Browse code

update vignettes

guangchuang yu authored on 03/01/2018 09:55:53
Showing33 changed files

... ...
@@ -2,7 +2,7 @@ PKGNAME := $(shell sed -n "s/Package: *\([^ ]*\)/\1/p" DESCRIPTION)
2 2
 PKGVERS := $(shell sed -n "s/Version: *\([^ ]*\)/\1/p" DESCRIPTION)
3 3
 PKGSRC  := $(shell basename `pwd`)
4 4
 
5
-all: rd readme check clean
5
+all: rd check clean
6 6
 
7 7
 alldocs: site rd readme
8 8
 
... ...
@@ -1,5 +1,6 @@
1 1
 # Generated by roxygen2: do not edit by hand
2 2
 
3
+S3method(collapse,ggtree)
3 4
 S3method(fortify,multiPhylo)
4 5
 S3method(fortify,obkData)
5 6
 S3method(fortify,phylo)
... ...
@@ -29,6 +30,7 @@ export(decimal2Date)
29 30
 export(expand)
30 31
 export(facet_plot)
31 32
 export(flip)
33
+export(fortify)
32 34
 export(geom_aline)
33 35
 export(geom_balance)
34 36
 export(geom_cladelabel)
... ...
@@ -61,6 +63,7 @@ export(get_balance_position)
61 63
 export(get_clade_position)
62 64
 export(get_heatmap_column_position)
63 65
 export(get_taxa_name)
66
+export(ggplot)
64 67
 export(ggsave)
65 68
 export(ggtree)
66 69
 export(gheatmap)
... ...
@@ -76,6 +79,21 @@ export(nodepie)
76 79
 export(open_tree)
77 80
 export(phylopic)
78 81
 export(range_format)
82
+export(read.beast)
83
+export(read.codeml)
84
+export(read.codeml_mlc)
85
+export(read.hyphy)
86
+export(read.jplace)
87
+export(read.jtree)
88
+export(read.mrbayes)
89
+export(read.newick)
90
+export(read.nexus)
91
+export(read.nhx)
92
+export(read.paml_rst)
93
+export(read.phylip)
94
+export(read.phyloT)
95
+export(read.r8s)
96
+export(read.raxml)
79 97
 export(read.tree)
80 98
 export(reroot)
81 99
 export(rescale_tree)
... ...
@@ -105,9 +123,11 @@ importFrom(ape,di2multi)
105 123
 importFrom(ape,extract.clade)
106 124
 importFrom(ape,getMRCA)
107 125
 importFrom(ape,ladderize)
126
+importFrom(ape,read.nexus)
108 127
 importFrom(ape,read.tree)
109 128
 importFrom(ape,reorder.phylo)
110 129
 importFrom(ape,rtree)
130
+importFrom(dplyr,collapse)
111 131
 importFrom(dplyr,full_join)
112 132
 importFrom(dplyr,mutate_)
113 133
 importFrom(ggplot2,Geom)
... ...
@@ -200,7 +220,21 @@ importFrom(treeio,Nnode)
200 220
 importFrom(treeio,Ntip)
201 221
 importFrom(treeio,as.phylo)
202 222
 importFrom(treeio,as.treedata)
223
+importFrom(treeio,read.beast)
224
+importFrom(treeio,read.codeml)
225
+importFrom(treeio,read.codeml_mlc)
203 226
 importFrom(treeio,read.fasta)
227
+importFrom(treeio,read.hyphy)
228
+importFrom(treeio,read.jplace)
229
+importFrom(treeio,read.jtree)
230
+importFrom(treeio,read.mrbayes)
231
+importFrom(treeio,read.newick)
232
+importFrom(treeio,read.nhx)
233
+importFrom(treeio,read.paml_rst)
234
+importFrom(treeio,read.phylip)
235
+importFrom(treeio,read.phyloT)
236
+importFrom(treeio,read.r8s)
237
+importFrom(treeio,read.raxml)
204 238
 importFrom(utils,modifyList)
205 239
 importFrom(utils,packageDescription)
206 240
 importFrom(utils,packageVersion)
... ...
@@ -1,5 +1,7 @@
1 1
 # ggtree 1.11.4
2 2
 
3
++ re-implement collapse as collapse.ggtree method by importing dplyr::collapse
4
+  generic to prevent function name collision (2018-01-03, Wed)
3 5
 + update treeVisualization vignette, with more layout examples added (2017-12-22, Fri)
4 6
 + update vignette (2017-12-21, Thu)
5 7
     - remove ggtreeUtilities.Rmd
... ...
@@ -59,15 +59,18 @@ is.viewClade <- function(tree_view) {
59 59
 ##' collapse a clade
60 60
 ##'
61 61
 ##'
62
-##' @title collapse
63
-##' @param tree_view tree view
62
+##' @title collapse-ggtree
63
+##' @rdname collapse
64
+##' @param x tree view
64 65
 ##' @param node clade node
66
+##' @param ... additional parameters
65 67
 ##' @return tree view
68
+##' @method collapse ggtree
66 69
 ##' @export
67 70
 ##' @seealso expand
68 71
 ##' @author Guangchuang Yu
69
-collapse <- function(tree_view=NULL, node) {
70
-    tree_view %<>% get_tree_view
72
+collapse.ggtree <- function(x=NULL, node, ...) {
73
+    tree_view <- get_tree_view(x)
71 74
 
72 75
     df <- tree_view$data
73 76
 
... ...
@@ -12,7 +12,7 @@
12 12
 ##' @importFrom ggplot2 GeomRect
13 13
 ##' @author Guangchuang Yu
14 14
 geom_hilight <- function(node, fill="steelblue", alpha=.5, extend=0, extendto=NULL) {
15
-  
15
+
16 16
   data = NULL
17 17
   stat = "hilight"
18 18
   position = "identity"
... ...
@@ -20,10 +20,10 @@ geom_hilight <- function(node, fill="steelblue", alpha=.5, extend=0, extendto=NU
20 20
   na.rm = TRUE
21 21
   inherit.aes = FALSE
22 22
   check.aes = FALSE
23
-  
23
+
24 24
   default_aes <- aes_(x=~x, y=~y, node=~node, parent=~parent, branch.length=~branch.length)
25 25
   mapping <- default_aes
26
-  
26
+
27 27
   l <- layer(
28 28
     stat=StatHilight,
29 29
     data = data,
... ...
@@ -40,7 +40,7 @@ geom_hilight <- function(node, fill="steelblue", alpha=.5, extend=0, extendto=NU
40 40
                   extendto=extendto,
41 41
                   na.rm = na.rm)
42 42
   )
43
-  
43
+
44 44
   return(l)
45 45
 }
46 46
 
... ...
@@ -67,15 +67,15 @@ stat_hilight <- function(mapping=NULL, data=NULL, geom="rect",
67 67
                          show.legend=NA, inherit.aes=FALSE,
68 68
                          fill, alpha, extend=0, extendto=NULL,
69 69
                          ...) {
70
-  
70
+
71 71
   default_aes <- aes_(x=~x, y=~y, node=~node, parent=~parent, branch.length=~branch.length)
72
-  
72
+
73 73
   if (is.null(mapping)) {
74 74
     mapping <- default_aes
75 75
   } else {
76 76
     mapping <- modifyList(mapping, default_aes)
77 77
   }
78
-  
78
+
79 79
   layer(
80 80
     stat=StatHilight,
81 81
     data = data,
... ...
@@ -101,7 +101,7 @@ stat_hilight <- function(mapping=NULL, data=NULL, geom="rect",
101 101
 ##' @export
102 102
 StatHilight <- ggproto("StatHilight", Stat,
103 103
                        compute_group = function(self, data, scales, params, node, extend, extendto) {
104
-                         
104
+
105 105
                          df <- get_clade_position_(data, node)
106 106
                          df$xmax <- df$xmax + extend
107 107
                          if (!is.null(extendto) && !is.na(extendto)) {
... ...
@@ -146,7 +146,7 @@ get_clade_position_ <- function(data, node) {
146 146
     y <- sp.df$y
147 147
 
148 148
     if ("branch.length" %in% colnames(data)) {
149
-        xmin <- min(x, na.rm=TRUE)-data[i, "branch.length"]/2
149
+        xmin <- min(x, na.rm=TRUE)-data[["branch.length"]][i]/2
150 150
     } else {
151 151
         xmin <- min(sp.df$branch, na.rm=TRUE)
152 152
     }
... ...
@@ -14,7 +14,7 @@
14 14
 ##' @return tree view with insets
15 15
 ##' @export
16 16
 ##' @author Guangchuang Yu
17
-inset <- function(tree_view, insets, width=0.1, height=0.1, hjust=0, vjust=0,
17
+inset <- function(tree_view, insets, width, height, hjust=0, vjust=0,
18 18
                   x="node", reverse_x=FALSE, reverse_y=FALSE) {
19 19
 
20 20
     df <- tree_view$data[as.numeric(names(insets)),]
... ...
@@ -6,6 +6,10 @@ ape::rtree
6 6
 ##' @export
7 7
 ape::read.tree
8 8
 
9
+##' @importFrom ape read.nexus
10
+##' @export
11
+ape::read.nexus
12
+
9 13
 ##' @importFrom tidytree groupOTU
10 14
 ##' @export
11 15
 tidytree::groupOTU
... ...
@@ -14,6 +18,18 @@ tidytree::groupOTU
14 18
 ##' @export
15 19
 tidytree::groupClade
16 20
 
21
+##' @importFrom dplyr collapse
22
+##' @export
23
+dplyr::collapse
24
+
25
+##' @importFrom ggplot2 fortify
26
+##' @export
27
+ggplot2::fortify
28
+
29
+##' @importFrom ggplot2 ggplot
30
+##' @export
31
+ggplot2::ggplot
32
+
17 33
 ##' @importFrom ggplot2 xlim
18 34
 ##' @export
19 35
 ggplot2::xlim
... ...
@@ -43,6 +59,59 @@ ggplot2::geom_label
43 59
 ggplot2::geom_point
44 60
 
45 61
 
62
+##' @importFrom treeio read.beast
63
+##' @export
64
+treeio::read.beast
46 65
 
66
+##' @importFrom treeio read.codeml
67
+##' @export
68
+treeio::read.codeml
69
+
70
+##' @importFrom treeio read.codeml_mlc
71
+##' @export
72
+treeio::read.codeml_mlc
73
+
74
+##' @importFrom treeio read.hyphy
75
+##' @export
76
+treeio::read.hyphy
77
+
78
+##' @importFrom treeio read.jplace
79
+##' @export
80
+treeio::read.jplace
47 81
 
82
+##' @importFrom treeio read.jtree
83
+##' @export
84
+treeio::read.jtree
85
+
86
+##' @importFrom treeio read.mrbayes
87
+##' @export
88
+treeio::read.mrbayes
89
+
90
+##' @importFrom treeio read.newick
91
+##' @export
92
+treeio::read.newick
93
+
94
+##' @importFrom treeio read.nhx
95
+##' @export
96
+treeio::read.nhx
97
+
98
+##' @importFrom treeio read.paml_rst
99
+##' @export
100
+treeio::read.paml_rst
101
+
102
+##' @importFrom treeio read.phylip
103
+##' @export
104
+treeio::read.phylip
105
+
106
+##' @importFrom treeio read.phyloT
107
+##' @export
108
+treeio::read.phyloT
109
+
110
+##' @importFrom treeio read.r8s
111
+##' @export
112
+treeio::read.r8s
113
+
114
+##' @importFrom treeio read.raxml
115
+##' @export
116
+treeio::read.raxml
48 117
 
... ...
@@ -4,9 +4,9 @@ ggtree: an R package for visualization and annotation of phylogenetic trees with
4 4
 
5 5
 <img src="https://raw.githubusercontent.com/Bioconductor/BiocStickers/master/ggtree/ggtree.png" height="200" align="right" />
6 6
 
7
-[![releaseVersion](https://img.shields.io/badge/release%20version-1.10.0-green.svg?style=flat)](https://bioconductor.org/packages/ggtree) [![develVersion](https://img.shields.io/badge/devel%20version-1.11.4-green.svg?style=flat)](https://github.com/guangchuangyu/ggtree) [![Bioc](http://www.bioconductor.org/shields/years-in-bioc/ggtree.svg)](https://www.bioconductor.org/packages/devel/bioc/html/ggtree.html#since) [![total](https://img.shields.io/badge/downloads-22621/total-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree) [![month](https://img.shields.io/badge/downloads-1218/month-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree)
7
+[![releaseVersion](https://img.shields.io/badge/release%20version-1.10.0-green.svg?style=flat)](https://bioconductor.org/packages/ggtree) [![develVersion](https://img.shields.io/badge/devel%20version-1.11.4-green.svg?style=flat)](https://github.com/guangchuangyu/ggtree) [![Bioc](http://www.bioconductor.org/shields/years-in-bioc/ggtree.svg)](https://www.bioconductor.org/packages/devel/bioc/html/ggtree.html#since) [![total](https://img.shields.io/badge/downloads-22863/total-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree) [![month](https://img.shields.io/badge/downloads-906/month-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree)
8 8
 
9
-[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![codecov](https://codecov.io/gh/GuangchuangYu/ggtree/branch/master/graph/badge.svg)](https://codecov.io/gh/GuangchuangYu/ggtree) [![Last-changedate](https://img.shields.io/badge/last%20change-2017--12--22-green.svg)](https://github.com/GuangchuangYu/ggtree/commits/master) [![GitHub forks](https://img.shields.io/github/forks/GuangchuangYu/ggtree.svg)](https://github.com/GuangchuangYu/ggtree/network) [![GitHub stars](https://img.shields.io/github/stars/GuangchuangYu/ggtree.svg)](https://github.com/GuangchuangYu/ggtree/stargazers) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://awesome-r.com/#awesome-r-graphic-displays)
9
+[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![codecov](https://codecov.io/gh/GuangchuangYu/ggtree/branch/master/graph/badge.svg)](https://codecov.io/gh/GuangchuangYu/ggtree) [![Last-changedate](https://img.shields.io/badge/last%20change-2018--01--03-green.svg)](https://github.com/GuangchuangYu/ggtree/commits/master) [![GitHub forks](https://img.shields.io/github/forks/GuangchuangYu/ggtree.svg)](https://github.com/GuangchuangYu/ggtree/network) [![GitHub stars](https://img.shields.io/github/stars/GuangchuangYu/ggtree.svg)](https://github.com/GuangchuangYu/ggtree/stargazers) [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://awesome-r.com/#awesome-r-graphic-displays)
10 10
 
11 11
 [![platform](http://www.bioconductor.org/shields/availability/devel/ggtree.svg)](https://www.bioconductor.org/packages/devel/bioc/html/ggtree.html#archives) [![Build Status](http://www.bioconductor.org/shields/build/devel/bioc/ggtree.svg)](https://bioconductor.org/checkResults/devel/bioc-LATEST/ggtree/) [![Linux/Mac Travis Build Status](https://img.shields.io/travis/GuangchuangYu/ggtree/master.svg?label=Mac%20OSX%20%26%20Linux)](https://travis-ci.org/GuangchuangYu/ggtree) [![AppVeyor Build Status](https://img.shields.io/appveyor/ci/Guangchuangyu/ggtree/master.svg?label=Windows)](https://ci.appveyor.com/project/GuangchuangYu/ggtree) [![Backers on Open Collective](https://opencollective.com/ggtree/backers/badge.svg)](#backers) [![Sponsors on Open Collective](https://opencollective.com/ggtree/sponsors/badge.svg)](#sponsors)
12 12
 
... ...
@@ -27,7 +27,7 @@ Please cite the following article when using `ggtree`:
27 27
 
28 28
 **G Yu**, DK Smith, H Zhu, Y Guan, TTY Lam<sup>\*</sup>. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. ***Methods in Ecology and Evolution***. 2017, 8(1):28-36.
29 29
 
30
-[![doi](https://img.shields.io/badge/doi-10.1111/2041--210X.12628-green.svg?style=flat)](http://dx.doi.org/10.1111/2041-210X.12628) [![Altmetric](https://img.shields.io/badge/Altmetric-325-green.svg?style=flat)](https://www.altmetric.com/details/10533079) [![citation](https://img.shields.io/badge/cited%20by-59-green.svg?style=flat)](https://scholar.google.com.hk/scholar?oi=bibs&hl=en&cites=7268358477862164627)
30
+[![doi](https://img.shields.io/badge/doi-10.1111/2041--210X.12628-green.svg?style=flat)](http://dx.doi.org/10.1111/2041-210X.12628) [![Altmetric](https://img.shields.io/badge/Altmetric-325-green.svg?style=flat)](https://www.altmetric.com/details/10533079) [![citation](https://img.shields.io/badge/cited%20by-64-green.svg?style=flat)](https://scholar.google.com.hk/scholar?oi=bibs&hl=en&cites=7268358477862164627)
31 31
 
32 32
 ------------------------------------------------------------------------
33 33
 
... ...
@@ -37,7 +37,7 @@ Please cite the following article when using `ggtree`:
37 37
 
38 38
 ### Download stats
39 39
 
40
-[![download](http://www.bioconductor.org/shields/downloads/ggtree.svg)](https://bioconductor.org/packages/stats/bioc/ggtree) [![total](https://img.shields.io/badge/downloads-22621/total-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree) [![month](https://img.shields.io/badge/downloads-1218/month-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree)
40
+[![download](http://www.bioconductor.org/shields/downloads/ggtree.svg)](https://bioconductor.org/packages/stats/bioc/ggtree) [![total](https://img.shields.io/badge/downloads-22863/total-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree) [![month](https://img.shields.io/badge/downloads-906/month-blue.svg?style=flat)](https://bioconductor.org/packages/stats/bioc/ggtree)
41 41
 
42 42
 <img src="docs/images/dlstats.png" width="890"/>
43 43
 
... ...
@@ -466,7 +466,7 @@
466 466
 
467 467
 			<aside class="copyright" role="note">
468 468
 				
469
-				&copy; 2017 Released under the Artistic-2.0 license &ndash;
469
+				&copy; 2018 Released under the Artistic-2.0 license &ndash;
470 470
 				
471 471
 				Documentation built with
472 472
 				<a href="https://www.gohugo.io" target="_blank">Hugo</a>
... ...
@@ -476,7 +476,7 @@ q + geom_text(data=d, aes(label=label))</code></pre>
476 476
 
477 477
 			<aside class="copyright" role="note">
478 478
 				
479
-				&copy; 2017 Released under the Artistic-2.0 license &ndash;
479
+				&copy; 2018 Released under the Artistic-2.0 license &ndash;
480 480
 				
481 481
 				Documentation built with
482 482
 				<a href="https://www.gohugo.io" target="_blank">Hugo</a>
... ...
@@ -361,7 +361,7 @@
361 361
 
362 362
 			<aside class="copyright" role="note">
363 363
 				
364
-				&copy; 2017 Released under the Artistic-2.0 license &ndash;
364
+				&copy; 2018 Released under the Artistic-2.0 license &ndash;
365 365
 				
366 366
 				Documentation built with
367 367
 				<a href="https://www.gohugo.io" target="_blank">Hugo</a>
... ...
@@ -409,7 +409,7 @@
409 409
 
410 410
 			<aside class="copyright" role="note">
411 411
 				
412
-				&copy; 2017 Released under the Artistic-2.0 license &ndash;
412
+				&copy; 2018 Released under the Artistic-2.0 license &ndash;
413 413
 				
414 414
 				Documentation built with
415 415
 				<a href="https://www.gohugo.io" target="_blank">Hugo</a>
416 416
Binary files a/docs/images/citation.png and b/docs/images/citation.png differ
417 417
Binary files a/docs/images/dlstats.png and b/docs/images/dlstats.png differ
... ...
@@ -346,7 +346,7 @@
346 346
 
347 347
 				<p><link rel="stylesheet" href="https://guangchuangyu.github.io/css/font-awesome.min.css"> <link rel="stylesheet" href="https://guangchuangyu.github.io/css/academicons.min.css"></p>
348 348
 <p><img src="https://raw.githubusercontent.com/Bioconductor/BiocStickers/master/ggtree/ggtree.png" height="200" align="right" /></p>
349
-<p><a href="https://bioconductor.org/packages/ggtree"><img src="https://img.shields.io/badge/release%20version-1.10.0-blue.svg?style=flat" alt="releaseVersion" /></a> <a href="https://github.com/guangchuangyu/ggtree"><img src="https://img.shields.io/badge/devel%20version-1.11.4-blue.svg?style=flat" alt="develVersion" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-22808/total-blue.svg?style=flat" alt="total" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-1218/month-blue.svg?style=flat" alt="month" /></a></p>
349
+<p><a href="https://bioconductor.org/packages/ggtree"><img src="https://img.shields.io/badge/release%20version-1.10.0-blue.svg?style=flat" alt="releaseVersion" /></a> <a href="https://github.com/guangchuangyu/ggtree"><img src="https://img.shields.io/badge/devel%20version-1.11.4-blue.svg?style=flat" alt="develVersion" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-22863/total-blue.svg?style=flat" alt="total" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-906/month-blue.svg?style=flat" alt="month" /></a></p>
350 350
 <p>The <code>ggtree</code> package extending the <em>ggplot2</em> package. It based on grammar of graphics and takes all the good parts of <em>ggplot2</em>. <em>ggtree</em> is designed for not only viewing phylogenetic tree but also displaying annotation data on the tree. <em>ggtree</em> is released within the <a href="https://bioconductor.org/packages/ggtree/">Bioconductor</a> project and the source code is hosted on <a href="https://github.com/GuangchuangYu/ggtree"><i class="fa fa-github fa-lg"></i> GitHub</a>.</p>
351 351
 <div id="authors" class="section level2">
352 352
 <h2><i class="fa fa-user"></i> Authors</h2>
... ...
@@ -355,7 +355,7 @@
355 355
 <div id="citation" class="section level2">
356 356
 <h2><i class="fa fa-book"></i> Citation</h2>
357 357
 <p>Please cite the following article when using <code>ggtree</code>:</p>
358
-<p><a href="http://dx.doi.org/10.1111/2041-210X.12628"><img src="https://img.shields.io/badge/doi-10.1111/2041--210X.12628-blue.svg?style=flat" alt="doi" /></a> <a href="https://www.altmetric.com/details/10533079"><img src="https://img.shields.io/badge/Altmetric-325-blue.svg?style=flat" alt="Altmetric" /></a> <a href="https://scholar.google.com.hk/scholar?oi=bibs&amp;hl=en&amp;cites=7268358477862164627"><img src="https://img.shields.io/badge/cited%20by-60-blue.svg?style=flat" alt="citation" /></a></p>
358
+<p><a href="http://dx.doi.org/10.1111/2041-210X.12628"><img src="https://img.shields.io/badge/doi-10.1111/2041--210X.12628-blue.svg?style=flat" alt="doi" /></a> <a href="https://www.altmetric.com/details/10533079"><img src="https://img.shields.io/badge/Altmetric-325-blue.svg?style=flat" alt="Altmetric" /></a> <a href="https://scholar.google.com.hk/scholar?oi=bibs&amp;hl=en&amp;cites=7268358477862164627"><img src="https://img.shields.io/badge/cited%20by-64-blue.svg?style=flat" alt="citation" /></a></p>
359 359
 <p><strong>G Yu</strong>, DK Smith, H Zhu, Y Guan, TTY Lam<sup>*</sup>. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. <strong><em>Methods in Ecology and Evolution</em></strong>. 2017, 8(1):28-36.</p>
360 360
 </div>
361 361
 <div id="featured-articles" class="section level2">
... ...
@@ -452,7 +452,7 @@ biocLite(&quot;ggtree&quot;)</code></pre>
452 452
 
453 453
 			<aside class="copyright" role="note">
454 454
 				
455
-				&copy; 2017 Released under the Artistic-2.0 license &ndash;
455
+				&copy; 2018 Released under the Artistic-2.0 license &ndash;
456 456
 				
457 457
 				Documentation built with
458 458
 				<a href="https://www.gohugo.io" target="_blank">Hugo</a>
... ...
@@ -81,13 +81,11 @@ http://dx.doi.org/10.1111/2041-210X.12628
81 81
       
82 82
       <guid>https://guangchuangyu.github.io/ggtree/tweets/</guid>
83 83
       <description>Leave me a message on 
84
-Subtrees as triangles with ggtree https://t.co/NNdur8PS2Y #rstats https://t.co/7PZbN9daHy
84
+Was just about to shout out at #ggtree and saw your tweet on the top of my newsfeed. #ggtree - where have you been all my life?
85
+&amp;mdash; Erin Becker (@ErinSBecker) November 20, 2017  Subtrees as triangles with ggtree https://t.co/NNdur8PS2Y #rstats https://t.co/7PZbN9daHy
85 86
 &amp;mdash; Jean 👨‍💻 (@JeanManguy) December 15, 2017  Drawing a tree with colored tips in R (ggtree)
86 87
 https://t.co/s9FYUbynCd @guangchuangyu
87
-&amp;mdash; Guanyang Zhang 张冠阳 (@GYZhang2) December 13, 2017  ggtree使ってみた。”注目する枝組み合わせの分布”みたいなものを可視化したいんだけどまだちょっと見にくい。 pic.twitter.com/w5xs7cd0jQ
88
-&amp;mdash; Kenji Fukushima (@kfuku0502) December 13, 2017  ggtreeにはhttps://t.co/8T3WU8h9pWからシルエット画像を取ってくる用の関数まで用意されているのか…。HGTの描画も楽そうだし、これはもう系統樹描画に関してはETEを投げ捨ててggtreeに鞍替えするべきか。https://t.co/gynekuwjCe
89
-&amp;mdash; Kenji Fukushima (@kfuku0502) December 11, 2017  I&amp;#39;ve had good experiences with ggtree https://t.co/eRLhDoc4Hm
90
-&amp;mdash; Philipp Bayer (@PhilippBayer) December 7, 2017  Thank you so much @guangchuangyu!</description>
88
+&amp;mdash; Guanyang Zhang 张冠阳 (@GYZhang2) December 13, 2017  ggtree使ってみた。”注目する枝組み合わせの分布”みたいなものを可視化したいんだけどまだちょっと見にくい。 pic.</description>
91 89
     </item>
92 90
     
93 91
   </channel>
... ...
@@ -345,7 +345,9 @@
345 345
 
346 346
 			<p><link rel="stylesheet" href="https://guangchuangyu.github.io/css/font-awesome.min.css"></p>
347 347
 <p><font size="4"><strong>Leave me a message on <a href="https://twitter.com/hashtag/ggtree"><i class="fa fa-twitter fa-lg"></i></a></strong></font></p>
348
-<p><blockquote class="twitter-tweet"><p lang="en" dir="ltr">Subtrees as triangles with ggtree <a href="https://t.co/NNdur8PS2Y">https://t.co/NNdur8PS2Y</a>  <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <a href="https://t.co/7PZbN9daHy">https://t.co/7PZbN9daHy</a></p>&mdash; Jean 👨‍💻 (@JeanManguy) <a href="https://twitter.com/JeanManguy/status/941463119490150405?ref_src=twsrc%5Etfw">December 15, 2017</a></blockquote>
348
+<p><blockquote class="twitter-tweet"><p lang="en" dir="ltr">Was just about to shout out at <a href="https://twitter.com/hashtag/ggtree?src=hash&amp;ref_src=twsrc%5Etfw">#ggtree</a> and saw your tweet on the top of my newsfeed. <a href="https://twitter.com/hashtag/ggtree?src=hash&amp;ref_src=twsrc%5Etfw">#ggtree</a> - where have you been all my life?</p>&mdash; Erin Becker (@ErinSBecker) <a href="https://twitter.com/ErinSBecker/status/932751454938415105?ref_src=twsrc%5Etfw">November 20, 2017</a></blockquote>
349
+<script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
350
+<blockquote class="twitter-tweet"><p lang="en" dir="ltr">Subtrees as triangles with ggtree <a href="https://t.co/NNdur8PS2Y">https://t.co/NNdur8PS2Y</a>  <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <a href="https://t.co/7PZbN9daHy">https://t.co/7PZbN9daHy</a></p>&mdash; Jean 👨‍💻 (@JeanManguy) <a href="https://twitter.com/JeanManguy/status/941463119490150405?ref_src=twsrc%5Etfw">December 15, 2017</a></blockquote>
349 351
 <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
350 352
 <blockquote class="twitter-tweet"><p lang="en" dir="ltr">Drawing a tree with colored tips in R (ggtree)<br> <a href="https://t.co/s9FYUbynCd">https://t.co/s9FYUbynCd</a>  <a href="https://twitter.com/guangchuangyu?ref_src=twsrc%5Etfw">@guangchuangyu</a></p>&mdash; Guanyang Zhang 张冠阳 (@GYZhang2) <a href="https://twitter.com/GYZhang2/status/941040327816892416?ref_src=twsrc%5Etfw">December 13, 2017</a></blockquote>
351 353
 <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
... ...
@@ -468,7 +470,7 @@
468 470
 
469 471
 			<aside class="copyright" role="note">
470 472
 				
471
-				&copy; 2017 Released under the Artistic-2.0 license &ndash;
473
+				&copy; 2018 Released under the Artistic-2.0 license &ndash;
472 474
 				
473 475
 				Documentation built with
474 476
 				<a href="https://www.gohugo.io" target="_blank">Hugo</a>
... ...
@@ -19,13 +19,11 @@
19 19
       
20 20
       <guid>https://guangchuangyu.github.io/ggtree/tweets/</guid>
21 21
       <description>Leave me a message on 
22
-Subtrees as triangles with ggtree https://t.co/NNdur8PS2Y #rstats https://t.co/7PZbN9daHy
22
+Was just about to shout out at #ggtree and saw your tweet on the top of my newsfeed. #ggtree - where have you been all my life?
23
+&amp;mdash; Erin Becker (@ErinSBecker) November 20, 2017  Subtrees as triangles with ggtree https://t.co/NNdur8PS2Y #rstats https://t.co/7PZbN9daHy
23 24
 &amp;mdash; Jean 👨‍💻 (@JeanManguy) December 15, 2017  Drawing a tree with colored tips in R (ggtree)
24 25
 https://t.co/s9FYUbynCd @guangchuangyu
25
-&amp;mdash; Guanyang Zhang 张冠阳 (@GYZhang2) December 13, 2017  ggtree使ってみた。”注目する枝組み合わせの分布”みたいなものを可視化したいんだけどまだちょっと見にくい。 pic.twitter.com/w5xs7cd0jQ
26
-&amp;mdash; Kenji Fukushima (@kfuku0502) December 13, 2017  ggtreeにはhttps://t.co/8T3WU8h9pWからシルエット画像を取ってくる用の関数まで用意されているのか…。HGTの描画も楽そうだし、これはもう系統樹描画に関してはETEを投げ捨ててggtreeに鞍替えするべきか。https://t.co/gynekuwjCe
27
-&amp;mdash; Kenji Fukushima (@kfuku0502) December 11, 2017  I&amp;#39;ve had good experiences with ggtree https://t.co/eRLhDoc4Hm
28
-&amp;mdash; Philipp Bayer (@PhilippBayer) December 7, 2017  Thank you so much @guangchuangyu!</description>
26
+&amp;mdash; Guanyang Zhang 张冠阳 (@GYZhang2) December 13, 2017  ggtree使ってみた。”注目する枝組み合わせの分布”みたいなものを可視化したいんだけどまだちょっと見にくい。 pic.</description>
29 27
     </item>
30 28
     
31 29
   </channel>
... ...
@@ -12,7 +12,7 @@
12 12
 
13 13
 <meta name="author" content="Guangchuang Yu School of Public Health, The University of Hong Kong" />
14 14
 
15
-<meta name="date" content="2017-12-27" />
15
+<meta name="date" content="2018-01-03" />
16 16
 
17 17
 <title>Annotating phylogenetic tree with images using ggtree and ggimage</title>
18 18
 
... ...
@@ -72,7 +72,7 @@ code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Inf
72 72
 <h1 class="title toc-ignore project-name">Annotating phylogenetic tree with images using ggtree and ggimage</h1>
73 73
 <h4 class="author project-author">Guangchuang Yu<br />
74 74
 School of Public Health, The University of Hong Kong</h4>
75
-<h4 class="date project-date">2017-12-27</h4>
75
+<h4 class="date project-date">2018-01-03</h4>
76 76
 </section>
77 77
 
