检索结果-内蒙古大学图书馆

TechRxiv 2023年

作者： Franco-Barranco, Daniel Lin, Zudi Jang, Won-Dong Wang, Xueying Shen, Qijia Yin, Wenjie Fan, Yutian Li, Mingxing Chen, Chang Xiong, Zhiwei Xin, Rui Liu, Hao Chen, Huai Li, Zhili Zhao, Jie Chen, Xuejin Pape, Constantin Conrad, Ryan De Folter, Jozefus Nightingale, Luke Jones, Martin L. Liu, Yanling Ziaei, Dorsa Huschauer, Stephan Arganda-Carreras, Ignacio Pfister, Hanspeter Wei, Donglai The Department of Computer Science and Artificial Intelligence University of the Basque Country Donostia-San Sebastian Spain San Sebastian Spain Ikerbasque Basque Foundation for Science Bilbao Spain Biofisika Institute CSIC UPV/EHU Bilbao Spain Harvard University All-ston MA United States The Department of Molecular and Cellular Biology Harvard University CambridgeMA United States The Wellcome Centre for Integrative Neuroimaging FMRIB Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom University of Science and Technology of China Anhui China The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China The National Engineering Laboratory for Brain-inspired Intelligence Technology and Application University of Science and Technology of China Anhui China The Georg-August University Goettingen Germany The Center for Molecular Microscopy Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda United States The Cancer Research Technology Program Frederick National Laboratory for Cancer Research Frederick United States The Francis Crick Institute London United Kingdom The Advanced Biomedical Computational Science Group Frederick National Laboratory for Cancer Research FrederickMD United States The Computer Science Department Boston College Chestnut Hill MA United States

In this paper, we present the results of the MitoEM challenge on mitochondria 3D instance segmentation from electron microscopy images, organized in conjunction with the IEEE-ISBI 2021 conference. Our benchmark dataset consists of two large-scale 3D volumes, one from human and one from rat cortex tissue, which are 3,600 times larger than previously used datasets. At the time of paper submission, 257 participants had registered for the challenge, 14 teams had submitted their results, and six teams participated in the challenge workshop. Here, we present eight top-performing approaches from the challenge participants, along with our own baseline strategies. Posterior to the challenge, annotation errors in the ground truth were corrected without altering the final ranking. Additionally, we present a retrospective evaluation of the scoring system which revealed that (1) the challenge metric was permissive with the false positive predictions and (2) the size-based grouping of instances did not correctly categorize mitochondria of interest. Thus, we propose a new scoring system that better reflects the correctness of the segmentation results. Although several of the top methods are compared favorably to our own baselines, substantial errors remain unsolved for mitochondria with challenging morphologies. Thus, the challenge remains open for submission and automatic evaluation, with all volumes available for download. © 2023, CC BY.

关键词： Electron microscopy

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Performance of the prototype shaftless small scale horizontal wind turbine for electricity generating from industrial exhaust air system

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IOP Conference Series: Earth and Environmental science 2020年第1期463卷

作者： Wachira Puttichaem Yuttanant Boonyongmaneerat Pakpachong Vadhanasindhu Sompong Putivisutisak Technopreneurship and Innovation Management Program Graduate School Chulalongkorn University Bangkok 10330 Thailand Metallurgy and Materials Science Research Institute (MMRI) Chulalongkorn University Bangkok 10330 Thailand Faculty of Commerce and Accountancy Chulalongkorn Business School Chulalongkorn University Bangkok 10330 Thailand Advanced Computational Fluid Dynamics Research Unit Department of Mechanical Engineering Faculty of Engineering Chulalongkorn University Bangkok 10330 Thailand

Man-made or unnatural wind from the industrial exhaust air system is an alternative wind resource for countries with natural low-speed or intermittent wind such as Thailand. It has strong and consistent wind speed when compared to the natural wind, with velocity about 5 to 10 m/s at a distance of 5 cm from the exhaust air outlet. However, some negative impacts to the exhaust air system performance was observed when a conventional wind turbine was employed. The objective of this research is to feasibility study the practicality of a prototype shaftless small scale horizontal axis wind turbine (SSHWT) to generate electricity from the exhaust air of the industrial exhaust air system. Aerodynamic, blade and generator designs were addressed in this study to maximize energy output and minimize negative impacts to the performance of the original exhaust air system. The performance of SSHWT was tested with a selected industrial fan that is widely used in industrial sections. The results showed that the SSHWT could generate electricity with less negative effect to exhaust air system performance. However, it still needs further improvements caused by the voltage output is too low. By the concept design, this innovative wind turbine is compact, thus needs only small space for installation. This SSHWT has high market potential for low wind speed countries to take advantage of unnatural wind resources which are better in terms of efficiency and economy for sustainable energy development.

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Jamming of Deformable Polygons

arXiv

引用

arXiv 2018年

作者： Boromand, Arman Signoriello, Alexandra Ye, Fangfu O'Hern, Corey S. Shattuck, Mark D. Beijing Natl. Laboratory for Condensed Matter Physics and Cas Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States School of Physical Sciences University of Chinese Academy of Sciences Beijing China Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Benjamin Levich Institute and Physics Department City College of City University of New York New YorkNY10031 United States

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Actively crosslinked microtubule networks: mechanics, dynamics and filament sliding

arXiv

引用

arXiv 2018年

作者： Fürthauer, Sebastian Lemma, Bezia Foster, Peter J. Ems-McClung, Stephanie C. Walczak, Claire E. Dogic, Zvonimir Needleman, Daniel J. Shelley, Michael J. Center for Computational Biology Flatiron Institute New YorkNY10010 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics Brandeis University WalthamMA02454 United States Department of Physics University of California Santa BarbaraCA93106 United States Physics of Livings Systems Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Medical Sciences Program Indiana University BloomingtonIN47405 United States Paulson School of Engineering & Applied Science and Department of Molecular & Cellular Biology Harvard University CambridgeMA02138 United States Courant Institute New York University New YorkNY10012 United States

Cytoskeletal networks are foundational examples of active matter and central to self-organized structures in the cell. In vivo, these networks are active and heavily crosslinked. Relating their large-scale dynamics to properties of their constituents remains an unsolved problem. Here we study an in vitro system made from microtubules and XCTK2 kinesin motors, which forms an aligned and active gel. Using photobleaching we demonstrate that the gel’s aligned microtubules, driven by motors, continually slide past each other at a speed independent of the local polarity. This phenomenon is also observed, and remains unexplained, in spindles. We derive a general framework for coarse graining microtubule gels crosslinked by molecular motors from microscopic considerations. Using the microtubule-microtubule coupling, and force-velocity relationship for kinesin, this theory naturally explains the experimental results: motors generate an active strain-rate in regions of changing polarity, which allows microtubules of opposite polarities to slide past each other without stressing the material. Copyright © 2018, The Authors. All rights reserved.

关键词： Photobleaching

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From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare

arXiv

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arXiv 2024年

作者： Li, Ming Xu, Pengcheng Hu, Junjie Tang, Zeyu Yang, Guang Bioengineering Department Imperial-X Imperial College London LondonW12 7SL United Kingdom National Heart and Lung Institute Imperial College London LondonSW7 2AZ United Kingdom Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Tri-Institutional Computational Biology & Medicine Program Weill Cornell Medicine of Cornell University New York United States Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London LondonWC2R 2LS United Kingdom

Federated learning holds great potential for enabling large-scale healthcare research and collaboration across multiple centres while ensuring data privacy and security are not compromised. Although numerous recent studies suggest or utilize federated learning based methods in healthcare, it remains unclear which ones have potential clinical utility. This review paper considers and analyzes the most recent studies up to May 2024 that describe federated learning based methods in healthcare. After a thorough review, we find that the vast majority are not appropriate for clinical use due to their methodological flaws and/or underlying biases which include but are not limited to privacy concerns, generalization issues, and communication costs. As a result, the effectiveness of federated learning in healthcare is significantly compromised. To overcome these challenges, we provide recommendations and promising opportunities that might be implemented to resolve these problems and improve the quality of model development in federated learning with healthcare. © 2024, CC BY-NC-ND.

关键词： Federated learning

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Slater insulator in iridate perovskites with strong spin-orbit coupling

arXiv

引用

arXiv 2017年

作者： Cui, Q. Cheng, J.-G. Fan, W. Taylor, A.E. Calder, S. McGuire, M.A. Yan, J.-Q. Meyers, D. Li, X. Cai, Y.Q. Jiao, Y.Y. Choi, Y. Haskel, D. Gotou, H. Uwatoko, Y. Chakhalian, J. Christianson, A.D. Yunoki, S. Goodenough, J.B. Zhou, J.-S. Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Materials Science and Engineering Program University of Texas at Austin AustinTX78712 United States Computational Condensed Matter Physical Laboratory RIKEN Wako Saitama351-0198 Japan Quantum Condensed Matter Division Oak Ridge National Laboratory TN37831 United States Materials Science and Technology Division Oak Ridge National Laboratory Tennessee37831 United States Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Department of Physics University of Arkansas FayettevilleAR72701 United States Advanced Photo Source Argonne National Laboratory ArgonneIL60439 United States Institute for Solid State Physics University of Tokyo 5-1-5 Kashiwanoha Chiba277-8581 Japan Department of Physics and Astronomy Rutgers University 136 Frelinghuysen Road PiscatawayNJ08854 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37966 United States Kobe Hyogo650-0047 Japan Wako Saitama351-0198 Japan

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SCA-AD, a Novel Method to Uniformly Identify Cells across Single-Cell RNA Sequencing Datasets, Applied to Adult and Pediatric Self-Renewing AML Cells

