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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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Correction to: Pharmacological Rescue With SR8278, A Circadian Nuclear Receptor REV-ERBα Antagonist As A Therapy for Mood Disorders In Parkinson's Disease

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Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 2022年第4期19卷 1432页

作者： Jeongah Kim Inah Park Sangwon Jang Mijung Choi Doyeon Kim Woong Sun Youngshik Choe Ji-Woong Choi Cheil Moon Sung Ho Park Han Kyoung Choe Kyungjin Kim Department of Brain Sciences Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Korea. Department of Anatomy College of Medicine Korea University Seoul Korea. Program in Neurosciences & Mental Health The Hospital for Sick Children Toronto ON Canada. Korea Brain Research Institute (KBRI) Daegu Korea. Department of Electrical Engineering and Computer Science DGIST Daegu Korea. Convergence Research Advanced Centre for Olfaction DGIST Daegu Korea. Department of Biological Sciences Ulsan National Institute of Science and Technology (UNIST) Ulsan 44919 Korea. Department of Brain Sciences Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Korea. kyungjin@dgist.ac.kr. Convergence Research Advanced Centre for Olfaction DGIST Daegu Korea. kyungjin@dgist.ac.kr.

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Optimization of culture and analysis methods for enhancing long-term Brugia malayi survival, molting and motility in vitro

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Parasitology Open 2018年 4卷 e3-e3页

作者： I. M. Lu T. Kassis A. M. Rogers A. Schudel J. Weil C. C. Evans A. R. Moorhead S. N. Thomas J. B. Dixon Parker H. Petit Institute for Bioengineering & Bioscience Georgia Institute of Technology Atlanta GA 30332 USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA School of Materials Science and Engineering Georgia Institute of Technology Atlanta GA 30332 USA School of Chemical & Biomolecular Engineering Georgia Institute of Technology Atlanta GA 30332 USA Department of Infectious Diseases College of Veterinary Medicine University of Georgia Athens GA 30602 USA Winship Cancer Institute Emory University Atlanta GA 30322 USA Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Atlanta GA 30332 USA

Lymphatic filariasis is a neglected tropical disease caused by roundworm parasites such as Brugia malayi that spread via a mosquito vector. In vitro culture of these parasites provides controlled conditions to understand parasite biology and provides a cheaper way to screen potential micro- and macrofilaricides. Published studies have used a wide array of approaches and metrics regarding in vitro cultures of B. malayi; as a result, drawing comparisons and identifying the reasons why inability to reproduce outcomes are difficult. This study sought to determine conditions that ensure reproducible outcomes and used evaluation metrics that are easily measured and can be automated to ensure objectivity. We found culturing B. malayi third-stage larvae (L3) in endothelial basal media supplemented with 20% fetal bovine serum and 75 µm ascorbic acid in a temperature- and humidity-controlled incubator produced better survival and molting rates as well as longer and more motile parasites than previously reported. The benefit of ascorbic acid seemed to be unique to L3 parasites, as the addition of ascorbic acid to adult parasites had no significant impact on survival or motility. The methods reported in this study will help in designing experiments for both parasite behaviour studies and drug screening applications for disease eradication.

关键词： Ascorbic acid Brugia malayi long-term culture lymphatic filariasis

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Corrigendum: 'electrical and optical properties of Zr doped β-Ga2O3 single crystals' [Appl. Phys. Express 12, 085502 (2019)]

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Applied Physics Express 2019年第10期12卷

作者： Muad Saleh Arkka Bhattacharyya Joel B. Varley Santosh Swain Jani Jesenovec Sriram Krishnamoorthy Kelvin Lynn Materials Science & Engineering Program Washington State University Pullman WA 99164 United States of America Center for Materials Research Washington State University Pullman WA 99164 United States of America Electrical and Computer Engineering The University of Utah Salt Lake City UT 84112 United States of America Lawrence Livermore National Laboratory Livermore CA 94550 United States of America School of Mechanical and Materials Engineering Washington State University Pullman WA 99164 United States of America Department of Physics Washington State University Pullman WA 99164 United States of America

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Large language models surpass human experts in predicting neuroscience results

arXiv

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arXiv 2024年

作者： Luo, Xiaoliang Rechardt, Akilles Sun, Guangzhi Nejad, Kevin K. Yáñez, Felipe Yilmaz, Bati Lee, Kangjoo Cohen, Alexandra O. Borghesani, Valentina Pashkov, Anton Marinazzo, Daniele Nicholas, Jonathan Salatiello, Alessandro Sucholutsky, Ilia Minervini, Pasquale Razavi, Sepehr Rocca, Roberta Yusifov, Elkhan Okalova, Tereza Gu, Nianlong Ferianc, Martin Khona, Mikail Patil, Kaustubh R. Lee, Pui-Shee Mata, Rui Myers, Nicholas E. Bizley, Jennifer K. Musslick, Sebastian Bilgin, Isil Poyraz Niso, Guiomar Ales, Justin M. Gaebler, Michael Murty, N. Apurva Ratan Loued-Khenissi, Leyla Behler, Anna Hall, Chloe M. Dafflon, Jessica Bao, Sherry Dongqi Love, Bradley C. Department of Experimental Psychology University College London London United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Physiology Anatomy & Genetics University of Oxford Oxford United Kingdom Max Planck Institute for Neurobiology of Behavior – caesar Bonn Germany Bilkent University Ankara Turkey Department of Computer Science University of Bristol Bristol United Kingdom The Alan Turing Institute London United Kingdom Department of Psychiatry Yale University New Haven United States Psychology Emory University Atlanta United States Faculty of Psychology and Educational Sciences Université de Genève Genève Switzerland Neurosurgery Novosibirsk State Medical University Novosibirsk Russia Federal Center of Neurosurgery FSBI Novosibirsk Russia Department of Data Collection and Processing Systems Novosibirsk State Technical University Novosibirsk Russia Department of Data Analysis Ghent University Ghent Belgium Psychology New York University New York United States Department of Cognitive Neurology University of Tübingen Tübingen Germany Computer Science Princeton University Princeton United States ILCC University of Edinburgh Edinburgh United Kingdom Philosophy Psychology and Language Sciences The University of Edinburgh Edinburgh United Kingdom Department of Culture Cognition and Computation Aarhus University Aarhus Denmark Department of Molecular Life Sciences University of Zurich Zurich Switzerland Bioengineering University of Pennsylvania Philadelphia United States Linguistic Research Infrastructure University of Zurich Zurich Switzerland Electronic and Electrical Engineering University College London London United Kingdom Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Institute of Neuroscience and Medicine INM-7: Brain and Behaviour Research Centre Jülich Jülich Germany Medical Faculty Institute of Systems Neuroscience Heinrich Hein

Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts. To evaluate this possibility, we created BrainBench, a forward-looking benchmark for predicting neuroscience results. We find that LLMs surpass experts in predicting experimental outcomes. BrainGPT, an LLM we tuned on the neuroscience literature, performed better yet. Like human experts, when LLMs were confident in their predictions, they were more likely to be correct, which presages a future where humans and LLMs team together to make discoveries. Our approach is not neuroscience-specific and is transferable to other knowledge-intensive endeavors. © 2024, CC BY.

关键词： Prediction models

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Author Correction: Cortical astrocytes modulate dominance behavior in male mice by regulating synaptic excitatory and inhibitory balance

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Nature neuroscience 2023年第12期26卷 2250页

作者： Kyungchul Noh Woo-Hyun Cho Byung Hun Lee Dong Wook Kim Yoo Sung Kim Keebum Park Minkyu Hwang Ellane Barcelon Yoon Kyung Cho C Justin Lee Bo-Eun Yoon Se-Young Choi Hye Yoon Park Sang Beom Jun Sung Joong Lee Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. Department of Physics and Astronomy Seoul National University Seoul Republic of Korea. Department of Molecular Biology Dankook University Cheonan Republic of Korea. Department of Electronic and Electrical Engineering Ewha Womans University Seoul Republic of Korea. Center for Cognition and Sociality Institute for Basic Science (IBS) Daejeon Republic of Korea. Department of Electrical and Computer Engineering University of Minnesota Minneapolis MN USA. Graduate Program in Smart Factory Ewha Womans University Seoul Republic of Korea. Department of Brain & Cognitive Sciences Ewha Womans University Seoul Republic of Korea. Department of Physiology and Neuroscience Dental Research Institute Seoul National University School of Dentistry Seoul Republic of Korea. sjlee87@snu.ac.kr.

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Ratiometric Analysis of In Vivo Optical Coherence Tomography Retinal Layer Thicknesses for Detection of Changes in Alzheimer's Disease

Translational biophotonics

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Translational biophotonics 2023年第3-4期5卷 e202300003页

作者： Shonit N Sharma Jordan W Marsh Michael S Tsipursky Stephen A Boppart Carle Illinois College of Medicine University of Illinois Urbana-Champaign Urbana IL USA. Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana IL USA. Department of Bioengineering University of Illinois Urbana-Champaign Urbana IL USA. Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign Urbana IL USA. NIH Center for Label-free Imaging and Multiscale Biophotonics (CLIMB) University of Illinois Urbana-Champaign Urbana IL USA. Interdisciplinary Health Sciences Institute University of Illinois Urbana-Champaign Urbana IL USA.

We analyzed ophthalmic retinal optical coherence tomography (OCT) images from patients with Alzheimer's disease (AD) to identify retinal layer thickness and ratio changes that may serve as image-based biomarkers for the disease. One three-dimensional volume before and one after diagnosis for each of 48 patients were segmented to identify retinal layer and total retinal thicknesses. Between before- and after-diagnosis retinal OCT images, there were significant thickness changes in six of ten (60%) retinal layers across all 48 patients. Through a comparison with age-matched healthy subjects, the significant changes were attributed to AD only (NFL and PR2 layers), age only (GCL, IPL, and RPE layers), or both AD and age (OPL layer). Analyzing ratios of retinal layer thicknesses, 53 of 90 (58.89%) ratios had significant changes. The four independently non-significant layers were assessed to be affected by neither AD nor age (INL layer) or both AD and age (ELM, PR1, and BM layers). The demonstrated image segmentation, measurement, and ratiometric analysis of retinal layers in AD patients may yield a noninvasive OCT image-based retinal biomarker that can be used to detect retinal changes associated with this disease.

关键词： Alzheimer Disease Biomarkers Early Diagnosis Retina Tomography, Optical Coherence

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Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study ( vol 14, 17380, 2024 )

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kember, Allan J. Zia, Hafsa Elangainesan, Praniya Hsieh, Min-En Adijeh, Ramak Li, Ivan Ritchie, Leah Akbarian, Sina Taati, Babak Hobson, Sebastian R. Dolatabadi, Elham Department of Obstetrics and Gynaecology University of Toronto 123 Edward Street Suite 1200 Toronto ON M5G 0A8 Canada Institute of Health Policy Management and Evaluation University of Toronto 155 College Street Suite 425 Toronto ON M5T 3M6 Canada Maternal-Fetal Medicine Division Department of Obstetrics and Gynaecology Mount Sinai Hospital 600 University Avenue Toronto ON M5G 1X5 Canada School of Health Policy and Management York University Stong College Room 314 4700 Keele Street Toronto ON M3J 1P3 Canada Temerty Faculty of Medicine University of Toronto Medical Sciences Building 1 King’s College Circle Toronto ON M5S 1A8 Canada Electrical Engineering and Computer Science National Cheng Kung University No.1 University Road Tainan City 701 Taiwan Regulatory Affairs Program Northeastern University First Canadian Place 100 King Street West Suite 4620 Toronto ON M5X 1E2 Canada Department of Computer Science University of Toronto 40 St. George Street Toronto ON M5S 2E4 Canada KITE Research Institute Toronto Rehabilitation Institute University Health Network 550 University Avenue #12-165 Toronto ON M5G 2A2 Canada Department of Biological Sciences University of Toronto Scarborough 1265 Military Trail Toronto ON M1C 1A4 Canada Vector Institute 661 University Avenue Suite 710 Toronto ON M5G 1M1 Canada

Sleeping on the back after 28 weeks of pregnancy has recently been associated with giving birth to a small-for-gestational-age infant and late stillbirth, but whether a causal relationship exists is currently unknown and difficult to study prospectively. This study was conducted to build a computer vision model that can automatically detect sleeping position in pregnancy under real-world conditions. Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present.

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Author Correction: A high-performance neuroprosthesis for speech decoding and avatar control

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Nature 2024年第8021期631卷 E13页

作者： Sean L Metzger Kaylo T Littlejohn Alexander B Silva David A Moses Margaret P Seaton Ran Wang Maximilian E Dougherty Jessie R Liu Peter Wu Michael A Berger Inga Zhuravleva Adelyn Tu-Chan Karunesh Ganguly Gopala K Anumanchipalli Edward F Chang Department of Neurological Surgery University of California San Francisco San Francisco CA USA. Weill Institute for Neuroscience University of California San Francisco San Francisco CA USA. University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering Berkeley CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Speech Graphics Ltd Edinburgh UK. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Neurological Surgery University of California San Francisco San Francisco CA USA. edward.chang@ucsf.edu. Weill Institute for Neuroscience University of California San Francisco San Francisco CA USA. edward.chang@ucsf.edu. University of California Berkeley-University of California San Francisco Graduate Program in Bioengineering Berkeley CA USA. edward.chang@ucsf.edu.

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Molybdenum Disulfide: Scalable Fabrication of Molybdenum Disulfide Nanostructures and their Assembly (Adv. Mater. 43/2020)

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Advanced Materials 2020年第43期32卷

作者： Yun Huang Kang Yu Huaizhi Li Kai Xu Zexi Liang Debora Walker Paulo Ferreira Peer Fischer Donglei (Emma) Fan Materials Science and Engineering Program Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA The Department of Advanced Electron Microscopy Imaging and Spectroscopy International Iberian Nanotechnology Laboratory Braga 4715‐330 Portugal Electrical and Computer Engineering University of Illinois at Urbana‐Champaign Urbana IL 61801 USA School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA Mechanical Engineering Department and IDMEC Instituto Superior Técnico University of Lisbon Av. Rovisco Pais Lisboa 1049‐001 Portugal Max‐Planck‐Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart 70569 Germany Institute of Physical Chemistry University of Stuttgart Pfaffenwaldring 55 Stuttgart 70569 Germany Walker Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA

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