Large-scale population sequencing requires reference genomes that accurately represent the genetic background of target populations. Although high-quality linear references such as T2T-CHM13 have improved the completeness of human genome representation, a single reference genome remains insufficient to capture genetic diversity across populations. Pangenome graphs provide a richer representation of multi-source genomic variation, but translating pangenome diversity into practical reference genomes remains challenging. First, the direct application of pangenome graphs in routine large-scale sequencing analysis is limited by graph complexity and poor compatibility with existing linear-reference workflows. Second, existing pangenome-driven personalized references mainly target individual samples rather than populations, and current path recommendation strategies may overlook local source consistency during branch-node selection. In this study, we propose PGpath, a pangenome-based method for recommending population-adapted linear reference genomes. PGpath generates a reference from a selected pangenome subpath, preserving pangenome diversity while retaining a linear format compatible with standard downstream pipelines. In this study, we characterize the shared genetic background of samples using population-level k-mer frequency features and reformulate reference recommendation as branch-node prediction in the pangenome graph. To encourage local source consistency, we develop a topology-constrained multi-task neural network incorporating source-consistency relationships between adjacent branch nodes. Experiments on Human Pangenome Reference Consortium (HPRC) population datasets and HG002 variant detection benchmarks showed that PGpath-derived references improved read mapping performance and reduced downstream variant detection errors compared with commonly used references. PGpath provides a practical strategy for translating pangenome diversity into population-adapted linear references for large-scale genomic analysis.

IntroductionCurrently, middle-aged and older adults face the compounded challenges of a dual "digital divide" and "health divide."MethodUsing data from the 2020 China Health and Retirement Longitudinal Study (CHARLS), this study constructed a digital literacy index via polychoric confirmatory factor analysis (Polychoric CFA). Ordered logit (ologit) models were employed to estimate the associations between digital literacy and physical and mental health among middle-aged and older adults. The Karlson-Holm-Breen (KHB) method with 1,000 bootstrap resamples was utilized to examine the mediating effect of social interaction in these relationships. Additionally, heterogeneity analyses across gender and household registration (hukou) status were conducted.ResultsThe findings reveal that digital literacy is significantly and positively associated with both the physical health and mental health of middle-aged and older adults. After incorporating social interaction, the indirect effect remains statistically significant, indicating that social interaction serves as an important pathway linking digital literacy to physical and mental health. Heterogeneity analysis further indicates gender-specific patterns: digital literacy maintains a significant positive association with physical health among men, but this association is not statistically significant among women; conversely, digital literacy exhibits a significant positive association with mental health among women, whereas this association does not reach statistical significance among men. Regarding household registration (hukou), formal inter-group difference tests reveal no statistically significant differences, suggesting that the positive associations between digital literacy and health outcomes are highly consistent and generalizable across urban and rural middle-aged and older populations.ConclusionBy disentangling the transmission pathway of "digital technology→social interaction→health outcomes," this study provides empirical evidence for developing countries to address digital health inequalities against the backdrop of population aging. We propose the following policy recommendations: (1) Advance national initiatives to enhance digital literacy among middle-aged and older adults, lower technological barriers, and help older cohorts overcome obstacles to digital technology adoption; (2) Improve the accessibility and age-friendliness of the digital society, with an emphasis on age-adapted product interfaces and community-based digital support networks; and (3) Formulate targeted health interventions for multiply vulnerable subgroups to enhance the precision and efficacy of public health policies.

