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The generation challenge programme platform: Semantic standards and workbench for crop science

International Journal of Plant Genomics 2008年第1期2008卷 369601页

作者： Bruskiewich, Richard Senger, Martin Davenport, Guy Ruiz, Manuel Rouard, Mathieu Hazekamp, Tom Takeya, Masaru Doi, Koji Satoh, Kouji Costa, Marcos Simon, Reinhard Balaji, Jayashree Akintunde, Akinnola Mauleon, Ramil Wanchana, Samart Shah, Trushar Anacleto, Mylah Portugal, Arllet Ulat, Victor Jun Thongjuea, Supat Braak, Kyle Ritter, Sebastian Dereeper, Alexis Skofic, Milko Rojas, Edwin Martins, Natalia Pappas, Georgios Alamban, Ryan Almodiel, Roque Barboza, Lord Hendrix Detras, Jeffrey Manansala, Kevin Mendoza, Michael Jonathan Morales, Jeffrey Peralta, Barry Valerio, Rowena Zhang, Yi Gregorio, Sergio Hermocilla, Joseph Echavez, Michael Yap, Jan Michael Farmer, Andrew Schiltz, Gary Lee, Jennifer Casstevens, Terry Jaiswal, Pankaj Meintjes, Ayton Wilkinson, Mark Good, Benjamin Wagner, James Morris, Jane Marshall, David Collins, Anthony Kikuchi, Shoshi Metz, Thomas McLaren, Graham Van Hintum, Theo Crop Research Informatics Laboratory International Rice Research Institute (IRRI) DAPO Box 7777 Metro Manila Philippines Crop Research Informatics Laboratory International Maize and Wheat Improvement Center (CIMMYT) DF 06600 Mexico Apdo. Postal 6-641 Mexico Centre International de Recherche Agronomique Pour le Développement (CIRAD) 34398 Montpellier Cedex 5 Avenue Agropolis France Bioversity International 00057 Maccarese (Fiumicino) Rome Via dei Tre Denari 472/a Italy National Institute for Agrobiological Sciences (NIAS) Tsukuba Ibaraki 305-8602 Kannondai 2-1-2 Japan Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) Brasilia DF 70770-900 Parque Estaçao Biologia Final W5 Norte Brazil Centro Internacional de la Papa (CIP) Lima 12 Avenida La Molina 1895 La Molina Peru International Crops Research Institute for the Semi-Arid Tropics Patancheru Andhra Pradesh 502324 India International Center for Agricultural Research in the Dry Areas Aleppo P.O. Box 5466 Syrian Arab Republic National Center for Genetic Engineering and Biotechnology 113 Thailand Science Park Klong Luang Pathumthani 12120 Phahonyothin Road Klong 1 Thailand Institute of Computer Science College of Arts and Sciences University of the Philippines Los Banos Laguna 4031 Philippines Department of Computer Science University of the Philippines Melchor Hall Diliman Quezon City 1101 Philippines National Center for Genome Resources Santa Fe NM 87505 2935 Rodeo Park Drive East United States Scottish Crop Research Institute Invergowrie Dundee DD2 5DA United Kingdom Department of Plant Breeding Cornell University Ithaca NY 14853 United States African Centre for Gene Technologies Lynnwood Ridge 0040 P.O. Box 75011 South Africa Department of Medical Genetics Faculty of Medicine University of British Columbia Vancouver BC V6T 1Z3 Canada School of Computing Science Simon Fraser Universtiy Burnaby BC V5A 1S6 8888 University Drive Canada Bioinformatics Graduate Program Genome Sciences Centre BC

The Generation Challenge programme (GCP) is a global crop research consortium directed toward crop improvement through the application of comparative biology and genetic resources characterization to plant breeding. A key consortium research activity is the development of a GCP crop bioinformatics platform to support GCP research. This platform includes the following: (i) shared, public platform-independent domain models, ontology, and data formats to enable interoperability of data and analysis flows within the platform;(ii) web service and registry technologies to identify, share, and integrate information across diverse, globally dispersed data sources, as well as to access high-performance computational (HPC) facilities for computationally intensive, high-throughput analyses of project data;(iii) platform-specific middleware reference implementations of the domain model integrating a suite of public (largely open-access/-source) databases and software tools into a workbench to facilitate biodiversity analysis, comparative analysis of crop genomic data, and plant breeding decision making.

