检索结果-内蒙古大学图书馆

Impact of bisphenol-A and synthetic estradiol on brain, behavior, gonads and sex hormones in a sexually labile coral reef fish

引用

Hormones and Behavior 2021年第0期136卷 105043-105043页

作者： Gonzalez, Jose A. Histed, Abigail R. Nowak, Ewelina Lange, Dominica Craig, Sarah E. Parker, Coltan G. Kaur, Achint Bhuvanagiri, Supriya Kroll, Kevin J. Martyniuk, Christopher J. Denslow, Nancy D. Rosenfeld, Cheryl S. Rhodes, Justin S. The Beckman Institute for Advanced Science and Technology University of Illinois d0e N. Mathews Ave Urbana 61801 IL United States The Neuroscience Program University of Illinois 405 N. Mathews Ave Urbana 61801 IL United States Department of Physiological Sciences and Center for Environmental and Human Toxicology College of Veterinary Medicine University of Florida Gainesville 32610 FL United States Department of Biomedical Sciences Bond Life Sciences Center Thompson Center for Autism and Neurobehavioral Disorders MU Institute for Data Science and Informatics and Genetics Area Program University of Missouri Columbia 65211 MO United States Department of Psychology University of Illinois 405 N. Mathews Ave Urbana 61801 IL United States

Endocrine disrupting chemicals, such as bisphenol A (BPA) and ethinylestradiol (EE2), are detected in the marine environment from plastic waste and wastewater effluent. However, their impact on reproduction in sexually labile coral reef fish is unknown. The objective of this study was to determine impacts of environmentally relevant concentrations of BPA and EE2 on behavior, brain gene expression, gonadal histology, sex hormone profile, and plasma vitellogenin (Vtg) levels in the anemonefish, Amphiprion ocellaris. A. ocellaris display post-maturational sex change from male to female in nature. Sexually immature, male fish were paired together and fed twice daily with normal food (control), food containing BPA (100 μg/kg), or EE2 (0.02 μg/kg) (n = 9 pairs/group). Aggression toward an intruder male was measured at 1, 3, and 6 months. Blood was collected at 3 and 6 months to measure estradiol (E2), 11-ketotestosterone (11-KT), and Vtg. At the end of the study, fish were euthanized to assess gonad morphology and to measure expression of known sexually dimorphic genes in the brain. Relative to control, BPA decreased aggression, altered brain transcript levels, increased non-vitellogenic and vitellogenic eggs in the gonad, reduced 11-KT, and increased plasma Vtg. In two BPA-treated pairs, both individuals had vitellogenic eggs, which does not naturally occur. EE2 reduced 11-KT in subordinate individuals and altered expression of one transcript in the brain toward the female profile. Results suggest BPA, and to a lesser extent EE2, pollution in coral reef ecosystems could interfere with normal reproductive physiology and behavior of the iconic sexually labile anemonefish. © 2021 Elsevier Inc.

关键词： 11-Ketotestosterone Aggression Bisphenol A EDC Endocrine disruptor Estradiol Ethinylestradiol Feminization Gonad Plastic Pollution Protandrous sex change Reproduction Vitellogenic Xenoestrogen

来源：评论

学校读者我要写书评

暂无评论

Multicenter target trial emulation to evaluate corticosteroids for sepsis stratified by predicted organ dysfunction trajectory

引用

Nature Communications 2025年第1期16卷 1-10页

作者： Rajendran, Suraj Xu, Zhenxing Pan, Weishen Zang, Chengxi Siempos, Ilias Torres, Lisa Xu, Jie Bian, Jiang Schenck, Edward J. Wang, Fei Tri-Institutional Computational Biology & Medicine Program Cornell University Cornell NY United States Division of Health Informatics Department of Population Health Sciences Weill Cornell Medicine New York NY United States Division of Pulmonary and Critical Care Medicine Department of Medicine New York-Presbyterian Hospital-Weill Cornell Medical Center Weill Cornell Medicine New York NY United States First Department of Critical Care Medicine and Pulmonary Services Evangelismos Hospital National and Kapodistrian University of Athens Medical School Athens Greece Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Gainesville FL United States Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN United States Regenstrief Institute Indianapolis IN United States

