Assessing genetic diversity is essential for the characterization and conservation of livestock. This study investigates the genetic diversity of the three indigenous goat breeds from northern Belgium (Flanders), Kempische Geit, Vlaamse Geit, and Belgische Hertegeit, using pedigree and genomic data. Pedigree analyses estimated inbreeding and effective population size, while genomic data were assessed using runs of homozygosity (ROH) and population structure analyses (F st , principal component analysis (PCA) and ADMIXTURE). Pedigree-based results revealed moderate inbreeding (F ped = 10.3%, 7.4%, and 9.0%) and critically low effective population sizes (N e = 17, 28, and 43, respectively). Despite these constraints, population trends since 2017 show encouraging growth, with active breeding females increasing 153% (Kempische Geit) and active breeders rising 90% (Vlaamse Geit). Genomic data from 280 individuals (88–97 per breed), genotyped using the GGP Goat SNP array, revealed inbreeding coefficients based on ROH from 8% to 15%, with individual values reaching up to 39%. Several ROH islands were detected, including two in Vlaamse Geit on chromosomes 10 and 13, which overlap with regions reported in other international breeds. Linkage disequilibrium-based estimates of effective population sizes (N e = 21–22) further highlight the endangered status of these breeds. Genetic differentiation was substantial (F st from 0.10 to 0.14) which was supported by PCA and ADMIXTURE. This study provides the first integrated pedigree and genomic assessment of goat diversity in Flanders, offering critical insights for the conservation and sustainable management of these local breeds. These data support comparisons with international populations and inform future breeding strategies. • Flanders (Northern Belgium) has three local goat breeds • All three goat breeds were analyzed based on pedigree and genotype data • Inbreeding coefficients based on runs of homozygosity were between 8% and 15% • Effective population sizes remain below critical thresholds for viability • The three goat breeds are considered endangered

Mitochondrial proteins are encoded by both mitochondrial- and nuclear-encoded genes. Because mitochondrial DNA (mtDNA) is maternally inherited, admixed individuals may have different ancestral sources for their nuclear and mitochondrial genomes. The potential incompatibility between these genomic components may cause suboptimal mitochondrial function and result in energy-related pathologies. This incompatibility, or 'mitonuclear discordance', is defined as the proportion of the nuclear genome not derived from the same ancestral source as the mtDNA. Based on this understanding, we hypothesized that increased mitonuclear discordance would be associated with lower mitochondrial copy number and increased risk of gout, type 2 diabetes and chronic kidney disease. We tested this prediction using genomic data from a cohort of 2301 New Zealanders with Polynesian ancestry (Indigenous Māori and Pacific peoples living in Aotearoa New Zealand). We observed that increased mitonuclear discordance was correlated with a decreased chance of gout (p = 5.08×10-5) and a decreased chance of diagnosis with type 2 diabetes, specifically in individuals having haplogroup B4a1a (p = 4.20×10-9), which was present in 86.0% of the Polynesian study cohort. No significant association was found between mitonuclear discordance and mitochondrial copy number (p = 0.93), risk of chronic kidney disease (p = 0.084) or gout flare frequency (p = 0.53). Overall, while these results contradicted our hypothesis, they can potentially be explained by a higher prevalence of disease-associated alleles for gout and type 2 diabetes in Polynesian genomes.

Spatiotemporal dynamics and reemergence of chikungunya virus in Bangladesh from 2008 to 2025.

Aquatic foods are essential sources of protein and micronutrients and play a critical role in global nutrition, trade, and livelihoods. However, their safety and sustainability are frequently compromised by microbial contamination and biofilm formation during farming, processing, storage, and retail. Biofilms persist on moist surfaces, resist conventional cleaning practices, and contribute to spoilage, cross-contamination, and economic loss. This article reviews emerging applications of artificial intelligence and Industry 4.0 technologies for biofilm prevention and control in aquaculture and seafood systems. Particular emphasis is placed on the use of continuous water quality sensing, imaging platforms for early detection and cleaning verification, genomic and omics tools for microbial trait-level insight, and digital twin frameworks for virtual simulation of sanitation strategies. Recent advances demonstrate that sensor telemetry can predict biofilm-favorable conditions, imaging can verify removal in real time, and genomic data can identify persistence traits and tolerance mechanisms. When integrated, these approaches enable facility-specific digital twins that anticipate surface-specific risks and recommend optimized interventions before implementation. The convergence of AI, sensor networks, imaging, and omics offers a shift from reactive to proactive biofilm management in aquatic food systems. Positioned within the transition to Industry 5.0, these innovations support earlier detection, targeted interventions, and measurable improvements in food safety, quality, sustainability, and resilience, while aligning production systems with human-centric goals.

