Publications by authors named "William A Schmitt"

Introduction: Pain catastrophizing, a measure of an individual's negative emotional and cognitive appraisals of pain, has been included as a key treatment target in many psychological interventions for pain. However, the neural correlates of pain catastrophizing have been understudied. Prior neuroimaging evidence suggests that adults with pain show altered reward processing throughout the mesocorticolimbic reward circuitry.

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The primary cannabinoid in cannabis, Δ9-tetrahydrocannabinol (THC), causes intoxication and impaired function, with implications for traffic, workplace, and other situational safety risks. There are currently no evidence-based methods to detect cannabis-impaired driving, and current field sobriety tests with gold-standard, drug recognition evaluations are resource-intensive and may be prone to bias. This study evaluated the capability of a simple, portable imaging method to accurately detect individuals with THC impairment.

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This cohort study examines the association between medical cannabis product use and exposure to Δ9-tetrahydrocannabinol and cannabidiol by quantifying levels of their metabolites in urine.

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The photosynthetic cyanobacterium Synechocystis sp. strain PCC 6803 uses a complex genetic program to control its physiological response to alternating light conditions. To study this regulatory program time-series experiments were conducted by exposing Synechocystis sp.

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Selective attention among offenders with psychopathy was investigated using 3 Stroop paradigms: a standard color-word (CW) Stroop, a picture-word (PW) Stroop, and a color-word Stroop in which the word and color were spatially separated (separated CW). Consistent with "overselective" attention, offenders with psychopathy displayed reduced Stroop interference on the separated CW and PW tasks relative to offenders who were not psychopathic. However, offenders with psychopathy displayed normal Stroop interference on the standard CW Stroop.

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Time-series profiles of gene expression generated by DNA microarrays possess sufficient information for building dynamic models of transcriptional behavior. This, however, requires properly designed experiments and sufficient independent data to validate such models. Here we report the use of AutoRegressive with eXogenous input (ARX) models to fit dynamic gene expression data obtained by subjecting cultures of the photosynthetic bacterium Synechocystis PCC6803 to consecutive light-to-dark transitions.

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Article Synopsis
  • Genome-wide and functional genomic tools aid in identifying genes linked to diseases and understanding disease regulatory patterns, highlighting the need for efficient bioinformatic tools.
  • This study introduces statistical methods to find genes that are differently expressed in malignant oral epithelial tissues compared to normal ones, employing techniques like Wilks' lambda score and Fisher Discriminant Analysis.
  • The research revealed 45 genes associated with oral cancer, with 6 already known to be linked to the disease and 2 being previously uncharacterized.
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Article Synopsis
  • Transcriptional profiling with microarrays offers insights into cellular and tissue expression, but it's expensive and time-consuming due to tissue sample limitations.
  • A power analysis method was developed to determine the minimum number of microarrays needed for reliable separation of different disease states or physiological differences.
  • The method successfully estimated sample sizes using existing data sets, demonstrating that using the suggested number of samples yields statistically significant differences in group means.
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Motivation: The increasing use of DNA microarrays to probe cell physiology requires methods for visualizing different expression phenotypes and explicitly connecting individual genes to discriminating expression features. Such methods should be robust and maintain biological interpretability.

Results: We propose a method for the mapping of the physiological state of cells and tissues from multidimensional expression data such as those obtained with DNA microarrays.

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