Corticosteroids decrease the duration of organ dysfunction in sepsis and a range of overlapping and complementary infectious critical illnesses, including septic shock, pneumonia and the acute respiratory distress syndrome (ARDS). The risk and benefit of corticosteroids are not fully defined using the construct of organ dysfunction duration. This retrospective multicenter, proof-of-concept study aimed to evaluate the association between usage of corticosteroids and mortality of patients with sepsis, pneumonia and ARDS by emulating a target trial framework stratified by predicted organ dysfunction trajectory. The study employed a two staged machine learning (ML) methodology to first subphenotype based on organ dysfunction trajectory then predict this defined trajectory. Once patients were classified by predicted trajectory we conducted a target trial emulation. Our analysis revealed that the association between corticosteroid use and 28-day mortality varied by predicted trajectory and between *** findings suggest that matching treatment strategies to empirically observed pathobiology may offer a more nuanced understanding of corticosteroid utility. © The Author(s) 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deciphering seasonal depression variations and interplays between weather changes, physical activity, and depression severity in real-world settings: Learnings from RADAR-MDD longitudinal mobile health study

arXiv

引用

arXiv 2024年

作者： Zhang, Yuezhou Folarin, Amos A. Ranjan, Yatharth Cummins, Nicholas Rashid, Zulqarnain Conde, Pauline Stewart, Callum Sun, Shaoxiong Vairavan, Srinivasan Matcham, Faith Oetzmann, Carolin Siddi, Sara Lamers, Femke Simblett, Sara Wykes, Til Mohr, David C. Haro, Josep Maria Penninx, Brenda W.J.H. Narayan, Vaibhav A. Hotopf, Matthew Dobson, Richard J.B. Pratap, Abhishek Department of Biostatistics & Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Institute of Health Informatics University College London London United Kingdom NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust London United Kingdom Health Data Research UK London University College London London United Kingdom NIHR Biomedical Research Centre University College London Hospitals NHS Foundation Trust London United Kingdom Department of Computer Science University of Sheffield Sheffield United Kingdom Janssen Research and Development LLC TitusvilleNJ United States Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom School of Psychology University of Sussex Falmer United Kingdom Teaching Research and Innovation Unit Parc Sanitari Sant Joan de Déu Fundació Sant Joan de Déu Barcelona Spain Centro de Investigación Biomédica en Red de Salud Mental Madrid Spain Department of Psychiatry Amsterdam UMC Vrije Universiteit Amsterdam Netherlands Mental Health Program Amsterdam Public Health Research Institute Amsterdam Netherlands Department of Psychology Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom South London and Maudsley NHS Foundation Trust London United Kingdom Center for Behavioral Intervention Technologies Department of Preventive Medicine Northwestern University ChicagoIL United States Boehringer Ingelheim Pharmaceuticals Inc. RidgefieldCT United States University of Washington Seattle United States

Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully quantified. This study analyzed real-world data from 428 participants (a subset;68.7% of the cohort) in the RADAR-MDD longitudinal mobile health study to investigate seasonal variations in depression (measured through a remote validated assessment - PHQ-8) and examine the potential interplay between dynamic weather changes, physical activity (monitored via wearables), and depression severity. The clustering of PHQ-8 scores identified four distinct seasonal variations in depression severity: one stable trend and three varying patterns where depression peaks in different seasons. Among these patterns, participants within the stable trend had the oldest average age (p=0.002) and the lowest baseline PHQ-8 score (p=0.003). Mediation analysis assessing the indirect effect of weather on physical activity and depression showed significant differences among participants with different affective responses to weather. Specifically, the temperature and day length significantly influenced depression severity, which in turn impacted physical activity levels (p Copyright © 2024, The Authors. All rights reserved.

关键词： Radar measurement

来源：评论

学校读者我要写书评

暂无评论

O63: Leveraging global and local genomic data to guide equitable carrier screening: From Genome Aggregation database (gnomAD) to Qatar Genome