Artificial intelligence is increasingly advancing both fundamental research and clinical applications in schizophrenia. This review surveys recent literature on artificial intelligence driven approaches for schizophrenia diagnosis, treatment, management, and characterization, using multiple data modalities such as neuroimaging, electrophysiology, electronic health records, and genomic data. Recent work shows substantial progress in leveraging machine learning and deep learning for diagnostic label prediction, treatment response modeling, and brain network characterization. While many studies continue to improve feature extraction and classification methods within single modalities, there is a growing trend to utilize multiple data sources to capture the complexity of schizophrenia from a comprehensive perspective. Emerging themes include multimodal fusion methodologies to identify linked correlates of schizophrenia, as well as data-driven approaches to learn subgroups, brain networks, and psychosis continua. The rise of large-scale multimodal datasets, foundation models, and mechanistic interpretability methods holds promise for scalable symptom assessment and biomarker identification, thereby better supporting early intervention and personalized treatment. Current literature highlights a shift from unimodal prediction to holistic, multimodal characterization of schizophrenia. Transforming these artificial intelligence models into clinical tools, however, requires careful attention to patient privacy and data bias, alongside rigorous validation across diverse populations and settings.

Developing multimodal data-driven diagnostic systems has become a key clinical strategy for improving breast cancer outcomes. However, effectively modeling multimodal features remains challenging due to substantial semantic heterogeneity, scale discrepancies, and the inherent difficulty of cross-modal alignment. Although existing studies have proposed various multimodal fusion methods, most rely on direct feature concatenation or shallow integration, which fail to capture fine-grained intra-modality semantics as well as the complex interactions between histopathological and genomic modalities. In this study, we propose a multimodal diagnostic framework based on Feature Enhancement and Semantic Collaborative Alignment (FESCA). The method incorporates a semantic-guided modality feature enhancement mechanism that effectively extracts and strengthens diagnostic cues from both pathological images and genomic data. In addition, a contrastive-learning-based cross-modal alignment strategy is introduced to map heterogeneous modalities into a unified semantic space and achieve deep semantic collaboration through contrastive optimization. To ensure robust breast cancer classification under varying modality availability, a multimodal collaborative diagnostic strategy is employed to dynamically adapt the feature representations. We evaluate FESCA on the TCGA-BRCA dataset, and the experimental results demonstrate that it outperforms state-of-the-art methods in breast cancer classification while significantly improving both intra-modality representation quality and cross-modal semantic alignment. To enhance accessibility and practical application, we developed a web-based breast cancer pathological staging diagnosis system to visualize and deploy the FESCA model, demonstrating a step toward clinical application and providing a benchmark for other research methods.

Phylogenetic analysis of Batrachospermaceae (Batrachospermales, Rhodophyta) based on plastid and mitochondrial genomes, with the description of two new species of different genera.

Phylogenomics of the woodland salamanders (Plethodon): Reticulate evolution and indistinct species boundaries.

Pleural mesothelioma (PM) is a rare and aggressive malignancy. The development of novel therapeutic strategies targeting PM remains an unmet clinical need. However, comprehensive genomic data from Asian populations, particularly from Japanese patients, are limited. This study aimed to elucidate the genomic landscape of PM in Japanese patients using the nationwide Center for Cancer Genomics and Advanced Therapeutics (C-CAT) genomic database. A total of 211 patients registered between June 2019 and March 2025 were analyzed. The most frequent genetic alterations were in BAP1, NF2, TP53, CDKN2A/B, and MTAP. The median tumor mutation burden (TMB) was 1.26, and no microsatellite instability-high patients were detected. The median overall survival (OS) after first-line treatment was 30.6 months. Patients treated with immune checkpoint inhibitors (ICIs) had a significantly better OS than those who did not receive ICIs. In univariate and multivariate analyses, TP53 alterations and high TMB (cutoff value of 1.6) were associated with poor prognosis. These results suggest that integrating clinical and genomic data can enhance prognostic stratification and contribute to the development of precision medicine for PM. This study provides the first large-scale genomic characterization of Japanese PM patients with C-CAT and highlights potential biomarkers for future therapeutic development.

