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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Spatio-temporal characterization of nonlinear forcing and response in turbulent channel flow

arXiv

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arXiv 2025年

作者： Huang, Yuting Toedtli, Simon S. Chini, Gregory P. McKeon, Beverley J. Mechanical and Civil Engineering California Institute of Technology PasadenaCA91125 United States National Center for Atmospheric Research BoulderCO80301 United States Program in Integrated Applied Mathematics Department of Mechanical Engineering University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering Center for Turbulence Research Stanford University StanfordCA94305 United States

The quadratic convection term in the incompressible Navier-Stokes equations is considered as a nonlinear forcing to the linear resolvent operator, and it is studied in the Fourier domain through the analysis of interactions between triadically compatible wavenumber-frequency triplets. A framework to quantify the triadic contributions to the forcing and response by each pair of triplets is developed and applied to data from direct numerical simulations of a turbulent channel at Reι ≈ 550. The linear resolvent operator is incorporated to provide the missing link from energy transfer between modes to the effect on the spectral turbulent kinetic energy. The coefficients highlight the importance of interactions involving large-scale structures, providing a natural connection to the modeling assumptions in quasi-linear (QL) and generalized quasi-linear (GQL) analyses. Specifically, it is revealed that the QL and GQL reductions efficiently capture important triadic interactions in the flow, especially when including of a small number of wavenumbers into the GQL large-scale base flow. Additionally, spatio-temporal analyses of the triadic contributions to a single mode representative of the near-wall cycle demonstrate the spatio-temporal nature of the triadic interactions and the effect of the resolvent operator, which selectively amplifies certain forcing profiles. The tools presented are expected to be useful for improving modeling of the nonlinearity, especially in QL, GQL, and resolvent analyses, and understanding the amplitude modulation mechanism relating large-scale fluctuations to the modulation of near-wall structures. © 2025, CC BY.

关键词： Navier Stokes equations

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The complex interplay between risk tolerance and the spread of infectious diseases

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Journal of the Royal Society Interface 2025年第225期22卷 20240486页

作者： Nguyen, Maximilian M. Freedman, Ari S. Cheung, Matthew A. Saad-Roy, Chadi M. Espinoza, Baltazar Grenfell, Bryan T. Levin, Simon A. Lewis-Sigler Institute Princeton University PrincetonNJ United States Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Miller Institute for Basic Research in Science University of California Berkeley BerkeleyCA United States Department of Integrative Biology University of California BerkeleyCA United States Biocomplexity Institute University of Virginia CharlottesvilleVA United States

Risk-driven behaviour provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of heterogeneity in the population with regard to risk tolerance. The interplay between behaviour and epidemiology leads to a rich set of possible epidemic dynamics. Depending on the behavioural composition of the population, we find that increasing heterogeneity in risk tolerance can either increase or decrease the epidemic size. We find that multiple waves of infection can arise due to the interplay between transmission and behaviour, even without the replenishment of susceptibles. We find that increasing protective mechanisms such as the effectiveness of interventions, the fraction of risk-averse people in the population and the duration of intervention usage reduce the epidemic overshoot. When the protection is pushed past a critical threshold, the epidemic dynamics enter an underdamped regime where the epidemic size exactly equals the herd immunity threshold and overshoot is eliminated. Finally, we can find regimes where epidemic size does not monotonically decrease with a population that becomes increasingly risk-averse. © 2025 The Author(s).

