CCInx/vignettes/CCInxUsage.html at master · BaderLab/CCInx · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
<!DOCTYPE html>
<!-- saved from url=(0095)file:///C:/Users/binnes/AppData/Local/Temp/RtmpQlgClT/preview-575c60ed429a.dir/ccinxfromde.html -->
<html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">


<meta name="generator" content="pandoc">

<meta name="viewport" content="width=device-width, initial-scale=1">

<meta name="author" content="Brendan Innes">

<meta name="date" content="2019-05-23">

<title>CCInx Usage</title>


<style type="text/css">code{white-space: pre;}</style>
<style type="text/css" data-origin="pandoc">
a.sourceLine { display: inline-block; line-height: 1.25; }
a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; }
a.sourceLine:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode { white-space: pre; position: relative; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
code.sourceCode { white-space: pre-wrap; }
a.sourceLine { text-indent: -1em; padding-left: 1em; }
}
pre.numberSource a.sourceLine
  { position: relative; left: -4em; }
pre.numberSource a.sourceLine::before
  { content: attr(title);
    position: relative; left: -1em; text-align: right; vertical-align: baseline;
    border: none; pointer-events: all; display: inline-block;
    -webkit-touch-callout: none; -webkit-user-select: none;
    -khtml-user-select: none; -moz-user-select: none;
    -ms-user-select: none; user-select: none;
    padding: 0 4px; width: 4em;
    color: #aaaaaa;
  }
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
div.sourceCode
  {  }
@media screen {
a.sourceLine::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; } /* Alert */
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
code span.at { color: #7d9029; } /* Attribute */
code span.bn { color: #40a070; } /* BaseN */
code span.bu { } /* BuiltIn */
code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */
code span.ch { color: #4070a0; } /* Char */
code span.cn { color: #880000; } /* Constant */
code span.co { color: #60a0b0; font-style: italic; } /* Comment */
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */
code span.do { color: #ba2121; font-style: italic; } /* Documentation */
code span.dt { color: #902000; } /* DataType */
code span.dv { color: #40a070; } /* DecVal */
code span.er { color: #ff0000; font-weight: bold; } /* Error */
code span.ex { } /* Extension */
code span.fl { color: #40a070; } /* Float */
code span.fu { color: #06287e; } /* Function */
code span.im { } /* Import */
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */
code span.kw { color: #007020; font-weight: bold; } /* Keyword */
code span.op { color: #666666; } /* Operator */
code span.ot { color: #007020; } /* Other */
code span.pp { color: #bc7a00; } /* Preprocessor */
code span.sc { color: #4070a0; } /* SpecialChar */
code span.ss { color: #bb6688; } /* SpecialString */
code span.st { color: #4070a0; } /* String */
code span.va { color: #19177c; } /* Variable */
code span.vs { color: #4070a0; } /* VerbatimString */
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */

</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
  var sheets = document.styleSheets;
  for (var i = 0; i < sheets.length; i++) {
    if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
    try { var rules = sheets[i].cssRules; } catch (e) { continue; }
    for (var j = 0; j < rules.length; j++) {
      var rule = rules[j];
      // check if there is a div.sourceCode rule
      if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
      var style = rule.style.cssText;
      // check if color or background-color is set
      if (rule.style.color === '' || rule.style.backgroundColor === '') continue;
      // replace div.sourceCode by a pre.sourceCode rule
      sheets[i].deleteRule(j);
      sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
    }
  }
})();
</script>


<style type="text/css">body {
background-color: #fff;
margin: 1em auto;
max-width: 700px;
overflow: visible;
padding-left: 2em;
padding-right: 2em;
font-family: "Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
font-size: 14px;
line-height: 1.35;
}
#header {
text-align: center;
}
#TOC {
clear: both;
margin: 0 0 10px 10px;
padding: 4px;
width: 400px;
border: 1px solid #CCCCCC;
border-radius: 5px;
background-color: #f6f6f6;
font-size: 13px;
line-height: 1.3;
}
#TOC .toctitle {
font-weight: bold;
font-size: 15px;
margin-left: 5px;
}
#TOC ul {
padding-left: 40px;
margin-left: -1.5em;
margin-top: 5px;
margin-bottom: 5px;
}
#TOC ul ul {
margin-left: -2em;
}
#TOC li {
line-height: 16px;
}
table {
margin: 1em auto;
border-width: 1px;
border-color: #DDDDDD;
border-style: outset;
border-collapse: collapse;
}
table th {
border-width: 2px;
padding: 5px;
border-style: inset;
}
table td {
border-width: 1px;
border-style: inset;
line-height: 18px;
padding: 5px 5px;
}
table, table th, table td {
border-left-style: none;
border-right-style: none;
}
table thead, table tr.even {
background-color: #f7f7f7;
}
p {
margin: 0.5em 0;
}
blockquote {
background-color: #f6f6f6;
padding: 0.25em 0.75em;
}
hr {
border-style: solid;
border: none;
border-top: 1px solid #777;
margin: 28px 0;
}
dl {
margin-left: 0;
}
dl dd {
margin-bottom: 13px;
margin-left: 13px;
}
dl dt {
font-weight: bold;
}
ul {
margin-top: 0;
}
ul li {
list-style: circle outside;
}
ul ul {
margin-bottom: 0;
}
pre, code {
background-color: #f7f7f7;
border-radius: 3px;
color: #333;
white-space: pre-wrap;
}
pre {
border-radius: 3px;
margin: 5px 0px 10px 0px;
padding: 10px;
}
pre:not([class]) {
background-color: #f7f7f7;
}
code {
font-family: Consolas, Monaco, 'Courier New', monospace;
font-size: 85%;
}
p > code, li > code {
padding: 2px 0px;
}
div.figure {
text-align: center;
}
img {
background-color: #FFFFFF;
padding: 2px;
border: 1px solid #DDDDDD;
border-radius: 3px;
border: 1px solid #CCCCCC;
margin: 0 5px;
}
h1 {
margin-top: 0;
font-size: 35px;
line-height: 40px;
}
h2 {
border-bottom: 4px solid #f7f7f7;
padding-top: 10px;
padding-bottom: 2px;
font-size: 145%;
}
h3 {
border-bottom: 2px solid #f7f7f7;
padding-top: 10px;
font-size: 120%;
}
h4 {
border-bottom: 1px solid #f7f7f7;
margin-left: 8px;
font-size: 105%;
}
h5, h6 {
border-bottom: 1px solid #ccc;
font-size: 105%;
}
a {
color: #0033dd;
text-decoration: none;
}
a:hover {
color: #6666ff; }
a:visited {
color: #800080; }
a:visited:hover {
color: #BB00BB; }
a[href^="http:"] {
text-decoration: underline; }
a[href^="https:"] {
text-decoration: underline; }

code > span.kw { color: #555; font-weight: bold; }
code > span.dt { color: #902000; }
code > span.dv { color: #40a070; }
code > span.bn { color: #d14; }
code > span.fl { color: #d14; }
code > span.ch { color: #d14; }
code > span.st { color: #d14; }
code > span.co { color: #888888; font-style: italic; }
code > span.ot { color: #007020; }
code > span.al { color: #ff0000; font-weight: bold; }
code > span.fu { color: #900; font-weight: bold; }  code > span.er { color: #a61717; background-color: #e3d2d2; }
</style>


<script type="text/javascript" src="./CCInxUsage_files/MathJax.js.download"></script><style type="text/css">.MathJax_Hover_Frame {border-radius: .25em; -webkit-border-radius: .25em; -moz-border-radius: .25em; -khtml-border-radius: .25em; box-shadow: 0px 0px 15px #83A; -webkit-box-shadow: 0px 0px 15px #83A; -moz-box-shadow: 0px 0px 15px #83A; -khtml-box-shadow: 0px 0px 15px #83A; border: 1px solid #A6D ! important; display: inline-block; position: absolute}
.MathJax_Menu_Button .MathJax_Hover_Arrow {position: absolute; cursor: pointer; display: inline-block; border: 2px solid #AAA; border-radius: 4px; -webkit-border-radius: 4px; -moz-border-radius: 4px; -khtml-border-radius: 4px; font-family: 'Courier New',Courier; font-size: 9px; color: #F0F0F0}
.MathJax_Menu_Button .MathJax_Hover_Arrow span {display: block; background-color: #AAA; border: 1px solid; border-radius: 3px; line-height: 0; padding: 4px}
.MathJax_Hover_Arrow:hover {color: white!important; border: 2px solid #CCC!important}
.MathJax_Hover_Arrow:hover span {background-color: #CCC!important}
</style><style type="text/css">#MathJax_About {position: fixed; left: 50%; width: auto; text-align: center; border: 3px outset; padding: 1em 2em; background-color: #DDDDDD; color: black; cursor: default; font-family: message-box; font-size: 120%; font-style: normal; text-indent: 0; text-transform: none; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; z-index: 201; border-radius: 15px; -webkit-border-radius: 15px; -moz-border-radius: 15px; -khtml-border-radius: 15px; box-shadow: 0px 10px 20px #808080; -webkit-box-shadow: 0px 10px 20px #808080; -moz-box-shadow: 0px 10px 20px #808080; -khtml-box-shadow: 0px 10px 20px #808080; filter: progid:DXImageTransform.Microsoft.dropshadow(OffX=2, OffY=2, Color='gray', Positive='true')}
#MathJax_About.MathJax_MousePost {outline: none}
.MathJax_Menu {position: absolute; background-color: white; color: black; width: auto; padding: 2px; border: 1px solid #CCCCCC; margin: 0; cursor: default; font: menu; text-align: left; text-indent: 0; text-transform: none; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; z-index: 201; box-shadow: 0px 10px 20px #808080; -webkit-box-shadow: 0px 10px 20px #808080; -moz-box-shadow: 0px 10px 20px #808080; -khtml-box-shadow: 0px 10px 20px #808080; filter: progid:DXImageTransform.Microsoft.dropshadow(OffX=2, OffY=2, Color='gray', Positive='true')}
.MathJax_MenuItem {padding: 2px 2em; background: transparent}
.MathJax_MenuArrow {position: absolute; right: .5em; padding-top: .25em; color: #666666; font-size: .75em}
.MathJax_MenuActive .MathJax_MenuArrow {color: white}
.MathJax_MenuArrow.RTL {left: .5em; right: auto}
.MathJax_MenuCheck {position: absolute; left: .7em}
.MathJax_MenuCheck.RTL {right: .7em; left: auto}
.MathJax_MenuRadioCheck {position: absolute; left: 1em}
.MathJax_MenuRadioCheck.RTL {right: 1em; left: auto}
.MathJax_MenuLabel {padding: 2px 2em 4px 1.33em; font-style: italic}
.MathJax_MenuRule {border-top: 1px solid #CCCCCC; margin: 4px 1px 0px}
.MathJax_MenuDisabled {color: GrayText}
.MathJax_MenuActive {background-color: Highlight; color: HighlightText}
.MathJax_MenuDisabled:focus, .MathJax_MenuLabel:focus {background-color: #E8E8E8}
.MathJax_ContextMenu:focus {outline: none}
.MathJax_ContextMenu .MathJax_MenuItem:focus {outline: none}
#MathJax_AboutClose {top: .2em; right: .2em}
.MathJax_Menu .MathJax_MenuClose {top: -10px; left: -10px}
.MathJax_MenuClose {position: absolute; cursor: pointer; display: inline-block; border: 2px solid #AAA; border-radius: 18px; -webkit-border-radius: 18px; -moz-border-radius: 18px; -khtml-border-radius: 18px; font-family: 'Courier New',Courier; font-size: 24px; color: #F0F0F0}
.MathJax_MenuClose span {display: block; background-color: #AAA; border: 1.5px solid; border-radius: 18px; -webkit-border-radius: 18px; -moz-border-radius: 18px; -khtml-border-radius: 18px; line-height: 0; padding: 8px 0 6px}
.MathJax_MenuClose:hover {color: white!important; border: 2px solid #CCC!important}
.MathJax_MenuClose:hover span {background-color: #CCC!important}
.MathJax_MenuClose:hover:focus {outline: none}
</style><style type="text/css">.MathJax_Preview .MJXf-math {color: inherit!important}
</style><style type="text/css">.MJX_Assistive_MathML {position: absolute!important; top: 0; left: 0; clip: rect(1px, 1px, 1px, 1px); padding: 1px 0 0 0!important; border: 0!important; height: 1px!important; width: 1px!important; overflow: hidden!important; display: block!important; -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none}
.MJX_Assistive_MathML.MJX_Assistive_MathML_Block {width: 100%!important}
</style><style type="text/css">#MathJax_Zoom {position: absolute; background-color: #F0F0F0; overflow: auto; display: block; z-index: 301; padding: .5em; border: 1px solid black; margin: 0; font-weight: normal; font-style: normal; text-align: left; text-indent: 0; text-transform: none; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; -webkit-box-sizing: content-box; -moz-box-sizing: content-box; box-sizing: content-box; box-shadow: 5px 5px 15px #AAAAAA; -webkit-box-shadow: 5px 5px 15px #AAAAAA; -moz-box-shadow: 5px 5px 15px #AAAAAA; -khtml-box-shadow: 5px 5px 15px #AAAAAA; filter: progid:DXImageTransform.Microsoft.dropshadow(OffX=2, OffY=2, Color='gray', Positive='true')}
#MathJax_ZoomOverlay {position: absolute; left: 0; top: 0; z-index: 300; display: inline-block; width: 100%; height: 100%; border: 0; padding: 0; margin: 0; background-color: white; opacity: 0; filter: alpha(opacity=0)}
#MathJax_ZoomFrame {position: relative; display: inline-block; height: 0; width: 0}
#MathJax_ZoomEventTrap {position: absolute; left: 0; top: 0; z-index: 302; display: inline-block; border: 0; padding: 0; margin: 0; background-color: white; opacity: 0; filter: alpha(opacity=0)}
</style><style type="text/css">.MathJax_Preview {color: #888}
#MathJax_Message {position: fixed; left: 1em; bottom: 1.5em; background-color: #E6E6E6; border: 1px solid #959595; margin: 0px; padding: 2px 8px; z-index: 102; color: black; font-size: 80%; width: auto; white-space: nowrap}
#MathJax_MSIE_Frame {position: absolute; top: 0; left: 0; width: 0px; z-index: 101; border: 0px; margin: 0px; padding: 0px}
.MathJax_Error {color: #CC0000; font-style: italic}
</style><style type="text/css">.MJXp-script {font-size: .8em}
.MJXp-right {-webkit-transform-origin: right; -moz-transform-origin: right; -ms-transform-origin: right; -o-transform-origin: right; transform-origin: right}
.MJXp-bold {font-weight: bold}
.MJXp-italic {font-style: italic}
.MJXp-scr {font-family: MathJax_Script,'Times New Roman',Times,STIXGeneral,serif}
.MJXp-frak {font-family: MathJax_Fraktur,'Times New Roman',Times,STIXGeneral,serif}
.MJXp-sf {font-family: MathJax_SansSerif,'Times New Roman',Times,STIXGeneral,serif}
.MJXp-cal {font-family: MathJax_Caligraphic,'Times New Roman',Times,STIXGeneral,serif}
.MJXp-mono {font-family: MathJax_Typewriter,'Times New Roman',Times,STIXGeneral,serif}
.MJXp-largeop {font-size: 150%}
.MJXp-largeop.MJXp-int {vertical-align: -.2em}
.MJXp-math {display: inline-block; line-height: 1.2; text-indent: 0; font-family: 'Times New Roman',Times,STIXGeneral,serif; white-space: nowrap; border-collapse: collapse}
.MJXp-display {display: block; text-align: center; margin: 1em 0}
.MJXp-math span {display: inline-block}
.MJXp-box {display: block!important; text-align: center}
.MJXp-box:after {content: " "}
.MJXp-rule {display: block!important; margin-top: .1em}
.MJXp-char {display: block!important}
.MJXp-mo {margin: 0 .15em}
.MJXp-mfrac {margin: 0 .125em; vertical-align: .25em}
.MJXp-denom {display: inline-table!important; width: 100%}
.MJXp-denom > * {display: table-row!important}
.MJXp-surd {vertical-align: top}
.MJXp-surd > * {display: block!important}
.MJXp-script-box > *  {display: table!important; height: 50%}
.MJXp-script-box > * > * {display: table-cell!important; vertical-align: top}
.MJXp-script-box > *:last-child > * {vertical-align: bottom}
.MJXp-script-box > * > * > * {display: block!important}
.MJXp-mphantom {visibility: hidden}
.MJXp-munderover {display: inline-table!important}
.MJXp-over {display: inline-block!important; text-align: center}
.MJXp-over > * {display: block!important}
.MJXp-munderover > * {display: table-row!important}
.MJXp-mtable {vertical-align: .25em; margin: 0 .125em}
.MJXp-mtable > * {display: inline-table!important; vertical-align: middle}
.MJXp-mtr {display: table-row!important}
.MJXp-mtd {display: table-cell!important; text-align: center; padding: .5em 0 0 .5em}
.MJXp-mtr > .MJXp-mtd:first-child {padding-left: 0}
.MJXp-mtr:first-child > .MJXp-mtd {padding-top: 0}
.MJXp-mlabeledtr {display: table-row!important}
.MJXp-mlabeledtr > .MJXp-mtd:first-child {padding-left: 0}
.MJXp-mlabeledtr:first-child > .MJXp-mtd {padding-top: 0}
.MJXp-merror {background-color: #FFFF88; color: #CC0000; border: 1px solid #CC0000; padding: 1px 3px; font-style: normal; font-size: 90%}
.MJXp-scale0 {-webkit-transform: scaleX(.0); -moz-transform: scaleX(.0); -ms-transform: scaleX(.0); -o-transform: scaleX(.0); transform: scaleX(.0)}
.MJXp-scale1 {-webkit-transform: scaleX(.1); -moz-transform: scaleX(.1); -ms-transform: scaleX(.1); -o-transform: scaleX(.1); transform: scaleX(.1)}
.MJXp-scale2 {-webkit-transform: scaleX(.2); -moz-transform: scaleX(.2); -ms-transform: scaleX(.2); -o-transform: scaleX(.2); transform: scaleX(.2)}
.MJXp-scale3 {-webkit-transform: scaleX(.3); -moz-transform: scaleX(.3); -ms-transform: scaleX(.3); -o-transform: scaleX(.3); transform: scaleX(.3)}
.MJXp-scale4 {-webkit-transform: scaleX(.4); -moz-transform: scaleX(.4); -ms-transform: scaleX(.4); -o-transform: scaleX(.4); transform: scaleX(.4)}
.MJXp-scale5 {-webkit-transform: scaleX(.5); -moz-transform: scaleX(.5); -ms-transform: scaleX(.5); -o-transform: scaleX(.5); transform: scaleX(.5)}
.MJXp-scale6 {-webkit-transform: scaleX(.6); -moz-transform: scaleX(.6); -ms-transform: scaleX(.6); -o-transform: scaleX(.6); transform: scaleX(.6)}
.MJXp-scale7 {-webkit-transform: scaleX(.7); -moz-transform: scaleX(.7); -ms-transform: scaleX(.7); -o-transform: scaleX(.7); transform: scaleX(.7)}
.MJXp-scale8 {-webkit-transform: scaleX(.8); -moz-transform: scaleX(.8); -ms-transform: scaleX(.8); -o-transform: scaleX(.8); transform: scaleX(.8)}
.MJXp-scale9 {-webkit-transform: scaleX(.9); -moz-transform: scaleX(.9); -ms-transform: scaleX(.9); -o-transform: scaleX(.9); transform: scaleX(.9)}
.MathJax_PHTML .noError {vertical-align: ; font-size: 90%; text-align: left; color: black; padding: 1px 3px; border: 1px solid}
</style></head>

<body><div id="MathJax_Message" style="display: none;"></div>


<h1 class="title toc-ignore">CCInx Usage</h1>
<h4 class="author">Brendan Innes</h4>
<h4 class="date">2019-05-23</h4>


