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Image Segmentation via Improving Clustering Algorithms with Density and Distance

Procedia computer science 2015年 55卷 1015-1022页

作者： Zhensong Chen Zhiquan Qi Fan Meng Limeng Cui Yong Shi School of Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing 100190 China Sino-Danish Center for Education and Research Beijing 100190 China Research Center on Fictitious Economy and Data Science CAS Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Image segmentation problem is a fundamental task and process in computer vision and image processing applications. It is well known that the performance of image segmentation is mainly influenced by two factors: the segmentation approaches and the feature presentation. As for image segmentation methods, clustering algorithm is one of the most popular approaches. However, most current clustering-based segmentation methods exist some problems, such as the number of regions of image have to be given prior, the different initial cluster centers will produce different segmentation results and so on. In this paper, we present a novel image segmentation approach based on DP clustering algorithm. Compared with the current methods, our method has several improved advantages as follows: 1) This algorithm could directly give the cluster number of the image based on the decision graph; 2) The cluster centers could be identified correctly; 3) We could simply achieve the hierarchical segmentation according to the applications requirement. A lot of experiments demonstrate the validity of this novel segmentation algorithm.

关键词： Image Segmentation Clustering Algorithm Feature Representation

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Erratum to: Meaurement of ϒ production in pp colliion at = 13 TeV

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Journal of High Energy Physics 2019年第5期2019卷 76-76页

作者： The LHCb collaboration R. Aaij B. Adeva M. Adinolfi Z. Ajaltouni S. Akar J. Albrecht F. Alessio M. Alexander A. Alfonso Albero S. Ali G. Alkhazov P. Alvarez Cartelle A. A. Alves S. Amato S. Amerio Y. Amhis L. An L. Anderlini G. Andreassi M. Andreotti J. E. Andrews R. B. Appleby F. Archilli P. d’Argent J. Arnau Romeu A. Artamonov M. Artuso E. Aslanides M. Atzeni G. Auriemma S. Bachmann J. J. Back C. Baesso S. Baker V. Balagura W. Baldini A. Baranov R. J. Barlow S. Barsuk W. Barter F. Baryshnikov V. Batozskaya V. Battista A. Bay J. Beddow F. Bedeschi I. Bediaga A. Beiter L. J. Bel N. Beliy V. Bellee N. Belloli K. Belous I. Belyaev E. Ben-Haim G. Bencivenni S. Benson S. Beranek A. Berezhnoy R. Bernet D. Berninghoff E. Bertholet A. Bertolin C. Betancourt F. Betti M. O. Bettler M. van Beuzekom Ia. Bezshyiko S. Bifani P. Billoir A. Birnkraut A. Bizzeti M. Bjørn T. Blake F. Blanc S. Blusk V. Bocci T. Boettcher A. Bondar N. Bondar S. Borghi M. Borisyak M. Borsato F. Bossu M. Boubdir T. J. V. Bowcock E. Bowen C. Bozzi S. Braun M. Brodski J. Brodzicka D. Brundu E. Buchanan C. Burr A. Bursche J. Buytaert W. Byczynski S. Cadeddu H. Cai R. Calabrese R. Calladine M. Calvi M. Calvo Gomez A. Camboni P. Campana D. H. Campora Perez L. Capriotti A. Carbone G. Carboni R. Cardinale A. Cardini P. Carniti L. Carson K. Carvalho Akiba G. Casse L. Cassina M. Cattaneo G. Cavallero R. Cenci D. Chamont M. G. Chapman M. Charles Ph. Charpentier G. Chatzikonstantinidis M. Chefdeville S. Chen S.-G. Chitic V. Chobanova M. Chrzaszcz A. Chubykin P. Ciambrone X. Cid Vidal G. Ciezarek P. E. L. Clarke M. Clemencic H. V. Cliff J. Closier V. Coco J. Cogan E. Cogneras V. Cogoni L. Cojocariu P. Collins T. Colombo A. Comerma-Montells A. Contu G. Coombs S. Coquereau G. Corti M. Corvo C. M. Costa Sobral B. Couturier G. A. Cowan D. C. Craik A. Crocombe M. Cruz Torres R. Currie C. D’Ambrosio F. Da Cunha Marinho C. L. Da Silva E. Dall’Occo J. Dalseno A. Davis O. De Aguiar Francisco K. De Bruyn S. De Capua M. De Cian J. M. De Miranda L. De Paula M. De Serio P. D Nikhef National Institute for Subatomic Physics Universidade de Santiago de Compostela University of Bristol Clermont Université Université Blaise Pascal CNRS/IN2P3 LPC University of Cincinnati Technische Universität Dortmund European Organization for Nuclear Research (CERN) University of Glasgow Universitat de Barcelona Petersburg Nuclear Physics Institute (PNPI) Imperial College London Universidade Federal do Rio de Janeiro (UFRJ) Sezione INFN di Padova LAL Univ. Paris-Sud CNRS/IN2P3 Université Paris-Saclay Tsinghua University Sezione INFN di Firenze Institute of Physics Ecole Polytechnique Fédérale de Lausanne (EPFL) Sezione INFN di Ferrara Università di Ferrara University of Maryland University of Manchester Ruprecht-Karls-Universität Heidelberg Aix Marseille Univ CNRS/IN2P3 CPPM Institute for High Energy Physics (IHEP) Syracuse University Universität Zürich Sezione INFN di Roma La Sapienza University of Warwick Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) Laboratoire Leprince-Ringuet Yandex School of Data Analysis Institute of Theoretical and Experimental Physics (ITEP) National Center for Nuclear Research (NCBJ) Sezione INFN di Pisa Centro Brasileiro de Pesquisas Físicas (CBPF) University of Chinese Academy of Sciences Sezione INFN di Milano Bicocca Università di Milano Bicocca LPNHE Université Pierre et Marie Curie Université Paris Diderot CNRS/IN2P3 Laboratori Nazionali dell’INFN di Frascati RWTH Aachen University Institute of Nuclear Physics Moscow State University (SINP MSU) Sezione INFN di Bologna University of Cambridge University of Birmingham Università di Modena e Reggio Emilia University of Oxford Massachusetts Institute of Technology Budker Institute of Nuclear Physics (SB RAS) Novosibirsk State University University of Liverpool Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences Sezione INFN di Cagliari Wuhan University Universitat Ramon Llull Università di Bologna Sezione INFN di Roma Tor Vergata Sezione INFN di Genova Università di Genov

