Preventing Pseudomonas aeruginosa and Chromobacterium violaceum infections by anti-adhesion-active components of edible seeds

<p>Abstract</p> <p>Background</p> <p><it>Pseudomonas aeruginosa </it>adhesion to animal/human cells for infection establishment involves adhesive proteins, including its galactose- and fucose-binding lectins PA-IL (LecA) and PA-IIL (LecB). The lectin binding to the target-cell receptors may be blocked by compatible glycans that compete with those of the receptors, functioning as anti-adhesion glycodecoys. The anti-adhesion treatment is of the utmost importance for abrogating devastating antibiotic-resistant <it>P. aeruginosa </it>infections in immunodeficient and cystic fibrosis (CF) patients. This strategy functions in nature in protecting embryos and neonates. We have shown that PA-IL, PA-IIL, and also CV-IIL (a PA-IIL homolog produced in the related pathogen <it>Chromobacterium violaceum</it>) are highly useful for revealing natural glycodecoys that surround embryos in diverse avian eggs and are supplied to neonates in milks and royal jelly. In the present study, these lectins were used as probes to search for seed embryo-protecting glycodecoys.</p> <p>Methods</p> <p>The lectin-blocking glycodecoy activities were shown by the hemagglutination-inhibition test. Lectin-binding glycoproteins were detected by Western blotting with peroxidase-labeled lectins.</p> <p>Results</p> <p>The present work reports the finding - by using PA-IL, PA-IIL, and CV-IIL - of rich glycodecoy activities of low (< 10 KDa) and high MW (> 10 kDa) compounds (including glycoproteins) in extracts of cashew, cocoa, coffee, pumpkin, and tomato seeds, resembling those of avian egg whites, mammal milks, and royal jelly.</p> <p>Conclusions</p> <p>Edible seed extracts possess lectin-blocking glycodecoys that might protect their embryos from infections and also might be useful for hampering human and animal infections.</p

Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio

The universe formation by a space reduction cascade with random initial parameters

In this paper we discuss the creation of our universe using the idea of extra dimensions. The initial, multidimensional Lagrangian contains only metric tensor. We have found many sets of the numerical values of the Lagrangian parameters corresponding to the observed low-energy physics of our universe. Different initial parameters can lead to the same values of fundamental constants by the appropriate choice of a dimensional reduction cascade. This result diminishes the significance of the search for the 'unique' initial Lagrangian. We also have obtained a large number of low-energy vacua, which is known as a 'landscape' in the string theory.Comment: 17 pages, 1 figur

Provision of pupulation of Sverdlovsk region with nutritional substances, affecting the development of cancer diseases

The aim was to evaluate the security of the rations of various population groups in Sverdlovsk region food substances that increase resistance to the effects of carcinogens.Целью исследований явилась оценка обеспеченности рационов различных групп населения Свердловской области пищевыми веществами, влияющими на устойчивость организма к воздействию канцерогенных факторов

Contrasting Diversity Patterns of Crenarchaeal, Bacterial and Fungal Soil Communities in an Alpine Landscape

International audienceBackground: The advent of molecular techniques in microbial ecology has aroused interest in gaining an understanding about the spatial distribution of regional pools of soil microbes and the main drivers responsible of these spatial patterns. Here, we assessed the distribution of crenarcheal, bacterial and fungal communities in an alpine landscape displaying high turnover in plant species over short distances. Our aim is to determine the relative contribution of plant species composition, environmental conditions, and geographic isolation on microbial community distribution. Methodology/Principal Findings: Eleven types of habitats that best represent the landscape heterogeneity were investigated. Crenarchaeal, bacterial and fungal communities were described by means of Single Strand Conformation Polymorphism. Relationships between microbial beta diversity patterns were examined by using Bray-Curtis dissimilarities and Principal Coordinate Analyses. Distance-based redundancy analyses and variation partitioning were used to estimate the relative contributions of different drivers on microbial beta diversity. Microbial communities tended to be habitat- specific and did not display significant spatial autocorrelation. Microbial beta diversity correlated with soil pH. Fungal beta- diversity was mainly related to soil organic matter. Though the effect of plant species composition was significant for all microbial groups, it was much stronger for Fungi. In contrast, geographic distances did not have any effect on microbial beta diversity. Conclusions/Significance: Microbial communities exhibit non-random spatial patterns of diversity in alpine landscapes. Crenarcheal, bacterial and fungal community turnover is high and associated with plant species composition through different set of soil variables, but is not caused by geographical isolation

Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.Comment: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Societ

Magnetic Field Amplification in Galaxy Clusters and its Simulation

We review the present theoretical and numerical understanding of magnetic field amplification in cosmic large-scale structure, on length scales of galaxy clusters and beyond. Structure formation drives compression and turbulence, which amplify tiny magnetic seed fields to the microGauss values that are observed in the intracluster medium. This process is intimately connected to the properties of turbulence and the microphysics of the intra-cluster medium. Additional roles are played by merger induced shocks that sweep through the intra-cluster medium and motions induced by sloshing cool cores. The accurate simulation of magnetic field amplification in clusters still poses a serious challenge for simulations of cosmological structure formation. We review the current literature on cosmological simulations that include magnetic fields and outline theoretical as well as numerical challenges.Comment: 60 pages, 19 Figure