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Reply to Romano and De Dreu: Why violent extremism cannot be reduced to laboratory games
Intentions poorly explain how and why people engage in offensive and defensive forms of violence
Hybridogenesis as an intermediate step between sexual reproduction and parthenogenesis in stick insects
Many organisms reproduce through noncanonical modes such as parthenogenesis or hybridogenesis (clonal transmission of one parent’s chromosomes), but whether these arise abruptly or stepwise from each other remains unclear. We address this in the stick-insect genus Bacillus , which harbors several hybrid lineages with diverse reproductive modes. From haplotype-resolved phylogenies of >500 wild-caught individuals, we infer a single, recent (~8,000 y) origin of all hybrids. The ancestral hybrid reproduced via hybridogenesis, which subsequently diversified into parthenogenesis and, twice independently, into triploid lineages. Laboratory crosses recapitulate this trajectory, where each step facilitated the next. These findings reveal how a single genomic perturbation can act as a catalyst for evolutionary innovation, turning the loss of sex into a driver of diversification rather than a dead end.
Topology-alloy interactions governing deformation and failure in LPBF-fabricated A286 and Inconel 718 lattice structures
Valuing ecological benefits in ecological restoration of abandoned railways based on social media analytics
Higher job exposures are associated with reduced return-to-work two years after rehabilitation in a nationwide cohort study based on German Pension Insurance data
Abstract Return-to-work (RTW) after medical rehabilitation is an important indicator of post-rehabilitation labour market participation, yet evidence on its association with occupation-based job exposure over longer follow-up periods remains limited. This nationwide retrospective cohort study examined the association between job exposure and RTW within 24 months post-rehabilitation using routine administrative data from the German Pension Insurance. The study included 621,695 individuals aged 18–63 years who completed medical rehabilitation between 2014 and 2019. Job exposure was assessed using the Overall Job Exposure Index, integrating physical and psychosocial exposures, and categorised into low, moderate, and high exposure. Initial RTW was defined as at least one month of employment, and stable RTW as at least four consecutive months within 24 months post-rehabilitation. Associations were analysed using Cox proportional hazards regression models with progressive adjustment for sociodemographic, work-related, and health-related factors. Within 24 months, 90.3% achieved initial RTW and 84.2% stable RTW. Higher job exposure was consistently associated with lower RTW rates. In fully adjusted models, moderate and high exposure were associated with a reduced likelihood of initial RTW (HR 0.903 [95% CI 0.897–0.909] and 0.872 [95% CI 0.866–0.878]) and stable RTW (HR 0.878 [95% CI 0.872–0.884] and 0.837 [95% CI 0.830–0.843]), compared with low exposure. These findings provide population-level evidence that occupation-based job exposure is associated with RTW after medical rehabilitation.
Sleeping but struggling: a qualitative study of the lived experiences of sleep in student-athletes
Abstract A growing body of evidence indicates that student-athletes experience suboptimal sleep health across multiple dimensions. However, limited research has explored sleep from the student-athlete’s own perspective. This study examined the lived experience of sleep health in a cohort of student-athletes known to demonstrate suboptimal sleep outcomes. Semi-structured interviews were conducted with 12 British rugby union student-athletes. During interviews, participants were presented with personalised sleep data from a prior actigraphy assessment, which served as a visual elicitation tool to facilitate reflection and discussion. Interview transcripts were analysed using reflexive thematic analysis. Nine lower-order themes were identified and organised into three higher-order themes: irregular patterns , reflecting structural and social demands that constrained control over consistent sleep; managing sleep behaviours , encompassing individual behaviours that often conflicted with established sleep recommendations; and connection to health , describing bidirectional relationships between sleep, wellbeing, and performance, whereby inadequate sleep contributed to negative physical, cognitive, and emotional outcomes. Overall, student-athletes identified multiple upstream influences that adversely affected their sleep health, with wide-ranging consequences for wellbeing. These findings contextualise previous quantitative evidence of poor sleep in student‑athletes and may inform the subsequent development of targeted sleep interventions grounded in behaviour change theory.
Robust deepfake video detection using spatio-temporal features and dynamic difference learning
Abstract Recent progress in facial manipulation technologies has made deepfake videos increasingly convincing, posing significant challenges to detection systems that rely solely on analyzing individual frames. Consequently, there has been a growing emphasis on investigating spatial and temporal inconsistencies within video sequences to more accurately distinguish between genuine and manipulated content. However, many existing approaches still depend on combining frame-level and sequence-level features without adequately addressing irregularities in facial motion, which can significantly constrain detection performance. To overcome these limitations, we propose a comprehensive deep learning framework that integrates both spatial and temporal analysis. Facial landmarks are extracted from each video frame using Dlib’s 68-point detector, providing geometric descriptors of facial structure. These landmarks are fed into a Transformer encoder to capture both short- and long-term motion dynamics, enhanced by a Dynamic Difference Module (DDM) that emphasizes abrupt, unnatural changes. Meanwhile, CNNs extract spatial features, and LSTMs model temporal dependencies. The performance was assessed using standard metrics, including precision, recall, and F1-score, ensuring a comprehensive evaluation of the framework’s effectiveness. Beyond a study, the full framework was experimentally validated across three benchmark datasets—FaceForensics++ (FF++), UADFV, and DFDC—achieving a remarkable 100% accuracy, thereby demonstrating its robustness and strong generalization capability.
Integrating generalized linear mixed models and XGBoost for safety performance function development on urban arterials
Swine wastewater-cultivated Chlorella sorokiniana reduces cadmium accumulation in rice grown on contaminated paddy soil
Thermoresponsive carbohydrate polymer mucoadhesive gel for synergistic delivery of acarbose and fluconazole against Candida biofilms
Influence of temperature and inoculum composition on standardized biodegradation tests of bioplastics in freshwater under aerobic conditions
Rapid monitoring of drought and salinity stress responses in wheat via potential Raman-derived biomarkers and traditional biochemical indicators
A homoeriodictyol sodium mouthwash reduces bitterness sensitivity in patients with gynecological cancer receiving carboplatin-based chemotherapy
Path planning based on integrating JPS-Theta* algorithm and improved APF algorithm
Spatial assessment of heat risk from deregulation of greenbelt in Seoul, South Korea
Reservoir operation impacts on floodplain wetland inundation and ecologically sensitive areas
Joint optimization of electric vehicle routes and charging locations through learning charge constraints using QUBO solvers
Abstract Optimal routing problems of electric vehicles (EVs) have attracted much attention in recent years, and installation of charging stations is an important issue for EVs. Hence, we focus on the joint optimization of the location of charging stations and the routing of EVs. When routing problems are formulated in the form of quadratic unconstrained binary optimization (QUBO), specialized solvers such as quantum annealers are expected to provide optimal solutions with high speed and accuracy. However, battery capacity constraints make it hard to formulate into QUBO form without a large number of auxiliary qubits. Here, we propose a sequential optimization method utilizing the Bayesian inference and QUBO solvers, in which the battery capacity constraints are automatically learned. This method enables us to optimize the number and location of charging stations and the routing of EVs with a small number of searches. Applying this method to a routing problem of 20 locations, we observed consistent convergence toward battery-feasible solutions across independent runs, demonstrating stable learning behavior of the proposed framework. Small-scale validation experiments using exhaustive enumeration show that the framework reliably discovers feasible configurations close to the global optimum, while runtime and QUBO-size analyses clarify its computational characteristics.