While extensive research has been conducted on the causes of intimate partner violence in the community, very little is known about rates and predictors of domestic violence perpetrated by offenders who have recently been incarcerated. Some evidence suggests that formerly incarcerated individuals may be at an increased risk to perpetrate intimate partner violence during the transition from prison to the community (e.g., Hairston & Oliver 2006; Hilton, Harris, Popham, & Lang, 2010; Oliver & Hairston, 2008). The primary goal of this dissertation was to examine the extent to which former inmates engage in domestic violence during the transition from prison to the community. A second goal of this dissertation was to determine the independent and interactive effects of selected individual, situational, and social-structural factors on post-prison domestic violence. The current dissertation project involved a retrospective study of data collected from n = 1,137 formerly-incarcerated male offenders who were released from state prison between 2004 and 2009. Data regarding individual-level factors of borderline and antisocial personality characteristics and exposure to family-of-origin violence were extracted from institutional records. Additional individual-level demographic characteristics including offenders' age, ethnicity, education need, marital status, number of children, crime of conviction, length of incarceration, and participation in correctional rehabilitation programs extracted from institutional records were also considered. The situational-level factor of offenders' employment after prison release was also collected from institutional records; and the social-structural factor of neighborhood disadvantage was collected from information available in offenders' community supervision records and Census tract-level data. The outcome measure of post-prison domestic violence was gathered from local law enforcement records. Data were entered into statistical models to predict post-prison domestic violence. Main effects on post-prison domestic violence were examined for each of the individual-level demographic characteristics, borderline and antisocial personality features, exposure to family-of-origin violence, employment, and neighborhood disadvantage. Interactive effects on post-prison domestic violence were examined between borderline and antisocial personality characteristics, exposure to family-of-origin violence, employment, and neighborhood disadvantage. Significant predicted main effects on post-prison domestic violence included age, ethnicity, education need, number of children, violent criminal history, attendance of substance abuse treatment in prison, witnessing interparental violence as a child, and neighborhood disadvantage. Significant predicted interaction effects on post-prison domestic violence included the interaction between physical abuse as a child and neighborhood disadvantage. Implications for policies regarding post-prison supervision sentencing, housing, and the advancement of programming to prevent intimate partner violence during the transition from prison to the community are discussed. Contributions to the literature on intimate partner violence, environmental transition theory, and ecological theoretical frameworks are also addressed.

In the analysis of survival outcome supplemented with both clinical information and high-dimensional gene expression data, traditional Cox proportional hazard model fails to meet some emerging needs in biological research. First, the number of covariates is generally much larger the sample size. Secondly, predicting an outcome with individual gene expressions is inadequate because a gene's expression is regulated by multiple biological processes and functional units. There is a need to understand the impact of changes at a higher level such as molecular function, cellular component, biological process, or pathway. The change at a higher level is usually measured with a set of gene expressions related to the biological process. That is, we need to model the outcome with gene sets as variable groups and the gene sets could be partially overlapped also. In this thesis work, we investigate the impact of a penalized Cox regression procedure on regularization, parameter estimation, variable group selection, and nonparametric modeling of nonlinear eects with a time-to-event outcome. We formulate the problem as a partially linear additive Cox model with high-dimensional data. We group genes into gene sets and approximate the nonparametric components by truncated series expansions with B-spline bases. After grouping and approximation, the problem of variable selection becomes that of selecting groups of coecients in a gene set or in an approximation. We apply the group Lasso to obtain an initial solution path and reduce the dimension of the problem and then update the whole solution path with the adaptive group Lasso. We also propose a generalized group lasso method to provide more freedom in specifying the penalty and excluding covariates from being penalized. A modied Newton-Raphson method is designed for stable and rapid computation. The core programs are written in the C language. An user-friendly R interface is implemented to perform all the calculations by calling the core programs. We demonstrate the asymptotic properties of the proposed methods. Simulation studies are carried out to evaluate the finite sample performance of the proposed procedure using several tuning parameter selection methods for choosing the point on the solution path as the nal estimator. We also apply the proposed approach on two real data examples.

