检索结果-内蒙古大学图书馆

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness Neuroimaging

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to ‘Scalable nano-crystallization approach over multiple passivation behavior for Fe-based amorphous coating under chloride exposure’ [J. Alloys Compd., vol. 1005 (2024) 176112 1–18/ JALCOM_176112]

引用

Journal of Alloys and Compounds 2024年 1005卷

作者： Miqi Wang Zehua Zhou Aihui Liu Ruhao Xie Jing Sun Fang Shi Faculty of Mechanical and Material Engineering Huaiyin Institute of Technology Huai’an Jiangsu 223003 PR China Jiangsu Key Laboratory of Advanced Manufacturing Technology Huaiyin Institute of Technology Huai’an Jiangsu 223003 PR China Research Laboratory of Computational Mechanics Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 PR China College of Materials Science and Engineering Hohai University Changzhou Jiangsu Province 213200 PR China Suzhou Chengtou Environmental Technology Development Co. Ltd. Suzhou Jiangsu 215000 PR China

来源：评论

学校读者我要写书评

暂无评论

Genome assembly and transcriptome analysis provide insights into the antischistosome mechanism of Microtus fortis

引用

Journal of Genetics and Genomics 2020年第12期47卷 743-755页

作者： Hong Li Zhen Wang Shumei Chai Xiong Bai Guohui Ding Yuanyuan Li Junyi Li Qingyu Xiao Benpeng Miao Weili Lin Jie Feng Mingyue Huang Cheng Gao Bin Li Wei Hu Jiaojiao Lin Zhiqiang Fu Jianyun Xie Yixue Li Bio-Med Big Data Center CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Shanghai Veterinary Research Institute Chinese Academy of Agricultural SciencesKey Laboratory of Animal ParasitologyMinistry of AgricultureShanghai 200241China Shanghai Laboratory Animal Research Center Shanghai 201203China Institute for Digital Health International Human Phenome Institutes(Shanghai)Shanghai 200433China Shanghai Center for Bioinformation Technology Shanghai Academy of Science and TechnologyShanghai 201203China School of Computer Science and Technology Harbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Shanghai Institute of Immunology Department of Immunology and MicrobiologyShanghai JiaoTong University School of MedicineShanghai 200025China State Key Laboratory of Genetic Engineering Ministry of Education Key Laboratory of Contemporary AnthropologyCollaborative Innovation Center for Genetics and DevelopmentSchool of Life SciencesFudan UniversityShanghai 200438China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhouZhejiang 330106China

Microtus fortis is the only mammalian host that exhibits intrinsic resistance against Schistosoma japonicum ***,the underlying molecular mechanisms of this resistance are not yet ***,we perform the first de novo genome assembly of ***,comprehensive gene annotation analysis,and evolution ***,we compare the recovery rate of schistosomes,pathological changes,and liver transcriptomes between *** and mice at different time points after *** observe that the time and type of immune response in *** are different from those in *** activates immune and inflammatory responses on the 10th day post infection,such as leukocyte extravasation,antibody activation,Fc-gamma receptor-mediated phagocytosis,and the interferon signaling cascade,which play important roles in preventing the development of *** contrast,an intense immune response occurrs in mice at the late stages of infection and could not eliminate *** mice suffer severe pathological injury and continuous decreases in cell cycle,lipid metabolism,and other *** findings offer new insights into the intrinsic resistance mechanism of *** against schistosome *** genome sequence also provides the basis for future studies of other important traits in ***.

