Probabilistic Nearest Neighbors Classification.

Analysis of the currently established Bayesian nearest neighbors classification model points to a connection between the computation of its normalizing constant and issues of NP-completeness. An alternative predictive model constructed by aggregating the predictive distributions of simpler nonlocal models is proposed, and analytic expressions for the normalizing constants of these nonlocal models are derived, ensuring polynomial time computation without approximations. Experiments with synthetic and real datasets showcase the predictive performance of the proposed predictive model.

Bayesian generalizations of the integer-valued autoregressive model.

We develop two Bayesian generalizations of the Poisson integer-valued autoregressive model. The AdINAR(1) model accounts for overdispersed data by means of an innovation process whose marginal distributions are finite mixtures, while the DP-INAR(1) model is a hierarchical extension involving a Dirichlet process, which is capable of modeling a latent pattern of heterogeneity in the distribution of the innovations rates. The probabilistic forecasting capabilities of both models are put to test in the analysis of crime data in Pittsburgh, with favorable results.

Prior Sensitivity Analysis in a Semi-Parametric Integer-Valued Time Series Model.

We examine issues of prior sensitivity in a semi-parametric hierarchical extension of the INAR(p) model with innovation rates clustered according to a Pitman-Yor process placed at the top of the model hierarchy. Our main finding is a graphical criterion that guides the specification of the hyperparameters of the Pitman-Yor process base measure. We show how the discount and concentration parameters interact with the chosen base measure to yield a gain in terms of the robustness of the inferential results. The forecasting performance of the model is exemplified in the analysis of a time series of worldwide earthquake events, for which the new model outperforms the original INAR(p) model.

TREATMENT EFFECTS: A BAYESIAN PERSPECTIVE.

This paper contributes to the emerging Bayesian literature on treatment effects. It derives treatment parameters in the framework of a potential outcomes model with a treatment choice equation, where the correlation between the unobservable components of the model is driven by a low-dimensional vector of latent factors. The analyst is assumed to have access to a set of measurements generated by the latent factors. This approach has attractive features from both theoretical and practical points of view. Not only does it address the fundamental identification problem arising from the inability to observe the same person in both the treated and untreated states, but it also turns out to be straightforward to implement. Formulae are provided to compute mean treatment effects as well as their distributional versions. A Monte Carlo simulation study is carried out to illustrate how the methodology can easily be applied.

Segmental dataset and whole body expression data do not support the hypothesis that non-random movement is an intrinsic property of Drosophila retrogenes.

BACKGROUND: Several studies in Drosophila have shown excessive movement of retrogenes from the X chromosome to autosomes, and that these genes are frequently expressed in the testis. This phenomenon has led to several hypotheses invoking natural selection as the process driving male-biased genes to the autosomes. Metta and Schlötterer (BMC Evol Biol 2010, 10:114) analyzed a set of retrogenes where the parental gene has been subsequently lost. They assumed that this class of retrogenes replaced the ancestral functions of the parental gene, and reported that these retrogenes, although mostly originating from movement out of the X chromosome, showed female-biased or unbiased expression. These observations led the authors to suggest that selective forces (such as meiotic sex chromosome inactivation and sexual antagonism) were not responsible for the observed pattern of retrogene movement out of the X chromosome. RESULTS: We reanalyzed the dataset published by Metta and Schlötterer and found several issues that led us to a different conclusion. In particular, Metta and Schlötterer used a dataset combined with expression data in which significant sex-biased expression is not detectable. First, the authors used a segmental dataset where the genes selected for analysis were less testis-biased in expression than those that were excluded from the study. Second, sex-biased expression was defined by comparing male and female whole-body data and not the expression of these genes in gonadal tissues. This approach significantly reduces the probability of detecting sex-biased expressed genes, which explains why the vast majority of the genes analyzed (parental and retrogenes) were equally expressed in both males and females. Third, the female-biased expression observed by Metta and Schlötterer is mostly found for parental genes located on the X chromosome, which is known to be enriched with genes with female-biased expression. Fourth, using additional gonad expression data, we found that autosomal genes analyzed by Metta and Schlötterer are less up regulated in ovaries and have higher chance to be expressed in meiotic cells of spermatogenesis when compared to X-linked genes. CONCLUSIONS: The criteria used to select retrogenes and the sex-biased expression data based on whole adult flies generated a segmental dataset of female-biased and unbiased expressed genes that was unable to detect the higher propensity of autosomal retrogenes to be expressed in males. Thus, there is no support for the authors' view that the movement of new retrogenes, which originated from X-linked parental genes, was not driven by selection. Therefore, selection-based genetic models remain the most parsimonious explanations for the observed chromosomal distribution of retrogenes.

