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Comparative and demographic analysis of orang-utan genomes (vol 469, pg 529, 2011)

NATURE 2022年第7924期608卷 E36-E36页

作者： Locke, Devin P. Hillier, LaDeana W. Warren, Wesley C. Worley, Kim C. Nazareth, Lynne V. Muzny, Donna M. Yang, Shiaw-Pyng Wang, Zhengyuan Chinwalla, Asif T. Minx, Pat Mitreva, Makedonka Cook, Lisa Delehaunty, Kim D. Fronick, Catrina Schmidt, Heather Fulton, Lucinda A. Fulton, Robert S. Nelson, Joanne O. Magrini, Vincent Pohl, Craig Graves, Tina A. Markovic, Chris Cree, Andy Dinh, Huyen H. Hume, Jennifer Kovar, Christie L. Fowler, Gerald R. Lunter, Gerton Meader, Stephen Heger, Andreas Ponting, Chris P. Marques-Bonet, Tomas Alkan, Can Chen, Lin Cheng, Ze Kidd, Jeffrey M. Eichler, Evan E. White, Simon Searle, Stephen Vilella, Albert J. Chen, Yuan Flicek, Paul Ma, Jian Raney, Brian Suh, Bernard Burhans, Richard Herrero, Javier Haussler, David Faria, Rui Fernando, Olga Darre, Fleur Farre, Domenec Gazave, Elodie Oliva, Meritxell Navarro, Arcadi Roberto, Roberta Capozzi, Oronzo Archidiacono, Nicoletta Della Valle, Giuliano Purgato, Stefania Rocchi, Mariano Konkel, Miriam K. Walker, Jerilyn A. Ullmer, Brygg Batzer, Mark A. Smit, Arian F. A. Hubley, Robert Casola, Claudio Schrider, Daniel R. Hahn, Matthew W. Quesada, Victor Puente, Xose S. Ordonez, Gonzalo R. Lopez-Otin, Carlos Vinar, Tomas Brejova, Brona Ratan, Aakrosh Harris, Robert S. Miller, Webb Kosiol, Carolin Lawson, Heather A. Taliwal, Vikas Martins, Andre L. Siepel, Adam RoyChoudhury, Arindam Ma, Xin Degenhardt, Jeremiah Bustamante, Carlos D. Gutenkunst, Ryan N. Mailund, Thomas Dutheil, Julien Y. Hobolth, Asger Schierup, Mikkel H. Ryder, Oliver A. Yoshinaga, Yuko de Jong, Pieter J. Weinstock, George M. Rogers, Jeffrey Mardis, Elaine R. Gibbs, Richard A. Wilson, Richard K. The Genome Center at Washington University Washington University School of Medicine Saint Louis Missouri USA Human Genome Sequencing Center Department of Molecular and Human Genetics Baylor College of Medicine One Baylor Plaza Houston Texas USA MRC Functional Genomics Unit and Department of Physiology Anatomy and Genetics University of Oxford Le Gros Clark Building Oxford UK Wellcome Trust Centre for Human Genetics Oxford UK Department of Genome Sciences University of Washington School of Medicine Seattle Washington USA IBE Institut de Biologia Evolutiva (UPF-CSIC) Universitat Pompeu Fabra PRBB Doctor Aiguader 88 Barcelona Spain Howard Hughes Medical Institute Seattle Washington USA CIBIO Centro de Investigação em Biodiversidade e Recursos Genéticos Universidade do Porto Campus Agrário de Vairão Vairão Portugal Instituto de Tecnologia Química e Biológica Universidade Nova de Lisboa Oeiras Portugal ICREA (Institució Catalana de Recerca i Estudis Avançats) and INB (Instituto Nacional de Bioinformática) PRBB Doctor Aiguader 88 Barcelona Spain Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Cambridge UK European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton Cambridge UK Center for Biomolecular Science and Engineering University of California Santa Cruz California USA Center for Comparative Genomics and Bioinformatics Penn State University University Park Pennsylvania USA Department of Biology University of Bari Bari Italy Department of Biology University of Bologna Bologna Italy Department of Biological Sciences Louisiana State University Baton Rouge Louisiana USA Center for Computation and Technology Department of Computer Sciences Louisiana State University Baton Rouge Louisiana USA Institute for Systems Biology Seattle Washington USA Department of Biology and School of Informatics and Computing Indiana University Bloomington Indiana USA Instituto Universitario de Oncologia Departamento de Bioquimica y Biol

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The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

arXiv

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arXiv 2020年

作者： Marinescu, Razvan V. Oxtoby, Neil P. Young, Alexandra L. Bron, Esther E. Toga, Arthur W. Weiner, Michael W. Barkhof, Frederik Fox, Nick C. Eshaghi, Arman Ansart, Manon Durrleman, Stanley Lu, Pascal Iddi, Samuel Li, Dan Thompson, Wesley K. Donohue, Michael C. Nahon, Aviv Levy, Yarden Halbersberg, Dan Cohen, Mariya Liao, Huiling Li, Tengfei Yu, Kaixian Zhu, Hongtu Tamez-Peña, José G. Ismail, Aya Wood, Timothy Bravo, Hector Corrada Nguyen, Minh Sun, Nanbo Feng, Jiashi Thomas Yeo, B.T. Chen, Gang Qi, Ke Chen, Shiyang Qiu, Deqiang Buciuman, Ionut Kelner, Alex Pop, Raluca Rimocea, Denisa Ghazi, Mostafa M. Nielsen, Mads Ourselin, Sebastien Sørensen, Lauge Venkatraghavan, Vikram Liu, Keli Rabe, Christina Manser, Paul Hill, Steven M. Howlett, James Huang, Zhiyue Kiddle, Steven Mukherjee, Sach Rouanet, Anaïs Taschler, Bernd Tom, Brian D.M. White, Simon R. Faux, Noel Sedai, Suman de Velasco Oriol, Javier Clemente, Edgar E.V. Estrada, Karol Aksman, Leon Altmann, Andre Stonnington, Cynthia M. Wang, Yalin Wu, Jianfeng Devadas, Vivek Fourrier, Clementine Raket, Lars Lau Sotiras, Aristeidis Erus, Guray Doshi, Jimit Davatzikos, Christos Vogel, Jacob Doyle, Andrew Tam, Angela Diaz-Papkovich, Alex Jammeh, Emmanuel Koval, Igor Moore, Paul Lyons, Terry J. Gallacher, John Tohka, Jussi Ciszek, Robert Jedynak, Bruno Pandya, Kruti Bilgel, Murat Engels, William Cole, Joseph Golland, Polina Klein, Stefan Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Netherlands Laboratory of NeuroImaging University of Southern California United States Center for Imaging of Neurodegenerative Diseases University of California San Francisco United States Department of Radiology and Nuclear Medicine VU Medical Centre Netherlands Dementia Research Centre The UK Dementia Research Institute UCL Queen Square Institute of Neurology United Kingdom Institut du Cerveau et de la Moelle épinière Paris France Alzheimer's Therapeutic Research Institute University of Southern California United States Department of Statistics and Actuarial Science University of Ghana Ghana Department of Family Medicine and Public Health University of California San Diego United States Ben Gurion University of the Negev Beersheba Israel The University of Texas Health Science Center at Houston Houston United States Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico University of Maryland College Park United States National University of Singapore Singapore Singapore Medical College of Wisconsin Milwaukee United States Emory University Atlanta United States Georgia Institute of Technology Atlanta United States Vasile Lucaciu National College Baia Mare Romania Biomediq A/S Denmark Cerebriu A/S Denmark University of Copenhagen Denmark School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom Genentech United States MRC Biostatistics Unit University of Cambridge United Kingdom IBM Research Australia Melbourne Australia Brandeis University Waltham United States Mayo Clinic ScottsdaleAZ United States H. Lundbeck A/S Denmark Clinical Memory Research Unit Department of Clinical Sciences Malmo Lund University Lund Sweden Center

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADASCog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 perce

关键词： Forecasting

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GENO-06A PAN-GLIOMA CHARACTERIZATION OF GENOMIC, EPIGENOMIC AND TRANSCRIPTOMIC ACTIVITIES REVEALS NOVEL RELATIONSHIPS BETWEEN HISTOLOGICAL SUBTYPES AND MOLECULAR SIGNATURES

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Neuro-Oncology 2015年第SUPPL_5期17卷 v92–v92页

作者： Barthel, Floris P. Ceccarelli, Michele Malta, Tathiane M. Sabedot, Thais S. Salama, Sofie R. Pagnotta, Stefano M. Murray, Bradley A. Morozova, Olena Newton, Yulia Brat, Daniel J. Cherniack, Andrew D. Zhang, Hailei Poisson, Laila Cooper, Lee Rabadan, Raul Laird, Peter W. Gutmann, David H. Noushmehr, Houtan Iavarone, Antonio Verhaak, Roel G.W. Department of Genomic Medicine Department of Bioinformatics and Computational Biology Department of Neuro-Oncology Department of Neurosurgery Department of Pathology University of Texas MD Anders Houston TX 77030 USA Department of Science and Technology University of Sannio Benevento Italy Qatar Computing Research Institute HBKU Qatar Qatar Department of Genetics Ribeirão Preto Medical School - FMRP São Paulo Brazil UC Santa Cruz Genomics Institute University of California Santa Cruz CA USA The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University Cambridge MA USA Winship Cancer Institute Emory University Atlanta GA USA Department of Neurology Department of Pathology Institute for Cancer Genetics Department of Systems Biology and Biomedical Informatics Columbia University Medical Center New York NY USA Henry Ford Hospital Detroit MI USA

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Entanglement-based quantum clock synchronization

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AIP Conference Proceedings 2020年第1期2241卷

作者： Ebubechukwu O. Ilo-Okeke Louis Tessler Jonathan P. Dowling Tim Byrnes 1)State Key Laboratory of Precision Spectroscopy School of Physical and Material Sciences East China Normal University Shanghai 200062 China 2)New York University Shanghai 1555 Century Ave Pudong Shanghai 200122 China 3)Department of Physics School of Physical Sciences Federal University of Technology P. M. B. 1526 Owerri 460001 Nigeria 4)RIKEN Cluster for Pioneering Research 2-1 Hirosawa Wako-Shi Saitama 351-0106 Japan 5)NYU-ECNU Institute of Physics at NYU Shanghai 3663 Zhongshan Road North Shanghai 200062 China 8)Hearne Institute for Theoretical Physics Department of Physics & Astronomy Louisiana State University Baton Rouge Louisiana 70803-4001 USA 9)CAS-Alibaba Quantum Computing Laboratory University of Science and Technology of China Shanghai 201315 China 6)National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo 101-8430 Japan 7)Department of Physics New York University New York NY 10003 USA

Synchronizing clocks using quantum entanglement works on the principle that two clocks Alice and Bob share between them a singlet state which is a stationary state that is immune to evolution under bare atomic Hamiltonian. A major obstacle to its realization is the hidden assumption of a common phase reference between the clocks. Without hidden assumption, a clock state of Alice or Bob is not a uniquely defined quantum state because the phase of the state is arbitrary. This results in an unknown relative phase in a two-particle entangled state defined by the clocks. We show that using entanglement purification, an entanglement-based clock synchronization is achieved despite earlier results showing the contrary. This closes the loophole for entanglement based quantum clock synchronization protocols, which is a non-local approach to synchronize two clocks independent of the properties of the intervening medium.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Using an online community of practice to explore the informal use of mobile phones by health workers

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Oxford open digital health 2023年第1期1卷 oqac003页

作者： Claire Glenton Josephine Nabukenya Smisha Agarwal Michele Meltzer Elisabeth Mukendi Immaculate Nakityo Lwanga Josephine Namitala Sunanda Reddy Geoff Royston Tigest Tamrat Neil Pakenham-Walsh Department of Health and Functioning Western Norway University of Applied Sciences 5020 Bergen Norway. Department of Information Systems School of Computing & Informatics Technology Makerere University 7062 Kampala Uganda. Department of International Health Johns Hopkins University Bloomberg School of Public Health Baltimore MD 20205 USA. Department of Medicine Thomas Jefferson University Philadelphia PA 19107 USA. Department of Health Senghor University Alexandria Governorate 5361022 Egypt. School of Public Health Makerere University 7062 Kampala Uganda. Department of Adult and Community Education Makerere University 7062 Kampala Uganda. Centre for Applied Research and Education on Neurodevelopmental Impairments and Disability-related Health Initiatives New Delhi 110075 India. Healthcare Information for All Global Healthcare Information Network Oxfordshire UK. UNDP/UNFPA/UNICEF/World Bank Special Program of Research Development and Research Training in Human Reproduction (HRP) Department of Sexual and Reproductive Health and Research World Health Organization Geneva 1211 Switzerland.

Health workers are increasingly harnessing mobile phones to develop their own solutions to work challenges. The mHEALTH-INNOVATE project aims to explore this topic further. In 2022, Healthcare Information for All organized an online discussion among health workers and other stakeholders to inform the project. Twenty-five people joined the discussion. Contributors' descriptions of the varieties of mobile phone use tallied with previous research, including for communication with patients and colleagues. In addition, they described increased mobile phone use in response to the COVID-19 pandemic and the increased need for communication, monitoring and reporting, including during lockdowns. Some solutions were health worker-initiated, including the establishment of WhatsApp groups. The discussion has helped develop a definition of informal mobile phone use.

关键词： digital health digital innovation health workforce informal mhealth mobile device mobile health

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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