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Author Correction: Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

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Nature neuroscience 2025年第1期28卷 216页

作者： Boyan Bonev Gonçalo Castelo-Branco Fei Chen Simone Codeluppi M Ryan Corces Jean Fan Myriam Heiman Kenneth Harris Fumitaka Inoue Manolis Kellis Ariel Levine Mo Lotfollahi Chongyuan Luo Kristen R Maynard Mor Nitzan Vijay Ramani Rahul Satijia Lucas Schirmer Yin Shen Na Sun Gilad S Green Fabian Theis Xiao Wang Joshua D Welch Ozgun Gokce Genevieve Konopka Shane Liddelow Evan Macosko Omer Ali Bayraktar Naomi Habib Tomasz J Nowakowski Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany. Physiological Genomics Biomedical Center Ludwig-Maximilians-Universität München Munich Germany. Laboratory of Molecular Neurobiology Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm Sweden. The Broad Institute of MIT and Harvard Cambridge MA USA. Moleculent Stockholm Sweden. Gladstone Institute of Neurological Disease San Francisco CA USA. Gladstone Institute of Data Science and Biotechnology San Francisco CA USA. Department of Neurology University of California San Francisco San Francisco CA USA. Department of Biomedical Engineering Johns Hopkins University Baltimore MD USA. Department of Brain and Cognitive Sciences MIT Cambridge MA USA. The Picower Institute for Learning and Memory MIT Cambridge MA USA. UCL Queen Square Institute of Neurology University College London London UK. Institute for the Advanced Study of Human Biology (WPI-ASHBi) Kyoto University Kyoto Japan. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA USA. Spinal Circuits and Plasticity Unit National Institute of Neurological Disorders and Stroke Bethesda MD USA. Institute of Computational Biology Helmholtz Center Munich - German Research Center for Environmental Health Neuherberg Germany. Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK. Department of Human Genetics University of California Los Angeles Los Angeles CA USA. Lieber Institute for Brain Development Baltimore MD USA. Department of Psychiatry Johns Hopkins School of Medicine Baltimore MD USA. Department of Neuroscience Johns Hopkins School of Medicine Baltimore MD USA. School of Computer Science and Engineering The Hebrew University of Jerusalem Jerusalem Israel. Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem Israel. Faculty of Medicine The Hebrew University of Jerusalem Jerusalem Israel. Department of Biochemis

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3D Ear Shape Matching Using Joint -Entropy

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Journal of computer Science & technology 2015年第3期30卷 565-577页

作者：孙晓鹏李思慧韩枫魏小鹏 Institute of Computer Systems School of Computer and Information Technology Liaoning Normal University Dalian 116029 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China School of Mechanical and Engineering Dalian University of Technology Dalian 116024 China Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian 116622 China

In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree （MST） on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.

关键词： joint a-entropy minimum spanning tree local shape feature ear matching ear recognition

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Tag Correspondence Model for User Tag Suggestion

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Journal of computer Science & technology 2015年第5期30卷 1063-1072页

作者：涂存超刘知远孙茂松 Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory on Intelligent Technology and Systems Tsinghua University Beijing 100084 China National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou 221009 China

Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence Model （TCM） to identify complex correspondences of tags from the rich context of microblog users. The correspondence of a tag is referred to as a unique element in the context which is semantically correlated with this tag. In TCM, we divide the context of a microblog user into various sources （such as short messages, user profile, and neighbors）. With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users＇ context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.

关键词： microblog user tag suggestion tag correspondence model probabilistic graphical model context

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Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

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Nature metabolism 2025年 2025 May 13页