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3D garment segmentation based on semi-supervised learning method

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Journal of Fiber Bioengineering and Informatics 2015年第4期8卷 657-665页

作者： Huang, Mian Liu, Li Liu, Ruomei Fu, Xiaodong Liu, Lijun Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China National Engineering Research Center of Digital Life State-Province Joint Laboratory of Digital Home Interactive Applications School of Data and Computer Science Sun Yat-sen University Guangzhou China

In this paper, we propose a semi-supervised learning method to simultaneous segmentation and labeling of parts in 3D garments. The key idea in this work is to analyze 3D garments using semi-supervised learning method which can label parts in various 3D garments. We first develop an objective function based on Conditional Random Field (CRF) model to learn the prior knowledge of garment components from a set of training examples. Then, we exploit an effective training method that utilizes JointBoost classifiers based on the co-analysis for garments. And we modify the JointBoost to automatically cluster the segmented components without requiring manual parameter tuning. The purpose of our method is to relieve the manual segmentation and labeling of components in 3D garment collections. Finally, the experimental results show the performance of our proposed method is effective. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

关键词： Random processes

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A survey of major techniques of clothing in vision

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Journal of Fiber Bioengineering and Informatics 2015年第4期8卷 725-732页

作者： Huang, Dongyan Liu, Li Fu, Xiaodong Liu, Lijun Huang, Qingsong Yunnan Provincial Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China National Engineering Research Center of Digital Life State-Province Joint Laboratory of Digital Home Interactive Applications School of Data and Computer Science Sun Yat-sen University Guangzhou China

Clothing in vision plays an important role in many applications of digital clothing, virtual fitting, fashion design and manufacturing. The research of clothing in vision is full of technical challenges, such as background detection and features extraction. In this paper, we introduce the latest remarkable works of clothing in vision area, including clothing segmentation, clothing recognition and clothing retrieval. There are many remarkable advances on the basis of the predecessors' achievements in the three fields. Firstly, we provide an overview of the techniques for clothing segmentation and clothing recognition. Then, we introduce and analyze the representative works on clothing retrieval. Finally, we forecast broader application prospect and future research work of clothing in vision. © 2015 Binary Information Press & Textile Bioengineering and Informatics Society December 2015.

