{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#microRNAs Functional Clustering \n", "\n", "microRNAs are small non-coding molecules produced by the genome to regulate its activity. These small ribo-nucleic acids (RNA) molecules are 22-24 nucleotids long and can recognize, by base-pairing (A binds U, C binds G), complementary sequences on messengers RNAs (mRNA). The consequences of the binding of a microRNA (miR) on its complementary targets' sequences (on various mRNAs) is an inhibition of mRNAs translation into proteins, such as cellular enzymes.\n", " \n", "Accordingly to the [mirbase database](ftp://mirbase.org/pub/mirbase/CURRENT/README), 35,828 microRNAs from 223 species were sequenced. For exemple, the human genome encodes 2,588 miRs and the mouse genome 1,915 miRs. Here I will use the most up-to-date [TargetScan](http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_70) database which has recently improved its target scoring algorithm to include up to 14 distinctive features [Agarwal et al. 2015](http://elifesciences.org/content/4/e05005).\n", "\n", "Despite the progresses made for the prediction of microRNA targets, the functions of the microRNAs are still poorly understood. While most studies address the biological function of individual microRNAs, it is anticipated from the highly connected network of miRs-mRNAs interactions that microRNAs cooperate for the regulation of the cell's transcriptome (the ensemble of expressed mRNAs). \n", "\n", "**Could we define some functional clusters of microRNAs accordingly to their common targets?**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##1. The TargetScan database\n", "\n", "I will use the `Conserved_Site_Context_Scores.txt` file from TargetScan to build a graph database in Neo4j." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Gene IDGene SymbolTranscript IDGene Tax IDmiRNASite TypeUTR_startUTR endcontext++ scorecontext++ score percentileweighted context++ scoreweighted context++ score percentile
0ENSG00000121410.7A1BGENST00000263100.39544mml-miR-23a-3p3142149-0.42897-0.38897
1ENSG00000121410.7A1BGENST00000263100.39544mml-miR-23b-3p3142149-0.42897-0.38897
2ENSG00000121410.7A1BGENST00000263100.39598ptr-miR-23b3143150-0.41997-0.41998
3ENSG00000121410.7A1BGENST00000263100.39598ptr-miR-23a3143150-0.41997-0.41998
4ENSG00000121410.7A1BGENST00000263100.39598ptr-miR-23c3143150-0.41997-0.41998
\n", "
" ], "text/plain": [ " Gene ID Gene Symbol Transcript ID Gene Tax ID \\\n", "0 ENSG00000121410.7 A1BG ENST00000263100.3 9544 \n", "1 ENSG00000121410.7 A1BG ENST00000263100.3 9544 \n", "2 ENSG00000121410.7 A1BG ENST00000263100.3 9598 \n", "3 ENSG00000121410.7 A1BG ENST00000263100.3 9598 \n", "4 ENSG00000121410.7 A1BG ENST00000263100.3 9598 \n", "\n", " miRNA Site Type UTR_start UTR end context++ score \\\n", "0 mml-miR-23a-3p 3 142 149 -0.428 \n", "1 mml-miR-23b-3p 3 142 149 -0.428 \n", "2 ptr-miR-23b 3 143 150 -0.419 \n", "3 ptr-miR-23a 3 143 150 -0.419 \n", "4 ptr-miR-23c 3 143 150 -0.419 \n", "\n", " context++ score percentile weighted context++ score \\\n", "0 97 -0.388 \n", "1 97 -0.388 \n", "2 97 -0.419 \n", "3 97 -0.419 \n", "4 97 -0.419 \n", "\n", " weighted context++ score percentile \n", "0 97 \n", "1 97 \n", "2 98 \n", "3 98 \n", "4 98 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "database = pd.read_csv(\"C:/Users/mgirardot/Desktop/neo4j/Conserved_Site_Context_Scores.txt\", sep=\"\\t\")\n", "database.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a database of genes (`Gene ID; Gene Symbol`) and transcripts (`Transcript ID`) for each species (`Gene Tax ID`). microRNAs predicted to bind these transcripts are reported in the `miRNA` column with its `type`(**1** : 7mer-1a(seed match 2-7+A); **2** : 7mer-m8 (seed match 2-8); **3** : 8mer (seed match 2-8+A); **4** : 6mer(seed match 2-7)), and binding position on the transcript (`UTR_start , UTR end`).\n", "\n", "Context++ score stands for a linear regression model of 14 features [Agarwal 2015](http://lens.elifesciences.org/05005/index.html): \n", "- TA_3UTR (3' UTR target abundance in all annotated 3'UTR [0.222]), \n", "- TA_ORF (ORF target site abundance in all ORF ), \n", "- SPS (predicted seed-pairing stability $\\Delta$G° [0.210]),\n", "- sRNA1 | 8 (identity of nucleotide at position 1 | 8 of the miR [-0.018:0.06]), \n", "- site1|8|9|10 (identity of nucleotide at position 1|8|9|10 of the target ), \n", "- local_AU (AU content near the site [-0.254]), \n", "- 3P_score (supplementary pairing at 3' miRNA [-0.04]),\n", "- SA (structural accessibility as Log10 probability that a 14nt segment centered on the match to miRNA pos 7-8 is unpaired [-0.115]),\n", "- min_stop (log10 distance from stop codon [0.118]),\n", "- len_ORF (log10 length of ORF [0.205]),\n", "- len_3UTR ( log10 length 3'UTR [0.310]),\n", "- off6m ( Number of offset 6mer sites in the 3'UTR [-0.02]),\n", "- ORF8m ( Number of 8mer sites in the ORF [-0118]),\n", "- Pct (Probability of conserved targetting [-0.103])\n", "\n", "Weighted context++ score take into account the relative abundance of alternative 3'UTRs targeted by the microRNA." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of genes: 13522\n" ] } ], "source": [ "# What is the number of genes?\n", "nb_genes = len(database.groupby(by=\"Gene Symbol\"))\n", "print(\"Number of genes: %d\" % nb_genes)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Human microRNA: 401\n" ] } ], "source": [ "# What is the number of microRNAs in Human (ID:9606)?\n", "nb_hsa_mir = len(database[database[\"Gene Tax ID\"] == 9606].groupby(by=\"miRNA\"))\n", "print(\"Number of Human microRNA: %d\" % nb_hsa_mir)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of mouse microRNAs: 399\n" ] } ], "source": [ "#What is the number of microRNAs in mouse (ID:10090)?\n", "nb_mmu_mir = len(database[database[\"Gene Tax ID\"] == 10090].groupby(by=\"miRNA\"))\n", "print(\"Number of mouse microRNAs: %d\" % nb_mmu_mir)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "#How many targets per miR in human?\n", "hsa_database = database[database[\"Gene Tax ID\"] == 9606]\n", "gene_per_mir = hsa_database.groupby(by=\"miRNA\").count()\n", "\n", "#How many miR per gene in human?\n", "mir_per_gene = hsa_database.groupby(by=\"Gene Symbol\").count()\n", "\n", "plt.subplot(1,2,1)\n", "plt.boxplot(gene_per_mir[\"Gene Symbol\"], patch_artist=True, showfliers=False)\n", "plt.ylabel(\"Number of mRNA targets per miR\")\n", "\n", "plt.subplot(1,2,2)\n", "plt.boxplot(mir_per_gene[\"miRNA\"], patch_artist=True, showfliers=False)\n", "plt.ylabel(\"Number of microRNAs per mRNA\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is about 500 targets per microRNA and each mRNA is recognized by about 10 microRNAs." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "score_grp = hsa_database.groupby(by=\"weighted context++ score percentile\").count()\n", "plt.plot(score_grp.index, np.cumsum(score_grp[\"Gene ID\"]), '-', color='b')\n", "plt.ylabel(\"Cummulative sum of targets\")\n", "plt.xlabel(\"weighted context++ score percentile\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The majority of microRNA affects mRNA regulation by a 60 to 100% score. It means that very few predictions of interactions will have a negligible effect individualy. However these weak regulators can still affect mRNA regulation when acting cooperatively." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##2. Building a graph database of microRNAs-mRNA interactions\n", "\n", "The network of microRNAs-mRNAs interactions are best represented as graph. Here I will use the [Neo4j](http://neo4j.com/) graph database. I am running a local instance of a Neo4j server. The REST API can be accessed with the [`py2neo`](http://py2neo.org/2.0/intro.html) client library. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from py2neo import Graph, authenticate, Relationship\n", "#Read the password for the local database\n", "f = open(\"C:/Users/mgirardot/Desktop/neo4j/pass_neo4j.txt\",'r')\n", "passwd = f.read()\n", "f.close()\n", "\n", "#create an instance of Graph bound to the http://localhost:7474/db/data\n", "graph=Graph()\n", "authenticate(\"localhost:7474\",\"neo4j\",passwd)\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Constructing the graph\n", "for i,row in hsa_database.iterrows():\n", " rna = graph.merge_one(\"RNA\",\"NAME\", row[\"Gene Symbol\"])\n", " mir = graph.merge_one(\"MIR\",\"NAME\", row[\"miRNA\"])\n", " graph.create_unique(Relationship(mir, \"TARGET\", rna, score=row[\"weighted context++ score percentile\"]))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " | Number_of_miRs | Number_of_Relationships | Number_of_mRNAs\n", "---+----------------+-------------------------+-----------------\n", " 1 | 401 | 250340 | 12424" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Use the cypher language to interrogate the graph database\n", "graph.cypher.execute('''\n", " MATCH (mir :MIR)-[r:TARGET]->(rna : RNA)\n", " WITH COUNT(DISTINCT mir) AS Number_of_miRs,\n", " COUNT(DISTINCT r) AS Number_of_Relationships,\n", " COUNT(DISTINCT rna) AS Number_of_mRNAs\n", " RETURN Number_of_miRs, Number_of_Relationships, Number_of_mRNAs;\n", "''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this project I am interested in the frequency of co-occurence of microRNA on targets to group them into functional clusters. I will then add a node feature to the `:MIR` to store the number of target of each miR." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.cypher.execute('''\n", " MATCH (m:MIR)-[r:TARGET]->(:RNA)\n", " WITH m, COUNT(DISTINCT r) AS nb_tar\n", " SET m.NB_TARGETS = nb_tar;\n", "''')" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " | m \n", "---+--------------------------------------------------\n", " 1 | (n156:MIR {NAME:\"hsa-miR-28-5p\",NB_TARGETS:240})" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Example of node enriched with a new feature\n", "graph.cypher.execute('''\n", " MATCH (m:MIR {NAME: \"hsa-miR-28-5p\"})\n", " RETURN m;\n", "''')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##3. Defining a distance mesurement between microRNAs\n", "\n", "Clustering methods are using a common metric such as euclidian distance between data points to group them accordingly. The metric to use here is the frequency of common targets between a pair of miR. The similarity of miRs based on their common targets is a Jaccard coefficient:\n", "$$ J(A,B) = \\frac{|A \\cap B|}{|A \\cup B|}$$\n", "Thus the Jaccard distance is:\n", "$$ J_{\\delta} = 1 - J(A,B)$$\n", "\n", "The intersection of miR-A and miR-B is the number of common targets and the union is the total number of targets of miR-A and miR-B without their common targets.