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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

引用

MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular Engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular Engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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THE COMPLEX INTERPLAY BETWEEN RISK TOLERANCE AND THE SPREAD OF INFECTIOUS DISEASES

arXiv

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arXiv 2024年

作者： Nguyen, Maximilian Freedman, Ari Cheung, Matthew Saad-Roy, Chadi Espinoza, Baltazar Grenfell, Bryan Levin, Simon Lewis-Sigler Institute Princeton University PrincetonNJ08544 United States Department of Ecology and Evolutionary Biology Princeton University PrincetonNJ United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Miller Institute for Basic Research in Science Department of Integrative Biology University of California Berkeley BerkeleyCA United States Biocomplexity Institute University of Virginia CharlottesvilleVA United States

Risk-driven behavior provides a feedback mechanism through which individuals both shape and are collectively affected by an epidemic. We introduce a general and flexible compartmental model to study the effect of heterogeneity in the population with regards to risk tolerance. The interplay between behavior and epidemiology leads to a rich set of possible epidemic dynamics. Depending on the behavioral composition of the population, we find that increasing heterogeneity in risk tolerance can either increase or decrease the epidemic size. We find that multiple waves of infection can arise due to the interplay between transmission and behavior, even without the replenishment of susceptibles. We find that increasing protective mechanisms such as the effectiveness of interventions, the number of risk-averse people in the population, and the duration of intervention usage reduces the epidemic overshoot. When the protection is pushed past a critical threshold, the epidemic dynamics enter an underdamped regime where the epidemic size exactly equals the herd immunity threshold. Lastly, we can find regimes where epidemic size does not monotonically decrease with a population that becomes increasingly risk-averse. Copyright © 2024, The Authors. All rights reserved.

关键词： Epidemiology

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Invasive or More Direct Measurements Can Provide an Objective Early-Stopping Ceiling for Training Deep Neural Networks on Non-invasive or Less-Direct Biomedical Data

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SN Computer science 2023年第2期4卷 161页

作者： Bartlett, Christopher W. Bossenbroek, Jamie Ueyama, Yukie McCallinhart, Patricia Peters, Olivia A. Santillan, Donna A. Santillan, Mark K. Trask, Aaron J. Ray, William C. Department of Pediatrics The Ohio State University College of Medicine Columbus OH United States The Steve and Cindy Rasmussen Institute for Genomic Medicine Battelle Center for Computational Biology The Abigail Wexner Research Institute at Nationwide Children’s Hospital Columbus OH United States Department of Computer Science and Engineering The Ohio State University College of Engineering Columbus OH United States Center for Cardiovascular Research The Abigail Wexner Research Institute at Nationwide Children’s Hospital Columbus OH United States Department of Obstetrics & Gynecology University of Iowa Hospitals & Clinics Iowa City IA United States The Interdisciplinary Graduate Program in Biophysics The Ohio State University Graduate School Columbus OH United States

Early stopping is an extremely common tool to minimize overfitting, which would otherwise be a cause of poor generalization of the model to novel data. However, early stopping is a heuristic that, while effective, primarily relies on ad hoc parameters and metrics. Optimizing when to stop remains a challenge. In this paper, we suggest that for some biomedical applications, a natural dichotomy of invasive/non-invasive measurements, or more generally proximal vs distal measurements of a biological system can be exploited to provide objective advice on early stopping. We discuss the conditions where invasive measurements of a biological process should provide better predictions than non-invasive measurements, or at best offer parity. Hence, if data from an invasive measurement are available locally, or from the literature, that information can be leveraged to know with high certainty whether a model of non-invasive data is overfitted. We present paired invasive/non-invasive cardiac and coronary artery measurements from two mouse strains, one of which spontaneously develops type 2 diabetes, posed as a classification problem. Examination of the various stopping rules shows that generalization is reduced with more training epochs and commonly applied stopping rules give widely different generalization error estimates. The use of an empirically derived training ceiling is demonstrated to be helpful as added information to leverage early stopping in order to reduce overfitting. © 2023, The Author(s).

关键词： Health Invasive Machine learning Model Non-invasive Overfitting

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Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling

arXiv

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arXiv 2024年

作者： Liu, Zhuoyi Grigas, Alex T. Sumner, Jacob Knab, Edward Davis, Caitlin M. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Integrated Graduate Program in Physical and Engineering Biology Yale University New HavenCT06520 United States Graduate Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs Nr that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, Nr/N 0.08, where N is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo. © 2024, CC BY.

