Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different "thinned" networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights. This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.

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Dropout: a simple way to prevent neural networks from overfitting

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The identification of genetically homogeneous groups of individuals is a long standing issue in population genetics. A recent Bayesian algorithm implemented in the software STRUCTURE allows the identification of such groups. However, the ability of this algorithm to detect the true number of clusters (K) in a sample of individuals when patterns of dispersal among populations are not homogeneous has not been tested. The goal of this study is to carry out such tests, using various dispersal scenarios from data generated with an individual-based model. We found that in most cases the estimated 'log probability of data' does not provide a correct estimation of the number of clusters, K. However, using an ad hoc statistic DeltaK based on the rate of change in the log probability of data between successive K values, we found that STRUCTURE accurately detects the uppermost hierarchical level of structure for the scenarios we tested. As might be expected, the results are sensitive to the type of genetic marker used (AFLP vs. microsatellite), the number of loci scored, the number of populations sampled, and the number of individuals typed in each sample.

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Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study.

Arlequin ver 3.0 is a software package integrating several basic and advanced methods for population genetics data analysis, like the computation of standard genetic diversity indices, the estimation of allele and haplotype frequencies, tests of departure from linkage equilibrium, departure from selective neutrality and demographic equilibrium, estimation or parameters from past population expansions, and thorough analyses of population subdivision under the AMOVA framework. Arlequin 3 introduces a completely new graphical interface written in C++, a more robust semantic analysis of input files, and two new methods: a Bayesian estimation of gametic phase from multi-locus genotypes, and an estimation of the parameters of an instantaneous spatial expansion from DNA sequence polymorphism. Arlequin can handle several data types like DNA sequences, microsatellite data, or standard multi-locus genotypes. A Windows version of the software is freely available on http://cmpg.unibe.ch/software/arlequin3.

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Arlequin (version 3.0): An integrated software package for population genetics data analysis

We present here a new version of the Arlequin program available under three different forms: a Windows graphical version (Winarl35), a console version of Arlequin (arlecore), and a specific console version to compute summary statistics (arlsumstat). The command-line versions run under both Linux and Windows. The main innovations of the new version include enhanced outputs in XML format, the possibility to embed graphics displaying computation results directly into output files, and the implementation of a new method to detect loci under selection from genome scans. Command-line versions are designed to handle large series of files, and arlsumstat can be used to generate summary statistics from simulated data sets within an Approximate Bayesian Computation framework.

Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows

Genome-wide association studies (GWAS) have identified more than 2,000 trait-SNP associations, and the number continues to increase. GWAS have focused on traits with potential consequences for human fitness, including many immunological, metabolic, cardiovascular, and behavioral phenotypes. Given the polygenic nature of complex traits, selection may exert its influence on them by altering allele frequencies at many associated loci, a possibility which has yet to be explored empirically. Here we use 38 different measures of allele frequency variation and 8 iHS scores to characterize over 1,300 GWAS SNPs in 53 globally distributed human populations. We apply these same techniques to evaluate SNPs grouped by trait association. We find that groups of SNPs associated with pigmentation, blood pressure, infectious disease, and autoimmune disease traits exhibit unusual allele frequency patterns and elevated iHS scores in certain geographical locations. We also find that GWAS SNPs have generally elevated scores for measures of allele frequency variation and for iHS in Eurasia and East Asia. Overall, we believe that our results provide evidence for selection on several complex traits that has caused changes in allele frequencies and/or elevated iHS scores at a number of associated loci. Since GWAS SNPs collectively exhibit elevated allele frequency measures and iHS scores, selection on complex traits may be quite widespread. Our findings are most consistent with this selection being either positive or negative, although the relative contributions of the two are difficult to discern. Our results also suggest that trait-SNP associations identified in Eurasian samples may not be present in Africa, Oceania, and the Americas, possibly due to differences in linkage disequilibrium patterns. This observation suggests that non-Eurasian and non-East Asian sample populations should be included in future GWAS. Citation: Casto AM, Feldman MW (2011) Genome-Wide Association Study SNPs in the Human Genome Diversity Project Populations: Does Selection Affect Unlinked SNPs with Shared Trait Associations? PLoS Genet 7(1): e1001266. doi:10.1371/journal.pgen.1001266 Editor: Joshua M. Akey, University of Washington, United States of America Received June 20, 2010; Accepted December 2, 2010; Published January 6, 2011 Copyright: 2011 Casto, Feldman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This research was supported by NIH grant GM28016 to MWF. AMC is also partially supported by the Medical Scientist Training Program (MSTP) at Stanford University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: morgan21@stanford.edu Introduction Genome-wide association studies (GWAS) have become a popular method for identifying genomic loci that contribute to complex traits [1]. In GWAS, large sample sets of individuals (now on the order of several thousand), whose phenotype for some trait has been assessed, are genotyped for common SNPs. Algorithms are then used to identify SNPs that demonstrate allele frequency differences between cases and controls or between persons representing opposite ends of the phenotypic range (for continuous traits such as height) [1]. It remains controversial how often the alleles at these SNPs themselves have direct effects on the phenotype under study; it is likely that in many cases these SNPs instead act as markers linked to the causal genomic variants [2,3]. Nonetheless, numerous individual SNPs have significant trait associations in more than one independent GWAS, confirming the association between these SNPs and particular human phenotypes [4,5]. As it is increasingly common for complex traits to have been the focus of multiple independent GWAS, it is now possible to discern which SNPs are most likely to have true trait associations and which are likely false positives [5]. Most GWAS are conducted on subjects of European ancestry [6]. As GWAS have reduced power to detect trait associations for SNPs with low minor allele frequency (MAF), this means that most ‘‘GWAS SNPs’’ have a relatively high MAF in Europe [6]. Outside Europe, the allele frequencies of some GWAS SNPs vary considerably. Perhaps the most cited examples of this are the SNPs associated with pigmentation phenotypes, many of which exhibit extreme allele frequency differences between continental groups [7]. Other examples of individual GWAS SNPs with large allele frequency differences between particular pairs of continents have been found, including variants associated with Type 2 Diabetes and Crohn’s disease [7,8]. However, most individual GWAS SNPs have allele frequency patterns that are indistinguishable from those of random SNPs with no known trait associations [8,9]. An understanding of the global allele frequency distributions of GWAS SNPs is important for two reasons. First, the frequency of a trait-associated allele determines to what degree it can contribute to variability in its phenotype in a given population. This is particularly true for SNPs that contribute directly to phenotypic variation rather than tagging causative variants [10]. Second, large allele frequency differences between populations for trait-associated SNPs may indicate that selection has acted upon the trait [8]. Past studies of the allele frequency patterns of GWAS SNPs have tended to be limited to particular human populations or pairs of PLoS Genetics | www.plosgenetics.org 1 January 2011 | Volume 7 | Issue 1 | e1001266 populations [9,11]. Such studies have also tended to focus on SNPs associated with phenotypes thought to be likely targets of selection (e.g. metabolic diseases like Type 2 Diabetes and resistance to infectious pathogens) [7,8]. Additionally, these studies have examined the allele frequency patterns of individual GWAS SNPs rather than looking for commonalities in the behavior of groups of SNPs associated with a particular trait or identified in a single study [7,9,11]. Here we examine approximately 1,300 GWAS SNPs associated with a wide variety of phenotypes. We explore the allele frequency patterns of these SNPs in 53 globally distributed populations using 4 different statistics. These statistics are calculated for different population groupings in order to detect individual GWAS SNPs and groups of SNPs that exhibit unusual allele frequency patterns on a local and/or global level. While pigmentation SNPs do seem to exhibit the most extreme variations in allele frequency, we found that groups of SNPs associated with blood pressure, infectious disease, and autoimmune disease also differ from random groups of SNPs in their allele frequency distributions.