The Levant region is an important recognized biological corridor that unites three major continents, Africa, Asia and Europe. Due to its intersectional positioning, the region has facilitated flora and faunal exchange between four biogeographical elements: Palaearctic, Palaeoeremic, Ethiopian and Oriental. The Levant's unique geological position, along with a distinguishable climate gradient and topographic heterogeneity, has likely contributed to the impressive solifuge biodiversity in a comparatively small area, making it an ideal and important gateway for beginning to interrogate the current solifuge diversity in the Old World. In this region, there are currently six families of solifuges and over 50 species described. However, solifuge taxonomy in the Old World has remained largely stagnant. While there exists a consensus that accurate taxonomic estimates are imperative for conservation efforts, such information is often in reference to undiscovered diversity, rather than the possible taxonomic inflation that may exist in understudied groups such as solifuges. The purpose of this study was to revisit the current standing taxonomic hypotheses using UCE phylogenomics, divergence dating, and analysis of SNPs recovered from solifuge genomes, using both newly generated genomic data derived from natural history collections and previously acquired genomic data. Our primary goal was to reevaluate the solifuge historical taxonomy of this region, with the intent of obtaining a better picture of shallow-level diversity patterns in the six native solifuge families of interest. Our molecular study provides evidence to suggest that the current reported solifuge diversity from this region should be synonymized to about one-third, as they represent junior synonyms of conspecifics. Our findings highlight longstanding taxonomic inaccuracies within Levantine Solifugae and illuminate the extent of unwarranted and excessive taxonomic splitting. Future taxonomic research should prioritize clarifying species boundaries and reorganizing the group based on a comprehensive understanding of what defines a meaningfully stable taxonomic unit, while remaining open to simplified scenarios with fewer taxonomic ranks.

The Klebsiella oxytoca complex inhabits diverse environments, including the human gut, and frequently causes opportunistic infections. This species complex carries the intrinsic β-lactamase gene blaOXY, which confers resistance to ampicillin, and may acquire resistance to other antimicrobials, such as carbapenems, through mobile antimicrobial resistance genes or chromosomal mutations. A subset of the K. oxytoca complex produces cytotoxins associated with antibiotic-associated hemorrhagic colitis. However, the epidemiological and genomic data from Japan are limited. In this study, we aimed to characterize the genomic and phenotypic features of K. oxytoca complex collected nationwide in Japan within the scope of a national antimicrobial resistance surveillance (JARBS) program. A total of 46 K. oxytoca complex isolates were obtained through the JARBS program targeting third-generation cephalosporin-resistant and carbapenem-nonsusceptible Enterobacterales in Japan. Whole-genome sequencing, antimicrobial susceptibility testing, plasmid analysis, and cell culture- and mass spectrometry-based cytotoxin detection were performed to investigate antimicrobial resistance and toxin production. Our analyses of clinical isolates revealed diverse genotypes and potential plasmid-mediated mechanisms for blaIMP acquisition. Phenotypic assays revealed multidrug resistance and cytotoxin production in a subset of isolates, and the corresponding genomic determinants were identified. Notably, we identified a multidrug-resistant, cytotoxin-producing lineage belonging to sequence type (ST) 176 that has disseminated across multiple regions of Japan. This study provides the first nationwide integrated genomic and phenotypic analysis of the K. oxytoca complex in Japan. The spread of the multidrug-resistant, cytotoxin-producing ST176 lineage represents a previously unrecognized high-risk linage in Japan, underscoring the need for continued genomic surveillance.

Clinical and Genomic Characteristics of Patients With Advanced NSCLC Who Have Long-Term Response to First-Line Immunotherapy: A Real-World Study.

Emerging biobank resources allow large-scale integration of eye-specific phenotypes with clinical, genomic, and multiomic data. This convergence enables unprecedented opportunities to systematically dissect the genetic architecture, epidemiology, and mechanistic pathways of both rare monogenic and common polygenic diseases. The review aims to critically examine how contemporary data extraction, multiomics, and analytic methodologies are reshaping disease classification, genetic discovery, and translational research in ophthalmology, while highlighting the associated challenges in leveraging these advanced tools. Recent literature demonstrates the utility of genome-wide and phenome-wide association studies, transcriptomic analyses, and artificial intelligence in uncovering novel risk loci, endophenotypes, and biomarkers relevant to eye diseases. Furthermore, advances in multiancestry sampling show substantial population-specific genetic variation, enriching disease models for conditions such as glaucoma and age-related macular degeneration. Finally. integrative approaches, including Mendelian randomization and enrichment analyses, are helping elucidate shared genetic architecture between ocular and systemic diseases, informing therapeutic target identification, and refining risk prediction models. The convergence of biobank-derived multimodal data and sophisticated analytic techniques is catalyzing a path to personalized medicine in ophthalmology. For these approaches to fully translate into clinical practice ensuring scientifically robust and equitable outcomes, future research must address cohort diversity, mechanistic validation, and practical cost-effectiveness.

