgtgtatcatttcctagctattatacggatatatttattatatcttatggtattgggttttgatgcaagcttcttgatcaaagtatcacgagtttgttatataatcgcaaacaaattctttaataaaacgatgcatttatgtatttttgatttgaaaatttggtataactccaattactcaggtatgatccaattcaaggacattgccaggtagggagtt
+>chrX_DS483648v1_random:9774-9829
+tcgttaatccattcatgcgcgtcacaaattagatgacgaggcatttggctatttt
+>chrX_DS483648v1_random:13026-13940
+ccttaagagagtcatagttactcctgccgatgacccgcgcttacttgaatttcttcactttgacattcagagcactgggcagaaatcacattgtgtcaacacccgctagggccatcacaatgctttgttttaattagacagtcggattccccaagtccgtgccgtccgttgattaatgaaaacataagtcttaagttagtcttaagacggtccaagaatttcacctctcgcgtcgtaatactaatgccctcaaactgcttctattaataattacctcttgatctaaaaccaatgaaagcagaacagaggtcttatttcattatcccatgcacagaatattcaggcatttgaagcctgctttaagcactctaatttgttcaaagtaatagtaccggcccacaataacactcgtttaagagcactaatgcagggttttaaataggaggaacatatgaaaaaatacaagtatttaatcacatataagaactccaccggtaatacgcttacatacataaaggtatagtactaaccacaattgtaagttgtactacccgtatgaagcacaagttcaactacgaacgttttaaccgcaacaactttaatatacgctattggagctggaattaccgcggctgctggcaccagacttgccctccaattggtccttgttaaaggatttaaagtgtacttattccaattacagggcttcggatatgagtcctgtattgttatttttcgtcactacctccccgagctgggagtgggtaatttacgcgcctgctgccttccttagatgtggtagccgcgtctcaggctccctctccggaatcgaaccctgattccccgttacccgttgcaaccatggtagtcctagatactaccatcaaaagttgatagggcaaacatttaaagat
+>chrX_DS483665v1_random:0-1878
+ccaatagcgtatattaaagttgttgcggttaaaacgttcgtagttgaacttgtgcttcatacgggtagtacaacttacaattgtggttagtactatacctttatgtatgtaagcgtattaccggtggagttcttatatgtgattaaatacttgtattttttcatatgttcctcctatttaaaaacctgcattagtgctcttaaacgagtgttattgggggccggtactattactttgaacaaattagagtgcttaaagcaggcttcaaatgcctgaatattctgtgcatgggataatgaaataagacctctgttctgctttcattggttttcagatcaagaggtaattattaatagaagcagtttgagggcattagtattacgacgcgggaggtgaaattcttggaccgtcgtaagactaacttaagcgaaagcatttgccaaagatgttttcattaatcaagaacgaaagttagaggttcgaaggcgatcagataccgccctagttctaaccataaacgatgccagctagcaattgggcgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaacttaccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatgggcgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgtgtttatgtaagtggagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggataaattgcgtcttgcaataatgagattggacaataacaggtctgtgatgcacttagatgtcctgggcccgcgtgcaggctacaatgaaagtatcaacgtgtacacgtttctagaccgagaggtccgggtaaaacgctgaaccactttcatgctcggaattgtgaactgaaactgttcacatgaacttggaatttccattaactcgcattcattacgtcccagccctgtgtacacactgccgtcgctactaccgattgaattatttagtgaggcctccggaagatggtgaattatacttgatcaggttgaagtcaggggaaaccctgatggaagactgaaaaagttctgacgtgcaaatcgattgtcagaattgagtataggggcgaaagaccaatcgaatttagtaagtaggaaggtacaatggtatacgtagaagtgtttggtgtcgatattttctcctcggaccttgaaaatttaacaggacgttccaatatccacagctggtctccaaggtgaagagtctctaggcgatagaataatgtaggtaataaaccaataacgtggtttatgtgttttttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatattttagttacgtagccaattgtggaactttcttgctaaaattttactatacggattaaaccaattagttcttattaattataacgattatcaatttagaactggcacagacttggggaatccgactgtctaattaaaacaaagcattgtgatgggcctagcttgtgttgacacaatgtgatttctgcccagtgttctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaatggagagagtaactaagactcctctta
