Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

Integrating diabetes self-management with the health goals of older adults: A qualitative exploration

Objective. This study investigates the life and health goals of older adults with diabetes, and explores the factors that influence their diabetes self-management. Methods: Qualitative in-depth interviews were conducted with 24 older adults with diabetes and other morbid conditions and/or their caregivers, when appropriate. ^ Results. Participants’ provided a consistent set of responses when describing life and health goals. Participants described goals for longevity, better physical functioning, spending time with family, or maintaining independence. Diabetes discordant conditions, but not diabetes, were seen as barriers to life goals for participants with functional impairments. Functionally independent participants described additional health goals that related to diabetes self-management as diabetes was seen often a barrier to life goals. Caregivers, co-morbid conditions, denial and retirement were among the factors that influenced initiation of diabetes self-management. ^ Conclusion. Participants endorsed health goals and diabetes self-management practices that they believed would help them accomplish their life goals. Functional capabilities and social support were key factors in the relationship between diabetes self-management and their broader goals. ^ Practice implications. When planning diabetes treatments, clinicians, patients and caregivers should discuss the relationship between diabetes self-management and health and life goals as well as the affects of functional limitations and caregiver support.^