Browsing by Author "Guo, Beibei"

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    Dispositional Mindfulness Predicts Enhanced Smoking Cessation and Smoking Lapse Recovery
    (Springer, 2016) Heppner, Whitney L.; Spears, Claire Adams; Correa-Fernández, Virmarie; Castro, Yessenia; Li, Yisheng; Guo, Beibei; Reitzel, Lorraine R.; Vidrine, Jennifer Irvin; Mazas, Carlos A.; Cofta-Woerpel, Ludmila; Cinciripini, Paul M.; Ahluwalia, Jasjit S.; Wetter, David W.
    Background: Although mindfulness has been hypothesized to promote health behaviors, no research has examined how dispositional mindfulness might influence the process of smoking cessation. Purpose: The current study investigated dispositional mindfulness, smoking abstinence, and recovery from a lapse among African American smokers. Methods: Participants were 399 African Americans seeking smoking cessation treatment (treatments did not include any components related to mindfulness). Dispositional mindfulness and other psychosocial measures were obtained pre-quit; smoking abstinence was assessed 3 days, 31 days, and 26 weeks post-quit. Results: Individuals higher in dispositional mindfulness were more likely to quit smoking both initially and over time. Moreover, among individuals who had lapsed at day 3, those higher in mindfulness were more likely to recover abstinence by the later time points. The mindfulness-early abstinence association was mediated by lower negative affect, lower expectancies to regulate affect via smoking, and higher perceived social support. Conclusions: Results suggest that mindfulness might enhance smoking cessation among African American smokers by operating on mechanisms posited by prominent models of addiction.
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    Statistical Methods for Bioinformatics: Estimation of Copy N umber and Detection of Gene Interactions
    (2011) Guo, Beibei; Guerra, Rudy
    Identification of copy number aberrations in the human genome has been an important area in cancer research. In the first part of my thesis, I propose a new model for determining genomic copy numbers using high-density single nucleotide polymorphism genotyping microarrays. The method is based on a Bayesian spatial normal mixture model with an unknown number of components corresponding to true copy numbers. A reversible jump Markov chain Monte Carlo algorithm is used to implement the model and perform posterior inference. The second part of the thesis describes a new method for the detection of gene-gene interactions using gene expression data extracted from micro array experiments. The method is based on a two-step Genetic Algorithm, with the first step detecting main effects and the second step looking for interacting gene pairs. The performances of both algorithms are examined on both simulated data and real cancer data and are compared with popular existing algorithms. Conclusions are given and possible extensions are discussed.