78 78
 
79 79
new file mode 100644
... ...
@@ -0,0 +1,160 @@
1
+<!DOCTYPE html>
2
+
3
+<html xmlns="http://www.w3.org/1999/xhtml">
4
+
5
+<head>
6
+
7
+<meta charset="utf-8">
8
+<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
9
+<meta name="generator" content="pandoc" />
10
+
11
+<meta name="viewport" content="width=device-width, initial-scale=1">
12
+
13
+<meta name="author" content="Guangchuang Yu School of Public Health, The University of Hong Kong" />
14
+
15
+<meta name="date" content="2018-01-03" />
16
+
17
+<title>Annotate a phylogenetic tree with insets</title>
18
+
19
+
20
+
21
+<style type="text/css">code{white-space: pre;}</style>
22
+<style type="text/css">
23
+div.sourceCode { overflow-x: auto; }
24
+table.sourceCode, tr.sourceCode, td.lineNumbers, td.sourceCode {
25
+  margin: 0; padding: 0; vertical-align: baseline; border: none; }
26
+table.sourceCode { width: 100%; line-height: 100%; }
27
+td.lineNumbers { text-align: right; padding-right: 4px; padding-left: 4px; color: #aaaaaa; border-right: 1px solid #aaaaaa; }
28
+td.sourceCode { padding-left: 5px; }
29
+code > span.kw { color: #007020; font-weight: bold; } /* Keyword */
30
+code > span.dt { color: #902000; } /* DataType */
31
+code > span.dv { color: #40a070; } /* DecVal */
32
+code > span.bn { color: #40a070; } /* BaseN */
33
+code > span.fl { color: #40a070; } /* Float */
34
+code > span.ch { color: #4070a0; } /* Char */
35
+code > span.st { color: #4070a0; } /* String */
36
+code > span.co { color: #60a0b0; font-style: italic; } /* Comment */
37
+code > span.ot { color: #007020; } /* Other */
38
+code > span.al { color: #ff0000; font-weight: bold; } /* Alert */
39
+code > span.fu { color: #06287e; } /* Function */
40
+code > span.er { color: #ff0000; font-weight: bold; } /* Error */
41
+code > span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */
42
+code > span.cn { color: #880000; } /* Constant */
43
+code > span.sc { color: #4070a0; } /* SpecialChar */
44
+code > span.vs { color: #4070a0; } /* VerbatimString */
45
+code > span.ss { color: #bb6688; } /* SpecialString */
46
+code > span.im { } /* Import */
47
+code > span.va { color: #19177c; } /* Variable */
48
+code > span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
49
+code > span.op { color: #666666; } /* Operator */
50
+code > span.bu { } /* BuiltIn */
51
+code > span.ex { } /* Extension */
52
+code > span.pp { color: #bc7a00; } /* Preprocessor */
53
+code > span.at { color: #7d9029; } /* Attribute */
54
+code > span.do { color: #ba2121; font-style: italic; } /* Documentation */
55
+code > span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
56
+code > span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
57
+code > span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
58
+</style>
59
+
60
+
61
+
62
+<link href="data:text/css;charset=utf-8,%40font%2Dface%7Bfont%2Dfamily%3A%27Open%20Sans%27%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A400%3Bsrc%3Alocal%28%27Open%20Sans%27%29%2Clocal%28OpenSans%29%2Curl%28data%3Aapplication%2Ffont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAE8YABIAAAAAhWwAAQABAAAAAAAAAAAAAAAAAAAAAAAAAABHREVGAAABlAAAABYAAAAWABAA3UdQT1MAAAGsAAAADAAAAAwAFQAKR1NVQgAAAbgAAABZAAAAdN3O3ptPUy8yAAACFAAAAF8AAABgoT6eyWNtYXAAAAJ0AAAAmAAAAMyvDbOdY3Z0IAAAAwwAAABZAAAAog9NGKRmcGdtAAADaAAABJsAAAe0fmG2EWdhc3AAAAgEAAAAEAAAABAAFQAjZ2x5ZgAACBQAADWFAABReBn1yj5oZWFkAAA9nAAAADYAAAA293bipmhoZWEAAD3UAAAAHwAAACQNzAapaG10eAAAPfQAAAIIAAADbLTLWYhrZXJuAAA%2F%2FAAAChcAAB6Qo%2Buk42xvY2EAAEoUAAABuQAAAbz3ewp%2FbWF4cAAAS9AAAAAgAAAAIAJ2AgpuYW1lAABL8AAAAKwAAAEyFNwvSnBvc3QAAEycAAABhgAAAiiYDmoRcHJlcAAATiQAAADyAAABCUO3lqQAAQAAAAwAAAAAAAAAAgABAAAA3AABAAAAAQAAAAoACgAKAAB4AR3HNcJBAQDA8d%2BrLzDatEXOrqDd4S2ayUX1beTyDwEyyrqCbXrY%2BxPD8ylAsF0tUn%2F4nlj89Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAHgBY2Bm2cY4gYGVgYN1FqsxAwOjPIRmvsiQxviRg4mJm42NmZWFiYnlAQPTewcGhWgGBgYNBiAwdAx2ZgAK%2FP%2FLJv9PhKGFo5cpQoGBcT5IjsWDdRuQUmBgBgD40BA5AHgBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ%2FBj3QYkS1m3sZ5lQAEsHgwiDBMZGP6%2FAfEQ5D8REAnUJfxnyv%2B3%2F1r%2Fv%2Fq3Eigi8W8PA1mAA0J1MzQy3GWYwdDP0Mcwk6GDoZGRn6ELAE09H%2F8AAAB4AXVUR3fbxhPfhRqr%2F6Cr3h8pi4wpN9K9V4QEYCrq7b2F0gC1R%2BXkS3rjKWXlfJeBfaF88jH1M6TfoqNzdWaXxZ0NM7%2FftJ2ZpXfzzeVILi0uzM%2FNzkxPTU68Md64GQZ%2Bvfa6d%2BP6tatXLl%2B6eOH8uVMnTxyvVg4fGisfhNfcV0f3luz%2F7Srmc9nMyPDQ4IDFWUUgjwMcKItSmEAASaNaEcFo069WAghjFIlAegyOQaNhIEhQxALHEqIeg2P0yHLjKUuvY%2Bn1LbktrrKrOgUI%2FMUH0ebLc5Lk73yIBO4YeUrL5GGUIimuSx6mKl2tCDD8oKmCmGrkaT5Xh%2Fp6rlphaS5PYp4kPAy3Un74OjeCdTi4nFosU6Qg%2BqRBsoazczLwHdeNqpVx3AW%2BoVjdhMThOo6YkGJTl862RFq5r263bbYSHyuswVrylsSBhHzVQKDU11g6hkfAxyOf%2FDVKJ1%2FHCvgBHtNRJ%2Bb7eSYepeQ4VLZBqAeMjgM7%2FzyJJF1kuGw%2FYFpEq458Xrr65YTUa6VCEKGKVdJ%2B2FoBYYNKCwV1K6B2s1mJnPB7Ww6GtyO04ya%2FHHWPHs5P4J65NyVa5VA0E0LocwPci45b6tvMvohm1BYc1h12Xd2GrbbHVkjB1pzs6IKtOHeYd%2BJYhFasmfs9Zt%2BSZlo9pu8eg0utWZAKB8vjaxBQx7cSbK3Qdr2nBwM27vrXcUHtLolLJyJjK3CAbDcFDo3hsPZ63IH2RrsoWyskdB47jiKitFtcAgqj4wQQxN3PB81RCiCo0Y1jnUVYlOj5JHhJd2JBevIEeSQxDWzTN8PEE3AL90KtP11dVrC5II1L1w331pHFq10vPBGYeyUCFRvB7PAEzMltdubhb%2BlZ4dw9w86yyNfG%2B%2Bu0ZWOBkmsb%2BGrsrKGIN4R0XPQimnAEcj3CI6ZDR35zzHJEZlcW5cQCTMwty4umkB5B4ajHwVNhQDqdMLSAmClnhLScgYgMbQJESALUrtIvjpQz9LVxuIPSiYgQkjusZ01l4BERrPtdO9KfDErKQLne6EUbJlXHqTccNzL163tuES26ickjo5va6FIkCyIyaFEYA%2BlejuqlFxLWIYKmQG9W0tlMe0yXu80wPe%2FOavEJrd8srSFziSal30wMj5H2mH7T6H218RQ93qOFysDEgtLBoRuQUeXjyPQKexdLjoa4vtAQJiBsEXYutEo9T1%2Fm5mUdBMbXFCzIq8Z6Yl5%2B7nyic%2B1mE3xisVatpBarpcC%2FmUs9%2Fs3Csty2GRPfLMo7FrfqcS1KDxIntwVjnkEtjRJoFKEVHWmelIyxd7Y9xlqGHTSA0VfbnBks08M4W21bHczuJBrTiYixiBnsMF7PepCwTAdrGcy8UqZb5uWGvIyX9QpW0XJSrqE7hNzjjGU5u1vgRe6k5DVv4DZvpVnP6Vi0yMKLOhUvPUq9tCzvFhi5mV9KVNMvWpfRJg1bggjEml6Uz6KmiiN92dh%2BGg19OHK4TmOC61TIcAFzsF7DPNQ0fkPjNzr4sMZHaEX5fk7uLZr9LHK9AW9KF2wU%2F%2F%2FBUfaOnlREfyrK%2Frv6Hyn3ISkAAAEAAwAIAAoADQAH%2F%2F8AD3gBhXwHfFRV1vg5974yvZdMQspkSIYkQkgmhdAyIIQQWsSADCLSpajUiMgiAkuJNGmhKyJGDCyybCiyiGBHRGQtyLIuf2UX19UPy7oWyFz%2B972ZBxOE72N%2BL2%2BYd%2Bbe0%2B5p99wBAscBBIN4ACjI4D4oUJEIVAbIL8wPYX4oP1TQ3um3%2B0v5dZz2bj44nsyKLhYPXKkaL1wCAhuuXcQ69dsWyAu7qF5PBMFqQzQRkzQgYvIQCuXleXYHlCXl2x1YZg%2BF7HxMDNAQLQoVetwuKZCZjRUTQqc%2Ff7RjebisqAeuEQJXmpZUdA%2F3KgcgsJA2kL1xDNPDZqCyQAWdXiIy5YOHThUq4%2FKB1XFpgPr5heVtJuSQvJzxOeKB6HfEplzKWCEA4Sc%2BVgqkw8bwIF16K7fg0ttNJr3DajEKBqfT5UlNkwXJKyD4hCRRlFySwU%2BTvTTJkJTh1wkms6l%2FpBWa08Fmt%2FWP%2BNz2AWYcYEez3WwXvU5qECE%2FVB5ylJXl5993Hyc3zw6hkHaPoerldxVjh7eMX%2FF3hYWxu0KF382pcKpXsV%2B9QlS93Mj%2FSz%2FujinsVE1dDTszcEk1u4LpPdjXmDdw6UAsqFlUg7rmf2J%2Bd3aGLmC757GBuEe55mHNXGxifZVrLtuNNUBhwbU6wSQ5IAOyoS2MCxcH7VmpXkHIdZlFP4BPtOvFdvlZZsncL0Kl1pZcS99Iam5eK1erfhFvrkviL9HDKc5X6OV%2FChUq7aGEvw5U6QuFVCbEhOSSZHegODM7WOzxhOzZ2cVFJaXFIbfHK2cH7WlELuK3EnR5vHZJEkzvHZw35S933n0ucur5ky%2FMO7SraN2mrVuqGiNPnIt%2BNnTy6HF4fMkfvf%2B6EEjfkpWPh7rtXrJgp%2BNAk9hzQScj6194%2F%2ByxlZE72Ow0KvcdloMLbPcBiDD%2B2jdSW%2FEk6MENfk55AfQMtwabaPC0aZWZ2a6Nob1NKgxRc3qemb%2FaF0jtk3xZPtkpc4Xjr3KVXE7WDfpi%2BsfVJ1RotwUyJVFVbE4ZV3JUPi0pLsq%2B%2BXMM4A9Vd%2B%2FYcXcVvrtx7bLN61av2oINVTU11dU1NVV4cuPaFRvXrV7xDGPNH6%2BheQJpbMQaHLiz8R9fXb5w8dLl5vO7XnzhD7uef37Xxa8u%2F%2F3ipa9pxpUqrt5AYeq1b8QPxVNg5BQWw13h9k4PpEqB3Lx2eW0DlmxfqkdfUhoy9Y6EnNZgW0t7MZ%2F6smlubka%2BI0NfFckQoDwPkjih%2Bd4yrpTleTdRqoinJE6Ts7AULcTt8mRxQbYjMeLcXMpYwucgMgaCkrrMn668Z97YBwZHJm%2F%2B%2FhnWZ%2FKwOzazl5c2DerS%2Bo2Xth9eshXXd7jTu7NHHeb98%2BVHfqw%2F%2Bz%2FCmp5zhvSZe3e%2FkSOubt2EO3tExnWrrbsy%2F51x94%2BaWFa%2F84V1k%2Fbfx2Z1fWE0%2B2It%2B2zfxGEfAaBiMbBctRiug0CpIBLFUpyK2R%2BOumYgYrZB%2BcZAdoT4%2BTfM0CpsksEggGCxGoNUsV4J5sVpc5SGJE6pwxvIJgM3r97%2B1Kq1S7et2UQKUI%2Fv7znOCn%2F8jpW80ohvKaN24aOatFEFAx8XLFYDFYItR0UbkQMljuIiEgx5HMS0efW2pWtXPbVdGZb9yjruPIInv%2FsR3z%2F%2BEisAhMFkrmCRXGCB9uEUKgoomw16o95qEwxoJiaT2cDtl84CUP5G4XWJOTBmWLK8olOmNOjMKhUpWZWHK5LZgl9279229we2OBUX50kuVjv5QDo7PBwnsvrhWJF%2BYDIuVagZDxeFHOF1MEKbsBMEQS%2BKJjOVdXJ1BKw61EH%2BfeqSTzTz3I7ZA3Zuv%2Bwhshy3sDFL2TjctJR6n2SDsfFJ3A0I5ewXfAgugw7s%2B0XQG0SAfFVWHOEsr6TyphSHW5NHFc9J6Wa%2B7B3Dfp42HguHAUINniPlZCpQ%2Fl0CogDIrW%2F8u85iv7sGv8ZzGzYAxjwV%2FMCxTwobJQCTWU8HRPQeruaaXpRqestVdUOXso7dupeF7px4Z8%2Bed3arKFc44AIg51W9ch4kIIiUEocmSk4sBpCcj15oUDRJXYYExl37RmirrkIv55rLASYJJF%2BS3t0nopeptU%2BE%2BmLrLK%2BlPgQyid3mCBU6UP1rVz8R2n770zc%2FXf7x8s%2FNn9fvaFi3rmFHPfmMLWRP4lycho%2FjNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ%2Fhbrd7GuYBwhgAIYtbTx%2F3%2Bd4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB%2FcXYlcQshhyMsZrI6PYLWc3lOG%2FvlA4rHr%2F3uTFD3r38%2Fr%2B3fMKOke9W4oJ9G566u7au84CpOz%2Fct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY%2B0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq%2BBLL299am8j%2Bzv774zj995%2FdgTOZsOfWr3rnTWPj2h8qGbo1%2FM%2F%2FkYYvmxfms7TtPrM54E7ns4vwBw0rFy%2FaNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk%2BhMQ5oJDqhrC%2Fl%2F%2FFxmAUlGYeK5Z6Jl5MDec2yJQdc%2Bl5ViNduL1avoZ805eGll04jy6COKheT8S%2BU6kQwdw%2BlW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M%2BWgUwldSkjbL1HPLrCf51d8MHbv66zu%2FmcGl5Kz0YNZ0%2Bmcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn%2F9i8n08OU3L%2F760UX2E369YuvqVUPrI9VryFR8CXc5V%2FrYefbW7svv%2FYNdxUHv%2FOnFVQ1V8yse2Dde0UcAIY%2FzU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF%2BmpaKOYunHhldNAlZhEyFGpz4R20C%2Bc47Vmu%2B6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf%2BvWjNvnr22b%2BbsfDJR7%2Be%2BcL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg%2F48%2B8C5U0euLuu%2Ff8ozr1xteHTRssdGru8V3kwfeHTMsN937%2FzksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0%2FIwe1eO%2B%2B6%2FRVHpg2mv%2FuPbBuguPMtfKLU%2BtuXfjkIFraEVzg2tlMuZg6O57%2FvXBP1C3kZ3H9od2PPV81RMVE%2FaNAy3HEcaokRS34Ta%2BLAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL%2BY3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj%2FT6FDccpXsmn6Rjlxv%2BknyrTFMR8%2BU%2FcF9%2BDiRwh%2FUCiChwdeXD58cDhSwsRjeikNNcTo83%2F0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up%2Bg5GIBDhGEr2BkRNVlMZTa%2FP3HKVyrMMKrF3H%2FKPYUAWjlGsXaRnXrxTIhrJwqp%2FbMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ%2Baq%2FctSSXgtmD6gf2emV91%2F9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj%2BzqPXn0w4On3e9nZ%2BNJLYFZ1yqkQ2ITFEM5zzwyA%2B1KLJ1kVwpAjsvSTgx3S%2BrQQeiisxv5Ky%2B9kGbnqUmllmSFEhOP6%2FG4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH%2FudKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt%2FDeaWvZt3%2B8wA7swe6Y%2F5cvjv3I1rHJn%2BAyhLM44ODVn14%2F7bBUDpq%2Fhpxb8c388XfdM%2BrU3veu%2BTws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz%2B5W8H0OIlBsz%2FtURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9%2FgxBMbluwe4SAjxpj%2FmcgN0ef3cCt2IAhVVLsR%2F7%2BTIjjZjU9PTeY1ew4I9%2FOvhn8cCeI%2FNf9BnK2Pk3%2FkZ7TF00%2B6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi%2FHK2DL%2B4emRymUNIE3%2BRo3WokKfbtNP37Cs0%2F7rxjQ0X2Cvs2Rex%2FNNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA%2BZuAPZTBx2Az2Uo2CY%2FhIHysic%2F1z59PI%2FdU5CtWz%2BaJB9gi9gKmYebVKZgHgMq89Bc%2Br1GJWSSDAQXQoWAyS%2FreEUlCQsTeEUKRr3B03DZmUZBwxy%2F6S%2FMZmh%2BdTYZHt5OF4oH1LKc%2BeilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4%2B8%2FHaphRyV9%2Brq5lT1xe9nfPc0a2IrDuKQL%2F%2F9bve3DrL%2Fso%2FQj0kbVrGXCYuWZWXjUhzzD7xn%2F%2BD6GvYau8Q%2BZe8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG%2FQbydi5f%2FDYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15%2BJJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC%2BUjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4%2BjCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0%2B0aio0pV%2FbIp9V%2BKIgpPrUZJOFCUev%2FJSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo%2Bbh4oGrVQLPOume7Uev%2FBCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj%2BWvNn%2BtsW4%2FRVB2xkGeEk582NR%2FnE3ZMwaxy2guAqFp99FZ5bu%2BIXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9%2Bv1%2Ffvn5h%2F9Eeb77LHuYa%2B94HIt1bArbxs6yU1iIuRjEAnYqZp%2BE8erqdUBRONnA%2Bc75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW%2FmlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp%2BZp8MeAIEa%2BOqmZtns6x0xC7KTL2yZM%2BMtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf%2FDnN%2BWMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7%2FVXTzl2vfEZGRLeJB94%2Fzf4%2BLjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D%2Bo5jgx%2B6jmBbwy4BEI%2B9d3rHnZ0I%2FGN%2B7usnL1ey%2BxM389WLx%2F1%2BINHRbWXfoDLjz%2B6Z07su%2BYN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij%2F2F%2FcJfFxrfee3ld8QDzf2vsC8wo5nuaa44%2BMabh%2BghQAAA4XW1%2FpMcNqJgMuooCJQqiPLlrxWvQhjgF8%2F%2FSgXTwej3O6M%2FNmF1x8zWHdVaFh%2F5uU3bnwXkmg1yXz6aT6km%2BQwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u%2BnL9p6UYpSa0Nne7yy%2B1EQ%2F7PaW6%2Fdbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj%2FPIY0oTXmKdjuAkfHg%2F60QWROeQZnI4%2Bgq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE%2BqKL%2BGCJdcNEhlrSb%2BQ6T8%2BR887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82%2F0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B%2BdkS6svyKX6HWC0A%2Bi1c2Kd5c2XRy3h0mgYbo%2F4spg%2FKNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA%2BKF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO%2BG5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU%2FLQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ%2Bs1ZoxwDW1OMStBHU83G1fm5MZ0%2B4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz%2BRw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o%2F98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc%2FVveH0F3HR77xCrNs%2FmPDWy89tOWB3js3Y1%2Bb1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0%2BD1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv%2F3%2Bk0l%2F7GwKzGzQ6Wa811i%2FqXFjfb0wlJ1jP%2FDXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9%2B2mlNGRBlVqGU88fB%2BdM97E%2BVvGCx2CV7ht%2FhtgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi%2BJOiG7ST%2F%2Fn2W%2B%2F%2B%2BTCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA%2Frh0V%2B%2Fmfny%2B4Gv3r54%2Bi%2BfxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632%2B%2BJsUuivNbh53Kb%2Bx%2F2JYp%2Fe%2F%2B7qFl8eecf%2FzBk65bfb7WQLstc2AZl1GMH9v3fJxx%2Fp2pttp%2F%2Bc%2FeGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE%2BzrxreeANH3lObN6LH8KHopW83l9G3%2B3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn%2BQzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm%2FJekTY2T7teEU9KnHUa%2Bzj%2F8pXd%2BrzbxD1uragaVBdAqDC%2BjaAUkrJv%2FOXKcGMXmJOnbhQXF%2FF3QsHJVnf87VhB3sSqoa%2Fte5X9jf3r7FdPzMgtC%2FccNOnTtwb3ZPb6ZWdOPLzh7amPD50%2F4z8%2F1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY%2B2PS66ypEhs2ZFOn5IO08%2FZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm%2F%2BGZhgadoWD%2FjBMnyJuLfn%2Fkk%2BjrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04%2F3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv%2FeNGZnLt9g16B6h%2BaqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW%2FXTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz%2BgdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv%2Fzx9HjQmV99dDDg8e8%2BheuMZq2cnxdUBBOApeiri69x23S22xcWW02g%2FV2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ%2Bq8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf%2FwOTZcvobX5lZ3PPffii88%2F10Cy2I%2FswyeR%2FAFNmMfeZ1f%2F8rfzH545p1j5vdyW1apU%2B6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y%2BgRHrThpU8Niry7c%2BPBf%2F%2Bf7yzvryabGFc8%2B6xowcMRg1kUqqh9azT5h%2F1GcNr14%2BGTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz%2F%2BY2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf%2FfJ57P0SQsajObpM%2Fd9mHXp3YunT59birloRDO2a6z%2F9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk%2FfTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq%2Frj%2F5t8pdueG5kbsG25Hfpq50%2Bj%2Fe%2F%2BtjA%2FbXzF82%2BdmN88r%2FevSPL3Z6ftEjj7Yds%2BJ13jSzsaHnpjbt7h4Uvrdr2aAH%2ByzaXLm4R1W3O7p2KO71FCCkX%2FuG7BQrwKPWJlwu3jPioEKS1%2BC0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9%2F83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS%2F4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau%2BHAQ0ms6hHK6jhiJZl%2BNX0NFTicIYQt7ER%2B76ptuiMte%2FtYyP4oI%2F8o0cx9iPtrx6K5UpSgI%2FWinsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY%2B3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR%2FdK5QyUaz6j5l%2B4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m%2FJktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z%2FsVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS%2FtYTsWfl%2FStKu2xq3cXzuCVn9wf%2Bpn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2%2F5cz0oeZ%2FksHR0%2BTL6D5y31Q6eN685sPxrixetlPl5%2FYlJxu9AFbZRbmnpqlpTq09K3F7TdV%2FbpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58%2F49MClCxdM44aRZaRxE%2BaPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN%2BtlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx%2BZ%2F9jjp45OnvHwVFIePIvB49LSPRvZ%2ByPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS%2FXAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK%2FsMJSfuLFn%2FUKvsVinhsvqH%2FRkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1%2FXt0PNKk%2FGszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg%2B07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY%2BtHv7My8sX%2FFdifTO%2Bxlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI%2BdGD1OMS3dlORL6h%2FR%2B3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2%2B99f%2FEEzUL%2B%2F1uGTs397MxS%2B7YtDz%2FxwtzsfO%2BU4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4%2BOLx8%2BeOq7t%2FUVXVgmF14%2BYuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD%2FCHvfj7JZN%2BN2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW%2FYmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg%2B33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd%2BARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p%2FYebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK%2Fn1%2FmCviN%2Btt%2FWK6OGGznh%2Fs4t9I39VVFmLztSUlwuwZdCiRC2l%2FKk33lG0dHD%2FqprTbw5%2FZmTxqMV9Z8yYvelw%2FcCqjf%2F%2B6K9P9H9t4KLl7R%2BcvmJR99W%2Ff6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH%2BklDl%2BfctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI%2FFxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t%2F2dq9X3n575qfMjIXZI%2FQ7b%2Fu6brOGD0zj0rT%2BwD%2F%2BwB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0%2FedPztWvHjM%2BbtnB%2BHauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI%2BCBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1%2Fdk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc%2BCdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS%2Bhp0Bqm51Fnr%2BL2UjHz5YPXLwfRNx36B%2Bl3eeXrwWxYbNVy%2F8n%2BpGrtwd7tNtSfXsNFaLo9jTdPZ89ub%2FpXB47YrkEiRpzW3r%2BoJ09UfBJLnmAoG5dBi5LJ5U83Z%2F2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c%2FfPn39g4Oqa%2Bfun195VPX3qwLunC2vmH9i%2FoGZlTdOCgdOm3l0zdZoiv%2FGASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz%2B0JMt%2Bsjy645vIQH91edGont0XbPj9msiaPXiIVI2%2FNHhk35IePbMLh0yeP6V6%2FZPPA4KflKlzBqAsnGkVRaCONIPUOstxn%2FMhJ%2BnrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd%2F2PpPpA3vsPbRzw4w1sz%2F8snbmA6Or7%2Bw%2BpUPP8mXDl2wVvqx%2BwJu%2F%2FYmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6%2BLeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz%2BZ%2F0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa%2F8RJkG7MortqVTEvILI6Z9PL1rzacn%2F%2Fov0pY1S3t%2FraYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz%2B7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog%2F71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu%2BbHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2%2FhFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V%2F3qF3eu3%2FB8lN07fsKwYRMeGCZM3nHw8LPP7T%2Bw%2FTH%2Bb%2FYjjwCBau4hdsY9BF%2BZRr1AgMrEoJdu5R%2F4fBhELEUxdqM72c5aTGef1%2BIQVnvjPTGxCb3wfhzek01IufGW24c%2BAOIZzq8gnCYLACAbHrsGKMNHNDV6EPR%2FosTBA8ziYuCw7Tjs%2BThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w%2B9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9%2BdbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB%2FyIz70jFvcU%2FeGRvmVKrdoPJ0bltbq9R1v%2FYaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA%2FfAdPM%2BwIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y%2FSAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG%2FAvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC%2FhhXgUA%2BgFip9id54Z5wod3t1glmAKcgCUk%2BrogS11erXC6%2FJJ%2BWL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf%2Bbn%2F07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS%2B3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0%2B0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa%2F3fbaLyfPTsaR%2BCIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0%2Ba32f2945YHTpRoDazQHnjnES1lrm3%2BFq4%2BYgL%2Fygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu%2F9zy%2FNwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO%2FfrHjvH9%2BpMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM%2BzzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq%2FB6ei%2FV%2B5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj%2Bvm0xpaZCBL2vZIQjWCo6Q2%2F2lnOTKUqE%2F1UYJv5ZAOKb36Lxv32p%2BOTCrfUnn27ofnjujZq094yVz2TcPf%2Fv7%2B58IPi6dX3OnPyC0L3b917LZdPTcF8w%2F0mVQxcHZN%2BcTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t%2B7FP1eajFc%2FGz689fe%2BUW3xc%2FvP58whegruiOKsCNGRZehzj%2BcwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF%2BWlJTe3ghi5y4pbYNtKyK%2BAqGgV6RD66BdECyZQU%2BxzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy%2F%2BqHSkOv7bLFExMz5TiAMaaVIb%2Fwg7NmPnUc0VVb4%2Ba%2F3xO8a6Hj%2F0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY%2F%2ByddrEUqp%2Fie%2B4eMYP%2F9%2ByRWGwjyVpav5k5sXH9%2F5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU%2BJFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4%2F9rD7PafGcS1R7PsEQk1d7TaLX%2FgqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0%2B9LqSttY3QmLJw6%2F3b430QyITiIlAqxdlBMcj%2FlHpUk%2B6gRVqnV4kwil39%2Be%2FsK5T%2F9sUYXdkp9n3vr4YN77ll3OW%2Bpzc8v7NpC3vppe0vPUtC7Ev2FzR%2FcQmlWcInr25%2BcGHXgtrefZ6cNHMlm8b%2BtaaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk%2F1HquySb%2Fe8drD6PPN2z4%2Bp45Ngi%2Bd8fu35a9%2Ff4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ%2FQQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP%2Fq3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M%2Bb2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt%2BDw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs%2B5ZLXnE%2FyxtZarrfrYDqw6wr2xGWIjpKsAWu%2BI2t%2BVyXex0jOkFJfNZpfsrQMOsKeYPHqqT%2BNdjB7q5euvRZPnb3oYUWsXUUomXo%2FW9JUVbx7J4HugOKR748Sz333%2Fyd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce%2FZMxo1nF6Nmgn7Y%2FTmxejV%2BpuEyuv9TaJArLfsb%2BIw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj%2BVwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8%2BRfs09fmXXEH%2Becs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs%2FNzup6pu8B%2BROnrBae6djz2%2BInL0aAOq4Y%2Fe8%2BQDVf9G154buPm5xvWCb3mrjKRjN%2B7vp4xEwtQh3q8Y%2Ba0KbPYz19MYDO5tw1mkLIPz3985rOPP%2F10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE%2FIgzx1QzhIE0DR3nLfB89CcmUMWLuFF2u%2BWPJGTu3C%2Bt3TBoiIAgpP5iG2lhdp%2BkEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn%2Bk78TN1N5wPn%2BSzg2gC%2FnKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB%2FTOLAxZRpmn39tucP%2FKjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff%2FFEyCCePMvngykw%2BK%2FeMIh5f8VUtYgffQ49lB7%2BR0HUNTpQenhP6WBBkscHEs5y%2BQZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk%2FbGKDPGARzmLjqmfcouq%2Fe4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1%2Bx3pUgbyrhA%2FvjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe%2Fei9JQJ4ELUkjOsxJ7m6%2BQYbnXvbTY2Ow6D6FHh%2F7lTTBZZSVLOtqB8g4iCCHzeZK%2BdC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD%2FURrIP3Wr6u%2BOqQ9OmDF94qRp5JtZj%2F9u9sx5C%2Ficym8TiHvgB8gGOwAEwU4c%2FM4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ%2B16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4%2BZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb%2F%2BILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT%2FdUDsCHUKOz4AWfRHQvA065Z1snHLxtW7%2FoddaNewgZANO4LY%2Bn9OPN%2BrQSxmD80rC7ed1%2FRm9%2FpuaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y%2BEuJtrjkUwGEVlI650ylKvE%2B5ABA%2FHNTwuf9lc%2BBgItUcf0%2FAgZwQedwuks0ypTyaYjSqY%2BiqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO%2B0ESk9LSjDup6913x%2FKzVKdeX9THFGzb1v5TDDfpQ45bECoJ9%2B43cBcf0nCXXr%2FF8%2F43notvxJ6rVEnqc1TWG05X9cp%2BAAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK%2FV%2BGK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua%2FdUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n%2FqjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786%2B2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d%2FYbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS%2Fohgnt9rG%2BooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc%2F4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8%2FsMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW%2BjOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX%2BV%2F3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr%2BPdo7x4dIHTKe5DFn%2BO%2Fj%2BW2VnE3ooW6isf0LIUENvZs1gf%2FLHojJwdpplCP5gn%2F5gi26FoYa19ZVFOJ6Sxuoz%2Fq2Ti20IKVJdnqvYJwnhfPH%2F2f6YHoQF30aZaK9J8T026RxH5fA%2FWPW%2F8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM%2BhdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe%2FuYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H%2FDXNOTeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDFOsGAADAurFtJw%2Fbt23btm3btm3btm3btq27UCik%2F1sq1CH0I9wl%2FDTSONInsjxyKcpGc0VrRNtGx0dXRF%2FFpFiV2KbYl3j%2B%2BJz4vkTaxKjEgcSXpJzMm6yb3ALkAnoCV0ARLAcOBjdCAJQJqgWNhJZDT2EbbgTPhz8h%2BZFJyDbkFSqgVdGh6Br0BhbFFCwHVhNrj43DXuH58V74WcIkahHvyDRkLXIGeY18SxWl%2BlMHaIVuSc%2Bh3zHpmNbMJOYuy7DF2E7sFvYMJ3Clf%2B3DHecNvjm%2Fm38g1BYmioxYS5wqbhZ3S0Wl2tJkab50U04pl5CHy9vlmwqlZFJaK4uVnco55YlaUK2kNla7qEPV6epi9aMW01jN0zJohbRZ2mptj3ZWu6e91wE9vT5LX63v0c%2Fq9%2FUPRiZjprHS2GmcNG4ar8yIOcycZC4yN5mHzMvmE%2FOrhVq6NcCaYC2wNlgHrAvWQ%2Ft%2Fe6w9115r77XP2fecrE4xp65zwM3lNnZnuBfdZ17E071sXj6vrTfP2%2BHd8F74lJ%2FeL%2BHv86%2F6D%2F23Qfogf1A%2BqB10CAYGk4LFwdaf2C%2BJfQAAAAABAAAA3QCKABYAVgAFAAIAEAAvAFwAAAEOAPgAAwABeAFljgNuBEAUhr%2FajBr3AHVY27btds0L7MH3Wysz897PZIAO7mihqbWLJoahiJvpl%2BWxc4HRIm6tyrQxwkMRtzNIooj7uSDDMRE%2BCdk859Ud50z%2BTZKAPMaqyjsm%2BHDGzI37GlqiNTu%2Ftj7E00x5rrBBXDWMWdUJdMrtUveHhCfCHJOeNB4m9CK%2Bd91PWZgY37oBfov%2FiTvjKgfsss4mR5w7x5kxPZUFNtEoQ3gBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFNSzVaxFAQfhP9tprgntWkeR2PGvd1GRwqaiyhxd1bTpGXbm%2FBPdAbrFaMzy%2BT75H4YoxiYFN0UaWoDWhP2IGtZtNuNJMW0fS8E3XHLHJEiga66lFTq0cNtR5dXhLRpSbXJTpJB5U00XSrgOqEGqjqwvxA9GsekiJBw2KIekUPdQCSJZAQ86hE8QMVxDoqhgKMQDDaZ6csYH9Msxic9YIOVXgLK2XO01WzXkrLSGFTwp10yq05WdyQxp1ktLG5FgK8rF8%2FP7PpkbQcLa%2FJ2Mh6Wu42D2sk7GXT657H%2BY7nH%2FNW%2BNzz%2Bf9ov%2F07DXE7QQYAAA%3D%3D%29%20format%28%22woff%22%29%7D%40font%2Dface%7Bfont%2Dfamily%3A%27Open%20Sans%27%3Bfont%2Dstyle%3Anormal%3Bfont%2Dweight%3A700%3Bsrc%3Alocal%28%27Open%20Sans%20Bold%27%29%2Clocal%28OpenSans%2DBold%29%2Curl%28data%3Aapplication%2Ffont%2Dwoff%3Bbase64%2Cd09GRgABAAAAAFIkABIAAAAAjFQAAQABAAAAAAAAAAAAAAAAAAAAAAAAAABHREVGAAABlAAAABYAAAAWABAA3UdQT1MAAAGsAAAADAAAAAwAFQAKR1NVQgAAAbgAAABZAAAAdN3O3ptPUy8yAAACFAAAAGAAAABgonWhGGNtYXAAAAJ0AAAAmAAAAMyvDbOdY3Z0IAAAAwwAAABdAAAAqhMtGpRmcGdtAAADbAAABKQAAAfgu3OkdWdhc3AAAAgQAAAADAAAAAwACAAbZ2x5ZgAACBwAADiOAABYHAyUF61oZWFkAABArAAAADYAAAA29%2BHHDmhoZWEAAEDkAAAAHwAAACQOKQeIaG10eAAAQQQAAAICAAADbOuUTaVrZXJuAABDCAAAChcAAB6Qo%2Buk42xvY2EAAE0gAAABugAAAbyyH8b%2FbWF4cAAATtwAAAAgAAAAIAJoAh9uYW1lAABO%2FAAAALcAAAFcGJAzWHBvc3QAAE%2B0AAABhgAAAiiYDmoRcHJlcAAAUTwAAADnAAAA%2BMgJ%2FGsAAQAAAAwAAAAAAAAAAgABAAAA3AABAAAAAQAAAAoACgAKAAB4AR3HNcJBAQDA8d%2BrLzDatEXOrqDd4S2ayUX1beTyDwEyyrqCbXrY%2BxPD8ylAsF0tUn%2F4nlj89Z9A7%2BtETl5RXdNNZGDm%2BvXYXWjgLDRzEhoLBAYv0%2F0NHAAAAAADBQ8CvAAFAAgFmgUzAAABHwWaBTMAAAPRAGYB%2FAgCAgsIBgMFBAICBOAAAu9AACBbAAAAKAAAAAAxQVNDACAAIP%2F9Bh%2F%2BFACECI0CWCAAAZ8AAAAABF4FtgAAACAAA3gBY2BgYGRgBmIGBh4GFoYDQFqHQYGBBcjzYPBkqGM4zXCe4T%2BjIWMw0zGmW0x3FEQUpBTkFJQU1BSsFFwUShTWKAn9%2Fw%2FUpQBU7cWwgOEMwwWg6iCoamEFCQUZsGpLhOr%2Fjxn6%2Fz%2F6f5CB9%2F%2Fe%2Fz3%2Fc%2F7%2B%2Bvv877MHGx6sfbDmwcoHyx5MedD9IOGByr39QHeRAABARzfieAFjE2EQZ2Bg3QYkS1m3sZ5lQAEscUDxagaG%2F29APAT5TwRIgnSJ%2Fpny%2F%2FW%2F%2Fv8P%2Fu0Bigj9C2MgC3BAqKcM3xgZGLUZLjNsYmQCsoGY4S3DfYZNDAyMIQAKyCHTAAAAeAGNVEd320YQ3oUaqwO66gUpi6wpN9K9V4QEYCquKnxvoTRA7VE5%2BZLemEvKyvkvA%2BtC%2BeRj6m9Iv0VH5%2BrMLEiml1XhzPdNn3n0rj6%2FEKn2%2FNzszO1bN29cv%2FbcdOtqGPjNxrPelcuXLl44f%2B7smdOnjh09crhe279vqrpXPuM%2BPbmzYj%2B2rVws5HMT42OjIxZnNQE8DmCkKiphIgOZtOo1EUx2%2FHotkGEMIhGAH6NTstUykExAxAKmEqSGMFl6aLn6J0svs%2FSGltwWF9lFSiEFfO1L0eMLMwrlT30ZCdgy8g2S0cMoZVRcFz1MVVStCCB8raOD2Md4abHQlM2VQr3G0kIRxSJKsF%2FeSfn%2By9wI1v7gfGqxXBmDUKdBsgy3Z1TgO64b1WvTsE36hmJNExLGmzBhQoo1Kp2ti7T2QN%2Ft2WwxPlRalsvJCwpGEvTVI4HWH0HlEByQPhx468dJ7HwFatIP4BBFvTY7zHPtt5Qcxqq2FPohw3bk1s9%2FRJI%2BMl61HzISwWoCn1UuPSfEWWsdShHqWCe9R91FKWyp01JJ3wlw3Oy2Ao74%2FXUHwrsR2HGHn4%2F6rYez12DHzPMKrGooOgki%2BHtFumcdtzK0uf1PNMOxwDhN2HVpDOs9jy2iAt0ZlemCLTr3mHfkUARWTMyDAbOrTUx3wAzdY%2BniaOaUhtHq9LIMcOLrCXQXQSSv0GKkDdt%2BcVypt1fEuSORsRUwgrZrAsamYJy8fu%2BAd0Mu2iYFhexjy9FIVLaLcxLDUJxABnH%2F97XOJAYQOOjWoewQ5hV4Pgpe0t9YkB49gh5JjAtb880y4Yi8AztlY7hdKitYm1PGpe8GO5vA4qW%2BFxwJfMosAk2X9n9X2cVVfnA36pzHNHJGbbITj75NTwpn4wQ7ySKfAu9u4kVOBVotr8LTsbMMIl4VynHBizBEJNVKBAfMNA9867j0InNX8%2BranLw2s6DOmqIHBIbDfQR%2FCiOVk4XBY4VcNSeU5YxEaGgjIEIUZOMi%2FoeJag4mEB3PUOweCaG4wwbWWAYcEMGKn9mR%2FsegY3R6zdYg2jipGKfZctzINQ%2FvxkJa9BOjR44W0OpTKAskcnjLTcKyuU%2FSVIWSKzKSHQHebYW9mfGYjfSHYfbT3%2Bv877XhsIwGzEUaleEwITyE2u%2F0q0Yfqq0%2F0dMDWuicvDanKbjsB2RY%2BTQwOnfvbMUhiNPFyDCRwhZhdjE69Ty6FjoOoeX0spZz6qKxxu%2Bed523KNd2do1fm2%2FUa6nFGqnkH8%2BkHv94bkFt2oyJj%2BfVPYtbzbgRpXuRU5uCMc%2BgFqEIGkWQQpFmUckZe2fTY6xr2FEDGH2px5nBcgOMs6WelWF2lmiKEiFjITOaMd7AehSxXIZ1DWZeymhkXmHMy3l5r2SVLSflBN1D5D5nLM%2FZRomXuZOi16yBe7yb5j0ns%2BiihRdlFbd%2FS91eUBslhm7mPyZq0MNzmezgspUUgVimQ3kn6ug48mntu3E1%2BMuBy8u4JnkZCxkvQUGuNKAoG4RfIfxKho8TPoEnyndzdO%2Fi7m8Dpwt4XrnSBvH45462t2hTEX4Bafun%2Bq8jIzK%2FAAEAAgAIAAr%2F%2FwAPeAF8egd8lFXW9zn3PmX6PNMnPZNJMRRDMkzmDYgZMRRDCEmMMUPJIgZEepHlRYyIiNhRUdYuS4ksy9reLDYsdOmLLC%2FLy7L2CgKrrCJkLt%2B9T2YyYPl%2BD8804J5zT%2Fn%2FzznPBQKbACSTvAEoqJAdtUhUJpQYjBJVAUrKSkIOJ1ZUOEKOUGkfV8ARiPB7E72m87WJZF58ibzhXPVE6QsAAnMufI4H9XXsUBh1UpOJSJLmQNWqNsasLkKhsrKnA%2FT1HCF9PQzSAPYtD5V5PW4lmFeIK86EcCRbObLp2lGjGxpH4%2Bf0wLkjjU3NDSNGxYSMxbSdDkzomhE1SypQalCISvniob1lDuTL7injC1O%2BMr%2FxmeJtxeRt%2FiJviJ8mmrjFOr0BJCZ3QAbkQFu0ypCZ45HcRqNJQkiT%2FLKsOO02s2Ryudze7CxVUnw%2Bv9%2BtmKTcgEEymzPRlgN2e5rHaeOXyeeiisnJFagMOSsqSkr45kL8Tr450SfM5%2Fy1V66pGvBwTV1BcYcDEX67QjQkbo8cigTplyVI2OHh%2F6zdXHO4%2BiR6SjoxMPzo8O21h2tPx7O2lmylNV%2FtY5Nwubj3fXUA%2F8BuFveBr74CoNB84V6pSnFCLhRCL7g7OijfR7Oy3FalR49AcXYRFBnsQUcgkAYO6H15j6wiAGu%2BI%2BAo6pleFDAWKJZMX%2BaImNunWOpiskIVH796ewAqEzvV9gqX9nQ4Qd8S%2F1V%2FScSM%2FrmsTP9FfNUNIvzuVlRPMFxY5PB6fY6iwsJw3%2FJIOOTx%2BlT%2BWzaR%2BxYWecrR7fWFFanqi%2F33nnn9%2Bv%2BMvXr7mk933%2Fv5Gy3PrN6yZjg7WFV1D5s2oGoh7nx%2Bk2vvTrkeDT0HKlieXvvakkfecj%2F5uKnhm6iNHRk27a6bevTL%2BclH3ulVkX3cBTJUXjip%2FCDvBiO4wQ95PB6qo%2Flen0%2BWTRpofo8nLa04mB3UgpeX5PbMLEzzKz4%2FtapOlXt5a1llpXhN7FF7r8zJ37o%2FiN15Q2XhvsE8RdajOqwFyrwFGETXr%2F0F9u9dNnZsWW9869X1azow9qe%2Fkpc7D52mPRf%2F%2FHcJFrR1npvf9sWX336EO7%2F9x7lqeUMn6frt8y%2B%2F%2FZD%2FJjzecOGEAnxvWdzjpTAzWtHbGjRhlhdMXqvLVZSWnl5kpSoChLJVtcwXSPea8vNLSrT0dEnTegyPaZIUqIlJLnSKhAV%2FpfBuhb9EbE53bYVIM%2F3S45hfiZ%2B7th8IFPHN5QuXcscms1vF8kiAZ2qBsEEEFQX7FnJDeNy%2B8nIF2JLZ7%2F77DPtk3rJhVV9vefPD%2B57CzCF98cr82%2Bs631s4%2FvbxrKPf1XjT0Iqrh%2F%2BuafTMxR%2B9e%2B%2BmxqZnxzzx5l8embstxo7PeX0Ju3DjoqYJA7C611hyd3hAtH%2FzpD5jAAVm4DM6Zjj5C5WIAIu9DuxCIB0kuvEBAKGBbSTz%2BL%2B3Qm7UZjaZqCSBqtrN%2BVQgmAMTua3joeaMhBTicTt9wULS8PSj5x58eNk9Z5c9RUrRiPte3MTKzvyHRd5Yh9vFygP4yq3JlfmyfHG%2Bso1LyP%2F5yqgRNVjuDPclRSGvk7Q%2B%2FejZJY89%2FOA5sTT7ifVb%2Bzru%2FOEM7tv0EisFhErSJGUpbrBBOOo3ms0ypVZUVc0umUyqilarYrDxpN1aJrKQuykJwvwz%2FyPMUOCTXSqlRa6CiEzJy8U4J8DWf%2FjpM%2FeeOMZeLMKpxYqbPTyx088Oz8MKtnMuFqefm4gzAKEZPpUqpG1g5qivGRSjkSKAxWo2giJRKOFCysqS4vjNhQXCAa4Bxz1HEI%2ByNlx0FBextqOk9SjezW49yhaIHbGzuBtOggKe1wgFWVapDCXbdSNt5ghfoNCgMxLA3X1v%2B%2BdV%2Beg%2FvIsdR9MJYWVcS5rISqDg%2BCuVQQLkSiTc7QoHPANIGq49dw6wi7GwgmvujZoUrrSRNsaMLqjsmfjnkYu4aU6SlJZ28xECNyqt0mMrM2pBricBidueiNS5iDcRA0ir4h%2By4yQgGJP%2FDwLVF05IQ%2BW9XLoPLou6LYoTFPCnGT0jYkaV2kfEaBok8y%2B1kkYCeeDQnIEyQI2nUrlDE3kkDT3PzsfZhXMoxZHGw2OmTRl7w%2BSpLeQoW8gexttwNi7C6ewO9hD7%2FusTaELr8eOAMA%2BA1nJtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae%2F8Tw%2F1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD%2BEQLt4TkOo6CU5f1drrhvrrVly%2FdspDayfe%2B8EtQx7fuJG0HcbZLyyc1r%2B5qXbojtE1xa0dt4x%2F5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8%2Fvq4tZ%2FGilfr8pX7VqJm1EzJQGeg3j5%2FxX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j%2Fo2ykPlEmSr335KxvYPvbZydev29P65KNrX58%2Bc92zfxv6%2BKil76PnU1Sl6fe%2Bl694%2F%2FzIweMjUO1ZPnH2TU3fxqa09%2Bl%2F6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5%2B98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4%2FHXxjyKYhkRU9LgYsIJ6e%2BpgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT%2Fn6efcC%2BPLN8xActmMGOhu%2B4bH6EpsV%2FyAgOoO0n9%2F%2BHnR2B5h7hr455LAPJ1%2Bwc%2B1i1AYGhXOs6eQf4IR%2BuigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd%2Fgvy8Q3E6TmsbErv%2B%2BZ2tRuuN%2F7f1X%2BzsNyv%2FvYhoN066sbVlcRuZiq%2FiWvuP7rEb%2F7LuhyPfsFPLMffdxfMnz7%2B1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U%2Bo3%2BQo%2FHnp1ttBtL%2BihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0%2FLqoW8qk7kShFiku3kK9cfCPVHyDedt%2FqpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg%2Bflxtq7zz3ZUzXhrU%2FO6sjqN73mrbXD2iY%2FKzm89vbBp7Y%2F3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR%2BNfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP%2F3%2BJIOl3xOz3v0nmr9Y%2Bf2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r%2B83%2FH0fEE%2B3DTXbdNum1%2BHfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU%2BxG7xcf6mPNQAQX6%2FIUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK%2FVYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304%2FFkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA%2BB2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m%2F7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44%2BjrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o%2BUfT%2Fqt9NdGau79%2FZhf73%2BexCP2T2Pz%2FZefZXez6I%2FgIyv%2FEkRs7Yf3IFpM1FG27n5x%2B%2BNQ9Q%2FotPPTGQSQBH%2FPd%2F9Yf%2Fvjjne1sx152gh0p6f3eKHwYW3%2FEZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik%2BLHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI%2Ft4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn%2FC%2FfPZh2fjx7C84%2FaZ8xev2nXHraxT3vDKpkVrHaacdQ%2B%2B%2FxGdXTuy8Zr4NrZo3PgNgDCXI%2FUBnh9eKI36VZeLN%2BNWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2%2B0Iw4l2BUdoTI%2BZiikBS%2B9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U%2FHmW846xyGi3DSZ3j9azd1FvUDImwoz%2BE2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO%2FxGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt%2Br%2BzlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr%2ByVfg43vTxgZtW%2FGXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu%2FUg004jIzW%2BQJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr%2FNdmE5uRrDZG78Xj5t2EIGAOCFiawBT%2BozgRw%2BbSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS%2BtfxVS0wMJpjIcRkNiOAzUBl2cq%2FUrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6%2FHf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI%2FEnAKwL1KEcM%2FJO9nv43fpSiwh81U7%2BqQGdrQtXseFv4FZvycdQPQ8%2BVKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj%2Bkj3BXubXE3gz1vNr%2FPlDb76Bs9nSNzaSY%2BxxdivejVP5tZCj0mP%2FOYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr%2FTSzoRn%2FZLXHoEyAo4ckJSx%2Bau%2BBBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb%2FgchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe%2BLX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C%2FWaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm%2BPz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n%2BRlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq%2BuhvwVHHWrRpn%2BIvGGoVFl%2FMhDadWMcJP9LZen9cr%2Bdin7JuOx%2FZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f%2Flkz5g4srx3e8%2Borqypveeqmzf8qL%2F13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k%2BdXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6%2B6CYLnlEXbyLfYFex%2FD9zz7BAf0ztqVZ%2B7EwHn6YufCPz33%2FDraBqjXfyHBI2K%2BRonRKAOiVZYkC3BDJ%2Bq9VNpUJOaj%2BsXtVx6h57CC2dmLTMMKdPlKFXO0a4DY%2BdTwvZeN%2FqJLhrqRy8gSsx%2BT0e52yQh%2Bv2ynlszMrKwci9mcnemSzdRvt6NJiOSi%2BEtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy%2Fnm1hKoRixV%2FmpUJxwVT85trLi1EAa6twb%2BaS%2B9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG%2BooUZ3VpjZj8Izex59h6fnXg56wfNmF%2FDGMtC5Pi%2BGHyHdka%2F47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA%2FLwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m%2FRXi%2F0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt%2FZIKYnGpfnBa6sUmAHY9%2FmJhqI4S4fJ%2BQL55xoKIY%2BVYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd%2FUoR8AzFIA%2BR3QIHeUTdBWVYkSTznFd6SVJko0DW%2BxLKLeyTRZYcwiGjADQ%2FjqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl%2F%2FvMAAGKNdIQ%2BqATAwK1gBjVKRVTIdwCUpB%2FrioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ%2Fz7GYIdmjju9pfm6E8Zq6OFTovBQvLy%2FP78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az%2Fy%2BZVYSeXlcUJWdMvMcN9%2B1u8h0omny9N6YT%2BhuGr1r0xzd%2BOr%2F5xbv%2FOn7T8Y9PswO%2FX3znY5MWPHHDsNfXvfono1K6rn7f%2BK3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX%2B7YNsMT%2BhpLLZbJM2ZOJJNvaZk%2BQ5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM%2B%2BlZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf%2FBTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi%2BobTZhqFy7xwIOIyi6fTTdPt5ft4%2BoT4Q%2BecShOXlPGioU%2FBLkji3iOnVPiAnZ9vHnOw9ON%2Fmw7Jv%2B1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y%2BDMEKROSUpzOLKcJlOm3%2BOkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs%2B%2FY3zDt7LdoiJfID985vmNc3Qb61PyZM%2Bd3NmAPdGAahth3Jx%2B789Eel5%2B4rCjB7nSOkgMeuCKa7SZElSn1%2BqwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l%2B9eu3bj%2Fufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz%2Bn4pxIOWz79gRHv0LneqJs2FQ76ewKfPao%2BpSsqEvmsj%2BykQFfCF6ZeRcGFyUQK8v26El%2F4WGzqS33OfxjpXbL2ndc3sTfYvm9%2BvP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5%2Fdrrmura10otT%2FceNqZjJ5Xzew187smt%2F1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D%2FJfU5336yUOPaKqJJEuCQeFQirWX7O%2B6YxfZjqapqE%2F61bQ958LsXt8S%2F40CwpeDekav%2Fvh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ%2FZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd%2FL0JgaDT%2FNcE9PAaBctOk%2FsjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd%2BeJXN6mThXRIWPMH%2FUn%2BNdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7%2BG7krl7%2BOC8ePqbjJDCRIiEfKFykdziVfBd8q%2Bke9n%2B%2BuvnTGL7vy529F437Xwso%2FdL097ZwvbVXz9jOnlw3rz12%2BLfSS1Lh1%2B%2FurZpy%2BF4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV%2FXLRG8TS0ierkyof%2BZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu%2FxUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl%2FI6Xt6%2F70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl%2BRVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p%2FtaZv8wfaPbt58TGf7vePdSx%2Fwsv5K9SPtbB87%2FT%2Fs7H10mU722JDgM67pTN1euaIq8dIsyh%2BTpOUZ%2Bfg6PcNnz%2FZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook%2BLIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9%2B6xqU%2FbcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy%2BJIqpdWewrCD%2B6iu3%2Fsre97yvSbCP7xLR8SXyH1LKxZTYkqp%2F1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX%2BvJ2%2F2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1%2FbDrDtW395oycuG8yJd%2F3%2FXu6MZHa5Zcv2zRrf2wZn1HILfzsvKx%2Bb0rCstHz73%2B8VXN%2F8y%2F%2FJriK%2FqHR%2F%2B30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu%2BTD4aeiNN0jGwgknTn4e%2F%2FyqK4UOT%2FGc4zM%2BcENZ1E8cDrfby3t%2Fj9NoJ7JNtumyPcmJ1sVDgItr7tQYgH%2BgrxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt%2FEpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X%2Fx0SbTfiOtaElf%2FINyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin%2FpGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH%2FPGvvE03jMkcer4XnuKKI5gApOW6bWqi%2BYoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu%2FmD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy%2BDRewhcJLzK17ycs1KTC73ZrXK0koahm%2FJgob%2FpNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu%2BvcfR9RErqqfl%2Bfkfr2%2FYcZNo8ic866XXnR8Z72xNZI450HXce2MIn%2BoKqkIYDYgmvQhAm8c7YR%2FMwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6%2B3xekDI4T38Id7D54ipCHUbcnIcfn%2BuNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm%2BREWRw08Coc18VTQ4xFQ6%2BEJhDmL2m6%2Fc%2FOZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg%2BknbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl%2F3%2F2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2%2B9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG%2FAg4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv%2FRAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii%2Bnphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA%2Bn4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8%2BePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN%2BrL%2FfHcyhjZk77c8arjmflv%2FBn9kZObzbAuFFEB4A0ST%2Bd2BztZXeaidFqTfd6iV%2FzO51ado7Fn%2BavjxnT0sDFqcleG3P6QR7xs%2BNNXUfUIJTSVqjbjT%2BpBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3%2Fmvbhr%2B6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM%2FOyYp9LSycWo2xQPZ4ctYBHz%2FYyHc11H2qb9S%2BiA4oURXyC3SM%2B0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt%2BNn93Y9siC8v1T6%2BqnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo%2FiNtYyYOnz6ttm7aDBHpCoDEp%2BPghZnR%2F7I53U6Plce2UaYyMYkJqxeRED%2FHBp%2FidDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448%2FvsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw%2F0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO%2FObSe%2B8uy39mVlqEzr76oeyi%2BbG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb%2Fbpj257c%2BOS5s4tvmrim7appHXPputbn8kPlVdURssit194%2FxklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0%2FYrQaWIvIdGcQ4pBB7dzU9snCdpLZJF%2FSOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0%2F7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7%2Fc7yzPc%2BbV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul%2Bc91p4c3j55tf%2FfvilPddGFx5b8zJqf5X9DCi9v%2Fm10vvcrj6U09uHsg%2F0Ke%2F29invHSBfX7VJ%2BTAv99nwkcNvfNd82xjlI%2F4%2FSu%2BrLyi3%2FObXaPaLTJb0b6xlBfCX%2BDHKMLqgAOoieZk65HLlmXXU56PLK%2FRmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy%2FKbYvMvxsYvGRLrPu%2FBCWegef0l%2BcNcmpeGP%2FqIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI%2BK5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12%2FoayC49HL1E%2Fs9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW%2BS5kUqBYFHUgcViJEbamxB9uT%2FROLFE8QLBIegdsp5%2BnaSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h%2FcO3K%2Bs89eFTJuceP%2Bdft9utd0xUFqDpyj3kqh3K1%2BH6uhrlzX%2FZctHQEckuSNLhJG8MjPTGCNLRbwWDZH%2BFr%2F6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg%2B7W0766bZL10emd02YWJ7G%2BNFAYSwiCGdcx%2BZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2%2Fh3kG0RVlFY12UNylYiiCAo%2FgZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF%2BJnjK%2F5%2BbriipGXeqiuf3np9%2B4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo%2BIr4APJkRy7enY7QbIl%2FLTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9%2Fn1P7N541ards5oXtJ3REgwFWsOdE%2Fb9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn%2BqLI0JEDyCnCdwXdyjui3uFjR%2FVNMjMIUk6ao6YiGZWHZ0i%2FDX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR%2FdrXNlOGl7yiCq4Y2Z%2BzTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF%2Bvigy%2Byj67YoVdz%2FPrAF7Z8wo%2F9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1%2F72IW5Tjkmn%2F3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr%2F%2B2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1%2F3PjAIiZPQYprQ3RWgnr3Xd88KXuOu%2FGW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD%2BWfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw%2FxTGK3kxc9YlKPgt969IarH3%2FwwP4nFG9dY%2BPEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79%2FOej1B9UBL%2B6NL0aoFonqQehRdg4ip%2FLxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y%2BTuAYvg1fgUA6eAP5Nrjj4L8IMFW%2BuJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as%2BEb9qmXpVwDBeWUH%2BLLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6%2F%2FYLru%2Bj0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2%2F%2FheIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj%2FJFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9%2FghbKIUT%2BhoLlq5jiPvcYqZDnXNrC6WKXZGjNP8%2BVlGYAXOBfY556p5%2BZaodTT0KC89ZE%2BUXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia%2BgnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0%2BBgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9%2Bd%2BCzzzahATIdd1Du%2FG5GoOPWnR9%2BofQoyl1qHsRXeDuriLez36eUA%2BdUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL%2BuB%2BVocqr6Rhetkf5tfL7vfj3qKxH%2BSMavZf%2B%2BVuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW%2BTMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO%2FyqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1%2FZBiOG0%2BWqzyvXWXXHDpANrEwNWGNfM3DSi%2FfHYJ%2Frbsp%2B8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv%2FagMUij5c2Vc18rz5%2FNZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH%2FaTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1%2FRVfoo3fmXTau%2BubIbfnTo2vnNjQ0TVjXsWQjbb4%2BhL9FfuGvkV%2BcNqai1JldVTJn7srmu%2B7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv%2FLO%2FazH5ASbVEBr5VQ%2BUtQfAPb2jbbEazY1vfvCE6Xna%2BkHfxhi6RUj001a%2BkAasPTikemClt4lAX%2B3T%2BGCYcUDmqJ%2FlKrwqwogTCEpQjeUQBBOgS2RydU1JDM%2FP2g3GoNBuabG7%2FGMKZPlsC%2FfW50fjVVXsyDp7OxQNJZtNo6aSoF3p%2BS0NFDHPHgbYiBJgQZGv%2FERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19%2F03NdfPRtr2nHzvKc%2Femdx%2Fd3LDyM4XkaJq%2Bcfm%2FbY8bqFq1fv6FyOvX%2B1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T%2F05acLVrNap952xtZhP0xWx0%2B0iY%2BfnCrjtT1FbQ2389oqStRWanr34n%2BeflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA%2Fy5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ%2BkEY0tOTnr6qEKLpcQNmZh%2BSJ2ImdjppB56CnnSKS02%2BRpiJifBU2MEnYC8izsQ2clwI9I%2B1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5%2F0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n%2Fm5lz4fKh8pLyPrJR8gyV%2Bhdtuva4%2FMv2Lj1ih27%2Blg74MwMf2tPV9%2FaEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS%2BgUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2%2BK1tf%2FPFpXNzVaf2ojO%2BKOwcEvTpva%2FPOG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx%2FC%2BzhmCNsJ9hQWsvOLVn0n5GBm1eUrt%2FzK5jR21o%2FOiJKy9AhwzKa%2F6alefjSoYJlXV2dVyL7IwUqpp%2BQes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977PkV5qi9zXh%2BBQ8XJOgmziYWsLhqD%2B1vHQZzli2Dxi8VWsCcbXDIRM6dEpOdxEnL%2BCQocxLLTDtnDWdWTT4Wyh0nAU7ot8Herhf%2F%2FuZLf5xv0ulUfvGjOONEDrXMYEgzK%2BCtE9qVsXpQVixvbB7mnLQ8CVqeut5Qc%2F0zNdcJKk9oH6byMk5M5VGJGk2mO108BE7wQmekxuJwGFF%2Bvs6WAeDL0umKLHa6drMgI7HQX0YznaWSNBddcwhCLotpRQ5tBcd%2BThplmiAy%2BBMMx2M6XcOLuERnVGvx%2B3WnH9vn31Wm9Cv3oTPQhPGbvaRDW9Q9dstdd%2FXVrfR7t8jpaBvqQuejTSZZXeCR145%2B8%2B1PDivZbnPyN%2BhT3SphMXhgNARhQWRMoMKEHQ6%2FX19RkWu3V%2BXr9aEchzvgiMYCATCbfxaNmc3YJNDOmfLEZnDT4VwQvFNiQupwHj45Cp00iOdT56kG4bniI7dDo6KTeT2fSk%2BLtyhf7dl5pPfHLSgb4QUvT7nsi2%2BR%2BbhTt2fL%2BU90tDx99FwN5Pu4fbWMBnC3%2FZprdiD9%2FciByqY1XcvYaf26naXlbOCeHGf7BhavuJhFHD0h%2FFXwSAVgZP0Zi5ozAMh6jE0ZWF4vsh39sg5pyx2NKqQzEZ2XGU%2BdFNAgrdc1Ne977elTUafn6kbhr2ed0XJ29tMLqh5sYBENqFX4M4lKD8Q9ehmS1eqmkUWyR8ay7CDxvRTYHVKNZ7qk8YhEdy1YcOklCy%2B67Pqa0tKaiorSGvGlCzavv%2BiCDZu7ykKhsrKqKkDwa%2BHPgkEygQuqIm4KNEUEQjLdBhvobPTrYvM6MzavFyCQ9fpZmoNENQebXw6qkISXvbF5mNVHiE23yjF6xRM27knfvXTUtKZoET%2B%2FfAk7F%2Buray7vKyjOr%2BKHAr4bGHqI3IN7%2BG5S%2BAS7SU0nbeih999Xlbp%2FqtQllG7Sj%2Fp4jIw7kiaIOqTTySBou5KZB5gLq7jGWhvCumKTs7N6sN5L%2Bp1zkG2h8t3HkHQFCVwRmQhIknSCRC8wvD8WUrffQHtNwbWDkz3iI84XlPdRySFI3luLeVIwEfnuWhIEtNuffHstwOzeZBl%2F%2BgzwRczUIGsiggSSZNFlkHRtI0Z%2BoT8E%2BbOoWSnwxY%2FoUzVPdILhSZyRP8ezp2Vz%2BE4SGJn%2FndpNDXwrMFMaMYjsRi%2BqN9Luoz60qB5QH885cqO31JNM8Ua1DBJFgVlJkOt5SRihMGIaeQcIpN7Ap91gROGgt0eWkkvbi2wunXrfKIyCdLA9wszuRplAgHssUq3uc6%2FavnXvvku37cGf9hzou3r%2FLbcAELbTizQXhfm75mXsYF6m6kEvys4gbKuXAofMQuS5LUhtbJnmP9AJy8gdX3yp56m7v%2BAps89kZzPacGPqPmctKUf%2BVkA7vpHbtCsijrgDV9RLQAg9pa0JI9VZmsxW0W%2FVN5vqlE12xKZeO24nRzp2bfoHPRPEf7z2SBs4vvHEBm8ApCxj83oe25YVSSeAEcaCFtqW8B8j5EX48mN%2F%2FIKMjge2AeK7BW0S%2B6EYdkQaJaL3%2BXI8RW5ntmywWIrSafaLika5cnP12dklBpdLzpRy83Knx0heRt66PJxOMvMy82yFPiiEabFCndlkMzXHbNp2YiNNoxZenyxzKUghO%2FCtQOhvro%2FH5DgKdA420DrVfS4oWELdb%2F7qWvq7BuL7XXhXXu9CVyrtGKN5yj0hZNq9ecn93ynPj9q6VMBLtvjQpG%2Be6ps7ebnwys5f3ucNFDzwTXgIxqK0Tx5wFVff9zVyT%2F%2FQ4%2BXsWgfzjp%2B0n6MTYDbdHRriMbs%2FSh7wQyNfQ04lboD45x8nfd7MPgcMBhzF34tPQRpYGbthFXUmWnBEBixim90k62TJikTRaiW6PJLPDTwBLSYu4RpNwn%2B8DhpfWI1CfA%2BzWrZnHP5%2BzefKBrTh0zXKHkmuzliH39q3rwfXHT%2FUN3Nu1gWuZ9Wn05u0pyuGRuJWn14KAMTT4QTpzcPp0q6k3PF0dS8BvtMDAcsjIIiIQGKXQLYPAt8FgTU2uvZ8EQDruB3sL%2FEV7krVDmZIWNNupYoPkxTdQ3NGKoYYgS4mKQ4q76sKS0JxHADfqZupKbq4gq9wuaT6%2FwCVeR0IAAAAAQAAAAEZmiehT9dfDzz1AAkIAAAAAADJQhegAAAAAMnoSqH7DP2oCo0IjQABAAkAAgAAAAAAAHgBY2BkYODo%2FbuCgYGr9zfPv0quXqAIKrgJAJZXBsIAeAFtkQOsGEEQhv%2Fbnd272rZtG0Ft27ZtW1G9dYMiamrbZlgrqN17M89K8uVfTna%2FoRs4AwCUGVBCU0zQl7DAlEIZWoPOfhXUs0BbVQAL1CG0ZepQd9STPdUW9dQ61FGN%2BU5LpOW1pswUpmU0hZj%2BTGOmWnQ2lPNyV2rEoO%2FA%2BmUw0CwATG8cNjkwyXzEYZrG9Of5NUyy%2BXBY7Q4Hm9a8tgCH%2FWU4bOcwPfmsjc7GvDcYPWk7StjU2G8qAf5xwHQE6D%2BzHRXUbqzi96bmrEQNEeim4V965jWnB%2Bho0sNRHnTn7E5H0V3nQAlaAGsawqkxWKfGhDPoO2Ts%2FGdwsk5fIecd011vh9O%2FOaegHO9toBWAfYLM5JBSxvoNquliyEeDvUucbeXvMd55vIqRtTGMJTnzAkP5bdnsXvTX6VGOPkbfYe%2ByRgh%2F6xHoLms6QDmmlvyFPThTB2PEtbczfMbr3XUu1JD7fmqUjaYre68jzpPD3wJIH6QH0RyQ5L6Ui%2FGeGFqDOZLiPj7iXnpkDsKJ5%2BTwO3LmEe8JYecb2fcazoXMC%2FEd4z0J7EFS3MdH3EuPJJX07gom%2Bff4%2FDMcpS1ee85bBLQNGO84cgiqPerpVcghUBEeK%2FS1jzBBfUZbwUv5X%2F7bkOlslqCEwJ5TBw4lBFsBJdRuHA4vYk%2Fown8RLYvLrQAAeAEc0jWMJFcQxvFnto%2F5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56%2F%2FCz%2BVaqqrat5rY8x7xnzxl3nvo%2B27jFnz8c%2FmI9Nmh2XBdMsilrBitsnD9rI8aiN5DI%2FjSftC9mIf9pMfIB4kHiI%2BhWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW%2FgLbzNbnfwLt7DJ%2Fp0TX4%2BUucji1hCnY%2FU%2BcijVB7D46jzkb3Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV%2FEaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK%2FUVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN%2B59b410iF0sUFO0l2UJtY%2F8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC%2FLzdhmV2XBvpBF25IlLJOvEFfRI%2BNjgCFGGGNK5Rs6Z7Ij%2F45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j%2F%2B58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go%2Flfr05F%2BUa7CCzGx10sYA9tiWLxCWs2BfyN%2BIa1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt%2FQOZPfmY3%2F%2FSs3Y5tNpTpL9ZQeGR8DDDHCGN%2FwbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h%2B1FeZTKY3gcT2KvTWUf9pMZIB4kHiI%2BxcQzxGfpfA7P4wW8yG4eT%2FkYYIgRxvgb9TWsYwObmOAITlI%2Fxf7TOIOzOIfzuEDlIi7hMq7gFbyK1%2FA63sBbeJtvdwfv4j28zyaP8QmVL%2FimL%2FENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI%2FhcTzJp73Yh%2F3kB4gHiYeIT%2BEZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe%2FgY%2F%2Begvq0YCAEoCNa1n%2BKVyTUl3Q0uIhoe%2B3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK%2F7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De%2Fxu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p%2Ff6oI%2F6pC%2FKSxvf9F0%2F1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu%2BkbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd%2BR3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI%2FWN54IuxXFS97oH58%2BMBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd%2B0lSVW5nNIL3nF6389h%2BY5NG3Thja0oQ1taEMb2tCGNrQn%2BQwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5%2F5wle%2B8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In%2FHCuNDGO%2BNOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I%2FD4%2FA4PA6Pw%2BPwODwOj8M%2Ff7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM%2B7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6%2Fh%2BP6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q%2F6H%2F0H%2B4P9yfPz82bdm2Y9ee%2FT355bS3%2FdivDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M%2FRm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP%2F84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y%2BrH1I%2BpH1M%2Fpn5M%2FZh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0%2F%2B9sBOGnTDshOF%2BDndyXG7k7vfh9%2Bn35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvfPDBBx988sknn3zxxRdfPHnyVPip8FPhp8JPhZ8KP78czLdxBDAMAMFc%2FbdAk4AERoMS5CpQOW82uWyPHexkJzvZyU52spOd7GQnu9jFLnaxi13sYhe72MVudrOb3exmN7vZzW52s8EGG2ywwQYbbLDBBnvZy172spe97GUve9nLJptssskmm2yyySabbLHFFltsscUWW2yxxX6%2B7P%2BrH%2Fqtf6%2B2Z3u2Z3u2Z3u2Z3u2Z3s%2BO66jKoYBGASA%2FiUFeLO2tqfgvhIgVkOshvj%2F8f%2FjF8VqiL8dqyG%2Bd4klllhiiSWWWGKJJY444ogjjjjiiCOO%2BPua0gPv7paRAHgBLcEDlNxQAADArI3Ydv7Vtm3btm3btm3btm3bD7VvBoIgLXVVqCf0ztXT9dzd3j3cvcX90CN5Snmae%2Fp45np2e356gbeH94HP8Q3x3feH%2FX38NwJwoHigQ2Ba4GBQCK4NfgxVDE0OnQr7w1nCI8P7wi8jdqR4ZGzkRDQSLRmdH%2F0UqxTrEVsbux%2FPHe8b3xh%2FlgglzESJRJfE6MS6ZChZJzkj%2BRouCA9GJKQuMhI5hsZRHR2A7kZ%2FYZWxldhtPDPeFd%2BIPybyE0OIy2SIrEy2IneSX8mvFKB6UpfodPQYeiOTjmnK3GOzsCPYpexaLjdXiRvBHeJ%2B8BX5Lvxe%2FqOACmWEnsJ60SsyYjqxiLhE3CoeE6%2BLL8RvUlRqJXWThkszpJXSbjkq83JaOZ9cXm4gd5IXKZACK4qSSSmiVFWmq0lVUtOr%2BdXyagO1oxbRSM3UsmnFtOpaC62nNkqbo7M60HPppfXaemu9j77X4IwUI49RxqhrtDWOGzeM92Y985lFWWWtcdZia4d10%2FpiU3YZu6%2B91j7rME5xp5szGVAgDcgBioDhYDpYDjaDE%2BAmeAW%2Bp8R%2FA5ajfCcAAAABAAAA3QCKABYAWAAFAAIAEAAvAFwAAAEAAQsAAwABeAF9jgNuRAEYhL%2FaDGoc4DluVNtug5pr8xh7jj3jTpK18pszwBDP9NHTP0IPs1DOexlmtpz3sc9iOe9nmddyPsA8%2BXI%2BqI1COZ%2FkliIXhPkiyDo3vCnG2CaEn0%2B2lH%2BgmfIvotowZa3769ULZST4K%2BcujqTb%2Fj36S4w%2FQmgDF0tWvalemNWLX%2BKSMBvYkhQSLG2FZR%2BafmERIsqPpn7%2ByvxjfMlsTjlihz3OuZE38bTtlAAa%2FTAFAHgBbMEDjJYBAADQ9%2F3nu2zbtm3b5p9t17JdQ7Zt21zmvGXXvJrZe0LA37Cw%2F3lDEBISIVKUaDFixYmXIJHEkkgqmeRSSCmV1NJIK530Msgok8yyyCqb7HLIKZfc8sgrn%2FwKKKiwIooqprgSSiqltDLKKqe8CiqqpLIqqqqmuhpqqqW2Ouqqp74GGmqksSaaaqa5FlpqpbU22mqnvQ466qSzLrrqprs9NpthprNWeWeWReZba6ctQYR5QaTplvvhp4VWm%2BOyt75bZ5fffvljk71uum6fHnpaopfbervhlvfCHnngof36%2BGappx57oq%2BPPpurv34GGGSgwTYYYpihhhthlJFGG%2BODscYbZ4JJJjphoykmm2qaT7445ZkDDnrujRcOOeyY46444qirZtvtnPPOBFG%2BBtFBTBAbxAXxQYJC7rvjrnv%2FxpJXmpPDXpqXaWDg6MKZX5ZaVJycX5TK4lpalA8SdnMyMITSRjxp%2BaVFxaUFqUWZ%2BUVQQWMobcKUlgYAHQ14sAAAeAFFSzVCLEEQ7fpjH113V1ybGPd1KRyiibEhxt1vsj3ZngE9AIfgBmMR5fVk8qElsRjHOHAYW%2BQwyumxct4bKxXkWDEvx7JjdszQNAZcekzi9Zho8oV8NCbnIT%2FfEXNRJwqmlaemnQMbN8E1OE7Mzb%2FP%2F8xzKZrEMA2hl3rQATa0Uxs2bN%2B2f8M2AEpwj5yQBvklvJ3AqRcEaMKrWq%2F19eWakl7NsZbyJoNblqlZc7KywcRbRnBjc00FeF6%2Fenoi05EcG62tsXhkPcdk87BHVC%2BZXleUPrOsUHaUI2tb4y%2F8OwbsTEAJAA%3D%3D%29%20format%28%22woff%22%29%7D%2A%7Bbox%2Dsizing%3Aborder%2Dbox%7Dbody%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dfamily%3A%22Open%20Sans%22%2C%22Helvetica%20Neue%22%2CHelvetica%2CArial%2Csans%2Dserif%3Bfont%2Dsize%3A16px%3Bline%2Dheight%3A1%2E5%3Bcolor%3A%23606c71%7Da%7Bcolor%3A%231e6bb8%3Btext%2Ddecoration%3Anone%7Da%3Ahover%7Btext%2Ddecoration%3Aunderline%7D%2Epage%2Dheader%7Bcolor%3A%23fff%3Btext%2Dalign%3Acenter%3Bbackground%2Dcolor%3A%23159957%3Bbackground%2Dimage%3Alinear%2Dgradient%28120deg%2C%23155799%2C%23159957%29%3Bpadding%3A1%2E5rem%202rem%7D%2Eproject%2Dname%7Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A%2E1rem%3Bfont%2Dsize%3A2rem%7D%2Eproject%2Dtagline%7Bmargin%2Dbottom%3A2rem%3Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E5rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dweight%3A400%3Bopacity%3A%2E7%3Bfont%2Dsize%3A1%2E2rem%7D%40media%20screen%20and%20%28max%2Dwidth%3A%2042em%29%7B%2Epage%2Dheader%7Bpadding%3A1rem%7D%2Eproject%2Dname%7Bfont%2Dsize%3A1%2E75rem%7D%2Eproject%2Dtagline%7Bfont%2Dsize%3A1%2E2rem%7D%2Eproject%2Dauthor%2C%2Eproject%2Ddate%7Bfont%2Dsize%3A1rem%7D%7D%2Emain%2Dcontent%3Afirst%2Dchild%7Bmargin%2Dtop%3A0%7D%2Emain%2Dcontent%20img%7Bmax%2Dwidth%3A100%25%7D%2Emain%2Dcontent%20h1%2C%2Emain%2Dcontent%20h2%2C%2Emain%2Dcontent%20h3%2C%2Emain%2Dcontent%20h4%2C%2Emain%2Dcontent%20h5%2C%2Emain%2Dcontent%20h6%7Bmargin%2Dtop%3A2rem%3Bmargin%2Dbottom%3A1rem%3Bfont%2Dweight%3A400%3Bcolor%3A%23159957%7D%2Emain%2Dcontent%20p%7Bmargin%2Dbottom%3A1em%7D%2Emain%2Dcontent%20code%7Bpadding%3A2px%204px%3Bfont%2Dfamily%3AConsolas%2C%22Liberation%20Mono%22%2CMenlo%2CCourier%2Cmonospace%3Bcolor%3A%23383e41%3Bbackground%2Dcolor%3A%23f3f6fa%3Bborder%2Dradius%3A%2E3rem%7D%2Emain%2Dcontent%20pre%7Bpadding%3A%2E8rem%3Bmargin%2Dtop%3A0%3Bmargin%2Dbottom%3A1rem%3Bfont%3A1rem%20Consolas%2C%22Liberation%20Mono%22%2CMenlo%2CCourier%2Cmonospace%3Bcolor%3A%23567482%3Bword%2Dwrap%3Anormal%3Bbackground%2Dcolor%3A%23f3f6fa%3Bborder%3Asolid%201px%20%23dce6f0%3Bborder%2Dradius%3A%2E3rem%3Bline%2Dheight%3A1%2E45%3Boverflow%3Aauto%7D%2Emain%2Dcontent%20pre%3E%20code%7Bpadding%3A0%3Bmargin%3A0%3Bfont%2Dsize%3A1rem%3Bcolor%3A%23567482%3Bword%2Dbreak%3Anormal%3Bwhite%2Dspace%3Apre%3Bbackground%3Atransparent%3Bborder%3A0%7D%2Emain%2Dcontent%20pre%20code%2C%2Emain%2Dcontent%20pre%20tt%7Bdisplay%3Ainline%3Bpadding%3A0%3Bline%2Dheight%3Ainherit%3Bword%2Dwrap%3Anormal%3Bbackground%2Dcolor%3Atransparent%3Bborder%3A0%7D%2Emain%2Dcontent%20pre%20code%3Abefore%2C%2Emain%2Dcontent%20pre%20code%3Aafter%2C%2Emain%2Dcontent%20pre%20tt%3Abefore%2C%2Emain%2Dcontent%20pre%20tt%3Aafter%7Bcontent%3Anormal%7D%2Emain%2Dcontent%20ul%2C%2Emain%2Dcontent%20ol%7Bmargin%2Dtop%3A0%7D%2Emain%2Dcontent%20blockquote%7Bpadding%3A0%201rem%3Bmargin%2Dleft%3A0%3Bfont%2Dsize%3A1%2E2rem%3Bcolor%3A%23819198%3Bborder%2Dleft%3A%2E3rem%20solid%20%23dce6f0%7D%2Emain%2Dcontent%20blockquote%3E%3Afirst%2Dchild%7Bmargin%2Dtop%3A0%7D%2Emain%2Dcontent%20blockquote%3E%3Alast%2Dchild%7Bmargin%2Dbottom%3A0%7D%2Emain%2Dcontent%20table%7Bwidth%3A100%25%3Boverflow%3Aauto%3Bword%2Dbreak%3Anormal%3Bword%2Dbreak%3Akeep%2Dall%3Bborder%2Dcollapse%3Acollapse%3Bborder%2Dspacing%3A0%3Bmargin%3A1rem%200%7D%2Emain%2Dcontent%20table%20th%7Bfont%2Dweight%3A700%3Bbackground%2Dcolor%3A%234CAF50%3Bcolor%3A%23fff%7D%2Emain%2Dcontent%20table%20th%2C%2Emain%2Dcontent%20table%20td%7Bpadding%3A%2E5rem%201rem%3Bborder%2Dbottom%3A1px%20solid%20%23e9ebec%3Btext%2Dalign%3Aleft%7D%2Emain%2Dcontent%20table%20tr%3Anth%2Dchild%28odd%29%7Bbackground%2Dcolor%3A%23f2f2f2%7D%2Emain%2Dcontent%20dl%7Bpadding%3A0%7D%2Emain%2Dcontent%20dl%20dt%7Bpadding%3A0%3Bmargin%2Dtop%3A1rem%3Bfont%2Dsize%3A1rem%3Bfont%2Dweight%3A700%7D%2Emain%2Dcontent%20dl%20dd%7Bpadding%3A0%3Bmargin%2Dbottom%3A1rem%7D%2Emain%2Dcontent%20hr%7Bmargin%3A1rem%200%3Bborder%3A0%3Bheight%3A1px%3Bbackground%3A%23aaa%3Bbackground%2Dimage%3Alinear%2Dgradient%28to%20right%2C%23eee%2C%23aaa%2C%23eee%29%7D%2Emain%2Dcontent%2C%2Etoc%7Bmax%2Dwidth%3A64rem%3Bpadding%3A2rem%204rem%3Bmargin%3A0%20auto%3Bfont%2Dsize%3A1%2E1rem%7D%2Etoc%7Bpadding%2Dbottom%3A0%7D%2Etoc%20ul%7Bmargin%2Dbottom%3A0%7D%40media%20screen%20and%20%28min%2Dwidth%3A%2042em%29%20and%20%28max%2Dwidth%3A%2064em%29%7B%2Etoc%7Bpadding%3A2rem%202rem%200%7D%2Emain%2Dcontent%7Bpadding%3A2rem%7D%7D%40media%20screen%20and%20%28max%2Dwidth%3A%2042em%29%7B%2Etoc%7Bpadding%3A2rem%201rem%200%3Bfont%2Dsize%3A1rem%7D%2Emain%2Dcontent%7Bpadding%3A2rem%201rem%3Bfont%2Dsize%3A1rem%7D%2Emain%2Dcontent%20pre%2C%2Emain%2Dcontent%20pre%3E%20code%7Bfont%2Dsize%3A%2E9rem%7D%2Emain%2Dcontent%20blockquote%7Bfont%2Dsize%3A1%2E1rem%7D%7D%2Esite%2Dfooter%7Bpadding%2Dtop%3A2rem%3Bmargin%2Dtop%3A2rem%3Bborder%2Dtop%3Asolid%201px%20%23eff0f1%3Bfont%2Dsize%3A1rem%7D%2Esite%2Dfooter%2Downer%7Bdisplay%3Ablock%3Bfont%2Dweight%3A700%7D%2Esite%2Dfooter%2Dcredits%7Bcolor%3A%23819198%7D%0Acode%20%3E%20span%2Ekw%20%7B%20color%3A%20%23a71d5d%3B%20font%2Dweight%3A%20normal%3B%20%7D%20%0Acode%20%3E%20span%2Edt%20%7B%20color%3A%20%23795da3%3B%20%7D%20%0Acode%20%3E%20span%2Edv%20%7B%20color%3A%20%230086b3%3B%20%7D%20%0Acode%20%3E%20span%2Ebn%20%7B%20color%3A%20%230086b3%3B%20%7D%20%0Acode%20%3E%20span%2Efl%20%7B%20color%3A%20%230086b3%3B%20%7D%20%0Acode%20%3E%20span%2Ech%20%7B%20color%3A%20%234070a0%3B%20%7D%20%0Acode%20%3E%20span%2Est%20%7B%20color%3A%20%23183691%3B%20%7D%20%0Acode%20%3E%20span%2Eco%20%7B%20color%3A%20%23969896%3B%20font%2Dstyle%3A%20italic%3B%20%7D%20%0Acode%20%3E%20span%2Eot%20%7B%20color%3A%20%23007020%3B%20%7D%20%0A" rel="stylesheet" type="text/css" />
