Paper List
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Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
This work addresses the core challenge of extracting reusable, interpretable, and high-performance biological algorithms from the opaque internal repr...
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MS2MetGAN: Latent-space adversarial training for metabolite–spectrum matching in MS/MS database search
This paper addresses the critical bottleneck in metabolite identification: the generation of high-quality negative training samples that are structura...
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Toward Robust, Reproducible, and Widely Accessible Intracranial Language Brain-Computer Interfaces: A Comprehensive Review of Neural Mechanisms, Hardware, Algorithms, Evaluation, Clinical Pathways and Future Directions
This review addresses the core challenge of fragmented and heterogeneous evidence that hinders the clinical translation of intracranial language BCIs,...
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Less Is More in Chemotherapy of Breast Cancer
通过纳入细胞周期时滞和竞争项,解决了现有肿瘤-免疫模型的过度简化问题,以定量比较化疗方案。
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Fold-CP: A Context Parallelism Framework for Biomolecular Modeling
This paper addresses the critical bottleneck of GPU memory limitations that restrict AlphaFold 3-like models to processing only a few thousand residue...
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Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database
This paper addresses the core pain point of fragmented biomedical data by constructing and federating large-scale, open knowledge graphs to enable sea...
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Predictive Analytics for Foot Ulcers Using Time-Series Temperature and Pressure Data
This paper addresses the critical need for continuous, real-time monitoring of diabetic foot health by developing an unsupervised anomaly detection fr...
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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
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.