The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Michael L. Tress - One of the best experts on this subject based on the ideXlab platform.
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Target Domain Definition and Classification in CASP8
Proteins: Structure Function and Bioinformatics, 2009Co-Authors: Michael L. Tress, Iakes Ezkurdia, Jane S RichardsonAbstract:In order to be successful CASP experiments require experimentally determined protein structures. These structures form the basis of the experiment. Structural genomics groups have provided the vast majority of these structures in recent editions of CASP. Before the structure prediction assessment can begin these target structures must be divided into structural Domains for assessment purposes, and each assessment unit must be assigned to one or more tertiary structure prediction categories. In CASP8 target Domain boundaries were based on visual inspection of targets and their experimental data, and on superpositions of the target structures with related template structures. As in CASP7 target Domains were broadly classified into two different categories: “template-based modeling” and “free modeling”. Assessment categories were determined by structural similarity between the target Domain and the nearest structural templates in the PDB and by whether or not related structural templates were used to build the models. The vast majority of the 164 assessment units in CASP8 were classified as template-based modeling. Just 10 target Domains were defined as free modeling. In addition three targets were assessed in both the free modeling and template based categories and a subset of 50 template-based models were evaluated as part of the “high accuracy” subset. The targets submitted for CASP8 confirmed a trend that has been apparent since CASP5: targets submitted to the CASP experiments are becoming easier to predict.
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Target Domain Definition and classification in CASP8.
Proteins, 2009Co-Authors: Michael L. Tress, Iakes Ezkurdia, Jane S RichardsonAbstract:In order to be successful CASP experiments require experimentally determined protein structures. These structures form the basis of the experiment. Structural genomics groups have provided the vast majority of these structures in recent editions of CASP. Before the structure prediction assessment can begin these target structures must be divided into structural Domains for assessment purposes and each assessment unit must be assigned to one or more tertiary structure prediction categories. In CASP8 target Domain boundaries were based on visual inspection of targets and their experimental data, and on superpositions of the target structures with related template structures. As in CASP7 target Domains were broadly classified into two different categories: "template-based modeling" and "free modeling." Assessment categories were determined by structural similarity between the target Domain and the nearest structural templates in the PDB and by whether or not related structural templates were used to build the models. The vast majority of the 164 assessment units in CASP8 were classified as template-based modeling. Just 10 target Domains were defined as free modeling. In addition three targets were assessed in both the free modeling and template based categories and a subset of 50 template-based models was evaluated as part of the "high accuracy" subset. The targets submitted for CASP8 confirmed a trend that has been apparent since CASP5: targets submitted to the CASP experiments are becoming easier to predict.
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Domain Definition and target classification for CASP7.
Proteins: Structure Function and Bioinformatics, 2007Co-Authors: Neil D. Clarke, Iakes Ezkurdia, Jürgen Kopp, Randy J. Read, Torsten Schwede, Michael L. TressAbstract:Experimentally determined protein structures formed the basis of the CASP7 prediction assessments. These target structures were assigned to one or more tertiary structure prediction categories and where necessary were divided into structural Domains. Boundaries for these Domains were based on visual inspection of the targets and superpositions of the target with template structures. Target Domains were classified into three different categories for assessment: "high accuracy modeling," "template-based modeling," and "free modeling." Assessment categories were determined by structural similarity between the target Domain and the nearest structural templates in the PDB and by the accuracy of the models submitted by the predictors or by whether or not template information was used to generate the predictions. In CASP7 108 of the 123 target Domains were evaluated in the template-based modeling category and the remaining 15 target Domains were classified as free modeling. A total of 28 target Domains from the template-based modeling category were also assessed in the high accuracy category and four overlapped with the free modeling category.
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Domain Definition and target classification for CASP6.
Proteins: Structure Function and Bioinformatics, 2005Co-Authors: Michael L. Tress, Iakes Ezkurdia, Chin-hsien Tai, Guoli Wang, Gonzalo Lopez, Alfonso Valencia, Byungkook Lee, Roland L. DunbrackAbstract:Assessment of structure predictions in CASP6 was based on single Domains isolated from experimentally determined structures, which were categorized into comparative modeling, fold recognition, and new fold targets. Domain Definitions were defined upon visual examination of the structures with the aid of automated Domain-parsing programs. Domain categorization was determined by comparison of the target structures with those in the Protein Data Bank at the time each target expired and a variety of sequence and structure-based methods to determine potential homologous relationships.