63
+
64
+</head>
65
+
66
+<body>
67
+
68
+
69
+
70
+
71
+<section class="page-header">
72
+<h1 class="title toc-ignore project-name">Annotate a phylogenetic tree with insets</h1>
73
+<h4 class="author project-author">Guangchuang Yu<br />
74
+School of Public Health, The University of Hong Kong</h4>
75
+<h4 class="date project-date">2018-01-03</h4>
76
+</section>
77
+
78
+
79
+<div id="TOC" class="toc">
80
+<ul>
81
+<li><a href="#annotate-with-bar-charts">Annotate with bar charts</a></li>
82
+<li><a href="#annotate-with-pie-charts">Annotate with pie charts</a></li>
83
+<li><a href="#annotate-with-other-types-of-charts">Annotate with other types of charts</a></li>
84
+</ul>
85
+</div>
86
+
87
+<section class="main-content">
88
+<p><code>ggtree</code> provides a function, <code>inset</code>, for adding subplots to a phylogenetic tree. The input is a tree graphic object and a named list of ggplot graphic objects (can be any kind of charts), these objects should named by node numbers. To facilitate adding bar and pie charts (e.g. summarized stats of results from ancestral reconstruction) to phylogenetic tree, <em>ggtree</em> provides <code>nodepie</code> and <code>nodebar</code> functions to create a list of pie or bar charts.</p>
89
+<div id="annotate-with-bar-charts" class="section level2">
90
+<h2>Annotate with bar charts</h2>
91
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">set.seed</span>(<span class="dv">2015</span><span class="op">-</span><span class="dv">12</span><span class="op">-</span><span class="dv">31</span>)
92
+tr &lt;-<span class="st"> </span><span class="kw">rtree</span>(<span class="dv">15</span>)
93
+p &lt;-<span class="st"> </span><span class="kw">ggtree</span>(tr)
94
+
95
+a &lt;-<span class="st"> </span><span class="kw">runif</span>(<span class="dv">14</span>, <span class="dv">0</span>, <span class="fl">0.33</span>)
96
+b &lt;-<span class="st"> </span><span class="kw">runif</span>(<span class="dv">14</span>, <span class="dv">0</span>, <span class="fl">0.33</span>)
97
+c &lt;-<span class="st"> </span><span class="kw">runif</span>(<span class="dv">14</span>, <span class="dv">0</span>, <span class="fl">0.33</span>)
98
+d &lt;-<span class="st"> </span><span class="dv">1</span> <span class="op">-</span><span class="st"> </span>a <span class="op">-</span><span class="st"> </span>b <span class="op">-</span><span class="st"> </span>c
99
+dat &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">a=</span>a, <span class="dt">b=</span>b, <span class="dt">c=</span>c, <span class="dt">d=</span>d)
100
+## input data should have a column of `node` that store the node number
101
+dat<span class="op">$</span>node &lt;-<span class="st"> </span><span class="dv">15</span><span class="op">+</span><span class="dv">1</span><span class="op">:</span><span class="dv">14</span>
102
+
103
+## cols parameter indicate which columns store stats (a, b, c and d in this example)
104
+bars &lt;-<span class="st"> </span><span class="kw">nodebar</span>(dat, <span class="dt">cols=</span><span class="dv">1</span><span class="op">:</span><span class="dv">4</span>)
105
+
106
+<span class="kw">inset</span>(p, bars, <span class="dt">width=</span>.<span class="dv">2</span>, <span class="dt">height=</span><span class="dv">1</span>)</code></pre></div>
107
+<p><img src="data:image/png;base64,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" /><!-- --></p>
108
+<p>Users can set the color via the parameter <em>color</em>. The <em>x</em> position can be one of ‘node’ or ‘branch’ and can be adjusted by the parameter <em>hjust</em> and <em>vjust</em> for horizontal and vertical adjustment respecitvely.</p>
109
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">bars2 &lt;-<span class="st"> </span><span class="kw">nodebar</span>(dat, <span class="dt">cols=</span><span class="dv">1</span><span class="op">:</span><span class="dv">4</span>, <span class="dt">position=</span><span class="st">'dodge'</span>,
110
+                 <span class="dt">color=</span><span class="kw">c</span>(<span class="dt">a=</span><span class="st">'blue'</span>, <span class="dt">b=</span><span class="st">'red'</span>, <span class="dt">c=</span><span class="st">'green'</span>, <span class="dt">d=</span><span class="st">'cyan'</span>))
111
+p2 &lt;-<span class="st"> </span><span class="kw">inset</span>(p, bars2, <span class="dt">x=</span><span class="st">'branch'</span>, <span class="dt">width=</span>.<span class="dv">2</span>, <span class="dt">height=</span><span class="dv">1</span>, <span class="dt">vjust=</span><span class="op">-</span>.<span class="dv">3</span>)
112
+<span class="kw">print</span>(p2)</code></pre></div>
113
+<p><img src="data:image/png;base64,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" /><!-- --></p>
114
+</div>
115
+<div id="annotate-with-pie-charts" class="section level2">
116
+<h2>Annotate with pie charts</h2>
117
+<p>Similarly, users can use <code>nodepie</code> function to generate a list of pie charts and place these charts to annotate corresponding nodes. Both <code>nodebar</code> and <code>nodepie</code> accepts parameter <em>alpha</em> to allow transparency.</p>
118
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pies &lt;-<span class="st"> </span><span class="kw">nodepie</span>(dat, <span class="dt">cols=</span><span class="dv">1</span><span class="op">:</span><span class="dv">4</span>, <span class="dt">alpha=</span>.<span class="dv">6</span>)
119
+<span class="kw">inset</span>(p, pies, <span class="dt">width=</span><span class="dv">1</span>, <span class="dt">height=</span><span class="dv">1</span>, <span class="dt">hjust=</span><span class="op">-</span>.<span class="dv">06</span>)</code></pre></div>
120
+<p><img src="data:image/png;base64,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" /><!-- --></p>
121
+</div>
122
+<div id="annotate-with-other-types-of-charts" class="section level2">
123
+<h2>Annotate with other types of charts</h2>
124
+<p>The <code>inset</code> function accepts a list of ggplot graphic objects and these input objects are not restricted to pie or bar charts. They can be any kinds of charts and hybrid of these charts.</p>
125
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">pies_and_bars &lt;-<span class="st"> </span>bars2
126
+pies_and_bars[<span class="dv">9</span><span class="op">:</span><span class="dv">14</span>] &lt;-<span class="st"> </span>pies[<span class="dv">9</span><span class="op">:</span><span class="dv">14</span>]
127
+<span class="kw">inset</span>(p, pies_and_bars, <span class="dt">width=</span>.<span class="dv">3</span>, <span class="dt">height=</span><span class="dv">1</span>)</code></pre></div>
128
+<p><img src="data:image/png;base64,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" /><!-- --></p>
129
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">d &lt;-<span class="st"> </span><span class="kw">lapply</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">15</span>, rnorm, <span class="dt">n=</span><span class="dv">100</span>)
130
+ylim &lt;-<span class="st"> </span><span class="kw">range</span>(<span class="kw">unlist</span>(d))
131
+bx &lt;-<span class="st"> </span><span class="kw">lapply</span>(d, <span class="cf">function</span>(y) {
132
+    dd &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">y=</span>y)
133
+    <span class="kw">ggplot</span>(dd, <span class="kw">aes</span>(<span class="dt">x=</span><span class="dv">1</span>, <span class="dt">y=</span>y))<span class="op">+</span><span class="kw">geom_boxplot</span>() <span class="op">+</span><span class="st"> </span><span class="kw">ylim</span>(ylim) <span class="op">+</span><span class="st"> </span><span class="kw">theme_inset</span>()
134
+})
135
+<span class="kw">names</span>(bx) &lt;-<span class="st"> </span><span class="dv">1</span><span class="op">:</span><span class="dv">15</span>
136
+<span class="kw">inset</span>(p, bx, <span class="dt">width=</span>.<span class="dv">2</span>, <span class="dt">height=</span><span class="dv">2</span>, <span class="dt">hjust=</span><span class="op">-</span>.<span class="dv">05</span>)</code></pre></div>
137
+<p><img src="data:image/png;base64,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" /><!-- --></p>
138
+<p>After annotating with insets, users can further annotate the tree with another layer of insets.</p>
139
+<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">p2 &lt;-<span class="st"> </span><span class="kw">inset</span>(p, bars2, <span class="dt">x=</span><span class="st">'branch'</span>, <span class="dt">width=</span>.<span class="dv">5</span>, <span class="dt">height=</span><span class="dv">1</span>, <span class="dt">vjust=</span><span class="op">-</span>.<span class="dv">4</span>)
140
+p2 &lt;-<span class="st"> </span><span class="kw">inset</span>(p2, pies, <span class="dt">x=</span><span class="st">'branch'</span>, <span class="dt">vjust=</span>.<span class="dv">4</span>, <span class="dt">width=</span>.<span class="dv">5</span>, <span class="dt">height=</span><span class="dv">1</span>)
141
+bx2 &lt;-<span class="st"> </span><span class="kw">lapply</span>(bx, <span class="cf">function</span>(g) g<span class="op">+</span><span class="kw">coord_flip</span>())
142
+<span class="kw">inset</span>(p2, bx2, <span class="dt">width=</span><span class="dv">2</span>, <span class="dt">height=</span><span class="dv">1</span>, <span class="dt">vjust=</span>.<span class="dv">04</span>, <span class="dt">hjust=</span>p2<span class="op">$</span>data<span class="op">$</span>x[<span class="dv">1</span><span class="op">:</span><span class="dv">15</span>]<span class="op">-</span><span class="dv">5</span>) <span class="op">+</span><span class="st"> </span><span class="kw">xlim</span>(<span class="ot">NA</span>, <span class="dv">6</span>)</code></pre></div>
143
+<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAYAAABEPM/FAAAEGWlDQ1BrQ0dDb2xvclNwYWNlR2VuZXJpY1JHQgAAOI2NVV1oHFUUPrtzZyMkzlNsNIV0qD8NJQ2TVjShtLp/3d02bpZJNtoi6GT27s6Yyc44M7v9oU9FUHwx6psUxL+3gCAo9Q/bPrQvlQol2tQgKD60+INQ6Ium65k7M5lpurHeZe58853vnnvuuWfvBei5qliWkRQBFpquLRcy4nOHj4g9K5CEh6AXBqFXUR0rXalMAjZPC3e1W99Dwntf2dXd/p+tt0YdFSBxH2Kz5qgLiI8B8KdVy3YBevqRHz/qWh72Yui3MUDEL3q44WPXw3M+fo1pZuQs4tOIBVVTaoiXEI/MxfhGDPsxsNZfoE1q66ro5aJim3XdoLFw72H+n23BaIXzbcOnz5mfPoTvYVz7KzUl5+FRxEuqkp9G/Ajia219thzg25abkRE/BpDc3pqvphHvRFys2weqvp+krbWKIX7nhDbzLOItiM8358pTwdirqpPFnMF2xLc1WvLyOwTAibpbmvHHcvttU57y5+XqNZrLe3lE/Pq8eUj2fXKfOe3pfOjzhJYtB/yll5SDFcSDiH+hRkH25+L+sdxKEAMZahrlSX8ukqMOWy/jXW2m6M9LDBc31B9LFuv6gVKg/0Szi3KAr1kGq1GMjU/aLbnq6/lRxc4XfJ98hTargX++DbMJBSiYMIe9Ck1YAxFkKEAG3xbYaKmDDgYyFK0UGYpfoWYXG+fAPPI6tJnNwb7ClP7IyF+D+bjOtCpkhz6CFrIa/I6sFtNl8auFXGMTP34sNwI/JhkgEtmDz14ySfaRcTIBInmKPE32kxyyE2Tv+thKbEVePDfW/byMM1Kmm0XdObS7oGD/MypMXFPXrCwOtoYjyyn7BV29/MZfsVzpLDdRtuIZnbpXzvlf+ev8MvYr/Gqk4H/kV/G3csdazLuyTMPsbFhzd1UabQbjFvDRmcWJxR3zcfHkVw9GfpbJmeev9F08WW8uDkaslwX6avlWGU6NRKz0g/SHtCy9J30o/ca9zX3Kfc19zn3BXQKRO8ud477hLnAfc1/G9mrzGlrfexZ5GLdn6ZZrrEohI2wVHhZywjbhUWEy8icMCGNCUdiBlq3r+xafL549HQ5jH+an+1y+LlYBifuxAvRN/lVVVOlwlCkdVm9NOL5BE4wkQ2SMlDZU97hX86EilU/lUmkQUztTE6mx1EEPh7OmdqBtAvv8HdWpbrJS6tJj3n0CWdM6busNzRV3S9KTYhqvNiqWmuroiKgYhshMjmhTh9ptWhsF7970j/SbMrsPE1suR5z7DMC+P/Hs+y7ijrQAlhyAgccjbhjPygfeBTjzhNqy28EdkUh8C+DU9+z2v/oyeH791OncxHOs5y2AtTc7nb/f73TWPkD/qwBnjX8BoJ98VQNcC+8AAEAASURBVHgB7N0HnNxVvf//95TtJdndZNM3IR1IoSZIQDAJRZR6FREEL/5E5QoiP0Tk7xW4Cj5UVCxX8T5AHxeu+lMRr0oVIoQg0kkgCSSk980mu9lep/y/Z5KZ7O7U3Z3ynZnX18c4M+ec7ynPs+x+8m3H4bc2sSGAAAIIIIAAAgggkCYBZ5raoRkEEEAAAQQQQAABBAICBKD8ICCAAAIIIIAAAgikVYAANK3cNIYAAggggAACCCBAAMrPAAIIIIAAAggggEBaBQhA08pNYwgggAACCCCAAAIEoPwMIIAAAggggAACCKRVgAA0rdw0hgACCCCAAAIIIEAAys8AAggggAACCCCAQFoFCEDTyk1jCCCAAAIIIIAAAgSg/AwggAACCCCAAAIIpFWAADSt3DSGAAIIIIAAAgggQADKzwACCCCAAAIIIIBAWgUIQNPKTWMIIIAAAggggAACBKD8DCCAAAIIIIAAAgikVYAANK3cNIYAAggggAACCCBAAMrPAAIIIIAAAggggEBaBQhA08pNYwgggAACCCCAAAIEoPwMIIAAAggggAACCKRVgAA0rdw0hgACCCCAAAIIIEAAys8AAggggAACCCCAQFoFCEDTyk1jCCCAAAIIIIAAAgSg/AwggAACCCCAAAIIpFWAADSt3DSGAAIIIIAAAgggQADKzwACCCCAAAIIIIBAWgXcaW2NxhBAAAEEEEAAgRQKPP7443rggQdCLXi9Xvn9/sB3l8slh8MRyisoKNC9996rY445JpTGh/QIEICmx5lWEEAAAQQQQCANAm+99ZZ2796t888/P9Daa6+9pp07dwY+L126VNXV1YHPnZ2devLJJ7V161YC0DTMy+AmCEAHi/AdAQQQQAABBLJaYMyYMbrnnnsCY/jqV78aCkCvv/56LViwIJC+b9++QACa1QPN4s4TgGbx5NF1BLJFYHqMjm6NkUcWAggggEBuCnATUm7OK6NCAAEEEEAAAQRsK0AAatupoWMIIIAAAggggEBuChCA5ua8MioEEEAAAQQQQMC2AgSgtp0aOoYAAggggAACCOSmAAFobs4ro0IAAQQQQAABBGwrQABq26mhYwgggAACCCCAQG4KEIDm5rwyKgQQQAABBBBAwLYCPAfUtlNDxxDIToGPf/zj2rFjhyZOnHh0AH/+89HPgz5dcsklg1Ky46tZZeULX/hCdnSWXiKQpQJvvvmm/uM//mNIvd+/f79GjRqV8D7mgfU//vGPEy5vCi5btkw33XTTkPah8EABAtCBHnxDAIERCrz66qvq6+tL+A9AR0fHCFvMzO7d3d2ZaZhWEcgjgcrKytDKRYkOe/Xq1YHfQYmWnzZt2sB/MCew45QpUxIoRZFYAgSgsXTIQwCBIQtMmDBBc+bM0cMPPxzad3roU/iHZ599NjyRFAQQQMASmDVrlu6+++4hWXzzm9/USy+9lPA+V111VeCIZsI7UDApAgSgSWGkEgRyR8DhiD4Wvz96HjkIIIAAAggkKsBNSIlKUQ4BBBBAAAEEEEAgKQIEoElhpBIEEEAAAQQQQACBRAUIQBOVohwCCCCAAAIIIIBAUgQIQJPCSCUIIIAAAggggAACiQoQgCYqRTkEEEAAAQQQQACBpAhwF3xSGKkEgfwQ8DiKow7U7ee5mFFxyEAAAQQQGCDAEdABHHxBAAEEEEAAAQQQSLUAR0BTLUz9CCCAAAIIIJA2AYf1MOM9e/boox/9aKDNAwcOyO0+HO7ccsstKioqCqR7PJ609YmGwgUcfmsLTyYFAQTyVSDWg+j7FP0U/NwYv0p86onKuTVqDhkIIIDA0AXMUpx//etfE9rRBKbXX3+9qqurEypPoeQJEIAmz5KaEMgJAQLQnJhGBoEAAgjYWoAA1NbTQ+cQyD6BxYsXh60Fn32joMcIIIAAAqkU4CakVOpSNwIIIIAAAggggECYAAFoGAkJCCCAAAIIIIAAAqkUIABNpS51I4AAAggggAACCIQJEICGkZCAAAIIIIAAAgggkEoBAtBU6lI3AggggAACCCCAQJgAAWgYCQkIIIAAAggggAACqRRgJaRU6lI3Ajkk4PP5dO2112rGjBmhlUQiDW/btm0qLo7+wPpI+5CGAAIIIJBfAjwHNL/mm9EiMGyBjRs3au7cuYHg0+VyRa2ns7NT48eP1759+6KWIQMBBBBAIL8FOAKa3/PP6BFIWKC0tDRQ9g9/+IMuuuiiqPsFH0QftQAZCCCAAAJ5L0AAmvc/AgDku8D0GACs0x4DhywEEEAAgWELcBPSsOnYEQEEEEAAAQQQQGA4AgSgw1FjHwQQQAABBBBAAIFhC3AKfth07IgAAkbA39oqf2eHuv3j5CpxymH9jw0BBBBAAIFYAgSgsXTIQwCBiAL+tjZ5310n3/Zt8nd1Bcrsdl8pj6dEHx53s/W9TV0HpZIxEXcnEQEEEEAgzwUIQPP8B4DhIzAUAXNss2bfXvX+7x8l67mgkTa3o1CjHTO07XFp9Cxp/GLJyW+aSFSkIYAAAnkrwDWgeTv1DByBIQr4/frX2TM1Zt+eqMHn4BqbN0nbn5a8vYNz+I4AAgggkM8CHJfI59ln7AgMQaBw3Ts6saZ6CHscLtptnYrf/bxUd67k4PLQIfuxAwIIRBbYsWOH9uyx/kGc4GZWcRs3blyCpSmWagEC0FQLUz8COSDgq98n99Ytwx5Jh7Uo0qENUvWxw66CHRFAAIEBAl/60pe0YYP1iyXBbfny5frZz36WYGmKpVqAADTVwtSPQJoEPI7o66+7/d0j6oV39Vsj2t/sfOBtqWqOdRSUC39GbEkFCCBgrgTy6dJLL9XXv/71EIf5vmvXLutsi0Ovv/56KP2GG26Q1+sNfedD5gUIQDM/B/QAAVsLmMcs+fbXj7iPXisGbt8tVdSNuCoqQAABBAICBQUFqqioCGk4nUf/hds/3eVyhcrwwR4CR2fKHv2hFwggYDMBc/o9WVvHyOPYZHWFehBAAAEEMihAAJpBfJpGICsErCOgydp6W5JVE/UggAACCGSzAKfgs3n26DsCaRDwe/oituJ3OtRrrXzUVeZXu3X5qW9vQcRy/RP9XILVn4PPCCCAQN4KEIDm7dQzcAQSE/AVF6q7wq2DxX69VrBbNXUN2j7bp2bHIfkc/kAlrpKLNW9n/F8nzvgxamKdohQCCCCAQFYLxP+LkdXDo/MIIDBSgb2TfXq5+xW1t7dr94x67Sjdad3JfvTqHWfxdPX0LUiomaLRCRWjEAIIIIBAjgsc/SuS4wNleAggMDyB8aOOlcP6X6TN4SzSJvdZGttaGik7LK1sUlgSCQgggAACeShAAJqHk86Qs1fArCQU7ZWqURUXVGhyzYkRq+8qO1Mt/jKNaYwfgBaNksrGR6yGRAQQQACBPBPgFHyeTTjDzU+BmSqKMfCeGHmHsxZMvkjv7/rngHKuoil6zzdXlSpQaUdhKM88HLqrq0v79g18fJO7ap/2vtgRKpdLH6ZMmaJp06bl0pAYCwJpFXj66afV3T20BTM6Oob2+6S+vl5//vOfEx5XVVWVzjrrrITLU3BoAgSgQ/OiNAJ5KVBZMk5zxl5gjf3hwPgdDre2FJ4tv0+a0Dfw6Ke5VrSxsVG7d+8OWb1dv1Irt/2/0Pdc+7Bw4UKtWbMm14bFeBBIm8A999yjhoaGIbVXVBTrH9bhVb333nu67bbbwjOipMybN48ANIpNMpIJQJOhSB0I5IFA3ejTtPVNnxznWY9fKjtdTT7rnLq1jWsfGICapfDMkYzgyiMFEzv1meNOs64d+FrOKo0fz7UFOTu5DCwtAn/7298CS2sOpbFPfOITQymuM888Uz/60Y8S3if4OyzhHSg4JAEC0CFxURiB/BbY+Y40uehC7XKfIPVZhz+tbWzTwAC0srJS5uWyzsrXnmzWf6+xSk0JlOX/EEAAgUgCpaUDf49EKjM4rf+ym4PzIn13u90qLy+PlEVaBgQIQDOATpMIDFfAf/ixm1F2j3791OYoewwnudI5SbfOnqvVLc16t7ldVV3FCj5f3mEtt1xsxZuVU6XRsxQIQofTBvsggAACCOS2AAFobs8vo0MgJQJuaxWkU60L9M1Lx1irIFmLJZng2Bz1ZEMAAQQQQCCeAAFoPCHyEUAgrgArHMUlogACCCCAQD8BngPaD4OPCCCAAAIIIIAAAqkXIABNvTEtIIAAAggggAACCPQTIADth8FHBBBAAAEEEEAAgdQLEICm3pgWEEAAAQQQQAABBPoJcBNSPww+IpBJgVtvvVWvv/66bZd0bGtrC/Bs3749k0y0jQACCIQEzO+jP/3pT6HvZiW24NY/3ayyNHny5GAW7zYQIAC1wSTQBQSMwBNPPKE9e/ZoqOsbp0svuE5zc3NzupqkHQQQQCCqQE1NjV5++WW99tprYWX81nPhbr/99gHpJ5xgLaDBZhsBAlDbTAUdyXeBY445JnD088knn7Qlxa5du1RXVyd+idtyeugUAnkn8N///d/q67MeQpzgVlBQkGBJiqVDgAA0Hcq0gQACCCCAAAJJFyCoTDpp2irkJqS0UdMQAggggAACCCCAgBEgAOXnAAEEEEAAAQQQQCCtApyCTys3jSGQPgF/d7d8Bxpk3dWkNsdMOYsKVFwjFVakrw+0hAACCCCAQCQBAtBIKqQhkMUCvvp98rzztvz79krWnaBmayycrJ7ewxfgF42Wao6XRs2UHI4sHihdRwABBBDIWgEC0KydOjqOwCABr1eel1+Sd/OmQRkDv/ZYT1Ha+5J0aKM0+WypoHxgPt8QQAABBBBItQDXgKZamPoRSIOAx9urv2/4sZoPbU24ta6D0rYnpN7WhHehIAIIIIAAAkkRIABNCiOVIJBZgVe3/o8a2jbphTFvqbsi8RMbni5p5wrJl/ij9DI7UFpHAAEEEMgJAQLQnJhGBpHPAnsOvaOdjW8ECHodfVo1YZ36il0Jk5gjoAfWJFycgggggAACCIxYgAB0xIRUgEBmBdbtHrhyUruzXa/UbZXXlfgdRk0bJG9PZsdB6wgggAAC+SNAAJo/c81Ic1CgrbtBTR07wkZ2wHlAbx/TIH+CMajfK7WGVxNWLwkIIIAAAggkQ4AANBmK1IFAhgQOtG2O2vJ29w5tmtYRNX9wRuf+wSl8RwABBBBAIDUCBKCpcaVWBNIi0N5t3coeY1tX9J72TrYObyaw9bUlUIgiCCCAAAIIJEGAADQJiFSBQKYEfObceZzttVHvq60g/p3xPl+cishGAAEEEEAgSQIEoEmCpBoEMiFQ5C6L22xnyWnqKYz/n7q7KG5VFEAAAQQQQCApAvH/KiWlGSpBAIFUCIwunRyzWldBtd7zzbbuiI9/eLO4OmZVZCKAAAIIIJA0AQLQpFFSEQLpFxg3arYKXMVRG24qPsO6E95lBaCH14SPWtDKqJgSK5c8BBBAAAEEkidAAJo8S2pCIO0CTodbM2rPiNiuu2iKtvjqAnkeZ+wjoOboZ0ltxGpIRAABBBBAIOkC8e9MSHqTVIgAAskUOH7Sh7X94KvqHnQb++6i062nyx9uKd4p+HGLktkj6kIAAQRGJvDNb35TTz31VKASv98vr/fwLzOHwyGXa+BKb6eeeqp+8pOfjKxB9k67AAFo2slpEIHkChS6S3X6rP+j59/7ifz+w0c6nSXHaq93bKghrzP6KfixJ0pl40NF+YAAAghkXGDDhg2qqqrSeeedp/r6ev3pT38K9GnOnDlatmxZqH//+Mc/tH79+tB3PmSPAAFo9swVPUUgqsC4yjn64Owv6KXNv5TH16ct7sVSv7PuvijXgI49QRq7MGq1ZCCAAAIZE5g9e7ZuuukmrVmzJhSAHnfccYG0YKc6Ozu1YsWK4Ffes0iAa0CzaLLoKgKxBCZWzdf58/8/1Yy9UId85QOKDr4GtLBSqjvHCj6tAJQNAQQQQACBdAtwBDTd4rSHQAoFKoprde7083VcV7fWNDdrR1eXDvX2ylUoFddYL+tmo0rrvqQy6+lN1qVUbAgggAACCGREgAA0I+w0ikBqBSaXFGtyCRd2plaZ2hFAAAEEhivAKfjhyrEfAggggAACCCCAwLAECECHxcZOCCCAAAIIIIAAAsMV4BT8cOXYDwEEEEAAAQRCAo2NjYE71svLy7V4sfUkDptv+/fv19q1awOPezr55JNt3tvc6x4BaO7NKSNCAAEEEEAgrQJvvfWWrrvuusBD4tva2jRlypTAg+QHPzQ+rZ2K0diqVav05S9/WW63Wy0tLVq4cKF+//vfWzdncndmDLakZnEKPqmcVIYAAggggEB+CZgjiZ/85CfV3t4eCOZ8Pp92796te++915YQO3bsCATLHR0dgf6aTq5bt04///nPbdnfXO0UR0BzdWYZFwJJFuju7g7UeMstt+juu+9Ocu25V93kyZNDD8/OvdExIgSOCrzxxhuqqKiQOfIZ3MzSmU8//bS+9rWvBZbRXLJkSTArofcu6xFytbW1CZU1p/5PO+20hMqaQoWFhSorK5MJQIOb6e8TTzyhL37xi8Ek3lMsQACaYmCqRyBXBMwptRNPPFEzZsxQSUlJrgwrZeMwASgbAvkgUFpaGnGY5vS22cxp7SuvvDJimWiJf/7zn6NlhaWb30fmCGyimzkCunLlyrDi0cYRVpCEpAgQgCaFkUoQyH2B4uJimeu82BBAAIH+AmeddVbgms9Nmzapr68vkGWCzx/+8IeBz06nU1/60pf67xL38yuvvBK3TLCACRyHUr/p40c+8hGZszoejydQjenj97///WCVvKdBgGtA04BME6kVMNeMR3ultmVqRwABBBAwwdsjjzwicye5ORVvzpb84he/0IIFC2yJU1BQoMcff1zz58/XqFGjNHXqVD300EOaNm2aLfubq53iCGiuzizjiiswU0VRy2xWT9Q8MhBAAAEEBgqYI54miMuWzVwH+rvf/S5bupuT/eQIaE5OK4NCAAEEEEAAAQTsK0AAat+5oWcIIIAAAggggEBOChCA5uS0MigEEEAAAQQQQMC+AgSg9p0beoYAAggggAACCOSkAAFoTk4rg0IAAQQQQAABBOwrQABq37mhZwgggAACCCCAQE4K8BimnJxWBhUU8DiKgx/D3/3hSaQggAACCGRewKyetGLFCi1evDiwlKfL5Qp06qmnntLzzz8f6mBnZ6fGjRsX+s6H7BEgAM2euaKnCCCAAAII5IXAzTffrHXr1iU0VrM8MFv2CRCAZt+c0WMEEEAAAQRyWuCUU06RebHlrgABaO7OLSMbgcD0OPtujZNPNgIIIIAAAghEF+AmpOg25CCAAAIIIIAAAgikQIAANAWoVIkAAggggAACCCAQXYAANLoNOQgggAACCCCAAAIpECAATQEqVSKAAAIIIIAAAghEFyAAjW5DDgIIIIAAAggggEAKBAhAU4BKlQgggAACCCCAAALRBQhAo9uQgwACCCCAAAIIIJACAQLQFKBSJQIIIIAAAggggEB0AQLQ6DbkIIAAAggggAACCKRAgAA0BahUiQACCCCAAAIIIBBdgKU4o9uQkyUCfn+sjnZHzdwcNYcMBBBAAAEEEEilAEdAU6lL3QgggAACCCCAAAJhAgSgYSQkIIAAAggggAACCKRSgAA0lbrUjQACCCCAAAIIIBAmQAAaRkICAggggAACCCCAQCoFCEBTqUvdCCCAAAIIIIAAAmEC3AUfRkICAggggAACCKRaoL29Xc8884y8Xm/UppxOp5YuXaqqqqqoZcjITgEC0OycN9v3+sQTT9T69etVVFRk+77apYMdHR2qqKiwS3foBwIIIJBSgRdeeEG333574O+E33qeXl9fX6A9h8OhgoKCwOeenh7dcccduuqqq1LaFypPvwABaPrN86JFE3hOnz5d//qv/5oX403GIO+//34C0GRAUgcCCGSFgAk6zbZq1SoVFxdr4cKFge9z5szRX/7yl9DnYLlAAv+XMwIEoDkzlfYaiDldMmbMGH3ta1+zV8ds3JsXX3xR/KK18QTRNQQQQACBpAkQgCaNkooQQCCTAtNjNL41Rh5ZCCCAAALpF+Au+PSb0yICCCCAAAIIIJDXAgSgeT39DB4BBBBAAAEEEEi/AKfg029Oi5kS8Hrk3bVL/vp98re1qtl1gro1XsXW0z3KJ0slYzPVMdpFAAEEEEAgvwQIQPNrvvNytH6fT7716+RZ+47U2xMy8FTMVVub1LZTOvC2VFwjjTtFKpsQKsIHBBBAAAEEEEiBAKfgU4BKlTYS6O5S35OPy/Pm6wOCz0g97G6UdvxNangrUi5pCCCAQH4KtLS06Itf/KLOOussfeQjH9H+/fsHQHR1dQWeeHLGGWdo2bJl2rx584B8viAQSYAjoJFUSMsJAb/1AOO+p56Uv6V5SOM5aB0o9Xmk8YuGtBuFEUAAgZwT6O3t1aJFi2RWJPJZZ5PMZlYmevrppzVlypTAo+NOOOEEuVyu0IpGF154oR555BHNmzcv5zwYUPIECECTZ0lNNhPwvGQ9V3OIwWdwCE3vSqW1UuW0YArvCAwU+M53vqOpU6cG/vAOzMm/bybQOO644/Jv4Hkw4ocffliFhYUygWhwM4HoD3/4Q91333168sknAysZmRWLgpvJv+GGG3TllVcGkyK+v/feexHTBye+9NJL6uzsHJw84Pu1114bWj1pQAZfbCtAAGrbqaFjIxHw7dkj384dw67iwIQOvdB5UNf56+S2loVjQ6C/wNq1awNLCPZPy+fPH/3oR/XYY4/lM0HOjt0sEezxWKeE+m0mwDx06FAgpc26kN4c/Ry81dfX6+c///ng5AHfB9c7ILPfF7NIx8svv9wvJfzj1VdfTQAazmLrFAJQW08PnRuugPfdtcPatbOiT29Or9dmd6vkld5ubtHJVaOHVRc75a5AeXl5YHC//e1vtXz58twdaIIjGz2a/0YSpMq6YkuWLNEvfvGLsH6ff/75gTRzer7/0VGTaJZiNmu333bbbWH79U94/PHHdcstt/RPivjZrKj3qU99KmIeidkrQACavXNHz6MJ9PXKt29ftNyI6V63T+/NOqDVpY3y6PD6xKbg2tZWAtCIYiQagVGjRmnsWJ7fxU9D7gqccsopuvnmm/WDH/xAlZWVges8P/jBD+qKK64IDHr69Om69957A2VMvjk6On/+/LjBZ+6KMbJEBQhAE5WiXNYI+JqarLuIDl8sn0ind9Y167WxDWpTX1jx3dbdnWwIIIBAPgt87nOfC9x4tGnTpsA/uExQ2n+74IILAkHnO++8E/hHmbkbng2BeAIEoPGEyM8+gQSDxtayHj0za5v2OKJf3N7aN/Dap+zDoMcIIIDAyAVmzpwp84q2mTvizYsNgUQFCEATlaJczgj0lrq1eVyT1ox5V3t8s2OOy8kNSDF90p05U0Uxmjx6F26MQmQhgAACCNhAgADUBpNAF5IsUFYWscK+Ype2jW/Vu0Ub5HP4VeQz5WIHoKML+E8kIiaJCCCAAAIIjECAv64jwGNXewo4q601NV3Wj7a19rvZvIUu7ZjQqbXF78rrsG5tP7K5PQelOP8FTI0SzAbr4D35Ah5HcfRKj94fFr0MOQgggAACtheI8+fX9v2ngwiEC1jPpHPW1cmze7t2je/R26Vr5HGEX8vp6zsQNwBdaN3VyYYAAggggAACyRUgAE2uJ7XZRMC78Hg9XfSYuhX9Lna/r1vlji61+0si9np8cbGOrTj8vMeIBUhEAAEEEEAAgWEJEIAOi42d7C5QPLpWU8d/QBvrn4vZ1WrHoYgBqLn56GOTJsrBTUgx/chEAAEEhivg9x++puahhx4asJpSY2Oj/uu//mu41bJflggQgGbJRNHNoQucUHeZWrr2qb4l+nrD5Wq2Kp4YVvmlEydoamnkI6NhhUlAAAEEEBiywKRJk1RRUSETgJpgtNg662Q2s7xnMAA1q47xeKch02bFDgSgWTFNdHI4Ak6nSx+c8296fetvtO3gKxGrKPZaD63vt9R7odOpf5k0QSextGBELxIRQACBZAmcdNJJeuONN5JVHfVkmQABaJZNGN0dmoDL6dZpMz+tujEna+3ux9XUvmNABS7v4RuRXNap9oWjKnX++HGqKigYUIYvyRdob49eZ4x74KPvRA4CCCCAQFYJEIBm1XTR2eEKTBw9T+bV2lWvhtb31dZ9QD6/R253peZUTNYs6zRPqXX3PFt6BCzu6Ju/O2re5qg5ZCCAAAIIZJMAAWg2zRZ9HbFAZcl4mRcbAggggAACCGROwJm5pmkZAQQQQAABBBBAIB8FCEDzcdYZMwIIIIAAAgggkEEBAtAM4tM0AggggAACCCCQjwIEoPk464wZAQQQQAABBBDIoAA3IWUQn6YRsJtAQ0ODurq65LSeh8oWXWDv3r3RM8lBAAEEEIgrQAAal4gCCOSPwNSpU9XdHf0xSPkjkdhIV6xYoQsuuCCxwpRCAAEEEAgJEICGKPiAAAKjRo0KBFTXXHMNGDEE9u/fr89//vP64Ac/GKMUWQgggAAC0QQIQKPJkI5AHgoUWKtAzZo1SxdffHEejj7xIW/bti1QuLCwMPGdKIkAAgggEBIgAA1R8AGB/BCYnh/DZJQIIIAAAjYW4E4DG08OXUMAAQQQQAABBHJRgAA0F2eVMSGAAAIIIIAAAjYW4BS8jSeHriGQ7wINre9rV9NqHWzfpr7uds3e+k25S5wqqZEqp0llE/NdiPEjgAAC2SlAAJqd80avEchpgaaOnXp962/V1LEjNE633PJ2+eXplLobpUPvS8XV0vjTpNLaUDE+IIAAAghkgQABaBZMEl3MXwGHI/rY/f7oedmcs/XAP/Xa1t/I7/cNGkb4gLubpO1PWUHoIqn62EHF+YoAAgggYFsBAlDbTg0dQ2D4AjNVFGPnnhh5mc3acfB1vbrlfyJ2whsWkB4pZsWl9a9KDuuK9qo5EXclEQEEEEDAZgIEoDabELqDQL4KtHcf1KtbIwefxiTGweAAmQlCS8dJRaPzVZBxI5AZgSeffFI333zzkBq/7777WEVsSGK5V5gANPfmlBEhkJUC7+z6i7y+vqh99znCT8H3L2wOkO5/Q6pb3j+VzwggkGoBszKY0+nUN77xjVBTb7zxhp544onA9/POO0+nnWZdrH1k+9a3vqX6+vrgV97zVIAANE8nnmEjYCeBNo9Ha3snqaq4Ud7ubTG6Fvs4aPtuqbdNKqyIUQVZCCCQdAETgF555ZWheh3WBezBAPTkk08ekPftb387VI4P+StAAJq/c8/IEbCNwMa2dm32TZFcU1RV3q46/wYVdq+T19s+oI9+xT4Kagq37ZJqjhuwG18QQAABBGwmQABqswmhOwjko8De7u7QsA/5y3VIp8hVfJKmOXepus8KRGMeFQ3tGvjQc2jgd74hgAACCNhPgADUfnNCjxDIO4EOrzdszF45tcU3VVtcUwNHRadaR0X91q1IsU/CywpWw6oiAQEEEEDAZgIsxWmzCaE7COSjgCvOoM1R0a6+ZXL44//KcsSrLE5bZCOAAAIIpF4g/m/z1PeBFhBAIM8FaopiPbfUPILJofk7xyakVFCeUDEKIYAAAghkUIBT8BnEp2kEEDgscExpSUyKWZ4KVTTHDlKDFZhngbIhgEC4QEtLi1588cVAxtlnn63ycv61Fq5ESroECEDTJU07CCAQVeCY0lJVFRToUF/k54AmevTTbcWx5ZOiNkMGAnkrsHv3bpnncZaUlMjn86mjo0MrVqzQlCnW0yfYEMiAAKfgM4BOkwggMFDAPDNwWW3kU+yzPJWqPFQ8cIco32rmHV6SM0o2yQjkpUCf9Q+7ZcuWyWM9b7etrS0QfBqIz372s4G0wSgmQDVlE32Z8kPdhtqG6YvfH/8xbEPtB+UzJ8AR0MzZ0zICIxLwOGIEZVn4e3pxdZXebmnVpvaBz/5csCNyYDoYr8QqVn3s4NTUfG8/0sdrrrkmcEQpNa1kttZPfvKT+t73vpfZTtB6UgSamppUVlYWCjyDlR44cEAmr7a2NpgUeL/jjjv0yCOPDEiL98XlSvzuPxNI3nvvvYFXvHr753/5y1/W9ddf3z+Jz1ksQACaxZNH1xHINYFP1U3WL7Zu174jzwUNHP1sjhFoHwEorJSmLE3f0c85c+bowx/+sMx7UZwbqLJ1jsw1gmy5IWCCT3MEcfBmTsOXWpe/DN4uvvhizZtnnU5IcPvnP/+pv//97wmWPlzMXA5w+umnD2mfBQsWDKk8he0tQABq7/mhdwjklUCpdRTl+unT9Ifde7WutVULdsU/+mmu+Zx4puSOH6cmzbKwsFBPPvlk0uqjIgRSKWBuNrrxxhv105/+VD09PaGmvvKVr0S8EenUU0+VeSW6dXV1DSkANZfcnHDCCbriiisSbYJyOShAAJqDk8qQEMhmgRIrCP301CnacahL2lOsjiiDKbXOGlYfL1VOjVKAZAQQCAlcd911mjp1qh588EGZAPDTn/60LrjgglA+HxBItwABaLrFaQ+BIQjEvuY++pI/m4fQRv+idrofdmqVdUv7+YdXNuo6IPVZ8ahZCt5tnTEsrpEKws8c9h8KnxFAYJDAueeeK/NiQ8AOAgSgdpgF+oAAAlEFXNap9XI7RcZRe0oGAggggECiAjyGKVEpyiGAAAIIIIAAAggkRYAANCmMVIIAAggggAACCCCQqAABaKJSlEMAAQQQQAABBBBIigABaFIYqQQBBBBAAAEEEEAgUQFuQkpUinIIpFig23r4eqv17MtnnnkmxS1Fr948mNr0gQ0BBBAYqkBvb29ol/4Pvvd6veqfx5KaIaa8/kAAmtfTz+DtJLB161Zt375dZoWQTG6ZDIAzOW7aRgCB4Qk4nc7ASkvz58+PWMF3v/tdmVf/bShLd/bfj8+5I0AAmjtzyUiyXODNN9/U7t27M7q2+BlnnKFLLrkkyyXpPgIIpFPgX/7lXzR58mT5fL6EmjUB6+LFixMqS6HcFSAAzfK5nR6j/1tj5JFlP4Hq6mqZVyY3s8Sk282vhUzOAW0jkG0CZqnPZcuWZVu36W+GBbgJKcMTQPMIIIAAAggggEC+CRCA5tuMM14EEEAAAQQQQCDDAgSgGZ4Amkcg6QJ9ffL2WNdiWeumsyGAAAIIIGBHAS72suOs0CcEhiLg98u3c4e8WzbLv79e/p4e7S/9iDq6x6totFRhraM+erZUUDaUSimLAAIIIIBA6gQIQFNnS80IpFzAd/CAPC+9KP+hQ2Ft+a2DoN1Nh18H10o186SxJ0gOznuEWZGAAAIIIJBeAQLQ9HrTGgJJE/Bu3SLPiy9Yp9rjn2s3wejBd6TO/VLdcslZkLRuUBECCCCAAAJDFuBYyJDJ2AGBzAv49uxOOPjs31sTgO56zkqJH7P2343PCCCAAAIIJFWAADSpnFSGQOoFzDWenn+sinnk0xGjGx37rKOh62MUIAsBBBBAAIEUCxCAphiY6hFItoBv/Tr5u7pGVO3BtyVf34iqYGcEEEAAAQSGLUAAOmw6dkQgAwLW9Z7eTRvjNhzvDLsJPlu2xq2GAggggAACCKREgAA0JayRK3VY50WjvSLvQSoCAwX8TY0JHf2MdQo+WGPH3uAn3hFAAAEEEEivAAFoer1pDYERCfiawx+3FKnCeEdAzT49zZH2JA0BBBBAAIHUC/AYptQb0wICSRPw9/TGrMvndqqz3KWWApdcnTGLytMTO59cBBBAAAEEUiVAAJoqWepFIAUCjoKj/8n6nQ51lTnVXu5VU3G79rsPqLnYpW3F56nT16QLu0eptK0wai9cPAs0qg0ZCCCAAAKpFTj61yy17VA7AggkQaCvulxbpnWpwd2o/a798jv6nWwvO0nvOBbL43NZLXn07OydOn/dNBX1RP7PvLAyCR2iCgQQQAABBIYhEPkv0zAqYpeRCXgcxVErcPu7o+aRkV8CBVXj9G7ZZnm8R8+fOxxFOlR+rrb6pg54wHyTerTy+J065+1pcnrDL/cunZBfdowWAQQQQMA+AuF/lezTN3qCAAKDBFxOt6bWnBpKdRVO0pbyqw4Hn6HUox/2Orr00rzd1pHSo2mBT9b30dMHpfEVAQQQQACBNAlwBDRN0DSDQLIEjp90vnYcfE09JSdpjU6Wzzc4uhzY0mZ3m0qO26tT1k8MZVQfK7lLQ1/5gAACCEQU2L59ux599NGIefES3W63rrnmGlVVVcUrSn4eChCAZsGkz1RRjF4ePRUboxBZOSRQVlSjGTNu1f/b25LwqNYWH1L57ELNfX+MCkdJtScmvCsFEUAgjwWeeuopPfDAA5o8eXKYQmdnp1paDv8eqqioUHl5eaiM1+vV3r17ddxxx+mcc84JpfMBgaAAAWhQgncEskjgpJrJaugr0N8PHEy41y9X7FfVnAItmT9KTu6AT9iNggjku0BZWZlWrFgRxvCnP/1Jt99+eyD92muv1ec///lQmYaGBp155pnyW6u3sSEQSYBrQCOpkIZAFgicP36crpg8SSUuc9d7/O24ygotOrVchUcPUsTfiRIIIIAAAgikQIAjoClAjVZl7H8IRr/TfXO0CknPe4GTq0ZrbkW5Xmps0lvNzWrstRZ577cVOJ2aXV6mJTU1mmW9syGAAAIIIGAHAQJQO8wCfUBgBAJl1oX+546rDbxa+/p0qM+jPp9P5W6XxhYVyeWIfZPSCJpmVwQQQAABBIYlQAA6LDZ2QsCeApUFBTIvNgQQQAABBOwswDWgdp4d+oYAAggggAACCOSgAAFoDk4qQ0IAAQQQQAABBOwsQABq59mhbwgggAACCEQReOGFFwKPOpo/f77OPvtstbe3RylJMgL2EyAAtd+c0CMEEEAAAQRiCmzevFnXX3+9zPM2e3t7tX//fn34wx9WV1dXzP3IRMAuAjl7E1Kz9UiaL3/5y1q4cKEc3AWc9p+39957j