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Protocol for Brain Magnetic Resonance Imaging and Extraction of Imaging‑Derived Phenotypes from the China Phenobank Project

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Phenomics 2023年第6期3卷 642-656页

作者： Chengyan Wang Zhang Shi Yan Li Xueqin Xia Xutong Kuang Shufang Qian Le Xue Lizhen Lan Yudan Wu Na Zhang Ji Tao Xumei Hu Wenzhao Cao Naying He Yike Guo Weibo Chen Jun Zhang Jingchun Luo He Wang Mei Tian Human Phenome Institute Fudan University825 Zhangheng RoadPudong New DistrictShanghai 201203China Department of Radiology Zhongshan HospitalFudan UniversityShanghai 200032China Department of Radiology Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China Department of Nuclear Medicine and Medical PET Center The Second Afliated Hospital of Zhejiang University School of MedicineHangzhou 310003China Department of Computer Science Hong Kong Baptist UniversityHong Kong Special Administrative Region 999077China Data Science Institute Imperial College LondonLondon SW72AZUK Philips Healthcare Co.Shanghai 200070China Department of Radiology Huashan HospitalNational Center for Neurological DisordersFudan UniversityShanghai 200040China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of EducationFudan UniversityShanghai 200433China

Imaging-derived phenotypes(IDPs)have been increasingly used in population-based cohort studies in recent *** widely reported,magnetic resonance imaging(MRI)is an important imaging modality for assessing the anatomical structure and function of the brain with high resolution and excellent soft-tissue *** purpose of this article was to describe the imaging protocol of the brain MRI in the China Phenobank Project(CHPP).Each participant underwent a 30-min brain MRI scan as part of a 2-h whole-body imaging protocol in *** brain imaging sequences included T1-magnetization that prepared rapid gradient echo,T2 fuid-attenuated inversion-recovery,magnetic resonance angiography,difusion MRI,and resting-state functional *** detailed descriptions of image acquisition,interpretation,and post-processing were provided in this *** measured IDPs included volumes of brain subregions,cerebral vessel geometrical parameters,microstructural tracts,and function connectivity metrics.

关键词： Human phenome project Phenobank Brain magnetic resonance imaging Imaging derived phenotypes Standard protocol

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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

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Computing and Software for Big science 2024年第1期8卷 1-21页

作者： Allaire, C. Ammendola, R. Aschenauer, E.-C. Balandat, M. Battaglieri, M. Bernauer, J. Bondì, M. Branson, N. Britton, T. Butter, A. Chahrour, I. Chatagnon, P. Cisbani, E. Cline, E.W. Dash, S. Dean, C. Deconinck, W. Deshpande, A. Diefenthaler, M. Ent, R. Fanelli, C. Finger, M. Fol, E. Furletov, S. Gao, Y. Giroux, J. Waduge, N. C. Gunawardhana Hassan, O. Hegde, P.L. Hernández-Pinto, R.J. Blin, A. Hiller Horn, T. Huang, J. Jalotra, A. Jayakodige, D. Joo, B. Junaid, M. Kalantarians, N. Karande, P. Kriesten, B. Elayavalli, R. Kunnawalkam Li, Y. Lin, M. Liu, F. Liuti, S. Matousek, G. McEneaney, M. McSpadden, D. Menzo, T. Miceli, T. Mikuni, V. Montgomery, R. Nachman, B. Nair, R.R. Niestroy, J. Oregon, S. A. Ochoa Oleniacz, J. Osborn, J.D. Paudel, C. Pecar, C. Peng, C. Perdue, G.N. Phelps, W. Purschke, M.L. Rajendran, H. Rajput, K. Ren, Y. Renteria-Estrada, D.F. Richford, D. Roy, B.J. Roy, D. Saini, A. Sato, N. Satogata, T. Sborlini, G. Schram, M. Shih, D. Singh, J. Singh, R. Siodmok, A. Stevens, J. Stone, P. Suarez, L. Suresh, K. Tawfik, A.-N. Acosta, F. Torales Tran, N. Trotta, R. Twagirayezu, F.J. Tyson, R. Volkova, S. Vossen, A. Walter, E. Whiteson, D. Williams, M. Wu, S. Zachariou, N. Zurita, P. Université Paris-Saclay CNRS/IN2P3 IJCLAB Orsay 91400 France Sezione di Roma Tor Vergata INFN Rome 00133 Italy Brookhaven National Lab Upton 11973 NY United States Meta Menlo Park 935025 CA United States National Institute for Nuclear Physics Genoa 16146 Italy Center for Frontiers in Nuclear Science Stony Brook University Stony Brook 11790-3800 NY United States Stony Brook University Stony Brook 11794 NY United States National Institute for Nuclear Physics Catania 95125 Italy Drexel University Philadelphia 19104 PA United States Messiah University Mechanicsburg 17055 PA United States Jefferson Lab Newport News 293606 VA United States Laboratory Nuclear and High-Energy Physics CNRS Paris 75005 France University of Michigan Ann Arbor 48104 MI United States National Institute for Nuclear Physics Roma Rome 00185 Italy Indian Institute of Technology Bombay Maharashtra Mumbai 400076 India Massachusetts Institute of Technology Cambridge 021339 MA United States Department of Physics University of Manitoba Winnipeg R3T 2N2 MB Canada William & amp Charles University Faculty of Mathematics and Physics Prague 18000 Czech Republic Accelerators and Beam Physics Group CERN Geneva 1211 Switzerland University of Regina Regina S4S0A2 SK Canada University of Virginia Charlottesville 22904 VA United States University of Victoria Victoria V8P 5C2 Canada Central University of Karnataka Kalaburagi 585367 India Facultad de Ciencias Físico-Matemáticas Universidad Autónoma de Sinaloa Ciudad Universitaria Sinaloa Culiacán 80000 Mexico Institute for Theoretical Physics TübingenUniversity Tübingen 72076 Germany The Catholic University of America Washington 20064 DC United States University of Jammu Jammu and Kashmir Jammu 180006 India Hampton University Hampton 23668 VA United States Oak Ridge National Laboratory Oak Ridge 37830 TN United States Virginia Union University Richmond 23220 VA United States Lawrence Livermore N