关键词： Conformations

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Confidence intervals for Markov chain transition probabilities based on next generation sequencing reads data

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Quantitative Biology 2020年第2期8卷 143-154页

作者： Lin Wan Xin Kang Jie Ren Fengzhu Sun NCMIS LSCAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences Fudan UniversityShanghai 200433China Quantitative and Computational Biology Program University of Southern CaliforniaLos AngelesCA 90089USA

Background:Markov chains(MC)have been widely used to model molecular *** estimations of MC transition matrix and confidence intervals of the transition probabilities from long sequence data have been intensively studied in the past *** next generation sequencing(NGS),a large amount of short reads are *** short reads can overlap and some regions of the genome may not be sequenced resulting in a new type of *** on NGS data,the transition probabilities of MC can be estimated by moment ***,the classical asymptotic distribution theory for MC transition probability estimators based on long sequences is no longer ***:In this study,we present the asymptotic distributions of several statistics related to MC based on NGS *** show that,after scaling by the effective coverage d defined in a previous study by the authors,these statistics based on NGS data approximate to the same distributions as the corresponding statistics for long ***:We apply the asymptotic properties of these statistics for finding the theoretical confidence regions for MC transition probabilities based on NGS short reads *** validate our theoretical confidence intervals using both simulated data and real data sets,and compare the results with those by the parametric bootstrap ***:We find that the asymptotic distributions of these statistics and the theoretical confidence intervals of transition probabilities based on NGS data given in this study are highly accurate,providing a powerful tool for NGS data analysis.

关键词： Markov chains next generation sequencing transition probabilities confidence intervals

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Pairwise Learning for Imbalanced Data Classification

Pairwise Learning for Imbalanced Data Classification

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International Conference on computational science and computational Intelligence (CSCI)

作者： Shu Liu Qiang Wu Computational and Data Science PhD Program Middle Tennessee State University Murfreesboro TN USA Department of Mathematical Sciences Middle Tennessee State University Murfreesboro TN USA

ISBN: (纸本)9781665458429

Imbalanced data classification problems appear quite commonly in real-world applications and impose great challenges to traditional classification approaches which work well only on balanced data but usually perform poorly on the minority class when the data is imbalanced. Resampling preprocessing by oversampling the minority class or downsampling the majority class helps improve the performance but may suffer from overfitting or loss of information. In this paper we propose a novel method called pairwise robust support vector machine (PRSVM) to overcome the difficulty of imbalanced data classification. It adapts the non-convex robust support vector classification loss to the pairwise learning setting. In the training process, samples from the minority class and the majority class always appear as pairs. This automatically balances the impact of two classes. Simulations and real-world applications show that PRSVM is highly effective.

关键词： Training Adaptation models Scientific computing computational modeling Support vector machine classification computational intelligence

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A Bottom-Up, Multi-Scale Model for Transient Mass Transport of Redox-Active Species Through Porous Electrodes Beyond the Pseudo-Steady Limit

SSRN

引用

SSRN 2022年

作者： Hamid, Md Abdul Smith, Kyle C. Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Program University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A transient, multi-scale model is introduced to capture the dynamic response of redox-active electrolyte flowing through porous electrodes under time-dependent current input. Unlike conventional models with time-invariant mass transfer coefficient, certain frequency-dependent transfer functions (TFs) are introduced to embed transient pore-scale transport physics into an up-scaled model. One of the introduced TFs is a spectral Sherwood number, that extends the film law of mass transfer to transient conditions. Another TF is introduced to capture the acceleration/suppression of solute advection resulting from inhomogeneity in local concentration distribution. The frequency dependence of these TFs is demonstrated for electrodes comprised of solid cylinders in crossflow. Three distinct spectral regions are observed, where with increasing frequency a transition from lagless response to semi-infinite Warburg response occurs. The results show orders of magnitude higher mass transfer rate for fast-changing applied current. These TFs are then embedded into an up-scaled model to obtain the time domain response of flow batteries. Analysis of concentration polarization and the average reactant concentration inside electrodes is used to construct non-dimensional maps of their operation space. From this analysis we find that fast current fluctuations can be sustained at conditions exceeding the limiting current that is expected for pseudo-steady operation. © 2022, The Authors. All rights reserved.

