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.

Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating the estimation of static parameters via a fully-adapted filter that utilizes conditional sufficient statistics for parameters and/or states as particles. State smoothing in the presence of parameter uncertainty is also solved as a by-product of PL. In a number of examples, we show that PL outperforms existing particle filtering alternatives and proves to be a competitor to MCMC.Comment: Published in at http://dx.doi.org/10.1214/10-STS325 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

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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-infectedrecovered (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.

The aim ofthis paper is to analyze extremai events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.