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Ivo D. Dinov , Ph.D., CCB Chief Operations Officer

Ivo D. Dinov , Ph.D., CCB Chief Operations Officer PI: Arthur W. Toga, Ph.D. Co-PI: Tony F. Chan, Ph.D. AWT. CCB Science, SW Development & Infrastructure. Science Developments CCB Grand Challenges SW & Computational Tool Development Internal Algorithm & SW design

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Ivo D. Dinov , Ph.D., CCB Chief Operations Officer

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  1. Ivo D. Dinov, Ph.D., CCB Chief Operations Officer PI: Arthur W. Toga, Ph.D. Co-PI: Tony F. Chan, Ph.D. AWT

  2. CCB Science, SW Development & Infrastructure • Science Developments • CCB Grand Challenges • SW & Computational Tool Development • Internal Algorithm & SW design • External SW design, policies, licenses • Software Management System (SMS) • Data Sharing • CCB SW Integration with other NCBCs • NCBC as a national infrastructure for biomedical computing

  3. CCB Science Developments • Non-Affine Volumetric Registration • Shape • Parametric & Implicit Shape Representation • Modeling & Parsing of Biological Shapes • Shape Analysis (e.g., using Integral Invariants) • Conformal Mapping (on D2 or S2) • Volumetric Image Segmentation • Biosequence analysis (e.g., alternative splicing) • Driving Biological Projects

  4. CCB – Driving Biological Projects (current) DBP 1: Mapping Language Development Longitudinally DBP2: Mapping Structural and Functional Changes in Aging and Dementia DBP3: Multiple Sclerosis and Experimental Autoimmune Encephalomyelitis DBP 4: Correlating Neuroimaging, Phenotype and Genotype in Schizophrenia

  5. CCB Science, SW Development & Infrastructure • Science Developments • CCB Grand Challenges • SW & Computational Tool Development • Internal Algorithm & SW design • External SW design, policies, licenses • Software Management System (SMS) • Data Sharing • CCB SW Integration with other NCBCs • NCBC as a national infrastructure for biomedical computing

  6. CCB Grand Challenges • Brain Mapping Challenges • Software & Hardware Engineering Challenges • Infrastructure & Communication Challenges • Data Management • Multidisciplinary Science Environment

  7. CCB Brain Mapping Challenges • Quantitative analysis of structural & functional data • Merging NeuroImaging and Clinical data (e.g., NPI) • NeuroImaging Interactions w/ Genotype-Phenotype • Understanding Temporal Changes in the Brain • Data Management (volume, complexity, HIPAA) • Data Integration Across Species, Modalities, Resol. • Efficient and Robust Neurocomputation (Grid) • SW & Tool Development and Management (Pipeline)

  8. CCB Science, SW Development & Infrastructure • Science Developments • CCB Grand Challenges • SW & Computational Tool Development • Internal Algorithm & SW design • External SW design, policies, licenses • Software Management System (SMS) • Data Sharing • CCB SW Integration with other NCBCs • NCBC as a national infrastructure for biomedical computing

  9. CCB Computational Atlas Framework

  10. CCB Computational Tools • Data Analysis • Image segmentation • Surface methods • DTI Analysis • Genotype-Phenotype analysis • Interaction • Grid Pipeline Environment • Pipeline/SCIRun Integration • Pipeline/Slicer Integration • Tools for Integration, Managing, Modeling & Visualization • Knowledge Management • Analytic strategy validation • Data Provenance

  11. CCB Infrastructure Computing Infrastructure Develop, implement and maintain the computing resources and network services required for computationally intensive science performed in the CCB Application Deployment Integrate the algorithms, techniques and tools developed in Cores 1 & 2 with the Computing Infrastructure to enable researchers to remotely access and use the computing resources of the CCB Computational Research Support Provide technical support and expertise to enable collaborators to use the resources of the CCB

  12. SW & Computational Tool Development • Internal Algorithm & SW design • Mixture of: • Sporadic Rapid Prototype development efforts • Structured library-based quality developments • SW stages: active development, a- & b-distributions • External SW design, policies, licenses • http://www.loni.ucla.edu/Policies/ • Integration/Interoperability (for now mainly with NAMIC) • Software Management System (SMS) • Based on: http://gforge.org/ • Summary | Admin | Home Page | Forums | Tracker | Bugs | Support | Patches | RFE | Lists | Tasks | Docs | Screenshots | News | CVS | Files|

