Wei Wang

Misinformation spreading on social networks is jointly shaped by community structure, higher-order reinforcement, and intervention. We formulate a sentinel-triggered competitive susceptible–infected–susceptible–AB model on a simplicial complex with two equal-sized communities. Sentinels respond to local misinformation exposure by temporarily becoming truth sources, coupling local detection and corrective response with higher-order reinforcement and cross-community invasion. We derive a heterogeneous mean-field description and compare its predictions with Monte Carlo simulations. The results reveal extinction, community-localized persistence, and network-wide spreading, separated by discontinuous transitions and initial-condition-dependent hysteresis in the examined parameter ranges. Increasing the sentinel fraction or the pairwise truth-spreading strength suppresses the high-misinformation state, whereas a higher sentinel activation threshold delays the response. Intercommunity coupling promotes misinformation invasion under weak intervention but can support truth spreading when sentinel intervention is sufficiently strong. Extensions to unequal community sizes and more general community structures remain topics for future work.

Traditional pairwise epidemic models fail to capture group-based infection risks in dense urban environments, where higher-order interactions in shared spaces drive explosive contagion. We develop a spatial activity-driven higher-order framework integrating agent-based mobility modeling with the microscopic Markov chain approach to simulate these complex contagion processes. Our results demonstrate that higher-order interactions significantly advance outbreak peaks by intensifying localized infection pressure, triggering explosive spreading patterns driven by cumulative infection pressure. Through systematic analysis of screening bias and testing scale, we find that targeted interventions based on activity levels are essential for containment, with their efficacy further amplified by the test scale. Crucially, the impact of isolation is highly contingent on screening bias: even the strictest isolation may fail if screening is poorly targeted. The derived basic reproduction number reveals three coupled dimensions governing epidemic thresholds: biological weight, higher-order enhancement, and structural spectral radius. This research establishes that the efficacy of strict isolation critically depends on the coupling between testing scale and screening bias in higher-order networks. By synchronizing high-resource allocation with precise targeting of spatial social hubs, public health authorities can effectively counteract the accelerated outbreak peaks induced by collective group interactions. Our findings provide fundamental insights into epidemic spreading in complex social systems and offer quantitative guidance for designing optimal intervention strategies against epidemic outbreaks.

Background and Aims: MASLD has become the most prevalent chronic liver disease globally. Although MVPA and plasma fatty acids have been individually studied in relation to metabolic health, their independent and combined associations with MASLD incidence remain unclear. We aimed to investigate these associations. Methods and Results: This study included 51,717 UK Biobank participants free of liver disease at baseline, with MVPA measured using wrist-worn accelerometers and plasma fatty acids quantified via NMR. Multivariable-adjusted Cox models and restricted cubic splines were used. Over a median follow-up of 7.8 years, 472 incident cases were identified. In fully adjusted models, meeting recommended MVPA levels together with higher n-6 PUFA concentrations was associated with a 71% lower risk (HR 0.29, 95% CI 0.18–0.45). The MVPA-MASLD association was nonlinear, with risk reduction plateauing at approximately 189 minutes per week. Higher n-6 PUFA was associated with reduced risk, whereas n-3 PUFA showed no significant association. Conclusions: These findings suggest that behavioral and metabolic factors may jointly influence MASLD risk. Further studies in diverse populations are needed to confirm these associations.

Herb–symptom interaction (HSI) prediction is crucial for understanding the multi-target mechanisms of herbal therapies and enabling data-driven precision traditional Chinese medicine (TCM). Existing computational approaches mainly employ graph neural networks (GNNs) to model herb–symptom relationships in biological networks. However, these methods are limited to low-order pairwise associations and fail to capture herbs’ high-order and indirect regulatory effects. In addition, random negative sampling from unobserved herb–symptom pairs often results in false negatives due to incomplete annotations. To address these limitations, this study proposes MHGNN, a novel hypergraph learning framework that models HSIs as a multiplex hypergraph integrating protein–protein interactions (PPIs) along with high-order herb–protein and symptom–protein relationships. MHGNN employs hierarchical message passing to aggregate high-order features across multiple edge types and adopts a network-based negative sampling strategy, which selects herb–symptom pairs with larger proximity distances on the human protein interactome as negative instances to reduce false-negative bias. Comprehensive experiments on two public TCM datasets show that MHGNN consistently achieves superior predictive performance compared with thirteen state-of-the-art baselines, highlighting its potential to advance mechanism-aware modeling and enable precision herbal medicine.

Low physical activity and poor sleep quality increase the risk of diabetes. This study investigates how physical activity and sleep patterns influence both