Disulfiram-induced psychosis still an important clinical entity: A case series.

Disulfiram, an FDA-approved medication for treating alcohol dependence syndrome (ADS), is often used surreptitiously, resulting in severe adverse drug reactions such as disulfiram-induced psychosis (DIP). DIP presents diagnostic challenges, despite being perceived as common; however, there is limited literature available on DIP in India. We describe four cases with a history of psychosis in the background of disulfiram exposure.

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications.

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean ( L. (Merr.

A novel bayesian multiple testing approach to deregulated miRNA discovery harnessing positional clustering.

MicroRNAs (miRNAs) are small non-coding RNAs that function as regulators of gene expression. In recent years, there has been a tremendous interest among researchers to investigate the role of miRNAs in normal as well as in disease processes. To investigate the role of miRNAs in oral cancer, we analyse expression levels of miRNAs to identify miRNAs with statistically significant differential expression in cancer tissues.

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations.

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus.

Adaptive-Rate Compressive Sensing Using Side Information.

We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. The first method uses extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprises the images in the video sequence.

A novel Bayesian semiparametric algorithm for inferring population structure and adjusting for case-control association tests.

While the population-based case-control approach is the popular study design for association mapping of complex genetic traits because of ease of data collection and statistical analyses, it suffers from the inherent problem of population stratification. There have been methodological developments for adjusting these studies for population substructure, but efficient estimation of the number of subpopulations (K), which has evolutionary significance, remains a statistical challenge. In this article, we propose a Bayesian semiparametric approach to estimate population substructure under the assumption that K is random.

Homography-based control scheme for mobile robots with nonholonomic and field-of-view constraints.

In this paper, we present a visual servo controller that effects optimal paths for a nonholonomic differential drive robot with field-of-view constraints imposed by the vision system. The control scheme relies on the computation of homographies between current and goal images, but unlike previous homography-based methods, it does not use the homography to compute estimates of pose parameters. Instead, the control laws are directly expressed in terms of individual entries in the homography matrix.

Model fitting and inference under Latent Equilibrium Processes.

This paper presents a methodology for model fitting and inference in the context of Bayesian models of the type f(Y | X, theta)f(X | theta)f(theta), where Y is the (set of) observed data, theta is a set of model parameters and X is an unobserved (latent) stationary stochastic process induced by the first order transition model f(X((t+1)) | X((t)), theta), where X((t)) denotes the state of the process at time (or generation) t. The crucial feature of the above type of model is that, given theta, the transition model f(X((t+1)) | X((t)), theta) is known but the distribution of the stochastic process in equilibrium, that is f(X | theta), is, except in very special cases, intractable, hence unknown. A further point to note is that the data Y has been assumed to be observed when the underlying process is in equilibrium.

A Bayesian approach to modeling dynamic effective connectivity with fMRI data.

A state-space modeling approach for examining dynamic relationship between multiple brain regions was proposed in Ho, Ombao and Shumway (Ho, M.R., Ombao, H.