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arXiv 2023年

作者： Zhang, Cunzhi Puligheddu, Marcello Zhang, Linfeng Car, Roberto Galli, Giulia Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States Materials Science Division Center for Molecular Engineering Argonne National Laboratory LemontIL60439 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Chemistry University of Chicago ChicagoIL60637 United States

Measuring the thermal conductivity (κ) of water at extreme conditions is a challenging task and few experimental data are available. We predict κ for temperatures and pressures relevant to the conditions of the Earth mantle, between 1,000 and 2,000 K and up to 22 GPa. We employ close to equilibrium molecular dynamics simulations and a deep neural network potential fitted to density functional theory data. We then interpret our results by computing the equation of state of water on a fine grid of points and using a simple model for κ. We find that the thermal conductivity is weakly dependent on temperature and monotonically increases with pressure with an approximate square-root behavior. In addition we show how the increase of κ at high pressure, relative to ambient conditions, is related to the corresponding increase in the sound velocity. Although the relationships between the thermal conductivity, pressure and sound velocity established here are not rigorous, they are sufficiently accurate to allow for a robust estimate of the thermal conductivity of water in a broad range of temperatures and pressures, where experiments are still difficult to perform. Copyright © 2023, The Authors. All rights reserved.

关键词： Thermal conductivity

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Dirac nodal lines and nodal loops in the topological kagome superconductor CsV3Sb5

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Physical Review B 2022年第8期106卷 L081101-L081101页

作者： Zhanyang Hao Yongqing Cai Yixuan Liu Yuan Wang Xuelei Sui Xiao-Ming Ma Zecheng Shen Zhicheng Jiang Yichen Yang Wanling Liu Qi Jiang Zhengtai Liu Mao Ye Dawei Shen Yi Liu Shengtao Cui Jiabin Chen Le Wang Cai Liu Junhao Lin Jianfeng Wang Bing Huang Jia-Wei Mei Chaoyu Chen Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China International Quantum Academy Shenzhen 518048 China Beijing Computational Science Research Center Beijing 100193 China State Key Laboratory of Functional Materials for Informatics and Center for Excellence in Superconducting Electronics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China National Synchrotron Radiation Laboratory University of Science and Technology of China Hefei 230029 China School of Physics Beihang University Beijing 100191 China Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and Technology Shenzhen 518055 China

The intertwining of charge order, superconductivity, and band topology has promoted the AV3Sb5 (A=K, Rb, Cs) family of materials to the center of attention in condensed matter physics. Underlying those mysterious macroscopic properties such as giant anomalous Hall conductivity (AHC) and chiral charge density wave is their nontrivial band topology. While there have been numerous experimental and theoretical works investigating the nontrivial band structure and especially the van Hove singularities, the exact topological phase of this family remains to be clarified. In this work, we identify CsV3Sb5 as a Dirac nodal line semimetal based on the observation of multiple Dirac nodal lines and loops close to the Fermi level. Combining photoemission spectroscopy and density functional theory, we identify two groups of Dirac nodal lines along the kz direction and one group of Dirac nodal loops in the A−H−L plane. These nodal loops are located at the Fermi level within the instrumental resolution limit. Importantly, our first-principles analyses indicate that these nodal loops may be a crucial source of the mysterious giant AHC observed. Our results not only provide a clear picture to categorize the band structure topology of this family of materials, but also suggest the dominant role of topological nodal loops in shaping their transport behavior.

关键词： Topological materials Topological phases of matter Topological superconductors Node-line semimetals Angle-resolved photoemission spectroscopy Density functional calculations

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Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources

arXiv

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arXiv 2023年

作者： Rajendran, Suraj Pan, Weishen Sabuncu, Mert R. Chen, Yong Zhou, Jiayu Wang, Fei Tri-Institutional Computational Biology & Medicine Program Cornell University NY United States Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New YorkNY United States School of Electrical and Computer Engineering Cornell University IthacaNY United States Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medical School New YorkNY United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Department of Computer Science and Engineering Michigan State University East LansingMI United States