Qin Zheng - One of the best experts on this subject based on the ideXlab platform.
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Semantic Web service composition using Graphplan
2009 4th IEEE Conference on Industrial Electronics and Applications, 2009Co-Authors: Yang Bo, Qin ZhengAbstract:Web service composition has become a key area of research in the services oriented architecture (SOA) community. The idea of using AI planner to compose Web services has been suggested in a lot of papers. Since the different application Domains and the constraint of precondition that AI planners have, none of the existing planner can solve this problem, perfectly. In this paper, we present an algorithm that translates Web service composition problem to AI planning problems, and using Planning Domain Definition Language (PDDL) to describe the problem which is supported by most of AI planners. The present paper also describes the architecture and several key procedures of the translating algorithm.
Blake Hannaford - One of the best experts on this subject based on the ideXlab platform.
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ICRA - Stable teleoperation with time Domain passivity control
IEEE Transactions on Robotics and Automation, 2004Co-Authors: Jee-hwan Ryu, Dongsoo Kwon, Blake HannafordAbstract:A new bilateral control scheme is proposed to ensure stable teleoperation under a wide variety of environments and operating speeds. System stability is analyzed in terms of the time-Domain Definition of passivity. A previously proposed energy-based method is extended to a 2-port network, and the issues in implementing the "passivity observer" and "passivity controller" to teleoperation systems are studied. The method is tested with our two-degrees-of-freedom master/slave teleoperation system. Stable teleoperation is achieved under conditions such as hard wall contact (stiffness >150 kN/m) and hard surface following.
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stable teleoperation with time Domain passivity control
International Conference on Robotics and Automation, 2002Co-Authors: Jee-hwan Ryu, Dongsoo Kwon, Blake HannafordAbstract:A new bilateral control scheme is proposed to ensure stable teleoperation under a wide variety of environments and operating speeds. System stability is analyzed in terms of the time-Domain Definition of passivity. A previously proposed energy-based method is extended to a 2-port network, and the issues in implementing the "passivity observer" and "passivity controller" to teleoperation systems are studied. The method is tested with our two-degrees-of-freedom master/slave teleoperation system. Stable teleoperation is achieved under conditions such as hard wall contact (stiffness >150 kN/m) and hard surface following.
Nicolas Arnaud - One of the best experts on this subject based on the ideXlab platform.
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Xyloglucans fucosylation defects do not alter plant boundary Domain Definition.
Plant signaling & behavior, 2018Co-Authors: Beatriz Goncalves, Julien Sechet, Nicolas ArnaudAbstract:The CUP-SHAPED COTYLEDON (CUC) transcription factors play a fundamental role in plant morphogenesis by defining boundary Domains throughout plant development. Despite their central roles in plant d...
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Xyloglucans fucosylation defects do not alter plant boundary Domain Definition
Plant Signaling and Behavior, 2018Co-Authors: Beatriz Goncalves, Julien Sechet, Nicolas ArnaudAbstract:The CUP-SHAPED COTYLEDON (CUC) transcription factors play a fundamental role in plant morphogenesis by defining boundary Domains throughout plant development. Despite their central roles in plant development, little is known about the CUC molecular network. In a recent work, we identified a role for MUR1, a protein involved in the production of GDP-L-Fucose, in this network and showed that fucose per se is required for proper boundary Definition in various developmental contexts. Which pathway involving fucose is required to determine boundary is not yet known. Here, we use a previously described mutant and transgenic line with reduced fucosylated xyloglucans (XyG) to explore one such pathway. By quantitatively comparing leaf shape, we show that defects in XyG fucosylation do not impact leaf serrations development suggesting that fucose absence in XyG does not impact boundary development in mur1-1 mutant. Thus another - not yet identified - pathway or fucosylated compound contribute to boundary Domain Definition.
Iakes Ezkurdia - One of the best experts on this subject based on the ideXlab platform.
