Investigation on radiation interactions with some quenched alloys used in nuclear reactors.

This study presents the investigation of the radiation interaction properties for SS304 and Incoloy 800H alloys, which are widely used in PWRs and HTGRs. First of all, theoretical and MC simulation evaluations are performed, then experiments are conducted for further analysis. The findings indicate no significant difference in mass attenuation coefficients (MAC) and gamma-ray radiation protection efficiencies (RPE) between the two alloys.

Loss of WWOX contributes to cisplatin resistance in triple-negative breast cancer cells by modulating miR-182 and miR-214.

Background/aim: WW domain-containing oxidoreductase (WWOX) loss frequently occurs in triple-negative breast cancer (TNBC). WWOX loss enhances cisplatin resistance in TNBC patients. Although WWOX loss has an effect on the selection of a DNA repair pathway that contributes to enhanced mutagenesis, the downstream expression changes in resistant cancer cells have not been fully explored.

A study on the interaction parameters of charged and uncharged radiation types with some indoor plants.

This study investigates how gamma rays, neutrons, and electrons interact with five commonly found indoor plants: Spathiphyllum wallisii (SW), Ficus elastica (FE), Dieffenbachia camilla (DC), Schefflera arboricola (SA), and Ficus benjamina (FB). Utilizing experimental measurements (with HPGe detector), Monte Carlo simulations (GEANT4 and FLUKA), and theoretical calculations (ESTAR and WinXCOM), some radiation interaction parameters for gamma rays, fast neutrons, thermal neutrons, and electrons were determined. Secondary particle generation was also analyzed to provide a comprehensive assessment.

Estimation of neutron and gamma-ray attenuation characteristics of some ferrites: Geant4, FLUKA and WinXCom studies.

Ferrites are ceramic oxide materials consisting of mainly iron oxide and they have become massively important materials commercially and technologically, having a multitude of uses and applications. The protection against neutron-gamma mixed radiation is crucial in several nuclear applications. From this standpoint, mass attenuation coefficient, radiation protection efficiency and transmission factor of some ferrites namely barium, strontium, manganese, copper and cadmium ferrite has been computed using Geant4 and FLUKA simulations.

MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.

Many individuals worldwide pass away as a result of inadequate procedures for prompt illness identification and subsequent treatment. A valuable life can be saved or at least extended with the early identification of serious illnesses, such as various cancers and other life-threatening conditions. The development of the Internet of Medical Things (IoMT) has made it possible for healthcare technology to offer the general public efficient medical services and make a significant contribution to patients' recoveries.

Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach.

Numerous studies have been conducted to elucidate the relation of tumor proximity to cancer prognosis and treatment efficacy in colorectal cancer. However, the molecular pathways and prognoses of left- and right-sided colorectal cancers are different, and this difference has not been fully investigated at the genomic level. In this study, a set of data science approaches, including six feature selection methods and three classification models, were used in predicting tumor location from gene expression profiles.

Targeting HIF1-alpha/miR-326/ITGA5 axis potentiates chemotherapy response in triple-negative breast cancer.

Purpose: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer that is frequently treated with chemotherapy. However, many patients exhibit either de novo chemoresistance or ultimately develop resistance to chemotherapy, leading to significantly high mortality rates. Therefore, increasing the efficacy of chemotherapy has potential to improve patient outcomes.

Predicted SARS-CoV-2 miRNAs Associated with Epigenetic Viral Pathogenesis and the Detection of New Possible Drugs for Covid-19.

Objective: The outbreak of COVID-19 caused by SARS-CoV-2 has promptly spread worldwide. This study aimed to predict mature miRNA sequences in the SARS-CoV-2 genome, their effects on protein-protein interactions in the affected cells, and gene-drug relationships to detect possible drug candidates.

Methods: Viral hairpin structure prediction, classification of hairpins, mutational examination of precursor miRNA candidate sequences, Minimum Free Energy (MFE) and regional entropy analysis, mature miRNA sequences, target gene prediction, gene ontology enrichment, and Protein-Protein Interaction (PPI) analysis, and gene-drug interactions were performed.

A deep learning approach for sepsis monitoring via severity score estimation.

Background And Objective: Sepsis occurs in response to an infection in the body and can progress to a fatal stage. Detection and monitoring of sepsis require multi-step analysis, which is time-consuming, costly and requires medically trained personnel. A metric called Sequential Organ Failure Assessment (SOFA) score is used to determine the severity of sepsis.

Prediction of Protein Metal Binding Sites Using Deep Neural Networks.

Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which vital function is fulfilled.

A Content-Based Retrieval Framework for Whole Metagenome Sequencing Samples.

Finding similarities and differences between metagenomic samples within large repositories has been rather a significant issue for researchers. Over the recent years, content-based retrieval has been suggested by various studies from different perspectives. In this study, a content-based retrieval framework for identifying relevant metagenomic samples is developed.

Retrieving relevant time-course experiments: a study on Arabidopsis microarrays.

Understanding time-course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta-analysis of available data obtained from other relevant experiments.

Eliminating rib shadows in chest radiographic images providing diagnostic assistance.

A major difficulty with chest radiographic analysis is the invisibility of abnormalities caused by the superimposition of normal anatomical structures, such as ribs, over the main tissue to be examined. Suppressing the ribs with no information loss about the original tissue would therefore be helpful during manual identification or computer-aided detection of nodules on a chest radiographic image. In this study, we introduce a two-step algorithm for eliminating rib shadows in chest radiographic images.

Retrieving relevant experiments: The case of microRNA microarrays.

Content-based retrieval of biological experiments in large public repositories is a recent challenge in computational biology and bioinformatics. The task is, in general, to search in a database using a query-by-example without any experimental meta-data annotation. Here, we consider a more specific problem that seeks a solution for retrieving relevant microRNA experiments from microarray repositories.

miSEA: microRNA set enrichment analysis.

Unlabelled: We introduce a novel web-based tool, miSEA, for evaluating the enrichment of relevant microRNA sets from microarray and miRNA-Seq experiments on paired samples, e.g. control vs.

TriClust: A Tool for Cross-Species Analysis of Gene Regulation.

We present a software tool, called TriClust, for multi-way analysis of gene expression data from paired conditions of multiple organisms. The analysis is based on a new concept called triclustering, which is an extension of biclustering over a third dimension that represents the organism where the microarray experiment is performed. TriClust provides a comprehensive analysis of co-regulated genes under a subset of experimental conditions over multiple organisms.

Computational prediction of microRNA function and activity.

Inferring microRNA (miRNA) functions and activities has been extremely important to understand their system-level roles and the mechanisms behind the cellular behaviors of their target genes. This chapter first details methodologies necessary for prediction of function and activity. It then introduces the computational methods available for investigation of sequence and experimental data and for analysis of the information flow mediated through miRNAs.

TRAINER: a general-purpose trainable short biosequence classifer.

Classifying sequences is one of the central problems in computational biosciences. Several tools have been released to map an unknown molecular entity to one of the known classes using solely its sequence data. However, all of the existing tools are problem-specific and restricted to an alphabet constrained by relevant biological structure.

A probabilistic approach to microRNA-target binding.

Elucidation of microRNA activity is a crucial step in understanding gene regulation. One key problem in this effort is how to model the pairwise interactions of microRNAs with their targets. As this interaction is strongly mediated by their sequences, it is desired to set-up a probabilistic model to explain the binding preferences between a microRNA sequence and the sequence of a putative target.

Variable context Markov chains for HIV protease cleavage site prediction.

Deciphering the knowledge of HIV protease specificity and developing computational tools for detecting its cleavage sites in protein polypeptide chain are very desirable for designing efficient and specific chemical inhibitors to prevent acquired immunodeficiency syndrome. In this study, we developed a generative model based on a generalization of variable order Markov chains (VOMC) for peptide sequences and adapted the model for prediction of their cleavability by certain proteases. The new method, called variable context Markov chains (VCMC), attempts to identify the context equivalence based on the evolutionary similarities between individual amino acids.

Subcellular localization prediction with new protein encoding schemes.

Subcellular localization is one of the key properties in functional annotation of proteins. Support vector machines (SVMs) have been widely used for automated prediction of subcellular localizations. Existing methods differ in the protein encoding schemes used.

SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees.

A new method based on probabilistic suffix trees (PSTs) is defined for pairwise comparison of distantly related protein sequences. The new definition is adopted in a discriminative framework for protein classification using pairwise sequence similarity scores in feature encoding. The framework uses support vector machines (SVMs) to separate structurally similar and dissimilar examples.

A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets.

In this study, n-peptide compositions are utilized for protein vectorization over a discriminative remote homology detection framework based on support vector machines (SVMs). The size of amino acid alphabet is gradually reduced for increasing values of n to make the method to conform with the memory resources in conventional workstations. A hash structure is implemented for accelerated search of n-peptides.