关键词： Textiles

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Querying Big data: Bridging Theory and Practice

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Journal of computer science & Technology 2014年第5期29卷 849-869页

作者：樊文飞怀进鹏 School of Informatics University of EdinburghEdinburgh EH8 9AB U.K. International Research Center on Big Data Beihang University School of Computer Science and Engineering Beihang University

Big data introduces challenges to query answering, from theory to practice. A number of questions arise. What queries are ＂tractable＂ on big data？ How can we make big data ＂small＂ so that it is feasible to find exact query answers？When exact answers are beyond reach in practice, what approximation theory can help us strike a balance between the quality of approximate query answers and the costs of computing such answers？ To get sensible query answers in big data,what else do we necessarily do in addition to coping with the size of the data？ This position paper aims to provide an overview of recent advances in the study of querying big data. We propose approaches to tackling these challenging issues,and identify open problems for future research.

关键词： big data query answering tractability approximation data quality

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Improved MILP modeling for automatic security evaluation and application to fox

Improved MILP modeling for automatic security evaluation and...

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作者： Qiao, Kexin Hu, Lei Sun, Siwei Ma, Xiaoshuang Kan, Haibin State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Data Assurance and Communication Security Research Center Chinese Academy of Sciences Beijing100093 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China

Counting the number of differentially active S-boxes is of great importance in evaluating the security of a block cipher against differential attack. Mouha et al. proposed a technique based on Mixed-Integer Linear Programming (MILP) to automatically calculate a lower bound of the number of differentially active S-boxes for word-oriented block ciphers, and applied it to symmetric ciphers AES and Enocoro-128v2. Later Sun et al. extended the method by introducing bit-level representations for Sboxes and new constraints in the MILP problem, and applied the extended method to PRESENT-80 and LBlock. This kind of methods greatly depends on the constraints in the MILP problem describing the differential propagation of the block cipher. A more accurate description of the differential propagation leads to a tighter bound on the number of differentially active S-boxes. In this paper, we refine the constraints in the MILP problem describing XOR operations, and apply the refined MILP modeling to determine a lower bound of the number of active S-boxes for the Lai-Massey type block cipher FOX in the model of single-key differential attack, and obtain a tighter bound in FOX64 than existing results. Experimental results show that 6, instead of currently known 8, rounds of FOX64 is strong enough to resist against basic single-key differential attack since the differential characteristic probability is upper bounded by 2-64, and thus the maximum differential characteristic probability of 12-round FOX64 is upper bounded by 2-128, where 128 is the key-length of FOX64. We also get the lower bound of the number of differentially active S-boxes for 5-round FOX128, and proved the security of the full-round FOX128 with respect to single-key differential attack. Copyright © 2015 The Institute of Electronics, Information and Communication Engineers.

