DC-DC Energy Conversion with Novel loaded Resonant Converter

This paper presents the direct current (dc)-to-dc energy conversion with novel loaded-resonant converter. Energy shortages and increasing oil prices have created the demand for a high energy conversion efficiency and performance. The growing electronic product market has increased the demand for high energy conversion efficiency and high power density of dc-to-dc energy power converters. The soft switching scheme is the most attractive dc-to-dc energy conversion topology in recent years. The soft-switching method can reduce switching losses and EMI of the switch-mode converter. Among the many advantages that resonant power conversion has over conventionally adopted pulse-width modulation include a low electromagnetic interference, low switching losses, small volume, and light weight of components due to a high switching frequency, high efficiency, and low reverse recovery losses in diodes owing to a low di/dt at switching instant. The proposed topology comprises a half-bridge inductor-capacitor inductor (L-C-L) resonant inverter and a bridge rectifier. Output stage of the proposed loaded-resonant converter is filtered by a low-pass filter. A prototype dc-to-dc energy converter circuit with the novel loaded-resonant converter designed for a load is developed and tested to verify its analytical predictions. The measured energy conversion efficiency of the proposed novel loaded-resonant topology reaches up to 88.3%. Moreover, test results demonstrate a satisfactory performance of the proposed topology. Furthermore, the proposed topology is highly promising for applications of switching power supplies, battery chargers, uninterruptible power systems, renewable energy generation systems, and telecom power supplies. The experimental results are clearly verified by simulation results

Mould incidence and mycotoxin contamination in freshly harvested maize kernels originated from India

BACKGROUND: In this study, mould incidence and mycotoxin contamination were determined in freshly harvested maize samples collected from different agroclimatic regions of India. A total of 150 freshly harvested maize samples from major maize-growing areas of India (Karnataka, Andhra Pradesh and Tamilnadu) were collected during winter seasons 2010-2011 and 2011-2012 to determine their toxigenic fungal incidences, and mycotoxins were analyzed and quantified by high-perfomance liquid chromatography. A total of 288 fungal isolates comprising Fusarium, Aspergillus and Penicillium species were tested for aflatoxin B1 (AFB1), ochratoxin A (OTA), trichothecenes (deoxynivalenol (DON) and T-2 toxin) and fumonisin B1 (FB1). Chemotype determination of fungal isolates was carried out by molecular and chemical analysis through polymerase chain reaction (PCR) and high-performance thin layer chromatography respectively. The diversity and distribution of the mycoflora among the studied samples were recorded in terms of frequency, density, importance value index and diversity indices. RESULTS: A total of 288 fungal isolates were recovered from the 150 maize samples, of which 28 were positive for AFB1, 20 for OTA, 58 for FB1, 23 for DON and 11 for T-2 toxin chemotypes by PCR. Species-specific PCR assays were in line with morphological analysis. Toxigenic fungal incidences were found throughout the study region, and most of the toxins under study exceeded the maximum legal limits. The range of observed toxin concentrations were 48-58 &micro;g AFB1, 76-123 &micro;g FB1, 38-50 &micro;g T-2, 72-94 &micro;g DON and &lt;5 &micro;g OTA kg(-1) grain sample. CONCLUSION: Owing to the high incidences of toxigenic moulds and mycotoxins in the study area, there is a need for the creation of mycotoxin awareness among maize farmers of India to control the chronic adverse health effects on humans and livestock due to mycotoxins.</p

Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images

This research paper explores the classification of knee osteoarthritis (OA) severity levels using advanced computer vision models and augmentation techniques. The study investigates the effectiveness of data preprocessing, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data augmentation using diffusion models. Three experiments were conducted: training models on the original dataset, training models on the preprocessed dataset, and training models on the augmented dataset. The results show that data preprocessing and augmentation significantly improve the accuracy of the models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the augmented dataset. Additionally, attention visualization techniques, such as Grad-CAM, are utilized to provide detailed attention maps, enhancing the understanding and trustworthiness of the models. These findings highlight the potential of combining advanced models with augmented data and attention visualization for accurate knee OA severity classification.Comment: Paper has been accepted to be presented at ICACECS 2023 and the final version will be published by Atlantis Highlights in Computer Science (AHCS) , Atlantis Press(part of Springer Nature

Ferredoxin:NADP(H) Oxidoreductase Abundance and Location Influences Redox Poise and Stress Tolerance