\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#compute the Jaccard distance for each pair of miRs and create a new relationship\n", "from py2neo.packages.httpstream import http\n", "http.socket_timeout = 9999 # fix for the socket timeout error\n", "\n", "graph.cypher.execute('''\n", " MATCH (mir1:MIR)-[r1:TARGET]->(n:RNA)<-[r2:TARGET]-(mir2:MIR)\n", " WITH mir1, mir2,tofloat(mir1.NB_TARGETS) AS mir1_tar,tofloat(mir2.NB_TARGETS) AS mir2_tar, tofloat(COUNT(DISTINCT n)) AS nb_tar\n", " WHERE mir1.NAME <> mir2.NAME\n", " WITH mir1,mir2,(1-nb_tar/(mir1_tar + mir2_tar - nb_tar)) AS Jaccard\n", " CREATE UNIQUE (mir1)-[:DISTANCE {JACCARD:Jaccard}]-(mir2);\n", "''')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " | mir1 \n", "----+-------------------------------------------------------------------\n", " 1 | (n1:MIR {NAME:\"hsa-miR-130a-5p\",NB_TARGETS:1531,SUBGRAPH:\"YES\"}) \n", " 2 | (n2:MIR {NAME:\"hsa-miR-23a-3p\",NB_TARGETS:1540,SUBGRAPH:\"YES\"}) \n", " 3 | (n3:MIR {NAME:\"hsa-miR-23b-3p\",NB_TARGETS:1543,SUBGRAPH:\"YES\"}) \n", " 4 | (n4:MIR {NAME:\"hsa-miR-23c\",NB_TARGETS:1539,SUBGRAPH:\"YES\"}) \n", " 5 | (n17:MIR {NAME:\"hsa-miR-424-5p\",NB_TARGETS:1864,SUBGRAPH:\"YES\"}) \n", " 6 | (n18:MIR {NAME:\"hsa-miR-15a-5p\",NB_TARGETS:1875,SUBGRAPH:\"YES\"}) \n", " 7 | (n19:MIR {NAME:\"hsa-miR-497-5p\",NB_TARGETS:1876,SUBGRAPH:\"YES\"}) \n", " 8 | (n20:MIR {NAME:\"hsa-miR-16-5p\",NB_TARGETS:1882,SUBGRAPH:\"YES\"}) \n", " 9 | (n21:MIR {NAME:\"hsa-miR-195-5p\",NB_TARGETS:1882,SUBGRAPH:\"YES\"}) \n", " 10 | (n22:MIR {NAME:\"hsa-miR-6838-5p\",NB_TARGETS:1878,SUBGRAPH:\"YES\"})\n", " 11 | (n23:MIR {NAME:\"hsa-miR-15b-5p\",NB_TARGETS:1868,SUBGRAPH:\"YES\"}) " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Select a subgraph for testing purposes\n", "ex_mir=[\"hsa-miR-23a-3p\",\"hsa-miR-23b-3p\",\"hsa-miR-23c\",\"hsa-miR-6838-5p\",\"hsa-miR-16-5p\",\"hsa-miR-195-5p\",\"hsa-miR-15a-5p\",\"hsa-miR-15b-5p\",\"hsa-miR-424-5p\",\"hsa-miR-497-5p\",\"hsa-miR-130a-5p\"]\n", "\n", "graph.cypher.execute('''\n", " WITH {N} AS coll\n", " MATCH (mir1:MIR)\n", " WHERE mir1.NAME IN coll\n", " SET mir1.SUBGRAPH =\"YES\"\n", " RETURN mir1\n", "''', {\"N\":ex_mir})" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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aHth8wQExW4AqfH/uQaffqU9ViHKnvjwyOUFAEBAEeh4Cs1fk3waF5n2RcYgu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PrNyvS/ffPt6e9ecdGbSdHRXtOy8h1Oo9GlKSwyGtBm2AU2+hzD3IFniIkeEz8mVmiQ0VTjsMIk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\n6LBCsDYke/1ZnRcD4VlHo1i/tdftW1xD2C6ynalaBkvFQM1Lk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q8ecKQpLCsz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xwGzaLSpBN5XADQl5LaWVasZTKlDjJAPGVXgyymIib3z2rDsY5A/WxssQn8wr1Rngcb1Y/JuT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\nHEkJbLKrf3f6RRIDF7Q7v3siugqot/pnbNXPJlZ2TLVMtM7rYkdSUSWtcf3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j1X9XJJ+y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"text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='pictures/subgraph.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The selected subgraph is composed of two visualy distinct clusters of microRNAs based on the Jaccard distances." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##4. Defining a quality metrics for clustering evaluation\n", "\n", "In order to use a clustering algorithm, one has to define quantitatively the quality of the clusters found. Accordingly to [Almeida H. 2011](http://link.springer.com/chapter/10.1007%2F978-3-642-23780-5_13), three quality metrics stood out when evaluating very large graphs: *Modularity, coverage and conductance*. Since all three gave similar results, I will use the coverage that is easier to implement:\n", "$$ coverage = \\frac{w(C)}{w(G)} $$ \n", "where\n", "$$ w(C) = \\sum_{i=1}^k w(E(v_{x},v_{y}))$$ \n", "with \n", "$$ v_{x},v_{y} \\in C_{i} $$\n", "\n", "The coverage of C (the set of all clusters) is the fraction of weight of all intra cluster edges versus the total weight of all edges in the whole graph G." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "graph.schema.create_index(\":MIR\",\"NAME\")\n", "graph.schema.create_index(\":MIR\",\"SUBGRAPH\")" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Whole graph edges weight: 179046\n" ] } ], "source": [ "whole_graph_edges = graph.cypher.execute('''\n", " MATCH (mir1:MIR)-[r:TARGET*2]-(mir2:MIR)\n", " WITH mir1,mir2,COUNT(DISTINCT r) as nb_tar\n", " WHERE mir1.SUBGRAPH = \"YES\" AND mir2.SUBGRAPH = \"YES\"\n", " RETURN SUM(nb_tar);\n", "''')[0][0]\n", "print(\"Whole graph edges weight: %d\" % whole_graph_edges)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Define the coverage function\n", "def coverage(mir_df):\n", " edges = 0\n", " #for each cluster\n", " for i in range(max(mir_df[\"cluster\"])+1):\n", " \n", " #get the list of mir in the current cluster\n", " list_mir = mir_df.mir[mir_df[\"cluster\"] == i]\n", " \n", " #iterate over the list of mir to define pairs\n", " for mir1 in list_mir:\n", " for mir2 in list_mir:\n", " result = graph.cypher.execute('''\n", " \n", " MATCH (mir1:MIR {NAME:{mir}})-[r:TARGET*2]-(mir2:MIR {NAME:{col}})\n", " WITH mir1,mir2, COUNT(DISTINCT r) AS nb_tar\n", " WHERE mir1.NAME <> mir2.NAME AND mir1.SUBGRAPH = \"YES\" AND mir2.SUBGRAPH = \"YES\"\n", " RETURN SUM(nb_tar)\n", " \n", " ''', {\"mir\":mir1, \"col\":mir2})[0][0]\n", " \n", " edges += result\n", " return(float(edges)/float(whole_graph_edges))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##5. Clustering of the microRNAs\n", "\n", "From the clustering algorithms implemented in sklearn, Affinity propagation do not require to define the number of clusters. This algorithm proceed by sending messages between pair of samples to elect an examplar data point of the cluster. The `preference` parameter influence the number of clusters found. The algorithm could either compute negative euclidian distances or accept precomputed distances (`affinity = \"precomputed\"`). Here I will use precomputed Jaccard distances." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Retrieve the pairs of miRs from the subgraph\n", "mir_pairs = graph.cypher.execute('''\n", " MATCH (mir1:MIR)-[d:DISTANCE]->(mir2:MIR)\n", " WITH mir1, mir2, d\n", " WHERE mir1.SUBGRAPH =\"YES\" AND mir2.SUBGRAPH =\"YES\"\n", " RETURN mir1.NAME AS mir1, mir2.NAME AS mir2, d.JACCARD AS distance\n", "''')" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of selected pairs: 55\n" ] } ], "source": [ "print(\"Number of selected pairs: %d\" % len(mir_pairs))" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " mir1 | mir2 | distance \n", "----------------+-----------------+----------------\n", " hsa-miR-15b-5p | hsa-miR-130a-5p | 0.901422107304" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mir_pairs[0]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -0. , -90.14221073, -100. , -32.30286738],\n", " [ -90.17396907, -0. , -100. , -100. ],\n", " [ -90.16763378, -23.46175963, -0. , -100. ],\n", " [ -32.67944717, -90.16446308, -90.18977163, -0. ]])" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#First get a list of unique miRs\n", "mir_lst = []\n", "for pair in mir_pairs:\n", " if pair[0] not in mir_lst:\n", " mir_lst.append(pair[0])\n", " if pair[1] not in mir_lst:\n", " mir_lst.append(pair[1])\n", "\n", "#Create a dataframe of miRs for future reference\n", "import pandas as pd\n", "mir_df = pd.DataFrame(mir_lst, columns=[\"mir\"])\n", "\n", "#Create a matrix\n", "import numpy as np\n", "len_mat = len(mir_df)\n", "mat = np.empty([len_mat,len_mat], dtype=float)\n", "mat.fill(-100.0)\n", "\n", "#Fill the matrix with distance values using the mir ordering found in the mir_df.\n", "#Jaccard distance are multiplied by -100 to comply with the clustering algorithm \n", "#that usually accepts negative euclidian distances.\n", "for pair in mir_pairs:\n", " mat[np.where(mir_df.mir == pair[0]), np.where(mir_df == pair[1])] = -100 * pair[2]\n", "\n", "#Finally the distance of a mir with itself is 0\n", "np.fill_diagonal(mat,-0.0)\n", "\n", "#Show a part of the matrix\n", "mat[:4,:4]" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-100 . . . . . . . . . -90 . . . . . . . . . -80 . . . . . . . . . -70 . . . . . . . . . -60 . . . . . . . . . -50 . . . . . . . . . -40 . . . . . . . . . -30 . . . . . . . . . -20 . . . . . . . . . -10 . . . . . . . . ." ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from sklearn.cluster import AffinityPropagation\n", "\n", "#values for preference\n", "mat_pref = range(-100, 0,1)\n", "\n", "cov_metrics = pd.DataFrame() \n", "cov=[]\n", "pref=[]\n", "nb_cl=[] \n", "\n", "for i in mat_pref:\n", " af = AffinityPropagation(preference = i, affinity = \"precomputed\")\n", " af.fit(mat)\n", " \n", " #get the label of the cluster for each mir\n", " mir_df[\"cluster\"] = af.labels_\n", " \n", " #get the coverage\n", " cov.append(coverage(mir_df))\n", " \n", " #store the preference\n", " pref.append(i)\n", " \n", " #store the number of clusters\n", " nb_cl.append(len(af.cluster_centers_indices_))\n", " \n", " if (i % 10 == 0):\n", " print i,\n", " else:\n", " print \".\",\n", " \n", "#Combine the results into a dataframe\n", "cov_metrics[\"coverage\"] = cov\n", "cov_metrics[\"preference\"] = pref\n", "cov_metrics[\"nb_clusters\"] = nb_cl\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.plot(cov_metrics.preference, cov_metrics.coverage, label=cov_metrics.nb_clusters)\n", "plt.xlabel(\"Affinity Propagation Preference\")\n", "plt.ylabel(\"Coverage of graph's clusters\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe that any preference values between -100 and -35 produce the best coverage. Thus, as suggested by the affinity propagation documentation we can choose the median of the distance matrix as preference. " ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-90.205523442517659" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(mat)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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coveragepreferencenb_clusters
100.769322-902
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" ], "text/plain": [ " coverage preference nb_clusters\n", "10 0.769322 -90 2" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cov_metrics[cov_metrics.preference == -90]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best coverage is for 2 clusters as suggested by visual inspection of the subgraph. " ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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mircluster
1hsa-miR-130a-5p0
4hsa-miR-23a-3p0
5hsa-miR-23b-3p0
0hsa-miR-15b-5p1
2hsa-miR-23c1
3hsa-miR-15a-5p1
6hsa-miR-16-5p1
7hsa-miR-424-5p1
8hsa-miR-497-5p1
9hsa-miR-6838-5p1
10hsa-miR-195-5p1
\n", "
" ], "text/plain": [ " mir cluster\n", "1 hsa-miR-130a-5p 0\n", "4 hsa-miR-23a-3p 0\n", "5 hsa-miR-23b-3p 0\n", "0 hsa-miR-15b-5p 1\n", "2 hsa-miR-23c 1\n", "3 hsa-miR-15a-5p 1\n", "6 hsa-miR-16-5p 1\n", "7 hsa-miR-424-5p 1\n", "8 hsa-miR-497-5p 1\n", "9 hsa-miR-6838-5p 1\n", "10 hsa-miR-195-5p 1" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#expected clusters: - mir130a;23b;23c;23a\n", "# - mir6838;195;497;424;15b;16;15a\n", "\n", "#Predicted clusters:\n", "af = AffinityPropagation(preference = -90, affinity = \"precomputed\")\n", "af.fit(mat)\n", "\n", "mir_df[\"cluster\"] = af.labels_\n", "mir_df.sort(\"cluster\", inplace=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "miR-23c is not in the expected cluster. This may due to the bias observed by [Almeida H. 2011](http://link.springer.com/chapter/10.1007%2F978-3-642-23780-5_13) when using coverage. Indeed the metrics in use favors larger clusters." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###How the partition of miR into clusters changes when using different preference values?" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mir_df = pd.DataFrame(mir_lst, columns=[\"mir\"])\n", "mat_pref = range(-100, 0,1)\n", "\n", "cov_metrics = pd.DataFrame() \n", "pref=[]\n", "nb_cl=[] \n", "\n", "for i in mat_pref:\n", " af = AffinityPropagation(preference = i, affinity = \"precomputed\")\n", " af.fit(mat)\n", " \n", " #get the label of the cluster for each mir and store them in a new column\n", " newcol = 'cluster_pref_%d' % i\n", " mir_df[newcol] = af.labels_\n", " \n", " #store the preference\n", " pref.append(i)\n", " \n", " #store the number of clusters\n", " nb_cl.append(len(af.cluster_centers_indices_))\n", " \n", "cov_metrics[\"preference\"] = pref\n", "cov_metrics[\"nb_clusters\"] = nb_cl" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Hu4Ce8ptfO/Xxx97/0t8ZV19t9K1vdy7jWpalN954Q1u2bFFUVJRycnK0du1a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\nt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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.pyplot import cm\n", "import random\n", "color = iter(cm.rainbow(np.linspace(0,1,11)))\n", "plt.figure(figsize=(15,10))\n", "for i in range(len(mir_df)):\n", " c=next(color)\n", " plt.plot(pref,mir_df.iloc[i,1:]+1+0.25*random.random(), '-', color=c, label=mir_df.iloc[i,0], lw =1.5)\n", "\n", "plt.xlabel(\"Preference\")\n", "plt.ylabel(\"Cluster\")\n", "plt.legend(frameon=False, loc='center left')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Members of the clusters do not change with preference changes.Thus, the median of the data seems to be a resonable default preference value for the clustering. Let's look at another subgraph example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##6. Clustering another subgraph example" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex_mir=[\"hsa-miR-23a-3p\",\"hsa-miR-23b-3p\",\"hsa-miR-23c\",\"hsa-miR-6838-5p\",\"hsa-miR-16-5p\",\"hsa-miR-195-5p\",\"hsa-miR-15a-5p\",\"hsa-miR-15b-5p\",\"hsa-miR-424-5p\",\"hsa-miR-497-5p\",\"hsa-miR-130a-5p\"]\n", "\n", "#First de-select the miR from the subgraph\n", "graph.cypher.execute('''\n", " WITH {N} AS coll\n", " MATCH (mir1:MIR)\n", " WHERE mir1.NAME IN coll\n", " SET mir1.SUBGRAPH =\"NO\"\n", " ''', {\"N\":ex_mir})" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " | mir1.NAME | mir1.SUBGRAPH\n", "----+-----------------+---------------\n", " 1 | hsa-miR-130a-5p | NO \n", " 2 | hsa-miR-23a-3p | NO \n", " 3 | hsa-miR-23b-3p | NO \n", " 4 | hsa-miR-23c | NO \n", " 5 | hsa-miR-424-5p | NO \n", " 6 | hsa-miR-15a-5p | NO \n", " 7 | hsa-miR-497-5p | NO \n", " 8 | hsa-miR-16-5p | NO \n", " 9 | hsa-miR-195-5p | NO \n", " 10 | hsa-miR-6838-5p | NO \n", " 11 | hsa-miR-15b-5p | NO " ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Verify the labels\n", "graph.cypher.execute('''\n", " WITH {N} AS coll\n", " MATCH (mir1:MIR)\n", " WHERE mir1.NAME IN coll\n", " RETURN mir1.NAME, mir1.SUBGRAPH\n", "''', {\"N\":ex_mir})" ] }, { "cell_type": "code", "execution_count": 221, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Select some random mir closeby miR-28, so they have a chance to be connected\n", "mir = graph.cypher.execute('''\n", " MATCH (mir1:MIR)-[:TARGET]->()<-[:TARGET]-(mir2:MIR {NAME:\"hsa-miR-28-5p\"})\n", " WITH mir1,mir2\n", " RETURN DISTINCT mir1.NAME LIMIT 20\n", "''')\n", "ex2_mir = []\n", "for i in iter(mir):\n", " ex2_mir.append(i[0])\n" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Label the mir as subgraph elements\n", "graph.cypher.execute('''\n", " WITH {N} AS coll\n", " MATCH (mir1:MIR)\n", " WHERE mir1.NAME IN coll\n", " SET mir1.SUBGRAPH =\"YES\"\n", " ''', {\"N\":ex2_mir})" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " | mir1.NAME | mir1.SUBGRAPH\n", "----+------------------+---------------\n", " 1 | hsa-miR-10b-5p | YES \n", " 2 | hsa-miR-10a-5p | YES \n", " 3 | hsa-miR-106a-5p | YES \n", " 4 | hsa-miR-519d-3p | YES \n", " 5 | hsa-miR-526b-3p | YES \n", " 6 | hsa-miR-93-5p | YES \n", " 7 | hsa-miR-3139 | YES \n", " 8 | hsa-miR-708-5p | YES \n", " 9 | hsa-miR-124-3p.1 | YES \n", " 10 | hsa-miR-506-3p | YES \n", " 11 | hsa-miR-124-3p.2 | YES \n", " 12 | hsa-miR-371a-5p | YES \n", " 13 | hsa-miR-340-5p | YES \n", " 14 | hsa-miR-101-3p.1 | YES \n", " 15 | hsa-miR-142-5p | YES \n", " 16 | hsa-miR-5590-3p | YES \n", " 17 | hsa-miR-224-5p | YES \n", " 18 | hsa-miR-1306-5p | YES \n", " 19 | hsa-miR-31-5p | YES \n", " 20 | hsa-miR-335-5p | YES " ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Verify the labels\n", "graph.cypher.execute('''\n", " WITH {N} AS coll\n", " MATCH (mir1:MIR)\n", " WHERE mir1.NAME IN coll\n", " RETURN mir1.NAME, mir1.SUBGRAPH\n", "''', {\"N\":ex2_mir})" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Retrieve the pairs of miRs from the subgraph\n", "mir_pairs = graph.cypher.execute('''\n", " MATCH (mir1:MIR)-[d:DISTANCE]->(mir2:MIR)\n", " WITH mir1, mir2, d\n", " WHERE mir1.SUBGRAPH =\"YES\" AND mir2.SUBGRAPH =\"YES\"\n", " RETURN mir1.NAME AS mir1, mir2.NAME AS mir2, d.JACCARD AS distance\n", "''')" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of selected pairs: 190\n" ] } ], "source": [ "print(\"Number of selected pairs: %d\" % len(mir_pairs))" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -0. , -94.93333333, -100. , -100. ],\n", " [ -96.97933227, -0. , -100. , -100. ],\n", " [ -96.92224622, -94.9197861 , -0. , -91.18060436],\n", " [ -97.10982659, -95.56313993, -100. , -0. ]])" ] }, "execution_count": 162, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#First get a list of unique miRs\n", "mir_lst = []\n", "for pair in mir_pairs:\n", " if pair[0] not in mir_lst:\n", " mir_lst.append(pair[0])\n", " if pair[1] not in mir_lst:\n", " mir_lst.append(pair[1])\n", "\n", "#Create a dataframe of miRs for future reference\n", "import pandas as pd\n", "mir_df = pd.DataFrame(mir_lst, columns=[\"mir\"])\n", "\n", "#Create a matrix\n", "import numpy as np\n", "len_mat = len(mir_df)\n", "mat = np.empty([len_mat,len_mat], dtype=float)\n", "mat.fill(-100.0)\n", "\n", "#Fill the matrix with distance values using the mir ordering found in the mir_df.\n", "#Jaccard distance are multiplied by -100 to comply with the clustering algorithms \n", "#that usually accepts negative euclidian distances.\n", "for pair in mir_pairs:\n", " mat[np.where(mir_df.mir == pair[0]), np.where(mir_df == pair[1])] = -100 * pair[2]\n", "\n", "#Finally the distance of a mir with itself is 0\n", "np.fill_diagonal(mat,-0.0)\n", "\n", "#Show a part of the matrix\n", "mat[:4,:4]" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-97.73159288687097" ] }, "execution_count": 163, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(mat)" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-100 . . . . . . . . . -90 . . . . . . . . . -80 . . . . . . . . . -70 . . . . . . . . . -60 . . . . . . . . . -50 . . . . . . . . . -40 . . . . . . . . . -30 . . . . . . . . . -20 . . . . . . . . . -10 . . . . . . . . ." ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from sklearn.cluster import AffinityPropagation\n", "#values for preference\n", "mat_pref = range(-100, 0,1)\n", "\n", "cov_metrics = pd.DataFrame() \n", "cov=[]\n", "pref=[]\n", "nb_cl=[] \n", "\n", "for i in mat_pref:\n", " af = AffinityPropagation(preference = i, affinity = \"precomputed\")\n", " af.fit(mat)\n", " \n", " #get the label of the cluster for each mir\n", " mir_df[\"cluster\"] = af.labels_\n", " \n", " #get the coverage\n", " cov.append(coverage(mir_df))\n", " \n", " #store the preference\n", " pref.append(i)\n", " \n", " #store the number of clusters\n", " nb_cl.append(len(af.cluster_centers_indices_))\n", " \n", " if (i % 10 == 0):\n", " print i,\n", " else:\n", " print \".\",\n", " \n", "#Combine the results into a dataframe\n", "cov_metrics[\"coverage\"] = cov\n", "cov_metrics[\"preference\"] = pref\n", "cov_metrics[\"nb_clusters\"] = nb_cl\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.plot(cov_metrics.preference, cov_metrics.coverage, label=cov_metrics.nb_clusters)\n", "plt.xlabel(\"Affinity Propagation Preference\")\n", "plt.ylabel(\"Coverage of graph's clusters\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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mircluster
0hsa-miR-93-5p0
16hsa-miR-31-5p1
3hsa-miR-506-3p1
6hsa-miR-124-3p.21
14hsa-miR-124-3p.11
5hsa-miR-142-5p2
8hsa-miR-5590-3p2
15hsa-miR-340-5p2
11hsa-miR-224-5p2
2hsa-miR-526b-3p3
4hsa-miR-106a-5p3
7hsa-miR-519d-3p3
12hsa-miR-101-3p.13
9hsa-miR-1306-5p4
10hsa-miR-10a-5p5
1hsa-miR-10b-5p5
18hsa-miR-371a-5p6
13hsa-miR-335-5p6
17hsa-miR-31397
19hsa-miR-708-5p7
\n", "
" ], "text/plain": [ " mir cluster\n", "0 hsa-miR-93-5p 0\n", "16 hsa-miR-31-5p 1\n", "3 hsa-miR-506-3p 1\n", "6 hsa-miR-124-3p.2 1\n", "14 hsa-miR-124-3p.1 1\n", "5 hsa-miR-142-5p 2\n", "8 hsa-miR-5590-3p 2\n", "15 hsa-miR-340-5p 2\n", "11 hsa-miR-224-5p 2\n", "2 hsa-miR-526b-3p 3\n", "4 hsa-miR-106a-5p 3\n", "7 hsa-miR-519d-3p 3\n", "12 hsa-miR-101-3p.1 3\n", "9 hsa-miR-1306-5p 4\n", "10 hsa-miR-10a-5p 5\n", "1 hsa-miR-10b-5p 5\n", "18 hsa-miR-371a-5p 6\n", "13 hsa-miR-335-5p 6\n", "17 hsa-miR-3139 7\n", "19 hsa-miR-708-5p 7" ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Predicted clusters:\n", "af = AffinityPropagation(preference = -97, affinity = \"precomputed\")\n", "af.fit(mat)\n", "\n", "mir_df[\"cluster\"] = af.labels_\n", "mir_df.sort(\"cluster\", inplace=False)" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mir_df = pd.DataFrame(mir_lst, columns=[\"mir\"])\n", "mat_pref = range(-100, 0,1)\n", "\n", "cov_metrics = pd.DataFrame() \n", "pref=[]\n", "nb_cl=[] \n", "\n", "for i in mat_pref:\n", " af = AffinityPropagation(preference = i, affinity = \"precomputed\")\n", " af.fit(mat)\n", " \n", " #get the label of the cluster for each mir\n", " newcol = 'cluster_pref_%d' % i\n", " mir_df[newcol] = af.labels_\n", " \n", " #store the preference\n", " pref.append(i)\n", " \n", " #store the number of clusters\n", " nb_cl.append(len(af.cluster_centers_indices_))\n", " \n", "cov_metrics[\"preference\"] = pref\n", "cov_metrics[\"nb_clusters\"] = nb_cl" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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27YNV58PKq5OeRpKSs33TSP9c9/BClDRvuMz3w76NsORGaGpJepqqCiHQ3/0i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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.pyplot import cm\n", "import random\n", "color = iter(cm.rainbow(np.linspace(0,1,21)))\n", "plt.figure(figsize=(15,10))\n", "for i in range(len(mir_df)):\n", " c=next(color)\n", " plt.plot(pref,mir_df.iloc[i,1:]+1+0.25*random.random(), '-', color=c, label=mir_df.iloc[i,0], lw =1.5)\n", "\n", "plt.xlabel(\"Preference\")\n", "plt.ylabel(\"Cluster\")\n", "#plt.legend(frameon=False, loc='upper center')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IUos87XLQPkSAL/cn0F9EX43uadMgeVqAfnOnvqe7hky7YzpC5iMpaA5pzeoW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\nzmZowNBIMnmN8rnMG43BCtQR0Y1GvR8JEAMleP3S0kSSRCs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nYJlRmA9tkl5qLug24IyY\nlonKZIgV+ycpo/uBM2ieKKYaqP4256yX5R/m0bW0qTD3s99/8HHYiu07KXxlo5uBZSO8bhXROXA/\nIaEmInKShnT90qL0qbj+OJs3Xv605gahPmJT5O1iBBtD/Z1w+zBClA1AH8Qb7dGL9pcWRZbbn/qg\ncyu4RnS+aCsbx2gNsRmJZkmOSPQsFhiBank6UYODWUJFsWsUvcaNRWuKMbv1RF2zYF+ubLC0HJRD\nyXa79PQdsMAtHXt5t2kW5ZrPEG+yMENzEqBlYqrovnQ1J3NkKkmM2Wi1oXibAQq3XhgX97/VY24G\nESEJf2Y7fmOUGq95CrvJaqlLoqWIsy+Sd7p8jz5cjn91kgjxvqF7myJtBi2QvFPnx31QIE7LK+8j\nTiRofafViISW+yAD4ZgoDOeEhtnt6eKm2682wTDvaVqbOME4abcbr43aiuOOzc5ApKX+uhE1iUCc\nRnlpHh9Xv/1rGdIFlCNtAG6h1zITUnm+mmVr8HMOknQzSJJRIDQUD58giE1bSjMFx3jCL8ODma6h\nyjb2/fRzYdz53orwpXAUxPVsJDbtQqIMVxWJDcnLWztXG/qdxPh+qlVQuCpz2g3CYeqK4kOjDSJR\n5CgztD03dB6o4vAbicyO4kN4/uvG9rjtU0CUHvtlgTJdbTtLDh1jURrc/iw1BP2j/T+pVQfXq2ln\njq7c3hsgWdzPtZ36q2UgLWsOM+DFrV2qyaJ0elRHVH1xGXqlfjGnwAp1nsrZs1aNgKvPHCO3abrc\naI2ilewrWLuob4oNaa2e3uRO0ZF6+jUGSXx73SfGy/fGNTs3JBTqJB21tsNlZVvyy8ocJQ5n2Kkx\nrbcGWSyO7/Yd/A1i1m6JPU75YsGvmweHBVlX/z975wEeRfW18dlN74UkJJBA6CX03gkgIFVQaTaC\niA1UROwNLKiAVBVRgYAUBZUOKtJ7770lhAAJpPe2+53f7E6yCQkJ6N8PIfd55kmy2Z2duXNn7nvf\n85731PBw3zx536FHGpb13v98g7rHhL1BSEUIgPACgjGAF1MvbBAgioc/kwmTBwwFD//PZcOUDgCG\nHqQwkMSxMjm8MaxWre++69X559TMrGNOU2bgCM7qnZU03jU88Ak7FNukLIpMaEa8XkyDyZy6zK/m\nAp2Ia3MzxwAfABIVlNjb/GMPexWgyHXJTsuSkE/eXCYhiuRKiemL+19I6D+nhtfZKHs9tgumQ5UJ\nB1CCXo/j+SfZZrQ5FLuG0SIEpTUBbHuHnoj1ahGd4qgY9QPFc+u83tp6TKcPQrqVb1xDFeD9F+vC\nEWoj/E9rEZ26vs2WMzWXnD63w9vJdaTUwGN88YwHbATKBliKVmvn6Q1Llxw/X6v/6tVQApTvIcsS\nSgr9kQZMYGrCZLuJIc/t2OJ/0UTWlhl1u+VjACjuK9gk7mfuHRzpAVA44AOgCBtyT2gFxwr7toJh\nt9bm88yL8xd/jPiehcr9JAan+dg19ZNSyPyAPAQakmGKPqm05fUAi5SUaFOCsGRVtp/bYzTZwfdc\n+0eB0tA145vkGHU7pJds7sW6N8VdfQz0UlWrd9EIFMjoeXrtOG+DwSa6MHsAS42SJVDShM2Esh4N\naKF4SbgsMTtVIZy2K+asUs3FV6nq5KssitieTwRtub+SAiXOrZfojxoISwWbVVBU3UdAEgBoypk1\nKhijwW4B2GYJIGog4bU2XjWVZSIE5yfACv0RDBFgb+3Vg7kAi88Oq9xJDbnBjHEehOLWyHvqS3hP\nY9fKCHjs4B2kvr7hzF5V57J5wsJJW2o0XicWzV+72dmyclXbgpOndz+x6o+u8mvCmNYtQqJT0lrb\nWunTHKytUx6tXm13RHLii4FurhH1fbwixNvI7mJcYltXOztfL0f7WGcbW3Pc1KilN2u7DcN2gD9S\nsrIcLiUm+YclJEU2LuuzxdvBIWXDpct14zLSMyq5uf68LfJKpREN6x2y0uuNSqb+cGGrUzGSHCps\nyORRjRu822jeIjxoWGkTmuChzkqbLDmy4Yps+Vgky3dZACVeZjI85GX/83e1PZemWes7C4uS21cM\nTmtbAUxScFo1PiW0pYa5hNGxuvmRQKhLijlLlEYehsIQGuSnyh6J/ohQmNbkkynVEzKjo+ytv4h3\nUA57pRnH3rCzMi0Y5J+2jmIuKyDt3zJ9NIE4AU5S3NOQbTrOaomZsZWSMj967NmxiPAIFa0YvGJ8\nkJw3mVNqJhNhy7udBafvCUVzjnL1ssUn6XKbK6lDhFvbuD3yqvLxzj1Zrja2555pEPRotyXLCVfl\na+YU+Iqif2NhwOwPY8qigJAZwATWlf7B+gTg8ndaoHzYMqOOrFX2TTgNjRNO4GSMPi2bZHiqx4Nf\nU6hsJXHlRoMGKuYnYXWOF5AIiGIWL5YREz1gnGnRkd+PLWTNhGHy+nelhcyLvvyZ4q0EqyltrURS\nYCHvufaPAiVB3iflTqt5r6ff3moUWGYDSBBhuJQ7gS1Qm2RNZMmEZe3iK5Eci54vCigBmhq5V1Zs\nZAJDo7RZssdgkdp61RKQoRN2KVpxE7aJDLTdsXlRmzsFSuijnqncUV0bfHMuv8QmJDBYcRYNlWVm\nHmwRTBavGyQcommraksYsW/5ZoqPCL2zDNnKPhFr/yTgy7JZvscyM45jr+7sp7JotL3CMvHZ5Ch5\njsmk/MuQcbuTo+NYBTu817KptYCiutIXzq9u2Dp9yv6DgAyoqMGy7ZSNEJqWjo+AlIfuLQUuFsdo\n/VhQzQEetnbtanl6nni+ft0jH+3c3aGqh1v9J+vU5GGuXE1OcT0dG9e4fYA/bu0W+5Uac2YjTAFl\n6i5d7G33SRc12BAeETlhzwGHJ2vXPLjw5BlXVzubtGfq1Dmx/tKlcpkG44aJHVofu5KUbFPGwT7H\nztb2ou7zKWGW/Wb2jQrV9p/7P52xr0VqPCtkqVFlfFV7bcjK8Y0ECXWXA+0uYA1DzSKbBp608BYp\nnUU2GYZy7quP/rIx+cjc31t1rV+zfaMnuyaEV/fLdU/HM83OSa7n/6NOMTM5XckUd/08fZPu55Qb\ncZOOvTqj3pvNmzxX9YHGTWbVKpMpfaNS4GgJ1bqTEr68m1pB7Zcwd8eFHXpq9obID+Q6hR2Iin7l\n4517lXb+5ZRn69dRnGzINVCWJWRnveY+eQaJCAiuCUUfiX/5+Z5n4mM/bzZvMbohtHUfy6YJHlk0\nsKEv+l/45BASJFRHWBC2EdYK8ARAQ6PEdyPMJyRIQkZuk7GNJdQmCalukjG+qaiFiXyAkDgeTjDD\nhOrZN6E9i32pVhTYefSxeDW3XIlok7hvG9/PJXAs+6uw37mnUq7zfJYFlF7faF6319Tn473U/jGg\nJN4ur4q3yyRWqWrxzvu4WWTCRUg2QEUJ4KozzdDVE67IssePrIm7ofDv3XiJCssApPOSKWGi0xmk\nPwk1ESJgFfratODgb15qVu+Z2NTM7WW+Ul0DCDfdLigq2BXcF/IAVbPZqDWF7oIZh+we2CcGOEJZ\nxN4cB3qoolrjCi4uM/7o37dSzTJuU6X23LYtkZGu3xw83O9KcnLQ8Eb19/WvWe3Ku1t31G1Xvnzl\nIK8yaaM2bg38sFXziDreZcrK5IdNQV7Tqa7HnH9VgXxMNKam04XK32Gm343UoEMvpbIkEmpK0E2Y\nkuvPMGTZlwHizdTdITunZs2ErIEHvOwPy2fLCfYuJ5PCTbbxxuycDJ2N9aUacWnVdlyPOWLjYnfs\n4Rz7GiLG3txq9Odotli5D3t7/kflr7k7DBThttpMYMNRwMbdUa6HB3kWiRem1a/a2l1LzXziTKxt\nZkbWnhGdKz107ciFn33rVVY1XjRCLQA9mOD/z8YCjGPPEiaJZm1U4h6ITDo74GRiV9jLF+rVq/Fp\ncKvdv545m/lQlcre3sLeWbSpwhyRqYaODjACe5TxerPGAz9u2+LLPktXv/f7hTAAIuwRoIISMlxX\nxo8lG4PlBn1DeA6fpcKE3iXtJkALjCru3YiwWeCYYoimcDTMBKslQnba6+o/c/2NxNtIwFIwmdUC\nnGR8C3CS+oCKlTC6BRYY8jEYW85xk+UBqoJ2nXGFfI77xaLpxoR09J+vMyjnYFudy3Kbl7aieiDX\nvsWUbAMbeE+1fwQo9Vs8ycHZ2RAuqzFvB09EkfePLqmo0UAIjowfWfG9M6f7aCZY5f3F41IkM8lR\nFbgLoCxtN/dAoUBJQprCItGXkdKX6C1oLPUD4l99QfTLxk69f1l1fsvly6weodby4kF31snMMpjn\nwQaS0YbOglUuKyXS/xG8Yh+AZilUtnwPcsuv/PWhHm+diYv7cOqBQxuuJaeSEg2oIHuuoHgbPQjA\njAwfanEVGoJT9SU0vWEy1gHyG4Z/6EvmqmFCwgc6nbADxqMyiWg2BYQU8pyuTQdIWNHd/H7RhhjT\nWaFnWek9E62VlxfW8upz0M3mxIqXp1aMuRDJBCvFZl82yoSLVmuD/E5WoGRMTQvxqRnQtc2rgya4\n+nurIu27PXyFroKwlVaIt0JSptLz8JUDzT77GqalUvmG1dZ3/uRZMi5gJlVvKmpSYr3wbwMmws2Z\nqRyraYhJmC1dBvdnU7ZG7nDLyVkn1zB+zPY9P4zdvqvq+OC2i19v1ghvKQCNyvJeTEh8t+r3c8ky\n03y80PCwqToknM7lfR2sJkwn9EXyA/cPYx7NkKU4mnsCkTVAiiw3gAzZdLz3n2zMSYiCAUua1UCx\n+1fvC51BQI8AJ6MxWD7AA0MYJwFGBp1YbOQtFAruTGWoCmmLq3ruXuvvdF/Vcyu2o4t4A6HgtDhJ\nPNTpdshilvF1T7V/BChJSElEpcYPrQQgOQpQKm3yBDIPHHnGJhszbQLmbL8wZlodr1cOejnc966u\ntzs+0GFQjkH6cv/sLZGPylhrr2Z6mR6IgexPJmwerohyCRMgEC1piE07HO4F2Co0DTRW4GGyscrG\n+4USDjBJpDGXSLCIw/SOK1enj9918L3l58/DOpHhRso0q/HhslmKt1ldE+bgdfQzRTaTc7V+pLhV\nE+JT9UhMmEwGMvEhMN9cnK2AvO8j6SEynqTpvhCQ9LuAr2D5Y4C8DiOFyaV8jzqpBsomQOmVQwNX\nrt3w86kzM+U7/QBrT3f0H5qTYfhJQlTVCNnZ/YcK9prKEYnRt4AR6xxDTkJs8hNLQj7eJOf6M936\neK9gH8fuzdyzKnjnuq2ywDFl6YnWSqwF/heN8BrPj2xZaFkK0uU6TdLrMz+f3e0dFZxoEzyASFiP\nTQLihig5OXJNdWh9El5av3nb5ohIm6PXbwB+GH+MWxYYeBEhqEY8rYsaPixCsi+3D1i5Fh0R15vz\nBcjzXpMg0ST4BhwxRtEDwSzx8+8IvQt23x2BpMKugWZ9IKcn94bKOrnJuBbgJOBJMR6S+0O9x4xv\njJTxblCTj6QTE+WZggv+Ie6nV1qW755oq29KTVLY0dJ26x5IvhZL7WUlW28ImN/tzTzH4nug4/42\nUHpm/dSyIpi8JD1kez9W577VGNCy4ConZv72/oGoh1dXcFF+qSwZyuJvQ1HT0layHtAc0NtcS40Z\neirmptDQ5aTk0wHfzmZyB6UTWoC5QXdR0mb5gFbDErLBtSM0BYyQ5ofnC8wKEwmsUlGNC9vih26d\nX+hfvWp1g17pI9oQdBGBsj0iGyE8wBjHihBMY47YN8wQQKdIkJdb0sSoHyKZW5sKHgQiXXOYpcgD\nNIUbDDh+mzPmdMulllof86qc7zc1K30lCWHQ35zz9MzXRmx9cd2msB+OHANE3tg94S3jjCAvUXQb\nba0FQNhLeaL/SikV7RTpaMASBWRpkftOh27/ZG77bzoH+4jfkNPzf26MOebnGtn8+YcSRafSSgBJ\nbgwO/RKAycYMnIrs8OL+weQCMCJ7MEPqGVoI5LMTU3M8byRffC3WEFshJfOzAjq0m8bJT6fOzivv\n4vR4eEJS1JOr//hVvtoUfjW5lDNuEWrDkBJ/xEYjp6mf72e7nuj/oADddnK9YTVxwgbM48E0VjY8\nishCQ+NEWRLuF0JzuHrfMvmguFO3+D/7xDOJe4BMUi3TDTSKnglTVY4pVLYiGdyivs+UBJHTwBSq\nU4FTfZVtUowyfnWWGcrLlCz9kCe7VjZYG6T0AJ2EJcD/c+j1Nvrx/+2tMEpqgodR99zcnqOxa7hn\n2t8GSkPWTJwsKHIkdLvqvl3acnuA0BshONrzxxMG3rDXOf1SxXVWacz79gaJVhD31aPX0+vFpqM1\nzyfASM7K2uNsa7vwbGzc2eo/zMPAjhh5Qf0EoRRCQ2guyDTTJhkNJKFRuEkPYT5SHta8H1aJVTbO\n2YU1vuO1pX17NU7Pyq49ZO2fp9JzcmCNDsjG5ATYwq9Jcy1mVc+KnOUqK/diQ4bmorCBRbl1l6Rn\nzY7fKhuV27L0HmJwuVRl6rRmyqRjYuQcIt9o3uiBVefCV0nB2dkDFo35xMHVSZ2ECzNRLclx3E3v\nsSxH5LL/Qs6AUzesWpcXYsXUBzCJRnnGGZsO7ZVVtnK5zuWqBtRMtdblcx5UMwfFCwrROhmEejII\n8YQis1CurEEyBtFJGSVjkMxBceqX38kkzH/Zc7JzTl49dPZqmarlP3lkyb4b+65G7XmmTu33Mw2G\nR+vMWQDoBrRcEVCMRi63xYmP1JNr/tz5SKUqH4Q0rD3rk52713+wbTdhN8Yu9hOwmRSeJQuOEDZf\nTJbbU4cHP1amXlmvLnKuXGsYVBglPoeO4lnZCM0hekZ4zT0A0GJRws9qsnHf4Yt0q1R+y8Mt+Dv3\nIW7thPYs90GYmu9HaN5ctuI0gbf6jtz/5emcjKLtu1kHOKap7+xwR5tphfnelegL7sM3ZaWkK+mJ\ncul0uuUSfrMQx//3O+NvAaWn1nxZXW804M0h3kD3h7Hk7V5yVqsUz5WOVguASrrpKXn81HAQnRKr\n8NJ26x4gDR2fDhGvZn2/OQIhdbRaq8NoRMOgNknd/1ayezIycgxd7Kz0tWT62SIL/wMWwk7oApgi\nVtj4GbFq5sEPo8JDmAc0egvS9CfLZumRxEObSYZVNuGLObIVlQXU4u0WTSePa9sqQ+q29RGRLUwM\nRW+ZoABImoM3h6151GCVjvaDVfQta7mYVsUiVtXrgmXlz7HCTrHqLRZgqR0lrejK6EYBRDomvrwm\n4QfJKKok5wHaByzwPYlDVk34XgTb9Idi6+Ko2El22L3Q0AGli4EerXJUkvL6mQTF3iDFiMdPo5+f\nko0+X549ekRHK51VzVejwvbvdNI3LN+4upOrn9cd10IR8JSdnZp+4OLWIyk+tfxHLRueT09TfVDN\nGr9O7tj2Ryk58qLobULMTGKt08889Ud1D3eTCFmulSQE9By3cx/asanCGtaXmoxbW8z/edn+a9Gc\nFGaPgA4AB+OQ+4exzjjkuobsfLz/xEtJSXsHrFiL/k4DK9i7A65mmj9HcgEaPQA0iw5CcjjOk/SA\noeoY2Sy1Terh3UEDEMFcsrjR3Lr5/UHZuJf/kZZnC5B/d1/W84445mkfUOqdVPJuNnkqCZGvU7Ki\ndHYua7u9fNvMX8m/7d99598CSiGrJ1Bz6+l7YUX5v+p29A+pN0yyF53RWN2o0wudbRx1rxbl/Kf7\nMa88jO5nMfEcaDbLC5bvCRHAqWaEyUr7Kyl3Mks0OtR2qyDvOW/S2+hgTRpk5eR4RSYlHw90c1tz\nOTlxV8CMOc3kdXRMgAwyx9AHoQ3SzPEQaQOg2H+obMUKxFmhHomP3jFu1z6DaHhY+QKmfpANNonv\ng23ifkGnxEq9ROJty/4UkLNUHkKHZeLGb6m/+Tvwi7mVc3G+S2IKr+UECtgMlKP4UPpIBOGEIXSS\nO2+8uTZDAX8mKU0kYU3jKJC/qt2wv7e0G2q9NAkh8NBvdCNNKfvrzq0CHBAvA3hfin3puQoe9naE\ngpQGcxdOPRx9gwSC8vb29lP9mlWvJ6kaDd0CvB2sbWyquDg7BnSs4F/zgKv1UTGGdHaJSqh68XrM\npeT4lNgyVf1/rNyq7vZDK7an7f9uKQwofQ8jAxhDBwdQIVT7ePfKgdtWPdL7JelywDYCesbqO0kj\nX+gsTKoAf8NICR/1kddx5EaQTYp/5KJeDy7oXbWyldPkbzAfhX3iHDQABCvFGKfxgGosdhjzF3Tv\nIkkL+uAC4mfei34JlpY0e7RtgCFAGfvk+AEzgDZsBhiPLEbutMFYISbH4mOTbNpChfkKVpb++dtN\nXXjkGLAgkOeATnRJsggx6DcBRGVu41w9SzOUb6+btSQmiSY/LEXitaoDt7eTu/DdfwsoiW9SlDzx\nfXCaxnG6tBXeA1rstmZ8xldWinHncXf7BaUGZiUbLWmxEvcW7UZhBYcTRj7XT6IWM7MMhqVS7LNp\npiGnmoCiLS52dmuEdVmupQh3qxI4cmDNam5P1a7lIe9tIk7drbMNhmPWVlb70ClkZRuP2H457Uk5\nItKimQSZtNRMRWlkurEiZxIrVDuk0vi2BmpBHdZPmIajNxMQGin0PT1kw9QPbUs/2XDwRixMWnSJ\nxNschAkgGkO/2LG/3VvbtrE/QiOIaQmdYOBXEmO+3E43iV2NobmC8NdfqSed7KlmDik6AaE6VvMq\nU6FNmkNWT3xRAkVqHS5HT1dVn3MvNljM1Bui65XB5XA4LGzGO1/Dvthsfazf7jbl/XKzB/uv+H3q\nktNnCD/CxDE+YN1UPZdsrlsHPbq1jX+5ugNW/L4pWzFcXdK7+6CYtNRxPl/9ADhnjM2VDX0MGjjA\n+ULZAC2MFcK7MKB08oenhj7VuoaH2zwBtONaLvjl9V1XrhhyRo8w6LOtl2rmpkz81xNTh724fqOr\nCLjLP1W31qq3mjZ+ZcW5C1l9l60GbLAQuCYbD+sxsgFwEN3CGqFDkiy4lyRkovtQwHiuk7r5GkN/\nQx1yL2ghORYDnDPnAIDivHmN4+fzhPAQkN+ujQDfA4tF1imfJVOU72D//1gz6f0UCWPnz4h74vfJ\nftY52VcwX3X2KbUFuJ0Oz5DQW6aE4GQMvSELW0D0PdHuGCg9vXZ8e4NBt8mKorfeJk1oaSu8Bwi9\nEYILiktXRh++rjzbPiBFCnI6la5Wbj1i0HAkXzNpsg12Dl7zHhhx0wN3cO3aQXNPnCDLp1w7f//v\nNw/qKw9nc6YLKRiS5XIwOvrkkDV/tTwcfZ3sFh7gnq82aXS0cVmvjl0CKyqONjZtrK10TgKg9iam\nZ+2U8N2e1guWWB28fp37g5UyJn2WqcraqiDnwFOPtTx0PXqhhP+2vvTXZlbT0M086JkMWYWzuiZe\nzwqbUAXOwZjooe0oVryt9ZCqK1IIA02nLhbny0OI70I3xdMc8MWEwsTFe27ZTBOiXirHT82nJTAx\nTsYxAqCCLXZg13Roj3eDHg5Ws+RIRCAh4V5uZJ6RjEEL23Zk9rhLGQ1alvMlRJvbfjx+cuZTa9bB\nrBDahe0B1GCfEFffx7vlocGD1L49cSN287g9+6rGpqWXl8SDnPPxCT+lZmVx/QBdMItcT0A6IVjG\nED5F+P7gLwSb9e73XR84/Uz92qOvp6avefbP9Q8uO3MeQA8bktvMru0hMKmxaWll9lyNcvZzcjLU\nKuPR4HJSil7MKJXfzp5TLiUmK1M7tTvV2NcH0GRqRgCasCo0nTA4/K0XMC4mlpbfYc4W4+ID0GFM\nI2QD9AEYAdEwnJ1kww6HThwAACAASURBVDASbyRAPX3D67fTOD/GN4JxFjEsCmBkAWEAvsq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tvNm1/dfiXiPRHo1hpQo5rN7mtRMUFenlFiDvhxtyXLEfKysgacEfIDzLC6Rb/BJPnimDYt1jpY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xgHo+WgLbkOYwpFL2MPoBdc/jcskFWAg12sCPtXt6/i3XDkxl86mZto/VLzCq18bG1s/9u2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\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J6a1D4u8Xjs/olieFXbuV1u9VqbRV9DOVHC0tN525d6/Wbxcve/WvWKFhqdm/HjWO+mCe\nXLdOqHlSWaHs8ZihdplwmTDQW+AFE3LSQhKIF/1Thw5aYWmubyLGrIPoorIEY3l0Eo94nvR3GCYm\nHkPGsCGNDXrDfyRYKQyo2UoJEVZbHHTpjTVXb5xLSE0f9VubFuW/P332PQFkACU8YcIPGAWAGBME\nkwE3A/BDuOWPTg4wExgo9kuokmvHKO668HaPTWP2HwpZeeXaYXnP5IRhOx41eMA7N2LjhobExmd4\nOdj5HA4Lt5WwiVLSxfmyVPDdN+fMBbKfdvVYNX6wAGo3rdTHi5a5Gpx475GZro945mqmLtePpvK7\nK2r08olNY5TkS1EBrUcCivI2wNMAByurgbU9PUpIvTBE0KeynyGhYIwbTCj9geeB1w0IOps09P2u\n3TdtnbDq8rUAeZ94vGe3+p52tltcba1u/Hb2Ssu+27fjnRNmA1TDFDEWAOWEoQFkOE2Ac8JXMAe8\nh0kFEAHAYJYYO4B3wAvAG40SFpq/MBjMR1yXBpTYF7/R9HzyMru0ic4grI+6RE5AnrAcfZeQHM4J\nYJDrhnUKlg3QRgiezNqfRYM1T3R+tl3Xb4Edq7HxjXZ1WxXzLqyky5Iuudc6ZK7jWsZV79OqUfmO\njRF7q3Of9T947VEWW06OTpAJJ0OxzVQMTe/ET3tj4H+QMdBwCnGW6B/MKehBmUsAy8w1jwydJXw4\ncNaV6JgmVX9d/JqsqzlZmKVXmy5dHXH0bjiOIAJuwDrSAvrek5y37NN45j+AL86LuQqgRP/CIUVS\nAZtEtKG5bDiRL0zLrVHa8q2sT2Z282U13z/+fJMkg4GQ1YBLUTfrhCfiVeVtiwUQQE8qqtdmLiJM\nXaa3GHpvNQNOXcbEWHlz4XyO64rmy0gx16m1MQivkQUCeP3/0DAiaQkpahVZrYkiamZCYsKw5Z3H\n5hY4yRe2d+pwd0twSAHWgdsbGmYcXr3ysnruhd5VLA2r94TcifVbuhKPGCPDJG4qBtQYHPRHpBcD\nRmBemDwxHnhWMhh1Yih0bUzA0lNvU1Ztq+zFaRXD7EyD8RX/Y6cr1CiQ/+smnt5zZPImk+GDrxo2\n/OGzWpXP7QoOqdt0+VomBOI2GDA8tOeR2cH4w5vnmgJlAzBiyFqJcRmWXaA0h2VSr1mtU2MMlAxI\nDGL/T2pV/7RlMe8IXxcnnbuN7dx5Fy6u7bt5h9sbsz9e4ODp6m4nWg+99J0XLXMVXSAtb7g978PX\nCsdSdJbw/9o7x3OVAXlUZ8GwSTFWaJcL81qPpBzF45qV1Lg58uPJs5Pf3xVIuAlQRHgJoT5eN6nZ\ngBVACP33h+B3+kxzsrZ912naTIwLwMe5V7nSG39u2dQ+NSPTpt7iFVfPRd7HEGE8CZsBSPgtfYSx\nQj9EDA07g9AbPQqsLvvC+PKec0EbAHsNA8RYwtFAlI0RgxGCsWA/NM6V//N5rqbWDNPp9oiWsbfM\nUR/KTTklf6dK34SxgjFbKBuAjf0CvtD70ThvDPb61t7eN3zdnAObeRce62lr987CoMtxk+o2bPGI\nBaFxDGGQVR3UmwtGd5VVIuYIQDNn/rOWFSP+Nkcx7415xvdoPlNiElUJg2jAjr4eHPtNm5uxAVgB\nGcPo25j3SOpAc4keDO0ZGwA0J+vtEYdzDBvY75KthdlUp+k/TZaSFf9pX6J4D4+Zs9HL4XTyW0Kp\nucDvM572k76mZb7xrNHiMj5I1PGTDWeOel5cB2G/XMkMz+HYf9suHgreyDIZxNnLvlys8NmfiYkH\nqsw4GLbBPi3zYY2STi+CRcOPIng+k0csmXOgPpsmzdd0SITWSOtGg/T/sbFkC4AJFo0mYOm4mWLe\nc3brD9ExqE1NE1YMpyQFWQlPTFRKuzgrlmZmhK/IoDnMAqHZX2VgMdnjceL9oG9i0p8tG0wOXheT\nJ5lEGARoeSbmhYYRg5uJcL27DMk3dJOnPZEuyAWQjKK10BklVKHziYxP/Npj1mzE11oZA4wMx/te\ngMmEy1FR8aXnqHWnmNiCFS9SAAAgAElEQVQIfzxPD40xiK6DcBy0eWINzwLfHu7euaXZJDXjTWOZ\nMDoCDodEC21ZRfoQrBhsFN9x7VS61J7BVSp2uPggynd7yK0j5n1a1LKp4G2plfp40TJXqYdGywuU\nHpUNy/fQK77mWU3ZJVmzpmVA1J3kaWjzSN0Wy7ZDQm8A1bwNRokw1k3pH4OkynqXCUdPwKrALBGC\nBXAAWAibwnYCoAA6Y7Z1ai+Zxbp9LVashnHhIty/f6WRvrQsjns3PsGhe/nSjZZfvjqh89rNGBk0\nb+j6OBbGjzFCv4SxAgTBWDGe2LefbIAWdFGEo7W+zNiBBeD3sLOE88jQA6ATgiakNlY2DGyulpW5\npqssToiE4rJa1kLMWWG5S1HRvzVZsqqUJFdglOnHnBfhOELaWho5YTgbf7+Gm4fXrDJ2/vmLm3pt\n2s64xqDS+B0aRNgw2DGAHdpB+vWYgtV8bzcd06e06OxKkb1rI9rZ/69zYt77R9FSipeqF6nTLS5q\nm9BzrN9YdVFjmRcDf70QtLT3pu0AJMKpXDPjH2ca4MEchGOoZb3l3b3Sq0LZzj8285vfd8uuqYsu\nXioT88G7tvYWFkHm/jMIt1EagD4GEMsVuntoR3/sA0AcrCfPDzuHRAIHF10cztvzFI//sTP7C7/9\nMFDa4D9JDMgIS1lxnMVxX7an34H0ZLyGeMUtJfP8xMNhhGWk82BLTW+vcYKE4jB6Mkgk9g87YdQF\nyuvTy0q4RG8ubPeN/KQuP1HX2hMm6Z/QZKVrJTlWaGWpMKszKnECWt6a22q4Cliy1yrTvFb1cgJv\nhyqyzEJ6dHyaZ5n58xnIeDywNITUiNlrTA2TOx41EwleDN40IAWtBZMqAxVqOV30Dv3k4C0ljDcg\nT60p9Zi5AZJxrdxjMT46HwGuY7O/z4NCqI1OCE+fyQrjM1YmNfGYDKsF1HEsMpqgtglV5BVeqsd6\nDk03tm7tL3qWL92j2M8BGA1YJgzggawJlnTgaXyGFmXqlGZ+5l8fPLrSxszMeaJfff2h0PDkWwlx\nt+x6NitgXqV4QSsp9WEppT5etMzV87GQLk9mlEwzV8s5FnpmoJSelCp1lBLQAC4QMTdZZqYNRgeg\njrEI/qZBg08bFy24vfbCpbvkPf2eZ0MfhrGhr4yVDR2Pt2wVV7Vv3UKWsbnbd8tOjBfzwBfSPwtv\nvxXyXfNlqxdIMcFAvc64VxIfdrdasRYmiKQGDCeAiLFEiIZQHP0VJgfnwlRQDYgDQMO6kgEF4MDT\nB1QB9gHklBbIEVXL64eaZsyVdL33I5gfzQmCYWonJTs2pSmG0VJTCgeHYwHOGKccY9yI6tX3fVCj\n4vh3t+6KlRBxhqyJiN5KM96EwgnboHEh7ETIEKcFgIAOqrxVPvtJA74e2DDex12tWaM6kbDskun7\n/7GlS2KPlFiRUFs2oaLTfxXQajiaxJx2+91+Nd1srfZcior6onLAYowFoAiACRBhToRJB9TCAt58\nzHWaf1anxvx3K1Xo5Ldk1WeNCxVaP71Zw9Nvbdh2ZvXVaySbeMvGvgnjPU/njv2ixTPNfOPZ15EN\n9n+zbPT9F6Y9DJS2+PspmcbdesmUkcq+L8yF/pUXoor0BCzppAjh3N1h4ukZAuV4TIK/N50q7A6U\nieW0YG6hr3UuEv7xO+Ju12xhKadaCWZ6SXwXelk8Jr3Fo/Sjf+UV/Pl9p0jGHxOE2ozK0IA2I6eK\nRwpNnyPyjhUaeuLRE8qIGlUVZxvLtVJVGrofrxbaDJ0OdL7WGHiEHAgvkGmDEaJD4nWSfgyzxKBE\n0zNFQNlxeQC+cjx/ARJoJXIDJJ3Q2xJik2cg+o6HinfydY6Hh0TYAiqcEAIXVDZ4QJ/dH+07cHtp\n0BU0BAAmBPaaR2xyys/nZeaIwUNF4F7e/ftfYCjwMjGORoDnnYTE2i2Wr3Zd0a5NVGkXp5qxaWlm\nLZetUSzNzc7LemShdxOSuF+JX30zxPFaxcIFrMThsRSH50XLXNXu9JNCb6aZq+mGDBF4XVezW6mL\nRjbs9oiz6hqOWiFawnkwUtdj7iqzT26EJZ0opT4w+Fhk+iLMjqYbIUQEk9JuTN1aNrL2m42UZuB7\n/D9YNkSthJ94DxMKGKjfr0K5NRlGY/+A8xcBBGRcqv002ZD+H9vvZmLQXitobx+5qWPbtpJladl2\n1YYdwsbCBpmKsxFKcyyMFTFI9EmmTUtsAJAA6AFasD04H1DAMBiAftgfQieaVilnH+rYVYzjsiu/\n59n972+zKnGTLafrfSMmJqzzui02J8IjYKcAaDGHu3c6XcvTU8ZjVpaWSckAnB7qUzHGYJhgoGDi\n7ssGI4KTFJg47L3wtIzMSU5WVs5vNyk6UhYE/0QrokvSkcq6/z+RJSDUBiBlivOY1XQyJxm/DWg1\nkoy/h5pkwH4mSzV96n/0xL5P9x2CgeTZ8GxhMdH4wDIBktApPqqhW/NJGz6o1qmIyDa1Fi7bO/2V\nhvc6lSr1vryedisuDkDNc+dZc3+fd+P5Md/QNwHnOAREC3A00Ub9Fcd83tfwTPt7CCjxqz6b/O+L\nkNgVoARgetmefAdM1ngrKWu8XcvKHDEGykSDB0eTiUwvVLfBT4y5n5g8P/kseoWPU/jGog61+YKF\nMEjE4HORUP+wG69l/nHa+dIM86cdvCPeuJEYBoyR1PhJU6afPK3I4rmKg6Wl0njZqh6BIaGax8NX\nAD5MnMF5Lp1+yuTBhA/zhPdF+ICJH696Y3a1amGfdI5yjyV8bBDvU1gjU4D08Lppj7vDADfCDXh0\nyaKvKis1lDJ+PnsetgBvj4zGJ+kH/tSTUxfDlXug1Z8RoyXhC117MRj9fz531qqGh+feWp4e4mnq\nRas0lfNhMoT2xoX19CtSqNynw3u9v6iEk2veUh9/6sT+JT9OlWKAatKCYhz2a+tR9DX6HH0TsE4Y\n7YhsgHEMxBv5rCxLSGglnxQnxcjDiqIzwwGgj9IweDCjp9+tVCk9n7X5txOPnCDTCbYo4XK/3pFS\nDuJjAfiESbxlg/35+ofmjVv2Ll+myIOktIaFf5xNgUbTENuz6j9w2GCiOG/+ErLjeqjzBGMF85Qr\nlftRIbfs63jSH11n3xILWhX3qStFUD0+2XvodiEHu7Bx9eq4JGdkvm035XsMfqKktxeUMgSnxx08\n+uHYA4cZv9wzmAlAAoCQ8gdDmxUt0n7Oq00jZMFp7rva0OyR6GJn7/CRTjF8IjIF1ThRHNlKGCZC\nc39HQ4dEf0GjSnNLzTQ4pxm+/LTLp2Ofcj5WQ6pVHji0WpUxM06ejvzu+ClYJMAt4597Qf8g1EpY\n91GNJBPA5blr/XuFXI+JmdFi+drPwt7rG7Aj+PYrPTdtA4yTnUY47HloKvOeA8wk4WNALXolHALm\n8+zIyYuz5tsjgZLolALkYntZOQhtb/97he6/oxP+fz9mpgySJCk0aZtpuDpzXxgeoYxoAUJGVdhI\np6fv/CwhHoxuTvtgzbc/x1qYqSmw0Mikwb4ILUNYpWRhl2guaZknPz4dPtstyYBeQm2rr14/IBOC\nLiQ+/sjt2HjCAFDEDHZi6gARQhtMALAoWsMzR5iIpwmrhL4CXQPiajLjmAj6NyhcsN/OTh0qmZub\np0sN8nB5BtK/dTdNFpalv7N/Jo0nUdHMuKR0cz4z17Vte/w1X+/LN+LjaxT/UV1MmAwfb9lgfDCK\nzxU0qeyYoj8ka7mVl7P0k2vYI30qUF5/IcaC8AR0PE27HjROMBbcJ4/lbVslp9Ur82B7+QIdCFPI\nsjbafXz59xnuABlKGVIOIz7swTsr+3/rIz9hXHPfYQYIqcGSor2BWewqZSV8rw/o1bze4lX9QmJi\nYDx5PgB7vg/zA1sJK4DeaOa7lSsk/nj6HKwoTErdKu5uLQ9071xEGCW8cPoyxwRkeYuup+bEhvX9\npLL+aNEKAW7+m8b5wjLBlNJnEGHj+RMqIS09q1qptKeF3J5wcPaLYzi+nlfBUhMb1nVdcfW6U3Fn\np/0Jaek/frxnP+eA8Z+ysn2rFlL9fui8M0G1Pz94kHsCIAAkLpCwvPOG118zFnNyhO0yabo9AiT9\ntA/6bp9Y0JCuJzOYa1EbcgX6u7m1hVpw969shNUypGxKRlpaDkCSG3BfWMiv5+wJYc4JyBZsy98n\nN5y8TLPMfccjwpfUXrgckERY0ls2WElajiZTXpvON4Qo6RMF6noVdN/RuYOrjblFiwa/LXNb0aH1\nOmFR+xaeNQ/w9Vc1APfmBW1a5OuxYWsTeY1TAeBtKht9fuNfdeD/9X4fCZR6bfTvLIZmKTFgW9fc\nEaT/9Qn+fz+eVoq+bEzq+pGnI0HVDzejpbfO319CPlmt96ZJo8XokdWihtrw+l+kliGVjdFsUXep\neFza7c9PRoh+Qwx9utmabL0DBh3vGE0QMXni99DDGBYmBQYaOqAcDlteo8nAcyGbAmoX1geBczJp\n/jfjY8cXyedQePP1kJhWJYqWk+KMlxWDbqBJ2QK8HCZj2CA8NFKYef2oxrgADGFMAFUpAl5mS52n\n2/mm/chkjzdPeIEQBmKyPzUhqEXkjFI2wmBsL3XS/dIyDPZfHzl+OjQubk+HYiUntV23LiKrdope\nMmV+NxZyXO7j55f69SxUevZ8qPv4qgXcq9fxLNDzWiXvEZ6dGkx/GUJ/zBN+wseJ92NFc5eh7Ppy\n3plbhy8CmAlfUX/o2xO9u3kN2hHY71BoGCwRfRHw0U90ZbYTjh33SE7PQNhKqYczshFaBjDxe5wB\nT9mm7OzcIW5zSFgF/yNHYFlsKubPP3fhay2KSS0xwBjOAOE6mFUATHAznyIlt3XsIOVGJMyWphv6\nKM3QH7xKxhzhNjL0coXdnjXk9rjjSamKEeMb1B0kNcDSLM0t3oxPTZ7SqXTJ8r3Kl7kg9cm21/9t\nOSzWpsPd36wjrOh+Af4YesoMwNKFSl2pfjcG9LK1MTcnq+/3ppNaTiaicu0f/TZOKJOpmH0s7F8u\nLZmZJYV4ZRPw9LyiImohXgFGAKQc/VHWiaQKi/2tYpvwbYDfWDW7RdMT8szkvLnmJzXrE9262Vf1\nctsh60met/7ue5ga+oymN8ulyZTPTcP+AHGcv6bvV6n4w3tVKro0WLQicWDlij9+1bD2e8svXC/T\neeNGwKifbLDPlJx4XuxSGXtLy8Gr2rUa2Hz5Gq4Rx5KGRgnA97yO85Tb99f/+5FAqcc2fzuzdKNK\nC2h1WP76U/lnHoGFcAEEUoCx3tzAYNEtyDIApg1Nkl7fQct0k8y2bkIZq8yTKtq2+WeItv/o0zFk\nsDJ2jFpzqWJU6qGh5+/9ZFor6v/YuwrwKK62O7PZuBFIQgLE8GAJ7hDcnRYpXgptoUCKlNL2gxqa\noqUUiluBCu4Ugge3oIEIFhLiLiv/ObM7YQlRyt9SyO0zT2iyOztz98695573vOfVn4/ZbsxuI2jh\nxEANCLUNZEf4e4pR5UY0yawf7loZLjgDbUczfZp/OQDPC2TywuIS9u8MDrEbVdOrkpiJkio6nxZ+\nDrUmjNkTsPI8XLTy8z3iLpw7br+4MR9ZJGSk7XT9eRUXF+orCNYYepH1BAXuIn2YEH5aIsAP2EfE\nbfDTn2CyzZZtmQff7bEeCwgpRoo7JU+V7OE4/YeJmBB/+bROzT4oKkzGre+eXt0cV1+/EbflVtCS\nj7fO2pBiolBYOsAiQPl6Cl8L3Gn/0Av5LPOZNkFJxGVdJv5pb2FWv5SlVUx1+xJWi1u3MIZ3l2u9\ndVt+C4yK4s6eglWGRipWLlH8aV0nR49112+RceRYZuYZNRwcrxzT1BExBH01ffwn5SISU753XbaC\nmUKmcJyfu61nlzoVflnNMU+AxBgOz01ATzDM52MuxsAXmGi8AJiHZjdpxN9JodTEQdNJgjSCOJ6L\nGiSG+chUUTDNxnmfnmU8f5a+6WVDbjgH2eDRtmamxXpXrFB9aFXPmU1+/Y0gj1l21K4MBhBMr1ai\nxISKxYtZIVP+h133Qo728ay47MeLl39D6RZmk7KvON6p0WmU7PtRsIXSmGJu+Yqhl1o4Lev/s/1j\nyLaZ7qKxsiPmgI4s7G74ZwIlAicFSztJJZ6M8BMzNlkn/r/+xaxKwAxmzllaMEas5sDxwN9RfyT7\nx0kdKIrReP0uMFe7VUbaPevaTnghs0yn39KuxnPtBo+8FjkAXH402XMyM7HuNjaX9r7T7bMHiUkP\nIOxnCHINDjqqv6DJNLg/hnz4PZMhLxU8YshKgM1oi3mL3wsZMWTDo6Sk5k02/i7PpQTIZPYI4Asa\nus2ty+Xfl9jXu3vA+uu3Z62/eZPjjvKIV5lll9/n/yN/zxEo8ZOH7PaDgFbbh/FfFsktai/2gJwd\ngyctEII9KcymS581BEsIm8AfCX/a9r6P6xI8djTpUrwxjrN5DAzuwOgvxdYtNOFq99AETMxgTtBS\nVJnHPjp4xBm6JZikRThDs3QNGo9jESkpZH64SFBMTUbnhUbxa7I6/esrkdGVwpOSg6Ah8nKyMN/z\n05Vri6H9IAh6jEmfjFU3TK7cmX6Pg/Fz2bqAAkQuWvkBJYolGR5kiK3tuUF9147Y/1fIpYinnMQI\nohge4ZEvoyTrjHD/DMt6pKnVx36/fSdo3vmrmosRERQGU8x+bmqjBl9/6F2tl/NPy6tB0DsDNeBc\nfr1558b5wX2HJ2eo++q1Kll9cnZg37Oh8fGn3t2xd+Wv3ToYdSvncdpt6ZrvnyYnd23x+cCybk1q\nFH8bxloew7BQf2KhUrLEdSNThI9v6EywV167IUSlpgowSRTMUB6mwvK13OEzW4sgnC7Y9A97hHBR\nrSfJKcneDvYZMelpp29FxzJEzDFEoM6wGsHAti1dO4jzz1+xVBqJiVimzTPUmn0n3uv9hWLOQgIO\nAngfHNRA8XPYCGoIzL/C/PKOVPbjWcamfH9ccJ1wcLHi51Fjx4eP2iiO9VAcZDsoCSAYeS7r7SVD\nblw/mPwwoE/lCveXtm3ddcGFS1umngxg2IzaIy70LXCcxkH21xwapm+/a9ygb4XixepeiYx62rCU\ns41CpSjNeo/4O8Pw1IHdw/NyAM8J7gUec9KcoQU4XLRavtm8fnKj3z4kfnaysfHwc45maZnQj+X1\neknbJIGkvBPDFFrhNrbEOxFM3b26/QT/glwLX6MvEDwWALdFNoBLEEwLiUU4OI5GTq5bd9kMn0Z/\norakEjXcyGKy/17QZOJ3XJQZlWBfU6dJL6wwWR97IDR0N/RK1SJHjag148zZa/POX6aOkSCG2jl+\nnuzwXtDbyPV1Om834XIexXn/9mf82yfIAyjN4eLOjAop++1V0Zf/9g2/ys+X2SQ8ZANWd560gSEU\nMkf6gUOhYjwGj5Q6eGjhVzU3lrfdjF1KhbcJfNJnia7lbKJW0X5V5/EUHQt/9erVffCB/VM/rVf7\n+ijv6mpk9lT4+tSZ2s1KlzbpX7XSUWMFpiXaJ0ilYLRk5cLArbjrGaQKSy9fTToU9tCja/my/vPO\nX9wD8MKdFXdJEtuEQ8Pv4ZtTZ8tj4uaEwt07G+P6nDS4S2coL69GrQh3pwRU479t2qBK0zKlXX1+\n/YPPBYESFyUCnBcoZmnhEbXNNRp1j+MPw5uvDrz50ExpdG1AlSpLmmzcTA0LRfw8NzUEzGyTzDrD\nRg75JDI1zbfu2k3HoDXq1rtS+azU030h93fAtG8ZfFIkYMZJMRO19qqu3DAFGW8LU3w/3oBadV4j\n9h9m+LJ15Y4NezUY1bOGEiF086IQej5fte7P9E+ij9L7t2KEJk9S7mRq1BX9zl4U+lepJLjZWAs3\nY2KedPxt+7HQhERmrJH1YwiY4KNePeeSIFK1D5Gx6FitRHGL3ajj18bV9QNXO+viZW2LBdmZms74\n406Q/cKWzQQ3Wxvh2INHAlzWUxHKOwoBdHuUQmGYjyFognOGpLhAyiCcOh6OkyD92GL6/FaEdbiY\nMvRi6J3EW+H4pLCcCyLPxfEus90EcFLJErlTXjLkxhB5nb6eFav19azUY/ShIwceJiRRi0nEwXFN\n5pUCObIi3GyQZSI7svLL+vU3tClbZlBdJ6cpZsaKzIjk1InOi5ezHxfjucXcKQ6RyyLpbEakxAVq\nEgvc8L4oUD9BQ1u6fK7Qaj21GtEZRWhrpkYnNDQvYSOa21ibKS1MDMWhGSiIHGNkqgzWZmqePrp0\nuyz0ainJT2Ogu0xZd3vPKYLdl2r6siOrs+mWCJT4/THcSqsUZjHOmtaggcvA6pUvgwE75bF0FfvT\nUJPJDR/BEdk3btL4vVbBQaDMf4vdypdbuKpD63eOPnz8DkwoIzWC+ijCcStPPQonG05d2kocr4pR\nUmDs/gFm26Tb1p3PMXkv1VGv6ZtyBUq8XoSJlgJoj/ivW8j/f/Q900DTE1MEK5UmcNHJRxBwcueD\nRVOj6QsnWYQtRQg9tclgNLjjAkM3h8ZzHUgBs6L729TkQpCaDFXS+VV7ht3YcZxaJIYCyP4wtk3q\nmACGGqJLYIyaLwu8UsPv7KVPvRzsk39q3VLjYGlWGdOvEsAJNreCqVYUTnx+9FTE8YeP4zb16jzH\ndeEyLiJy4+L1gaOFRfeRXtXqdKngsbbe2s0MTXHS5mdwMQLTl28zpMZdSpib7780uG//5pu2Lg6J\nkxTrkkYKh/mk+rXrKESxp1qjaY0snwolLcyjR3hX3z7t1Om4P28FR4IpYxiB7AJDI5zQmIVEa4PV\nODhJMrU2I+3TUd/tuBvcFAzRqD29uvp2KOsux/0NLlYkUzAXSNIuOSPTyXrBkuQq9iUqnh3wboNa\nazZ9eic2ljqZjc5V3Tzbzx4diFVLtHJEzTeEG4pa7j1ARiEpgl2H3OyTjwTzdHUKmCGFo7l5VkbL\n05S0h1hwMr4NOOuG0EtG5RJ2h4NiYhPOPYnsCEZlrJlSnHbk/sPDALuD/Zo3jihuZlaq1/bdV04+\nCt8NofJH71aqYPdlw7qC8pnYeA02UxsxTvcvvRLIsYGMTcl7iUCM44P/T7bgOTBuGNaJT8xsWWzJ\nEjJOZHEYxuKzRcaBY50AnGCJf5OB0nMlS14y5MZumrSnd7cmAY+e1Pkj6N6C61FRBPWyhxKvnSnu\nZLH4fDNEzc4lo0GGjIs8dQfFb70/qBWKO6ejJA+wgYKL+DvYJHUuLDDiBRk23heejwn9dh5YuPPe\nPTJoDJdS/8XPZf/QYkTTsla1ZdPr1G5f09rqXs+te8J2B4fwddzIMHwpA1VdhsrfaLnolqhLI6A9\ngYNMOsHkE8xdH958f6Ab5pSzdgt/Zpo95zR+fwTEvC72M4EW5w3Op5wneG90gR+2uLWPJ5IGqilU\nRs2DU2K/U2k0XSotX8cMQklD9SpbnO/oWrZKxR8Yx9xYvpEtz5lz4A4/DyMjrRT+oKj7dTX4+qe/\nGcayk1FdnD8/vBEVUj8yNecBohX7iX4LNgEkkeocTYqX/agvAv9PX/a/+nmpsbqCkPh5d/Pg778S\n1Gp6I7FxN8yJKwG7rm7onMuynoshtvORT2adfhxecXQtr0AYWlYHUNokqMVdgpG2DszB7X66fKVt\nbFp6ySkN6tgpjRTcccYh2e0yfEU0ZSzM9/bcsbdva3fXvqmqzBWzz1ykEJvhALJa2Xl2efJkCIAL\njWHj3yjyafRJLe+ZVeyL3/3q+OnzlsbKdzxLlLBrUqZUCTdba/Xcc5cylQrhjl/zlh8337SJwI+7\neqJiaq34HJFepyidu2rqVgi0mPZN6pw7vc/grrt+esDZRFzre9ObNvb9vEFthg2z2oOExCsuNtay\n7YQQl55+wdrY5LtUlaqvWtCGFlvwM72e2KcUGW9v9/2Ixs7eFSzMUSuLvjNFLfceyMT4TMM4TY6K\nu70lMLGSx9LVX58d1McKTJAvgI2URrXu+u0Rg/bs32xjavpTKUuL2r0rVXiAenv17cxMzFu7uUZc\niIgsU7l4ceFiRKQwFjYYIfHxAurBfeS6dPUJNxsb72+a1F86qKpnFosx/ezZyV8cDXCNHPXBx46L\nfyEDw7HBRZzaIQqUuWBTW8QFkICa+iVmQ0njV2JbRGFqeGJq31JLfiGzwNfThJJZdhyD1NixlIqU\ncaa/ewIv6lTCXzLkJrGZK28Hnrj6NEpRw86hNWrUEajRa4pjnRlAHPf8XNmTjJ9HEMRGHReBE0OA\nsxAy/qJvxQpHwP70x6TaFr8bLr1Pq1iQSw1J/Wny/rG6UyenO9ERD1q6uRxtvXkrWWQa1zJTURZK\n81lkGJ7Zh9vRl2Ox+fg6LCHp5Mh9h78/EBbGkKHhXMDvhRtiJnpQaJ3FXhfogvCifHRLfEAp4eDP\nm7VLlmxOnyVnK8sI159XMnmA3yWffYq5udEjaCIrTp0cQSq/V/a5ZOR7tG+vn1CPML7Wmo0Rqb6j\nPDVazUrL+UsIWsn8kHWn4Fq3M/ibDX0Xl5s56d889Wvx9ny3mGCVZgMPTKRtPMuaFDVsj8AkkVEy\n1mgPLjv6CPQuaVBtNsMpXTrr4D1+HUStlmySFP5gGORtbNytU6/EbKKwU9eOHfl+LbVD8i5ZnNyw\nbqsWZUrvsjE2OeHp4DDjRlTkzFqOJV2j01NDlly65vVpvZo/2InmfjkIItmhTNm+Lrlos2YejDyl\nn2D5nqQkl1t+5XqZZi6l1JXs7K6VtLJkmj0KeTHdXoHQqETnk9lieI5+LtzREZxkFySavutZYZKR\nqPhocSsfxxlnzqmwaMKOXIwuaWH2O/QW3PFxAmbGB8EPJ1Wey9A6gAsJG8X81JOQQufYIA3OsIs5\nzCYnDd/31+1VgTc4eSswAfF8UnuYmHT7fycCrq4ErS7/Tv6JVVPle+h4lQWXLg3B78heheIoW/O9\nNju8+retyuwfljMparn3gGyaqkpP/2Lu4bAvIKzffyXy6ePmrmVifu3crq+zpWWFs08iptRft5kb\nI4qkCVgYjrlazTQGJHgAACAASURBVN6+flJG+mosasU7lfUQRtf2ArmsFb4POCf08aw0od7aXxn+\nmjS5fm3ljGaNqT8S7sbGpUDvRNDui++ZvlnUozDdnQsi9UljcRD8MLmBLBDnGC7cZIm4+eK4Ufr5\nNB3Yv0rF+U4WlvMUfgtpxcHsW5ZSYWP4jjolMttkczhGp+Fg1ib1MwUyltSfS/qRNuETjw3Xbx6G\nyabp1cgoeYyTDeF5CXS4gPP/yaDy8wj4yC4xBLcdB0NNDB1RM8hszcnYJOGaxakUPUsfYqzurtNi\nYfVRiNOEdGS9PV9A1/CScvt3JWQjLpnauG6jdK3gaea3iH3IsDezv3Js+kSLaZjTB8elZiy1W/Qz\nvytqvghWGRpkfxKkkPFjP/M+DbPQCnJdeemWXng/JQSXI6PKo6jwWjh5c14gM8S5hPMIN1/cHLFR\n48bNF/v75NiaNStMb95wV7om8yuVIF4wERXn+u7c992+4FCOHUNQnbc4K/87cpzerNHx/pUrTnVf\ntlreAOf/rv/QK/IFSsP3zy2uUqk5GCzpx0Kfire5MQsiEVkx7DgAoGarOk86rvO90cLcy7A7xR2o\njj14WNvS2/GoNysS1GJWz4C4OxrzplbQPL0WXGH350skttKjWLEPRlSvMjcxM9NqfL1aajA1yb/f\nCkr8/PjpkhBra0EbcyLgosHdU24ZFWRRuHgRoPA1oTgouOVOl+JuABkR8X0td9XQ1MP4E7uw8OQk\nL7jiJk5rXO+am43t2V9v3rYMeBgesaBN80OoypKwNSio3657of0eJyWVaeHqEtS9YtkdJUzNh26+\nfXvNuL+OccfJSZ8AixMUJy1+Jnd93KmRGqf1QXSZBlXqa9NUw5t82meruUMx/F5rpkrNfNooPlXb\n/HFGiXMnLzR9nJTcBEVuK2LB5DkYuvBAEdXPoI2BCFi8Umftpq/BHNldH/YetQZZDSvng+uR0U9q\nrNlAATzDirx3qZXzqdmv6cT+G5HvI1g58TSG7yz6t2EPJCPsRnCD+E/VFYcf/HQ9KuaHaqvWke37\niGNlY5cOUbBeaGmqVKTNOXPpr3kXLtLri0kDBDAMp0YtbdNqzDuVy0+3MzOlQljAAifMOnMhFsyf\nVcPSztcn1q29FvUOe8Bhuunw/X/dXRN4k+Vp6mEO6agvgkwWkvpGgndOttNwEBxRo8TPINsgC3IZ\nRmF47XZJCwuLcXVqjp9Qt+b5J/Fpw1yWL+d5KAynLQHD0i94fb1MyI0MVHB83NEpx07t2HzrDsPm\n9jgIjOhnxhAfmQ+Gk8iYkjGipmYgDmpt2E+8D2px+Byy/zheeY9qvbYzDv3AMLnUpPJDomYIxi1A\nALyJFOKC3Gplyu8x+OlcyspyKuwzBlsbG6P0z0LaMTBT9SoO9iPZuezsMd/u4OXosGxTl/Z1S5iZ\nFZty/NSd5Vevr8bvuali5pj8/JGxo2iebFmhWy66JZ6HjBzBMo8wXmvAe++equ3kaLL26u12YO9o\np8A5hmCJiSC8Jm64GHrjuCEA5biVbAow3fpvvHWnZVxq2nyEeO2Mf/iRrBV3Tfz+uKn6u2n8pof6\n9PTzcS0do5yziJnFb1wr0LSJ0BG/mO9oFW+BauRvc6MwWSoEKwq/ItOt/4tZblm9E3MaVMMyzxIW\nDLlZOWBtLFBvv9m9y9AGQxxIr120quOEMbzbMwP73jRViJV33AuRXLuxUKU3Xv/bpStPo5ghSDqZ\nE+8cHNwpEZywcVfF3StBCn/HyZc7Kk5iDDO44Mgm7v5kSEhC4gSvVRuPwBOJYlbuhisBqA0KGjbo\n0PZ79/r7P3zYuIGzU5n+VSpbXH8aZQRBrqpBqZL3l12+kY5MncCpjeptwPeI8IboCSEtd8dcvEjB\n0/ukkkMltwlVujfJgPVDY2jRRAt7O8HYVFkMW7Z8415KlTrJKV1r8shKyXDFDU2mdq/vn5e/K2dn\nW2nCXyeaLL12bQh+vwoLipwNhdVF2Izh1U5fKDc0h9Gj7PnLZ7dsStmXo3uxyRtibJrDff6tX7HA\nM7V0Umh4wDfeKLWx28ColOcmnc5FQAWNUVPfOrWqHQp7oJl55vw+hHkp9mcoiVqTyRGjPmjtaGnu\nBJxE0C4ldOhCLmownaIPdDNdLqYkevwe+TiybYsGW3fERFrXtrDuGWhnejbcWJH65No9d5Qxula6\nZsUdGEeh+75Y2hrXdSQ2JJxMBjcEDLWQ2SDTQa0bQ20MDX56YXDfyjeiYrosvnhteUB4OIEBr4uZ\npAzpEbgw9PLSxpIEV1OOnY6cEXCOIENO36fJJvuHpoNkkPgMkm2jZw/HPdcPmqLyetkn3NBQ3E1w\nxZAYNxhGPcuVm/VHr87IetOMy146RfIa0wAwaSXjWYb3CHz4/GVvPL8PDgIBPkeJuGYKyzvaL/pl\nVnRqKoEZw+ucewhEnsv+05+M8w3vb2/g0IFdSlqa/2pspLi5Kzh05ICd+8h4kR0jk81NGN9faEZJ\nvuhC+C0NvvfBkP4OFmZlPVesP4q0f7JiBL/8PmkLwWxiapmoqZLsZ/TN7sfWPts+8KpWwvSHH3+J\nGj1iKJzkd5r88GMo/k6Q+LydjcEbC/NPHaDVonbmcz5vhTnFa/3aAi3d72x5x8jSqh6KuArV3mZh\ntyzgNtJq1aMCo5fVjE07gz7pi2/YDj/TsIBWBVVsDxSA32vrj29UKirGxMheLkz6Wo+Ef+ji1KBp\nUqJ0Po/NIpLbDL0VMxV9R5dt4SiygBByEuzMzNSLLl55DGE0w1EU0nIHTe0FwwoETNwt03uEYQ8f\nHJyk+W9+F5yQye7k5JZt8uDD9/9IUWV4Qth4A0LbK5u7dAxec+PmOKTVO8Ekz+L4o8cq1EuaufbG\n7Zt/3rrbtUrx4n+OqVvLbs+9e93Wdmp7B3qg6pcjol087YvV7Lptl7qMlVX4e73b3rziYmt519bU\nPc3MmJP+iw1WSdSmSfo0+rmICni0QFUEBkOj/5nT2xDeTdKEx964curKzfunbs6LvBNyBUBJR5WL\nUgo5iQt3LBpDcvxc/HLA1hltEfJFiAesUsli/++uxbldx+v6e34HyU+hOcTP638enXduxa5U9YQx\nTbUaYapy7kKCcDI3XPCpb2Eo9DN41WjgMJ3ZqJQzmD9xXrllqzOC4+MppL6ry9ISARq02N2Llzu2\nLNfOUZ3eEZsD+vy0wffFMcJQbSGbGI8xExx68lqKnYfTl9s+9COjweti9mVKKWvrsQgP/mmuVIyo\n4WDfI12jXrviynWaulJXQyaFoSOGXVT5hNzIOBBIkJUgGCJrxtCdPw4yF9RYsS8oeubrqDkiMCLr\nS5aD45IZdQRGZJvIqrrjILij+JjAjfogbnK4wSDw4Saof8Xidn8EDnuvh7FK6ZFbqE23OVWgj1Fc\nXBTnw4Rzjf61BJHcAPE74vNP77RFKDQcb6wQjw3es//g2uu3eP+8RkO7EPzvc41AiZMUw5MEXD99\n0aBu28kN6pRAseJbow/6Xw1PTmZfUlNGYJITK5X9nLn+/4GePR2bujuvVoqiy/cnz3eZFhBApi37\n/MXv+b1tPTpPr+PkKMw9c7nJ3IsXyWZxTiQjRCaPgJWbRUPwR2ZjiHriJ63UGm3Yuzv3Bq7t0Oan\n9/cdXvTb7TvsHwk4/93WvWzZ6vPbNL/qvnRVgTDF3/28f/r9Bb6pQXt/qAmAcAohY7O3MYykFyJL\n30+9p+lff3Q9MieKkQ/+Yyxa7YfunjMSq9nPCrBwlm85C5d9UMusXKOIVNUHN6OyRFsQZQtfnQgQ\nelUsJ6y5fvMQwhLM7uBkwMWKExPZG07KnMBI4VNL5I6DXiRM02daNYEUQ2/cLXLXZZit0qBmSYeP\n/fv28jESRavZZy+aj/SudqS4iemh8UdPevx06YoPXk96nSm7nCipS+BixEWAYkl+NkMZe3fMnND2\nrI1R6UfOdtZaE6Ms8Z4IEGSE8LQxNH06czsc/AmglHcjYMKIgbmdBgfSlKUyGjTulBvgFXTq2j39\n7sU1rBUW+0WjFb8qLw7pN/e9XfvHbA+6R+1CrqVUBu+YtRXX0d0ERXJNUSS3qD3rAblGoUOa+tqc\nXpOZeTYtadzHPjeiYmfWW/8rFx6CDDI3HAf00OLiQl3cgFk+TbZ3Lecxubi5WTtYWowtvnDpMrIf\n0caKK0s8HRZmGmnfuW9tXB7g6HkNI8YFDRBl00MFDRDh0cTG8B+SHaSfkvmhBv9WERs/LyWBEWJ4\nsP8l06g7D0/c3HkiwdO+eKfl7Vp+23jD7w6f1a9/cUazBl/uDwkLQ9q2JQrschNBMfNWgCSEXETv\nnJyu8XcytdTyEEjQAJZggDoqPgvsC/YP5z4OTG5geFH+OLg54bPGUKCkydQ3AimyQHyGqQMk+CL4\nYMYZwRSff26GCLwIrKbg+nitzZExrNMr5dKyheW2ffyX/7olF64wJCWL1qmR4n18BvD6w4YbtysO\n2L2fMhKCM7JzuTVuxMiC8TVy9tuR+k5OdRe39fkIcwgMPMWpRnMWUgz9dxtBGUHOta09OrVo5+HW\ntN2W7VePP3xEBpAWENn1Q3Z/9el5GtdgUmfNpmUA5wy3Q/Yhse70SeL8l/09zQCgR14fNqDzxKPH\ng1u4lvnzvSqVe6J/yUS9ktbcxaXulPq1d7Qt60b/RQJzjK+FlB68ES2/2fu5mxy21683Hl7umuAq\n/fZk0WjIgkTHS7pC6JK+gC5pur4KNulOwxYvK/8BlEIwWt3N0E9k4Yrasx4gg5IcqWOVBl19st4t\nJCoeOpyY4IREy0lHjrcdVqNq0IXwSBV8lZgJxt08X8xJlf/mrpGTKnUcnJg5qXHxomCVjYCGQnGC\nmm6t3Fw27uzZNdJcadTsdkxs9823gp4MqFrpD2cLq65TT53avvLaraezmjfqsOXW3aoHQsM48fPh\n5i6ROgZ+v5zcuWPnxPlupxE9qlXo0LBVnIkoGYyy0WNMKpcAgMSkh1fZyMBpUDaB4UqCJ7nh33Fe\ndyLuf/pUcxkCXoYomSUlizrll3FBor6mYfVeLU7UHtaReiq47UPvrl+UX+W1/hfPpYHmMBmaQ7Zx\ngU+v1IhKKzbv/KULKEYbA/DzCJM9wTnHE0ECtT5cXLiAMcREUMA+jgE7qfYoZlvpnY97q+9Xd42A\nIzrFsllNKqmBRA4jfUmN/IHzi71J0CQBaKmMBpyice1ZDVYFqZeDjTMcbPpsHTmb1zlbaWRki8y8\ncs3KlFZHZaSP2nc3xPOPbp2O9axYbiscqb1z0foQJBG4yOEbMhZkgLiYMxzFe2ZImwsyGQz2Ayc4\nAgomJ1C0TnZXZjW4xhAM8b3MxnLHwc2IrDWkMJ7PGMEYmSsCrlT9/LqtICaGurCmZhySHd6ffPSU\nCB3OF81+/X0NzsPvjaChzboObc+39nBbvjLw2uIvjgVw/shPwMyNGb9DhumYccjO5r2M/bJR/avf\nNm6AkCEct+mS7jffH79/mUa2h9ot9pfUH7Ju6cdLV/bCsZybP9pDZG+W14a8t9fcWOlVb91mTUxa\nGudBbh7pwE0wn1MzOT+of73aTvY7GaaHy9x8Vkow1IO9zA1I10sRvlZiLQ3a87X5Xubcr9N7CgWU\neOFDd/l9phW13K1LeiXqlt7kRvt6GJQJaljao61c3Wki2QtBipmrNdQAGDSdeyxquU3CwJlVVCsv\n95GRnoDMweRU9uuedd0mU3xISpxbak4OnHgpUuRkRlqZExYnXk7M3NEyo4cpxJzEuDtknF0qxcDv\n5dND/hPDk1N7Q2tkC2F44r24+Muoe/RTjRUbjW/ExHBn/Gcrd9eQnT06T72fkJQA12X76g4lBNsF\nPxMkcWdGESwFst0hMO8MjVLIhH6dzU952FV6WKa4NODJBhhbmkns0T/1DEilFGCGSEd4CuPZjLRC\nyINLt3898NVy+uTIO2npTzgYhuBOnqGNAe1mjJzkXKN8lbfJ8FTqpDyazG5GBAaH7P1sybcfe3s1\n+qJhncalrC2dsSz6i7MkHzSyItQh0fuH2YrUqPAn/8bsJ2HwLj9kdGk/B3Hozv8nECJo/v8s0qoD\nTag/RuYRgNqg7VSlps5Z3/t/RO0DAODq4jnwBK95aGC1ylU+rV1znpHfIo4VLtQEE0wcIBvOZ4Oh\nazKqDJcRBFFDxOePCzGfQfr1EJgT7BA0knVhlieFwQRYZIV4MQy5sX+4gSHTQe8kgjC+nu+nPpCb\nGY5RjlXOpQSlFCTL8ys+Q+FTAD8l3ifnCtXs5o1nbL5990NsvIzTVOqrHcuV7z364EF+drR20tit\nAAfUjFH8XJjGdZJ9RSaRMgFu1nZIjJZCuxofHYIJYWghRObyZ/OczHLkeGJfS426pejUlO1w779S\nZskKzkc5NXFdp7Y/NXcp3evgvYfeEHhzbObb9HYSg6XsQmNNKIBe978B9HR2B8bIIBa0ZPKeNcgC\n8io5k++FvmYvKDRQ4vUP3e23DITwB9xJM+Wdi8ab2mSnXkx9R1d3muBjeJ8GDtz8NRdtCB0UQ4e1\nKLMc/VOO6dhMyy5qL/YAF/3kp7pacHf2nGl9avHvNfAq0vEUnBIcEQBxMiYjwgFG4MQdE7PJGHLj\nzrM6TCBdvqhfO923Xm2IfrQM09khvT7R0dLiVLpa9RO8jThJc7InC0D/EJ5nDCYjmwyN6iRKoWXF\noZAOHg/tQXRyZqZT+7Luyg+qV01ZFfUkKa2Nl6CqWJoLiHQlplYWggmE0f9mU6Vm6IAm7BbYME7v\nm9nZjFvTeSLFqmwEfQSdvHdmI1UqXbvisDbffEDWDOFgsErG2Rwt/s0b+hc+m4VNOQbZYDTZ4Pdh\nM7gQhuGIlZ5tEfo3PMhzz1+6OP7IcepuyDKSQaI+jmGpe22+Hm7s7FW+P/pSYpC4OWJ4U8oO/gen\nRTJjqtR0IT0Jj5CeK9GqtRv3T/nZ/ElgcDzG+wKE3/xnnD0nhMcnN0BiABMCaDvA8BTHB9kg+hxR\nkE4mlc8Yz0QGib5BBDUEhRSNEyARgJNt4nNKtpVMBp8lji+yLzKwpJUB+5V9Ru0Qz8EEDeqE+Cxz\nw0OQ9kLLYityrpMmv57ibV4jheP0jmIzOdCnV4PGzo4fGBsZDZjkf+KcpbHxrO+aNrBGLHwJimV3\nKAA4oAie18x74q6E3yaNHQkK6b3E+UdCpzpNGjL3RJSuyhC/LqSNAZk4Mlxkrfg9kN6sXsba2jRk\n5BA3pUIsY1Anjps0Xgevh99D7ZhPRtSyMzf7MJf6fy92Kq930thtUt1NQYuxjHqTmYpyhbzm586r\nt1TA+SQwqWss1/LyTFuO1/1v/vKlH2VkwpHmb8ldE8Nw1GK8aS0tPknavWMwPRSVYqNV7cZLu52s\nsSChaaByUYzDA+LNrJYQa9N139QqaSVlujm+MSHa/5evFuUBpN3wuyHxqR3CEvajH/2RFQT/Kcnb\niKwRKXkaqBHgcDJktsxuF2vrP1Amwrl7+bIpDUo5FUfF7RsT6taC6FQqc0C9kMwAMExMgMMJjn4t\nZEJHx34yMqmYuelkfVaS7rnGsfXOvVAsJMmxqenRKSp1UPPlE0UzawvuoKVmAgbJGCDpddoYcHxy\ncTQIwxzE9Y1b2WECd+hc3Mm2cVHk7r7y0D0/2CE13bfIVwlxXJirZwJc1HycEDsmKIHV6cmK6MYD\nsrukf2gU/kmq9NmLL141n3zsJDUpBNqnvPq2alu6duURjlXcdQwjNo0Ez/92mJ0bEGbwEUTL7fHF\n2xcnWTp8US80bsu4I0eTZzRrlAzn53LXoqIjIpJSTrX2cFmA3T8XfVnnImeGyUiaLBGfJZ6UCRbs\nJ7JDZEMIiphxygw2Pkby7wgkdDFNXSMbTKDJnSMZIOq+auEgYCJ7laPtR06WAQbnZN/z8xgaJ2sq\nL0K8F177h6ZGRl0/q1f74bSmDVuh8jSfCbJLp3G/zJjNq/E6CcLI9NCuA94aUniJHmYMy43HQZ0k\ngc6vF/r3P1artP2X9F/CejENY4khyII23gfnKXf9G3idXGs02erEsY8oM+BPMnOHcQRC3N4Dc+e8\nHGrJ5fj5etNLzLHa6fjG/HDNO9Ef/B5euumNSy9lnaAIKOm6YvCuuaUVovoQnozKBAUES4zDvwmN\nAtq0+ESdJkQUU/D4dcqtCKI0kDGZyuj5451zvk9RCFOKwhv5j4RMgKQ0gCXUIzn587EwP523EQ+t\nF4vmjjp41KKes9OpLh7l11+MCu+B0gI9FrVsXnn5tRuPwpOSws+HRybvCw2jFxMFopyoSa9QaErT\nM06gbsXNTTOLmZhWhAfS3e+bNSz2/elz3igLEo9spWRjI7EMKpRLkytn+DPhT441XL+lS+MxfR0q\ntKtN3QAnQsHYwlRaBF9nXQ8XRmZlkqFD2CcWe93Bq7pMIEAiWGJIhLqVy/12TD9kamTMCc35bR6j\nsoCbfVU9OrWF79WoNVgwLunBkvjoo2FflrKyKmGg4SAAr27tXiKmx6LPvgEYJfCUshglgITi4a9T\now6QgEmyMmHDuMg4dPH6xgWbLoSPev/R6UdPxmy4cedpt/IextDxKUtYmNmkZqpOIjPKDSaZF6HT\nojkkhdBkT6gfIiCixog/GTqjgJg6PYIkMgnUvzGZgn8jq8RsO8PGpAhufJgMweeTY5MbdYb5mF4v\nhc6zt2eL+ouWAXgtgQuNKznGGT6k3ocaRYYQqbF6LrlBx1ApqM1xxUOyoJCeTLw0giXeL/uD90jR\n+gUcBJiSTYBOkiGF4/6ufimrK7L5LTFywTmLiS7UcBLwVVvSpsXND2tW71xgsCTVotQcwbj4Djs/\nhDw1fbJbMuT0feT1OwOrHCbRBMiZuO9cn2ZiE2rlrFYonJG16YyyLM6Q72QiBBCu1igwHtKfeJzP\nDJ82bdrfyh4s7PUW5vUvzSjxQ2hGCbHpWtTd4o5fMLOFcBmLyn+5seJ9WlyiLvMEaF2jUAxa22H8\nM6Scz80hLHkabH2DorBb/qOA4vikJ2Tf8ZhmqOzX9vic1DOo7LE1oJr3Q9HQNltu39U6WpiLpx8/\nSf2mcYOAuk4ll5dcsqJEZFISQwXUC3DXyt3k2g9r1vCAeLXzlw3qPcJU0hwTf03sKBV+5y6moKp7\nCjxUNAdCH1yJSE7m5BY30NPz2NrOrevoHYDdMjSalA983FojA2ktJtPyug2A9X+mdA/7Mz0OPl8A\noGzhV+79jNCLHLokcKTf04XuSyd1L1bGQQrRvY3WFQxRpUql+mBxHZ/ae2P//23FgmwjaS10YOnj\ngVU8N33VqJ53xeVryGJSTyMgmaUKQlzrscjW5BJvAnbR1IoA6W9No/k/KH/jFdQuEUSTOWO7e+hc\nxIl5WxacHdDvFoS9q3Av05C9FVXRzs7u9gcD07FjWKqC/QmKAatPPAwXTz5+fL+Cnd278A8im0SW\nKN7D1rYRSrPQ4ZpCbIbLmdrPnzk1AkyaUjLbjD8Z0uNDz2w6MjYUyZOpkygwXcFftbvhoq37HRb1\nDEV2ywAyMQzH80vgOWk/wN06N0sM4TNM9lzTC78jsCnDAq2FV4a4FaG4NQUMEzGBhEwPD7JqBH5E\noi8YT74C/dLz180+ELSr4f92y2bBEkZzGCaVhWlkvAaEf/y+wsnK8vsCgyUda4owq4g+g2FyhsJZ\nUCphuZAxHyCHgLDQ7fScyfsuOlo4xZkoygbZmIbhySiFlZQAM9+G10bitTAVxncjagNQxHjPms4T\nOFf/6+2VPOFD9vrNBzKlh4a0u6J9wH+xGVa6xwO0XSWaDFrfYUyWyC6/exq0c6arQmEUxnnT2ons\nbFHLrwfo1E1hcoNIuMbehB6ZOiPsOPBTQtyRKckXSi5ecRgaA5SQsNBWti9uGpOaVsLMyCitqn2J\nR7FpGUYp6kzbsbW9zOs4OmrvxiUEVXUovqvLHztcUtXqI/5hD6mloGeLZAaIg5M1wQMnN+5IOaEK\ni1u3GFOihff4fd6laFQpMqRsBpD0OoXZ8utL+e8yW8L/R/9u2DLoW+o3KILnxM4V03n4qHcvqzrW\npTGn8DY57qvAEqcig5UNXlbjVnUcr61d0rHnhk5tD1aysV8sg6Xpp8/f7VO5/PDyv6zhJml6x9mj\nvR2ruhEoWFO8b2Zn9VozjNnHCkO0lBKwJUfHn905Zn7mJ56e02Y3aeS3825wwsC9B888HTU8LTIl\n5cuFF64IH3lXF1DIN9LBwjypcWln18SMTFVQXHxIUkZG2fXXbxkFPAqfHJyQsAanY5/QPJL+SFK4\nyOCzqTskQOccSnaHejnqa5BeL1kVkFViaFi3W0LTgSItNDRid0MRtz4ERcuAzngZw34EZsxOJbvC\nZ5saRBpMslF35Y7D0Hgx67IQzluCOXqIoDGpDFDAsBU0RlqUWxDni7MW8J5yawRKHA9kv8iKkXHj\n5oN2BrzHF4wnC6Nfwj3y/hhSy7HlUSeOfcD7vQ9WZ0ChwnATx/rjKwvAZnEc6OgYvNcBYFkJ3ZJd\nQXRL72xZbGVhmQqPMG1HvK8LriFHUERpjgKFMHF+3XODTR03dvSSoy0KQ8Y5NbwcYUhxF167B4lU\nDNf+K+2VACVe+ZDdftCFaKW4rDEEzFxkXuON1gudLVe41/1BnAvhNmPQhWpD9vhBVKddUqT/KHi3\nZSTDETkhWagal/ZowqWni6Es6ImpljqCrBaZmpqIopYnwRTd93YsUePTIydqmxgpxA+9a0RWt7dP\n+fTIUY+BVSvHYlI/tvr6rfTP69Sf57JsGUXcnFC52+IkSmqcEx13Y9QA0FZAMqT7tFYt4/MtK60r\n26IW9QZSGMXMlpvd/24zZE2wQAakPozutX3Sj3JmTJvYMR9u2uxhUzygooPk88Q6jtTZvMmNgmfJ\n5gM/mz9KzOh46fEnjlZWZ/eGhByHW3bKOzv2lJSzeJIzVXGxaalOLj+vbNZwVK9fK3VsAANJjA1Y\nfdDy47/YtgiSxgAAIABJREFU1OkAiZAU8P6ToxPiTy387fSj87dGjPCqtqOlq0spD1sbuw03bhmD\nSRMy8JpvT59V/Xz5Ghf/WdD1VTwQdn8VigELrVzLZKBIcNDT1NQL7+89VH/XvRBmbXEt4YJNVod0\nPDU+ZHvoik2hN+dTLqJ8HRmk5/Sehv0phXBEcayBiFn6M7ytjg3YvS9pW1AwkxToY8XPYIhcznoj\nw8TPph6KgINO1S80fcYywkPaechS5uZJVypFoSVgag6QNl8wAss0c35otjdzE0U9FTP5GHbkfMJ7\nY7YezTdz9DEzrB+Xm35JFkTTxT2na9b3GwdeMgAVAefYXJijEhB497YzN/88O9jMsS8kna0am3sR\nfhPSJlLX8tAY+fhPU7olWX0ogSNdaDar6axSTCS7FPQhNppGwGAFgxkES3xG5exeJqwYAiiEyZMA\nmACOxd1YnwnO/7FWsDso4OUALHXCbmAdOtyOky+zg173UBxDbenQdsjp1jD1G7Wq0wRmcxS6wRZg\nB1H1mxCCLPTNv+Qb5MwjI0GRvNw/jGDG/cVTiUl7g0PufnTQ3/HHds2n+h487no3Lo5KeepuyJQ0\ncbW13nyqX++g0tbWDfHQ++BJt8V3gVAKBOKC9nIuOzU6JL+7cMX/Zl90spYEdqbWlmBFXy+9yUt2\nLVLHVVIYmQsjHvQ78C18Z03XiVefjH6/b0lzSxrVCYur2gvnHcyl8KJFcUhNXumM8LJX/v/zPpm9\nhI5rv8W0DbNLmZuvm1y/TqnD9x8KSAgQupRzl+w9uGhplZoLqWqV/fB6Jf+EQecQaWy8ASVgqF1K\nQ3iW8x2MTTUBi/64f+fA2Slpvh/XfJycMnH9jVtkkrDGiYK5UinUcHRIqOFQYnuVEnbdfTZtDYQ2\nkLYCbbqUK9u4XVlXs7ZubsoKdrauaq3m+NY7wSo8f9MabdhMKwE+T7T8oAcTgRHTx2mKyg0K9U8E\nKLmy9WBi+B43wxDQR7W9Wpa1sto9oV7dhmCbmD1HbRKZJW6ECIoIYpj6z89lOF6f//fieALY+B1/\nbYHzP0f960EUs9j4nR+Ze+7yyfH+x8hQydlvTDShczYF1dRkMZSo8+rIp+WlXypACRDOdwSb1GJq\nfmjZZPoob68RKNr8bf31W2hOKd8rM38jwMyl5sTMZb9E7STf4ZggCDazpWfrrG6yvx5JXJ9gTkUi\njDarAoHkEWams8J41ZstZvWq0uEblorxqnqGQzFNXcKsNnNNpwlky//f2yufFofs/KGyYKRZja+N\nC5jkMcNw3OuWJs+JgmwGjdukJgphokYzclXnzyjUK3Qb4r/aTEiOwsOjVVgi200qVVHUCtQDyVHx\n8IFRCb7XnmbUiElHcgpUbzqTOrn5YkLjQ7sUB3eopGAZPmMmHDNeZB0OU3o5wap1taHU3oYCcfri\n4P8BnrT+MnACuAd9rv2UvjcMp7xpRZ+5Q2N2F8c77vGySik0WXvwAXZl2DmjZYAS/76Wo3Dfyvg/\nyQQXaIBhCZEy3KDdQs20uB1j5t9VJqX596hYdtidmLjiXg72wvdNGwlWJsanzOYu5mJ0DTq5cvNr\n2F++UtxMmiPfNIPdrJqVuLfYsIjpvsdCusOTJ8skc+mVQCE6NU24GBEptPdwS0Hh6G1PU6QUYIZv\nuZNgogN/Miv1wtNPhve58CTKt5Wri5XSSCx9Myrm/snH4Y/XBN5yPPHwERkYzqu0B2AjEEFNt5wF\n3PJ3iu/gMhd7Az8eO4TVN39S06viZ/4nt8ZnZMhiaob3TuDIbria6/DQ11hDWF7zUU6AQMfwqLsj\nNeJrhohQbGja3IDLf00+cYK0q2H22xD8P0Hbc0ZWeY3LnPRLBQBKDGO2wsFNPBmlO5Pr1zUdXbPG\nt9BWXiy7bNVzzA4//1kYU4MabC+CHvTvZMyJX2C+fZEizeaDNHjPHOjQxM9huEzgK3mEkV0lc/RP\nZbwT5NNHToV1W+9ryH0d5nTFzFWdx7/Uup3X92T4t1cOlOSTA3liAAF5Clpa4ksZcdyRvWrn4oLe\nqPw6NVBpZiKyQfSCVxSFwK5GnL6q8wQi9ZduEkBUaG4SUVs65MaevvTp3+g3ypN2qbTMr6b3mkJa\nXS7a6SOxQ1otK4pzQuLOjpktzLJhajGzW6izoWiTOiSaGzHNlXqJ51pWUVKUbtCBJ23zPW42D3/z\nsJX8kSyKQ7RN75s3sQEoMOTE3ZmFWti7+PgDTqrcIRDNKx9amghzvO2FBPgqGYFFkPQ3b0gYjoyl\nBBTpN5Wpytj71S8REdeCgxe2al4ROhxnhJCe+8ZnBFz4Ysrxk0uH7p79NRbJUWTGKSOgq/ab1uTa\nlbyvMYHRyWUfxd2iXukdz4re889dsuvrWVGwNzcT5py9ICy4eOVaXFo6RdtkVBiGJDNEPyXqdMgE\nk2Ggp9LcaQ0aWE9qVLN2fFrmIDOlspmF0ijlTHhEdDWHEj+uv3n76piD/nw/1x6KpnJNo8/y5xG1\nQwzE3ZZbunX4/WDIA6tfrgYy44sicAIXMklS1YiCNj0QKw0gxnvJtWUrlbLaIFuOQJGTPU0zCWCo\nuSpwe06/pAbIUwgT8igqy8w6sloEpnRJ5+epIRsoP9TLczv0mplipsLHQFdEoqJ91OiRu0uYm+I9\n2nGGYAn3PhMgtBYIDYbjGULM3gIx51ZHhQmK7SdjCpFkCUaYI2iRQnnNv9mkLF8AJtkWBeG9bdpM\n9azVXT+TvbRe6eX9vwEl+SqH7pkNkbc4BaBcEo6ygyVH43/YSoATJQWNcrosbhyXJMxUKUxnFkaw\nnVvvD949uwXCdoc5odKEs6gVvAeyUrUF8VuEPblbyqtx9/M5DoIkUuCc5Aie6C8ipWyjMTyQK+3O\nFwzdNaudVlRI4kDqkV639O6C917BXknAkBKTIE0sLcNTngy8E30NPUQGTmqo8SL84llcuGtjKrlK\nU4fzurHABbvTZ69SpSGDlZocZLAq4lOeGK85ZN/WzPZsz0oec2JS07fOP39ZOBcRqelbuYKiX+WK\ngglEpljQL4xQRbnUGthemq8sStj843NVYe/z77w+PREO+ZAeYD6Mv7UroHfan8eHXxzUt4/hOe/G\nxgkQc8dNP3Wuye9379IskiHv1TiG4KAtAMEK2Q4UAZYAEzPcaFDJ8hun43w/urT3boivtYlJ95au\nZRxTVarUQ2EPn6RkZv46pFLVBQaLOxM4mBRE5koCUHt79Ghe2bHYDitTs5YOC5dIGVA/tmlT6npU\nxE3vkiW+GrnvCFnmPDVJufWPntnZj6y3dgXJetOx1JoheG7IiB0Bk7EA7zuDfxP4Mey3AodhNlq+\nX42BfgnGm2IwMi7JHOXU3PFLfq4rDm4EyaYz/MeNXm/NxE/s8NyOMdAtcQfAQ6X3OPInWJJOLCqg\nb5LqsWHzoLADL1gMVSam4f/0gElUR1goVdNqOwWkGUnmo6+lDQYn+EyMXckWhau5dG/CGtMHNh8s\nHTmyQOHQfL8g/Qv+34ESP+edLdNMLC0tJota8XMEVSQBCOk6qT4WgBNBU0EFXwW9Mb5OoukY38xe\nF0kUfhIzQNd1f95AsjDnzv7aYbt/6Acp2kalmalgjh15USt4D8jZhpG3ws7sGf8jJ9wczef0Z+T4\noWMufVrIJBEo0YuFwlG6JnN3x5RuTqqcTLIyauQrGrhjRnkjpfEpPF0O1CNRl/Q2NIbfqNNh63o/\n4XSP4Hj4sIhMfb/IcCS1XKMblRmUbKyQUoP/y3otOUmA94Fabse2fzz3clUb28OLWjWbce5JhGdE\nSgpEyS5CRTvb23XXbREtjJWWm7t2uJXiWabqGi8nlid5I+xOCjKu02ITpVqC6fHJ92aeeHDb1dKS\nIl3uJFOAmU2AnBHW0ixI1whXzPwWheFPTFEiqKGXELPWaNHBDQ7DT0z7J0tBWwC6c7O0Dp9XjjP6\nMI1Z0b4NMsW0H75TuYLS2ti4DRa3yymZ6lO+h49aLLsSyPPynMxg40Lf08LYePmCls3ihntW95ZB\n1dLWrdufe/pk2/rAm4dQ6oOZd3lqknLrB7A6Ufj8B4UoEMvJou8ohLxGeFV3AlujgsZt95abd2fs\nCg6mw/gzp8+CdL7+NdrPoMlCpp8uFSzP+nHsV9qi8H7pas7vigDodja/pdWGH58FliBswH8GIY9n\nWiS9Pmva5RIWHbBpsk1RiqbcNFE6829XIsirK3MwWT0Hb8NBq7uMLxTDl9dn/CNASb6AYXunO2i0\nxuPxRdF3iYtZVpNrIjH8QdD0ckUjdbWwCIwIkAw5BZyPu5zd8JL8aV3nCbSLf6Vt6J45n2KI/0D3\nZlObt2PhfVUdyO8L2hGWk7jw25DvqYF4Ic0222dx/EzDQS8gZshR+9AfB7cV/D3FpHQT5g53mf73\n8ilEiO5P4JWN3sbsxEzQ1WnIMmQbHBS3yedx4u3sNZlAt09FR7If/5MZgLw/3ifb/YDrJw5/u5rM\nBu+n/vs1qkzv6OFR5VxEhDChTi0BAEkYfegoiiNfT3Co7Fq2w8yP66Icicl/2eak0M+lQWi2bEJG\