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Target Domain Definition and Classification in CASP8
Proteins: Structure Function and Bioinformatics, 2009Co-Authors: Michael L. Tress, Iakes Ezkurdia, Jane S RichardsonAbstract:In order to be successful CASP experiments require experimentally determined protein structures. These structures form the basis of the experiment. Structural genomics groups have provided the vast majority of these structures in recent editions of CASP. Before the structure prediction assessment can begin these target structures must be divided into structural Domains for assessment purposes, and each assessment unit must be assigned to one or more tertiary structure prediction categories. In CASP8 target Domain boundaries were based on visual inspection of targets and their experimental data, and on superpositions of the target structures with related template structures. As in CASP7 target Domains were broadly classified into two different categories: “template-based modeling” and “free modeling”. Assessment categories were determined by structural similarity between the target Domain and the nearest structural templates in the PDB and by whether or not related structural templates were used to build the models. The vast majority of the 164 assessment units in CASP8 were classified as template-based modeling. Just 10 target Domains were defined as free modeling. In addition three targets were assessed in both the free modeling and template based categories and a subset of 50 template-based models were evaluated as part of the “high accuracy” subset. The targets submitted for CASP8 confirmed a trend that has been apparent since CASP5: targets submitted to the CASP experiments are becoming easier to predict.
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Target Domain Definition and classification in CASP8.
Proteins, 2009Co-Authors: Michael L. Tress, Iakes Ezkurdia, Jane S RichardsonAbstract:In order to be successful CASP experiments require experimentally determined protein structures. These structures form the basis of the experiment. Structural genomics groups have provided the vast majority of these structures in recent editions of CASP. Before the structure prediction assessment can begin these target structures must be divided into structural Domains for assessment purposes and each assessment unit must be assigned to one or more tertiary structure prediction categories. In CASP8 target Domain boundaries were based on visual inspection of targets and their experimental data, and on superpositions of the target structures with related template structures. As in CASP7 target Domains were broadly classified into two different categories: "template-based modeling" and "free modeling." Assessment categories were determined by structural similarity between the target Domain and the nearest structural templates in the PDB and by whether or not related structural templates were used to build the models. The vast majority of the 164 assessment units in CASP8 were classified as template-based modeling. Just 10 target Domains were defined as free modeling. In addition three targets were assessed in both the free modeling and template based categories and a subset of 50 template-based models was evaluated as part of the "high accuracy" subset. The targets submitted for CASP8 confirmed a trend that has been apparent since CASP5: targets submitted to the CASP experiments are becoming easier to predict.
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Domain Definition and target classification for CASP7.
Proteins: Structure Function and Bioinformatics, 2007Co-Authors: Neil D. Clarke, Iakes Ezkurdia, Jürgen Kopp, Randy J. Read, Torsten Schwede, Michael L. TressAbstract:Experimentally determined protein structures formed the basis of the CASP7 prediction assessments. These target structures were assigned to one or more tertiary structure prediction categories and where necessary were divided into structural Domains. Boundaries for these Domains were based on visual inspection of the targets and superpositions of the target with template structures. Target Domains were classified into three different categories for assessment: "high accuracy modeling," "template-based modeling," and "free modeling." Assessment categories were determined by structural similarity between the target Domain and the nearest structural templates in the PDB and by the accuracy of the models submitted by the predictors or by whether or not template information was used to generate the predictions. In CASP7 108 of the 123 target Domains were evaluated in the template-based modeling category and the remaining 15 target Domains were classified as free modeling. A total of 28 target Domains from the template-based modeling category were also assessed in the high accuracy category and four overlapped with the free modeling category.
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Domain Definition and target classification for CASP6.
Proteins: Structure Function and Bioinformatics, 2005Co-Authors: Michael L. Tress, Iakes Ezkurdia, Chin-hsien Tai, Guoli Wang, Gonzalo Lopez, Alfonso Valencia, Byungkook Lee, Roland L. DunbrackAbstract:Assessment of structure predictions in CASP6 was based on single Domains isolated from experimentally determined structures, which were categorized into comparative modeling, fold recognition, and new fold targets. Domain Definitions were defined upon visual examination of the structures with the aid of automated Domain-parsing programs. Domain categorization was determined by comparison of the target structures with those in the Protein Data Bank at the time each target expired and a variety of sequence and structure-based methods to determine potential homologous relationships.