Database error: Invalid SQL: select count(id) from pwn_comment where pid='136824' and iffb='1'
MySQL Error: 1194 (Table 'pwn_comment' is marked as crashed and should be repaired)
#0 dbbase_sql->halt(Invalid SQL: select count(id) from pwn_comment where pid='136824' and iffb='1') called at [D:\wwwroot\s154\wwwroot\includes\db.inc.php:55] #1 dbbase_sql->query(select count(id) from {P}_comment where pid='136824' and iffb='1') called at [D:\wwwroot\s154\wwwroot\comment\module\CommentContent.php:65] #2 CommentContent() called at [D:\wwwroot\s154\wwwroot\includes\common.inc.php:524] #3 PrintPage() called at [D:\wwwroot\s154\wwwroot\comment\html\index.php:13] Database error: Invalid SQL: select * from pwn_comment where pid='136824' and iffb='1' order by id limit 0,10
MySQL Error: 1194 (Table 'pwn_comment' is marked as crashed and should be repaired)
#0 dbbase_sql->halt(Invalid SQL: select * from pwn_comment where pid='136824' and iffb='1' order by id limit 0,10) called at [D:\wwwroot\s154\wwwroot\includes\db.inc.php:55] #1 dbbase_sql->query(select * from {P}_comment where pid='136824' and iffb='1' order by id limit 0,10) called at [D:\wwwroot\s154\wwwroot\comment\module\CommentContent.php:167] #2 CommentContent() called at [D:\wwwroot\s154\wwwroot\includes\common.inc.php:524] #3 PrintPage() called at [D:\wwwroot\s154\wwwroot\comment\html\index.php:13] 网友点评--家居饰品商城|苏州大宗商品交易中心毛
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发布于:2017-11-28 03:47:52  访问:7 次 回复: 篇
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The computational speed of this scan is largely governed by the volume of time spent calculating summary Title Loaded From File statistics to produce the feature vectors (also as simulating instruction information, ifPLOS Genetics | DOI:10.1371/journal.pgen.March 15,21 /Robust Identification of Soft and Tough Sweeps Applying Machine LearningFig 7. Constant with this can be the observation that our candidate window also overlaps a area identified by Green et al. [65] as getting an excess of derived alleles inside the human genome relative for the number observed in Neanderthal. The computational speed of this scan is largely governed by the amount of time spent calculating summary statistics to generate the function vectors (also as simulating instruction information, ifPLOS Genetics | DOI:10.1371/journal.pgen.March 15,21 /Robust Identification of Soft and Really hard Sweeps Applying Machine LearningFig 7. Heatmaps showing the fraction of regions simulated beneath Tennessen et al.‘s European demographic model located at varying distances from sweeps inferred to belong to every class by S/ HIC, SFselect+, and evolBoosting+. The location of any sweep relative for the classified window (orPLOS Genetics | DOI:10.1371/journal.pgen.March 15,22 /Robust Identification of Soft and Challenging Sweeps Employing Machine Learning"Neutral" if there is no sweep) is shown on the y-axis, even though the inferred class on the x-axis. Here, U(503, 505). These three classifiers had been educated from simulations with equilibrium demography. A) Outcomes for S/ HIC. B) SFselect+. C) evolBoosting+. doi:ten.1371/journal.pgen.1005928.gabsent), as the education and classification tasks are fairly inexpensive (normally requiring several minutes for the former and only seconds for the later). The approximate runtime for calculating our set of summary statistics within the 4 Mb region encompassing LCT is 30 minutes (employing code from https://github.com/kern-lab/shIC). As a result, if a compute cluster is accessible, 1 can subdivide the genome into segments of this size and carry out these calculations in parallel, and classify each and every window in the human genome in beneath an hour.Fig eight. Browser screenshot displaying patterns of variation around a putative selective sweep in Europeans within L3MBTL4 on chr18. Values of , Tajima‘s D, Kelley‘s ZnS, and Nielsen et al‘s composite likelihood ratio, all from Pybus et al. [59], are shown. Beneath these statistics we show the classifications from S/HIC (red: challenging sweep; faded red: hard-linked; blue: soft sweep; faded blue: soft-linked; black: neutral). This image was generated working with the UCSC Genome Browser (http://genome.ucsc.edu). doi:ten.1371/journal.pgen.1005928.gPLOS Genetics | DOI:10.1371/journal.pgen.March 15,23 /Robust Identification of Soft and Hard Sweeps Using Machine LearningDiscussionDetecting the genetic targets of recent adaptation and also the mode of constructive selection acting on them--selection on de novo mutations versus previously standing variants--remains an important challenge in population genetics. The majority of efforts to this finish have relied on population genetic summary statistics designed to uncover loci where patterns of allele frequency [e.g. 8, 36, 66] or linkage disequilibrium [e.g. 9, 10] depart from the neutral expectation. Recently, powerful machine understanding techniques have begun to become applied to this dilemma, showing great promise [18, 37, 39, 40, 43].
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