Ivo Dinov - US grants

Mathematical Sciences (21). This project is developing a suite of dynamically linked instructional materials, tutorials, demonstrations, experiments, graphical interfaces, and computational and visualization tools for improving statistics and probability education. Intellectual merit: The proposed interactive tools target both lower and upper division undergraduate students including those enrolled in a wide range of cross-listed service courses in disciplines such as economics, biochemistry, education, engineering, biomedicine, neuroscience and psychology. The project team combines faculty expertise in computational and statistical modeling research and open-source software development, with staff in a Center for Teaching Statistics and an office for Educational Technology Service. Broader impacts: Undergraduate and graduate students figure prominently in the design, implementation, and validation of the resources, via an iterative cycle of design-and-analysis with instruction, training and learning taking place simultaneously. The Web-based nature of the resources also facilitates the involvement of a diverse population of users. Collaborative efforts with other institutions further bolster the functionality and effectiveness of the resource as a contemporary pedagogical instrument. Finally, the materials serve to advance understanding in the general population of basic probability and statistical modeling techniques, and data and result interpretation for informed and scientific decision-making on social, biomedical and environmental issues.

This project develops a new computational and educational resource, the Distributome, for exploring, discovering and interacting with varieties of probability distributions. The Distributome project leverages the results of successful NSF projects that have been sustained over decades of work in developing interactive learning materials, forging technological advances, and building and sustaining digital libraries; all integrated with an effective dissemination and professional development infrastructure to ensure on-going use. There are several novel features of this project. This resource builds the infrastructure for community based development, expands and validates the distributions' meta-data that is stored, processed, searched, traversed and updated by experts, learners, and educators. The Distributome provides a graphical user interface for interactive exploration of diverse distribution resources, as well as a web-service for query, discovery and computational utilization of these distribution resources by other software programs and tools.

Specifically, this project provides an open (development and utilization), platform-agnostic, extensible and broad framework for navigation, discovery and usage of probability distributions in diverse applications. The entire framework is built using XML/JAVA/HTML/Wiki/MathML/LaTeX and is freely made available to the entire community via www.Distributome.org. The user-base of the Distributome infrastructure includes both educators (integrating these graphical tools and instructional materials in their course curricula and participating in a unique virtual community led by a cadre of activists) and most importantly learners (exploring, validating and understanding the use of probability distributions and models for practical problem solving). Probability modeling is at the root of solving driving biological, engineering, health, physical science, and social problems fundamental to the modern STEM curriculum. The Distributome infrastructure enables representation, demonstration, computation and visualization of a large number of probability distributions, their interrelations and their applications integrated with associated class and out-of-class activities to advance learning.

Core C: Summary/Abstract The Udall Center Biostatistics and Data Management Core will provide vital biostatistical support and data management for the Udall Center and contribute to the national Udall program and wider Parkinson's disease research community. The two specific aims of the Core are designed to ensure broad and reliable support of all Udall Center investigators, as well as external collaborators and the wider Parkinson's community. The first specific aim of the Core is to provide computational and statistical support of the Center projects, including design of experiments, statistical analyses using modern, state-of-the-art statistical models and methods, and interpretation of analyses leading to dissemination of research findings in manuscripts and scientific presentations. The second aim is to develop a federated database for imaging, clinical, and biomarker data that supports the research studies, collaborative interactions within the Center and nationally across all Udall Centers, and ensures that data management and quality controls are applied for all projects. Core investigators will assist Udall investigators, collaborators, and trainees with formulation, testing and validation of appropriate research hypotheses, and data collection, management, processing, visualization and interpretation. To ensure uniform formats and vocabularies that facilitate analyses and sharing of resulting data, the NINDS Parkinson's Disease Common Data Elements, CDEs, (www.CommonDataElements.ninds.nih.gov/PD.aspx) will be used. Electronic case report forms (eCRFs) will be incorporated into the 21 CFR Part 11-compliant web-based relational database OpenClinica® (see Facilities and Resources for more information on OpenClinica®). De-identified data will be deposited into the Imaging Data Archive (IDA) and the NINDS Data Management Resource (DMR). The IDA data management service (http://ida.loni.usc.edu) will utilize the established PPMI data schema (www.ppmi-info.org). The NINDS DMR repository requires the use of a Global Unique Identifier (GUID) that facilitates data aggregation without exposing/transferring Personally Identifying Information. Data standardization will comply with NINDS Parkinson's Disease Biomarkers Program (PDBP) protocols for storage and access. Clinical data will be entered into the PDBP DMR via the ProFoRMS interface in order to broadly share data with investigators through the NINDS PDBP DMR. The Center web-site will provide direct links to access summary statistics (data dashboard), manage community requests for samples and data, and disseminate research findings and computational protocols. The impact of the Biostatistics and Data Management Core will be to enhance the success of the Udall projects, to facilitate PD-related research locally, and to contribute unique clinical, biomarker and imaging data to national repositories for broad community use.

