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Physical Sciences and Mathematics Commons™
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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Dynapenic Obesity And The Effect On Long-Term Physical Function And Quality Of Life: Data From The Osteoarthritis Initiative, John A. Batsis, Alicia J. Zbehlik, Dawna Pidgeon, Stephen J. Bartels
Dynapenic Obesity And The Effect On Long-Term Physical Function And Quality Of Life: Data From The Osteoarthritis Initiative, John A. Batsis, Alicia J. Zbehlik, Dawna Pidgeon, Stephen J. Bartels
Dartmouth Scholarship
Obesity is associated with functional impairment, institutionalization, and increased mortality risk in elders. Dynapenia is defined as reduced muscle strength and is a known independent predictor of adverse events and disability. The synergy between dynapenia and obesity leads to worse outcomes than either independently. We identified the impact of dynapenic obesity in a cohort at risk for and with knee osteoarthritis on function.
Wordless Intervention For Epilepsy In Learning Disabilities (Wield): Study Protocol For A Randomized Controlled Feasibility Trial, Marie-Anne Durand, Bob Gates, Georgina Parkes, Asif Zia
Wordless Intervention For Epilepsy In Learning Disabilities (Wield): Study Protocol For A Randomized Controlled Feasibility Trial, Marie-Anne Durand, Bob Gates, Georgina Parkes, Asif Zia
Dartmouth Scholarship
Epilepsy is the most common neurological problem that affects people with learning disabilities. The high seizure frequency, resistance to treatments, associated skills deficit and co-morbidities make the management of epilepsy particularly challenging for people with learning disabilities. The Books Beyond Words booklet for epilepsy uses images to help people with learning disabilities manage their condition and improve quality of life. Our aim is to conduct a randomized controlled feasibility trial exploring key methodological, design and acceptability issues, in order to subsequently undertake a large-scale randomized controlled trial of the Books Beyond Words booklet for epilepsy.
New Malignancies After Squamous Cell Carcinoma And Melanomas: A Population-Based Study From Norway, Trude E. Robsahm, Margaret R. Karagas, Judy R. Rees, Astri Syse
New Malignancies After Squamous Cell Carcinoma And Melanomas: A Population-Based Study From Norway, Trude E. Robsahm, Margaret R. Karagas, Judy R. Rees, Astri Syse
Dartmouth Scholarship
Skin cancer survivors experience an increased risk for subsequent malignancies but the associated risk factors are poorly understood. This study examined the risk of a new primary cancer following an initial skin cancer and assessed risk factors associated with second primary cancers.
Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit
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.
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.