A model of the roles of essential kinases in the induction and expression of late long-term potentiation.

The induction of late long-term potentiation (L-LTP) involves complex interactions among second-messenger cascades. To gain insights into these interactions, a mathematical model was developed for L-LTP induction in the CA1 region of the hippocampus. The differential equation-based model represents actions of protein kinase A (PKA), MAP kinase (MAPK), and CaM kinase II (CAMKII) in the vicinity of the synapse, and activation of transcription by CaM kinase IV (CAMKIV) and MAPK. L-LTP is represented by increases in a synaptic weight. Simulations suggest that steep, supralinear stimulus-response relationships between stimuli (e.g., elevations in [Ca(2+)]) and kinase activation are essential for translating brief stimuli into long-lasting gene activation and synaptic weight increases. Convergence of multiple kinase activities to induce L-LTP helps to generate a threshold whereby the amount of L-LTP varies steeply with the number of brief (tetanic) electrical stimuli. The model simulates tetanic, -burst, pairing-induced, and chemical L-LTP, as well as L-LTP due to synaptic tagging. The model also simulates inhibition of L-LTP by inhibition of MAPK, CAMKII, PKA, or CAMKIV. The model predicts results of experiments to delineate mechanisms underlying L-LTP induction and expression. For example, the cAMP antagonist RpcAMPs, which inhibits L-LTP induction, is predicted to inhibit ERK activation. The model also appears useful to clarify similarities and differences between hippocampal L-LTP and long-term synaptic strengthening in other systems.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

MULTIPLE POSTTRANSCRIPTIONAL REGULATORY FEATURES CONTROL EXPRESSION OF ETHANOLAMINE UTILIZATION GENES IN ENTEROCOCCUS FAECALIS

Enterococcus faecalis is a Gram-positive bacterium that lives as a commensal organism in the mammalian gastrointestinal tract, but can behave as an opportunistic pathogen. Our lab discovered that mutation of the eutK gene attenuates virulence of E. faecalis in the C. elegans model host. eutK is part of the ethanolamine metabolic pathway which was previously unknown in E. faecalis. I discovered the presence of two unique posttranscriptional regulatory features that control expression of eut locus genes. The first feature I found is an AdoCBL riboswitch, a cis-acting RNA regulatory element that acts as a positive regulator of gene expression. The second feature I discovered is a unique two-component system, EutVW. The EutV response regulator contains an ANTAR family domain, which binds RNA to trigger transcriptional antitermination. I determined that induction of expression of several genes in the eut locus is dependent on ethanolamine, AdoCBL and the two-component system. AdoCBL and ethanolamine are both required for induction of eut locus gene expression. Additionally, I discovered eutG is regulated by a unique mechanism of antitermination. Both the AdoCBL riboswitch and EutV response regulator control the expression of the downstream gene eutG. EutV potentially acts through a novel antitermination mechanism in which a dimer of EutV binds to a pair of mRNA stem loops forming an antitermination complex. My data show a unique mechanism by which two environmental signals are integrated by two different posttranscriptional regulators to regulate a single locus.

Uptake And Metabolism Of 5’-AMP In The Erythrocyte Play Key Roles In The 5’-AMP Induced Model Of Deep Hypometabolism

