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Modeling temporal networks with bursty activity patterns of nodes and links

Physical Review Research 2020年第2期2卷 023073-023073页

作者： Takayuki Hiraoka Naoki Masuda Aming Li Hang-Hyun Jo Department of Computer Science Aalto University Espoo 00076 Finland Asia Pacific Center for Theoretical Physics Pohang 37673 Republic of Korea Department of Mathematics University at Buffalo State University of New York Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New York 14260-5030 USA Department of Zoology University of Oxford Oxford OX1 3PS United Kingdom Department of Biochemistry University of Oxford Oxford OX1 3QU United Kingdom Department of Physics The Catholic University of Korea Bucheon 14662 Republic of Korea

The concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node, and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation, and we show that it leads to heavy-tailed interevent time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems.

关键词： Collective behavior in networks Dynamics of networks Scaling laws of complex systems Social systems Communication networks Multiple time scale dynamics Networks & random structures Social networks Evolving network models Network Models Non-Markovian processes Time series analysis

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Gravitational effects on global hemodynamics in different postures:A closed-loop multiscale mathematical analysis

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Acta Mechanica Sinica 2017年第3期33卷 595-618页

作者： Xiancheng Zhang Shigeho Noda Ryutaro Himeno Hao Liu Graduate School of Engineering Chiba University Computational Science Research Program RIKEN Shanghai Jiao Tong University and Chiba University International Cooperative Research Center (SJTU-CU ICRC)

We present a novel methodology and strategy to predict pressures and flow rates in the global cardiovascular network in different postures varying from supine to upright. A closed-loop, multiscale mathematical model of the entire cardiovascular system (CVS) is developed through an integration of one-dimensional (1D) modeling of the large systemic arteries and veins, and zero-dimensional (0D) lumped-parameter modeling of the heart, the cardiac-pulmonary circulation, the cardiac and venous valves, as well as the microcirculation. A versatile junction model is proposed and incorporated into the 1D model to cope with splitting and/or merging flows across a multibranched junction, which is validated to be capable of estimating both subcritical and supercritical flows while ensuring the mass conservation and total pressure continuity. To model gravitational effects on global hemodynamics during postural change, a robust venous valve model is further established for the 1D venous flows and distributed throughout the entire venous network with consideration of its anatomically realistic numbers and locations. The present integrated model is proven to enable reasonable prediction of pressure and flow rate waveforms associated with cardiopulmonary circulation, systemic circulation in arteries and veins, as well as microcirculation within normal physiological ranges, particularly in mean venous pressures, which well match the in vivo measurements. Applications of the cardiovascular model at different postures demonstrate that gravity exerts remarkable influence on arterial and venous pressures, venous returns and cardiac outputs whereas venous pressures below the heart level show a specific correlation between central venous and hydrostatic pressures in right atrium and veins.

关键词： Gravitational effects Cardiovascular system (CVS) Postural change Multiscale cardiovascular model Multibranched junction model Venous valve

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Brain-network clustering via kernel-ARMA modeling and the Grassmannian

arXiv

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arXiv 2019年

作者： Ye, Cong Slavakis, Konstantinos Patil, Pratik V. Muldoon, Sarah F. Medaglia, John NY14260 United States Department of Mathematics Computational and Data-Enabled Science and Engineering Program University at Buffalo SUNY NY14260 United States Department of Psychology Drexel University PA19104 United States Perelman School of Medicine University of Pennsylvania PA19104 United States

Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis. Notwithstanding, most of the brain-network clustering methods revolve around state clustering and/or node clustering (a.k.a. community detection or topology inference) within states. This work answers first the need of capturing non-linear nodal dependencies by bringing forth a novel feature-extraction mechanism via kernel autoregressive-moving-average modeling. The extracted features are mapped to the Grassmann manifold (Grassmannian), which consists of all linear subspaces of a fixed rank. By virtue of the Riemannian geometry of the Grassmannian, a unifying clustering framework is offered to tackle all possible clustering problems in a network: Cluster multiple states, detect communities within states, and even identify/track subnetwork state sequences. The effectiveness of the proposed approach is underlined by extensive numerical tests on synthetic and real fMRI/EEG data which demonstrate that the advocated learning method compares favorably versus several state-of-the-art clustering schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Unsupervised particle sorting for high-resolution single-particle cryo-EM

arXiv

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arXiv 2019年

作者： Zhou, Ye Moscovich, Amit Bendory, Tamir Bartesaghi, Alberto Department of Computer Science Duke University Durham United States Program in Applied and Computational Mathematics Princeton University United States School of Electrical Engineering Tel Aviv University Tel Aviv Israel Department of Biochemistry Duke University School of Medicine Durham United States Department of Electrical and Computer Engineering Duke University Durham United States

Single-particle cryo-Electron Microscopy (EM) has become a popular technique for determining the structure of challenging biomolecules that are inaccessible to other technologies. Recent advances in automation, both in data collection and data processing, have significantly lowered the barrier for non-expert users to successfully execute the structure determination workflow. Many critical data processing steps, however, still require expert user intervention in order to converge to the correct high-resolution structure. In particular, strategies to identify homogeneous populations of particles rely heavily on subjective criteria that are not always consistent or reproducible among different users. Here, we explore the use of unsupervised strategies for particle sorting that are compatible with the autonomous operation of the image processing pipeline. More specifically, we show that particles can be successfully sorted based on a simple statistical model for the distribution of scores assigned during refinement. This represents an important step towards the development of automated workflows for protein structure determination using single-particle *** Codes 62P10 Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

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A dynamical system model for predicting gene expression from the epigenome

arXiv

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arXiv 2020年

作者： Brunner, James D. Kim, Jacob Downing, Timothy Mjolsness, Eric Kober, Kord M. Center for Individualized Medicine Microbiome Program Mayo Clinic RochesterMN55901 United States Department of Biological Sciences Columbia University New YorkNY10027 United States Henry Samueli School of Engineering University of California Irvine IrvineCA92697 United States Department of Computer Science and Mathematics University of California Irvine IrvineCA92697 United States Department of Physiological Nursing Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA94143 United States

Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigenetic processes (e.g., DNA methylation). Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data. In contrast, dynamical systems can be used to generate a model to predict gene expression using epigenetic data and a gene regulatory network (GRN) which can also serve as a mechanistic hypothesis. Here we present a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given GRN. We provide an evaluation of the model using real patient data and a GRN created from robust reference sources. Software for dataset preparation, model parameter fitting and prediction generation, and reporting are available at https://***/kordk/stoch_epi_lib. Copyright © 2020, The Authors. All rights reserved.

关键词： Gene expression

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Frontier Orbitals of Dehydrogenated Tetrahydrocurcumin in Water Solvent: A Theoretical Study

Frontier Orbitals of Dehydrogenated Tetrahydrocurcumin in Wa...

引用

International Conference on Computation in science and engineering, ICCSE 2017

作者： Boli, Lusia S.P. Khoirunisa, Vera Saputro, Adhitya G. Agusta, Mohammad K. Rusydi, Febdian Rachmawati, Heni Dipojono, Hermawan K. Engineering Physics Research Group Institut Teknologi Bandung Indonesia Engineering Physics Program Institut Teknologi Sumatera Indonesia Research Center for Nanoscience and Nanotechnology Institut Teknologi Bandung Indonesia Research Center for Computational Quantum Engineering Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia School of Pharmacy Institut Teknologi Bandung Bandung Indonesia

We studied two frontier orbitals - the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO)- of tetrahydrocurumin (THC) using density-functional theory (DFT) in water solvent. These orbitals were observed in THC molecule without one hydrogen atom (dehydrogenated THC). The loss of hydrogen atom is due to the transfer of the atom from THC molecule toward reactive oxygen species (ROS) (Hydrogen atom transfer -HAT- mechanism). We began our investigation by optimizing dehydrogenated THC at three X-H sites. Then, water solvent was added by using polarized continuum model (PCM) method. This study observed that dehydrogenated THC at two O-H sites has wider gap of HOMO-LUMO compare to C-H site. © Published under licence by IOP Publishing Ltd.

