检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

A surrogate-model assisted approach for optimising the size of tidal turbine arrays

引用

International Journal of Marine Energy 2017年 19卷 357-373页

作者： Culley, D.M. Funke, S.W. Kramer, S.C. Piggott, M.D. Applied Modelling and Computation Group Department of Earth Science and Engineering Imperial College London London United Kingdom Center for Biomedical Computing Simula Research Laboratory Oslo Norway Grantham Institute Imperial College London London United Kingdom

The new and costly nature of tidal stream energy extraction technologies can lead to narrow margins of success for a project. The design process is thus a delicate balancing act - to maximise the energy extracted, while minimising cost and risk. Scenario specific factors, such as site characteristics, technological constraints and practical engineering considerations greatly impact upon both the appropriate number of turbines to include within a tidal current turbine array (array size), and the individual locations of those turbines (turbine micro-siting). Both have been shown to significantly impact upon the energy yield and profitability of an array. The micro-siting arrangement for a given number of turbines can significantly influence the power extraction of a tidal farm. Until the layout has been optimised (a process which may incorporate turbine parameters, local bathymetry and a host of other practical, physical, legal, financial or environmental constraints) an accurate forecast of the yield of that array cannot be determined. This process can be thought of as 'tuning' an array to the proposed site to maximise desirable outcomes and mitigate undesirable effects. The influence of micro-siting on the farm performance means that determining the optimal array size needs to be coupled to the micro-siting process. In particular, the micro-siting needs to be repeated for any new trial array size in order to be able to compare the performance of the different farm sizes. Considering the large number of design variables in the micro-siting problem (which includes at least the positions of each turbine) it becomes clear that algorithmic optimisation is a key tool to rigorously determine the optimal array size and layout. This paper proposes a nested optimisation approach for solving the array size and layout problem. The core of this approach consists of two nested optimisation procedures. The 'outer' optimisation determines the array size. At each 'outer' iteration t

关键词： Turbines

来源：评论

学校读者我要写书评

暂无评论

CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

引用

arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Engineered Whole Cut Meats Assembled of Cell Fibers Constructed by Tendon-Gel Integrated Bioprinting

Research Square

引用

research Square 2020年

作者： Kang, Dong-Hee Louis, Fiona Liu, Hao Shimoda, Hiroshi Nishiyama, Yasutaka Nozawa, Hajime Kakitani, Makoto Takagi, Daisuke Kasa, Daijiro Nagamori, Eiji Irie, Shinji Kitano, Shiro Matsusaki, Michiya Division of Applied Chemistry Graduate School of Engineering Osaka University Suita 2-1 Yamadaoka Osaka565-0871 Japan for Advanced Cell Regulatory Chemistry Graduate School of Engineering Osaka University Suita 2-1 Yamadaoka Osaka565-0871 Japan Department of Anatomical Science Hirosaki University Graduate School of Medicine 5 Zaifu-cho Hirosaki036-8562 Japan Nipponham Foods Ltd. 3-3 Midorigahara Ibaraki Tsukuba300-2646 Japan Cell Therapy Project Research & Development Division Kirin Holdings Company Limited 1-13-5 Fukuura Kanazawa-ku Yokohama236-0004 Japan Biomedical Business Center Healthcare Business Group Ricoh Company Ltd. 3-25-22 Tonomachi LIC 322 Kanagawa Kawasaki-shi210-0821 Japan Solution Planning Product Solution Technologies Production Printing Industrial Solutions Ricoh Japan Corporation 4-2-8 Shibaura Minato-ku Tokyo108-0023 Japan Department of Biomedical Engineering Faculty of Engineering Osaka Institute of Technology 5-16-1 Omiya Asahi-ku Osaka535-8585 Japan

With the current interest in artificial meat, mammalian cell-based cultured meat has mostly been in minced form. There is thus still a high demand for artificial steak-like meat. Herein, we demonstrate in vitro construction of engineered steak-like meat assembled of three types of edible bovine cell fibers, such as skeletal muscle, adipose, and blood capillary fabricated by tendon-gel integrated printing (TIP) technology. Because actual meat is an anisotropically aligned assembly of the fibers connected to tendon for the actions of contraction and relaxation, TIP was discovered to construct the fiber assembly connecting tendon gels with engineered structures. In this study, a total of 72 fibers comprising 42 muscle, 28 adipose, and 2 blood capillary were constructed by TIP and subsequently assembled to fabricate a steak-like meat with a diameter of 5 mm and a length of 10 mm by consulting histological images of actual Wagyu beef steak. The TIP discovered here could be a powerful manufacturing technology for fabrication of the desired types of steak-like cultured meats. © 2020, CC BY.

关键词： Cells

来源：评论

学校读者我要写书评

暂无评论

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning

arXiv

引用

arXiv 2020年

作者： van Eijnatten, Maureen Rundo, Leonardo Batenburg, K. Joost Lucka, Felix Beddowes, Emma Caldas, Carlos Gallagher, Ferdia A. Sala, Evis Schönlieb, Carola-Bibiane Woitek, Ramona Centrum Wiskunde & Informatica Amsterdam1098 XG Netherlands Medical Image Analysis Group Department of Biomedical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Department of Radiology University of Cambridge CambridgeCB2 0QQ United Kingdom Cancer Research UK Cambridge Centre University of Cambridge CambridgeCB2 0RE United Kingdom Mathematical Institute Leiden University Leiden2300 RA Netherlands Centre for Medical Image Computing University College London LondonWC1E 6BT United Kingdom Cancer Research UK Cambridge Institute University of Cambridge CambridgeCB2 0RE United Kingdom Foundation Trust CambridgeCB2 0QQ United Kingdom Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Biomedical Imaging and Image-guided Therapy Medical University Vienna Vienna1090 Austria

Deep learning is being increasingly used for deformable image registration and unsupervised approaches, in particular, have shown great potential. However, the registration of abdominopelvic Computed Tomography (CT) images remains challenging due to the larger displacements compared to those in brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate the use of the commonly used unsupervised deep learning framework VoxelMorph for the registration of a longitudinal abdominopelvic CT dataset acquired in patients with bone metastases from breast cancer. Methods: As a pre-processing step, the abdominopelvic CT images were refined by automatically removing the CT table and all other extra-corporeal components. To improve the learning capabilities of the VoxelMorph framework when only a limited amount of training data is available, a novel incremental training strategy is proposed based on simulated deformations of consecutive CT images in the longitudinal dataset. This devised training strategy was compared against training on simulated deformations of a single CT volume. A widely used software toolbox for deformable image registration called NiftyReg was used as a benchmark. The evaluations were performed by calculating the Dice Similarity Coefficient (DSC) between manual vertebrae segmentations and the Structural Similarity Index (SSIM). Results: The CT table removal procedure allowed both VoxelMorph and NiftyReg to achieve significantly better registration performance. In a 4-fold cross-validation scheme, the incremental training strategy resulted in better registration performance compared to training on a single volume, with a mean DSC of 0.929 ± 0.037 and 0.883 ± 0.033, and a mean SSIM of 0.984 ± 0.009 and 0.969 ± 0.007, respectively. Although our deformable image registration method did not outperform NiftyReg in terms of DSC (0.988 ± 0.003) or SSIM (0.995 ± 0.002), the registrations were appr

关键词： Computerized tomography

来源：评论

学校读者我要写书评

暂无评论

The overlapping burden of the three leading causes of disability and death in sub-Saharan African children

