Basic Medical Science Publications (UMKC)
https://hdl.handle.net/10355/14253
The items in this collection are the scholarly output of the faculty, staff, and students of the Department of Basic Medical Science.2024-03-28T19:03:51ZAssociations between the built environment and physical activity in public housing residents
https://hdl.handle.net/10355/15094
Associations between the built environment and physical activity in public housing residents
Heinrich, Katie M; Lee, Rebecca E; Suminski, Richard R; Regan, Gail R; Reese-Smith, Jacqueline Y; Howard, Hugh H; Haddock, C Keith; Poston, Walker SC; Ahluwalia, Jasjit S
Abstract
Background
Environmental factors may influence the particularly low rates of physical activity in African American and low-income adults. This cross-sectional study investigated how measured environmental factors were related to self-reported walking and vigorous physical activity for residents of low-income public housing developments.
Methods
Physical activity data from 452 adult residents residing in 12 low-income housing developments were combined with measured environmental data that examined the neighborhood (800 m radius buffer) around each housing development. Aggregated ecological and multilevel regression models were used for analysis.
Results
Participants were predominately female (72.8%), African American (79.6%) and had a high school education or more (59.0%). Overall, physical activity rates were low, with only 21% of participants meeting moderate physical activity guidelines. Ecological models showed that fewer incivilities and greater street connectivity predicted 83% of the variance in days walked per week, p < 0.001, with both gender and connectivity predicting days walked per week in the multi-level analysis, p < 0.05. Greater connectivity and fewer physical activity resources predicted 90% of the variance in meeting moderate physical activity guidelines, p < 0.001, and gender and connectivity were the multi-level predictors, p < 0.05 and 0.01, respectively. Greater resource accessibility predicted 34% of the variance in days per week of vigorous physical activity in the ecological model, p < 0.05, but the multi-level analysis found no significant predictors.
Conclusion
These results indicate that the physical activity of low-income residents of public housing is related to modifiable aspects of the built environment. Individuals with greater access to more physical activity resources with fewincivilities, as well as, greater street connectivity, are more likely to be physically active.
2007-11-12T00:00:00ZA Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process
https://hdl.handle.net/10355/14799
A Bayesian Analysis for Identifying DNA Copy Number Variations Using a Compound Poisson Process
Chen, Jie; Yiğiter, Ayten; Wang, Yu-Ping; Deng, Hong-Wen
To study chromosomal aberrations that may lead to cancer formation or genetic diseases, the array-based Comparative Genomic Hybridization (aCGH) technique is often used for detecting DNA copy number variants (CNVs). Various methods have been developed for gaining CNVs information based on aCGH data. However, most of these methods make use of the log-intensity ratios in aCGH data without taking advantage of other information such as the DNA probe (e.g., biomarker) positions/distances contained in the data. Motivated by the specific features of aCGH data, we developed a novel method that takes into account the estimation of a change point or locus of the CNV in aCGH data with its associated biomarker position on the chromosome using a compound Poisson process. We used a Bayesian approach to derive the posterior probability for the estimation of the CNV locus. To detect loci of multiple CNVs in the data, a sliding window process combined with our derived Bayesian posterior probability was proposed. To evaluate the performance of the method in the estimation of the CNV locus, we first performed simulation studies. Finally, we applied our approach to real data from aCGH experiments, demonstrating its applicability.
2010-08-17T00:00:00Zdelta-Tocotrienol and quercetin reduce serum levels of nitric oxide and lipid parameters in female chickens
https://hdl.handle.net/10355/15010
delta-Tocotrienol and quercetin reduce serum levels of nitric oxide and lipid parameters in female chickens
Qureshi, Asaf A.; Reis, Julia C.; Qureshi, Nilofer; Papasian, Christopher J.; Morrison, David C.; Schaefer, Daniel M.
Abstract
Background
Chronic, low-grade inflammation provides a link between normal ageing and the pathogenesis of age-related diseases. A series of in vitro tests confirmed the strong anti-inflammatory activities of known inhibitors of NF-κB activation (δ-tocotrienol, quercetin, riboflavin, (-) Corey lactone, amiloride, and dexamethasone). δ-Tocotrienol also suppresses β-hydroxy-β-methylglutaryl coenzyme A (HMG-CoA) reductase activity (the rate-limiting step in de novo cholesterol synthesis), and concomitantly lowers serum total and LDL cholesterol levels. We evaluated these compounds in an avian model anticipating that a dietary additive combining δ-tocotrienol with quercetin, riboflavin, (-) Corey lactone, amiloride, or dexamethasone would yield greater reductions in serum levels of total cholesterol, LDL-cholesterol and inflammatory markers (tumor necrosis factor-α [TNF-α], and nitric oxide [NO]), than that attained with the individual compounds.
Results
The present results showed that supplementation of control diets with all compounds tested except riboflavin, (-) Corey lactone, and dexamethasone produced small but significant reductions in body weight gains as compared to control. (-) Corey lactone and riboflavin did not significantly impact body weight gains. Dexamethasone significantly and markedly reduced weight gain (>75%) compared to control. The serum levels of TNF-α and NO were decreased 61% - 84% (P < 0.001), and 14% - 67%, respectively, in chickens fed diets supplemented with δ-tocotrienol, quercetin, riboflavin, (-) Corey lactone, amiloride, or dexamethasone as compared to controls. Significant decreases in the levels of serum total and LDL-cholesterol were attained with δ-tocotrienol, quercetin, riboflavin and (-) Corey lactone (13% - 57%; P < 0.05), whereas, these levels were 2-fold higher in dexamethasone treated chickens as compared to controls. Parallel responses on hepatic lipid infiltration were confirmed by histological analyses. Treatments combining δ-tocotrienol with the other compounds yielded values that were lower than individual values attained with either δ-tocotrienol or the second compound. Exceptions were the significantly lower total and LDL cholesterol and triglyceride values attained with the δ-tocotrienol/(-) Corey lactone treatment and the significantly lower triglyceride value attained with the δ-tocotrienol/riboflavin treatment. δ-Tocotrienol attenuated the lipid-elevating impact of dexamethasone and potentiated the triglyceride lowering impact of riboflavin. Microarray analyses of liver samples identified 62 genes whose expressions were either up-regulated or down-regulated by all compounds suggesting common impact on serum TNF-α and NO levels. The microarray analyses further identified 41 genes whose expression was differentially impacted by the compounds shown to lower serum lipid levels and dexamethasone, associated with markedly elevated serum lipids.
Conclusions
This is the first report describing the anti-inflammatory effects of δ-tocotrienol, quercetin, riboflavin, (-) Corey lactone, amiloride, and dexamethasone on serum TNF-δ and NO levels. Serum TNF-δ levels were decreased by >60% by each of the experimental compounds. Additionally, all the treatments except with dexamethasone, resulted in lower serum total cholesterol, LDL-cholesterol and triglyceride levels. The impact of above mentioned compounds on the factors evaluated herein was increased when combined with δ-tocotrienol.
2011-02-28T00:00:00ZGene selection for classification of microarray data based on the Bayes error
https://hdl.handle.net/10355/15040
Gene selection for classification of microarray data based on the Bayes error
Zhang, Ji-Gang; Deng, Hong-Wen
Abstract
Background
With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.
Results
In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes.
Conclusion
The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.
2007-10-03T00:00:00Z