Pulmonary infection with an exaggerated inflammatory response is the major cause of morbidity and mortality in cystic fibrosis (CF). The objective of this study was to determine whether differences in the innate immune system underlie the exaggerated immune response in CF. We established a model that recapitulates the exaggerated immune response in a CF mouse model by exposure to Pseudomonas aeruginosa LPS and assessed the pulmonary cellular and cytokine responses of wild-type (WT) and CF mice. Compared with WT mice, CF mice had increased numbers of neutrophils and increased proinflammatory cytokines in their bronchoalveolar lavage fluid after LPS exposure. Based on the increased levels of IL-1a, IL-6, granulocyte colony-stimulating factor (G-CSF), and keratinocyte chemoattractant, all of which are known to be produced by macrophages, we tested whether two populations of macrophages, bone marrowderived macrophages and alveolar macrophages, directly contribute to the elevated cytokine response of CF mice to LPS. After in vitro stimulation of bone marrow-derived macrophages and alveolar macrophages with LPS, IL-1a, IL-6, G-CSF, and monocyte chemoattractant protein-1 were higher in CF compared with WT cell supernatants. Quantitative analyses for IL-6 and keratinocyte chemoattractant revealed that LPS-stimulated CF macrophages have higher mRNA and intracellular protein levels compared with WT macrophages. Our data support the hypothesis that macrophages play a role in the exuberant cytokine production and secretion that characterizes CF, suggesting that the macrophage response may be an important therapeutic target for decreasing the morbidity of CF lung disease.

The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQ and assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-dopa pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.

Barry and Hartigan (1993) propose a Bayesian analysis for change point problems. We provide a brief summary of selected work on change point problems, both preceding and following Barry and Hartigan. We outline Barry and Hartigan's approach and offer a new R package, bcp (Erdman and Emerson 2007), implementing their analysis. We discuss two frequentist alternatives to the Bayesian analysis, the recursive circular binary segmentation algorithm (Olshen and Venkatraman 2004) and the dynamic programming algorithm of (Bai and Perron 2003). We illustrate the application of bcp with economic and microarray data from the literature.

Methodology extending nonparametric goodness-of-fit tests to discrete null distributions has existed for several decades. However, modern statistical software has generally failed to provide this methodology to users. We offer a revision of R's ks.test() function and a new cvm.test() function that fill this need in the R language for two of the most popular nonparametric goodness-of-fit tests. This paper describes these contributions and provides examples of their usage. Particular attention is given to various numerical issues that arise in their implementation.Y. Xiao and Y. Cui. CvM2SL2Test: Cramer-von Mises Two Sample Tests, 2009b. R package version 0.0-2.

The R package bcp is available on CRAN (R Development Core Team, 2008). The O(n) version is available in version 2.0 or higher, with support for NetWorkSpaces in versions 2.1 and higher.