Bubbling Over: Soda Consumption and Its Link to Obesity in California

Based on 2005 Health Interview Survey data, examines the link between soda consumption and the prevalence of overweight and obese adults and children, geographical differences in soda consumption, and the social and environmental factors that affect it

Refining interaction search through signed iterative Random Forests

Advances in supervised learning have enabled accurate prediction in biological systems governed by complex interactions among biomolecules. However, state-of-the-art predictive algorithms are typically black-boxes, learning statistical interactions that are difficult to translate into testable hypotheses. The iterative Random Forest algorithm took a step towards bridging this gap by providing a computationally tractable procedure to identify the stable, high-order feature interactions that drive the predictive accuracy of Random Forests (RF). Here we refine the interactions identified by iRF to explicitly map responses as a function of interacting features. Our method, signed iRF, describes subsets of rules that frequently occur on RF decision paths. We refer to these rule subsets as signed interactions. Signed interactions share not only the same set of interacting features but also exhibit similar thresholding behavior, and thus describe a consistent functional relationship between interacting features and responses. We describe stable and predictive importance metrics to rank signed interactions. For each SPIM, we define null importance metrics that characterize its expected behavior under known structure. We evaluate our proposed approach in biologically inspired simulations and two case studies: predicting enhancer activity and spatial gene expression patterns. In the case of enhancer activity, s-iRF recovers one of the few experimentally validated high-order interactions and suggests novel enhancer elements where this interaction may be active. In the case of spatial gene expression patterns, s-iRF recovers all 11 reported links in the gap gene network. By refining the process of interaction recovery, our approach has the potential to guide mechanistic inquiry into systems whose scale and complexity is beyond human comprehension

BMI-for-age graphs with severe obesity percentile curves: Tools for plotting cross-sectional and longitudinal youth BMI data

Abstract Background Severe obesity is an important and distinct weight status classification that is associated with disease risk and is increasing in prevalence among youth. The ability to graphically present population weight status data, ranging from underweight through severe obesity class 3, is novel and applicable to epidemiologic research, intervention studies, case reports, and clinical care. Methods The aim was to create body mass index (BMI) graphing tools to generate sex-specific BMI-for-age graphs that include severe obesity percentile curves. We used the Centers for Disease Control and Prevention youth reference data sets and weight status criteria to generate the percentile curves. The statistical software environments SAS and R were used to create two different graphing options. Results This article provides graphing tools for creating sex-specific BMI-for-age graphs for males and females ages 2 to <20 years. The novel aspects of these graphing tools are an expanded BMI range to accommodate BMI values ˃35 kg/m2, inclusion of percentile curves for severe obesity classes 2 and 3, the ability to plot individual data for thousands of children and adolescents on a single graph, and the ability to generate cross-sectional and longitudinal graphs. Conclusions These new BMI graphing tools will enable investigators, public health professionals, and clinicians to view and present youth weight status data in novel and meaningful ways

Community Functioning and Cognitive Performance in Schizophrenia: The Nature of the Relationship

Although cognition is one of the most important predictors of community functioning in schizophrenia, little is known about the causes of this relationship. This study is the first to our knowledge to examine the extent to which this correlation is genetically and/or environmentally mediated and its degree of specificity to schizophrenia. Six hundred and thirty-six participants from 43 multigenerational families with at least two schizophrenia relatives and 135 unrelated controls underwent diagnostic interview and functioning assessment along with the Penn Computerized Neurocognitive Battery, Trail Making Test and California Verbal Learning Test. Exploratory factor analyses yielded one general cognition factor and one functioning factor while a social cognition factor was comprised of the average of two tasks. SOLAR (Sequential Oligogenic Linkage Analysis Routines) (Almasy & Blangero, 1998) was used to conduct family-based analyses quantifying genetic and environmental effects on the cognition-functioning correlation. As expected, among the 103 relatives with schizophrenia, there was considerable variation in functioning and cognitive performance and a significant correlation between the two (Rp=0.335, p=0.005). Shared genetic effects were significant contributors to this relationship (Rg=0.956, p<0.001) whereas idiosyncratic experiences were not. In contrast, shared genetic effects were not significant among relatives with major depression, substance abuse or no psychopathology. Furthermore, functioning in schizophrenia was not significantly predicted by cognition in relatives from other diagnostic groups. Across all analyses, the contributions of social cognition to functioning were similar to and fully accounted for by general cognition. The cognition-functioning correlation in schizophrenia is largely attributable to genetic factors specific to the disorder that also encompass genetic effects on the association between social cognition and functioning. These findings provide a foundation from which heritable factors contributing to functioning in schizophrenia can be differentiated from those contributing to functioning in psychiatric disorders in general, which suggest that investigations of specific genetic variants contributing to this association are warranted

Book Review: Crossing Borders: International Women Students In American Higher Education

Review of Crossing Borders: International Women Students In American Higher Education by Dongxiao Qi

Estimating the Distribution of Random Parameters in a Diffusion Equation Forward Model for a Transdermal Alcohol Biosensor

We estimate the distribution of random parameters in a distributed parameter model with unbounded input and output for the transdermal transport of ethanol in humans. The model takes the form of a diffusion equation with the input being the blood alcohol concentration and the output being the transdermal alcohol concentration. Our approach is based on the idea of reformulating the underlying dynamical system in such a way that the random parameters are now treated as additional space variables. When the distribution to be estimated is assumed to be defined in terms of a joint density, estimating the distribution is equivalent to estimating the diffusivity in a multi-dimensional diffusion equation and thus well-established finite dimensional approximation schemes, functional analytic based convergence arguments, optimization techniques, and computational methods may all be employed. We use our technique to estimate a bivariate normal distribution based on data for multiple drinking episodes from a single subject.Comment: 10 page