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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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Comparing CUDA, OpenCL and OpenGL implementations of the cardiac monodomain equations

Comparing CUDA, OpenCL and OpenGL implementations of the car...

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9th International Conference on Parallel Processing and Applied Mathematics, PPAM 2011

作者： Sachetto Oliveira, Rafael Rocha, Bernardo Martins Amorim, Ronan Mendonça Campos, Fernando Otaviano Meira Jr., Wagner Toledo, elson Magalhães Dos Santos, Rodrigo Weber Federal University of São João del Rey Department of Computer Science Brazil Federal University of Minas Gerais Department of Computer Science Brazil Federal University of Juiz de Fora Department of Computer Science Computational Modeling Program Brazil National Laboratory of Scientific Computing Brazil Medical University of Graz Institute of Biophysics Austria

ISBN: (纸本)9783642314995

computer simulations of cardiac electrophysiology are a helpful tool in the study of bioelectric activity of the heart. The cardiac monodomain model comprises a nonlinear system of partial differential equations and its numerical solution represents a very intensive computational task due to the required fine spatial and temporal resolution. Recent studies have shown that the use of GPU as a general purpose processor can greatly improve the performance of simulations. The aim of this work is to study the performance of different GPU programming interfaces for the solution of the cardiac monodomain equations. Three different GPU implementations are compared, OpenGL, NVIDIA CUDA and OpenCL, to a CPU multicore implementation that uses OpenMP. The OpenGL approach showed to be the fastest with a speedup of 446 (compared to the multicore implementation) for the solution of the nonlinear system of ordinary differential equations (ODes) associated to the solution of the cardiac model, whereas CUDA was the fastest for the numerical solution of the parabolic partial differential equation with a speedup of 8. Although OpenCL provides code portability between different accelerators, the OpenCL version was slower for the solution of the parabolic equation and as fast as CUDA for the solution of the system of ODes, showing to be a portable way of programming scientific applications but not as efficient as CUDA when running on Nvidia GPUs. © 2012 Springer-Verlag.

关键词： Nonlinear systems

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A node placement optimization method for watershed underwater sensor networks 9

A node placement optimization method for watershed underwate...

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9th ACM International Conference on Underwater Networks and Systems, WUWNet 2014

作者： Barbosa, Gentil V. Letouze, Patrick Xavier, Adilson e. Department of Computer Science UFT - Universidade Federal do Tocantins PESC Brazil Department of Computer Science UFT - Universidade Federal do Tocantins MCS - Postgraduate Program of System's Computational Modeling Brazil PESC - Postgraduate Program of System's Engineering and Computation UFRJ - Universidade Federal do Rio de Janeiro Brazil

ISBN: (纸本)9781450332774

Given a set of underwater sensors coordinates that are usually determined by a specialist for the design of a watershed monitoring system, where should be placed the sinks and the base station? The answer may be the minimization of the Variable Internode Distance. In such cases, a two dimensional architecture is acceptable because it is a shallow water system. Hence, in this paper, it is proposed a two-level node distance minimization for Underwater Wireless Sensor Network for monitoring systems. The mathematical method employed for optimization is the Hyperbolic Smoothing approach and the proposed method includes the placement of the base-station in the algorithm. Copyright 2014 ACM.

关键词： Base stations

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Adaptive Time Step for Cardiac Myocyte Models

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Procedia computer science 2011年 4卷 1092-1100页

作者： Ricardo Silva Campos Marcelo Lobosco Rodrigo Weber dos Santos Department of Computer Science and Master Program in Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil

The modeling of the electrical activity of the heart is of great medical and scientific interest as it provides a way to better understand the underlying biophysical phenomena, supports the development of new techniques for diagnoses and serves as a platform for drug tests. At cellular level, the electrical activity of cardiac myocytes may be simulated by solving a system of ordinary di_erential equations (ODes) describing the electrical behavior of the cell membrane. Because the biophysical processes underlying this phenomenon are non-linear and change very rapidly, the ODe system is challenging to solve numerically. Furthermore, the implementation of these models is a hard task. This paper presents a tool to describe models using Ordinary Differential equations. It is based on CellML standard and automatically generates C++ source-code, with numerical methods to solve the model's equations. The aim of this work is to present a numerical method with adaptive time step based on the euler Method and Second Order Runge-Kutta method. The proposed method accelerated the execution and kept numerical errors under control. Preliminary results suggest this adaptive method is up to 25 times faster than the explicit euler method with fixed time step.