<div id="ccinx-usage" class="section level1">
<h1>CCInx Usage</h1>
<p>CCInx takes cell type transcriptomes (generally from clustered scRNAseq data) and predicts cell-cell interaction networks. It generates both node and edgelists appropriate for importing into graph visualization software such as Cytoscape, and figures showing bipartite graphs for predicted interactions between pairs of cell types.</p>
<div id="ranking-nodes-by-differential-expression-between-conditions" class="section level3">
<h3>Ranking nodes by differential expression between conditions</h3>
<p>Here we’ll demonstrate the standard use case using data from <a href="https://www.biorxiv.org/content/10.1101/440032v1">a recent study of aging mouse brain</a>, where differential expression testing was performed between young and aging neuronal cell types. The input data is a list of data frames, where each named list entry represents a cell type, and its data frame contains the differential expression statistics for genes in that cell type.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(CCInx)</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="co">#&gt; Loading required package: shiny</span></a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">load</span>(<span class="kw">system.file</span>(<span class="st">"DemoData/DemoDE.RData"</span>,<span class="dt">package=</span><span class="st">"CCInx"</span>))</a>
<a class="sourceLine" id="cb1-4" title="4"><span class="kw">lapply</span>(deL,head)</a>
<a class="sourceLine" id="cb1-5" title="5"><span class="co">#&gt; $DOPA</span></a>
<a class="sourceLine" id="cb1-6" title="6"><span class="co">#&gt;                pval       padj      logFC DetectPctYoung DetectPctOld</span></a>
<a class="sourceLine" id="cb1-7" title="7"><span class="co">#&gt; mt-Nd4  0.000007580 0.03565106 -0.3873342     1.00000000    0.9948718</span></a>
<a class="sourceLine" id="cb1-8" title="8"><span class="co">#&gt; mt-Cytb 0.000012300 0.03565106 -0.3685228     1.00000000    1.0000000</span></a>
<a class="sourceLine" id="cb1-9" title="9"><span class="co">#&gt; Fam3c   0.000013900 0.03565106  0.3439192     0.03731343    0.2307692</span></a>
<a class="sourceLine" id="cb1-10" title="10"><span class="co">#&gt; mt-Co3  0.000069600 0.12934011 -0.3110772     1.00000000    1.0000000</span></a>
<a class="sourceLine" id="cb1-11" title="11"><span class="co">#&gt; Sepw1   0.000098700 0.15294014 -0.6145720     0.56716418    0.5487179</span></a>
<a class="sourceLine" id="cb1-12" title="12"><span class="co">#&gt; mt-Nd1  0.000151239 0.18006463 -0.3097862     1.00000000    1.0000000</span></a>
<a class="sourceLine" id="cb1-13" title="13"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-14" title="14"><span class="co">#&gt; $GABA</span></a>
<a class="sourceLine" id="cb1-15" title="15"><span class="co">#&gt;             pval     padj      logFC DetectPctYoung DetectPctOld</span></a>
<a class="sourceLine" id="cb1-16" title="16"><span class="co">#&gt; Malat1  1.11e-59 1.43e-55  0.3297557      0.9965659    0.9982379</span></a>
<a class="sourceLine" id="cb1-17" title="17"><span class="co">#&gt; mt-Nd1  1.36e-46 8.72e-43 -0.2975538      1.0000000    0.9995595</span></a>
<a class="sourceLine" id="cb1-18" title="18"><span class="co">#&gt; mt-Nd4  5.53e-46 2.37e-42 -0.3230167      0.9986264    0.9969163</span></a>
<a class="sourceLine" id="cb1-19" title="19"><span class="co">#&gt; mt-Atp6 2.23e-45 7.16e-42 -0.2668197      1.0000000    1.0000000</span></a>
<a class="sourceLine" id="cb1-20" title="20"><span class="co">#&gt; mt-Cytb 4.04e-45 1.04e-41 -0.2907866      1.0000000    0.9995595</span></a>
<a class="sourceLine" id="cb1-21" title="21"><span class="co">#&gt; mt-Co2  1.12e-33 2.39e-30 -0.2465769      1.0000000    0.9986784</span></a>
<a class="sourceLine" id="cb1-22" title="22"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb1-23" title="23"><span class="co">#&gt; $GLUT</span></a>
<a class="sourceLine" id="cb1-24" title="24"><span class="co">#&gt;             pval        padj      logFC DetectPctYoung DetectPctOld</span></a>
<a class="sourceLine" id="cb1-25" title="25"><span class="co">#&gt; PISD    4.67e-30 5.77000e-26  0.5634818      0.7088036    0.8787879</span></a>
<a class="sourceLine" id="cb1-26" title="26"><span class="co">#&gt; Malat1  1.01e-19 6.21000e-16  0.3880324      1.0000000    0.9983165</span></a>
<a class="sourceLine" id="cb1-27" title="27"><span class="co">#&gt; Gm26917 7.02e-14 2.89000e-10  0.3507149      0.2911964    0.5101010</span></a>
<a class="sourceLine" id="cb1-28" title="28"><span class="co">#&gt; Nap1l5  1.38e-08 4.27000e-05  0.3612765      0.5959368    0.7424242</span></a>
<a class="sourceLine" id="cb1-29" title="29"><span class="co">#&gt; mt-Nd4  4.05e-08 1.00002e-04 -0.2316669      0.9977427    0.9983165</span></a>
<a class="sourceLine" id="cb1-30" title="30"><span class="co">#&gt; mt-Nd1  5.30e-08 1.09025e-04 -0.2269154      1.0000000    0.9983165</span></a></code></pre></div>
<p>The CCInx network is built using the list of gene expression data frames. The output of <code>BuildCCInx</code> is a list of cell type pairs, with each entry storing both the edge list and node metadata. These can be exported as .csv files for use in Cytoscape.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">inx &lt;-<span class="st"> </span><span class="kw">BuildCCInx</span>(<span class="dt">GeneStatList=</span>deL,</a>
<a class="sourceLine" id="cb2-2" title="2">                  <span class="dt">GeneMagnitude=</span><span class="st">"logFC"</span>,</a>
<a class="sourceLine" id="cb2-3" title="3">                  <span class="dt">GeneStatistic=</span><span class="st">"padj"</span>,</a>
<a class="sourceLine" id="cb2-4" title="4">                  <span class="dt">Species=</span><span class="st">"mmusculus"</span>)</a>
<a class="sourceLine" id="cb2-5" title="5"><span class="co">#&gt; Scaling node weights per cell type...</span></a>
<a class="sourceLine" id="cb2-6" title="6"><span class="co">#&gt; Building node metadata...</span></a>
<a class="sourceLine" id="cb2-7" title="7"><span class="co">#&gt; Building edge list...</span></a>
<a class="sourceLine" id="cb2-8" title="8"></a>
<a class="sourceLine" id="cb2-9" title="9"><span class="kw">head</span>(inx<span class="op">$</span>edges)</a>
<a class="sourceLine" id="cb2-10" title="10"><span class="co">#&gt;                                 nodeA         nodeB edgeWeight</span></a>
<a class="sourceLine" id="cb2-11" title="11"><span class="co">#&gt; Acvr1_DOPA~Acvr1b_DOPA     Acvr1_DOPA   Acvr1b_DOPA  0.2790543</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="co">#&gt; Acvr1_DOPA~Bmpr1a_DOPA     Acvr1_DOPA   Bmpr1a_DOPA  0.1935704</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="co">#&gt; Acvr1b_DOPA~Acvr1c_DOPA   Acvr1b_DOPA   Acvr1c_DOPA  0.4319298</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="co">#&gt; Acvr1b_DOPA~Bmpr1a_DOPA   Acvr1b_DOPA   Bmpr1a_DOPA  0.2919875</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="co">#&gt; Acvr1b_DOPA~Hsp90aa1_DOPA Acvr1b_DOPA Hsp90aa1_DOPA  0.4434583</span></a>
<a class="sourceLine" id="cb2-16" title="16"><span class="co">#&gt; Acvr1b_DOPA~Snx2_DOPA     Acvr1b_DOPA     Snx2_DOPA  0.2785116</span></a>
<a class="sourceLine" id="cb2-17" title="17"><span class="kw">head</span>(inx<span class="op">$</span>nodes)</a>
<a class="sourceLine" id="cb2-18" title="18"><span class="co">#&gt;                    node   gene cellType proteinType nodeWeight        pval</span></a>
<a class="sourceLine" id="cb2-19" title="19"><span class="co">#&gt; Fam3c_DOPA   Fam3c_DOPA  Fam3c     DOPA      Ligand  0.6700919 0.000013900</span></a>
<a class="sourceLine" id="cb2-20" title="20"><span class="co">#&gt; Cst3_DOPA     Cst3_DOPA   Cst3     DOPA  ECM/Ligand -0.7916701 0.000389130</span></a>
<a class="sourceLine" id="cb2-21" title="21"><span class="co">#&gt; Cntn5_DOPA   Cntn5_DOPA  Cntn5     DOPA    Receptor  0.6451822 0.001858835</span></a>
<a class="sourceLine" id="cb2-22" title="22"><span class="co">#&gt; Acvr1b_DOPA Acvr1b_DOPA Acvr1b     DOPA    Receptor  0.3774713 0.006177912</span></a>
<a class="sourceLine" id="cb2-23" title="23"><span class="co">#&gt; Gabra1_DOPA Gabra1_DOPA Gabra1     DOPA    Receptor  0.4729222 0.010928797</span></a>
<a class="sourceLine" id="cb2-24" title="24"><span class="co">#&gt; Gabrd_DOPA   Gabrd_DOPA  Gabrd     DOPA    Receptor  0.5560968 0.011594485</span></a>
<a class="sourceLine" id="cb2-25" title="25"><span class="co">#&gt;                   padj      logFC DetectPctYoung DetectPctOld</span></a>
<a class="sourceLine" id="cb2-26" title="26"><span class="co">#&gt; Fam3c_DOPA  0.03565106  0.3439192     0.03731343    0.2307692</span></a>
<a class="sourceLine" id="cb2-27" title="27"><span class="co">#&gt; Cst3_DOPA   0.32885040 -0.4877179     0.83582090    0.8000000</span></a>
<a class="sourceLine" id="cb2-28" title="28"><span class="co">#&gt; Cntn5_DOPA  0.67958515  0.3311345     0.08955224    0.3025641</span></a>
<a class="sourceLine" id="cb2-29" title="29"><span class="co">#&gt; Acvr1b_DOPA 0.76506521  0.1937341     0.05223881    0.1487179</span></a>
<a class="sourceLine" id="cb2-30" title="30"><span class="co">#&gt; Gabra1_DOPA 0.99181188  0.2427234     0.11940299    0.2871795</span></a>
<a class="sourceLine" id="cb2-31" title="31"><span class="co">#&gt; Gabrd_DOPA  1.00000000  0.2854121     0.28358209    0.4769231</span></a></code></pre></div>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="kw">PlotCCInx</span>(<span class="dt">INX=</span>inx,</a>
<a class="sourceLine" id="cb3-2" title="2">          <span class="dt">cellTypeA=</span><span class="st">"GABA"</span>,<span class="dt">cellTypeB=</span><span class="st">"DOPA"</span>,</a>
<a class="sourceLine" id="cb3-3" title="3">          <span class="dt">proteinTypeA=</span><span class="st">"Receptor"</span>,<span class="dt">proteinTypeB=</span><span class="st">"Ligand"</span>,</a>
<a class="sourceLine" id="cb3-4" title="4">          <span class="dt">TopEdges=</span><span class="dv">50</span>)</a>
<a class="sourceLine" id="cb3-5" title="5"><span class="co"># Also check out ViewCCInx(inx) for a Shiny viewer!</span></a></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAIAAAB0NULIAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOy9TYzjaJrn9zA+FKH4zMr67Oqs6ulpKacmHINZYPdEHbZ8MBZSwkAejPAxsRcKsLGQMNiEL2n32M6LkYuBhMUaEC+LPDrgQwJGihj44J5D6DK9mPZ2THQ5qe6prsqqyvrIrIxPKRSRQR9einpJvqQofkgK6v+DDiHy5UtGiKE/n+d9PiTDMAgAAAAA6WJu0hcAAAAAgPiBwAMAAAApBAIPAAAApBAIPAAAAJBCIPAAAABACoHAAwAAACkEAg8AAACkEAg8AAAAkEIg8AAAAEAKgcADAAAAKQQCDwAAAKQQCDwAAACQQiDwk6etlQsFqU+hUK5rbfegen9IWbPt0MqSiEJBMIvnJGBqEX2+4nukrdW5G6lQKGvttv88bJBzGtwkAKQFA0wSvSYLPxa5pnuOU5r8nqbi/eHaR/pMAqYW78/Xdo943EiDMd7z2G8F3CQApAZY8JOkXb9XbRGRrDTN72G9qchERK3qI958aj/dbRHJiiITkfpEYFkJv6XtI4dPAqYW/vM1b5JW9V7dNL89byRujGsevWneJQ+5IbhJAEgRiT9CAE9Mo8ptZ8tKranbNxGRXNNN4eaP8JyEyG7k+U0Cphbfz9fcLB5j3ywc49qImwSAFAELfnK0n+0TESl3i7bNucreXqNSzHEDn+62iGj7di53ZyeIZdXW2BHyzh1rmpEnAVOM+RnS/rM2kfZEJXLfSFS8q1hjBLS1+kOViEjeyvc34SYBIEVA4CeHftAi/tvVC/NbV7lbtL7Y3d+7aomLncqXqi0iufa44tR3/0nAtSF3e9uxRXAj5bdkImod6INN3H3C7hIi5UH/NsFNAkCqgMBPD4PwZVsMM/+tO9L3bmt3sI4fehKQYthaUKNv+OMmASBdQOAnh8C+EmB+61qmV56ZXc7vXUeQXbMmE7XUkvmUEHAScG0w13c4BDeSwEdku0/sa0G4SQBIGRD4yWF6Wa0g5lxlzzAMR0JT/1vXhe/3bq5YecCtv4abBEwxTLtp+3bOWmu3RcMTUdtcYt++nXMf7wY3CQBpAwI/QYr3a2Yq06DcSFurP+FsM/Nb154Wz54AfL9323UWeBVpEjCdtLV6oaQSkVy7XySyFJ6/kdpa2cycM8cMnRQ3CQCpI/a4fDAKulf9EVlpGsJsN8OwZzf5FbqhoJOAqSXmQjdeHzduEgDSByz4yZIrNgy9WVMG385mEvxeoziwqrhsNyIa+GT9LCtZNuOnokwCphJ2j+xVuA80V9nTmzVFln3G+ICbBIAUIhmGMelrAAAAAEDMwIIHAAAAUggEHgAAAEghEHgAAAAghUDgAQAAgBQCgQcAAABSCAQeAAAASCEQeAAAACCFQOATQitLIgqFQVXa8dNua+VCGTVLph3z5hF/UmbTwfF+jL5XBACYTiDw46XVUkv5CX1Ntuv38iVV3FAEAABAyoDAJ4ujiysr+O1s+wVAcMymg41ALWQAADMMBH6c5MwmrhzternQd9+XHe0+NWuXc5/HUX1Hat06slCosyWBdr3Q7+9d4nyt3ERSoVzXHFMV6ub+SS4sABsuF731GRbKWtvpS+dvIv5escYN7gDPe6xQ1p6N7dcDAMTHRFrczADCNlz9ll/9re5OYVY3L0F3sP4+76M8Oo8pTdd8no3olKZoKmeTMZAovj3czE/S3OnRRc7cK/iEvffwp/SfFgBwLYAFnyxqiQ+xy9s6dLfrD1UaSKpek4la1UcaEZH2qMp152Zft63dp23/o/rItaZuGIbeHCwJ5Cp7vDI0iqSVSyoNZjIHqyX7VEpTNwz9ccCmZGDMmPeJ+SH2P3Br7xPuVtFrosZw1q3CBN/ca5/WS+4BANMNBH6cmJ1gTbXUD0yXeZ5Tf/YV2362T0SkPGBDzWXXvUrO96g+yoNKMUdEuaK5JNA60F3X0j9Fs1Fk5+gPtk91t5gjykHepxPzQ5Rr9+0fuEmxYRiGcZ/0erlcyFdV9wSDW+Uud6Dr3nAvLQEAph8IfLIwn2bfPmrtHwx2md+iQkwVF+B3lIm8lbd+zm952V6CU5iD959ZS7H8VGD6MD/E7dvWE5jtA2cL8PlSqaqOlD3hvje87yMAwNQCgR8HuWLD9LKrpXuOCHrX8najGOD7VHiUCW+vez8pCE7hlgsw3bgfyfgPXHtUUlum30jXzWX3IM9s/tMCAK4JEPgxkas8ZguZraop8bk7OzKx9fM2Ednin3O3t4mI1If1/r4CC2pv+x3VRy2V+6HzD9ky+10up8r82u6fwhqriQaDqcb8EAd3g/kZEtHAf799t1LM5aj+ROCiDzEtAODaEH/cHjAMcSC0Fas0PB5eGNfkGQDtH0Vvzcntlmu6VxQ9PxbR8xPBI8SdfR6+UfSyzOVpiKdhB7ruT/sGRNEDkAJgwY+RXGWPfY32rfhiQ2/W5P5XqazU9H4AHuUqe3pTsb5lZaXW1E03vM9RDKWp18xDbXuL9/ubaft2fyLxOcA1IVfZ4z7s5t6D7cG+YsPaRbLSNCNBOMd7sGlJVpo1BNkBcP2QDMOY9DWAuNDKUkklUpqoczYjmJ84KU29UcxZ9Yxk10MfAGD2gAUPwPWln91mz5qUd+5A3QEAEHgArjHFht5UZG7BXLBkAwCYTeCiBwAAAFIILHgAAAAghUDgAQAAgBQCgY+AVpakglmYTivbas0IadcLjpI0rulsDJ8RgIiIb0r+1o4wr9lu1jnR4E7ndg357+CH4R8DgEBA4GOh35ktCu1n+47CMt65bs//6XPHK+rZQerY//S/d7zGdmqtLOWrZN7M+s5untNkrSyV9vuVmbar+ZGeI7Sy2V0JuOCLWpqPV9Ge0cD1BwI/NegHrWBl4IVyDo0HPEI5H5fGa09Ukmv9HsO5yuOarD6sW9WTB7uKjabi7HXsPWtZkqI/RwMwQ0DgI6PXC1JJZa3fBw57aeCErNvdnc/Evsv2s/0gZeB9hBwaDxg+Qh5G4zlv+2DhqF0vSIW6JrqZtSeqPRU/d3ubWrtP20Ttp7v2x9j8lmxrUOzx38Eq+sg13buML+DgG0yDGQYCH5l8Zc9oKqxO914lZ34XWTXdt6v2PtxqdXfHdF3WiHNQ6gctef+hJPx2A2BStOuFfHWbK1mvlix/e6ta6u9qKq1q3ndt3GpyaOtnZ/a16eP138Fa26dRr/qO9brZUEqSCgXWYsqkbe2QJKlQtn0vtOvWQWXtmW0HXPSAIPDx064/VElp9tfPiw2HyeFwXfYdlNoTlVq0Yy3BPzgYbXESgPCoJUd858AVrh+0eM9SscEHh8g1rkGC5YfPb8m2nsX9vnbk1XZ2UB7f478j/ajVkmr+aVqtasl6VtLKeWsHEbXUwWNUu17IV62D1FIVyxfACQQ+bhxfiINqoia8fzJ3e7v/7ea0UIp3gy9OAhANV5e4wVNp8a5CnNFux3kzMz98rvJAIbXUf0Bt1+8FDozz+O+YAeRaUzcMQ2+yPn7ms5L2xOw0wJwaChH1VzS0R+yvau0U9v8Dsw0EPmYGxooYm3+S8lue/5XOxUkAJkKxYeg1eWDjc1Jvv5ntxzSVVtWsj3/wwGgqbLD4hrdkPfB/R+pQHlSKOSLKFSsPWMfJA53M537jPun1crmQ521084tGaTaKOSLzqQoAOxD4mHGsKbqwuS49PJYWnl+gAIwRM2SLWfYD09wXJk1mumf72f5AxT28965dw/470gT/n84/17AF+HypVFVV+x/D/ceZpechEBAIfNy4LG+HTc/7HNvP9s1wY3eVD4+suVs//xOvM/vsAjPF9q/+Q4hdQSg2mspAhplH3sQVOz+g/XS3xSQsd2dHtv0LOFa0xP8d6Yd/suGkW3tUUltEslJr6no/g4D9JU055/5gM/Q8BIICgY8N81/NXIDkgmTsC5Ct6j0rly5fbSkPKjnrKO65QHuiyrX7wqw5oZBD3QGPUMjDqLujpp32ROXMTf5mLqlk3swsMtx6XNXK+Sr1g+dylQeK/SjbbS7+75gB1FJZswoFEBEpd4t920Devlsp5nJUf8K56E1XYav6SGsTUVszjwOAxwChaSrUrz1nBiX1K9FxkfNKs59Dx+JglNogGsYR28TH27vCngJx9vow4u8EZgx2U4qC7KyyirY8kP7WITczf4y9PqMtHozbNey/g5s43D/HdCLO67d/rTgwf32PsLrBV437Lw9mDQh88ozrK+ns9SF7JX8qMPOIHwzAqJgarjT1msIEW1Y4Wba2kqw0ddNHb+237a0pEHjgYEH0DAgi0C8M0s8VZl5IfWiJutD8+jffLy5cEdFfbr/fOTwios7hUXZzI7ETgmvA2YvveienmY31lffemfS1gCDkKo29SmPI1qJhNLz3Gs7jA5W+BikGa/Bxk6vsNRWucEiJmolX4Lq4nCOiP375ytJ1pvRgNoG6zzLtekFCSx5ARESw4BOg2LA/ZyfL8nKm2+0R0evDi599RNnNDag7SJxcZc+oTPoigIB+NL08qKcJZhXJMIxJXwOIyq9/8z0RLS5c/eLnG2urWepb8HDUzyBnL74jIpqbIyJY8ADMMnDRpxmY8rNJ7+SUoO4AzDwQ+PRwcTn3+386OjntEGe7Q+NnCma+ZzbWJ30hAIDJA4FPA//in73r3giNBwCAWQYCn2ag8TMFVt8BADwQ+FRxcTn38lWHeekZiLObEUx1B2BUtHI/p9e3jZDnsMEOd8NBMFkg8ClheTnDfnh9eOHYxTQeRvxMAPMdjIRWlkr7/Qrb29W8h8b7DGs/23eUzEN23tSANLn0wJLliOiT/BoRsXw5hqXuMOhTCTPfVz547+y7HwgCD4LSrhfyuzu6VYlLK0slaroU2neYxzFgGoAFPxNgMR6AGaJdL/j7281hT3ftTaldza6HD2s/2+cb/oKpAgKfQl6+6rg3QuPTCsx34CRX2TMeHOSHrqsTcc1/yWpCO8ow/aAl7z+UAi3jg3EDgU8P1jL89y/fEBEfaseAxgMwMxQbhmEYxmO65xn51q9qa2f/WTv4MO2JSi3asZbgHxx4LeODCQCBTw/bn2wOHYM1+JRhme+TvhAwpeQqe4ZhGPrWw0Ss62LDMPhuWsW7Sqv6CGH0UwIEPp28PrwkkRFPCKpPKfDPAyHtekGSJCl/8MDd1TK/JQuOcDWZDThsMNi9jA8mAwQ+VVhe+hffdYcOhsZfd2C+A2/M9PR79Ng3d611oA/etJ/te8wWcBjDtmAPJggEPlXwXnqWJudjxBM0Pi3AfAc22vWC9HBLN+zucxe5OzuybcldP2gJQuJ9hrXrBcfyvn7Q8jLuwdhBHnzasLLhP3hv+cbmAtkT4nmQHH+t4c13CDwIiVnBZq+Ss/8ceJgjDV4rSw+3hFOACQALPm1YXvqhwI4HYNYpNvQaVfOSJEl2dbfb5p7DqNgwmlSy0uSe3PX1GYDxAgs+bex/dtjt9tjP/+Kfvctc9F5GPMGOv57AfAcADAUWfNoIkizHA10HAIBUAoEfEa3cL9akld3FI9r1gnNbIq2WvvjiS8dLOOz518c+oXYWSJy7XsB8BwAEAQIfI1o5X3WWfEqg1ZJQzvmNIyXL8UDjAQAgNSxM+gLSglaWSqpgu37Qou0H8QWdeBnrbNfHH39ERNufbFqx9ES0tpo9Oe2cnHZ8VuKzmxtM3TuHR3DaTzPBc98P/uu/cmzZ+r/+xmc8u4VlcRS15xEj9BFr1wv5gwdoOzYKv/rVr4TbP/3007FeB7iewIKPA60slVS5phtNxbHHu9WSVpYK9XqZ99rz3vw4HPlERM+/Pg44EkH10w9T98Fbb/+8W929Npq06w9VWVHkxOqMivxbwBcvdfffBYAFBD4OnPWYB/i2WmpVd1ktikbRsoYMwzAMvSarpUiFo4Mny/FA468FQ813HyH32sU6gt69v5NInVGtLIkdXMATS8I/deEYAIAXEPhEGdJqSd65kxuMlGv3TVM/d2dHtteGDA1bhg8SaseAf35qSbIwbfvpbouUu8XcHafCs87iGito7tEP1H+Mt38LeMGru3tvUP/8wCXoay0MHSaIHbYdHsbdGPXaEgleTh8Q+EQZ0mqJq+hYbFjjtLIU2ZnJJ8sF99IzEFR/LYgzfl57VG2x50um8A/tX6ataqm6zbxLTaVVzYu+S73HePu3gD8+Qs52+RnxZr059olsV72auA4f5r+2opVDuGaiX1sCwcupBAI/XrxbLbUt+6dETb0mat80Gg4vfXAj3gIaPz0k2ldGe6Ja3qRc5YFCrd2ntm9cuaab35/Fhl6TnQ8AgceAcdGuP1Tl2mPzsarYaIqbuA4blsjaShzXhor3wYDAjx9Rq6V2/V61pTSHP4uyOPmRdo2aLEdYjJ964jTf2/WHKr9aVLyrkOMbl/8qzd3edj0ABB0DxgQLqeA+EbFl4T9s6NpKu16QSiqRWuI86JzvXGyYx3Ft3sHLwAYEPkmCt1pydHHSD3xc9EIhd28ctaSdA2j89JCo+d5+utsiapl1xiXJNNn4b9wg3T/RIXTasH0iudvbIw8buraSq+wZTYVIafbHaWWppPYNleZ2Ne+xNB712nyDl8EACHyS5CoPFNs3pS2Ujsfx5FxSiWz9GR18/PFHjpf/hbBl+FG99ND4a4pPvrtrFwuvaxo2mgolEUwPxoXYQnB9owQcFhTmVO+v1FCx0VRc8RyxXNuQ4GVgAYFPlqCtlnKVxzVZNQc+3NKNpkKt6r2Rblq3codLluNBUP3EcZvvQfzzQo0XbByE1/EU79f4UDubt51fsOcIMgakGpdTnfJbcWUD2RkSvAwsUMluRIqNfvu9wU+ivfZtDb+JGLnKnlHh3ouP8sTfLn/xXffWh+sUrKqdA1bkDhXurh3+desY2hOV5JpbiXN3duRqdfdpu3KHiKhVvVe/Y/YCL6mkNAVPqUHGgJH41a9+5RVIz+LnPcPs81sy7bu2uhYHAw4LjdD5nsS15bdkqj7RGkUsy9uBBZ8ShO73KMlybuCoHz+Jrr6z8DrlgUiJWTB93ypSaju7Zi9wVfGoTRtkDAiIfzWbYCVubKZz+5lbLEcaFgrP6ZK4NkSBCIDApxy3lz5EvhwW46eH4PHznePjzrHvU12usueds1FscAkdtyt7gnzjYoN/Kx7jmBG6HxRe4x04BgjI3dmRbcvajije0YYFxDUb6Qctt+5Gv7bgwcszDwQ+PYRQ7uBA48dPsuY7mHqGFrrxJld5oAyCeLRySRzdG3DYEPoyzGazahyxpRq3gyjytQUPXgYGSBHHJ2fHJ2f8lt/+7vXf/8N37OUzLCBnrw/ZK+qFggCcfvPt6Tff2rZ8+/3pt98HPPzs6Ojs6CjqReg1mVxR9iHGgLHDlcvii745Py2PYRz9VDghZpK8dSiXNO8xXTzXxifn497zQjLcUWHgOsMseD6Gzmod+8F7yyzUTjgsIMyCR8Bd0gjN95H880SUXV9P5uoAANcAuOjTT/RkOR5Uqh8DjrawAAAQAgh82vBZiedr1kZfsIfGJ01o8x0AAAgCPwvEmyxHCLhLmOixdfDPAwAIAp9K3Na50EsfxYiHxo8ZmO8AgFFBJTsQElbhjohQ5C5GkBoHeP76r/96pO0A8MCCTyde1vmL77qxeOkZ0PUpBP751OCj4hB4EAQI/Eyw/clm7F56BoLqY8TLfId/fgaxJPyvXTgGeDJoze7ba81n2NAZ2vWCR0dYdrj3viQvO8i1zQYQ+NSSaGE7N9B4AOLCX8UDme9aWSrtm8VhmttVr36qPsOGz6CV81VRU9f+3pI6/Dpjv+xA1zYzjL+2DhgbfMU6YUk797BwoMJddNx16wa7xl/ADkyUX/7yl7/85S8jjNFrsq32m0cxOp9hw2YYVJLzqiPnWwAvqcsOeG2zAiz4NMMb8bEny/EgqD45RvLPYwEeEAlas+e3ZFv19qHD/GfQylJJlWu6rWCsbeZ6QSqpRGqJ86APnOoeXvXolx3k2mYJCPwMEW9JOwfQ+CggeB7Eja2Nm7Az+7Bh3ruKDcMw9kR9hvujK3uWXc3GaWXWR9gwDOZV91gfj3zZQ69tloDAp5wgK/FxrdZD42MH4XUgDPqBaPnZ1sp1yLCAMwSlXX+oyjW93y242GgqpD50mvHRLxvYgcDPIvEmy/EgcS4EcZnv8M+DKcXlVKf8lkytA31ylzQbQODTj2WgeyXLUawh90icA2DC5LdkwVabwg4ZFnCG0Aid79EvG9iBwIOkgMYHwcd8h39+xhla6MY3X85mH7ef7Y8+LOAMofCcLvplgwEQ+JnAbaC7vfSxG/EEjR9GjG1h4Z9PE1Hz4HN3dmTborR+0CLlbjH4sIAzBMQ1G+kHLXugXDyXDexIhmFM+hquLWaxhb1Kjkgrl6nRiHSLaWXJXhhCaRrRZuRhyr22mt3/7LDb7RHRB+8t3/pwXTgmljNa6o6FeS/8V9+RIDfj+Av5EJm3fzsNfg4+LMgMWlkqkcf3lP2YfhR9o+h8E/NlB7q2WQEWfCyEqtnkoP1s31a8wfC5M7/44kvHa+j0Yy5sR7DjhxGjuoNUEqkWfbGh16ialyRJsutfu16QBilqnsN8dwWieFehVjVv5rwXG0ZTUUuSNZ34+y36ZQMOWPARGDw5xvGsGHgOLzn/+OOP/A9k6v75lz0vC57iNuKpr+4w4t3AfAcAJAos+MjogppNtpJN9TJfuInbZav00H62H2QZycdYH2rHM9men7tkb5NLluNBUL0QmO8AgKSBwEcm76zZ5CzZVB14700r3TAMw9BrsloaCL9+0JL3Hw4p5RgHH/10xaekXXKefGj8dNOuFwaPns77L0BbMOv45G5dAMBIQODjpl1/qJLStJVs6qM9UeXafXNP7s4OV+pBe6JSi3asJfgHB159lCLhMOLHAxbjHcRbmNbLP//F//y/O16eU2hlScrv7nDdO7attVMKFmKiPaq2WAgJlkMBmBIg8HHjytgo3rUUvtgwuAhVWzdDZwHl4l2lVX00hmbGbi99EkY8ND44sfjnhXIu1vh2vVBSlaZNmIsNvSa3qvdGe8ZEqREApgoIfMz4l1wYuEFL1NRronpMfcR9lAJx9sMr9hLuZfq9ujJHRC++64Y5QSig8Ywx9JXxMdbdu9pPd1sDv5JFrvKg2dyr5Nxtwey+/LJmbx3m0UIEhOO/9GDS1wWuBxD4mPFuf0TUrt+rtvqL80Ei7l2FIEbEUnqH3n/005VI84YFsfRDGXt4nbtKeJ9isUiutmDteiFf3baabDcVtSSVdW7MbKcdx4uPkEPjQRAg8HHjsrwHNr3De8/3RLInefb3Cr53fXLhrF0r79xceeeme4Cl9HOdDhEtLlyx7ePx0jNmPKg+dvM9coKcsKKY/3DuHi42IOkJYUn4/+PCMcCTQcaObzyPz7BwuxjtesHHnRMgbHO0Sx3PZV83IPCxYZZOzFUeKKSW+reIVh4stdu03wxcsh3FPRfY4vHsCDXevZHJvPVy7F247KzMXaxcnR29eCWdJZ4s52ZmNf56U7yrkFpK0TfgdMKru3uvcKMTs0qHFTPpoWc+w8LtskbYYoxce0NUBkv0Nwp02dcQA4SmqVC/9pwZKt+vRMdFzitNy3lpGNyyu1zT2bBB9TruqP4BI3J2fHJ2fDJ02On3L0+/f/nsH7/83X/+4z/+p8+f61+xLexlDTs+OTs+OQtzHUOv8/UheyUx+dRy+s23p9986zfg2+9Pv/1+pDnPjo7Ojo4cG//41//B52Ufq9fkoXcbdwdbhzhvVMcYEIlPP/30008/jTBGr8m2upgeH4/PsHC7rPf+X2Mh7pZEf6OAl339gAUfgWLDCnwvNgzDGITBm28Nw+G/zFX2+tv3Kjln6Dx3VBi351cvTtgPnZNT/5HMpr9cyJ5LS2dzzvV4y5PfOzrpHZ2MfB0BQMBdLDj8815hlb6424D00coeIXODu7ipEF/LAUwLrsgKccyuz7Bwu8gsA2KZL6JrE1QGs5cGE91Pif5GQS77egKBjxvnarpWLnk622Pht7/74de/+fbXv/n2mxen2bVVtnGoxhPRm6sFIlpcuDJW1l/1Foeu3PtE5odg1jQ+ueB590fz8S//O6/B7l25OzuyICOzXX+o0rAbt9hoKvbGnWBqsEVWeAf/+gwLtcuZ8Osi56wM5ioNlvdYAkryNxp62dcTCHzc5Cp7g54KLB8u2fvmL/7cFnE9ksZnMgtkT5bjl+1v/uRdIupevOEPiVHsEVTPM2r8/NkPr86PTs49XCxCjRcLf/+O5Syndr2Qr7bk2uPBjWsa+Zqt7jJpT9TIuR4gfvgA3gEuR43PsHC7wtCuP1Tlmm4rDaY+dJrxif5G6WVh0heQRooNw2hM5MxfvTj56Qdr2bVVpu6dk4FN72b7k839zw6JLq1wejeZjTUiWun3nnGIuuOt0AHgT3Zzo3N41Dk8SrfYx2i+W3/z3vk5EWWWlsjjL+9jxzspNgzjblnKS9X+FrmmG4O1o7sKqdW8tFvT9xpGkx8oo5EXiATznD/g7qH8lkzqgU6E+yoyEPg0sLQ0f37+hoi+eXH60w/WiCigxvM8//rY3VxubTV7cto5Oe2wxDleSNwWPL9lVLFPscYzdR8yZpj5LvSXLG2sxddBzu+x1LZPPLDYQF/K6SG/JZO74pYr7dZvWLhdcSB0vif7G6UWuOjTgMNLzwjoq59fWCail6/ORj2pfxpe8JX7GVmMD2e+C/+A7A++tLEW06WBKWVooRvffDlbbIR3hU2fYeF2RcZzukR/o3QCgU8bViw9BdD47U82h04YsOiNj9jTsJX7FGt8aOe8l66zPy8awKcb/2o2w0vcuHIjXC0yhg0LtysE7jQOYe2lRH+jFJNsFh4YF//54Pu//4cX7OXYxZLjvfLjf/u713//D9/95rcvPv/ipXCAETkhnk+yd78G15nG5C2L4cgAACAASURBVPihue+GPf3d5+/DI0x/BynjU1+GHMxV6bD9HHxYuF22mf3y4B0FQPiCCuLjEv2NAl329QMWfErgvfS8EU+jxNW7a9bGQkDj3ri4PD8+oRTZ8cHN997JqZfJntTFganHxwM/vJhdsaHXqJpnqTz7XCykPZHXc1jYXYEo3lWoZXUkLjaMQeZRab+mi8uAJPobpRTJQGhMWvjt735goXYr2fmtP3Ouylvq7oi52//skIi63d7iwtWNzcWffSRWFOaiX+uH08eF22Pf63aJKLO8TKHC8qeKoQLPfv3e0TERZTZMf/vQ3xr+eQBAEBBFnx4215e+Oz8jorPOG/der7j6frLcZHCI2dkPrzLLy71ut9ftZpaXo6fhTRAfded/L0vdr9dvBwCYfuCiTw8f3xqYdA4vPcPfV39xOUdEf/zSr4t8Ev3leMz48PW1pfU1ZsrzJFFQb8y4Lz6ztppZW4W6AwBiBxZ8qrAS4g8Puywh3oGXHb+8nOl2e68PL25sLo7tar1g1W+W1teIC7CPvcZOojjMd/cTCbvgEN3f4Z8HAAQEa/Cp4ovnx9/9YGa0/+SDVaHGk2g9fv+zw263R0Tvvj3/9s2s11p7Qivx4ovsh9q5C+D4W/DTIPZmcZsFwQO0rVgQBB4AkBiw4FPFx7fWD4/PmRHvg3+du5evOuORcH+YHU+iInfulXuft+PX+9e//5yIMr7XDEAQJEkSbodhBoIAgU8tXl56hlvjmZd+fn7xzZsLqzbtZLE03p/gBXQpSaEV+hV8ThfCfAczhZe6s13QeDAU3CVpI6CXnmH56n///JKIps1Lz2AaH65S/Xic+bao+MMjIrrxiz8ZfhT888AbS93dX9E+uwDgQRR92vj41vrS0nzAwZZ//he3Br6c+flFSj5gflTCVb+Jq2C+EPeBK+/czKyuZFZXhh8L8x144y/hMeu6Vu73ti44m7QGHBZuF6NdL3i0fwfRgcCnmcNDZ6aZG0vjM9K5tfHlK091H0++HE+MleqjFMx3D3PPHGNbWAB8hJzt8vHhB0Urs9pxhmEYze1q3kOGfYaF22WNyFdFbdpBPMBFn0JG8tIzOienX351SkRHHemD95aJ6MbmAnm44sfvpSffoPpYGOrMH7rEbrWFDSLw8M8DH5hy+385BxkzjHa9kN/dGZRs1cpSiZquSrE+w8Ltst6rRESkuE8JYgEWfAoZyUvPsOz45YWLF98Nt/vHT9Id57yM+97JSe/k5PXnX7Af2MvHDZCQugMQDa3sdpK3n+62bB3R81syqU+cDnOfYeF2kanuck1nzWVAQkDgU87hYVdY1c7N7T8zlWl54YKIXh9ekocrfvxeekZCtrsbXrkza2v8z+zldubDOQ+mk3a9IEmSJD25awh7q9gas+Zub3tM4zMs1K5iwzDEFwRiBAI/IlrZihbp/+f4h6YEx5ovhjk315eYES+sS+9Fz1jKLC4QEb05HzZ2MjCNT7TdnHt9/caffMxePpF6vdOz3ulZcjV04Z8HI2KGt+UPHhiGYQh94PqBaP3b1px9yLBwu8C4QB58aLRH1ZYcW8fBdr2Qt+Zr1wv5al468FyZav/+uWNL7he3+Lcf31r/7e9MkfZPiHfQM5aILl8f9m5s0trq+slpR5gTv7aa9do1HtzVbyLiVU3WjTvtnqXGsco2jqB6wYngnwdJ064X8tXtpmFgZXu2gQUfCX6FKRLtp7stUprm00KusqfXRIthbKxL3YUbN9eXrJ8Deum3P9kkou6lWY7++Zc/BDlqzMS7GO8TEh/kcDaSpcbFnoYHZhz/QjfkE2GXq+wZTWId1n1y0PJbsmCr61vNZ1i4XWBcQOBDodcLUkklUkvsH0grS1JZG6R88v9Tg6227VpZKtTrZXNzrrJnd6Llbm8L3VlCdffaFcJLT6ykXV/j540rmrKVeIpD4310fdTqN/zq+9A0vNd/+GPv5JTmRvu/g39+1rCUW6jxwbLjig3DMAxD33roq/OtA33wpv1s32M2n2HhdoFxAIEPRb6yZzQVIqU5WNxSSw+3dMMwDL0mqyVzDd3MDDGc24mIqFXdZUcIHPHaEzXi0y7fPTZ4qN2AedMBcOFq2zoNhNP4GHV9KInW2AGph9d4B44B/uQqe4ZhGMbdJ+7AntydHdlmRugHLVLuOr+OfIaF2wXGBQQ+NuTaY9PBfmdHNh9dtSeqXLtv3tLc9v4hO3fECq6VSyoNjgyLFWoXghffdZnGLy9lLrrdaStsRyMG1Sek68GD501n/tpqxtXdh6D3QMTQQjejUGwIgtZzlQdKq3rPFH6tXFKF3zo+w8LtAmMCAh8bA3N7kA7C/VNpZclVs0lsobP6D9aCfBwE99KzZXiLl0dX7AehHT9BLz1jaFC9sJpsEvZ6EKzwOn/jnux6n7R/nl9CEi4mgQlieBDbCYoNvUbVvCRJklTa54KG2/UCdxt4Dgu7C4wFRNEnCwuOJyIipanXHuarQw7o13+I5V/BEUtPRAHD6VlnOfYzq4HTPe/9+PLHt95+K/pVJYEjqH7Uxm7hiCv33b/77fnRCREZ5xfCwT48/5v/6Nhy66/+tXio84bTylJJwhfybJCr7BmV4Zs9hoXfZVJsoJhqcsCCT5J2/V61pZhL8MNrMbbrhRjVnRHVS09EXJ07q/ucxZQY8UTUOTwa5xJ7csRSMN+t7l4bRRQbTYVau0+RsgzAdQYCnySOqBJx5QcTZusrzSHVnRz57gF3UQQv/fOvj4mI2e7d855b4yeOcXF5fnxyfnzS49YRktb1Uc33cOnvK+/cXNpYW9pYGylSz0fIg2p8fmsQL2KrwBQgQ6RdL0iFujY4KqZKUACA0YDAJ4mj+HJJJa9KTu36vWpLrulBWi4IhdxL3UPH0i8vZxxbFpeX2Q8OjZ+UEc8LW6Z/bdLiwvWy1/1xLMAPNe5jO7F+0GJ1Rq2SKYymopZMLR+SIVLqH9VUWtU81vQBGD8Q+CTJVR7XZJWVm5AebulGU6FBWClH++lui6hlhqMMr1eb+8Utx8vnKqJ46XnWVrOLy8vd8x6JfPXjROiHv3Hrw6X1oAX7ol7AWMx3f4ZG6kVAK5dUUh5Uci4vVLHRX20aliFiPa0WG3pNVh/Cigdg3KBdbHo4Ojrb2Fhxb//i+fHh8fn5+RsiWsnOb24uDw212//skIhYnN0H7y3f+tC0IJmZzqrfELc2P54GskGqybKI+kTb0ozUFtY8JKzAhwuh9/fDO0LtBk07ObhAkH5Sh08QSX8KZdAmtLrNH2D2BUfMHgBjBRZ8qjg6OnNv/PjW+qhGvHAZnvoS/kYybxvLjk/USx+immyi3WjMC0i+cdz4CtjJNd2ehWVLdWIOeNda+2BpvkRNvWavS2prIwYAmAgQ+LQh1PhwuJfhLXzi6uMiXEh80m3jQ6TGXf/uMv1iaIbRVMhcax8xQwQAMH4g8OlB6J93cNZ5M2rZWitZjmEZ6w6Nj8uIj57qlrTGTz+e+e6+u4LA8ucO9KEZIrYcO+2J6l21EQCQFBD4VME03m3Ex+il54nXjo8xhT0hjY+rsk0QIvrnhUIeRt21si3YU3uiMvf7sAwRe41SM2IPADBOUMluRvnqxUmQqnZ8STsLvhl8dm2VqXvn5JSkMM+LwRuxj0R2c4Ope+yd40digv75iMa6SbFhNMtSXuqXYLTC7yqPa7v5kqSaG43mI6lUvVe/s8cKlym1nd3+Ub4RegCApEAUfQphFrzDY89i6Yno/PzNqLH0H7y3TERWLD25wuaZxmfXVoOH04+nmmyMQfXhzPcxx89bx06yt6w7ih4AMAngok8tDkc989KHm4otw/NeeseKO/PVB3HUj7ma7NBuNIkyKXXvdbpsBgDALAOBTyFBou2IaGioHVuG94mld8NS5N2hdhOvEh9R48e5+h4F6DoAwAICn068ou2IaGlpnsXSRzyF0Igne2PZies6zVJQPVP3XqebyS5P0kWfq+whcw6AKQACn3J4jY/ipQ8C0/jlpcxFt/vqm++np6tbRI2/FqvvvLqPekYAQCqBwKeWII764Anx7mV4EuW+G93z8+Pjy7POm8tB//Jp6P4yI3Y8U/dJmu8AgKkBAp9mvBz1wb30AZfhbY3dFs3B0vLSxHWdJ1ws/ZjN93DwS+9QdwAAAwI/W7i99CNVtXNw9sOruU6nd3TSvRg0m1955+a7H324sJKdwubxkw2qTwgE1gEAhEDgU46PEU9EI4XaWV76IKFzXs3jp4SAGj/+4PlRF+Ad6g7zHQBgAYGfFXiNZ0b8WeeN9/ABvJdeOjuWzmyKsvLOzZs/eTezseZIjZue5vEOxrAYf/27ywAA0gAEPv04ou0+vuU08oZ66ZcujpcujleuBo8IAUPnptOOD6jx05/77jD3Yb4DAHgg8DOB0FE/1EsvdMUbK+uveouOkcJWcl7N46eBoRrP1H3MjOSft5zz42sbDwC4VkDgZxSHl5434oW6fr64frVy45sTp7QHYQzN40MQJKg+hPk+Hv+8pe7QdQCAFxD4WYE34i0vPd9A1id0Lv8Xt4bO798Pfpo13m3ET7lznld3mO8AAC8g8DOHO6K+88NLOrWJXFzVaXjVn06NZ8QVcBfFfB9Vqi11BwAAIRD4GcIRbffWQm/p8pROTZH48bAbRNeFJe0Y/kY8TaXGuxfjr4X5zj8HwHwHAAiBwM8WGxsr58en33/1/TvLZinZhcU5IqLVDVr1W5MO0VmO4dWTZpo1fmrhTXY45wEA/kDgw6CVJQFljR/Bvws+a6HejvNCORxL7Kfnb4jofGH1fGG1t7gavbkcY6gRT9Ot8a//+JzCmu9J++cRWAcAGAkIfFjkmm7wNBW11NdnrVxSEz15+/fPHS+vke7QudWl+aX11aX11avsuk8s/VCEXvqATLPGTzlM3WG+AwCGAoGPi2KjqVBr92lSFvgAoZw7NvpXk2WL8Tc2zBB6PpZ+KMvLGbYM74XbiBea9VOo8UanS0TSVLZb5RUdsXUAgCBA4OMjvyVT6+BpvSCVVCK1JEmFertdL0iFulYv9B35nBNeK0uFer3s8u8TEbXrBUnksfcx1q1d4RqxMy+9jxHPluFjZAo1nhFiMT5R/7xQ0WG+AwD8gcDHh37QInnrTmXPaCpEStMw9io5IqJWtVTdbpqO/FY1z4l5q7q7pRuGYTSK3EzteiFfbSnN/gSh8Nd1ZsRfXl4wL/2bq9DnERDQiKdp0ngWPH/jZ2bG//QE3DmW3uGcBwAEBAIfF1q5pJLyQKjIck03BbzY0Guy+nBgmMs7d5xHaGWm7jbNH4XgKezv3Fy+vLyw3gYPtfNJlhuV6dF4Roig+uSq1wnVHQAAggCBD0urmrfF0Jf2a7qXJm/fHoh47vY2v1TP7yIi2r0nldTBA0EoOj++DvJafNPrnZ5Jl+c3li6XrrqZN93Mm0Be+iDJcsGNeJoCjXfkvo8zcS6IRe7YC/MdABAECHxYnFH0Pu50eSsfdNZWi2rNGlXvJZYuZ2N1eYGILq8MIroyiIioe0rdU5/HgszVWebqLCt1pfNT6dxvZG8UtZ64xjuYhuR4h/Yn5JwX5nyOnuRpp10vsEmsH/wGuqJTAABxsDDpCwA25NrjSjF3W6mW7tXvhF2Bz751I/jgt+czP7zqnl9cEVHW6HbOLrIri68Oz29uLgU5/NXr7s0b4rDzbGahc3L66uQ0mzFvs17vkog6vXOv2Xq9HhH1Tk8zGYGTYKTfKzhepeuymxtB1D0h/3x0b/xX6v/h2PJT5b8VD5VrOn+zteuFvCRFWSXKVfaMChGRr2ZrZeauMio586QHEc4JAHAAgR8Drd2n7Ur/+1N7opJccy282yjer8n56iOt4v6yy/3illcgfe4Xw1vCuNnYWPnhVXc1O3fauVq+sXl0fJ5dXiai