Browsing by Author "Chen, Wei"
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Item Association of Symptons of Depression and Obesity With Hypertension: The Bogalusa Heart Study(2006) Kabir, Azad Alamgir; Whelton, Paul K.; Khan, M. Muhmud; Gustat, Jeanette; Chen, WeiBackground: There is growing evidence that symptoms of depression influence the development of cardiovascular disease. The objective of this study was to evaluate the direct and indirect relationships between symptoms of depression, body mass index (BMI), and hypertension in a biracial (African American–white) rural population. Methods: This is a cross-sectional study with 1017 study participants (aged 12 to 62 years, 60% white, and 52% women) from 561 families of the Bogalusa Heart Study. A two-stage modeling approach was used to evaluate the relationship between symptoms of depression, BMI, and hypertension. Generalized estimating equation methods (GEE) were used to account for within family correlations. Adjusted coefficients (95% confidence interval [CI]) and odds ratios (OR) were used to explore relationships. Results: Mean ( SE) BMI of the study population was 28 (7). Thirty-two percent of those studied had presumptive depression and 13.4% had hypertension. The indirect effect of a 5 unit higher symptoms of depression score was associated with a 14% (OR: 1.14; 95% CI: 1.01–1.28; P .02) higher likelihood of being hypertensive due to presence of a higher level of BMI in both whites and African Americans. The direct effect of a 5 unit higher symptoms of depression score was found to be nonsignificant (OR: 1.05; 95% CI: 0.92–1.20; P .22) in whites and significant (OR: 0.81; 95% CI: 0.68–0.95; P .004) in African Americans. Conclusions: The presence of a significant indirect effect of symptoms of depression (mediated through higher level of BMI) in both whites and African Americans suggests that BMI can be an intermediate variable linking symptoms of depression and hypertension.Item Data-Driven Geometric Design Space Exploration and Design Synthesis(2019) Chen, Wei; Fuge, Mark D; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)A design space is the space of all potential design candidates. While the design space can be of any kind, this work focuses on exploring geometric design spaces, where geometric parameters are used to represent designs and will largely affect a given design's functionality or performance (e.g., airfoil, hull, and car body designs). By exploring the design space, we evaluate different design choices and look for desired solutions. However, a design space may have unnecessarily high dimensionality and implicit boundaries, which makes it difficult to explore. Also, if we synthesize new designs by randomly sampling design variables in the high-dimensional design space, there is high chance that the designs are not feasible, as there is correlation between feasible design variables. This dissertation introduces ways of capturing a compact representation (which we call a latent space) that describes the variability of designs, so that we can synthesize designs and explore design options using this compact representation instead of the original high-dimensional design variables. The main research question answered by this dissertation is: how does one effectively learn this compact representation from data and efficiently explore this latent space so that we can quickly find desired design solutions? The word "quickly" here means to eliminate or reduce the iterative ideation, prototyping, and evaluation steps in a conventional design process. This also reduces human intervention, and hence facilitates design automation. This work bridges the gap between machine learning and geometric design in engineering. It contributes new pieces of knowledge within two topics: design space exploration and design synthesis. Specifically, the main contributions are: 1. A method for measuring the intrinsic complexity of a design space based on design data manifolds. 2. Machine learning models that incorporate prior knowledge from the domain of design to improve latent space exploration and design synthesis quality. 3. New design space exploration tools that expand the design space and search for desired designs in an unbounded space. 4. Geometrical design space benchmarks with controllable complexity for testing data-driven design space exploration and design synthesis.Item Mechanism of pH-dependent activation of the sodium-proton antiporter NhaA(2016) Huang, Yandong; Chen, Wei; Dotson, David L.; Beckstein, Oliver; Shen, JanaEscherichia coli NhaA is a prototype sodium-proton antiporter, which has been extensively characterized by X-ray crystallography, biochemical and biophysical experiments. However, the identities of proton carriers and details of pH-regulated mechanism remain controversial. Here we report constant pH molecular dynamics data, which reveal that NhaA activation involves a net charge switch of a pH sensor at the entrance of the cytoplasmic funnel and opening of a hydrophobic gate at the end of the funnel. The latter is triggered by charging of Asp164, the first proton carrier. The second proton carrier Lys300 forms a salt bridge with Asp163 in the inactive state, and releases a proton when a sodium ion binds Asp163. These data reconcile current models and illustrate the power of state-of-the-art molecular dynamics simulations in providing atomic details of proton-coupled transport across membrane, which is challenging to elucidate by experimental techniques.Item Results from a prostate cancer admixture mapping study in African-American men.(2009) Bock, Cathryn Hufford; Schwartz, Ann G; Ruterbusch, Julie J; Levin, Albert M; Neslund-Dudas, Christine; Land, Susan J; Wenzlaff, Angela S; Reich, David; McKeigue, Paul; Chen, Wei; Heath, Elisabeth I; Powell, Isaac J; Kittles, Rick A; Rybicki, Benjamin AThere are considerable racial disparities in prostate cancer risk, with a 60% higher incidence rate among African-American (AA) men compared with European-American (EA) men, and a 2.4-fold higher mortality rate in AA men than in EA men. Recently, studies have implicated several African-ancestry associated prostate cancer susceptibility loci on chromosome 8q24. In the current study, we performed admixture mapping in AA men from two independent case-control studies of prostate cancer to confirm the 8q24 ancestry association and also identify other genomic regions that may harbor prostate cancer susceptibility genes. A total of 482 cases and 261 controls were genotyped for 1,509 ancestry informative markers across the genome. The mean estimated individual admixture proportions were 20% European and 80% African. The most significant observed increase in European ancestry occurred at rs2141360 on chromosome 7q31 in both the case-only (P = 0.0000035) and case-control analyses. The most significant observed increase in African ancestry across the genome occurred at a locus on chromosome 5q35 identified by SNPs rs7729084 (case-only analysis P = 0.002), and rs12474977 (case-control analysis P = 0.004), which are separated by 646 kb and were adjacent to one another on the panel. On chromosome 8, rs4367565 was associated with the greatest excess African ancestry in both the case-only and case-control analyses (case-only and case-control P = 0.02), confirming previously reported African-ancestry associations with chromosome 8q24. In conclusion, we confirmed ancestry associations on 8q24, and identified additional ancestry-associated regions potentially harboring prostate cancer susceptibility loci.