In linear photosynthetic electron transport, ferredoxin:NADP(H) oxidoreductase (FNR) transfers electrons from ferredoxin (Fd) to NADP(+). Both NADPH and reduced Fd (Fd(red)) are required for reductive assimilation and light/dark activation/deactivation of enzymes. FNR is therefore a hub, connecting photosynthetic electron transport to chloroplast redox metabolism. A correlation between FNR content and tolerance to oxidative stress is well established, although the precise mechanism remains unclear. We investigated the impact of altered FNR content and localization on electron transport and superoxide radical evolution in isolated thylakoids, and probed resulting changes in redox homeostasis, expression of oxidative stress markers, and tolerance to high light in planta. Our data indicate that the ratio of Fd(red) to FNR is critical, with either too much or too little FNR potentially leading to increased superoxide production, and perception of oxidative stress at the level of gene transcription. In FNR overexpressing plants, which show more NADP(H) and glutathione pools, improved tolerance to high-light stress indicates that disturbance of chloroplast redox poise and increased free radical generation may help “prime” the plant and induce protective mechanisms. In fnr1 knock-outs, the NADP(H) and glutathione pools are more oxidized relative to the wild type, and the photoprotective effect is absent despite perception of oxidative stress at the level of gene transcription

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

The Reactome pathway Knowledgebase

The Reactome Knowledgebase (www.reactome.org) provides molecular details of signal transduction, transport, DNA replication, metabolism and other cellular processes as an ordered network of molecular transformations-an extended version of a classic metabolic map, in a single consistent data model. Reactome functions both as an archive of biological processes and as a tool for discovering unexpected functional relationships in data such as gene expression pattern surveys or somatic mutation catalogues from tumour cells. Over the last two years we redeveloped major components of the Reactome web interface to improve usability, responsiveness and data visualization. A new pathway diagram viewer provides a faster, clearer interface and smooth zooming from the entire reaction network to the details of individual reactions. Tool performance for analysis of user datasets has been substantially improved, now generating detailed results for genome-wide expression datasets within seconds. The analysis module can now be accessed through a RESTFul interface, facilitating its inclusion in third party applications. A new overview module allows the visualization of analysis results on a genome-wide Reactome pathway hierarchy using a single screen page. The search interface now provides auto-completion as well as a faceted search to narrow result lists efficiently

Reactome knowledgebase of human biological pathways and processes

Reactome (http://www.reactome.org) is an expert-authored, peer-reviewed knowledgebase of human reactions and pathways that functions as a data mining resource and electronic textbook. Its current release includes 2975 human proteins, 2907 reactions and 4455 literature citations. A new entity-level pathway viewer and improved search and data mining tools facilitate searching and visualizing pathway data and the analysis of user-supplied high-throughput data sets. Reactome has increased its utility to the model organism communities with improved orthology prediction methods allowing pathway inference for 22 species and through collaborations to create manually curated Reactome pathway datasets for species including Arabidopsis, Oryza sativa (rice), Drosophila and Gallus gallus (chicken). Reactome's data content and software can all be freely used and redistributed under open source terms

The Reactome pathway knowledgebase

Reactome (http://www.reactome.org) is a manually curated open-source open-data resource of human pathways and reactions. The current version 46 describes 7088 human proteins (34% of the predicted human proteome), participating in 6744 reactions based on data extracted from 15 107 research publications with PubMed links. The Reactome Web site and analysis tool set have been completely redesigned to increase speed, flexibility and user friendliness. The data model has been extended to support annotation of disease processes due to infectious agents and to mutation

Reactome: a database of reactions, pathways and biological processes

Reactome (http://www.reactome.org) is a collaboration among groups at the Ontario Institute for Cancer Research, Cold Spring Harbor Laboratory, New York University School of Medicine and The European Bioinformatics Institute, to develop an open source curated bioinformatics database of human pathways and reactions. Recently, we developed a new web site with improved tools for pathway browsing and data analysis. The Pathway Browser is an Systems Biology Graphical Notation (SBGN)-based visualization system that supports zooming, scrolling and event highlighting. It exploits PSIQUIC web services to overlay our curated pathways with molecular interaction data from the Reactome Functional Interaction Network and external interaction databases such as IntAct, BioGRID, ChEMBL, iRefIndex, MINT and STRING. Our Pathway and Expression Analysis tools enable ID mapping, pathway assignment and overrepresentation analysis of user-supplied data sets. To support pathway annotation and analysis in other species, we continue to make orthology-based inferences of pathways in non-human species, applying Ensembl Compara to identify orthologs of curated human proteins in each of 20 other species. The resulting inferred pathway sets can be browsed and analyzed with our Species Comparison tool. Collaborations are also underway to create manually curated data sets on the Reactome framework for chicken, Drosophila and rice