关键词： epidemic size heterogeneous behaviour intervention adoption and usage risk tolerance

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Motor cortex repetitive transcranial magnetic stimulation in fibromyalgia: a multicentre randomised controlled trial

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British Journal of Anaesthesia 2025年第6期134卷 1756-1764页

作者： Silva, Valquíria A. Baptista, Abrahão F. Fonseca, Alessandra S. Carneiro, Adriana M. Brunoni, André R. Carrilho, Paulo.E.M. Lins, Catarina C. Kubota, Gabriel T. Fernandes, Ana Mércia B.L. Lapa, Jorge.D.S. dos Santos, Lucas M. Sasso, Ivo Monte-Silva, Katia Poindessous-Jazat, Frédérique Mori, Nobuhiko Miki, Kenji Baltar, Adriana Tanaka, Clarice Teixeira, Manoel J. Hosomi, Koichi Bouhassira, Didier Attal, Nadine Ciampi de Andrade, Daniel LIM 62 - Pain Center Department of Neurology São Paulo Brazil Service of Interdisciplinary Neuromodulation (SIN) Department and Institute of Psychiatry São Paulo Brazil Medical-Surgical Nursing Department University of São Paulo São Paulo Brazil Center for Mathematics Computation and Cognition Federal University of ABC São Paulo Brazil Mood Disorder Program (Pro-GRUDA) Department of Psychiatry University of São Paulo São Paulo Brazil Medicine School “Professor Orival Alves” State University of Western Paraná (UNIOESTE) Paraná Brazil Neurosurgery Unit Hospital de Cirurgia Sergipe Aracaju Brazil Department of Medicine Federal University of Sergipe Sergipe São Cristóvão Brazil LIM 54 - Physiotherapy Research Laboratory University of São Paulo São Paulo Brazil Applied Neuroscience Laboratory Universidade Federal de Pernambuco Cidade Universitária Pernambuco Brazil UVSQ-Paris-Saclay University Ambroise Paré Hospital Boulogne-Billancourt U987 France Department of Neurosurgery Osaka University Graduate School of Medicine Osaka Japan Faculty of Health Science Osaka Yukioka College of Health Science Osaka Japan Center for Pain Management Hayaishi Hospital Osaka Japan Center for Neuroplasticity and Pain (CNAP) Department of Health Science and Technology University of Aalborg Gistrup Denmark

Background: Despite affecting 2–4% of the population worldwide, fibromyalgia often remains refractory to treatment. Here we report the first international randomised double-blind, sham-controlled trial developed to assess the efficacy of repetitive transcranial magnetic stimulation (rTMS) as an add-on therapy for fibromyalgia. Methods: Women aged ≥18 yr with fibromyalgia refractory to best available treatment were enrolled in Brazil, France, and Japan, and randomised to 10 Hz motor cortex (M1) rTMS, 3000 pulses day⁻¹, or sham stimulation. This included 10 induction sessions over 2 weeks, followed by weekly maintenance (6 weeks), and fortnightly extended maintenance (8 weeks). Primary outcome was ≥50% pain reduction at week 8 compared with baseline. Secondary outcomes included pain interference, mood, global impression of change, and Fibromyalgia Impact Questionnaire (FIQ) scores at weeks 8 and 16. Results: We randomised 101 women (mean age 48 [range 25-83] yr) into active (n=52) or sham (n=49) arms. Bayesian analysis revealed a 99.4% probability of ≥50% pain reduction at week 8 in the active group vs sham (odds ratio [OR] 3.04;95% credible interval [95% CrI] 1.26–8.06), with a number needed to treat of 4.54. Frequentist analysis confirmed that relative pain reduction was higher in the active than in the sham group (40.4% vs 18.4%, P=0.028). At week 16, this probability reduced to 34.2% (OR 0.815;95% CrI 0.313–2.1), but the likelihood of FIQ score reduction was 79.1%. The intervention appeared safe. Conclusions: Add-on M1-repetitive transcranial magnetic stimulation reduced pain intensity up to 8 weeks in women with fibromyalgia. Although analgesic effects waned, functional improvements remained during extended maintenance at week 16. © 2025 The Authors

关键词： chronic pain clinical trial fibromyalgia neuromodulation transcranial magnetic stimulation

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Simulation of Impurity Diffusion in a Strained Nanowire Using Off-Lattice KMC

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Communications in Computational Physics 2007年第1期2卷 164-176页

作者： Weidong Guo Tim P.Schulze Weinan E Department of Mathematics University of TennesseeKnoxvilleTN 37996-1300USA Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJ 08544-1000USA.