+XX0q4+8ga7u7v1+uuv60c/+tHIK8uTGmqsR1xdffXVeTJahmkXgTvvvFNmtaHg5rOefGGCzxtvvFHTpk0LJo/4fe3atSOq449//KNee+21EdUR3NmstGT+rrPlhkDOBqBmhYaHHnpILush3QSg6f9h9Xg8ampqSn/DtDgigT7rMU4rV67UqlWrRlRPPu1cXV2tj33sYyopKcmnYTPWDAuYZTAHbyYg3bhxo3bs2DE4a9jfg0ttDreC9evXa9u2bcPdfcB+5r81AtABJFn9JWcD0Lq6usDEmB/+OXPmZPUkZWPnzakgE/yzZZeAOXPAhgAC9he47LLLZM409V/q0gSljz/+uCZNmpS0Adx///168MEHh12fOVJ77rnnDnt/dsxdAa4Bzd25ZWQIIIAAAjkqYC77OP/88wOjGz16tMzrN7/5TVKDzxylY1g2EcjZI6A28aUbCCCAAAIIpETAXKttrvlsbGzUrFmzuO4+JcpUmioBAtBUyVIvAggggAACKRaYMWOGzIsNgWwTIAC18Yz5zTPdrGt6ujVW7lKHCspt3Fm6hgACCCCAAAIIJChAAJogVLqK+dvb5F2/Tt7t1l2DR57ntsPxafn8brmKpMqpUvVxUtHodPWIdhBAAAEEEEAAgeQKEIAm13P4tfn98r6zRp6310jW89wibd4e6dD71muTVGMFobUnSw5uI4tERRoCCCCAAAII2FiAANQOk2MFnJ4Xnpd3x/aIvQk8R9/fL8v63LjeOkDaKNUtl5zMYj8cPiKAAAIIIICA3QU4fmaDGfK88nLU4DNW9zrrpb0vxipBHgIIIIAAAgggYD8Bjp1leE58e3bL+/6GYfei1VrwomWLNIqbIIdtyI4IIIAAAtEFenp69K1vfSuswJYt1h+fI9tzzz0XWJc++J016YMSvEcTIACNJpOmdM/qN0fcUsNq6+ak6db1oI4RV0UFCCCAAAIIhATmzp2ryZMn6x//+EcoLfjBrD8fXHXJPIt0cJmZM2cmdV36YLu854YAAWgG59FvLXvoP3hwxD3oM09rsk7Hl00YcVVUgAACCCCAQEjgQx/6kMyLDYFkC3ANaLJFh1Cfd++eIZSOXbRjX+x8chFAAAEEEEAAAbsIEIBmcibaWpPWem/yqkpan6gIAQQQQAABBBCIJMAp+EgqaUrz9/ZGbMlT7FKv9dD57iK/Ooslzx6H4l3e6euLWBWJCCCAAAIIIICA7QQIQDM4JZ1VbjVPtILMgh61uTvV7GwJvLwOT6hXveVn60QrAI03Uc7C0C58QAABBBBAAAEEbC0QL66xdeezvXMHxvr0env0u+Bd7kpt0LE6MYGBsjRnAkgUQQABBBBAAAFbCHANaAanYcJoaz3NGFtr8Qfk8btilDiaVT7p6Gc+IYAAAggggAACdhYgAM3g7JQV1Wj8qGMj9sBVMEbv+2cG8uJNUnG1VDImYjUkIoAAAggggAACthOIF9vYrsO51qGFdZdYNxiF32J0sHiJ/BHSI42/9uRIqaQhgAACCCCAAAL2FCAAzfC8VJfVaUHdRQN64S6aom2+KaE0f+hT+Idq6yw+p9/DXUhBAAEEEEAAAfsKEIDaYG6Om3i+jp14bqgnuws/EPpsPkQ6QmrSR8+Sxp1qPrEhgAACCCCAAALZI8Bd8DaZqxPqLlV12RS9Wb9ae321MXvlLLACz1Okqjkxi5GJAAIIIIAAAgjYUoAA1EbTUldzisZVnaTaQ816p6VVu7q61OfzBXrosI5VF9dIlVMPH/l0WQ+qZ0MAAQQQQAABBLJRgADUZrNW5HRqSU114GW61uHxqGiW9SB6E3CG36tks97THQQQQAABBBBAIL4AAWh8o4yWKHNbU8QsZXQOaBwBBBBAAAEEkivATUjJ9aQ2BBBAAAEEEEAAgTgCBKBxgMhGAAEEEEAAAQQQSK4AAWhyPakNAQQQQAABBBBAII4AVxfGASIbAQQQQAABBPJLwGc9gaazs3NIgy4uLpbb3LfBlpAAUgkxUQgBBBBAAAEE8kXgrrvu0u9///shDXfBggV65JFHhrRPPhcmAM3n2WfsCCCAAAIIIBAm0NraqunTp+v//t//OyDv/vvv1/r16wNpd955p8aOHRv4/Ic//EG7du0aUJYvsQUIQGP7kIsAAggggAACeShQVVWlc845Z8DI+x/hPOOMM1RXVxfIf/nllwlAB0jF/0IAGt+IEgggYAOB6TH6sDVGHlkIIIAAAvYT4C54+80JPUIAAQQQQAABBHJagAA0p6eXwSGAAAIIIIAAAvYTIAC135zQIwQQQAABBBBAIKcFCEBzenoZHAIIIIAAAgggYD8BbkKy35zQIwSyXsDhiD6EPhVHzXT7u6PmkYEAAgjkg0B3d7d+97vfyTwKas6cOXruuefU1NSkD33oQ7ryyitzhoAANGemkoEggAACCCCAQDYL9Pb2avny5Wpvb1dPT4/MikzBbdWqVXr44Yf19NNPB5Oy+p0ANKunj84jgAACCCCAQCSBtra2QCAXKS9eWldXl/x+f7xiA/K9Xq/27ds3IG0oX4qKinT77berublZfX19EXfdtm2bfvOb3+iqq66KmJ9NiQSg2TRb9BUBBDIm8MYbb2jRokVyWNcXmJfZbrrpJv3gBz/IWJ9oGAEEoguYVYt++ctfRi8QJ+ekk06KU2JgdkNDg84+++yBiUP4Zn6/1NfXRw0+g1W9/vrrBKBBDN4RQACBXBcwQac5InLjjTcGlugz4x3JH5tc92J8CGRa4LLLLtPJJ588rG48+OCDQ97PrJz0jW98Y8j7BXcw+993332BFZViHX0Nrr4U3C9b3zkCmq0zR78RQCCtAi6XK9De5ZdfriVLlqS1bRpDAIGhC8ycOVPmNZztsccekzmiOZStuLhYy5YtG8ouYWXvueeewPKfwX/whhWwEsyZl1zYCEBzYRYZAwI5IjBTRTFG0hMjjywEEEAg+wXM0c3XXntNd9xxR+Ba0Llz5+qFF14I3BF/6qmn6rvf/a6C/xjO9tESgGb7DNJ/BBBAAAEEEMgZgVGjRunHP/5xaDzmxqRc3HgQfS7OKmNCAAEEEEAAAQRsLEAAauPJoWsIIIAAAggggEAuChCA5uKsMiYEEEAAAQQQQMDGAgSgNp4cuoYAAggggAACCOSiAAFoLs4qY0IAAQQQQAABBGwswF3wNp4cuoYAAggggAACmREw67Lv2bNnQOPd3d2h72bVouAjkTo6OkLpfEhMgAA0MSdKIYAAAggggECeCBQUFGjt2rVaunRp1BFfffXVA/JmzZo14DtfYgsQgMb2IRcBBBBAAAEE8kzg3//933XNNdcMadS1tbVDKp/vhfMiAJ0eY5a3xsgjCwEEEEAAAQTyT8A8DH7+/Pn5N/A0jjgvAtA0etIUAghYAn5/LIaj11ANLrV5cALfEUAAAQRyUoC74HNyWhkUAggggAACCCBgXwECUPvODT1DAAEEEEAAAQRyUoAANCenlUEhgAACCCCAAAL2FSAAte/c0DMEEEAAAQQQQCAnBQhAc3JaGRQCCCCAAAIIIGBfAQJQ+84NPUMAAQQQQAABBHJSgMcw5eS0MigEpLvvvlv333+/pk+fDkcSBFpaWgK1bN26VUuWLElCjVSBAAII5K8AAWj+zj0jz3GBTZs2qampSWeeeWaOjzQ9wyssLAw0VFRUlJ4GaQUBBBDIYQEC0ByeXIaW3wKTJ09WVVWVfve73+U3RJJGv2bNGp144omaNGlSkmqkGgQQQCB/BQhA83fuGXmSBGKd4Gap1yQhUw0CCCCAQE4J5EwAOlODTostl2b4C/URLbAmrCenJo3BIIAAAggggAAC2SzAXfDZPHv0HQEEEEAAAQQQyEKBrDoC6nEURyf2R88iBwEE7Cvga2qUf/9++Tva1eKYJ39BqYprpNLxktNl337TMwQQQACB4QtkVQA6/GGyJwII2E3At3WLPG+vkb+lOdS1tuKZ6uwuDXx3FkijZ0tjratoXIOusAntwAcEEEAAgawUIADNymmj0whkr4C/p0eeVSvl27M75iB8fVLTeqnVupNr0llSmXVElA0BBBBAIDcECEBzYx4ZRQoFwm5wC2uLm9zCSKIk+Ht75fnbUzKn3RPdPF3SzmelumVWEDox0b0ohwACCCBgZwHbBaAOR3Qu64AIGwIpEeD64pSwhlXqefGFIQWfwQr8XmnX89aTLS6WCsqDqbwjgAACwxMwz/V96KGHhrVzWVmZ7rjjDgUXpxhWJewk2wWgzAkCCOSmgHfHdvl27Rz24Mwp+frXpClLh10FOyKAAAIBgVWrVulvf/ubTjjhhJBIW1ubNm/eHPg+duxYmcU8Bm9mSV5T5nOf+5zq6uoGZ/N9CAIEoEPAoigCCAxfwLv27eHvfGTPNit+7bHuWSoaPeKqqAABBPJcoLKyUr/97W9DCi+99JI+85nPBL4vX748cJQzlHnkgwlcr7vuusHJfB+GgHMY+7ALAgggMCQBf3ub/AcPDmmfaIVbd0TLIR0BBBBAIFsEbHcE1B/zeZ7dUV0PHzQ/mr1ixQqdc8452rBhg+bMOZrOJwQQSL+Ar6EhaY12Ja+qpPWJihBAAAEEhibAEdCheVEaAQSGI9DRMZy9Iu7Tl7yqItZPIgIIIIBA6gVsdwQ09UOmBQQQSLfAzlFN2jFrj/wOv/zyWf+z/t9v/b/53i+ttXS7Ju0u0pgDZVG7GPssSdTdyEAAAQQQsJEAAaiNJoOuIJCrAv6SQu1z7Ys7vIMFh/RW3XZNnVyuBbvHRAxEC0riVkMBBBBAAAGbCxCA2nyC6B4CuSBQVZrY40rc/t7AcHc427Wjrt0KRMt0wp5aVTccXp7TZBZZ68SzIYBAbgr4rVMcHdYlO263W8XFxbk5SEYVEOAaUH4QEEAg5QJVZZNVXjwmbjsFOhyABgvucHboL1O26bkTt6uptjOQXDk1mMs7AgjkksCBAwd02WWX6fTTT9fChQv1q1/9KpeGx1gGCRCADgLhKwIIpEZgzvilcSt2+SMvaxoMRN89rV6ltXGroQACCGSZQK+1TO8ZZ5yhd999Vz09h38P/OhHP9Jf//rXLBsJ3U1UwPan4C+99FJNmDBBFRUViY4pUG7Tpk2B9+AP8pB2pjACCCRdYGbtB7Wl4SU1d+6JWne0ANTsYFbpXVRXGXXfVGeYP5Bm+/nPf57yP4oTJ07UTTfdlOohUT8CthEwD3gvLy9Xe3t7qE/m77c5CnrRRRfpvvvu0/bt20N5I/1gYgRzun+427e+9S2Vlh69NGhwPeZB9hdeeOHgZL73E7B1ANrV1aU///nPqqqqCvxg9ut33I/BH+KDSXr4ddwGKYAAAjEFnE6Xzpj9OT2z7nvq9UR+lpLbYa23GeVvwkUTJ6guxi/8mI0nIdP8sSkqKtLzzz8fuD4tCVVGrYIANCoNGTkq4PP55HSGn5QNHkRqbW1VMv+ed3YevqRnuJxNTU2KVUesvOG2mWv72ToADWJ///vfDy2PFUyL9x58EP2kSZPiFSU/TwQc5hBalM0Ke9jSIFBRXKvlx92iFzb+TB09jWEtOn3WqbdB8+S0Ju7iCeN1ek11WPl0JsybN0/d3dEXw0hnX2gLgVwT+MAHPqDgWYbg2MxNSFdeeWXg65133hlMTsr7T37ykwHLcA61UnNElrXgh6o2sHz4PzcG5vMNAQQQSKrAqNIJ+vCCf9ecCcvkdAz8N7DDPzDAm15Wqi/NmJ7x4DOpAFSGAAJhAuYyuyeeeCKQbk7Fm+DzU5/6lK6++uqwsiTkhsDA3/65MSZGgUBEgdiX+wwMfPpXMHiZ1/55fB6eQIGrWCdN/ZiOn/Rh7W5arQNtWwNHRH3uMZpXVKmJxUWaN6pSE3gMy/CA2QuBLBSYPHmy1q5dq7179wYuuxszJv6TM7JwmHT5iAABKD8KCCCQMYEid5lm1J4ReGWsEzSMAAK2ESgsLNS0adNs0x86kjqB7A5A+3rlsy4E9nX1qtNRpwJr9b5i6zIxBxcWpO4nhpoRQAABBBBAAIERCmRlAOrbvUved9fLt2+vdcestZZ0YZl29x5eacVpjajCelC1v7NohDTsjgACCCCAAAIIIJAKgawKQHusR7fsWf+MJq1uimrh80gtW6zsPdN1Rt1lUR/pErUCMhBAAAEEEEAAAQRSKpA1J6u7elv0rPX8wFe7nlFbjSsuinnA7MmTzlPbW6Pl98UtTgEEEEAAAQQQQACBNAlkRQDql08vbPiZ2robAiyvjHlXPndiXe9tKFL9K2nSpBkEEEAAAQQQQACBuAJZcQq+1bFOpZ1H14huc7Tp/SmtmrutPO4ATYFD70uV06SyiQkVp1ASBDZu3Kjdu3eLx2gkAXOYVZiVQ7xe7zD3ZjcEEEAgtwXMiok33nhjaJCNjUcXxzBLg/bPCxZK5mpMwTrz9d32AajL6mGr8z2N1/QBc/Ru0QZNHLNYlQcT+wPbsEY6hgB0gGEqv9x8881avXq1pk617ghjy4jAY489pvfeey8jbdMoAgggYGeBU045RW+88Yaam5tD3TRLgZr04NY/L5jmdru1dOlSjR07NpjE+zAFbB+AjrHiF5+/N+LwXqlZp2Ut85TIyfgu6+x9X7tUkNhB04jtkZi4QKR/OSa+NyWTIWCWtTMPdGZDAAEEEBgocPrpp8u82DInkEjslrneWS2PHj9oYeh+vWl3dGjjlEP9UmJ/7NgXO59cBBBAAAEEEEAAgdQL2D4ALa6IjbCh8H01VftjFzqS29uWUDEKIYAAAggggAACCKRQwPYBqLkGNNLmkEOuwlo5yhbq+YnW8kcJbP7ELhdNoCaKIIAAAggggAACCAxXIEp4N9zqkr9f35Gb313uSvkLJqrbNV7NGqMG69XrP9x9h8OhhU6/nL7op+tNz1wsjpT8Ccrs9267AAA2EklEQVTBGq21tVTf8p6qt3fI2diiDvdstfvqVDT68JMUSmtzcNAMCQEEEEAAgTQK2D4A3dk0QxtKrlVn0cmHWSKcbTcBQ3dJn0o7CmPSFVXFzCYTAW098E+t3fW4OnsPaWnjIo0+4FNvxUS1Hrl844D1NAUTiI47VSqfBBgCCCCAAAIIDEfA9gFo+74udTrL4o6tp8xrBaDRizmtxZPKxkfPJye/BTzeHr20+Zfae2htCOKg9QM1WiWh78EPPdZTO3Y+K1UfK41fZKXGPvAe3I13BBBAAAEEEDgiYPtrQL1796jSF38tzc6iyI9qCs505XTJWRD8xjsCRwW8Po9WbvjPAcGnyd1buP9ooQifmqxHbO59KUIGSQgggAACCCAQU8D2Aajp/byerpiDMJldxZ6oZUzgOfbEqNlk5LnAmp1/0oG2zWEKB10H5S20Dp3H2Jqt3Q5tjFGALAQQQAABBBAIE8iKAHScx6MPjqkJ63z/hHZ3X/+vRz9bp0cnLrEeQF96NIlPCAQFmjv3aNP+lcGvYe/tlWFJYQn735CsM/hsCCCAAAIIIJCgQFYEoGYsF04Yr9Nroj9uqbMw/Aio1+dV+bzWwDrwCXpQLM8ENtY/J78/wp1tRxwayzrjivisf/s0b4pbjAIIIIAAAgggcEQgawJQ099LJ07QJ6dMVoW1Fuvgrd058Aioo6xHj6y/V0WT45++H1wX3/NDwASeu5us29pjbPuKYl8HGty1bWfwE+8IIIAAAgggEE8gPJKLt0eG808aPUrzKyu0pqVV61pbtbuzS21er7pcXhVYqyaZZzRWTrNuDtm4Tfvbt2W4tzRvZ4GOnoPq9cQ+wrnf2aC5Cdy81tUo60iqdUM8d8TbecrpGwIIIICATQSyLgA1bgVOp06tGh14DXCc1+8bN4b0w+BjJIGuvpZIyWFpjSXtin0rkhV8Wqts+awHMbDYQRgfCQgggAACCIQJZNUp+LDek4DASASiX/ppHcm0ls0qPUEHKj6uZ8bVyOuIUTjYB45+BiV4RwABBBBAIKZAVh4BjTkiMhFIUKB00NJYDodbjpJZanLN1g7/RHn91nFP8whaR7fWH9ugBe+Oi1qz0/ovyRV7Ia6o+5KBAAIIIIBAvgkQgObbjDPekEBZUY1KCker11mtloI5VtA5RT1+K4qMsO7BmyUHNWF8ucbWR16Vq2RsqFo+IIAAAggggEAcAU7BxwEiO7cF3OP/Ta+7LtD7vhmHg88Yw31+4m71FIU/7svsYm58Y0MAAQQQQACBxAQ4ApqYE6VyVOD0MeP1j6Y263R7/Gs8OxwevTJ3r856u26AhttaLn7UjAFJfEEAAQQQyBOBvr4+HThwIOYzpQ2Fw3pMyoQJEwLveUITc5gEoDF5yMx1gerCAi0bO0bPNBxIaKhb3W2aPKNJM7YcXRRhwmmSuQaUDQEEEEAg/wTuv/9+/exnP0to4N/5znd06aWXJlQ21wvxZzPXZ5jxxRVYXjtW9T09esd6tmwi2z9H12vs6FJVNhdrzAKpYmoie1EGAQQQQCAXBbq7u1VTU6Nf/OIXoeF99rOfVUtLi0pLS/XQQw/Jaz2v/IorrlBXF4vjBJEIQIMSvOetgDktcpW1wla5u17/bGyK6+CRXy/O2KPPOGeo5vi4xSmAAAIIIJDjAoWFhVqwwDoicWRzH1mx0Wk9t9ykmwCUbaAAAehAD77lqYDTCkLNUq/zrFW2nt7foJ3WClvRtrkV5bpg/DjVFEcrQToCCCCAAAIIxBIgAI2lQ17eCcwqL5d5NXT3aHNHhw709KrX51Wxy6XxxUWaXV6hUQX8Z5N3PxgMGAEEEEAgqQL8JU0qJ5XlikCtFWyaFxsCCCCAAAIIJF+A54Am35QaEUAAAQQQQAABBGIIEIDGwCELAQQQQAABBBBAIPkCnIJPvik1IoAAAggggEAWCJhHJZnHJzU2NurMM8/UhRdeaKteNzU1Bfp36NAhLV++XOedd56t+jeSzhCAjkSPfRFAAAEEEEAgKwXM8zvPPvtsmZWMzOsvf/mLVq5cqR/84Ae2GE9ra6s+9KEPyePxBF5//etfdc011+jrX/+6Lfo30k4QgI5UkP0RsKmA31pe1Dx7rqGhwaY9tFe3ysrKZF5sCCCQHwK33HKLeqxFSPo/o/P555/XqlWrNGrUqIQRzDKciW579uzR22+/nVBxE2ia/pnf5cHt0Ucf1cUXX6x58+YFk7L2nQA0a6eOjiMQW+Ctt94KBJ/jxo2LXZDcgEBVVZXM6S42BBDID4GdO3cOCD7NqE2w99prr+mBBx4YEsL48eMTKv/ggw/KvIa7uaxHAu7atYsAdLiA7IcAAqkX+OlPf6r//d//1THHHJP6xnKghUT/gOTAUBkCAghYAjNmzNCWLVsGBKGdnZ1avHjxkK61fPjhh/X6668nZHrdddclXPe3v/1trV69esARUHNafurU3Fj/mSOgCf3IUAiB7BOYNWuWvvrVr2Zfx+kxAgggkAaBu+66S0899ZQKCgoC14Cad3Ojj7kZaSjb2LFjEy4+ceJEzZ8/P6HyP/7xjwN9Mct6mutAzdHPyy+/XMcdd1xC+9u9EAGo3WeI/iGAAAIIIIBA0gVGjx6td955Rz/5yU9k7jJfsmSJPvKRjyS9neFWWFtbGzgCas5mNTc3a+nSpTrnnHOGW53t9iMAtd2U0CEEEEAAAQQQSIdAUVGRbr311nQ0Naw2SktLddtttw1rX7vvxIPo7T5D9A8BBBBAAAEEEMgxAQLQHJtQhoMAAggggAACCNhdgADU7jNE/xBAAAEEEEAAgRwTIADNsQllOAgggAACCCCAgN0FCEDtPkP0DwEEEEAAAQQQyDEB7oLPsQllOAgggAACCCCQXoHe3t7AI5OCrZrndprNLPNpHibv8/mCWbwfESAA5UcBAQQQQAABBBAYpkBJSYkaGxt1xRVXhNXQ1dU1IN08VontsAABKD8JCCCAAAIIIIDAMAWuv/56fexjHxuwZGakqpxOp8aNGxcpKy/TCEDzctoZNAIIIIAAAggkQ8AslTlhwoRkVJVXdXATUl5NN4NFAAEEEEAAAQQyL0AAmvk5oAcIIIAAAggggEBeCRCA5tV0M1gEEEAAAQQQQCDzAgSgmZ8DeoAAAggggAACCOSVAAFoXk03g0UAAQQQQAABBDIvQACa+TmgBwgggAACCCCAQF4JEIDm1XQzWAQQQAABBBBAIPMCBKCZnwN6gAACCCCAAAII5JUAAWheTTeDRQABBBBAAAEEMi9AAJr5OaAHCCCAAAIIIIBAXgkQgObVdDNYBBBAAAEEEEAg8wIEoJmfA3qAAAIIIIAAAgjklQABaF5NN4NFAAEEEEAAAQQyL0AAmvk5oAcIIIAAAggggEBeCRCA5tV0M1gEEEAAAQQQQCDzAgSgmZ8DeoAAAggggAACCOSVAAFoXk03g0UAAQQQQAABBDIv4M58F+gBAggggAACCGRKYNeuXfrGN76h3t7eUBd8Pp/8fn/gu9PplMPhCOUtWbJEX/ziF0Pf+YDAcAQ4AjocNfZBAAEEEEAgRwQ2bdqkl19+WeXl5aqtrQ289uzZozVr1gReZWVlofT6+nqtWLEiR0bOMDIpwBHQTOrTNgIIIIAAAjYRuOuuuzRx4sRAb8wRzmCgefvtt2vGjBmBdPN5w4YNNukx3chmAY6AZvPs0XcEEEAAAQQQQCALBQhAs3DS6DICCCCAAAIIIJDNAgSg2Tx79B0BBBBAAAEEEMhCAQLQLJw0uowAAggggAACCGSzAAFoNs8efUcAAQQQQAABBLJQgAA0CyeNLiOAAAII5KeAeTaneW6neRwSGwLZLMBjmLJ59ug7AggggEDeCLS0tOjaa6/Vzp071dHRodmzZ+vRRx+V282f8rz5IcihgXIENIcmk6EggAACCOSmgDnyuWjRIq1fv15tbW0yKxVt3LhRd955Z24OmFHlvEDO/rPJ/EvRbLfeeqtGjRqVtomsrKzUfffdp8LCwrS1SUMIIIAAArktsGXLFpkVicyRz+BmgtInn3xS99xzTzAp8L5371595StfGZAW60tzc3Os7LA8s0rSlVdeGZYeKcH046STToqURVqeC+RsAGpWc6iqqpL5D3H//v1pm2YTgJr1dAlA00ZOQwgggEDOC5i12COdau/r6wsbu8vl0rhx48LSk5Vg+pFo/UVFRclqlnpyTCBnA9APfOADampqyrHpYjgIIIAAAvkoYJbCNNd8rl69Wh6PJ0BggruLLroojMMEh+ZMXKLbc889pzfffDPR4oHgcyj1J1wxBfNKgGtA82q6GSwCCCCAQLYK/OpXv9LYsWNljnCWlJQEToPffffd2Toc+p3nAjl7BDTP55XhI4AAAgjkmIC5tGvlypWBy7zM0LjUK8cmOM+GQwCaZxPOcBFAAAEEsluAwDO754/eHxbgFDw/CQgggAACCCCAAAJpFSAATSs3jSGAAAIIIIAAAggQgPIzgAACCCCAAAIIIJBWAQLQtHLTGAIIIIAAAggggAABKD8DCCCAAAIIIIAAAmkV4C74tHLTGAIIIIAAAvYUuOGGG0KPdtq+fbuczsPHqG655RYVFxcHOr1jxw6NHz/engOgV1klQACaVdNFZxFAAAEEEEiuwLx583TxxReHVlgytZvlrCNtJn3RokWRskhDYEgCBKBD4qIwAggggAACuSVQW1ur733ve7k1KEZjewGuAbX9FNFBBBBAAAEEEEAgtwRy5gjo9BjzsjVGHlkIIIAAAggggAAC6RXgCGh6vWkNAQQQQAABBBDIewEC0Lz/EQAAAQQQQAABBBBIrwABaHq9aQ0BBBBAAAEEEMh7AQLQvP8RAAABBBBAAAEEEEivAAFoer1pDQEEEEAAAQQQyHsBAtC8/xEAAAEEEEAAAQQQSK8AAWh6vWkNAQQQQAABBBDIewEC0Lz/EQAAAQQQQAABBBBIrwABaHq9aQ0BBBBAAAEEEMh7AQLQvP8RAAABBBBAAAEEEEivAAFoer1pDQEEEEAAAQQQyHsBAtC8/xEAAAEEEEAAAQQQSK8AAWh6vWkNAQQQQAABBBDIewEC0Lz/EQAAAQQQQAABBBBIrwABaHq9aQ0BBBBAAAEEEMh7AXfeCwCAAAIIIIBAjgvs2rVLhw4dSmiU06dPV3l5eUJlKYTAcAUIQIcrx34IIIAAAghkicBll12m1tbWhHp73XXX6Stf+UpCZSmEwHAF0n4K3uGQor2GOwj2QwABBBBAAIHoAh6PRyawXLlyZeBlPge3r33ta6H08ePHy5RlQyDVArY6AupxFA8Yb4H1rU9F0v/5N3ms11y/f0D+wC89A7/yDQEEEEAAAQRCAhUVFZowYULgu/kc3EaPHh1Kd7ttFRYEu8h7Dgrwk5aDk8qQkiswPUZ1W2PkkYUAAggggAACkQXSfgo+cjdIRQABBBBAAAEEEMgXgbQfAY15Fl3dA9y7urpUWlqqX/7yl/rMZz6jzQNy+YIAAggggAACCCCQjQIcAc3GWaPPCCCAAAIIIIBAFgsQgGbx5NF1BBBAAAEEEEAgGwXSfgo+G5HoMwIIIIAAAqkQMI88+s///E+tXr1akyZN0p133qmiIuvpL2wI5LgAAWiOTzDDQwABBBCwr8DixYvV29sbeLlcLj366KN69dVXZR6NxIZALgsQgOby7DK2rBN4+eWXAzfeZV3HI3R4xowZLOcXwYUkBIICf/jDH9Td3R168LvX65V5Due9996re+65J1gs9N7X1xd4YHwoYQgffD5fQqX91p3CO3bs0LPPPhu3/KhRo7Ro0aK45SiAQCQBAtBIKqQhkAGBgwcP6vTTT89Ay6lp8gtf+ILuv//+1FROrQjkgEBDQ4NM0Nl/M6fkzbrtkbaOjg7dcMMNkbLipg3lAfPPPfeczCveNn/+fP3xj3+MV4x8BCIK2DoADf6HaX7AzWOY2BCIJ2CWeY22xX4EWLS90p9+1113admyZelvOMktHnfccUmukeoQyC2BY489NnC9pzkKGtwc1i+xU089Nfh1wLs54rhq1aoBaYl+Oe+88xItqssvvzyhQLegwKxXyIbA8ARsHYCa62HMNmvWrOGNjr0QyEKBuXPn6owzzsjCntNlBBAYioD5h+bSpUv15JNPyul0qri4OHAJzo033hixGhOcjhs3LmJevESzbyKbKVdWVjbsdhJpgzIIGAFbB6DBKVq4cGHwI+8IDFvA4yiOuq/bf/QIRNRCZCCAAAJJFrjvvvt04YUX6v3331d1dbU+/vGPJ7kFqkPAngJZEYDak45eIYAAAgggMHIBcxTUvNgQyCcBHkSfT7PNWBFAAAEEEEAAARsIEIDaYBLoAgIIIIAAAgggkE8Ctj4F7z/UpKUTxmva/n1q/ec2tfuPUdEoqWyiVFydT9PEWBFAAAEEEEAAgdwRsGUA6tu7R54335B/f70umVanmpZmeeqb1dx6FL64Rhp38uFg9GgqnxBAAAEEEEAAAQTsLmCvANR6UKPntVfkfe/duG7djdKOZ6SqudKExVbxxJ4wEbdeCiCAAAIIIIAAAgikVsBWAejbm/6gGdu7NZRH2x7aIHl7pMlnpRaK2nNbYKaKYgzQ+gFjQwABBLJcYNOmTXrmGevIjbWZz8Ft3bp1gWd/mu+dnZ3BZN4RSKmAbQLQDftW6N2mldo9ZZTO2nWsCro8CQ+8dZvUOEaqOT7hXSiIAAIIIIBA3ghUVlbqscceC7wGD/rXv/61zCu4mRWX2BBItYAtAtDuvla9s+uvgbG2Olv0wpR3A0Gooy3xILThLWnUdOvJ+iWpJqN+BBBAAAEEsktgxYoV6u3tTajTZiUkNgRSLWCLAPT9+hfk9fWFxtrqbLWC0Pe0ZFviS3D6vVKTdTq+9sRQNXxAAAEEEEAAAUvArNvO2u38KNhJwBYB6O7W7WEmJghdNWWjWnyJX3/XZlVDABpGmVcJ1n1sMbboy21ujrEXWQgggAACCCCQXIGMP4i+2+vT876leq/sOjVXXCJ/+SK5Cq0HfVq3tbc4m7WqboO6E7wrqadF6ncgNblS1IYAAggggAACCCCQFIGMHwFt7Tt86r3Lugt5q2+yNSjrVbBIhYUejfHu1Xat1HPTDmlyYWLj7euQikYnVpZSCCCAAAIIIIAAAukXyHgAGm3IvX63Gn118vUuV/Ghq9RYuFC27Wy0QZCOAAIIIIAAAgggECaQ8Ziu0rowuv9W6yvWMV2VmnigQuUNbrU3nKipB8fIPSaxqwUKuHmvPyefEUAAAQQQQAAB2wlkPAAtdjl1krtalQeKNb6+XCWdRwNSjz/xxzAZ2ULr0WXOo7vbDpsOIYAAAggggAACCFiPzbQDwvLuCTqwdeQ9GTVt5HVQAwIIIIAAAggggEBqBWwRgI6eIx1cJ5lneQ53c7gkUw+bfQWqq6vV3t4ut9sWP3a2g/L5fIE+Pffcc/rEJz5hu/7RIQQQQAABBJIlYItIoKD08PM7978x/GGZ53+aetjsK+B0OnX66afrggsusG8nM9izjo4OffOb39SCBQsy2AuaRgABBBBAIPUCtghAzTBr5kldjZJZ132oW+Uxh/cf6n6UT69ASUmJTjvtNH31q19Nb8NZ0trBgwcDAeiYMWOypMd0EwEEEEAAgeEJ2CYANd2f/EFpX5F0yFpSM9Gtaq40YXGipSlnF4HpMTqShMuBY9ROFgIIIIAAAghkWsBWAai1+JEmnCZVTJEa3pLa90fnKa6xTtufJJVPil6GHAQQQAABBBBAAAH7CdgrAD3iY4JK8zq0x6N/3PknnTZqriqmzpXLesSSWeWodIJUYgWgbAgggAACCCCAAALZJ2DLADTIWFzt15t7/6bOcZdryoes8/NsCCRJwN/ZKd/O7fIfOCCfdWd+veM8uUrcMkfWK6Zaz5StSFJDVIMAAggggAACYQK2DkDDeksCAiMV6O5S3+uvybfNutL0yGOPTJVdLr+81mPAWqyb4MzTGCrqpPGLrCcrlI+0QfZHAAEEEEAAgcECBKCDRfieswK+/fXqe/7vUnd33DG27ZQ69kmTzjwcjMbdgQIIIIAAAgggkLBAYgusJ1wdBRGwp4C/oUF9z/wtoeAzOAJfn7TrealtVzCFdwQQQAABBBBIhgBHQJOhSB22FvD39KhvpXXk0+sZej/90p5V0oyLOR0/dDz2QACBVAs8++yzuvfee+X3W7+sjmxmVTWvuabI2lwul8wiIIM3k3bnnXcGFgcZnMd3BNIhQACaDmXayKiA9+01MjcdDXczR0Ib3rROx5813BrYDwEEEEiNwIYNG7Rv3z5dccUVoQbeeecdrVmzJvB9yZIlmjFjRigv+OF//ud/tHHjRgLQIAjvaRcgAE07efY16LCezxpt6/eP7mhFMpvu8ci3aeOI+9C6XRp3quRmudcRW1IBAggkV6C0tFRf//rXQ5Xef//9oQD0oosu0iWXXBLKC374zW9+E/zIOwIZESAAzQh77jTqcRRHHYzbH/9mn6g7JynDu2e3/H3WIcwRbibQNjcmmZW32BBAAAEEEEBgZALhF4aMrD72RsBeAk2NSetPV/KqSlqfqAgBBBBAAIFsFOAIaDbOGn1OWMDf2RW1rN9cWuByymfeA/8XtWggwzP8y0hjV0wuAggggAACeSZAAJpnE55vw91Ue1C7HdvlcXjk8XvkdXrV5++T1+GV3/qf2RyOQk069AnV1Mf5z4HzBfn248N4EUAAAQRSJBDnL26KWqVaBNIk4Cst1EHnwcOtBW+mCr4f6YOjoEY7xrRYAWjsO4wKYmenaUQ0gwACCCCAQPYLcEwn++eQEcQQqCmfHiP3cJbfPUabi1qtQ6FHn6MXaafS2kippCGAAAIIIIDAUAUIQIcqRvmsEhhXOVvFBZUx+9zrrFKHPDpYG/0iT6dLKp8SsxoyEUAAAQQQQCBBAQLQBKEolp0CDodTx05cHrPznY7Rgfzt1mn4aNvoOdb9SoXRcklHAAEEEEAAgaEIcA3oULQom5UCs8ct1faDr+tQR+RF3Vs0KjCurSVtOsVcHzroTHxBuVR7YuqH7rEemm+2u+++Ww888EDqG6SFiALnnHOObrvttoh5JCKQaoHGxkZdfvnlCTfT2tpq3Ug56ML2BPc2D6z/9a9/nWDpgcU+97nP6ROf+MTARL4hMAQBAtAhYFE0OwWc1vnzM2d/Qc+uv1ddvc2DBuHQIb8VYVpbh3WX/IHaDo3dXxYq4yqS6pZJzoJQUso+VFdXa86cOZowYYLKyo72IWUNUnFEAbOqDBsCmRIoKirS8uWxz9r075tZcnPbtm39kxL+PHv2bB1//PEJl+9fcOrUqf2/8hmBIQsQgA6ZjB2yUaCsqFrnHH+r/vH+f6mpY2doCE73aHn91gWeR7YdY1tDAWihdenolKVS0eEz9MEiKXsvLCyUWdeZDQEE8legvLxct99+e8IAP/3pT7V9+/aEy/cvuGzZMl177bX9k/iMQNoECEDTRp1/Dc2Udfiw31ZknQH/o+4LvKSefjnp+WiC0HPn3ab361dqw74V6uw9JEfBmAGNb7Huhl9cMkHV1pKbNfOsI59HY9MB5fiCAAIIIIAAAsMX+P/bu/vYuKo7jePPjGf8/hI7NrExzovjhJCAAyTdFlC0kAJbSOiqVK3UwrJbQChsECgFIUGlqq1AohUgBFIFkejuP12VF5FsC0kRQrSQkqDQkKQkS14dExKS2InjOPbYnre9d2Am9tjjmbF9Z+698z1oNPflnHPP+ZwEfty59xwC0MnbFUxJcx301Cn1eu8Hkwq1tLTojjvu0FNPPZV0Jne75ktJlzat1MLGG3S6v0PHBvpVGp4Za0C1z6dLysrUuiQqr3dyz1TlridcCQEEEEAAAecKEIA6d+xo+RQEzIf26405QuuNxz+XTqEeiiKAAAIIIIBA9gJMw5S9GSUQQAABBBBAAAEEpiBAADoFPIoigAACCCCAAAIIZC9AAJq9GSUQQAABBBBAAAEEpiBAADoFPIoigAACCCCAAAIIZC/AS0jZm1HCEOjs7MzaIRgMKhKJZF2OAggggAACCCDgLgECUHeNZ056EzXmZZo7d+6krrVhwwb95je/mVRZCiGAAAIIjBYwZ/To7e3VypUrEyf6+/vlM6aVM9Ovf/1rPf/884lz8Y1wODzpJTzjdfCNwFQECECnolegZeN3Me+//37deuutGSvceeedWS0xl3HFZEQAAQQKVGDVqlUyg8n4v5czZfB6vbrpppsyzU4+BKZdgAB02kkLp8L29natXr064w5XVVWppqYm4/xkRAABBBCYWGDevHl66KGHJs7EWQRsKMBLSDYcFJqEAAIIIIAAAgi4WYAA1M2jS98QQAABBBBAAAEbChCA2nBQaBICCCCAAAIIIOBmAZ4BdfPo5rhvkZMnFOnoUKTrlDTQr6Pe2yV/qcrqpao5ksf4h4QAAggggAACCBCA8mdgygLRnjMKbv2boqeMwHNECht/ukL90tBZ6exB6V9bf6ryEPOAjiBiEwEEEEAAgYIUIAC16bC3TtCuwxOcy/WpSMdhBbe8L2MekLSXri5uUEt/m7p2Sg1Xps1OBgQQQAABBBBwqQAB6DQMrDEPcMpkzNnu2lRr/MwefP8vUpadNANQT5FUf4VraegYAggggAACCEwgwEtIE+BwKrVAtd+v+adOZB18xmvs2iEFuuJ7fCOAAAIIIIBAIQkQgBbSaE9jX29taVbRFNZ1N2+antg+jQ2iKgQQQAABBBBwjAA/wVs8VCFPacor+KKDKc/Z+kQwqG80GK+2TzEFjHeWBk9LpTOnWBHFEUAAAQQQQMBRAtwBddRw2aOxkePH5DfWEZ6O1Hd0OmqhDgQQQAABBBBwksD0RBFO6jFtnbJAtKdnynXEKxicvqriVfKNAAIIIIAAAjYX4Cd4mw+QLZs3lPrRgajXo3CxV6FijyKB9FMAhFNXZcuu0ygEEEAAAQQQmLoAAejUDQuuhnPVEe3wHVPtRcbty9ZzGigKaMAzoPOefg16LkSUC07foPKu2RP6mNMxkRBAAAEEEECgsAQIQAtrvKelt911IR1p+1L76g6pqLgkZZ391afSBqDFVSmLcwIBBBBAAAEEXCrAM6AuHVgru9VQ1ZZR9efL00/0WT4ro6rIhAACCCCAAAIuEiAAddFg5qorM8qbdf5M+uc7+32j14ZPbp/XL1W1JB9lHwEEEEAAAQTcLsBP8G4fYYv617nTCEC/O3HlUU+v+quHVHFu/J/pZy6RzCCUhAACCCAwViAcDuv9999X0Jh7OZPU2Nio9vb2TLKSB4G8CxCA5n0InNmArk6pLDLx7UtPuFen6wLjBqAltawF78yRp9UIIJArgU8++URr1qzJ+HK1tbXatm1bxvnJiEA+BfgJPo/6bSpRqk8em5XxpWeEvqn6qtaU+aPBs+qqDIw5X1wpzf62xBvwY2g4gAACCCQEouaaxUbauHGj9u7dm/hceeWViTwffvhh7PjatWsVz584yQYCNhYgALXx4Ni9aV75dcOihzSv4VvjNjUSGVB3ce+ocwO+Ls1dJfmNIJSEAAIIIJBewGusPFdUVJT4eDyeRKH4uZHHEifZQMDGAgSgNh4cJzTNV1Ssb83/d3178To11SyWx/hnZBr2nlHIH1GZsXT8+8f+R0crtshXNjIH2wgggAACCCBQaAI8A1poI25Rfy+qXijzMxwa0On+IxoYOhP7OegbpfPVssCjEuM9pCP379Z1usKiFlAtAggggAACCDhFgADUKSPlkHYW+8pjd0Id0lyaiQACCCCAAAJ5EOAn+Dygc0kEEEAAAQQQQKCQBQhAC3n06TsCCCCAAAIIIJAHAQLQPKBzSQQQQAABBBBAoJAFeAZ0Gkb/66naUtQ0mOK4dDDlGU4ggAACCCCAAALuFSAAde/Y2q5n5nJyhw4d0ubNm23XNrs2yO/36/rrr5fPx19Vu44R7UIgncArr7yiUCiULtuY852dxpJzWSTzGr///e+zKPFV1jvuuCPrMhRAYKoC/FdtqoKUz1jg7Nmzev3112OfjAuRUe+9914sCIUCAQScKfDEE09oeHjY8sab1/jVr36V9XUIQLMmo8A0CBCATgMiVWQmcODAAQ0ODsZW88isBLnMO5+zZ88GAgEEHCywe/fuSS2TuX37dt11110Z97y8vFy7du3KOD8ZEcinAAFoPvUL7NotLS0F1mO6iwACCEjmMpmTWSrTXGYz2zSZMtleg/wITIdA9n+6p+Oq1IEAAggggAACCCBQsAIEoAU79HQcAQQQQAABBBDIj4CjfoJfunSpPv30U54hzM+flcRVI5FIbHvTpk1as2ZN4jgbCCCAAAIIIIBAJgKOCkArKyu1cOFC3XfffZn0jTwWCZgB6COPPKJly5ZZdAWqRQABBBBAAAE3CzgqAJ0xY4Zqamq0bt06N4+J7fsWDodjAeisWbNs31YaiAACCCCAAAL2E+AZUPuNCS1CAAEEEEAAAQRcLUAA6urhpXMIIIAAAggggID9BBz1E7z9+GgRAggggAAC1gq8+eabMielj6eurq74pl577TWZE9Cbk92TEHCSAAFoLkfLWAUo3HlE0VMnFT1/Xl3eFQr7qlU6U6oy5mg3v0kIIIAAAgiYAnV1dfL7/Vq/fn1KkGeeeSZx7rLLLktss4GA3QUIQHMxQsb6vKEdf1d4/2fS11MYmZcdKg0rMCj1HZW6dkplF0lN3yQQzcWQcA0EEEDA7gLz58+PTT1o93bSPgQmI0AAOhm1LMpEes4o9O47sTue6YoFTkkdb0mN/yTVLkqXm/MIIIAAAggggIAzBQhALRy3aG+vgps3ScNDGV8laszx/uW2r26UzlyccTEyIoAAAggggAACjhHgLXirhsr4qT34l3ezCj5HNuWk8bz5wIXnzEeeYhsBBBBAAAEEEHC0gKvugLZOMBSHJzhnxanwZ3sV7emZfNVR6cRHUuvqyVdBSQQQQAABBBBAwI4C3AG1aFTCe/dMuebBbuMuqPFcKAkBBBBAAAEEEHCTAAGoBaMZPX06o5eOMrl03+eZ5CIPAggggAACCCDgHAECUAvGKnLauHU5TSkwfVVNU4uoBgEEEEAAAQQQmJoAAejU/MYtHQ0Exj2efDDqNV55T5PCmVWVphZOI4AAAggggAAC9hFw1UtIdmH1eD0pmzJc6dOJ2oAOlnXqTPUWlfZdq7YOY7WL4aLxy/C/COO7cBQBBBBAAAEEHCtAAGrB0HkqKkfVGiwtUvfMoA5XfKGT3gtvFfmHd2hbZZt2XNGtKwJ1urRjpkoCo4fEXzGqKnYQQAABBBBAAAHHC4yOdhzfHXt0wNNwkcJ+r87MjKij8oS+8H0xbsPCw6d0cUmXjkca9Peybu1afFqXDc3Q4s56lfcVx8qUG8tzkhBAAAEEEEAAATcJEIBaMJqeqir9bWmXus93pK29MbRHx73Xx/KFFNU/Snr06cIeLQzW6PKj9WqbW5q2DjIggAACCCCAAAJOEuAJQ4tGa9HFN2VUs2fwM5Vp9FKdxhz02ufv1YGrulRcnVE1ZEIAAQQQQAABBBwjQABq0VC11F2l5tr2tLVHoyG1eg+OyVdeVKTvNjWNOc4BBBBAAAEEEEDA6QIEoBaO4Lfm/4dmlF+S9gplgZ2j8vg8Hv3b7BbV+HlCYhQMOwgggAACCCDgCgECUAuHsdhXphuX/FQXz7h8wqtEQj2a7T0Ry1Pj92tN6zy1VfL6+4RonEQAAQQQQAABxwpwi83iofMXlemfF63V56c/1qfHNqt34Pi4V2yO7tf8hst1Q0O9yoyf30kIIIAAAggggIBbBQhAczSys2cul/k5O3BM3X2H1D90RpFoWKX+KtVWtKihaoGKvAxHjoaDyyCAAAIIIIBAHgVsGfGEPF9NPeQ3YIIqke75T4WMz/8a+4uiUbWZx8ZNo98mHzdLng/OKG82ngttznMruDwCCCCAQL4EHn/8cb311lujLh+JXFia2WO8B2B+4un73/++fv7zn8d3+UbAFQK2DEBdIUsnEEAAAQQQGEego6NDc+bM0erVqxNnN2/erL1798b2f/zjH6vp61lQNmzYoCNHjiTysYGAWwQIQN0ykvQDAQQQQMAxAvPnz9d9992XaO/BgwcTAej3vvc9tbd/NY3fjh07NDw8nMjHBgJuEeAteLeMJP1AAAEEEEAAAQQcIpC3O6AjHm8ZQxUcc4QDCCCAAAIIIIAAAm4R4A6oW0aSfiCAAAIIIIAAAg4RIAB1yEDRTAQQQAABBBBAwC0CefsJ3phNaYI0GDsXCARUXl6ul19+WXfffbdWrVqlhbdGtWnTpgnKcgoBBBBAAAEEEEDAzgJ5C0DtjELbEEAAAQQKW6C7u1vbtm2L3QRZuXKlozE+/vhjdXV1ad68eVq0aJGj+0Lj3SNAAOqesaQnCCCAAALTILBr1y7dddddKi4ujk2BNDg4qJ07d6qsrGwaas9tFQ8//LDee+89FRlLPJ87d05r167Vgw8+mNtGcDUExhHgGdBxUDiEAAIIIFCYAj09PfrhD38oM+g0Azbz2wzeHnvsMceBvPbaa3rzzTfV398f64vZgRdffFFbt251XF9osPsEuAPqvjHNWY9++ctf6oUXXsjZ9bK50HXXXaf169dnU4S8CCCAgPbs2aOamhr19vYmNMLhsD744IPE/siNP//5z3r00UdHHkq7HTVegmhsbEybL57BfBQgPjF9/Fgm3yOX94znN4+Zfbnmmmvih/hGIC8CBKB5YXf2Rc27ATfffLMaGhpUXV1ty85cffXVtmwXjUIAAXsLVFRUaLzALVWrFyxYkPVP2n/4wx9SVTfu8ZaWFv3gBz8Y99xEB83g2AyoR/bH7/fHAuyJynEOgVwIOCIAPXr0qHbv3i3zp5GSkpJcuHCNNAJvv/12mhycRgABBJwncNVVV2nZsmWxn6mHhoZiHfD5fCl/7TGX1DQ/2aR33303m+xqbm7Wvffem1UZM7M5c8yNN94o846r+TFvHpjLev7kJz/Jui4KIDDdArZ+BtT8y2KmX/ziF1q6dGnsXwgfffTRdBtQHwIIIIAAAgmBl156SbfddptmzZqltra22HOT1157beK8Uzaampr017/+VQsXLtQll1wi821+87+h5stVJATyLWDrO6DmXxJzzs/S0lKZ/we6bt06R76FmO9B5voIIIAAAtkJPPnkk9kVsGnu+vp6/fGPf7Rp62hWIQvYOgA1B+aWW25JjI/5f6PmzwgkBBBAAAEEEEAAAecK2PoneOey0nIEEEAAAQQQQACBVAIEoKlkOI4AAggggAACCCBgiQABqCWsVIoAAggggAACCCCQSsD2z4BGjh9XpOOwIqdO6O7KMnVEvqMDr0olM6WqFqmmVfLavhep+DmOAAIIIIAAAggUnoBtQ7ee/i+09/BGLdtZLk/4qxePzEmZPB6PggOKfc4flU7tkGYtl2a0Fd7g0WMEEEAAAQQQQMCJArYMQD8//XdtPfjfikRDar7oajV9mfpJgfCgdHyLNHBSuticps3jxGGgzQgggAAChSJg3kjZvHmz3nnnnUSXzeU+zeNm+tGPfpTYDoVCMpcWJiHgNgHbBaBfnt2jDw+8LGPdhpj1zqr/U2PXFfKEIhPanz1g3h2Vmpw3V/CE/eIkAggggIC7BH72s59p3759GXdq8eLFGeclIwJOEbBVADoUPK8PD/5XIvg0EQPegI7PGlbzsfRN7dkvVTYbz4bOcQo/7UQAAQQQKDSBJUuWyPyQEChkgdS/bedBZe/xtzUc6h9z5V2Vnyniz6ypJz82ijNX/RhDDiCAAAIIIIAAAnYRyCyqy0Frw5GQDp0yHuYcJw16BnWscXicM2MPDfdJfV+MPc4RBBBAAAEEEEAAAXsI2CYA7e47pKD5RlGKtKtirwY9Ez8HGi96/lh8i28EEEAAAQQQQAABuwnYJgA9OzBx1DisYX1W3ZWR31BPRtnIhAACCCCAAAIIIJAHgfRv9uSoUac9zeqrXqUiY+olbzQsr4LyRIPGt7EvY3oKY/sfdXu0vNeftkXhobRZyIAAAggggAACCCCQJwHbBKCD3jodCM9LzWBMsdRddF7Fakid5+szHnPGehICCCCAAAIIIICALQVs8xP8zJLitEDLjzWlzWNmKK7KKBuZEEAAAQQQQAABBPIgYJs7oK0VFbGVH6LR8edQujRUo96AsQB8Bqm8MYNMZEEAAQQQQAABBBDIi4Bt7oBW+XxaaASh4yWvsb7m0o6Lxjs15pj583v13DGHOYAAAggggAACCCBgEwHbBKCmx02zxn++8/KhWlWcS/8TvVlH3WWSr9TcIiGAAAIIIIAAAgjYUcBWAeic8nKtqB/9M7vPuPu55FB9RnbFNVLDlRllJRMCCCCAAAIIIIBAngRsFYCaBrc1Naq9pjrBcdVAvUoD6ade8hm/3s+5SfLa5qnWRBfYQAABBBBAAAEEEBghYLsA1JhtSXe2XKJvN9SrwuPTpQdH3xEd0fbEZoXxcvy8VZK/MnGIDQQQQAABBBBAAAGbCtjyfqHH49F3GmdpRWWDenu8Ove5FAmOFjSyqHyWVGs881k9Z/Q59hBAAAEEEEAAAQTsK2DLADTOVVHpVcUKqclYAn7ojPTphs1S1KM1/3KLSo0bo0WZvZcUr45vBBBAAAEEEEAAARsI2DoAjft4jAcFSo33kE6FDsmcJ9T8yZ2EAAIIIIAAAggg4EwB2z0D6kxGWo0AAggggAACCCCQqYAj7oBm2hnyIYAAAgggkG+BEydOaNOmTVk3Y+7cuVq5cmXW5SiAgBMFCECdOGq0GQEEEEDAtgJ/+tOf9PTTT6u2tnZUG4PBoAKBQOxYSUmJzE88mcerq6sJQOMgfLtegADU9UNMBxFAAAEEcilgvqtQVlambdu2jbqsGZg+8sgjsWP33HOPHnjggcT5Z555Rhs3bkzss4GA2wV4BtTtI0z/EEAAAQQQQAABmwkQgNpsQGgOAggggAACCCDgdgECULePMP1DAAEEEEAAAQRsJkAAarMBoTkIIIAAAggggIDbBQhA3T7C9A8BBBBAAAEEELCZAAGozQaE5iCAAAIIIIAAAm4XIAB1+wjTPwQQQAABBBBAwGYCBKA2GxCagwACCCCAAAIIuF3AURPR9/X1qaenR7/73e8cNy4rVqzQggULHNduGowAAggUqsDzzz+vSCSSdfc/+eSTrMuYBQYHB/Xcc89lXHb27Nm6/fbbM85PRgTsJOCoAPTkyZPav3+/zBUknJYefvjh2NJsTms37UUAAQQKVeDVV1+dVABqLqtproaUbRoeHpZ5zUzT8uXLCUAzxSKf7QQcFYDu27cvsY6u7STTNMhclo2EAAIIIOAcgS1btkyqsevXr9dvf/vbrMuaa8F/8MEHWZejAAJOFHBUAGoCE8g58Y8ZbUYAAQQQQAABBC4I8BLSBQu2EEAAAQQQQAABBHIgQACaA2QugQACCCCAAAIIIHBBgAD0ggVbCCCAAAIIIIAAAjkQIADNATKXQAABBBBAAAEEELggQAB6wYItBBBAAAEEEEAAgRwIEIDmAJlLIIAAAggggAACCFwQIAC9YMEWAggggAACCCCAQA4EHDcPaA5MuAQCCCCAAAJTEggGg3r22WdH1XHgwIHE/tatW2WufBRP27dvj2/yjUBBCBCAFsQw00kEEEAAgVwJtLa2qq6uTm+88caoS5rrytfW1saOdXR0qLOzc9T59vb2UfvsIOBmAY+xXm32C9a6WYS+IYAAAggggAACCFgqwDOglvJSOQIIIIAAAggggECyAAFosgj7CCCAAAIIIIAAApYKEIBaykvlCCCAAAIIIIAAAskCBKDJIuwjgAACCCCAAAIIWCpAAGopL5UjgAACCCCAAAIIJAsQgCaLsI8AAggggAACCCBgqQABqKW8VI4AAggggAACCCCQLEAAmizCPgIIIIAAAggggIClAgSglvJSOQIIIIAAAggggECyAAFosgj7CCCAAAIIIIAAApYKEIBaykvlCCCAAAIIIIAAAskCBKDJIuwjgAACCCCAAAIIWCpAAGopL5UjgAACCCCAAAIIJAsQgCaLsI8AAggggAACCCBgqQABqKW8VI4AAggggAACCCCQLEAAmizCPgIIIIAAAggggIClAgSglvJSOQIIIIAAAggggECyAAFosgj7CCCAAAIIIIAAApYKEIBaykvlCCCAAAIIIIAAAskCBKDJIuwjgAACCCCAAAIIWCpAAGopL5UjgAACCCCAAAIIJAsQgCaLsI8AAggggAACCCBgqQABqKW8VI4AAggggAACCCCQLEAAmizCPgIIIIAAAggggIClAgSglvJSOQIIIIAAAggggECyAAFosgj7CCCAAAIIIIAAApYKEIBaykvlCCCAAAIIIIAAAskCBKDJIuwjgAACCCCAAAIIWCrw/83Sv4wYGqvKAAAAAElFTkSuQmCC" /><!-- --></p>
144
+</div>
145
+</section>
146
+
147
+
148
+
149
+<!-- dynamically load mathjax for compatibility with self-contained -->
150
+<script>
151
+  (function () {
152
+    var script = document.createElement("script");
153
+    script.type = "text/javascript";
154
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
155
+    document.getElementsByTagName("head")[0].appendChild(script);
156
+  })();
157
+</script>
158
+
159
+</body>
160
+</html>
0 161
new file mode 100644
... ...
@@ -0,0 +1,60 @@
1
+
2
+# Tree annotation with analysis of R packages
3
+
4
+## annotating tree with ape bootstraping analysis
5
+
6
+```{r results='hide', message=FALSE}
7
+library(ape)
8
+data(woodmouse)
9
+d <- dist.dna(woodmouse)
10
+tr <- nj(d)
11
+bp <- boot.phylo(tr, woodmouse, function(xx) nj(dist.dna(xx)))
12
+```
13
+
14
+
15
+```{r fig.width=6, fig.height=6, warning=FALSE, fig.align="center"}
16
+library(treeio)
17
+tree <- as.treedata(tr, boot = bp)
18
+ggtree(tree) + geom_label(aes(label=bootstrap)) + geom_tiplab()
19
+```
20
+
21
+## annotating tree with phangorn output
22
+
23
+```{r results='hide', message=FALSE, fig.width=12, fig.height=10, width=60, warning=FALSE, fig.align="center", eval=FALSE}
24
+library(phangorn)
25
+treefile <- system.file("extdata", "pa.nwk", package="treeio")
26
+tre <- read.tree(treefile)
27
+tipseqfile <- system.file("extdata", "pa.fas", package="treeio")
28
+tipseq <- read.phyDat(tipseqfile,format="fasta")
29
+fit <- pml(tre, tipseq, k=4)
30
+fit <- optim.pml(fit, optNni=FALSE, optBf=T, optQ=T,
31
+                 optInv=T, optGamma=T, optEdge=TRUE,
32
+                 optRooted=FALSE, model = "GTR")
33
+
34
+phangorn <- phyPML(fit, type="ml")
35
+ggtree(phangorn) + geom_text(aes(x=branch, label=AA_subs, vjust=-.5))
36
+```
37
+
38
+![](figures/phangorn_example.png)
39
+
40
+
41
+## phylo4d
42
+
43
+`phylo4d` was defined in the `phylobase` package, which can be employed to integrate user's data with phylogenetic tree. `phylo4d` was supported in `ggtree` and the data stored in the object can be used directly to annotate the tree.
44
+
45
+```{r fig.width=6, fig.height=5, warning=FALSE, fig.align="center", eval=FALSE}
46
+dd2 <- dd[, -1]
47
+rownames(dd2) <- dd[,1]
48
+require(phylobase)
49
+tr2 <- phylo4d(tree, dd2)
50
+ggtree(tr2) + geom_tiplab(aes(color=place)) +
51
+    geom_tippoint(aes(size=value, shape=place, color=place), alpha=0.25)
52
+```
53
+
54
+
55
+![](figures/phylobase_example.png)
56
+
57
+## jplace file format
58
+
59
+`ggtree` provides `write.jplace()` function to store user's own data and associated newick tree to a single `jplace` file, which can be parsed directly in `ggtree` and user's data can be used to annotate the tree directly. For more detail, please refer to the [Tree Data Import](treeImport.html#jplace-file-format) vignette.
60
+
... ...
@@ -1,15 +1,17 @@
1 1
 % Generated by roxygen2: do not edit by hand
2 2
 % Please edit documentation in R/clade-functions.R
3
-\name{collapse}
4
-\alias{collapse}
5
-\title{collapse}
3
+\name{collapse.ggtree}
4
+\alias{collapse.ggtree}
5
+\title{collapse-ggtree}
6 6
 \usage{
7
-collapse(tree_view = NULL, node)
7
+\method{collapse}{ggtree}(x = NULL, node, ...)
8 8
 }
9 9
 \arguments{
10
-\item{tree_view}{tree view}
10
+\item{x}{tree view}
11 11
 
