The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities.

Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce.

Conceptualizing a resilience research framework at The National Institutes of Health.

The National Institutes of Health (NIH) have recently gathered internal and external input towards a shared understanding of resilience in the wide context of human health and the biomedical sciences that would help accelerate advances in human health and its maintenance. This shared view is that resilience refers in general to a system's capacity to recover, grow, adapt, or resist perturbation from a challenge or stressor. Over time, a system's response to a challenge might show varied degrees of reactions that likely fluctuate in response to the type of challenge (internal and/or external), severity of the challenge, the length of time exposed to the challenge, other external factors and/or biological factors (innate and/or external). We have embarked on this special issue as an opportunity to explore commonalities amongst viewpoints on the science of resilience covered by the various NIH Institutes, Centers, and Offices (ICOs) with respect to the characterization of various systems, stressors, outcomes measures and metrics, and interventions and/or protective factors that are shared within each domain and across multiple domains. Here, resilience is characterized broadly by four areas of scientific study: molecular/cellular, physiologic, psychosocial and spiritual, and environmental/community resilience. Each area or domain provides general frameworks for designing studies that may help advance the science of resilience within the context of health maintenance. This special issue will also acknowledge the remaining gaps that impede advancement of the science of resilience and offer considerations for potential next steps towards addressing the research gaps.

Next steps: Operationalizing resilience research.

The National Institutes of Health have recently gathered internal and external input towards a shared understanding of resilience in the wide context of human health and the biomedical sciences that would help accelerate advances in human health and its maintenance. We suggest the current view that resilience refers in general to a system's capacity to recover, grow, adapt, or resist perturbation from a challenge or stressor. To help harmonize the design and reporting of resilience research studies across multiple domains we have developed and are proposing a Resilience Research Design (ResD) Tool. Researchers can use the Resilience ResD Tool to proceed through a flowchart of six questions that will guide identification of key features in a resilience research study. Through this special supplement, we have shown the application of the Resilience ResD Tool and suggest opportunities and gaps with respect to next steps towards operationalizing resilience research.

Anatomy of data integration.

Producing reliable information is the ultimate goal of data processing. The ocean of data created with the advances of science and technologies calls for integration of data coming from heterogeneous sources that are diverse in their purposes, business rules, underlying models and enabling technologies. Reference models, Semantic Web, standards, ontology, and other technologies enable fast and efficient merging of heterogeneous data, while the reliability of produced information is largely defined by how well the data represent the reality. In this paper, we initiate a framework for assessing the informational value of data that includes data dimensions; aligning data quality with business practices; identifying authoritative sources and integration keys; merging models; uniting updates of varying frequency and overlapping or gapped data sets.

Multifaceted inventory of data assets.

The same data are viewed and handled differently by different specialists. For a physician, patient records are separate entities allowing him to treat every patient individually. A database administrator sorts out data elements from the records into tables assigning them data types and data validation rules. Researchers, studying the causality of diseases, group data into demographics, occupational, genetic, etc. Proper management of these diverse coexisting conceptualization schemes requires creating a multifaceted conceptual data model (CDM).

The Arabidopsis SeedGenes Project.

The SeedGenes database (http://www.seedgenes.org) presents molecular and phenotypic information on essential, non-redundant genes of Arabidopsis that give a seed phenotype when disrupted by mutation. Experimental details are synthesized for efficient use by the community and organized into two major sections in the database, one dealing with genes and the other with mutant alleles. The database can be queried for detailed information on a single gene to create a SeedGenes Profile. Queries can also generate lists of genes or mutants that fit specified criteria. The long-term goal is to establish a complete collection of Arabidopsis genes that give a knockout phenotype. This information is needed to focus attention on genes with important cellular functions in a model plant and to assess from a genetic perspective the extent of functional redundancy in the Arabidopsis genome.