Kinetic Monte Carlo(KMC)is a stochastic model used to simulate crystal ***,most KMC models rely on a pre-defined lattice that neglects dislocations,lattice mismatch and strain *** this paper,we investigate the use of a 3D off-lattice KMC *** test this method by investigating impurity diffusion in a strained FCC *** faster than a molecular dynamics simulation,the most general implementation of off-lattice KMC is much slower than a lattice-based *** improved procedure is achieved for weakly strained systems by precomputing approximate saddle point locations based on unstrained lattice *** this way,one gives up some of the flexibility of the general method to restore some of the computational speed of lattice-based *** addition to providing an alternative approach to nano-materials simulation,this type of simulation will be useful for testing and calibrating methods that seek to parameterize the variation in the transition rates for lattice-based KMC using continuum modeling.

关键词： Off-lattice KMC nanowire strain rate impurity diffusion.

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Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes

arXiv

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arXiv 2025年

作者： Wu, Da Wang, Zhanliang Nguyen, Quan Wang, Kai Raymond G. Perelman Center for Cellular and Molecular Therapeutics Children's Hospital of Philadelphia PhiladelphiaPA19104 United States Applied Mathematics and Computational Science Graduate Program University of Pennsylvania PhiladelphiaPA19104 United States Bioengineering Graduate Program University of Pennsylvania PhiladelphiaPA19104 United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States

Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Children’s Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes;in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes. Conclusions: Integrating CoT and RAG enhances LLMs’ understanding of clinical notes in the context of rare disease diagnosis, and can facilitate various downstream medical tasks. © 2025, CC

关键词： Chains

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Optimization-Based String Method for Finding Minimum Energy Path

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Communications in Computational Physics 2013年第7期14卷 265-275页

作者： Amit Samanta Weinan E Program in Applied and Computational Mathematics Princeton UniversityPrincetonNew JerseyUSA Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNew JerseyUSA Beijing International Center for Mathematical Research Peking UniversityBeijingChina

We present an efficient algorithm for calculating the minimum energy path(MEP)and energy barriers between local minima on a multidimensional potential energy surface(PES).Such paths play a central role in the understanding of transition pathways between metastable *** method relies on the original formulation of the string method[***.B,66,052301(2002)],*** evolve a smooth curve along a direction normal to the *** algorithm works by performing minimization steps on hyperplanes normal to the *** the problem of finding MEP on the PES is remodeled as a set of constrained minimization *** provides the flexibility of using minimization algorithms faster than the steepest descent method used in the simplified string method[***.,126(16),164103(2007)].At the same time,it provides a more direct analog of the finite temperature string *** applicability of the algorithm is demonstrated using various examples.

关键词： Rare events string method minimum energy path dislocation nucleation

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MORSE AND MELNIKOV FUNCTIONS FOR NLS PDES

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COMMUNICATIONS IN MATHEMATICAL PHYSICS 1994年第1期162卷 175-214页

作者： LI, Y MCLAUGHLIN, DW 1. Department of Mathematics Princeton University 08544 Princeton NJ USA 2. Department of Mathematics Program in Applied and Computational Mathematics Princeton University 08544 Princeton NJ USA