关键词： Genome assembly Microtus fortis Schistosome Immune Transcriptome

来源：评论

学校读者我要写书评

暂无评论

A machine learning-based high-precision density functional method for drug-like molecules

Artificial Intelligence Chemistry

引用

Artificial Intelligence Chemistry 2024年第1期2卷

作者： Jin Xiao YiXiao Chen LinFeng Zhang Han Wang Tong Zhu Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development Shanghai Frontiers Science Center of Molecule Intelligent Syntheses School of Chemistry and Molecular Engineering East China Normal University Shanghai 200062 P.R. China Program in Applied and Computational Mathematics Princeton University Princeton NJ 08544 USA AI for Science Institute Beijing 100080 P.R. China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 P.R. China NYU-ECNU Center for Computational Chemistry at NYU Shanghai Shanghai 200062 P.R. China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518005 P.R. China

In computer-aided drug discovery, accurately determining the structure and properties of drug-like molecules is of utmost importance. This necessitates the use of precise and efficient electronic structure methods. Here, we developed two deep learning-based density functional methods, namely DeePHF and DeePKS, specifically tailored for drug-like molecules. Notably, DeePKS incorporates self-consistency into its framework. With a limited dataset labelled at the CCSD(T)/def2-TZVP level, both models have been able to achieve chemical accuracy in calculating molecular energies and have demonstrated excellent transferability. We anticipate that further advancements in this field will lead to the development of high-quality density functional methods designed specifically for drug discovery purposes. This research showcases the capabilities of deep learning approaches in simplifying the construction complexity associated with traditional DFT methods.

关键词： Machine Learning Density Functional Theory DeePHF DeePKS

来源：评论

学校读者我要写书评

暂无评论

DPA-2: a large atomic model as a multi-task learner

arXiv

引用

arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

来源：评论

学校读者我要写书评

暂无评论

Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report

arXiv

引用

arXiv 2024年

作者： Huijben, Evi M.C. Terpstra, Maarten L. Galapon, Arthur Pai, Suraj Thummerer, Adrian Koopmans, Peter Afonso, Manya van Eijnatten, Maureen Gurney-Champion, Oliver Chen, Zeli Zhang, Yiwen Zheng, Kaiyi Li, Chuanpu Pang, Haowen Ye, Chuyang Wang, Runqi Song, Tao Fan, Fuxin Qiu, Jingna Huang, Yixing Ha, Juhyung Park, Jong Sung Alain-Beaudoin, Alexandra Bériault, Silvain Yu, Pengxin Guo, Hongbin Huang, Zhanyao Li, Gengwan Zhang, Xueru Fan, Yubo Liu, Han Xin, Bowen Nicolson, Aaron Zhong, Lujia Deng, Zhiwei Müller-Franzes, Gustav Khader, Firas Li, Xia Zhang, Ye Hémon, Cédric Boussot, Valentin Zhang, Zhihao Wang, Long Bai, Lu Wang, Shaobin Mus, Derk Kooiman, Bram Sargeant, Chelsea A.H. Henderson, Edward G.A. Kondo, Satoshi Kasai, Satoshi Karimzadeh, Reza Ibragimov, Bulat Helfer, Thomas Dafflon, Jessica Chen, Zijie Wang, Enpei Perko, Zoltan Maspero, Matteo Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Radiotherapy Department University Medical Center Utrecht Utrecht Netherlands Computational Imaging Group for MR Diagnostics & Therapy University Medical Center Utrecht Utrecht Netherlands Department of Radiation Oncology University Medical Center Groningen University of Groningen Groningen Netherlands GROW School for Oncology Maastricht University Medical Centre Maastricht Netherlands Department of Radiation Oncology LMU University Hospital LMU Munich Munich Germany Department of Radiation Oncology Radboud University Medical Center Nijmegen Netherlands Wageningen University & Research Wageningen Plant Research Wageningen Netherlands Department of Radiology and Nuclear Medicine Amsterdam UMC Location University of Amsterdam Amsterdam Netherlands Cancer Center Amsterdam Imaging and Biomarkers Amsterdam Netherlands School of Biomedical Engineering Southern Medical University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China School of Biomedical Engineering ShanghaiTech University Shanghai China Fudan University Shanghai China Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Indiana University Bloomington United States Advanced Development Engineering Elekta Ltd Montreal Canada Infervision Medical Technology Co. Ltd. Beijing China Department of Biomedical Engineering Shantou University China Independent researchers Department of Computer Science Vanderbilt University Nashville United States Australian e-Health Research Centre CSIRO HerstonQLD Australia Los AngelesCA United States University Hospital Aachen Aachen Germany Center for Proton Therapy Paul Scherrer Institut Villigen Switzerland Department of Computer Science ETH Zurich Zurich Switzerland University Rennes 1 CLCC Eugène Marquis INSERM LTSI Rennes France Subtle Medical Shanghai China MedMind Technology Co.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥ 0.87/0.90) and gamma pass rates for photon (≥ 98.1%/99.0%) and proton (≥ 97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning. © 2024, CC

关键词： Radiotherapy

来源：评论

学校读者我要写书评

暂无评论

Packaging, containerization, and virtualization of computational omics methods: Advances, challenges, and opportunities

arXiv

引用

arXiv 2022年

作者： Alser, Mohammed Waymost, Sharon Ayyala, Ram Lawlor, Brendan Abdill, Richard J. Rajkumar, Neha LaPierre, Nathan Brito, Jaqueline Ribeiro-Dos-Santos, André M. Firtina, Can Almadhoun, Nour Sarwal, Varuni Eskin, Eleazar Hu, Qiyang Strong, Derek Kim, Byoung-Do Abedalthagafi, Malak S. Mutlu, Onur Mangul, Serghei Department of Information Technology and Electrical Engineering ETH Zürich Zürich8092 Switzerland Department of Computer Science UCLA Los AngelesCA90095 United States Department of Translational Biomedical Informatics University of Southern California Los AngelesCA90089 United States Department of Clinical Pharmacy University of Southern California Los AngelesCA90089 United States Department of Computer Science Munster Technological University CorkT12 P928 Ireland Department of Genetics Cell Biology and Development University of Minnesota MinneapolisMN55417 United States Department of Bioengineering UCLA Los AngelesCA90095 United States Institute for Systems Genetics NYU Grossman School of Medicine New YorkNY10016 United States Department of Computational Medicine University of California Los AngelesCA90095 United States Department of Human Genetics University of California Los AngelesCA90095 United States Office of Advanced Research Computing UCLA Los AngelesCA90095 United States Center for Advanced Research Computing University of Southern California Los AngelesCA90089 United States Genomics Research Department Saudi Human Genome Project King Fahad Medical City King Abdulaziz City for Science and Technology Riyadh Saudi Arabia King Salman Center for Disability Research Riyadh12512 Saudi Arabia Riyadh Saudi Arabia

Omics software tools have reshaped the landscape of modern biology and become an essential component of biomedical research. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging, virtualization, and containerization are different approaches to satisfy this need by wrapping omics tools in additional software that makes the omics tools easier to install and use. Here, we systematically review practices across prominent packaging, virtualization, and containerization platforms. We outline the challenges, advantages, and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers, and system administrators. We also propose principles to make packaging, virtualization, and containerization of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research. © 2022, CC BY.

关键词： Containers

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

A dual-purpose binder-free FeNiS 2 -Decorated Ti 3 C 2 T x nanocomposite for supercapacitor and catalytic hydrogen evolution reaction

引用

Journal of Power Sources 2025年 649卷

作者： Brindha Devi Sankar Sankar Sekar Veeramuthu Vignesh Jrjeng Ruan Rajkumar Nirmala Youngmin Lee Sejoon Lee Pei-Chien Tsai Shang-Cyuan Chen Yuan-Chung Lin Vinoth Kumar Ponnusamy Rangaswamy Navamathavan Department of Physics School of Advanced Sciences Vellore Institute of Technology (VIT) Chennai Vandalur - Kelambakkam Road Chennai 600127 India Department of Material Science and Engineering National Cheng Kung University Tainan 70101 Taiwan Department of Semiconductor Science Dongguk University-Seoul 04620 Republic of Korea Quantum-functional Semiconductor Research Center Dongguk University-Seoul 04620 Republic of Korea Division of Advanced Materials Engineering College of Engineering Research Center for Advanced Materials Development (RCAMD) Jeonbuk National University (JBNU) Jeonju 54896 Republic of Korea Department of Biotechnology Hindustan College of Arts and Science Affiliated to University of Madras Padur Chennai 603103 India Department of Medicinal and Applied Chemistry Kaohsiung Medical University (KMU) Kaohsiung city 807 Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu 602105 India Department of Environmental Science and Occupational Safety and Hygiene Tajen University Pingtung 907 Taiwan Center for Emerging Contaminants Research National Sun Yat-sen University Kaohsiung city 804 Taiwan Institute of Environmental Engineering National Sun Yat-sen University Kaohsiung city 804 Taiwan Research Center for Precision Environmental Medicine Kaohsiung Medical University (KMU) Kaohsiung City 807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital (KMUH) Kaohsiung Medical University Kaohsiung City 807 Taiwan