n4JSL4f9ruWlrHfiYrQXjEQHLhG2sBl5jxYbvr0VHc5GWBczMAmNNN25WmPlGE0rOrcw4I61OIMVQ\nCN3MqUkiYOLmhXEbhul4npTj/d7p/TApcRwE4saoSVcvRaUKMDdS7h+w54DZxhu3CJbatXZ3sexT\nuVLwB/sOURvFEJT3uUF9e9V2cqypmL2wRaHvWf8GXYYdypooRM98arXxHhnW573xHshaH13attXI\nes6O73mXdMB8I27Deb5+iZpvzznBF6J+HHfkDKHrhbayZAGGl8/MU7O6BqCQwEGSwmQ12OCAxaIA\nXGrwAfwYSIribonEILP8Mmvxy34ff+d9hmF2AJs4hM4HIDuOFhJ/u/2jQMnwagftnuWpEI2AhrWd\nmH2Q/U4k/xqmFiLFUPJg4IF/k1tDsoaUNohaRvg3ZHa5+DAADINVUOzGM75nZacJpEj/39qQXX5z\nMJlMMANIYmixqBW8BxgWTYGgG2GhK2u6fkbanxkepLIJfBgeI6VtuFPjJMz6UnK2C0uWMPuFNDzT\nhukMS+qf8XWG6bLeC0C7FoB2IOucWSBE+l+ZBArem/m/Ui5IbK4REsdfjVhZ/gs/HSVv0MZtn/NB\nvFJATcfXNyEj+zUTcJOGl+vfDboTm9rSdzqzYtbj+GukV/WlLjZWHwyqWllYcvma0NbNVbgU+VRQ\naTRJc64GLmm9+NN3TG0t3JXmJpLR6NvUpNAsrRMQpry1+3RYwE9/kjViqj+fvWh9Flq3eJWmV7F5\nP9IVOqdGbzJqPVFOSHLLZ8HdNTjI+HLB4gaHOkMyT6E4yELxWacYWwJQKE80fnP39oGO5hYNytkW\nqx8QHl7hQGhY3OAqVRdNP3NuYoZK7b/6+o04vJbAy9XPp+lHvnW8l+qL/MrXlFUktyDfIe4tDsvK\nIWwY5NB9Tm9j6RZ+LpNHCPjkkCMBVJ+mLqVLT2/a0KNJmdLQ8qBUilagaeP2gnw+XyOXzJE3LbJ+\nCR21xvOXtaFBcXHsSwJTMnrcILIsk2TQm73l5LeE81HbRLb+hYbrlHCAnNjCf/9nmXYpcUPHkOqa\nOBZgiSD+b7V/DSgZXvXInT/YZ4iqTkBDHbGINQI0cgKqLbR6EoseH87LNKdSaJW7V3b2ZXmLf6QN\n3eO3Dp87oMgaoPDdTd1MUmQsM99i13b/nBMPtTM0dqNHCSco5oVmZ5kYO6fRG2l/TnAUODLcRqEy\nd7KkqKmlGIpDolAG75rTA+D5TwoyLOxtJeHy29rI4BFYVEzMuDSl7xcUxr/Qfvt52sfnHMzmR5oZ\nSdYJr0tCRvYLzW7xgb9joRKHrTx6f03Pbbv3bwu6txq/M53t09g3AiU9HMzNylW0K2Y26+wF14T0\nDGV4UrLQ6n9Dkq29y1tKNglvqcaQLHxqjGRCLsQEPZi8Y9xCAiI+SxRs70dopxv6FX35nDCYawgB\nEp9V4mp6JZEVplCbOkLS63wmGZLT2YPrNIVMvOAzzffQfFIGUHBmluq00dRxRg17+/6TG9ZpAA2Z\nDSwc2iy8eMU5Ijn17pRGdWcBQF0cc/Co7+33B3SzM7NoDssA1pLjWCbLRW8j/n++TRI2C1pf6HVK\n5mGySDNFXj8ZMoYUOdcwA3cgDoJAht24MYsDS4U+UAzBOd1oYok6oGvyM2/MDpR40QQ8d5Njf3a1\nse6CpIMv4KDO/vwAB7WZ/Cwabd7M7Qaf1YkTAV61tBDIoWmFcEvj96bUK/Uuwn485xuh2ZS1d7wf\nzPkLV3WcKBliv2x7LYBSThc/8ICfozEs1TWi2hl/dwII4k86NhuDUqPFebioFp+ojTThokYZ7tEp\nIXya+O/VigFQOgSg1Cp79lQ/18ZCCRNr4ce7/4ypqPx5wckRQpuSNeCHosvq4WwUmRYvbH10VriR\nwOfs+da5VG2hZjF3YfODU8KdxHChlp2H0KtMfcFaaQ7TdY1wG79bGXJESNfkr5FztbAXhnm0EIqb\n6LRa8ZkpwrqwY9J5c2uJ4dHEL9pVnSZm91XgJCvrHbK/nb+n+JHZL3NwcJYnyKIQl668BFIEXpyk\ntWCTzgCI1yti/SAyAYOAUKfUn6JGrIe6cCwa/FzTG/913ljBzv5QKStPjKFnCRnIfGEGzL/ZUD4J\nYs6UrN2jObKHXZLSl0/p/yXtIqQq6bGpqXuLL1omD3h//VgiS0JWg67sfbzb1i/rPbZ3BYb8CZLe\nlIy/l/luMgAkoVXiW+/ACZlhGlJrBB4HcJzUewEhFIeNTIboCwDACYE7dm5SWXSazyCfYWZd8fnj\nQ8/wt6wVpAaJDCadrcmOGDZ+b3yWqU+i+SHPS7aYzLEEepLGfPjezpCQFfBrulXPqWSl2LT0mJKW\nFhcxNqvWWbt5zaWICDLL9Dcik8xNFIEaN1EEXtx86WzZDZpeUE2gt8Sgtl/2l/E8U3FwzaQQmsw1\nP4sLMG0KOMVyruHGjsy2nLnLex2M1Xo1QOYCQ7fx566BDtlaFNP1m++f7YP7DK5eRbu6fRuwWFq3\nqSfOrPvm9BmOW94HQ2a0Q8ndL4qhRUGk5kvXRG0gPocgj++1DbdQhs/xKmkSa6oowSgOMzz/7cz0\n7B3/sv9v6EYPsLQbYImu7i/VXlug9FJ38y++CXYI1EFUsbAv9twC8k8DJbkLOjrXFKrauAhzbuuI\nGHdLB6GfaxPhcWqMsCaUYXZdczS1EToBJFWxKS2kqDKEDfePS4CmP15b0sxW+PX+Sek8De0rCL8/\nCBBuJcrmzrl3dh27skLX0nWEnY8vCOdiSOrk38gokVlSGSlLrW/vy8mVO046a5MupjkdJ7Lcmswu\n8X3ULNFigCwIz8Ed30p8Px74uarIvuFZF2ZpecCyre44kX2d1QyqgXPRqpdsZNT7gKvVrt0uNsZq\nRMH5QprJGiN9nCzMP9loPJeRbJDBKsI3C+zD3BOPHtplarHYwmxUIaJYtYa6EbNsix/nPLKMXMQZ\njq/fa8XnlaydirvCWFIwseSQebub7GuG5dd3deeJkvEjDjIq3HBITIdgrJkPyOAlCY/nzOffuWGh\nXQCZHAIUxj5ydd/OoYcJRLi5oRcSNTIEGCwPwrR4MidZACdzwie+8Hca2WrTn0d/69ZpS3sP1+q4\nlk9xvS7QN93ecy/0cZfy7t+azf2JgmeOVYrzOS8wfMXr5H2wVl0WgAL7Qs1RP9Y7y4P94djh5owL\nLsP5LABMCQCZN4IlhvvJrjFkmdX09eVwP+IQKSwH0JS9VApYrdyAEm0PCNBWLWzlU/29KpW+sTJR\nXl169foE2Cywv2UndMOPlP6t/1xk50nAzuCCFC0IyFQTR/eYUr/M4khzI2c+www30w7jTWpMYElF\n4XXJ4RsZcZjn8B0UvhUBpcL3WY7vgAAWxXCFBtyRGi4cBErlrJzAzEDkrTAG0Hgk/HzvoNDByVto\n7lhVsDAyEVLUGcLhiGvCnaRwoa9LY8HZ3E5icc7H3hM2hNFH7VkzPJ9SNBIuxgUL1W1dBTOc+0yM\nrgA3GSwySoZAqQKu4R2XhsKjbECJ7I83mCRUdBa8bN2EXx+clIDS6PLtheiMRAkoVbR2Ft7Fe489\nvSncT4nK9xrb495a4N7IZvGQ78Pw2k0USuFmwiMJtJGlIqPEdmDqcs/H52+/g3+yRABTbY9oPxvb\nF5PL0XxEknw9vZRYUZvxePq9yKguCPF3TJraChQn0iitqKE3MHkkRZBV0gpjA6PueUen0RgQJn8I\nm2i1fgbZMdzRc5Exe2yp/GqcnbaRZflSWQZ3nFylDFaUt1CaETS9+mmFYSGW4GC4kMZzcoP+cJFC\nLcxc0W1SFoLPKkehlUB2FAZhC4Oxw4tjOjnD8gH9N3/dFcVtV/IerGAUW9QQ59YnV6Cf406sO+Qe\n/NshskPMlHrOBVnPOM7beufeZIQ4+aVwV0bDTj6D9FwKLUR/8nsh4CL7x1B7PRzMvuT3KnkG4aBG\nh6G1XdDcrFh+7UYpiLupgdqg1+Vc3nj99gm3YlZGjUuVBkjWekkmsxBZZ6q1x0x+WEhtI5kUisg5\n4WQBKLiQ113RvnXNfpUrjBH9FuVVmYEDnNfQGgfBllQCBY3CVAJvhsd08cscmi4zDXXVGJYTwDJl\ngmWaPx/hulyBEs9C93ACVYrYtx3v1/vzWk6OI2PT0zeWNrEelxuwA6O6Cs/xkBcuA30CLVQLlNxC\nFQ3tgDddr0nmOZUaWBJvovA/gCXq4QrVXv2MVqiPf3NejEH3JwZdD3M7mNMZmHERHFS1KYOQ1mmh\njHlxoU7xclJ4q21JLwmc/BURKFxP0G28yOK4WdoLq0L8pTBYbbz21/snhHtJz8gUw/PVxd/LWjpK\n565m6yK4WTgIYSlPs4BSYUJvZKDq2uHzcgFK/QDgzgG4FTO2zPcaB7g1FUrjXpfeOySUsSgudCtV\nVzgEIFjWylG6zl2PLwqmkL20cqwmHHhyRTgWcRMapRiK8lPWdJ1Ein9K2oRP0kwVCtYPHCIt2Eao\nHP5i7SV5AHGXR6qdAkfu7p6rsEgvL5h9z6dLPLVJRe1ZD8geVjaZ6osLTj7GxCnWQ9YNJnOJjdM3\nMQ0TDAYhJxpxKlB890emirZbPGyf3CxpZZYpSt45uoYxbQiaXta1V673RP0RQRJFxgbtAT5nj0ar\nXrC202e5azToe6PWhOB98VKVeF3ogwvklzgoxg0auH36bVhFVGSmKjNWi5quB1JhI6HCbvzO3oAb\np378gyVBCEheSH0n84iyptvGHz4evODCJWaekqXh88isZnouhb5kn3L8EdCsxiFrjYhkyeD47eja\nVXsy4tFl2Aus+/JEAHVUQpzv6Fo/nDl78lDow9anHz8+mWUyC4NZ+EB1hmN4GWNRcUBpZARzR2R7\n6eYTroFkpLs/+HBYDTtTU2+rBUt4vpxCdQytkXmi/qouDm7GyPa442CYkiHdAjU5FZ+fqxubYOO0\n4riC+DnhPcoHw4fblLGz+B+ufjBuYRoy13I07tRtGjQ+eA1ZJZjtEqABzTYsvSDGFH5Kb4leUwb/\nvHdMUe8hDEd9WYFbEVAqcFfl/UKknC/GYP84u3khgY29qbWwKGifQGBD8PL7wwBJv9PJuZZgY2yB\nOlOZwtGnNyQWiH93NismAYlkVbqk7eldpoHghN+ReXmQEi0hY56P5/YAUJp+c6sgh9oepka/wCg1\nd6gi+OD4KzJQOBF1S5ji2SPrfFsAshgeyw8oyYzS0/SEfK9RPid7jGwUWbKzYLvsTCwR6rMVFgSR\npcYsWqmrBBJ3PTgv6WVKpqo0M8+E58b9gtUQOTHpmgITsFYbmvX/GYor2XdWnCjrpt7LqNateTBe\nW5Lxd2NkNBU1gx4A/kh6qgt7YtLsvcr/wZcvUPV4+eOk5PRSVhamGkGRdCcm+t7ekPu76jo77226\nYfPJoXv96gI8dcSpmNXEBSSrSRMM2BpmrZK1IXDiT+ohJOgDAESGiEBIYopwGcxkfUF1IYoXG4cn\nK90S0ne29Z1OoFOgpnNfVnyCG6ydqdFOvBcXpxl7+GjggZD71h1mjzpZsqr7pKJw7ItdSYBKg1K0\ndHW66Lqu5wSW6ZAbQQxRJZkTFZ+z30Ju7zoY+sAF7NKSqNRU6gXJvFC/lHNZ+GfnYsjNBwdDdbT1\nkAXffAVYIclG4GscdP9mYyiKYTkHsCrHz7z37rdKhdJHr/2x7VGh3M5f2rcU7Rf90tTgevmez1Z0\nbPPzsKqVAeDgzK9z/NeGxCfe2njzluhsZjVsmHdVRzD5p4/cfzCq9Zat9H8yDNWRTaMInbokFmTl\njov/Tx0V74EMGv+dv4jT4ML0Ycwh+NU8+klR/J09LGfw8hf+mVf9uJzex9cvq1x83GkHc0ncbI6K\nBG9a2abc+svAiV6DWahRYTLhi4BSXqOwEH8bsmvOVxjo32T3XzHUKBkCJVnYTIamt0sDwR7hsgRV\nisBwWkB0kFDB2kkob+mUxfDIl2J4voICJb63C3RIDLHJYu3st5YdKJHdImBbAQG3t5270MS+srAN\nQvBmsAvJ7xo/KNtKCrmRGeN9MBS3J/yS4FXMLYtdKwHw2MKhqvT7k09uSDtY51RV6PQz4e45dXtM\nWvpRa1MTI2OFArQ8AZLohIkOAn9Rp0gWJKfZZ3SRrq5bnaWlTTMCKjgUZ8YWXZaL2os9IAt4HdPU\nUbMCHj+rIG7w0jSV6qmJ0ujqk6TkWpP8Tx2t4+xYDD47Xt83bWjnaV/cXwKxmOjv2CjL3y5m3udP\nd9sgM63WJ00UGS4odEPyRjQ2BOfxHe5RqzP3rOv6+V0pbIFsIkPfl/xOjBDNaowNf5Rv4OI2JuiD\nQVpnS6ueP1++5nXp/RaWxsWtjd60Om759UlB/y5nRuJ7nYdwBQ1d2ajxYTq/7L5NR3yKi00HV/P8\nY7Z80syqAAAgAElEQVRPk7qOZpbdC8iMEHBRKC57LRGEPBMe69gehk8JyBjWeqHpxgTEyhlgnOfP\nZ8ZdB9X4T941UohboU2jvopxdtoQMKWehXOz2rQmDXyKmZjMGOFVLQ6hNxTTlrIlwbhokuOTVDWK\nLVnCQr4M1RH4UetYHgfnG4YGaXJJCxNeIz+XRaYZinuphnGKHCUtQo1StlxzXMd8TKJr8mDRuVmg\nxEAq2ZFT/bicLmTo7h8aYUtC7RcMZaHJg5nk29Tkcln40u6YaBSNl3YZT0CcbysCSvl2UcFegPDO\ncIR3flFC/8KJV265ASWCplrFygrG8IqiRulo5HWJRWpq7ylV7g5OjhRswTYdeHIV2iOaUOvaywIl\n6qOGl20psVE/3WV06/mWHSgx641MlhW0VRqkisnaqu6l6+Z7jVUQauxRup7gCDF4Gu7NH/e298ll\n6dorWjlLLBrbObBMmxCGVKWiWCnqcWHO2bjK/yEmJRimUV8gN1EM2x8cegimbvHIcElMV6nbIl1W\nDTCGjBz960RpAtMzTmKo7q2i06T6Tu2emhmJb3Qtt4IN0TxfRUaPmXANTt5ZNzLTnIvCcw2uZXG3\nomO+s1CafORezHq93u+Fi2ZjTNI3kTqno/Z1rJK9pA3RilXTlYrVJ5xtD28sZxWrQeYqQmrOYJck\nkCsF8hTIitIITzDGwkUjo3AjjSb8scLkyd4OYwyZhaxrob+MfgEs0F3r/Wjc8R6GUxi2YbbVzck/\nT9n0xMWuT1E4NvduVGfA3wzeSmxahVB+TYeJfK6Yhcb0eGqHyKSMwMGyGgQjifq6aMiKE6fmFg4y\n+ESGq3xwMEuM/yZzxOeY52fGSX5slO7aJo2Zhh/dZHdtfVgLc4GCTBMpZLpXM8HjWfn5HACUlMCg\n1Q7H2GWGmXgrJvYhfJ3W/nL1WpTv4eOk1wiEOIaYCchz8f4ZwuHkFYqDGooCXbNBH+juAUBJ9jPS\nXz9ZsyFSWE4DwPRiNlz2U8jn4fum4X0AjzC/hP5JfuGwvQsdtNqMUyDSyjMRg1Y2b2OjBYZkSA2j\nYmR2Si70+bUioJRfDxXw7xALY4HQ7qI+w7x4EXORU7fllgGYkYSU5ESmJItzYQ5GMSgnDt0DL7FE\n4hUUizTM3KBvEjUEBEZkkbSgsKEo1md3iGp3TBQfHjcXVCvruTRWoIaXpSO1qEUttx6QTSi9Q6LD\nelyLzMCOXF3aylKqFC63I6GPRk4LCHDz79tzNECNF3a7tAtwx5El8pUWm0wsGkbaJtA6sUgpFj3q\nI7KEtRLzhMkf3+n8Z6HU/6evJltV9rL4GD6clwdum3EFIKmGuZ0NNIWSTVRRy6EHuIHhRgahbt/V\nHaQMOIbCZPdtvoNrSAsczMDi39VZFgLM8MpEVpzBYp3tI2j58j4OsjP0UeK5eO7PcDADjiWKCtRo\nBYEXxgKcMaMR4ElimqZ22Lz9/X1hYQQL2T31KBbPCUAJt94fGDf5+KnEKfXqjanjbO/9JCl1hLOV\nhdOpR+EPQxISzgY+jdk/88w5MjkUjxKAUUvAZ4GZtQR7hS5jYgiU5Bu+0L+//fnoJ36dy3n012i0\n4k+Xr13cHhS89EZ09O94ja4WUQ7NoH7cc/olrFH4frRjmf5v8RavUdQ/pkQl6EL+Wu3INZ0n0b4i\nz1YElPLroQL+fei+2bW0avFC0Q419w7LDSjJi3S3sITH3UPi6XWSCKCjxN6sEejoUvh3FCbA3Cp8\nk8FgRoyhUZX4g0+T8f5Vnb8s0bEeiLm3d/dUwOGLjDKdJgVmr1dRmJhFPrPch/379vZpVNppX601\nm44ERkVV/F+jeqbdKpS1qL1mE3f+ZBJk/Yj8cQ0m1au9tHN5j5LNNv7OTEQ6FGv1bIM3mCTvjTdu\nN197/aZ7qloV3b18+VPj6nj9iRzJy4UET5y/GDvgwpSHl8yz3TovsP8fM8qamCnv0ZXdyolRk6KW\nWw/IbK9HUmbk/85H/HQu4klMnx17O1oqlPMDY2JITbPf5RAas8Wy3KJlN2+grB4G3yufY3olcbHP\nkTXE7wma3HEQLBWo6cAB6xVq4Ii9aDXfpAu7CnG5eCMxjEb2JzuAMm3n7rb9u2YNW9cp5VQemwFS\nagvbubkt2f1ul5LnHkd+6OXo4G2OMjcqteb4rdjYo9UcHf7A68LwOm7yyDqxMkCBmx7M47qf2wzy\n/ewHekKtx2uapWtVNJlrvjXo3iP/h48mLrl49TkTXmmTImq6Z7l7S8kMEjvvdsTF5n9ry9kyTCpY\nIqFFgcSWt7llAvynMYohisGrO05gH+fZioBSfj1UwL+/v312KbVSfIRsCsG6ZNHkW8Buk14mU6GD\ngmJDWjxK4q7s+SaKTBk+hCk5VCremoNwO9s7yBy8P2j7zPYQ6tbKnolYmGt7m16bGAH7G+y2Dn65\n7MdHl4IIWKkXoWbpCyw6Y7HgEDwRlOzIGD96R2xq2t6SPy2nAJdCX8aHufAwM2liiu/H7titXbKc\nv4RUHndsXEjkxswbhnC+jhs7qu7wfft9v2hQNw1hvSpfnjjleSkyMtlcaRyVmJGRWM2++Ipd90LJ\nVFCQy7RePlw0x+TnsO4YvbN4TWQhclqgSt7/cFjg3pCwD0bu/4usgzDgz+/HQcA6722s9VfY8UyR\nfVKEzityzpnHs+xT1U4RKanlp50MqGtramLyfvWqN+Iy0pKXXAqMndey+QFbU+PzUOqHydoafS21\naVDrT9MDGH5vHFehOKjxkVPpZdBLMTTtG2hV8ZymKL9r1wEF1KVDcVkCM9kyAMNynDh7kfTdF6BJ\nTFPm+NGfIry/EmOeY86wZAnBS02wZrviU1LbBsclDK3haF+GtWyPPnyc7GZrvaS8hV2+TtyG15GN\n9TT8E8s5cXzzGZMa2brwhMQxxc3MPoJe8wA2F/PksJweGIJFelboVnoPMt+WViuxM8DBzKpo0/is\ne7P8wgRxEiIZ1KHl2oqAUgGenIK+BO7cV/DA1GDojSG4olaAHsAEk8iJGPvSqRci57knprMu1PNN\nVOzDRHsHdIc1JkGAIAi3dUlTCN1ImXCh+N1lQwA16OACV0VGRlgRa1CA70D/Eu6wuNN6sOlI6qM9\np5+Wtyv2YGhVz0BkMZnXc3bu0XDDlnRMGEapKtWcxW1bHB5azfPogN0HRv95+y4HO4W43LHSz6oj\nJu2RyHajfxHLIlCIK4lO9Y2LDVkr2c+E2UMMuUgiU07sVyMjm844e74PanmxcG3VScdO3ofo9q/P\nj54scSEislRcWjrLZLDeF8cB2QGCKeqQbuDgvMbPpdOzek+vblWvPH2a+PmxUyzFcbfTD6P/cKjs\n5lDkqVWwsUGmkYzjlc2Hd1xau5flRrLct6vYF5/gbGWZ/Eu7luEeNrYQTiPMKoINYho6Q6zUHGkV\nKMaq7YL/v/Y0LXW048JlZIHJ1NBviYwkM10Ze5+Gg5sc2hHQLDJXljC3K9f5FInzEPKrqfMnkliW\nI3qxd5ZeJ487l5gm7WdjxuDTP1pz/Z7bkN27yYrS0426LLJGvDaGBY2O9Os1tcWvf5xv4eoydk7z\nxpdqO5csj3tHVh2E4YIInR4TCfKu+ZYHUKJ5JUN71GuxTAqNPNm4aWinnjA6GfVSkb0Gg1WWStFq\n+Qxw9LPPW8gs3uD9c+uJKrUkhqcEgVKEovbMLwzEclxSUrLLb+9Oo9Ysx1YElF7hiAFQmgGgNJlF\ncVkmo6jl3wOyvwUAzelVHSc0yhKDPp/B9jUmP5/nAZIiAOApCZOCKSY0iElFd/ydfihgnYTQdeXs\nkg+XsepUxBrk/x3Ir5DpaLPHsTG9Dt08WsHO9s7667c8vB0dmrhY25T46/6DhJtRMWb3ExOtbU1M\nhFUdWiM3XJvRasufj8+HR1LcSi+XB93Kl+vyW7eOHUx+WESDwFnS1K3LbpIbxb/0veIiwEYnZu7o\nJKCExkwlimYZnqGHTo2Wri7v13FyLBaZnOq9qI2PaGWspBuzv//9R/dH7D9U7X5icsl0lcoHr6V2\nLYuxwr9V05s2Oj+gSuUQ16UrdxhbmI1+77dv6fEjWIH5ZdmSopZ3D2QkQ0OYkCxE3XlwdZf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ksIJRoB+lB8tshGcbaMuW7b5sCcVafq7niWeQBWEfzGHI5cWuDUlt8rx4wdBSZW5pzPaxIqLeJC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"text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='pictures/graph_ex2.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this second exemple, the clustering by visual inspection is less obvious, thus there is no \"expected\" clusters. The model with the most stingent condition (`preference = -100`) gives a clustering into 6 groups. Interestingly, microRNAs from the same family are grouped together: miR-124-3p.1 & 3p.2; miR-10a-5p & 10b-5p; miR-3139 & 708-5p. This suggest that microRNAs with the same targets are indeed clustered together properly." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##7. Clustering the whole graph" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Get all the pairs\n", "mir_pairs = graph.cypher.execute('''\n", " MATCH (mir1:MIR)-[d:DISTANCE]->(mir2:MIR)\n", " WITH mir1, mir2, d\n", " RETURN mir1.NAME AS mir1, mir2.NAME AS mir2, d.JACCARD AS distance\n", "''')" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of selected pairs: 69472\n" ] } ], "source": [ "print(\"Number of selected pairs: %d\" % len(mir_pairs))" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -0. , -95.90027701, -96.22991347, -100. ],\n", " [ -99.93468321, -0. , -100. , -100. ],\n", " [ -99.92486852, -91.17870722, -0. , -100. ],\n", " [ -99.89212513, -91.62257496, -93.38999056, -0. ]])" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Get a list of unique miRs\n", "mir_lst = []\n", "for pair in mir_pairs:\n", " if pair[0] not in mir_lst:\n", " mir_lst.append(pair[0])\n", " if pair[1] not in mir_lst:\n", " mir_lst.append(pair[1])\n", "\n", "#Create a dataframe of miRs for future reference\n", "import pandas as pd\n", "mir_df = pd.DataFrame(mir_lst, columns=[\"mir\"])\n", "\n", "#Create a matrix\n", "import numpy as np\n", "len_mat = len(mir_df)\n", "mat = np.empty([len_mat,len_mat], dtype=float)\n", "mat.fill(-100.0)\n", "\n", "#Fill the matrix with distance values using the mir ordering found in the mir_df.