? DESCRIPTION (provided by applicant): The Center for Complexity and Self-Management of Chronic Disease (CSCD) will address the growing problem of chronic disease. As knowledge and technology addressing disease advances, and the focus on health promotion and illness prevention continues to lag behind, the burden of chronic illness burgeons. Many are concerned about the aging population and the increase in chronic disease, calling for improved efficiencies in the provision of health care. Improved self-management is an important strategy to improve health outcomes in a cost effective manner. Despite much research in self-management, critical gaps in knowledge persist. Current research is limited by designs, methods and analytics that reduce the high number of dynamic, interacting variables into linear equations with select variables that result in missed opportunities for quantum improvements in the desired outcomes. Self-management involves a cluster of behaviors, processes and context, and relationships among the self and provider, health care system, and community. In addition, individuals, families and populations too often are confronted with challenges of multiple chronic illnesses. Research in this complex arena requires methods and analytics that can address non-linear, dynamic relationships between many variables. Though a challenge for the status quo, the Center for Complexity and Self-Management of Chronic Disease (CSCD) is positioning itself to address the following specific aims: 1) to leverage complexity to advance the science of self-management for the promotion of health in chronic illness; 2) expand the number and quality of research investigators who are successful in independently funded careers in self-management research to improve health outcomes; 3) facilitate the dissemination of research findings to the scientific and, when applicable, to the clinical communities and 4) develop plans to sustain the CSCD and the interdisciplinary teams who are in its membership. To accomplish these aims, we will establish three cores, administrative, pilot and methods/analytics. The Center will be guided by Individual and Family Self-Management Theory that has been informed by Chaos Theory. In short the overall purpose of the Center for Complexity and Self-management of Chronic Disease is to address the need for innovative research that encompasses complexity in order to advance the science in self-management to achieve clinically important outcomes such as minimizing disability, optimizing function and living well. In addition, the Center will facilitate interdisciplinary approaches and expand the pool of interdisciplinary research teams who are equipped to successfully develop and implement externally funded programs of research in self-management. We plan to change the landscape of self-management research and thereby advance the science in efficient, meaningful ways.

Methods & Analytics Core (MACORE) - Summary Chronic diseases and conditions (e.g., arthritis, diabetes, heart disease, injury-based disabilities) affect more than one-third of adults 65 or older, one-fifth of the population age 60 and older (e.g., 10 million people have diabetes), and one-seventh of the Americans who die annually. The need for self-management programs that enable patients to learn the disease characteristics, practice healthy behaviors, and handle chronic disorders is critical. This research will focus on the complexity of self-management by focusing on participant-centered outcomes, modifications of existing and development of innovative strategies for health self-management through interaction with healthcare providers and reinforcement of positive outcomes. Building on our significant prior experience in developing techniques for management, processing, analysis and visualization of complex multidimensional dataset, the Methods and Analytics Core (MACORE) of the Complexity of Self-management in Chronic Disuse (CSCD) Center will provide the necessary infrastructure and resources to support engagement of patients, family, caregivers, researchers and stakeholders. Specifically, MACORE will support CSCD research projects (pilots, collaborations and services) with data collection and aggregation, formulation and testing of clinically-relevant research hypotheses (across space, time, disease state, phenotypes and treatment regiments), and algorithms to elicit the intricate relations, complex associations, causal connections and complex patterns in the self-management data. The MACORE specific aims are designed to support the broader CSCD-wide Center aims by leveraging complexity to advance the science of self-management for the promotion of health in chronic illness. We will explore new strategies for mentorship by interdisciplinary teams, utilizing innovative methods for analyzing the effects of complex interventions and facilitating development of symposia focused on complex methodology. MACORE will link novice investigators with resources to facilitate development as independent researchers and to lead interdisciplinary teams and develop a range of techniques and supporting documentation for selecting appropriate methods to address complexity for self-management study designs, data processing, and analytical protocols. Finally, the Core will provide methodological and analytic expertise and tools to collaborators and pilot project investigators and develop the CSCD computational infrastructure enabling efficient and reliable end-to-end computational workflow solutions for advanced data analytics.

