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arXiv 2022年

作者： Li, Wenfei Ou, Qi Chen, Yixiao Cao, Yu Liu, Renxi Zhang, Chunyi Zheng, Daye Cai, Chun Wu, Xifan Wang, Han Chen, Mohan Zhang, Linfeng AI for Science Institute Beijing100080 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States HEDPS CAPT College of Engineering School of Physics Peking University Beijing100871 China Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China DP Technology Beijing100080 China

Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost. In this work, we demonstrate that this issue can be largely alleviated with Deep Kohn-Sham (DeePKS), a ML-based DFT model. DeePKS employs a computationally efficient neural network-based functional model to construct a correction term added upon a cheap DFT model. Upon training, DeePKS offers closely-matched energies and forces compared with high-level QM method, but the number of training data required is orders of magnitude less than that required for training a reliable ML potential. As such, DeePKS can serve as a bridge between expensive QM models and ML potentials: one can generate a decent amount of high-accuracy QM data to train a DeePKS model, and then use the DeePKS model to label a much larger amount of configurations to train a ML potential. This scheme for periodic systems is implemented in a DFT package ABACUS, which is open-source and ready for use in various applications. © 2022, CC BY.

关键词： Density functional theory

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Microano-fabrication technologies for cell biology

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Medical & Biological engineering & Computing 2010年第10期48卷 1023-1032页

作者： Qian, Tongcheng Wang, Yingxiao Department of Bioengineering and the Beckman Institute for Advanced Science and Technology University of Illinois at Urbana–Champaign Urbana USA Department of Biomedical Engineering Peking University Beijing People’s Republic of China Department of Integrative and Molecular Physiology Center for Biophysics and Computational Biology Institute for Genomic Biology Neuroscience Program University of Illinois at Urbana–Champaign Urbana USA

Micro/nano-fabrication techniques, such as soft lithography and electrospinning, have been well-developed and widely applied in many research fields in the past decade. Due to the low costs and simple procedures, these techniques have become important and popular for biological studies. In this review, we focus on the studies integrating micro/nano-fabrication work to elucidate the molecular mechanism of signaling transduction in cell biology. We first describe different micro/nano-fabrication technologies, including techniques generating three-dimensional scaffolds for tissue engineering. We then introduce the application of these technologies in manipulating the physical or chemical micro/nano-environment to regulate the cellular behavior and response, such as cell life and death, differentiation, proliferation, and cell migration. Recent advancement in integrating the micro/nano-technologies and live cell imaging are also discussed. Finally, potential schemes in cell biology involving micro/nano-fabrication technologies are proposed to provide perspectives on the future research activities.

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Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level Optimization Objective

Improved Accuracy of Cardiac Tissue-Level Simulations by Con...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： E. Pouranbarani L. A. Berg R. S. Oliveira R. W. dos Santos A. Nygren Department of Electrical and Computer Engineering University of Calgary Alberta Canada Department of Computer Science and the Graduate Program of Computational Modeling Federal University of Juiz de Fora Juiz de Fora Brazil Department of Computer Science University of São João del-Rei São João del-Rei Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Cardiac cellular models are utilized as the building blocks for tissue simulation. One of the imprecisions of conventional cellular modeling, especially when the models are used in tissue-level modeling, stems from the mere consideration of cellular properties (e.g., action potential shape) in parameter tuning of the model. In our previous work, we put forward an accurate framework in which membrane resistance (R m ) reflecting inter-cellular characteristics, i.e., electrotonic effects, was considered alongside cellular features in cellular model fitting. This paper, for the first time, examines the hypothesis that considering R m as an additional optimization objective improves the accuracy of tissue-level modeling. To study this hypothesis, after cellular-level optimization of a well-known model, source-sink mismatch configurations in a 2-dimensional model are investigated. The results demonstrate that including R m in the optimization protocol yields a substantial improvement in the relative error of the critical transition border which is defined as the minimum window size between source and sink that wave propagates. Model developers can utilize the proposed concept during parameter tuning to increase the accuracy of models.