关键词： Molecules

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Advances of machine learning in molecular modeling and simulation

arXiv

引用

arXiv 2019年

作者： Haghighatlari, Mojtaba Hachmann, Johannes Department of Chemical and Biological Engineering University at Buffalo State University of New York BuffaloNY14260 United States Computational and Data-Enabled Science and Engineering Graduate Program University at Buffalo State University of New York BuffaloNY14260 United States New York State Center of Excellence in Materials Informatics BuffaloNY14203 United States

In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第6期55卷 1078页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Samsung Genome Institute Samsung Medical Center Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University School of Medicine Seoul South Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Department of Haematology University of Cambridge Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson.org. Quantitative and Comp

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Core Collapse Supernova Gravitational Wave Emission for Progenitors of 9.6, 15, and 25 M☉

arXiv

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arXiv 2022年

作者： Mezzacappa, Anthony Marronetti, Pedro Landfield, Ryan E. Lentz, Eric J. Hix, W. Raphael Harris, J. Austin Bruenn, Stephen W. Blondin, John M. Messer, O.E. Bronson Casanova, Jordi Kronzer, Luke L. Department of Physics and Astronomy University of Tennessee 1408 Circle Drive KnoxvilleTN37996-1200 United States Physics Division National Science Foundation AlexandriaVA22314 United States National Center for Computational Sciences Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831-6164 United States Physics Division Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831-6354 United States Joint Institute for Nuclear Physics and its Applications Oak Ridge National Laboratory P.O. Box 2008 Oak RidgeTN37831-6374 United States Department of Physics Florida Atlantic University 777 Glades Road Boca RatonFL33431-0991 United States Department of Physics North Carolina State University RaleighNC27695-8202 United States Physics Program Community College of Denver P.O. Box 173363 DenverCO80217-3363 United States Department of Aerospace Engineering and Mechanics University of Alabama Box 870280 TuscaloosaAL35487-0280 United States

We present gravitational wave emission predictions based on three core collapse supernova simulations corresponding to three different progenitor masses. The masses span a large range, between 9.6 and 25 M☉ , are all initially non-rotating, and are of two metallicities: zero and Solar. We compute both the temporal evolution of the gravitational wave strains for both the plus and the cross polarizations, as well as their spectral decomposition and characteristic strains. The temporal evolution of our zero metallicity, 9.6 M☉ progenitor model is distinct from the temporal evolution of our Solar metallicity, 15 M☉ progenitor model and our zero metallicity, 25 M☉ progenitor model. In the former case, the high-frequency gravitational wave emission is largely confined to a brief time period ∼75 ms after bounce, whereas in the latter two cases, high-frequency emission does not commence until ∼125 ms after bounce or later. Nonetheless, the physical origin of the high-frequency emission in all three cases corresponds to convection in the proto-neutron star of both Schwarzschild and Ledoux type, and convective overshoot. The low-frequency emission in all three models exhibits very similar behavior. At frequencies below ∼250 Hz, gravitational waves are emitted by neutrino-driven convection and the SASI. This emission extends throughout the simulations when a gain region is present. In all three models, explosion is observed, at ∼125, ∼500, and ∼250 ms after bounce in the 9.6, 15, and 25 M☉ progenitor models, respectively. At these times, the low-frequency gravitational wave emission is joined by very low frequency emission, below ∼10 Hz. These very low frequency episodes are the result of explosion and begin at the above designated explosion times in each of our models. Our characteristic strains tell us that in principle all three gravitational wave signals would be detectable by current-generation detectors for a supernova at a distance of 10 kpc. However, our 9.6 M☉ progeni

关键词： Strain

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