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arXiv 2023年

作者： Wei, Zhuoran Zhu, Hanbing Curhan, Sharon Curhan, Gary Wang, Molin Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Shanghai China Harvard Medical School BostonMA United States Channing Division of Network Medicine Department of Medicine Brigham and Women’s Hospital BostonMA United States Division of Renal Medicine Department of Medicine Brigham and Women’s Hospital BostonMA United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States

Due to the nature of pure-tone audiometry test, hearing loss data often has a complicated correlation structure. Generalized estimating equation (GEE) is commonly used to investigate the association between exposures and hearing loss, because it is robust to misspecification of the correlation matrix. However, this robustness typically entails a moderate loss of estimation efficiency in finite samples. This paper proposes to model the correlation coefficients and use second-order generalized estimating equations to estimate the correlation parameters. In simulation studies, we assessed the finite sample performance of our proposed method and compared it with other methods, such as GEE with independent, exchangeable and unstructured correlation structures. Our method achieves an efficiency gain which is larger for the coefficients of the covariates corresponding to the within-cluster variation (e.g., ear-level covariates) than the coefficients of cluster-level covariates. The efficiency gain is also more pronounced when the within-cluster correlations are moderate to strong, or when comparing to GEE with an unstructured correlation structure. As a real-world example, we applied the proposed method to data from the Audiology Assessment Arm of the Conservation of Hearing Study, and studied the association between a dietary adherence score and hearing loss. Copyright © 2023, The Authors. All rights reserved.

关键词： Audition

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Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks

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Nucleic acids research 2023年第13期51卷 6578-6592页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Bo-Jia Chen Yu-Te Lin Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Microbiology and Public Health College of Veterinary Medicine National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Program in Integrative & Complex Diseases Department of Nutrition Texas A&M University College Station TX 77843 USA. Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

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Coupled chemical reactions: Effects of electric field, diffusion, and boundary control

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Physical Review E 2023年第6期108卷 064413-064413页

作者： Shixin Xu Robert Eisenberg Zilong Song Huaxiong Huang Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University 8 Duke Ave Kunshan Jiangsu 215316 China Department of Applied Mathematics Illinois Institute of Technology Chicago Illinois 60616 USA and Department of Physiology and Biophysics Rush University Chicago Ilinois 60612 USA Math and Statistics Department Utah State University Old Main Hill Logan Utah 84322 USA Research Center for Mathematics Advanced Institute of Natural Sciences Beijing Normal University Zhuhai Guangdong 519088 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai Guangdong 519088 China Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing 100875 China and Department of Mathematics and Statistics York University Toronto Ontario Canada M3J 1P3

Chemical reactions involve the movement of charges, and this paper presents a mathematical model for describing chemical reactions in electrolytes. The model is developed using an energy variational method that aligns with classical thermodynamics principles. It encompasses both electrostatics and chemical reactions within consistently defined energetic and dissipative functionals. Furthermore, the energy variation method is extended to account for open systems that involve the input and output of charge and mass. Such open systems have the capability to convert one form of input energy into another form of output energy. In particular, a two-domain model is developed to study a reaction system with self-regulation and internal switching, which plays a vital role in the electron transport chain of mitochondria responsible for ATP generation—a crucial process for sustaining life. Simulations are conducted to explore the influence of electric potential on reaction rates and switching dynamics within the two-domain system. It shows that the electric potential inhibits the oxidation reaction while accelerating the reduction reaction.

关键词： Continuum mechanics Dissipative dynamics Electric field effects

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Coupled Chemical Reactions: Effects of Electric Field, Diffusion and Boundary Control

arXiv

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arXiv 2023年

作者： Xu, Shixin Eisenberg, Robert Song, Zilong Huang, Huaxiong Zu Chongzhi Center for Mathematics and Computational Sciences Duke Kunshan University 8 Duke Ave Jiangsu Kunshan China Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States Department of Physiology and Biophysics Rush University Chicago IL60612 United States Math and Statistics Department Utah State University Old Main Hill Logan UT84322 United States Research Center for Mathematics Advanced Institute of Natural Sciences Beijing Normal University Guangdong Zhuhai519088 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Guangdong Zhuhai519088 China Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China Department of Mathematics and Statistics York University TorontoONM3J 1P3 Canada

MSC Codes 92E20, 92B05, 35Q92Chemical reactions involve the movement of charges, and this work presents a mathematical model for describing chemical reactions in electrolytes. The model is developed using an energy variational method that aligns with classical thermodynamics principles. It encompasses both electrostatics and chemical reactions within consistently defined energetic and dissipative functionals. Furthermore, the energy variation method is extended to account for open systems that involve the input and output of charge and mass. Such open systems have the capability to convert one form of input energy into another form of output energy. In particular, a two-domain model is developed to study a reaction system with self-regulation and internal switching, which plays a vital role in the electron transport chain of mitochondria responsible for ATP generation-a crucial process for sustaining life. Simulations are conducted to explore the influence of electric potential on reaction rates and switching dynamics within the two-domain system. It shows that the electric potential inhibits the oxidation reaction while accelerating the reduction reaction. © 2023, CC BY.

关键词： Open systems

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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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Introducing the DREAMS Project: DaRk mattEr and Astrophysics with Machine learning and Simulations

arXiv

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arXiv 2024年

作者： Rose, Jonah C. Torrey, Paul Villaescusa-Navarro, Francisco Lisanti, Mariangela Nguyen, Tri Roy, Sandip Kollmann, Kassidy E. Vogelsberger, Mark Cyr-Racine, Francis-Yan Medvedev, Mikhail V. Genel, Shy Anglés-Alcázar, Daniel Kallivayalil, Nitya Wang, Bonny Y. Costanza, Belén O'Neil, Stephanie Roche, Cian Karmakar, Soumyodipta Garcia, Alex M. Low, Ryan Lin, Shurui Mostow, Olivia Cruz, Akaxia Caputo, Andrea Farahi, Arya Muñoz, Julian B. Necib, Lina Teyssier, Romain Dalcanton, Julianne J. Spergel, David Department of Astronomy University of Florida GainesvilleFL32611 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Department of Physics Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions CambridgeMA02139 United States Department of Physics and Astronomy University of New Mexico 210 Yale Blvd NE AlbuquerqueNM87106 United States Institute for Advanced Study School for Natural Sciences PrincetonNJ08540 United States Institute for Theory and Computation Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Physics University of Connecticut 196 Auditorium Road U-3046 StorrsCT06269-3046 United States McWilliams Center for Cosmology and Astrophysics Carnegie Mellon University PittsburghPA15213 United States Facultad de Ciencias Astronómicas y Geofísicas Universidad Nacional de La Plata Observatorio Astronómico Paseo del Bosque La PlataB1900FWA Argentina Department of Astronomy School of Physical Sciences University of Science and Technology of China Anhui Hefei230026 China CAS Key Laboratory for Researches in Galaxies and Cosmology School of Astronomy and Space Science University of Science and Technology of China Anhui Hefei230026 China Theoretical Physics Depa

We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over dark matter physics, astrophysics, and cosmology in modeling a range of systems-from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of Warm Dark Matter, each comprised of 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a (25 ℎ−1 M☉)3 volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the Warm Dark Matter particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and Convolutional Neural Networks, to demonstrate how such simulation suites can be used to disentangle the effects of dark matter and baryonic physics on galactic properties. The DREAMS project can be extended further to include different dark matter models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of dark matter on these scales. © 2024, CC BY.

关键词： Galaxies

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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