THE ORIGIN OF MASS IN NEUTRONS AND PROTONS

@IAC

Hydrogen sulfide protects renal grafts against prolonged cold ischemia-reperfusion injury via specific mitochondrial actions

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/ajt.14080 This article is protected by copyright. All rights reserved.Accepted manuscript online: 15 October 2016Ischemia-reperfusion injury (IRI) is unavoidably caused by loss and subsequent restoration of blood flow during organ procurement and prolonged IRI results in increased rates of delayed graft function and early graft loss. The endogenously produced gasotransmitter, hydrogen sulfide (H2 S), is a novel molecule that mitigates hypoxic tissue injury. The current study investigates the protective mitochondrial effects of H2 S during in vivo cold storage and subsequent renal transplantation (RTx) and in vitro cold hypoxic renal injury. Donor allografts from Brown Norway rats treated with University of Wisconsin (UW) solution + H2 S (150 μM NaSH) during prolonged (24-hour) cold (4°C) storage exhibited significantly (p1000-fold compared to similar levels of the non-specific H2 S donor, GYY4137 and also improved syngraft function and survival following prolonged cold storage compared to UW. H2 S treatment mitigates cold IRI-associated renal injury via mitochondrial actions and could represent a novel therapeutic strategy to minimize the detrimental clinical outcomes of prolonged cold IRI during RTx.This work was supported by grants from Physicians Services Incorporated and the Canadian Urological Association (AS) and by a Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award from the Canadian Institutes of Health Research (IL). MW and MEW would like to thank the Medical Research Council UK (MR/M022706/1) for their generous research support. RT would like to acknowledge the Brian Ridge Scholarship for support

Performance improvement of perturb and observe maximum power point tracking technique for solar PV applications

© Springer Nature Switzerland AG 2020. The renewable energy market has increased significantly over the last decade, and the solar photovoltaic (PV) power generation is becoming important in many countries globally with a particular interest in the field of distributed electric power generation. A regular and accurate examination and evaluation of the photovoltaic system performance and efficiency are very essential in the sense that they provide vital information of the system’s quality evaluation for the users, installers, as well as the manufacturers. The maximum power point of a solar panel varies with the irradiation and temperature and the control algorithms are commonly used for the maximization of the power extraction from PV arrays known as maximum power point tracking (MPPT) algorithms. Perturb and Observe (P&O) algorithm is one of the popular techniques frequently used due to its easy implementation and low cost. The MPPT technique is mainly used for obtaining the maximum power from the solar PV module and conversion circuit to the load and improving the power quality of PV power generation for grid connection. Perturb and Observe maximum power point tracking (MPPT) is extensively used in charge controllers for extracting maximum power from photovoltaic (PV) module irrespective of irradiance, temperature and load variation. The standard P&O MPPT technique has drawbacks bordering on fast convergence time to a maximum power point, poor system response to fast-changing irradiance and steady-state oscillation with a fixed step size. This chapter discusses the detailed operation and implementation of an improved P&O algorithm technique to resolve the various challenges of the standard P&O algorithm. This technique segments the operational region of the PV array into four operating sectors based on the sector location from the maximum power point (MPP), step size modifications are implemented. Furthermore, the critical comparison is made between the new P&O method and the standard P&O method. Finally, the hardware implementation of both MPPT algorithms is discussed in order to evaluate their performance and efficiency. The measured results show that the average efficiency of the proposed system is 96.89% which is more than 4% higher than the standard system

Multi-scale pedestrian intent prediction using 3D joint information as spatio-temporal representation

