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Functional magnetic resonance imaging (fMRI) data sets are large and characterized by complex dependence structures driven by highly sophisticated neurophysiology and aspects of the experimental designs. Typical analyses investigating task-related changes in measured brain activity use a two-stage procedure in which the first stage involves subject-specific(More)
Granulomatous amebic encephalitis (GAE) from Balamuthia mandrillaris, a free-living ameba, has a case fatality rate exceeding 90 % among recognized cases in the USA. In August 2010, a GAE cluster occurred following transplantation of infected organs from a previously healthy landscaper in Tucson, AZ, USA, who died from a suspected stroke. As B. mandrillaris(More)
To assess the spectrum of illness from toxigenic Vibrio cholerae O1 and risk factors for severe cholera in Haiti, we conducted a cross-sectional survey in a rural commune with more than 21,000 residents. During March 22-April 6, 2011, we interviewed 2,622 residents ≥ 2 years of age and tested serum specimens from 2,527 (96%) participants for vibriocidal and(More)
There is strong interest in investigating both functional connectivity (FC) using functional magnetic resonance imaging (fMRI) and structural connectivity (SC) using diffusion tensor imaging (DTI). There is also emerging evidence of correspondence between functional and structural pathways within many networks (Greicius, et al., 2009; Skudlarski et al.,(More)
Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some(More)
The field of statistics makes valuable contributions to functional neuroimaging research by establishing procedures for the design and conduct of neuroimaging experiments and providing tools for objectively quantifying and measuring the strength of scientific evidence provided by the data. Two common functional neuroimaging research objectives include(More)
We present a statistical and graphical visualization MATLAB toolbox for the analysis of functional magnetic resonance imaging (fMRI) data, called the Bayesian Spatial Model for activation and connectivity (BSMac). BSMac simultaneously performs whole-brain activation analyses at the voxel and region of interest (ROI) levels as well as task-related functional(More)
Cancer detection using mammography focuses on characteristics of tiny microcalcifications, including the number, size, and spatial arrangement of microcalcification clusters as well as morphological features of individual microcalcifications. We developed state-of-the-art wavelet-based methods to enhance the resolution of microcalcifica-tions visible in(More)
Data-driven statistical approaches, such as cluster analysis or independent component analysis, applied to in vivo functional neuroimaging data help to identify neural processing networks that exhibit similar task-related or restingstate patterns of activity. Ideally, the measured brain activity for voxels within such networks should exhibit high(More)
In this study, hollow-fiber ultrafiltration (UF) was assessed for recovery of Escherichia coli, Clostridium perfringens spores, Cryptosporidium parvum oocysts, echovirus 1, and bacteriophages MS2 and ΦX174 from ground and surface waters. Microbes were seeded into twenty-two 50-L water samples that were collected from the Southeastern United States and(More)