Autonomic cardiovascular dysregulation as a potential mechanism underlying depression and coronary artery bypass grafting surgery outcomes.

BACKGROUND: Coronary artery bypass grafting (CABG) is often used to treat patients with significant coronary heart disease (CHD). To date, multiple longitudinal and cross-sectional studies have examined the association between depression and CABG outcomes. Although this relationship is well established, the mechanism underlying this relationship remains unclear. The purpose of this study was twofold. First, we compared three markers of autonomic nervous system (ANS) function in four groups of patients: 1) Patients with coronary heart disease and depression (CHD/Dep), 2) Patients without CHD but with depression (NonCHD/Dep), 3) Patients with CHD but without depression (CHD/NonDep), and 4) Patients without CHD and depression (NonCHD/NonDep). Second, we investigated the impact of depression and autonomic nervous system activity on CABG outcomes. METHODS: Patients were screened to determine whether they met some of the study's inclusion or exclusion criteria. ANS function (i.e., heart rate, heart rate variability, and plasma norepinephrine levels) were measured. Chi-square and one-way analysis of variance were performed to evaluate group differences across demographic, medical variables, and indicators of ANS function. Logistic regression and multiple regression analyses were used to assess impact of depression and autonomic nervous system activity on CABG outcomes. RESULTS: The results of the study provide some support to suggest that depressed patients with CHD have greater ANS dysregulation compared to those with only CHD or depression. Furthermore, independent predictors of in-hospital length of stay and non-routine discharge included having a diagnosis of depression and CHD, elevated heart rate, and low heart rate variability. CONCLUSIONS: The current study presents evidence to support the hypothesis that ANS dysregulation might be one of the underlying mechanisms that links depression to cardiovascular CABG surgery outcomes. Thus, future studies should focus on developing and testing interventions that targets modifying ANS dysregulation, which may lead to improved patient outcomes.

Qualitative analysis of the interdisciplinary interaction between data analysis specialists and novice clinical researchers.

BACKGROUND: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time. METHODS/PRINCIPAL FINDINGS: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of "what if" situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal. CONCLUSION/SIGNIFICANCE: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

Disturbance, response, and persistence in self-organized forested communities: Analysis of robustness and resilience in five communities in Southern Indiana

We develop an analytic framework for the analysis of robustness in social-ecological systems (SESs) over time. We argue that social robustness is affected by the disturbances that communities face and the way they respond to them. Using Ostrom's ontological framework for SESs, we classify the major factors influencing the disturbances and responses faced by five Indiana intentional communities over a 15-year time frame. Our empirical results indicate that operational and collective-choice rules, leadership and entrepreneurship, monitoring and sanctioning, economic values, number of users, and norms/social capital are key variables that need to be at the core of future theoretical work on robustness of self-organized systems. © 2010 by the author(s).

A systematic approach to institutional analysis: Applying Crawford and Ostrom's grammar

In 1995, Crawford and Ostrom proposed a grammatical syntax for examining institutional statements (i.e., rules, norms, and strategies) as part of the institutional analysis and development framework. This article constitutes the first attempt at applying the grammatical syntax to code institutional statements using two pieces of U.S. legislation. The authors illustrate how the grammatical syntax can serve as a basis for collecting, presenting, and analyzing data in a way that is reliable and conveys valid and substantive meaning for the researcher. The article concludes by describing some implementation challenges and ideas for future theoretical and field research. © 2010 University of Utah.

R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics

Nolan and Temple Lang argue that “the ability to express statistical computations is an es- sential skill.” A key related capacity is the ability to conduct and present data analysis in a way that another person can understand and replicate. The copy-and-paste workflow that is an artifact of antiquated user-interface design makes reproducibility of statistical analysis more difficult, especially as data become increasingly complex and statistical methods become increasingly sophisticated. R Markdown is a new technology that makes creating fully-reproducible statistical analysis simple and painless. It provides a solution suitable not only for cutting edge research, but also for use in an introductory statistics course. We present experiential and statistical evidence that R Markdown can be used effectively in introductory statistics courses, and discuss its role in the rapidly-changing world of statistical computation.

Finding our way through phenotypes.

Despite a large and multifaceted effort to understand the vast landscape of phenotypic data, their current form inhibits productive data analysis. The lack of a community-wide, consensus-based, human- and machine-interpretable language for describing phenotypes and their genomic and environmental contexts is perhaps the most pressing scientific bottleneck to integration across many key fields in biology, including genomics, systems biology, development, medicine, evolution, ecology, and systematics. Here we survey the current phenomics landscape, including data resources and handling, and the progress that has been made to accurately capture relevant data descriptions for phenotypes. We present an example of the kind of integration across domains that computable phenotypes would enable, and we call upon the broader biology community, publishers, and relevant funding agencies to support efforts to surmount today's data barriers and facilitate analytical reproducibility.