The Integrative Biostatistics and Informatics Core (IBIC) will provide expertise in experimental design, data management and analysis for preclinical, clinical and translational research studies conducted by MNORC Research Base investigators. Core will focus on providing assistance in data base design that can accept multiple data types, including molecular phenotypes derived from multiple ?omics technologies. The latter will require proper data architecture and tools to integrate and visualize data. The IBIC will assure that all clinically relevant databases will be Health Insurance Portability and Accountability Act (HIPAA) compliant. The IBIC will continue support for Research Base investigators with the Michigan Institute for Clinical and Health Research (MICHR), the Department of Computational Medicine and Bioinformatics (DCMB) and the BRCF Bioinformatics Core, to provide additional biostatistics and informatics services. The Core will also continue to develop new tools for data mining of relevant databases as well as assistance and training in the use of various software, Cloud and service tools for analysis of genomic, transcriptomic, metabolomic and proteomic data. Finally, IBIC personnel will collaborate with other Cores and the Weight Management Program to develop appropriate data formats and database constructs to integrate clinical, molecular, neurobehavioral and other phenotypic data into formats that ease analysis by biostatistical and informatics methodologies.

Novel neuroscience tools and techniques are necessary to enable insight into the building blocks of neural circuits, the interactions between these circuits that underpin the functions of the human brain, and modulation of these circuits that affect our behavior. To leverage rapid technological development in sensing, imaging, and data analysis new ground breaking advances in neuroscience are necessary to facilitate knowledge discovery using data science methods. To address this societal grand challenge, the project will foster new interdisciplinary collaborations across computing, biological, mathematical, and behavioral science disciplines together with partnerships in academia, industry, and government at multiple levels. The Big Data Neuroscience Spoke titled Midwest: Advanced Computational Neuroscience Network (ACNN) is strongly aligned with the national priority area of neuroscience and brings together a diverse set of committed regional partners to enable the Midwest region to realize the promise of Big Data for neuroscience. The ACNN Spoke will build broad consensus on the core requirements, infrastructure, and components needed to develop a new generation of sustainable interdisciplinary Neuroscience Big Data research. ACNN will leverage the strengths and resources in the Midwest region to increase innovation and collaboration for the understanding of the structure, physiology, and function of the human brain through partnerships and services in education, tools, and best practices.

The ACNN will design, pilot and support powerful neuroscientific computational resources for high-throughput, collaborative, and service-oriented data aggregation, processing and open-reproducible science. The ACNN Spoke framework will address three specific problems related to neuroscience Big Data: (1) data capture, organization, and management involving multiple centers and research groups, (2) quality assurance, preprocessing and analysis that incorporates contextual metadata, and (3) data communication to software and hardware computational resources that can scale with the volume, velocity, and variety of neuroscience datasets. The ACNN will build a sustainable ecosystem of neuroscience community partners in both academia and industry using existing technologies for collaboration and virtual meeting together with face-to-face group meetings. The planned activities of the ACNN Spoke will also allow the Midwest Big Data Hub to disseminate additional Big Data technologies resources to the neuroscience community, including access to supercomputing facilities, best practices, and platforms.

This award received co-funding from CISE Divisions of Advanced Cyberinfrastructure (ACI) and Information and Intelligent Systems (IIS).

Neuroscience is advancing by dissolving disciplinary boundaries and promoting transdisciplinary research between psychologists, cognitive neuroscientists, computer scientists, and engineers, to name a few. The success of this scientific endeavor would be enhanced by establishing software mechanisms to improve reproducibility of scientific results. This project develops a software platform that facilitates publication of publicly-accessible data and implementation of data-analysis algorithms. Both functions will be achievable within high-performance computing environments. The platform will enable publication of reproducible code, and access to national supercomputers. It will also make available reference datasets for validating results and data quality. It is expected that the open online platform will promote voluntary data submissions in exchange for access to the system. In addition, this platform will provide a reusable database of "data derivatives," which are data at different stages of preprocessing, including cortical segmentations, meshes, functional maps, brain connectivity matrices, or white-matter tracts. This open-derivatives database will allow computer scientists, mathematical scientists and engineers to use these data to develop and improve methods in their domains. Most generally, providing easy-to-use published data and methods will promote understanding the brain and allow diverse communities of scientists to use reproducible methods, and reuse the "long tail" of neuroimaging data.