7FtrwvG9byUioiX65rD3wXtspNim7Pz42rElm1no9C47vUtL8h1kMhlT43s9t8a7J4xI9q0b/m1hmcZ3Do/GnCXvrmkzqvnuVne20VPjeXKVveaBVHpYv1+MEOU5FPav8Ng8Ra7yuLabT/qcAMwScNEnxf6zgfHSslzufqF4HLnKA4XUkti1KRTycOrOI0lvfjz0tK0tgifLZd+6cfP9dzJrq28yS9m3brBXZm01s7ZqvXW/Nt9/L7O6mlldpcVFfnu0X05M58fXvW631+1KSxnPhYbTs97p2eHXL4R7X//xea/TlRYXwj18+Mu2Q92DI1T3obtsFO/X5Fb1EbsBtbIklbWBL99es1Hk2Hd45p+JHPHaE9UeY+oITwEARAQWfBIU7yqkVvPSbk1/TESk1HZ281KViIgCOj6Ljaailkrlu8LR0eWc50//5OYfPh+kzh8edjc3l796cfLTD8RGvIPnXx/f+jCoZbm2mj057ZycdljMnZDs2ipbie+cnFpr87FrfOfH1+eHR0S05GudZ7LLvU6XiHqdbsa3Bk4Ije91z4mILt+4N27+5H3H4PHG1uVub7On1CJTYLX0sKYbRo7a9UK+VNjS9yo50spSiZqGUSSybXegVndrurFn+uHzBRINMmkd6EQw4QGIAwh8GIoNwxg6oEFERO06EdHt/pKk3zSCt43IlxqQhYXFlWU6615srK8cHQ+34ylw45kgiu5GqPHxkn3rhnHeowCPDtm3BqF2Dl+9cXHJZohr+cCUfO5xgW3JLC8FOUWMj0H5LZnUgdpavvTcnR25Wj3QiXLaE1Wu6eYDKLfdOZXTD89WnxwnIGo/24/r4gEABIEHDl4fnc1JQSvXvviuy5bhkyBpjR+pLawVcMevx/PhdSGUtXN8nFlb4e1ytoXIadNPA4N8ztztbfOnYsPou5e0slRSiWjbfaQ7TdT0DFQeKNWSZfS36/eqrcQuH4BZBGvwgIjo41vrCwuL1lv/srWj1qx1ZMAH6TjHmKrcuaTj7PjAukHkwcJ8Zm1l8yfv+0QtJBSpwAoz+id4DpLcStTUa7J4lH2W/JY1rNhgpR0lSZKk/MEDo6mMklMKAPAHAp8wucqecW0yfzbWVogouzzyXRFLSTshCWn8SOb74GI2NyjhzHiHQZ/ciYbRfrbvLsPkGFK/V20prAaz3z3eOtC5d/oBb6cXG1YhiUax/Wx/yCkBACMAgQdOTjuD4rX+DWSHdpazCG3E05TZ8YzO4VHE9PfgRWxCxNb55MIFSpMjIu1RtSXX7vs+meoHLVLuFvm3YviMkvazfVF9ZiKi9tPdFgx4AOIDAg9MPr61TkSGMU9E2eU5/7r0sXeW8ydejQ9nvptX0nfU984DhSIGQWipR6xbJxTyoOrerhdK6iAyzov8lkzqEzMXTiuXVLJruQWfJpqvtvppolqZT63TyvkqDT0nACA4CLIDNjbXl45OzoiIaLQ4r6HJco5w+lGj68cQVx+Q7ObG6y+/IiJpKVAegT/umjZxEVTOiTVWqPIbAiVzspD4kqQSsdLNzUdSqXqvfmfPkTHikSZabBhNkszjXeX0AABRkYwhCV9ghvji+TERHR6fS9KbOYmI5jc3l4lImBC//9kh++HGhukHGpoNz3zylqI73gbBsuBDa3wU830wyXc/9M7PlzbWKVTwHW+dCy31a98Ttl0v5KvbqDsLwESBix4MYF76zfUlw5jPLi8GNOIDLsPTiEvvQqZnPT6zFKhWvz8+6n79wXI6ABMGAg/8uLjokUeonXsZftRY+nB6H0Xj4zLfiWjlvXciBtX7C/n1Nd+1siRxK+0AgEkBgQdiTs6ussuLZ2cXQ0cGLGnHiG7E0zTZ8YyRNN6h62lzzluZb/DOAzBpIPDAhuWlJ6LFxUXqG/H+BPfSOwit9yE0Phbz3XkZodrGs+L2dM2FHAAw5UDgQSACeumDEIsRTyNqvH9b2OC409/DaTyJ1D0F5jsAYHqAwANPfjw831hfOTu7CGLEM0KUtIui96Pa8fGa74PLGEXjmfnupe4AABAXEHjghPfSW/hr/EjL8BSfEU/BND4u57xP9bqAyXKH331PRD5tZ2G+AwDiAgIPhmI2l/MvWxt6GT46UxJzNzSo3qemDZzzAIDYgcADP348PN9YXzo7uyIPIz50shxvxCeaH59EbJ0//o56H/MdAABiBAIPBAi99ORrxI/qpY+dpO34IN1lfBbjO8fHvU43k12G+Q4AGA8QeDCcjfWlxcUMDVuJH9VLH68RTyKNH7/5LtR4nwA6xNYBABICAg+G8OPhORFZzeWOjs4cA8bcWc6fpPrHj9Ic1qHxloQjtg4AME4g8ECM20vPjPhXvm1kGcGT5WIMp7ewNP71869pvOb74Br6Gn/4rS3/Pn116wAAUwsEHgRiY33p8LDLNN5txDMiLsMnkTvXu7yMPlvIa+AS5yDhAIDxA4H3p10vSAMK9fYoxxTqbccMwSaYLjbXl5iX3uLVYddL40MkyyVhxBORcXJKREs3NqP76kfyz9uYk4iIrsQdmWG+AwASBQLvjVaWpPzujm70aW5X88M1WntUbck13TCMvQrVC/n+O0Ovya1qXipr0S/tmf7c8Yo+pxvmpbfYMAvUC8z06J3lGPGKfWZhgf0wkfx4PnqOOep5LUdsHQAgaSDwHrTrhZKqNI09rudlsaHX5Fb13nAzfPt2joio/XS3RUrTnCNX2dNrMqlPIiq8UM4T0ngHLNTOx1EfzksfuxFvBc9PqgaOpd+b75sRAL0zwW8H8x0AkBwQeDHtp7stuXbf2fEyV3nQbFqab/fflzW2paQSqSVJksparrJnb5uZu71NtP+sTURaWSrU62XrULbJNpsIHyFPTuOFXnqvwRFL2sUu9hE1PoR/3lGxzp04B+c8AGAMQOCFtJ/utvpmuJ1i0dTrdr2Qr243Lfe9opaksl7ZM5oKkdIUN8TWnqg0mLdV3d3S+62ztbJUInM6vSarpalYrxd66UlkxEf00seo6+7c94nY8ZZ+d46PMyvZzPIyjd5xDgAAQgOBF6IftEjeyg8boty1NLzYEEs6j1YuqcT7BeSdO/1nCO2JOtiTu7MjU+tAD3v5SXJot93djvrJlrTzagsbTuNDm+9u63xgx8N8B0EwHYTRY3Z4zyCPzYRoa/VywfJIFgrlujYFBgaICAQ+LMW7Cqml4P9+WlkqqYMFeSIi3kdQbFjL/VpZyldbsV5sdNxe+uOzq3hPYRnx0a15Ye77GOx4/9C57OZGr9sVLsYDMEG0ciFfqqot61un1VKrpfxUOBFBFCDwQvJbwy3oYoO50oetmhOZ6i7XdB8bf7CgX6KmXpNDX3vceHnpiWhjY4VERvykOssNLUybqMYLm8WJ7XWPxDkAkkOx1hNNBhZFSW0RyUrTzBjSm+z7J1BAMZhmIPBCcnd2ZDMYzoFW5rQ8V9mzluDJa9WcBd7JNZ233d2D7lVb/f/AIZ7+iWN56a3eM0zjoyfLxWjEexFc48Olv/v43rEYDyLRrpcHTnSnD93aWShrba081OgYHPdQJSK59rhRNL+gcsXK45osK7XmY5/vLHANgMCLyd3ZkVvVR87/j3b9oX0VvU+x0VRENn+7XshXW450OwGOFX39wMtFfzt/y2sOn12xYHnp3Ua8g4kswwfvK5OEHR98Zd2n4xwAnmhlKV9VB050tVrKl/sa364XrJ0ttZQvqUGnbT/dbZEtHIiIiHKVvb1GpQh5v+ZA4D3IVfaailriA1HaZtUa9lSrlW1RKtoT1R2W167fq7bkmj7cJM9vcQnyWrmkEok9COQh5Imqu8NL78DLUR+OMRjxFLfGB6lawz8BjEfjv/nbv3O8fIcHK9rYrhdiKdUERsP8TiDTjW460dUSs0G0Ryxoh+3te9gdDFYTBZ+wMGUIXHsg8N4UG2bxOpN8lWq6ZYoXG7adpX23D549HLcGg7y/O3OVx4P1/IdbutFU/FbAbudvOV5s++vDeFR2KG4vvQNrGT5cSbtRCdEW1l/jg/vnhUvv/K7s+rr7CYCvVJ8EQjn31PigRRu18vSFf84E7Wf7RERK03Sj54qVBwoRMavA3CvX7hdz/E4w8yxM+gKmnGLDMBqj7Sw2DDOEKlfZMyp+B/PvnYP9TiyGqfvrw7MbmwK3eSwwL/1bm0u8l56INjZWjo7Ojo7Otj/Z3P/skKJ56ddWsyennYRsd57s2ipT987JqaX3I+Gj7oLT2cdkNzc6h0edw6PYxd7HWP/mb//uJ//qX9o29Ys28n6mYkOv7eer9+p3rKdWlgYCJoJgzS6/JRO1mJ+P7eWscHOnDcdHbGf/WZvgkE8fsODTQ3K6TiIvvdCIj8tRzwjopQ9hvlsI7fhRw+v81X3o8vxkF+MDFW200kCaAtOQc+4X6lq9wJn+QYozguHkt1xOd32g6uZebk3PO4jHSe7OjkxErd2ndneNVi4UylobQfTXGwh8qmAan6ij3uoQ7zbiHSOjeOkTXYB3EHo93l+5g4TdTUHAXYCijWTWcRIGimplaVDRsbldLQ18+NNZnPFakru9TUSklsywurbGYt9ZZK65t1V9pLX5ncGmNhW+em8QsqfVCyW11VJL957iE7vWQODTSaIa76h4Y8E0/uMPF9nbWGLphyp9FPPdIoTGB2wHN1TmJ63xAYo2+tCuP1RJafZdv8UGV8Hh2hRnvA4UG8x3opbykiRJefYc1f/LF++zPzvbmy/t0gh1NHKVvaYis+h7M9rIfErrBxSDawsEPm1YjvrYNT6glz4uxmnEE6fxr7/6hob55wMuvfc6gQr+TFrjI+BI77TsQaLpL854zSg29GZNsYRbVmrNQXpOrrKn93fKSnPvwfaIc+/ZJidZVmpN38od4FoAgU8hyWk8w8tLT30jXqKLJM7rJhbz3cLS+N5FoOv3r2lDRJnssv+wwVQJB9V7E6Rooydm+LbX3mktzng9MMtoDQLjcsVKo19Zy7BlqWtlScpX1ZbS1A1jjHk3RAAAIABJREFUr1Hsh9Uzz0yxEaB+lm1yA0nwKQECn04SDbgjby+9BdP4iMlyzIhnjMeUN846RLS0ueHjqw9Y04aZ78GbyjCNj8WId8bJ++0KWLRRDNf+2MW1Ks54zSneZbGPfSc7c5c4i9eA2QMCn1qSCLgL4qXf2Fj56EPz8SLRknbxmu8WmUUzhkCo8QGX3gM654Ukp/HCjaMWbbRRvOso4GgWRqMRijOC6BQbelOROR+JrPjXxgazAfLg008SmfE+XnpijvqvzyS6MGgx4olYTjz72SpvlzRe+fEBl94t//yoPWFZZjwRxZIc72PH28hV9poHUkniSjX1izbqQyWieL8m50uFLXZou36v2iIW4ZXfkqn6RGsUi2QrzgjPbyLkio294oiVM0DqgQWfZhJdjHd76d2hdhJdJFTSLgnznU9/d8fVB69pM6pznmcyAXf+RRt9yVX2Bofmq9u1/lr7iMUZAQCxIxkGOlemHEvd47Ljv3h+TESHx+dvbZrmuzQnsR9++sGaNewfP/uBiAxavLFhPkfe+jCM5pF9AX5tNcvUnZIUeIal7tm11eDtZA6/+56INt97N/SVWOoe2o5nf7HxODxstOuF/O4O3MMATAGw4NNPQnb8psg5z8P888lF1Me++u7GsuMPf/iBxqXuFDmofmLqDgCYJiDwM0FCQfWWlz5gQnxoLz3Tqm63R4nF1nmVp82urfa6HSLqnQ9JHKDAIXhBiDGoHgAwm0DgZ4UYg+qtWHr/UDvqG/GHR8OlMQgsJr97eRXLbAHpHB9nlpaIKLOcDVjnjqW/x3YBI2r8hM33XGUv2OI9ACBpIPAzR9ItZS0jfvuTTWvjRbDSMT6Yhe1evqYxmu8Wm++Yu4bmx8eo7iEC7sZW+A8AMP1A4GeIJBbjLS+9cSWO1oyeKceTmZNojDLGB9YFqVcfJX5eSLigeqy+AwAIAj9rxKXxbi+9DwYtHh5dMCM+SrLc3PExEV1lx6Re7gV1H42P3XwfnDSwxiO2DgDAg0I3M8eNzZUxeOlZvtz2J5v7nx0S0eLifFyTs5X4GIveCP3zXlnvwho4McbWCYm3AA64Fuz/V/9mnKfb/r///ThPB8YDLPhZJN4qtkO99NR31EdZiWfB8zdvfUD9cPrk8K9p4++rj9E/b5t2mK7DfAcAOIDAzzRRNN7fS++VL3dxcRFvSbvk8Gvizml88AI4Ua8HiXNgbGjlflnDgnfxwUG/QPc4bp/PDCBZIPAzSkLVb/hKdg5OO+G99Hzu+9pqdnk50+32Ygm1E1SvCybYg96yAfLj48Wt8TDfQZxoZam0X9MNw2AljIUKbTUsMAzD0Gtyq5q3mg9qZbPcsWEYhr6zmx/SlhAkBAR+dolR4y0vvds655PlFhejOuqTZqTVdKsGDklz47HjJ1OpHkwCaWF+nC/uzO36Q1WuPTaLGRQbTUXQbZD1DVSaZs2DXGVPr8mkPtGIiLQnKg1mYF0JHsKMnwAQ+NAMfFiSNLRx9uAgn3Hjd2pFrHAXzktPREdHIzxSuEvXxWXEO8z34O1krPGsBg6N0Y53azzMdxAn7ae7Ldq+PahVlN+ylJsjV9kzjAbXTTh3e5v1C2T6zjejz93eptbuUyj82IHAh0IrS1KJmoZFU1FLw1XZbJoppF0v5Kvb5pSebjGT3/1/zx2v0L9K7G3jfbz01DfiX/4Yvl160oxqhVs1cCai8ahsAxJA3soP3uRubwc5Rnuiku3BwEHrQI9+YWA0IPBh0J6opDT5p9diQ6/JIkdW4CkfVVty7b45ZfG+z2xCOY+i8YyIGu/jpR+c4uiK+hof0Ij3qjzPV6ePhVF97LbxxhWNUss2Oo6gepjvIDb0g5Zg6/4zf+tFK5dUMr/B8luyXc7bz/ZjvUQQFAh8CIS3q91jZQsvlcoa21JSidQSc8BrZalQr5f7+4sNI1AJbx8hD63xURbjg3jp+WX4kbDawgqxEuJDTs7556Mnslu++nFqfOf8ond6BnVPJdLC3Dhfka5VK0sllV+Qf6CQWur7INv1e1XRMwNIHgh8CMz713PZ3eZtN933Zb2yZzQVIqVpKXmrurulG4Z9JYtNcI+358dAvEH1PgnxjMXFxZc/dgMa8V6V5+My4kddeicPcz9ILdskQMAdiJP8lizY6u1718pSSZVrOv81VmwYTaVVzUuSJEn5gwdGU7G7/cF4gMCHotgw9JqslsQRdvpBi5S7RW6wW8OJiOyBKAytLElSvtoaxKCOi+gtZa9Ebd4coXbMS2/ho/FB2sJGNOIpgro73gavVx8XJ6edzOpKdmmRoPEgZoI62JlfUq7pbu9jsWFFKDWK7Wf7fsvzICkg8GHJVfbM29eS+r5LqnjX18AfILjlzX8LfWc3P/76EOEC7iwvPY8j1M7tpWcr8REJbcQ74udDpLd5HTJmOx6JcyBmcnd2ZNuSu8NgsWCJ8Epz+Npi++luCwb8JIDAR8eU+qbSquaZqPsb+MHmfKBMMLEkuqM+SLm6o5M35GHEBzHfGVGM+BDJ60EOsTQ+OfjUOGh8KpncGnyu8kBpVe+Z5oVWLqmi5UJzHVEX+Sa1Mv+tp5XzVRq7RxIQQeDD0K4XRJpdvKtwsaYDA7/Jx5sExxWJOh4iLsYH8dLf+tCpjiOlxTsIYcQz813KDu+DFxqm8ZMKqgcgEsWGXiNzBb20zzngB9997ae7LaL+MrujXm2xwSKPJPcMYKxA4Ecnd2dHVPiBiDyc7k1lqFS7Hxo8/GJ//me3vObw2TUSITQ+iJfegvWXY2xsCBb+g5vvoen1zAeCiOb7UIM+CY0XVrZBpXoQIwMDxeaAH+QKcQN4rMHcEnyg9CCQCBD4EPSj6Hmz3AwlvV9kP/P7tCcqF0EqzidlUw6qObJqkeIweqGQWxujWMMW0QPuyO6l/+rFiVeyHNP4KJf9ztubRPTDy8OhIx2EUPcRJkdQPQBgoqAffCiKDcO4W5ZKeana3yTXdMOq3mw0y9JgnxVkWryrkFrNS7s1fe+2e0r+IKVp7HlnyXkZ60wmj47OhJbxSLC28a8Pz0YV+6srmus/N/70gzWfgrVE9PzrY4fHPmnz/ey7H3q93tLmeri68SMdJWweHxGfwrRoG58mpPnwzZkAYEiGMSRlGVwvLFM4usZbLvqAGv/Fc9PGtQT+1ofrlsD/9IM13jl/Y2POGmNd9sbGSmiBZxY8s+Z9eP38ayIaVeCFrviAMXqWBR9d461YQp/iNmHVnRUr6SPMfAJj5LP/5n8Y5+k++T//t3GeDowHuOjThqXr0X310avfBPTSW3z/5QtK0nwPp+4Rid1X71+6zlL3b/727xwvr0Pa9YK9twILsUKHTwCuNxD4FDJxjedj6f17z1D/ISC6v2HoSnyImjb8gVGeCWLR+JG6xgnlXKzx7fq9aktp8rWYWPNPtQSJB+A6A4FPJ7zGR5T54Gvwwlh6L9zJcgtnJ0R0uTLkgSAimUxmpPFesXWjqv44Y+58jHX3LnubI4vcnR15kMXh7q1AxKJJy9qgbzKeB2LkOtWiB9MKPtfUwtvEsWh8OEe9w0tv/cyvx8eFT9GbzvHx+eFxJpOxqteNRCwu/Sgan1jT9/azfVHJZKJcZc+06YW9FfparpYesoYKek0OU+8BAJAYEPg0s7GxEqO7nkbReC8vvXAZ/vnXxyy27t2PPqAIl+pV9CZ0s7joznkHk8qd80Y/aA2rEu7bW8FqmpC7szOR0kwAAC8g8OknFo0PuBgv9NJ7GfGWl146fu04JPSluo14S91Hdc77EEX4Q2h8Yua7gIHD3XK6+/ZWGDwc5G5vj+H6AACBgcDPBOPUeAt+lV0Yaufw0rPg+YjRdl5GvNE5J667TBBiN98tps2O54svDSqQNRVuW8TeCmBksAYPooPPdVYYv8ZbafEOHF56Zr7zqXERa9vxRnxokQ7t1Q9IcI1P2Hwv3g3U1ShybwUAwNiBwM8QsYTWDw2qH8lLLxxgETFuwBJpR3PY4AifDOKy7JOz43/yr/5l8F3F+zW5VX0U2CgP1FsBADAFQOBni1hC64MH1Q/10t+c6xCRsX7D5zpHZW01u7ycOTk66ZxfUATzfQzFcIZqfGjzXajxYuHPVR7XZHdrBamkmqF1vr0VAABTC2rRzxy8Azxi1fqhleq/eH48J3qGPD/vLS1liOjrbzvZZfFT5sbGCvM0hLjCXscMshtnxbpw+NSrD9fk3sLHjneQq+wZFY1vn0DEVb7x7K0AEgS16EF0UIt+dolYtd6/Ur27Lj3ZS9P/+PrN8vlrIlp+9yY/wH2FIS6vc3zcOb+4kuaXlzNzp6c0in/e33xPyLgX1qsfZ/A8mDb0e//TOE+Xf/y/jPN0YDzART+7RAy7Cxhw565YF3xAuGg7psGZbBhdTDq2zgu3rz6iur8+PPvqm9evXsdQ/AAAcE2BwM80cWm8D16x9AsnL4mou+RcfRcS/PIshWYr8d1ur3v5JuCxFhNx7Mcbc3d61iOimzeiVvgHAFxfIPCzTsTQeq+AO69YeneonX/N2pH88+52MpdnZxTYPz+22DovLI3/8eWPFM18J6JsNrbCPmD8IA8eRAefK4gntN7HUe92wktHP25sLLrNd2Gy3KiOel6hM/NzFDlazWIMTwCWxl90u6EngfkOACAIPGBEqVo/dDHey0t/fm7Wmxu6Th/kwtzqy8LrrpaWg0w+cfPd4o00R0TLS5lwvnqY7wAABgQeDIhX47289G/RGREZG29ZG4d2lhvqqPcJjvNpMRfk8ImwuGw+lITQeJjvAAAGBB7YiK7xQoQ2+gfvZy0j3kLopSdfR7176d1ieWGeRKXphfib7+Mx8a3g+WmrVw/GzfzcWF8gjeBzBU4iarzQUW956aXj1z8enn94+yPHgIBeejde6s6Xp/U34qfHOe8ghMbDPw8AsIDAAwERQ+stjee99KEl3HFVwuvxkWev/nJTiDv3fVSNh38eAGABgQdiwoXW+wfcscZxjsrzzEvPL8N7eend1+NlfLu7y3gZ8QHN9zFY+V4OhuAaD/MdAMADgY9Ou17w6pHdrhfYHq3s1UV70MnDe8yECBda76Xxzz/7cvBzPyH+g/cH1moQE59/7IgeGTdtsXXkkfseUONhvqcJaWF+nK9J/7ogESDw0clVHihE6hO3NmuPqi2zI1e87B88d7xiP4VFFI0nu5f+rc0ld+M44vLlgl/Pq29fsrdBrGpeNd2G8jSsvg8tTDtU42G+AwAcQODjoHhXqPDaE5Xk2v249V0o59Op8ZYRP3fymohWPniPH+Ooajc0Wc6NUJsDdn8P7nWfkig8xNUDAEYCAh8LQoXXnqgk79xx99VkPn1JkiSpUH/mnk0rS4V6vSxJAs+/j5BPm8YzeEc9X/HGWmjnvfTuvUIWpTdEdGGM4Fd0GPFT5ZwP3lfGS+NfH56dnvWy2Qz88wAACwh8PBTv12SHwnvou1aW8tXtpmEYhmE0t6tVVTRfq7q7pRuGYfXkngJGDa23HPXLV6dEdLV2g7wX2pmXPsgyPK/NoQvrMiZulIcAdvyMIM3PjfM16V8XJAI+15jI3dmxK7xY39v1hyopzb5sFxtNRTyf0PSfOKOG1lsa3zPEA9y9Z/yx1P3m+28LBwz1z6+tZnudTuf8IqC6J+2fD9EW1qHxMN8BAEIg8HFhV3gm5A8qTpHWDxxRd8W7YoXfvj2F+k40emh9pnNCRMayqUlCL72Ff7Kco6bNqE1omIjG1XhmssCOBwAMBQIfG7nKA6Wv8O2nu8Lw+faz/fFfWBKMpPFrmbm3by5fXFywt24/PFuGD+ildxvT1jUECa/rdTrZpcVMNjsNSh/CfLewNP706ISQHQcAcAGBj5HiXYXUh/W2p75T7vb2BK4rGYJo/NmL74gLnrc0niegl17oKh+pWzwRzV9dElHnXHAZ4yf6E0Z2bfX07JyIMnNXcVwRmCKk+flxvib964JEgMDHSfGuQq3dp9rT3ZZHelx+yxGLN6pNv711K8SuhAhux1uL8RcXFz5e+vPzntBL7xP0Pqqjnogy2WxAd/0YEuTCme8WC0vh+84BANINBD5Wivdrcmv34W7LK0YuV3lck9USK11HpJXz1daoJxEK+fjVneETWu8w33/x85vsh4uLC4cf/qcfrPmUtPNpFmc73TD/PJvn5js3aQpW4qM45y3M8Lr1tbXVJYLGAwDsQODjJXdnR261PPWdiHKVPb1G1bwkSZJU2q/VPMLofdneuuV4RbnoiAQPrV9cXHRvHFp5nuGj7uwCTntvgsxjgZg7AEC6kQzDI4EJXDd+/Zvv37m5/CcfTyy321L3jY0Vh/nOYM55thL/i5/ftKT91ofrX704efGtqbX//C/ftXa9vU4UwEl+dHR2fnRMRO/eel84gHe2W9azvxmdnH8+TvOdy46z1N3Se3B9+ad/++/Gebqf/7t/O87TgfEACz4N6H84+vVvvieiH151P/9iYjXahi7J83XpHa1o+FC7wTL8Rfflq26QUy90z4hoaUMsxo4lfMt2T5MRTyF13eqHZGPQAik64jPYdw8qO8ZzTgAAQeDTQf5PNzY3liZ9FUR9jT8/PDq9uHKY7xaWo77XM6PZmb1uLcOfn/dufbhOF6a0B7ShVzPz5LtGMCV16xIy3xnZtVVL5r/5VcvxinLGZGjXC/lqS67phmEYek1uVfPT1VERgOsMBD5tTNaIJ6KFsxP2g4/WLi4uvnzVvfnWsqXxZDfiBzb34vLQM/qH1wk97UON+ClpMBMaoZxPncazfNLmHisIlavs6a6KzwCA0EDgU8L0GPFEtLo4t7S5QaLQestLLwy4c7K4TIGj8ChUytyYict8J9/OsD5CPrLGmw52rcx70Tm3Om9vDwaZ3ZI4j/szvsNSf2uusmdvt5C7vU20/wx+etSiB3GAzzWFTNCIt2LrgoTWs+R4ZsTzKr481/vD50e3fvZu6MuwzuhjiF/rlfjTsx6NsXqdWnq4xXvR8wcPDPOtWjIlXitLJVXx6qKkVnd3dPMYqua91tq1J+oUl2kG4JoBgU8PDiN+so56ClC1/ovnxzffWiZuMf6f/+W7y3O9kc7i8M/zDxahe8Im5J+PxXxPBLUk2SnZexzKtcemF/3OjkxkVXEaNGAY1kXJmoGVgmhVHwn88Fq5pJJHiSgAwMhA4NMG0/gfggWfx4swNc6t8Xws/aDdXO+COEnuXnn6n4ficNQH0ekxGPFxTZ5I77i+4W3hkGeHTS0wsYd1UeIPEfvhtbJUUgcL8gCAyEDgU0X+Tzf4txM34hn+dnwmYy7G/+HzV+wHpu7/6f/93hrjtQzvE1532el0znr+6j5+5/w0mu9xMKzisryV597lt2THfq0slVS5ptsW5Gec+fmxvkAagcCnkIkY8ULz3cJL4794fnzrw3Wm8XNvzk/Pev/FX/zU2ju0s5wXozahIU7sk/DPx+WcHxpeNymGdVFqHejcO/2Aj/Rr1wtM3WG7AxAvEPi0MZ1GPNk1/saG02LISG+I6OTEtgD/1YuTcOfqHB9nVzIL2ezQiPprF2EXJLzuJ586LeQguyIxrIsS75FvP9unfjFnlgivNA2oOwCxA4FPJ7wRPwaN9zffLXjD+vJykAHP6tEuLCxczS+9PjxbWhJYqG4vvZd/PnRsHRP72JvJTsR8Fwp5UupORLnKA4WsiHp3F6VW9R7fYEl5UMkRUbt+r9qSazoc8wAkwcKkLwDET/5PN/Q/HG1uLB0enU/6WpzwEXCXlxcLC4tffv79O28vExEtLhMLpzdMiX3xbYevSz8S2fX1bD8RP4THfjoZKbwuQTkXUWwYTZJKEou/V5pNpVSi2zmiNhGRUtvZzUtVc5+51N5+utsiomp/hwm89UREyE0H0UGzmXSi/+GIiA6Pzt+5aVaCS64JTUDz3cHR0RlzMCzSm3feXn7ZF/Fe7+LmW8uff3H05mqeiD54P2tcmbeoY0leaME7VtD5/jc+F2MZ2Z3j4875hdUwPjrxmu/sL+Ev8NOSjKeVpRI1ETUXlj/+j/9+nKf72f/6b8Z5OjAe8JCYZjY3liaSLxcEprhzb87fvLnMrq9b4s0C7hYWFubnnO1feVN+aPd3/izpYKTiNq9en716PcaKfs6OMlq5pCKjHYDJAoFPJ45QO0psJT6c+c5YJFPCrQQ5hpUcT0Qvvu1Ic1LACYUB8EHq1/KhdtmlRYop7G78q+/sjL0Lg8ZY546IKFfZaypcvZwSIW4OgEmDNfiUw4x45qj//IvjCXaLd9A5Pn7n7eUfXnYvyOwCd3FxwQrUP//6ePvP39n/3Q90eckc9Q58nPM+DF2M73U62aXF7Pr6NQqq55nwZRcbhtGY5AWkC2kBuekgKrDgU4tlxCfXhCaK+c4wFpYXFsxCN2+/tXxx4Yxgdzvq/RHmrwdx1PMh9LHkzsW7Fj7UP29d7QTMdwDAVAKBnwmslfgYHfVM3cPh8KUvLCy+PjKFfKDxktU2vnd0bKYDeEXUD61OM+ZGczEa00H889bpJh9bBwCYGiDwaWYMRnwI8533pfN16S07++Liggn5wmKWiA4Pe9ns4EYNGF4X7qoyWVMgYzHix2a+W6djgXUw3wEABIGfHeI14kM75y11F1rbP//ZTfbDxcXF9iebRCRcgxfOObS47FAjPq7wuhid88HNd9juaWN+bqwvkEbwuaYcoRE/qfq1/ur+xfNjImJxdsQpMdP4w/7bHw9tiX8h6tYFdNRPSRVbf/OdvzyY7wAAHgi8kHa9ILkp1NsBDy0Lml2PdtJA5xqRi8t4ihpFjK1zqDvvpWcwjX/5Y1eii+UliYgOD3srK4u93oV0ekgi/3zA3jBe0XaWDyC6qI/TfMfSOwDABwi8N44m2U2lVc2HUu4gsKYbck03DMPQa3Krmvd5TNg/eO54+UztzomnSRjxAR3prOIN0/iPPlwhovn5K2bEd7oXvaswc/KMFG03JUa8EIe6w3wHADiAwAemeL/maJgVI+2nuy1SmmZpkFxlT/c+mVDO/TXeIroRH858D+hIZ156xuLi4tHJGyJaXJifn7/KZDILl10iOl9cCVedPiBRRD12892rNi1s99Qjzc+N8zXpXxckAj7X4ORub9vbXmplT4/6s4G73blLeFSusmfYyna7TmbiI+Q+uyZrxPsvvZPLS8/XnDfIXJK/uOitLdvqMoVu3O4w4v3nmWYj3lJ3mO8AADcQ+OC0n+0Tbd82y29qZamk9r34zW27R12t7u7oBvO2UzU/EHLfo3i0Jyp3sjjhjfhRNT7K6nsIJWYwjX99eHFh0JveJRH1ehehe8LyxJsWP2bzHbY7AMAfCHxQ2vV71RYpd4vmu4cq18e62GgqpD60dFyuPTbrcOcqj2tyq/pIC3DUAK1cUineXh28ET/OgrWj2tm8l56IbmzMEdHCwvLSVZeIehlT1VhseeiHBv/adg6rPYgRPzYT330imO8AACEQeG+41hmSJOWrLauPNVsyt9nX+S2ZWge6+Y7fNXC2DzvKRCtLJXWwIJ8ArJksI7gRH8J8D25n+3jpTfqOh+75hXTZOTnpnQdIkfeBafyrb19ShAcFBzGa73zHHQssvc8Q8/NjfYE0AoH3xoqi12syESlNv9bWudvbgzfyVp7bld+SAx1FRKa6yzU9iT7aYzbihy69B2epd7SwML+4vrmyskhEc3NzhnFFfS2MSOes5944khGfhM/cbZcL1R3mOwDACwh8AHKVvaZCaskvN739bH/wxm6U6wetQEdRu15g6j6GPpujGvGjmu9R1N3hpe92TQF+8W1ncTFjNpldzHa6AmEeievYLR62OwAgIBD4YBQbTYVa1XumxOfu7Mj2GHf9oMUZ7vyu9rN9knfu5IYcxRLhlWFdtLe3boXYZeFlxCcUUT+Sunt56T94b3mw9WLQ77zbvep0exGN+OxKhoJF23kZ8fGa717hdcKzwHwHAPgAgQ+KXeJzlQd83RutXFJJeWBJ8+BRQCvnq63+Hu+j2vV71RYXf+eHUMitjS9fnQb8jfQ/HAV01Icz3+Na2GbV684zG0tLGSJaWVm8oIxV0TaKxrPrXMiKtXl6EuSm4RrAmJEW5sb5mvSvCxIBn2twivdr8kC6iw2jqfTj8Er79lVzpbazm2d71EFonvdR7ae7LaJWNR+wNu721i3Hi21n6u6v8cKceIrPiI8lgc3hpb+4eENEGeq+ft2jRVOPFxcXV7MZiqbxNEptO7fkj8F89wqsg/kOAPBHMox46pODKcFS97dvrnqN4Rfg83+6YUm70KAfyXyPHlhnSTvz2D//+phZ8D++Wc9Q9+Li8sa7m4eHXSLaWF+69eH60dHZacdcjP/J+zdGOpflabDU3b0q75Bw/7cRcQu8T9g8BD7dPP+b/zjO0936q389ztOB8QALPm1Yuu5jxwc34pm6j0pcznnq++eN1c0MdYnIWFzj9z7/+nhjY2W1345lpMI1/DpC8Gg73ohPQt2F2XFQdwBACCDwKSSIxls4VuKFjvqRzPe41N3mpb/o9i4urXebm8tEdHR8zt5ubKww2/200wtdnM7LUT/mlXheuVGxbqZBHjyIDAQ+nQzVeIcRH6NzPiJ8LP3/z967xLaVpmmaL+8XidTdkh0OR2QEaUe51Jmd1TnVDWnTA8xG8mIMNOBBr2IzoJbSJnZeehcbaWliNrFqjIEBYhYhbQbo2ljoyq7O7MxyucIm4+bwRbLuFMU7eWbxHf78+Z8LDw8vtqnvARGgeA4Pj+yIePl+18Lbw6mJsDY2QT9WEAZwclo3fWMkHERvGk90fDsp7uHRGQZp322+UgzGvks7EtTyj501FxuQd9ZMi0gsXmYYpv+wwI8sssbbW3k5JQ9X1XZ9nGljQrUEYHImXjJrfBeb5SYnos413jTY4DxQb3onvSOUuy8T617/459NLKWAAAAgAElEQVSUh9WZ2a1lj2cVrfXImU3Yrit2zc7aarrvF2UYxhwW+FFGrrMzary9iXdu3wen7scnpUqlMjMdNh5SovREVxpvimmg3hilD4eDLi5uhWLf7dXdoX03lXNzjc9ufSmPYQaa+4rTq32WeJZ3hhkqLPAjzsz0mJOUfO8mvo/qTlF678UZgNBEDAACbRpvFaWHQeP7uy+OIAHur8bDoNw9enfnh3a+3tg12WuUuHNvqblaSXmD5ZbkrLwk+bn6Plq1tJly/GtccnzeoT6YUYT/Xi8FVhpvZeJ9+dOj47Jz+z6QyDwA1OWLL1wJG2PjufOyiNITQuP1Ewwab3/PDk38gIrv7AvrBpB9bw1aVEisPzYZu2S973hnzZPcWGwd2Wg369mtLzew+c36zf7dOsMw9rDAXxY6+ngy8V0toelXYZ0Nh0clmC2X+2hhnKL0ppDGN5ojHtz5eNN39b2yXY7PVyoDSe3bknmqbDi0w3rfcXbrQVrax7TycDvV/kaS94HvWGAYpgUL/CXCVOONPfG+/CmA+vikfaB+oIV1s8EyUNdCUc1vqeJOcKfxDqvt+mjij08LlUplfCyEodp3E9qL6duT8Db7jjNPd9EW0V+521J4lneGeSewwF8u7H08mfiZ6VDH6wy2bN7Wy1KU/sn3Z+IVpdSOEDVrisY7zCkYA/XjY5FSqVIqVQZh38Xv+0663uUFSCsPm6X02w6y5c19x+1rEdtheXeFx+cd5uNd/7rMQOC/10uHsX1ONvFUPF8f12e+2pv4Aal74e0hAGHfaeINRenlzXIfLbSm2ilpeMJK4/tIX0w8WfPxsZCNug/Mvq/cTWH30XcuG9Obwt4UerNTvnu0K61ZWE3rSxcG0ITHMEwbLPCXEZv2ubeHJXTaJDvgwjrdvn/86VzHM23S8ITQeBF1LxYqxYKjVLdi4vMXxXA4GA4H+1VYp9j3Pnr3a//+75wfWvlqc2l342sncmuz7zh5ewnpb6WLCE+fWH+sSWyngKXNjKY5WZzIMEwvsMBfUpT2OTLxgcIpmr3vVtV2Qyis64jDKD1BGn96VpAz611ZefuTezTxTt7owr6bary58CfWv9lcSq+29bztrHk8q2mofXLW+471iyzLW5Kd3y3DMIPA/65vgHmXzEyPGTfMZn7MKZV3P784J70fdOodwOnL1wAmr1+TX3zx8vzG9dj1a7GXr88XroT33pbo9Y8WxmmzHICXr8+NxfZtVz4rhLz1QDRY1XwAcrlCx2K6eDxK6i4Xz+cvivmLYo+Gm+y73+/JX5QBLFzpbg+eDef5Ymw8Alsfr5BYf6yt76x5kp4N8Vr75BvBykNtG55VD/XALW1mtGZuPbH+OIPlpH6Npc3N1MaGdWKe6QjPh2d6htfFMrq6V87OyxeVanQSUlZejs9/eiM26OB88fy8fHYOSeCN22MB7L0t0ZyZxS8mXu3lhcbfvjVrdWUS1FqpOBYN2u+HNXJwcAogEg3JZl0R+G7b507PCuVyZWwslL8oB4NBG3felX0/z+vxANJ45sPlVfr/HubHfZT6P4b5ccxw4BA9g5npscrZOYCy5lEOyYH6Qau7IBjsPCROTLyRS+1sEMn4i0IFkq47j9U36n37KkzfNpyoe1fI6t5xAQHDMCMPCzwDABMRL4B6MFqtVmGYXAvAWy8dHJYGeg9k34PBYPSKiRGXa+kFchoeFrX0gpC3DsAf1q2tQ43PXxQj0VAo1PrOYZp07zYTXy5X4OCrjHP7rqg72kspGYa5hLDAM3pr3NXfLMzPhQFUq1WSeeLTGzFvXZf2Idh3BXl7rICa5YSJF7X0NqV2xFg0iKaBRrvG28u81bZ4d5C6U/a9L/bdqO7MBw/Pomd6hv9emRay53v67IieiMK6hi/sYgmNQ+ztu4zp2NpuP86o8TDTb6vMei8mfv/tKYCxsVC/7Ltp3p3tO8MwLPCXHWUt7O1bM4FAgJ4LLzg3G274dJc8CI132HonCu5seLWXt/mISCwmkvGyxjsJ1/fFxPdl0bsppO4cnGcYRsACz5hAGr9/UHr75hBAJBbragmNO6wcrWmUniiVKpSGj8f08bqiot4Go8bDLCVvZd/pBBcmPn9RvLgoA5i/MtnRnXdl37lmnmEYIyzwlxrFvhP60JtAIIAKgPO8mtjur4knb60VywA6xueJ69di8sxamMXt7REaL9PRxztcQmOPXK/XIyI4T7B9HyU8Pt8wH+/612UGAgu8c9r3bHmsl265Jbu13LygPFXMhL88eak8+vH5bfgaJZ/Pd5ar+kJ63dYQTHxH3EXpTRv8xIQ7+UUh4dT7brTvHQP1Viae7HsoFJyciPbFvnPLO8Mw9rDAO6e1Z0vLbC7RQG2dPszVzm4tJzcWt/WB3YsbSUuNN5VzFxpvat+J1iS7gPn2uX6ZeIf2ve9RehkrjS8WyjZCnssVnLfG9Wt8vcCo7mzfGYZRYIF/T9j5emN3afMr/YuC9QIQGyHvo48n3a17wwCOT+v04tHxRWy89S9M7xrfy1j73qP0sEjGA/D6PGjG0o0a3zFQrwg/PemjfWfvzjCME1jg+8POWlucPru17FnbAXbWPMtbW2ttYXwp0i+Z9JWHmvZ4iCuzbew76e7VefOeK1nj+0K3vfXdRuntB/AZNZ70OBIN2aTkRaC+o4kXh/qVejdVd7bvI4jfO9QHM4rw32t/WLmbkrZlZr97tNtcxLW78eh2RoTxd9Y8q+mUFIg3Td9nt76U/fwwMV0nk/kxJ2+fozkt6M3EC92l7e8dy+vso/T0RETpu8K04I6U28VEW/ntQt1rNc3qgxTs7buNujMMwyiwwPcJWeGz3z3aXbqd1I8s3bvTNObZrQfppc1MM2O/8nA7hfSDtlz7zprH40lu7C5tfjM4P29l3xV1V3bKoekRZ2fCQuPfIXJMXplZe3ZWsmqIN0UU3Blb46w03klbPH3zkK/mYvGrwD4yz/adYRgFFvh+0VL47HePdiVVX7wp9P27R7vSjwCSt5ew+zQjX4Yq+TL3HiU7ldIPCtNotphOr/h4dya+x701IkpvTMMrJr7bD6I+dQV7H09LaKyi9CK64AQb+bdSdw7OMwxjBQt83xAKn3kq67stiZuLFgfW76ew++i7ASi8vX1XtNBo4gHMTI/NzoThVuPl2jqH8XnCSZTeNZMTUfp1KJyuYDq13qbaTuh9OByk7L6T8joruKruMuLzDfXBjCIs8P1j5W4KT55nd75NO9V3ZJ8/sTpkNPcAgN8tXrd6h80hAam7EScF7fKKOUXjXdCXvTV9jNIDGBvT3b9SVE+YTq2nFxUTL57MzkzA8R+RlfzbqDvbd4ZhbGCB7yPJ20u7j758YKnviTv3lvDkueTKM093sXQ7qc+4aau3yzzdRbNQrx1TIXei7gLFvpsW1glMTTzaY/XPMkcOP1qOE3Rl3xWMUXoy8cYovUNIleevTNqc43BqPUGpd4r5OymvM4W9O8MwrmGB7yOJO/eWdndt4vOJ9fup3Y1kq2FuNY3U/fWEHpKX6u2oHM+yjP53i9eVh5P7s2mNgzNLreyJv5WcEc+HU8ttjNLbtb9Xi8enpW5DBaYT7mQUjVdMvFKmR91x+YuifXze9Ki9urN9H3F4XSzTM/z32k8Sd+4twTY+v/JQ206lV6kNfvXJZkZrldRT2xyRfHq/67b4Z9mzZ9mzzue146QMzcrEAwiFgqRhh0eljhrfY22dc8jEFwrVjmcSpntlnGs8USyUlVIAuoKI/HcFe3eGYXrEo2kmJUWMS7Jby1/im2EOrGkipP1WYsL0BFP7bh+cl5G9u6L3VGRHmebZmbCVp1Q+y3V8Xh50Q4b+n/7ngXjlD/92DsDL1+e583IAlWg08NHHc/YXNF3hKtTdJrouZ+JzuUI+XwiGAuFwUFxHlNftvT0FsGAR/zfa947qzvZ95Hn96Lthfty1e3eG+XHMcGAH30+y3z2C0/q6QeHcxDtXd9iaeFpCIya12fv43u27MUpPoq5Aq/AQiDgstVPsu9UUWxnZx5fLlUDAXym3YgZC3Z18uoxDdWcYhrGHBb5PZLeWPZ7ko3sDHE9ji2zcjRpvtO9dqbuCkokXhELBw6MSzBRoaMF5QSQccHKa1dJ3dKnxACqVWiDgLxbUTvrj00IwGBwfC5n2yiv23Xlknu37aMPrYpneYYHvE4n1x9pwp8kbsArO29CV4nY08XDs43upn5cxzqWXw/Vobpbrtl9O4MR/x+NRWk4Ti0UbDQDIXxTd2Xcn6s7BeYZhHMICP5rIJt7Kvvfip61MPIDzfIOeHB1fkBr13b53jNLPxODkE23su6BjUb1YTgMgEglWKjWjiYfFnnjZvnNVHcMw/YUFfqRwYuJ7WdLqxMRD0ngAb98cKmf2y74PGVONlwv0KFzv9/vzhXK5XKFvBg6n1zlU967s+08//6o8TE+Tthu2YzUpeWeteUzeodi2TzG7tdzpKgzDDBwW+JGFTLxi33tJvSsYTbys8fL2ufN8eXDZd2OU/uXrtlesovRO7DthlYw3lt/H49FIJAigUOxs4oX2D8K7m8q5lcZjaTOjGeicb1p5qIk2zxbZreXkxqK0MZE13hXcB8/0DP+9jhr21XZEL3JrY+JlqHcuGmgA8IUifS/8Nkbpw2E9/X96lANw/ZM5uF0ga8Sm4E75iiDK7vb3T1/vnaKTfXeu7s7tu6WQ2x7qEztfy6uOV77aXNrd+NpkJzLDMIOGBX4EERofLJ7CYN/7aKbtTTwRG9cllvRpcPH5xS+6qDF0bt8FSsWc1RVOzwpTk7Gg31er1WDYgiNMPNn3gN9Dr7/nefe2kPtz8XJbWL7JysN3XGzKMIwOC/yIs3+gG8ReUu8KDk38i58OAERiMWE6B9TAbYzSHx2X5B97rKUXKGvjbZiYHGtoqFZr49Fgx6n1TtT9HRbP76x52kLuG2nn781ufSn7eYZhhgkL/GhyKzFB9r0SmURfU+8KVibeWy8BODjUhZaUqZLLnxXq/bLvplH6sI/m24TRHFNvjNK7sO8y5XKFVsiY2ncAkxNROuTV/XnbRNvxsUj+olypVMi+O1f3AbIrZiQbauyyWw/SSG23Jipvp5xdc2fN4/EkN3aXNt/VbIgPHF4Xy/QMC/zo80Nmn570Ud3tTbwI1Dd8YfGi7OMHrVh7b0tKqR3xai/f0Xzb428G1U3XxssEgn75R8XHV6u1bj96gPbdrMhOD7Mbthqu3HWm8CsPNU3TtMy9R0kupWeYdwIL/GhS2Hs7Pxch+w7g9V5hcHXsRhNPAQNSd6q2IyaiLaPQX42nKP3iFxPBgN941GjiXdt3AGNjIZrnoxTcycNtKMUeDplo/PFpQdMQCQfK5cp7HpwHkH3+pLcLJNbvp7D76DtWeIYZOizwIwi1xgG4lZjwayUANU/Y9h1usDfxc7PqJ1J53dVP5/uYkjdG6a2gNHy9VkUP6i5i+05G1AWDwUg0RDt45Kn1+bz+tUCU/feFXK4gggS/+fRjq9NsDpmSuLnY020BSN5ewu7TTK+XYRimW1jgR5bowhW5sM7FJlnnyCbeWKsvm3hiQGV39NGlehDNKL1xW/zxScnknQ5QYvvKhDujfafuOJpwl8sVSONPcwUAPg9mZyaM1zTixL7L0m6v8d2qO0DynP5WKpXv4OmzW8tKab0hyM84gvvgmZ7hv9dRQ0y2IbW7thAdhH0njCZe/koht8wNQeMpSv/xR1FTZxyPhci++/yOltAomK6UJWym2NLJZOJzuYLH6wkE/ADC4c6l9U6QpR1APB6Vl9/85tOPlYebz0isf7O5lF5tZtF31pIbu/bn308h/UAk3bNbD9JcRs8w7wQW+BFHdtJDM/HiQ4XGewtnMLS/90XjnUfpCwW9Jb3bfjkrdReB+tdvTmG9WoZMfKlUyV+UAdy4rv8hNOoabE28jX23l/b9gy4bAs2q6EUdfWL9cWYT+hmrTzY3OxTZrTzUthfFBZNP73NbPMO8Gzya1qEYmPmAUOy7EFpZ2l0snbNHSPv1OQ8Mtfrk3Ungb3xx3fh2oe6u68jIu3tqpdmZcCQWe/L9GY2XWbiiN8u9fH1er1cB+AO6uf9oYdz59e3b6k7PCvTV4drVSZgNn89fFEulSqOulSq18fEoHSJ5LpcrkWjI6sqmAq9Yf1nXCaHu83Nd/I7M+8ab7f86zI+7uvq/DvPjmOHADn4EMc606buom/Jm38SMfnojRureiJrfQx/b52gbPZr1a3tvXabbZTo2zXcsuKNvG6VKze/3C+EnYQ6FgsVC2dTEG9Xd3rUrsLp/8HAOnukZ/nsdHci+e5pSZNUX1/dAPWXifY2SzYcSxkw8IcuYC41XovTGmbVk332+ANXSw3GU3knT/OlZIRoNRqPB07OC0b6LUfPj4y3jTgh5LhbKii9X/hCcSzvZd1Z3hmHAAj9iVKpVemIU2oGaeFJ3K2anW1V+NhrfS0reUysB0Pwm5YQvX5/PTDe3vXWze8amsM6KYrFt8jypeygUrNW1SqUyHg3CoPHUUl8uV4xldzPTY1259q5T7wzDjDQmU0GY95x/+Id/MH3977+4HZqMw8HEumfZs0Hofd0bzvyYU0rrqf39xhfXraRdZmZ6jNT96PjCXUr+xctzMvThcLBUquy9Lc3N+AC/z9dd8bxDdZe74169OYU0nlZeE3dRKAOIx6NGFY/Ho/kLT7FQRrOb7uj4olSuxMZCpnbfCk69MwyjwA6+7+ysmRQkE2s70mIud+M7rdQdwB+/fwpbdXeySdYFxfPzq/ORutdpM5690rvz8bMzbZ8uovQBfwPA0bFJpN0mSu/CuwOgTfAATs8KsrofnxaCweD4WCh/USSdVmR+fCwiRuLs75+Wym1hAHvXrsDqPjr4fUN9MKMIC3zf0Wdwa5qmZTaX2sZ8P1zRt2VnNFcrNYW6T80mlAe9/o//43/YX6Hvxt1Y0GecXEsYN8la0a3G0z0Y4/OiIV6271rDadtIR3WX7TtBGl9uKrTNJFrTPvhisVIsV6vVWmwsRLruUNo59c4wjBEW+HfA4k03bcGyuhuPmr5oTx9NfCQWM51ca7X9vWO4vpd8vNge6/PW0FR340g7WJh4d7vmRHmdz+cBUCiUSd3F62ITvFGzc7kC9cR7vQAQMBunbwOn3hmGMYUFfohkt5Y9q2kgvUrh+p01z/LW1pqI3kMK4EuvtWEj5HTIJoZP9NHEG6fSEk5MfFca70TmrSbeULOcvFzOpgne+a45o32HlHcPhYI2E+5EoF6poavVtdh4NDYWgqFyviNs3xmGUWCBHyKJ9cfadgpIbVO4HsDuxqPbGT16j+zWcnIDzZD+dqo1IHRQ9LdlzrS8TrHvzgP1cNY+Z/Ul49OPgwB8FnX1ppdyl3pH06YHmptk56/oS/yo7E7ummvddrEil9zH49G5uUkAlUpFtvgdNZ6D8yML98EzPcN/r++YpXt3mpac0vPfNFPzKw9d5emd0BcTb6WshJWJl3FYV+8iXC+i9DL2Ufqu1N1o35W8uzgkqzhd+eDgNJcrULa+WKyIRDv9drFYGO2FdTYaz8F5hmFsYIF/x7Ty8dnnT9ym53vBnYk31tYR9jtkia4C9YRDjRdR+kajCqDe0JPZzqP0LjbJHp8WyuXK+FgI7VV1VMw3Fm1tvsnlCtQOR0QiwUhE3zpDv1cwGIT0VcOJxoPtO8MwFrDAv1cs3U4O6ZP60jJn05LnL+X2D0vG8jqiq0A94cLHU7Ncx7Xrr/by7grrCJuaebLpb/ZPRUKdxtp4fR7TCnlj67+NxnNwnmEYe1jg3yt2n2aG92GuA/X2wXknJl7GoYmHmcab3gnZ9+OTms2lXr4+Fyae1sg6R47Py/3u8jmUlfd7USxVAFw0A/XxeJSWy4kfAbx6cyJ+O1Fsr5yDdo3n4Pzo4/MN9cGMIizw7w2Jm4vAk+cdqupODi1PoEP/8T/+Rxcf3t9qO38pB6AWjttk4l2YeDjw8dNT3fWYCXV3Yd/P80XaAHt1flI5VCpVaMfMWCQIIBIONrTO0+iskDU+lyvw0DqGYZzAAv/+sPLV5tLuxpfNwvns1nJbp5xQblONtxF+G1yYeHv7TszPOp1qRzg38WjX+PN82fQcr7dtMG04HDTdLCfU/ey87vDThX2XO+LkE3K5wuu9UwA0eT4ejwr5p/cqHr1a0yBV4BtPIOQvB5VKBazuDMN0ggX+PSKx/jiziY0kdcEnNxa39Wa6JrLGKw/9jOAXP/zSuXzdFCcm3qq2zgaHJt6FxtfLRQCFauvfYRJFoe4vXp4bN8sJXr4+n54KA/D5uxtTL31cORQKii44m8UwHVfKwkFHHF3w7LwEIBLi/3IZhumAR9OcTu5k3hMsR9kEvwAQjfhmpkOff9JFIlyWdntP78S+i/Z3Ie32WXkh7d0G7Yvn5+f5si8UQVPyRa2caJO7cT325PszAKVSZeFKW1yhXq/OTEeEd7eZfiNQ7DuZ7+nJthUy+UIlHA6a9r7LyXu61XK5gebWOLTbdNO6PwrOVyqViWY3Xcd7Zj5Q3uz+92F+3NWl/2WYH8cMB94m9+FhmmV/lj07Oa2UKvVCsY7jMpBzrvG3EhPO7XvHVXVGjCvmTPn5xXm3Gh8bDxWqAHB0fBEKeWGdSqcovdB42hAv82ov70TjIRXWASiVKjkpQhGPR2uNDm8/PSsYDT0tmqNtch1v4OOPpukLgcPzGYa5nHCgb0S4lZiYmgyGgz4AhWL96LjsLlbfx2o7h+X07qrtxLcNkZI/PzfJsptG6c/zuuE+Pas5FHU0/TeNmgdwni+XSvqKdzTj52LyvOkVhK6fnhXGxyLn56VKpaK0xolggDETL/fFOWyRZxjmMsMCPzrIGk841/iO1XYO7bvVdhkng+3QZSZeMDM9RnVnFPGGxVx6aoiv16vRSADtK+YImwWyRLlcKRbKxUJZXknX1TpXofEvX50oh+wvYuyLU0rrHd4AwzCXBxb4ESQc9JGJRzcaLzCaeBe1dYQLE+9O42nCKxyPwZHV3WH2/fy8QOY9HA7mCxUA1xYmZVW2t+8CofGVSk3cNqFsixcm3qovrqup9cyHhebzDfPxrn9dZiCwwI8UZOLhSuM7zrZzbd8JexPfVaBeCSdQHPvqwhT9qGyfk+fSHx1TmD1wmjPvrzMllyucn+vyGQ4H4/FoOBzsOCDPhnpdAzAx0SoMdIJpX5zDqfUMw1xCWOBHDVnjAZDGO3+v8UXXtXVEt4Pt4NbEo33U61i09e82peF93lowqFaVKpvllCg9Rb9pZA2AK1cmrRLtDu27gKbYXlyofztGE392VqpVq/Zd76zxDMMYYYEfTUjjC0W9B6wvgfre6aOJFyjtZPL2OTElHoDPq0+upeD8ZDwkX8QYpReJ7VKpUq7UgkH/x9fNIxPdQtEFuslQKHhxUTY18fTpzkfS9qzx2a1lTwvXi4p31qThTAzDvEtY4EcQYcR7DNSjG/tuE5/vr4mXb8kqvq1o/M8vjulHsV8OgFWU/udfj4VGlkqVSDQUDPqVcXUK3dp3QiTjFR+vVNv5A4GJibCTSD69Ubz9eeal8rB8586ax5N8dC+jNdle3Ei6EfmdtdV0t+9hzOF98EzP8N/raGIaqO/Wx/+Q2e/7jTk38c4D9aa97zPTYzSoTvh4UvfTnNqoTlH6jxbGq9VKtdoy/R6vJxINFQrlUCgoxNidlgtk+w5gciJKXx32357Kp5FI//rqGF2OpJXV3XjUXOOzW8ur6dS29ni9tap45WGmbW4ywzAfICzwowxpfKMOdKPxsol/vVdwnX2XcW7inQfqO+54bepovVqt/c3NGeWoHKU3Tpn1ePV+d3vvjp4l32qKLY2kpfY/0+n0NtiYdeOh7HePdpc2v1pRXk6s39/eljV/Z80sfr+z5lne2hKHVtNAerWHCD/DMP2DBX5kIZ12rfF+rQSg5nG0Nsa+fl7BYU88zEx8txV/gYCu05kfjhqG6XVnuUK1Wq1Wq9L5wUAg+MtLvUmdyt0HZN8FY2MhNAfpEPsH+WAwCGAi1t3anu7JfvdoF4s3E8YjKyst0d9Z86ymU9tS/L6Vad/deHS7GdzfTgGpba0tGsAwzDuCBX6UkTV+aqJlWLuK1fer2q6/Jr6jfadzZmcigYAf0Nt8SeNPcw1F149OSvF49NOPpwHUmivmlBXvpjiXfJsGffotyuWKrPEAPv5oGs2iuW5NvGMyT3exdDtpe05260F6aTPT3Hy08nA7hfQDYdKX7t1hOe8/vA+e6RkW+MvC0XF5aiIkuubsNb54fn5tIToI+070YuIdIrTQ6w2EQsFQKPjR1QgANEoe6BIejQQE9Iqs7vJuGPRs3wmjfSfIxAM4PSvII2nfC4wmP3l7CbtPM/pPpv6fYZh3Dgv8iCObeIcab5xb925NvFHjndh3Qj7nLFf2euoAtEYdAOm6XEsvtsj4/YGOY2vRvX23Une6z7GxULlcOcuVRNs92tviB2Pi27XaIYmbi329CYZhBgEL/OgjazwAhz4+Eot1nG3XCx1NvDFQ7zwBL38DuHE9VqtVyZp7vD4ADc0Xj4WUt5C6T0+G/f4AmtVtTva49xHh4432fWATbBJ37i3hyXOTmridNY/HY97Unn3+ZDB3wzBMH2GBv0RMTQZJ2m3y8YqIdlxC02183kVPPNpNfEf7LntcpTxeQwDeMIBXb4rCKJ/myvVa9fi4iPa8+8lZaz2d0az30b4Lzs5K4XBwIh6Wk/FyW7xDE38zed35ocSde0u7G1+rQp7depAGVdcbvwM4SdwzPcJ98EzP8N/rpUDWadL4Rr111GHN3ftg4jsixK9R12Rp9/sDfn8gPu4F4PUFAExNhkulymQ8RHF7NNXd+QLZ/nJRqAOoNUv/jBrflYk31Xhz4U+sP95OpVfl1olVWbMAACAASURBVLbs1nJyY3dp8xsqhk+s30/tbiSbbn5nbTWN1H2rQnnTcADDMMNHHc3NjCq3EhPPsmdTk8GTU925NurwthfPmsbA6Y2m13RRXgcg+VnceZGd4MVPB3Oz4brXD2v7nr8oFgtltDev0zoWeeUMmhoPoFgsao0GAJ+/tV+O4vPUqIah2HcxknZiIjw+ptf3nZ4VjDmC8bFI/qKYvyh2LEGw8fEqKw817e6aJ+nZaL6ytJnR2gbfaNvwrHrSZgflC91NIb2R9DzazHCnHMO8azyapnU+ixkJhE6fnFZmpkMATs7K9ATAtWm9ZdyY5JYFXg4GuBN4tHv3jkF7is9766WG1pidj8NC4A8O9GFwQt3l4LYQ+Fy+NckuFCgDqFTq9YZ3eioK4Pq1GICfXhyjKfAfLYwPTeDn58ZFAkI4eKHx5ODj8ajzGkPmw+X1X/9lmB937bd/O8yPY4YDh+gvEUKb5WS8KLjbPyjCooSt79V2XWXilUC9UdjkRHsoFCTLrkx0F1CUHoDXU1uYjwUD+pz5Vkr+rDA1ERb23cjg1F1+0aq+L5crDKwnnnmP4H3wTO+wwF8uZI3Xn0yEAPjqJQCvjy3DOcZqO9f2XcFJuJ7su+YNHB7V5NeFtNPA+bm5SStdv3G97VuC19NcLucPiM3ucn+a4MdfjuGq/d1mso2Msi9OFm/SeGHlrX41hmEYU1jgu4JWaqqtQztrYvj2zppVZ1FnenmvG4R377bgbvgmfm5WHbkju/ZyuRKJhiJRtfPNCqHuombe6/MVijUAL1+dAJiciFqV2nU77sZJ8Tw6jbVRNJ5NPMMwTmCBd0F61VKHVx5q2kN1b0f/+cuTl8rD+XuNgXpfo9RooO7TRdRK4zu2zA2OYpm62AMAfvjpWK4n9/o8JO3Oc9Llcg1AQ/M/+f6Mku4AvD4fgItCNZ8vS2dW0IN9dxeclxGBemWK7cDa4hmGGR1Y4LtnaWlJGsQ9fEzl3LXG+xp6t3dXPv7HJ7+iH/F5wj5KT7X9s/Pxer1ar1cBHByVAMTjUa9PLwx0ou4Upa/Xq5MTgYbW1j9Cm+W8Xg+ASCSoRNfFVLu+TKsVKMF5geLOFY0XgXo28SMO98EzPcN/r92zeP+bTRgngwBQw+wU0W8i2/62I/KB560DpmECGyHvSuMF1EZR94YBfP5JK2xuqvGyid9726u0OIzSF8vVSrnWqGtzM3qMwecLGMe/OKFuWCgnUyhWAwFd+I+OLxq1KhysizXifLINnM2cVwruXLTFMwxzCWGBd0Ni/X7KJk5PZLeWkxuL+oZNTdtOpVd1zc5uLSc3sJlpHRAzRtIbj+5lNE3TMptL0sv9h6SadsK+PW3uVP0x50TjA6UcgGrYzUw6K0xNPCXaK+VWYZ3PF/D5AgB+fnHebbeYGDXv8wUKBb2erhWl1+oAgsFAsdTqo6tWWh89CPtupe5Gd64U3FmdxjAMI2CBd8fKV5ud4vSZp7tI3RX5+JWHmp6e3/laGhJGB8RQkNbwsDv3XCwB6QpjRT2cabyg92o7KxMvaugK53kA0dg4db6JljmtYefFjZC6z85E6PvBwhW1ai8aaQ26mZkeE/67VKxAitJ3xIl9twrOO0EO1LOJZxjGBhZ4lyTWv9nExpc2Er9yNwVh2iWyz59Yb9hsvT74hV2U26adsI1Gq0Guo8YvXBnIiBUy8cr0+GDIHwz5lQ4xUveDg87T3Ajh3a1WvFcqVQDw+MRyuePTwth4aHZmDECpWHFYbeekNU6ou31w3srEo93HN+oa2MSPJH7fUB/MKMIC7xoa0G2eigcArDykQLtJTv3dL+ogdb+20FIsG42XofZ3EZ/vo4nXGlVZ2kUNXSQUkM+fndHT5B5vwMm2eCt1F1F6ANNTbYb+5Wv9ssKIVyu1U2n3jD1Osu/u1r3LGs9t8QzD2MMC3wMrD7dT9nH6xPpjkYKHlFMfbOzdOcpOWKuAudHE97dlTmtUyZRT4R5F421c6dxchFrmOmJUd2XiDYAn3+vfUaiW/jRXlv3657+ZrtVqALQG7HEenHen7vpNSgV3pPFs4hmGMYUFvidWvtrExpcPHCzHXnm4nSJdT9xc7GHj1u8WLTeI2BxSsFqsLttx58n4Xkw8BeRFzN/jbSuPrxSLkVBAvk9jYZ2NibePzLfS8FoVwGefTltdJxBsldbb/zr2dJt6t6qhMy24YxiGUWCB743E+v3U7u6u2SFpvh0A7Hyb1iPzK19tLu220vfZreXuJtiZCrlzdTci23H7ZLw8nrbHAfVKrt3jDZApp0w8qZoSnJelruMm2Y55d7RH6a2g4vnxGGlq+ej4wlTmnbfG9WLfFUSgnk386MGz6JneYYHvlZWH2ymLA9r24kaymYFffSI2aCbWH2c20TyU3Fjc7nb63e8WrysPej3zY67jaHernbDiuRyoVzSeJsyYvss5irRTQL5jT7xQL2NhndHE26t7W5Req6I97q1p9UKpVii05v5MToQBhCOtXgMXVt5dcN7exEPy8bQnl2EYRsDrYkcHJztYSd3hYCesuBpd6odfcv5CDkAtGpcl32qTrBFjT5dSJibdf/XqfNTXqIn7tFJ3WdqFp3fi3Wl77N7bUjTiAQBPYPGLCQAvX5+fnBUATE1Er1+Lid53uU0uHNTHBgi/3tG+O6ycN8Wm3V+ou9cDAFSTyGtkR4OXP/08zI+7/ptPh/lxzHBgBz86KM7b5syudsLSpazy8U5MvGLZ0XTtymkdTbxRuoyBeifqLpiZ8lUqNXhaiQAqr/N41IilvHtG0fWO9KLu9ggfT3kVNvEMw8iwwI8U9hpvVVsnsAnUAxCTYmFRc2fMxJtG4zv1d1UBvNlvzbFxOLHu5xfnztXdWEuvcJor//DTESx632WNd5h9d63u9uPq5ORCKBQsFsqciR8R3vks+p21Zn5xgCM1mYHCAj9qWGm8CM475Fn2TL7UT09fAvjkb1qlfELjTa2/qbQ7v3lagROJxTrKlTDxjeZsOyfeHUC1WgVQq+tm/cn3Z8enhampyNREh/ukcD0pev6inL+w882998V1hDS+wak2po/srHlWn+jjtLcXN5Ks8R8kLPAjiI2Pt7HvhKLW8qX2D0uwiNXL73In7URTzgMA3uwXbQrrFEjdDw6LDtVdsHAlbFpLr2l1tNt344Z4h9tge8fhzPmGxiae6QvZrQfp1jjtlYfb9jO9mPcVFvjRRMlndwzOyyhpdWNq3Cofr2lVTau+3tfVvStpR1PArs7rb9GgT5bpqO4z03qfutfZbDsAp2eFmelwIGA3LadQqomRdqYcHV+Mj4XGx0KQYvVGBmrfCRGovyh2bvxjmA5kv3u02zZOO3l7CelvWeE/OFjgR5+OjXM2UMjdX8oBqIXj4lKKxudyhatXWmLZrbTLjI9FKD7f8Abe7Bc6qjvl3edmI15ns+2s0LQqgOnJ6PVrsUjIA7NSO4FcVC/7eFnj+xuc72jiSeMj4WCtATbxI0C14Rnmw/D5beO0B78ZgxkILPAji5zPfrNfdGjfCSVQPz/bKq9TNL5RrzakJeseT8DjCbiYeyNX0l2djzTtewfNNq2qc2jiZShK/+pNh9lwcpRerq2Tt8/R6/0KzneFXHDHFfWMezJPzWZ3uR6/ybwzWOBHmeRncXLD6N7HC40PlHJ7b4u/ud0qr8v8mKNEu5D2/cNiPB79Ijkrzullfm2x3PrGYHPbirp3nG0noPbxyYko1dIvXAn7vHUAHk8AzdF1ZN/Fcjmzi6iHhMb/9MsxPRlCcF5hciJKJn5ubnLIH80wzPsGC/yIc3U+AqDuDaMHjScoJCAWwwCYn40A8PoCXl+AkvHuZtvJ9v348BjA/NUJe/tu3xHnwsTLTE21rqmk4cnE05J4Y5GdeKVQqPRd3R2W2gG4Ou9Q3VuNUAashifvrHU3WJn5AEneXjJ51WrHNfP+wgI/ylBt3WeJK+IV1/n4Z9mzXK4gpH3vbbE5YnZGnKM0x7sO1KO9sM54z1bqLpt4K40X9l28UqlUABSLGoB//tdDANOTUdosB1sTb0qtrgGIx0I9bqZxjfjV/vLkpfIwnLvyUGuS2VzCkt4WpWlat8OTmX5Tr3uG+TB8ftvGy+xzBwu1mPcPFvhLgfMhdwofx6sAKqEIlcfT2jdaDGNVcNetiTfOsaEdM1aD7ey9u/NAPUFR+om4Gi24fs3yOmTfw5GgXGpHUOo9GtWn1vdd452beDM5N3+RYVQSd+4ttaXcM093kbrL3/k+OFjgRxalNc6dxl9U6rHJ1pKVeDx6M9Gy7FYaL553ZeLzF0Xjfljlg7qaRGs08Ub7LiMK6ZXX5Sj90fHF5ETI/nPn58aNo+6GiY2QO9f49i2Hym5ENGP1rSC/HLeXQ/8cz/8ASazfT7U2Xu6sraaXNr9iff/wYIEfTUzn1nWl8blc4eDlPoBQPIb28nhTby1rvD/QivjZa7yw76au1MrE26u7k0C9gErqgsEggEq5Jh+yidKLzXKyiVf64qza53rEuYnvkcT6N5tL6QdbWahzT1qkVx/czugB/vSq/gVgZ82zim0R+BevMx8SKw9bGy+lTZjMhwUL/ChjtMJONN44is6mPF6+iJXGd0TIlbIAXvkgsu9OvLtpoN7Kvk9PRwCIQvon3+u/oDFKL1rjjFPtTPvijO1zHxRk477ewc7XGzCRd0CIfuLOvSU9a7vzreT2pNeZ7mg0hvowklh/3CzIYHX/UGGBH0Hs59bZLG1TpH0s6BsL6sNebMbbWWn8aU6fqmZl4hUbajrTpvVBWtV41Ak2Jl5sg4VZGt4FppXzH7DGrzzcTqVXPavp1H3T/8W36qpbk1BWHgo92FnzJDfMOqoZhhkGLPCXGtJm0nXFtftLBQDRK7PGdxkD9aYaPzsTEhpvhNS9VNJPGB+L2H0v0aoA9vYLzkfNKybePvtOhfT1hs90Lj2AJ98fWm2Ne7WX7zi0rr8aP7QoPYCVuymgu/qq7Naynn5fxXZm06zhimGYYcACP2o4GTsvtFlrVJ9nj+RDNlNmjVvjnPt4KxMfDgfRadr8ghiC63Fpsjtm4ilKLxBR+sUvTL7fECJKb79NTvBh+vjs1oN0KpVKrzoulctufbmxm9rmXjuGefewwI8UznfCzs/6jU3tHQfIG1vgrAL+pPGzMyGYabzsPu3VXZTNk7p31eMnm/jDo5JyVMTnRZ3dwpUwLGjU6zCz70LdnYy16aPG25v43y1eN33d/pCR7NaXG9j86uFXotquM0pDlfnQU6Yz9cZQH8xIwgI/gtjbdxGNN21qJwpvD2ERnxcYTblyEVnjjYjgPGEaeBDqnvx8Bq4gja/VqrCOzytYRelfvbEcL1+rO72fobXPmQp5V+oO7Hy9sZu6v55oVds5oG3v2M7aaho8xJxh3hEs8KNDx+C8MdFu2tRuj/NAvYxs4vMXxVKp0jE4b9Xy7noYnxyoV+z79KQ+l97I4hezZN+9PnW5nN8HALQu1jkDap8TiM693y1eVx5dXaet+Xnlq80lZ3F66q5bpST8g9sZbTuFVkc1wzBDxKNp2ru+B6Y/2Ai8rOsAlFC8LJnJz+Jd2Xeh98pFxHOae3N4VAYwGQ/eSkwcHp0BCIeDsrorN2+q7uIjbBoBjJyeFQ6PSn5/AFLQ3ijwAF68PN97W0JzFN3iFxMAjo4vyLt7fb7JeEj0zlFh3WmuLATe2Dtnj1B3Y+TfIcYhgGgKfLc3w7xv/OuzoY4d/Jtb3X3/Yz4I2MGPDpFYzKjuRtduTLS7HmQLSentk/HEaa5CNX0u1F2mRxNvqu4EpeGVKH004if7bpx4c8tt7gCDKbsT9t04Rpf5sKjXh/pgRhIW+J6Rh3jurA1nMKeDPSKOpF0gy/P+oVqPZsQYqJcxJuNFJr5cqZyclF1E5tGlcSeoOy7x2bR4xaai3hilJ+lNfKaqeMe+OIf0qPFKqZ0s6uzgGYZhge8jeknRoLHfI2La1N6xPB5N+fSXcgBe5YOdTu9QUW+q8dFwDUC+aJkVcj5qvlsTb5xtZ7TvTnj5+tx0aB3cmuZ++XhWd4ZhFFjgPzA67hGRtdyhtAtcx+pNA/XKFeZn9S72StX7j386EK+L+LwTde82+w5D8XylWjk8KnbU9UKh8td/OURTgMVc+otm9F7Y997VtBeNJxP/4uWJeIXVnWEYggW+T2S2lj2raSC9KgfsxUqt5a2ttnVcJuu22rd3NU9ys6jDYVO7KfOzlr3gRkwD9aYaT5XzV+ejhZKfXlGWx3e1Js4dsom3su83rsdsuuEBFIo1WAfnXWe+e2mfOz5ppVRY3UcG7oNneocFvk8k1x9r2ykgta2vZthZ86ymmxO9thc3NlrRe/N1W4k796QOYgA736axdO/OENc8UP18LawrtBMTb7r93eizqes9HA5OxvXg/+FRWWh8oaSP3OlK3e1vz8q+AwgGgodHHea8ej1tk22oeL5arQOoVtUUQ19ktff2OVZ3hmFkWOAHQ3brQRqp7eaozpWH2ylxzGrdlqLw7vW9xyEqvQfqZTI/5qgKjCrnbyUmhMYD+PnHt4VSNRiJwLG6uyi1E8zORNCU+VyuQ+lwpVITM2sBBPxd7Mdzh4vtc6/28j5/AEC95nIZD8MwowoL/GBQBnY2l3bQU6t1W4n1+ymh8D37d5J548PqfLn9vSuN7xSor77ZP0d7uzZp/OFRuaE1znIVuI3Md/X9g8LyNzs1tt24Hpua8AJoaOpkGwCBgM/YLCfTe3+ac42XP2t6qovcCsMwlwEW+IGQff7E7qjluq2Vu02F70Xf7QenOBR+1xrffgXdVp6c1ZSTJ+PBoL9RLNYBvD3qzn12NPE2u+P0yfOBICxa5kxldf8gPzkRCgR0yX/5Wn1jf8PjTjReVvcb16cwrP1yzHBo1If6YEYSFviB0NqObcR23VZT4Xe+TcN0B3fHPSKU2KZgr/Fhc89nhfpZoS5L/vSkr1rVpde5VxYmPn9RvDofBeqAD2hbBHcrMQHUolE/AK+np38JXQy9oUC9PWTfaeKN6IsTtfRDwF7juSmOYZiOsMAPhraVG4Ds6e3XbZHCr32btt7B3XGPyA+/5JQadYGV8FdyeQDBuCoV83PhapOnz45sHL/16Bvd9cpKTDXzPq1Sr2P/1ANDUX1HbEy8qX0n407IxfOKiaffS95t8+d/PqQncuX8oKP0hJXGm6r7MJfEMwzzQcAC32f0xVmUThfbOXbWWqn2Duu2Vu6mkE7b6DtgvUdElsmuJHMi6puI+kwd//xcK7m7f9DqyDIG+Wen/bVmqZdQmqvzUWHfSeNJ3a8tRD3wjkX97m5YxoWJl1vmTAP1SrMcqbsYRI/BR+kJ+/Y59u4Mw9jAAt9HVu6msLuRpN71lYfadqq5VWsV29spYPFmovO6LSrHs9V3S+TB73AsmTbbZUjmb9+aCTQ5Pq3bh/prtWqjUfr1Ve78vESPtlD/D0f0RFTVTcaDtIrG+Q0TpibeuX03zrYjBW3/7erVak327g6j9H0cBa+0z/GQ+UsC98EzvcPb5IaF3vyuJt0HhKKUivArOFkfB+t9cTJHxxdvD1sKNBkPBoN6U9z+Qcnj0Yt5rsyO1ctFAAdnoD1vp7mKmFdvf7f2t+Q6Pv/pjZgi8H/80x49iUYjtFkOwMvX5xSfJ6WXPT0hBLjv9vro+OL0rAwgHAlaXd90vxzzIfJPfx7qNrk//J63yY0g7OAHgzqWrm259hBwZ+XtcVJXHwp5P/4o7vP6vd6wz+uPxcIi1E+9ZwA0qf1s4UqkVqvWatXxqKdaqdLj+8yRwy5w5XuGTfH8RaEMw+g62cQb7Xs8RpkFtVmOpN0+DT8IShX9u3ipWOHgPMMwHWGBHwyJ9cetAD3F6EXz+5AwarypzDu074TDITMa/B7UALx6o09un5keGx8P+30+vz9Mcf7YeCg23hburtdbwaSDo1K3ffxW3znIvo9FO4TWD4/adug1K+dbtfSE0bJb0d9YOl2NvPvkRMjqz4FL7RiGEbDAD4yVh5rEkELz7Xz+SXwQVp4wCqqID390tTWrjirqqbBOVKf7GqU3+8VILDYzPfa3X8z6/QF6fJGcCQQDgWAAwMGR+dZaRe8pwV+tVuv1Krqx7wSZ+HK5AuA8r6ciSd2nJjun24dTaid/V/j8N/rq2z5ukWfeQzgHz/QOC/zo01+NtwrUK67x2tWoBr1CXiySsXk78Sx7RndLMn96Xnfex1+v11+9ySvy78S+x8bb/isQXe9ybZ08sxaOo/R9L4ijbw/92jDLMMxowwJ/KVCsvAjXdxWfFxhFWqj7+FjEUOdVe71XgFQ2b4zzKz30yq3KZ5o28d++NaNpdQAej5ovv8iXj48vLvJlraHZx/lDobbZdqTuolmuqyh9f0286bcEm/Y5jtIzDEOwwF8i+mjl2zVe73xTirdpXB09f73XEkhaAF/3hiGZeGXYrY3Gm+Lz+Xw+n+jiowepcijoN54vlP6XX4/zF+XZmXC5XCmXK8ViBZJ3v3G9c8bdGKXvIzYT63rfPscwzGjDAn+5kIXTX8gdHJW6te8CWePf7Bdk40jPz/PFawuU9lYl9up866uAaaAe3TTLAVi4YtIYNjYeghdj46GPr085ifNXq/V6vX5wUJC9vtdTp9WxplH6jvQSpe84j9Zq+xyb+BGgXh/qgxlJWOAvHVbhehdI62QCik5LeXdd3ZVlssY4v+nGGnGTNrdB3XE+X9u8PBtkpR8fC33y8fTM9NitpF4AGAj45Ir6iXgAgNdTLxWLQvVLpUqpVGnU61Zp+N6j9M6nzXNKnmEYU1jgLyl9Cdc318noUKx+fCwi1D02HlHy6xSfj8RisC24M5r4jneopPaPTwsXhfJYNGRaPG9k/yA/NxcdH4+EQsFQKCi+BCTN1stOTeq5+Ua9XipVsj8eHVn38rkw8d3ukjFq/PhYhMfdMMwlhwX+klJ4ezg3E65F3Vt5EQFOftaSwDf7BVnd6YmNNVdU2bixpqPGmw63cTKdXp5sIyrnBfKQu4bmo4cc5w+Hg9GISXZfXDwc9JSKFXp0bOK3wnkkwK2P13cXL4tRyYKdNfmA2HFscma3H+bxeNrGQDEMMxBY4C81rhvl5bJ5yLF6DXv7bTXzClRRb4pVoN65jxdfF2q1alf2nZifGzcOqJf5458O5B/D4aDX5ytVtHA46CTHD7MNPX1x/AR9tLiBf/rzS+Vh897dR98pur3zbVr+6euN3aXNjKa5n9eU3VpObmAzo2maRnsaWONt4D54pndY4Bn34XoRBNZj9Zpeq2NUd1m2FY3vGKiHtcbbzKbN5ao2d26073LXOyFM/N//3ZzxCkqznFxL33L5kSA9ShXNofD/8NOx8P3hoMfmLabI6m48aqnxqVRKVfidb9OpVKrtrMWbvYxizH73aBep+/rXg5WH26n2hcoMw/QbFvjLiLH9vavKO9ONJqVSRR/b7lEL7ohbiQm/VgJQ84SVQ4rGm4b0Hfr45GfxWq3aaNS9Xp+VfZcdszE433GTrIx9Lb2xsc30IU6gXTL6lSdCcOb4jdiYdYtDd+8qCr/zbXrp9m16nt1a9qymgfRqK7JO8Xs9Zr+1Jkfu5Th8L/F8hmF6hAWeaeHEyhubr/IXxVKpEgoFF+aj8DgqZVcq6mE95V4+U9F4G/vu5B5mpsdMh9bBbJOs4MVLE8mnWnrXDfEk86WKJhz/57+Z7j3U3xUr7Qq/82166d6dm/RDYv2xtp0CUtv6zOWdNc9qOrVNQ5i3Fzc2WsH8tjh8ZhMbyabGJ+7cW0L6gf7Tztpq2uVOZIZhHMICf+mwn15nv6JGSb0TpZI+xCY2HrGfRNtsiwfMNF5+o7Haznh7bw/N+7zHx72AvoXWiHFrnDE4L0MmPhrVp+vvvW110FGUvi8N8aZl8x0dfz9J3l7afZrRfyB9twjIZ7cepJHabm5XWHm43YrkU6r+Gz0On1h/vJ3a3fh6p/mTtr24kaTtS082M+9mQcOHQqMx1AczkrDAMypWlXem6n54dAYgFAqaTqKVNZ4a5D5Pzlt9rpNAPZoaX69VARyd1pSjNHze6/UZb0DBKvVOKCZe7IPvlm4b4h02xfVf+BN37i01c+K2+o7M0912771yt6nwO9+m0f7O5O2lZqp9Z422KpK5v/coyQF8hhksLPCMOarG/2wyj9bYEUc42RyPToF6+Y3KmTb5eFoc9zc3TZrXIdl3Y+rdBiUTbxqlJ+yj9FYmvl87aXrT+JbC2+t79vkT2+vs6h6dSG7s6m9r9/2J9W82l4S5ZxhmELDAXy662i6jWPn9g8L+Yas0/TxfpC2rszMm7tZG463C78Y3er2WZeRXZltfKYTGm+6Os/mGYR+cV6rtTKP0xGQ8ZLNZzt6UdzvTZnA0FT77/ImNviNxc9H2Mkt6Al7ZlZx5uoul20nlOk+es4dnmIHBAs904PNP4tCqAKiAjtRUePdwOGj1Rlmqf8y+RXOAHWxH38Ci4E7+KkDldTcNM+bkve/Giyj23V7dCVnjTaP0HTfL2TMgdf/D76+7OKQr/NZ3j3ZtG+KkqDvR8vQrd1NoZfIN7zIe6q3zbqThWfRM77DAXyLcLYfNXxTn59oq1bM/He0fFMrlSjgctJ+H2r6QxrwmzqbaDu0m3uZM+9592cR3FZyXkQP1fYnSD9S7mwq5nboDZKrTGxu79tXtifX7KaRXm1NqdtZEGB5Y+WpzKb3ayq3vrDV75ZR3ZbeWV9NLm19xmR3DDA4WeMYOUVj3+aczFK7XGnqU/uxcLXAzxSpW7zpQL3fHyRmEp8/eAvj42pTpRci+1+oa/ejEvhOyiRdReiPdRumHEJn/w++vK4+Ob6FyuY7daysPaRId1cNjezslvHhi/XFmEyINv5pObTdn37W9K7mBzYzroXgMwziBBd412jjcqwAAIABJREFU0qiPDhM9dtZak7d31izPbb/gezCt21g2f2VG7z3zeABATsnbcH3OA8MCeHQTqD851TvxjF8FZI0/Pze/n2q1un/QSpw7V3eF/IV+G8ZmOecoJXXvNu9OzWutfrWVh5ryU0ud27raVh62ZdgNVzQ/Kr3L/chbhmEcwgLvhuzWstTx05zoYSnI6v8cLS76/IlSoGT9nr88eak8Ot6zu/g8JHWnvPv8XHThSiti73BFjbwA3hTT8LvQ+JlpvW6u0ajCMNzm80/i5VIJQCgcVm5G/pZA9t2FugsTvzAXLlf0dGW3UXpZyPtVNv9uyG4tt3353FnjYPsA4Fn0TO+wwHdPduvLjd3Utqy/ifXHmc2lVobRDZmnu85qjkzl3InGd4Uyj1bpiJufaw2tQ5fbZp0H6mWEiX+zb7KuJhazvJlqtQqg0dAODtXqd+cIjQ8EfFbn2EfpBWfSOe/avrsisf64FWqnGD3bcYZ5H2GB7xoa1mUwLIk795aaycudNX1AN8XZ5RB9GzS1myL22edPnIzutBHyPmq8Mo9WVnch/A5X1IgF8E6S8UZkE0+34fUGlK8Cv74+AXD71hXTm5mfC0vP+yCoZOLdRelJ3emfH6S6E3Ko3Ul0imGYdwELfLdknz+BaZtwYv2x9H+63Y1HtzO2//fLbi0nN3ZFDVLm6e7SkwcD2tLRVXzedGIdzHbEdbWiBg4G4NgH6sfHAhcFu8o+4wAcqq1rNDQAoVDQyZ54K8jEL8yF0dR417X0+KDVnWGYDwQW+G5xGkm3mxUCvblIivPvfJvGLu6JFPz9p8l3MsjTqO7km0ndTffIdbVt1lTjOwbqk5/F6/UqgFrNp5ymDLcxanzuvDw3q+6vcwdpvH2U3uqQnHefcDa+nrnMcA6e6R0W+F5pr31vReLtvgQ8+tKzml7azEjufuVhe2Hxyt1UvwZ5uiivM6be7bFaUSPi8/LRjhpvw8x0SCTjCXm4jXIz1Ur19V6eettCoaDyiT0iqu0URBreysSTun/YdXYMw3wIsMC7QR6w2UpHSku17Nndxeb2Jja+tLPohnlhQ8C+sM7UvgusVtSYYh+rNzXxC1dan0sa/yx7ZjqbFu1fOA6PS/Nz41braLtFbovfe1sSUXqRhjc18ULO2bszDDM0WOC7ZeVuCvLmbBcsbX6zvrJ+P7VrL/Fon909aGwK65xfRNH4/QPLAIBR420C9TTc5ovkDJotc6TxRvsumIz5qtWa1+cLh4NdFfl3ZGEuTFF6KxOvYDXThk08wzADhQW+a1a+6s8arLbrqL3Flrn+3y1aziOjQ4o0OozPK6l3o7rb23cZw4qaopW+2mt8x7fkzotnZxWjfQdwdHyROy+Pj5mvhO8xSi+b+ONTk744OUpvVHcur2Oc0KgP9cGMJCzw3ZNY/2ZzKb3aVui+s+bxrKad9LnJ12lN56bnUkR+51vL4SGmGi+/+Cx7Zt9TrtBR3V3w+SdxX70EoO4LwzpcbxOrF7+CPJuW3kIm3uvVABxYT9OLRoNiF84Pv+T6FaUHMD6mX/Y8X7OJ0ufOP/CWd4ZhPmRY4N2QWH+saduL0uLr1XRqu/uO4JWH20LiVx5q22hND/n2rt3wkN8tXlce9Lqs611pPGzV3bl9l5mfazvfqolO0V2Ho28m4l4AxZJmPET2PRoNzs+NW22O79HEL34xIWrpTU08nKk7R+kZhhkcHk0z+V8k8+Eii2KglFu4ErGPz8vibaPu6F7gRQm9outKnh7tckt6L36L+bkADLNpAfzr86NCsdJoeOp139RkUP5a8NMvxwBI4OkV+QZENLJHQ//k+7OTU2og9P/bRf1PWFTOn+bKYkXO7Vsmf/5C2tnZM6Zs/399nk1pz+r/1nkXEfPBwQ5+1LiVmJDVbu9t0cYHy4V19pF51+oOB43yNoF608G0x6eF8XFvNBKs132QptjCTN2VGzg5K5t+UC/8zyeH9ERE6dsX4NnBJp4xpd7Qhvl4178uMxBY4EeTW4mJQCkHoBpuM8QysjW3Und3wXkjHWfeKRpP31Fotcz+gXmWfV5qnFN+QeNIWvHpM9MhofE9MjVJYY8a2rfFy8H5eMy8L46NO8Mwg4YFfmSRG8dhkEAn6t537K28qcYbOT4tXBTKY9HQ9GRUbJk7Oa08y56Rff/NJ9MdP500vhcTv/jFBIBQUJ1qZ1T3jmNrGYZhBgEL/CizcCWi1KwpMm+v7q7tu+kAO8K5xn+fOQLg9QZgXW0nKuoBUEbcHvp0vZO+Tz6eOM2Vf37R1hRn5d2NcJSeYZhBwAI/msjt74oVfpY9E8o9NO8uYx+ulzXe42mlsknjjaPrxPk+b+PwqGRl3+VPF89Pzso9ZuKj0aBi4icmwnCm7hylZ2zgWfRM77DAXwpkjfeg9vpN4fWbgr269yv7boWNlZc13msoVzNOr/sPf5jzeSlP73Ui2J9/Ivn+fvh4SsOfX5SPT8xXznOUnmGY4cMCP4KYTq+j6noP9HWrGvB6r4ABeHeb+LyC1YoaAHMzfgA+XwBSkfzTZ4cwmzy/f5APBn0A6g3f0bEjwZY/+o9/PnDyFiOUhgcQCvoq1XqxVAdwfFJyHpwXcJSeYZi+wwJ/ichfFK9djQIQPTGv9ypWZ2KQ9l1gs6JGFAnKG+RevzmHYfh8oVCJxwL1hj6Y9r/9kyPB/vvfzxk/1AXRaLBer49F9EB9bCwkmuVkTE08R+kZhhkcLPCXjqsL0WsLJJB+mFXeKVtnhoCi8c9/OAIwOREVsfqZ6VDuvAjA61Wr1vcP8gDisdB/+ENLsB1m1qcmdKvt0PebUi5XAOTOawBiYyG0t8yZir0pbOIZmXp9qA9mJGGBHzWstsuQbIu5hbcSM/JRY5m6O/vuPD6voFj5t4f6iho5H99o1NE+fH7/IF8oVADMTI8BmJrUR8QfHTuqnpMv7s7ET022vnAszEXFmhnnsIlnGGZAsMBfCkjdSyU90E15d2N1PYYYnDfl80/i9VoVgM8fgCS6tVp1fCwgJs/TrZJ3h1SyfisxITTeIbKJdx2oDwZ9Pl9baOH4pLVQTrzIpXYMwwwTFvjRR6h7KBREe1WdMtf2Wfbs9RuT0bDD5MqsuqJGROXHxwIXhZp8tFCoxGMhsu8Kzk280Hh06eONQfXpSf1S8VhIaDxH6RmGeSewwI8Uxvi8jboLhMZTjf2rN+aVd0OANsPe/HxGDtfXqlVv89/Tet1HBXd/fXoIs3Zz2cQ71Hg0fTxl4h1qvCzG9Ge797YEwNv9f1LvNkq/s+YxZW2nwxuzW8t0prw3GdmtZfWt4kTDyYw13AfP9A4L/CjjRN0JY7i+222z6CEBb4pSeUfCqQ+hOy0C2Nu/QDP7LqME6jtqPGXiu/Lxsrr/u9/NAYhGTVIDZOK7itL30cT/P9/9qjwsT13azGgqnVYf73y9sUvvk/Ya76wlN3bbTstuLSebJ2qZzaXdjWTn7w4Mw/QDFvjRwbS8zom6Ex9dDV67GtXgF6+40PheIPsub4b9/JP41ETrfoKhwMlZmTS+XKr4/WpFvQxpPJly5z5elNPbaLyswabm+9MbMbRHFzpG6ftu4k3l3E7jXbF4M9H6YWfN41lNK2dkv3u0i9S2/h0gsf44s7mE9Les8AwzBFjgRxay7w7VXWBaefdumZOy8jPToZNTqhLwFgq1w+Oa6Vvot3BecCfK6Tv6eHt1pyj9i5ctmy4y8cPERshdaLwUw1/e2lrzeJa3draWPatpIL3ajOXvrHlW00ubGW071fbmxPrj9mBA4uYi8OQ5x+kZZvCwwI8mctm8E3WXi+eNlXdOZL7H+LzRvqM5eR7A3GzEHwgAKJUqY2N+v68BoN7w0RI5+yt3ZeIBNKSeYBsfL6s7jbSTo/Rk4vXf4qT8rqL0vbOz5llNp7Ypar+9uLGRBoDk+mNtOwWktpux/JWH7bF66wt+m1acP2MK5+CZ3mGBHxHk+LySenfH+2blP/8kPh7V/3UdHw95vChV6pAG2SrIJt6Jxss98couHPG8K+n99EZsaFH6Fy/P6eH6CuZktx6kkdpuOvCVh4o/75qdtdU0lja/6pDfZximH7DAjxrOC+uUtxh739+txpN9V6bSzk6HfV4PgKmpKKBrvNVsWmOg3qGPz/yYM2p8x9S7jKK1SqDeaOJJnk9PS/QQgu3w4eSXsmN3I2laQp95uovUXUmNV+72oPA7a57VdCshzzDMgPF3PoX50HCh7laQRgpppyeK8GMw8XlCyDyNtZmfG/f7PAdHJQAffxT99VVBaLw8qlZhajJ4dFwWG+SsSH4Wl78BfP5JXNh3J+pOM/VevLqIjev/WXm9KJb1KgGS4bNzXekbtkHR83w5Nt71xhr3LG1mzFQ3+/wJsNifj2gm6VndGWZosMCPCCI435W6C+xH191KTMj2/Vn2zKjx/UXoOj0RQ+uOji8AfJGcAfDDLznSeDStfMc7B5D5MSdH4xXO83rA/09/PZydDuVyFQDVah3AWNQHIDYeMnXM8XFvwXZE0Ju3F1evmAzkkYmNh87zLovyblyP9bjb3kjiZn/UXbTKsbo7p8Hz4Zme4RB9R2hMh9q6u7PmaGaHw9MMH+iyU1gZRtsR54Np+9Iob4oT+04/zs+1WWelUf4fdvcgZaPFI39RyV9UAgHsvS2c5yvn+cqf/npoFeuebXf5M1MhUncAF4V6V66aNs3Lon582hJvYeUFN67HblyPTU6G6eHze+iVjo9yRStXNFnd/9Odj63uyuaQCcnbSk9b9vmTLt5Ob9laTm7sprYdleAxDNNH2ME7JL26drfj7A+VnbXVNLDU5XuSG7vokOn8819eKq/8/nfXAczOTJzni31f8U4o4Xo0rXx/59sAyP54BCAYDFYqFQBvD4oAopFg5ocjAKFQ8KKge+iAz/PZjbEfftF//K+PX0/GgzNTbTI8NxOmkH404rso1MaifgCHx+XZThF7Omcs6rso1AEEAr6jk7JycYVoNBgFCoXKwpUwgBvXYwC8Xj0HH4+FPr0REwl407K7jxbGnZfyGS27SFX8pzsfGzviulN3AIn1+6mNVfGvvj7Gppt/obNbX27sLm1muv1Ph2GY3mGBd8bS0lL6wdZXK92YEJL3bqAqpE4Y1Z1eJI13ru7u9soYw/U35k3iQA4rv6rVKoBAIJA7Nz+/XKnTHhfaymrK55/EhMbTsHobjRcvWmn83/12Vginz++ZnAxPTuLouDQ2FgRQrWtK5EBw43rsyfd2UY3cefnnF/D3478502j80XF5ajIoEihdy7kZKw+1bXhWPfSvZWp7O7W6CudNbtnvHu0C2Eh6NuSXOVrPMMOABd4Zi/e/ufcg+fXOuokT2VnzPLi9SS3Cqe2mzc9uPUgvbW4ubmw8Uc8WIt46G1IV0s2vPauWN2Kq7uIQaXxfsFHoSNgLgCQTwI8/VwDEpvrzueTayb4LdY9GgiTwVo1/8fFALl8FUK01qjWtWtfQHsMvllpVbV6vh56Qw7aiUKgUCpidDQP47e1ZUXD3wy85K41XePHy/Mb1GDl148Sbl6/P7XvnXu3llYI+qyx78rP4s+xZt5v0iJWHYoew9QkPmz/srHV4X/vLifXH2rqLm2K4N53pAyzwTkms3095rOL0uxuP7mU0reVJsltfbmAzs37z6zbnsrPmWcW2pq2AcpOry7eFlRH/H+3fGE8bkW40qgC83sDxicsmK7LFYW8FQKkRLB2V5mbCXV1B2HfjoRvXY8enhVyuFIkE5+fGj44vxqJh061x4vxn2bOT00qpUi8U6zguz0yHZCU2VtvBuuAu+Vn8L08OxY+ksnJRvb3Gm86lF9RqHUy8aZTeXtrlX62f9Y/ZreXkxmLra+jO2mp6aTPD4XaG+SBggXfOylebS0mLOP3SvTvSi015TyhivfOt9L/HxJ17SxsbTzNAv2OV/R94YsHcTPj8pBU5P2jXeHt/DLPyuuPTAhCh4nmh7g5vhiScNB7AkUHjBY2GJky8qcbL+lootJyUc43fe1uiNLxgeip0fFLOnZdpk+y0bS5fvhP5BmTotpVvLX3ubkisP95+KgL0AFLb2mOWd4b5QGCB74LE+jebj5Jfbt0x5g/l2ZsteVdZeag1/+/YjNT3qcu4O4R9tz+to0IDKE4AwIt9XYQoEu5EZmyK5wHkciXxnFrjbOy7zNRk8OS00qjD62tpPIDPP4nLJl5ueVc0ntR9djZ8eFgyenF7jV/8YkJJw4so/cvX56Tx8lGrKP1HC+MihKDcg3yrg1V3oi1AzzDMhwQLfFdQVfHXO+vW/8uzlnc0W4YAAKntzOaD5IbZWb0il4+ZirS78job+tIor/S+d2Xf5dswajwMYkx3aN84LpRV+QbgxMfbR+ntTbxNNF7+cVBheeb9gHPwTO9wH3yXrDzcTqUfWHe2Z797tCtN/lxN63NA13b0lqHm4o4B9g3FxoPUG12umJRB9VHd5Qa5PjbKk30nde/KvqN9PC2NCpE3wPoDHvkOxXOhqXJw3mYejtWw+o7YR+YzP+aM6l4oVJKfxRXjzurOMExHWOC7ZuWrTWx8+cBi4Edi/bEmsZ0CljYzpOfKZO/M093uP92mTp4OKQpBmiFetB9M2yPKGjpYj6+3yL63RtL2eBtoavzUREjeAAtA1nhZNTM/5mzm0Rp1t6PG0+pYGEoics0RNxSuf/n6XPk7IqLR4OxsmB7y68MIyzMMMxKwwHdPYv1+anfXhTi3zQXTu+RdbMY21XjxopXvJAl5s19AX4PzRnpcUVMsVgB8emMa3dt3I2Tflamfp7mKuDfxx1UoVA4PdUkW6m5j4mEYpUfQ6lgYovQi104mnhbN/fr64uDIZDatYtkFrO4MwziHBd4NLtdmJta/2VxKr1Lw/sHtjLadwu7Gl11OsgWA3//uuvKQj5I8mClEFcCb/appKLhbbAbY2W+U72jf4/EwmuruDuOu2Ea9pcezM6HTXEXIfPKzOO2JIdytbe0qUP/pjdjhcemiqO+hyV+0Pt30L45CC0pYntV9tKnXh/pgRhKP1mHKBTMKNOW8CgBQi+ftfaoVTibUmjpOK4GvVhv9te/06SenFbFHLvmZXh932PTNk/Hg+LhPePdoNKj8acjfhEz/oGRpp+8QopBezKwFcON67OXrc+HX63UNQLVWBxAO+X//b2ZNfwWS9rcHxWikFQ9gab8M/F//RZ00PFD+z//ch7mHzPsGO/hLQfKz+NX5wNX5qFHdYcjT9xFjuN5G3elH2b73ou4C2hVLz8Wi99kZXfJPc5W3B0XKc1NQXflz6Pjtx5iMN43SZ37MFUutL9MTcf0GxiL+uRm7yjsaxV8o6i6f1Z1hGIewwF8urKP3QDdK73zBjBJMfrNfoDoAk2sWK/F4WOyO6x3xuUaN//yTOGl8vV4vlupHxyW5ls3qT8DqdfuCu8Pj8uGxSaJ9djo8FvEDOD4piw00Cvl8W/CU1Z1hGOewwF8KjK1xTpS+jzdAyiQG7IjQvWzfybujr/Zd1njxIv1qn38Srzdzj/mLWqncUIrqxXMnKQyjxp/lKme5ipB2sWl+biY0NxNKfhb/9EaMSu1gNqke7QmOK3MRVvdLRb0x1AczkrDAX3ZslL6/oXurRnl5aN1AaTRaEXJqivvkY72ejlba/PBLzuszf6/8RqtDQuOPjssHR3o7ohyWT34WF9F4suyf3jAPgShliVfmImjv0WcYhukIC/zo43CyjXOld7cA/vSscHU++jc3W9Vk9Xp1b78QiQQBBAJeANOT0T7ad0L+YiE0XjTFRcK+makIpLK7k7NWMF+80WEdYqOuF+2PRX2Vaiu6PjsdotmCNuvj/vov+mxapTLx9/9mzslHMwzDKLDAMyodQ/dv9nualiMrbq1W3z8odTuStpdPlJviDg9LJJ+T8SCaGj8zHTLVeIHxRfkLkDxXx+PFRDwoxgabLgH67d+21c/bd7qziWcYxjk8i37EcT2YVmi8qci92S/WDzS4bbG7lZg4Pi28fFWCxwfg6bNDALdvzfbdvht5lj2jRTIAotFg5sccTbAnjSdmpkNkxKcmQmIQvbyfRmD6hzM1EfL6UKuXAeTyFSAYHzf5Jm3cNPPf/3wQj+m3IUu76QJZZrThvDjTOyzwo0xfBtMqSu9rlADUvWH5RXRSemN3XC5XiscCuXwjFAzU61UA//L94cKVQY3YE+twCsVKoYgrcxHR+y5Tq2o0y5Y0/uSsrEy6JWwy8dKfQ27/QNf4+HirRJ82y8lvmZ4K/fpKn+qTO6/EY0Gup2MYpnc4RD/69GswLYXur86bX62rijyxL+63t9sC1Htvi4Oz77cSE6KbHO3L4mRBVQbQnpyV//jnA3qe/Cx+fFKmh3JxYwWDfJ2j5vmmUfqLQiMUalX32as7W3mGYRzCDn5k6ftOWMHV+QhV2NmnqIXamdp3NPfFzc0GgMCbvQIAv1/voBuEhZWlMZ+vy+ti5V9E2nWbE93zf/zzgWLlxb5Xm9BFfDyYy1cAxOPm22Nfvj6/KLSFYudmwj+/ODdW13OUnmGYbmEHz3SBUj/vbmzOzy+OAUQibZqnBOddr5q1gtSR+s1o7Ku8aQaA16tukv38k/jMdKhYrNPj9V4B7ftebX53hf2D8pFk+kXq/a2UJvj4o7GxqJ92zf38wnzuDXN5qDe0YT7e9a/LDAQW+NFkcPbdFBulr9ere2+LB0c1RenJvlOsXmtoAP72C7WevF8yL3tfpevMSuMBZH7MNeq4ttAKPLzeK5yclaenQvTomI9Y/GIiPt76HkMaL6L0srr/3W/n0GlbvOmvwzAMYwULPNNPOnr6f366f56v0joZI843yrtD2RRHF5fv9uS0Qv/8b/90IF4UGh+J+GamQx2H4SiQxlOUnjT+WfZMVnflV7Yy8e7W3DEMc2lhgR9BhmzfTSGZn5vxA/D59A035bJe40ahe9m+y7V1/dV4o99VVtmiqfHHJ2WPB6/31NaD5GfxawvRSMSH5vJZ+0Z5I/HxIFXUA3jxqm0N7lhU/2+Q4vZs4hmG6Rcs8IxT3A2wW7gSIbEvlyuaVgcQDuvfPPbfFvffFvcPTNrV7DfKO0cWQtkBK98hFJ0uluoAjo7LIhqR/Cw+NRGiOruj4zINwxEyb4PYLEc+/qJQA3BwVByLBgFckTbcGBmKic9uLXs8azvKqztrHs/yVtb0HXbHlOt6PE5OZczgffBM73AV/ajxPth3Qimen5sN0764o+OaOKder3u9vuPT+vFpDoaKdNG8Tkj17Y6wUncZisbTwvjpqdDxSTka8RWKdQBTk0H5E2nQzdRE6OSsLKrrT87KYhJOR6KRAAk8AH/AY6XudBu0h8a0ot4h/+X/VReK/+f/fWg7v3e+3thd2sw8Xk8M6xMZhlFhBz9S9GWyzSDYP8iLbbBki2u1Kqm7fJqx8N51uN5e3TM/5rxeD2Xc0Qy8A/j3/25ueip0/VoUzXy8DAk5+fiZ5gDak7OycUWsgrD7kbAPwFjUD+CwWVcv5t6I6nongXr7KL1R3a1eHByLN1ndGeZdwgI/ggzCvruLzxP7ByZSNH8lMhELzM+FA4GA8ais9C7C9TbqLn+BECINqedNqbkz/aypiVBDimoeHdtpvIjkvz0qA7jx0RiAXK4SGw8emm2JVVAC9U6i9DZC7k7jRbzd41neet52aGfN0zq0tebxLG/tbC17VtNAetVjEv1nGGZYsMD//+2dS2wbSZrnv9SDpGRbsl52dVW1a1BFurq16i50V/VUIzkD1MyN1GENLCBgT74sSMxhQAILHwYwsLOAgD0YC5CYw4LEXnzVyReTt0ZhZkXUdNfsbmM1nrKZbaCqXe6ynpZsSST1yD1EMhgZ+WDyTaX+PxAFMiMzmDKz8p//L774wj8MT3CeLPF5bt/Zx93XR4dvq5OTgbnZKx4n07dn5UU5dCq0N3M9wKLxfEvkwylR+0XE85RK39hq/FNt/8ZcY7dPP1kgojnBoDtp/JiH0bP+pNoVk0okvVTQdV3X9cJSOp0Xm+L5hNwUSa3rhQRRoqDrei7Wh1P0I1gPHnQOBB70Fmbfp6bcsskYTZVeDKqTs5WXZM+p3g77rl9/1pgWb9ub1cSLZ6ifE8+8I6I/fHsgyrx4IM/RY0gab43Si7Rh4lsjH1fMxLmIa9nVPCUKdZ2O5QoJD00AgCEASXYXjy+//NJ2+2e/+rxH9r3V+Dy370zdj49r4+P29t32cJeF7JixNpZ6u24UpBPNvajuR0fntpbdPZVP2McoVcs0XlpzlvXMEuJmZ4JiRv0fvj04PTGVBuPSvvHNPl9ZbnxUuXY18OZtjYi296pWaf+zW9e4tHeSbdecREFy2sWkEt8gIqLykxIl7guNsTsJym80aQIADAFw8BcMJ3Unoq9/9899PJHm8KF3L/bdFidPz2R+73WNvbg8c3Xf3q4cHdmEHZsWl3UK1Dsl3HFmpoM8U297p/r6oPb6wDjk4/A0nyxHRLfeb+i0tLCNxIuXzUW911F67ZmjYLs0gc5BiB50DgTeOyzTSE4aEmYFF5Od5RSZUpnsJg9zdZ+4FpZe0g4DRBp9Hx8fISJu34no8G2VWlz03ar0c7NBUX2/+nqr9Nsftrcr7MVXinM6XMJa+oYfyN+Lte1EmIknopnp4EcfTG3vNKz864Oa93l9PLZvG6Unc6C+b1XtwreX2mgCAAwDEPhWycd7kxesZaOmVKaIrPGiulsPt93YFdrLn2f2nRWcF2HV6yQB9o4k1Vzjq7WTk9Pz1we1w+NTsXPv68E4KbE4SC8F/MWemcZ/9fXW9frCcfNzwfm5oPsMuu9evJmvD8a/eVuz3bmNyLzLfPeWp8JHFlXKPxIu+YZxd2kCAAwBEPgWUVU1v9qD2lysMMi9er7SvYxaSj+weZJwEXLWNFgTz+x7tWYuAxHFAAAgAElEQVSE/Jicd2jfbeFKryhG4TlGtUp8vXaP0m5FGpJng/1EtLNbtdX42Zng0fHZi5dHbOP1qcDnv2w8FkiZdxvf7ItR+vffvTYvJNxJGs+T7xi2q8yxKD1LOeRnbivk7RS6CaceZtR8vP64WUxG0iXedD8hPPCKTQCAIQAC3yJL9x9myFZ6JWzmB2tUD+M3Gut3x1hO131W9stq3//4Yo86sO8iPDd+diY4e32M++aJ0Kiuk66b9vHYp1Og/uPwtLvG89F3Itp7bYTlpfH1iQnTUnUSksZLUXrRxHON51H6za1jqd4fe/Mf//2PpZfLCbgQTq2XM5SOsPT6jUymkSsfy+mFRD0FP06FQgLlbboFStWCzoHAt0w4dT/RLE5vPz+4Tj6+uljWdV0vi95IQMveFf38AGkpPi/a95sLV632ndGhfZc0++jIyGULfzj1zo1Gz6Loeld6p0C9qPFkjtUzQWUl6o4rZ+IEeknjD97KmXokrB47PxN8U99B9PHMxDsF6je3jono6LjRc0vVfInCqXWbyerS82Y4ta4brKdSprZYrt5i6iWWwxR4AAYNBL4NYvcyrnH6ZvOD1cxDdn8ML6+oVHpSFtqKSUVRIukS3+disX9gs3IMY2f3kIiuXPW0WpotVpFm6j4/H5qfD733zlUmw+xFRDu7VVHmqUVPb507x7ulusazfdj2yYnRyYnR3T2Txf/ogylR5g/e1pjMS1F69kb08VXzRDsRZuKfavtv35qcl3Wx3d4ir1NTTMbzQ/FYCgAgIgh8e4RTDzOUvusk8eUnJUrckeYHCzSCmNZEZMMQlVfWIhd3HS5b+87E2Grom+KkypOTI0zaSYhXc5HjSi9pvHuf5BqoJ/N4vNj6688WeAH5XUtxOsnKlxwS8qU9pSJ33MRv7VSkh48bCxN9lXZGOLXeCNCzGL3fhpkAuNBA4NsjnLqfsM+C604ucTh1P0GltccXR+Ff7x/tH1RCoYB16J3ate9OGhz5cGpysnHpWueMiWo3cz1wfq43LYXrdLgtf3x5SMLkeLa/+BX//C+yhH/0wdTNBdNA+05dv3mUnu/J3zONF1PttnYqRHTwphGTv7EwQYNaIV4I0KMsbXfBPHjQORD4donlCgn7OH135gdHFuXoPRERHb9x1HzW9MUXX3Th240OWxiAF4Pzndt3F2mXpNppRrh1iRom8y0pPdmZ+HO9ETznKXWMzz9dIGc++mCKrQrPmJoK7Fi8PtNypvHXrgZI8PFPtX0+PYGIDt7UPg5P/+Jnbt8IALjMQODbJ3YvQ+m7q1a73sb8YHk40ybOz5XbVuNdhL8PfPvHXSLq3L67143n2uzdrdouUeNleZuREUU6isEkORQcPaqcsmI7TiP6VhPPYBr/aqsqdmgtbiP6+G9fHPJzmLoWIKKFudDCnFwfcDAmHgAwrEDgOyCcup8olaxTf9uYH8wOaQQEtOyqTb6SqPHSS9phIDB1b8++e7fs7gu9W7FqPBdLd6Vna9vwODw7kC8S8947k3x0Xzxz0cRb/yJWs3bqakC08jt71f/z/7atJ8A0/vDwlIgOjxoxeS7tLNuub1XtAAAXCwh8RzgtoNXG/OBYTi8sGZONFSXy5L59vpKLhHdX3b3H55l9/+DHs7atTe27d2mn1tWdYU0vty4Q56L0e69rv/lff+JKPzcTZDLvNHFOzLZzydj/6IOpqalGDzwUz0fcn2r71aoxHMCK+bA/pIerzoChAWPwoHMUXXecjQO6RjGpxElesWu48Sjwr7beVio1qgu8ZN+Zuh8d1a5cDUoO3kn5XMrPtafuIpKuuyTT8dPb2a0eVU7Z+8nQGBH9+rMFsR8xmC+ug8dy6WdngtJftPGNcSxz89y7T00ZK8vNzwQPhZVytnePiejKlbFrVwM8bi+WtPuzW9c6/5cBw8Z//Yfv+vl1/+Vvb/Xz60B/gIPvAZdpfrCo7rZY1b0ly87pioZZM+9s14AnB0/PB93FlenFfWz/LqdHGab0PB5wUF997tsXh+JuE6HRK1fGyFysXqptB1EHAFiBwPeAiz8/2Lt9J6Lp+mqwtvadB+ddpp57XxKGuuFQ3cP14van2v7M9UAoOBoKjk6GxqQp9WyE/quvt2w1ngXqmY8X/2px6ViqV7xhGn94eMpG3JlrZ6f6y58v2BarR6AeAOAOBL43XIL5wTw4z1eGtcLS63Z2T1103Yu0dz0/vKnGi1uYyZbK5ogladlKsry+Tfn5AV+Ehu/jXj5va6dyfq7Pz07wLWPjyth4I/jvsiANmSP2yKX3BxiDB50DgQftwLw7NbPvb4/OX20eWw9vybL3aIDZJVxvHarne85cD/CTFzWe6gvDs5fo40Xhl86BRelZ7RoientYYzXtWUyezFpuLVYvmnhE6QEAEhB4IOMxPl+p1KanQrb2vfz84NVW5dWWXJfeu2Xn9Dp9zGUSHTlUdxcn04uVa8mcP8//K/p4Bo/S7+1Xn2r7V680culvzIc+eN+0GM8fvj3gdeldfDxMPABAAgIPWkYaeifBvrNR9pOTEyJSFCKisbFxatGyc/qTHM4lfGevyl9k0X7bGvXS8jYiXON396rf/8lYKl408Xv7phF9sXbN/EwwOG5aXpZPohO3/+HbA9HEn9QwIwYA0GBs0CcALhg8OC9v3zze2T21bm9D1xn9nPr1cXjaKdXO+27n5zp7CLDWsJsIjYqB+vNz3aru9U5opP7U/dEHU9ymX7sa2N6rMgcvbv/DtwcffTAl2nfgD7BGO+gcOHhgwmN8PhgYIaLr05Pl5wf/9mzn1eYxc+pExOz7Ozcnbt6Y+Ontuc5PqQ+jy0y2eZU69sZd8kUTL238/NOF2Zkgi8yzAjWco+OzFy+PXv5wXKsZ209O6ePwtLh6rIi02uz2XpVZeXGjGKvf3TOGRRClBwBA4EELMPvOStL+sHlszRo7OTm5uRC6uSCXSW+VfuqTKOTqr26ov7ohNrnUxrHVeAbPon/vR40cBbGm1JWJsVrtbERR9g9q5AAvaSetNvt//3Vb2nhW7/raldbW6wMA+BgIPPAKD85/U975YfN4dHSciE5PT0gYaGfSrowo1NbS74y+BeetKXXSG76b+NF9MVmx0D3feHR8JlWMVBQKBY0BMqk8wLndnKWPPpi6JpSvZ65dEn4GTDwAgAGBBw1c4vOvtt5ubVe2tisHb2T9uXljguXQsalxc7NXrId7p5/qLn50Samz7mzdbqv6kQ+ndnarTNpZrP7ddybef3fy/XcniahaNaL0m9tVIqrW9O3d6vauvIAs56MPpqQsejE4L5p4rvHg4oJ58KBzIPCgOeXnB1vbFSIKBgNnZydExOz73OzYzRsTbTt1K0Oi7nyj00R520C9dQsrhMe3H1fORkYUHr3/8Xv2/27bu9WtnerWTpWEKD2HaTyfEC8iRwkAAJcbCDxwg0WPq9UaEQWDDa2yTnvj9t26XKxHBqLutjPdRZysvPUocUvpd5t8z5nrgYnQ6OTE6OSEkUsv/tNNTwWmp+QpdkypmcyL0Xu2bPy8ZWL97uv66vKvDe/OTDyi9ABcZiDwwAaxbjxTd8bC3Ng7NyZ+Epkju3Xfu0Xv1N1p0N0d92I4ZFZ9Po1ePPzXnzUWiZcm0bEkO/bA9Mufz9uegLWM//xMUByAn70eZBp/fTq487qCVDsAAEHgAYcNwL/Y0m2rpi/9dN5lRnt37XuPaClXTsJ2RXlroF78CqlazuefLoiHi/+Y+wc1vobs/GyQvWxPo/z8QIze22o8Ee28rvzsp/PvvXMV9WsvLhiDB50DgQcGf3p1/KdXct34arW2MB9a+uk8Eb3eP6L60jKikDN174Q+BOc7UXeno0SN39mrWifTO30LM/GRD6duzBs7WyfLjY3RwlxwYS749tDUxOra8ui9OFGeafy5Tp/+fIFaRstGlQbRrNZoKiZNyx8DAC4GqGR32eF+fdS8PfLh1Kutt3TN64z2tu17n9W9PWmXDhfT6P748nByYoyIdvaqTN25xksu//NPF3h8nq04F/lwanPbtOXW+9e+e/GGiKavGZ0szAXZ0Lv7YvMjo0REs9cboft/ePidtPPf3r1l/1cVk0o8r2bK+nqYb4goa5nyxVrlGABgAg7+8iIO646eV4jobCRE9fFgPuudlbURsdr3tqfG9Vrd2xt0b4po3Ccnxo6OT/lHa+69eKC4Gg37x7dm2DlhXapHNPfnZ7SzW3VRd6eNpGWj8XyioItiHsuVM2opfVf08QCACwYE/jIiZWxxrLnxXN3F+Hy36IO6ix+7pe4cnkzHNJ4tG+/+XZEPp5wWid/crko/Cq9XL02W+8XP5ln0Xup8ZtrYYi/kDk3a47WSmrkXkzaHU/cLBdnAS7F6hO57yOlZX1/Al0DgLxHWZGxG5MOpH92c+NHNCb5FLEkr4WTfO0mqv1jqzqMCXNHnZoKTE2Msf95lWjyHaTybMhcMKKKJLz8/4HXpeZTeiYW54ERIaXs5HyJi+k5Lt20i8bGYLPoAgAsFxuAvHl9++aXt9i+++MLpEFu/TvVcbqmAnXW9uF7b967Ti7C8tWeG+qsbT7X9uZkgM/RM41mg3rb+XeTDqfLzg9mZIF9fbv+gNj0V4Hl25ecHwYBiPVDk/Xeviba+A40vPymRuhJp93AAwPACB982xaTilHVss2cLkUwtG3Xe3UndnZq8R+Mlemrfexec79Ggu7VnEubOsf/y+LzVx9sqvVXjWc1aEWlVWQAA8A4Evh20bFRR4lTQ65QzlI44qnIsp+s5r+HOYjKSLjm0cQlXAmHpJe3gEo13l3ZrcL7r9r2n6i5+7K66Sz1ba9eLW7iPt+2B//uzWD2bLCfOlKvW5KKz1pq1Xpo8EFlUqfSk3EEPoCdgHjzoHAh862jZu+lSoiBqdji1Xs6o+XiHGUfFpKLE8w6NorpbW/nGVi27GJ+3BudFupI8f+HU3X3FWAlxqVmrxrcEW3XGZRiezZ1j/P7Ja/Zq/XvCyysqbTyziT8Vkwpy6AC4yEDgW6b4IG2bdby8oibusK3FpBLNZlkIP1k0hejZ+0Z4v3ED5XORCwmXb7dVd7Hp++/+t7S9aTReoql9lyrbeI/P90fdm5aX995nU+NuRf3VjTkhT/6pts+T7W1NPDmPoDONd4/Sv3xVeflKXjvOcb67XVN4eUUtpR/IQq5lV/Nkc50LDwPasw2XMwMADBoIfKtozzZIXVm2ymw4tS54+lJ6bbGs67ax+Xx8lbWVM2o+Xh+9j+V0Xe9iYZGm0XgJl8x5slPxTpaF7am6d73PNnpmGs+kneffOVF+fsAT6Te3q+wnY6u/i6vHiqF45te5tFerJ3w7e2Or8fbCH06tFxL5uJhGomWjkXRJzTyULsfIokqltcca2+eu41gSAGAYQBZ9q5SflGjpfnMZtn0IMJrqN87w8oqaTj8pE3W1Xph3UefxedvgvIt95+renn3vFr0Iy3co7Wx/nlfPy9uxN2LQnqXTs/dLP5le/