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BPS2025 - Cross-scale conundrum: Slow masticatory myosin in jaw muscles that are both fast and strong

Biophysical Journal 2025年第3期124卷 516a-516a页

作者： Varun V. Dhorajia Nicholas LaFave Brandon Reder Robert Brocklehurst Nicolai Konow Sam Walcott Jeffrey R. Moore Biomedical Engineering and Biotechnology Program University of Massachusetts Lowell Lowell MA USA Department of Biological Sciences University of Massachusetts Lowell Lowell MA USA Department of Mathematical Sciences Worcester Polytechnic Institute Worcester MA USA Department of Bioinformatics and Computational Biology Worcester Polytechnic Institute Worcester MA USA

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High-resolution spatio-temporal model for county-level COVID-19 activity in the U.S.

arXiv

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arXiv 2020年

作者： Zhu, Shixiang Bukharin, Alexander Xie, Liyan Santillana, Mauricio Yang, Shihao Xie, Yao School of Industrial and Systems Engineering Georgia Institute of Technology Harvard Medical School Computational Health Informatics Program Harvard University School of Engineering & Applied Sciences

We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases one-week ahead of the current time, at the county-level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (a) temporal auto- and pairwise correlation of the two local time series (confirmed cases and death of the COVID-19), (b) dynamics between locations (propagation between counties), and (c) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model’s high-dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top ten metropolitan areas in the nation, which we refer (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multi-variate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability. Copyright © 2020, The Authors. All rights reserved.

关键词： Dynamics

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Bacterial biofilms use chiral branches to escape crowded environments by tracking oxygen gradient

arXiv

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arXiv 2022年

作者： Basaran, Mustafa Yüce, Tevfik Can Yaman, Yusuf Ilker Vetter, Roman Kocabas, Askin Department of Physics Koç University Sarıyer Istanbul34450 Turkey Bio-Medical Sciences and Engineering Program Koç University Sarıyer Istanbul34450 Turkey Computational Physics for Engineering Materials ETH Zurich Zurich8093 Switzerland Koç University Surface Science and Technology Center Koç University Sarıyer Istanbul34450 Turkey Koç University Research Center for Translational Medicine Koç University Sarıyer Istanbul34450 Turkey

Bacterial biofilms collectively develop distinct and ordered structures, including fibers, bundles, and branches. Often, it is unclear how these structural motifs convey specific advantages to bacterial strains under challenging conditions. In oxygen-limited environments, dense bacterial aggregates generally deplete oxygen, which leads to arrest of bacterial growth. However, we observed that biofilm-forming Bacillus subtilis could use branching patterns to escape from these crowded regions by tracking the oxygen gradient. The process depends on the chain-forming ability of the biofilm. As a result of collective branching triggered by bending and mechanical buckling, bacteria can extend from the oxygen-depleted biofilm core to the oxygen available periphery. Remarkably, these bacterial branches are strongly chiral and curve in a clockwise direction. Our analysis revealed that the surface friction and axial rotation of the twisting cell wall break left-right symmetry on solid surfaces and drive chiral bending of bacterial chains. We further observed that the chirality of individual branches could propagate across a large scale and shape the macroscopic morphology of the colony under a limited spatiotemporal growth profile. Taken together, our results provide new insights into how simple physical interactions lead to bacterial collaboration and promote the survival of biofilm-forming strains in challenging environments. © 2022, CC0.

关键词： Biofilms

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Tempered enthusiasm by interviewed experts for synthetic data and ELSI checklists for AI in medicine

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AI and ethics 2025年第3期5卷 3241-3254页

作者： Laura Y Cabrera Jennifer Wagner Sara Gerke Daniel Susser Department of Engineering Science and Mechanics Pennsylvania State University W-316 Millennium Science Complex University Park PA 16802 USA. Department of Philosophy Pennsylvania State University University Park PA 16802 USA. Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Rock Ethics Institute Pennsylvania State University University Park PA 16802 USA. Bioethics Program Pennsylvania State University University Park PA 16802 USA. School of Engineering Design and Innovation Pennsylvania State University University Park PA 16802 USA. Department of Anthropology Pennsylvania State University University Park PA 16802 USA. Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Institute for Computational and Data Sciences Pennsylvania State University University Park PA 16802 USA. College of Law and European Union Center University of Illinois Urbana-Champaign Champaign IL 61820 USA. Department of Information Science Cornell University Ithaca NY 14853 USA.