Machine learning (ML) in healthcare presents numerous opportunities for enhancing patient care, population health, and healthcare providers' workflows. However, the real-world clinical and cost benefits remain limited due to challenges in data privacy, heterogeneous data sources, and the inability to fully leverage multiple data modalities. In this perspective paper, we introduce "patchwork learning" (PL), a novel paradigm that addresses these limitations by integrating information from disparate datasets composed of different data modalities (e.g., clinical free-text, medical images, omics) and distributed across separate and secure sites. PL allows the simultaneous utilization of complementary data sources while preserving data privacy, enabling the development of more holistic and generalizable ML models. We present the concept of patchwork learning and its current implementations in healthcare, exploring the potential opportunities and applicable data sources for addressing various healthcare challenges. PL leverages bridging modalities or overlapping feature spaces across sites to facilitate information sharing and impute missing data, thereby addressing related prediction tasks. We discuss the challenges associated with PL, many of which are shared by federated and multimodal learning, and provide recommendations for future research in this field. By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models. This paradigm promises to strike a balance between personalization and generalizability, ultimately enhancing patient experiences, improving population health, and optimizing healthcare providers' workflows. © 2023, CC BY.

关键词： Data privacy

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Bayesian non-parametric clustering of single-cell mutation profiles 24th

Bayesian non-parametric clustering of single-cell mutation p...

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24th Annual Conference on Research in computational Molecular Biology, RECOMB 2020

作者： Borgsmüller, Nico Bonet, Jose Marass, Francesco Gonzalez-Perez, Abel Lopez-Bigas, Nuria Beerenwinkel, Niko Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland SIB Swiss Institute of Bioinformatics Basel Switzerland The Barcelona Institute of Science and Technology Barcelona Spain Research Program on Biomedical Informatics Universitat Pompeu Fabra Barcelona Spain Barcelona Spain

Cancer is an evolutionary process characterized by the accumulation of mutations that drive tumor initiation, progression, and treatment resistance. © Springer Nature Switzerland AG 2020.

ISBN: (纸本)9783030452568

Cancer is an evolutionary process characterized by the accumulation of mutations that drive tumor initiation, progression, and treatment resistance. © Springer Nature Switzerland AG 2020.

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Shin et al. Reply:

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Physical Review Letters 2023年第5期131卷 059602-059602页

作者： Dongbin Shin Nicolas Tancogne-Dejean Jin Zhang Mahmut Sait Okyay Angel Rubio Noejung Park Max Planck Institute for the Structure and Dynamics of Matter and Center for Free-Electron Laser Science Luruper Chaussee 149 22761 Hamburg Germany Department of Physics and Photon Science Gwangju Institute of Science and Technology (GIST) Gwangju 61005 Republic of Korea Materials Science & Engineering Program Department of Chemical & Environmental Engineering Department of Physics & Astronomy and Department of Chemistry University of California-Riverside Riverside 92521 California USA Nano-Bio Spectroscopy Group Departamento de Fsica de Materiales Universidad del Pas Vasco 20018 San Sebastian Spain Center for Computational Quantum Physics (CCQ) The Flatiron Institute 162 Fifth avenue New York New York 10010 USA Department of Physics Ulsan National Institute of Science and Technology (UNIST) UNIST-gil 50 Ulsan 44919 Korea

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Vishakha D Bhandarkar Aruna Sri Rongali Mohamed Dawood Shamout Prof. Sachin Pund Gigih Forda Nama Shrishailappa Patil Applied Mathematics and Humanities Yeshwantrao Chavan College of Engineering India Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Department of Management College of Business Administration University of Sharjah Sharjah United Arab Emirates Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Computer engineering Vishwakarma Institute of Technology Pune

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the “performing Scalable Inference” technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way..

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Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

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Cell Reports Physical science 2025年第5期6卷

作者： Cesaro, Angela Wan, Fangping Shi, Haoyuan Wang, Kaiyang Maupin, C. Mark Barker, Matt L. Liu, Jiqian Fox, Stephen J. Yeo, Jingjie de la Fuente-Nunez, Cesar Machine Biology Group Departments of Psychiatry and Microbiology Institute for Biomedical Informatics Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Departments of Bioengineering and Chemical and Biomolecular Engineering School of Engineering and Applied Science University of Pennsylvania Philadelphia PA United States Department of Chemistry School of Arts and Sciences University of Pennsylvania Philadelphia PA United States Penn Institute for Computational Science University of Pennsylvania Philadelphia PA United States Lab for Engineering Living Materials Sibley School of Mechanical and Aerospace Engineering Cornell University Ithaca NY United States Chemical and Biological Signatures Group Pacific Northwest National Laboratory Richland 99352 WA United States Procter and Gamble Cincinnati OH United States Procter & Gamble Singapore Singapore