关键词： Integer programming

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Dense matter with eXTP

arXiv

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arXiv 2018年

作者： Watts, Anna L. Yu, Wenfei Poutanen, Juri Zhang, Shu Bhattacharyya, Sudip Bogdanov, Slavko Ji, Long Patruno, Alessandro Riley, Thomas E. Bakala, Pavel Baykal, Altan Bernardini, Federico Bombaci, Ignazio Brown, Edward Cavecchi, Yuri Chakrabarty, Deepto Chenevez, Jérôme Degenaar, Nathalie Santo, Melania Del Salvo, Tiziana Di Doroshenko, Victor Falanga, Maurizio Ferdman, Robert D. Feroci, Marco Gambino, Angelo F. Ge, MingYu Greif, Svenja K. Guillot, Sebastien Gungor, Can Hartmann, Dieter H. Hebeler, Kai Heger, Alexander Homan, Jeroen Iaria, Rosario in’t Zand, Jean Kargaltsev, Oleg Kurkela, Aleksi Lai, Xiaoyu Li, Ang Li, XiangDong Li, Zhaosheng Linares, Manuel Lu, FangJun Mahmoodifar, Simin Méndez, Mariano Miller, M. Coleman Morsink, Sharon Nättilä, Joonas Possenti, Andrea Prescod-Weinstein, Chanda Qu, JinLu Riggio, Alessandro Salmi, Tuomo Sanna, Andrea Santangelo, Andrea Schatz, Hendrik Schwenk, Achim Song, LiMing Šrámková, Eva Stappers, Benjamin Stiele, Holger Strohmayer, Tod Tews, Ingo Tolos, Laura Török, Gabriel Tsang, David Urbanec, Martin Vacchi, Andrea Xu, RenXin Xu, Ypeng Zane, Silvia Zhang, Guobao Zhang, ShuangNan Zhang, Wenda Zheng, ShiJie Zhou, Xia Anton Pannekoek Institute for Astronomy University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Shanghai Astronomical Observatory Shanghai200030 China Tuorla Observatory Department of Physics and Astronomy University of Turku Väisäläntie 20 PiikkiöFIN-21500 Finland Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-10691 Sweden Institute of High Energy Physics CAS Beijing100049 China Tata Institute of Fundamental Research Mumbai400005 India Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Institut für Astronomie und Astrophysik Tübingen Universität Tübingen Sand 1 TübingenD-72076 Germany Leiden Observatory Leiden University PO Box 9513 Leiden2300 RA Netherlands Research Center for Computational Physics and Data Processing Silesian University in Opava Bezrucovo nam. 13 OpavaCZ-74601 Czech Republic Physics Department Middle East Technical University Ankara06531 Turkey INAF Osservatorio Astronomico di Roma Via Frascati 33 Monteporzio CatoneI-00078 Italy New York University Abu Dhabi PO Box 129188 Abu Dhabi United Arab Emirates Dipartimento di Fisica Enrico Fermi University of Pisa PisaI-56127 Italy INFN Italian National Institute for Nuclear Physics Pisa Section Pisa56127 Italy Department of Physics and Astronomy Michigan State University East LansingMI48824 United States Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Mathematical Sciences and STAG Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom MIT Kavli Institute for Astrophysics and Space Research CambridgeMA02139 United States DTU Space Technical University of Denmark Elektrovej 327-328 Kgs Lyngby2800 Denmark INAF/IASF Palermo via Ugo La Malfa 153 PalermoI-90146 Italy Universita degli Studi di Palermo Dipartimento di Fisica e Chimica via Archira 36 Palermo90123 Italy

In this White Paper we present the potential of the Enhanced X-ray Timing and Polarimetry (eXTP) mission for determining the nature of dense matter;neutron star cores host an extreme density regime which cannot be replicated in a terrestrial laboratory. The tightest statistical constraints on the dense matter equation of state will come from pulse profile modelling of accretion-powered pulsars, burst oscillation sources, and rotation-powered pulsars. Additional constraints will derive from spin measurements, burst spectra, and properties of the accretion flows in the vicinity of the neutron star. Under development by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of science, the eXTP mission is expected to be launched in the mid 2020s. Copyright © 2018, The Authors. All rights reserved.

关键词： Pulsars

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Observation of the decay Λ0b → ψ(2S)pπ−