引用

Nature Communications 2022年第1期13卷 1-14页

作者： Reiner, Robert C. Welgan, Catherine A. Troeger, Christopher E. Baumann, Mathew M. Weiss, Daniel J. Deshpande, Aniruddha Blacker, Brigette F. Miller-Petrie, Molly K. Earl, Lucas Bhatt, Samir Abolhassani, Hassan Abosetugn, Akine Eshete Abu-Gharbieh, Eman Adekanmbi, Victor Adetokunboh, Olatunji O. Aghaali, Mohammad Aji, Budi Alahdab, Fares Al-Aly, Ziyad Alhassan, Robert Kaba Ali, Saqib Alizade, Hesam Aljunid, Syed Mohamed Almasi-Hashiani, Amir Al-Mekhlafi, Hesham M. Altirkawi, Khalid A. Alvis-Guzman, Nelson Amare, Azmeraw T. Amini, Saeed Amugsi, Dickson A. Ancuceanu, Robert Andrei, Catalina Liliana Ansari, Fereshteh Anvari, Davood Appiah, Seth Christopher Yaw Arabloo, Jalal Aremu, Olatunde Atout, Maha Moh’d Wahbi Ausloos, Marcel Ausloos, Floriane Ayanore, Martin Amogre Aynalem, Yared Asmare Azene, Zelalem Nigussie Badawi, Alaa Baig, Atif Amin Banach, Maciej Bedi, Neeraj Bhagavathula, Akshaya Srikanth Bhandari, Dinesh Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bhutta, Zulfiqar A. Bijani, Ali Birhanu, Tesega Tesega Mengistu Bitew, Zebenay Workneh Boloor, Archith Brady, Oliver J. Butt, Zahid A. Car, Josip Carvalho, Felix Casey, Daniel C. Chattu, Vijay Kumar Chowdhury, Mohiuddin Ahsanul Kabir Chu, Dinh-Toi Coelho, Camila H. Cook, Aubrey J. Damiani, Giovanni Daoud, Farah Gela, Jiregna Darega Darwish, Amira Hamed Daryani, Ahmad Das, Jai K. Davis Weaver, Nicole Deribe, Kebede Desalew, Assefa Dharmaratne, Samath Dhamminda Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Dorostkar, Fariba Dubljanin, Eleonora Duko, Bereket Dwyer-Lindgren, Laura Effiong, Andem El Sayed Zaki, Maysaa El Tantawi, Maha Enany, Shymaa Fattahi, Nazir Feigin, Valery L. Fernandes, Eduarda Ferrara, Pietro Fischer, Florian Foigt, Nataliya A. Folayan, Morenike Oluwatoyin Foroutan, Masoud Frostad, Joseph Jon Fukumoto, Takeshi Gaidhane, Abhay Motiramji Gebrekrstos, Hailemikael Gebrekidan G. K. Gebremeskel, Leake Gebreslassie, Assefa Ayalew Gething, Peter W. Gezae, Kebede Embaye Ghadiri, Keyghobad Ghashghaee, Ahmad Golechha, Mahaveer Gubar Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Malaria Atlas Project University of Oxford Oxford United Kingdom Imperial College London London United Kingdom Department of Laboratory Medicine Karolinska University Hospital Huddinge Sweden Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Public Health Debre Berhan University Debre Berhan Ethiopia Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Population Health Sciences King’s College London London United Kingdom Centre of Excellence for Epidemiological Modelling and Analysis Stellenbosch University Stellenbosch South Africa Department of Global Health Stellenbosch University Cape Town South Africa Department of Epidemiology and Biostatistics Qom University of Medical Sciences Qom Iran Faculty of Medicine and Public Health Jenderal Soedirman University Purwokerto Indonesia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center Department of Veterans Affairs St Louis MO United States Institute of Health Research University of Health and Allied Sciences Ho Ghana Department of Information Systems College of Economics and Political Science Sultan Qaboos University Muscat Oman Infectious and Tropical Disease Research Center Hormozgan University of Medical Sciences Bandar Abbas Iran Department of Health Policy and Management Kuwait University Safat Kuwait International Centre for Casemix and Clinical Coding National University of Malaysia Bandar Tun Razak Malaysia Department of Epidemiology Arak University of Medical Sciences Arak Iran Medical Research Center

Despite substantial declines since 2000, lower respiratory infections (LRIs), diarrhoeal diseases, and malaria remain among the leading causes of nonfatal and fatal disease burden for children under 5 years of age (under 5), primarily in sub-Saharan Africa (SSA). The spatial burden of each of these diseases has been estimated subnationally across SSA, yet no prior analyses have examined the pattern of their combined burden. Here we synthesise subnational estimates of the burden of LRIs, diarrhoea, and malaria in children under-5 from 2000 to 2017 for 43 sub-Saharan countries. Some units faced a relatively equal burden from each of the three diseases, while others had one or two dominant sources of unit-level burden, with no consistent pattern geographically across the entire subcontinent. Using a subnational counterfactual analysis, we show that nearly 300 million DALYs could have been averted since 2000 by raising all units to their national average. Our findings are directly relevant for decision-makers in determining which and targeting where the most appropriate interventions are for increasing child survival. © 2022, The Author(s).