+>chrX_DS483685v1_random:1973-2331
+attgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgtagaataagtgggagatattagacctcggtttggtatcgtcaatgaaataccactactcttattgttttcttacttacttgattaagtggaacgtgtatcatttcctagccattatacggatatatttattatatcttatggtattgggttttgatgcaagcttcttgatcaaagtatcacgagttttttatataatcgcaaacaaattctttaataaaacgatgcatttatgta
+>chrX_DS483685v1_random:10687-11582
+tttgatttgaaaatttggtataactccaattactcaggtatgatccaattcaaggacattgccaggtagggagtttgactggggcggtacatctctcaaataataacggaggtgtcccaaggccagctcagtgcggacagaaaccacacatagagcaaaagggcaaatgctgacttgatctcagtgttcagtacacacagggacagcaaaagctcggcctatcgatccttttggtttaaagagtttttaacaagaggtgtcagaaaagttaccatagggataactggcttgtggcggccaagcgttcatagcgacgtcgctttttgatccttcgatgtcggctcttcctatcattgtaaaattcaccaagcgttggaatgttcacccatgcaagggaacgtgagctgggtttagaccgtcgtgagacaggttagttttaccctactaatgacaaaacgttgttgcgacagcattcctgcgtagtacgagaggaaccgcaggtacgaaccaatggcacaatacttgttcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtctccgtgctggactgcaatgataaataaggggcaatttgcattgtatgccttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgggaggatcttcgatcacctgatgccgcgctagttacatataaaagcattatttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagctgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrX_DS483821v1_random:0-765
+acgaaagttagaggttcgaaggcgatcagatgccgccctagttctaaccataaacgatgccagctagcaattgggtgtagctacttttatggctctctcagtcgcttcccgggaaaccaaagcttttgggctccgggggaagtatggttgcaaagctgaaacttaaaggaattgacggaagggcaccaccaggagtggagcctgcggcttaatttgactcaacacgggaaaactttccaggtccgaacataagtgtgtaagacagattgatagctctttctcgaatctatgggtggtggtgcatgggcgttcttagttcgtggagtgatttgtctggttaattccgataacgaacgagactcaaatatattaaatagatatcttcaggattatggtgctgaagcttatgtagccttcattcatgttggcagtaaaatgcttattgtgtttgaatgcgtttatgtaagttgagccgtacctgttggtttgtcccattataaggacactagcttcttaaatggacaaattgcgtctagcaataatgagattgagcaataacaggtctgtgatgcccttagatgtcctgggctgcacgcgcgctacaatgaaagtatcaacgtgtatttactagaccgagaggtccgggtaaaccgctgaaccactttcatgcttgggattgtgaactgaaactgttcacatgaacttggaattcccagtaagtgtgagtcattaactcgcattgattacgtccctgccctttgtaca
+>chrX_DS483888v1_random:1893-1922
+atgagtgacgcgcatgaatggattaacga
+>chrX_DS484057v1_random:571-809
+aaaatagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacgagattcctactgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgtagaataagtgggagatattagacctcggtttggtatcgtcaatgaaatacca
+>chrX_DS484088v1_random:2042-2115
+agtagatagggacagtaggaatctcgttaatccattcatgcgtgtcactaattagatgacgaggcatttggct
+>chrX_DS484101v1_random:2161-2904
+ttatatacaacctcaactcatatgggactaccccctgaatttaagcatattaattaggggagcaaaagaaactaacaaggacttagtagcggcgagcgaaaagaaaacagttcagcactaagtcactttgtctatatggcaaatgtgagatgcagtgtatggagcgtcaatattctagtatgagaaattaacgatttaagtccttcttaaatgaggccatttacccatagagggtgccaggcccgtataacgttaatgattactagatgatgtttccaaagagtcgtgttgcttgatagtgcagcactaagtgggtggtaaactccatctaaaactaaatataaccatgagaccgatagtaaacaagtaccgtgagggaaagttgaaaagaactctgaatagagagttaaacagtacgtgaaactgcttagaggttaagcccgatgaacctgaatatccgttatggaaaattcatcattaaaattgtaatatttaaataatattatgagaatagtgtgcattttttccatataaggacattgtaatctattagcatataccaaatttatcataaaatataacttatagtttattccaattaaattgcttgcattttaacacagaataaatgttattaatttgataaagtgctgatagatttatatgattacagtgcgttaatttttcggaattatataatggcataattatcattgatttttgtgtttattatatgcacttgt