This study is focused on developing novel electrode material, FeNi-based sulfide nanoparticles (FNS) incorporated onto titanium carbide (Ti 3 C 2 T x , TC, MXene) deposited on Ni foam (NF), termed as FNS@NF/TC, for energy conversion and storage application. This nanocomposite offers an improved conductivity, and stability for electrocatalytic hydrogen evolution reaction (HER) and electrochemical capacitor applications. This FNS@NF/TC nanostructured electrode is fabricated by using a binder-free technique, which is simple electrochemical deposition. The fabricated electrode shows a higher specific capacitance of 1460 F/g at the current density of 2 A/g with capacitance retention of 91.8 % and coulombic efficiency of 92 % after 5000 cycles. In the case of electrocatalytic water splitting HER, the lower overpotential is calculated at around 104 mV at the current density of 10 mA/cm 2 and decreased Tafel slope of around 65 mV/dec for the FNS@NF/TC nanostructured electrode with good stability after 12 h in chronopotentiometry technique. Overall, the deposition of FeNiS 2 on the Ti 3 C 2 T x @NF composite enhances ion transport and storage capacity, positioning it as an up-and-coming candidate for efficient and sustainable energy conversion and storage solutions in the energy sector.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Placental Vessel Segmentation and Registration in Fetoscopy: Literature Review and MICCAI FetReg2021 Challenge Findings

arXiv

引用

arXiv 2022年

作者： Bano, Sophia Casella, Alessandro Vasconcelos, Francisco Qayyum, Abdul Benzinou, Abdesslam Mazher, Moona Meriaudeau, Fabrice Lena, Chiara Cintorrino, Ilaria Anita De Paolis, Gaia Romana Biagioli, Jessica Grechishnikova, Daria Jiao, Jing Bai, Bizhe Qiao, Yanyan Bhattarai, Binod Gaire, Rebati Raman Subedi, Ronast Vazquez, Eduard Plotka, Szymon Lisowska, Aneta Sitek, Arkadiusz Attilakos, George Wimalasundera, Ruwan David, Anna L. Paladini, Dario Deprest, Jan De Momi, Elena Mattos, Leonardo S. Moccia, Sara Stoyanov, Danail Department of Computer Science University College London United Kingdom Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Italy The BioRobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Italy Fetal Medicine Unit Elizabeth Garrett Anderson Wing University College London Hospital United Kingdom EGA Institute for Women's Health Faculty of Population Health Sciences University College London United Kingdom Department of Development and Regeneration University Hospital Leuven Belgium Department of Fetal and Perinatal Medicine Istituto Giannina Gaslini Italy ENIB UMR CNRS 6285 LabSTICC 29238 France Department of Computer Engineering and Mathematics University Rovira i Virgili Spain ImViA Laboratory University of Bourgogne Franche-Comté France Physics Department Lomonosov Moscow State University Russia Fudan University China Medical Computer Vision and Robotics Group Department of Mathematical and Computational Sciences University of Toronto Canada Co. Ltd China NepAL Applied Mathematics and Informatics Institute for Research Nepal Redev Technology United Kingdom Sano Center for Computational Medicine Poland Quantitative Healthcare Analysis Group Informatics Institute University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. The challenge provided an opportunity for creating generalized solutions for fetoscopic scene understanding and mosaicking. In this paper,

关键词： Pixels

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：