When the delicate hormonal balance in early pregnancy is disrupted, the consequences can be significant. We have a poor understanding of the "cross-talk" in the fetal/placental/ovarian axis that occurs throughout pregnancy and is essential for normal fetal development. This lack of knowledge challenges our ability to recognize disruptions in this axis that may be a signal for future disease. As a result, our ability to apply preventive measures against adverse obstetric outcomes, such as preterm birth (PTB), are quite limited. Attempts to predict PTB using biomarkers of feto-placental health have been largely unsuccessful, but no one has considered the inclusion of ovarian biomarkers in these models. Anti-Mullerian hormone (AMH) is a biomarker of ovarian activity that has recently been found to decline in early pregnancy at a time that corresponds to the involution of the corpus luteum (CL). The signal for CL involution is believed to originate from the placenta; therefore, the AMH levels in pregnancy may reflect the degree of ovarian up or down-regulation based on feto-placental needs. As the major function of the CL in pregnancy is the production of progesterone, which acts as an anti-inflammatory agent in the placental bed, changes in CL-derived progesterone could result in higher or lower degrees of placental inflammation. Therefore, monitoring the changes in AMH levels may provide insight into the inflammatory state of the placenta which could then be used as a signal for possible adverse obstetric outcomes resulting from a pro-inflammatory state, such as PTB. The first aim of this project was to test the hypothesis of an association between AMH levels in early pregnancy and PTB risk. When the differences in AMH levels between the 1st and 2nd trimesters of pregnancy were stratified by the level of maternal serum alpha-fetoprotein (MSAFP) and controlled for maternal weight gain between trimesters, small or absent decreases in AMH levels were associated with a higher probability of preterm birth. However, when AMH was modeled alone, no significant associations were found. The need for changes in multiple biomarkers in the fetal/placental/ovarian axis suggests that a change is only significant if it can impact multiple axis points. Therefore, models that included two biomarkers from different part of the axis would find stronger associations than two biomarkers from a single point (e.g. two feto-placental biomarkers), and monitoring these changes may help identify women at risk for PTB. The strategy of the second aim was to determine if the changes in AMH levels in early pregnancy could be used to predict time to delivery. Again, only when the risks of AMH and MSAFP were combined was a significant, dose-dependent relationship found with time to delivery. In women with an MSAFP of >1 multiple of the median (MoM), smaller declines and/or elevations in AMH levels were significantly associated with shorter times to delivery. In fact, 19% of women in the highest risk group delivered prior to 32 weeks gestation compared to 7% in the lowest risk group, and all infants who delivered prior to 24 weeks gestation were in the highest risk category. Thus, the amount of change in the AMH level when MSAFP is elevated may reflect the level of disruption in the fetal/placental/ovarian axis, which can then be used to predict time to delivery. Finally, the third aim of this study was to determine if AMH levels were associated with a pro-inflammatory placental state other than PTB. The degree of placental inflammation is known to vary by fetal gender, with male placentas having higher levels of inflammation compared to female placentas. When AMH levels were compared between women with male vs. female fetuses in early pregnancy, 1st trimester AMH levels were found to be lower when carrying a male fetus. Further, sexually-dimorphic patterns in AMH levels were seen between genders when stratified by birth outcome (term vs. preterm delivery). The stronger ovarian response seen in women with female fetuses suggests a better survival function and may account for the discrepancies between PTB rates in males and females. This also strengthens our hypothesis that the dynamic changes in AMH levels reflect the degree of placental inflammation and the need for CL-derived progesterone. This project demonstrates that the changes in AMH levels may be representative of the cross-talk occurring in the fetal/placental/ovarian axis in early pregnancy. Further, changes in AMH levels may be an indication of the amount of inflammation in the placenta and the physiologic need for higher levels of progesterone to control this inflammatory state when considered along with MSAFP. Therefore, the consideration of AMH levels as a biomarker of ovarian activity along with biomarkers of feto-placental health may provide clinically useful information about the development of future diseases such as preterm birth.