arXiv

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arXiv 2018年

作者： Aaij, R. Adeva, B. Adinolfi, M. aidala, C.A. Ajaltouni, Z. Akar, S. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Albero, A. Alfonso Ali, S. Alkhazov, G. Cartelle, P. Alvarez Alves, A.A. Amato, S. Amerio, S. Amhis, Y. An, L. Anderlini, L. Andreassi, G. Andreotti, M. Andrews, J.E. Appleby, R.B. Archilli, F. d’Argent, P. Romeu, J. Arnau Artamonov, A. Artuso, M. Arzymatov, K. Aslanides, E. Atzeni, M. Audurier, B. Bachmann, S. Back, J.J. Baker, S. Balagura, V. Baldini, W. Baranov, A. Barlow, R.J. Barsuk, S. Barter, W. Baryshnikov, F. Batozskaya, V. Batsukh, B. Battista, V. Bay, A. Beddow, J. Bedeschi, F. Bediaga, I. Beiter, A. Bel, L.J. Beliy, N. Bellee, V. Belloli, N. Belous, K. Belyaev, I. Ben-Haim, E. Bencivenni, G. Benson, S. Beranek, S. Berezhnoy, A. Bernet, R. Berninghoff, D. Bertholet, E. Bertolin, A. Betancourt, C. Betti, F. Bettler, M.O. van Beuzekom, M. Bezshyiko, Ia. Bhasin, S. Bhom, J. Bifani, S. Billoir, P. Birnkraut, A. Bizzeti, A. Bjørn, M. Blago, M.P. Blake, T. Blanc, F. Blusk, S. Bobulska, D. Bocci, V. Garcia, O. Boente Boettcher, T. Bondar, A. Bondar, N. Borghi, S. Borisyak, M. Borsato, M. Bossu, F. Boubdir, M. Bowcock, T.J.V. Bozzi, C. Braun, S. Brodski, M. Brodzicka, J. Gonzalo, A. Brossa Brundu, D. Buchanan, E. Buonaura, A. Burr, C. Bursche, A. Buytaert, J. Byczynski, W. Cadeddu, S. Cai, H. Calabrese, R. Calladine, R. Calvi, M. Gomez, M. Calvo Camboni, A. Campana, P. Perez, D.H. Campora Capriotti, L. Carbone, A. Carboni, G. Cardinale, R. Cardini, A. Carniti, P. Carson, L. Akiba, K. Carvalho Casse, G. Cassina, L. Cattaneo, M. Cavallero, G. Cenci, R. Chamont, D. Chapman, M.G. Charles, M. Charpentier, Ph. Chatzikonstantinidis, G. Chefdeville, M. Chekalina, V. Chen, C. Chen, S. Chitic, S.-G. Chobanova, V. Chrzaszcz, M. Chubykin, A. Ciambrone, P. Vidal, X. Cid Ciezarek, G. Clarke, P.E.L. Clemencic, M. Cliff, H.V. Closier, J. Coco, V. Coelho, J.A.B. Cogan, J. Cogneras, E. Cojocariu, L. Collins, P. Colombo, T. Rio de Janeiro Brazil Rio de Janeiro Brazil Center for High Energy Physics Tsinghua University Beijing China Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS IN2P3 LAPP Annecy France Clermont Université Université Blaise Pascal CNRS IN2P3 LPC Clermont-Ferrand France Aix Marseille Univ CNRS IN2P3 CPPM Marseille France LAL Univ. Paris-Sud CNRS IN2P3 Université Paris-Saclay Orsay France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Fakultät Physik Technische Universität Dortmund Dortmund Germany Heidelberg Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany School of Physics University College Dublin Dublin Ireland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Milano Milano Italy INFN Sezione di Cagliari Monserrato Italy INFN Sezione di Padova Padova Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma Tor Vergata Roma Italy INFN Sezione di Roma La Sapienza Roma Italy Nikhef National Institute for Subatomic Physics Amsterdam Netherlands Nikhef National Institute for Subatomic Physics VU University Amsterdam Amsterdam Netherlands Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences Kraków Poland AGH - University of Science and Technology Faculty of Physics and Applied Computer Science Kraków Poland Warsaw Poland Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest-Magurele Romania Gatchina Russia Moscow Russia Institute of Nuclear Physics Moscow State University SINP MSU Moscow Russia Institute for Nuclear Research Russian Academy of Sciences INR RAS Moscow Russia Yande

The Cabibbo-suppressed decay Λ0b → ψ(2S)pπ− is observed for the first time using a data sample collected by the LHCb experiment in proton-proton collisions corresponding to 1.0, 2.0 and 1.9 fb−1 of integrated luminosity at centre-of-mass energies of 7, 8 and 13 TeV, respectively. The ψ(2S) mesons are reconstructed in the µ+µ− final state. The branching fraction with respect to that of the Λ0b → ψ(2S)pK− decay mode is measured to be B B( ( ΛΛ0b0b→→ψψ(2S)pK(2S)pπ−− ) ) = (11.4 ± 1.3 ± 0.2)% , where the first uncertainty is statistical and the second is systematic. The ψ(2S)p and ψ(2S)π− mass spectra are investigated and no evidence for exotic resonances is found. Copyright © 2018, The Authors. All rights reserved.

