Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Computer-assisted (2)
- Humans (2)
- Methods (2)
- Adipose tissue (1)
- Aged (1)
-
- Algorithms (1)
- Automated (1)
- Breast (1)
- Chromosome mapping (1)
- Cytology (1)
- Diagnosis (1)
- Female (1)
- Gene expression profiling (1)
- Genetic predisposition to disease (1)
- Genetic testing (1)
- Genetics (1)
- Hemoglobins (1)
- Image processing (1)
- Magnetic resonance imaging (1)
- Metabolism (1)
- Middle aged (1)
- Multigene family (1)
- Near-infrared (1)
- Neural networks (computer) (1)
- Parkinson disease (1)
- Pattern recognition (1)
- Polymorphism (1)
- Protein interaction mapping (1)
- Single nucleotide (1)
- Spectroscopy (1)
Articles 1 - 3 of 3
Full-Text Articles in Medicine and Health Sciences
Reduction In Urinary Arsenic With Bottled-Water Intervention, Arun B. Josyula, Hannah Mcclellen, Tracy A. Hysong, Margaret Kurzius-Spencer, Gerald S. Poplin, Stefan Stürup, Jefferey L. Burgess
Reduction In Urinary Arsenic With Bottled-Water Intervention, Arun B. Josyula, Hannah Mcclellen, Tracy A. Hysong, Margaret Kurzius-Spencer, Gerald S. Poplin, Stefan Stürup, Jefferey L. Burgess
Dartmouth Scholarship
The study was conducted to measure the effectiveness of providing bottled water in reducing arsenic exposure. Urine, tap-water and toenail samples were collected from non-smoking adults residing in Ajo (n=40) and Tucson (n=33), Arizona, USA. The Ajo subjects were provided bottled water for 12 months prior to re-sampling. The mean total arsenic (microg/L) in tap-water was 20.3+/-3.7 in Ajo and 4.0+/-2.3 in Tucson. Baseline urinary total inorganic arsenic (microg/L) was significantly higher among the Ajo subjects (n=40, 29.1+/-20.4) than among the Tucson subjects (n=32, 11.0+/-12.0, p<0.001), as was creatinine-adjusted urinary total inorganic arsenic (microg/g) (35.5+/-25.2 vs 13.2+/-9.3, p<0.001). Baseline concentrations of arsenic (microg/g) in toenails were also higher among the Ajo subjects (0.51+/-0.72) than among the Tucson subjects (0.17+/-0.21) (p<0.001). After the intervention, the mean urinary total inorganic arsenic in Ajo (n=36) dropped by 21%, from 29.4+/-21.1 to 23.2+/-23.2 (p=0.026). The creatinine-adjusted urinary total inorganic arsenic and toenail arsenic levels did not differ significantly with the intervention. Provision of arsenic-free bottled water resulted in a modest reduction in urinary total inorganic arsenic.
Imaging Breast Adipose And Fibroglandular Tissue Molecular Signatures By Using Hybrid Mri-Guided Near-Infrared Spectral Tomography, Ben Brooksby, Brian W. Pogue, Shudong Jiang, Hamid Dehghani, Subhadra Srinivasan, Christine Kogel, Tor D. Tosteson, John Weaver, Steven P. Poplack, Keith D. Paulsen
Imaging Breast Adipose And Fibroglandular Tissue Molecular Signatures By Using Hybrid Mri-Guided Near-Infrared Spectral Tomography, Ben Brooksby, Brian W. Pogue, Shudong Jiang, Hamid Dehghani, Subhadra Srinivasan, Christine Kogel, Tor D. Tosteson, John Weaver, Steven P. Poplack, Keith D. Paulsen
Dartmouth Scholarship
Magnetic resonance (MR)-guided near-infrared spectral tomography was developed and used to image adipose and fibroglandular breast tissue of 11 normal female subjects, recruited under an institutional review board-approved protocol. Images of hemoglobin, oxygen saturation, water fraction, and subcellular scattering were reconstructed and show that fibroglandular fractions of both blood and water are higher than in adipose tissue. Variation in adipose and fibroglandular tissue composition between individuals was not significantly different across the scattered and dense breast categories. Combined MR and near-infrared tomography provides fundamental molecular information about these tissue types with resolution governed by MR T1 images.
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie
Dartmouth Scholarship
The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.