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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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WaveNano:a signal-level nanopore base-caller via simultaneous prediction of nucleotide labels and move labels through bi-directional WaveNets

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Frontiers of Electrical and Electronic engineering in China 2018年第4期6卷 359-368页

作者： Sheng Wang Zhen Li Yizhou YU Xin Gao Computational Bioscience Research Center (CBRC) King Abdullah University of Science and Technology (KAUST)Thuwa23955-6900Kingdom of Saudi Arabia Department of Computer Science University of Hong KongHong Kong SAR 999077China School of Science and Engineering Shenzhen Research Institute of Big DataThe Chinese University of Hong Kong Shenzhen 518172China

Background The Oxford MinION nanopore sequencer is the recently appealing third-generation genome sequencing device that is portable and no larger than a cellphone. Despite the benefits of MinION to sequence ultra-long reads in real-time, the high error rate of the existing base-calling methods, especially indels (insertions and deletions), prevents its use in a variety of *** In this paper, we show that such indel errors are largely due to the segmentation process on the input electrical current signal from MinION. All existing methods conduct segmentation and nucleotide label prediction in a sequential manner, in which the errors accumulated in the first step will irreversibly influence the final base-calling. We further show that the indel issue can be significantly reduced via accurate labeling of nucleotide and move labels directly from the raw signal, which can then be efficiently learned by a bi-directionalWaveNet model simultaneously through feature sharing. Our bi-directional WaveNet model with residual blocks and skip connections is able to capture the extremely long dependency in the raw signal. Taking the predicted move as the segmentation guidance, we employ the Viterbi decoding to obtain the final base-calling results from the smoothed nucleotide probability *** Our proposed base-caller, WaveNano, achieves good performance on real MinION sequencing data from Lambda *** The signal-level nanopore base-callerWaveNano can obtain higher base-calling accuracy, and generate fewer insertions/deletions in the base-called sequences.

关键词： nanopore sequencing bi-directional WaveNets base-calling third generation sequencing deep learning

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Adaptive Structure-Constrained Robust Latent Low-Rank Coding for Image Recovery

Adaptive Structure-Constrained Robust Latent Low-Rank Coding...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Lei Wang Sheng Li Yang Wang Zheng Zhang Zhengjun Zha Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Georgia Athens GA Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information Science and Technology University of Science and Technology of China Hefei China

In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accurately, AS-LRC seamlessly integrates an adaptive weighting based block-diagonal structure-constrained low-rank representation and the group sparse salient feature extraction into a unified framework. Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part. To enforce the block-diagonal structures adaptive to different real datasets for the low-rank recovery, AS-LRC clearly computes an auto-weighting matrix based on the locality-adaptive features and multiplies by the low-rank coefficients for direct minimization at the same time. This encourages the codes to be block-diagonal and can avoid the tricky issue of choosing optimal neighborhood size or kernel width for the weight assignment, suffered in most local geometrical structures-preserving low-rank coding methods. In addition, our AS-LRC selects the L2, 1-norm on the projection for extracting group sparse features rather than learning low-rank features by Nuclear-norm regularization, which can make learnt features robust to noise and outliers in samples, and can also make the feature coding process efficient. Extensive visualizations and numerical results demonstrate the effectiveness of our AS-LRC for image representation and recovery.

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Genomic basis for RNA alterations in cancer (vol 578, pg 129, 2020)

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NATURE 2023年第7948期614卷 E37-E37页