关键词： Adaptive Time Step Cardiac modeling

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Perturbed Solving Method for Interdecadal Sea-air Oscillator Model

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Chinese Geographical science 2012年第1期22卷 42-47页

作者： MO Jiaqi LIN Yihua LIN Wantao CHeN Lihua Department of Mathematics Anhui Normal University Wuhu 241003 China Division of Computational Science E-Institutes of Shanghai Universities at Shanghai Jiaotong University Shanghai 200240 China State Key Laboratory of Numerical Modeling for Atmospheric Sciencens and Geophysical Fluid Dynamic Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Department of Mathematics and Computer Science Fuqing Branch of Fujian Normal University Fuqing 350300 China

A coupled system of the interdecadal sea-air oscillator model is studied. The e1 Nifio-southem oscillation （eNSO） atmospheric physics oscillation is an abnormal phenomenon involved in the tropical Pacific ocean-atmosphere interactions. The oscillator model is involved with the variations of both the eastern and western Pacific anomaly pat- terns. This paper proposes an eNSO atmospheric physics model using a method of the perturbation theory. The aim is to create an asymptotic solving method for the eNSO model. employing the perturbed method, the asymptotic solution of corresponding problem is obtained, and the asymptotic behaviour of the solution is studied. Thus we can obtain the prognoses of the sea surface temperature anomaly and related physical quantities.

关键词： nonlinear equation perturbation e1 Nifio-southern oscillator model interdecadal sea-air oscillator

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An information theoretic framework for eukaryotic gradient sensing 19

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20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Kimmel, Joseph M. Salter, Richard M. Thomas, Peter J. Computer Science Program Oberlin College Oberlin OH 44074 United States Department of Mathematics Biology and Cognitive Science Case Western Reserve University Cleveland OH 44106 United States Computational Neuroscience Graduate Program University of Chicago United States Center for Computation and Modeling United States

ISBN: (纸本)9780262195683

Chemical reaction networks by which individual cells gather and process information about their chemical environments have been dubbed "signal transduction" networks. Despite this suggestive terminology, there have been few attempts to analyze chemical signaling systems with the quantitative tools of information theory. Gradient sensing in the social amoeba Dictyostelium discoideum is a well characterized signal transduction system in which a cell estimates the direction of a source of diffusing chemoattractant molecules based on the spatiotemporal sequence of ligand-receptor binding events at the cell membrane. Using Monte Carlo techniques (MCell) we construct a simulation in which a collection of individual ligand particles undergoing Brownian diffusion in a three-dimensional volume interact with receptors on the surface of a static amoeboid cell. Adapting a method for estimation of spike train entropies described by Victor (originally due to Kozachenko and Leonenko), we estimate lower bounds on the mutual information between the transmitted signal (direction of ligand source) and the received signal (spatiotemporal pattern of receptor binding/unbinding events). Hence we provide a quantitative framework for addressing the question: how much could the cell know, and when could it know it? We show that the time course of the mutual information between the cell's surface receptors and the (unknown) gradient direction is consistent with experimentally measured cellular response times. We find that the acquisition of directional information depends strongly on the time constant at which the intracellular response is filtered.

