Paper List
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An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
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Hierarchical Molecular Language Models (HMLMs)
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Stability analysis of action potential generation using Markov models of voltage‑gated sodium channel isoforms
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Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution
This paper addresses the core challenge of inferring the relative contributions of multiple, simultaneous generative mechanisms in network formation w...
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EnzyCLIP: A Cross-Attention Dual Encoder Framework with Contrastive Learning for Predicting Enzyme Kinetic Constants
This paper addresses the core challenge of jointly predicting enzyme kinetic parameters (Kcat and Km) by modeling dynamic enzyme-substrate interaction...
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Tissue stress measurements with Bayesian Inversion Stress Microscopy
This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which ...
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DeepFRI Demystified: Interpretability vs. Accuracy in AI Protein Function Prediction
This study addresses the critical gap between high predictive accuracy and biological interpretability in DeepFRI, revealing that the model often prio...
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.