A Multinational Analysis of Mutations and Heterogeneity in PZase, RpsA, and PanD Associated with Pyrazinamide Resistance in M/XDR Mycobacterium tuberculosis.

Pyrazinamide (PZA) is an important first-line drug in all existing and new tuberculosis (TB) treatment regimens. PZA-resistance in M. tuberculosis is increasing, especially among M/XDR cases. Noted issues with PZA Drug Susceptibility Testing (DST) have driven the search for alternative tests. This study provides a comprehensive assessment of PZA molecular diagnostics in M/XDR TB cases. A set of 296, mostly XDR, clinical M. tuberculosis isolates from four countries were subjected to DST for eight drugs, confirmatory Wayne's assay, and whole-genome sequencing. Three genes implicated in PZA resistance, pncA, rpsA, and panD were investigated. Assuming all non-synonymous mutations cause resistance, we report 90% sensitivity and 65% specificity for a pncA-based molecular test. The addition of rpsA and panD potentially provides 2% increase in sensitivity. Molecular heterogeneity in pncA was associated with resistance and should be evaluated as a diagnostic tool. Mutations near the N-terminus and C-terminus of PZase were associated with East-Asian and Euro-American lineages, respectively. Finally, Euro-American isolates are most likely to have a wild-type PZase and escape molecular detection. Overall, the 8-10% resistance without markers may point to alternative mechanisms of resistance. Confirmatory mutagenesis may improve the disconcertingly low specificity but reduce sensitivity since not all mutations may cause resistance

A fixed combination of probiotics and herbal extracts attenuates intestinal barrier dysfunction from inflammatory stress in an in vitro model using Caco-2 cells.

Background: Inflammatory Bowel Diseases (IBD), are considered a growing global disease, with about ten million people being affected worldwide. Maintenance of intestinal barrier integrity is crucial for preventing IBD onset and exacerbations. Some recent patents regarding oily formulations containing probiotics (WO2010122107A1 and WO2010103374A9) and the use of probiotics for gastrointestinal complaints (US20110110905A1 and US9057112B2) exist, or are pending application. Objective: In this work, we studied the effect of a fixed combination of registered Lactobacillus reuteri and Lactobacillus acidophilus strains and herbal extracts in an in vitro inflammation experimental model. Methods: Caco-2 cell monolayer was exposed to INF-\u3b3+TNF-\u3b1 or to LPS; Trans Epithelial Electrical Resistance (TEER) and paracellular permeability were investigated. ZO-1 and occludin Tight Junctions (TJs) were also investigated by mean of immunofluorescence. Results: Pre-treatment with the fixed combination of probiotics and herbal extracts prevented the inflammation-induced TEER decrease, paracellular permeability increase and TJs translocation. Conclusions: In summary, the fixed combination of probiotics and herbal extracts investigated in this research was found to be an interesting candidate for targeting the re-establishment of intestinal barrier function in IBD conditions

An evolution strategy approach for the balanced minimum evolution problem

Motivation: The Balanced Minimum Evolution (BME) is a powerful distance based phylogenetic estimation model introduced by Desper and Gascuel and nowadays implemented in popular tools for phylogenetic analyses. It was proven to be computationally less demanding than more sophisticated estimation methods, e.g. maximum likelihood or Bayesian inference while preserving the statistical consistency and the ability to run with almost any kind of data for which a dissimilarity measure is available. BME can be stated in terms of a nonlinear non-convex combinatorial optimization problem, usually referred to as the Balanced Minimum Evolution Problem (BMEP). Currently, the state-of-the-art among approximate methods for the BMEP is represented by FastME (version 2.0), a software which implements several deterministic phylogenetic construction heuristics combined with a local search on specific neighbourhoods derived by classical topological tree rearrangements. These combinations, however, may not guarantee convergence to close-to-optimal solutions to the problem due to the lack of solution space exploration, a phenomenon which is exacerbated when tackling molecular datasets characterized by a large number of taxa. Results: To overcome such convergence issues, in this article, we propose a novel metaheuristic, named PhyloES, which exploits the combination of an exploration phase based on Evolution Strategies, a special type of evolutionary algorithm, with a refinement phase based on two local search algorithms. Extensive computational experiments show that PhyloES consistently outperforms FastME, especially when tackling larger datasets, providing solutions characterized by a shorter tree length but also significantly different from the topological perspective