Widely distributed species are experiencing a continual pattern of range shifts due to anthropogenic expansion and climate change, forcing these species into novel environments and out of critical habitat. The ability to estimate current and forecasted states of demographic parameters of species distributed along a gradient of environments is becoming increasingly important in a time of large-scale environmental change. Consulting models that provide temporally relevant estimates of population dynamics based on the latest realizations of environmental conditions can allow for informed, quick and decisive conservation and management actions. Modelling the drivers of demography across a wide range of environmental conditions will provide a more comprehensive understanding of how species will respond to novel environments. In this study we provide an example of relating seasonal-environmental variables to survival in a widely distributed ungulate species. We used a mule deer (Odocoileus hemionus) survival dataset collected in Utah with seven sites distributed across the multiple ecoregions of the state, allowing for the elucidation of relationships across a variety of environmental conditions. Multivariate analyses predicting survival of young and adult females were performed using geographic location, elevation, and seasonal satellite-derived primary productivity data and weather variables. We developed frameworks for estimating past and current states of survival and predicting short-term (sub-year) forecasts of survival. Furthermore, we investigated adaptive modelling techniques for increasing the certainty of the forecasted predictions of survival. We found that increased winter precipitation had a negative effect on survival across the state. Survival was lower in the northern region of the state and in higher elevations. Furthermore, measures of summer primary productivity had a positive relationship with survival. Lastly, our adaptive modelling demonstration shows that uncertainty of forecasted survival predictions can be reduced with the addition of data. This study provides a framework for developing models that will provide invaluable information to managers in a time of large-scale environmental change.

Studies of predator-prey and predator-predator interactions are needed to provide information for decision-making processes in land management agencies. Mountain lions (Puma concolor) are opportunistic carnivores that prey on a wide variety of species. In the Sierra National Forest, CA, they have not been studied since 1987 and their current interactions with their prey and other predators are unknown. Forest managers in this region are concerned with declines of fishers (Pekania pennanti) and studies have shown intraguild predation to be a leading cause of fisher mortality in this area. Managers are interested in learning more about mountain lion predation patterns with regard to prey preference, but also how lions traverse and use the landscape and how anthropogenic activities may be increasing lion predation risk on fishers. Using GPS radio-collar technology, we examined mountain lion kill rates and prey composition at 250 kill sites. We found mule deer (Odocoileus hemionus) to be their main source of prey (81%) with gray foxes (Urocyon cinereoargenteus) comprising 13.2% of prey composition. We did not detect any fisher predation during our 2-year study; however, during our study, the Kings River Fisher Project experienced extremely low juvenile fisher survival. To gain a better understanding of seasonal resource selection by mountain lions, we developed resource selection functions (RSF) while they were moving through the landscape and when killing prey. We developed RSF models for all data across the study area, as well as, for a subset of data encompassing an area where LiDAR (Light Detection and Ranging) data had been collected. Within the LiDAR study area, we digitized unmapped roads and skid trails using a Bare Earth data set. We found mountain lion ‘moving’ locations showed selection for close proximity to streams during summer months and selection for ruggedness and steeper slopes during both summer and winter. With 3 of the 4 RSF models at kill sites showing high risk of predation within close proximity to either digitized roads/skid trails or mapped roads, we recommend managers map all anthropogenically created linear landscape features and consider restoring these linear features to pre-treatment landscape conditions following timber harvest.

The objective of the present study is to investigate key aspects of ovarian and breast cancers, which are two main causes of mortality among women. Identification of the true behavior of survivorship and influential risk factors is essential in designing treatment protocols, increasing disease awareness and preventing possible causes of disease. There is a commonly held belief that African Americans have a higher risk of cancer mortality. We studied racial disparities of women diagnosed with ovarian cancer on overall and disease-free survival and found out that there is no significant difference in the survival experience among the three races: Whites, African Americans and Other races. Tumor sizes at diagnosis among the races were significantly different, as African American women tend to have larger ovarian tumor sizes at the diagnosis. Prognostic models play a major role in health data research. They can be used to estimate adjusted survival probabilities and absolute and relative risks, and to determine significantly contributing risk factors. A prognostic model will be a valuable tool only if it is developed carefully, evaluating the underlying model assumptions and inadequacies and determining if the most relevant model to address the study objectives is selected. In the present study we developed such statistical models for survival data of ovarian and breast cancers. We found that the histology of ovarian cancer had risk ratios that vary over time. We built two types of parametric models to estimate absolute risks and survival probabilities and to adjust the time dependency of the relative risk of Histology. One parametric model is based on classical probability distributions and the other is a more flexible parametric model that estimates the baseline cumulative hazard function using spline functions. In contrast to women diagnosed with ovarian cancer, women with breast cancer showed significantly different survivorship among races where Whites had a poorer overall survival rate compared to African Americans and Other races. In the breast cancer study, we identified that age and progesterone receptor status have time dependent hazard ratios and age and tumor size display non-linear effects on the hazard. We adjusted those non-proportional hazards and non-linear effects by using an extended Cox regression model in order to generate more meaningful interpretations of the data.

