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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Ab initio evaluation of complexation energies for cyclodextrin drug inclusion complexes

arXiv

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arXiv 2020年

作者： Oqmhula, Kenji Hongo, Kenta Maezono, Ryo Ichibha, Tom School of Materials Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan School of Information Science JAIST 1-1 Asahidai NomiIshikawa923-1292 Japan

We examined the reliability of exchange-correlation functionals for molecular encapsulations combined by van der Waals forces, comparing their predictions with those of diffusion Monte Carlo method. We established that functionals with D3 dispersion force correction and including sufficient proportion of exact-exchange in long-ranged interaction can comparatively reliably estimate the binding strength. Our finding agrees with a previous ab initio study on argon dimer. However we found that even such functionals may not be able to distinguish the energy differences among different conformations. Copyright © 2020, The Authors. All rights reserved.

关键词： Van der Waals forces

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Dynamics of surface electrons in a topological insulator: cyclotron resonance at room temperature

arXiv

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arXiv 2025年

作者： Mohelsky, I. Le Mardelé, F. Dzian, J. Wyzula, J. Sun, X.D. Cho, C.W. Piot, B.A. Shankar, M. Sankar, R. Ferguson, A. Santos-Cottin, D. Maršík, P. Bernhard, C. Akrap, A. Potemski, M. Orlita, M. Laboratoire National des Champs Magnétiques Intenses EMFL CNRS UPR3228 Univ. Grenoble Alpes Univ. Toulouse Univ. Toulouse 3 INSA-T Grenoble France Department of Physics Université de Fribourg Chemin du Musée 3 Fribourg1700 Switzerland Faculty of Mathematics and Physics Charles University Ke Karlovu 5 Prague121 16 Czech Republic Scientific Computing Theory and Data Division Paul Scherrer Institute Switzerland Institute of Physics Academia Sinica Nankang Taipei11529 Taiwan Department of Physics Faculty of Science University of Zagreb Zagreb10000 Croatia Institute of High Pressure Physics PAS WarsawPL-01-142 Poland CENTERA CEZAMAT Warsaw University of Technology WarsawPL-02-822 Poland

The ability to manipulate the surface states of topological insulators using electric or magnetic fields under ambient conditions is a key step toward their integration into future electronic and optoelectronic devices. Here, we demonstrate – using cyclotron resonance measurements on a tin-doped BiSbTe2S topological insulator – that moderate magnetic fields can quantize massless surface electrons into Landau levels even at room temperature. This finding suggests that surface-state electrons can behave as long-lived quasiparticles at unexpectedly high temperatures. Copyright © 2025, The Authors. All rights reserved.

关键词： Electron cyclotron resonance

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Hacc cosmological simulations: First data release

arXiv

引用

arXiv 2019年

作者： Heitmann, Katrin Uram, Thomas D. Finkel, Hal Frontiere, Nicholas Habib, Salman Pope, Adrian Rangel, Esteban Hollowed, Joseph Korytov, Danila Larsen, Patricia Allen, Benjamin S. Chard, Kyle Foster, Ian High Energy Physics Division Argonne National Laboratory LemontIL60439 United States Argonne Leadership Computing Facility Argonne National Laboratory LemontIL60439 United States Department of Physics University of Chicago ChicagoIL60637 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States

We describe the first major public data release from cosmological simulations carried out with Argonne's HACC code. This initial release covers a range of datasets from large gravity-only simulations. The data products include halo information for multiple redshifts, downsampled particles, and lightcone outputs. We provide data from two very large ΛCDM simulations as well as beyond-ΛCDM simulations spanning eleven w0-wacosmologies. Our release platform uses Petrel, a research data service, located at the Argonne Leadership computing Facility. Petrel offers fast data transfer mechanisms and authentication via Globus, enabling simple and efficient access to stored datasets. Easy browsing of the available data products is provided via a web portal that allows the user to navigate simulation products efficiently. The data hub will be extended by adding more types of data products and by enabling computational capabilities to allow direct interactions with simulation results. Copyright © 2019, The Authors. All rights reserved.

关键词： Cosmology

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How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

arXiv

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arXiv 2024年

作者： Bunne, Charlotte Roohani, Yusuf Rosen, Yanay Gupta, Ankit Zhang, Xikun Roed, Marcel Alexandrov, Theo AlQuraishi, Mohammed Brennan, Patricia Burkhardt, Daniel B. Califano, Andrea Cool, Jonah Dernburg, Abby F. Ewing, Kirsty Fox, Emily B. Haury, Matthias Herr, Amy E. Horvitz, Eric Hsu, Patrick D. Jain, Viren Johnson, Gregory R. Kalil, Thomas Kelley, David R. Kelley, Shana O. Kreshuk, Anna Mitchison, Tim Otte, Stephani Shendure, Jay Sofroniew, Nicholas J. Theis, Fabian Theodoris, Christina V. Upadhyayula, Srigokul Valer, Marc Wang, Bo Xing, Eric Yeung-Levy, Serena Zitnik, Marinka Karaletsos, Theofanis Regev, Aviv Lundberg, Emma Leskovec, Jure Quake, Stephen R. Department of Computer Science Stanford University StanfordCA United States Genentech South San FranciscoCA United States Chan Zuckerberg Initiative Redwood CityCA United States School of Computer and Communication Sciences School of Life Sciences EPFL Lausanne Switzerland Arc Institute Palo AltoCA United States KTH Royal Institute of Technology Science for Life Laboratory Department of Protein Science Stockholm Sweden Department of Bioengineering Stanford University StanfordCA United States Department of Pharmacology University of California San DiegoCA United States Department of Bioengineering University of California San DiegoCA United States Department of Systems Biology Columbia University New YorkNY United States Cellarity SomervilleMA United States Vagelos College of Physicians and Surgeons Columbia University Irving Medical Center New YorkNY United States Chan Zuckerberg Biohub New YorkNY United States Department of Molecular and Cell Biology University of California Berkeley BerkeleyCA United States Department of Statistics Stanford University StanfordCA United States Chan Zuckerberg Biohub San Francisco CA United States Chan Zuckerberg Institute for Advanced Biological Imaging Redwood CityCA United States Department of Bioengineering University of California BerkeleyCA United States Microsoft Research RedmondWA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Google Research Mountain ViewCA United States NewLimit San FranciscoCA United States Schmidt city United States Calico Life Sciences LLC San FranciscoCA United States Chan Zuckerberg Biohub Chicago IL United States Northwestern University EvanstonIL United States Cell Biology and Biophysics Unit European Molecular Biology Laboratory Heidelberg Germany Department of Systems Biology Harvard Medical School BostonMA United States Department of Genome Sciences University of Washington SeattleWA Unit