Genetics in Medicine Open

引用

Genetics in Medicine Open 2025年 3卷 102632-102632页

作者： Aarabi, Mahmoud Mbarek, Hamdi Bashar, Aryan Soman, Vishal Al-Mulla, Hajer Nkrumah, Esther Schmitz, Matthew Darabi, Helia Wang, John Kiehl, Paris Sethi, Rahil Saad, Chadi Rajkovic, Aleksandar Yatsenko, Svetlana Chandran, Uma Departments of Pathology and Obstetrics Gynecology and Reproductive Sciences University of Pittsburgh School of Medicine； Medical Genetics and Genomics Laboratories University of Pittsburgh Medical Center (UPMC) Pittsburgh PA Qatar Genome Program Qatar Foundation； Qatar Precision Health Institute Doha Qatar Data Science Graduate Program University of Colorado Boulder Boulder CO Department of Biomedical Informatics University of Pittsburgh School of Medicine Pittsburgh PA Joan H. Marks Graduate Program in Human Genetics Sarah Lawrence College New York NY Temerty Faculty of Medicine University of Toronto Toronto ON Canada Medical Genetics and Genomics Laboratories University of Pittsburgh Medical Center (UPMC) Department of Pediatrics University of Pittsburgh School of Medicine Pittsburgh PA Department of Microbiology Immunology and Pathology Colorado State University Fort Collins CO Department of Pathology and Institute of Human Genetics University of California San Francisco San Francisco CA Department of Pathology Stanford University School of Medicine Stanford CA

来源：评论

学校读者我要写书评

暂无评论

Federated learning for computational pathology on gigapixel whole slide images

arXiv

引用

arXiv 2020年

作者： Lu, Ming Y. Kong, Dehan Lipkova, Jana Chen, Richard J. Singh, Rajendra Williamson, Drew F.K. Chen, Tiffany Y. Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Dana-Farber Cancer Institute BostonMA United States Department of Pathology Northwell Health NY United States

Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non human-identifiable features from histology such as molecular alterations. However, the development of robust, adaptable and accurate deep learning-based models often rely on the collection and time-costly curation large high-quality annotated training data that should ideally come from diverse sources and patient populations to cater for the heterogeneity that exists in such datasets. Multi-centric and collaborative integration of medical data across multiple institutions can naturally help overcome this challenge and boost the model performance but is limited by privacy concerns amongst other difficulties that may arise in the complex data sharing process as models scale towards using hundreds of thousands of gigapixel whole slide images. In this paper, we introduce privacy preserving federated learning for gigapixel whole slide images in computational pathology using weakly-supervised attention multiple instance learning and differential privacy. We evaluated our approach on two different diagnostic problems using thousands of histology whole slide images with only slide-level labels. Additionally, we present a weakly-supervised learning framework for survival prediction and patient stratification from whole slide images and demonstrate its effectiveness in a federated setting. Our results show that using federated learning, we can effectively develop accurate weakly supervised deep learning models from distributed data silos without direct data sharing and its associated complexities, while also preserving differential privacy using randomized noise generation. Copyright © 2020, The Authors. All rights reserved.

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

引用

The Lancet Psychiatry 2022年第6期9卷 477-486页

作者： Gumley, Andrew I Bradstreet, Simon Ainsworth, John Allan, Stephanie Alvarez-Jimenez, Mario Aucott, Lorna Birchwood, Maximillian Briggs, Andrew Bucci, Sandra Cotton, Sue M Engel, Lidia French, Paul Lederman, Reeva Lewis, Shôn Machin, Matthew MacLennan, Graeme McLeod, Hamish McMeekin, Nicola Mihalopoulos, Cathy Morton, Emma Norrie, John Schwannauer, Matthias Singh, Swaran P Sundram, Suresh Thompson, Andrew Williams, Chris Yung, Alison R Farhall, John Gleeson, John Glasgow Institute of Health and Wellbeing University of Glasgow Glasgow United Kingdom Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences Faculty of Biology Medicine and Health Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Orygen Melbourne Melbourne VIC Australia Centre for Youth Mental Health University of Melbourne Melbourne VIC Australia School of Computing and Information Systems Faculty of Engineering and Information Technology University of Melbourne Melbourne VIC Australia Centre for Healthcare Randomised Trials (CHaRT) University of Aberdeen Aberdeen United Kingdom Centre for Mental Health and Wellbeing Research Warwick Medical School University of Warwick Warwick United Kingdom Department of Health Services Research and Policy London School of Hygiene & Tropical Medicine London United Kingdom Greater Manchester Mental Health NHS Foundation Trust Manchester United Kingdom School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia Department of Psychiatry School of Clinical Sciences Monash University Melbourne VIC Australia School of Medicine Deakin University Melbourne VIC Australia Department of Psychiatry Manchester Metropolitan University Manchester United Kingdom Department of Psychiatry University of British Columbia Vancouver BC Canada The Usher Institute University of Edinburgh Edinburgh United Kingdom School for Health in Social Sciences University of Edinburgh Edinburgh United Kingdom Mental Health Program Monash Health Melbourne VIC Australia Department of Psychology and Counselling La Trobe University Melbourne VIC Australia NorthWestern Mental Health The Royal Melbourne Hospital Epping VIC Australia Healthy Brain and Mi

Background: Early warning signs monitoring by service users with schizophrenia has shown promise in preventing relapse but the quality of evidence is low. We aimed to establish the feasibility of undertaking a definitive randomised controlled trial to determine the effectiveness of a blended digital intervention for relapse prevention in schizophrenia. Methods: This multicentre, feasibility, cluster randomised controlled trial aimed to compare Early signs Monitoring to Prevent relapse in psychosis and prOmote Well-being, Engagement, and Recovery (EMPOWER) with treatment as usual in community mental health services (CMHS) in Glasgow and Melbourne. CMHS were the unit of randomisation, selected on the basis of those that probably had five or more care coordinators willing to participate. Participants were eligible if they were older than 16 years, had a schizophrenia or related diagnosis confirmed via case records, were able to provide informed consent, had contact with CMHS, and had had a relapse within the previous 2 years. Participants were randomised within stratified clusters to EMPOWER or to continue their usual approach to care. EMPOWER blended a smartphone for active monitoring of early warning signs with peer support to promote self-management and clinical triage to promote access to relapse prevention. Main outcomes were feasibility, acceptability, usability, and safety, which was assessed through face-to-face interviews. App usage was assessed via the smartphone and self-report. Primary end point was 12 months. Participants, research assistants and other team members involved in delivering the intervention were not masked to treatment conditions. Assessment of relapse was done by an independent adjudication panel masked to randomisation group. The study is registered at ISRCTN (99559262). Findings: We identified and randomised eight CMHS (six in Glasgow and two in Melbourne) comprising 47 care coordinators. We recruited 86 service users between Jan 19 and Au

关键词：

来源：评论

学校读者我要写书评

暂无评论

Germline-encoded recognition of peanut underlies development of convergent antibodies in humans

引用

science Translational Medicine 2025年第802期17卷

作者： Orlee Marini-Rapoport Léna Andrieux Tarun Keswani Guangning Zong Dylan Duchen Gur Yaari Jungki Min Isabelle R. Lytle Alexander F. Rosenberg Christopher Fucile James J. Kobie Michael S. Piepenbrink Timothy Sun Victoria M. Martin Qian Yuan Wayne G. Shreffler Antti E. Seppo Kirsi M. Järvinen Johannes R. Loeffler Andrew B. Ward Steven H. Kleinstein Lars C. Pedersen Monica L. Fernández-Quintero Geoffrey A. Mueller Sarita U. Patil Center for Immunology and Inflammatory Diseases Division of Rheumatology Allergy and Immunology Massachusetts General Hospital Boston MA 02124 USA. Food Allergy Center Massachusetts General Hospital Boston MA 02124 USA. Département de Biologie École Normale Supérieure de Lyon Université Claude Bernard Lyon 1 Lyon 69342 France. National Institute of Environmental Health Sciences National Institutes of Health Research Triangle Park NC 27709 USA. Department of Pathology Yale School of Medicine New Haven CT 06510 USA. Department of Biomedical Informatics and Data Science University of Alabama at Birmingham Birmingham AL 35294 USA. Division of Infectious Diseases University of Alabama at Birmingham Birmingham AL 35294 USA. Division of Pediatric Gastroenterology Hepatology and Nutrition Massachusetts General Hospital Boston MA 02124 USA. Pediatric Allergy and Immunology Department of Food Allergy University of Rochester School of Medicine Rochester NY 14642 USA. Department of Integrative Structural and Computational Biology Scripps Research Institute La Jolla CA 92037 USA. Department of Immunobiology Yale School of Medicine New Haven CT 06510 USA. Department of Biomedical Informatics and Data Science Yale School of Medicine New Haven CT 06510 USA. Program in Computational Biology and Biomedical Informatics Yale University New Haven CT 06510 USA.