62ppB1vkqL0Yhw+GBzn0m7FxcfLxHK6fiepRJR0fYuaKevWh81war3wRImz/RKFQiIe9/oVoEUwLg46BwLfKcWkOGzeGJq3nVpMUlP49lJPT84bLkXrGJ0vHNeL4HzX1b1zaRcPZBrPPnKxd9qfmXhxphxv2t6tjo/TcUUnop3XjhVJPlm83t6p1onldD3n3GS/G1agB2CIQYi+HcScpFiunkrvOnY+/Dipu0h79v2yqbs7pd9t8vdSrIUF6vlkORalf/P2ZHfPEH5bv/7uzdC7N0Od5dIDAHwIBL5VYncSfBjSD+wfVMhO3Xl8vkP73mt17zylrr1kuqZIPYiZd6XfbVqfJ1hxG7Gq3autypu3TNGVavVU2v+TxeufLF7/81+0sXAcAOBSgBB9y8TuZdRI+kEx5XlqezfRa5pTIr1e08i1np3E8Zs3+wcVJTDRfFciamtqXNfVvbvGvdeuXSxmx3oW7TsbjxeH2xnV2vmrLTkx/vy8EQx3isa/ePlGnDsHLjSoDw86Bw6+dcKphxnVnHVcn8FenyfXC7hyMyGXsN3okfbse0vO/hKqu+3XidXurOfw8oejze0KEV27Zvj40dHR0zND2sfHlHdvhqzqDlEHANgCB98O4dS6niqaso7JXPmmN3zxxRes3I2TnHu37+QcnLelQ/veOV1U935Ku2jimWVnH8XJ8a+2KyOKqfj8zm51bjb45k2NiMZGldERZWxcIaKtnSpB0QEA3lB05MFeNNpYbMbKD99vEZESmPBi33llG+/pdd0NzndrpnufXbv1e0W939mrcndeq52HgqNEdHR8NjY2QkRzs0EimgyNvHlbI6Kjo9OxcQoGxojoFz+bl/oXM+wg//4g+feOa/72gtzfe55UCS4OcPAXj5aE3Jam8+Js8W7fu6juF9S4u5zDx+Hpf/zqFftYqZ4xXQ8ERvh7IroxH1r6iXFu5ecHb97WJifHDg9rwYCxRUq/lxaXAz4A8+BB52AM/vIyPRWybnSy76K6u9v3IVT3HuXJe4d/16vtyj9+9Wp6yhiJDwVH+XsiqlTPRkcpGBghoo1vjBPmWj4eGDk6PiWit4eNZWkAAMAJOPhLx6utt3rteHoqxBeA7yLDpu7D4NoZlZq+f2BT6Gb/oDo9FQwFFCLae10josPj02kKiPvMzwa3d6uB8bHDWqMSjlO9QuTSAwAYEPjLhft6cV7su0e6qO6dGHdpS//V3XaFNybqTO9vzofYWYln+2rrKBAY5R9vvX+NFaVnJn5yYowsGo8oPQBAAgJ/GbENznvEJT7flbR53xh3q7RzUSeiUEAJzRu/AhuYZ2f41ddbfH+xcD0z8UQ0OTHGAvVXrwRcfDy46JxiDB50DAT+EsHs+1RIp/oC8La0Z9+7EpzvXN0HLu3ui7LzaDzZzaAjornZ4OH3RtG6zZ0qkaTxVK2dMo2/eiVg6d4AUXoAACHJ7vLgHpwnh7XjJJzsO9Sd6ku4uuzAisvyj6JBF7n13pVQcKxWO+O7PdX2b71/jYjmZxsZebt7VTIvSwNRBwCIwMFfCsR5cWyJWCfasO9dV/eLJe3uok6WyrKScefbpcq1oeDYuVCj4qm2PxEaIaIrk2OnpzQ5MVar6bt71dmZIAL1AABbIPCXCK7u1vi8lF7HK9u0RHvq3nXjPrTSzpFq1FufThhSebutncrCXIiIxsbo9JQCAcVF4xGlv+icnivNdwLAFYTo/Q+z715q2kgReMm+28bnO0ys61CbbSe4d3I+XmCheBd1Z6F4jwu0SyaezGvInpw2/qKrVwJbO5VaTSeihbkgEc3OBMkcq4eoAwA4EHif03TonSyj730LzneizQOpXdPqKLsLTqfK/qgb88ZY+/5Bzbrz4eEp2Wm8BGbNAXDJgcBfCtqw701bu6jurWpz/0fcu2jZOeI5S+dvHVOXdnjx8nBzuzIRakRxd/eqMPEAABGMwfsZKThvOwBvO/re1L53ou4dGndpS0+lve1R9laxZtvxj+u/3Yr++QLV/9J/+f3W+PjoyckZEW1uV37584Xy8wPu4JFw5xuwHjzoHAi8b/ESnOdYF46zbWUfB6LuQyXt3dJ1lwy76J8vrP92y7p9YS60tVPhH59q+yMjyuxMcHevyhPuuLNHqh0AlxmE6H2Oe3C+7dF3hv/U3WMCXRe/0fYPkf5ecbI7ES3Mha5cMSbKHx7Vzs/1nd2qOBgPUQcAEBy8X7FmzjtNkKNm9l2i7bT59ma691Pa3Xforq435am2Pz0VYEl2Irfev/bdizfT14JEdHJiTJSfuR7Y2a2yWXVsMF4cngcAXE4g8D7EY3De4+i7GJ9vLzjfnnHvj7QPg667BOoZm9tVIpvB9Rvzoc1tI1w/cz2w97rGNf7zTxd6c7KgT6AWPegchOj9hli0zsv+TL/1c73pnj5T9y7Oeesc8a/bqWfMjY/R9JRjwXki2tuv3qgvRkdEM9cDuk5Hx2dQd9B9iknFIJrVvO1u3lHLRlvqAXQOBN6fSOruEp8XcbHvfVP3Xk9w726Zmi7C/8a5maDtDnwkntWl57x4+UbU+L/6i3fY+//+P7+TXna9Gnddmzsuu6EbDcWkoiSL5mNwn748FJNKfCNT1nVd1wtL6UizX76YjOflDiJpMnoor6xFGpcT6B0QeF/hvWgdi88z387su0tuXefq7kWk+yPtLjsMRNdt4SZ+b7/KTPzmtlzKhg3Dc9g/FP/nspVzB40nIiqtPZbu2MVH4h06ltP1XMxoeZAuqZmyruvrqXDzPwZceLTsal7NPDR+7ViukCilH7jos0XeqfgoT40ewqmHGTW/isfDngOBv3h86UBL8+K8YC1u41HdJan2aNylLX2T9kFZdiuiiWcaf2MuuLffkHYpnZ6IxFZ3dXdrSiQSssIXH+UTiYTjqS7dhrT3ltPzvr7c0B6vlUy/eGRRpfwjJ4VnjwMZ8eIpPsqTurLc6CF8e8nmoRJ0Gwh8c0yhyib7tRqzNEU+DbRs1Dl89eWXXzr19W//+jXZ2XdrfL5t++6FVsPyvTPu/Z/z1jm2gXrrSLw1St/Z1965Iyl88VFeXVwUPhsXqpaNKvE8UT6uWK9c4F/UxUjjQ/j2kuOOWvZumjIPU7eb91l6Uu7CmQEXIPDN0LKreTWRUN1jUt2jmIykSw5tXN3PR8PSi21nGt9FXu83aqp4se8tqXuvpd2pdXgsuztioJ5vFE28FKXvhJhZ4YuP8urKss09Opxa1wsJokRBb8Tsgb8pP7G7I208szEzdXmX4juRRdUs59qzja6eIrAHAt8EFp26c2/FLSbVLYpJRZEHrziiultbbTfa4tG+774+er1fCQYN79iqujeV6l7E5C+iZbdiDdQT0fRUwDoMzxDlv30ii2rjDsz0HUF40BoO8k4UTt1PUD5eD3Bq2buOLgZ0Ewi8O9rjtRIl7sTCy7YKb5r48czU1JhUokSzWR69LyaNj5YIZzGpxPNqpqwXnEc+XYWcNUkxfI/581Za8u4tDbr3wrhfoAQ6L4j/GjfmgmRn4qUofaeEl1fU+gUOfR8CTs6Ufr7cTiWyqNpstaRhOMo7EVEspxcSpXREURRFiTy5rxcS5rA/6AUQeFdYvvC9GLv/kTnvs5hUIumlgl6fOpLOi03xfMK2iaiUXlss65YIZyzXh7Rkj/b9+be7RMTsuxd1Fz+2ZNw7lPahnfPWLUQTf6671SroeBi+ofDQ90uO4Ft4VlHzALv2eK1EdQlnochSOiL4mFhOr5OLac82kKnZeyDwboi5n+HU/YSY96llV/OUKNRFOpZrGG+XJiIiGoa75+FbT3HdLqq7rXH3cg62+MyyWxED9ZXKKXvPo/R8JJ4Nw0tR+v/8n245devSVFd47dnGMFyhYGCEU+tcitdTYeZuxCH38pMSJe7EnI/Sdb2QIFIzZYdMDe3xWgkGvvdA4J3RsqumuR2xOwlqpNpZrvHYnbqMuzQRUR+nGEnxedG+T04GyMG+e0+b9zjo3t2YvL8tuwj/J7oyaZSUlky8S5TeVshd1J2orvDZx2slWCsgEk7dT5TSdw0zX0zG8yyu6Z1iUhyTLCYjzsF80EVQi94R7fFaiYjSESUtbs4/KuZiMRaksp8r4tI0JBy+rU5OBpzUvVqtEVEwGHCx7y0Zd2lLe9I+DEXjB8XcTPC77w9DIdP/rdal36XFYZvIuQ3h20uUTqcpUUB6/MB5nHl/0KcgEMuVM9GIcS9UM2U+lqhlo5H0UqHZjIpYTi+QEleMsUqxB9BDIPBOsPQ66cItJpU4U3iXmaBuk0QHzeFRbWIiwOy7FY8V6zyqe3+k3ce6TuZ1aCqV01Bo7PxcHxkxpURNXwvuv6nu7VdnpjudNRe7k6B83hJ8BYDCqXU95XkzG3G3bMj15NSAIxB4B1h63UPpThe7l1Hz6dXsvVgqsqhSmrl5RiPxxKWpY0bONKdE+pEzjYi++OIL21YWn5+YCDjZd6buzL5/+MGs0wl4UfeuSPtltuwiTONDoTE2Er+5U31nIcSamIn/7kUn6XXmG7T5Jizco8XbteXWDQAYSjAGb4+ltKJBeHlFZal2rJwyn9kp1qcxJn3WR5zcSte0AFduJuQS1o3WCXKHRzWqj75LiN79+nTI6Ry8DLp3ru6+T6BrAzZZjmn8ufPSfx3n0gMA/AME3g6WBn/fbpCIJdOnHxSJwqn1coaMaSHxDbH4ciynFxL5uDFfhAqFRFdS60SNl17SDhKifSfnwrTVau36dEgsPs/xMtO9w2Q63895axv2b8jG4JnG7+ya0ultc+kBAJccRUe0rQ8Uk0qcmiaieMSpHL2k7qKDf71/xEbfbee+c/vuJPBNw/IdunZE473wT/+8SUSVyumt964QER+JZ1H6/TdVIpqZDop5dgCAywwEvgfIiaXGWsr9TBuV1J2IznX75HkxOD8RGiHz8nHUS3WHrrcEF3giuvXelb3XtblZI6suGFAg8AAACQh8bygmxaLycjJ+77Ha9/Mz3Tr3vSV1h7QPHK7xVybH5maC3MRzgSdoPACgDrLoe8PQTAlxGX2XJsWx1d857sa9R9IOXXdnZtq0PPzObpWZ+GpNZ5PlBndqAIChAwLvQ6T8eW7fm6o7t+9dV3dY9i4SCo0dHp0Sq1Rf13gAAJCAwPsZyb7bYi1o46LuXZd26HpLLP1keuOb/b39Ksuo39mrjihGlH57tzo+TlTPpUeUHgAAgfc5Te07EYn23WnQvVVph2XvHWKgfuZ6gJn4a1cD4kg8AABA4P1G0wXgXerRdkXdIe39ga0ka2viAQCAIPA+Rkye5/bdSd1/eHW0tX3CP3L99i7t0PV+MjMd3Nypjo4QEZ3rujQSjyg9AIAg8JcKW3XffX30w6uj0dGG9WMSDmkfTtgwPBHNTAfHx2hnz4jJMw4nNEgAABLzSURBVI1HlB4AwIHA+woen7fadxfvLvbgXd2h68MAC9QfVU4nQ2OEKD0AQAAC71uqlZPxcZvfV1T3J0+3iYjZd0j7hePklNgvHAqOHlVOaZcCAWM8fm+/ihA9AJccCLwPYfZ9fHzM1r4zbOfCNa1KizlvwwCP0lN9MVkeqL92NbCzd3RlcoyIXrx8A40H4DIDgfcnzL47BeetQu5u3GHZhx/BxON/agAAEQTeT7AB+Or5KLfv5KruZ2cnRDQ6Ou5i3GHZhx/RxB9VTk9OlfFxujI5hig9AJccrAfvQ6qVEyKam73i7t3fuWmz7jtTd49Ls3fxnEF7sFj9x+HpuZlgKDhKRONjo7z1xcs3AzszAMCggYP3FYdHter5KLPvkrpLNv3s7OSHVyfW2XGIxl8IxGF4iaPK6fiYcnh0ykbiAQCXFjh4n8Di82OhCW7feZOk7jwhi/NxeLpS071Ydqj70CKa+JPTc7ZRXHoOAHDZgMD7isOjGhFJ9l1S97mZIB99JyIm7S59QteHHDGjvr5NqdXO2YpzAIBLCwTePxwe1VjyfKWm841v356xN8y4z800CppWarq4pwQs+5Cz9BObwoLcxJ+d67XaOWEYHoBLDEbp/ACLzx8cnRNR7UQPjRERbW4dT04EyCLtr7Yr09dGiYgU+94g6hcXllH//Q9HfxV9b9DnAgAYMHDwPoEF509O9dBEgBzU/dV2RVB3+dkOlt0ffBye/uu/+FEbBxaTiqIo0azW9VMCAAwERdcdg7TgYvFvT3+Ymr5KdXXnyXSnZ8ZPPD0VJCLST4lMAg9Rv6CIifTWiP1/+x/fSVv+7m9uOfalZaORtaUE5fNLBT0X695JAgAGBRx8TxiIGfrpx+8Q0ebW8XFFZ+p+eqYzdZ+eCtqqOyz7hcZ2GJ5hVXenjQzt8VqJlu7cW1Ep/6jYndMDAAwWCHwP0LKreTWRUEvpBz26Vf7mn15IL7b9vXeuHh2fvj06fXt0Kht3AUTj/Y2LkDs0aY/XSpS4EwsvywqvZaNKNFvMRhUD+bGVPczaNWmNgxD5B2AAQOC7T6/NEJdz242HxzoRsVTqhnFn6Kc/fm/yk3833/1zAhea4oN0Sc3cixExhV81y3EpHU8vFXRd1/VCopSOJPlVXUwq8XyCNemFpXREqbdp2WgkTZmyruu6Xs5QOgKNB6DPYAy+62jZaCS9VNBzkfqbmNCytlJYWYunS0REpGbK66lwkyYzturO+eu/fJ+I/vGrV5JrZ2Z99/UREc1et6lQ6xu+/n2TSnw+QyxlMzMd/OyT6+Tq4MluJL6YVOIb/IIzf6oLNb8a2aVaXk+FxbdyR+ZOjCbC6D4A/QTT5LoNM0MPY0S0vKKm06vZezFBqUvpeClR0Ndj7JYXSd7m9zyXptY4rtLxlnHfn7sepLrsjSinRPT821pHfyAYJmamg3v71aPjMyI6OWmrbp2WXc2TmlmuX6SxOwnKpx8UU42rb+l24woO316i0tpjLZWix2slWrovPIZGFlXKPykTlR+ZujSa0o+KuRgUHoB+gRB9lyk+ypO6wu5s4dT9BJXWHpsik2qmbNw4Y7lyRhWjoS5NrTJ3PchefAtT93Mdj3R+YHO7yl9M3dtGe7xWIiqlI3y8PJ4nMo0uqYsRb32Fby81PohdKkrECE0BAPoGBL6rMDO0YjJDUqqdnRlq3tQSoq4DfyAq+ua2vVM/OW1D6Vl6nTGKXqeQoLbyR7RnG40PqjEAL4AAPQD9BH6umzAzROmIkhY354XIpIsZ8uyTWudS2Xc2Dn3RkZIJbsy7PbRJi8r83d/cchqGlwfghRElgdi9jJpno0tEZETkjafPYiP6vryipteeaRSrP5iWn5RIXYkQhe8kWKzeJo0EANAnLsUdv1/UzZDJpxSTSjzf37FHq8JdhvS6C03nuYE/uhESP9pqvE16nWWsnBFeXlHT6bXHWmqZiKiUvptdXk+FiYrJeJ4ShRQfg0o3kkXEtti9jBqJRxfFzL28U+YoAKAnIIu+e1gShxmNNGSSE5IbR1hylR06I3JOpGcp9LZA4IeNzhVdfIzjJe1cSt/YoMnzPESMGXDlxdVIeimT2Ugbg+i2T7DsrSTgWjYqjLzLxwEAeg0Evms4SjIX7+XH0Ui6xG+D9TnEuRi/F9o12WHVeKj7kNNdRZdwr1nbEa4PAQCAYQYh+i6hZVcboUszLJCZflBcXiSiRGZlrT5GL0m4S5OEi5yDIaGnii6x9JNpUeMBAIAg8F0jnFrXU06NsZyu54i07CoR3Xbe06WpXWDf+0aHiu6P3EAAwPAAgQegTYZT0Te+2e9mlN71yRUAMMxA4P0M7Ht3GU5FZyBKDwCQQJKdn4HAd8gwK7qVNnPpAQA+BQ4egAYXS9EBAMAFCLxvgX33gi8VvcvD8ACAiwkEHlwufKnoDAzDAwBEIPD+BPad42NFBwAAFyDwwFf0s7zMMIMoPQAAAu9DLpV9h6KLIEoPAOBA4MEFA4oOAABegMCDYQeKDgAAbQCB9xs+iM8jLa4rYBgegEsOBB4MHih6F8EwPACAAYH3FRfFvkPRAQCg10DgQT+AogMAQJ+BwIOeAEUfBjAMD8BlBgLvHwYbn4eiDw8YhgcAEAQetA0UHQAAhhkIvE/og32Hol9EEKUH4NICgQf2oLzMhQZRegCAouv6oM8BDAVQdAAA8BMjgz6BoaKYVASiWc3LQVo2qijJYotNTTps+aA2+Pr3r8VXGz189sl18dX1MwR9RMtGW7/4i0nxShW68Hg8AKCHIERvoGWjkXQpUdD1WGNDRHlS0HOxvp5IMRlJlyjhvtNv/umFtOWv//L9pl3Do18qltPyRfI443CRFJNKPK9myvp6mG+IKGuZ8noq7PINxWQ8T4Uc+6Blo5H0UkFfjxnHR6nJ4QCA3gKBJyIiLXs3XUqYxDycWi9TNBJP3umfxBeTSjzfdC+rurONVo1HWtylxarubKONxmvZaDxvvvgplitnNiLpu9llrxpdfJAuqZmHRh+xexk1kn5QTPX58RgAIIAQPVH95nRPvheFl1fUxJ3GVjGCL8fQnzlHJ+2bikklms0mG51xF1Vws++26i41tR11R8jdH9iqu1OT9njN9uJP3S8UuLqb4/fJItsSzxPl4+yyjuV0HYYdgKECDp6ItGcbpK4sW29O4dR6rv6+mFTiZETwtWw0Eo8uChHIfHrNCHBq2WjEFJ10biql11bKum7sF8vpeo6IqOPh92+/P/a45+TkuPix+OV2p98NBkrsi/kWj9Aer5Vo6b6NMsdiwmgVD76z/xMUKujr+m32/4TVpWvZu6KfBwAMBDh4Iio/KdHSbXfzUXyUb9ic8PKKSqUn5UazmnloqHY49TCjltIPil6a7J4qesrk5Lj46u+XgyGk/KRE6mKk2S5CJCuW03XnYatiUlGUSLrUuOwBAAMCAm+DOZmeBeNjOR6ALCaVSLpkPkR8PgjfXiLaeKa11tQjoOigU2J3EpSPe5zZEcvpuq7r5ZW1CFLpARgsCNEbbDzTKCYHy1lcnsHS7ImIKFEoZ1YjaeFoswWKLKq04aGpV/yH+I96/RXAL0QWVco/KRO5PGvGcnp5MRqJK0YGaKL53JJw6n4iHV97rKXg4wEYFHDwxBxKae2xm9uop9nrum140hSup/KTkqcmAAZOeHlFFYJKAsWkkEwaTq2zi18vJCgf9+DNI4vSMBYAoM9A4InYpB5xbNyKNAxp0WnxDinl7Lk0tYHLfHcvU+HBZcBxvrtdU3h5xe7i17KrebLJrqdYrpCwKre1ppP0vwwAoO9A4InISH/Lx02DhsWkosTzxj0qsqhS/lGx3hLPk1m5S+m7xqHFZCRdStxvBCZdmtrDVsih7kDEVuPthT+cWi8kzBc/G4+q58kVk+YJno/ywsCT8X9BOHU/QflVvpeWXc3bPR4AAPqIDhpIM9DrIXld13W9nFHr2/lkdTVTNhoSmUZz4zCXJr2QkL7AQwsAPcN88auZsmNjo83YXN8g7oUrGICBg8VmAAAAAB+CED0AAADgQyDwAAAAgA+BwAMAAAA+BAIPAAAA+BAIPAAAAOBDIPAAAACAD4HAAwAAAD4EAt8jzCvScaLRZHFwK2xpWjEZ9bYoGBggxsVj/0tp2ahz40DOCAAwnEDg+0uplI9HBnSb1LJ3I/E8FrsBAIBLAQS+t5gLdhrlboWS3QC0iLGuW7P1WgEAlx4IfD8Jp+4npE1aNhmth++TZt3XirxJbnM4qh5IzfIjo9EsGxJoLGefjwuxVqEjJZrMFqWuolmjfZADC8CEJUTPf8NosqjJsXTxIhKvFb5f4wpwvMaiyeKzvv15AIDuMZAK+JcAY90NWwfPtxZkuW+s4yGsbSO3OR9lbeFfJ/WXKDjsnijYdSUtPQJ6i+3FU8f4JY1Gm+tEaLX5hZ1bxK907xYAcCGAg+8t+biYYsc8dH0VTS27mqeGpJYzKlF9Ye7iA2PXMm+i0tpjzf2oOmqmUNZ1vVxoDAmEU+uiMuRi9UVv6z0ZO+fj5q4ShbKulx92usQt6A3GdWL8iPUfnLc+Ei6VciZBRI1Fjxn8UmGCb7Sau3WSewDAcAOB7ydqIlMo6+uGWpafGCHziKD+7BarPdsgIqqvHW8Mu66nwq5H1UncT8XCRBSOGUMCpSdly7nUv6KQi7HvqO9s7upOLEwUhrwPJ8aPqGbumX9wg1hO13X9HpWzyWQ0ks5bO2hcKneEAy3XhnVoCQAw/EDgewuLadb9UWnjSaPJuIvaYqi4DW5HGaiLEf4+sujkvWy+wth54xkfihW7AsOH8SMu3eZPYKYfnA3AR+LxdL6l2RPWa8P5OgIADC0Q+H4QjuWMKHs+flfKoLcMb+diHu6ntkcZiH7d+UnB5iuscgGGG+sjmfiDFx/E8yUjblQuG8PuXp7Z3LsFAFwQIPB9Ipx6yAYyS2lD4sPLKyqx8XONiEz5z+HbS0RE+dVsvS3Kkto1t6Pq5OPJeur8KhtmvyPMqTJu2/Wv4PsW7XYGQ43xIzauBuM3JKJG/H7pTioWDlP2kU2Ivo1uAQAXhu7n7QFdt0+E5rlKzfPhbfOaHBOg3bPoeZ9Cs5opO2XRi/sie34gOKS4s9/DNYteVYV5GvbdsAMt16d5A7LoAfABcPB9JJxaZ7fRuouP5cqFjFq/laqJTLmegEfh1Hq5kOB3WTWRKZSNMLzLUYxEoZwxDjW1xu7VN9PS7XpH9t8BLgjh1LrwYxfW7y812mI53kRqomBkggiBd2/dkpooZJBkB8DFQ9F1fdDnALpFManE80SJAuqcXRKMX5wShXIuFub1jFTLQx8A4PIBBw/AxaU+u808a1JdWYa6AwAg8ABcYGK5ciGhCgPmNkM2AIDLCUL0AAAAgA+BgwcAAAB8CAQeAAAA8CEQeAAAAMCHQOAHj1ZMRoU1u03Lsjd2ytZ3MRWt4wt7y/CF4D11AoYWu9/X/hrRilnhQopGk0VNc++H7SR3g4sEAL8w6Eo7lxynhTjlEnLifuZyYo5Vzyx7unQChhbn39d0jThcSI19nPsxXwq4SADwDXDwg0TL3mXzluuru+v16nXSCu/a47USkZpIqGRZ0Zthe5c279m8EzC0iL+vcZHwVQ1cLiRhH0s/9dXj86vCLrhIAPARPX+EAI7Y1avX9XJGZevGmzYRkZopm+uQu3dCZDZ5bp2AocX193UqLG9zqO0+lo24SADwEXDwg8NY7UtevS2cWl/PpWJCqRLmqmjpdthYS66Zs9KK7AixolnLnYAhxvgNWWH54iP7ZQCNMndOxef5InGNFWRxkQDgJyDwg4Otsd18fW7jrpu4E+M3dut9Nx8Xcqci8XSJSM08TMn67t4JuDAYK7oK2FxIbF330pNyY5NwnbCrhChxv36Z4CIBwFdA4IeHRvqyKYdZvOu2dN8trTXG8dvuBPgY1byAIC4SAPwFBH5w2PgrG4y7LrdebDkRy31XSrIrZFSiUj5uPCV47ARcGIzxHQGbC8kmRmS6TsxjQbhIAPAZEPjBYURZeRJzOLWu67o0oal+17Xget8Nx1L3hfHX9joBQwzTblq6HeZj7aZseCLSjCH2pduelp7BRQKA34DAD5DYvYwxlalRbkQrZh8J3sy465qnxbMnANf7rpZliVcddQKGE62YjcbzRKRm7sWIuMKLF5JWTBoz54x9mnaKiwQA39H1vHzQCmWn+iNqoqDbznbTdfPsJrdCN+S1EzC0dLnQjdPPjYsEAP8BBz9YwrGcXi5kEo27szEJfj0Xa7gqYbYbETVism7OSlWN/KlOOgFDCbtGTKu+h1Pr5UJGWBneZh8XcJEA4EOwHjwAAADgQ+DgAQAAAB8CgQcAAAB8CAQeAAAA8CEQeAAAAMCHQOABAAAAHwKBBwAAAHwIBB4AAADwIRB4AAAAwIdA4AEAAAAfAoEHAAAAfAgEHgAAAPAhEHgAAADAh/x/oZctx29kTM8AAAAASUVORK5CYII="><!-- --></p>
</div>
<div id="ranking-nodes-by-expression-magnitude" class="section level3">
<h3>Ranking nodes by expression magnitude</h3>
<p>If no comparisons have been made experimentally, CCInx can use gene expression magnitude to rank nodes in its predicted interactions. Here we use a subset of data from the <a href="https://github.com/BaderLab/MouseCortex">developing murine cerebral cortex</a> to demonstrate.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw">load</span>(<span class="kw">system.file</span>(<span class="st">"DemoData/DemoExpr.RData"</span>,<span class="dt">package=</span><span class="st">"CCInx"</span>))</a>
<a class="sourceLine" id="cb4-2" title="2"><span class="kw">show</span>(e13cortex)</a>
<a class="sourceLine" id="cb4-3" title="3"><span class="co">#&gt; Loading required package: SingleCellExperiment</span></a>
<a class="sourceLine" id="cb4-4" title="4"><span class="co">#&gt; Loading required package: SummarizedExperiment</span></a>
<a class="sourceLine" id="cb4-5" title="5"><span class="co">#&gt; Loading required package: GenomicRanges</span></a>
<a class="sourceLine" id="cb4-6" title="6"><span class="co">#&gt; Loading required package: stats4</span></a>
<a class="sourceLine" id="cb4-7" title="7"><span class="co">#&gt; Loading required package: BiocGenerics</span></a>
<a class="sourceLine" id="cb4-8" title="8"><span class="co">#&gt; Loading required package: parallel</span></a>
<a class="sourceLine" id="cb4-9" title="9"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-10" title="10"><span class="co">#&gt; Attaching package: 'BiocGenerics'</span></a>
<a class="sourceLine" id="cb4-11" title="11"><span class="co">#&gt; The following objects are masked from 'package:parallel':</span></a>
<a class="sourceLine" id="cb4-12" title="12"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-13" title="13"><span class="co">#&gt;     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,</span></a>
<a class="sourceLine" id="cb4-14" title="14"><span class="co">#&gt;     clusterExport, clusterMap, parApply, parCapply, parLapply,</span></a>
<a class="sourceLine" id="cb4-15" title="15"><span class="co">#&gt;     parLapplyLB, parRapply, parSapply, parSapplyLB</span></a>
<a class="sourceLine" id="cb4-16" title="16"><span class="co">#&gt; The following objects are masked from 'package:stats':</span></a>
<a class="sourceLine" id="cb4-17" title="17"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-18" title="18"><span class="co">#&gt;     IQR, mad, sd, var, xtabs</span></a>
<a class="sourceLine" id="cb4-19" title="19"><span class="co">#&gt; The following objects are masked from 'package:base':</span></a>
<a class="sourceLine" id="cb4-20" title="20"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-21" title="21"><span class="co">#&gt;     anyDuplicated, append, as.data.frame, basename, cbind,</span></a>
<a class="sourceLine" id="cb4-22" title="22"><span class="co">#&gt;     colnames, dirname, do.call, duplicated, eval, evalq, Filter,</span></a>
<a class="sourceLine" id="cb4-23" title="23"><span class="co">#&gt;     Find, get, grep, grepl, intersect, is.unsorted, lapply, Map,</span></a>
<a class="sourceLine" id="cb4-24" title="24"><span class="co">#&gt;     mapply, match, mget, order, paste, pmax, pmax.int, pmin,</span></a>
<a class="sourceLine" id="cb4-25" title="25"><span class="co">#&gt;     pmin.int, Position, rank, rbind, Reduce, rownames, sapply,</span></a>
<a class="sourceLine" id="cb4-26" title="26"><span class="co">#&gt;     setdiff, sort, table, tapply, union, unique, unsplit, which,</span></a>
<a class="sourceLine" id="cb4-27" title="27"><span class="co">#&gt;     which.max, which.min</span></a>
<a class="sourceLine" id="cb4-28" title="28"><span class="co">#&gt; Loading required package: S4Vectors</span></a>
<a class="sourceLine" id="cb4-29" title="29"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-30" title="30"><span class="co">#&gt; Attaching package: 'S4Vectors'</span></a>
<a class="sourceLine" id="cb4-31" title="31"><span class="co">#&gt; The following object is masked from 'package:base':</span></a>
<a class="sourceLine" id="cb4-32" title="32"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-33" title="33"><span class="co">#&gt;     expand.grid</span></a>
<a class="sourceLine" id="cb4-34" title="34"><span class="co">#&gt; Loading required package: IRanges</span></a>
<a class="sourceLine" id="cb4-35" title="35"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-36" title="36"><span class="co">#&gt; Attaching package: 'IRanges'</span></a>
<a class="sourceLine" id="cb4-37" title="37"><span class="co">#&gt; The following object is masked from 'package:grDevices':</span></a>
<a class="sourceLine" id="cb4-38" title="38"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-39" title="39"><span class="co">#&gt;     windows</span></a>
<a class="sourceLine" id="cb4-40" title="40"><span class="co">#&gt; Loading required package: GenomeInfoDb</span></a>
<a class="sourceLine" id="cb4-41" title="41"><span class="co">#&gt; Loading required package: Biobase</span></a>
<a class="sourceLine" id="cb4-42" title="42"><span class="co">#&gt; Welcome to Bioconductor</span></a>
<a class="sourceLine" id="cb4-43" title="43"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-44" title="44"><span class="co">#&gt;     Vignettes contain introductory material; view with</span></a>
<a class="sourceLine" id="cb4-45" title="45"><span class="co">#&gt;     'browseVignettes()'. To cite Bioconductor, see</span></a>
<a class="sourceLine" id="cb4-46" title="46"><span class="co">#&gt;     'citation("Biobase")', and for packages 'citation("pkgname")'.</span></a>
<a class="sourceLine" id="cb4-47" title="47"><span class="co">#&gt; Loading required package: DelayedArray</span></a>
<a class="sourceLine" id="cb4-48" title="48"><span class="co">#&gt; Loading required package: matrixStats</span></a>
<a class="sourceLine" id="cb4-49" title="49"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-50" title="50"><span class="co">#&gt; Attaching package: 'matrixStats'</span></a>
<a class="sourceLine" id="cb4-51" title="51"><span class="co">#&gt; The following objects are masked from 'package:Biobase':</span></a>
<a class="sourceLine" id="cb4-52" title="52"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-53" title="53"><span class="co">#&gt;     anyMissing, rowMedians</span></a>
<a class="sourceLine" id="cb4-54" title="54"><span class="co">#&gt; Loading required package: BiocParallel</span></a>
<a class="sourceLine" id="cb4-55" title="55"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-56" title="56"><span class="co">#&gt; Attaching package: 'DelayedArray'</span></a>
<a class="sourceLine" id="cb4-57" title="57"><span class="co">#&gt; The following objects are masked from 'package:matrixStats':</span></a>
<a class="sourceLine" id="cb4-58" title="58"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-59" title="59"><span class="co">#&gt;     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges</span></a>
<a class="sourceLine" id="cb4-60" title="60"><span class="co">#&gt; The following objects are masked from 'package:base':</span></a>
<a class="sourceLine" id="cb4-61" title="61"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb4-62" title="62"><span class="co">#&gt;     aperm, apply, rowsum</span></a>
<a class="sourceLine" id="cb4-63" title="63"><span class="co">#&gt; class: SingleCellExperiment </span></a>
<a class="sourceLine" id="cb4-64" title="64"><span class="co">#&gt; dim: 843 323 </span></a>
<a class="sourceLine" id="cb4-65" title="65"><span class="co">#&gt; metadata(0):</span></a>
<a class="sourceLine" id="cb4-66" title="66"><span class="co">#&gt; assays(1): logcounts</span></a>
<a class="sourceLine" id="cb4-67" title="67"><span class="co">#&gt; rownames(843): 1600012H06Rik 1700066M21Rik ... Zfyve27 Znrf3</span></a>
<a class="sourceLine" id="cb4-68" title="68"><span class="co">#&gt; rowData names(0):</span></a>
<a class="sourceLine" id="cb4-69" title="69"><span class="co">#&gt; colnames(323): TACTAGATGCTA AAATTCGTCGGT ... GCAAAATTTCAC</span></a>
<a class="sourceLine" id="cb4-70" title="70"><span class="co">#&gt;   TAAAACCTAATT</span></a>
<a class="sourceLine" id="cb4-71" title="71"><span class="co">#&gt; colData names(1): cellTypes</span></a></code></pre></div>
<p>We can automatically generate the <code>GeneStatList</code> input for <code>BuildCCInx</code> from a <code>Seurat</code> or <code>SingleCellExperiment</code> object by using one of the functions from <a href="https://baderlab.github.io/scClustViz/">scClustViz</a>, repurposed here in the following function:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1">gsl &lt;-<span class="st"> </span><span class="kw">BuildGeneStatList</span>(<span class="dt">inD=</span>e13cortex,</a>
<a class="sourceLine" id="cb5-2" title="2">                         <span class="dt">cl=</span><span class="kw">colData</span>(e13cortex)<span class="op">$</span>cellTypes,</a>
<a class="sourceLine" id="cb5-3" title="3">                         <span class="dt">assayType=</span><span class="st">"logcounts"</span>)</a>
<a class="sourceLine" id="cb5-4" title="4"><span class="co">#&gt; Loading required package: scClustViz</span></a>
<a class="sourceLine" id="cb5-5" title="5"><span class="co">#&gt; Registered S3 methods overwritten by 'ggplot2':</span></a>
<a class="sourceLine" id="cb5-6" title="6"><span class="co">#&gt;   method         from </span></a>
<a class="sourceLine" id="cb5-7" title="7"><span class="co">#&gt;   [.quosures     rlang</span></a>
<a class="sourceLine" id="cb5-8" title="8"><span class="co">#&gt;   c.quosures     rlang</span></a>
<a class="sourceLine" id="cb5-9" title="9"><span class="co">#&gt;   print.quosures rlang</span></a>
<a class="sourceLine" id="cb5-10" title="10"><span class="co">#&gt; -- Calculating gene detection rate per cluster --</span></a>
<a class="sourceLine" id="cb5-11" title="11"><span class="co">#&gt; -- Calculating mean detected gene expression per cluster --</span></a>
<a class="sourceLine" id="cb5-12" title="12"><span class="co">#&gt; -- Calculating mean gene expression per cluster --</span></a>
<a class="sourceLine" id="cb5-13" title="13"><span class="kw">lapply</span>(gsl[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>],head)</a>
<a class="sourceLine" id="cb5-14" title="14"><span class="co">#&gt; $ProjectionNeurons</span></a>
<a class="sourceLine" id="cb5-15" title="15"><span class="co">#&gt;                DetectRate MeanDetectGeneExpr MeanNormGeneExpr</span></a>
<a class="sourceLine" id="cb5-16" title="16"><span class="co">#&gt; 1600012H06Rik 0.034188034         0.96027540        -3.846653</span></a>
<a class="sourceLine" id="cb5-17" title="17"><span class="co">#&gt; 1700066M21Rik 0.042735043         0.60205847        -3.881925</span></a>
<a class="sourceLine" id="cb5-18" title="18"><span class="co">#&gt; 4931414P19Rik 0.034188034        -0.08063235        -4.823379</span></a>
<a class="sourceLine" id="cb5-19" title="19"><span class="co">#&gt; Abca1         0.017094017         1.26484824        -4.502413</span></a>
<a class="sourceLine" id="cb5-20" title="20"><span class="co">#&gt; Abhd15        0.008547009        -0.67777189        -6.893249</span></a>
<a class="sourceLine" id="cb5-21" title="21"><span class="co">#&gt; Acvr1         0.008547009         0.87558854        -5.736874</span></a>
<a class="sourceLine" id="cb5-22" title="22"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb5-23" title="23"><span class="co">#&gt; $CorticalPrecursors</span></a>
<a class="sourceLine" id="cb5-24" title="24"><span class="co">#&gt;               DetectRate MeanDetectGeneExpr MeanNormGeneExpr</span></a>
<a class="sourceLine" id="cb5-25" title="25"><span class="co">#&gt; 1600012H06Rik 0.01904762        -0.11515394        -5.599577</span></a>
<a class="sourceLine" id="cb5-26" title="26"><span class="co">#&gt; 1700066M21Rik 0.03809524        -0.11207464        -4.709306</span></a>
<a class="sourceLine" id="cb5-27" title="27"><span class="co">#&gt; 4931414P19Rik 0.02857143         0.05859855        -4.931774</span></a>
<a class="sourceLine" id="cb5-28" title="28"><span class="co">#&gt; Abca7         0.04761905        -0.43947291        -4.715466</span></a>
<a class="sourceLine" id="cb5-29" title="29"><span class="co">#&gt; Abcc8         0.13333333        -0.04928220        -2.926440</span></a>
<a class="sourceLine" id="cb5-30" title="30"><span class="co">#&gt; Ackr3         0.04761905         0.05591052        -4.252934</span></a>
<a class="sourceLine" id="cb5-31" title="31"><span class="co">#&gt; </span></a>
<a class="sourceLine" id="cb5-32" title="32"><span class="co">#&gt; $IntermediateProgenitors</span></a>
<a class="sourceLine" id="cb5-33" title="33"><span class="co">#&gt;               DetectRate MeanDetectGeneExpr MeanNormGeneExpr</span></a>
<a class="sourceLine" id="cb5-34" title="34"><span class="co">#&gt; 1600012H06Rik 0.06930693          0.3824473        -3.423116</span></a>
<a class="sourceLine" id="cb5-35" title="35"><span class="co">#&gt; 1700066M21Rik 0.08910891          0.1107141        -3.335895</span></a>
<a class="sourceLine" id="cb5-36" title="36"><span class="co">#&gt; 4931414P19Rik 0.03960396         -0.6783537        -5.172877</span></a>
<a class="sourceLine" id="cb5-37" title="37"><span class="co">#&gt; Abca1         0.00990099         -2.0267104        -7.507622</span></a>
<a class="sourceLine" id="cb5-38" title="38"><span class="co">#&gt; Abca7         0.00990099         -1.6165331        -7.311758</span></a>
<a class="sourceLine" id="cb5-39" title="39"><span class="co">#&gt; Abcc8         0.07920792          0.2986867        -3.317917</span></a></code></pre></div>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1">inx &lt;-<span class="st"> </span><span class="kw">BuildCCInx</span>(<span class="dt">GeneStatList=</span>gsl,</a>
<a class="sourceLine" id="cb6-2" title="2">                  <span class="dt">Species=</span><span class="st">"mmusculus"</span>)</a>
<a class="sourceLine" id="cb6-3" title="3"><span class="co">#&gt; Scaling node weights per cell type...</span></a>
<a class="sourceLine" id="cb6-4" title="4"><span class="co">#&gt; Building node metadata...</span></a>
<a class="sourceLine" id="cb6-5" title="5"><span class="co">#&gt; Building edge list...</span></a>
<a class="sourceLine" id="cb6-6" title="6"></a>
<a class="sourceLine" id="cb6-7" title="7"><span class="kw">head</span>(inx<span class="op">$</span>edges)</a>
<a class="sourceLine" id="cb6-8" title="8"><span class="co">#&gt;                                                                    nodeA</span></a>
<a class="sourceLine" id="cb6-9" title="9"><span class="co">#&gt; Abca1_ProjectionNeurons~Pltp_ProjectionNeurons   Abca1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-10" title="10"><span class="co">#&gt; Abca1_ProjectionNeurons~Slc1a5_ProjectionNeurons Abca1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-11" title="11"><span class="co">#&gt; Abca1_ProjectionNeurons~Slc7a1_ProjectionNeurons Abca1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-12" title="12"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr1b_ProjectionNeurons Acvr1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-13" title="13"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr2a_ProjectionNeurons Acvr1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-14" title="14"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr2b_ProjectionNeurons Acvr1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-15" title="15"><span class="co">#&gt;                                                                     nodeB</span></a>
