Assessment of sealants for the mitigation of PFAS leaching from contaminated concrete.

There has been limited research on the presence, distribution and management of per- and poly-fluoroalkyl substances (PFAS) in concrete infrastructure exposed to historical use of aqueous film-forming foams (AFFF). Concrete sealants may constitute a rapid, effective in-situ mitigation option for leaching from PFAS contaminated concrete pavements and ex-situ for PFAS contaminated concrete waste piles. This laboratory study, using leaching and imbibition investigations, revealed six commercially available sealants as possible options to mitigate PFAS leachability from contaminated concrete.

Prediction of miRNA Targets in Plants.

MicroRNAs (miRNAs) are the central component of an important layer of post-transcriptional regulation of gene expression. In plants, miRNAs target the transcripts in a manner that is generally highly sequence complementary. Extensive research is being made to study the genome-wide miRNA-mediated regulation of gene expression, which has resulted in the development of many tools for an in-silico prediction of miRNA targets.

Contamination of per- and poly-fluoroalkyl substances in agricultural soils: A review.

Numerous reviews have focused on the chemistry, fate and transport, and remediation of per- and poly-fluoroalkyl substances (PFAS) across various environmental media. However, there remains a significant gap in the literature regarding a comprehensive review specifically addressing PFAS contamination within agricultural soils. Recognizing the threat PFAS pose to ecosystems and human health, this review critically examines the sources of PFAS in agricultural environments, their uptake and translocation within plant systems, and recent advancements in soil remediation techniques.

Size-Dependent Effects of Electron Solvation on the Kinetics of Ammonia Revealed on the Molecular Scale.

The high relevance of electron solvation in several branches of physics, chemistry, and environmental science arises from its efficient electron transfer mechanism. The effect of solvated electrons on solvent structure has been considered local and transient due to a lack of real-space studies. An experiment was designed to study the impact of solvated electrons on the ammonia structure while adsorbed to Cu(110) using low-temperature scanning tunneling microscopy with an adjoined femtosecond laser.

Mapping Molecular Pathways of Multiple Sclerosis: A Gene Prioritization and Network Analysis of White Matter Pathology Transcriptomics.

Objectives: Rapid advances in transcriptomics have driven efforts to identify deregulated pathways in multiple sclerosis (MS) tissues, though many detected differentially expressed genes are likely false positives, with only a small fraction reflecting actual pathological events. Robust, integrative methods are essential for accurately understanding the molecular mechanisms underlying MS pathology.

Methods: We conducted a gene prioritization analysis of MS white matter pathology transcriptomic studies.

Harnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach.

The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.

Cell state-dependent allelic effects and contextual Mendelian randomization analysis for human brain phenotypes.

Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.

Bifurcation and stability analysis of within host HIV dynamics with multiple infections and intracellular delay.

Human immunodeficiency virus (HIV) manifests multiple infections in CD4+ T cells, by binding its envelope proteins to CD4 receptors. Understanding these biological processes is crucial for effective interventions against HIV/AIDS. Here, we propose a mathematical model that accounts for the multiple infections of CD4+ T cells and an intracellular delay in the dynamics of HIV infection.

Comprehensive analysis of inhibin-β A as a potential biomarker for gastrointestinal tract cancers through bioinformatics approaches.

Inhibin, β, which is also known as INHBA, encodes a protein that belongs to the Transforming Growth factor-β (TGF-β) superfamily, which plays a pivotal role in cancer. Gastrointestinal tract (GI tract) cancer refers to the cancers that develop in the colon, liver, esophagus, stomach, rectum, pancreas, and bile ducts of the digestive system. The role of INHBA in all GI tract cancers remains understudied.

Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.

Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral data cube for turbidity prediction in inland water. Nine machine learning regressors Cat Boost, Decision Trees, Extra Trees, Gradient Boost, Light Gradient Boost (LightGBM), Recursive Feature Elimination (RFE), Random Forest, Support Vector Regressor (SVR), and Xtreme Gradient Boost (XGBoost) have been used to compute the feature importance of the hyperspectral bands for predicting turbidity.

Bitter peptide prediction using graph neural networks.

Bitter taste is an unpleasant taste modality that affects food consumption. Bitter peptides are generated during enzymatic processes that produce functional, bioactive protein hydrolysates or during the aging process of fermented products such as cheese, soybean protein, and wine. Understanding the underlying peptide sequences responsible for bitter taste can pave the way for more efficient identification of these peptides.

Circulating microRNAs and isomiRs as biomarkers for the initial insult and epileptogenesis in four experimental epilepsy models: The EPITARGET study.

Objective: Structural epilepsies can manifest months or years after the occurrence of an initial epileptogenic insult, making them amenable for secondary prevention. However, development of preventive treatments has been challenged by a lack of biomarkers for identifying the subset of individuals with the highest risk of epilepsy after the epileptogenic insult.

Methods: Four different rat models of epileptogenesis were investigated to identify differentially expressed circulating microRNA (miRNA) and isomiR profiles as biomarkers for epileptogenesis.

Hospitals and Superutilization.

A small percentage of patients consume most of the health services in the US. These cases of superutilization affect hospitals, but little is known about what it is, the impact on hospitals, or how hospitals can identify potential cases of superutilization. We conducted exploratory research using the Medical Expenditure Panel Survey (MEPS) datasets for 2019 to examine superutilization as it relates to hospitals.

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing.

Non-coding RNAs as therapeutic targets and biomarkers in ischaemic heart disease.

The adult heart is a complex, multicellular organ that is subjected to a series of regulatory stimuli and circuits and has poor reparative potential. Despite progress in our understanding of disease mechanisms and in the quality of health care, ischaemic heart disease remains the leading cause of death globally, owing to adverse cardiac remodelling, leading to ischaemic cardiomyopathy and heart failure. Therapeutic targets are urgently required for the protection and repair of the ischaemic heart.

The distribution, fate, and environmental impacts of food additive nanomaterials in soil and aquatic ecosystems.

Nanomaterials in the food industry are used as food additives, and the main function of these food additives is to improve food qualities including texture, flavor, color, consistency, preservation, and nutrient bioavailability. This review aims to provide an overview of the distribution, fate, and environmental and health impacts of food additive nanomaterials in soil and aquatic ecosystems. Some of the major nanomaterials in food additives include titanium dioxide, silver, gold, silicon dioxide, iron oxide, and zinc oxide.

Recommendations for detection, validation, and evaluation of RNA editing events in cardiovascular and neurological/neurodegenerative diseases.

RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding.

HCG18, LEF1AS1 and lncCEACAM21 as biomarkers of disease severity in the peripheral blood mononuclear cells of COVID-19 patients.

Background: Even after 3 years from SARS-CoV-2 identification, COVID-19 is still a persistent and dangerous global infectious disease. Significant improvements in our understanding of the disease pathophysiology have now been achieved. Nonetheless, reliable and accurate biomarkers for the early stratification of COVID-19 severity are still lacking.