关键词： Flow batteries

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Interpretable prediction of drug synergy for breast cancer by random forest with features from Boolean modeling of signaling pathways

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Scientific Reports 2025年第1期15卷 1-18页

作者： Taoma, Kittisak Ruengjitchatchawalya, Marasri Kusonmano, Kanthida Termsaithong, Teerasit Sutthibutpong, Thana Liangruksa, Monrudee Laomettachit, Teeraphan Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand School of Information Technology King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Biotechnology Program School of Bioresources and Technology King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand Systems Biology and Bioinformatics Research Group Pilot Plant Development and Training Institute King Mongkut’s University of Technology Thonburi Bangkok 10150 Thailand Learning Institute King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Theoretical and Computational Physics Group Center of Excellence in Theoretical and Computational Science King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand Department of Physics Faculty of Science King Mongkut’s University of Technology Thonburi Bangkok 10140 Thailand National Nanotechnology Center (NANOTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani 12120 Thailand

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson’s correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model’s input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein’s contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs’ actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs. © The Author(s) 2025.

关键词： Breast cancer Drug combinations Drug resistance Machine learning Signaling pathways

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Efficient conversion of low-concentration nitrate sources into ammonia on a Ru-dispersed Cu nanowire electrocatalyst

Efficient conversion of low-concentration nitrate sources in...

引用

2023 AIChE Annual Meeting

作者： Chen, Feng-Yang Wu, Zhen-Yu Gupta, Srishti Rivera, Daniel J. Lambeets, Sten V. Pecaut, Stephanie Kim, Jung Yoon Timothy Zhu, Peng Finfrock, Y. Zou Meira, Debora Motta King, Graham Gao, Guanhui Xu, Wenqian Cullen, David A. Zhou, Hua Han, Yimo Perea, Daniel E. Muhich, Christopher L. Wang, Haotian Department of Chemical and Biomolecular Engineering Rice University HoustonTX United States School for Engineering of Matter Transport & Energy Chemical Engineering Program Arizona State University TempeAZ United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States Structural Biology Center X-ray Science Division Argonne National Laboratory LemontIL United States Canadian Light Source SaskatoonSK Canada Electron Microscope Center Department of Materials Science and NanoEngineering Rice University HoustonTX United States Advanced Photon Source Argonne National Laboratory LemontIL United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN United States Department of Materials Science and NanoEngineering Rice University HoustonTX United States Environmental Molecular Sciences Laboratory Earth and Biological Sciences Directorate Pacific Northwest National Laboratory RichlandWA United States School for Engineering of Matter Transport & Energy Materials Science & Engineering Program Arizona State University TempeAZ United States Department of Chemistry Rice University HoustonTX United States

Electrochemically converting nitrate ions, a widely distributed nitrogen source in industrial wastewater and polluted groundwater, into ammonia represents a sustainable route for both wastewater treatment and ammonia generation. However, it is currently hindered by low catalytic activities, especially under low nitrate concentrations. Here we report a high-performance Ru-dispersed Cu nanowire catalyst that delivers an industrial-relevant nitrate reduction current of 1 A cm-2 while maintaining a high NH3 Faradaic efficiency of 93%. More importantly, this high nitrate-reduction catalytic activity enables over a 99% nitrate conversion into ammonia, from an industrial wastewater level of 2, 000 ppm to a drinkable water level 4Cl and liquid NH3 solution products, which suggests a practical approach to convert wastewater nitrate into valuable ammonia products. Density functional theory calculations reveal that the highly dispersed Ru atoms provide active nitrate reduction sites and the surrounding Cu sites can suppress the main side reaction, the hydrogen evolution reaction. © The Author(s), under exclusive licence to Springer Nature Limited 2022.

关键词： Ammonia

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Artificial neural network approach for turbulence models: A local framework

arXiv

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arXiv 2021年

作者： Xie, Chenyue Xiong, Xiangming Wang, Jianchun Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China

A local artificial neural network (LANN) framework is developed for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by artificial neural network (ANN) based on the local coordinate system which is orthogonal to the curved walls. We verify the proposed model for the flows over periodic hills. The correlation coefficients of the RANS unclosed terms predicted by the LANN model can be made larger than 0.96 in an a priori analysis, and the relative error of the unclosed terms can be made smaller than 18%. In an a posteriori analysis, detailed comparisons are made on the results of RANS simulations using the LANN and Spalart-Allmaras (SA) models. It is shown that the LANN model performs better than the SA model in the prediction of the average velocity, wall-shear stress and average pressure, which gives the results that are essentially indistinguishable from the direct numerical simulation (DNS) data. The LANN model trained in low Reynolds number Re = 2800 can be directly applied in the cases of high Reynolds numbers Re = 5600, 10595, 19000, 37000 with accurate predictions. Furthermore, the LANN model is verified for flows over periodic hills with varying slopes. These results suggest that the LANN framework has a great potential to be applied to complex turbulent flows with curved *** Codes 76F40, 76F55, 76N99 Copyright © 2021, The Authors. All rights reserved.

关键词： Neural networks

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