12 12
 \item{node}{clade node}
13
+
14
+\item{...}{additional parameters}
13 15
 }
14 16
 \value{
15 17
 tree view
... ...
@@ -4,8 +4,8 @@
4 4
 \alias{inset}
5 5
 \title{inset}
6 6
 \usage{
7
-inset(tree_view, insets, width = 0.1, height = 0.1, hjust = 0,
8
-  vjust = 0, x = "node", reverse_x = FALSE, reverse_y = FALSE)
7
+inset(tree_view, insets, width, height, hjust = 0, vjust = 0, x = "node",
8
+  reverse_x = FALSE, reverse_y = FALSE)
9 9
 }
10 10
 \arguments{
11 11
 \item{tree_view}{tree view}
... ...
@@ -7,10 +7,18 @@
7 7
 \alias{reexports}
8 8
 \alias{read.tree}
9 9
 \alias{reexports}
10
+\alias{read.nexus}
11
+\alias{reexports}
10 12
 \alias{groupOTU}
11 13
 \alias{reexports}
12 14
 \alias{groupClade}
13 15
 \alias{reexports}
16
+\alias{collapse}
17
+\alias{reexports}
18
+\alias{fortify}
19
+\alias{reexports}
20
+\alias{ggplot}
21
+\alias{reexports}
14 22
 \alias{xlim}
15 23
 \alias{reexports}
16 24
 \alias{theme}
... ...
@@ -24,6 +32,34 @@
24 32
 \alias{geom_label}
25 33
 \alias{reexports}
26 34
 \alias{geom_point}
35
+\alias{reexports}
36
+\alias{read.beast}
37
+\alias{reexports}
38
+\alias{read.codeml}
39
+\alias{reexports}
40
+\alias{read.codeml_mlc}
41
+\alias{reexports}
42
+\alias{read.hyphy}
43
+\alias{reexports}
44
+\alias{read.jplace}
45
+\alias{reexports}
46
+\alias{read.jtree}
47
+\alias{reexports}
48
+\alias{read.mrbayes}
49
+\alias{reexports}
50
+\alias{read.newick}
51
+\alias{reexports}
52
+\alias{read.nhx}
53
+\alias{reexports}
54
+\alias{read.paml_rst}
55
+\alias{reexports}
56
+\alias{read.phylip}
57
+\alias{reexports}
58
+\alias{read.phyloT}
59
+\alias{reexports}
60
+\alias{read.r8s}
61
+\alias{reexports}
62
+\alias{read.raxml}
27 63
 \title{Objects exported from other packages}
28 64
 \keyword{internal}
29 65
 \description{
... ...
@@ -31,10 +67,14 @@ These objects are imported from other packages. Follow the links
31 67
 below to see their documentation.
32 68
 