关键词： Mathematical model Optimization computational modeling Resistance Tuning Fitting Protocols

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Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation

arXiv

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arXiv 2024年

作者： Yang, Soojung Nam, Juno Dietschreit, Johannes C.B. Gómez-Bombarelli, Rafael Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Institute of Theoretical Chemistry Faculty of Chemistry University of Vienna Vienna1090 Austria

In molecular dynamics simulations, rare events, such as protein folding, are typically studied using enhanced sampling techniques, most of which are based on the definition of a collective variable (CV) along which acceleration occurs. Obtaining an expressive CV is crucial, but often hindered by the lack of information about the particular event, e.g., the transition from unfolded to folded conformation. We propose a simulation-free data augmentation strategy using physics-inspired metrics to generate geodesic interpolations resembling protein folding transitions, thereby improving sampling efficiency without true transition state samples. This new data can be used to improve the accuracy of classifier-based methods. Alternatively, a regression-based learning scheme for CV models can be adopted by leveraging the interpolation progress parameter. © 2024, CC BY.

关键词： Geodesy

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Concurrency measures in the era of temporal network epidemiology: A review

arXiv

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arXiv 2020年

作者： Masuda, Naoki Miller, Joel C. Holme, Petter Department of Mathematics State University of New York Buffalo United States Computational and Data-Enabled Science and Engineering Program State University of New York Buffalo United States School of Engineering and Mathematical Sciences La Trobe University Australia Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Diseases spread over temporal networks of interaction events between individuals. Structures of these temporal networks hold the keys to understanding epidemic propagation. One early concept of the literature to aid in discussing these structures is concurrency-quantifying individuals' tendency to form time-overlapping "partnerships". Although conflicting evaluations and an overabundance of operational definitions have marred the history of concurrency, it remains important, especially in the area of sexually transmitted infections. Today, much of theoretical epidemiology uses more direct models of contact patterns, and there is an emerging body of literature trying to connect methods to the concurrency literature. In this review, we will cover the development of the concept of concurrency and these new approaches. © 2020, CC BY.

关键词： Epidemiology

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Waiting-time paradox in 1922

arXiv

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arXiv 2020年

作者： Masuda, Naoki Hiraoka, Takayuki Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States Department of Computer Science Aalto University Espoo00076 Finland

We present an English translation and discussion of an essay that a Japanese physicist, Torahiko Terada, wrote in 1922. In the essay, he described the waiting-time paradox, also called the bus paradox, which is a known mathematical phenomenon in queuing theory, stochastic processes, and modern temporal network analysis. He also observed and analyzed data on Tokyo City trams to verify the relevance of the waiting-time paradox to busy passengers in Tokyo at the time. This essay seems to be one of the earliest documentations of the waiting-time paradox in a sufficiently scientific manner. Copyright © 2020, The Authors. All rights reserved.

关键词： Queueing theory

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Study of self-interaction-errors in barrier heights using locally scaled and Perdew-Zunger self-interaction methods

arXiv

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arXiv 2022年

作者： Mishra, Prakash Yamamoto, Yoh Johnson, J. Karl Jackson, Koblar A. Zope, Rajendra R. Baruah, Tunna Computational Science Program University of Texas at El Paso El PasoTX79968 United States Department of Physics University of Texas El PasoTX79968 United States Department of Chemical & Petroleum Engineering University of Pittsburgh PittsburghPA15261 United States Physics Department Science of Advanced Materials Program Central Michigan University Mt. PleasantMI48859 United States

We study the effect of self-interaction errors on the barrier heights of chemical reactions. For this purpose we use the well-known Perdew-Zunger [J. P. Perdew and A. Zunger, Phys. Rev. B, 23, 5048 (1981)] self-interaction-correction (PZSIC), as well as two variations of the recently developed, locally scaled self-interaction correction (LSIC) [R. R. Zope et al., J. Chem. Phys. 151, 214108 (2019)] to study the barrier heights of the BH76 benchmark dataset. Our results show that both PZSIC and especially the LSIC methods improve the barrier heights relative to the local density approximation (LDA). The version of LSIC that uses the iso-orbital indicator z as a scaling factor gives a more consistent improvement than an alternative version that uses an orbital-dependent factor w based on the ratio of orbital densities to the total electron density. We show that LDA energies evaluated using the self-consistent and self-interaction-free PZSIC densities can be used to assess density-driven errors. The LDA reaction barrier errors for the BH76 set are found to contain significant density-driven errors for all types of reactions contained in the set, but the corrections due to adding SIC to the functional are much larger than those stemming from the density for the hydrogen transfer reactions and of roughly equal size for the non-hydrogen transfer reactions. Copyright © 2022, The Authors. All rights reserved.

关键词： Local density approximation

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Quantifying gender imbalance in East Asian academia: Research career and citation practice

arXiv

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arXiv 2023年

作者： Nakajima, Kazuki Liu, Ruodan Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Gender imbalance in academia has been confirmed in terms of a variety of indicators, and its magnitude often varies from country to country. Europe and North America, which cover a large fraction of research workforce in the world, have been the main geographical regions for research on gender imbalance in academia. However, the academia in East Asia, which accounts for a substantial fraction of research, may be exposed to strong gender imbalance because Asia has been facing persistent and stronger gender imbalance in society at large than Europe and North America. Here we use publication data between 1950 and 2020 to analyze gender imbalance in academia in China, Japan, and South Korea in terms of the number of researchers, their career, and citation practice. We found that, compared to the average of the other countries, gender imbalance is larger in these three East Asian countries in terms of the number of researchers and their citation practice and additionally in Japan in terms of research career. Moreover, we found that Japan has been exposed to the larger gender imbalance than China and South Korea in terms of research career and citation practice. Copyright © 2023, The Authors. All rights reserved.

关键词： Geographical regions

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Text and data mining for biomedical discovery 18

Text and data mining for biomedical discovery

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18th Pacific Symposium on Biocomputing, PSB 2013

作者： Gonzalez, Graciela Cohen, Kevin Bretonnel Kann, Maricel G. Greene, Casey S. Leaman, Robert Hahn, Udo Shah, Nigam Ye, Jieping Department of Biomedical Informatics Arizona State University ScottsdaleAZ85259 United States Computational Bioscience Program U. Colorado School of Medicine AuroraCO United States Department of Biological Science University of Maryland Baltimore County BaltimoreMD21250 United States Department of Genetics Geisel School of Medicine at Dartmouth HanoverNH03755 United States National Center for Biotechnology Information BethesdaMD20894 United States Friedricdh Schiller University Jena Language and Information Engineering Lab. Jena Germany Stanford Center for Biomedical Informatics Research StanfordCA94305 United States Computer Science and Engineering Arizona State University TempeAZ85287 United States

ISBN: (纸本)9781627480161

The biggest challenge for text and data mining is to truly impact the biomedical discovery process, enabling scientists to generate novel hypothesis to address the most crucial questions. Among a number of worthy submissions, we have selected six papers that exemplify advances in text and data mining methods that have a demonstrated impact on a wide range of applications. Work presented in this session includes data mining techniques applied to the discovery of 3-way genetic interactions and to the analysis of genetic data in the context of electronic medical records (EMRs), as well as an integrative approach that combines data from genetic (SNP) and transcriptomic (microarray) sources for clinical prediction. Text mining advances include a classification method to determine whether a published article contains pharmacological experiments relevant to drug-drug interactions, a fine-grained text mining approach for detecting the catalytic sites in proteins in the biomedical literature, and a method for automatically extending a taxonomy of health related terms to integrate consumer-friendly synonyms for medical terminologies.

关键词： Data mining

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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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