Heat stress severely limits tomato growth, yield, and biochemical stability, highlighting the need for thermotolerant genotypes. This study evaluated six advanced tomato lines using morphological, biochemical, genetic, and molecular analyses under heat stress conditions. Significant genotypic differences (p < 0.001) were observed across the evaluated traits, with TAL101-2 and TAL101-1 exhibiting better growth and reproductive performance under high-temperature conditions. TAL105 showed better growth and antioxidant activity, whereas TAL104 exhibited strong biochemical adaptation, as evidenced by elevated phenolic content and antioxidant activity. TAL101-2 maintained relatively high fruit set despite limited vegetative growth. High heritability and genetic advance for reproductive and phytochemical traits indicated strong genetic control. Correlation and principal component analyses revealed positive relationships between vegetative vigor and yield, whereas biochemical traits were associated with oxidative stress tolerance. Molecular and phylogenetic analyses confirmed clear genetic diversity among the lines. Stress-responsive gene profiling also revealed distinct marker activity patterns. TAL105 and TAL104 were identified as promising candidates for breeding heat-tolerant tomato cultivars.

Since its development, the Fear of Missing Out (FoMO) scale has been widely used to measure addictive behaviour towards information and communication technologies. However, its French adaptation and associated psychometric properties have not been validated. This study aimed at addressing this gap in the literature and proposed a validated measure for French-speaking populations. Specifically, we investigated the factorial structure of the scale in French with a lifespan sample and test its age, sex, education, and time invariance. Data were derived from an online longitudinal study (2-waves, 2 months interval) with a lifespan sample (N = 543, age range18 to 98 years, M = 46.8). The results showed that the French adaptation had a different factorial structure than the original scale. Specifically, the selected model had excellent fit and identified three lower-order factors, based on 8 out of 10 original items, which were then loaded to one higher-order factor. Moreover, measurement invariance testing revealed that the scale can be used for mean level comparisons (scalar invariance) between men and women, high vs. low educated individuals and across measurement points, while caution is suggested when comparing young vs. older individuals (partial scalar invariance). Lastly, the findings supported convergent and discriminant validity of the scale. In conclusion, the French adaptation of the FoMO scale can be used in longitudinal studies and for mean comparisons across sexes, educational levels and time, while the factorial structure (three lower-order factors loading to one higher-order factor) can be used when modelling FoMO with structural equation methods.

Reference genome assemblies are foundational resources across the biological sciences as they begin to expose the fundamental building blocks of each species and the molecular toolkits vital for adaptation and survival. High-quality genomes enable insights into evolution, facilitate ecosystem monitoring and protection and conservation of species, access to new biomaterials and biomedicine, advances in agri- and aquaculture, and support planetary health. The amount of information gathered from a species' genome assembly is directly dependent on its quality. However, different sequencing technologies and software solutions generate a wide range of quality outcomes. Here, the Earth BioGenome Project's Sequencing and Assembly committee, together with a large community of researchers performing eukaryote sequencing and assembly, outlines quality standards for a "reference" assembly and formulates recommendations to ensure these standards are met.

Protein conformational dynamics often govern protein-protein interaction mechanisms and function, as flexibility enables adaptation to different environments. Yet, the extent to which current physics‑based and emerging AI‑driven methods can predict flexibility‑dependent physicochemical properties remains to be determined. Here, we studied the HyHEL10 (Hy10) antibody lineage binding to lysozymes with different conformational flexibility. Contrary to traditional models, the affinity-matured Hy10[4X] antibody achieves high affinity by actively destabilizing its CDRH1 loop, enabling the capture of mobile antigen variants via entropy-buffered conformational selection. This structural plasticity presents a significant challenge to standard computational protocols. We found that the polarization effect is enhanced with increasing protein flexibility. Our benchmarking of MM/PBSA and AI-driven structure predictors reveals that both approaches initially fail to accurately rank affinities in more flexible systems, primarily due to sampling limitations and inaccurate structure prediction. However, the inclusion of structural templates in Chai-1 effectively mitigated these limitations, leading to enhanced correlation with experimental affinities. In the future, by coupling AI's quick screening with robust physics-based evaluations, we may have a potential pathway for in silico engineering of therapeutic antibodies against the most elusive, shapeshifting targets in the proteome.