Correlations in Networks associated to Preferential Growth

Combinations of random and preferential growth for both on-growing and stationary networks are studied and a hierarchical topology is observed. Thus for real world scale-free networks which do not exhibit hierarchical features preferential growth is probably not the main ingredient in the growth process. An example of such real world networks includes the protein-protein interaction network in yeast, which exhibits pronounced anti-hierarchical features.Comment: 4 pages, 4 figure

Topological Parallax: A Geometric Specification for Deep Perception Models

For safety and robustness of AI systems, we introduce topological parallax as a theoretical and computational tool that compares a trained model to a reference dataset to determine whether they have similar multiscale geometric structure. Our proofs and examples show that this geometric similarity between dataset and model is essential to trustworthy interpolation and perturbation, and we conjecture that this new concept will add value to the current debate regarding the unclear relationship between overfitting and generalization in applications of deep-learning. In typical DNN applications, an explicit geometric description of the model is impossible, but parallax can estimate topological features (components, cycles, voids, etc.) in the model by examining the effect on the Rips complex of geodesic distortions using the reference dataset. Thus, parallax indicates whether the model shares similar multiscale geometric features with the dataset. Parallax presents theoretically via topological data analysis [TDA] as a bi-filtered persistence module, and the key properties of this module are stable under perturbation of the reference dataset.Comment: 18 pages, 6 figures. Preprint submitted to NeurIPS 202

A Class Representative Model for Pure Parsimony Haplotyping under Uncertain Data

The Pure Parsimony Haplotyping (PPH) problem is a NP-hard combinatorial optimization problem that consists of finding the minimum number of haplotypes necessary to explain a given set of genotypes. PPH has attracted more and more attention in recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from mapping complex disease genes to inferring population histories, passing through designing drugs, functional genomics and pharmacogenetics. In this article we investigate, for the first time, a recent version of PPH called the Pure Parsimony Haplotype problem under Uncertain Data (PPH-UD). This version mainly arises when the input genotypes are not accurate, i.e., when some single nucleotide polymorphisms are missing or affected by errors. We propose an exact approach to solution of PPH-UD based on an extended version of Catanzaro et al. [1] class representative model for PPH, currently the state-of-the-art integer programming model for PPH. The model is efficient, accurate, compact, polynomial-sized, easy to implement, solvable with any solver for mixed integer programming, and usable in all those cases for which the parsimony criterion is well suited for haplotype estimation

Internal rotation of red giants by asteroseismology

We present an asteroseismic approach to study the dynamics of the stellar interior in red-giant stars by asteroseismic inversion of the splittings induced by the stellar rotation on the oscillation frequencies. We show preliminary results obtained for the red giant KIC4448777 observed by the space mission Kepler.Comment: 3 pages, 4 figures, the 40th Liege International Astrophysical Colloquium Liac40, 'Ageing low mass stars: from red giants to white dwarfs', to be published on EPJ Web of Conference

Diffusion-annihilation processes in complex networks

We present a detailed analytical study of the $A+A\to\emptyset$ diffusion-annihilation process in complex networks. By means of microscopic arguments, we derive a set of rate equations for the density of $A$ particles in vertices of a given degree, valid for any generic degree distribution, and which we solve for uncorrelated networks. For homogeneous networks (with bounded fluctuations), we recover the standard mean-field solution, i.e. a particle density decreasing as the inverse of time. For heterogeneous (scale-free networks) in the infinite network size limit, we obtain instead a density decreasing as a power-law, with an exponent depending on the degree distribution. We also analyze the role of finite size effects, showing that any finite scale-free network leads to the mean-field behavior, with a prefactor depending on the network size. We check our analytical predictions with extensive numerical simulations on homogeneous networks with Poisson degree distribution and scale-free networks with different degree exponents.Comment: 9 pages, 5 EPS figure

Diffusion-annihilation processes in complex networks

We present a detailed analytical study of the $A+A\to\emptyset$ diffusion-annihilation process in complex networks. By means of microscopic arguments, we derive a set of rate equations for the density of $A$ particles in vertices of a given degree, valid for any generic degree distribution, and which we solve for uncorrelated networks. For homogeneous networks (with bounded fluctuations), we recover the standard mean-field solution, i.e. a particle density decreasing as the inverse of time. For heterogeneous (scale-free networks) in the infinite network size limit, we obtain instead a density decreasing as a power-law, with an exponent depending on the degree distribution. We also analyze the role of finite size effects, showing that any finite scale-free network leads to the mean-field behavior, with a prefactor depending on the network size. We check our analytical predictions with extensive numerical simulations on homogeneous networks with Poisson degree distribution and scale-free networks with different degree exponents.Comment: 9 pages, 5 EPS figure