Hybrid Composite Laminates from ESOA-BisGMA Blend and 2-Hydroxyethyl Acrylate (HEA) Treated Jute Fiber

The development of an inter-cross-linked polymer network of thermoset-thermoset blends have been extensively studied due to their enhanced mechanical properties. Among various polymer blends, modifications of vinyl ester resin (VER) i.e. styrene cross-linkable Bisphenol-A glycidyldimethacrylate (BisGMA) with epoxidized soybean oil acrylate (ESOA) combinations are an attractive route to promote the performance of the thermoset matrix and to overcome the inferior properties of both the components. The primary goal of this research is to develop hybrid composite laminates from ESOA-BisGMA blend (50:50 wt%) using both untreated and 2-hydroxyethyl acrylate (HEA) treated jute fiber as reinforcement and then to characterize thereof. The mechanical properties like tensile strength, bending strength bending E.modulus, dynamic mechanical analysis, corrosion and ageing studies have been investigated. The results suggested improved properties of the hybrid systems with the incorporation of ESOA-BisGMA blend as the composite matrix. Moreover, HEA treatment of jute fiber enhanced the composite properties further, which interestingly, outperformed the parent ESOA-BisGMA blend and untreated jute-ESOA/BisGMA blend based composite. In this investigation 5 ply of jute fabric has been reinforced into ESOA-BisGMA blend matrix, so that at a low cost thin sheets can be produced. This may be used as an alternate material to wood, which has not been carried out elsewhere

Research Performance of National Institute of Technology Rourkela: A Scientometric Analysis

This paper\u27s main objective is to perform a scientometric study on the National Institute of Technology, Rourkela, research publications, as reflected in the Scopus Database. The study evaluated the quantitative growth, author and Institute collaborations, using different scientometric dimensions. Appropriate keywords were used to cover the entire spectrum of research publications that yield 9233 results from the database. To make the analysis concise to get better outcomes, authors have limited the study period to the publications produced from 2000 to 2019. This study analyzed different aspects of research productivity, such as year-wise growth of publications, most preferred sources for the publications, authorship pattern, subject-wise distribution of papers, etc. Furthermore, the study also explored the international research collaboration patterns of the authors. The analysis identified 2018 as the most productive year with a publication of 1339 research papers and 102692 citations for all publications during the selected period, with an average of 11.12 citations per paper. The majority of the papers have double authorship patterns, and the degree of collaboration and collaborative co-efficient is apparent with a total of 0.97 and 0.61, respectively. The study further identified that Mahapatra, S. S was the most productive author with 295 articles and 5650 citations and the Core/shell nanoparticles: classes, properties, synthesis mechanisms, characterization, and applications by Ghosh Chaudhuri R Paria S. published in Chemical Reviews of 2012 is the most highly cited (2045) paper and \u27IOP Conference Series Material Science and Engineering\u27 is the topmost preferred source of publication. Scientometric studies are useful tools for measuring the scientific and technological progress that cannot be directly measured. Various scientometric indicators are used as analytical tools to perform such assessments

Existing plaques and neuritic abnormalities in APP:PS1 mice are not affected by administration of the gamma-secretase inhibitor LY-411575

The γ-secretase complex is a major therapeutic target for the prevention and treatment of Alzheimer's disease. Previous studies have shown that treatment of young APP mice with specific inhibitors of γ-secretase prevented formation of new plaques. It has not yet been shown directly whether existing plaques would be affected by γ-secretase inhibitor treatment. Similarly, alterations in neuronal morphology in the immediate vicinity of plaques represent a plaque-specific neurotoxic effect. Reversal of these alterations is an important endpoint of successful therapy whether or not a treatment affects plaque size. In the present study we used longitudinal imaging in vivo with multiphoton microscopy to study the effects of the orally active γ-secretase inhibitor LY-411575 in 10–11 month old APP:PS1 mice with established amyloid pathology and neuritic abnormalities. Neurons expressed YFP allowing fluorescent detection of morphology whereas plaques were labelled with methoxy-XO4. The same identified neurites and plaques were followed in weekly imaging sessions in living mice treated daily (5 mg/kg) for 3 weeks with the compound. Although LY-411575 reduced Aβ levels in plasma and brain, it did not have an effect on the size of existing plaques. There was also no effect on the abnormal neuritic curvature near plaques, or the dystrophies in very close proximity to senile plaques. Our results suggest that therapeutics aimed at inhibition of Aβ generation are less effective for reversal of existing plaques than for prevention of new plaque formation and have no effect on the plaque-mediated neuritic abnormalities, at least under these conditions where Aβ production is suppressed but not completely blocked. Therefore, a combination therapy of Aβ suppression with agents that increase clearance of amyloid and/or prevent neurotoxicity might be needed for a more effective treatment in patients with pre-existing pathology

