Browse Articles

Co-development of the educational content of a proposed educational mobile health application prototype on oral cancer using Delphi technique

Super mitochondria-enriched extracellular vesicles enable enhanced mitochondria transfer

Cerchar abrasiveness index prediction based on rock properties leveraging hybrid soft computing techniques

Deciphering splicing heterogeneity at single-cell resolution by SCSES

Abstract Alternative splicing (AS) plays a critical role in generating cellular transcriptomic heterogeneity. While single-cell RNA sequencing (scRNA-seq) has become a standard approach for exploring this heterogeneity, it remains challenging to accurately characterize splicing changes at the single-cell level due to high dropout rates, inevitable noise, and limited coverage. To address this, we developed SCSES (Single-Cell Splicing EStimation), a computational framework designed to enhance the AS profiles. SCSES infers and completes the missing splicing changes by sharing information across similar cells and events with data diffusion. Through systematic simulation studies, SCSES outperforms existing algorithms in recovering percent spliced-in (PSI) values and diversity across cell populations. When applied to various datasets, SCSES uncovers substantial splicing heterogeneity and cell subgroups with exclusive splicing patterns, which cannot be captured by conventional single-cell gene expression clustering. Together, our study provides SCSES as a valuable tool in deciphering splicing heterogeneity and is widely capable of handling different biological scenarios, species and sequencing platforms.

RGB image encryption using SPN with a novel block cipher over simple graph adjacency matrices and Galois fields

UNICORN: Towards universal cellular expression prediction with a multi-task learning framework

Optimal control and economic evaluation of diphtheria disease model with booster immunization and hospitalization

Sex-specific associations of gene expression with Alzheimer’s disease neuropathology and ante-mortem cognitive performance

Habitual video gaming predicts multitasking performance while the role of cognitive capacity remains inconclusive

Robust zwitterionic hydrogels enabled by consolidated supramolecular networks and spatially hierarchical structures

Genetic association analysis of grain yield-related traits in Aegilops tauschii under drought and non-stress conditions

Conductive binary Li borate glass coating for improved Ni-rich positive electrode in sulfide-based all-solid-state Li batteries

Exogenous neoagaro-oligosaccharides accelerate seed germination and alleviate the impact of growth retardants on germination and seedling growth of garden cosmos (Cosmos bipinnatus Cav.)

The combined impact of fisheries and climate change on future carbon sequestration by oceanic macrofauna

Extraction and characterization of antibacterial marine polysaccharide K-Carrageenan from Kappaphycus alvarezii against multidrug-resistant wound associated bacteria

Enhancing radiostrontium uptake by a layered titanate perovskite via sustainable electrochemically switched ion exchange

Adaptive identity-regularized generative adversarial networks with species-specific loss functions for enhanced fish classification and segmentation through data augmentation

Abstract Traditional fish classification systems suffer from limited training data and imbalanced datasets, particularly for rare or morphologically complex species. This paper presents a novel Generative Adversarial Network architecture that integrates adaptive identity blocks to preserve critical species-specific features during generation, coupled with species-specific loss functions designed around distinctive characteristics of marine species. Our method introduces adaptive identity blocks that learn to maintain species-invariant features while allowing controlled morphological variations for data augmentation. The species-specific loss function incorporates morphological constraints and taxonomic relationships to ensure generated samples maintain biological plausibility while enhancing dataset diversity. Experimental evaluation on a comprehensive fish dataset containing nine species demonstrated significant performance improvements. Our proposed method achieved 95.1% ± 1.0% classification accuracy, representing a 9.7% improvement over baseline methods and 6.7% improvement over traditional augmentation approaches. While demonstrated on a dataset of 9000 images across nine fish species, these results provide a solid foundation that warrants validation on larger, more taxonomically diverse datasets to establish broader generalizability. Segmentation performance achieved 89.6% ± 1.3% mean Intersection over Union, representing a 12.3% improvement over baseline methods. Critically, our approach showed substantial improvements for morphologically complex species, with expert evaluation by marine biology specialists confirming 88.7% ± 2.0% overall quality and achieving 87.4% ± 1.6% biological validation score. Statistical significance testing confirmed all improvements at p  < 0.001 with large effect sizes, and cross-validation demonstrated exceptional consistency across folds. The results validate the effectiveness of our biologically-informed approach for generating high-quality synthetic fish data that significantly improves classification and segmentation performance while maintaining biological authenticity.

Mechanism of trinucleotide repeat expansion by MutSβ-MutLγ and contraction by FAN1

Abstract Triplet repeat expansion underlies multiple pathologies, including Huntington’s disease, often arising in somatic non-dividing tissues such as the brain. Despite identification of genetic modifiers, mechanistic insights remain limited. Using purified human proteins, we show that MutLγ (MLH1-MLH3), stimulated by MutSβ (MSH2-MSH3), incises DNA opposite an extrahelical loop on the 5’ side. This activity, with a moderate sequence preference, generates DNA nicks enabling Polδ-mediated displacement synthesis with the loop as a template, leading to expansion. PCNA confines these MutLγ incisions near the loop. FAN1, instead, preferentially targets the looped strand. RFC-PCNA stimulate and direct FAN1 nuclease to the 3’ boundary of the loop while restricting its exonuclease activity. No pre-existing nick is required. Following FAN1-RFC-PCNA action, Polδ removes the loop and resynthesizes DNA, causing contraction. FAN1 also directly inhibits MutLγ, preventing its activation by MutSβ. Our study illuminates both repeat expansion and contraction mechanisms and reveals the protective function of FAN1.

Identification of key biomarkers associated with necroptosis and immune infiltration in hepatitis B virus-related acute-on-chronic liver failure

DNA methylation patterns facilitate tracing the origin of neuroendocrine neoplasms

Abstract Neuroendocrine neoplasms (NEN) are thought to originate from diffuse neuroendocrine networks and therefore most frequently arise in the gastrointestinal tract and lungs. The liver is a frequent site of metastasis of NEN but also the existence of primary hepatic NEN has been proposed. Due to the impact on disease management, it is urgently required to discriminate the origin of hepatic NEN metastases and to identify clinically relevant subgroups. Using a comprehensive set of NEN ( N  = 212) from two independent cohorts, we show that the DNA methylation profiles of NEN of distinct anatomical localizations differ significantly and primary tumor-metastasis pairs cluster together, enabling the identification of the tumor origin. Furthermore, the subgroup of hepatic NEN without clinically detectable primary tumor, thus classified as primary hepatic NEN, does not form a distinct cluster by DNA methylation analysis but colocalizes with various subgroups of extrahepatic NEN. Organ-specific subtyping of NEN delineates a foregut-like epigenetic profile for hepatic NEN with unknown primary. We propose a classifier with high prediction accuracy for each of the different organ sites. In conclusion, our results demonstrate that DNA methylation profiling enables precise prediction of NEN origin and suggests that a substantial proportion of presumed primary hepatic NEN may in fact represent misclassified secondary hepatic NEN of unknown primary.