842 research outputs found

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

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    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

    Correlations in Networks associated to Preferential Growth

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    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

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    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

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    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

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    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

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    We present a detailed analytical study of the A+AA+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 AA 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

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    We present a detailed analytical study of the A+AA+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 AA 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

    Spectroscopic survey of Kepler stars. I. HERMES/Mercator observations of A- and F-type stars

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    The Kepler space mission provided near-continuous and high-precision photometry of about 207 000 stars, which can be used for asteroseismology. However, for successful seismic modeling it is equally important to have accurate stellar physical parameters. Therefore, supplementary ground-based data are needed. We report the results of the analysis of high-resolution spectroscopic data of A- and F-type stars from the Kepler field, which were obtained with the HERMES spectrograph on the Mercator telescope. We determined spectral types, atmospheric parameters and chemical abundances for a sample of 117 stars. Hydrogen Balmer, Fe i, and Fe ii lines were used to derive effective temperatures, surface gravities, and microturbulent velocities. We determined chemical abundances and projected rotational velocities using a spectrum synthesis technique. The atmospheric parameters obtained were compared with those from the Kepler Input Catalogue (KIC), confirming that the KIC effective temperatures are underestimated for A stars. Effective temperatures calculated by spectral energy distribution fitting are in good agreement with those determined from the spectral line analysis. The analysed sample comprises stars with approximately solar chemical abundances, as well as chemically peculiar stars of the Am, Ap, and λ Boo types. The distribution of the projected rotational velocity, vsin i, is typical for A and F stars and ranges from 8 to about 280 km s−1, with a mean of 134 km s−1
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