33 69
 \describe{
34
-  \item{ape}{\code{\link[ape]{rtree}}, \code{\link[ape]{read.tree}}}
70
+  \item{ape}{\code{\link[ape]{rtree}}, \code{\link[ape]{read.tree}}, \code{\link[ape]{read.nexus}}}
35 71
 
36
-  \item{ggplot2}{\code{\link[ggplot2]{xlim}}, \code{\link[ggplot2]{theme}}, \code{\link[ggplot2]{ggsave}}, \code{\link[ggplot2]{aes}}, \code{\link[ggplot2]{geom_text}}, \code{\link[ggplot2]{geom_label}}, \code{\link[ggplot2]{geom_point}}}
72
+  \item{dplyr}{\code{\link[dplyr]{collapse}}}
73
+
74
+  \item{ggplot2}{\code{\link[ggplot2]{fortify}}, \code{\link[ggplot2]{ggplot}}, \code{\link[ggplot2]{xlim}}, \code{\link[ggplot2]{theme}}, \code{\link[ggplot2]{ggsave}}, \code{\link[ggplot2]{aes}}, \code{\link[ggplot2]{geom_text}}, \code{\link[ggplot2]{geom_label}}, \code{\link[ggplot2]{geom_point}}}
37 75
 
38 76
   \item{tidytree}{\code{\link[tidytree]{groupOTU}}, \code{\link[tidytree]{groupClade}}}
77
+
78
+  \item{treeio}{\code{\link[treeio]{read.beast}}, \code{\link[treeio]{read.codeml}}, \code{\link[treeio]{read.codeml_mlc}}, \code{\link[treeio]{read.hyphy}}, \code{\link[treeio]{read.jplace}}, \code{\link[treeio]{read.jtree}}, \code{\link[treeio]{read.mrbayes}}, \code{\link[treeio]{read.newick}}, \code{\link[treeio]{read.nhx}}, \code{\link[treeio]{read.paml_rst}}, \code{\link[treeio]{read.phylip}}, \code{\link[treeio]{read.phyloT}}, \code{\link[treeio]{read.r8s}}, \code{\link[treeio]{read.raxml}}}
39 79
 }}
40 80
 
... ...
@@ -9,7 +9,7 @@ weight: 1
9 9
 
10 10
 <p><link rel="stylesheet" href="https://guangchuangyu.github.io/css/font-awesome.min.css"> <link rel="stylesheet" href="https://guangchuangyu.github.io/css/academicons.min.css"></p>
11 11
 <p><img src="https://raw.githubusercontent.com/Bioconductor/BiocStickers/master/ggtree/ggtree.png" height="200" align="right" /></p>
12
-<p><a href="https://bioconductor.org/packages/ggtree"><img src="https://img.shields.io/badge/release%20version-1.10.0-blue.svg?style=flat" alt="releaseVersion" /></a> <a href="https://github.com/guangchuangyu/ggtree"><img src="https://img.shields.io/badge/devel%20version-1.11.4-blue.svg?style=flat" alt="develVersion" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-22808/total-blue.svg?style=flat" alt="total" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-1218/month-blue.svg?style=flat" alt="month" /></a></p>
12
+<p><a href="https://bioconductor.org/packages/ggtree"><img src="https://img.shields.io/badge/release%20version-1.10.0-blue.svg?style=flat" alt="releaseVersion" /></a> <a href="https://github.com/guangchuangyu/ggtree"><img src="https://img.shields.io/badge/devel%20version-1.11.4-blue.svg?style=flat" alt="develVersion" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-22863/total-blue.svg?style=flat" alt="total" /></a> <a href="https://bioconductor.org/packages/stats/bioc/ggtree"><img src="https://img.shields.io/badge/downloads-906/month-blue.svg?style=flat" alt="month" /></a></p>
13 13
 <p>The <code>ggtree</code> package extending the <em>ggplot2</em> package. It based on grammar of graphics and takes all the good parts of <em>ggplot2</em>. <em>ggtree</em> is designed for not only viewing phylogenetic tree but also displaying annotation data on the tree. <em>ggtree</em> is released within the <a href="https://bioconductor.org/packages/ggtree/">Bioconductor</a> project and the source code is hosted on <a href="https://github.com/GuangchuangYu/ggtree"><i class="fa fa-github fa-lg"></i> GitHub</a>.</p>
14 14
 <div id="authors" class="section level2">
15 15
 <h2><i class="fa fa-user"></i> Authors</h2>
... ...
@@ -18,7 +18,7 @@ weight: 1
18 18
 <div id="citation" class="section level2">
19 19
 <h2><i class="fa fa-book"></i> Citation</h2>
20 20
 <p>Please cite the following article when using <code>ggtree</code>:</p>
21
-<p><a href="http://dx.doi.org/10.1111/2041-210X.12628"><img src="https://img.shields.io/badge/doi-10.1111/2041--210X.12628-blue.svg?style=flat" alt="doi" /></a> <a href="https://www.altmetric.com/details/10533079"><img src="https://img.shields.io/badge/Altmetric-325-blue.svg?style=flat" alt="Altmetric" /></a> <a href="https://scholar.google.com.hk/scholar?oi=bibs&amp;hl=en&amp;cites=7268358477862164627"><img src="https://img.shields.io/badge/cited%20by-60-blue.svg?style=flat" alt="citation" /></a></p>
21
+<p><a href="http://dx.doi.org/10.1111/2041-210X.12628"><img src="https://img.shields.io/badge/doi-10.1111/2041--210X.12628-blue.svg?style=flat" alt="doi" /></a> <a href="https://www.altmetric.com/details/10533079"><img src="https://img.shields.io/badge/Altmetric-325-blue.svg?style=flat" alt="Altmetric" /></a> <a href="https://scholar.google.com.hk/scholar?oi=bibs&amp;hl=en&amp;cites=7268358477862164627"><img src="https://img.shields.io/badge/cited%20by-64-blue.svg?style=flat" alt="citation" /></a></p>
22 22
 <p><strong>G Yu</strong>, DK Smith, H Zhu, Y Guan, TTY Lam<sup>*</sup>. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. <strong><em>Methods in Ecology and Evolution</em></strong>. 2017, 8(1):28-36.</p>
23 23
 </div>
24 24
 <div id="featured-articles" class="section level2">
... ...
@@ -8,4 +8,4 @@ weight: 50
8 8
 
9 9
 <p><link rel="stylesheet" href="https://guangchuangyu.github.io/css/font-awesome.min.css"></p>
10 10
 <p><font size="4"><strong>Leave me a message on <a href="https://twitter.com/hashtag/ggtree"><i class="fa fa-twitter fa-lg"></i></a></strong></font></p>
11
-<p>{{% tweet "941463119490150405" %}}{{% tweet "941040327816892416" %}}{{% tweet "940738021514547201" %}}{{% tweet "940337892030910467" %}}{{% tweet "938702392450617344" %}}{{% tweet "936241103585517568" %}}{{% tweet "920370392451178496" %}}{{% tweet "912971120198012929" %}}{{% tweet "907543915686793217" %}}{{% tweet "910671384069722112" %}}{{% tweet "903786057182957568" %}}{{% tweet "899635001247059969" %}}{{% tweet "898729425168080897" %}}{{% tweet "891144389715783680" %}}{{% tweet "883301626022375424" %}}{{% tweet "883246854695256064" %}}{{% tweet "877576023109390336" %}}{{% tweet "875594411584782337" %}}{{% tweet "871775262152810496" %}}{{% tweet "867408666852511744" %}}{{% tweet "857665638600507393" %}}{{% tweet "840181267542994944" %}}{{% tweet "839831849736232962" %}}{{% tweet "830023569887391744" %}}{{% tweet "829398997735313408" %}}{{% tweet "824264788456984576" %}}{{% tweet "822383911258890242" %}}{{% tweet "811666431452450816" %}}{{% tweet "811661275889549312" %}}{{% tweet "797041236867686400" %}}{{% tweet "797037016403820546" %}}{{% tweet "794278716859879424" %}}{{% tweet "831952966701678592" %}}{{% tweet "788751417746059264" %}}{{% tweet "693123105707986944" %}}{{% tweet "691762302492790788" %}}{{% tweet "687933344219312128" %}}{{% tweet "684411780412526592" %}}{{% tweet "665188011047563268" %}}{{% tweet "664040586001915904" %}}{{% tweet "639507420054749184" %}}{{% tweet "671673570259509248" %}}{{% tweet "667490462693912576" %}}{{% tweet "660278158663401473" %}}{{% tweet "641251456595685376" %}}{{% tweet "559734740488818688" %}}{{% tweet "559762891478667264" %}}{{% tweet "561514846291001344" %}}{{% tweet "559542705663905793" %}}{{% tweet "561056244782227456" %}}{{% tweet "664085841766195200" %}}{{% tweet "664431087683260416" %}}{{% tweet "667706456045694976" %}}{{% tweet "663937183791759360" %}}{{% tweet "663771499589861376" %}}{{% tweet "667433534756397057" %}}{{% tweet "640573716733169664" %}}{{% tweet "664407764001779712" %}}{{% tweet "630500659960254464" %}}{{% tweet "547497335412899840" %}}{{% tweet "610364467939913728" %}}</p>
11
+<p>{{% tweet "932751454938415105" %}}{{% tweet "941463119490150405" %}}{{% tweet "941040327816892416" %}}{{% tweet "940738021514547201" %}}{{% tweet "940337892030910467" %}}{{% tweet "938702392450617344" %}}{{% tweet "936241103585517568" %}}{{% tweet "920370392451178496" %}}{{% tweet "912971120198012929" %}}{{% tweet "907543915686793217" %}}{{% tweet "910671384069722112" %}}{{% tweet "903786057182957568" %}}{{% tweet "899635001247059969" %}}{{% tweet "898729425168080897" %}}{{% tweet "891144389715783680" %}}{{% tweet "883301626022375424" %}}{{% tweet "883246854695256064" %}}{{% tweet "877576023109390336" %}}{{% tweet "875594411584782337" %}}{{% tweet "871775262152810496" %}}{{% tweet "867408666852511744" %}}{{% tweet "857665638600507393" %}}{{% tweet "840181267542994944" %}}{{% tweet "839831849736232962" %}}{{% tweet "830023569887391744" %}}{{% tweet "829398997735313408" %}}{{% tweet "824264788456984576" %}}{{% tweet "822383911258890242" %}}{{% tweet "811666431452450816" %}}{{% tweet "811661275889549312" %}}{{% tweet "797041236867686400" %}}{{% tweet "797037016403820546" %}}{{% tweet "794278716859879424" %}}{{% tweet "831952966701678592" %}}{{% tweet "788751417746059264" %}}{{% tweet "693123105707986944" %}}{{% tweet "691762302492790788" %}}{{% tweet "687933344219312128" %}}{{% tweet "684411780412526592" %}}{{% tweet "665188011047563268" %}}{{% tweet "664040586001915904" %}}{{% tweet "639507420054749184" %}}{{% tweet "671673570259509248" %}}{{% tweet "667490462693912576" %}}{{% tweet "660278158663401473" %}}{{% tweet "641251456595685376" %}}{{% tweet "559734740488818688" %}}{{% tweet "559762891478667264" %}}{{% tweet "561514846291001344" %}}{{% tweet "559542705663905793" %}}{{% tweet "561056244782227456" %}}{{% tweet "664085841766195200" %}}{{% tweet "664431087683260416" %}}{{% tweet "667706456045694976" %}}{{% tweet "663937183791759360" %}}{{% tweet "663771499589861376" %}}{{% tweet "667433534756397057" %}}{{% tweet "640573716733169664" %}}{{% tweet "664407764001779712" %}}{{% tweet "630500659960254464" %}}{{% tweet "547497335412899840" %}}{{% tweet "610364467939913728" %}}</p>
... ...
@@ -11,6 +11,7 @@ weight: 50
11 11
 