  13. Example: Shape Representation –New Codebase Specification • Explicitly represent points, edges, faces and solids • Allow any number of arbitrary objects (e.g., scalars, vectors, tensors, colors) to be associated with topological primitives • Modular Java-based architecture designed for collaborative development, including documentation sufficient to support independent development http://www.loni.ucla.edu/twiki/bin/view/CCB/ShapeToolLibraryProgram

  14. Grid Pipeline • The algorithms have been implemented • Possibly located on different platforms, different machines • The data has been gathered • Possibly located on different machines, have different forma • Grid Pipeline Processing Environment • A data flow execution environment • Useful for… • Any task where you can draw the steps in a flowchart • Any task where you need to write instructions for someone

  15. Pipeline GUI v 3.0

  16. Data Mediation

  17. Data Visualization Mutation Pathways Of HIV-1 Protease Additional functionality Is integrated via the extension architecture.

  18. Grid Engine Integration

  19. DB of Current CCB SW Developments Tool Categories • Viewers • Java-based • C++/VTK/ITK • Analysis • Segmentation • Sequence Analysis • Data Alignment • Shape Analysis • Preprocessing (skull stripping) • Atlasing Tools • Construction • Mapping • Analysis (variation) • Data Processing (Pipeline, Debabeler) • Data Integration (BrainGraph) DB Template Example 1. SW Label (acronym) ShapeViewer 2. Short Description: Provides 3D interactive user interface for viewing parametric shapes commonly used in CCB 3. Data INPUT (format, parameters, etc.): Current version requires UCF format 4. Data OUTPUT (format, parameters, etc.): Scenes, multiples shape objects, associated view reloading 5. Implementation Language: Java 1.4, requires Java3D runs as Application or as Applet 6. Platform(s) tested: Macintosh, PC, Sun 7. Version, date, Stage: 1.0, May 24, 2005, 8. Author(s): Ma, Schwartz, Woods, Dinov 9. URL: http://www.loni.ucla.edu/Software/Software_Detail.jsp?software_id=18 http://www.loni.ucla.edu/CCB/Software/

  20. CCB Science, SW Development & Infrastructure • Science Developments • CCB Grand Challenges • SW & Computational Tool Development • Internal Algorithm & SW design • External SW design, policies, licenses • Software Management System (SMS) • Data Sharing • CCB SW Integration with other NCBCs • NCBC as a national infrastructure for biomedical computing

  21. CCB Data Sharing • CCB does not acquire data • CCB utilizes other resource for test data • Test data for internal CCB use: • Algorithm Development (All Problems outlined in CCB SIG Challenges) • Tool Implementation Testing and Validation • Online: http://www.loni.ucla.edu/CCB/About/Inside_CCB/CCB_Resources.jsp

  22. CCB Science, SW Development & Infrastructure • Science Developments • CCB Grand Challenges • SW & Computational Tool Development • Internal Algorithm & SW design • External SW design, policies, licenses • Software Management System (SMS) • Data Sharing • CCB SW Integration with other NCBCs • NCBC as a national infrastructure for biomedical computing

  23. CCB SW Integration with other NCBCs • CCB–NAMIC • SLIPIE (Slicer-LONI Pipeline Integration Environment) • Java  JNI mediation C/C++ tools • Level-set segmentation techniques • CCB–I2B2 • HIVE cells  Neuroscience Pipelines • Pipeline modules  HIVE Objects • Neurogenetics (e.g., Huntington’s), DB and biosequence analysis • CCB–SimBios • Structure Modeling Tools  Pipeline Modules • CCB Compute/Viz Libs  SimTK • CCB–Collaborators (many directions) • Diffeomorphic shape representation • … • Integration of Gene expression maps and Macro-imaging

  24. CCB Science, SW Development & Infrastructure • Science Developments • CCB Grand Challenges • SW & Computational Tool Development • Internal Algorithm & SW design • External SW design, policies, licenses • Software Management System (SMS) • Data Sharing • CCB SW Integration with other NCBCs • NCBC as a National Infrastructure for Biomedical Computing

  25. NCBCs-Nat’l Infrastructure for Biomed Computing • Inter-Center Infrastructure • Intra-Center Infrastructure • Center-Collaborators Infrastructure

  26. Center for Computational Biology

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