Browse Articles

Mechanical properties and sealing performance evaluation of packers in high temperature underground gas storage

Deciphering the biosynthesis pathway of gamma terpinene cuminaldehyde and para cymene in the fruit of Bunium persicum

A reliable framework for supply chain management using cluster based blockchain for intelligent industrial automation

Light controls gene functions through alternative splicing in fungi

Light controls important biological processes in fungi by regulating transcriptional gene activation. Here, we found that beyond the regulation of mRNA transcript abundance, light regulates alternative splicing (AS) in the filamentous fungi Aspergillus nidulans , Trichoderma guizhouense, and Neurospora crassa . Blue light-regulated AS was involved in ergothioneine biosynthesis and conidiation in T. guizhouense , which required the blue light receptor BLR1. Blue light activated the MAPK HOG (Sak) pathway which then transmitted the signal via the serine/threonine kinase SRK1 to the AS key regulator SRP1. SRK1 and SRP1 are important for light-induced conidiation. The light-activated HOG pathway led to an increase of the SRK1 protein level and its phosphorylation status. Phosphorylated SRK1 translocated from the cytoplasm to the nucleus to interact with SRP1, thereby regulating AS efficiency. This study unravels another level of complexity of fungal environmental sensing and responses and also first describes the entire cascade from an environmental signal to the splicing machinery.

Diagnosing malignant transformation potential in oral submucous fibrosis with leukoplakia: an approach utilizing deoxyribonucleic acid image cytometry

Establishing a robust genetic sequencing and gene expression data library in cardiovascularly healthy cats

Abstract Cardiovascular disease is a leading cause of increased morbidity and mortality in cats, with hypertrophic cardiomyopathy (HCM) significantly overrepresented. While feline HCM is hereditary, the genetic etiology of disease remains poorly understood. Establishing a cohort of well-phenotyped and -genotyped healthy control cats is essential to fuel future genetic/pharmacogenetic discoveries. We sought to construct a robust genetic sequencing and gene expression library from cardiovascularly healthy cats using whole genome sequencing (WGS) and RNA sequencing (RNA-Seq). Fifty-four client-owned cats ( $$\ge$$  10 years) were screened, of which 18 cats (cohort 1) were prospectively enrolled after being deemed cardiovascularly healthy by clinicopathology, biochemistry, and echocardiography. DNA isolated from blood samples was submitted for paired-end WGS at ~ 30X coverage. Standard pipelines were employed for variant calling across sequenced cats. A second cohort of 15 purpose-bred cats were euthanized for non-cardiac reasons. Flash-frozen left ventricular (LV), interventricular septum (IVS), and left atrium (LA) tissues from 11, 14, and 13 cats, respectively, were submitted for stranded mature RNA-Seq at 50 million reads/sample. Gene variants and expression profiling were catalogued for both meticulously selected cohorts. Transcriptomic and WGS data libraries were generated to serve as an open-access resource in future investigations of feline cardiovascular precision medicine.

Pre-exposure prophylaxis knowledge as a mediator between eligibility and intention to use among voluntary counselling and testing clients, Thailand

Spatially varying polarization of guided fundamental mode in a titanium indiffused periodically poled lithium niobate waveguide

Nesting niche partitioning between two sympatrically breeding Chlidonias Tern species revealed by remote sensing

Abstract Sympatrically breeding avian species may have similar environmental requirements for nesting sites, resulting in interspecific competition. It may be reduced by partitioning resources in space or/and time, allowing relatively stable coexistence in the shared habitat. Here, we investigated nesting niches of sympatrically breeding, Black Terns (BT, Chlidonias niger) and Whiskered Terns (WT, Chlidonias hybrida) in Druzno Lake (Poland) in 2024. We compared nesting site proximity characteristics between both studied species at two different spatial scales using remotely sensed from drone surveys: (contribution of water and vegetation) and indices derived from satellite imagery: Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation density, and Normalized Difference Water Index (NDWI) as a proxy for open water contribution. Both species partitioned location (they bred in separate locations) and resources within common breeding ground. BT’s nests were situated significantly closer to the lake shore (mean ± SD 102.25 ± 62.47 m) and further away from each other (mean ± SD 22.22 ± 14.81 m) compared to WTs (mean ± SD 257.24 ± 109.64 m & 11.24 ± 10.67 m, respectively). BTs, unlike WTs prefer to nest in areas with higher open water contribution and lower vegetation density. The nesting niches of BT were wider than those of WT, suggesting that BT use more diversified habitats for breeding.

A robust resilient-oriented design for the cyber-physical distribution system against sequential typhoons

Trade-offs between agricultural production and ecosystem services under different land management scenarios in the Loess Plateau of China

Artificial sweeteners differentially activate sweet and bitter gustatory neurons in Drosophila

Trophic behavior and parasite communities in kelp gulls from the northern Patagonian coast, Argentina

Multiomics integration prioritizes potential drug targets for multiple sclerosis

Multiple sclerosis (MS) is an immune-mediated disease with no current cure. Drug discovery and repurposing are essential to enhance treatment efficacy and safety. We utilized summary statistics for protein quantitative trait loci (pQTL) of 2,004 plasma and 1,443 brain proteins, a genome-wide association study of MS susceptibility with 14,802 cases and 26,703 controls, both bulk and cell-type specific transcriptome data, and external pQTL data in blood and brain. Our integrative analysis included a proteome-wide association study to identify MS-associated proteins, followed by summary-data-based Mendelian randomization to determine potential causal associations. We used the HEIDI test and Bayesian colocalization analysis to distinguish pleiotropy from linkage. Proteins passing all analyses were prioritized as potential drug targets. We further conducted pathway annotations and protein–protein interaction network analysis (PPI) and verified our findings at mRNA and protein levels. We tested hundreds of MS-associated proteins and confirmed 18 potential causal proteins (nine in plasma and nine in brain). Among these, we found 78 annotated pathways and 16 existing non-MS drugs targeting six proteins. We also identified intricateAQ PPIs among seven potential drug targets and 19 existing MS drug targets, as well as PPIs of four targets across plasma and brain. We identified two targets using bulk mRNA expression data and four targets expressed in MS-related cell types. We finally verified 10 targets using external pQTL data. We prioritized 18 potential drug targets in plasma and brain, elucidating the underlying pathology and providing evidence for potential drug discovery and repurposing in MS.

Microbial dynamics in rice ecosystem under supplementation of organic sources of nitrogen with inorganics and their concomitant impact on yield

The BioSUD Biobank as a genomic resource for substance use disorders in Italy

Design and numerical evaluation of a high sensitivity plasmonic biosensor based on MISM nanoring for versatile virus detection

Smartphone addiction and its association with accident risk in Iranian adults

A novel S-scheme heterojunction photocatalyst, Yb6Te5O19.2/g-C3N4: synthesis, characterization, photocatalytic activity, and mechanism

When the crowd gets it wrong – the limits of collective wisdom in machine learning

Abstract This study examines collective decision-making dynamics using a machine learning framework, drawing parallels between a previously established synthetic population model and a newly introduced ensemble machine learning counterpart. Grounded in the “wisdom of crowds” principle, the research explores scenarios where the accuracy of group decisions may unexpectedly decrease as group size increases, particularly when individuals share highly correlated information. By replicating these conditions with machine learning ensembles, such as decision trees and support vector machines, the study identifies circumstances where collective accuracy declines, challenging the assumption that larger groups inherently make better decisions. The findings reveal the limitations of collective models in machine learning and provide valuable insights for data-scarce environments.