作者： Calabrese, Claudia Davidson, Natalie R. Demircioglu, Deniz Fonseca, Nuno A. He, Yao Lehmann, Kjong-Van Liu, Fenglin Shiraishi, Yuichi Soulette, Cameron M. Urban, Lara Greger, Liliana Li, Siliang Liu, Dongbing Perry, Marc D. Xiang, Qian Zhang, Fan Zhang, Junjun Bailey, Peter Erkek, Serap Hoadley, Katherine A. Hou, Yong Huska, Matthew R. Kilpinen, Helena Korbel, Jan O. Marin, Maximillian G. Markowski, Julia Nandi, Tannistha Pan-Hammarstrom, Qiang Pedamallu, Chandra Sekhar Siebert, Reiner Stark, Stefan G. Su, Hong Tan, Patrick Waszak, Sebastian M. Yung, Christina Zhu, Shida Awadalla, Philip Creighton, Chad J. Meyerson, Matthew Ouellette, B. F. Francis Wu, Kui Yang, Huanming Brazma, Alvis Brooks, Angela N. Goke, Jonathan Ratsch, Gunnar Schwarz, Roland F. Stegle, Oliver Zhang, Zemin European Molecular Biology Laboratory European Bioinformatics Institute Hinxton UK European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany CIBIO/InBIO - Research Center in Biodiversity and Genetic Resources Universidade do Porto Vairão Portugal Berlin Institute for Medical Systems Biology Max Delbruck Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) partner site Berlin Germany German Cancer Research Center (DKFZ) Heidelberg Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine Berlin Germany German Cancer Consortium (DKTK) Partner site Berlin Berlin Germany European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Division of Computational Genomics and Systems Genetics German Cancer Research Center (DKFZ) Heidelberg Germany ETH Zurich Zurich Switzerland Memorial Sloan Kettering Cancer Center New York NY USA Weill Cornell Medical College New York NY USA SIB Swiss Institute of Bioinformatics Lausanne Switzerland University Hospital Zurich Zurich Switzerland Computational Biology Center Memorial Sloan Kettering Cancer Center New York NY USA Department of Biology ETH Zurich Zürich Switzerland Department of Computer Science ETH Zurich Zurich Switzerland Korea University Seoul South Korea Computational and Systems Biology Program Memorial Sloan Kettering Cancer Center New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine New York NY USA National University of Singapore Singapore Singapore Genome Institute of Singapore Singapore Singapore Computational and Systems Biology Genome Institute of Singap

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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

arXiv

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arXiv 2020年

作者： Sekuboyina, Anjany Kumar Husseini, Malek E. Bayat, Amirhossein Löffler, Maximilian Liebl, Hans Li, Hongwei Tetteh, Giles Kukačka, Jan Payer, Christian Štern, Darko Urschler, Martin Chen, Maodong Cheng, Dalong Lessmann, Nikolas Hu, Yujin Wang, Tianfu Yang, Dong Xu, Daguang Ambellan, Felix Amiranashvili, Tamaz Ehlke, Moritz Lamecker, Hans Lehnert, Sebastian Lirio, Marilia de Olaguer, Nicolás Pérez Ramm, Heiko Sahu, Manish Tack, Alexander Zachow, Stefan Jiang, Tao Ma, Xinjun Angerman, Christoph Wang, Xin Brown, Kevin Kirszenberg, Alexandre Puybareau, Élodie Chen, Di Bai, Yiwei Rapazzo, Brandon H. Yeah, Timyoas Zhang, Amber Xu, Shangliang Hou, Feng He, Zhiqiang Zeng, Chan Xiangshang, Zheng Liming, Xu Netherton, Tucker J. Mumme, Raymond P. Court, Laurence E. Huang, Zixun He, Chenhang Wang, Li-Wen Ling, Sai Ho Huynh, Lê Duy Boutry, Nicolas Jakubicek, Roman Chmelik, Jiri Mulay, Supriti Sivaprakasam, Mohanasankar Paetzold, Johannes C. Shit, Suprosanna Ezhov, Ivan Wiestler, Benedikt Glocker, Ben Valentinitsch, Alexander Rempfler, Markus Menze, Björn H. Kirschke, Jan S. Department of Informatics Technical University of Munich Germany Munich School of BioEngineering Technical University of Munich Germany Department of Neuroradiology Klinikum Rechts der Isar Germany Department for Quantitative Biomedicine University of Zurich Switzerland Friedrich Miescher Institute for Biomedical Engineering Switzerland Institute of Biological and Medical Imaging Helmholtz Zentrum München Germany Department of Computing Imperial College London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Austria Gottfried Schatz Research Center: Biophysics Medical University of Graz Austria School of Computer Science The University of Auckland New Zealand Computer Vision Group iFLYTEK Research South China China Department of Radiology and Nuclear Medicine Radboud University Medical Center Nijmegen Netherlands Shenzhen Research Institute of Big Data China School of Biomedical Engineering Health Science Center Shenzhen University China NVIDIA Corporation United States Zuse Institute Berlin Germany 1000shapes GmbH Berlin Germany Damo Academy Alibaba Group China Department of Mathematics University of Innsbruck Austria Department of Electronic Engineering Fudan University China New York University United States France Deep Reasoning AI Inc United States Technical University of Munich Germany East China Normal University China Chinese Academy of Sciences China Lenovo Group China Ping An Technologies China College of Computer Science and Technology Zhejiang University China Real Doctor AI Research Centre Zhejiang University China The University of Texas MD Anderson Cancer Center United States Department of Electronic and Information Engineering The Hong Kong Polytechnic University China Department of Computing The Hong Kong Polytechnic University China The School of Biomedical Engineering University of Technology Sydney Australia Department of Biomedical Engineering Brno University of Technology Czech Republic India