UPTAKE AND METABOLISM OF 5’-AMP IN THE ERYTHROCYTE PLAY KEY ROLES IN THE 5’-AMP INDUCED MODEL OF DEEP HYPOMETABOLISM Publication No. ________ Isadora Susan Daniels, B.A. Supervisory Professor: Cheng Chi Lee, Ph.D. Mechanisms that initiate and control the natural hypometabolic states of mammals are poorly understood. The laboratory developed a model of deep hypometabolism (DH) initiated by uptake of 5’-adenosine monophosphate (5’-AMP) into erythrocytes. Mice enter DH when given a high dose of 5’-AMP and the body cools readily. Influx of 5’-AMP appears to inhibit thermoregulatory control. In a 15°C environment, mice injected with 5’-AMP (0.5 mg/gw) enter a Phase I response in which oxygen consumption (VO2) drops rapidly to 1/3rd of euthermic levels. The Phase I response appears independent of body temperature (Tb). This is followed by gradual body temperature decline that correlates with VO2 decline, called Phase II response. Within 90 minutes, mouse Tb approaches 15°C, and VO2 is 1/10th of normal. Mice can remain several hours in this state, before gradually and safely recovering. The DH state translates to other mammalian species. Our studies show uptake and metabolism of 5’-AMP in erythrocytes causes biochemical changes that initiate DH. Increased AMP shifts the adenylate equilibrium toward ADP formation, consequently decreasing intracellular ATP. In turn, glycolysis slows, indicated by increased glucose and decreased lactate. 2,3-bisphosphoglycerate levels rise, allosterically reducing oxygen affinity for hemoglobin, and deoxyhemoglobin rises. Less oxygen transport to tissues likely triggers the DH model. The major intracellular pathway for AMP catabolism is catalyzed by AMP deaminase (AMPD). Multiple AMPD isozymes are expressed in various tissues, but erythrocytes only have AMPD3. Mice lacking AMPD3 were created to study control of the DH model, specifically in erythrocytes. Telemetric measurements demonstrate lower Tb and difficulty maintaining Tb under moderate metabolic stress. A more dramatic response to lower dose of 5’-AMP suggests AMPD activity in the erythrocyte plays an important role in control of the DH model. Analysis of adenylates in erythrocyte lysate shows 3-fold higher levels of ATP and ADP but similar AMP levels to wild-type. Taken together, results indicate alterations in energy status of erythrocytes can induce a hypometabolic state. AMPD3 control of AMP catabolism is important in controlling the DH model. Genetically reducing AMP catabolism in erythrocytes causes a phenotype of lower Tb and compromised ability to maintain temperature homeostasis.

THE EXPRESSION PROFILE OF A GWAS DETECTED GENE (NINJ2) IN THE BRAIN OF A MOUSE MODEL OF ISCHEMIC STROKE

Stroke is the third leading cause of death and a major debilitating disease in the United States. Multiple factors, including genetic factors, contribute to the development of the disease. Genome-wide association studies (GWAS) have contributed to the identification of genetic loci influencing risk for complex diseases, such as stroke. In 2010, a GWAS of incident stroke was performed in four large prospective cohorts from the USA and Europe and identified an association of two Single Nucleotide Polymorphisms (SNPs) on chromosome 12p13 with a greater risk of ischemic stroke in individuals of European and African-American ancestry. These SNPs are located 11 Kb upstream of the nerve injury-induced gene 2, Ninjurin2 (NINJ2), suggesting that this gene may be involved in stroke pathogenesis. NINJ2 is a cell adhesion molecule induced in the distal glial cells from a sciatic-nerve injury at 7-days after injury. In an effort to ascribe a possible role of NINJ2 in stroke, we have assessed changes in the level of gene and protein expression of NINJ2 following a time-course from a transiently induced middle cerebral artery ischemic stroke in mice brains. We report an increase in the gene expression of NINJ2 in the ischemic and peri-infarct (ipsilateral) cortical tissues at 7 and 14-days after stroke. We also report an increase in the protein expression of NINJ2 in the cortex of both the ipsilateral and contralateral cortical tissues at the same time-points. We conclude that the expression of NINJ2 is regulated by an ischemic stroke in the cortex and is consistent with NINJ2 being involved in the recovery time-points of stroke.

Multiple site phosphorylation of tyrosine hydroxylase: A potential role for protein kinase C in the regulation of catecholamine synthesis

Tyrosine hydroxylase (TH), the initial and rate limiting enzyme in the catecholaminergic biosynthetic pathway, is phosphorylated on multiple serine residues by multiple protein kinases. Although it has been demonstrated that many protein kinases are capable of phosphorylating and activating TH in vitro, it is less clear which protein kinases participate in the physiological regulation of catecholamine synthesis in situ. These studies were designed to determine if protein kinase C (PK-C) plays such a regulatory role.^ Stimulation of intact bovine adrenal chromaffin cells with phorbol esters results in stimulation of catecholamine synthesis, tyrosine hydroxylase phosphorylation and activation. These responses are both time and concentration dependent, and are specific for those phorbol ester analogues which activate PK-C. RP-HPLC analysis of TH tryptic phosphopeptides indicate that PK-C phosphorylates TH on three putative sites. One of these (pepetide 6) is the same as that phosphorylated by both cAMP-dependent protein kinase (PK-A) and calcium/calmodulin-dependent protein kinase (CaM-K). However, two of these sites (peptides 4 and 7) are unique, and, to date, have not been shown to be phosphorylated by any other protein kinase. These peptides correspond to those which are phosphorylated with a slow time course in response to stimulation of chromaffin cells with the natural agonist acetylcholine. The activation of TH produced by PK-C is most closely correlated with the phosphorylation of peptide 6. But, as evident from pH profiles of tyrosine hydroxylase activity, phosphorylation of peptides 4 and 7 affect the expression of the activation produced by phosphorylation of peptide 6.^ These data support a role for PK-C in the control of TH activity, and suggest a two stage model for the physiological regulation of catecholamine synthesis by phosphorylation in response to cholinergic stimulation. An initial fast response, which appears to be mediated by CaM-K, and a slower, sustained response which appears to be mediated by PK-C. In addition, the multiple site phosphorylation of TH provides a mechanism whereby the regulation of catecholamine synthesis appears to be under the control of multiple protein kinases, and allows for the convergence of multiple, diverse physiological and biochemical signals. ^