Data availability: Only publicly available data were used.Copyright © 2023 The Author(s). There has been a rise of use of Autonomous Vehicles on public roads. With the predicted rise of road traffic accidents over the coming years, these vehicles must be capable of safely operate in the public domain. The field of pedestrian detection has significantly advanced in the last decade, providing high-level accuracy, with some technique reaching near-human level accuracy. However, there remains further work required for pedestrian intent prediction to reach human-level performance. One of the challenges facing current pedestrian intent predictors are the varying scales of pedestrians, particularly smaller pedestrians. This is because smaller pedestrians can blend into the background, making them difficult to detect, track or apply pose estimations techniques. Therefore, in this work, we present a novel intent prediction approach for multi-scale pedestrians using 2D pose estimation and a Long Short-term memory (LSTM) architecture. The pose estimator predicts keypoints for the pedestrian along the video frames. Based on the accumulation of these keypoints along the frames, spatio-temporal data is generated. This spatio-temporal data is fed to the LSTM for classifying the crossing behaviour of the pedestrians. We evaluate the performance of the proposed techniques on the popular Joint Attention in Autonomous Driving (JAAD) dataset and the new larger-scale Pedestrian Intention Estimation (PIE) dataset. Using data generalisation techniques, we show that the proposed technique outperformed the state-of-the-art techniques by up to 7%, reaching up to 94% accuracy while maintaining a comparable run-time of 6.1 ms

Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning

Genomic data analysis is a fundamental system for monitoring pathogen evolution and the outbreak of infectious diseases. Based on bioinformatics and deep learning, this study was designed to identify the genomic variability of SARS-CoV-2 worldwide and predict the impending mutation rate. Analysis of 259044 SARS-CoV-2 isolates identified 3334545 mutations with an average of 14.01 mutations per isolate. Globally, single nucleotide polymorphism (SNP) is the most prevalent mutational event. The prevalence of C > T (52.67%) was noticed as a major alteration across the world followed by the G > T (14.59%) and A > G (11.13%). Strains from India showed the highest number of mutations (48) followed by Scotland, USA, Netherlands, Norway, and France having up to 36 mutations. D416G, F106F, P314L, UTR:C241T, L93L, A222V, A199A, V30L, and A220V mutations were found as the most frequent mutations. D1118H, S194L, R262H, M809L, P314L, A8D, S220G, A890D, G1433C, T1456I, R233C, F263S, L111K, A54T, A74V, L183A, A316T, V212F, L46C, V48G, Q57H, W131R, G172V, Q185H, and Y206S missense mutations were found to largely decrease the structural stability of the corresponding proteins. Conversely, D3L, L5F, and S97I were found to largely increase the structural stability of the corresponding proteins. Multi-nucleotide mutations GGG > AAC, CC > TT, TG > CA, and AT > TA have come up in our analysis which are in the top 20 mutational cohort. Future mutation rate analysis predicts a 17%, 7%, and 3% increment of C > T, A > G, and A > T, respectively in the future. Conversely, 7%, 7%, and 6% decrement is estimated for T > C, G > A, and G > T mutations, respectively. T > G\A, C > G\A, and A > T\C are not anticipated in the future. Since SARS-CoV-2 is mutating continuously, our findings will facilitate the tracking of mutations and help to map the progression of the COVID-19 intensity worldwide

Thermoplastic Composites reinforced with Multi-layer Woven Jute Fabric: A Comparative Analysis

Three commonly available thermoplastic matrices - High-density polyethylene (HDPE), polypropylene (PP) and nylon 6 – are reinforced with hessian jute fabric in multi-layer sequence to prepare composite laminates by compression moulding technique. The composite had a nominal fibre content of 18% in terms of weight and a nominal thickness of 6.5 mm. The mechanical and fracture behaviours of the resultant laminates are tested and compared. It was found that the Nylon-Jute composite exhibited the highest values of tensile strength, Young’s modulus, flexural strength, flexural modulus and hardness. On the other hand, HDPE-Jute composite showed relatively poor performance. Interestingly, the HDPE-Jute composite exhibited the highest impact strength and the Nylon-Jute composite was the poorest in this regard. The amount of water absorption by the composites from highest to the lowest was found in the following order: Nylon-Jute > HDPE-Jute > PP-Jute