作者： Anne Loft Margo P Emont Ada Weinstock Adeline Divoux Adhideb Ghosh Allon Wagner Ann V Hertzel Babukrishna Maniyadath Bart Deplancke Boxiang Liu Camilla Scheele Carey Lumeng Changhai Ding Chenkai Ma Christian Wolfrum Clarissa Strieder-Barboza Congru Li Danh D Truong David A Bernlohr Elisabet Stener-Victorin Erin E Kershaw Esti Yeger-Lotem Farnaz Shamsi Hannah X Hui Henrique Camara Jiawei Zhong Joanna Kalucka Joseph A Ludwig Julie A Semon Jutta Jalkanen Katie L Whytock Kyle D Dumont Lauren M Sparks Lindsey A Muir Lingzhao Fang Lucas Massier Luis R Saraiva Marc D Beyer Marc G Jeschke Marcelo A Mori Mariana Boroni Martin J Walsh Mary-Elizabeth Patti Matthew D Lynes Matthias Blüher Mikael Rydén Natnael Hamda Nicole L Solimini Niklas Mejhert Peng Gao Rana K Gupta Rinki Murphy Saeed Pirouzpanah Silvia Corvera Su'an Tang Swapan K Das Søren F Schmidt Tao Zhang Theodore M Nelson Timothy E O'Sullivan Vissarion Efthymiou Wenjing Wang Yihan Tong Yu-Hua Tseng Susanne Mandrup Evan D Rosen Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark. anlo@bmb.sdu.dk. Section of Endocrinology Diabetes and Metabolism University of Chicago Chicago IL USA. margo@uchicago.edu. Department of Medicine Section of Genetic Medicine University of Chicago Chicago IL USA. Translational Research Institute AdventHealth Orlando FL USA. Department of Health Sciences and Technology Institute of Food Nutrition and Health ETH Zurich Schwerzenbach Switzerland. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Molecular and Cell Biology University of California Berkeley Berkeley CA USA. Center for Computational Biology University of California Berkeley Berkeley CA USA. Department of Biochemistry Molecular Biology and Biophysics Institute on the Biology of Aging and Metabolism The University of Minnesota-Twin Cities Minneapolis MN USA. Center for Functional Genomics and Tissue Plasticity (ATLAS) Department of Biochemistry and Molecular Biology University of Southern Denmark (SDU) Odense Denmark. Laboratory of Systems Biology and Genetics Institute of Bioengineering School of Life Sciences École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland. Department of Pharmacy and Pharmaceutical Sciences Faculty of Science National University of Singapore Singapore Singapore. Department of Biomedical Informatics Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Genome Institute of Singapore (GIS) Agency for Science Technology and Research (A*STAR) Singapore Singapore. Precision Medicine Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Cardiovascular-Metabolic Disease Translational Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapor

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The Human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data.

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Interactive Dissection of Digital Organs Based on Metaballs 16

Interactive Dissection of Digital Organs Based on Metaballs

引用

Proceedings of the 33rd computer Graphics International

作者： Junjun Pan Shizeng Yan Hong Qin State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Department of Computer Science Stony Brook University (SUNY Stony Brook) New York USA

ISBN: (纸本)9781450341233

This paper describes an interactive dissection approach based on metaballs for digital organs in virtual surgery. For organ's geometry, we employ a novel hybrid model comprising both inner metaballs and high-resolution surface mesh with texture. Using metaballs, the organ interior is geometrically simplified via a set of overlapping spheres with different radii. For physical representation, we present a hybrid framework to interlink metaballs with moving least squares (MLS) based meshfree method. MLS enables the direct and rapid transition from metaballs to local nodal formulations in physical modeling. For soft tissue dissection, the nature of our MLS-driven meshfree method also facilitates adaptive topology modification and cutting surface reconstruction. As nodal points only exist temporarily in localized regions adjacent to the cutting path, our method could generate accurate cutting surface without sacrificing real-time computational efficiency.

关键词： Virtual Surgery Meshfree Metaballs Dissection

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Skyrmion dynamics in a frustrated ferromagnetic film and current-induced helicity locking-unlocking transition

arXiv

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arXiv 2017年

作者： Zhang, Xichao Xia, Jing Zhou, Yan Liu, Xiaoxi Zhang, Han Ezawa, Motohiko School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Electrical and Computer Engineering Shinshu University 4-17-1 Wakasato Nagano380-8553 Japan SZU-NUS Collaborative Innovation Center for Optoelectronic Science and Technology Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Optoelectronic Engineering Shenzhen University Shenzhen518060 China Department of Applied Physics University of Tokyo 7-3-1 Hongo Tokyo113-8656 Japan

The helicity-orbital coupling is an intriguing feature of magnetic skyrmions in frustrated magnets. Here, we explore the skyrmion dynamics in a frustrated magnet based on the J1-J2-J3 classical Heisenberg model explicitly by including the dipole-dipole interaction. The skyrmion energy acquires a helicity dependence due to the dipole-dipole interaction, resulting in the current-induced translational motion with a fixed helicity. The lowest energy states are the degenerate Bloch-type states, which can be used for building the binary memory. By increasing the driving current, the helicity locking-unlocking transition occurs, where the translational motion changes to the rotational motion. Furthermore, we demonstrate that two skyrmions can spontaneously form a bound state. The separation of the bound state forced by a driving current is also studied. In addition, we show the annihilation of a pair of skyrmion and antiskyrmion. Our results reveal the distinctive frustrated skyrmions may enable viable applications in topological magnetism. Copyright © 2017, The Authors. All rights reserved.

关键词： Dipole moment

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CirKet: A Performance Efficient Hybrid Switching Mechanism for NoC Architectures

CirKet: A Performance Efficient Hybrid Switching Mechanism f...