Multiple GABA receptors in rabbit retina

The task of encoding and processing complex sensory input requires many types of transsynaptic signals. This requirement is served in part by an extensive group of neurotransmitter substances which may include thirty or more different compounds. At the next level of information processing, the existence of multiple receptors for a given neurotransmitter appears to be a widely used mechanism to generate multiple responses to a given first messenger (Snyder and Goodman, 1980). Despite the wealth of published data on GABA receptors, the existence of more than one GABA receptor was in doubt until the mid 1980's. Presently there is still disagreement on the number of types of GABA receptors, estimates for which range from two to four (DeFeudis, 1983; Johnston, 1985). Part of the problem in evaluating data concerning multiple receptor types is the lack of information on the number of gene products and their subsequent supramolecular organization in different neurons. In order to evaluate the question concerning the diversity of GABA receptors in the nervous system, we must rely on indirect information derived from a wide variety of experimental techniques. These include pharmacological binding studies to membrane fractions, electrophysiological studies, localization studies, purification studies, and functional assays. Almost all parts of the central and peripheral nervous system use GABA as a neurotransmitter, and these experimental techniques have therefore been applied to many different parts of the nervous system for the analysis of GABA receptor characteristics. We are left with a large amount of data from a wide variety of techniques derived from many parts of the nervous system. When this project was initiated in 1983, there were only a handful of pharmacological tools to assess the question of multiple GABA receptors. The approach adopted was to focus on a single model system, using a variety of experimental techniques, in order to evaluate the existence of multiple forms of GABA receptors. Using the in vitro rabbit retina, a combination of pharmacological binding studies, functional release studies and partial purification studies were undertaken to examine the GABA receptor composition of this tissue. Three types of GABA receptors were observed: Al receptors coupled to benzodiazepine and barbiturate modulation, and A2 or uncoupled GABA-A receptors, and GABA-B receptors. These results are evaluated and discussed in light of recent findings by others concerning the number and subtypes of GABA receptors in the nervous system. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Metastatic lung cancer in a new mouse model inheriting p53 and latent K-ras mutations

Lung cancer is a devastating disease with very poor prognosis. The design of better treatments for patients would be greatly aided by mouse models that closely resemble the human disease. The most common type of human lung cancer is adenocarcinoma with frequent metastasis. Unfortunately, current models for this tumor are inadequate due to the absence of metastasis. Based on the molecular findings in human lung cancer and metastatic potential of osteosarcomas in mutant p53 mouse models, I hypothesized that mice with both K-ras and p53 missense mutations might develop metastatic lung adenocarcinomas. Therefore, I incorporated both K-rasLA1 and p53RI72HΔg alleles into mouse lung cells to establish a more faithful model for human lung adenocarcinoma and for translational and mechanistic studies. Mice with both mutations ( K-rasLA1/+ p53R172HΔg/+) developed advanced lung adenocarcinomas with similar histopathology to human tumors. These lung adenocarcinomas were highly aggressive and metastasized to multiple intrathoracic and extrathoracic sites in a pattern similar to that seen in lung cancer patients. This mouse model also showed gender differences in cancer related death and developed pleural mesotheliomas in 23.2% of them. In a preclinical study, the new drug Erlotinib (Tarceva) decreased the number and size of lung lesions in this model. These data demonstrate that this mouse model most closely mimics human metastatic lung adenocarcinoma and provides an invaluable system for translational studies. ^ To screen for important genes for metastasis, gene expression profiles of primary lung adenocarcinomas and metastases were analyzed. Microarray data showed that these two groups were segregated in gene expression and had 79 highly differentially expressed genes (more than 2.5 fold changes and p<0.001). Microarray data of Bub1b, Vimentin and CCAM1 were validated in tumors by quantitative real-time PCR (QPCR). Bub1b , a mitotic checkpoint gene, was overexpressed in metastases and this correlated with more chromosomal abnormalities in metastatic cells. Vimentin, a marker of epithelial-mesenchymal transition (EMT), was also highly expressed in metastases. Interestingly, Twist, a key EMT inducer, was also highly upregulated in metastases by QPCR, and this significantly correlated with the overexpression of Vimentin in the same tumors. These data suggest EMT occurs in lung adenocarcinomas and is a key mechanism for the development of metastasis in K-ras LA1/+ p53R172HΔg/+ mice. Thus, this mouse model provides a unique system to further probe the molecular basis of metastatic lung cancer.^

Obesity and risk of multiple myeloma: A case-control study

Introduction. Several studies have reported a positive association of body mass index (BMI) with multiple myeloma; however, the period of adulthood where BMI is most important remains unclear. In addition, it is well known that body fat is associated with both sex-steroid hormone storage and with increasing insulin levels; therefore, it was hypothesized that the association between obesity and multiple myeloma may be attributed to increased aromatization of androgen in adipose tissue. Objective. The overall objective of this case-control study was to determine whether multiple myeloma cases had higher BMI and greater adult weight gain relative to healthy controls. In addition, we tested the hypothesis that hormone replacement therapy use among women will further increase the association between BMI and risk of multiple myeloma. This study used data from a pilot case-control study at M.D. Anderson Cancer Center (MDACC), entitled Etiology of multiple myeloma, directed by Dr. Sara Strom and Dr. Sergio Giralt. Methods. The pilot study recruited a total of 122 cases of histopathologically confirmed multiple myeloma from MDACC. Controls (n=183) were selected from a database of random digit dialing controls accrued in the Department of Epidemiology at MDACC and were frequency matched to the cases on age (±5 years), gender, and race/ethnicity. Demographic and risk factor information were obtained from all participants who completed a self-administered questionnaire. Items included in the questionnaire include demographic information, height and weight at age 25, 40 and current/diagnosis, medical history, family history of cancer, smoking and alcohol use. Statistical analysis. Initial descriptive analysis included Student's t-test and Pearson's chi-squared tests. Odds ratios and 95% confidence intervals were calculated to quantify the association between the variables of interest and multiple myeloma. A multivariable model will be developed using unconditional logistic regression. Results. MM cases were 1.79 times (95% CI=0.99-3.32) more likely to have been overweight or obese (BMI > 25 kg/m2) at age 25 relative to healthy controls after controlling for age, gender, race/ethnicty, education and family history of cancer. Being overweight or obese at age 40 was not significantly associated with mutliple myeloma risk (OR=1.42, 95% CI=0.86-2.34) nor was being overweight or obses at diagnosis (OR=1.43, 95% CI=0.78, 2.63). We observed a statistically significant 2-fold increased odds of multiple myeloma in individuals who gained more than 4.7 kg during between 25 and 40 years (OR=1.97, 95% CI=1.15-3.39). When assessing HRT as a modifier of the BMI and multiple myeloma association among women (N=123), no association between obesity and MM status was observed among women who have never used HRT (OR=0.60, 95% CI=0.23-1.61; n=73). Yet among women who have ever used HRT (n=50), being overweight or obese was associated with an increase in MM risk (OR=2. 93, 95% CI=0.81-10.6) after adjusting for age; however, the association was not statistically significant. Significance. This study provides further evidence that increased BMI increases the risk of multiple myeloma. Furthermore, among women, HRT use may modify risk of disease. ^

CARMA3 serves as a critical mediator of NF-kappaB in multiple signal transduction pathways

The central dogma of molecular biology dictates that DNA is transcribed into RNA, which is later translated into protein. One of the early activators in this process is the transcription factor NF-κB. We have determined that an NF-κB inducer, CARMA3, is required for proper neural tube closure, similar to other NF-κB inducers. Using a genetic knockout of CARMA3, we demonstrated that it is required for Gαq-coupled GPCR-induced NF-κB activation. This is facilitated through a MAPK and IKK phosphorylation-independent mechanism, most likely by controlling NEMO-associated ubiquitination. We have also shown that CARMA3 is required for EGF and HRG-induced NF-κB activation. This activation requires the activity of both EGFR and HER2, as well as PKC. Again, we observed no defect in IKK phosphorylation, although we determined a clear defect in IKK activation. Finally, we have begun to determine the role of CARMA3 to both EGFR and HER2-induced tumorigenicity. By overexpressing a constitutive active mutant of HER2 in our CARMA3 WT and KO MEF cells, we have shown CARMA3 is important for HER2-driven soft agar colony growth. We have also shown that knockdown of endogenous CARMA3 in the EGFR-overexpressing A431 cell line abolishes EGF-induced NF-κB activation. These same cells have a dramatically reduced capacity to form colonies in soft agar as well. Using both mouse xenografts and a transgenic model of HER2-induced breast cancer, we have initiated studies which will help to determine the role of CARMA3 to in vivo tumorigenesis. Collectively, this work reveals novel roles for the CARMA3 protein in development, GPCR and EGFR/HER2 signaling. It also suggests that CARMA3 is involved in EGFR/HER2 mediated tumorigenesis, possibly indicating a novel therapeutic target for use in treatment of cancer. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