关键词： Mass spectrometry

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Nat Genet:单细胞分析解开结直肠癌细胞的神秘面纱

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现代生物医学进展 2017年第15期17卷 I0003-I0003页

作者： Huipeng Li, Elise T Courtois, Debarka Sengupta, Yuliana Tan Shyam Prabhakar Elise T Courtois, Yuliana Tan Paul Robson Debarka Sengupta Kok Hao Chen, Jolene Jie Lin Goh Paul Jongjoon Choi Say Li Kong, Axel M Hillmer Iain Beehuat Tan Clarinda Chua Iain Beehuat Tan Lim Kiat Hon Wah Siew Tan Mark Wong Lawrence J K Wee Iain Beehuat Tan Paul Robson Paul Robson Paul Robson Computational and Systems Biology Genome Institute of Singapore Singapore Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India Synthetic Biology Genome Institute of Singapore Singapore Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Department of Medical Oncology National Cancer Centre Singapore Singapore Department of Pathology Singapore General Hospital Singapore Department of Colorectal Surgery Singapore General Hospital Singapore Data Analytics Department Institute for Infocomm Research Singapore Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA Department of Biological Sciences National University of Singapore Singapore.

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测... 详细信息

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测序。”JAX科学家、这篇发表在Nature Genetics上的文章共同第一作者Elise Courtois说道。”我们可以根据肿瘤中的细胞组成将结直肠癌进一步分类。因为每种亚型的肿瘤病人生存率存在差别，我们的方法将为肿瘤医生提供更多的关于肿瘤预后和治疗的信息。”

关键词：单细胞分析结直肠癌癌细胞 Nat 细胞组成肿瘤病计算机技术细胞生物学

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Abstracts and Workshops 7th National Spinal Cord Injury Conference November 9-11, 2017 Fallsview Casino Resort Niagara Falls, Ontario, Canada Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2017年第6期40卷 813-869页

作者： [Anonymous] a NET Team Toronto Rehabilitation Institute - University Health Network Toronto ON Canada. fo Neural Engineering and Therapeutic Team Lyndhurst Centre Toronto Rehabilitation Institute - University Health Network Toronto ON Canada. c Rick Hansen Institute Vancouver BC Canada. ev Rick Hansen Institute Vancouver BC Canada. fc Stan Cassidy Hospital University of New Brunswick Fredericton NB Canada. fd Dalhousie University Halifax NS Canada. e University of Saskatchewan Saskatoon SK Canada. aa University of Calgary Calgary AB Canada. y Dalhousie University Halifax NS Canada. fa Health Sciences Centre Winnipeg MB Canada. fb University of Manitoba Winnipeg MB Canada. h University of Alberta Edmonton AB Canada. i Universite Laval Quebe c City QC Canada. bd Rick Hansen Institute Vancouver BC Canada. fz Clinician Investigator Lyndhurst Centre Toronto Rehabilitation Institute University Health Network and Assistant Professor Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto. v University of Toront o Toronto ON Canada. n Institute of Biomaterials and Biomedical Engineering University of Toronto Toronto ON Canada. o Toronto Rehabilitation Institute - University Health Network Toronto ON Canada. ga Clinical Scientist Lyndhurst Centre Toronto Rehabilitation Institute University Health Network and Assistant Professor Department of Physical Therapy University of Toronto. dx Department of Electrical and Computer Engineering University of Toronto Toronto ON Canada. fm Lawson Health Research Institute London ON Canada. r Parkwood Institute London ON Canada. bi Parkwood Institute London ON Canada. s University of British Columbia Vancouver BC Canada. t Simon Fraser University Burnaby BC Canada. u DeltaQuest Foundation Concord MA USA. z Institut national d'excellence en santé et en services sociaux (INESSS) Quebec City QC Ca

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