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The article discusses the development of ontology-centered syndronic surveillance for bioterrorism in the United States. Syndronic surveillance could prevent widespread illness and death, but public-health analysts face many technical barriers. To resolve such technical barriers, experts have developed BioSTORM which can help them by supporting ontology-based data integration and problem-solver deployment. BioSTORM or the Biological Spatio-Temporal Outbreak Reasoning Module is an experimental end-to-end computational framework that integrates disparate data sources and deploys various analytic problem solvers to support public health analysts in interpreting surveillance data and identifying disease outbreaks.

Presents a rejoinder to Craig Hemmens' article 'The Supreme Court: Lapdog of Law Enforcement or Principled Expounder of Constitutional Protections?' United States Supreme Court (SC) and the Fourth Amendment (FA); Criticism of the claim that the SC's FA agenda is to support virtually any and all law enforcement practices regardless of the costs to individual liberties.

Examines and responds to the views expressed in 'Bright Lines, Blanket Rules and the Fourth Amendment: The Supreme Court's Half-Hearted Endorsement of the Knock and Announce Rule,' published in the April 1994 issue of 'The Justice Professional.' United States drug war; Civil rights and law enforcement; Cases.

Developing promising treatments in biomedicine often requires aggregation and analysis of data from disparate sources across the healthcare and research spectrum. To facilitate these approaches, there is a growing focus on supporting interoperation of datasets by standardizing data-capture and reporting requirements. Common Data Elements (CDEs)-precise specifications of questions and the set of allowable answers to each question-are increasingly being adopted to help meet these standardization goals. While CDEs can provide a strong conceptual foundation for interoperation, there are no widely recognized serialization or interchange formats to describe and exchange their definitions. As a result, CDEs defined in one system cannot be easily be reused by other systems. An additional problem is that current CDE-based systems tend to be rather heavyweight and cannot be easily adopted and used by third-parties. To address these problems, we developed extensions to a metadata management system called the CEDAR Workbench to provide a platform to simplify the creation, exchange, and use of CDEs. We show how the resulting system allows users to quickly define and share CDEs and to immediately use these CDEs to build and deploy Web-based forms to acquire conforming metadata. We also show how we incorporated a large CDE library from the National Cancer Institute's caDSR system and made these CDEs publicly available for general use ., (©2019 AMIA - All rights reserved.)

The Center for Expanded Data Annotation and Retrieval is studying the creation of comprehensive and expressive metadata for biomedical datasets to facilitate data discovery, data interpretation, and data reuse. We take advantage of emerging community-based standard templates for describing different kinds of biomedical datasets, and we investigate the use of computational techniques to help investigators to assemble templates and to fill in their values. We are creating a repository of metadata from which we plan to identify metadata patterns that will drive predictive data entry when filling in metadata templates. The metadata repository not only will capture annotations specified when experimental datasets are initially created, but also will incorporate links to the published literature, including secondary analyses and possible refinements or retractions of experimental interpretations. By working initially with the Human Immunology Project Consortium and the developers of the ImmPort data repository, we are developing and evaluating an end-to-end solution to the problems of metadata authoring and management that will generalize to other data-management environments., (© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Public health surveillance is critical for accurate and timely outbreak detection and effective epidemic control. A wide range of statistical algorithms is used for surveillance, and important differences have been noted in the ability of these algorithms to detect outbreaks. The evidence about the relative performance of these algorithms, however, remains limited and mainly qualitative. Using simulated outbreak data, we developed and validated quantitative models for predicting the ability of commonly used surveillance algorithms to detect different types of outbreaks. The developed models accurately predict the ability of different algorithms to detect different types of outbreaks. These models enable evidence-based algorithm selection and can guide research into algorithm development.

The Open Biomedical Ontologies (OBO) Foundry is a coordinated community-wide effort to develop ontologies that support the annotation and integration of scientific data. In work supported by the National Database of Autism Research (NDAR), we are developing an ontology of autism that extends the ontologies available in the OBO Foundry. We undertook a systematic literature review to identify domain terms and relationships relevant to autism phenotypes. To enable user queries and inferences about such phenotypes using data in the NDAR repository, we augmented the domain ontology with an information model. In this paper, we show how our approach, using a combination of description logic and rule-based reasoning, enables high-level phenotypic abstractions to be inferred from subject-specific data. Our integrated domain ontologyinformation model approach allows scientific data repositories to be augmented with rule-based abstractions that facilitate the ability of researchers to undertake data analysis.