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Redford B. Williams - One of the best experts on this subject based on the ideXlab platform.

  • developing a synthetic psychosocial Stress Measure and harmonizing cvd risk data a way forward to gxe meta and mega analyses
    BMC Research Notes, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p < 0.0001). This evidence of the validity and the detailed account of our data harmonization approaches demonstrated that it is possible to overcome the inconsistencies in the collection and Measurement of human health risk variables.

  • Developing a synthetic psychosocial Stress Measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses
    BMC, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Abstract Objectives Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. Results We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p 

Abanish Singh - One of the best experts on this subject based on the ideXlab platform.

  • developing a synthetic psychosocial Stress Measure and harmonizing cvd risk data a way forward to gxe meta and mega analyses
    BMC Research Notes, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p < 0.0001). This evidence of the validity and the detailed account of our data harmonization approaches demonstrated that it is possible to overcome the inconsistencies in the collection and Measurement of human health risk variables.

  • Developing a synthetic psychosocial Stress Measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses
    BMC, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Abstract Objectives Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. Results We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p 

Daniel A Tortorelli - One of the best experts on this subject based on the ideXlab platform.

  • Combined shape and topology optimization for minimization of maximal von Mises Stress
    Structural and Multidisciplinary Optimization, 2017
    Co-Authors: Haojie Lian, Daniel A Tortorelli, Asger N. Christiansen, Ole Sigmund, Niels Aage
    Abstract:

    This work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal Stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology. By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises Stress using the p -norm Stress Measure with p -values as high as 30, provided that the Stress is calculated with sufficient accuracy.

  • Stress based topology optimization for continua
    Structural and Multidisciplinary Optimization, 2010
    Co-Authors: Julian A Norato, T E Bruns, Daniel A Tortorelli
    Abstract:

    We propose an effective algorithm to resolve the Stress-constrained topology optimization problem. Our procedure combines a density filter for length scale control, the solid isotropic material with penalization (SIMP) to generate black-and-white designs, a SIMP-motivated Stress definition to resolve the Stress singularity phenomenon, and a global/regional Stress Measure combined with an adaptive normalization scheme to control the local Stress level.

Michael A. Babyak - One of the best experts on this subject based on the ideXlab platform.

  • developing a synthetic psychosocial Stress Measure and harmonizing cvd risk data a way forward to gxe meta and mega analyses
    BMC Research Notes, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p < 0.0001). This evidence of the validity and the detailed account of our data harmonization approaches demonstrated that it is possible to overcome the inconsistencies in the collection and Measurement of human health risk variables.

  • Developing a synthetic psychosocial Stress Measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses
    BMC, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Abstract Objectives Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. Results We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p 

Ilene C. Siegler - One of the best experts on this subject based on the ideXlab platform.

  • developing a synthetic psychosocial Stress Measure and harmonizing cvd risk data a way forward to gxe meta and mega analyses
    BMC Research Notes, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p < 0.0001). This evidence of the validity and the detailed account of our data harmonization approaches demonstrated that it is possible to overcome the inconsistencies in the collection and Measurement of human health risk variables.

  • Developing a synthetic psychosocial Stress Measure and harmonizing CVD-risk data: a way forward to GxE meta- and mega-analyses
    BMC, 2018
    Co-Authors: Abanish Singh, Michael A. Babyak, Beverly H. Brummett, William E. Kraus, Ilene C. Siegler, Elizabeth R. Hauser, Redford B. Williams
    Abstract:

    Abstract Objectives Among many challenges in cardiovascular disease (CVD) risk prediction are interactions of genes with Stress, race, and/or sex and developing robust estimates of these interactions. Improved power with larger sample size contributed by the accumulation of epidemiological data could be helpful, but integration of these datasets is difficult due the absence of standardized phenotypic Measures. In this paper, we describe the details of our undertaking to harmonize a dozen datasets and provide a detailed account of a number of decisions made in the process. Results We harmonized candidate genetic variants and CVD-risk variables related to demography, adiposity, hypertension, lipodystrophy, hypertriglyceridemia, hyperglycemia, depressive symptom, and chronic psychosocial Stress from a dozen studies. Using our synthetic Stress algorithm, we constructed a synthetic chronic psychosocial Stress Measure in nine out of twelve studies where a formal self-rated Stress Measure was not available. The mega-analytic partial correlation between the Stress Measure and depressive symptoms while controlling for the effect of study variable in the combined dataset was significant (Rho = 0.27, p