\n", "#Jaccard distance are multiplied by -100 to comply with the clustering algorithms \n", "#that usually accepts negative euclidian distances.\n", "for pair in mir_pairs:\n", " mat[np.where(mir_df.mir == pair[0]), np.where(mir_df == pair[1])] = -100 * pair[2]\n", "\n", "#Finally the distance of a mir with itself is 0\n", "np.fill_diagonal(mat,-0.0)\n", "\n", "#Show a part of the matrix\n", "mat[:4,:4]" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-100 . . . . . . . . . -90 . . . . . . . . . -80 . . . . . . . . . -70 . . . . . . . . . -60 . . . . . . . . . -50 . . . . . . . . . -40 . . . . . . . . . -30 . . . . . . . . . -20 . . . . . . . . . -10 . . . . . . . . ." ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from sklearn.cluster import AffinityPropagation\n", "#values for preference\n", "mat_pref = range(-100, 0,1)\n", "\n", "cov_metrics = pd.DataFrame() \n", "cov=[]\n", "pref=[]\n", "nb_cl=[] \n", "\n", "for i in mat_pref:\n", " af = AffinityPropagation(preference = i, affinity = \"precomputed\")\n", " af.fit(mat)\n", " \n", " #get the label of the cluster for each mir\n", " mir_df[\"cluster\"] = af.labels_\n", " \n", " #get the coverage\n", " cov.append(coverage(mir_df))\n", " \n", " #store the preference\n", " pref.append(i)\n", " \n", " #store the number of clusters\n", " nb_cl.append(len(af.cluster_centers_indices_))\n", " \n", " if (i % 10 == 0):\n", " print i,\n", " else:\n", " print \".\",\n", " \n", "#Combine the results into a dataframe\n", "cov_metrics[\"coverage\"] = cov\n", "cov_metrics[\"preference\"] = pref\n", "cov_metrics[\"nb_clusters\"] = nb_cl\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.plot(cov_metrics.preference, cov_metrics.coverage, label=cov_metrics.nb_clusters)\n", "plt.xlabel(\"Affinity Propagation Preference\")\n", "plt.ylabel(\"Coverage of graph's clusters\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 186, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-100.0" ] }, "execution_count": 186, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.median(mat)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Predicted clusters:\n", "af = AffinityPropagation(preference = np.median(mat), affinity = \"precomputed\")\n", "af.fit(mat)\n", "\n", "\n", "mir_df[\"cluster\"] = af.labels_\n" ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of clusters: 96\n" ] } ], "source": [ "print(\"Number of clusters: %d\" %len(af.cluster_centers_indices_))" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Add the cluster number as a node property\n", "for cluster in range(len(af.cluster_centers_indices_)):\n", " mir_names = list(mir_df.ix[mir_df.cluster == cluster,0])\n", " graph.cypher.execute('''\n", " WITH {N} AS coll, {C} AS cluster\n", " MATCH (mir :MIR)\n", " WHERE mir.NAME IN coll\n", " SET mir.CLUSTER = cluster\n", " \n", " ''', {\"N\":mir_names, \"C\":cluster})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##8. Clusters' statistics" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Get the number of miRs per clusters\n", "nb_mir = []\n", "for i in range(96):\n", " nb_mir.append(graph.cypher.execute('''\n", " MATCH (mir1 :MIR)\n", " WHERE mir1.CLUSTER = {n}\n", " RETURN COUNT(DISTINCT mir1)\n", " ''', {\"n\":i})[0][0])" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(3, 26), (4, 21), (2, 19)]\n" ] }, { "data": { "image/png": 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1sIRkTEuhjucQdUKBjidARLyYPtySpE9wJQU7njBonVCg4ylpT+AvgMvZXFfV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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#the frequency counts\n", "import collections\n", "nb_mir.sort()\n", "f = collections.Counter(nb_mir)\n", "print(f.most_common(3))\n", "plt.plot(f.keys(),f.values(), 'o-', color='b')\n", "plt.xlabel(\"Number of miRs per cluster\")\n", "plt.ylabel(\"Number of clusters\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, most clusters are formed of 2 to 5 microRNAs.\n", "\n", "What is this large cluster of 36 miRs?" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " | Count | Cluster\n", "---+-------+---------\n", " 1 | 36 | 0\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 1\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 2\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 3\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 6 | 4\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 5\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 1 | 6\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 7\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 8\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 9\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 10\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 8 | 11\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 6 | 12\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 13\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 14\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 15\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 7 | 16\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 6 | 17\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 18\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 19\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 20\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 7 | 21\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 22\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 23\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 24\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 6 | 25\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 26\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 27\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 28\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 29\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 30\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 31\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 32\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 33\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 34\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 35\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 36\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 37\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 1 | 38\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 39\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 7 | 40\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 41\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 42\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 43\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 1 | 44\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 45\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 46\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 47\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 9 | 48\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 49\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 50\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 9 | 51\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 52\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 53\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 54\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 55\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 56\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 57\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 58\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 59\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 60\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 61\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 62\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 63\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 64\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 11 | 65\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 66\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 67\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 68\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 69\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 7 | 70\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 71\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 72\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 73\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 74\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 75\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 76\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 77\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 78\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 79\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 80\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 81\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 6 | 82\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 83\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 84\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 85\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 7 | 86\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 87\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 88\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 2 | 89\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 90\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 91\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 92\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 5 | 93\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 4 | 94\n", "\n", " | Count | Cluster\n", "---+-------+---------\n", " 1 | 3 | 95\n", "\n" ] } ], "source": [ "for i in range(96):\n", " print(graph.cypher.execute('''\n", " MATCH (mir1:MIR {CLUSTER:{n}})\n", " RETURN COUNT(DISTINCT mir1) AS Count, {n} AS Cluster\n", " ''', {\"n\":i}))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cluster of 36 microRNAs is the cluster 0. After examination in Neo4j browser, it is composed of mostly unrelated microRNAs that only have very few targets. It seems to be the default cluster to put the miRs unrelated to any other clusters.\n", "\n", "More importantly, the cluster 11 of 8 microRNAs is the family of let7 microRNAs that have very similar sequences and are expected to cluster together." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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YzH8gJX1DoMdGmzWrF2EA08sKZ2JpZao/6sjziOlCKp9uOB6kIhRgTw80b238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"text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename=\"pictures/let7_graph.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##9. Conclusion\n", "\n", "I showed in this notebook how Neo4j could handle efficiently a very connected graph such as the biological network of microRNAs-mRNA predicted interactions. Here I showed that microRNAs can be clustered accordingly to their common targets. I found 96 clusters (or 95 if we remove the suspicious cluster 0) that are composed mostly of 2 to 5 microRNAs. I used Affinity propagation as the clustering algorithm for this unsupervised task, however other algorithms exist (such as DBSCAN) that could do a clustering without knowing the number of clusters beforehand. These other clustering algorithms need to be tested in regard to the results obtained with the Affinity Propagation algorithm. I could use the metrics implemented here (coverage, part 4) to evaluate the efficiency of each algorithms." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }