Associations between teacher support and students' fruit and vegetable consumption in low-income elementary schools in central Texas

Background. Similar to parent support in the home environment, teacher support at school may positively influence children's fruit and vegetable (FV) consumption. This study assessed the relationship between teacher support for FV consumption and the FV intake of 4th and 5th grade students in low-income elementary schools in central Texas. Methods. A secondary analysis was performed on baseline data collected from 496 parent-child dyads during the Marathon Kids study carried out by the Michael & Susan Dell Center for Healthy Living at the University of Texas School of Public Health. A hierarchical linear regression analysis adjusting for key demographic variables, parent support, and home FV availability was conducted. In addition, separate linear regression models stratified by quartiles of home FV availability were conducted to assess the relationship between teacher support and FV intake by level of home FV availability. Results. Teacher support was not significantly related to students' FV intake (p = .44). However, the interaction of teacher support and home FV availability was positively associated with students' FV consumption (p < .05). For students in the lowest quartile of home FV availability, teacher support accounted for approximately 6% of the FV intake variance (p = .02). For higher levels of FV availability, teacher support and FV intake were not related. Conclusions. For lower income elementary school-aged children with low FV availability at home, greater teacher support may lead to modest increases in FV consumption.^

Physical and functional interaction between the dopamine transporter and the synaptic vesicle protein synaptogyrin-3.

Uptake through the dopamine transporter (DAT) represents the primary mechanism used to terminate dopaminergic transmission in brain. Although it is well known that dopamine (DA) taken up by the transporter is used to replenish synaptic vesicle stores for subsequent release, the molecular details of this mechanism are not completely understood. Here, we identified the synaptic vesicle protein synaptogyrin-3 as a DAT interacting protein using the split ubiquitin system. This interaction was confirmed through coimmunoprecipitation experiments using heterologous cell lines and mouse brain. DAT and synaptogyrin-3 colocalized at presynaptic terminals from mouse striatum. Using fluorescence resonance energy transfer microscopy, we show that both proteins interact in live neurons. Pull-down assays with GST (glutathione S-transferase) proteins revealed that the cytoplasmic N termini of both DAT and synaptogyrin-3 are sufficient for this interaction. Furthermore, the N terminus of DAT is capable of binding purified synaptic vesicles from brain tissue. Functional assays revealed that synaptogyrin-3 expression correlated with DAT activity in PC12 and MN9D cells, but not in the non-neuronal HEK-293 cells. These changes were not attributed to changes in transporter cell surface levels or to direct effect of the protein-protein interaction. Instead, the synaptogyrin-3 effect on DAT activity was abolished in the presence of the vesicular monoamine transporter-2 (VMAT2) inhibitor reserpine, suggesting a dependence on the vesicular DA storage system. Finally, we provide evidence for a biochemical complex involving DAT, synaptogyrin-3, and VMAT2. Collectively, our data identify a novel interaction between DAT and synaptogyrin-3 and suggest a physical and functional link between DAT and the vesicular DA system.

A case-control study of hepatitis C virus and its interaction with hepatitis B virus on the development of hepatocellular carcinoma in the USA

Objective. The aim of this study was to assess the independent risk of hepatitis C virus (HCV) infection in the development of hepatocellular carcinoma (HCC). The independent risk of hepatitis B virus (HBV), its interaction with hepatitis C virus and the association with other risk factors were examined.^ Methods. A hospital-based case-control study was conducted between January 1994 and December 1995. We enrolled 115 pathologically confirmed HCC patients and 230 nonliver cancer controls, who were matched by age ($\pm$5 years), gender, and year of diagnosis. Both cases and controls were recruited from The University of Texas M. D. Anderson Cancer Center at Houston. The risk factors were collected through personal interviews and blood samples were tested for HCV and HBV markers. Univariate and multivariate analyses were performed through conditional logistic regression.^ The prevalence of anti-HCV positive is 25.2% in HCC cases compared to 3.0% in controls. The univariate analysis showed that anti-HCV, HBsAg, alcohol drinking and cigarette smoking were significantly associated with HCC, however, family history of cancer, occupational chemical exposure, and use of oral contraceptive were not. Multivariate analysis revealed a matched odds ratio (OR) of 10.1 (95% CI 3.7-27.4) for anti-HCV, and an OR of 11.9 (95% CI 2.5-57.5) for HBsAg. However, dual infection of HCV and HBV had only a thirteen times increase in the risk of HCC, OR = 13.9 (95% CI 1.3-150.6). The estimated population attributable risk percent was 23.4% for HCV, 12.6% for HBV, and 5.3% for both viruses. Ever alcohol drinkers was positively associated with HCC, especially among daily drinkers, matched OR was 5.7 (95% CI 2.1-15.6). However, there was no significant increase in the risk of HCC among smokers as compared to nonsmokers. The mean age of HCC patients was significantly younger among the HBV(+) group and among the HCV(+)/HBV(+) group, when compared to the group of HCC patients with no viral markers. The association between past histories of blood transfusion, acupuncture, tattoo and IVDU was highly significant among the HCV(+) group and the HBV(+)/HCV(+) group, as compared to HCC patients with no viral markers. Forty percent of the HCC patients were pathologically or clinically diagnosed with liver cirrhosis. Anti-HCV(+) (OR = 3.6 95% CI 1.5-8.9) and alcohol drinking (OR = 2.7 95% CI 1.1-6.7), but not HBsAg, are the major risk factors for liver cirrhosis in HCC patients.^ Conclusion. Both hepatitis B virus and hepatitis C virus were independent risk factors for HCC. There was not enough evidence to determine the interaction between both viruses. Only daily alcoholic drinkers showed increasing risk for HCC development, as compared to nondrinkers. ^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

The Skip To Breakfast project: Development, implementation, and feasibility evaluation of an intervention to increase healthful breakfast consumption among fifth grade students and their families

Children and adults frequently skip breakfast and rates are currently increasing. In addition, the food choices made for breakfast are not always healthy ones. Breakfast skipping, in conjunction with unhealthy breakfast choices, leads to impaired cognitive functioning, poor nutrient intake, and overweight. In response to these public health issues, Skip To Breakfast, a behaviorally based school and family program, was created to increase consistent and healthful breakfast consumption among ethnically diverse fifth grade students and their families, using Intervention Mapping™. Four classroom lessons and four parent newsletters were used to deliver the intervention. For this project, a healthy, "3 Star Breakfast" was promoted, and included a serving each of dairy product, whole grain, and fruit, each with an emphasis on being low in fat and sugar. The goal of this project was to evaluate the feasibility and acceptability of the intervention. A pilot-test of the intervention was conducted in one classroom, in a school in Houston, during the Fall 2007 semester. A qualitative evaluation of the intervention was conducted, which included focus groups with students, phone interviews of parents, process evaluation data from the classroom teacher, and direct observation. Sixteen students and six parents participated in the study. Data were recorded and themes were identified. Initial results showed there is a need for such programs. Based on the initial feedback, edits were made to the intervention and program. Results showed high acceptability among the teacher, students, and parents. It became apparent that students were not reliably getting the parent newsletters to their parents to read, so a change to the protocol was made, in which students will receive incentives for having parents read newsletters and return signed forms, to increase parent participation. Other changes included small modifications to the curriculum, such as, clarifying instructions, changing in-class assignments to homework assignments, and including background reading materials for the teacher. The main trial is planned to be carried out in Spring 2008, in two elementary schools, utilizing four, fifth grade classes from each, with one school acting as the control and one as the intervention school. Results from this study can be used as an adjunct to the Coordinated Approach To Child Health (CATCH) program. ^

Dissecting the Interaction between p53 and TRIM24

Dissecting the Interaction of p53 and TRIM24 Aundrietta DeVan Duncan Supervisory Professor, Michelle Barton, Ph.D. p53, the “guardian of the genome”, plays an important role in multiple biological processes including cell cycle, angiogenesis, DNA repair and apoptosis. Because it is mutated in over 50% of cancers, p53 has been widely studied in established cancer cell lines. However, little is known about the function of p53 in a normal cell. We focused on characterizing p53 in normal cells and during differentiation. Our lab recently identified a novel binding partner of p53, Tripartite Motif 24 protein (TRIM24). TRIM24 is a member of the TRIM family of proteins, defined by their conserved RING, B-box, and coiled coil domains. Specifically, TRIM24 is a member of the TIF1 subfamily, which is characterized by PHD and Bromo domains in the C-terminus. Between the Coiled-coil and PHD domain is a linker region, 437 amino acids in length. This linker region houses important functions of TRIM24 including it’s site of interaction with nuclear receptors. TRIM24 is an E3-ubiquitin ligase, recently discovered to negatively regulate p53 by targeting it for degradation. Though it is known that Trim24 and p53 interact, it is not known if the interaction is direct and what effect this interaction has on the function of TRIM24 and p53. My study aims to elucidate the specific interaction domains of p53 and TRIM24. To determine the specific domains of p53 required for interaction with TRIM24, we performed co-immuoprecipitation (Co-IP) with recombinant full-length Flag-tagged TRIM24 protein and various deletion constructs of in vitro translated GST-p53, as well as the reverse. I found that TRIM24 binds both the carboxy terminus and DNA binding domain of p53. Furthermore, my results show that binding is altered when post-translational modifications of p53 are present, suggesting that the interaction between p53 and TRIM24 may be affected by these post-translational modifications. To determine the specific domains of TRIM24 required for p53 interaction, we performed GST pull-downs with in vitro translated, Flag-TRIM24 protein constructs and recombinant GST-p53 protein purified from E. coli. We found that the Linker region is sufficient for interaction of p53 and TRIM24. Taken together, these data indicate that the interaction between p53 and TRIM24 does occur in vitro and that interaction may be influenced by post-translational modifications of the proteins.