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Web Host Access Tool: A Support Vector Machine Approach

Web Host Access Tool: A Support Vector Machine Approach

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Banerjee L. Cassel Department of Computing Sciences Villanova University Villanova PA USA Graduate Program Director of Computing Sciences Villanova University Villanova PA USA

Search engines are an integral part of web information gathering and retrieval process, but they are significantly dependent upon the words or word phrases input by the end user. The search engine usually contributes no additional semantic data toward the information acquisition process. This paper presents an intelligent search agent "Web host access tool" (WHAT) based on support vector machines (SVM), which introduces the notion of queries conducted within a specific contextual meaning. Given a context and associated keywords that personalize the search history and preferences of the user, WHAT performs more intelligent resource filtering than conventional search engines, providing more relevant results while filtering the irrelevant references. Search results obtained in the form of text from different search engines are processed by the SVM based word classifier that arrange the results obtained according to user preference obtained from previous search processes. The text materials are processed by Latent Semantic Indexing (LSI) for creating a document matrix that gives the probability of a word occurrence in a specified context. For simplicity, this paper considers 5 different contexts: business, education, entertainment, news and information and tourism. The LSI coefficients were used by SVM to yield confidence levels for each search result and according to that the results were sorted and presented to the end user. As an alternative, least square support vector machines (LS-SVM) are also studied in this paper. The system is updated by the user that provides feedback regarding the search relevance, based on which the LSI coefficients are updated to ensure increased relevance in future searches. Three different types of kernel function were considered in this paper -Linear. Radial basis function (RBF) and polynomial kernel. Results claim that Linear SVM perform better than the others, not only in terms of classification accuracy but also in terms of training s

关键词： Support vector machines Search engines Large scale integration Machine intelligence Filtering Support vector machine classification Kernel Information retrieval Intelligent agent History

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Spotlights on network operations and management research in Latin America - A report on LANOMS 2005

Spotlights on network operations and management research in ...

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作者： Granville, Lisandro Zambenedetti Gaspary, Luciano Paschoal Schulze, Bruno De Souza, José Neuman Brazil Special Interest Group on Computer Networks and Distributed Systems Brazilian Computer Society Brazil Brazilian Laboratory on Computer Networks Brazil IFIP/IEEE im Brazil Graduate Program on Applied Computing Universidade do Vale do Rio dos Sinos Brazil IEEE Communications Society Japan National Lab. for Scientific Computing - LNCC Brazil Brazilian National Research Council CNPq Brazil ACM Brazil IEEE Brazil Brazilian Computing Society Brazil Federal University of Ceará Brazil

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A Supervised Constructive Neuro-Immune Network for Pattern Classification

A Supervised Constructive Neuro-Immune Network for Pattern C...

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International Joint Conference on Neural Networks (IJCNN)

作者： H. Knidel L. Nunes de Castro F.J. Von Zuben Laboratory of Bioinformatics and Bioinspired Computing-LBiC Department of Computer Engineering and Industrial Automation-DCA School of Electrical and Computer Engineering-FEEC State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program in Computer Science Catholic University of Santos Sao Paulo Brazil

This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.

关键词： Pattern classification Immune system Neurons Supervised learning Vector quantization Classification algorithms Prototypes Clustering algorithms Unsupervised learning Artificial neural networks

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Real-time organ motion tracking and fast image registration system for MRI-guided surgery

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Systems and Computers in Japan 2006年第1期37卷 83-92页