Humans develop immunoglobulin G (IgG) antibodies to the foods they consume. In the context of food allergy, allergen-specific IgG antibodies can sequentially class-switch to pathogenic IgE. However, the mechanism underlying the antigenicity of food proteins remains uncharacterized. Here, we identified convergent antibodies arising from different antibody gene rearrangements that bind to the immunodominant peanut allergen Ara h 2 and characterized allelic and junctional constraints on germline antibody specificity. Structurally, we found similar epitope-paratope interactions across multiple gene rearrangements. We demonstrate that these germline-encoded epitope-specific convergent antibodies to peanut occur commonly in the population because of the worldwide prevalence of the relevant gene rearrangements, allelic independence, and junctional malleability. As a result, serum IgG to this public epitope is prevalent among diverse cohorts of nonallergic peanut-consuming infants and peanut-allergic children and adults. This work demonstrates that IgG recognition of dietary antigens can be intrinsically programmed by the germline antibody repertoire.

关键词：

来源：评论

学校读者我要写书评

暂无评论

MiRNA Changes in the Mouse Placenta Due to Bisphenol a Exposure

引用

Epigenomics 2021年第24期13卷 1909-1919页

作者： Jiude Mao Jessica A Kinkade Nathan J Bivens Cheryl S Rosenfeld 1 Christopher S Bond Life Sciences Center University of Missouri Columbia MO65211 USA2 Biomedical Sciences University of Missouri Columbia MO65211 USA 2 Biomedical Sciences University of Missouri Columbia MO65211 USA 3 Genomics Technology Core Facility University of Missouri Columbia MO65211 USA 2 Biomedical Sciences University of Missouri Columbia MO65211 USA4 MU Institute for Data Science & Informatics University of Missouri Columbia MO65211 USA5 Thompson Center for Autism & Neurobehavioral Disorders University of Missouri Columbia MO65211 USA6 Genetics Area Program University of Missouri Columbia MO65211 USA

Aim:To determine small RNA expression changes in mouse placenta induced by bisphenol A (BPA) ***:Exposing female mice to BPA two weeks prior to conception through gestational day 12.5; whereupon fetal placentas were collected, frozen in liquid nitrogen and stored at -80°C. Small RNAs were isolated and used for small ***:43 small RNAs were differentially expressed. Target mRNAs were closely aligned to those expressed by thymus and brain, and pathway enrichment analyses indicated that such target mRNAs regulate neurogenesis and associated neurodevelopment ***:BPA induces several small RNAs in mouse placenta that might provide biomarkers for BPA exposure. Further, the placenta might affect fetal brain development through the secretion of miRNAs.

关键词： EDC endocrine disrupting chemicals miRNA phenolic compounds RNA-sequencing trophoblast

来源：评论

学校读者我要写书评

暂无评论

Biclustering Method to Capture the Spatial Pattern and to Identify the Causes of Social Vulnerability in Indonesia: A New Recommendation for Disaster Mitigation Policy

引用

Procedia Computer science 2019年 157卷 31-37页

作者： Puspita Anggraini Kaban Robert Kurniawan Rezzy Eko Caraka Bens Pardamean Budi Yuniarto Sukim Computational Statistics Department Polytechnic of Statistics - STIS Jakarta 13330 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Indonesia College of Informatics Chaoyang University of Technology Taichung City 41349 Taiwan (R.O.C.) Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Geographically, Indonesia is a meeting point of three continental plates. Scilicet, the Eurasian Plate, the Indo-Australian Plate, and the Pacific Plate. Therefore, Indonesia is part of the infamous volcanic zone called the ”Ring of Fire” and one of the areas prone to natural disasters such as volcanic eruptions, earthquakes, tsunamis, floods, and landslides. This study aims to capture the spatial pattern and identify the causes of social vulnerability in the districts/cities in Indonesia using the biclustering method. The data is extracted from the Indonesian National Socio-Economic Survey (SUSENAS) by BPS-Statistics in 2014. The biclustering result indicates that each district/city has its own social vulnerability characteristics and shows that the vulnerable aspects of each district/city are different. The adjacent observations tend to have social vulenrability characteristics. The results of this study can be used as a reference for national disaster mitigation policy in Indonesia.

关键词： biclustering disaster mitigation social vulnerability SoVI spatial pattern

来源：评论

学校读者我要写书评

暂无评论

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：