Assessing childhood difficulties: Comparing the SDQ and the BASC-2

This study of the Behavior Assessment System for Children, Second Edition (BASC-2), had two objectives. First, was to compare the Strengths and Difficulties Questionnaire (SDQ) and the BASC-2. Participants were students from SBISD, identified as having difficulties, assessed with the BASC-2 and completed the SDQ. Based on the small sample (N=8), scores from the SDQ and the BASC-2 were found to correlate highly with one another on most conceptually similar scales. With both Parent and Teacher raters, diagnostic concordance was high for nearly all behavior and emotional problem scales. While the diagnostic concordance of the SDQ and BASC-2 looks promising, results need to be replicated with a larger sample. ^ The second objective was to assess the BASC-2 inter-informant concordance (parent, teacher and child). Participants were 145 students, 3-17 years, 78.6% male, 28% Hispanic, 37% White, 34% Black, and 64% were economically disadvantaged. Of the four dyads, teacher-teacher pairs had the highest correlations and agreement levels, especially on externalizing scale items, regardless of the subjects' age group, gender or ethnicity. ^ Overall, parent-teacher pairs had low to moderate concordance for most scale items, with slightly higher agreement for externalizing problems, with better concordance for preschool children, very low correlations with girls' ratings, but moderate correlations with boy ratings. Correlational results were generally moderate for teachers and parents of White children and low for teachers and parents of Hispanic and Black children. ^ Parent-child self-reports had low concordance for nearly all scale items evaluated, particularly with girl self-raters, but moderate with the boys. Conversely, Teacher-Girl pairs had larger correlations than with Boy. Parents reported substantially higher frequency of disorder endorsement than reported by the children, regardless of the child's ethnicity or gender. While generally low, Teachers and Black students had higher concordance on internalizing measures than Hispanic or White students. Parents of Black students had higher frequency of disorder endorsements than other ethnicities. ^ The difference in format and lack of externalizing measures on the self-report version (SRP) hinders inter-rater comparisons. Future studies using the revised, BASC-2 with children in a school-based setting are needed to assess further its rater reliability. ^

Association of part-time employment and sexual risk-taking behaviors among middle school students

Background. Previous research shows inconsistent results as to the association between part-time employment and sexual behavior among younger teens. Studies of older teens cannot be generalized to younger teens because of the wide differences in types of work performed, nature of work environments, and work intensity. Objective. Examine the relationship between part-time employment and sexual behavior in a cross-sectional sample of public middle school students in Houston, Texas. Methods . The study presents a secondary analysis of data from the It’s Your Game…Keep it Real baseline data collection (11/2004–1/2005). It’s Your Game… is an intervention program for middle school students designed to prevent Sexually Transmitted Infections. Statistical analysis. Univariate and multivariate logistic regression analyses were conducted to examine the association between part-time employment and vaginal intercourse: (a) ever had sex; and (b) current sexual activity. Results. Overall, 13.2% of students worked for pay; male students were 1.5 times as likely as females to be working. Of all the students, 11.0% had had sexual intercourse; students who worked were 3 times more likely to be sexually experienced than those who did not. Among students who were sexually experienced, 67.0% were currently sexually active. After adjusting for the other covariates, Hispanic students were almost 3.6 times more likely to report current sexual activity compared to students in other racial/ethnic groups. In univariate analysis, students who worked 1-5 hrs/week were more likely to be sexually experienced than those not currently employed, and the likelihood increased with number of hours worked. There is a similar pattern in the multivariate model, but the odds ratios are too close for the evidence to be more than suggestive. Of sexually experienced students, students working 1-5 hrs/week were 2.7 times more likely to report current sexual intercourse than those not working; those working >5 hrs/week were 4.7 times more likely. The multivariate model showed a similar increase in likelihood, and adjustment for covariates increased these associations: students who worked 1-5 hrs/week were 3.6 times more likely to report current sexual intercourse, and students who worked >5 hrs/week were 4.5 times more likely, than students not currently employed.^

Design, Calibration, and Evaluation of Depth-of-Interaction-Capable PET Detector Modules

High-resolution, small-bore PET systems suffer from a tradeoff between system sensitivity, and image quality degradation. In these systems long crystals allow mispositioning of the line of response due to parallax error and this mispositioning causes resolution blurring, but long crystals are necessary for high system sensitivity. One means to allow long crystals without introducing parallax errors is to determine the depth of interaction (DOI) of the gamma ray interaction within the detector module. While DOI has been investigated previously, newly available solid state photomultipliers (SSPMs) well-suited to PET applications and allow new modules for investigation. Depth of interaction in full modules is a relatively new field, and so even if high performance DOI capable modules were available, the appropriate means to characterize and calibrate the modules are not. This work presents an investigation of DOI capable arrays and techniques for characterizing and calibrating those modules. The methods introduced here accurately and reliably characterize and calibrate energy, timing, and event interaction positioning. Additionally presented is a characterization of the spatial resolution of DOI capable modules and a measurement of DOI effects for different angles between detector modules. These arrays have been built into a prototype PET system that delivers better than 2.0 mm resolution with a single-sided-stopping-power in excess of 95% for 511 keV g's. The noise properties of SSPMs scale with the active area of the detector face, and so the best signal-to-noise ratio is possible with parallel readout of each SSPM photodetector pixel rather than multiplexing signals together. This work additionally investigates several algorithms for improving timing performance using timing information from multiple SSPM pixels when light is distributed among several photodetectors.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.