The theory of the focusing NLS equation under periodic boundary conditions, together with the Floquet spectral theory of its associated Zakharov-Shabat liner operator L, is developed in sufficient detail for later use in studies of perturbations of the NLS equation. ''Counting lemmas'' for the non-selfadjoint operator L, are established which control its spectrum and show that all of its eccentricities are finite in number and must reside within a finite disc D in the complex eigenvalue plane. The radius of the disc D is controlled by the H-1 norm of the potential q. For this integrable NLS Hamiltonian system, unstable tori are identified, and Backlund transformations are then used to construct global representations of their stable and unstable manifolds - ''whiskered tori'' for the NLS pde. The Floquet discriminant DELTA(lambda;q) used to introduce a natural sequence of NLS constants of motion, [F(j)(q) = DELTA(lambda = lambda(j)c(q);q), where lambda(j)c denotes the j(th) critical point of the Floquet discriminant DELTA(lambda)]. A Taylor series expansion of the constants F(j)(q), with explicit representations of the first and second variations, is then used to study neighborhoods of the whiskered tori. In particular, critical tori with hyperbolic structure are identified through the first and second variations of F(j)(q), which themselves are expressed in terms of quadratic products of eigenfunctions of L. The second variation permits identification, within the disc D, of important bifurcations m the spectral configurations of the operator L. The constant F(j)(q), as the height of the Floquet discriminant over the critical point lambda(j)c, admits a natural interpretation as a Morse function for NLS isospectral level sets. This Morse interpretation is studied in some detail. It is valid globally for the infinite tail, {F(j)(q)}\j\>N, which is associated with critical points outside the disc D. Within this disc, the interpretation is only valid locally, with the s

关键词： 34L05 35Q55 58F05 58F19

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Relativistic Quantum Simulation of Hydrogen Sulfide for Hydrogen Energy via Hybrid Quantum-Classical Algorithms

arXiv

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arXiv 2025年

作者： Hwang, Chi-Chuan Su, Cheng-Fang Yang, Jung-Fan Department of Engineering Science National Cheng Kung University Tainan City701 Taiwan Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu City30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu City30010 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan City33551 Taiwan

We present a hybrid quantum-classical computational framework for simulating relativistic quantum chemical systems using the variational quantum eigensolver (VQE). Our approach integrates relativistic electron integrals derived from the Dirac–Coulomb Hamiltonian with quantum circuit models constructed via Jordan–Wigner transformation. By employing both heuristic and chemistry-inspired ansätze, we compute ground-state energies and potential energy surfaces (PES) for benchmark molecules including H2, H2O, and H2S. Active-space reduction and Hartree–Fock embedding techniques are used to minimize the number of qubits and Pauli terms, enabling efficient simulation on near-term quantum devices. Our results demonstrate that VQE can reproduce classical full-configuration-interaction energies with millihartree-level accuracy, even under relativistic Hamiltonians. We observe consistent relativistic energy shifts and orbital contraction effects, particularly for the sulfur-containing H2S molecule. The constructed PES allow for equilibrium geometry prediction and activation energy analysis. This work highlights the feasibility and scalability of applying quantum algorithms to relativistic electronic structure problems, paving the way for future simulations of heavier-element systems in quantum chemistry. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular orbitals

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Comparative Analysis of Quantum Support Vector Machines and Variational Quantum Classifiers for B-cell Epitope Prediction in Vaccine Design

arXiv

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arXiv 2025年

作者： Hwang, Chi-Chuan Su, Cheng-Fang Hong, Yi-Ang Department of Engineering Science National Cheng Kung University Tainan City701 Taiwan Department of Applied Mathematics National Yang Ming Chiao Tung University Hsinchu City30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu City30010 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan City33551 Taiwan

Quantum computing offers new opportunities for addressing complex classification tasks in biomedical applications. This study investigates two quantum machine learning models-the Quantum Support Vector Machine (QSVM) and the Variational Quantum Classifier (VQC)-in the context of B-cell epitope prediction, a key step in modern vaccine design. QSVM builds upon the classical SVM framework by using quantum circuits to encode nonlinear kernel computations, while VQC replaces the entire classification pipeline with trainable quantum circuits optimized variationally. A benchmark dataset from the Immune Epitope Database (IEDB) is used for model evaluation. Each epitope is represented by 10 physicochemical features, and dimensionality reduction via Principal Component Analysis (PCA) is applied to assess model performance across different feature spaces. We also examine the effect of sample size on prediction outcomes. Experimental results show that QSVM performs well under limited data conditions, while VQC achieves higher accuracy in larger datasets. These findings highlight the potential of quantum-enhanced models for bioinformatics tasks, particularly in supporting efficient and scalable epitope-based vaccine development. Copyright © 2025, The Authors. All rights reserved.

关键词： Epitopes

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