+>chrX_DS484519v1_random:0-589
+cctcggaccttgaaaattatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctcttaaggtagccaaatgcctcgtcatctaattagtgacgcgca
+>chrX_DS484519v1_random:864-1963
+attgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaatctggcagtgtaaggagacataagaggtgtagaataagtgggagatattagacctcggtttggtatcgtcaatgaaataccactactcttattgtttccttacttacttgattaaatggaacgtgtatcatttcctagccattatacggatatatttattatatcttatggtattgggttttgatgcaagcttcttgatcaaagtatcacgagtttgttatataatcgcaaacaaattctttaataaaccgatgcatttatgtatttttgatttgaaaatttggtataactccaattactcaggtatgatccaattcaaggacattgccaggtagggagtttgactggggcggtacatctctcaaataataacggaggtgtcccaaggccagctcagtgcggacagaaaccacacatagagcaaaagggcaaatgctgacttgatctcggtgttcagtacacacagggacagcaaaagctcggcctatcgatccttttggtttaaagagtttttaacaagaggtgtcagaaaagttaccatagggataactggcttgtggcggccaagcgttcatagcgacgtcgctttttgatccttcgatgtcggctcttcctatcattgtgaagcaaaattcaccaagcgttggattgttcacccatgcaagggaacgtgagctgggtttagaccgtcgtgagacaggttagttttaccctactaatgacaaaacgttgttgcgacagcattcctgcgtagtacgagaggaaccgcaggtacggaccaatggcacaatacttgttcgagcgaacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtatccgtgctggactgcaatgataaataaggggcaatttgcattgtatggcttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgggaggatcttcgat
+>chrX_DS484611v1_random:0-608
+ggtctaaacccagctcacgttcccttgcatgggtgaacaatccaacgcttggtgaattttgcttcccaatgataggaagagccgacatcgaaggatcaaaaagcgacgtcgctatgaacgcttggccaccacaagccagttatccctatggtaacttttctgacacctcttgttaaaaactctttaaaccaaaaggatcgataggccgagcttttgctgtccctgtgtgtactgaacaccgagatcaagtcagcatttgcccttttgctctatgtgtggtttctgtccgcactgagctggccttgggacacctccgttattatttgagagatgtaccgccccagtcaaactccctacctggcaatgtccttgaattggatcatacctgagtaattggaattataccaaattttcaaatcaaaaatacataaatgcatcgttttattaaagaatttgtttgcgattatataacaaactcgtgatactttgatcaagaagcttgcatcaaaacccaataccataagatataataaatatatccgtataatggctaggaaatgatacacattccttggctgtggtttcgctagatagtagatagggacaat
+>chrX_DS484611v1_random:1163-1235
+tagggacagtaggaatctcgttaatccattcaagcgcgtcactaattagatgacgaggcatttggctatttt
+>chrX_DS485072v1_random:0-362
+aacagtggtatgacgctacgtccgttggattatgcctgaacgcctctaaggtcgtctccgtgctggactgcaatgataaataaggggcaatttgcattgtatgccttctaaaccatttaaagtttataatttactttataaacgacaatggatgtgatgccaatgtaatttgtaacatagtaaattgagaggattttcgatcacatgatgacgcgctagttacatataaaatcattttttaatacaatgacaaagcctagaatcaattgtaaacgacttttgtaacaggcaaggtgttgtaagtggttgagcagccgccatactgcgatccactgaagcttatcctttgcttgatgattcga
+>chrX_DS485111v1_random:702-837
+ctcctccgatatctctcgacggcttctttatggtcgttcctgttgccaggatgagcacgaggcccatatttaataacaaacggatactcaacaggttacggaattggaaccgtattccctttcgttcaaaattat
+>chrX_DS485111v1_random:878-1238