作者： Tokuda, Junichi Hirano, Masaya Tsukamoto, Tetsuji Dohi, Takeyoshi Hata, Nobuhiko Graduate School of Information Science and Technology University of Tokyo Tokyo 113-8656 Japan GE Yokogawa Medical Systems Ltd. Hino 191-8503 Japan International Society for Computer-Aided Surgery Japan Society for Computer-Aided Surgery Japan Society for Magnetic Resonance in Medicine GE Yokogawa Medical Systems Ltd. Imaging Application Tech. Center MR Group R and D Laboratory Technology Center GE Yokogawa Medical Systems Ltd. Society for Magnetic Resonance in Medicine Graduate School of Information Science and Technology Society for Life Support Technology Japan Society for Organ Preservation Biology and Medicine Society for Precision Engineering Society for Medical Image Computing and Computer Assisted Intervention International Union for Physics and Engineering Sciences in Medicine JSMEBE Japan Society for Life Assistance Technology Brigham and Women's Hospital Harvard Medical School MR Therapy Program

The advantage of magnetic resonance imaging (MRI)-guided surgery, in which MR images taken during surgery are used to guide the surgery, has been recognized recently. However, there is a problem, due to long imaging time of MRI, that it is difficult to capture the respiratory organ motion. It is often a constraint that the patient must hold his or her breath, especially in the treatment of the abdominal region. Consequently, this paper proposes a method of eliminating this constraint in MRI-guided surgery by real-time monitoring of the movement of the organ involved in the surgery by means of the signal derived from MRI. Specifically, the navigator echo is extracted in imaging during surgery. The movement of the organ involved in surgery is detected on the basis of the change in the calculated projection profile, and the results are presented to the surgeon. In this study, the procedure is built into a 1.5-T clinical MRI system. The feasibility of the proposed system was examined under clinical conditions by evaluating its accuracy and delay in a phantom experiment, and also in an in vivo experiment on the liver of a volunteer. The results are reported in this paper. © 2005 Wiley Periodicals, Inc.

关键词： Motion estimation

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Exploiting multithreaded programming on cluster architectures

Exploiting multithreaded programming on cluster architecture...

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19th International Symposium on High Performance computing Systems and Applications, HPCS 2005

作者： Cordeiro, Otávio Corrêa Peranconi, Daniela Saccol Real, Lucas Correia Villa Dall'Agnol, Evandro Clivatti Cavalheiro, Gerson Geraldo H. Post-Graduate Program on Applied Computing Vale do Rio Dos Sinos University São Leopoldo RS Brazil

ISBN: (纸本)0769523439

The development of programs for parallel architectures focusing to achieve high indices of performance is a hard task. The programmer must deal with two classes of problems, one related to describing the application concurrency and data dependencies, and other related to exploiting efficiently the processing resources of the architecture, in order to obtain an efficient implementation for a specific hardware configuration. Since cluster technology allows building parallel computers are highly scalable-particularly in terms of number of nodes, processors and amount of memory-programming and execution levels must be considered independently to achieve performance portability. This paper presents Anahy, an environment for exploiting High Performance computing on cluster architectures. It is highlighted the Anahy programming interface and some features of its implementation as well as a performance evaluation. © 2005 IEEE.

关键词： Multiprocessing systems

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Congestion control with ECN support in poll-based multicast protocols

Congestion control with ECN support in poll-based multicast ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： M.P. Barcellos A. Detsch Post-graduate Program on Applied Computing PIPCA/UNISINOS Sao Leopoldo Rio Grande do Sul Brazil

Most of the traffic in the Internet nowadays is transmitted using TCP, or transport control protocol. The stability of the Internet depends on the congestion control being performed by this protocol, as well as equivalent mechanisms employed by other protocols. The ECN technique (explicit congestion notification), in which packets forwarded by routers are marked whenever congestion is about to occur (or already occurring), allows a transmitter to reduce the sending rate accordingly without relying on packet drops. ECN has not been fully explored, particularly regarding multicast protocols. This paper presents models of poll-based reliable multicast protocols and extends them with a new single-rate congestion control mechanism that harnesses ECN. Simulation results show that it provides fairness between flows from multiple sources, and is TCP-friendly. Further, they demonstrate the substantial efficiency gain obtained with the use of ECN in multicast.

关键词： Multicast protocols Sliding mode control Internet Transport protocols Multicast algorithms Scalability Signal generators Stability Transmitters Grid computing

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Exploiting multithreaded programming on cluster architectures

Exploiting multithreaded programming on cluster architecture...