Re-analysis of the larval testis data on meiotic sex chromosome inactivation revealed evidence for tissue-specific gene expression related to the drosophila X chromosome.

BACKGROUND: Meiotic sex chromosome inactivation (MSCI) during spermatogenesis has been proposed as one of the evolutionary driving forces behind both the under-representation of male-biased genes on, and the gene movement out of, the X chromosome in Drosophila. However, the relevance of MSCI in shaping sex chromosome evolution is controversial. Here we examine two aspects of a recent study on testis gene expression (Mikhaylova and Nurminsky, BMC Biol 2011, 9:29) that failed to support the MSCI in Drosophila. First, Mikhaylova and Nurminsky found no differences between X-linked and autosomal genes based on the transcriptional profiling of the early testis development, and thus concluded that MSCI does not occur in D. melanogaster. Second, they also analyzed expression data from several D. melanogaster tissues and concluded that under-representation on the X chromosome is not an exclusive property of testis-biased genes, but instead, a general property of tissue-specific genes. RESULTS: By re-analyzing the Mikhaylova and Nurminsky's testis data and the expression data on several D. melanogaster tissues, we made two major findings that refuted their original claims. First, the developmental testis data has generally greater experimental error than conventional analyses, which reduced significantly the power to detect chromosomal differences in expression. Nevertheless, our re-analysis observed significantly lower expression of the X chromosome in the genomic transcriptomes of later development stages of the testis, which is consistent with the MSCI hypothesis. Second, tissue-specific genes are also in general enriched with genes more expressed in testes than in ovaries, that is testis-biased genes. By completely excluding from the analyses the testis-biased genes, which are known to be under-represented in the X, we found that all the other tissue-specific genes are randomly distributed between the X chromosome and the autosomes. CONCLUSIONS: Our findings negate the original study of Mikhaylova and Nurminsky, which concluded a lack of MSCI and generalized the pattern of paucity in the X chromosome for tissue-specific genes in Drosophila. Therefore, MSCI and other selection-based models such as sexual antagonism, dosage compensation, and meiotic-drive continue to be viable models as driving forces shaping the genomic distribution of male-related genes in Drosophila.

Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model.

In this article, we use Google Flu Trends data together with a sequential surveillance model based on state-space methodology to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model [a susceptible-exposed-infected-recovered (SEIR) model] within the state-space framework, thereby extending the SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time and provides updated estimated odds of a pandemic with each new surveillance data point. We show how our approach, in combination with sequential Bayes factors, can serve as an online diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the United States during 2003-2009 and in nine separate U.S. states chosen to represent a wide range of health care and emergency system strengths and weaknesses. This article has online supplementary materials.

Direct evidence for postmeiotic transcription during Drosophila melanogaster spermatogenesis.

Extensive gene expression during meiosis is a hallmark of spermatogenesis. Although it was generally accepted that RNA transcription ceases during meiosis, recent observations suggest that some transcription occurs in postmeiosis. To further resolve this issue, we provide direct evidence for the de novo transcription of RNA during the postmeiotic phases. These results strengthen the newly emerging notion that postmeiotic transcription is dynamic and integral to the overall process of spermatogenesis.

Stage-specific expression profiling of Drosophila spermatogenesis suggests that meiotic sex chromosome inactivation drives genomic relocation of testis-expressed genes.

In Drosophila, genes expressed in males tend to accumulate on autosomes and are underrepresented on the X chromosome. In particular, genes expressed in testis have been observed to frequently relocate from the X chromosome to the autosomes. The inactivation of X-linked genes during male meiosis (i.e., meiotic sex chromosome inactivation-MSCI) was first proposed to explain male sterility caused by X-autosomal translocation in Drosophila, and more recently it was suggested that MSCI might provide the conditions under which selection would favor the accumulation of testis-expressed genes on autosomes. In order to investigate the impact of MSCI on Drosophila testis-expressed genes, we performed a global gene expression analysis of the three major phases of D. melanogaster spermatogenesis: mitosis, meiosis, and post-meiosis. First, we found evidence supporting the existence of MSCI by comparing the expression levels of X- and autosome-linked genes, finding the former to be significantly reduced in meiosis. Second, we observed that the paucity of X-linked testis-expressed genes was restricted to those genes highly expressed in meiosis. Third, we found that autosomal genes relocated through retroposition from the X chromosome were more often highly expressed in meiosis in contrast to their X-linked parents. These results suggest MSCI as a general mechanism affecting the evolution of some testis-expressed genes.