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. © The Author(s) 2024.

关键词： Artificial Intelligence Deep learning EIC ePIC Machine learning Physics QCD

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GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

arXiv

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arXiv 2023年

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong Ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Department of Radiology Netherlands Cancer Institute Amsterdam Netherlands National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China Shenzhen RayShape Medical Technology Co. Ltd China Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness. © 2023, CC BY.

关键词： Medical applications

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‘Flux+Mutability’: a conditional generative approach to one-class classification and anomaly detection

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Machine Learning: science and technology 2022年第4期3卷 045012页

作者： Fanelli, C. Giroux, J. Papandreou, Z. Laboratory for Nuclear Science Massachusetts Institute of Technology CambridgeMA02139 United States The NSF AI Institute for Artificial Intelligence and Fundamental Interactions WilliamsburgVA23185 United States William & Mary Data Science WilliamsburgVA23185 United States Jefferson Lab Newport NewsVA23606 United States Department of Physics University of Regina ReginaSKS4S 0A2 Canada

Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration. © 2022 The Author(s). Published by IOP Publishing Ltd.

关键词： Anomaly detection

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FENDL: A library for fusion research and applications

arXiv

引用

arXiv 2023年

作者： Schnabel, G. Aldama, D.L. Bohm, T. Fischer, U. Kunieda, S. Trkov, A. Konno, C. Capote, R. Koning, A.J. Breidokaite, S. Eade, T. Fabbri, M. Flammini, D. Isolan, L. Kodeli, I. Kosál, M. Kwon, S. Laghi, D. Leichtle, D. Nakayama, S. Ohta, M. Packer, L.W. Qiu, Y. Sato, S. Sawan, M. Schulc, M. Stankunas, G. Sumini, M. Valentine, A. Villari, R. Žohar, A. NAPC–Nuclear Data Section International Atomic Energy Agency Vienna Austria Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear Havana Cuba University of Wisconsin-Madison MadisonWI United States Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Nuclear Science and Engineering Center Japan Atomic Energy Agency Ibaraki Tokai Japan Josef Stefan Institute Ljubljana Slovenia Lithuanian Energy Institute Laboratory of Nuclear Installation Safety Kaunas Lithuania United Kingdom Atomic Energy Authority Culham Science Centre Abingdon OxonOX14 3DB United Kingdom Fusion for Energy Barcelona Spain ENEA Fusion and Technology for Nuclear Safety and Security Department C.R. Frascati Italy Industrial Engineering Department University of Bologna Bologna Italy Research Center Rez Ltd Husinec Rez25068 130 Czech Republic Rokkasho Fusion Institute National Institutes for Quantum Science and Technology Aomori Rokkasho Japan NIER Engineering Castel Maggiore Italy