Research Performance of National Institute of Technology Rourkela: A Scientometric Analysis

Scientometric studies are useful tools for measuring the scientific and technological progress that cannot be directly measured. Various scientometric indicators are used as analytical tools to perform such assessments. This study performed a scientometric analysis on the research publications of National Institute of Technology, Rourkela from 2000 to 2019 as reflected in the Scopus Database. The study analyzed different aspects of research productivity for 9233 publications and identified 2018 as the most productive year with a publication of 1339 research papers and 102692 citations for all publications with an average of 11.12 citations per paper. The majority of the papers have double authorship patterns, and the degree of collaboration and collaborative co-efficient is apparent with a total of 0.97 and 0.61, respectively. Study identified Mahapatra, S. S as the most productive author with 295 articles and 5650 citations and the "Core/shell nanoparticles: classes, properties, synthesis mechanisms, characterization, and applications" by Ghosh Chaudhuri R Paria S. published in Chemical Reviews of 2012 is the most highly cited (2045) paper and 'IOP Conference Series Material Science and Engineering' is the topmost preferred source of publication

Human protein reference database—2006 update

Human Protein Reference Database (HPRD) () was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein–protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein–protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data

Trade-Offs Between Reducing Complex Terminology and Producing Accurate Interpretations from Environmental DNA: Comment on “Environmental DNA: What\u27s behind the term?” by Pawlowski et al., (2020)

In a recent paper, “Environmental DNA: What\u27s behind the term? Clarifying the terminology and recommendations for its future use in biomonitoring,” Pawlowski et al. argue that the term eDNA should be used to refer to the pool of DNA isolated from environmental samples, as opposed to only extra-organismal DNA from macro-organisms. We agree with this view. However, we are concerned that their proposed two-level terminology specifying sampling environment and targeted taxa is overly simplistic and might hinder rather than improve clear communication about environmental DNA and its use in biomonitoring. This terminology is based on categories that are often difficult to assign and uninformative, and it overlooks a fundamental distinction within eDNA: the type of DNA (organismal or extra-organismal) from which ecological interpretations are derived

Gene-Based Tests of Association

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%–50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis

Combined searches for the production of supersymmetric top quark partners in proton-proton collisions at root s=13 TeV

A combination of searches for top squark pair production using proton-proton collision data at a center-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated luminosity of 137 fb(-1) collected by the CMS experiment, is presented. Signatures with at least 2 jets and large missing transverse momentum are categorized into events with 0, 1, or 2 leptons. New results for regions of parameter space where the kinematical properties of top squark pair production and top quark pair production are very similar are presented. Depending on themodel, the combined result excludes a top squarkmass up to 1325 GeV for amassless neutralino, and a neutralinomass up to 700 GeV for a top squarkmass of 1150 GeV. Top squarks with masses from 145 to 295 GeV, for neutralino masses from 0 to 100 GeV, with a mass difference between the top squark and the neutralino in a window of 30 GeV around the mass of the top quark, are excluded for the first time with CMS data. The results of theses searches are also interpreted in an alternative signal model of dark matter production via a spin-0 mediator in association with a top quark pair. Upper limits are set on the cross section for mediator particle masses of up to 420 GeV

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe