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Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

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Physical Review Research 2022年第2期4卷 023257-023257页

作者： Naoki Masuda Prosenjit Kundu Department of Mathematics State University of New York at Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise.

关键词： Network resilience

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Computer Vision Techniques for Cancerous Cells Analysis in FIB-SEM Images

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Microscopy and Microanalysis 2019年第S2期25卷 1196-1197页

作者： Guillaume Thibault Jessica Riesterer Kevin Stoltz Kevin Loftis Geoffrey Schau Erin Stempinski Claudia López Young Hwan Chang Joe W. Gray OHSU Center for System Spatial Biomedicine (OCSSB) Biomedical Engineering Portland OR USA. Multiscale Microscopy Core Oregon Health & Science University (OHSU) Portland OR USA. Computational Biology Program OHSU) Portland OR USA.

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Expanding the computational Toolbox for Single-Particle Cryo-Electron Microscopy

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Microscopy and Microanalysis 2019年第S2期25卷 2686-2687页

作者： Alberto Bartesaghi Department of Computer Science Trinity College of Arts & Sciences Department of Biochemistry Duke University School of Medicine Department of Electrical and Computer Engineering Pratt School of Engineering Duke Institute for Brain Sciences Computational Biology and Bioinformatics Program Duke University Durham NC USA.

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Social network analysis of manga: similarities to real-world social networks and trends over decades

arXiv

引用

arXiv 2023年

作者： Sugishita, Kashin Masuda, Naoki Department of Transdisciplinary Science and Engineering Tokyo Institute of Technology Tokyo152-8550 Japan Department of Mathematics State University of New York at Buffalo BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States Center for Computational Social Science Kobe University Kobe657-8501 Japan

Manga, Japanese comics, has been popular on a global scale. Social networks among characters, which are often called character networks, may be a significant contributor to their popularity. We collected data from 162 popular manga that span over 70 years and analyzed their character networks. First, we found that many of static and temporal properties of the character networks are similar to those of real human social networks. Second, the character networks of most manga are protagonist-centered such that a single protagonist interacts with the majority of other characters. Third, the character networks for manga mainly targeting boys have shifted to denser and less protagonist-centered networks and with fewer characters over decades. Manga mainly targeting girls showed the opposite trend except for the downward trend in the number of characters. The present study, which relies on manga data sampled on an unprecedented scale, paves the way for further population studies of character networks and other aspects of comics. Copyright © 2023, The Authors. All rights reserved.

关键词： Population statistics

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Numerical study on high intensity focused ultrasound therapy using array transducer

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Physics Procedia 2010年第1期3卷 315-322页

作者： Kohei Okita Kazuyasu Sugiyama Kenji Ono Shu Takagi Yoichiro Matsumoto RIKEN CIPS VCAD System Research Program Functionality Simulation and Information Team 2-1 Hirosawa Wako Saitama 351-0198 Japan The University of Tokyo Department of Mechanical Engineering Tokyo Japan RIKEN Computational Science Research Program Integrated Simulation of Living Matter Group Organ and Body Scale Team Saitama Japan

The development of the HIFU therapy for the deeply placed cancer such as the liver cancer and brain cancer has been desired. One problem is the displacement of the focal point due to the reflection and refraction of ultrasound. In the present study, the HIFU therapy for the brain cancer through a skull with a bowl-shape array transducer is performed numerically. Our approach is to solve the mass and momentum equations for mixture with the equation of state of media. The nonlinearity is mainly taken into account through the equation of state of the media. The three-dimensional controllability of the focal point by the array transducer with a phase delay is examined. As the result of the ultrasound propagation through the skull with the phase delay, we obtain a clear focal point where the peak pressure is higher than that without the phase delay. Therefore, the array transducer with the appropriate phase delay enables to assign the focal point to the target adequately, even if the ultrasound propagates through the inhomogeneous medium as a human skull.

关键词： Theraputic ultrasound HIFU Numerical simulation Voxel phantom Array transducer

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Data-driven model reduction and transfer operator approximation

arXiv

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arXiv 2017年

作者： Klus, Stefan Nüske, Feliks Koltai, Péter Wu, Hao Kevrekidis, Ioannis Schütte, Christof Noé, Frank Department of Mathematics and Computer Science Freie Universität Berlin Germany Department of Chemical and Biological Engineering & Program in Applied and Computational Mathematics Princeton University United States Zuse Institute Berlin Germany

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis (TICA), dynamic mode decomposition (DMD), and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Dynamical systems

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Facile Anhydrous Proton Transport on Hydroxyl Functionalized Graphane

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Physical Review Letters 2017年第18期118卷 186101-186101页

作者： Abhishek Bagusetty Pabitra Choudhury Wissam A. Saidi Bridget Derksen Elizabeth Gatto J. Karl Johnson Computational Modeling and Simulation Program University of Pittsburgh Pittsburgh Pennsylvania 15260 USA Department of Chemical and Petroleum Engineering University of Pittsburgh Pittsburgh Pennsylvania 15261 USA Department of Chemical Engineering New Mexico Tech Socorro New Mexico 87801 USA Department of Mechanical and Materials Science University of Pittsburgh Pittsburgh Pennsylvania 15261 USA

Graphane functionalized with hydroxyl groups is shown to rapidly conduct protons under anhydrous conditions through a contiguous network of hydrogen bonds. Density functional theory calculations predict remarkably low barriers to diffusion of protons along a 1D chain of surface hydroxyls. Diffusion is controlled by the local rotation of hydroxyl groups, a mechanism that is very different from that found in 1D water wires in confined nanopores or in bulk water. The proton mean square displacement in the 1D chain was observed to follow Fickian diffusion rather than the expected single-file mobility. A charge analysis reveals that the charge on the proton is essentially equally shared by all hydrogens bound to oxygens, effectively delocalizing the proton.

关键词： Anomalous diffusion Graphene Membrane structures

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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

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NATURE CHEMICAL BIOLOGY 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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Data-augmented modeling of intracranial pressure

arXiv

引用

arXiv 2018年

作者： Wang, Jian-Xun Hu, Xiao Shadden, Shawn C. Mechanical Engineering University of California BerkeleyCA United States Aerospace and Mechanical Engineering Center of Informatics and Computational Science University of Notre Dame Notre DameIN United States Department of Physiological Nursing Department of Neurological surgery Institute of Computational Health Sciences UCSF Joint Bio-Engineering Graduate Program University of California San FranciscoCA United States

Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction. Copyright © 2018, The Authors. All rights reserved.

关键词： Diagnosis

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Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior

Gaussian Process Port-Hamiltonian Systems: Bayesian Learning...

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IEEE Conference on Decision and Control

作者： Thomas Beckers Jacob Seidman Paris Perdikaris George J. Pappas Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA graduate program in Applied Mathematics and Computational Science University of Pennsylvania Philadelphia PA USA Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfavorable in two ways: The models are not as data-efficient as they could be by incorporating physical prior knowledge, and the model itself might not be physically correct. We propose Gaussian Process Port-Hamiltonian systems (GPPHS) as a physics-informed Bayesian learning approach with uncertainty quantification. The Bayesian nature of GP-PHS uses collected data to form a distribution over all possible Hamiltonians instead of a single point estimate. Due to the underlying physics model, a GP-PHS generates passive systems with respect to designated inputs and outputs. Further, the proposed approach preserves the compositional nature of Port-Hamiltonian systems.

关键词： Uncertainty Gaussian processes Probabilistic logic Data models Bayes methods Behavioral sciences Kernel

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