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IEEE International Symposium on High Performance computing Systems and Applications (HPCS)

作者： O.C. Cordeiro D.S. Peranconi L.C.V. Real E.C. Dall'Agnol G.G.H. Cavalheiro Post-Graduate Program on Applied Computing Vale do Rio dos Sinos University São Leopoldo RS Brazil

The development of programs for parallel architectures focusing to achieve high indices of performance is a hard task. The programmer must deal with two classes of problems, one related to describing the application concurrency and data dependencies, and other related to exploiting efficiently the processing resources of the architecture, in order to obtain an efficient implementation for a specific hardware configuration. Since cluster technology allows building parallel computers are highly scalable - particularly in terms of number of nodes, processors and amount of memory - programming and execution levels must be considered independently to achieve performance portability. This paper presents Anahy, an environment for exploiting high performance computing on cluster architectures. It is highlighted the Anahy programming interface and some features of its implementation as well as a performance evaluation.

关键词： Concurrent computing Computer architecture High performance computing Parallel architectures programming profession Hardware Parallel machines Central Processing Unit Runtime environment Scheduling algorithm

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Virginia Tech-Wake Forest University School of Biomedical Engineering & Sciences: Providing new tools for a new program

Virginia Tech-Wake Forest University School of Biomedical En...

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2005 ASEE Annual Conference and Exposition: The Changing Landscape of Engineering and Technology Education in a Global World

作者： Scales, Glenda R. Peed, Cheryl Boehme, John Brewer, Kevin Virginia Tech. United States Wake Forest University United States Wake Forest University School of Medicine United States Office of Academic Computing Wake Forest University School of Medicine United States Virginia Tech. College of Engineering United States Commonwealth Graduate Engineering Program United States

The Virginia Tech-Wake Forest University School of Biomedical Engineering & Sciences (SBES) was established in 2001 to provide graduate programs and enhance research collaboration between the universities.1 This school enables students to earn M.S. and Ph.D. degrees in Biomedical Engineering, a joint M.D./Ph.D. through the Wake Forest University School of Medicine, and a joint D.V.M./Ph.D. through the Virginia-Maryland Regional College of Veterinary Medicine. Given that students may take face to face classes on either campus or classes via distance learning while residing on their home campus in either Blacksburg or Winston-Salem, new instructional technology solutions and infrastructure are required to support this initiative. A project team, consisting of members from Wake Forest and Virginia Tech, was established to develop this innovative learning environment. A key feature of this learning environment is the ability for the professor to easily interact with students using multiple types of instructional technologies. The use of these technologies is particularly important given the course content from the medical and engineering disciplines. This paper will address network infrastructure solutions and lessons learned from the development of this state-of-the-art distance learning environment.

关键词： Engineering education

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Handling Data Sparseness in Gene Network Reconstruction

Handling Data Sparseness in Gene Network Reconstruction

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： G.B. Bezerra T.V. Barra F.J. von Zuben L.N. de Castro Laboratory of Bioinformatics and Bio-Inspired Computing (LBiC) Department of Computer Engineering and Industrial Automation State University of Campinas-UNICAMP Sao Paulo Brazil Graduate Program on Informatics Catholic University of Santos Sao Paulo Brazil

One of the main problems related to regulatory network reconstruction from expression data concerns the small size and low quality of the available dataset. When trying to infer a model from little information it is necessary to give much more precedence to generalization, rather than specificity, otherwise, any attempt will be fated to overfitting. In this paper we address this issue by focusing on data sparseness and noisy information, and propose a density estimation technique that achieves regularized curves when data is scarce. We first compare the proposed method with the EM algorithm for mixture models on density estimation problems. Next, we apply the method, together with Bayesian networks, on realistic simulations of static gene networks, and compare the obtained results with the standard discrete Bayesian network model. We intend to demonstrate that adopting a discrete approach is not justifiable when only a small amount of information is available.

关键词： Intelligent networks Bayesian methods Genetics Bioinformatics Computer networks Reverse engineering Cellular networks Noise robustness Laboratories Data engineering

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