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The structure and dynamics of membranes are crucial to ensure the proper functioning of cells. There are some compounds used in therapeutics that show nonspecific interactions with membranes in addition to their specific molecular target. Among them, two compounds recently used in therapeutics against COVID-19, remdesivir and favipiravir, were subjected to molecular dynamics simulation assays. In these, we demonstrated that the compounds can spontaneously bind to model lipid membranes in the presence or absence of cholesterol. These findings correlate with the corresponding experimental results recently reported by our group. In conclusion, insertion of the compounds into the membrane is observed, with a mean position close to the phospholipid head groups.

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We introduce a marginal version of the nested Dirichlet process to cluster distributions or histograms. We apply the model to cluster genes by patterns of gene-gene interaction. The proposed approach is based on the nested partition that is implied in the original construction of the nested Dirichlet process. It allows simulation exact inference, as opposed to a truncated Dirichlet process approximation. More importantly, the construction highlights the nature of the nested Dirichlet process as a nested partition of experimental units. We apply the proposed model to inference on clustering genes related to DNA mismatch repair (DMR) by the distribution of gene-gene interactions with other genes. Gene-gene interactions are recorded as coefficients in an auto-logistic model for the co-expression of two genes, adjusting for copy number variation, methylation and protein activation. These coefficients are extracted from an online database, called Zodiac, computed based on The Cancer Genome Atlas (TCGA) data. We compare results with a variation of k-means clustering that is set up to cluster distributions, truncated NDP and a hierarchical clustering method. The proposed inference shows favorable performance, under simulated conditions and also in the real data sets.

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Using a new type of array technology, the reverse phase protein array (RPPA), we measure time-course protein expression for a set of selected markers that are known to coregulate biological functions in a pathway structure. To accommodate the complex dependent nature of the data, including temporal correlation and pathway dependence for the protein markers, we propose a mixed effects model with temporal and protein-specific components. We develop a sequence of random probability measures (RPM) to account for the dependence in time of the protein expression measurements. Marginally, for each RPM we assume a Dirichlet process model. The dependence is introduced by defining multivariate beta distributions for the unnormalized weights of the stick-breaking representation. We also acknowledge the pathway dependence among proteins via a conditionally autoregressive model. Applying our model to the RPPA data, we reveal a pathway-dependent functional profile for the set of proteins as well as marginal expression profiles over time for individual markers.

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We discuss inference for repeated fractional data, with outcomes between 0 to 1, including positive probability masses on 0 and 1. The point masses at the boundaries prevent the routine use of logit and other commonly used transformations of (0, 1) data. We introduce a model augmentation with latent variables that allow for the desired positive probability at 0 and 1 in the model. A linear mixed effect model is imposed on the latent variables. We propose a Bayesian semiparametric model for the random effects distribution. Specifically, we use a Polya tree prior for the unknown random effects distribution. The proposed model can capture possible multimodality and skewness of random effect distribution. We discuss implementation of posterior inference by Markov chain Monte Carlo simulation. The proposed model is illustrated by a simulation study and a cancer study in dogs.

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We discuss the analysis of data from single nucleotide polymorphism (SNP) arrays comparing tumor and normal tissues. The data consist of sequences of indicators for loss of heterozygosity (LOH) and involve three nested levels of repetition: chromosomes for a given patient, regions within chromosomes, and SNPs nested within regions. We propose to analyze these data using a semiparametric model for multi-level repeated binary data. At the top level of the hierarchy we assume a sampling model for the observed binary LOH sequences that arises from a partial exchangeability argument. This implies a mixture of Markov chains model. The mixture is defined with respect to the Markov transition probabilities. We assume a nonparametric prior for the random mixing measure. The resulting model takes the form of a semiparametric random effects model with the matrix of transition probabilities being the random effects. The model includes appropriate dependence assumptions for the two remaining levels of the hierarchy, i.e., for regions within chromosomes and for chromosomes within patient. We use the model to identify regions of increased LOH in a dataset coming from a study of treatment-related leukemia in children with an initial cancer diagnostic. The model successfully identifies the desired regions and performs well compared to other available alternatives.

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We analyse data from a study involving 173 pregnant women. The data are observed values of the ß human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.

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Glutathione (GSH) plays an important role in the defence of microorganisms and plants against different environmental stresses. To determine the role of GSH under different stresses, such as acid pH, saline shock, and oxidative shock, a GSH-deficient mutant (Bradyrhizobium sp. 6144-S7Z) was obtained by disruption of the gshA gene, which encodes the enzyme gamma-glutamylcysteine synthetase. Growth of the mutant strain was significantly reduced in liquid minimal saline medium, and the GSH content was very low, about 4% of the wild-type level. The defect, caused by disruption of the gshA gene in the growth of mutant strain, cannot be reversed by the addition of GSH (up to 100 micromol/L) to the liquid minimal saline medium, and the endogenous GSH level was approximately the same as that observed without the addition of GSH. In contrast, the wild-type strain increased the GSH content under these conditions. However, the growth of the mutant strain in a rich medium (yeast extract--mannitol) increased, suggesting that at least some but not all of the functions of GSH could be provided by peptides and (or) amino acids. The symbiotic properties of the mutant were similar to those found in the wild-type strain, indicating that the mutation does not affect the ability of the mutant to form effective nodules.

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Este documento contiene la obra completa titulada "Corrupción en la Corte" de Peter Muller, lista para que los aficionados puedan ponerla en escena.

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We propose a class of longitudinal data models with random effects that generalizes currently used models in two important ways. First, the random-effects model is a flexible mixture of multivariate normals, accommodating population heterogeneity, outliers, and nonlinearity in the regression on subject-specific covariates. Second, the model includes a hierarchical extension to allow for meta-analysis over related studies. The random-effects distributions are decomposed into one part that is common across all related studies (common measure), and one part that is specific to each study and that captures the variability intrinsic between patients within the same study. Both the common measure and the study-specific measures are parameterized as mixture-of-normals models. We carry out inference using reversible jump posterior simulation to allow a random number of terms in the mixtures. The sampler takes advantage of the small number of entertained models. The motivating application is the analysis of two studies carried out by the Cancer and Leukemia Group B (CALGB). In both studies, we record for each patient white blood cell counts (WBC) over time to characterize the toxic effects of treatment. The WBCs are modeled through a nonlinear hierarchical model that gathers the information from both studies.

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