+ttttgttttacttgaaaattttcggctttcgccttgaacttaggaccgactaactcgtgatcaaccactgttcacacgaaacccttctccacttcagtcctccaaggtctcattcgattatttgctactaccaccaagatctgtaccaatggcagctccatgcaggcttacgccaaacacttctacgcataccattgtaccttcctactcactaaagtttcaaaatttatatcacaagtaatataaatcatctactttagcgggtaatgtataggtatacaacttaagcgccatccattttaagggctagttgcttcggcaggtgagttgttacacactccttagcggatttcgacttcc
+>chrX_DS485225v1_random:629-1200
+ccttaagagagtcatagttactcccgccgttgacccgcgcttacttgaatttcttcactttgacattcagagcactgggcagaaatcacattgtgtcaacacccgctagggccatcacaatgctttgttttaattagacagtcggattccccaagtccgtgccagttctgaattgattgttaattgataatcgttataattaataagaactaattggtttaacccaaatagtattcttaaaaattttagcaagaaagttccacaattggctacgtaactaaactatccggggaacaagtaactaacataaatgctagaaactctatttacccagaacgagcacataaaccatgttattgtttcccaatcaagcccgactatctcaatcttcagagccaatccttatcccgaagttacggatctaatttgccgacttcccttacctacattattctatcgactagagactcttcaccttggagaccagctgcggatattggtacggcctgttgagaagtttgcgtgtccccaccataaattttcaaggtccgaggagaaaatatcgacacaa
+>chrX_DS485459v1_random:531-560
+atgagtgacgcgcatgaatggattaacga
+>chrX_DS485537v1_random:0-572
+ttgtgtcgatattttctcctcggaccttgaaaatttatggtggggacacgcaaacttctcaacaggccgtaccaatatccgcagctggtctccaaggtgaagagtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattgagatagtcgggcttgattgggaaacaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctctttagat
+>chrX_DS485599v1_random:0-540
+agtctctagtcgatagaataatgtaggtaagggaagtcggcaaattagatccgtaacttcgggataaggattggctctgaagattaagatagtcgggcttgattgggaaccaataacatggtttatgtgctcgttctgggtaaatagagtttctagcatttatgttagttacttgttccccggatagtttagttacgtagccaattgtggaactttcttgctaaaatttttaagaatactatttgggttaaaccaattagttcttattaattataacgattatcaattaacaatcaattcagaactggcacggacttggggaatccgactgtctaattaaaacaaagcattgtgatggccctagcgggtgttgacacaatgtgatttctgcccagtgctctgaatgtcaaagtgaagaaattcaagtaagcgcgggtcaacggcgggagtaactatgactctcttaaggtagccaaatgcctcgtcatctaattagtgacgcgcatgaatggattaacgagattcctactgtccctatct
+>chrX_DS485672v1_random:0-563
+tccctgtgtgtctgaacaccgagatcaagtcagcatttgcccttttgctctatgtgtggtttctgtccgcactgagctggccttgggacacctccgttattatttgagagatgtaccgccccagtcaaactccctacctggcaatgtccttgaattggatcatacctgagtaattggagttataccaaattttcaaatcaaaaatacataagggcatcgttttattaaagaatttgtttgcgattatataacaaactcgtgatactttgatcaagaagcttgcatcaaaacccaataccataagatataataaatatatccgtataatggctaggaaatgatacacgttccatttaatcaagtaagtaaggaaacaataagagtagtggtatttcattgacgataccaaaccgaggtctaatatctcccacttattctacacctcttatgtctccttacactgccagattagagtcaaagctcaaaagggtcttctttccccgctaattattccaagcccgttcccttggctgtggtttcgctagatagtagatagggacaat
+>chrX_DS485691v1_random:109-491
+taatgatccttccgcaggttcacctacggaaaccttgttacgacttttacttcctctaaataatcaagttcggtcaagttttgcgaaacaaccgtaacacgcaaggcgtcacagtgatcacgtccggagacctcactaaataattcaatcggtagtagcgacgggcggtgtgtacaaagggcagggacgtcaatgcgagttaatgactcacacttactgggaattccaagttcatgtgaacagtttcagttcacaatcccaagcatgaaagtggttcagcggtttacccggacctctcggtctaggaaatacacgttgatactttcattgtagcgcgcgtgcagcccaggacatctaagggcatcacagacctgttattatt
+>chrX_DS485959v1_random:430-536
+attgtccctatctactatctagcgaaaccacagccaagggaacgggcttggaataattagcggggaaagaagacccttttgagcttgactctaagggaagtggaag