Synthetic data are increasingly being used in data-driven fields. While synthetic data is a promising tool in medicine, it raises new ethical, legal, and social implications (ELSI) challenges. There is a recognized need for well-designed approaches and standards for documenting and communicating relevant information about artificial intelligence (AI) research datasets and models, including consideration of the many ELSI challenges. This study investigates the ethical dimensions of synthetic data and explores the utility and challenges of ELSI-focused computational checklists for biomedical AI via semi-structure interviews with subject matter experts. Our results suggest that AI experts have tempered views about the promises and challenges of both synthetic data and ELSI-focused computational checklists. Experts discussed a number of ELSI issues covered by previous literature on the topic, such as issues of bias and privacy, yet other less discussed ELSI issues, such as social justice implications and issues of trust were also raised. When discussing ELSI-focused computational checklists our participants highlighted the challenges connected to developing and implementing them.

关键词： Artificial intelligence computational checklists ELSI Synthetic data

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Machine learning assisted phase and size-controlled synthesis of iron oxides

arXiv

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arXiv 2023年

作者： Liu, Juejing Zhang, Zimeng Li, Xiaoxu Zong, Meirong Wang, Yining Wang, Suyun Chen, Ping Wan, Zaoyan Zhao, Yatong Liu, Lili Liang, Yangang Wang, Wei Wang, Zheming Wang, Shiren Guo, Xiaofeng Saldanha, Emily G. Rosso, Kevin M. Zhang, Xin Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99354 United States Department of Chemistry Washington State University PullmanWA99164 United States Materials Science and Engineering Program Washington State University PullmanWA99164 United States Department of Industrial and Systems Engineering Texas A&M University College StationTX77843 United States Energy and Environment Directorate Pacific Northwest National Laboratory RichlandWA99354 United States National Security Directorate Pacific Northwest National Laboratory RichlandWA99354 United States

The controllable synthesis of iron oxides particles is a critical issue for materials science, energy storage, biomedical applications, environmental science, and earth science. However, synthesis of iron oxides with desired phase and size are still a time-consuming and trial-and-error process. This study presents solutions for two fundamental challenges in materials synthesis: predicting the outcome of a synthesis from specified reaction parameters and correlating sets of parameters to obtain products with desired outcomes. Four machine learning algorithms, including random forest, logistic regression, support vector machine, and k-nearest neighbor, were trained to predict the phase and particle size of iron oxide based on experimental conditions. Among the models, random forest exhibited the best performance, achieving 96% and 81% accuracy when predicting the phase and size of iron oxides in the test dataset. Premutation feature importance analysis shows that most models (except logistic regression) rely on known features such as precursor concentration, pH, and temperature to predict the phases from synthesis conditions. The robustness of the random forest models was further verified by comparing prediction and experimental results based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models are in good agreement with the experimental results. Additionally, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with desired phase and particle size from previous studies in the dataset. This study lays the foundation for a closed-loop approach in materials synthesis and preparation, from suggesting potential reaction parameters from the dataset and predicting potential outcomes, through conducting experiments and analysis, to enriching the dataset. Copyright © 2023, The Authors. All ri

关键词： Particle size

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A deep potential model with long-range electrostatic interactions

arXiv

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arXiv 2021年

作者： Zhang, Linfeng Wang, Han Muniz, Maria Carolina Panagiotopoulos, Athanassios Z. Car, Roberto Weinan, E. DP Technology Beijing China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States School of Mathematical Sciences Peking University Beijing100871 China AI for Science Institute Beijing China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make possible molecular simulations with the accuracy of quantum mechanical density functional theory, at a cost only moderately higher than that of empirical force fields. However, the majority of these models lack explicit long-range interactions and fail to describe properties that derive from the Coulombic tail of the forces. To overcome this limitation we extend the DP model by approximating the long-range electrostatic interaction between ions (nuclei+core electrons) and valence electrons with that of distributions of spherical Gaussian charges located at ionic and electronic sites. The latter are rigorously defined in terms of the centers of the maximally localized Wannier distributions, whose dependence on the local atomic environment is modeled accurately by a deep neural network. In the deep potential long-range (DPLR) model, the electrostatic energy of the Gaussian charge system is added to short-range interactions that are represented as in the standard DP model. The resulting potential energy surface is smooth and possesses analytical forces and virial. Missing effects in the standard DP scheme are recovered, improving on accuracy and predictive power. By including long-range electrostatics, DPLR correctly extrapolates to large systems the potential energy surface learned from quantum mechanical calculations on smaller systems. We illustrate the approach with three examples, the potential energy profile of the water dimer, the free energy of interaction of a water molecule with a liquid water slab, and the phonon dispersion curves of the NaCl crystal. © 2021, CC BY.

关键词： Electrostatics

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Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles

Privacy-Preserving Knowledge Transfer with Bootstrap Aggrega...