The present study evaluated the effects of graded dietary inclusion of Gracilaria tenuistipitata on growth performance, meat composition, intestinal morphology, gut microbiota, immune response and intestinal immune gene expression in broiler chickens. A total of 200 one-day-old commercial broiler chicks were randomly allotted to four dietary treatments containing 0% (control), 1%, 2% and 4% dietary red seaweed and reared for 35 days. Average daily gain (ADG) and average daily feed intake (ADFI) were significantly (P < 0.05) reduced during the starter and early grower phases at 4% inclusion, whereas birds receiving 1-2% red seaweed maintained growth performance comparable to the control. Feed conversion ratio was transiently impaired at 4% inclusion during early growth but did not differ among treatments over the entire experimental period. Overall ADG was significantly (P < 0.05) lower at 4% inclusion, while livability remained unaffected. Breast meat proximate and mineral composition were not altered by seaweed supplementation; however, selective enrichment of docosahexaenoic acid (DHA) was observed at 2% inclusion. Histomorphometric evaluation revealed improved villus development and mucosal adaptation, particularly at 1-2% inclusion. Caecal microbiota analysis demonstrated reduced Escherichia coli and increased Lactobacillus populations in seaweed-fed groups. Humoral immune response against Newcastle disease virus remained unaffected, whereas increased expression of TLR2A, IL6 and CLDN2 indicated modulation of intestinal immune and barrier-regulatory pathways. Overall, dietary inclusion of G. tenuistipitata at 1-2% appears to support favourable changes in gut health and intestinal immune-related responses while maintaining overall growth performance comparable to the control, whereas higher inclusion levels may impose nutritional limitations on growth.

Biodiversity across Africa faces escalating threats from agricultural expansion, urbanisation, resource overexploitation, and pollution, particularly at the margins of protected areas (PAs). These land‑use changes intensify interactions between people and wildlife by compressing habitats, increasing competition for space and resources, and disrupting ecological processes, thereby exacerbating human-wildlife conflict and threatening conservation outcomes. In this context, conservation practitioners play an important role in managing these conflicts by implementing conservation management interventions. A total of 49 conservation practitioners across KwaZulu‑Natal, Limpopo, and Mpumalanga in South Africa were interviewed using a semi-structured questionnaire to document the implementation of key conservation measures and to examine their association with practitioner-reported poaching occurrence. Most reserves implemented perimeter fencing and shared boundaries with adjacent local communities. Community engagement and environmental education programmes were also widely reported. The proportion of practitioners reporting poaching was highest in KwaZulu‑Natal (84.2%), followed by Limpopo (71%) and Mpumalanga (61.5%). Binary logistic regression revealed that community engagement was significantly associated with lower odds of poaching. Sustaining PAs requires integrated strategies that combine enforcement, community engagement, and adaptive management.

Enterococcus spp. from food animals represent important reservoirs of antimicrobial resistance; however, the mechanisms underlying tigecycline resistance remain poorly understood. In this study, we investigated the genetic basis and evolutionary dynamics of tigecycline resistance in Enterococcus hirae. Among 118 Enterococcus isolates recovered from 150 swine-derived samples, E. hirae was the predominant species (68/118, 57.63%). Tigecycline resistance was identified in ten isolates (10/118, 8.47%), all belonging to a single E. hirae clone. Whole-genome sequencing of a representative isolate, EG2361, revealed three plasmids, among which pEG2361-2 harbored an IS1216E-associated multidrug resistance region capable of generating two circular translocatable units (TUs). TU1, carrying the promoter-variant tet(L) locus together with tet(M) and poxtA, underwent tandem amplification, leading to plasmid structural heterogeneity. Functional analyses demonstrated that tet(L) reduced tigecycline susceptibility, whereas enhanced resistance mainly associated with variation in the upstream promoter region rather than Tet(L) amino acid substitutions. Experimental evolution revealed a dynamic resistance trajectory. The initial increase in tigecycline resistance was associated with TU1 amplification and marked upregulation of tet(L) expression. Subsequently, tet(L) expression declined, whereas tigecycline minimum inhibitory concentrations (MICs) remained elevated and stable. Recurrent rpsJ mutations emerged independently across evolved lineages and were associated with high-level resistance. Together, these findings support a stepwise evolutionary model in which insertion sequence (IS)1216E-mediated amplification of the promoter-variant tet(L) locus drives rapid early adaptation, followed by the emergence of chromosomal rpsJ mutations associated with stable high-level resistance. This study expands current understanding of tigecycline resistance evolution in enterococci and highlights the interplay among mobile genetic elements, transcriptional regulation, and chromosomal adaptation in shaping antimicrobial resistance.