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Euromicro Symposium on Digital System Design

作者： Mohamad FallahRad Ahmad Patooghy Hesamedin Ziaeeziabari Ebadollah Taheri Zahn Innovation Center City University of New York New York USA Dependable Systems and Architectures Laboratory School of Computer Engineering Iran University of Science and Technology Tehran Iran

In this paper, we propose and evaluate a hybrid switching mechanism for Network-on-Chips (NoCs). We propose the use of pseudo circuit-switching along with packet-switching in NoC routers. To do this, packets traversing NoC channels are categorized into high and low priority packets which are routed using pseudo circuit and packet switching respectively. Each output port of NoC routers are equipped with a one-bit flag register indicating that the traversing packet is either of low or high priority packet. Using pseudo circuit switching, high priority packets reserve the intermediate routers till the tail flit passes the router. The proposed switching mechanism offers its highest efficiency for applications in which the traffic is dominated by streams. We have used Booksim2 which is a cycle accurate NoC simulator to evaluate the proposed switching technique. Results show that the proposed switching mechanism 1) offers at least 10% improvement in overall flit latency in all tested scenarios as compared with a packet-switched NoC, 2) reduces the power dissipation in the router logic by at least 4%, and 3) imposes a negligible area overhead to NoC router circuitry.

关键词： Switching circuits Switches Throughput Packet switching Ports (computers) Bandwidth

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Free-space creation of ultralong anti-diffracting light beam with multiple energy oscillations adjusted using ‘optical pen’

arXiv

引用

arXiv 2017年

作者： Weng, Xiaoyu Song, Qiang Li, Xiaoming Gao, Xiumin Guo, Hanming Qu, Junle Zhuang, Songlin Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Optoelectronic Engineering Shenzhen University Shenzhen518060 China Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China Optics Department Telecom Bretagne Institut Mines-Telecom CS83818 Brest Cedex 0329238 France

A light beam propagating with an infinite anti-diffracting distance requires infinite power to preserve its shape. However, the fundamental barrier of finite power in free space has made the problem of diffraction insurmountable over the past few decades. Here, to overcome this limitation, we report an approach that employs the multiple energy oscillation mechanism, thereby permitting the creation of a light beam with an ultralong anti-diffracting distance in free space. The anti-diffracting distance is no longer restricted by finite power in free space but instead depends on the number of energy oscillations. This unprecedented propagation behavior is attributed to a new understanding of non-diffractive light beam: when an anti-diffracting light beam completely discharges its energy, it cannot recharge again. A versatile ‘optical pen’ is therefore developed to manipulate the number, amplitude, position and phase of energy oscillations for an arbitrary numerical aperture of a focusing lens so that energy recharge can occur in free space and multiple energy oscillations can be realized. A light beam with a tunable number of energy oscillations is eventually generated in free space and propagates along a wavy trajectory. This work will enable extending non-diffractive light beams to an expanded realm and facilitate extensive developments in optics and other research fields, such as electronics and acoustics. Copyright © 2017, The Authors. All rights reserved.

关键词： Lenses

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

arXiv

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arXiv 2018年

作者： Binder, Alexander Bockmayr, Michael Hägele, Miriam Wienert, Stephan Heim, Daniel Hellweg, Katharina Stenzinger, Albrecht Parlow, Laura Budczies, Jan Goeppert, Benjamin Treue, Denise Kotani, Manato Ishii, Masaru Dietel, Manfred Hocke, Andreas Denkert, Carsten Müller, Klaus-Robert Klauschen, Frederick Machine-Learning Group Department of Software Engineering and Theoretical Computer Science Technical University of Berlin Marchstr. 23 Berlin10587 Germany Systems Pathology Lab Institute of Pathology Charité University Hospital Charitéplatz 1 Berlin10117 Germany Department of Internal Medicine Infectious Diseases and Pulmology Charitéplatz 1 Berlin10117 Germany Institute of Pathology University of Heidelberg Im Neuenheimer Feld 224 Heidelberg Germany Laboratory of Cellular Dynamics Immunology Frontier Research Center Osaka University 2-2 Yamada-oka Suita Osaka565-0871 Japan ISTD Pillar Singapore University of Technology and Design 8 Somapah Road Singapore487372 Singapore Charité Berlin Charitéplatz 1 Berlin10117 Germany Department of Brain and Cognitive Engineering Korea University 145 Anam-ro Seongbukgu Seoul136-713 Korea Republic of Max-Planck-Institute for Informatics Saarbrücken Germany Department of Pediatric Hematology and Oncology University Medical Center Hamburg-Eppendorf Hamburg Germany

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment. Copyright © 2018, The Authors. All rights reserved.

关键词： Fluorescence microscopy

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