OBJECTIVE: Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain. MATERIALS AND METHODS: DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health-funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium. It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries. In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine. RESULTS AND CONCLUSION: Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services. Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

Background: Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings. Methods: We developed a web-based tiered informed consent tool called informed consent for clinical data and bio-sample use for research (iCONCUR) that honors granular patient preferences for use of electronic health record data in research. We piloted this tool in 4 outpatient clinics of an academic medical center. Results: Of patients offered access to iCONCUR, 394 agreed to participate in this study, among whom 126 patients accessed the website to modify their records according to data category and data recipient. The majority consented to share most of their data and specimens with researchers. Willingness to share was greater among participants from an Human Immunodeficiency Virus (HIV) clinic than those from internal medicine clinics. The number of items declined was higher for for-profit institution recipients. Overall, participants were most willing to share demographics and body measurements and least willing to share family history and financial data. Participants indicated that having granular choices for data sharing was appropriate, and that they liked being informed about who was using their data for what purposes, as well as about outcomes of the research. Conclusion: This study suggests that a tiered electronic informed consent system is a workable solution that respects patient preferences, increases satisfaction, and does not significantly affect participation in research.

BACKGROUND: Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. OBJECTIVE: The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. MATERIALS AND METHODS: Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. RESULTS: The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. DISCUSSION AND CONCLUSION: Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.

SUMMARY: MAGI is a web service for fast MicroRNA-Seq data analysis in a graphics processing unit (GPU) infrastructure. Using just a browser, users have access to results as web reports in just a few hours->600% end-to-end performance improvement over state of the art. MAGI's salient features are (i) transfer of large input files in native FASTA with Qualities (FASTQ) format through drag-and-drop operations, (ii) rapid prediction of microRNA target genes leveraging parallel computing with GPU devices, (iii) all-in-one analytics with novel feature extraction, statistical test for differential expression and diagnostic plot generation for quality control and (iv) interactive visualization and exploration of results in web reports that are readily available for publication. AVAILABILITY AND IMPLEMENTATION: MAGI relies on the Node.js JavaScript framework, along with NVIDIA CUDA C, PHP: Hypertext Preprocessor (PHP), Perl and R. It is freely available at http://magi.ucsd.edu.

Recent studies recognize that health is influenced broadly by a multitude of factors of different types, including medical, genetic, environmental, social and behavioral factors. Developing successful health interventions therefore requires taking into account all these factors as well as the interactions between them. However, intervention designers have traditionally had access only to a very limited subset of health data (typically medical record data). Other health data, such as environmental or physical activity data, although already collected and stored, have been very difficult to access, since they are maintained by different providers and isolated in their own proprietary silos. This prevents physicians and intervention designers from acquiring a true overview of all factors influencing a condition and acting towards its prevention or cure. To solve this problem, we propose DELPHI: a platform allowing the integration of disparate health data into a single Whole Health Information Model (WHIM), providing a 360-degree view of an individual's health. DELPHI supports the integration of data and thus enables the design of applications and services that utilize the WHIM to offer the next generation of health services. In this paper, we describe DELPHI's architecture, outline the technical challenges encountered and describe an asthma management use case that will be enabled by DELPHI.

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

Automotive and avionics systems are complex, distributed, software-intensive systems-of-systems (SoS). Consequently, system integration is a central challenge in both domains. Important cross-cutting requirements aspects, such as security, authorization, and failure management, are best understood as properties of the interplay among sub-systems. Yet, traditional development processes address the integration challenge only late, at the level of implementation and deployment. Consequently, potentials for reuse within and across product lines are left unrealized. Furthermore, late integration leads to high calibration, configuration and redesign costs. Service-Oriented Architectures (SOAs) have emerged as a solution to the integration challenge. However, inappropriate application of SOA-principles results in a high degree of fragmentation and scattering of functionality-this leads to additional difficulties in requirements traceability and quality assurance. In this article, we give a comprehensive overview of these SOA-challenges, and present Rich Services as a hierarchical SOA blueprint and development process enabling SoS integration in a dependable way. Rich Services introduce services as hierarchical, partial interaction patterns; these interactions are then augmented with infrastructure elements to inject behaviors that address cross-cutting requirements aspects. Rich Services also seamlessly address the mapping from logical to deployment architectures. Using end-to-end failure management as an example, we illustrate the utility of Rich Services.

Existing embedded software development methodologies can hardly cope with platform changes such as CPU upgrades, different RTOS or distributed systems. The timing definition language (TDL) enables the development of deterministic real-time components regardless of the deployment platform. The proposed virtual execution environment enables parallel execution and deterministic run-time composition of real-time TDL components with data dependencies. We focus on embedded code generation and the virtual machine that controls the execution of TDL components and their interaction patterns.

One of the key challenges to successful systems-of-systems integration using Web services technologies is how to address crosscutting architectural concerns such as policy management, governance, and authentication, while still maintaining the lightweight implementation and deployment Jlavor that distinguishes Web services fiom earlier attempts at providing interoperable enterprise systems. To address this challenge, this article introduces the notion of a Rich Service, an extension of the standard service notion, based on an architectural pattern that allows hierarchical decomposition of system architecture according to separate concerns. Rich Services enable the capture of dzfferent system aspects and their interactions. By leveraging emerging Enterprise Service Bus technologies, Rich Services also enable a direct transition from a logical to a deployed service-oriented architecture (SOA). This results in immediate beneJits not only in SOA design, implementation, deployment, and quality assurance, but also in the traceability of architectural requirements to an SOA implementation.

This paper introduces the notion of transparent distribution of real time software components. Transparent distribution means that (1) the functional and temporal behavior of a system is the same no matter where a component is executed, (2) the developer does not have to care about the differences of local versus distributed execution of components, and (3) the components can be developed independently. We present the design and implementation of a component model for real time systems that is well suited for transparent distribution. The component model is based on logical execution time, which abstracts from physical execution time and thereby from both the execution platform and the communication topology.

Embedded computers are increasingly tighter integrated with each other forming distributed embedded systems that interact through a shared communication medium. Often these embedded systems perform services for safety-critical operations that require deterministic computation and predictable communication. However, since these systems are embedded, they must cope with limited resources. One fundamental challenge is to make efficient use of the shared communication medium, especially in the timed model, in which communication tends to cumulate at the end of harmonic periods of tasks. In this article, we present an approach that uses Microtasks for splitting computation and communication into several sequentially executed steps to allow for better balanced load on the communication medium in the timed model. We discuss the approach and describe its implementation in OSEK/Works with TTCAN.