Fluorescent proteins (FPs) are widely used in biological imaging, yet their limited programmability restricts adaptability for advanced nanoscale applications. Synthetic molecular containers offer designable cavities but struggle to simultaneously achieve large dimensions, biocompatible interfaces, and precise microenvironment control. Here we report DNA framework (DNF) dots, highly programmable fluorescent probes constructed by encapsulating Cy3 within three-dimensional DNA-encoded hydrophobic nanocavities. Using amphiphilic tetrahedral DNA frameworks with inward-oriented alkyl chains, we create well-defined nanocavities (4.8-8.9 nm) with tunable size, shape, and internal chemistry. Single-molecule analysis reveals that DNF dots enhance photostability (up to 15-fold) and total photon output (up to 58-fold), as compared to single Cy3 tagged on single-stranded DNA. The exceptional photostability and brightness of DNF dots enable stable binding event frequencies exceeding 7500 frames in DNA-PAINT super-resolution imaging. Furthermore, integration into light-inducible DNA hydrogels yields rewritable information encoding platforms. DNF dots thus establish a versatile platform that combines the environmental shielding of fluorescent proteins with the programmability of synthetic molecular containers, introducing a DNA framework-encoded microenvironment engineering paradigm for ultrastable fluorescent probes.

This paper proposes a fully data-driven and end-to-end adaptive framework for the complete black-box identification of piecewise affine ARX (PWARX) systems, in which model structure discovery, submodel parameter estimation, recursive parameter fine-tuning, and explicit polyhedral partition identification are integrated into a unified two-stage learning architecture. In Stage 1, an unsupervised process determines the number and order of submodels, computes consistent initial submodel parameter estimates, and constructs a compact labeled training set. In Stage 2, a self-tuning semi-supervised support vector machine (STS3VM) iteratively learns the submodel classifiers, while adaptive-forgetting recursive least squares fine-tunes the submodel parameters. The learned decision rule is then converted into explicit polyhedral partitions, with stage-wise convergence guarantees. Extensive simulations demonstrate high accuracy, reproducibility, robustness under diverse noise conditions, and scalability.

Surface electromyography (sEMG) gesture recognition technology offers significant potential for enhancing human-computer interaction and rehabilitation engineering by providing an intuitive and noninvasive method of engagement. However, challenges such as electromagnetic interference (EMI), signal feature processing, and adaptability to individual variability remain prevalent. To address these issues, this paper presents a comprehensive framework that encompasses material selection, device design, feature selection algorithms, integration strategies, and system layout to create a skin-integrated electronic system functioning as a wireless control interface between humans and machines. This system features a flexible electrode that incorporates fractal design elements, serpentine structures, honeycomb configurations, and multilayer graphene, along with an efficient feature selection algorithm grounded in data dispersion principles. It is characterized by enhanced resistance to EMI, conformal attachment capabilities, rapid motion recognition, and high accuracy. Our work stands out with a dramatically higher shielding efficiency of 99.98% and a classification accuracy rate of 97.8%. This methodology significantly enhances the performance of sEMG-based human-computer interfaces, emphasizing rapid recognition speeds and high accuracy in real-time applications such as remote-control systems and virtual reality environments, thereby broadening its applicability across diverse contexts.