computational methods have proven effective in identifying virucidal agents, contributing to the discovery of novel molecules. In this study, we successfully developed a machine learning (ML) model, trained on a small and imbalanced dataset, to predict inhibitors of human enterovirus 71 (EV71), a significant pathogen that causes severe illness in children and immunocompromised adults. Despite the compact size of the dataset—36 compounds, 5 of which were active against EV71—our ML framework demonstrated significant predictive accuracy. Experimental validation confirmed that five out of eight compounds predicted from the Chinese cosmetic material list exhibited virucidal activity. Molecular dynamics simulations further validated the inhibitory effects of the most active compounds. These findings underscore the potential of our AI-driven approach to accelerate the identification of active molecules against viral pathogens, even with limited data. © 2025 The Authors

关键词： anti-EV71 compounds data-scarce setting deep learning machine learning molecular dynamics simulations molecular modeling for viral capsid prediction of virucidal compounds representation learning virucidal compound interaction

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A gas-only bioreactor system maintains stable culture environments and reveals that moderate pH deviations trigger transcriptome-wide responses in human cells cultured in physioxia and physiological buffers

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Life Medicine 2023年第2期1卷 236-240页

作者： Silvia Arossa Samhan M. Alsolami Shannon G. Klein Yingzi Zhang Gerardo Ramos-Mandujano Alexandra Steckbauer Anieka J. Parry Juan Carlos Izpisúa-Belmonte Carlos M. Duarte Mo Li Red Sea Research Centre (RSRC) and Computational Bioscience Research Center (CBRC) King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia Bioscience Program Biological and Environmental Science and Engineering Division (BESE) King Abdullah University of Science and Technology Thuwal 23955 Saudi Arabia Gene Expression Laboratory Salk Institute for Biological Studies 10010 North Torrey Pines Rd. La Jolla CA 92037 USA Altos Labs Inc. 5510 Morehouse Drive Suite 300 San Diego CA 92121 USA

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The Role of Heterogeneity in Autonomous Perimeter Defense Problems

arXiv

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arXiv 2022年

作者： Adler, Aviv Mickelin, Oscar Ramachandran, K. Sukhatme, Gaurav S. Karaman, Sertac Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Program in Applied and Computational Mathematics Princeton University Princeton NJ08544 United States Department of Computer Science University of Southern California Los AngelesCA90089 United States Department of Aeronautics and Astronautics Massachusetts Institute of Technology CambridgeMA02139 United States

When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic programming

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A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

arXiv

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arXiv 2024年

作者： Ive, Julia Bondaronek, Paulina Yadav, Vishal Santel, Daniel Glauser, Tracy Cheng, Tina Strawn, Jeffrey R. Agasthya, Greeshma Tschida, Jordan Choo, Sanghyun Chandrashekar, Mayanka Kapadia, Anuj J. Pestian, John University College London Institute of Health Informatics United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center University of Cincinnati CincinnatiOH United States Advanced Computing in Health Sciences Computational Sciences in Engineering Division Oak Ridge National Laboratory United States Division of Neurology Cincinnati Children's Hospital Medical Center University of Cincinnati CincinnatiOH United States Department of Pediatrics Cincinnati Children's Hospital Medical Center University of Cincinnati CincinnatiOH United States Department of Psychiatry College of Medicine University of Cincinnati CincinnatiOH United States

Introduction Healthcare analytics and Artificial Intelligence (AI) hold transformative potential, yet AI models often inherit biases from their training data, which can exacerbate healthcare disparities, particularly among minority groups. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data like clinical notes, where bias and data sparsity introduce unique challenges. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop and evaluate a de-biasing method for mental health text data. Methods We examined classification parity across demographic groups, identifying biases through analysis of linguistic patterns in clinical notes. Using interpretability techniques, we assessed how gendered language influences model predictions. We then applied a data-centric de-biasing method focused on neutralizing biased terms and retaining only the salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients-a crucial application given the rise in youth anxiety post-COVID-19. Results Our findings show a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced this diagnostic bias by up to 27%, demonstrating the approach's effectiveness in enhancing equity across dem

关键词： Pediatrics

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