The Impact of Creative Movement Presentations on Dance Participation and Student Attitudes Towards Dance in a Montessori Early Childhood Classroom

This study sought to examine how time management skills would impact the self-efficacy of Teacher Leaders working in a teacher-led school model. The participants of the four-week study were three Teacher Leaders from two teacher-led primary Montessori schools in an urban area. The Teacher Leaders incorporated time management skills including time analysis, establishing goals, prioritization, and planning/scheduling.Data was collected on Teacher Leader productivity, distribution of time among teaching and administrative roles, self-efficacy, and time management behavior through pre- and post- questionnaires, daily to-do lists, and daily activity logs. The study concluded that although the results were not statistically significant, two out of three Teacher Leader’s productivity, time management behavior, and self-efficacy did improve over the course of the study. Further research is needed to determine how these time management skills impact Teacher Leader’s experienced stress, perceived productivity, and to further investigate how Teacher Leaders’ distribution of time among teaching and administrative roles impacts stress and self-efficacy

Normal ranges of 2D speckle tracking left ventricular segmental longitudinal, radial and circumferential maximum strain post-systolic time delays used for dyssynchrony analysis

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The pathophysiology of intestinal lipoprotein production

Intestinal lipoprotein production is a multistep process, essential for the absorption of dietary fats and fat-soluble vitamins. Chylomicron assembly begins in the endoplasmic reticulum with the formation of primordial, phospholipids-rich particles that are then transported to the Golgi for secretion. Several classes of transporters play a role in the selective uptake and/or export of lipids through the villus enterocytes. Once secreted in the lymph stream, triglyceride-rich lipoproteins (TRLs) are metabolized by Lipoprotein lipase (LPL), which catalyzes the hydrolysis of triacylglycerols of very low density lipoproteins (VLDLs) and chylomicrons, thereby delivering free fatty acids to various tissues. Genetic mutations in the genes codifying for these proteins are responsible of different inherited disorders affecting chylomicron metabolism. This review focuses on the molecular pathways that modulate the uptake and the transport of lipoproteins of intestinal origin and it will highlight recent findings on TRLs assembly

Model Averaged Double Robust Estimation

Existing methods in causal inference do not account for the uncertainty in the selection of confounders. We propose a new class of estimators for the average causal effect, the model averaged double robust estimators, that formally account for model uncertainty in both the propensity score and outcome model through the use of Bayesian model averaging. These estimators build on the desirable double robustness property by only requiring the true propensity score model or the true outcome model be within a specified class of models to maintain consistency. We provide asymptotic results and conduct a large scale simulation study that indicates the model averaged double robust estimator has better finite sample behavior than the usual double robust estimator

Statistical Methods for Effect Estimation in Biomedical Research: Robustness and Efficiency

Practical application of statistics in biomedical research is predicated on the notion that one can readily return valid effect estimates of the health consequences of treatments (exposures) that are being studied. The goal as statisticians should be to provide results that are scientifically useful, to use the available data as efficiently as possible, to avoid unnecessary assumptions, and, if necessary, develop methods that are robust to incorrect assumptions. In this dissertation, I provide methods for effect estimation that meet these goals. I consider three scenarios: (1) clustered binary outcomes; (2) continuous outcomes with a binary treatment; and (3) continuous outcomes with potentially missing continuous exposure. In each of these settings, I discuss the shortfalls of current statistical methods for effect estimation available in the literature and propose new and innovative methods that meet the previously stated goals. The validity of each proposed estimator is theoretically verified using asymptotic arguments, and the finite sample behavior is studied through simulation