<a class="sourceLine" id="cb6-16" title="16"><span class="co">#&gt; Abca1_ProjectionNeurons~Pltp_ProjectionNeurons     Pltp_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-17" title="17"><span class="co">#&gt; Abca1_ProjectionNeurons~Slc1a5_ProjectionNeurons Slc1a5_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-18" title="18"><span class="co">#&gt; Abca1_ProjectionNeurons~Slc7a1_ProjectionNeurons Slc7a1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-19" title="19"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr1b_ProjectionNeurons Acvr1b_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-20" title="20"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr2a_ProjectionNeurons Acvr2a_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-21" title="21"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr2b_ProjectionNeurons Acvr2b_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-22" title="22"><span class="co">#&gt;                                                  edgeWeight</span></a>
<a class="sourceLine" id="cb6-23" title="23"><span class="co">#&gt; Abca1_ProjectionNeurons~Pltp_ProjectionNeurons    0.1713428</span></a>
<a class="sourceLine" id="cb6-24" title="24"><span class="co">#&gt; Abca1_ProjectionNeurons~Slc1a5_ProjectionNeurons  0.2671990</span></a>
<a class="sourceLine" id="cb6-25" title="25"><span class="co">#&gt; Abca1_ProjectionNeurons~Slc7a1_ProjectionNeurons  0.1981018</span></a>
<a class="sourceLine" id="cb6-26" title="26"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr1b_ProjectionNeurons  0.3138731</span></a>
<a class="sourceLine" id="cb6-27" title="27"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr2a_ProjectionNeurons  0.2667038</span></a>
<a class="sourceLine" id="cb6-28" title="28"><span class="co">#&gt; Acvr1_ProjectionNeurons~Acvr2b_ProjectionNeurons  0.3765668</span></a>
<a class="sourceLine" id="cb6-29" title="29"><span class="kw">head</span>(inx<span class="op">$</span>nodes)</a>
<a class="sourceLine" id="cb6-30" title="30"><span class="co">#&gt;                                                            node</span></a>
<a class="sourceLine" id="cb6-31" title="31"><span class="co">#&gt; 1600012H06Rik_ProjectionNeurons 1600012H06Rik_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-32" title="32"><span class="co">#&gt; 1700066M21Rik_ProjectionNeurons 1700066M21Rik_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-33" title="33"><span class="co">#&gt; 4931414P19Rik_ProjectionNeurons 4931414P19Rik_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-34" title="34"><span class="co">#&gt; Abca1_ProjectionNeurons                 Abca1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-35" title="35"><span class="co">#&gt; Abhd15_ProjectionNeurons               Abhd15_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-36" title="36"><span class="co">#&gt; Acvr1_ProjectionNeurons                 Acvr1_ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-37" title="37"><span class="co">#&gt;                                          gene          cellType</span></a>
<a class="sourceLine" id="cb6-38" title="38"><span class="co">#&gt; 1600012H06Rik_ProjectionNeurons 1600012H06Rik ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-39" title="39"><span class="co">#&gt; 1700066M21Rik_ProjectionNeurons 1700066M21Rik ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-40" title="40"><span class="co">#&gt; 4931414P19Rik_ProjectionNeurons 4931414P19Rik ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-41" title="41"><span class="co">#&gt; Abca1_ProjectionNeurons                 Abca1 ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-42" title="42"><span class="co">#&gt; Abhd15_ProjectionNeurons               Abhd15 ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-43" title="43"><span class="co">#&gt; Acvr1_ProjectionNeurons                 Acvr1 ProjectionNeurons</span></a>
<a class="sourceLine" id="cb6-44" title="44"><span class="co">#&gt;                                 proteinType nodeWeight  DetectRate</span></a>
<a class="sourceLine" id="cb6-45" title="45"><span class="co">#&gt; 1600012H06Rik_ProjectionNeurons      Ligand 0.38054423 0.034188034</span></a>
<a class="sourceLine" id="cb6-46" title="46"><span class="co">#&gt; 1700066M21Rik_ProjectionNeurons      Ligand 0.37689638 0.042735043</span></a>
<a class="sourceLine" id="cb6-47" title="47"><span class="co">#&gt; 4931414P19Rik_ProjectionNeurons      Ligand 0.27952939 0.034188034</span></a>
<a class="sourceLine" id="cb6-48" title="48"><span class="co">#&gt; Abca1_ProjectionNeurons            Receptor 0.31272425 0.017094017</span></a>
<a class="sourceLine" id="cb6-49" title="49"><span class="co">#&gt; Abhd15_ProjectionNeurons             Ligand 0.06545941 0.008547009</span></a>
<a class="sourceLine" id="cb6-50" title="50"><span class="co">#&gt; Acvr1_ProjectionNeurons            Receptor 0.18505395 0.008547009</span></a>
<a class="sourceLine" id="cb6-51" title="51"><span class="co">#&gt;                                 MeanDetectGeneExpr MeanNormGeneExpr</span></a>
<a class="sourceLine" id="cb6-52" title="52"><span class="co">#&gt; 1600012H06Rik_ProjectionNeurons         0.96027540        -3.846653</span></a>
<a class="sourceLine" id="cb6-53" title="53"><span class="co">#&gt; 1700066M21Rik_ProjectionNeurons         0.60205847        -3.881925</span></a>
<a class="sourceLine" id="cb6-54" title="54"><span class="co">#&gt; 4931414P19Rik_ProjectionNeurons        -0.08063235        -4.823379</span></a>
<a class="sourceLine" id="cb6-55" title="55"><span class="co">#&gt; Abca1_ProjectionNeurons                 1.26484824        -4.502413</span></a>
<a class="sourceLine" id="cb6-56" title="56"><span class="co">#&gt; Abhd15_ProjectionNeurons               -0.67777189        -6.893249</span></a>
<a class="sourceLine" id="cb6-57" title="57"><span class="co">#&gt; Acvr1_ProjectionNeurons                 0.87558854        -5.736874</span></a></code></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="kw">PlotCCInx</span>(<span class="dt">INX=</span>inx,</a>
<a class="sourceLine" id="cb7-2" title="2">          <span class="dt">cellTypeA=</span><span class="st">"ProjectionNeurons"</span>,<span class="dt">cellTypeB=</span><span class="st">"CorticalPrecursors"</span>,</a>
<a class="sourceLine" id="cb7-3" title="3">          <span class="dt">proteinTypeA=</span><span class="st">"Ligand"</span>,<span class="dt">proteinTypeB=</span><span class="st">"Receptor"</span>,</a>
<a class="sourceLine" id="cb7-4" title="4">          <span class="dt">GeneMagnitudeThreshold=</span>.<span class="dv">5</span>)</a>
<a class="sourceLine" id="cb7-5" title="5"><span class="co"># Also check out ViewCCInx(inx) for a Shiny viewer!</span></a></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAMACAIAAAB0NULIAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nOy9e5wb1ZXvu6pf7ofdfrvdNjY2ltqm3eZhAjYlCIYkEKm5GXMnGALJxyTcSDOecKTDnZ4zD8+9c84495xM53Kk8QxzJQ4JvkkYMPkkvjlYCiQEEujCDmAebhq7S34AftB+27jd7677xy5t7araVSq91dL6fvSHVFWqKrWq9avf2muvJSiKAgiCIAiClBdVxT4BBEEQBEFyDwo8giAIgpQhKPAIgiAIUoagwCMIgiBIGYICjyAIgiBlCAo8giAIgpQhKPAIgiAIUoagwCMIgiBIGYICjyAIgiBlCAo8giAIgpQhKPAIgiAIUoagwCMIgiBIGVI5Ah/zCUZcLl8sntn+4iGXIAiCK5Th+8lO4jGfyxdjd6i+yoLEJ9XuSV2a/e4rgHgs5HO5mIsklOlVQnaX029Zs4dcX9UIgpQRlSPwXCQp4nEWTfPioc1OT0TK1+4jHlTzDIj5XE5PICLR70WSIgGPM+M7uTx/yxyKe1UjCFIqVJzAe6NKAjkaFAEAItsy+e12+HsURVF6/I4cnZm6w7A7R/sDlPgMiPk8EQlA9EZl7VUiBTZnFaxRycO3DDm8qhEEKSMqTuAZHG7/Vi8AgNQnQyLa6QqFfGoIlPw+xtXXgiAIbKzWEKJnNnT5tD+u8ZhmH3H1/c6ABAAQ8ZDDGYK3ZodOhtuTW+gPqWL1M29ywoZYvmZBmn+llKcaj7HB8GKHluOhbREAEIM7wm71xs3h9u8IiqI3GN3B3Mul+Lz07+NyCSm+Zd61YVxj9v0asXVVp3mtQiZXhfU3a/cPaGNXCIKYoVQKUS8AcL0OiEE5uYEKbxmoe5AVRVFk8mayHWdDdQXdUL9Ot9gbVRckTtDi0Nx1ybcmPqnXy56I7vObn7DhD6VZkOZfyd6pctYUBe13aor9zyt6vZbfssm1YXIQ8ibNHjK5qtO/VnkHSnFVWH6zafwBU+0KQRBzKk7gOejExhuVFUWW9UuYn05vVNGKAf3NJb9RvJ9g9XdSIyHcH3vtLyb30HStGIzKiqLIya2ZtVRMND/rOpEwP+EUP+U2/0rWp6oVVJvymkd091h87H3e5N8n9bfM+/xkFb2Z9Br+aDxlZTG7qjO8VtO8Kqy+2TT/gKV2kSDI1KGyBV4UvcHEWKvxJ4zzc89sw/7SWNhUK80w/+m3PrS9X1vm6OCNWjkuzQnb37mdv5Jd5yeK3mAwKhf5V9uOwKf7edP7lvUHi0aDTBDAnsBbX9UZXqtpXhUW32zaf8ASu0gQZOpQcWPwml+vnp6w361NkhPbnYmncp8h9dnZLgIA9PZrhy37e02Px9mJDWwfOgUOdTw24vHtYhZbnbAtMvkr8XCH5aBXBJKpHvA4ndnOO8wJlidu6/Myfx8LrK4NMhTu9HgCERsZ+Pav6txfq7yjWH2zaf8BS/MiQZApQMUJfBqoPzss6o9TRxsvdd4QNgy7uTvJw6HNcYeJAYpEIvZOuMCn6vCHexRFTlrVHGWrZ4Sjc5MIANLO3doziPlcLl8sHgfI5VdjdW3EutVk/mBUlhPD0PZuG2yRw2vVDLNvNoM/YEldJAgydUCBN8fR1gFA5pqR1OMYybEG70atECZ0IdAdiwNAnE0wTuxkWyixzlAfh2MYbR/aBgmJt3nCiR/gyC51nXpkM7I51cRxQ+Bwu/3hrk25FJhMoH+YzTRVOx4LuTwRSYp4Nu+OQ+af1+JbNl4bqs8WOzb63Q4HhHZZfgUZfcI0r9U0rwqLbzbdP2DJXSQIMnXIZby/pDGO7XE30FobfrqvoijZZdFzksjFYNROFr3JZ0kxIMqcQuoseuvE9zT/SilOlfe3KXb+VNTLVRDmtFJ/Xs1nsPiWTa8N/ndgOgaf1lWd9bVqXGs8iuU3m94fsBQvEgSZEqCDt8QdlqPB5A++6A1GZW4c2x2Wo0FRTG4n0wo4Dn+PHOXvw91Fd97RlumhbeDw79D/SFqcMB3zBABRDEaDppnaWZ+qw98jM+/UnkeRcId7NB9HTVpjTivNz2vxLZteG5rvwBtVJx+kl4Bhej6ZXavpXRWW32x6f8BSvEgQZEogKIpS7HOYmqiFarzRXFclQxAEQZDsqSn2CUxJklXoEARBEKQkwRB9JiRm+ojeKNp3BEEQpBTBED2CIAiClCHo4BEEQRCkDEGBRxAEQZAyBAUeQRAEQcoQFHgEQRAEKUNQ4AuMpiqsnnjIZb6yKGeE5BP1T6/H5aKFcotAPB7zufBqQJAyAAUeQUoMSYp4nEWS2Hhos9Njo38dgiClDwp8KeHw9yhKZk3dkKmLtpK8Wno9sg37pSEIkhUo8KWEIUQfD5GGXoLLF4vrY+mJbl/qBlQP6Hb03exazW4Fly/WX7CPh9jB4d+qL/Oe/L7036TmIjD9ljVraHc2+k6XK6Q2dqMVGiMe5kpjdiS4fKGYbleukLq+mAMLCIJwKEaHm0rGsv2XnLLVGFh197LT9yvVbpECwr0YEt+Pjb5/nK8ysc78XRaXh25/pn3tvFHerrDDG4KUGOjgS5ZYNzFT5NdUjmp/fGO7InSdIpPWXpFdmnFbMai+k/wMq2u1uzWTe6SARDxsih3x0GKwyw0AECfN0hOSKgdF0swdgH6VqrCSb1LauTtu/a4E9PJIDgk4/D3sPWbYDTGfh/RqV68WsnHEo92VNyorirwDO7whSGmBAl+qxPt7AcgPvQMAHG5t5NYdVhRF6QI55PO5nIGIcQferX71nRuZN6q79UbDbgcANyCMFBXRG4zKSqIfqtr2IOJxMupPbtYSX+VWv/pN9iiKQt5o8a4EyctDvQKkPtlwLoarJbGxdlcb3Q4AB8o7gpQYKPClivob3dFGfzed7YzZJmOvTo8nEEkr5znRJyeJZrdIESDB+ESkRertS65SFZaL8au08y4Vsd1Jn5tfAZxDqBszrenZXSEIUkKgwJcqxh9S9tc21u2JSKrbk2V1MNTOL631bpHi4XCH1Sh7xLNZl0FvGN4Ou23cmXHfpcL6dfMrgHMI440ngiAlCgp8qeJo6wAgI6dxAIjHyLAqIRG/79jodzscENrFCdFnsFukuDj8O8gQuBRQJd7RuUkE9utiZlKoX2VkWyixzkWS2uNW70oQ8fgSqfPkCvBuZGZnqjeAiUPQbWO8jREEKVFyn7eHWGGSw0zslmUWvSgy2dX83ZA3GnKztQswi75k4GXR068ndT4896s0nUphnUVP98msFoOyWRY9uy1mzyNIiYIOvnRx+HvkoJf8hIveaM/WjuQ6d5iuAtEbVcdvmcC7vd2C6I0GMcmulHD4e8iXmXDx7rAcDYoJJRe9QTmRgAcOf48c9VKRF73BqKyG4S3eRfBGmYuLWevuohdHR1tiR/xjIAhS2giKohT7HBAuMZ/gUec6hd0OWoVENPxUI0gaqNeVN4oVExGkzEEHX7IkZrdp5zqJmzpR3REEQZCUoMCXLu6wHPWKzCgrJ9CKIAiCIFwwRI8gCIIgZQg6eARBEAQpQ2qKfQJIJtwrbNIteVHZWZQzQUoWz/zv6JZET/+oKGeCIEhRQAefK9RWry5jF+9EU03+FLaYL7Eu+cwao7qbLUQqFqO68xaqFXB4+GIQ8+mK4yAIMqVAgc8xpJsXS8x+mTkbWAg5ajxC4Ko7bxVpWaQoCimaw5aswTl0CDLlQYHPKV6vV6/wsV0RrzdHtWRSSjhqPGKh7jY3QBCkPECBzy0bN+oUPrYrIra3azdSo/kkcN/P3xHZho3Yo3gjKcm9ePcnL9ZEuD7mE1yhkKa+PRvrx6g+gpQIKPA5xq1V+NiuiLips43ZIOYTnIGORDHvjgCvlTspWueNKhnMesf7ACQl9u8DIoGdm2Q1hB/x0BtOKbCzXU5E8mM+wQNRGulnNkMQpJigwOcaZ7uYbMVJ9J0R6XhoWwS80cQApzvM6eUR8xF1x1FQpOiIwR3kJtPRuUlkmswyl3VsV0QMdqkXq24zBEGKCE6TyzWOzk1iYFcs7Har+i47QNt627uVUW73Ri9EepOvd24WJEkMYj8PpCRI9n1XW8caloM7rCQu1kT/BHZLJDMKHIrDebZlCTr4nOPo3CRGdsWA498TfdzNkSQIRoOQaAeOIKVPMqfEA1GTbsQIghQeFPjck1D4eH+vvjWM1gVxEIM7/G7/Vq+UscTjnTiSklxWvImHNgekRId4DDyVB0wesJ3iHPGQyzy30m59DyQPoMDnAaLwod07JSaQSXC2i0DsvQrX07u7gqIU6Nb+x9hU7scfezrtE0bKhSLUqpP7JPBudLMvkSkNyfFViyLIQVEKOC2nRsR8pNOlyUpPLuuAIOmBAp8PHG0dEAkEJOZ3L7HGv4NNMzb713D4t3oh4tH/V6XU+Et3rDwQP4kaX8msetBlvUGObwI096zqz3lvPxq27Kkq7CNBfPdOCbxRdQaPw98jB3WuhCHmEwQLBUd5LzIo8HmB9HI36juo/zAQID3ePb3BIL8Ijjsc5Um8lcafcTnHJpTLQyOo8ZXMgfhJ+MI1Zmtzb/HVe1YSzd3WLitRL2Q+woQUHYe/RzvW4mjr4N+zxXyCJyIGZYUzFwiAzBkSzX7hkEKA7WKnJMYM2zMuJwAIggAAs6fXA8AqR+sT2x8t/LkhReTxx54+ED+pvnj7sG4tNpuZQtwrPFjIw72oPGe2KuYTPBHLebtqJQTtBvGQy7lzk9zT1i14eoNyBjU9kKzBaXJTEurjH3nulTffPFQdP9V8cQgAFEURBOHy0Mj0hmkA8PhjT6PGVw6sup+vnoR1ywDg8vqrbl/nfPIrtxfzzJCpS8zniUCy0IFd4qHNAQjKfgdgYcMigiH6qc0zD37p1ltXTDgWXJrZQBfSQD1gzl3Fg+o+VRGqC/rgQgob0AF52xB534GmvdigwJcDt9664vysBlbjCajxlYMmOJ/g8vqrinIySDlAh9jTnfyI8l4yoMCXCc3zZgDApZkNJKmCmHi6FjW+vOF+vyQ4X/iTQcqAeMhF1D2DofP47p0SSGoeMUmylwJO7EJUDFDgpzzPPPglACCBegBgfTwN1ANqfLnD2vfz1ZNFPBNkqpNNsytQ0/CTRL0AYlDGKkjFAAW+rDg/qwEALjbXA8DYhDo/AjW+vDELzhP7jgPwUxSlrqqQj+SB46HNAWyHUSagwJcD1MSTQD0ADM5qJE9IoB41vqLAoXckY+K7d0rARNg19WrjIReG2qcSOA++THjkuVcA4M03DwFAdfxUTVUVADRduFJbLQDA9IZpqxytdGOcO1c2GO37+epJdvQdHfwUpbP+W4U83O7hnxTycEhhQAdfVtx66woAOD+rYXxyEhgfD9oxWvTx5UHK3DpUdwSpZFDgywQSpSc0z5tBBuMB4MKMBjAE6gE1vlwwjr4jZcK06oI+kHIEBb7cICaeDsYDQFXLTECNLzu4X9+KtctxahyCIAQU+LLldPM08uTclVGi8UZQ46co5IvT2fcVa5e/1jBi8g4EQSoOFPjygUbpqYkfXdBM11a1zDSaeECNn7Jwg/OsfccBeASpcFDgyxlleh3R+HNXRgE1vlwwC84X/kyQ/FG0efBIGYHfa1mhM/EsVOPJS9T4KYpZcB4AJjYsLc45IQhSkqDAlzlrVy1mA/UAcKW5nrslavyUZvv3H2ZfYnweQRAU+LKFmPh3jp2mGk9MPCQ03jiIixpf4ljYdwRBEB01xT4BJMc88+CXSFU7ALj11hWkth0AjC5orjt1iW62Yu3yQ/uOHIifZCvcAcDjjz2Nde5KE4uhd519J3zlWr9uyW8+CuXjxJB8oNTi3HQkW9DBVwRrVy0mT6iJ3x8/aeb80MeXMmaVbbb85nX2pVHdzRYmiPmMpcfNt7RXjzwectnbJYIgeQAFvsy59dYV7xw7DbzBeABYsXY5VzBQ40uNtOy7/B9+brYfrsbHQy5B8ECUdviUgxBwmoq4O2yr9SdpOioGZbJHUbLYJYIgeQAFvgxhy9bqGF3QzJp4QI2fCtDvgv2mzAIwFupO0Gt8PLQ5IHmjrGY7/D1yUIx4stHj+O6dEnijaktxskeI7EKFt4lSU1XIR7E/LpIX8Hstf+iUORqop6DGTyG435HOvqdUdyOx7oAkBrv0jtzRuUn0biRLYz7BFQqREL4vZgjRs8H95HKHv0dr9B1tHQC9/RinR5BCgQJfEZAoPYE18RQcjy9Z7JS10Q3Ap4Qx8fH+XhA3dToM2zj8PYw8S4Gd7bKiGGPzMV8yuC8HxYjHdKg9tisC0NFmPBKCIPkBBb480UXpdSaeHYzfn/CFYzg/vvSwDs5zk+fTRO6TbKku9yYAILYrkrT/js5NIkh9Mu/9MZ8nApxIAYIgeQMFvlKgJp5o/GfTk3KOGj8VyYW6c9Am0ycj7iY3Ae6woo6yQ8wnOAOS2U49keSAPGIDpaa6kI9if1wkL6DAly1mJt4a1PjSwWZuXbrxeSPswLg7nEilj3rtvDc5E84DUTkoGreI+QRPRAzKdjLvEQTJISjwlYiFiQcAs0I3qPGFxHroPUv7zlS8cW/0grRzd4apb4kMfIU3PA9E/j0RMSijd0eQwoMCX0HULUkOvRONd665mi6hGv/Y3/0MNb50MKtswyWDWnXurqAoBbozm74m90mQyLVXXyYhE+G9UQXVHUGKAgp8OWMxIZ7w+pEBVuMpqPHFJWP7HujasfretdY7198EOPw7gmLEo6k0F/MJgifCajcfZzsztz3m80SABvzjoc0BSQzKGJjPDJwHj2QPfq+VxbNd99HnxMS/fmSALmED9ajxxYL981L7bjaPkR2A75EOkicWGs+1+A5/j6JEOwJOmlzniXij/KC7/p3k5kAQBEHY1i4rUS9Igc2hOKlzAxKzT6xXiyAFRlAUpdjngOQR2niGMvrpJfblvgPHb1/eIu//mC5Zk2g/Q5yimZxjT5o8kdbUOCrwPdLBOy/Xsqs+fHGfbs/YbGYKcXfb9wp5uJf7/6WQh0MKAzr4MscYpWdNPMEiUA+Yc1dY7JS1MUK9O8vqe9f+5qMQ+8jNKSIIMkVAga84HnnuFWOgHpiEO12gHlDjiwE3OM+171x1B4Bg9+a8nR2Sd5Tq6kI+iv1xkbyAAj8l6Zz5iO5hsXHKVLu1qxazI/Gg1XgCanwBsP5jcnPrqLrr4vOo7giCoMBbo5bx4KQGkYpfiRW03EcWSUR2m2dz5dxa440YA/WgnTVHISYeUOPzTFq5ddaguiMIAijwNjFWAontirCvuhONrzOd8mu3ebaFkNvXeGPmHTXxFoF6QI0vHtb2nQXVHUEQAgq8Dbxer17hY7siXq+2lGdWfbLsNc9OKeFmG3Cj9DoTb9ZMloAan1cysO/X/+f/YVyI6l42TNZUFfJR7I+L5AX8Xu2wcaNO4WO7ImJ7O3lOqnECRDzJ5hz8DtnxkEtwhWLJSHwyDm+jeXa6QfgM0Jl4M1Djc0tKdTfa90DXDvalbgAeQRAEUOBt4tYqfGxXRNzU2UZeOPw9StQLQCuDWHbIlgKeQEdUbeYhBZy8MLx6iMyCAjbvA0iU3szEWwfqATW+qOjUnYL2HUEQFhR4ezjbxWSfa6LvZtqbokN2sninOywHxcg2XjZdHppnp8ylJwxOqyFPMtZ4JC3Ste9E3V+dPqbbD6o7giA6UODt4ejcJCaGxK31PVWHbNaVO9o6ePl7BW2ezU2ntyClxqOJt0/KsjZcdddx5+VaVPfyY7K6qpCPYn9cJC/g92qTpMKn0PcUHbLFdqflcQrYPNuYS0+wNvE6UONzRcqucRiZRxAkLVDg7ZJQ+Hh/r6W+p+qQbUFOmmfvvviM2SqzKL3RxNsP1JuBGp+StOy7mbq7xJU5PzEEQcoDFHjbEIUP7d4pWea+WXbI1s2oj+2KQOJuIWXzbAvlzhgzE89iXf0GMOEuI3R/HGLfzebF6dSdDsCjuiMIYgEKvH0cbR0QCQQk6ybZFh2yAQDUZpp0pXer3wF2m2en1PjGbV+33iADE0/RBepR4/OKRdN3AlH3J79ye0FOByk0kzXVhXwU++MieQEFPg3cG70AYK3vFh2yCd7gpp3OZM9touj2m2dbaHzv/3ZbFh+Oo/FPbb2fPEk5Mx5Q49MhG/tOQO+OIEhKsB98AYmHXM5ARzQH+XPGye6vP3rLbKEOAG5ZtvCFx+61eK8xLE9t/UPdv9StahoZp89Jz3jaLZ6gM5rYPD4ldtSd/lW56j569zX0OTr4cuWOL/xVIQ/3+7f/qZCHQwoDOvgpye6Lz5DHyl97D//f97z+6C0AcF4ZtfNeiwnxFiYeEj7eIlAP6ONzClfdjTPgEQRBuKDAT22euFXULfnj0c/u3/5irvZv9PRGUOPtY9++20mbt7bv67+4VffI+LSRwoPz4JHswe+1gBgKzucE9+qrF7fPzmYPbNA+AxNvBDWeS8p5cZBK3e1PeefKuaXGa7oV2217HPOZ1Vo223MW/ZQRBEkPFPiygkTpU5p4m2VrCQ91/zLdQD2gxtvArLKNhbpv+c3rdvZsIeT8VTGfIDh3bpKVBNGOgDO1FquTRCyx2wcZQZCcgwI/5XniVpFr4tMK1FubeB0pZ8arJ4Yaz2DTvtv37mbx+ZSheP0G8ZDLE9EVYHCH5aDITv7IFHt9kBEEyQco8BVKNiaekDJQD6jxCYyf90D8pHFeXJ4i89bEd++UOH2NHP6t0YQux3yC4IsleyD7Ytouya5QnLsN2OqDjHDBMXgke/B7LR+IiadRekjTxLNwTXwGgXpAjefBDc5Xj44bF0J+Ss0z9wHx3Tslfl9it5sV/YhnW7vMdEAGpktywvvrt+GpeMZ9kBEESRcU+HKAROkhofGZYV221phObzNQDxWv8XaC8/29n3Lfy6q7zQH4NJH7pJQdkAAAxOAONcxu6ICcxjZ56IOMIIgZKPBlS0oTbx2lT2niCcZAPWo8i83g/LVtrWDAwrsXvr5N0nQ72joy26awfZARBEGBLx+yN/HWEBOfwaw5QsVqfEq4wflCNYF1tpv58dxSwD7I5cFETVUhH8X+uEhewO+1DKEl7dIdiddF6Y0mPptAPVSextux79zgvFHdcxuf3/OHbYmnjs5NIj/pLeZL5splR076ICMIki4o8GUCKWmXrolPK5eeogvUO9dcbTNQD5Wn8TqM6s4NzltjEZ9nlNsujs5NohTo1gt5PLQtN6PlKfsgIwiSJ1Dgy5ycp9MbA/UE1HgdXPvOvrSTWJcBKTVev4HD3xP1RjxsXZtEdZodqTU5xYw3e32QESPj1VWFfBT74yJ5Ab/XcoOY+MZrm3TLbWq8MZc+Zd0bsNdPlqXsNT5l5jxRd6N9z8nQu4XG81e5w2rxOhVnAIJyasvt3ugFybLknf0+yAiC5BwU+PLB2HjGDhlE6bkmPq1APVSAxutg7Xu63j2DAfi1f3+PcaGluXeHFQaNuLvDmmI1yZfqe3r8DpNtHP4ehQNG6xGkEKDAly23r1lKnpAoPeTBxKPGc7HOraPqrrPvNr27/Qlya//+HvLY84dt5GHzjQiClAco8GUIidK/M36Warw1GZt4m1SUxlsH5/M07q4jPyVxkIIyUV1VyEexPy6SF/B7LSvMovTpmngj2Zh4a8pP43XQ4Dyr7qx9t1b3LNW68CVxEAQpEVDgyxNi4oEJ1KeFddlaAjXxWQbqoYw03sK+Z6buOlCtEQSxDwp8OfPO+Fn63NrE24nS20mnJ6RV/YZQBhrPPdWx5nqLt+SjXB3G5xEEIaDAlxs0Sp+libeDmYkH2/VrWcpA483g2vcCFKNFxz91GauuKuSj2B8XyQv4vVYidkbiuVF6CxNvDNQbt7E28TCVNd7CvtssRssF7TiCIBmDAl8REBNPo/TA0/jMytZapNMPN9QZF5alxttX9wwK01Ls2HG8IUAQhIICX864V1/NDsOni00Tn26gviw1nkthJsWZgfF5BKlwUODLEO5kufyZeAvSLWFLmUIab2bfjepO7Hv+1B3tezmBY/BI9uD3WubQVDvIXbZdWiY+s0A9TBGN557ME9sfzYm6ZyPYaN8RBEGBL390UXprE6/DbEK8/Ww7KHeNN3LPn/w37vJsvDsKNoIg6YICX56wUfq0THzGUfq0itcSprrGc89hsr7GuPDattZ8j7tjfB5BEB0o8JVIWibejOxNvE1KU+PN1P2jfk5eYQbqjvH5Cme0uqqQj2J/XCQv4PdaESxYOZ0+N5p4a423U7aWwJr4HAbqoVQ13iYv/X9/neUeULARBMkAFPiyJbP28JBOlN5+8VrC9u8/bFw4FTXevn1v61hSgPPB+DyCIEZQ4CuR29csZaP0kEWgXoe1if/uthe475paGp+WunPvafIK2n0EQQichCCk9LnjC3+lW/L7t//J+i0/feTubz7zMn15+5qlI5+P2jzcI8+9Ymbrn+26zyK97qmt9+tEfbihrn7I7nGNPLH9Ua6+Pv7Y02byXwAOyiecOS34n5YjN9v4xi//g27Ju7/VL0FKluEaodingEx50MFPPYzqbraQ5fE3Jd0SCxOfTcWblOn02QTqodg+3ngUou75s+9pOXK6sVHdzRYiCFKuoMCnIuYTBFconmKreMglCIIgCC6fzyUIvlgGR6L7EATzQ1oIuXGVbhj+p4/czb5Mq+6NRaqd9Ug8N1A/pTWexcy7FyU4T7EQcuOqmE/gk/qqj/kyudQNB8zovwVBkJSgwOeEWHdAEoOyoig9Xe0Z7SEecjkT+1DkoCgFnMYfvpQ2PeUGOgpj4nM+GA9F0njdzg/KJ8gTnX3PMrEuy/h8SpvO2UC97rT0+B32z8M+8f5e3fHC7nwcB0EQFPic0dGWxe9hfPdOCbxR9TfV4e+RgyJEdrEKn654G3n8TSkbE29Buun0YBKoT4sCazxX3Y32nah7rux7uvH5KRCEl/uk7P5XKoTPq6sK+Sj2x/PS6ggAACAASURBVEXyAn6v6cGEF12hkE8QXKFYyCV4IgARDxNt7E+G25ORznjIpW5vWOXw92idjKOtA6C3P1WQ1IjuPiDdyXIW6fT2J8SDPROfZaAeiherp94dtPa9MJPisieN+wCLi5bAXOpsyIkNxLPL4/294N2Inh1BCgAKfBrEfIIn4o2SuGK0IxCIAAA4/T1K1AvgjSajjZHAzk0kCCkHIeBkfhOlgCfQQXYR9UoBp9n4Y2xXJMuggE2mzdDXn6Ean1aU3qIDDaHAGp9b2DsGqu46+07VvWCj74Wa/m510dJLXQ6KEY96pcd8ggfUfxR2OQDIfZLYuy2NgX4EQTIFBd428dC2CHijCZvtDke9ptuKwR3qAKbDvyMoSoHuWHKVrO7CHZaDYmQb7xcu5vNEQAx25dzoGKP0ZQBX43No4i3Undr3XHn3jDU7q+nvUsBpmWNncdHSS93RuUkEqU8GgNiuSPLiZZaTVSDBJjoEv7XPiRqPIHkCBd42cp+kjS26N5oqPGu9dcF2/Spp527d71vMJ3giyQH5rEkZpbcw8Tqso/TFMvGQf43nwlX3HNr3wpWs4SXZsZefxUWbXOVo61CfucP03TGf4AwwEzTdYe2u3Ru97O0vQhmsrirko9gfF8kL+L3aJd7fa3tbsd3JvHK2i2arjMR8giciBuWMU4tTVrwBw3w5AHjhsXu5W6abS58y2y5/Gs8le41PGZwv1rh7NvH5NCvepLpoDSRnfHogKgetbjCd7fpsUgRBcgQKvF2S/iQ1NCAJAMT62yIechF153p3O8oNAA88udve0TSwRe4ImZl4Ixm0kaUUPeEuZXBep+5Z2vfs4/MlUasuHtockBK5KnZuVNO+gUAQxA4o8LYxWA0LT8+mv8f7e0Hc1KlKtiYiH9sVoavIRHhv1Gr6cUqNX/idOyCVxpOSdlPCxKdL/pLq2bR5M3KbW2cdn7e4FUip8enfBJhetHx0g1nsDW48pC8DhbPmECRvoMDbxuHf6oWIJ/HrFPNpxha1SIHNauZQzOcMSN6tSdFmV3kioK6KhzYHpGQqkzkWGk/UnaDT+Mw6y+WjAw2hwIPxkJHG07ew6m5h34uF7lbg68FfxdvmmW2cmcXnX7RmaG6FYz5PBOgtL/knYm6TNfl4CEu1UNAHUo6gwKeBO6xEvREPHVyMes1msnmDm3aSvGRPxBvVhCm5q+K7d0qcXGZ+fjFX41l1J6Qbq//mMy8bTTxX41NG6TOoe0MoHY23UHdCboPzOeTtY6dHxic/cM42rjJVd14WPXv5WVzPHBz+HUEx8X+yrV1Wot7kLYI7rETBQ4+xa2O+KuYhCCIoilLsc5iyqLN9bafDxUMuZ6AjjTekB1fRn9/SSZ7oms0QT68bev/pI3cbFZ2ovk7UU8btja7dqPrGgrVPbb3fTM7TVVAzObc5b568XReZp/bd6N2zF3hd1N0iRG+x5deDv3qp/5OR8cmJavX/utnZfP7xb2d+Wnm+aBEzhId+WMjDKc/+ZSEPhxQGdPC20Q8fxnye0oouUi1noapvJ0pv38SnxI6JNwbqIXdWOBsfX3h112F/gpxO3Yl9p+pes6xx0aym3J4bgiBTBRR42zj8PckAPYnRl1x00VrjjXCL3phl27Gkm0sP9tLpiafP1ay5zDTeQt2hZMbdzTh7ZYh92Vhb8+F3NhXrZJCsqK4q6AMpR/B7TQd3mK0EknbQ0lBwPh/Y1Hhje3iCcb4cANy//cUMmstlZuJLQePNcuYn6mqMC3Ni3+1PkDPbktp3uiQ39r0gFy2CIPkABb4MMdN4bpQ+YxOfAdnMiSfkW+O5C4l9D3ZvNq7KR25dBvH5rwd/BQn7TuLzNcsaG2tr7lq+OOenhyDIVAEFvjxJN1bPYmbidUvsROlzbuIzw6bGWwTng92bc1JZL3+8fey0bsmiWU3b73QV5WSQHIDT5JCsQYEvW7gav/u3nIl3Nk18BlF6I1wTn+9APdjWeG5wnuvdoRhT47jxeRKcP3tliMbniX0v7KkhCFJyoMCXM9YabzYMb0auTHxagfqCafzjjz1tVHeaW5dX+55ZhVoSnyfBecpEtVKzrBEAFs1qwvg8glQ4KPBljk7jb5g7F0x8PAuJ0htN/ODpId7mOcDMxOcWM43vvPu/mKl7ge27/envFJ19BwBi3zE+jyAVDgp8+WMnVp/XJvE2TXwBAvVgovFXroxcuTxsXE7UvQRH37n2HRLBebTv5QCOwSNZgwJfEZhpvEWU3szEv7bvKPvS5oT4jIvXEvKq8e++e5g8YTXeuWaphbqXQmFaou6sfRdWNBT7pBAEKSFQ4CsFqvEkSk9gfbyZiddp/M1LWnJyPmmZeDOy13iq7gSi8VTdC4DNAXibm1H7jvF5BEFQ4CuIdOfOcefLgcHE28SmiS/ArDnCE9sf5ebMX7k8TNW9wPbd5gz4J79yO2vfycKGRViSFkEQDSjwlYVR4987e5ZqvE0TryODsrWEdOve5DZQH+jawV1+443XWNS5K6ngPHk5Mj7ZsKhpuGGS2PeinhqSO6qEgj6QcgQFvuJ4fksnG6UnpNVb9uYlLUUx8ZA7jafq3ji9nl1+443XkCePP/Z0AXLrsozP6yrPEzA+jyAIAQW+EuHG6rnQKL11tl3OTXwBBuPl/Z+QJ1TjGxunsRsc2ndE95a82neb8flTH54HtO9IaRCPx1PMuEWKCgp8hdL5ZX0fPGLibc6XyzjVLst0esiFyga6dlB1JzROr29snLZyZXJq2YH4SeBpfOFh7ftLr8r0ObHvZOid1q1D+44UkFi30+kUXCEU+VIFBb5yoRr/3tmz5Ak3UG/TxGdMBiY+m0A9d+h9pXORUd0JVONLYfT9xgXzQGvf6SqjfRe+9YTuUbDzRHLAlJkH39FWWk2zkSQo8BUKt7McADzw5G4LE2+RbWc/Sm/fxOdc44m66+w74Yntj5rVucuTj0+3Qq2Zfa9qVv+L2eI2XDlHjUdyirsr6hUh4nH5QrFYLM5S7FNDAAAFvsIxBuqB5+O58+VIlD6vJj4DLDTeTN1XOhdRaX9i+6OsfafUXuLUucshZgPwxpsAat+rFiTL2lD7TuLzFkJuvioecglJbIVdYz5B8MWSzzUkViDlS6zbE5EAQIoEPB6PkwW//pIABb7SIRpPo/SEsStjZtvnpFW8/Q406Zp4M8wmxa10LtItWbF2uW7JKkcrmLSKLxgvvSqT+LyOhTMayRNq31PadM4GMZ8gOHdukpUE0Y5AemOr8f5eEIPJHShK2G37zQiC5AUU+MqFRum5Pv6NvmPsS7OiN8CY+Ixz6a3JPlBP1V1n34m6s5F58l6jxhNyqPFpxedpcJ7Y97NXhhYun0OWnBI0oYVMgvDxkMsT8UaVHn/yMnCH5aAoBTbblni5T8Kx2NwyBcbg3WHFDLy/KwlQ4BEAnsavW9ai03gKNfHZlK3Nso0sJaXG21d3FqrxxL5T8uHjbcbnaXAeAE4NDkHCvguj4ydPXXxtT//E7zi5BVzY+4D47p2SGOzS/x47/FujUVXz2VC88SUAMfDejfibXsHgyHspggKPqCy+cbZxoZnG68hr3RvIopms/cg8GKx/AXx8Snp/8cELP3370vGLcHro3IXBcxcGT1z4vLq5dnhkbHhkbODs51eGRwFgsVKT6RHiu3eaeG+3275gy32S2LstvRF8pCyIx3wuQRAEp9PpdAqCILh8Mfz2SwUU+IpGl0vPLYBDNZ6N0ptNmcsySm9h4jMI1LPqztp3qu5m9p0SjW3lLs+Vxv/hj/E//DG+5e//nfvo/cUHH10ZJVueVibIk8Z5DUOCAgANQtXQdKWpKst/YblPArHdmd1OYrsiIEFyDH9rnxM1vhKIh1xOkmeXRIp48NsvFTK+8UfKk+e3dNIs+nXLWvYeHQCAN/qO3dZ+FXf7m5e0vPXpQMaHe7brvpyk0G///sM6/9330XEAWLOiBeypu1n6/RPbH+XK+eOPPW19f7Dl7//dbNUf6lPn5PeeOWdcOAyTJKeuQUjqOrHvt1+pN25fKNxhRQmzrzd6PZ7umB8HYrNgCvRoj3UHJAAAb1Du8jscAPF4rNvpiYC0c3fc78eUjKKDDh5JQtrDW/j4kjXxZuw/pLn54EbmIVXXOK6Qf3ToM7fn+2bm20LddXxx2FSYqX0fqa0mT6h9J2Rs35WfPJ546mwXQeqTrbbOAGe7CJFdOFOqIvBGw34HEXOHwx2OegHycEkhmYACX+lwK95QjV+3LJlGZxyPJxqfZYf4tIrXGjWewgbqiX0nUPvOqru1+f6w/8SH/SdYtR6e1fjRoc/YB9nyyDuHLfaTMb1nzlF1p1TNU0vlE/s+NF0Bw+j7ny9rhfRwdG4SobefE1GN+bKbzZ514B8pdZztIoDu6on39wJ++aUCCjzCx8zHW8yXg2LUvWFNPNF4Vt2vfD50bmIStOo+PKuRFe+77u8mik4fALBqlX5IYvlN13BPIF2NtxOfZ6H2XQdr3/f/zbef/MdvkAfjzvnoNnB0bhKlQLdeyOOhbRFgsuuTv+LkF1y7rUt3K4Cz5ioCR+fWoAhSYLMvUcYu5tsckMAb3NEJWNKuBECBR0whGs+aeDD4eG7dm3Sj9FmaeFbjD3746eXBYfoYn5gcn5g8NTpuNN8WGNWdkCuNp5jF51n7PmdW05xZTS0r582fO0Opr1k2b2bL3BnCvNrWBTNbF8zcsL5tw/o23dstNJ6zyuHviXojHjb1PR5yOQOSGNxBhlGd7SJIO3fHyarNAcmwh61eTUQ+tivCmXmHpMUUmAcf6/YESCW7RBk7knEXCWBJu9IAk+wQDY+/KbFBezbnjvLAk7t1/p5NtXtt39ENa5dleRoPdf+Sq/pkbPuDI/q0PrI8vv9jduHkmJp5Xl1TdX58fHZNDRhE+sABW/MAKctvuoYr50feOWwm/9Y8+Y/f0C1Z8bc/al48kzw/fWmoCeDOdde8cVRTQJfUpiXV67jt45SfPG4semMq/O6womz0CU4hkFgiBmWFJkk5/D3RPsFDVnujUa/HY9xB1CcICZnwRpUelHcEKTaCoiipt0LKHZJeRzCOyi/7qx/plqxb1sJq/P3bX2Rz6TesXfbMg19K9xwe6v7le0x4YMNVnMqslHcMGj/zcjL0fXFolFV38mR2TY1Rg40Cb2bfWcwsu27/RvEGbfkabombFX+r/rVPXxqaU18PALUtDQBwanCIFqatmV0NlgKPTHWE//ivhTyc8t//opCHQwoDOngkNUf/6TtGjTf6eMpr+44+Aq/oND5lYvl7x85odnLsjIXG37S8hdV44dSlSwDNjXXk5cyGuvNjQ8CoOwCcHx/XVa5J175TiI+/dsVC/YoLV1LMnUtVofbrwV/plhjtO6vuCFI84vG4w8HLtIiHQrLfj0GcooMOfkqy/ov6Aix7/rAtmx2yDh54Jv6bz7ysG30nY/NU4x1/9uSpyQnNW1qsLDiX17Qab9PEC6cukSdU4EfOXQaAkfpadvvGmY3AGHSuutO1XPOtw6zcjYXGW9v3rwd/9e4p9S9w+tIQAPzpHe1E3dG+VxrCXz5ZyMMpP9yS/ptiPmEbBLd2+d1JlY/HfJs9EckbxXL0JUAlJNkZc3wByCSg7OotMf01DTtKNs/Upi7ZmXcUD7kstzKqu9lC+5i1hyds+ft/bz50tm5s/OyFQfp4p/eTd3o/cWz5N7Np378bHczmlMCg9zpuWt4CjLoDwKUro5BQ98bpDezGRN0hoetP/uM3VrctMj5oLrqd0zMT8lzVuSPxeQJV9y86FwHad6RUkCIBj1OtThuPhXzGynZIEcEQfYbEfIInkshEiodcTqGP3rLGfIKnV10V8wlOF8g9tqs6xXzOgARes9UWQr7+i1sz8/FEnqVZQ8kl0Y+5W7Y2TTs5OEKeD4yPt9TUAMDLZy/cPXfW3fNnv3z6PGviT5wfgpamtM5kw1XzXksnUD/z8vAl7ZJLV0bnNk6jk+I++FRt0HJtW3Ka3PZ//IZ1ZRv7ZFznzojRvtPgPGvfpTOZ1w1EkJzi7ooGez0BSYp4nJHkYtEb3YH2vSRAgc+M2K4I0FlE4PDvCO50bgt1uf0OiIe2RcSgrK5yh6NewW7VzphP8EQs1qe06UaNt19SjUWaNSReaNAt/PL0Gb+9/Dmr8RSq8T8dsPLcOYG114GuHR9qp72Nnrm88vpl9OV1S+Z/8Onpa1cvYbfJlboT7Gu8xQC8cej9T+9op89Z+y6dGaD2HePzSHFxuP09SpvPlXTtYjANN4Pkm0oI0duAibYnq3fFQy7BFYrxAvGxXREQN3Umr2NHW4c6U9jQnUtftbPfJLJPYwJRvn23GYTPrGAqABgV3YzWJrWk2sD4OF348tkLxi0z0Pu+Hz92Q/tV7ONCcz2NnOvUHQBWM5luo2cuNzQ39GkT7H+76691h/jw4IkPD55I98QsyCBWb9YiFhL2HQB0uXVIZTEF5sGToLwmJi8FnNhOrnRAB69W9eigM3djPsEjQFQJOwFACngkdVJvzCd4nL42i9QRqU8GcIKuTqOjrYPdKBLYGZSVHjWwz4TvacOO7ApD7Pv9h2vvWJ3VLswhJh60sXrKy2cvfLNlnoWo2xzbttOBhu0Ut3rFQuLjG5rVe5S+IwPty1MU0P3w4InVK9W4fcb2nZJNrJ7YdxKfJ+q+2DmXrq2qUn98iX3P8jwRJHfEuj2BCAANypP8OinicUYwya4kqBgHH/EIWpKxcLlPAu9GejG6w4qSvDbFoKw+d4floBjZFooDp0NHsnyn3MdLMUkW+tQG9nlFQrNmf/yk9SMnR6E+nnJTx9Kzrc1zZjWyj9MLZqSVucZFp/fcLu+jZy4bFxJxZfU7t97deCwzzOLzxuD8Yufc9YtazOw7xueRkkL0RmWlJ+x2ADjc4R5FDnqtEnaRglIxAu+NKlqSgXD3Ri9EPCaJ62ywPRmHJ7U5PYkQO698pxn6HfIbfeQXM+E/c2GQPticOwCgIn3L9cvo40/ElePTp93UsZQ8yJaCtsXZW58O3L/9xXTP0KJ4LVfdV69YSO07oe/IAKu4XI9OxD57+07harzR2ZvF52lwnsLa96zPDkFyi7Mroe1JHP5wjxwNtpu9BykkFSPwVrjDihwUkx6fkXrTpkjusBL1SgGnIAiC4OzbqkS9ZGPSYEkPlXXtDvkbZ8uRiYkjExOpt2NYtXrJqtVL3NMWzp47gzwmqqvGptXQx3e3vUAeDccu7u39ZG+v2qLN2Cf+lqULzlzQC1X2EBPPVXcAqBoeN8bkdc1kt3//YaN9z7mht6nxBGrfaf48Cc4T+36btjUcm16HlD9VhX1kAr/KDYDDjVVuSgMcgyc4/D2KHwDIULvH1S73+FO9hw6aAwDEQ9ugY6MDIA7qYHzi0o/39wLQYXjNKhLP35SzTwEA8NGimTMmAQCoxruvW5bTI6hQjQeAyeFxdpVQDcdPf754/gy65P7tL3Lb0lhgHIm3UHfjwiXr9bdm3OR5Ogyfb448s3f5I+vYJbrg/OlLQ0TdaXxe+kSdIGC07xifR4pHPOTr7oP2rnAiXT4ejyfFPuZzbeuFjq09OAZffNDB63GHo97k+HqihxYAcHLnk8R375SIOTf019YN8bOr4v29Zjs0YnOO+1i1MFRXPVRXPX1GA3m8fmTgdUPldltHnM6vVLNh1kzdkrsWztUtqa+vOX7687MXr5y9eOXXvUfe+egYjQGwD/sn89le2dhjBgCC3Zvp85QmnivnXOHPBqOJ/+jceQA48sxeO29H+46UNnJfJBKJ0BykmE/XNU6SJKx1Uxqgg6d1aRKzN8kU94T9kwKbQ509pGSNJwLeKNkq5hM8yUTRmM8ZgMTUd4d/qzfgYd8lBuXkvSy7Q2dA8kZ77E8a3fOHbdaT5d5a19pwclipqZrTOG0c4JblySlkRKuf7brPWlbXX24y03ULXrtw8a6Fc3/32VnyckF1zamJcQAYHp2or1Pbmb9y4uyXFunvA6xPpglgcFoNAHy2V+ZuEOzerIt+ty9v6TsywNr372574amt91MVX71yEQ3LU71/7O9+lsOReDBPqj/yzN7Yz/4StPb93VNnqH03gva9Qsl06hqCUFDgE60uk60yE6Ua4gAA3uCmnYlVzMwPd1iJguARItq3JFbKQZdTfZe+8IPJDu1iofF7b19cNQ5DrfVwcvjclZE5jdP+eOQzVuOBjGRPqwHLLLbH35R+s7ff+jQ2zJr52oWLuoWsxgNAfX3N8PA40fjTE2Pzq2shffYdON50/Dx9+fr+T+YkCs4vW3vNV+/9v8Bg3I3BeS46N18wjedOnKPqPj46CQC3LWul8XkEQZDMqASBT46vs7jDTJsdzXC6ljbuu63fY3JIutRvOb6vOTMOXI3fe/tiAFAAhKqqkdZGOHnl3JURADBqPIEMb5vJ/FfWtdHn3DL1Zs6bajw18cD4eK6Jt2bJ+cFzvOXL1iYbs+oq2yxpaX6/X5M6t+GBH65xJCPerInPK0TjSXye5Ut/8t9m36mmGav2vaWRuwdi3zE+jyBIBlSCwJch7Hj8wh07Th0agkkFFFBqQBiHySqAOY2jAPOa6s+dvPjHI5+BNlxP4cr8E7eKuuZyRp7aev83n3mZXTIE8NNH7ibPH3hy99t9n0LCxAPA8OgENGTi4I2cuzK69rZVAPDpHk7cntj369sWsRp/aXCk5/2jzczEfecNy/bv19TbX+NozbmJB4Antj/qfviH9OW1c2a//8lpADj/ah/ReDY4b8e+Y3weQRCboMBPeTZ3OLePfjj86YQyqYAC1VXCxKQyMg2mjcCZweF5rTPHAWouDptZeWDKyFjE7Y389JG7dRpPeX5L5wNP7v7jJ6cA4PSEOqJ/anzcvWbZBQCaTp8yye7ovsMAMKex7tyVUbpwcPFssFR3HePnOAVwAMC55mqZ0XhS/2fDAz90rrmau/1TW++3Plszlj+yjpteRzXeDDr6jva9Eqku9gmkJB6PW7xESgDsB18OND711OjpyclhRZlUBAVqJoWJSaVqEqaNAADMa6oHgPXLWt5+/2MzjWd5tus+1sFbdJI1Cjw18QCQ1PhzqsbPbpy2fkUrMBpvAdtIhgo8UXcAWDZwyfgWncATE88KPDHxVMLl/fymeWYanxLuTQCpYUc0fvSyZkbf2SvDI/euIs9PXhic39jA2ndufN6+gxf+YrtuifKvj9l8L1J0hP/z/ynk4ZT//Ge2t03VFAsgo/wiJPegwJcDt/zPn/d+dn740wkAgEllelXt8Nj4xKTSkKg3QzWevKy6pC8jr+ODc2fv/lrSXNrXeFbgAWD5X/8YtAIPAOtXtKYUeOOUdzJHbuE6JwCcfv0AAKy5egEwA/Bc+/7OHn22YHPTNFa/uRqfscAb6alJTkmY9oeP59Rq6vsOwgQAXLxrBQCMj06SqXFE4Kl9r5+RDLNlo+4Eo8bzfq2xJVjxQYFHsgfnwZcDdy5aBABV9QIAQJUwODleX1tTXSUI09Uw35nBYQDYc1TVwsnmadbR+OvmzH35V33kkdaZ6PT+jhuWAsD8OWpX+PNXRgBgz6GT1sVrzQrasOpOaV/e0r685dcv/u1TW+/XPeqHRpsNBfN14s3VcjNnnyUXl87/fHCYPs5eHhoZGx8ZG69/6eDhzy6cOndZev9oTg5kpu6mq8SgzFRxloMQcGo6HSIIgzuspATVvSRAB18mEBNPAvVVAI1QAwDDY+PThCrlslrSTufjn/N5wNDHhfLBueSEt+vmzLW4IbA28Y8898rv3/sEEj6emHgA+DT4Xe7ezNQ92L2ZnCor8MTEg0mBWDL3Xddcx7nmamMgfcMDPwQDOfHx1MEfvzw898wkAMyLnyJLxuqSE50nFBi5aSkADChjALCwTk1IPFU/0SokkxPF0UZIlQ1goe4U1sfrykCYLkMKi/BfCuvg/w/7Dp5DPB4HGcBtUrsWKRKYZFcm3LloUe9n5+vmVw1/OjEJUFUrTI4p9bU1yvikML2aaPyZweF5TfV7jg4QjX8wHAWA5xLKbdGh9YNzZ62n1bF885mXdRp/xw1LicYDwPkrI0TjlwSeMmq8hbqTo3/la//VuNZC3QFgjaOVaryZZrOT6JIMjdKk+rSK7lFYdacLzzgWzIufEuqqASbJkgkFqgFm7Tt2Ya2+sL9R3a1P5n+czNns+WRXpHjI5Uw2U2Jjr0ysVgwGOwIBvCuoOOIh3+YA6QjvjSobu4VtENwRxougNECBLxN+cJO4o1e+NDpaVS9MDitD4xNXN0//7NIVoaZKGZ+cP6MBAE5/PqTTeAB4MBwlVp6IN5X56+bMZU08gZtvb5FODwDPPPilR557hWg8HYwnPPDk7ue3dNKX1uoOvJYt+z8+9Ztf/Y3ZoXVQdSe17dhV27//sHXBWjsp9NY3AcS+E844Fsz/RPO3rROqAKDxnU9gbSu17/lD+IvtFgl38dC2iDequNUXLmegI6r0kJcxn+ARIKqE3dpqjqrUY6PQykJ770eQIgFnL+CNXkmAY/Dlw9I5TQBQN7+qql6YUCZPDQ0tbG4EAKGm6owyCgBE5gl0PB4SVp7wbNd9dmz6Q92/tHD8XMh4PIvUf+KBJ3eT5ynVnUBj8tbo1Npo0I1izJ0Bn1aZemMSwIabVmy4acVITfW8WU1rVyXdee28hoM3qTdYE9ohsuX7kgMK1Q2F+vdMtEUkOANSsmWCrpWCO6yOr8ZD2yLgjSZLOyb7LyOVQnz3TglADMr0y3d3Bb2g6+GBFA8U+PLhzkWLmuvUMq5QLwyNTwAA0fgFMxqpxpOEOzBovFHm2UR6o5sHc5k3M/Rfv+taSKTaEYjG21F3btnX+bevsnmfYWdAPXuNN/ISkze3dtVVa1dddceXrhWvX9Y6r3n4npXTamuqq4TWGY2zMXcksAAAIABJREFUptdDTRUA1FVVzfzjMeN+aHw+L2iT7JSoF6SA2j3EvdELEQ/bQpmgE36yIZJDqoSCPjJB7pOAHcwBAId/oxcg2a4LKSoo8OXDD24SqYkHAGLiAWBhc+PpoeEFM1SFmD+jQUj8P7MaD1orDwBP3Cre/bV2Vua5PNT9y8nTg3vfPmK2wTMPfok+nzerQbf23fePRi9xatGYqTs18fNvX8U9HFeSjTH2zIbV7UOmv3PZc0L9sx9YqylLUFdbM3tGw+wZDcs+OvuVL7bddbODPmhUIK/nrELseGQXUfiwIgfFiCfh74nUx/t7C3EmyBSAbZBJmnUBkNaaSLFBgS8rqImvm19FTTzh9NBw1fRkyoVNjSfYkXkA2Pv2ESrzZiZ+w9plOo0/OTIGADqN10XmrWFNPFfdiTW3o/G5NfHUvt9YozbYnbF4hm6bGx65HQAuDI8CM8UAAA49vSd5Vsz0d+NAAPuweWIpK94429nRdIe/J+ntIx5XKA6Otg6bx0LKF3dXUASQAk6SahnZ5hJI1qXtJthIfkGBLyuoiSc0NdVSE0+WZKzxkIjbm43Q35ww1qzMc9mwdll1nXrhXbiQTLujGq9Td2Nwfs3VC8zse/bkSuNfMkxq/3nga+zLkxcGW2c1AcCi+7+g27J9/mzQarxNclWrTu6TuCbMHY56SfjV2S4mPD4BPX0F4vDviHqT94KkDbzoDe7ADLvSAAW+DKEmfmhijC4kgXoAEFckM86EKsHlXAQAe44OWAzJE2j9WjuJeHvfPuL8Xph6azZKT1i/opWqOzHxABC9dDmlugNvXhw5kIV9J2QTqM9yMB4Avv3Cq8DE51ku3HENa98PnL9AnuRD41PfBMR8ngh4t/odABDzCQJT9Ca2K0KU3+Hf6oWIJzE2H/MZsqmR7Kgu7CNDHO5wjyLL0QSyrPTgJLmSAQW+3CBV7QjCPKFqWhUx8ZDw8XsGTrMaLx066UpURTVaeYsitUaZv5mX4s5NxNuwdtn7HxxtreXM0qR59dbY7ItjtOMZB+rTYsUPf6JbwrXv6xe1QKI27ZKHbubuqvYXB7jLLbCQcP4qbRa94OkNyonJ7u6wEu1Irmbq37jDStSbGJv3QDTq1eVbIZWCw+FOgJVuSgoU+HKDROlpOv3QxFjrzGTQvrq2CtLUeOvDWcftKQ91//LNNw/Rl5+++P7F4VEAoBpPTPyN1y9j32XTvhNu/8YT1ueQFtkE6r8e/BX78saamT8PfI3YdzNI5Xmi8SQ+z8L9O1jDFXLuQl7dUe0cZs0WmlXMGixNWpnEYyGfi4Nh2gVSDLBUbRnyn96RSNEbAJheXTt+RpkcmVzQkExtmxibXN8yHwCkQ8lZ1+KK1h5Z7aFOy+AAwHtnzgCA5y71d93C0wOA83th3RLW1r9z7DQAXPnsYlttNQB82qDehZwcGwcAzy3JMd/nt3TaUXc2NvDpviMAcMM1yTO3MOJG187NUEsZ8+fy9eCv3h2/SF/eWDOT5taR+DwdfV+/qEXXWgYAju98m93bqtmzyBOzO5vikCiAkxB2rG6bY4T/qv9XyivK3/gyeJd52xlsNlMSoIMvT6iJvzwxBgCsiQeAO5yL9wycBgBxRSu18sTH0yF5uvEN8+YBQPR3tgpXyP/iW/eF5RYbXPnsIgD0j030j00QEw8ArbU1s2Y1Sf0n6GY2A/UUou72yd9gvE7dwRCcp5D4PGjV/amv3bHi0fX0JVV3yMjH5xGHvycZoCcxegXVPZdMgTF4MiUOQPQGg1EtXThNrhRAgS9DfnCTxmTXzBMGhodIK1LCG0dPUo0HJu2OGHqjxhNsajwArPvCcvIAgLc+PkWX154fYjerG50YGB4bGB67/rpl6gkkNP69fUd2D2k2Bp6FNQ4NvHdYPe3sx9Ez2IkuOA8Ah/7yWzQ4r7PvkBh9N8JqPEtpabw2uI9+rVLxRnvCfr9bC47FlwQo8OXJnYsW0flylyfGliye/qY2edtM4wkWGr/q+/+v9aHZTjNU5gHgg/2fcLefObNx78HjwvikcRWr8WYB6me77kvXvhNsmvicl7ejwXnyUmffH3u1hzyfGhqPVDSkeqG20A1SSqDAlyfExNNUu5MjgwDAmnjCHc7F9DnReDoqT8L1RONJlJ6SMvNOR9X8pgWnLnFXLWRm7Qvjk+c+H5L6T7zHCLbRx+swau17hwdsOu/carzRvt9z/TJdbt3JC4Ngm+hP/3fuctR4pERwdwW9IAU2+0KxuJZinxkCACjwZQw7Xw4A6uZWv3liQBeoB4BpzXV0iU7jAYBqvA5rjde1iyV8uWXWl1tm/enSZM5d48KZAHAbk4U3t2HawLnPB8bGBsaSM/h3Dw1Z55exiXWFhOvj6QD8PdoZASQ+T4PzACB98hlr37mYfXDU+PKnqrCPTIj5nIEIAEiRgMepBbPoSwIU+HJGl2p3V/tVwPPxNFAPaWq8TSt/4uXeD44ne9W01Va31VYTdTfSoMAVRQEAVubNcu64Ertk7XL7ne6yMfEsRvsOAEMXxtmX1L7T+LzmTJj4PAtqPIIgmYECX7boUu1Ojgy+MXDSOBIP2kA9mGi8UJu8VMjEOYKZxlMTf+JlTgXTJfdef+utK8jjnWOnqYm/eHEQABqYmZsDY2PDTXWQKq+emvgla61y+LlkH6jnBueNG6dl39n686jxSEnCq6GAOZelBAp8OUNS7ZI9ZHkm3hioB57Gr503n9V4Fgsfz1X3Na1z2Ze33rqCJMMTdScok6rIz5zVBAB7Dh7fc/D41X7NlFvrTLd029XbIeVgPDtB7tVDyVl/e04MWI++m9l3Cmo8UsrE4/F4DEfeSw4U+Erh8sQYa+KNgfqUGg8AaWm8Tt1plN7YKe6R51755F98LbW17EKq8SxX+yNmyn3DNS0Z2HdCNtPi9x05dfKPh9gl91y/7NVDJ9bP1RTupfadFLexb98pqPGVxRSYBw8AEA/5XILgdDqdnm4ZYj7B5Quh0JcKKPDlzA9uEs1MPAsx8QDws+/cwy4303jPXQ42Sk/QDckHunZct3guGCDqbuw9Q1SqpbaWPMhCT8cy4x56jp1+qPuXqx/5Z+MqHWmZ+MwC9fuOnNJtwA3Opzj01+6wuSVqPFJSxEMuZyCi7TIkRQJOF2p8aYACX0GQVLs3Bk42zKoFXqD+Oz9/1ULj186bTxbGXj9sbeUDXTu4a8+s1ocNCCd+sQ8A7m1qpEtaamvvvqXtjY9Pua6ab9x+94cfA8Dvjp8lD7Jw+/cfttmBJku4gXrWxLPBeTC0j+MWt7GOz7OgxiMlQ3z3TglADMpRr7rE3RX0AoC0czcqfCmAAl/mkFQ7auLJhHiKMVBvrfEs1bX8uN7KP3+SPmdN/KK7OyzOs3fgHGg1HgDWrVz8bNd9XI0/BsnCOKzM68iHiYeExuvs+8G9B7j2/c72ZA4jyZ/XFbfR75wXn2dBjUdKA7lPAl0LQYd/oxcApD65WCeFMKDAlz+6CfEEYuJZaKAeTGL1OvadOW3U+HffOwoAscuDscuaOwmq7t985mXyhEbpiX3Xcf2N6mj6/dtffLbrvo9D3vUrVZm8cPEKecJq/PWrl5C+tFmaePsabwzOz29ugIR9pwPwe04MvNp3PJtT4oIaX/5UKQV9ZI62kh2pTy+2Yy36UgAFvoJorqujqXYA8KOv3wkmJh54Gi8dOkmj9JTq2urnfB7ynKg7hWj8dYvnWnh3o7rf29RI1Z3l+S2d61cupjJPIBp//eoldMlD3b98q//EW0zfmnyk0wNA6y0rjAsP/Pw93RKdfeem19mPz7OgxiPFxt0VFAGkgJO0lItscwmku5y4qRNr0ZcCKPDlD0210y3/i9++QZ5Qjacm3kzjuft/+EcvPefz6NSdELs8aMyZN4NE6Y3cv/1F8uT5LZ379n+8oKFuQUMyZ5D18YSb2xYBACvzOQ/Uk4nvrMZPm1EDAGcHR+re+JTd0tq+ZxCfZ0GNR4qLw78j6k3W25AkAADRG9yBfQVLAxT4yoKOxLMmnkWn8dbsO6OWwHv4Ry+5p+tvIADgxhuWPRiOvnHgGLuQRunn9Bw1vuWJ7Y++8Ni9Zkf86hy1/h2R+QUNdbNmNvYcO222vc7N24TbG55iLGuzdrlmRtyCP6pZdQ2zatjldmrTpgtqPFJUHO5wjyLLtE2sLCs9YZT3UgEFfkpy45f/QfdI+RZq4kkuPcFo4nXoTDwwufQs77x7+NfDV9zTm1iZv/GGZeTJ+uULdRoPJgpkJlfExJOSMlTj1Z2vXLx+5WKdxhMTT3mr/4Tzu/+aTazebCSeNfFnB0d0a1n7bixPS+x7ZvF5FrM/mvAf/kX3yPJASEGZIvPgAQAcQEfcZTkWwwT6kgEFPhviIZcgCAJn0mfMx65Qt+NumT5cObfWeLZsLZ0TrzPxNgP1Rt559zB58uvhKwBANJ6qO4XVeGridTzy3CvkidHE00A9MBq/ds3V5Mn6lYsnmjSFenQaTyCJeOYfJYlZoN5o33ue/Y9gUPfDsf0/vl8THbFj39OKz7MYNf6/C5wufFyNJ5eqHrZXSMyX+84h8ZBLwH4kUxjym5b4CuVuD8NmnAVfKqDA5wDjpE+SSUpfdQckMSgritKTdezKQsjt+Hhq4tn5ckaNp6TUeKruBFbjWdYvXwgAbxw4RmR+4H+qyWgdLXPoNov+17UWZ/5er6advM7HEyaa6p7tus8skZ4dks/MzRvV/eeBrwHAxIalxo2vf+iHFruyX9zGJqzGc9WdwPfx5OpMEvVGPIm70ZjPE+G8JStiPmdASr0ZUqokvsDINq2WiyIAgBToxnu30gAFPmu8Xq9e4WO7Il6vV7OVZq5opqSUcIsNSKqdcTmN0rOwU+YIXI2/eOjcpdEx3cJfD18Jdm9+zueh2fU6fvu7XgDYf0Izc52qO9fEE3X/9ZmL7Fvkf9ti3DnpSUNknmviKUTmLZTeaOL3GQYaCGxlm1l1dQBwXetcAKjbq26/flEL175nH59nIRpvoe4EG7F6dzjqzVexkphPzbRGrCnhdrGqgRG9UU02nTfa0yMHRQCI7EKFLwlQ4LNn40adwsd2RcT2dvI8HnIJnghAxEMCWjGfIPhiybCoNhRKI6SuUMini+jbMeg20aXagY1APTAaT4bhLx7iJ73fdOM1D//oJfKc1Xhi4gn7m5LZZ6yJ12GRbUd4fkuncSHtO2fUeG7CnYXMsxr/yrHToNV4Yt8Jo7clp+pd1zr3D0c0d0jc6nU6Mo7Ps6RUd7s420WQ+nYzV68rFI+HXIIrFEsMOGku0JhPvWgFQfCFkptot4z5BE9EDMpK1Gt+bKTEUae6B3eE3Qbb4ujchApfOqDA5wC3VuFjuyLips428sLh71GiXgBvNNlCMeLZ1i4riqLIQTHiYX/6vFE1RNoRCGRocqzvA6xT7QhmgXpgNN5C3ckTrsYDwPCJC+QJq/H2g/PUxNN6sXY0njwsDgGp4vavGBL1qbp/+wX1j8NqPKVu7zGjfc95fD5dUpt4uU8Csb2TuXrV4SUp4Al0RNVAvhRwMneoUmAnua7Dfn8PDffLQRHAu5W82x3OyUAVUgKwQUlnu9fr9bY7AcDRZlWxEiksKPC5wNkuJkszEn23+gkTE9NEHZ2bRFLUMR7aFgFvNNFE2R3Oh8OxSLUDyylzLD/7zj26cXcapafqTmA1nsg8VXfCsxcvRkeGjAliNEoPJiZeVw3eWuMpROatZ80Z4/YpA/V7ziZL2o3etuS61rkfnNSMPrzzi7e5x8ptfD6nxHyeCBVlHWJQVi9Td1gOiuwoLOe6j4c2B6TkhY2UJw5/OBwms+Pi/ZwW0UiRQIHPBY7OTWIiJpVa35lbX3q3K/dJ4N3I/Aq6N+YxhmlMtSMm3k6gPtC146v1jc11+kq3B7b/ufFAVOMB4DmfZ1Z9nXGbB8NRY3M5irHpu24knmCh8bqcu5vbFtkpZ8vK/Pnp03RrZ19Wc+apfSeQCrUXhkfZhXOa6uVffUBfcu17TuLzmSMFnJqAuqc3KCsmoswaN0dbBztUb0g0iYc2ByQx2IXyngHVSkEfaeBsF8GYYEcg/We0P2ZI0UCBzwlJhbeh7xwKdtfLptoZTTwLN1BP28R9tV7TFYaMu3MT8R7+0UtE5h9/7GnPtAbd2uE5jWDSTp7y1XmahPkbOjgp62DbxwMAKVlvkW/PbrnC96Ru4ZcSzW906g4AP77/zss3aKLxisL/6Swt+67PoreIoqdRZFyVd6xqVm44/Fu9ACAFnK4QO+k9Hgu5SHY96nuJgAKfGxIKH+/vzaQMcw7Hrd797T/Y2YxWrjUz8RQ2UP+bqqQ3vW5Sla6ZK+aQknZmGg8A125RZfKhmUm1rl80iz7//f5Pfr8/OdZOovRG+05g58SzmGm8hZDbkfl3PzomKHDxkiZ/jUyLZ+PzlNF1V9Hnc5rqyRNi4kvEviv//L28HwPlvYxxh0mFWingYWI/Tk9AAgARh2RKBhT4HEEUPrR7p5TRhDhnu6hNPDV6epvK/a2f/CblNtTEV1UL5Alr4s2mxe99SxORWzVrFgDMXKHJgbfQ+NiEGtZeMzi+ZnCcLt9z5DMAWLe0BQBYjWehJp7a93Q1XrdEl09nJvN748k/y6wZDRcvDdWNqaXv/zB8WXr/KLvxR3/2wLdfeJV0f2c1nsIG6vNE/pVbM2Ektiti0lcE5b3McYd7ZLYMPUEUvVG5B+W9ZECBzxWOtg6IBAJSZsEph3+rFyKeREqySSGQlBrfsdkFqTSeptpZtJ+hEI1/4+hJqu6siX/wkS8Y92/U+H1vHyJPiMavWTQXAL58Fz9oQTX+jm//s8WnsICr8bo6d8DrQKOL27PqTpjfMA0AXj8y8PqRgXPHNakAd67gZOnPbtQP3hPyGp9PqfEZ3ASwDUGlQKJQmXkqHsp7Dij5drEOd7hHURRZluVoVJZlWVF6ejgz55DigQKfM0haXMaDT+4wqR9GkpwgGvVyq+NYaDxRd4IdH09YMmM6W9WOwAbqb1vWevrM5yeEZNM2VuPdt2sy5wlE47lWnmh8y/9yAwDctkq1uayJB4Df7/8k+rI+gPHVeTN1o+9mJh5MNN6iJ40OM0O/dpXa+/Xs5DgAnDt+8fhbyZx83ZC80cSvaZljHHTIR3zeQsLTV3f3Ri9IASed8+4NbtpJwrKeiDfKTcUjiVb6zD2sTFueOBwOcDoBAOIMxT4rBAAABLMkIKTIxHyCB/i/n7zJ7qy6U37yra9w9/2f3pEA4NUTJz45NwgAkxNK67QmALitpRUA/vXLtwGTNr/3rThR90VK8nbwK5N1we7Nj7+phhlirx8GQx8aIvB09J3loye3sOXo3zhwjJTB2fuJ2odt8OQlAHigdR77ru3ff1gn6taVcIyR+T0Hj7uu0pyk2QA8rUr77kfqpLh1DnXAYt+B40TgRxK+56o50zfcvWrowjiJzxOEOsG1eCEbll9DS/p8eVnyQ+VtAN442T3bAH485HIGOswuSiSnCE9x/nHyh/JdTl3I1MQTaXV6TO79kMKCDr40iIdcGosT83kiFtOL3v3tP+geXC038/G6KL2FiSeReSLtrIm/tG4pADxxq7ofMx//+GNPu6v1Yeq1X1jBTp8DxsoTiLoDwPMnz9CFurnvBAsTDzwfb+w7l5Ibr73qxmuvOhRO/vwRddfxyu6PpB75TF9y567FC9kNqLp/ePb8h8+/m9Y5ZIbyz9/TPQpwUKSSiIc2Y0+BkgYFvjRw+HuSAXoSo0+74FdaGg9Mqt3xK6q6k1Q7OhLfvDeZ8sZq/LqbHWBoGM/VeBKQN2o8APTENQVnaqbVABOlp7x04dJLFy5RdU9ZvFZHylg9t3qdsakMMHH7OTM1UwTrq4QzFwYB4NzQCACc6Tt9pu+0UKdmLzq/dp3F6RV5+jtSykyBdrFyn5o1r5tlmazaiRQXFPiSwR3O/v/DvsYbTbxuAzrlXQdRdy4t185gX5LcOp3Gr/2C2kBdvKZVp/Gk4B217ywWE+WtTTwYNH79ysVgOR5v1jKOMG3ZLNHlvNdz/QiTlzQ8qZy7eIV9y6R86fXX+slz59euW2Oot18YE59jHP4e/OVGkpCKNx0bMbGuVEGBLzfS9fEUEqUnJv7u7wTJwq9MJpPPFylV8+fNYKfFf+fnr9IoPYEm1tHMeYq7ehpVdwqr8WRU/lpFYDe4MDS69rqrgdF461bxXKx9vP3WsWwaXfXy6dXLp9dXCQAwbVJzzkLiv+r11/rJg0YgPjx7nm42JTUeQZI4/DuCIkQ8rlAsrqPYp4YAAAp8WWJf40mUfumcpuNXBqmJl5/V1E6nGr/uZodFHxqKMXmezoDXrRKvaQWtxj/2dz+7btFcncZTrAveWcNqPDHxwPPx1vadZeG8ZgCYt2LW2NVNADA6MTk6pO+cS1jbOu+h7l++d3hAt3z13NlmlXwQZKrQIZJ6NzpwzkRJgAJfntjReLb3DOHkyOCFg5zSbKCNzHOb0BBIXn3Lu/q+6bGJEdJUhjt3jmj8x69+9N4xNavuWkUgMt/U2sxu+WA4+mA4moGJB0sfT0y8tbobC9N+Y90q8mTO4pl1CxrrFjSODo3Nb66f01BvPNCStcvfOzxw9vTllOeJIABTYB48SbKLYJZdCYMCX7bY9PE01Y5y8KpaAOhjg8k/+C59Tkvf0CUXjo+wb3/8saeNx2Uz51mNJyYeDDl3BKLxdO4chavxdqAaT008pDM/nrLn7ClS2ebkuMa11y1ohGuaTd4E55Y0A8DZ05fJgyxEE49MWdQkuyAm2ZUqKPDljP1YPYnSC5+PGFcFuzfrlhgD9S+/96luCTdznqvxhMGBSwcTmefXLZpLniy/Z/Uda/itZYzhejsmHsx9/NIt/6ZbaG3fCQvnNS8Yrjk3rP7drhkTznxy/vYNbbdvaCNL1mqn8o+vUF8219W9d3iAxO1R45GpiZpk14ZJdqUKCnyZY63xbJR+6OwgAJyfKQBj4qm6p+wWTzT+4L+9SZcYM+fBUuMB4GCdQKP0y+9ZTZ7csWap0cQDQHVd1d7DpoMFFhCNZ038mcvDZ0c0Xtxs6J1A7Purh04AgHj9Mrr8SKMyp37a6+8eBgAi87SQzvunz3J2BEBkHjUemYI4Ord6McmulEGBL39S+vg7Fy0688Fnc4cmhycmGmpMp8Tq6teCQeOJuh+8eIEu4WbO6zRevKZ1cCA5Ne5gnfDy5cvUxBPuWLP0OZ+He1asxts08WDi42P7j3I31tl3bgc5ALhmTJ8bSHrH6Qrfjq+YRxv1Ut47PMB2oEeQqTAGH+v2RCTAJLvSBQW+IrDQ+B/cJO4Pv8Uupybe+dAXjO1nKGyg/pb5CwDgg9l63QITp27t489fGTm3Yp5xuVHjxVWLQavx9nl+Sycx8WcuD9OFe48OQDr2ff3cBXtODMxbMgsAjjQmfyWJiWe5y33tXe5ryfMla5cbd/vpviMAgDKPIEiuQIGvFMw0nhS0WfXxBADMHZoEAGLiZ61cYNxeF6i/bVkrNfEXD5wCg8ZbZM5TjZ9z6Ay369rrH2lS8UmHeAsfT2TevokHgOe3dLLqDgDnPh+qrtPEMMxG33Xcf89qAJhTn/wgbOt32j6OyPyzXfdZaDygzCNTA3eXLBvz6zDJrnRAga8gjBr/9r7DL41rFG54YgK06q4z8cbBeAD4455+9iWJ0hN1J1hr/N3TNXX0mlqapcMn11+9UKfxBFLwjr4UVyWH0jPQePeaZbolew4ep11qjOr+4/vvhIR9BwC2u4yO7257gbuclKd9tuu+G67R1+XVQWQelR4pVWLdTmeyzyBSeqDAVxasxr+9Tw0jH2nSzNu+atHcmjoBElXtrKEmfmat6t25gXow0fiV/jB58sCCuStHOQOBXI0HSyuf6pSTkInvc6fV0iXzpqt/CmMnOsKW37xuXHhn++LX4if+5NZrdcvNNJ6w/fsP33BNi07mqYlnQZmvRKqVgj4yh9PVGikRUOArDqLxVN0J79dWkSj9SucC2nuGYm3i6z77/Lj2B+KD2XWsfafoNP7t94/qNlg5qjS1qPPIpYRUU40nUXoK1XjWxAPA3sMn7Zh4WtbmlrbFROOpuu85eBwAlv/Nj3Vv0dn39f8/e28f5VZ1pvm+xzb+KrABY5cp25gPyUClukkCOFjCBkOASM7KuHvAw3TCmNATaTq5udLkxp2eO14zs+51r+60c2np0kmnlG6CB0gYh572zQpSAoQvIxmcwCTdbidYgmCM7bKNAYO/XUb3j320tbXPPp8650glPb+ltSKdc6RSBbkePc9+9/vOUaxiMJZPbmYSPJ83wlrYMpnnSq/UeILMg64jsa7IqujT+VKphCr67gMC34/M+Se5q8ysqVN/fc6ki36/OeHUoYmXwnnG7Kvm8VHxEkYf/+RRveXLxxdeRETxSLPxzg2L9fdj4eOZzBs13lVfW67xFjB1F2H5PLPv7MjyT8gj9SQTbz0+jsu8mcYTcnvQRfAq+kI2mUyiir77gMD3Hayq7o4pcjvVC6Jzjr9+mhrz5SSsTfyCs9reyXWe0pOq9Q2Habxo37nGL155NbVqPIdpvGTiGcq4vvLbvRYab+xKe+Nl8+ML5/KHT+x4k4iee/VN6TJu3yX49Pfln7hctO9E9MwvrOyMcc69MbdXApkHAFgDge9fRI2/7pO69XxjxxFqTIi3NvEP3rlStO8Lzuq7wGdfpQfXxjk0Fjx59KgodUzjWakdP2jm44nosXRSMvEMpcYre86zreqixjOYxjP7blyAF+27ksrU4xZnGUaNJ6I57x+XNtArsZB57Rt/K91s3wnoIibAPvjWEdeoou8+IPD9hTTlnWk8U/dPzJlLRPNnzHhjxxEnJt7Yc37v5DpXd2sufP0d48HPP/izR+69nT+08PFKjD6LYardAAAgAElEQVS+8tu91JhP4+RdcQ6fOUNEhxoj3rmPb7Hvp9R/E7/3uZu+t/4u6SAz8Rb5vFLjWXs75zIvKr1SzpUHS2lNgUnCWkprQtF0Ka28sJaPm74A6FGw9N6VQOD7CEndGb/567TxIPPxRHTzJUNkbuJvPzVFfLjgrLb3cLNAb/uhgxYmXtoad901l5KwcY4RjwxVDFXxV/xXxW/BeOuvv/Spy1v65DONJ8HKW4yMMzPxSxfNUxbVc/vO83mOmca7hbewdSLz1FB6C7OuPhXLte5mLqYKScXup1I6WXDx5kFfUMun45qmL71rWjyNXXPdAwS+X1CqO0PZA2d37QgRvTjWoq/cxCtHxi29YckfXCX3bzFqPBctSeMZ9Y9anHE8MiSm9Azpe4CEpPGcu0eLTsa9v3z0yOBcPcDgJp6IZv0f3+X3x46pg3exuQ05y+c5ShMvwWTeWul/+M6Y9Ys4yOoTo8UUVTY/0fKXGvIODNTy8WjrxNhKIRvFzvhuAQLfF5ipO58lwzSep/Ts4JuvHaGGiTeDm/ilN+jz00QTzxA1XpqqwjT+OmFeCxlGx2qT5B7vZK7xbIysqPHcxBPRy28dfPktdSd5xrRLz2d3uMYvXdRcdNhb1Ufozp8yY+WwvuRvtO8MycTHTs+03hZPlkG9hJnM26q7U6LDMarsrDYP1PIbCrFcLmW4clc+rsz1heP4e++FCbAGT6WN2QoRpfi82GouRkSV7Eas0HQFEPjex1bdGUYff3x8/M3XjjATL6b0kn2Xg/o5A1zjtx+yUlP96QYfH7v8Yknjl1+9UHz40u4xcqDxkpUvv6a/Jtd4657zTOPZnvjte/SncI0nIuvyuq8+W77lerkDiI8aT45zeyP2Jr66s0Kx4WjjYS2/Nku5TZklhgsL2c1rqvqf9kKyKeX8eL2aI3i63qS0pUBEqeIonxcbyZSLKSIqbIHCdwMQ+B7HobozHr7nNtHED0ybYrwm8fn/x3iQ23ciWjp33oI5co0eM/FKoXrgzz+vbHInavxDd98qaTzj8w/+zElcX/ntXq7ujJffOmhUd9aYdsXIYn7kc5cp0ovXf3OI23cRKZ8XiZ2eafEmnWAxT9ZJbu+SUjpZoNT6jP5nuyHvqoZlscbxyKo1ounnxymS2ZSLwdMBED4Q+F7GlbozjD7+zdeOWJfakUmDegllwR03rNYa/4WHniSDj+cYNZ6ZeIbFkrx1df2nzp1NRPGFc5/Y8ebh06fEU0+++IZZeZ2EXybeCd41vpKNttTQJ3fkqnyjk5W8i31KI0tGlMf1Mzt2wcP3GtHhGBEVkulS479tTa/UENIf0EEg8D2LB3VnrFp+Kb+vTSYieuT537KHb3z/ZSL6zbvvi9ezrrRmGs9T+pdefd3654oaHzNRZa7xLKXnWPv4j87W6+MfiUfiV+rWnGu8OFdGNPEkrMczLjn/PCLaW3tPPCjZd7E9bdBBvQ/IVfT1Mpdza3k3f8WWP/BMCIArJsAafCSzPkVEVEhGNS0e1zQtygoxm+kP6CgQ+N7Es7oT0TevjZFQasc4PXbmyM6x31wqD5JR9pxn4+GN/Lx+QnxolDGjj+cmfvzkWXbHoY8XTTzjU5fo74qrO0Pp45l9J6KXjx4hIu1CfbXi5Pg4ER06eoIMGm+GdXta02cFqfH1b/6JwytrT2yuCAY/WdDtvt0295YCParuVDcuBhOdxGi1mGLf3ir6f+NYKldFm5suAQLfRzhRd4Zo4gcHZhDRwRMn3jnZrGaXTDzDIqjn9l3SeCNc47mJLxtq2ZZfvXD51QslE08mPn5btTHa9Y0xrvESl69/SDoixt3vTB2nhsbPnNqsS7hoxvRntv+OLFffGcZt8bYm3gxrjXeu3A6JZMqtG+R1u2/7F1xM5Gu7dlBszSp4ul4kkhgt1+vVarFYLBar1Xq9PAr33jVA4HsQpX13ru6MVcsvZSb+4MkTRHT8+MlpkyeJGk8m9t0hrlaamcZzE88wK7vj93/01c9ydWe89MbYnvv/vdTzjlXIP//r3c//ejc7wufKMPvOWRC94KKZLcEGETGNF1GOj/Og8d4W42013vcvAUoq2bV64XwpHc1WkNm6ZtJHod7aIxJJJBKJRAT/kbsLCHyv4Yu6s5Se+/jjx0/yU1vnT2N3Tv2rK41PVJr4n277jfjQg4knlY8nk9I86/V4htk4ea7xpKpZO3DgyO03Xs7yeRGz4fHkNZ9vPt1TUG8h4eGoOxGlcms2R/VYP1VEa/LeomZLp98hICIirV732OIAdCHtLL1LfOOVChE9u2/fGzuOnHr3+Pg5k4ho5rh26uxHs4+e/f2j45d/8VPf/vSNyueygnlWXvdu7TARnT+tZfH+hk9eYVt4z3RaalU7afrk5a096h+6+1aloj963x2sb91LbzST/GXRIRKW5+8eLfIN7pyli+Yx+Wdldy/s2M0ieiKaPP7R3bGrn6vt21t776IZ+qie64b01rb/48ur2B3RwUsCb3TtRmdvxGx7ofWzjJvdQ1N30D7aP9wf5o+r/+uvOb62lNZsmxriS11XAAffO/io7tQw8SuHhmafkTvTHTl38hvXzCfD+Bl/Ye5cKqf/qDWlF6+UuORP9W48N1yu72Rj6i5i9PGsb524g05UdyL64XM7iWhB5ALjT2Q+XpnPc3xcjLel/s0/kW4B/SAAQHcCgZ+QaH+ck27+qjtn5wPbiGjeefWZZzUiOj6lPm3ypKmzpxPR06/uNnsWd+fMvhPR+6dO87M3fPIKcjZMVqncyoFyyiu37zukfNm7HvgJu/PFHz170zWLb7pmsfIyFtcPzTmPH5l/VC9BiC+Yf8vSy0iw7wwpq28zn2++Tsi75kA3MLke6s0Fqimx1WIuxfdCxlK5dbDv3QAE3jO1Zq9t23bbzSmbJhM2DS9o8XraH+eMB/PvKrZstanujOE3zhDR+QPj4sFDJ08Q0dOv7jYz8Q/euVLaLHerNoMa6s7wpvEHPzwhavy9j/3ceOXW3+oXMI2/4fL5RvsuwjRebDvPK+/Ey/ZP/YiI9r+t7yBgGi/x0+dtlh+9mXhoPOhWaqV8Oh5NspkzsVSxikL6rgEC74lSWtOijV7b9Xq9XhzJRtuaqVHLx6PZit5wpJqLVbJR5RcBpbozJI1vX93PPNRSHPexWVOIaN7g+YMDzcarFj5e6myjrK1zovG/23Cv+PCimdPJ0sdzdWds33fI2JX2rgd+Ina2oYbGSxw+dWrf6+/T+2cnjyvKjL/3uZv4ujvj1YPvkCeN9ww0HnSQWikd1yRtT0DbuwcIvHtq+XiykCoK3b6IEqPVXKy5M8j9az6xuUKpov6SkUy5mosZBzZYqDuDa3z76q4cCDv8icGDJ2SdvvKv1BrDLDvn/VOnRfvuinhrYd3BD1veAzfxZF5Xb+x789wvW7a3ff+ulY+lk2xV3lh5d+ydUzyfnzNj+rP/9CY/JWk846fP11zl855NPACdQdd2Xdpz0PauBALvmtoTmysx4xJTJLO+WBQ0vyZOy9zVeq1xwmYkU25tHmJs322r7hxfknkRltK/d/oUEV165WwiGhyYyVJ6xj0PPyU9hTlLqXieVPvonJj4R+69XdR4CxN/Z+7HS1vXxZdftZAMe+d++bbpmDtj5d28c6fPmDm1JuwVlGAaz+w7x2LvHCGoB7Z07z54UdsplipWN61bFSVsk+tCIPBuqT2xudIySYOTSHCBLqW1aHakyOP7bMumErMJmyKlLQVS/xgblIvxbuH2PXre+fzgnE8MrVw8REQzL5Fle/v+g6LGc8kRTfyFkTlEdN/jz3rTeGr18czESxrP9sUREdd4pu6MqQPnbP/dGAnqzk0872xDjco78WUPNaT92Punj72vVwsee+1d8RrJx39y3kVkp/HegMaDTlPdUmi2Hq4UklEFdr2MQShA4N0iDcpWUctvKFCq2DDkidFiquW82YTNJqV0skCKmCAUlOH8nauuI6IXDujdZm5fulgy8aTy8UQ0fKJpDtjOeIdyLvHIvbcrjzONF1N6huTjGTeOLGIab8tN1yyetnAGEc07dzoRnTgzPmPmVCKaNnnygYPyvkHGZ25SfB2z0Hh/t8xB4wEAEhD4AKjurFBqtSDOidUtCm82YVOHtZFIiXl/JxFNPBExE09Ety9VVKXd8/BTktJcfcEF1LDvnPZNPEvpGUzjuX3nvPVXik66h0+cOvRB83vJc7/8nWTfxYuHrjif2ffpk6eIx88cPk1Ef7TxH/kRtv09HI3HYjzoKKptcjLoctMVQODdEh1Wm+4mtV07vL98Ka0lCzFhGnfIqGvrvrpMOvLi2H4iWnTFbG7it+/Xc++nTsoG9+47Py4d8SWoF0vtHn/xNekyVjlvrLkbHDyPiLjGX7dQPX6GsfKKoYVXXnDijL5FcNGcWfxUed871KrxjM/cFGH5vIjvWT2C+t5nAoyLBd0OBN4tkVVrYq3Vbw1KaVY0p7TlTqjl40zd2/Hu9b/Pen6uUt35RJmVQ0NE9MKBfdzE33zJ0KIrZosX/+JXvyOVxt/+8UWe3xWHp/RSUT3jpzVFzR0RPXrfHVzmX3rrAD/ONV7se6N8hYFFAwsub8YYF04+h90p73uHybyEsq7eTOM9B/XQeNAF1Er5dFwB1uC7Agi8ayKr1sQq2Y3y57eW39BYNo8OS1vcnHh6thFe2n0n0o5ytwlrWyvBTDwRTV+g19xxE0+Cxks6xJbhqT0TT0TxyNBFM6czE3/gsP6zeOs648Z30cozE09EH50xLR5+6fDBlVcMvbBn/74jx9kRpvHzzh+Qrlz8H79nbE8bjsYrgcaD0Cilo8lsoaKg0+8MEBEE3guRTLmYKiTFvjaNNjWseC6S2SSWx5fS0azd572WX5utxHJV62DeVuODs+8MpYknok9/cjERHdndUsDPNf7+ZTFSmXgPGi+W2jEfz9WdoWxrw5h6nlz8T0Ss5s7Y90Zk/vQZxNrTXnfp9QvmLrtqgXTBz7Y2W/rw7e+uNN4bWIwHHaW0hW0PiqVyuWIr6yzrkEFIQOA9kRjVm9fpRLOUqwreO5IpV3Okn0/uyOVSli/H9t5RpfmCpv1qLSQ8UHVXmngRpvHvnzwtHnz/KqsV7vaZd+HAqfFmD913j58iy3GxKz6mf8kYHDxvzgx97i3TeKnvzcor1K1tlXNmx947+rOtr4syz3Cu8QjqgUz37oOXSBXLo5lMohVMhu8KIPCeaS0lNQTrkUy5eS4z2rgiMdpSYKo/FC62fFEiMhHyjgT4LKW/+ZKhFw/s/82PfjU8rrhG3DvHTDxP6altEz9++OQ00sSzyrY2JKyvc40XKe1sabj70uGDRCTm8yKPpZO7//pL/OF7dIbfh8aDfoJtD1JWJIGuAAI/Ian/fVa6tfNqTsJ5jjGlJ6J3a/ri9/B408Rf/3F9Fss9Dz/FUnoyqbbzthi/lU+CGddnyA7OGXhpt2KbuxS/r/jYohsuGbxxpOWdHPrgBDfxon3n+TwRfe9zN/Hj8aGWUvmx944S0ScHL+JzZjkhZPUAdITEulyKKtm16Xyphk52XQgEHihQqrtFqd2c6dOrQ/pnydbHS5hpuYXGiyZ+zozpRHSRoSeuRVBPjZo7pvGHT5xiB8X98USktO+cH6z7A0njOeI4eYZDjYeJBxOKUjqaLRBRpZCV29mhir4rgMD3O0r7bg0z8dRoevN8Rd6DPnT0NLfvnJ9ue5Pduf3ji8SUnkyCemu2/lqeYnfnjVdKR5jGG6vnWGcb5ViaHz2zk519Yc9+6ZRo3znTF067eOG57D4z8ZyOa/zfzD6q/dWoeHPyOqBbmDBr8KB7gcD3Na7CeQnetpYhmvir7/r4w/fcJl1/7dy5XOOVOA/qjU3rJl8wjd8XU3qjjxf71j163x1TZsnWf8mfP0yt9p3l80Z+sO4P2B2m8RfQOdIF3jTeF/5m9lHjQaXGl9KaAtGAldKwY0CFRUs7dLLrCiDwwAXGlL6+58i+6YpQ/itPv2jUeGr4eOWWOXKj8dcvHuT3WUpPRMuvXihd9tJbB174lz3KV2BINXdzZkzbv/eDHzz3G/aQLcCTiX0Xt78zjd/2273SNVJc70Tj2zfxSnVnqH18LFdt+essbgMtpZMFxVMAsACL8N0BBL5/ace+81I78eCc6dOJ6Oq7mo1pLTTec5cbo31ffs1iItpa20cqjScirvGifadGen/XLcPGpxzbf+LY/hNkbt8Ztzcm3F/70XnxoYssVuX5/aA13kLdGQ6y+sRoMUWVzU/grzSwoaZoZKdpWtTQCQx0Agh8n+JZ3UUTf3CH3vmVm3ie0hPRV55+kcw1/mvb1M1/3Jr45dfIM2+WX72QpfRiY1prHz931gwimjNj2rsnTxHRhwf1aru9bx2xeJaSSR+cUh53q/He8G2hnY1ceCIf15IFokJS0+L5Wi0f1+L5Uj6uaNRQSmvxfD4t5vti+I+Q3zUTYQ2+lI4qGtn5+/8DaAMIPPAIL7VbeHYyP/iHt1xjvJJr/LVzmyNcf7rtTTMtt9B4o31nLFs4SA0TT0zjBXVnnP6wpQmPWHx383WXMY0nolNnzxLR+Hunp0+eTETP/uot63yem3iOshkOudR4f/vXSth/D2BTkVdlyvViiihV5E0ZKtlkdqSoB/mVrFgtXcluHq421l9LaS1J7Lp6VWzsCHoHvZNdTO/kFUsVi7kYEaWKWIPvDiDw/Ug74Tzn1e+/SkTvnjrJHnIT/+KBZv05M/Fk4uPvefgpV8XzRnUXG85IKBvaWOydu27hvKWXzSeisx81J2vNnTHD+lmMaz86T3z4Rxv/8bF0Uinz4pJ8ZzXeklI6WaDUemWfpWZD5cRoNRcrbGgKd2zNqsYzSlsKbDADkT6gyXoEI5i4pNZnMmwg9nA0kdmUi1EhicSmO4DA9x3tq/s3r40xdY/u1bWQmfibYldSozu9EaPGv3LokHF/vNtqOyXl3WMvvrFP1PgbLtGL8sz2zv3oq58losELZ06ZrgcSJw6fJKJPLZxHdhrPy+k5bIysrZXvlh44cpPk5A7zgcUjS5qyH1kyIi7VC6cSo7wPYymt2Q9jABOXHbtqFB2OEfsosGGaLcO2QMeAwAP/EU28yMP33Cam9IwzJ+QifKXGP7vzrVc+aBlmIw2VkVJ6BtN4ru4Ms71zc6+avefIMSLiGm/2LOP4ODPa1PiQTLxcRW860pAoNuxsikiNr9QnqVjN2UwyAEYmwBo8a1VbyUbT1VVrYvoXRWy66CIg8P2FL+G81CKNpfT/btkIHyArwlN6hjKrf8Gwu0zi2Z1vSUe4uotd7Rjlxj74F9/YR6qs3rg8b+TcganTJk8+835LxZzSx7PxcWYmniw1nsl8+Bpf/9N0m69gQy2/NltJFbElusdJjFaLqRgRUSSzXpio1VyeAR0FAt9H+KLujKtmX8DusJT+lhuvFs+y8TNmz/3Mskv5/VcOHSKiZYsHJY03C+olEy/BTPyBD1s6zn7/rpXGpnViUT3fO/fCnv0Dl5/Lj889f6bxR1hk9UaN55gtyVPDyvuV1Qeu3K2b50pbCuLCe5PqzgqlVifEh6AniSRGy+w7XGK0XmWjYqtV8wQIhAoEHrhD2eGcbYhfuXjIiYm/f1lM1HiGhcZL9v2VD96TwnnRxB86qd6lxjWe23dp49yXn9oqdq9jzXOWXjb/xR3y/ror/+IR5Y8wwk08w7PGuzLxthrv4UuAOC+skl2rl9VZlOJFh2PNZVi9VQ6GjvUwtVqtVqoRRsV2GRD4fsFH+87gJv7f3P4J41mzUjuOUeNJldUbK+dXDl9iW3DHTXz9rNXi4gv/skdqfUNEA5efyzbIXTs0l0w0/smX9LlzLJ/nWJh4hnVc70rjLbCQcPfqnlidoko2yve8p3JrNrOSvGTBdDtUJLMpFysk2SL8huFqvZgSvhkAJ0yANXgi1ulG06LRaDS5sUqltBZP4z9z1wCB7wv8UndXA8qU++VEJI1ftniQWjWeCfm1sy6w/VnMxG/bd3DuzOlm1zx63x3G1XceuT+2Qx7lzmHb5yS4xlsjmXiyi+udZ/XWK/FKIVceTIxa1dQRbzjOr1mSKSsajidGWxfbI82rypkIexEEt71GLR+PZgut6y+VQjaKpgddAgQeOEWp7p/84ifZHWNKb2viiegzyy5ly/AMSeN5OM81fuXwJeyO0cRv23dQOhJfLAuzVHDHquuNy+prbhlm9p1z+tgZdueXB5rvVqnxtiae4VbjyX21Xf1P09LNyRsDwDG1JzZXiGK5arFRYJdYl0sRoc1xtwCB7318D+c5PKXnXe0kzEz8/cua26bMsnqm7ryq7tpZF3B1Z1gH9Tylv/exn7M7bO+7sgHO7P9T3trDtsWLGCv1rhuca9sDh1QmnmGh8dpkTTrYgc3xoLNoH4V68wIrnhQ7I1CE9bxBW6PuAALf4wQazhtnkJN7E0+te+eWNcbEvfOh3iPPunKe8cUfPbsiuoDdZyn93OnTzC42bo4/OX6WjZYhovnTZyh70971wE8uvOxc43Gjxjs08WQZ1ys1vnO97YhIj92x6Q200lo9ydrXOm2XAIIFAg88Iqn7yqEhabgcw3Ylnoi+tq0i7Y8/cOQYCRpPRI9nP+e8vR1Td9705t7Hfi61rhN9/Mtv69m+ND7OaOKJ6PYbLmN3rhtsZvi2Pt7MxDPMNH7b6/KWhM5rPABNEutyrL0Na25T2BDX+9wo906C8IHA9zIh1NYZJ8QT0crFTeNuZuLFlJ7BNf7M8TP8INP4WQv1Zu9KjefizU082xBvgbg5/tDJ5r75Y/tPKO0746fPVEnQeJHPP/gzUeadm3iGUuNjS4aUGu/qlQEIjkhmk97ohoiI2CC5WCq3CeWU3QEEvmcJbumdTMJ5EibEKzfEk7mJZ3CNv/Ccqfyg6OOdsGxoHr/PTDzvbSfBNP7k+Fl+hG2Q44gm/lcH3+H3lRpPllbe2sSTSVzPNF6S+Q8ulDvwwMT3IJPOhnrzSCQxWq5X9Q43xWKxWq2XRyHv3QIEHthguzWOm3izUrsp0+TlZCN8PPyJwyekU4OzB1747V6exksm/sU395d3N/WPm3gJ1raWIe59n3lRy7a6uTNmWOftzMQ/sDJurLlj8Ke7NfEMpcaTIa6HxoPOUauVWqk2K+qqVXaohir6bgAC35t0xL6LmHW1I8HEG1N61tbm+nlzqGHiB2cPsFMWGk9EXOO5ePOU/n/usNqwXt47NnDxDP7QODvOuBLPNJ5UdfUMs68Itiae4VDjnxk77OTVAPCb6sakLRtRRd8NQOB7EB/V3VVnGzIvtXNi4kWYxksY+9y9+KbiO4Rx/IyIaN+/9OPnx4QF+OlTmvm8JNJiPi/y6H13KGWePd1iAo01FhrPZf664UWSxsPEg5CJmdLpdwaICAIPLHC+Nc621E5CuRIvdaW9ft6c1b9/uXRNZIPeB/7BO1dK6l7evd/Yd1ZsTS+m9BLMxH82slg8yDVeMvEfn3fRXQ/8RDxiofGeMS7JM40nwcpD43uZidCqtlKpEI2sWb9+U1kCuym7Agh8rxFoOO8QZamdtYn/SfG1fzrcolWPZz/38D238W3xHK7x8cUXS6ekXXM8pedNbyT7Xt7bLL4Tg3oOF+nP3GKzrddM4z2beIatxhOyehA2idFqtZhLsfL5SqWQTSajmqZp8Xg6n8fqezcBge99fAznLVbflaV2FiZef2+GZXgJ5fz4ex5+StrXTkQ3XnoxNTSet6a3fnGRu0euMBPprz5bNh6XTDy50XhXWGj8ttf3Xze8iFo1HiYeBE4kksiMjpbr9Xq9Wi3mcqlYjJjWZ5PRqKZpWrrU6fcICALfYyjtu184qa0z4mS/3E+Kr0lnxYGwtfVfkM5u231g62t7STDxTN0tOPDhifji+bxtLUNcgGcoRZr1nGcm/uPzLrL4KQ6zelcmngxxPdd4IoLGg84SiSQymdFNm4q6yoNuAgLfO3Swtk7CVamdqO4spZfGvZNB4w8eO05EksZzmIk/Z+AcarStNSLl80T0nduWszsWGi9hNPFmT9fmyLvaPGCh8ewOsvqeovvX4Gu1Uj6fjsc1TYtGk9kC63NDsVgql1uHVrXdAAQeOMKJffdWavf7cxQF80ZWXNWywV3U+F1f/yPpYmML27nTp7GmN5KJZ9w9coX4UBRpPj7uyZd+Z1yJd67xlQMt6uvWxDPMNP7MtCkHTp0mQeNh4kFg1PLxuKarui7rsVgqlytWq/V6vV4uj2YyEfS66QYg8D1C99h3IxaldlLlPKnsO+PBO1cyjWfSTq0aL//EN/dTo+mN0cR/6cfPkyqfF1GK9Lu/O2rxFOunx4cXSRrvDTGuFzX+wlkzofEgFKo7G149lcoVi9Vqtbpp07pVq6JEtQYdfouAAYHvBYJWdw+r72JKb2Hipf3ln01eyVvaKZk0Xf7ELls8eM/DT1m0viHVZDkxnx+aPZPn8yKP3neHOP2dYex7ozTx5MDHezPxDKXGE5Gk8QAESaVQyCaTURUosusKpnT6DQAvaH+ck478x5nndeSdSHzz2tg3XlEo9Itj+2+cry+WT5mmjZ+qE9HPntnFjvz+nDnSHjklD965csm3fjB/zrljh3Unzd38PQ8/9fA9t7Fknu+PFzWesbW2b/LAFCKaInxRWLHItEDv9hsu46vvbHzc5x/82Y+++lkzUZd49L47xAq7+PCi8s49lQOHY4OOViWseSydvHu0SESxJUOVXfuI6MJZM9/94PiBU6cHp019ZuzwG8P0d9/5rviU+pf/Q/s/F4TDRd77wwOgAwffJrV8XLElpJTWtHheHVNZnZNeV9NUlxrVnYj++viH4sPO2neGtYnn6s5N/GeTV1q/4Bd/9CwrqZs/R5/LPm9g5rbdB9j9ex5+yviUFdEFy1ysC9cAACAASURBVIbmScPlDh5vhvNDs23K34xzZYxV8RZ6b+bjmZVvx8STENdzH3/hrJlEdODU6TeGFddrrXpP1PppLKXtbZf68668DB6uV0mM1m1Bo5uuAALfnZQ2ZiuxXLVer5dbJzMp1Z3BNd7HtjYe1F1Zaidh3fTGOqVnzJ9z7rwBWZvPnBg3trcTHx46e2rsg2MkLMBb2Hfl9nfG1IFzbN8hR9T4+HBzDr0vS/LUiOvFrP7YJ6aaXazQeJ1Smg319oFSOpq1/08IAAgYCHz3MrJELkS1UHeG5ONd4XttncQ5gqgX/9cb4qlfHXxn1//9Reun8842fF8cr6vnJp6IPjrZsuHnxksvnnquQoyPnTpDDfuuXIAXuf2Gy1g+L7L9dy277KxDe7OxNJUDh9s08QxR4y3UnWGu8X5QSmuab98UAADtAIEPA563a1o8v6vlVCmtNU/l05oWz5fycS1ZICokWxpC2ao7o7P2XUK5IZ6pu9gx5o5blrh62fjii1lnG2nvHFN6vkLPeWnsIE/pT46fPX7yjKsfRyqFvnFkkaTxDl9BNPHkt8bvuWLcy5OrwkeuGdgbPpl8sWiX+IEWlpBKaS1ZiOWq9WKqvd8G0KB2Nsxbp39dEAgQ+MAppbVodqTIVqaKI9lsQTyVLKTkU9FMuV5MEaWKXpayHH4PkPDXvpul9OJ+uV9POiKeUo6f4Rgb03KYxosmft7ATKbxvL3disgCIjp0Vh88c3Y6EdGxU2dc5fNKFy5qvG3lndnoOX+zeifIJl78yJUzEbNPZoNCdvOaar1er9erOcpGmxqfGDUuKgEAOgUE3g8KSa2VZkZZy28oUKrY0OnEaNPbWJzqAtq37wypNb0YzjMTr7Tv1svw379rpbgvjmn8/7ez2WzOuDz/0tjBwYHmOJnxsx/NnDKFHOTzRPTAyji7I8nzjSOLyKXGsxeRTDwRXfr1v7N9YnjYfTJjuU26iEcym3KxSnYj6ukA6EIg8H7QsDqc5h/E6s4KpVYLNjyxOmV/KlyCXn3nKf2PX339nSmn+fFfTzoiqvupY+p+mRb2nSNl9UT0qdZV88nTJh88euLkeLtRpK2Pd/giRo1vc7ysn9h9MsXqkMiSEaIdu9DYBIDuAwIfLLVdOzycChN/t8ZxxJReNPFzJ7WUgH370zdav45R3fnIV6m5zfw55/Jt8YwXfqtocseYUtcGZ854pqq+wKJ+nlo1npl4EjTe+RZ56Uh5554u0Xi7T2ZsWOzYGx3GiJEgmD3pbJi3Tv+6IBAg8MESWTLi4VQ71P8+G8TLtskLB/b9+NXXpYPJT1ye2doipc+81VRcJ5vluMbz3XGixi9bPMg0vrxnjIhOnj2rNRbgiWj/B8ecvHOez4tYa7xD3vzWvzceDEfjrTve2H0yKzurwqPqTuyIA6A7gcAHTHQ4RoUtwhJl0x5ZnFIRhHIHZN8Zoomvvf4uv69NJSJKfuJy9lA08fE5LVVvFvZdCW+As2zxIL/DffzMaecQUf1UfUpdmz5lMhHdcKG8+c05ZjvfyLGJJyKppV155x5qT+P96VVn98kUE/narh0UW7MKdXUAdB8Q+ICJZDblYoVkc+9RswVIJLM+RYVkYyOcg+4gthrf/pcAv9RdYu57LjrDmKFU9wfvXCk2tzEG9UQ0dkg9JGZrbZ9xuJx1Pi/CNd6zif/Buj9Qtq0NVOMtLtCV2+6TWcmuFT/PqfWomwegG4HAB04kU67mKBtl5fU7crlmvVJitF5MNUrwk1QsplTtbVqwkHC36h50bR1ny49fI6J3hOltsy5uFrSbpfRPvrrnFwcPOnl9aST8nde2DHXVJhMRvbn/CBHRFE0jbfyU0+nXynxexOjjmcY7N/HU6uOZiafANN78VGJ1iirZKNvYbv3JTOXWbNY/z4VUEV1JA+Fc7aMwb53+dUEgaPV6vdPvATQopbUkOfmDadzs7sG7GwU+CPt+5X/9Pr9fv3gKEX3uk1cQ0ZlTzQ9efnlc3Ac/bWASET356h4iun7ePHbQLJxnMb7YkpZtfz9zQm/58tLbB4jorfeOEhENTKKPqE51Ilp0jr6V7vX19/DnSvbdVuBJUOIXd+zhB5deNp9Uo+eUsEY3fDe8WGBvsRBgi7FjnfcA3/EnE/jFHb/438L8cT+7/m/C/HEgHDBNrnPU8vFodqT5V7OUThZiuaqTv6HtR/Gh2XeRd06euGj6DONxo4kff8edpYgvvphpPG9uc86MKWdOjDN154wfHZ914fRjrZ3s7n3s5w/dfavxNZ2oOxlGxjG2/26MabxzYoNzmMaXd+7hGv/5B3/mWeO9y3kbn0wAQPeAiL5zRDLlZgzKktCQuoAFWlsncmfux793QcsaM7Pv1Nqanuz2y1nbdzMevuc2dufQ8ZMzpk0hoimTJh1///SMKVOI6NCpUzbv3jFMg/lKPGP778YcBvU/WPcH7I7ZenzY2+c698kEAPgIBL6jtM5d7LEA9M7cj6UjX1puuv9KNPF7dh/d3yiUc7gMT0KDes6Vf/3Dmz8miO4UIqLpUyaf+FBvtsM0npfaOS+vM2Km8W5fh2k8X4nnhK3xPf3JnBAMTDob5q3Tvy4IBAh83xGafVcizp5Rmvhn/uXtedNaYnzn9l1qfcPgGj/zHH1BasY5U6af1H+0mY93mM+LKLP0xV9z1IOWm3gy8fHUVa3uAAATAQg8CCqc5/dZSr/6c1dSa1c7CWklfr9ht5s1yu8BL7y+r7x3bGjoPPZw5vRm0cnJ482V+K01eeSdNx697w7JxJMnYY4NzjGaeG8vBQDoWyDw/UU4tXXGcJ6pu5H88hajfPw9eZDr7Z9cpGxpZ7b6zk08615HRPsPHyMipvHzBmbMnz2TiOadN5MaGs9M/NWFx8x/IRcYffyLOxz1oBVNPBHFBufUDyu+4kDjAQAOgcD3O6GF8xwxpf/6dnVvH5bST7nI6efTLMY/eEzffD9pmrZg8Sx2n2k8a2bHNX7f+83OtR7yeZHd9yt60HpDGftD4/uBmZPGw7x1+tcFgQCB7yM6Zd8fz37ObPaMyFPVt1/+8OANQ4O2P8K6eP7BO1dy+85gJn7Footv/cQlRLT04nnUMPHUmtX7hbRHzpuJJ6I/2viP0HgAgDcg8H2N7/Zdqe7GyyxK7TiLFp9LjcZ2tuPhpSMrrmj5DjF/1sy9B/RutUzjebN6xrETp08cG//vv/itxU9xxY+++lm/NJ7g4wEAnoDA9wsd6WwjYm3in6q+ze5wEy8NnuHYjof/0o+fZ3d4Pj/WWq/H9scvWzx4yQXn8YNnj509cWyc2s7nrfGgyqzPHTQeAOAWCHxfEM7WOIf23cgHY6elI7d8bCG/L86QNWK2+s5N/PxZM4lo/sDMZ3+zl4i+c9tyEnrgiJw9dvbpqqJ23RtKE08OVFlp4gka32fM0M6Geev0rwsCAQIPgsJa3cWU/henWhrKvvzhQbPGds7tOxFdPGfA4sqH77lt2eLB6VMmn9WaXfF37XrX4iluMWo8w60qMxNP0HgAgBsg8L1Pp+y7EWVK/9NdbxHRp86bx0/dFl0oPfGZt/Yql+EtxsOX945Rw75zmInnPHzPbf9q5DLxyOS6FtnwiOUv4RpR4/lAGmtVNjPxBI0HADgGAt+PdE84z+Emnqv7tz99o7QMb2vfjay8esHYsePzB5oyf9/jLS9y/sKZ0lPeeu/Dex5+yu0PMoMNlGvfx3MTT9D4/mC6djbMW6d/XRAIEPgep+O1dRZMnqzbdyNSVzslRvvO83lm340sU+3B+/Kqa/T3U9fr+bftPuCjxjO4xotTZS1U2cLEEzQeAOAACHzf0Vn7Lqb0Ep86b54xnOds2vqa88EzjJsjQ0T0b6+LSsclE09E586bJj48+OFxHzWeT4Vv08eLJp7MNR4yDwBgQOB7mRDsezvhfHH7ngMnTvCHUqldZmvZmNJzLOy7yAt79hPRyqsXSMeZxvPxcUPnD0wemMzPnhw/S0H6eNHEk7nGW5t4MtF4ixcEAPQVEPj+IvzGtEZEEz9/vLkE/pkll0it6Tl7dh8lx+NnWD7P7LuIMp9nfDq6aNE5M6fPPMd4yheN5yaeTDTeIZKJJ0uN1x78jnTz8BNBp5g+6WyYt07/uiAQIPA9S/fbd+NBqTU9M/HiEZbSO7TvRLRikR4AmJl4kbnTpnGNP/jhcSLatvsA+aTxIsas3rmJd6jxP6DXjQeVGl9Ka1o8X1Mc1bR0id1Ll5rHm/cBAN0NBL43CWFrnF/qrn2oEdFnllzCHhpN/Kmxpr1waOKN9p0M0+JfGmtZEVgeGSJB41lKT/5pvGjiiWjpZfMdBvVOePS+O0SZV6o7w5GPL6W1ZCFVrNdHE0SJUfa/7ESy4PlNAgBCBgIPOsB1F82zv6iBNH7GYu87tdbPswV4C37+L7rEPrAy/tDdt/LjTOOZief4rvFGlBrvxMQzmMZbqDvDRuNLaS25I1flog4AmKhA4HuQLrfvxu3s3L6TIaUffvB/SBd/5ekXpSO2+Tw1OtRKJp4EjSfBxBPR9JnnSCae/M7qjSaeHGu8GbbqboOu7uVMRDykpUtEtXxcSxaICklVpA/8Zro2Huat078uCAQIfF/QPepuJLl00eTJLUeMKf3FjU41ixafWz5s48uV+byElM8zuInnGs9NvF8abwzqjdc4yerNTLxD1CZeoe4CkUy5XkwRpYr1usklAICuAgLfa3RzZxsS7LtFSs9NPBsx9/KHB6+f13KxaOJF++4knzea+GOHm6Nulrd+OZh3gdznjvz28aePKabRGzXeuYn3SCmtYX0dgN4CAt/7dLl9Z3ecmHg2Id4abt+N+TzHqPHKinoiOnjsJE3Rj3ATT+1pvHEl3qHGS7Rp4luoZKPJHblqNRerZKMokwegR4DA9xRB2/c21V1afbcuteMT4ono5Q9betiVD+9nJt7MvlvD+9sYeejuW7mJZxpPRCuu0rfY+aXxRpy0ng3SxMdy1XImEsmUiykqJCHxXQB60YP2gcD3DuFMjfMRi3r4r2+viPPliGjHV/7NLZfIe9kluH2XLLuRWz+mJwc3zNdL9I0mXoRrvIhnjTea+Lse+ImH9vKeTXz9vi+3PI6tWaUvqidGiykqJFFFB0APAIEHTvHXvhsRU3rjEBrj+JmP/a1cYM/58lNbzU5x+8413oK506YdPHbyhdpeami8aOLJV40n87Z0HFsTLyu3BxKj1Vyskl0LiQdgogOB7xGCtu++qzuz7xazZ35x6gA38XwIDTfxUi19ee+YWfG8hZvn9p3BTLyY0pMQ1JPfGi9x1wM/IZXGuzXxthpve0Eks8liMX7HLig/ABMDCHzP0s3hvBnMxJvNkL1/mfxtoKJad7fN5zlOqu2IiJt48lXjzfreWGu8k5V4Cwl3ZvEjmU25GBWSmqTxidUpqmSj2AcfAtMmjYd56/SvCwIBAt8LTKzaOmpdfbc28STYd5bSMxO/Z99R52+AY1FeJ2Jh4snv9XgRZuKVWPh45Uq8UsiVBxOjqn3tkUy5Xq/XRxMtrWopMVqv17EPHoCJAQS+N+mecN6Ida/Zw7tPiQ+lUjslUj5vsQAv8sDKODk28TfMn8dNPBGtuGqBZOLJk8Y7N/EkaLzDcvr6fV+Wbm7fHgBgQgOBn5Bo/+lvxdvffPRBp9+RKba1dRK/fKdlR5z0beC5PfuoYd/HTh4nQ0ov5fNO4nqjxjNEE7/t7QM3zG/5quGXxktwE++qqN7PPfEAgF4BAj/x0P7T3xoPiho/4ew7T+mNM2S/vr0iNr1ZfvHFRDR07oB4jZPetOQ4nyei+x5/Vpw9Y4YvWb1yyxy7Y6HxzifQgAkK9sGD9oHAW1DLx7UmZoVFtXxcrkWyer02m4go1Z0Rgo93q+5u7Tvj+ml6cbs4hIbz33+1S3y4/9jxrXubFfWu8nmOw6D+9FG55dw5M6YYL/OxB46HzfEAAMCAwJtQSmtadPOaar1BcURZPVxKR7MV5SsEgIW6M/7mow8Cte9tYr36zu27lNJLJp7BTPzYyeMLLmq6eQ/5vDVSSk+qrwLta7yFiSdzjYeJBwBYA4FXUcvHk4VUsaVYWNH/o5TWQhzQYavu/tJ+OO/Kvku19NdPG1TadyKaP7M5AObYGX17j2jildjm80blnjRF8a/jCw89aTz48D23GQ+2qfEizjUeAAA4EHgFtSc2V2K5dQnpcCSzvljkml9Ka8lCLFetF1OKV2iG+/F8KR8Xrf8u8VxNeAK70mZBwJaQvweYYb01zvZ6ycRzfv7GXiLaP36cDCvxzv26lM9zjBpf3jO23LDAr8zz29d4CWnLnMOsHia+Z5imjYd56/SvCwIBAm+k9sTmCo0sUez0TSSaop8YNdsPXEpr0exIkSf7yZYMv5BtBP/VHGWjgpBXssnG04qpzk718r22zgni7Bk+ZY6hTOmJ6NwLp+wfP8buf+XpFx0uwHuGpfRfeOjJIDTe2sSTicbDxAMAzIDAG6nurFBsOOrx2bX8hgKlio3WIInRaq4lfI7lNunfCvSOoBtLzVNV/WmJ0WouVtjQmX5hQYTzruw741tL5QY4zL4T0fyZM/ePH9939NjCVhMvIhp65/XzknIvv2xIaeLN8NfHG/veOGlkCxMPAGBA4P2murNCqdVCvB9ZtUZUKjEaiCwZEXt7y6cqm5/oiY6g1upuhNn3b7zSUrz49e2VZRe0tI5fdHFT3VcsHCKirW/bT4w1y+c5ZtviGdYmntrTeOtqO4ZR47U5MwkAAAxA4I1Eh2NU2Vn19uTarh2W51ujgehwzOyUV+p/8SftPD3k2jrpemlCvGjin3xd3iK/99hxIlp47gBL6V/Yo66zc27flViY+CA03huVA4fFhzDxPcA07WyYt07/uiAQIPBGIqvWxNQzs0ppzW4ju2TKDbR+c6judL7Frk3ldkIQS+/e7LsZ3MQPzVKH88zEmxXc2dp3hrLazskTqW2N92bi48OLJI0HAAAIvILIqjUta+M6tfyGAimq61tJrE61qnjtic2iioviX9u1g2JrVunBfEsiX9pSEE7p2Gp8CF8CrGnHvhuRUvqXj8kqy0z8v/vYla5+aJvwlJ4s83wfNd6IsuBO1HiYeAAABF5FJFMupgpJca9aLR+PZivNCjlzEutysUKy8dRafm1rI5zmVvpSOpqtpNY3X1A8lSyQeIpjIeFdGM67te/fv2ultCGepfRiPr/sgsH9R49ZvIi4Eu85nxeVe/llQ0Q0aZr6H4tFFzy/snrllDlJ4+PDi8iQ1QMA+hkIvAmJUb15nU40S7mqszGZkUy5+dRodiTXUkafyq3ZzE4lC6licxCn9SkRpZB3XN094MTuK038gvPP5UduWbyQiMr7fSivkzAq96FTLZPueNMb3zXeiYknOx8PEz+hmaqNh3nr9K8LAgECb4E+/FrHTNxb5mUrnjq6qnEwkinX66MZNmlbn7bdwhLzU63U/+JPpJu339BHfLHv7I5k4o+MyR3gGRdPH6DGfDmRFYsu/srTL7r60Q4ZnDWD3WEpvUN88fFmo+JFjWcmnuDjAQBEBIEH1B21dc45Z8akG+fNNx5fvlA+2Gb9PAnWnKX05MnEkyeNd2jiydLHw8QD0M9A4PsdX9Tdw9Q4h0/56a/f3v5Oi12uvN/cDrfovHOJ6OF/rhJRZczKVbvN5zmScluYeN81XsLMxJOg8dzEE9EP5+774dx92kPf5jfnPwsA0ANA4IMnkinbLt6z9N66Pn/i4MG+S0+RUvpPDeju/PYrdAFjJp6NhxdZsUg/4ntKb2bineNW451smeNIPn73iOJNKjW+lG5j8oER59OTgSXoRQ/aBwLf13S/fZeOPL37bWo18Yy9J45Ra0q/dZ/NfDnnWFfbifPlbOfKB9oDh2l8fHiRUt0ZAfv4UKcnAwCsgcD3LwFVzrdv341sf+fApwbmc/vO+KfP3yVdJqX0zzfq7zzn80qsS+381XhXJp6IHr3vjh9MsnHjQWl8uNOTAQC2QOBBWwRn36WU3ozxM3Xx4bc/faPb9+MEJts8pSdzE++ENjXeAl/Fu5TW4vl8WtM0TUuXrCcaW05PBh5Aq1rQPhD4PiWgcN5H+/6lHz8vPtz+zgGWzzOUKf0tjY7xPJ9/3rCJzjOiNecm3vZKhmTiqb2s3trEO8Hx94BKdvNwtblt03yisfn0ZABAp4DA9yMdaWvDcOX4l17UHB/3mWsWxs5vltR9evFCIlIOiZdMvL/5vMihU6d4Si+ZeM8ar8SVifcXqV1yl0w0BgA4AQIPPBKmfSeip3bLBXdf3y6Xcz23Z9/XtlWk7e8+ltM/eOdKntIzE++qot6Jxodm4h0ijjCm3p1oDEBPAoHvOzpYW+dhwZ7xmWsWEtFLH+5nJp7ZdyL68lNbb5w3/+IBeSD6TYt0GV4+JO+jCw3rufIchxrvttouGPyZaAycMFU7G+at078uCAQIfH/hl7p7lmoRJ/ZdTOmtWXS+unMt+W3ipSM/fv0tdsdYauckqFcS6PB4Iqrf+5VAXx8A0A1A4IEPBG3feT7/0octtXWs1E7sXPv2B8deHNtPgon3neq6z7M7vNTupbGDZhf7WHDnxMQHrNz2E40BAN0DBL6PmBD23Yi0X+5Xd61hd8SiemNKT635fEDjZxgHj51kd9zul+N41ngjthrfzpcAJxONAQBdAgS+f/Grct5f+24sr7OGmfiVlzT9+t5j+rT4gPbEUwAmnrxunFOuxFtIuPpUpTkXWbHHXcDhRGPQPmhVC9oHAt8vGO27N0K279/73E1ffmqreOQbr1S+tVTugcP2y739gS7tLKWX8NfExxcpJtqRiYn3UeMdbplTCrnyYOtQZHE2smoQsu1EY+X0ZABAJ4DA9wXBhfPh2PcbztPD9tsa9fMizMSLg2f2HjvGtr8HZ+I5zMQfPHbSwsQ7x4OPNyunr9/7FenW/tsDAEwgIPAgbNqx75zjR+SNPcqmNxI+mviH7r7VaOKZxrdj4smBxnew7w0AYAIBge99Jrp953D7/o1XWlrcsG3xUn+bzFb9YWgm3hYfNV4ijD3xvTXRuPuZqn0U5q3Tvy4IBAh8jxNcV1oPfetcPet7n7tJfMhTeiWsq92CgQH2cOGsga37fRsXa4Fo4lktvS9BPdlpfHf0vQEAdDUQeOAIb7V1bdp3s3z+qdf2vXTogNiannHj/JYj3MSL+JvSk6ra7qWxg2b75Vy1vgl0eDwAoOeBwPcyPWPfieg7ty1XDpBl+fwze/aW391HgokXCbPUzvZKvzQeJh4AYA0EvmfxUd2Dtu+eMZp4Ilo4S9d4s5S+syZeiV8aD3oG9KIH7QOBBzb4MjXO+lmu8nnOS4cOGA+apfRBm/j4ovkOS+3I8RwajvOsHiYeAMCBwPcmHZz4zmjHvivzeXbnm9fGnnpNHifzzJ697A5L6W920IU+uM61PKW3NvFu59CYaTxMPADADAh8DxJoOB+OfXfId25bLnW1Y/1tOFv37w/axLOUnlQm3rqi3i+Nl47AxAMAGBB44AKH6t6+fbfN5xnfeKUibX//+vbK/ctinTXxwaHU+OnnTwv/nYCgwT540D4Q+F6j47V1Rtq07zyfJ6IvP7X1hrnyhPj4hbqc37JogfIVwlyJp9Za+shfPmJxvYeB8UqN3z72jvgQJh4AQBD4HiPQpfdus++3XTlERJWD6lL5ME08T+mJ6A8/dpl01lqzfdH4FVctgMYDACQg8EBNCPbdCaJ953AT/3RNLrijRlc7jrRZLoQ98RIv1Pb6/ppOfDwAoM+BwPcOE86+t1NeJxG/cEjM5+9fJrfEUXa1o2BW4v/wY5dJHW98N/Fk0PgVVy2gVo2HiZ/QTKN6mLdO/7ogECDwPYK/6t4R+26bz5udMqb0zMSbpfRBmHgxpSeiudNaCt9sTXxAGg8A6Gcg8EDG89Y43+27Mp9nvPpHd0r5vLRfToSl9GGa+OWRIVcmXokHjWdwjYeJB6CfgcBPSLT/67vSTbog5LY2SjzYd89I29+pkdJ3lYl3G9Q7RNR4ZuKJaPvYO49f9MbjF72h/fD/5Tdvrw8AmKBA4J1SSmtq4vkaERHV8vHWAwZq+bimpUtmP4G/gOWLEBnlnIj+4YOmnfU3nA/TvjusnzciTYgnQ6kdI2QT7/Yp3oJ6Uvn4t0Y+MF6m0nj2qZM/laW05UdQuM7889x8JZtravm43SV9yFStHuat078uCAQIvBtiuWrdQDkTISIqbcxW2Hn9gDtq+Xi08QL1ai5WyUaVf/SU6s4QNd4vPNfAt2/fpXxe0v6vPluOzZMnzShTembijYNnQiinr4/LfzdtBbt9jV9x1QKlujNMfHwh6UVhS+lkwf2zDC8SzSq+iwEA2gYC7ycjSzxIOxER1Z7YXKFUUf9uEMmUq7kYFbZIf3Qt1J3xDx/s60htXQiD40Qk7f90ZIhUJn7S5PDeEkNK6Ynohvnz+P0g9suJMI1/ZMpvrC9TaHwsFitscGDY/aaU1jQfviMAAJRA4P2glo9ryQJRIcnSThZLNkP9Fne0q5nEN0PQSKZcr48mmldFlowQ7dgl/M21VXffCci++5jPOzTxIh1P6YMz8eRA3dWMrN+Uo+xGcxMvLFDpH1rxM29YpZI/9MJnvnm4lNaShViuWi+mvLxnAIAdEHg/iGTK9WKKKFWsc5kuJDcMN/L2QrIp5YXs5jUs6K/mKBs1W+gsbSl4SwQ8fw/olH33ls+7+hFhltpJbHv7gNHEB6rxTjCa+Ehmfcospy+ltWQhVWQrUsURtngkfubLmYi+yESNRaxiSvjQ8898y7+FxKjX9ay+YGq4N9CTQODdUMlGTWvsZGK5TXrevmpNjCo7q/JximQ25WIVpW8qpZMFiuXWJYynwqJr7bsyn2cYU/oXDjTrEvgyfKAmXkrpPZTaTmoDugAAIABJREFUdYjEupwyp6/lNxRiuWojXkqMFlOkuI7VoDQ+25QYFcTb7N8CACBQIPBuUBXZmVmQpvuOLBlRHlfl8ES6YWouyIdCN9t3Ccm+f/NaOZMXU/qbh3R9hYm3JZLZlKPsWkm6a09srrRGSdFhhUjXdu0wT5zM/i0AAAIFAh8yseGo8Cg6LKsTX5gcDc+9+zX03faJPvamZRi3v1ubeE74Jl7UeEb3aXwksz6ljpRarjIT6dbPNmiPczQtzFunf10QCBD4kGm1PtWdohyxuqVYrtqOd6//l//g+blu8cu+W+fztum9tYnn3LxoyLhZjjoxfqbj1P/t/64+kRgtpuzq6Wu7dqhPIHsHoLuAwIeNmMjXdu2g2JpVEaLGRvhU0TTzD0i5u82+W+fzk6e4/sTylF4izHL6bW8fIMHE8/1yvpt4U+V2TGJdjrJrN3ANj6xaE2tdRqrurBjNunq1CQDQSSDwYVPhq5yldDRbSa3PRIioll+brQilTGpsNb79LwHO1T2I1XdXGPN5hkVKH9pKvFmpnTGot8V3jbe5IJJZn6pUKq2Ps9FGgX0pnSyQ/qklaop6Yl0u1vxs23VtBACEAATeDaoqemctPZukcms2s1dJFlJFfam99sTmiuLlFa9sIeEe1N3HBjWefX+b+TzDYUpPqpZ2RoIw8RLOTbwSzxrvxOInRlt3pidG68VUIck+k8kdzQKRxOoUVbJR9jmNZMrVHDU+wtHsSDHMOpLeYypNCvPW6V8XBIJWr6ML8cTDuNndm3eXBL4d++42n1cuwBvzefGslM9LDl407kzvxXb0z+3bt2JwiIie27Nv+cXN9jj55c0XEXW9fU9/72M/l46MnzzL7rw0dpCIVkSaA+xtJ80YFd32KcbN7u0H+CA0Pnz7mjB/3HkLf21zBVtEZPdT5t/ebC+r5ePRnevx7S8UpnT6DQAv+LIe30H77rC8zgKzfJ7xjVcq37w29q2lMePImZsXDZ01tIg38pWnX2xT4x+6+1ajxou8UNsrarw1D965UtL4+x5/1lrjIefAN0ppbcNwtV6P6A+SGqnE2/4yNnoAvQtDAslMn9JObZ3bbwa+lNfZYkzpzRBTerHULuhy+kfuvZ3d8bBljjqxMx4AItKbHenVwMTqLYyTMuwvw+iB0IHAg3bxbN9tEf391r1jrn6KVGp389CQbamdhO8r8aKhZxovjp+BWgORLlqDrz2xuSIIt2J0hoPLMHqgE0Dg+5Euse+u8vmbFzdV2Syfd15qJxKciTcOl+Mm3hsw8aADVHdWaGRJpDlxSF1XbH0ZRg90Agg88F4A7+G5Zva9zXzeIdzEi7V1FoRg4kUQ1IMupLZrB1EhqW1Zrbfnrq7ZHDXugXR4GQgTCHzf0U5tXZhz39vJ5xkspVea+K9tk4vvOEGbeJEb5s8Lekg8AH4gNumIZNanVGvwzi8DIQGB73cCte9h5vMMi1I71tLOVWv6ILBO6WHiAeMcmhzmze7ttA4Sig4rq+wcXwZCAgLfX4RpwY14y+e92XcRpYnvhpSeVCYeGg86Sy0fF5ttmUwXktsVO7wMhAkEvq9xZd8D2h3nCmv7bsTYtpYjpfQTqNQOgECJZMot47ATq+WoXa+na32aw8tAiEDg+wh/7Xs75XW2+bznBjjkJqXvlImXgIkH3U1iXS4mDBms5TcUYrl1hjY3Di8D4YFOdv1Cm1PjgrPvtvm8uADvAYuudhKZrWUu+d/+9I3B6fq9j/38kXtv/8JDT/Ijyop622a0HtrbgYnCpIW/7PRbaBLJlOtL0lpjbLww0rqWjzfnDphfBjoDHDzwQqD23Qzn+bxtVztm4r+2rRKOibeupfcX+HgQCInRltxex9DzRn2Z9DpoRB8SEPi+IGT77pxwtr8zWKmd2Xh4Rmida41t6k8fPSMd8RbUAwAAAwLfj7SzNc7J09ssr+MWv/36eYZFqR2FtRLvpNTOm1pjMR4AoAQC3/u06b/bt+/t5PN8Ad5t/bzDlN6aoMfPSIir8gyHUg2NBwAYgcD3Hd1j38PM58262hlb2lk0vfG37I6V2kkHIdUAAL+AwPc4XWXfneBjPm9m4vkyvJP9cj6aeCeldkYT7xB8MwAASEDg+4vO2vfQ8nkL3M6Xk/B975yPJl564vdnlbUnN4i3dt4nAGDCAYHvZbrZvoeZzzPMSu1YSh+OiZcw1tIT0RceerL92vjvz1J8R1FpvNiWVNOs54GaUcvHNYwNA6DrgMD3LG1ujTMSpn33q37eeUovEZCJV6b0TjrXujLxSnVnqH18qlgXKaYq2SgEG4CJDgQeqOmIfVd+CfAxn2eYpfQdMfFmeA7qLdSdYZ/VJ9blMAUMgIkPBL43Cd+++wiz7212qFWiTOlfOLDPYjy8Eh9X4s1Sem+v5tNCe2TJCNGOXTU5fS+l9fS+lNbi+Xxa07Tm2V3NsN9dwg8ACAYIfF/gVp492Hd/y+s47dt3ZUr/raUx65Z2ZEjp/TLxzlP64Arj7b4H1Hbt0Ed7RzKb+AARNjxkk95/tJLdPFyt13nb0UJ285pqvV6v16s5ykah8QB0HAh8D+J7Z1kP9t2vfN53fOlq5y8WJr4jm99q+bXZCqVWM+GOZNanKtmNJSptzBKXdyKKrVklthpvSn8ksykXq2Q3IuIHoLNA4Hufjtt3VwSRz3vuatdtpXZBUUiKJfTRbCVVFOaBJEaLqUJSSxZS68XpIdKgb/FhM+IHAHQOCHyv0YX2vYP5vAVSqR1fhu+eUrvwTDyvoq/mYkSUKkrTvhKrU0Tc0yuJDUeFR9Fh+24DAICAgcD3FO3X1gVt38Pf/m6Ep/TiMnzIJl6CpfRmJt73kXH129erT0Qy5WKKCsnWFfRafkMhlUoVkhZb5yo7q8Kj6k53VYsAgACAwAMr/F19t4C5fL+2vxsxK7VTXhyCiW9zQryZiTdVbuckRospqmTXcomv5ddmKbdudB2vtlMiJvK1XTvkJXoAQOhA4HuHjth3Wzzk82wBPtB8XonbzXIUQOdaCb5fznlQb6vxthe0SnxpY7aSWp+JNKvt1DS/E5TSUf0pAIBOAoHvWdrfue7kFSZcPs9oJ6UPaCXeOqV3hYWEO7P4iXW5GFPsUjpZiOXWJfhh05w+lVuzOappmqYlCy01egCADgGB7xHaN9++2Hdv5XVB5/MMv1J6Cc8m3mFK78HEk4mQqw5GMuW6UY0jmXK9Xi9nIonRer3MvTi/ODEqPokdzmTKjV63UHcAugEIfG8yce17aPm8ckP8dVseV17cJSbercZLN7/eJwBgQgCB7wW60753IUoT/8vVdyov7kITDwAAzoHAT0i04l+Kt4cGXhbPdol978j4uOAIx8Qz/DLxAIB+BgLvlFJaMTZbHKzBB2t77cLdMpnb4kW04l8aD0oa74oQ7LtFPm/8HhBa/bwypf/69ubBEEy8hLJtLbWaeN+3xQMAehIIvBtiuWrdgF6CVNqYrbDzZS/7g2r5eLTxAvVqLlbJRjVVubJS3RlM47vEvruC2/cgJshJKFP6FYPNn/vcPnUtPQVj4s1Selfl9DDxAAAjEHg/kbpzu6D2xOYKpYr6d4NIplxVTeS2UHeGBx8fkH3vzva0Zty/rEX1O2vinYCgHgBgCwTeD2r5uJYs6DM70iUqpTUtXWqG+qIVF6P+5nHDZiXjuA5bdfeFIOa+u8rnQ4an9KKJd04QK/FOUnoAALAFAu8HkUy5XkzpMzsa47GTG4YbeTvv7V1Ka0kq8rkecs9vgdKWgrdEwNX3AG/2PaDyuhDyeYbtcLnn9u0TTbyIRWt68mrivaX0MPEAAGsg8G6oZKOmNXYyfDx2ZNWamD6Ko7Sl2RVMPG6glE4WqHlpWHiz757L64x0JJ9nJv7+ZTEzE2+d0oc5Yk4y8dB4AIAFEHg3qIrszErqmu47smREv5cY5ZeX0lo0qzaJpbSWLDQX5AMjIPs+IbA18RZYm3hf4Cl9J4fEAwAmOBD4UGnuhEtSkc3eliiltWQhlquG3+3TL/vuPJ//8lNbu3b7u5TSB11q53y4HEw8AMAhEPgQqeXXZiupomm7blarF8tV2/Hu9eSfObksHPvuPJ+/efFQR/J5hm1KLxFC0xvnJh7b4gEASiDwIVLdWaHU6oT4sAnbCJ8qmmb+DpXbG0Gsvnc5TlL67jTxtsDEAwAIAh8q0WFhb3spnSwQ3wlXy6/NVmK5qnUwb6vxgdp3J0zc9rRduF/ODON+OQT1AAAjEHg3qKroXbSmjWQ25WKFJHvWhuFqvZhiQ7dZnxvjyyte2ULCPVt8h/bd33xe+irQkXze2sRbtLSjYPbLSaDUDgDQDhB4pyRGjV1qxTp6cUJ267Rs4WGkOTK7nImw1yxnIuJx+wp9pZA7V3e/7LuP+XxoO+BtkVrakZuUvn0T77nUjmDiAQAGIPATknryz6Sb55fy0b5P3Hye4TalD8HEc5yYeFTbAQBEIPD9RWj2vfvzeYaTlD5MEy9h1raWHHSu/f6ssvbkBvHm73sDAHQ5EPi+phvse/fk8wyW0nfKxFuk9K5M/PdnKd6YQeNNZyCzOQltT0AGAHQSCHwf0SX2XaKD9t2IckK8RPeYeDOU6s5o1XihsKSai7V0ahxNtDsBGQDQYSDw/UsQg+MmIm5TeokwTbyEMqW3UHeGq6ze+wRkAECngcD3C57tu+/5/O899CNv7yQcvvFKxTal79T4GduU3reFdsUE5Hg+nxanHDf7LksTkQEA3QEEvk/xbN/9zef/+d67vL0Nf3Hb1c4V7ZfTt1Nqp8T+e4BiAnIlu5kNQB5N6I0XqRHpF1MWk48BAB0CAt8XBGrfexhm4q073pAqpW/TxLdZahcQsTWrGnk9W57f1FiaT4xinR6A7gMC34/4a997KZ9n8JTeAuet6cnvPfES3ky8B5rr8bVdO7A8D0DXA4HvfcK07xMxn2e0mdL7buIlxJS+OzrXxoajnX4LAABLIPB9R3Cr7z2J51K7NnE1XM6tia/fvt7l2zFS2Vlt+zUAAEECge9xgrbvrvL5rz5rs4Ore5A2xPNl+DD3y1lgZuL9UG4HRJaM8EGIAIBuBQLfXwRq323z+ed2N6vVli+Y7+2dBIeU0vNl+C7ZL+ew442txvvxJSCxLhdjgxCJSN8xh51yAHQZEHjgHVf2XcLVan1HcNLVzhVuTbznlN5Cwv2y+JFMuZqjxnzjaHakKM5PBAB0AxD4XqaryusmRD5vUWrnLaXvVKmdUsit1D2SKcs73SwmIDeeIXa2BQB0GRD4PqKz5XVdns8raT+llwjNxBNR/fb10s3VjwYATHQg8D1LN5fXdX8+zzBL6Tto4kW6Y78cAKBLgcD3C50trxPpcvtultKvGBwya2kXZtMb61K70JreAAC6Hwh8b4LetH5h29LOiL8m3jqlh4kHAJgBge8LfLfvbvN5cQF+YuEwpe+ezrUAAMCAwPcgnu27N9zm892/AG+R0nt+TR9X4pHSAwCcAIHvfZzb9yDK63qArQea8YO4DO+5qx25NPFI6QEAHoDA9xo+2ndfyusmxPZ3I6KJv2VR07i3s18OJh4AECYQ+B7Hd/vuAb4A3+X18/4CEw8A6CwQ+J4iBPveZj7f/QvwShym9GGaeGtg4gEAEPheJmj73qv5PMNDSi/hr4mXkFJ6mHgAgAQEvncIefXdIT2Tz4smXiQ0E++qbS3BxAPQ90Dge5Yg7Hu/5fNm++U+Ouv0FWxNvI/AxAMARCDwPUL49r2383kjYkov0ammNw4nxAMA+hMIfG/iuXVdQEz0fJ4hpvTfWuq6hS0HKT0AIAQg8L1AO/Y9oHye2feJ26GWYzEh3iGBltpJIKUHAHAg8D1IO/bdr3xehNn3CbcAb83XtrXE8p3aL2eb0sPEA9C3QOAnJFrxL8XbQwMve3ud0MrreoNbFg15S+l9NPG2KT038Y9eUmE37Zn/xm4OfwQAoDeAwE88tOJfGg9yje8G+94z+TzDYUrfVSb+0UsUffKh8QD0FRB4p5TSmpp4vkZERLV8vPVAez/L5FWU6s5w6+PDGf3ee/k8N/Ff21bpThOvVHeGUePNPtjpkv6ZTpeUr1RKm54yUMvHHV8LAPALCLwbYrlq3UA5EyEiKm3MVth5/YBnSulkQX3GQt0ZnrN6srTvyOe5ibfYLGekI51rbW264gLVB3s04dc7KqWjWdNvHACAwIDA+8nIkvaknchC3m3VPRz6bfu7KywGyDrBczk9T+m7LoQvpTXN7PsqACBYIPB+UMvHtWSBqJBk0SZLL5vZp5hOiomoMbWs5TcUYrlcyvt7cfg9IOh8ni3A98b2dyM+pvTeTLzbDfES7r4H7GosPqkXjsw/7aW0lizEctV6sY0PNADAKxB4P4hkyvViiihVbEabheSG4Wq9Xq9Xc7FCUv/DWEprSSqyCFQ8rlPLr81SblNmSei/QaD5fG8swDtM6dvpake+7on3i0J285qq/pGlbFRZHKL8tFNi1IcVKwCAVyDwbqhko6Y1djKx3Cb2ly2yak2MKjurRFTaUojl1ulrm8JxRkPeg/976Nm+I59X0hETLxFc21r+SaZIZlMuVsluNJbLqT7tAIAOA4F3g6oWycygNNfjI0tG9HuJUX55Ka21Vh6FJ+8GfLfvvZ3PM8yGyxkJyMS3mdK3YPjmKi4eiZUlkSUjRDt2Gb7Tqj7tAIAOA4EPFb6VTktSsZqLCSf8kvd68s+sLwjOvvv4rO5EuSFeamlHdqV23WDi67f8t5bHhm+uQgl9bDgqXBkdbrdzLwAgLCDwIVLLr81WUkXFLqTaE5srgo1KFnRTJRopW+X2ho+j3/snn2fL8M672oVm4mXl9oHWvL26ExveAJgoQOBDpLqzQqnVCfGhTiRTFv1TMaWbKmkvsq3G+/sloJ3yul7N5711tZMIzsSTA413+yVATORru3ZQbM0qlM0BMBGAwIdIdDhGhS26Kdf3uyvWMy2xkHAn6h5CeV3PdKh1Dkvp/TXx3mApvYWEe7D4lexavZC0lI5mK6n1KIsHYGIAgXeDqoreRWvaSGZTLlZIsmdtGK7Wiynhr6djlELuzbv7mM+LcPveSwvwEsaU3l/aLLVTCrm3AD+VW7M5qi8dpYo+drgDAASLVq/XO/0eQEgY7bvz+nlXDr7nBf4br1SI6Jk9+4ho+eAQEd2/LEaGZF7y9FIyr/T0oq47DO2l2jo/q+sBABMZOPj+xbl9x/Z3W4JI6b01vQluQzwAYGIBge8XwpkdJ9r3nsc2pQ+n1A6WHQCgBALfp1jbd1/Gx/VqPk+WtfTdYOIBAIAg8H1CO/Yd+bwtzMQbO97YEtB+OaT0AACCwPcnAdn3vsrnmYm3HQ9vTOmDMPFI6QEARiDwwArPMXsP5/O2OJ89YwZMPACgfSDwvU+g5XXI5xn+ltpJwMQDADwAge87giuv6598nmEstePL8O2X2vnVuRYA0LdA4HucQMvrRPqwQy2DL8N77mrnl4mXQEoPQJ8Dge8v/O1Na5bP988CvOfZM0GYeKT0AAARCHwv49a+I59vH4cpvURAJh4A0M9A4PsIV/bd1fb3vs3nGU5S+nBMvARSegD6GQg88AHRvvdPPs/w3NXOA7YmHik9AIADge9ZwszngYhZSztv++Vg4gEA3oDA9wvB5fOAzFN6f1vTOwEmHgDAgMD3JmHa97PjH3l+bm/gsJbeFpTaAQB8BALfF/hr3wN6bs/QJaV241c+xm5f+F9fYjcPLwIAmLhA4HuQoEe/I583Ipl4cRk+/P1yD9196/iVjxmPQ+MB6Csg8L2PrX1HPu8LtpPlGCGYeAshl0+V0poWz9f0++mS8x9iQi0f1xr48HIAAO9A4HuNNu078nlfcFVqJ9Gmibe16SYXlNLJgu3PtaGU1tbSpjqjmCokofEAdBAIfI/jb29aQj5vjkVKb4tfJr6jIXwtv6EQW7Mqoj9MrMvFqLAFCg9Ap4DA9zvt5PMPrPR/l9eExnNK74F2yunl7wHVfFxLFogKSTGw51F7PJ8XkvyWU4JFrz2xuSLoO1EkU67XRxOe3yUAoD0g8D1FyPk8euNYEERKH9QM2WimXC+miFLFer2ciRCV0lqykCrqWftINttM70tpLUn6mWouVkg2hL+6s0IjSyJN9W9+IwAAdAIIfC/je3mdRT6PBXhyk9J7K7WTCGpPfC2/oUCpYsN8J0aLKX6utKUQy63Tz0RWrYlRZWeViKi2awdRIaltWa2vwVfXbI5iDR6ADgKB7x1Ctu/I55WIKf1EMvEi1Z0VSq0WsvXEaq7wiVHm8YmolNai2dbvKbFclWfykcz6FNbgAeggEPieBeV1HaGdrnahmfhHPvE9i7O1XTuszvJ9cEkqVnOtv+3IkojwKDqMKjsAOggEvkfwYN/bLK/DAnyb2JbaeTDx1srtkMiSEdNztfzabKWxON9SQGfyrNhwtP13BADwAgS+N3Fr39tcQccCvBnXb3lcfGib0rdv4m013v5LgMF5Nz29lN5Xdza/pCRWy4m8XnZn89MAAAEBgQeOQD7vnF+svtPhlZ73y1mvxFtIuLW679hVI2qsnicbBXKldHOpvUX79d44+rMosS4XK2zgpfO1/AahHg8AEDoQ+F4A+Xw3841XWlTc9652pFqJVwq5pbonVqeoko2yzW2JUdaIrrHWXkzp6+uRzKZcrHFiw3C1XkxRJbs2r38xKNfX74zqS/TRzWuqjXo8AEAHmNLpNwD8B/l8x1k+OMRL6J/Zs4+udfHc/PK4E13/9qdvtK6wc7Qenxit14W7o61n+MNSmh+PZMr1jPAKrc+SXwUA0Dng4Cc8Qc+OI+Tz7rl/mVOb7rnULkBq+XjLpJhSOomwHYCJBwR+QqI9/5/57e9mP/l3s5/kp5zYd7SnDRm3KX2Hm95EMuVmQM8y+jrCdgAmHBB4p4j9t1toNOTk+4M9d+gUJm1avYb2/H82HhQ13hUeAnYswDth+WCz480ze/ZZXBnEfrl2SYzWBdBQHoCJCATeDbFctW5AtzaljdkKO+/N69Ty8Wh2pNn9O6rWeKW6M/5u9pNB2HfrfB4L8GZYp/TdbuIBABMfCLyftLHnl30/aCxzJtblYpXsRrkHmIW6O7xAAu1pQ0NK6f0ijM61AICJCQTeD2rCtE0tXaJSWtPSpWaoL07cUI/apMSonfV3K94BgXzeOWJKb41fpXYw8QAADgTeDyLCtM3GemUhuWG4Ko/UNB212Uotv1b0866w/h6AfL5TPLNnX0CldjDxAAAlEHg3VLJRTcasHC6W28QMuTBS03TUZpNSWtO0aLbCnx4cyOeD5v5lMZh4AECngMC7QVVkZ5arN9fjm1M4rEdtNi7hs7Q9l+P7BLa/Bw1MPAAgOCDwoWI5alMkklmfosrmJ3xV+Pa3v2MBvh2MKb1bYOIBAM6BwIeI+ahNBdFhRYDvgPpNf+7ksvaXz7EA74T2U3qYeACANyDwIWIxalNuDmq42LFym+FveR3wC1ezZwgmHgDgGAh8iFiM2mQDOu1GbdpqfED2Hfm8X9im9DDxAAC/gMC7QVVF76I1reWozcRovTjCXz+6c726fM9Cwtu0+K5APu8c25TerYl3CEw8AH2OVufTIsHEwbjZ3VrdJfNtK89SPm908BB4V3xtW4VPj71l0RARffPaFlEXjbtS76Vk3sLTi7oOTw9AP4N58BOSdsw6tr93BHFCvDVf314xarzDIfESX3n6RWg8AH0LInrgCCzA+4VyspyPpXZQdAAAAwLf+/heP4983i3GyXLBldpxZg5+d+bgd9f98xf4zdXTAQATHQh8f4F8voP4W2pnndjPHPyu8aBS48XpR4riUeMGTgDABAECD0DYKFN6iXZMvFLdGWofr+rBbDPcEADQ9UDgexxf8nkswLePk5Tel/1ytlE8snoA+gQIfB/hSz6PBfh2cN621gnGlD4o8d7FhygIbR9q+bgWz5dUpwAAXQAEvpeB8+5aWEofQqmdErffAwrZzWtYiF/NUTYqCHklm8yOsPEKxVQlG8VqPQDdAwS+X3DivJHPBwpL6cMstXOKqkWjaMdjuU36gnwksykXq2Q3lpqnqvrcpMRoNRcT2i0DADoMBB6oQT7fDQRn4ltQFdmJNXYjS5r3I0tG+AgFxSm/ZxwDADwDge9ZMD6uyzFL6Ttj4q2IDUeFR9HhmNkpAEA3AYHvC7D9vUtwktI7oU0Tv/H3HnFzeWVnVXgkjjkGAHQxEHhgBRbgQ8BDqZ0EN/EuldspzUSeqLZrB8XWrNKD+ZZEvrSlIJwCAHQYCHxvEkQ+jwV4H2m/1M7MxNtqvIcvAXyoMZXS0Wwltb65Pi+eShZIPAUA6CwQ+N4H+XzX4qSlHTkz8SIWEq4+paqiF+voU7k1m9kVyUKqWNfL5u1OAQA6CwQemIJ8PgiMLe0ogFI7pZArDyZGjV1qhTr6SKZcr49mMuXGUVnCl5ifAgB0FMyD70GQz08InE+IN8N6SHxA6/EAgIkCHHyPg3y+y/ExpQ9+vxwAYCIBge81kKt3Pz6m9P43vXEOS+8RywPQrUDgexnP7WkJXxRCwdWGeJh4AIArIPCgiVk+jwX4QOEp/cQz8QCALgYC31PAdk8UeErv7wBZAADgQOB7FuTzvQdSegCAcyDwQAf5fDeAlB4A4BcQ+N4B4+MmKMsHhxxuliOYeACAYyDwvQm2v3c/ys1ySmDiAQAegMD3CD6ummMBvrNYD5dzCEw8AAAC34O0U17n4aVA+/iS0sPEAwBEIPAA+XzHMEvp2589AwAAEPheAKF6D4CudgAAf4HA9xpt5vP4rtBBrFN6lNoBAFwBge93LPJ5LMCHgPOU3gMw8QD0MxD4CQ88d8/gS0oPEw8AYEzp9BsAXlj3z1/g9wfmExEdG0sT8vle51tLY05W3xnL5v8XInqzicW3AAAXjElEQVSs2jxyd/TnQbwrAEB3AgevoJTWtHi+Zn1RLR/X0qVw3lArorpzBuaPengp5PPdhrgMb5vSW5h4pu4Sj1VvNRyr5eOaZvgkO/on4Pgyww/szD8cAPoNCLw3Sulo1oc1Uvco1Z3hTeNBxxGX4W1Teieldiohtz5VSHrQ3FI6WXD/nA79wwGgD4HAu+f/b+9uXqTX8gKOn8jVnWsdGAUh1SNtq4vRxaTwD0gapFcNs2rcJApiFRea2TS46c2lZUhAkMquQRB61QhP8gdI5S7UhdLTaCcgjBf8A2anMHGRlzqpvNdLVyr5flZPV1Inqcp58qvzOyfn+Jai9L+zHUJDdO+4A/PPn5cdGvEN0b12B03T3MeeLfH+4f10/3GAaSLA9+RbiuFqdhh7Znljku5MSU0i36p6OXLmimL5m41zJ5KL2GpTtQbvvrby83TAD8T//W/LDg2N+NboXu3q4dkWy6fKRrxvKXPHsUqV99HVbLv0n6C6qouW/zgAjoEA35O+iuN4vVDLWyJnPlsKO4zjOI5jz3SNpHfStxRDeMmroa1lL6dc4/EyTDcFy5kye3/Y7LlLV2XD74DuzXc64D9Z94VnDqL8O0BdPJi1FS5YviSVdKUnL0TO3VLYz4uL4n5NVb3+Pw6AIyHAH4r/tAw0+zm7g+mr9Hbmv7qafZ/eGNXrW00E79LA5vwt6vWtJkS+q3p9qwn39WiDkZie9lzsPNSuJ/3ers3Ta7fXUmTOwvt2sG6p6gA+GQH+QKKPNyGuLsoNFH2Vt1t8SymNMNp6S1UJn4H8/KDsk6Xfmbp4tsXyrirEy9WyLry3VnUAn4wAf0Da5azi1U2vuiG80D7VmiHk5weuIUv/WY14dfFgBjVd8an68C4GU9UBJAjwB1SVkIycu2Vgpv2SWRfmMT39/t+37kN+fvi++WFLfNynEV87442+8sym8fTRl5dABMtZFsddkfxl+Seo6gCaEeAPRL24EuLto3RnDN8DYd7o8p876hK5MXH7z1Wn39tieff4Vr1VXaxjiWcKodlhEs8PV9UBHAQB/lD0e1sLNj2Y6TNwYnYpjZVLHx2u+B3QTWuMr9uhOT9PB/wAff1tIT52z9K3xviWHdTFgxkEOwTng1Z1APsjwNfYpCGlh9QbqYt1aIvsbbPllRevdKEunm3NNZIXHy/D2DNFUD2SqZOGGN+xid+cn6cD/oS2uuF3ztI3hPAuTXx9tdOz6oeu6gD2pMRxfOpzQG/lh92bo7vcgi8HeLkFT4A/Lbnh/tMfaXLDvTLebw2vk0N++WF3FpsBJoUAP35b+fnmCewI8Ke1lZn/1V8rbG2N8cd4fA7AmSJFPy3k5weub5YeAOoQ4IGz8VkPxAMYAwL8yLF83Fn7+tvgqA/EAxgxAvyENA+vw0DsufAMjXgACQL8mPVqvtMBfxYqs/Q04gGUEeCngulpzxdD7QDsgAA/XeTnz8LWg3NdkKUHIAjwI0Z+/nw1d8OTpQfQBQF+EsjPn7u+WXoa8QAI8MDQlbP0NOIBtCLAj1Nrfp4O+IErZ+kZagegFwL8+LXm5+mAHyWy9MDEEeCBM0CWHkBfBPgRYnracdg/S08jHpgyAvzIVebn6YAfBxrxABoQ4KeODvhzkWTpGWoHoKOvTn0CODDy82Py0x9pO8xkl/iz3/xzIcTPfr555fd++98PclYAzgIt+DEjPz96dVn6JLpv+dnP/6D8om8pytyJuhxss6tvKYrl9zxXAJ+LAD8qfZvv5OfPUWuWvjKQt24CMDIE+NFietrx6ZKubw3hxHhgIgjwwKA1Lzwjiln6gwfvyJkrqbnzUd7uW8rccSxFUcjZA0NDgB+PLvl5OuDHYc+x9B1/B/iWMlteeXEcx3HsXS2XbtVewfLlMozjOF7p+5wTgEMjwI9Tl/w8HfDnqDJLXznUbl+R8+gK08vCtr7yzOodtdtr9fCHB7AvAjwwdJVZ+qM/EB++B8K8kVrl+k11hL+6IL4DQ0SAHwny8zis6OPt1KcAYC8E+BEiPz9u+2fpu8x4o15c9TgnAMNDgAfOQMcs/SHnqptdasJ9lUbG06YHzgsBfgyYnha51hjf9UeAuni2NdfIZrnzrdmStemAc0KAH5u6/Dwd8KOXZ+kbQnj1pmA5UwqSqK4u1qEt0m3Gm23XDKMHMEhKHMenPgfsS27BdwnwdMCfo62u9zxpL/e+y0n78sPuLDYDTAqryZ098vMT0WVluZ/8a5DHeMI5MHEE+LP0t//xp/m/f/A9IYT4z//5iSA/P0nf/FA7ykQ3AM4cffBd+ZZSLRuElM/a3XHtzfYj1MztLUf33A++903Hw5CfH4ed14kHMBEE+D40O4xL1gtVCCH8p2WQbE9f6Cv6eNs6QNXc3pXRPdE9xuNMtS48I440bS2AM0SAP6S95uwM34O2Ahqie8cdMEpHn7YWwBkiwB9C5MwVwxXCNZLEum8piuVvUu5yrl1OxFtbs4gUZv7etnPwpgN+rMjSA2hAgD8EdbGOPVMI09sk1l3jMVlEM5RmC/EtxRDp8pvy60KI8D3Q3h5LPfu9Nf8OoAP+3JGlB9ARAb6P0nwgDZFYs5+Tvnj1+lYTwXsohPBfXc2+T1vp0uvJJhGI27wL/uF9tnOMxwSRpQewhQDfR9Ugu7ohdZvu9M2qHfoq3923lMLMn/qqWJZ+YwbLp+ph9EAbGvEACPCfKn+UTjGEF9pNba7SUh+7oQN+3ORueBrxAGQE+E8UOXfLwPTqn4Hbpl3Oeh/kL3/3H+s20QE/Dl264QGAAP+JwvdAHigfvm8aX5Ez35rYpvTUXEPkBsrI0gMTR4D/RIWsu28ZrhDi7SMSQgh18WAWMvKF8XiZ1hi/tQP5+SkgSw+gEgG+j6pR9D2eaEsX2E7e9XgZxp4pguVd8nZ9FXvCyAt9vakevtcQ45vDP/n5MemYpacRD0wZy8WepfLD7pXRnSViR0xuuG/F+7oFZAFMCqvJnSX64yH7+tuAkXcAtpCiHy064MeNLD2AZrTgJ4H8/KQkK8Q//MZfJH/+4rv09V///r+d7JwAfDpa8MAI5dFd9ovv/rD+HfIqSJ1HjpYf79xT5MwPWh4wZQT4cSI/PzXymLuGQF65KXLmymYVpDiOQ1ssZycItL5VmL8ZwF4I8ONHfn6sKrvhG5vpVTtkEyxKcyuqi3Voa67xmSHetxTFcD/veMD4EeCB8WiN7mX+0zIoz6kk1OtbTZp2sZDB3477H/kSC3Jq37eUueNY8jtqC/EtxXA1O4w9s+/5A6hDgAdGQs7SN5N+B0Qfb0K7vS7PqaQu1nmb3rc2GfzQ1lyj0EXvLl/SdY5DWyzlZY6D5ctlmK280FTI9mKKAA6AAD9CdMBPx96Pv5fWPKhQmDdZvb7VRPAebjZr9nMamtXFs63JyxxLPx1aCgFwcAT4kaMDHr0UB9MneXR9lbeufUspjYOTfx+oF1f5AgvFTS2FADg4AjwwdZuAnCbL4ziO5e7wKO9kN4QX2sWkQXFR49llXUqhsRAAh0eAHxvy81OzQ5ZemvFGvzFF8PKl6an3bJh9nPWmFxUz7fIiyD0KAXB4BPgxIz8/NX/933/X9y36fbHbvCx8D4Q0oL4UwuUEQO2YvbZCABwcAR4Yldb5aLd3yFYxlkfGpw+lJxF5dqkJ99XPthiuKAb1fMnjZKIa86FqNHxbIQAOjgAPjMrX3wYNMb5yk7pYx7F3tZzlg+sM1/TyVHr2C0BRFEV5vAxjz5SCuhCmffsy27ytOv/eVgiAg2M9+FHZ6oAnRT8d5eXhy5PesNgMMCmsJjdaRPeJI5wDE0eKHhib7lPaARgxAjwwBntPaQdgbAjw48ET8ACAHAF+nOiAnziy9AAI8MBIkKUHICPAjwT5eQCAjAA/QuTnIcjSA5NHgAfGgyw9gBwBHgCAESLAjwEd8ACALQT4saEDHjm64YEpI8ADo0I3PIAEAf7skZ8HAJQR4EeF/Dy2kKUHJosAD4wNWXoAggAPAMAofXXqE8Au/um//jj/949VIYT4h+inJzsbDNLf/NZfJf/45XfpK7/y/X852dkA+HS04PfiW0q1uRM1vs3yNwVk/46ceacC5Oie+7H6NR3wyOXRXfbL7/6o/KJvtVXXrnapzACOhwC/N80O45L1Qq19g28ZbunFyJnPlkFaVmhrwXKW3yxlldG9dRMmpTKQt246nK6VGcBREeCHIfryEgjTS38XqIt1aGvCfd26KbaGcGI8WkP40WN8t8oM4NgI8EcUOfNNzjJJhVrWXDFcIVyjmLhUF+s4Xumb96oXV0K8fUipTYI3Wh0+eEu9UHmFrajYchK+Q2UG8AkI8EekLp5tzX10IiFE5Dy6mv28Wq1jzxTC9JrT+MJ/dYW4umjYowa/A9Cq6+8A31IM1/SSbifvKsu0V1Tsprq8e2UGsA8C/N6C5ax2jJ26eDCD5ZMv/KelaLkLynzLcIVm3+vtuwJHksTuMGuL6yvPFGlY71OxqczAifCY3N40O2xoi+srz1QMxRWmF3cM776lGO6mDxM4iejLSyCuHqRKOLvUhPseCqF2rthUZuB0aMEfnX5jCiHMm24NGN9SDFezw0IfJjAE6sWV9Fd7xaYyAydFgD+2yHl0TdN0jfbnhCJnntwQ92nu/Mnv/PPO78VE7DjjTfTxJv3RXLEPUpkB7IMAf1yRc7cU9v3qPh+UVL/nfLYMTK927B2RG60OOVeden2rFce+h++B0C5nQrRV7NbKDOATEOCPyn9aBubDQt0MSkptPzMUOXfLQBrQVK01xvMjAK0xvvOPgKTSzjazLhquMB8Wqmio2EJ0rcwAjo0Av7eqUfTJOHrfMtx89LB+b2tJOlO/MUWwnMnPDkdfXoKKkiqm+GwI4UR3JBpCePWmupqnr2LPdI3kNeMt602vqdip7pUZwFEpcRyf+hzQW/lhd6I7tpQfdmexGWBSCPAAAIwQKXoAAEaIAA8AwAgR4AEAGCECPAAAI0SABwBghAjwAACMEAH+k/mWoiiKUj9/d+3Gk5wRjin96kuTJM0t/3STwkSRb82pDcAIEOCBgQkC15idKMRGzt3McIOTHBvAYRHgh0RdrOM4ZnHNiTG9WBLamhBCtKxNBABtCPBDUkrRR441T7K2lh9t59IjP92Y7JDHg3y//N3y1kKxytzyPz7t46ELdfFgbr20uV7bV7JQCWqvcmFLVj2c/J3zuZN0CSSLwAkhhGtINU0qSJlbjr9V1NxJt5+yYwFAhRifyktu3sVGWyZtvKUb07+2pVu97SjQtEU+ZHOx+ESVlSG7Ptmr5cup2WFx14pt9e9qqB5b5Zleze6mV1VUfloAhoEW/GD5T0ljKrmbhl7x5uu/uvm2OLRNIYRwXwv9tpqdvjO5Dadbi8XWhXt8omy5tkTShs4Wa4ucR+lCJ5crW541vZRpYE2uZPDyJWp+VyavHpsuAXWxln9jrvRkidhNSenOrlEsyvTCOA6fWfsdGBYC/FBFH29CJDd6VQih6sXMrb6K4zi+F6FjWfPZ0i0XYD4s0nfeSG9MizW9la4KUZkQxklppu2F8TqNluF7mjKfSdE/+bGWXcpkhfZsAMd6oTa+K7OpHmkNCN7D0rmUaku2c7GoG10VQiW8AwNDgB+q9B59dZHfN2eXUmM76XudGcbS7TXmOS1WUigWJ5Ak47NMS/D2vtmURthK5UvZ5V0p7XKW/7u+BlQcIt357SPvb5eLAjAgBPihKt9I5but/2S4QdraC8O0M7TLnba5WJyOqq/SLLtr3G2NoC91b6/0Dr/MKt+Vktvr9TWg4hDlH54ABooAP1TqxZUQSc9pJISI/KRbNZHl769uFrqqCue1IkW/Q7E4LXXxnHSBB8s0xKvXt5qQL5f0JEV6Kd1HJ9s2Twa1R03vyriGlQ2dT2qAeSM9nZn+AMwOke/rV+0MYKAOP24PTWrGMCfNrcZR9Jomja6uLiZ5Y2lsdvEFRtEPRtUo+vzytI+Hr7yUtY9SNI+iz8uUNmt2WDeKXt6X0fPAQNGCHy51sQ5tM7mFa6a3frjabNNX+SahmV7afysl3rsVKzTTsxlkNyTqYp1czKwVr69Cz9aySK6ZdpgNwBPqYh16Zh7kNdP2wjQN3/CuhOlJlUvaqt/nlePqIiuo+hgAhk2J4/jU54BKvqUY6bNOK13NZyHRSrdqoIe0XpkeMyYCI0cLfrCyp9uKzzppt9dEdwBAKwL8cOmr0DM1qZe1ItEKAEAlUvQAAIwQLXgAAEaIAA8AwAgR4AEAGKHpBPh0Nq+i+XznNazTtdvnzj5rYEeRb83TCcZKi8HvKvukxZKqZjNDtch3rPlcqiTOXiudH/YqF0o4dK0GMCLTCfCVgsA1ZieLeZFzNzN6LRXTi2sQzXfgW/OZsXSD/LoEgbs0Zjv/kjvyVa5w2loNYCgmF+ClaUHllbD7FySvznkQaYGHnH2EEN+bb6Wr+HhhsZbk08Pv5whXWRywVgMYkckFeMnWSthJtnPuOFaaAk3uj1H6t6IoipyrLaXopR3nVvHmmq0DIm9LJ6YTQrhGcrhS8rbu0Jt0+2aP7UOmmm7zNSdcyuUXXuj5LbWeauTLyfBTp5bThVc0+zldAF0IVV8825pm2t6z9Fuu5fPm3898rrRc5aq6Ud5Sd33LOtXqnnVV7FIrmq9s1y+wQ1EA6nzWpPcnV17YI2vrpKtlFFfVqHpNpCWEcZwv9JGutNFvRRBpYRmp1MJKM02HrluxprjOiGma8olsff7WxUhq1qrp+S11O9WKLSdRvKa1un9ezTQbr3JN3ag5SLpuvFzCLrW6f12tOlBLrWi8sj2+wLaiANSbXICvsBVsTC+M4zDcfkW6dZpeXAwG+T03uUdV3YLT+2QhhFTe7It3zMpD51s12wvjOA43e0tb82BSuK1vBYn6E265lXf8lppPtRhQO4bXI9r6jVWt2+fdfD/tV7nq8yeb8h+TZulLq4qssrpavWNd7Vkrmq5szy9waJUEOB/TDvCaZtpZX2v5FlZxu5f2ke80Dc3UpphRf+tvPnS3u610dGF6TS2uwgl3L7zLt9S15adppm174Ynv2l0CfN/P2+8qbx/M82wpCdAtwDfX6h3ras9a0XBle3+BA6skwPmYXB984e61Xq8WenGQnHY5y/4ZvpeGPs8uNSFKy7JGH2+1x6sopIPOh26hpv2xrmG9Si83nXAnu3xLVfJVb4PAXS6N2Wzf5w4PovHEO31e6ftp0FQ3kq7wmWEs3Q4j8LvX6sPX1aqjNF3Z3l/gMCsJcAYmF+B7SG87svTmdHVRNXS+lDZc6ZWFHOHQ9fRV0gByXbfbCX/yqaqL1TqOw01T9UCj1XeiXt9qQojg5UvxDHxrPrf8KBLikJemqW74T+lgftsLw6wbutvPhk4OWFfr1F3ZHb7AQVUS4HwQ4OupF1dCJM+aJUOP/WSMtTBvioEwiwvLJz8SQkTyAOOskEcn21aaH6eiwdj50B1kIb7jCWc3YPc13ZYeuc4+p5od1xGqri9W97eHDDC7yL+Yu3yoduQ7c8MNAte4+xKJ3T9vw1Uu1420na1d3Sx0VRXOa+Ml2OkT9qyrPWtFw5Xt+wUOrpIA5+OQ+f5BK/ftVe5QbNpUD/eN43i/UfQVg8g12+syir7ms7R0iEqn0D6Kvnnge89vqeVUq76bU4+f8szKCCKdVvvnLXyGhqtcWzeqr0FtH3yvWr13XS1vLR+l8cr2+wKHWEmAs0ALvpG+Cj17c8PXTNsLK/PY+ir07Hzt9sLC7epiHXrVZej3eeFXF7seugN18bx9k2w44bzPUwihabZn147U3vtU1cU6lN45iBXv9dW68HHSQWvSafX8vA1XubZuFK6B6aUPH/QbgFF7PrvV1X61ovHK9vsCh1hJgLPAevC7SieqMb1Dz0oGAMD+vjr1CZylzSx0AAAMEin6XWRP+mimR/MdADBEpOgBABghWvAAAIwQAR4AgBEiwAMAMEIEeAAARogADwDACBHgAQAYIQI8AAAjRIAHAGCECPAAAIzQ/wNivF6CFHq4DgAAAABJRU5ErkJggg=="><!-- --></p>
</div>
</div>


<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
  (function () {
    var script = document.createElement("script");
    script.type = "text/javascript";
    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
    document.getElementsByTagName("head")[0].appendChild(script);
  })();
</script>


</body></html>