12 12
 
13 13
 ````{r echo=FALSE}
14
+blogdown::shortcode('tweet', '932751454938415105')
14 15
 blogdown::shortcode('tweet', '941463119490150405')
15 16
 blogdown::shortcode('tweet', '941040327816892416')
16 17
 blogdown::shortcode('tweet', '940738021514547201')
17 18
new file mode 100644
... ...
@@ -0,0 +1,104 @@
1
+---
2
+title: "Annotate a phylogenetic tree with insets"
3
+author: "Guangchuang Yu\\
4
+
5
+        School of Public Health, The University of Hong Kong"
6
+date: "`r Sys.Date()`"
7
+output:
8
+  prettydoc::html_pretty:
9
+    toc: true
10
+    theme: cayman
11
+    highlight: github
12
+  pdf_document:
13
+    toc: true
14
+vignette: >
15
+  %\VignetteIndexEntry{Annotating phylogenetic tree with images}
16
+  %\VignetteEngine{knitr::rmarkdown}
17
+  %\usepackage[utf8]{inputenc}
18
+---
19
+
20
+```{r style, echo=FALSE, results="asis", message=FALSE}
21
+knitr::opts_chunk$set(tidy = FALSE,
22
+		   message = FALSE)
23
+```
24
+
25
+
26
+```{r echo=FALSE, results="hide", message=FALSE}
27
+library(ape)
28
+library("ggplot2")
29
+library("ggimage")
30
+library("ggtree")
31
+```
32
+
33
+
34
+`ggtree` provides a function, `inset`, for adding subplots to a phylogenetic tree. The input is a tree graphic object and a named list of ggplot graphic objects (can be any kind of charts), these objects should named by node numbers. To facilitate adding bar and pie charts (e.g. summarized stats of results from ancestral reconstruction) to phylogenetic tree, *ggtree* provides `nodepie` and `nodebar` functions to create a list of pie or bar charts.
35
+
36
+## Annotate with bar charts
37
+
38
+```{r fig.width=7, fig.height=7}
39
+set.seed(2015-12-31)
40
+tr <- rtree(15)
41
+p <- ggtree(tr)
42
+
43
+a <- runif(14, 0, 0.33)
44
+b <- runif(14, 0, 0.33)
45
+c <- runif(14, 0, 0.33)
46
+d <- 1 - a - b - c
47
+dat <- data.frame(a=a, b=b, c=c, d=d)
48
+## input data should have a column of `node` that store the node number
49
+dat$node <- 15+1:14
50
+
51
+## cols parameter indicate which columns store stats (a, b, c and d in this example)
52
+bars <- nodebar(dat, cols=1:4)
53
+
54
+inset(p, bars, width=.2, height=1)
55
+```
56
+
57
+Users can set the color via the parameter *color*. The *x* position can be one of 'node' or 'branch' and can be adjusted by the parameter *hjust* and *vjust* for horizontal and vertical adjustment respecitvely.
58
+
59
+
60
+```{r fig.width=7, fig.height=7}
61
+bars2 <- nodebar(dat, cols=1:4, position='dodge',
62
+                 color=c(a='blue', b='red', c='green', d='cyan'))
63
+p2 <- inset(p, bars2, x='branch', width=.2, height=1, vjust=-.3)
64
+print(p2)
65
+```
66
+
67
+## Annotate with pie charts
68
+
69
+Similarly, users can use `nodepie` function to generate a list of pie charts and place these charts to annotate corresponding nodes. Both `nodebar` and `nodepie` accepts parameter *alpha* to allow transparency.
70
+
71
+```{r fig.width=7, fig.height=7}
72
+pies <- nodepie(dat, cols=1:4, alpha=.6)
73
+inset(p, pies, width=1, height=1, hjust=-.06)
74
+```
75
+
76
+
77
+## Annotate with other types of charts
78
+
79
+The `inset` function accepts a list of ggplot graphic objects and these input objects are not restricted to pie or bar charts. They can be any kinds of charts and hybrid of these charts.
80
+
81
+```{r fig.width=7, fig.height=7}
82
+pies_and_bars <- bars2
83
+pies_and_bars[9:14] <- pies[9:14]
84
+inset(p, pies_and_bars, width=.3, height=1)
85
+
86
+d <- lapply(1:15, rnorm, n=100)
87
+ylim <- range(unlist(d))
88
+bx <- lapply(d, function(y) {
89
+    dd <- data.frame(y=y)
90
+    ggplot(dd, aes(x=1, y=y))+geom_boxplot() + ylim(ylim) + theme_inset()
91
+})
92
+names(bx) <- 1:15
93
+inset(p, bx, width=.2, height=2, hjust=-.05)
94
+```
95
+
96
+
97
+After annotating with insets, users can further annotate the tree with another layer of insets.
98
+
99
+```{r fig.width=7, fig.height=7}
100
+p2 <- inset(p, bars2, x='branch', width=.5, height=1, vjust=-.4)
101
+p2 <- inset(p2, pies, x='branch', vjust=.4, width=.5, height=1)
102
+bx2 <- lapply(bx, function(g) g+coord_flip())
103
+inset(p2, bx2, width=2, height=1, vjust=.04, hjust=p2$data$x[1:15]-5) + xlim(NA, 6)
104
+```
0 105
deleted file mode 100644
... ...
@@ -1,196 +0,0 @@
1
-title: "Advance Tree Annotation"
2
-author: "Guangchuang Yu and Tommy Tsan-Yuk Lam\\
3
-
4
-        School of Public Health, The University of Hong Kong"
5
-date: "`r Sys.Date()`"
6
-bibliography: ggtree.bib
7
-biblio-style: apalike
8
-output:
9
-  prettydoc::html_pretty:
10
-    toc: true
11
-    theme: cayman
12
-    highlight: github
13
-  pdf_document:
14
-    toc: true
15
-vignette: >
16
-  %\VignetteIndexEntry{05 Advance Tree Annotation}
17
-  %\VignetteEngine{knitr::rmarkdown}
18
-  %\usepackage[utf8]{inputenc}
19
-
20
-```{r style, echo=FALSE, results="asis", message=FALSE}
21
-knitr::opts_chunk$set(tidy = FALSE,
22
-		   message = FALSE)
23
-```
24
-
25
-
26
-```{r echo=FALSE, results="hide", message=FALSE}
27
-library("ape")
28
-library("treeio")
29
-library("ggplot2")
30
-library("ggtree")
31
-```
32
-
33
-
34
-# Visualize tree with associated matrix
35
-
36
-<!--
37
-At first we implemented `gplot` function to visualize tree with heatmap but it has [an issue](https://github.com/GuangchuangYu/ggtree/issues/3) that it can't always guarantee the heatmap aligning to the tree properly, since the line up is between two figures and it's currently not supported internally by ggplot2. I have implemented another function `gheatmap` that can do the line up properly by creating a new layer above the tree.
38
-
39
-The `gheatmap` function is designed to visualize phylogenetic tree with heatmap of associated matrix.
40
-
41
-In the following example, we visualized a tree of H3 influenza viruses with their associated genotype.
42
-
43
-```{r fig.width=8, fig.height=6, fig.align="center", warning=FALSE, message=FALSE}
44
-beast_file <- system.file("examples/MCC_FluA_H3.tree", package="ggtree")
45
-beast_tree <- read.beast(beast_file)
46
-
47
-genotype_file <- system.file("examples/Genotype.txt", package="ggtree")
48
-genotype <- read.table(genotype_file, sep="\t", stringsAsFactor=F)
49
-colnames(genotype) <- sub("\\.$", "", colnames(genotype))
50
-p <- ggtree(beast_tree, mrsd="2013-01-01") + geom_treescale(x=2008, y=1, offset=2)
51
-p <- p + geom_tiplab(size=2)
52
-gheatmap(p, genotype, offset = 5, width=0.5, font.size=3, colnames_angle=-45, hjust=0) +
53
-    scale_fill_manual(breaks=c("HuH3N2", "pdm", "trig"), values=c("steelblue", "firebrick", "darkgreen"))
54
-```
55
-
56
-The _width_ parameter is to control the width of the heatmap. It supports another parameter _offset_ for controlling the distance between the tree and the heatmap, for instance to allocate space for tip labels.
57
-
58
-
59
-For time-scaled tree, as in this example, it's more often to use x axis by using `theme_tree2`. But with this solution, the heatmap is just another layer and will change the `x` axis. To overcome this issue, we implemented `scale_x_ggtree` to set the x axis more reasonable.
60
-
61
-<!-- User can also use `gplot` and tweak the positions of two plot to align properly. -->
62
-
63
-
64
-
65
-```{r fig.width=8, fig.height=6, fig.align="center", warning=FALSE}
66
-p <- ggtree(beast_tree, mrsd="2013-01-01") + geom_tiplab(size=2, align=TRUE, linesize=.5) + theme_tree2()
67
-pp <- (p + scale_y_continuous(expand=c(0, 0.3))) %>%
68
-    gheatmap(genotype, offset=8, width=0.6, colnames=FALSE) %>%
69
-        scale_x_ggtree()
70
-pp + theme(legend.position="right")
71
-```
72
-
73
-
74
-# Visualize tree with multiple sequence alignment
75
-
76
-With `msaplot` function, user can visualize multiple sequence alignment with phylogenetic tree, as demonstrated below:
77
-```{r fig.width=8, fig.height=6, fig.align='center', warning=FALSE}
78
-fasta <- system.file("examples/FluA_H3_AA.fas", package="ggtree")
79
-msaplot(ggtree(beast_tree), fasta)
80
-```
81
-
82
-A specific slice of the alignment can also be displayed by specific _window_ parameter.
83
-
84
-```{r fig.width=7, fig.height=7, fig.align='center', warning=FALSE}
85
-msaplot(ggtree(beast_tree), fasta, window=c(150, 200)) + coord_polar(theta='y')
86
-```
87
-
88
-# Annotate a phylogenetic tree with insets
89
-
90
-`ggtree` provides a function, `inset`, for adding subplots to a phylogenetic tree. The input is a tree graphic object and a named list of ggplot graphic objects (can be any kind of charts), these objects should named by node numbers. To facilitate adding bar and pie charts (e.g. summarized stats of results from ancestral reconstruction) to phylogenetic tree, *ggtree* provides `nodepie` and `nodebar` functions to create a list of pie or bar charts.
91
-
92
-## Annotate with bar charts
93
-
94
-```{r}
95
-set.seed(2015-12-31)
96
-tr <- rtree(15)
97
-p <- ggtree(tr)
98
-
99
-a <- runif(14, 0, 0.33)
100
-b <- runif(14, 0, 0.33)
101
-c <- runif(14, 0, 0.33)
102
-d <- 1 - a - b - c
103
-dat <- data.frame(a=a, b=b, c=c, d=d)
104
-## input data should have a column of `node` that store the node number
105
-dat$node <- 15+1:14
106
-
107
-## cols parameter indicate which columns store stats (a, b, c and d in this example)
108
-bars <- nodebar(dat, cols=1:4)
109
-
110
-inset(p, bars)
111
-```
112
-
113
-The sizes of the insets can be ajusted by the paramter *width* and *height*.
114
-
115
-```{r}
116
-inset(p, bars, width=.06, height=.1)
117
-```
118
-
119
-Users can set the color via the parameter *color*. The *x* position can be one of 'node' or 'branch' and can be adjusted by the parameter *hjust* and *vjust* for horizontal and vertical adjustment respecitvely.
120
-
121
-
122
-```{r}
123
-bars2 <- nodebar(dat, cols=1:4, position='dodge',
124
-                 color=c(a='blue', b='red', c='green', d='cyan'))
125
-p2 <- inset(p, bars2, x='branch', width=.06, vjust=-.3)
126
-print(p2)
127
-```
128
-
129
-## Annotate with pie charts
130
-
131
-Similarly, users can use `nodepie` function to generate a list of pie charts and place these charts to annotate corresponding nodes. Both `nodebar` and `nodepie` accepts parameter *alpha* to allow transparency.
132
-
133
-```{r}
134
-pies <- nodepie(dat, cols=1:4, alpha=.6)
135
-inset(p, pies)
136
-```
137
-
138
-
139
-```{r}
140
-inset(p, pies, hjust=-.06)
141
-```
142
-
143
-## Annotate with other types of charts
144
-
145
-The `inset` function accepts a list of ggplot graphic objects and these input objects are not restricted to pie or bar charts. They can be any kinds of charts and hybrid of these charts.
146
-
147
-```{r}
148
-pies_and_bars <- bars2
149
-pies_and_bars[9:14] <- pies[9:14]
150
-inset(p, pies_and_bars)
151
-```
152
-
153
-```{r}
154
-d <- lapply(1:15, rnorm, n=100)
155
-ylim <- range(unlist(d))
156
-bx <- lapply(d, function(y) {
157
-    dd <- data.frame(y=y)
158
-    ggplot(dd, aes(x=1, y=y))+geom_boxplot() + ylim(ylim) + theme_inset()
159
-})
160
-names(bx) <- 1:15
161
-inset(p, bx, width=.06, height=.2, hjust=-.05)
162
-```
163
-
164
-
165
-After annotating with insets, users can further annotate the tree with another layer of insets.
166
-
167
-```{r fig.width=10, fig.height=7}
168
-p2 <- inset(p, bars2, x='branch', width=.06, vjust=-.4)
169
-p2 <- inset(p2, pies, x='branch', vjust=.4)
170
-bx2 <- lapply(bx, function(g) g+coord_flip())
171
-inset(p2, bx2, width=.4, height=.06, vjust=.04, hjust=p2$data$x[1:15]-4) + xlim(NA, 4.5)
172
-```
173
-
174
-# Plot tree with associated data
175
-
176
-For associating phylogenetic tree with different type of plot produced by user's data, `ggtree` provides `facet_plot` function which accepts an input `data.frame` and a `geom` function to draw the input data. The data will be displayed in an additional panel of the plot.
177
-
178
-```{r warning=F, fig.width=10, fig.height=6}
179
-tr <- rtree(30)
180
-
181
-d1 <- data.frame(id=tr$tip.label, val=rnorm(30, sd=3))
182
-p <- ggtree(tr)
183
-
184
-p2 <- facet_plot(p, panel="dot", data=d1, geom=geom_point, aes(x=val), color='firebrick')
185
-d2 <- data.frame(id=tr$tip.label, value = abs(rnorm(30, mean=100, sd=50)))
186
-
187
-facet_plot(p2, panel='bar', data=d2, geom=geom_segment, aes(x=0, xend=value, y=y, yend=y), size=3, color='steelblue') + theme_tree2()
188
-```
189
-
190
-
191
-
192
-
193
-
... ...
@@ -225,6 +225,7 @@ the [Tree Annotation](treeAnnotation.html) vignette.
225 225
 + [Tree Annotation](treeAnnotation.html)
226 226
 + [Phylomoji](https://cran.r-project.org/web/packages/emojifont/vignettes/phylomoji.html)
227 227
 + [Annotating phylogenetic tree with images](https://guangchuangyu.github.io/ggtree/vignettes/ggtree-ggimage.html)
228
++ [Annotate a phylogenetic tree with insets](https://guangchuangyu.github.io/ggtree/vignettes/ggtree-inset.html)
228 229
 
229 230
 
230 231
 **ggtree homepage**: <https://guangchuangyu.github.io/ggtree> (contains more
... ...
@@ -29,81 +29,27 @@ knitr::opts_chunk$set(tidy = FALSE,
29 29
 library("ape")
30 30
 library("ggplot2")
31 31
 library("cowplot")
32
+library("treeio")
32 33
 library("ggtree")
33
-```
34
-
35
-
36
-# Rescale tree
37
-
38
-Most of the phylogenetic trees are scaled by evolutionary distance (substitution/site). In `ggtree`, users can re-scale a phylogenetic tree by any numerical variable inferred by evolutionary analysis (e.g. *dN/dS*).
39
-
40
-
41
-```{r fig.width=10, fig.height=5}
42
-beast_file <- system.file("examples/MCC_FluA_H3.tree", package="ggtree")
43
-beast_tree <- read.beast(beast_file)
44
-beast_tree
45
-p1 <- ggtree(beast_tree, mrsd='2013-01-01') + theme_tree2() +
46
-    ggtitle("Divergence time")
47
-p2 <- ggtree(beast_tree, branch.length = 'rate') + theme_tree2() +
48
-    ggtitle("Substitution rate")
49
-
50
-library(cowplot)
51
-plot_grid(p1, p2, ncol=2)
52
-```
53
-
54
-```{r fig.width=10, fig.height=5}
55
-mlcfile <- system.file("extdata/PAML_Codeml", "mlc", package="treeio")
56
-mlc_tree <- read.codeml_mlc(mlcfile)
57
-p1 <- ggtree(mlc_tree) + theme_tree2() +
58
-    ggtitle("nucleotide substitutions per codon")
59
-p2 <- ggtree(mlc_tree, branch.length='dN_vs_dS') + theme_tree2() +
60
-    ggtitle("dN/dS tree")
61
-plot_grid(p1, p2, ncol=2)
62
-```
63 34
 
64
-In addition to specify `branch.length` in tree visualization, users can change branch length stored in tree object by using `rescale_tree` function.
35
+CRANpkg <- function (pkg) {
36
+    cran <- "https://CRAN.R-project.org/package"
37
+    fmt <- "[%s](%s=%s)"
38
+    sprintf(fmt, pkg, cran, pkg)
39
+}
65 40
 
66
-```{r}
67
-beast_tree2 <- rescale_tree(beast_tree, branch.length = 'rate')
68
-ggtree(beast_tree2) + theme_tree2()
69
-```
70
-
71
-# Zoom on a portion of tree
41
+Biocpkg <- function (pkg) {
42
+    sprintf("[%s](http://bioconductor.org/packages/%s)", pkg, pkg)
43
+}
72 44
 
73
-`ggtree` provides _`gzoom`_ function that similar to _`zoom`_ function provided in `ape`. This function plots simultaneously a whole phylogenetic tree and a portion of it. It aims at exploring very large trees.
74
-
75
-```{r fig.width=9, fig.height=5, fig.align="center"}
76
-library("ape")
77
-data(chiroptera)
78
-library("ggtree")
79
-gzoom(chiroptera, grep("Plecotus", chiroptera$tip.label))
45
+inset <- ggtree::inset
80 46
 ```
81 47
 
82
-Zoom in selected clade of a tree that was already annotated with `ggtree` is also supported.
83 48
 
84
-```{r fig.width=9, fig.height=5, message=FALSE, warning=FALSE}
85
-groupInfo <- split(chiroptera$tip.label, gsub("_\\w+", "", chiroptera$tip.label))
86
-chiroptera <- groupOTU(chiroptera, groupInfo)
87
-p <- ggtree(chiroptera, aes(color=group)) + geom_tiplab() + xlim(NA, 23)
88
-gzoom(p, grep("Plecotus", chiroptera$tip.label), xmax_adjust=2)
89
-```
90
-
91
-
92
-# Color tree
93
-
94
-In `ggtree`, coloring phylogenetic tree is easy, by using `aes(color=VAR)` to map the color of tree based on a specific variable (numeric and category are both supported).
95
-
96
-```{r fig.width=5, fig.height=5}
97
-ggtree(beast_tree, aes(color=rate)) +
98
-    scale_color_continuous(low='darkgreen', high='red') +
99
-    theme(legend.position="right")
100
-```
101
-
102
-User can use any feature (if available), including clade posterior and *dN/dS* _etc._, to scale the color of the tree.
103 49
 
104 50
 # Annotate clades
105 51
 
106
-`ggtree` implements _`geom_cladelabel`_ layer to annotate a selected clade with a bar indicating the clade with a corresponding label.
52
+`r Biocpkg("ggtree")` [@yu_ggtree:_2017] implements _`geom_cladelabel`_ layer to annotate a selected clade with a bar indicating the clade with a corresponding label.
107 53
 
108 54
 The _`geom_cladelabel`_ layer accepts a selected internal node number. To get the internal node number, please refer to [Tree Manipulation](treeManipulation.html#internal-node-number) vignette.
109 55
 
... ...
@@ -151,13 +97,34 @@ Users can also use `geom_label` to label the text.
151 97
 p+ geom_cladelabel(node=34, label="another clade", align=T, geom='label', fill='lightblue')
152 98
 ```
153 99
 
154
-# Highlight clades
100
+## Annotate clades for unrooted tree
101
+
102
+`r Biocpkg("ggtree")` provides `geom_clade2` for labeling clades of unrooted
103
+layout trees.
155 104
 
156
-`ggtree` implements _`geom_hilight`_ layer, that accepts an internal node number and add a layer of rectangle to highlight the selected clade.
105
+
106
+```{r fig.wdith=7, fig.height=7, fig.align='center', warning=FALSE, message=FALSE}
107
+pg <- ggtree(tree, layout = "daylight")
108
+pg + geom_cladelabel2(node=45, label="test label", angle = 10) +
109
+    geom_cladelabel2(node = 34, label="another clade", angle=305)
110
+```
111
+
112
+# Labelling associated taxa (Monophyletic, Polyphyletic or Paraphyletic)
113
+
114
+`geom_cladelabel` is designed for labelling Monophyletic (Clade) while there are related taxa that are not form a clade. `ggtree` provides `geom_strip` to add a strip/bar to indicate the association with optional label (see [the issue](https://github.com/GuangchuangYu/ggtree/issues/52)).
157 115
 
158 116
 ```{r fig.width=5, fig.height=5, fig.align="center", warning=FALSE}
159 117
 nwk <- system.file("extdata", "sample.nwk", package="treeio")
160 118
 tree <- read.tree(nwk)
119
+ggtree(tree) + geom_tiplab() + geom_strip(5, 7, barsize=2, color='red') + geom_strip(6, 12, barsize=2, color='blue')
120
+```
121
+
122
+
123
+# Highlight clades
124
+
125
+`ggtree` implements _`geom_hilight`_ layer, that accepts an internal node number and add a layer of rectangle to highlight the selected clade.
126
+
127
+```{r fig.width=5, fig.height=5, fig.align="center", warning=FALSE}
161 128
 ggtree(tree) + geom_hilight(node=21, fill="steelblue", alpha=.6) +
162 129
     geom_hilight(node=17, fill="darkgreen", alpha=.6)
163 130
 ```
... ...
@@ -170,7 +137,8 @@ ggtree(tree, layout="circular") + geom_hilight(node=21, fill="steelblue", alpha=
170 137
 
171 138
 Another way to highlight selected clades is setting the clades with different colors and/or line types as demonstrated in [Tree Manipulation](treeManipulation.html#groupclade) vignette.
172 139
 
173
-# Highlight balances
140
+## Highlight balances
141
+
174 142
 In addition to _`geom_hilight`_, `ggtree` also implements _`geom_balance`_
175 143
 which is designed to highlight neighboring subclades of a given internal node.
176 144
 
... ...
@@ -180,14 +148,17 @@ ggtree(tree) +
180 148
   geom_balance(node=19, fill='darkgreen', color='white', alpha=0.6, extend=1)
181 149
 ```
182 150
 
183
-# labelling associated taxa (Monophyletic, Polyphyletic or Paraphyletic)
151
+## Highlight clades for unrooted tree
184 152
 
185
-`geom_cladelabel` is designed for labelling Monophyletic (Clade) while there are related taxa that are not form a clade. `ggtree` provides `geom_strip` to add a strip/bar to indicate the association with optional label (see [the issue](https://github.com/GuangchuangYu/ggtree/issues/52)).
153
+`r Biocpkg("ggtree")` provides `geom_hilight_encircle` to support highlight
154
+clades for unrooted layout trees.
186 155
 
187
-```{r fig.width=5, fig.height=5, fig.align="center", warning=FALSE}
188
-ggtree(tree) + geom_tiplab() + geom_strip(5, 7, barsize=2, color='red') + geom_strip(6, 12, barsize=2, color='blue')
156
+
157
+```{r fig.width=5, fig.height=5, fig.align='center', warning=FALSE, message=FALSE}
158
+pg + geom_hilight_encircle(node=45) + geom_hilight_encircle(node=34, fill='darkgreen')
189 159
 ```
190 160
 
161
+
191 162
 # taxa connection
192 163
 
193 164
 Some evolutionary events (e.g. reassortment, horizontal gene transfer) can be modeled by a simple tree. `ggtree` provides `geom_taxalink` layer that allows drawing straight or curved lines between any of two nodes in the tree, allow it to represent evolutionary events by connecting taxa.
... ...
@@ -197,67 +168,40 @@ ggtree(tree) + geom_tiplab() + geom_taxalink('A', 'E') + geom_taxalink('F', 'K',
197 168
 ```
198 169
 
199 170
 
200
-# Tree annotation with analysis of R packages
171
+# Tree annotation with output from evolution software
201 172
 
202
-## annotating tree with ape bootstraping analysis
173
+The `r Biocpkg("treeio")` package implemented several parser functiions to parse
174
+output from commonly used software in evolutionary biology.
203 175
 
204
-```{r results='hide', message=FALSE}
205
-library(ape)
206
-data(woodmouse)
207
-d <- dist.dna(woodmouse)
208
-tr <- nj(d)
209
-bp <- boot.phylo(tr, woodmouse, function(xx) nj(dist.dna(xx)))
210
-```
176
+Here, we used [BEAST](http://beast2.org/)[@bouckaert_beast_2014] output as an
177
+example. For details, please refer to the
178
+[Importer](https://bioconductor.org/packages/devel/bioc/vignettes/treeio/inst/doc/Importer.html) vignette.
211 179
 
212 180
 
213
-```{r fig.width=6, fig.height=6, warning=FALSE, fig.align="center"}
214
-library(treeio)
215
-tree <- as.treedata(tr, boot = bp)
216
-ggtree(tree) + geom_label(aes(label=bootstrap)) + geom_tiplab()
181
+```{r warning=FALSE, fig.width=5, fig.height=5, fig.align='center'}
182
+file <- system.file("extdata/BEAST", "beast_mcc.tree", package="treeio")
183
+beast <- read.beast(file)
184
+ggtree(beast, aes(color = rate))  +
185
+    geom_range(range='length_0.95_HPD', color='red', alpha=.6, size=2) +
186
+    geom_nodelab(aes(x=branch, label=round(posterior, 2)), vjust=-.5, size=3) +
187
+    scale_color_continuous(low="darkgreen", high="red") +
188
+    theme(legend.position=c(.1, .8))
217 189
 ```
218 190
 
219
-## annotating tree with phangorn output
220
-
221
-```{r results='hide', message=FALSE, fig.width=12, fig.height=10, width=60, warning=FALSE, fig.align="center", eval=FALSE}
222
-library(phangorn)
223
-treefile <- system.file("extdata", "pa.nwk", package="treeio")
224
-tre <- read.tree(treefile)
225
-tipseqfile <- system.file("extdata", "pa.fas", package="treeio")
226
-tipseq <- read.phyDat(tipseqfile,format="fasta")
227
-fit <- pml(tre, tipseq, k=4)
228
-fit <- optim.pml(fit, optNni=FALSE, optBf=T, optQ=T,
229
-                 optInv=T, optGamma=T, optEdge=TRUE,
230
-                 optRooted=FALSE, model = "GTR")
231
-
232
-phangorn <- phyPML(fit, type="ml")
233
-ggtree(phangorn) + geom_text(aes(x=branch, label=AA_subs, vjust=-.5))
234
-```
235
-
236
-![](figures/phangorn_example.png)
237
-
238
-# Tree annotation with output from evolution software
239
-
240
-In `ggtree`, we implemented several parser functions to parse output from commonly used software package in evolutionary biology, including:
241 191
 
242
-+ [BEAST](http://beast2.org/)[@bouckaert_beast_2014]
243
-+ [EPA](http://sco.h-its.org/exelixis/web/software/epa/index.html)[@berger_EPA_2011]
244
-+ [HYPHY](http://hyphy.org/w/index.php/Main_Page)[@pond_hyphy_2005]
245
-+ [PAML](http://abacus.gene.ucl.ac.uk/software/paml.html)[@yang_paml_2007]
246
-+ [PHYLDOG](http://pbil.univ-lyon1.fr/software/phyldog/)[@boussau_genome-scale_2013]
247
-+ [pplacer](http://matsen.fhcrc.org/pplacer/)[@matsen_pplacer_2010]
248
-+ [r8s](http://loco.biosci.arizona.edu/r8s/)[@marazzi_locating_2012]
249
-+ [RAxML](http://sco.h-its.org/exelixis/web/software/raxml/)[@stamatakis_raxml_2014]
250
-+ [RevBayes](http://revbayes.github.io/intro.html)[@hohna_probabilistic_2014]
251
-
252
-Evolutionary evidences inferred by these software packages can be used for further analysis in `R` and annotating phylogenetic tree directly in `ggtree`. For more details, please refer to the [Tree Data Import](treeImport.html) vignette.
192
+# Tree annotation with user specified annotation
253 193
 
194
+Integrating user data to annotate phylogenetic tree can be done at different
195
+levels. The `r Biocpkg("treeio")` package implements `full_join` methods to
196
+[combine tree data to phylogenetic tree object](https://bioconductor.org/packages/devel/bioc/vignettes/treeio/inst/doc/Importer.html).
197
+The `r CRANpkg("tidytree")` package supports [linking tree data to phylogeny
198
+using tidyverse verbs](https://cran.r-project.org/web/packages/tidytree/vignette/tiytree.html).
199
+`r Biocpkg("ggtree")` supports mapping external data to phylogeny for
200
+visualization  and annotation on the fly.
254 201
 
255
-# Tree annotation with user specified annotation
256 202
 
257 203
 ## the `%<+%` operator
258 204
 
259
-In addition to parse commonly used software output, `ggtree` also supports annotating a phylogenetic tree using user's own data.
260
-
261 205
 Suppose we have the following data that associate with the tree and would like to attach the data in the tree.
262 206
 
263 207
 ```{r}
... ...
@@ -273,6 +217,7 @@ dd <- data.frame(taxa  = LETTERS[1:13],
273 217
 dd <- dd[sample(1:13, 13), ]
274 218
 row.names(dd) <- NULL
275 219
 ```
220
+
276 221
 ```{r eval=FALSE}
277 222
 print(dd)
278 223
 ```
... ...
@@ -281,52 +226,114 @@ print(dd)
281 226
 knitr::kable(dd)
282 227
 ```
283 228
 
284
-We can imaging that the _`place`_ column stores the location that we isolated the species and _`value`_ column stores numerical values (e.g. bootstrap values).
229
+We can imaging that the _place_ column stores the location that we isolated the
230
+species and _value_ column stores numerical values (*e.g.* bootstrap values).
285 231
 
286
-We have demonstrated using the operator, _`%<%`_, to update a tree view with a new tree. Here, we will introduce another operator, _`%<+%`_, that attaches annotation data to a tree view. The only requirement of the input data is that its first column should be matched with the node/tip labels of the tree.
232
+We have demonstrated using the operator, `%<%`, to update a tree view with a new
233
+tree. Here, we will introduce another operator, `%<+%`, that attaches annotation
234
+data to a tree view. The only requirement of the input data is that its first
235
+column should be matched with the node/tip labels of the tree.
287 236
 
288
-After attaching the annotation data to the tree by _`%<+%`_, all the columns in the data are visible to _`ggtree`_. As an example, here we attach the above annotation data to the tree view, _`p`_, and add a layer that showing the tip labels and colored them by the isolation site stored in _`place`_ column.
237
+After attaching the annotation data to the tree by `%<+%`, all the columns in
238
+the data are visible to `r Biocpkg("ggtree")`. As an example, here we attach the
239
+above annotation data to the tree view, _p_, and add a layer that showing the
240
+tip labels and colored them by the isolation site stored in _place_ column.
289 241
 
290 242
 ```{r fig.width=6, fig.height=5, warning=FALSE, fig.align="center"}
291 243
 p <- p %<+% dd + geom_tiplab(aes(color=place)) +
292 244
        geom_tippoint(aes(size=value, shape=place, color=place), alpha=0.25)
293
-p+theme(legend.position="right")
245
+p + theme(legend.position="right")
294 246
 ```
295 247
 
296 248
 Once the data was attached, it is always attached. So that we can add other layers to display these information easily.
249
+
297 250
 ```{r fig.width=6, fig.height=5, warning=FALSE, fig.align="center"}
298 251
 p + geom_text(aes(color=place, label=place), hjust=1, vjust=-0.4, size=3) +
299 252
     geom_text(aes(color=place, label=value), hjust=1, vjust=1.4, size=3)
300 253
 ```
301 254
 
302
-## phylo4d
303 255
 
304
-`phylo4d` was defined in the `phylobase` package, which can be employed to integrate user's data with phylogenetic tree. `phylo4d` was supported in `ggtree` and the data stored in the object can be used directly to annotate the tree.
256
+# Visualize tree with associated matrix
305 257
 
306
-```{r fig.width=6, fig.height=5, warning=FALSE, fig.align="center", eval=FALSE}
307
-dd2 <- dd[, -1]
308
-rownames(dd2) <- dd[,1]
309
-require(phylobase)
310
-tr2 <- phylo4d(tree, dd2)
311
-ggtree(tr2) + geom_tiplab(aes(color=place)) +
312
-    geom_tippoint(aes(size=value, shape=place, color=place), alpha=0.25)
258
+<!--
259
+At first we implemented `gplot` function to visualize tree with heatmap but it has [an issue](https://github.com/GuangchuangYu/ggtree/issues/3) that it can't always guarantee the heatmap aligning to the tree properly, since the line up is between two figures and it's currently not supported internally by ggplot2. I have implemented another function `gheatmap` that can do the line up properly by creating a new layer above the tree.
260
+-->
261
+
262
+The `gheatmap` function is designed to visualize phylogenetic tree with heatmap of associated matrix.
263
+
264
+In the following example, we visualized a tree of H3 influenza viruses with their associated genotype.
265
+
266
+```{r fig.width=8, fig.height=6, fig.align="center", warning=FALSE, message=FALSE}
267
+beast_file <- system.file("examples/MCC_FluA_H3.tree", package="ggtree")
268
+beast_tree <- read.beast(beast_file)
269
+
270
+genotype_file <- system.file("examples/Genotype.txt", package="ggtree")
271
+genotype <- read.table(genotype_file, sep="\t", stringsAsFactor=F)
272
+colnames(genotype) <- sub("\\.$", "", colnames(genotype))
273
+p <- ggtree(beast_tree, mrsd="2013-01-01") + geom_treescale(x=2008, y=1, offset=2)
274
+p <- p + geom_tiplab(size=2)
275
+gheatmap(p, genotype, offset = 5, width=0.5, font.size=3, colnames_angle=-45, hjust=0) +
276
+    scale_fill_manual(breaks=c("HuH3N2", "pdm", "trig"), values=c("steelblue", "firebrick", "darkgreen"))
313 277
 ```
314 278
 
279
+The _width_ parameter is to control the width of the heatmap. It supports another parameter _offset_ for controlling the distance between the tree and the heatmap, for instance to allocate space for tip labels.