Disease risk factors and humoral antibody response to periodontal-associated pathogens: A model to evaluate periodontal disease in older adults

Periodontal diseases (PD) are infectious, inflammatory, and tissue destructive events which affect the periodontal ligament that surround and support the teeth. Periodontal diseases are the major cause of tooth loss after age 35, with gingivitis and periodontitis affecting 75% of the adult population. A select group of bacterial organisms are associated with periodontal pathogenesis. There is a direct association between oral hygiene and prevention of PD. The importance of genetic differences and host immune response capabilities in determining host, susceptibility or resistance to PD has not been established. This study examined the risk factors and serum (humoral) immune response to periodontal diseased-associated pathogens in a 55 to 80+ year old South Texas study sample with PD. This study sample was described by: age, sex, ethnicity, the socioeconomic factors marital status, income and occupation, IgG, IgA, IgM immunoglobulin status, and the autoimmune response markers rheumatoid factor (RF) and antinuclear antibody (ANA). These variables were used to determine the risk factors associated with development of PD. Serum IgG, IgA, IgM antibodies to bacterial antigens provided evidence for disease exposure.^ A causal model for PD was constructed from associations for risk factors (ethnicity, marital status, income, and occupation) with dental exam and periodontitis. The multiple correlation between PD and ethnicity, income and dental exam was significant. Hispanics of low income were least likely to have had a dental exam in the last year and most likely to have PD. The etiologic agents for PD, as evidenced by elevated humoral antibody responses, were the Gram negative microorganisms Bacteroides gingivalis, serotypes FDC381 and SUNYaBA7A1-28, and Wolinella recta. Recommendation for a PD prevention and control program are provided. ^

Hispanic incorporation and access to health care in Houston and Harris County, Texas: Adopting an Hispanic perspective to the Aday and Andersen model

This study examines Hispanic levels of incorporation and access to health care. Applying the Aday and Andersen framework for the study of access, the study examined the relationship between two levels of Hispanic incorporation into U.S. society, i.e., mainstream versus ethnic, and potential and realized measures of access to health care. Data for the study were drawn from a 1992 telephone survey of 600 randomly selected Hispanics in Houston and Harris County.^ The hypotheses tested were: (1) Hispanics who are incorporated into mainstream society are more likely to have better potential and realized access to health care than those who are incorporated into ethnic-group enclaves regardless of their socioeconomic status (SES), health status and health needs, and (2) there is no interaction between the levels of incorporation (mainstream or ethnic) and SES, health status, and health needs in predicting potential and realized access.^ The data analysis supported Hypothesis One for the two measures of potential access. The results of bivariate and multiple logistic regression analyses indicated that for Hispanics in Houston and Harris County, being in the "mainstream" incorporation category increased their potential access to care, having "health insurance" and a "regular place of care". For the selected measure of realized access, having a "regular check-up", the analysis did not demonstrate statistically significant differences in having a regular check-up among Hispanics incorporated in the ethnic or mainstream incorporation categories.^ Hypothesis Two, that there is no interaction between the levels of incorporation and socioeconomic characteristics, health status, and health needs in predicting potential and realized access among Hispanics was supported by the data. The results of the logistic regression analysis showed that, after adjusting for socioeconomic status, health status, and health needs, the association between "level of incorporation" and the two measures of potential access ("health insurance" and having a "usual place of care") was not modified by the control variables nor by their interaction with level of incorporation. That is, the effect of incorporation on Hispanics' health insurance coverage, and having a usual place of care, was homogenous across Hispanics with different SES and health status.^ The main research implication of this dissertation is the employment of a theoretical framework for the assessment of cultural factors essential to research on migrating heterogeneous subpopulations. It also provided strategies to solve practical and methodological difficulties in the secondary analyses of data on these populations. ^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^