Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
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Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
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Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
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Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
Module control in youth symptom networks across COVID-19
School of Biomedical Engineering and Informatics, Nanjing Medical University | Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University
30秒速读
IN SHORT: This paper addresses the core challenge of distinguishing whether a prolonged societal stressor (COVID-19) fundamentally reorganizes the architecture of youth psychopathology or merely redistributes influence across a stable symptom network scaffold.
核心创新
- Methodology Applies a minimum-dominating-set (MDS) based module control framework to repeated cross-sectional symptom network data, enabling the quantification of how control is redistributed across symptom communities over time.
- Biology Reveals a dual-timescale response: symptom community structure (mesoscale scaffold) remains conserved, while intermodule control dynamically shifts from stress-centered to a distributed pattern across emotional, cognitive, and social domains.
- Methodology Systematically evaluates the robustness of network control metrics (node strength, ACF, AMCS) via extensive resampling (bootstrap and case-dropping), establishing intermodule control (AMCS) as a stable feature for cross-phase comparison.
主要结论
- Symptom community organization was broadly conserved across five pandemic phases (2020-2023), indicating a stable mesoscale scaffold resilient to macro-level shocks.
- Intermodule control, quantified by Average Module Control Strength (AMCS), reconfigured significantly: early phases were dominated by stress-related symptoms (STR domain), while later phases showed distributed control across Emotional (EMO), Cognitive/Social (CSF), and Self-perception/Physiological (SPF) domains.
- Resampling analyses (1000 bootstraps) demonstrated high stability for node strength (correlation with full-sample ~0.95), moderate stability for module-to-module control (AMCS correlation ~0.70-0.80), and lower robustness for within-module control (ACF).
摘要: The COVID-19 pandemic exposed young people to a prolonged and evolving societal stressor, yet it remains unclear whether symptom networks were reorganized or whether control was redistributed across a conserved modular scaffold. Here we analysed repeated cross-sectional data on 47 self-reported mental-health symptoms from 14,181 U.S. young adults aged 18–24 years across five COVID-19 phases between 2020 and 2023. For each phase, we estimated Gaussian graphical models, identified symptom communities, and characterized minimum-dominating-set-based module control. Symptom networks showed broadly conserved community organization across phases, indicating a stable mesoscale scaffold despite marked temporal variation. By contrast, intermodule control shifted from an early configuration centered on stress-related symptoms to a later, more distributed pattern spanning emotional, cognitive and social domains. Resampling analyses showed high stability for node strength and moderate stability for module-to-module control, whereas average within-module control was less robust. These findings suggest that prolonged crisis may preserve the modular architecture of youth psychopathology while redistributing control across symptom domains, and they identify intermodule control as a comparatively robust mesoscale feature for cross-phase comparison.