/pdf/genome-wide-association-study-snps-in-the-human-genome-36zqx3nvnw.pdf

Genome wide association study SNPs in the human genome diversity project samples: does selection affect unlinked SNPs with shared trait associations?

Touching for shape recognition has been shown to activate occipital areas in addition to somatosensory areas. In this study we asked if this combination of somatosensory and other sensory processing areas also exist in other kinds of touch recognition. In particular, does touch for texture roughness matching activate other sensory processing areas apart from somatosensory areas? We addressed this question with functional magnetic resonance imaging (fMRI) using wooden abstract stimulus objects whose shape or texture were to be identified. The participants judged if pairs of objects had the same shape or the same texture. We found that the activated brain areas for texture and shape matching have similar underlying structures, a combination of the primary motor area and somatosensory areas. Areas associated with object-shape processing were activated between stimuli during shape matching and not texture roughness matching, while auditory areas were activated during encoding of texture and not for shape stimuli. Matching of textures also in- volves left BA47, an area associated with retrieval of relational information. We suggest that texture roughness is recog- nized in a framework of ordering. Left-lateralized activations favoring texture might reflect semantic processing associ- ated with grading roughness quantitatively, as opposed to the more qualitative distinctions between shapes.

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Seeing Shapes and Hearing Textures: Two Neural Categories of Touch

ter of our humanity. But it is equally certain that few organs are the subject of more misinformation in scienti1⁄2c and public discourse–especially in the widespread notion that most behaviors controlled by our marvelous brain are somehow programmed into it genetically. A typical treatment in the popular press is this overexcited claim by columnist Nicholas Wade in the New York Times: “When . . . [the human genome] . . . is fully translated, it will prove the ultimate thriller–the indisputable guide to the graces and horrors of human nature, the creations and cruelties of the human mind, the unbearable light and darkness of being.”1 Wade may get a pass for being a journalist, but some scientists are equally confused. Molecular biologist Dean Hamer wrote: “People are different because they have different genes that created different brains that formed different personalities,” and “[u]nderstanding the genetic roots of personality will help you ‘1⁄2nd yourself’ and relate better to others.” As distinguished a neurobiologist as Michael Gazzaniga is guilty of

Genes, environments & behaviors

Excess female mortality has existed throughout Chinese history. Since the 1980s the sex ratio at birth has risen significantly, resulting in a severely unbalanced sex ratio in the Chinese population. Data from the five censuses, with appropriate adjustments, permit estimates of the numbers of missing females from the period 1900–2000. The analysis is broken down by historically important periods and probes the reasons for the missing females in each period. Our estimates are compared with those of other investigators.

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Estimation of the Number of Missing Females in China: 1900-2000

The dynamics of allele frequencies changing under migration and heterogeneous selection in a subdivided population are investigated Using perturbation techniques, a stationary state is obtained when migration and selection are both small Heterogeneous selection leads to a positive correlation between values of F-statistics and heterozygosities when these are compared among sets of subdivided populations This contrasts with a negative value of the correlation obtained under Wright's classical model of homogeneous selection, and with the absence of correlation in the completely neutral situation

Marcus W. Feldman

Papers

Genome wide association study SNPs in the human genome diversity project samples: does selection affect unlinked SNPs with shared trait associations?

Seeing Shapes and Hearing Textures: Two Neural Categories of Touch

Genes, environments & behaviors

Estimation of the Number of Missing Females in China: 1900-2000

Heterogeneous selection in subdivided populations