Immune dysfunction contributes to comorbid depression in patients with multiple sclerosis.

Explainable machine learning framework for the molecular classification of triple negative breast cancer.

Out of North Africa: Evolution and biogeography of Afro-Arabian dwarf tarantulas (Theraphosidae, Ischnocolinae).

Detecting optimal biomarkers in ovarian cancer cells from high-dimensional mRNA expression data using machine learning.

Genomic data can advance precision medicine; however, to continue developing more targeted treatments, genomic datasets need to be integrated with health care data and become more disease-focused. This integration, in turn, amplifies existing challenges in health care data management, such as handling large data volumes, adhering to data standards, and protecting sensitive information. Addressing these challenges calls for unified digital ecosystems that combine data collection, standardization, analysis, and governance within a single platform, thereby reducing the technical burden for users. Currently, a clear set of indications about functional and nonfunctional requirements to help designers translate stakeholder needs into actionable design specifications is missing. This scoping review aimed to identify the functional and nonfunctional requirements most frequently discussed in the literature from the perspective of end users (eg, clinicians and data analysts) to inform the design of a health and genomic data management platform that supports data sharing and analysis in clinical settings by conducting a PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Review) review. We searched for peer-reviewed English studies that focused on platforms for managing genomic data from a user-centered perspective. We considered studies from 2014 to 2024 that were extracted from Scopus, PubMed, Web of Science, and Google Scholar for the scoping review. Insights were extrapolated for a thematic analysis to develop an initial set of requirements. We charted the functional and nonfunctional requirements according to their frequency of occurrence in the literature to provide a structured overview of the most commonly reported requirements. From 410 initial items, 210 items were preliminarily selected, and 53 items were included in the final analysis. Three primary groups of 26 interface functional requirements emerged: (1) general data management (acquisition, standardization, and sharing), (2) data processing and analysis (preprocessing and analysis pipelines), and (3) data visualization and reporting. Twenty nonfunctional requirements were identified and organized in 4 groups: (1) communication and support, (2) platform technical infrastructure, (3) user experience and user interface characteristics, and (4) security and compliance. We also investigated the issues that need to be resolved to develop an ideal platform. We identified and mapped the most frequently reported functional and nonfunctional requirements of clinical and data professionals when discussing a health and genomic data management platform. The 3 key functional requirements should be supported by nonfunctional requirements such as secure technical infrastructure and governance mechanisms that enable compliant data processing and sharing. Designers may use these insights and mapping to develop standardized data platforms that promote efficient data exchange between institutions and experts while ensuring regulatory compliance and secure access, as proposed by the European Health Data Space.

While GWAS (genome-wide association studies) have identified over 1000 obesity-associated loci, their functional impact on gene expression remains unclear. Moreover, many studies have not fully captured the genetic architecture of obesity in high-risk populations or considered the complexity of adiposity beyond traditional measures. To address these gaps, this study explores the genetic and transcriptomic pathways of obesity using diverse obesity phenotypes in a high-risk population. We analyzed genomic and whole-blood transcriptomic data from the CCHC (Cameron County Hispanic Cohort), performing GWAS on 13 obesity-related traits. Differential expression analysis was conducted for genes near GWAS-identified single nucleotide polymorphisms (P<5×10-6) followed by expression quantitative trait loci mapping and GWAS-expression quantitative trait loci colocalization. GWAS identified 486 trait associations, including 6 genome-wide significant (P<5×10-8) loci, with 3 novel signals linked to abdominal subcutaneous adipose tissue, body fat percentage, and waist circumference. Among 3024 genes near these loci, 60 showed differential expression. Further expression quantitative trait loci analysis suggested 2 single nucleotide polymorphism-gene-trait relationships: rs543314376-MAPK11, associated with subcutaneous adipose tissue volume in females, and rs963018484-PER1, linked to body mass index in females. Both genes play key roles in obesity-related pathways, including inflammation and circadian rhythm regulation. This integrative genomic-transcriptomic analysis uncovers 2 novel candidate genes for obesity and underscores the critical need for involving all populations and comprehensive adiposity measures in obesity research. By expanding beyond body mass index in a Hispanic/Latino population, we move closer to a deeper and more inclusive understanding of obesity's genetic architecture.

A probabilistic approach for predicting indole-3-acetic acid synthesis in bacteria using genomic data.