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach. © 2024, CC BY.

关键词： Virtual environments

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Improving solution accuracy and convergence for stochastic physics parameterizations with colored noise

arXiv

引用

arXiv 2019年

作者： Stinis, Panos Lei, Huan Li, Jing Wan, Hui Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA99354 United States Atmospheric Sciences and Global Change Division Pacific Northwest National Laboratory RichlandWA99354 United States Department of Computational Mathematics Science and Engineering Department of Statistics and Probability Michigan State University East LansingMI48824 United States

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. [7, 8] demonstrated that a correction term to various deterministic numerical schemes, known in stochastic analysis as the It^o correction, can help improve solution accuracy and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the It^o correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the It^o correction is derived for noises of any color. The formulation is applied to a test problem described by an advection-diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized It^o correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying);alternatively, for the same target accuracy, the generalized It^o correction allows for the use of significantly longer time steps and hence helps to reduce the computational cost of the numerical *** Codes 65C30, 86A10 Copyright © 2019, The Authors. All rights reserved.

关键词： Advection

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Deep learning for cardiac image segmentation: A review

arXiv

引用

arXiv 2019年

作者： Chen, Chen Qin, Chen Qiu, Huaqi Tarroni, Giacomo Duan, Jinming Bai, Wenjia Rueckert, Daniel Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom Department of Computer Science City University of London London United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Data Science Institute Imperial College London London United Kingdom Division of Brain Sciences Department of Medicine Imperial College London London United Kingdom

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers

arXiv

引用

arXiv 2023年

作者： Huppenkothen, Daniela Ntampaka, Michelle Ho, Matthew Fouesneau, Morgan Nord, Brian Peek, J.E.G. Walmsley, Mike Wu, John F. Avestruz, C. Buck, Tobias Brescia, Massimo Finkbeiner, Douglas P. Goulding, Andy D. Kacprzak, T. Melchior, Peter Pasquato, Mario Ramachandra, Nesar Ting, Yuan-Sen van de Ven, Glenn Villar, Soledad Villar, V.A. Zinger, Elad SRON Netherlands Institute for Space Research Niels Bohrweg 4 Leiden2333CA Netherlands Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States UMR 7095 98 bis bd Arago ParisF-75014 France Königstuhl 17 HeidelbergD-69117 Germany Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Jodrell Bank Centre for Astrophysics Department of Physics & Astronomy University of Manchester ManchesterM13 9PL United Kingdom Dunlap Institute for Astronomy & Astrophysics University of Toronto 50 St. George Street TorontoONM5S 3H4 Canada Leinweber Center for Theoretical Physics University of Michigan Ann ArborMI48109 United States Department of Physics University of Michigan Ann ArborMI48109 United States Universität Heidelberg Interdisziplinäres Zentrum für Wissenschaftliches Rechnen Im Neuenheimer Feld 205 Heidelberg69120 Germany Universität Heidelberg Zentrum für Astronomie Institut für Theoretische Astrophysik Albert-Ueberle-Straße 2 Heidelberg69120 Germany Department of Physics "E. Pancini " University Federico II of Napoli Via Cinthia 21 NapoliI-80126 Italy INAF Astronomical Observatory of Capodimonte Salita Moiariello 16 NapoliI-80131 Italy Department of Physics Harvard University 17 Oxford St. CambridgeMA02138 United States Harvard-Smithsonian Center for Astrophysics 60 Garden St. CambridgeMA02138 United States Department of Astrophysical Sciences Princeton University PrincetonNJ08544 United States Swiss Data Science Center Paul Scherrer Institute Villigen5303 Switzerland Center for Statistics & Machine Learn

Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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P0578QUININE EXPOSURE AND THE RISK OF ACUTE KIDNEY INJURY: A POPULATION BASED OBSERVATIONAL STUDY OF OLDER PEOPLE

引用

Nephrology Dialysis Transplantation 2020年第SUPPLEMENT_3期35卷

作者： Duncan, Andrew Hapca, Simona De Souza, Nicosha Morales, Daniel Bell, Samira University of Dundee Division of Population Health and Genomics School of Medicine Dundee United Kingdom University of Stirling Division of Computing Science and Mathematics Faculty of Natural Sciences Stirling United Kingdom Health Data Research U.K. Scotland NHS Tayside Renal Unit Dundee

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