The project focuses on providing seamless public access to data, computing, and reproducible algorithms, while promoting code sharing and upcycling the long tail of neuroscience data. It has three main objectives. First, to develop a platform to capture brain data, publish algorithms as reproducible applications, and perform data-intensive computing on high-performance compute clusters, as well as public clouds. Second, to develop novel algorithms for mapping brain-connectome individuality and variability. The algorithms will enhance discovery by leveraging the online platform for data intensive processing of large datasets. Third, to collate a large data set of brain data and data derivatives (processed data), such as connectome matrices, multi-parameters tractography models, cortical segmentation and functional maps. These derivatives will benefit scientists to develop algorithms for functional mapping, anatomical computing, and model optimization. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). It has also received funding from the CISE Office of Advanced Cyberinfrastructure.

This project builds on a prior Midwest Big Data Hub effort. In 2015 stakeholders in the Midwest region of the United States formed a consortium of partners and working groups called the Midwest Big Data Hub (MBDH). MBDH aimed to help member organizations working in Big Data coordinate current activities and launch new collaborative projects. The project included stakeholders in the twelve states of the Midwest Census region (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) and six leading universities that support hundreds of researchers, technologists, and students. This hub provides a basis for collaboration and outreach that increases the potential for benefitting society.

The current award is a collaboration among five academic sites (Indiana University, Iowa State University, UIUC/NCSA, the University of Michigan, the University of North Dakota, and the University of Minnesota - Twin Cities). The project focuses on priority areas that are important to the region and can also be influential on the national stage.
- The five thematic areas of focus, and the institutional partner leading that thematic area, are: Digital Agriculture (led by Iowa State); Smart, Connected, and Resilient Communities (Indiana University); Water Quality (University of Minnesota); Advanced Materials and Manufacturing (UIUC); and Health and Biomedicine (University of Michigan).
- Three cross-cutting areas that are emphasized across the project are: data science education and workforce development; cyberinfrastructure, data access and use; and communication and community development.
The priority areas have regional relevance and also have the prospect for integration into societal contexts at the national level. The overall goal is to enable the use of existing and emerging cyberinfrastructure and best practices to improve access to and use of data. The project plans to reach out to the Midwest community at large and to connect people, resources, and organizations. Ties to Big Data Hubs in three other regions provide a means to advance knowledge across these fields at the national level.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

This is a new application to support a novel Biomedical Informatics and Data Science Training Program (BIDS-TP) at the University of Michigan (UM). The overarching goal of the BIDS-TP is to train a cadre of data- savvy, computationally-skilled, and highly-motivated biomedical scholars in an intellectually-stimulating environment using an effective competency-based curriculum. To enhance their scientific, clinical, and translational abilities, all BIDS-TP students will be trained in collecting, managing, processing, interrogating, and analyzing large amounts of complex high-dimensional biomedical information with rigor and transparency. Throughout the 5-year funding cycle, the Program will annually support 12 Fellows (6-Year 1 and 6- Year 2). Program participants will also include additional 10 Trainees that are fully-engaged, but funded by other mechanisms. The BIDS-TP program represents a unique collaboration between the UM?s Department of Computational Medicine and Bioinformatics (DCMB) and the Michigan Institute for Data Science (MIDAS). This partnership will provide immersive synergistic activities, translational education, transdisciplinary research projects, co-mentoring, and career development for all BIDS-TP Fellows and Trainees. Feeder graduate programs with eligible pre-doctoral trainees include DCMB and MIDAS doctoral students from engineering, mathematics, statistics, public health, and information sciences. Thirty-six UM faculty members from 6 UM Schools and Colleges will provide breadth and depth of scholarly research, co-mentoring, career coaching, and student-specific curriculum development. The BIDS-TP curriculum requires all trainees to complete the add-on graduate data science certificate program, and to actively participate in BIDS-TP workshops, seminars, and short-courses on biomedical informatics, health analytics, and computational data science. Capitalizing on the extensive database of successful DCMB, MIDAS, and Rackham Graduate School alumni, the Program will support professional networking, practical career mentorship, employment and career opportunities to promote the next generation of biomedical and health data science leaders. All scholars will be encouraged to focus their energy to design rigorous experiments, and develop effective techniques to tackle critical challenges, address unmet needs, and bridge scientific knowledge gaps. The strong, interdisciplinary, and trainee-mentor tailored curriculum plans will facilitate trainee?s growth, employability, and positioning to contribute to the NIH mission to discover, model, understand and treat complex human disorders. BIDS-TP will increase the capacity, ability, and efficacy of the US workforce to address known and unexpected biomedical, health and environmental challenges using advanced bioinformatics and data science techniques. As a premiere global institution, UM is dedicated to research, education and training of biomedical and data science graduate students. There are a number of complementary and synergistic UM activities that will support the BIDS-TP trainees and mentors in fertilizing interdisciplinary, collaborative, and cutting edge research.