引用

VLDB workshops: International Workshop on Polystore Systems for Heterogeneous Data in Multiple Databases with Privacy and Security Assurances, Poly 2020, and 6th International Workshop on Data Management and Analytics for Medicine and Healthcare, DMAH 2020

作者： Yoon, Hong-Jun Klasky, Hilda B. Durbin, Eric B. Wu, Xiao-Cheng Stroup, Antoinette Doherty, Jennifer Coyle, Linda Penberthy, Lynne Stanley, Christopher Christian, J. Blair Tourassi, Georgia D. Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37830 United States College of Medicine University of Kentucky LexingtonKY40536 United States Louisiana Tumor Registry School of Public Health Louisiana State University Health Sciences Center New OrleansLA70112 United States New Jersey State Cancer Registry Rutgers Cancer Institute of New Jersey New BrunswickNJ08901 United States Utah Cancer Registry Huntsman Cancer Institute University of Utah Salt Lake CityUT84132 United States Information Management Services Inc. CalvertonMD20705 United States Surveillance Research Program Division of Cancer Control and Population Sciences National Cancer Institute BethesdaMD20814 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

ISBN: (纸本)9783030710545

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance. However, knowledge transfer is difficult because of restrictions that are in place to ensure data security and privacy. Institutions are not allowed to exchange data or perform any activity that may expose personal information. With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models. The BATE algorithm is based on and provides enhancements to the PATE algorithm, maintaining competitive task performance scores on complex datasets with underrepresented class labels. We conducted a proof-of-the-concept study of the information extraction from cancer pathology report data from four cancer registries and performed comparisons between four scenarios: no collaboration, no privacy-preserving collaboration, the PATE algorithm, and the proposed BATE algorithm. The results showed that the BATE algorithm maintained competitive macro-averaged F1 scores, demonstrating that the suggested algorithm is an effective yet privacy-preserving method for machine learning and deep learning solutions. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Composite B-Spline Regularized Delta Functions for the Immersed Boundary Method: Divergence-Free Interpolation and Gradient-Preserving Force Spreading

arXiv

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arXiv 2024年

作者： Gruninger, Cole Griffith, Boyce E. Department of Mathematics University North Carolina Chapel HillNC United States Department of Applied Physical Sciences University of North Carolina Chapel HillNC United States Department of Biomedical Engineering University of North Carolina Chapel HillNC United States Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel HillNC United States Computational Medicine Program University of North Carolina School of Medicine Chapel HillNC United States McAllister Heart Institute University of North Carolina School of Medicine Chapel HillNC United States

This paper presents an approach to enhance volume conservation in the immersed boundary (IB) method by employing regularized delta functions derived from composite B-splines. These delta functions are constructed using tensor product kernels, similar to the conventional IB method. However, the kernels are B-splines whose polynomial degree varies according to the normal and tangential directions of each velocity component. The conventional IB method, while effective for fluid-structure interaction applications, has long been challenged by poor volume conservation, particularly evident in simulations of pressurized, closed membranes. We demonstrate that composite B-spline regularized delta functions significantly enhance volume conservation through two complementary properties: they provide continuously divergence-free velocity interpolants and maintain the gradient character of forces corresponding to mean pressure jumps across interfaces. By correctly representing these forces as discrete gradients, they eliminate a key source of spurious flows that typically plague immersed boundary computations. Our approach maintains the local nature of the classical IB method, avoiding the computational overhead associated with the non-local Divergence-Free Immersed Boundary (DFIB) method’s construction of an explicit velocity potential which requires additional Poisson solves for interpolation and force spreading operations. Through a series of numerical experiments, we show that sufficiently regular composite B-spline kernels can maintain initial volumes to within machine precision. We provide a detailed analysis of the relationship between kernel regularity and the accuracy of force spreading and velocity interpolation operations. Our findings indicate that composite B-splines of at least C1 regularity produce results comparable to the DFIB method in dynamic simulations, with errors in volume conservation primarily dominated by truncation error of the employed time-stepping s

关键词： Delta functions

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ON THE STABILITY OF POSITIVE SEMIGROUPS

arXiv

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arXiv 2021年

作者： Del Moral, Pierre Horton, Emma Jasra, Ajay Centre de Recherche Inria Bordeaux Sud-Ouest Talence 33405 France Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science & Technology Building 1 Thuwal23955-6900 Saudi Arabia

The stability and contraction properties of positive integral semigroups on Polish spaces are investigated. Our novel analysis is based on the extension of V -norm contraction methods, associated to functionally weighted Banach spaces for Markov semigroups, to positive semigroups. This methodology is applied to a general class of positive and possibly time-inhomogeneous bounded integral semigroups and their normalised versions. The spectral theorems that we develop are an extension of Perron-Frobenius and Krein-Rutman theorems for positive operators to a class of time-varying positive semigroups. In the context of time-homogeneous models, the regularity conditions discussed in the present article appear to be necessary and sufficient condition for the existence of leading eigenvalues. We review and illustrate the impact of these results in the context of positive semigroups arising in transport theory, physics, mathematical biology and signal processing. Copyright © 2021, The Authors. All rights reserved.

关键词： Banach spaces

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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