The Fusion Evaluated nuclear data Library (FENDL) is a comprehensive and validated collection of nuclear cross section data coordinated by the International Atomic Energy Agency (IAEA) nuclear data Section (NDS). FENDL assembles the best nuclear data for fusion applications selected from available nuclear data libraries and has been under development for decades. FENDL contains sublibraries for incident neutron, proton, and deuteron cross sections including general purpose and activation files used for particle transport and nuclide inventory calculations. In this work, we describe the history, selection of evaluations for the various sub-libraries (neutron, proton, deuteron) with the focus on transport and reactor dosimetry applications, the processing of the nuclear data for application codes (e.g. MCNP), and the development of the TENDL-2017 library which is the currently recommended activation library for FENDL. We briefly describe the IAEA IRDFF library as the recommended library for dosimetry fusion applications. We also present work on validation of the neutron sub-library using a variety of fusion relevant computational and experimental benchmarks using the MCNP transport code and ACE-formatted cross section libraries. A variety of cross section libraries are used for the validation work including FENDL-2.1, FENDL-3.1d, FENDL-3.2, ENDF/B-VIII.0, and JEFF-3.2 with the emphasis on the FENDL libraries. The results of the validation using computational benchmarks showed generally good agreement among the tested neutron cross section libraries for neutron flux, nuclear heating, and primary displacement damage (dpa). Gas production (H/He) in structural materials showed substantial differences to the reference FENDL-2.1 library. The results of the experimental validation showed that the performance of FENDL-3.2b is at least as good and in most cases better than FENDL-2.1. Future work will consider improved evaluations developed by the International nuclear data Eva

关键词： Libraries

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An Efficient Digital Twin Based on Machine Learning Svd Autoencoder and Generalised Latent Assimilation for nuclear Reactor Physics

SSRN

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SSRN 2022年

作者： Gong, Helin Cheng, Sibo Chen, Zhang Li, Qing Quilodrán-Casas, César Xiao, Dunhui Arcucci, Rossella SJTU-ParisTech Elite Institute of Technology Shanghai 200240 China Data Science Institute Imperial College London LondonSW7 2AZ United Kingdom Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610041 China Faculty of Sciences and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom

This paper proposes an approach that combines reduced-order models with machine learning in order to create an digital twin to predict the power distribution over the core during the operation stage. The operational digital twin is designed to solve forward problems given input operation parameters, as well as to solve inverse problems given some observations of the power field. The forward model is realised using SVD autoencoder reduced order model and machine learning methods. The inverse model is based on a generalised latent assimilation method. The effectiveness in the sense of accuracy and real-time solver of the digital twin is illustrated through a real engineering problem in nuclear reactor physics - reactor core simulation in the life cycle of HPR1000 affected by control rod inserting step, burnup, power level and inlet temperature of the coolant, which shows potential applications for on-line monitoring purpose. © 2022, The Authors. All rights reserved.

关键词： Inverse problems

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Back-n white neutron source at CSNS and its applications

arXiv

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arXiv 2021年

作者： Tang, Jing-Yu An, Qi Bai, Jiang-Bo Bao, Jie Bao, Yu Cao, Ping Chen, Hao-Lei Chen, Qi-Ping Chen, Yong-Hao Chen, Zhen Cui, Zeng-Qi Fan, Rui-Rui Feng, Chang-Qing Gao, Ke-Qing Gao, Xiao-Long Gu, Min-Hao Han, Chang-Cai Han, Zi-Jie He, Guo-Zhu He, Yong-Cheng Hong, Yang Hu, Yi-Wei Huang, Han-Xiong Huang, Xi-Ru Jiang, Hao-Yu Jiang, Wei Jiang, Zhi-Jie Jing, Han-Tao Kang, Ling Li, Bo Li, Chao Li, Jia-Wen Li, Qiang Li, Xiao Li, Yang Liu, Jie Liu, Rong Liu, Shu-Bin Liu, Xing-Yan Long, Ze Luan, Guang-Yuan Ning, Chang-Jun Niu, Meng-Chen Qi, Bin-Bin Ren, Jie Ren, Zhi-Zhou Ruan, Xi-Chao Song, Zhao-Hui Sun, Kang Sun, Zhi-Jia Tan, Zhi-Xin Tang, Xin-Yi Tian, Bin-Bin Wang, Li-Jiao Wang, Peng-Cheng Wang, Zhao-Hui Wen, Zhong-Wei Wu, Xiao-Guang Wu, Xuan Xie, Li-Kun Yang, Xiao-Yun Yang, Yi-Wei Yi, Han Yu, Li Yu, Tao Yu, Yong-Ji Zhang, Guo-Hui Zhang, Lin-Hao Zhang, Qi-Wei Zhang, Xian-Peng Zhang, Yu-Liang Zhang, Zhi-Yong Zhou, Lu-Ping Zhou, Zhi-Hao Zhu, Ke-Jun State Key Laboratory of Particle Detection and Electronics Department of Modern Physics University of Science and Technology of China Hefei230026 China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang621900 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing102413 China Beijing100049 China Spallation Neutron Source Science Center Dongguan523803 China State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Northwest Institute of Nuclear Technology Xi’an710024 China University of Chinese Academy of Sciences Beijing100049 China Department of Engineering and Applied Physics University of Science and Technology of China Hefei230026 China