关键词： Biogeography Epidemiology Infectious diseases Malaria

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Building bridges for innovation in ageing: Synergies between action groups of the EIP on AHA

引用

The Journal of nutrition, health and aging 2023年第8期27卷 688-688页

作者： Jean Bousquet M. Bewick A. Cano P. Eklund G. Fico N. Goswami N.A. Guldemond D. Henderson M.J. Hinkema G. Liotta A. Mair W. Molloy A. Monaco I. Monsonis-Paya A. Nizinska H. Papadopoulos A. Pavlickova S. Pecorelli A. Prados-Torres R.E. Roller-Wirnsberger B. de Oliveira-Alves CHRU University Hospital 371 Avenue du Doyen Gaston Giraud 34295 Montpellier Cedex 5 France MACVIA-LR Contre les Maladies Chroniques pour un VIeillissement Actif en Languedoc-Roussillon European Innovation Partnership on Active and Healthy Ageing Reference Site Paris France INSERM VIMA: Ageing and chronic diseases Epidemiological and public health approaches U1168 Paris France UVSQ UMR-S 1168 Université Versailles St-Quentin-en-Yvelines Paris France iQ4U Consultants Ltd London UK Institute of Biomedical Imaging and Life Sciences (IBILI) Faculty of Medicine University of Coimbra Coimbra Portugal Universidad Politecnica de Madrid (Life Supporting Technologies) Madrid Spain Department of Pediatrics Obstetrics and Gynecology University of Valencia Valencia Spain Computing Science Department Umeå University Umeå Sweden Four Computing Oy Helsinki Finland Technical University of Catalonia Barcelona Spain Universidad Politécnica de Madrid (Life Supporting Technologies Research Group) Madrid Spain Medical University of Graz Graz Austria Integrated care & technology University Medical Center Utrecht Utrecht The Netherlands EIP on AHA European Innovation Partnership on Active and Healthy Ageing Reference Site Scottish Centre for Telehealth and Telecare NHS 24 Glasgow UK TNO Delft The Netherlands Biomedicine and Prevention Department University of Tor Vergata Roma Italy Directorate of Finance eHealth & Pharmaceuticals Scottish Government Health Department Edinburgh UK University College Cork Ireland AIFA - Agenzia Italiana del Farmaco Area strategia e politiche del farmaco Ufficio coordinamento OsMed ed attività HTA Roma Italy Polibienestar Research Institute–University of Valencia Valencia Spain University of Lower Silesia Wrocław Poland Division of Applied Technologies National Center for Scientific Research (NCSR) «Demokritos» Athens Greece Rector of the University of Brescia Brescia Italy IIS Aragón Aragon Health Sciences Institute (IACS) Zaragoza

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

The European Reference Genome Atlas: piloting a decentralised approach to equitable biodiversity genomics