引用

Blood 2024年 144卷 2218-2218页

作者： Yoonkyu Lee Wen Wang Timothy K Starr Klara E Noble-Orcutt Chad L Myers Zohar Sachs Masonic Cancer Center University of Minnesota Minneapolis MN Bioinformatics and Computational Biology Program University of Minnesota Minneapolis MN Division of Hematology Oncology and Transplantation Department of Medicine University of Minnesota Minneapolis MN Department of Computer Science and Engineering University of Minnesota Minneapolis MN Department of Obstetrics Gynecology and Women's Health University of Minnesota Minneapolis MN

Introduction Single cell RNA sequencing (scRNAseq) is commonly used to determine cell identity. Several scRNAseq analysis methods are available. Most methods use arbitrary scores to represent the similarity of the query cell to a reference profile and do not assess the statistical significance of those scores. We previously presented a method to identify cells that express a profile of interest (the reference profile) within a scRNAseq dataset. Our method, called Single Cell Correlation Analysis (SCA), identifies cells in a query dataset that are similar to a reference profile. SCA represents a novel advance in the field because it calculates the statistical significance of the correlation coefficient using a permutation-based false discovery rate (FDR) estimation. Here, we advanced SCA to uniformly identify cells across multiple scRNAseq datasets. The new method is called SCA-Across Datasets (SCA-AD). We tested the performance of SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow, benchmarked SCA-AD to other methods, and used SCA-AD to identify and compare self-renewing cells in human AML samples from adult and pediatric patients. Methods and Results SCA-AD uses a universal scoring system to assign cell identity across datasets. Spearman's correlation is used to score the similarity of query cells to a reference profile. SCA-AD integrates a common background dataset into each query dataset which ensures heterogeneity in the data and allows SCA-AD to establish a threshold value for cell identity across the datasets. FDR calculations are used to establish the threshold value. We tested SCA-AD in validated scRNAseq datasets of normal murine and human bone marrow. In both cases, the self-renewing compartment was experimentally-validated using gold-standard, in vivo self-renewal assays. We extracted a reference normal bone marrow self-renewal profile from the in vivo- validated murine scRNAseq dataset (Rodriguez-Fraticelli et al. Nature 2020).

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Technology dictates algorithms: Recent developments in read alignment

arXiv

引用

arXiv 2020年

作者： Alser, Mohammed Rotman, Jeremy Taraszka, Kodi Shi, Huwenbo Baykal, Pelin Icer Yang, Harry Taegyun Xue, Victor Knyazev, Sergey Singer, Benjamin D. Balliu, Brunilda Koslicki, David Skums, Pavel Zelikovsky, Alex Alkan, Can Mutlu, Onur Mangul, Serghei Computer Science Department ETH Zürich Zürich8092 Switzerland Computer Engineering Department Bilkent University Bilkent Ankara 06800 Turkey Department of Computer Science University of California Los Angeles Los AngelesCA90095 United States Department of Epidemiology Harvard T.H. Chan School of Public Health MA02115 United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA02142 United States Department of Computer Science Georgia State University AtlantaGA30302 United States Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles Los AngelesCA90095 United States Division of Pulmonary and Critical Care Medicine Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Biochemistry & Molecular Genetics Northwestern University Feinberg School of Medicine United States Simpson Querrey Center for Epigenetics Northwestern University Feinberg School of Medicine ChicagoIL60611 United States Department of Computational Medicine University of California Los Angeles Los AngelesCA90095 United States Computer Science and Engineering Pennsylvania State University University ParkPA16801 United States Biology Department Pennsylvania State University University ParkPA16801 United States The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16801 United States The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow119991 Russia Bilkent-Hacettepe Health Sciences and Technologies Program Ankara Turkey Department of Clinical Pharmacy School of Pharmacy University of Southern California Los AngelesCA90089 United States

Massively parallel sequencing techniques have revolutionized biological and medical sciences by providing unprecedented insight into the genomes of humans, animals, and microbes. Modern sequencing platforms generate enormous amounts of genomic data in the form of nucleotide sequences or reads. Aligning reads onto reference genomes enables the identification of individual-specific genetic variants and is an essential step of the majority of genomic analysis pipelines. Aligned reads are essential for answering important biological questions, such as detecting mutations driving various human diseases and complex traits as well as identifying species present in metagenomic samples. The read alignment problem is extremely challenging due to the large size of analyzed datasets and numerous technological limitations of sequencing platforms, and researchers have developed novel bioinformatics algorithms to tackle these difficulties. Importantly, computational algorithms have evolved and diversified in accordance with technological advances, leading to today’s diverse array of bioinformatics tools. Our review provides a survey of algorithmic foundations and methodologies across 107 alignment methods published between 1988 and 2020, for both short and long reads. We provide rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read aligners. We separately discuss how longer read lengths produce unique advantages and limitations to read alignment techniques. We also discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology, including whole transcriptome, adaptive immune repertoire, and human microbiome studies. Copyright © 2020, The Authors. All rights reserved.

关键词： Bioinformatics

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

引用

arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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