Back-streaming neutrons from the spallation target of the China Spallation Neutron Source (CSNS) that emit through the incoming proton channel were exploited to build a white neutron beam facility (the so-called Back-n white neutron source), which was completed in March 2018. The Back-n neutron beam is very intense, at approximately 2×107 n/cm2/s at 55 m from the target, and has a nominal proton beam with a power of 100 kW in the CSNS-I phase and a kinetic energy of 1.6 GeV and a thick tungsten target in multiple slices with modest moderation from the cooling water through the slices. In addition, the excellent energy spectrum spanning from 0.5 eV to 200 MeV, and a good time resolution related to the time-of-flight measurements make it a typical white neutron source for nuclear data measurements;its overall performance is among that of the best white neutron sources in the world. Equipped with advanced spectrometers, detectors, and application utilities, the Back-n facility can serve wide applications, with a focus on neutron-induced cross-section measurements. This article presents an overview of the neutron beam characteristics, the experimental setups, and the ongoing applications at Back-n. © 2021, CC BY-SA.

关键词： Neutron sources

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Phase transitions and spectrum evaluation in mixed traffic model concerned with Interruption Probability Effect under CAVs and HDVs surroundings

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Physica A: Statistical Mechanics and its Applications 2025年 672卷

作者： Zhou, Tong Tian, Yudan Zhu, Meilan Peng, Guanghan School of Big Data and Internet of Things Chongqing Vocational Institute of Engineering Chongqing402260 China Guangxi Key Laboratory of Nuclear Physics and Technology Guangxi Normal University Guilin541004 China College of Physical Science and Technology Guangxi Normal University Guilin541004 China

In the context of connected autonomous vehicles (CAVs) navigating real-world roadways within mixed traffic environments, network communication security is paramount. However, network communication disruptions interruptions often occur. To address this, we have incorporated the Interruption Probability Effect (IPE) into a novel Heterogeneous Lattice Hydrodynamic Model (LHM) designed for mixed fleets comprising both CAVs and human-driven vehicles (HDVs). The stability requirement, influenced by the IPE, is inferred through linear stability analysis in heterogeneous vehicle scenarios. Additionally, nonlinear analysis reveals a significant relationship between the equations governing heterogeneous vehicles and the IPE. Our simulations demonstrate that the increase of the IPE, alongside the permeability of CAVs and the sensitivity of flux information, results in a reduction of density and limit loops. This indicates that the IPE can enhance traffic characteristics similarly to the permeability of CAVs and the sensitivity of flux information. Furthermore, our spectral analysis, focusing on power spectrum and spectral entropy, confirms that the stability of heterogeneous vehicles is bolstered by the IPE, the permeability of CAVs, and the sensitivity of flux information. © 2025 Elsevier B.V.

关键词： Nonlinear analysis

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Erratum to: Jet charge identification in the e+e− → Z → process at Z pole

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Journal of High Energy Physics 2024年第7期2024卷 1-1页

作者： Cui, Hanhua Zhao, Mingrui Wang, Yuexin Liang, Hao Ruan, Manqi Institute of High Energy Physics Chinese Academy of Sciences Beijing China University of Chinese Academy of Science (UCAS) Beijing China Science and Technology on Nuclear Data Laboratory China Institute of Atomic Energy Beijing China Niels Bohr Institute University of Copenhagen Copenhagen Denmark China Center of Advanced Science and Technology Beijing China

A correction to this paper has been published: https://***/10.1007/JHEP05(2024)210

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Erratum to: Jet charge identification in the process at Z pole

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Journal of High Energy Physics 2024年第8期2024卷 1-1页

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