An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers

BARS (DiMatteo, Genovese, and Kass 2001) uses the powerful reversible-jump MCMC engine to perform spline-based generalized nonparametric regression. It has been shown to work well in terms of having small mean-squared error in many examples (smaller than known competitors), as well as producing visually-appealing fits that are smooth (filtering out high-frequency noise) while adapting to sudden changes (retaining high-frequency signal). However, BARS is computationally intensive. The original implementation in S was too slow to be practical in certain situations, and was found to handle some data sets incorrectly. We have implemented BARS in C for the normal and Poisson cases, the latter being important in neurophysiological and other point-process applications. The C implementation includes all needed subroutines for fitting Poisson regression, manipulating B-splines (using code created by Bates and Venables), and finding starting values for Poisson regression (using code for density estimation created by Kooperberg). The code utilizes only freely-available external libraries (LAPACK and BLAS) and is otherwise self-contained. We have also provided wrappers so that BARS can be used easily within S or R.

An Implementation of Bayesian Adaptive Regression Splines (BARS) in C with S and R Wrappers

BARS (DiMatteo, Genovese, and Kass 2001) uses the powerful reversible-jump MCMC engine to perform spline-based generalized nonparametric regression. It has been shown to work well in terms of having small mean-squared error in many examples (smaller than known competitors), as well as producing visually-appealing fits that are smooth (filtering out high-frequency noise) while adapting to sudden changes (retaining high-frequency signal). However, BARS is computationally intensive. The original implementation in S was too slow to be practical in certain situations, and was found to handle some data sets incorrectly. We have implemented BARS in C for the normal and Poisson cases, the latter being important in neurophysiological and other point-process applications. The C implementation includes all needed subroutines for fitting Poisson regression, manipulating B-splines (using code created by Bates and Venables), and finding starting values for Poisson regression (using code for density estimation created by Kooperberg). The code utilizes only freely-available external libraries (LAPACK and BLAS) and is otherwise self-contained. We have also provided wrappers so that BARS can be used easily within S or R

Gpr124 is essential for blood-brain barrier integrity in central nervous system disease

Although blood-brain barrier (BBB) compromise is central to the etiology of diverse central nervous system (CNS) disorders, endothelial receptor proteins that control BBB function are poorly defined. The endothelial G-protein-coupled receptor (GPCR) Gpr124 has been reported to be required for normal forebrain angiogenesis and BBB function in mouse embryos, but the role of this receptor in adult animals is unknown. Here Gpr124 conditional knockout (CKO) in the endothelia of adult mice did not affect homeostatic BBB integrity, but resulted in BBB disruption and microvascular hemorrhage in mouse models of both ischemic stroke and glioblastoma, accompanied by reduced cerebrovascular canonical Wnt-β-catenin signaling. Constitutive activation of Wnt-β-catenin signaling fully corrected the BBB disruption and hemorrhage defects of Gpr124-CKO mice, with rescue of the endothelial gene tight junction, pericyte coverage and extracellular-matrix deficits. We thus identify Gpr124 as an endothelial GPCR specifically required for endothelial Wnt signaling and BBB integrity under pathological conditions in adult mice. This finding implicates Gpr124 as a potential therapeutic target for human CNS disorders characterized by BBB disruption

Cannibalism, Kuru, and Mad Cows: Prion Disease As a "Choose-Your-Own-Experiment" Case Study to Simulate Scientific Inquiry in Large Lectures.

Despite significant efforts to reform undergraduate science education, students often perform worse on assessments of perceptions of science after introductory courses, demonstrating a need for new educational interventions to reverse this trend. To address this need, we created An Inexplicable Disease, an engaging, active-learning case study that is unusual because it aims to simulate scientific inquiry by allowing students to iteratively investigate the Kuru epidemic of 1957 in a choose-your-own-experiment format in large lectures. The case emphasizes the importance of specialization and communication in science and is broadly applicable to courses of any size and sub-discipline of the life sciences

Changes in student perceptions about biology.

<p>Student survey data were collected in five independent sections of Introduction to Biology over two years (<i>n</i> = 195). Students were given a nine-question survey (upper panel) immediately before and following the activity. Questions 1–7 were selected for relevance from the CLASS-Bio survey [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002351#pbio.1002351.ref008" target="_blank">8</a>], while questions 8 and 9 were added to address issues that are specifically relevant to the activity but not present in CLASS-Bio. Expert responses are affirmative for questions 1–5 and 9, and negative for questions 6–8. Shifts in student responses were analyzed using a Wilcoxon signed-rank test. Individual test results with <i>p</i>-values less than 0.005 are indicated. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002351#pbio.1002351.s010" target="_blank">S8 Text</a> for additional details regarding data collection and analysis.</p

Case introduction to be read aloud in class to begin the activity.

<p>Students are alerted in advance that this is a true story as part of the initial case study PowerPoint (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002351#pbio.1002351.s001" target="_blank">S1 PowerPoint</a>).</p

Summary statistics of student reactions and self-reported learning following the activity.

<p>Student affective and self-reported learning data were collected and pooled from five courses at three schools. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002351#pbio.1002351.s008" target="_blank">S6 Text</a> for survey questions and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002351#pbio.1002351.s010" target="_blank">S8 Text</a> for additional details regarding data collection and analysis; a representative listing of various student responses for each theme shown is given in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002351#pbio.1002351.s010" target="_blank">S8 Text</a>.</p