关键词： Ligands

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An evolutionary Analytic Center Classifier 9th

An Evolutionary Analytic Center Classifier

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9th Brazilian Conference on Intelligent Systems, BRACIS 2020

作者： Goulart, Renan Motta Villela, Saulo Moraes Borges, Carlos Cristiano Hasenclever Neto, Raul Fonseca Postgraduate Program in Computational Modeling Federal University of Juiz de Fora Juiz de ForaMinas Gerais Brazil Department of Computer Science Federal University of Juiz de Fora Juiz de ForaMinas Gerais Brazil

ISBN: (纸本)9783030613761

Classification is an essential task in the field of Machine Learning, where developing a classifier that minimizes errors on unknown data is one of its central problems. It is known that the analytic center is a good approximation of the center of mass of the version space that is consistent with the Bayes-optimal decision surface. Therefore, in this work, we propose an evolutionary algorithm, relying on the convexity properties of the version space, that evolves a population of perceptron classifiers in order to find a solution that approximates its analytic center. Hyperspherical coordinates are used to guarantee feasibility when generating new individuals and enabling exploration to be uniformly distributed through the search space. To evaluate the individuals we consider using a potential function that employs a logarithmic barrier penalty. experiments were performed on real datasets, and the obtained results indicate concrete possibilities for applying the proposed algorithm for solving practical problems. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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Semi-analytical finite element method applied for characterizing micropolar fibrous composites

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Applied Mathematics and Mechanics(english edition) 2024年第12期45卷 2147-2164页

作者： J.A.OTeRO Y.eSPINOSA-ALMeYDA R.RODRIGUeZ-RAMOS J.MeRODIO School of Engineering and Sciences Monterrey Institute of TechnologyEstado de Mexico 52926Mexico Institute of Engineering and Technology Autonomous University of Ciudad JuarezCiudad JuarezChihuahua 32310Mexico Postgraduate Program in Computational Modeling in Science and Technology Federal Fluminense UniversityRıo de Janeiro 27255-125Brazil Faculty of Mathematics and Computer Science University of HavanaLa Habana 10400Cuba Department of Mathematics Applied to ICT School of Informatics Systems EngineeringTechnical University of MadridMadrid 28031Spain

A semi-analytical finite element method(SAFeM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FeM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FeM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFeM may serve as the reference points for validating the outcomes of experimental and numerical computations.

关键词： semi-analytical approach fiber-reinforced composite(FRC) effective property finite element method(FeM) asymptotic homogenization method(AHM) micropolar elasticity

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The Importance of Systematic Review as a Scientific Research Method for computer science: A Quantitative Systematic Review

The Importance of Systematic Review as a Scientific Research...

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2014 International Conference on Society,education and Psychology(ICSeP 2015)

作者： Valéria M.Silva José I.M.Souza Júnior David N.Prata Patrick Letouze Department of Computer Science UFT-Universidade Federal do Tocantins Palmas Brazil Postgraduate Program of Systems’ Computational Modeling-UFT

The importance of Systematic Review to computer science research as a scientific method has to be assessed, because interdisciplinary and Internet scientific research are demanding it. Hence, this work is a quantitative systematic review of Systematic Reviews in the domain of computer science. Scientific Journal papers have been extracted from a number of representative databases between the years 2006 and 2012. From an initial amount of 3,097 papers, only 138 were selected resulting in a survival rate of 4.46%.

关键词： computer science Research,computer science Teaching,Research education,Scientific Methodology,Systematic Review

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Stochastic Petri Net Models for the Study of Macrophage Reprogramming

Stochastic Petri Net Models for the Study of Macrophage Repr...

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Ieee International Conference on Bioinformatics and Biomedicine

作者： elvis H. Ribeiro Alexandre B. Pigozzo Carolina R. Xavier Rodrigo W. dos Santos Marcelo Lobosco Department of Computer Science Federal University of Sao Joao Del Rei Graduate Program in Computational Modeling Federal University of Juiz de Fora

ISBN: (纸本)9781728118680

In recent years, Petri nets are being used for modeling complex biological systems. examples include the modeling of biochemical reactions, protein interaction networks, gene regulation networks and the immune system. In this work, Petri nets are used to model the innate immune response to a bacterial infection. The objective of this work is to study how changes in macrophage phenotype impact the regulation and resolution of the immune response. From simulations performed, we have observed that the conversion between a M1 proinflammatory phenotype to a M2 regulatory phenotype during an inflammatory response is an important process to avoid a state of persistent inflammation.

关键词： stochastic Petri net innate immune response macrophage reprogramming chronic inflammation Petri nets Innate Immune Response Chronic inflammation Macrophages reprogramming Inflammatory Response modeling Immune System

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