Filtering coupled Wright-Fisher diffusions.

Coupled Wright-Fisher diffusions have been recently introduced to model the temporal evolution of finitely-many allele frequencies at several loci. These are vectors of multidimensional diffusions whose dynamics are weakly coupled among loci through interaction coefficients, which make the reproductive rates for each allele depend on its frequencies at several loci. Here we consider the problem of filtering a coupled Wright-Fisher diffusion with parent-independent mutation, when this is seen as an unobserved signal in a hidden Markov model.

An R Package for Nonparametric Inference on Dynamic Populations with Infinitely Many Types.

Fleming-Viot diffusions are widely used stochastic models for population dynamics that extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic population, whose individuals undergo random genetic drift and at birth can mutate to a new allelic type drawn from a possibly infinite potential pool, independently of their parent. Recently, Bayesian nonparametric inference has been considered for this model when a finite sample of individuals is drawn from the population at several discrete time points.

Perfect Sampling of the Posterior in the Hierarchical Pitman-Yor Process.

The predictive probabilities of the hierarchical Pitman-Yor process are approximated through Monte Carlo algorithms that exploits the Chinese Restaurant Franchise (CRF) representation. However, in order to simulate the posterior distribution of the hierarchical Pitman-Yor process, a set of auxiliary variables representing the arrangement of customers in tables of the CRF must be sampled through Markov chain Monte Carlo. This paper develops a perfect sampler for these latent variables employing ideas from the Propp-Wilson algorithm and evaluates its average running time by extensive simulations.

An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.

Background: The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development.

Results: In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table.

Intracortical inhibitory and excitatory circuits in subjects with minimal hepatic encephalopathy: a TMS study.

Minimal hepatic encephalopathy (MHE) is the earliest form of hepatic encephalopathy (HE) and affects up to 80 % of patients with liver cirrhosis. By definition, MHE is characterized by psychomotor slowing and subtle cognitive deficits,  but obvious clinical manifestations are lacking. Given its covert nature, MHE is often underdiagnosed.

Effects of theta burst stimulation on referred phantom sensations in patients with spinal cord injury.

To further explore the mechanisms underlying cortical reorganization in patients with phantom sensations after deafferentation, a repetitive transcranial magnetic stimulation study was carried out in two patients with referred phantom sensations (RPS) after incomplete spinal cord injury at the thoracic level. We delivered continuous (inhibitory), intermittent (excitatory), and placebo theta burst stimulation to the contralateral primary motor cortex (M1), primary somatosensory cortex (S1), and secondary somatosensory cortex (S2). Perception of RPS was significantly and transiently disrupted by inhibitory theta burst stimulation applied over S1 and, to a lesser extent, S2.

CAVEOLIN-1 expression in brain metastasis from lung cancer predicts worse outcome and radioresistance, irrespective of tumor histotype.

Brain metastases develop in one-third of patients with non-small-cell lung cancer and are associated with a dismal prognosis, irrespective of surgery or chemo-radiotherapy. Pathological markers for predicting outcomes after surgical resection and radiotherapy responsiveness are still lacking. Caveolin 1 has been associated with chemo- and radioresistance in various tumors, including non-small-cell lung cancer.

Rediscovery of Good-Turing estimators via Bayesian nonparametrics.

The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics, designs of experiments, machine learning, etc. A full range of statistical approaches, parametric and nonparametric as well as frequentist and Bayesian, has been proposed for estimating discovery probabilities. In this article, we investigate the relationships between the celebrated Good-Turing approach, which is a frequentist nonparametric approach developed in the 1940s, and a Bayesian nonparametric approach recently introduced in the literature.