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://***/nqjyw/, https://***/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://***/anjany/verse. © 2020, CC BY-NC-ND.

关键词： computerized tomography

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The repertoire of mutational signatures in human cancer (vol 578, pg 94, 2020)

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NATURE 2023年第7948期614卷 E41-E41页

作者： Alexandrov, Ludmil B. Kim, Jaegil Haradhvala, Nicholas J. Huang, Mi Ni Ng, Alvin Wei Tian Wu, Yang Boot, Arnoud Covington, Kyle R. Gordenin, Dmitry A. Bergstrom, Erik N. Islam, S. M. Ashiqul Lopez-bigas, Nuria Klimczak, Leszek J. McPherson, John R. Morganella, Sandro Sabarinathan, Radhakrishnan Wheeler, David A. Mustonen, Ville Getz, Gad Rozen, Steven G. Stratton, Michael R. Department of Cellular and Molecular Medicine Department of Bioengineering Moores Cancer Center University of California San Diego CA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Cellular and Molecular Medicine and Department of Bioengineering University of California San Diego La Jolla CA USA UC San Diego Moores Cancer Center San Diego CA USA University of California San Diego San Diego CA USA Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Massachusetts General Hospital Boston MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama Japan Computational Biology Program Ontario Institute for Cancer Research Toronto ON Canada Programme in Cancer & Stem Cell Biology Duke-NUS Medical School Singapore Singapore Centre for Computational Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore Programme in Cancer and Stem Cell Biology Centre for Computational Biology Duke-NUS Medical School Singapore Singapore SingHealth Duke-NUS Institute of Precision Medicine National Heart Centre Singapore Singapore Singapore Cancer Science Institute of Singapore National University of Singapore Singapore Singapore Institute of Molecular and Cell Biology Singapore Singapore Laboratory of Cancer Epigenome Division of Medical Science National Cancer Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Human Genome Sequencing Center Baylor College of Medicine Houston TX USA Dan L. Duncan Cancer Center Baylor College of Medicine Houston TX USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Genome Integrity and Structural Biology Laboratory National Ins

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Detecting Points of Interest in a City from Taxi GPS with Adaptive DBSCAN

Detecting Points of Interest in a City from Taxi GPS with Ad...

引用

ICT International Student Project Conference (ICT-ISPC)

作者： Uriwan Angkhawey Veera Muangsin Department of Computer Engineering Chulalongkorn University Bangkok Thailand CU Big Data Analytics and IoT Center (CUBIC) Faculty of Engineering Chulalongkorn University Bangkok Thailand

Points of Interest (POIs) are popular places that create activities and transportation demands in a city. As cities constantly evolve, keeping an up-to-date map of POIs is difficult. However, by observing digital footprints of people, including taxi pick-up and drop-off locations, we can detect hotspots of activities that indicate POI locations by using techniques such as point clustering. DBSCAN is a popular density-based clustering algorithm for geographic data points. However, the effectiveness of the algorithm relies on determining appropriate parameters that match the distribution pattern of data points. That is hard because pick-up and drop-off locations are distributed differently in different areas. This paper proposes a method to automatically determine the parameters of DBSCAN according to point distribution in each dataset. The algorithm has been applied to pick-up and drop-off locations of taxis in Bangkok. The experimental results show that the proposed method can effectively discovered POIs in areas with different distribution patterns.

关键词： Clustering algorithms Public transportation Global Positioning System Partitioning algorithms Urban areas Histograms

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Online Emerging Topic Detection on Twitter Using Random Forest with Stock Indicator Features

Online Emerging Topic Detection on Twitter Using Random Fore...

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International Joint Conference on computer Science and Software engineering (JCSSE)

作者： Ekapop Verasakulvong Peerapon Vateekul Apivadee Piyatumrong Chatchawal Sangkeettrakarn Department of Computer Engineering Chulalongkorn University Big Data Analytics and IoT Center (CUBIC) Faculty of Engineering Chulalongkorn University Bangkok Thailand National Electronics and Computer Technology Center (NECTEC) a member of NSTDA Pathumthani Thailand

Social media is one of the most impactful and fastest communication methods. By monitoring Twitter streams, we are able to detect emerging topics and understand events around the world. There are some prior attempts that aim to online detect topics on Twitter. However, they can only detect bursty topics by using user-defined keywords a long with simple rules. In this paper, we propose an algorithm to detect emerging topics on Twitter streams. To detect emerging topics, a clustering technique has been applied to aggregate a set of keywords. Since an emerging topic occurs continuously, the emerging topics are merged with stateful technique to accumulate topics from different time intervals. To detect both high signal topics and small-medium signal topics, we use statistical features based on average, acceleration, and z-score. Moreover, we propose to include the stock indicator features: Relative Strength Index (RSI) and Stochastic Oscillator (STOCH). They are common features in trend (oversold and overbought) detection in stock analysis which is similar to our topic detection in twitter. To capture any event patterns, Random Forest (RF) has been proposed as a classifier to detect emerging keywords by utilizing the stated above five features. To evaluate the performance, we created and published a corpus by collecting Twitter data for 10 days with over 80 million tweets and then labeling possible topics in total161 events along with related keywords. The experiment was conducted on our collected data. The F1-results show that our model outperforms all baselines: TwitterMonitor, SigniTrend, and TopicSketch, in terms of detected keywords and topics.

关键词： Feature extraction Twitter Training Indexes Microsoft Windows Forestry computer science

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The Past, Present, and Future of the Brain Imaging data Structure (BIDS)

arXiv

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arXiv 2023年

作者： Poldrack, Russell A. Markiewicz, Christopher J. Appelhoff, Stefan Ashar, Yoni K. Auer, Tibor Baillet, Sylvain Bansal, Shashank Beltrachini, Leandro Bénar, Christian G. Bertazzoli, Giacomo Bhogawar, Suyash Blair, Ross W. Bortoletto, Marta Boudreau, Mathieu Brooks, Teon L. Calhoun, Vince D. Castelli, Filippo Maria Clement, Patricia Cohen, Alexander L. Cohen-Adad, Julien D'Ambrosio, Sasha de Hollander, Gilles de la Iglesia-Vayá, María de la Vega, Alejandro Delorme, Arnaud Devinsky, Orrin Draschkow, Dejan Duff, Eugene Paul DuPre, Elizabeth Earl, Eric Esteban, Oscar Feingold, Franklin W. Flandin, Guillaume Galassi, Anthony Gallitto, Giuseppe Ganz, Melanie Gau, Rémi Gholam, James Ghosh, Satrajit S. Giacomel, Alessio Gillman, Ashley G. Gleeson, Padraig Gramfort, Alexandre Guay, Samuel Guidali, Giacomo Halchenko, Yaroslav O. Handwerker, Daniel A. Hardcastle, Nell Herholz, Peer Hermes, Dora Honey, Christopher J. Innis, Robert B. Ioanas, Horea-Ioan Jahn, Andrew Karakuzu, Agah Keator, David B. Kiar, Gregory Kincses, Balint Laird, Angela R. Lau, Jonathan C. Lazari, Alberto Legarreta, Jon Haitz Li, Adam Li, Xiangrui Love, Bradley C. Lu, Hanzhang Maumet, Camille Mazzamuto, Giacomo Meisler, Steven L. Mikkelsen, Mark Mutsaerts, Henk Nichols, Thomas E. Nikolaidis, Aki Nilsonne, Gustav Niso, Guiomar Norgaard, Martin Okell, Thomas W. Oostenveld, Robert Ort, Eduard Park, Patrick J. Pawlik, Mateusz Pernet, Cyril R. Pestilli, Franco Petr, Jan Phillips, Christophe Poline, Jean-Baptiste Pollonini, Luca Raamana, Pradeep Reddy Ritter, Petra Rizzo, Gaia Robbins, Kay A. Rockhill, Alexander P. Rogers, Christine Rokem, Ariel Rorden, Chris Routier, Alexandre Saborit-Torres, Jose Manuel Salo, Taylor Schirner, Michael Smith, Robert E. Spisak, Tamas Sprenger, Julia Swann, Nicole C. Szinte, Martin Takerkart, Sylvain Thirion, Bertrand Thomas, Adam G. Torabian, Sajjad Varoquaux, Gael Voytek, Bradley Welzel, Julius Wilson, Martin Yarkoni, Tal Gorgolewski, Krzysztof J. Department of Psychology Stanford University StanfordCA United States Max Planck Institute for Human Development Berlin Germany University of Colorado Anschutz Medical Campus AuroraCO United States School of Psychology University of Surrey Guildford United Kingdom McConnell Brain Imaging Centre Montréal Neurological Institute McGill University Montréal Canada Department of Bioengineering University of California San Diego La Jolla CA United States School of Physics Astronomy Cardiff University Wales United Kingdom Institut de Neurosciences des Systèmes Marseille France Neurophysiology Lab IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Center for Mind/Brain Sciences - CIMeC University of Trento TN Rovereto Italy Brigham and Women’s Hospital BostonMA United States Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Rackspace Technology San AntonioTX United States NeuroPoly Lab Polytechnique Montréal MontréalQC Canada Georgia State Georgia Tech Emory AtlantaGA United States University of Florence Sesto Fiorentino Italy Bioretics srl Cesena Italy Department of Medical Imaging Ghent University Hospital Ghent Belgium Department of Diagnostic Sciences Ghent University Ghent Belgium Department of Neurology Boston Children's Hospital BostonMA United States Dipartimento di Scienze della Salute dell'Università degli Studi di Milano Milan Italy Department of Clinical and Experimental Epilepsy University College London United Kingdom Zurich Center for Neuroeconomics Department of Economics University of Zurich Zurich Switzerland UMIB-FISABIO Valencia Spain The University of Texas at Austin AustinTX United States SCCN University of California San Diego La Jolla CA United States Department of Neurology NYU Langone Medical Center New YorkNY United States Department of Experimental Psychology University of Oxford Oxford United Kingdom UK Dementia Research Ins

The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS. © 2023, CC BY.

关键词： data structures

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Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

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Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

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