NPJ Biodiversity

引用

NPJ Biodiversity 2024年第1期3卷 28页

作者： Mc Cartney, Ann M. Formenti, Giulio Mouton, Alice De Panis, Diego Marins, Luísa S. Leitão, Henrique G. Diedericks, Genevieve Kirangwa, Joseph Morselli, Marco Salces-Ortiz, Judit Escudero, Nuria Iannucci, Alessio Natali, Chiara Svardal, Hannes Fernández, Rosa De Pooter, Tim Joris, Geert Strazisar, Mojca Wood, Jonathan M. D. Herron, Katie E. Seehausen, Ole Watts, Phillip C. Shaw, Felix Davey, Robert P. Minotto, Alice Fernández, José M. Böhne, Astrid Alegria, Carla Alioto, Tyler Alves, Paulo C. Amorim, Isabel R. Aury, Jean-Marc Backstrom, Niclas Baldrian, Petr Baltrunaite, Laima Barta, Endre Bedhom, Bertrand Belser, Caroline Bergsten, Johannes Bertrand, Laurie Bilandija, Helena Binzer-Panchal, Mahesh Bista, Iliana Blaxter, Mark Borges, Paulo A. V. Dias, Guilherme Borges Bosse, Mirte Brown, Tom Bruggmann, Rémy Buena-Atienza, Elena Burgin, Josephine Buzan, Elena Cariani, Alessia Casadei, Nicolas Chiara, Matteo Chozas, Sergio Jr, Fedor Čiampor Crottini, Angelica Cruaud, Corinne Cruz, Fernando Dalen, Love Biase, Alessiode Delcampo, Javier Delic, Teo Dennis, Aliceb. Derks, Martijn F. L. Diroma, Maria Angela Djan, Mihajla Duprat, Simone Eleftheriadi, Klara Feulner, Philine G. D. Flot, Jean-François Forni, Giobbe Fosso, Bruno Fournier, Pascal Fournier-Chambrillon, Christine Gabaldon, Toni Garg, Shilpa Gissi, Carmela Giupponi, Luca Gomez-Garrido, Jessica González, Josefa Grilo, Miguel L. Grüning, Björn Guerin, Thomas Guiglielmoni, Nadege Gut, Marta Haesler, Marcel P. Hahn, Christoph Halpern, Balint Harrison, Peter W. Heintz, Julia Hindrikson, Maris Höglund, Jacob Howe, Kerstin Hughes, Graham M. Istace, Benjamin Cock, Mark J. Janžekovič, Franc Jonsson, Zophonias O. Joye-Dind, Sagane Koskimäki, Janne J. Krystufek, Boris Kubacka, Justyna Kuhl, Heiner Kusza, Szilvia Labadie, Karine Lähteenaro, Meri Lantz, Henrik Lavrinienko, Anton Leclère, Lucas Lopes, Ricardo Jorge Madsen, Ole Magdelenat, Ghislaine Magoga, Giulia Manousaki, Tereza Mappes, Tapio Marques, Joao Pedro Redondo, Gemma I. Martinez Maumus, Florian McCarthy, Shane A. M Genomics Institute University of California SantaCruz CA United States TheVertebrate Genome Laboratory The Rockefeller University NewYork NY United States Department of Biology University of Florence Sesto Fiorentino Italy InBios-Conservation Genetics Laboratory University of Liege Liege Belgium Leibniz Institut für Zoo und Wildtierforschung Berlin Germany Berlin Center for Genomics in Biodiversity Research Berlin Germany Department of Biology University of Antwerp Antwerp Belgium Institute of Zoology University of Cologne Cologne Germany Department of Chemistry Life Sciences and Environmental Sustainability University of Parma Parma Italy Institute of Evolutionary Biology (CSIC-Universitat Pompeu Fabra Barcelona Spain Naturalis Biodiversity Center Leiden Netherlands Neuromics Support Facility VIB Center for Molecular Neurology VIB Antwerp Belgium Neuromics Support Facility Department of Biomedical SciencesUniversity ofAntwerp Antwerp Belgium Tree of Life WellcomeSanger Institute Cambridge Hinxton United Kingdom School of Biology and Environmental Science University College Dublin Belfield Ireland Aquatic Ecology & Evolution Institute of Ecology & Evolution University of Bern Bern Switzerland Department of Fish Ecology & Evolution Eawag Kastanienbaum Switzerland Department of Biological and Environmental Science University of Jyvaskyla Jyvaskyla Finland The EarlhamInstitute NorwichResearchPark Norwich United Kingdom Digital Science London United Kingdom Barcelona Supercomputing Center Spanish National Bioinformatics Institute ELIXIR Spain Getafe Spain Leibniz Institute for the Analysis of Biodiversity Change Museum Koenig Bonn Bonn Germany CE3C—Centre for Ecology Evolution and Environmental Changes & CHANGE—Global Change and Sustainability Institute Faculdade de Ciências Universidade de Lisboa Campo Grande Lisboa Portugal Centro Nacional de Análisis Genómico (CNAG) Barcelona Spain Universitat de Barcelona (UB) Barcelona Spain CIBIO Cen

A genomic database of all Earth’s eukaryotic species could contribute to many scientific discoveries;however, only a tiny fraction of species have genomic information available. In 2018, scientists across the world united under the Earth BioGenome Project (EBP), aiming to produce a database of highquality reference genomes containing all ~1.5 million recognized eukaryotic species. As the European node of the EBP, the European Reference Genome Atlas (ERGA) sought to implement a new decentralised, equitable and inclusive model for producing reference genomes. For this, ERGA launched a Pilot Project establishing the first distributed reference genome production infrastructure and testing it on 98 eukaryotic species from 33 European countries. Here we outline the infrastructure and explore its effectiveness for scaling high-quality reference genome production, whilst considering equity and inclusion. The outcomes and lessons learned provide a solid foundation for ERGA while offering key learnings to other transnational, national genomic resource projects and the EBP. © The Author(s) 2024, corrected publication 2024.

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：