Listeria monocytogenes is a major foodborne pathogen whose environmental persistence and virulence pose significant threats to food safety. The ferric uptake regulator (Fur) is essential for iron homeostasis; however, its role in the acid and oxidative stress responses, pathogenicity and its potential as a target for food safety interventions remain poorly understand. In this study, a Δfur mutant was constructed to investigate these questions. Deletion of fur severely impaired survival under acidic stress and disrupted iron homeostasis, resulting in intracellular Fe[2+] accumulation. The mutant also exhibited enhanced resistance to oxidative stress, elevated glutathione (GSH) levels, and increased swimming motility. Additionally, the mutant showed reduced adhesion, invasion, cell-to-cell spread, and phagosome escape which correlated with attenuated virulence and lower bacterial burdens in mice. Electrophoretic mobility shift assays (EMSA) and qRT-PCR analysis showed that Fur modulates the expression of some PrfA-regulon virulence genes by binding to the promoters of prfA and hly in the presence of Fe[2+] and GSH. PrfA also bound to the fur promoter, suggesting a cross-regulatory loop between Fur and PrfA. In the wild-type strain, expression of fur and its downstream genes was upregulated under iron-limited conditions. Building on this regulatory insight, a Fur-overexpression strain CΔfur_Phelp was engineered. This strain displayed heightened sensitivity to stresses in PBS at both neutral (pH 7.4) and acidic (pH 3.5 or 5.5) conditions, and exhibited poor survival under refrigeration, particularly in milk. The iron chelator 2, 2'-dipyridyl (Dip) and sodium phytate inhibited bacterial viability in PBS at neutral pH, and the inhibitory effect of Dip was further enhanced under citric acid conditions (pH 3.5-5.5) upon prolonged incubation. This work establishes Fur as a master regulator linking metabolism to virulence, and proposes novel anti-listeria strategies targeting Fur or iron availability.

Lipid autoxidation of eicosapentaenoic acid (EPA; 20:5 n-3) and docosahexaenoic acid (DHA; 22:6 n-3) generates volatile carbonyl compounds-including propanal, hexanal, (E)-2-nonenal, and (E,Z)-2,4-decadienal-that contribute to fishy, rancid, and cardboard-like off-flavors and limit the commercialization of omega-3-enriched foods. Seventy-six percent of the global population fails to meet recommended EPA + DHA intake thresholds, driving demand for stable functional food applications (Calder et al., 2025). This review integrates off-flavor formation chemistry with a comparative assessment of conventional and emerging mitigation strategies across eight performance dimensions. Particular attention is given to the divergence between simplified model lipid systems and commercial fish or microalgal oils, in which minor components-heme-derived iron, transition metals, tocopherols, phospholipids, pigments and residual cellular material-modulate hydroperoxide decomposition and therefore the resulting volatile profile. The frequent assumption that odor activity values (OAVs) are additive is examined against experimental evidence of aroma masking and odorant synergy in marine and algal oils, and the oxidative fishy note is explicitly distinguished from the ammoniacal odor of microbially derived trimethylamine. Among conventional approaches, steam deodorization is industrially mature and effective for volatile removal, although severe thermal conditions may compromise lipid quality and promote the formation of process contaminants. Green extraction technologies-supercritical CO2, ultrasound-assisted, and enzymatic-assisted extraction-offer reduced oxidative damage during oil production and are gaining industrial relevance (Jamalluddin, Ismail, Ab. Mutalib, & Sikin, 2022). Clean-label interfacial engineering using protein-polyphenol complexes and multilayer emulsification represents an emerging strategy for simultaneous emulsion stabilization and off-flavor suppression (Hennebelle et al., 2024). Emerging non-thermal technologies-ozonation and cold plasma dielectric barrier discharge-offer selective volatile degradation mechanisms but their industrial adoption remains constrained by the absence of clearly established, application-specific regulatory pathways for omega-3 oil deodorization; hybrid cold plasma-antioxidant configurations remain at an early technology readiness level. Matrix-specific mitigation options for microalgal omega-3 oils are summarized separately. A conceptual Precision Oxidation Management (POM) framework is introduced, defining selective volatile degradation, hybrid process architecture, and AI-supported adaptive monitoring as three pillars for industrial translation.