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Full-Text Articles in Medicine and Health Sciences

Multiple Subject Barycentric Discriminant Analysis (Musubada): How To Assign Scans To Categories Without Using Spatial Normalization, Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini Dec 2012

Multiple Subject Barycentric Discriminant Analysis (Musubada): How To Assign Scans To Categories Without Using Spatial Normalization, Hervé Abdi, Lynne J. Williams, Andrew C. Connolly, M. Ida Gobbini

Dartmouth Scholarship

We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can …


Measuring Infertility In Populations: Constructing A Standard Definition For Use With Demographic And Reproductive Health Surveys, Maya N. Mascarenhas, Hoiwan Cheung, Colin D. Mathers, Gretchen A. Stevens Aug 2012

Measuring Infertility In Populations: Constructing A Standard Definition For Use With Demographic And Reproductive Health Surveys, Maya N. Mascarenhas, Hoiwan Cheung, Colin D. Mathers, Gretchen A. Stevens

Dartmouth Scholarship

Background: Infertility is a significant disability, yet there are no reliable estimates of its global prevalence. Studies on infertility prevalence define the condition inconsistently, rendering the comparison of studies or quantitative summaries of the literature difficult. This study analyzed key components of infertility to develop a definition that can be consistently applied to globally available household survey data.

Methods: We proposed a standard definition of infertility and used it to generate prevalence estimates using 53 Demographic and Health Surveys (DHS). The analysis was restricted to the subset of DHS that contained detailed fertility information collected through the reproductive health calendar. …


A Comparison Of Individual Versus Community Influences On Youth Smoking Behaviours: A Cross-Sectional Observational Study, Anna M. Adachi-Mejia, Heather A. Carlos, Ethan M. Berke, Susanne E. Tanski, James Sargent Jul 2012

A Comparison Of Individual Versus Community Influences On Youth Smoking Behaviours: A Cross-Sectional Observational Study, Anna M. Adachi-Mejia, Heather A. Carlos, Ethan M. Berke, Susanne E. Tanski, James Sargent

Dartmouth Scholarship

Objectives: To compare individual with community risk factors for adolescent smoking. Design: A cross-sectional observational study with multivariate analysis.Setting: National telephone survey.Participants: 3646 US adolescents aged 13–18 years in 2007 recruited through a random digit-dial survey.


Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit May 2012

Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit

Dartmouth Scholarship

There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.