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Bacteriophages are the natural predators of bacteria and are available abundantly everywhere in nature. Lytic phages can specifically infect their bacterial host (through attachment to the receptor) and use their host replication machinery to replicate rapidly, a feature that enables them to kill a disease‐causing bacteria. Hence, phage attachment to the host bacteria is the first important step of the infection process. It is reported in this study that the receptor could be an LPS which is responsible for the attachment of the Sfk20 phage to its host (Shigella flexneri 2a). Phage Sfk20 bacteriolytic activity was examined for preliminary optimization of phage titer. The phage Sfk20 viability at different saline conditions was conducted. The LC–MS/MS technique used here for detecting and identifying 40 Sfk20 phage proteins helped us to get an initial understanding of the structural landscape of phage Sfk20. From the identified proteins, six structurally significant proteins were selected for structure prediction using two neural network systems: AlphaFold2 and ESMFold, and one homology modeling software: Phyre2. Later the performance of these modeling systems was compared using various metrics. We conclude from the available and generated information that AlphaFold2 and Phyre2 perform better than ESMFold for predicting Sfk20 phage protein structures. [ABSTRACT FROM AUTHOR]

We propose Bayesian methods for Gaussian graphical models that lead to sparse and adaptively shrunk estimators of the precision (inverse covariance) matrix. Our methods are based on lasso-type regularization priors leading to parsimonious parameterization of the precision matrix, which is essential in several applications involving learning relationships among the variables. In this context, we introduce a novel type of selection prior that develops a sparse structure on the precision matrix by making most of the elements exactly zero, in addition to ensuring positive definiteness--thus conducting model selection and estimation simultaneously. More importantly, we extend these methods to analyse clustered data using finite mixtures of Gaussian graphical model and infinite mixtures of Gaussian graphical models. We discuss appropriate posterior simulation schemes to implement posterior inference in the proposed models, including the evaluation of normalizing constants that are functions of parameters of interest, which result from the restriction of positive definiteness on the correlation matrix. We evaluate the operating characteristics of our method via several simulations and demonstrate the application to real-data examples in genomics. [ABSTRACT FROM AUTHOR]

We propose a new approach to model a sequence of spatially distributed time series of extreme values. Unlike common practice, we incorporate spatial dependence directly in the likelihood and allow the temporal component to be captured at the second level of hierarchy. Inferences about the parameters and spatio-temporal predictions are obtained via MCMC technique. The model is fitted to a gridded precipitation data set collected over 99 years across the continental U.S. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Nous considérons I'inférence semi-paramétrique bayesienne pour un modèle log-linéaire. Ce modèle consiste en une composante paramétrique pour les coefficients de régression et en une composante non-paramétrique pour la distribution inconnue de I'erreur. L'analyse bayesienne est étudiée pour le cas d'un a priori paramétrique sur les coefficients de régression et un a priori de type mélange de processus de Dirichlet pour la distribution inconnue de I'erreur. Une méthode chaǐne de Markov Monte Carlo (MCMC) est développée pour calculer les caractéristiques de la distribution a posteriori. Nous étudions une méthode de sélection de modéle pour obtenir un ensemble plus parcimonieux de prédicteurs. La méthode ajoute des variables indicatrices à I'équation de régression. L'ensemble des variables indicatrices représente tous les sous-ensembles possible devant ětre considérés. Une méthode MCMC est développée pour rechercher de façon stochastique le meilleur sous-ensemble. Ces procédures sont appliquées à deux exemples, dont I'un contient des données censurrées. [ABSTRACT FROM AUTHOR]

Cet article fournit un interprétation du paramètre d'échelle d'un processus Dirichlet lorsque le but est d'estimer une fonctionnelle linéaire d'une distribution de probabilité inconnue. Nous fournissons les premier et second moments a posteriori exacts pour de telles fonctionnelles sous les spécification a priori informatives et non-informatives. Le cas non-informatif nous donne une approximation normale du bootstrap bayesien. [ABSTRACT FROM AUTHOR]