Cyclooxygenase (COX) is the rate limiting enzyme that catalyses the production of prostanoids, which are crucial to vascular homeostasis. Evidence suggests that endothelial dysfunction and inflammation play a role in vascular complications in aging and diabetes. Previous animal studies by our laboratory at RMIT University reported enhanced COX expression with aging in rat aortas, platelets and monocytes. Potentially, alteration in COX expression may result in an imbalanced prostanoid production favoring the synthesis of vasoconstrictors and hence increase the risk of cardiovascular events in the aging population. The regulation of altered COX expression in aging, however, is not clear. It has been suggested that histone hyperacetylation may be an important mechanism that regulates COX levels during the aging process as increased histone acetylation has been shown to occur with aging. Thus, we hypothesized that COX expression is modulated by histone hyperacetylati on. This was investigated by measuring COX expression in histone hyperacetylated cultured endothelial cells. In the case of diabetes, studies have reported that the development of diabetes and its complications is associated with persistent inflammatory activity, evident with increased inflammatory markers in the circulation. COX-mediated pathways may be involved in this inflammatory process in diabetes. Furthermore, the formation of advanced glycation end products (AGEs) is accelerated in diabetes. AGEs can bind to receptors for AGEs (RAGE), which has also been suggested to play a role in inflammation in diabetes. We hypothesized that COX- and RAGE-mediated pathways contribute to increased inflammation in diabetes and potentiate the development of diabetic vascular complications. This was investigated by measuring changes in COX-mediated pathways in both rat and human diabetic models. The current thesis reports: 1) in cultured endothelial cells, histone hyperacetylation was associated with increased COX expression; 2) an overall increase in inflammation was observed in diabetes involving COX- and RAGE-mediated pathways. This was supported by increased platelet COX-1 and monocyte COX-2 levels in Zucker rats, increased monocyte COX-2 in human Type 1 diabetes and elevated plasma TXB2 and PGE2 levels in both human Type 1 and Type 2 diabetic subjects. Up-regulation of RAGE expression was further found in platelets and monocytes in both human diabetes types. When treated with NSAIDs, plasma prostanoid levels, COX and RAGE expression were reduced significantly in both platelets and monocytes in human diabetic subjects. 3) It is unclear how COX and RAGE expression was regulated, but histone modifications may be one of the mechanisms. Data from cultured cells indicated that increased COX expression was associated with increased histone acetylation levels induced by TSA. Concurrent increases in histone acetylation and COX-2 levels were also observed in human Type 1 diabetes, but similar findings were not observed in human Type 2 diabetes. In addition, we failed to find an age-dependent increase in monocyte histone H4 acetylation in human Type 2 diabetes despite an age-dependent increase in monocyte COX-2 expression. Thus, whether histone hyperacetylation modulates COX expression and in what conditions require further investigation.

Thesis (M.A. in anthropology)--Washington State University, December 2007. / Includes bibliographical references (p. 184-202).

Thesis (M.A. in anthropology)--Washington State University, December 2007. / Includes bibliographical references (p. 157-180).

La première partie concerne l'estimation séquentielle du paramètre de régression pour le modèle de Cox pour des données censurées à droite. Il est ainsi possible de définir des règles d'arrêt garantissant une bonne estimation. Celles-ci conduisent alors à des estimateurs dépendant de tailles d'échantillons aléatoires pour lesquels le comportement asymptotique est le même que celui des estimateurs non séquentiels. Les propriétés démontrées sont étendues au cadre multidimensionnel et illustrées par des simulations. Cette première partie s'achève par l'étude théorique du comportement de la variable d'arrêt dans le cadre d'intervalles de confiance séquentiels. La règle d'arrêt normalisée est alors asymptotiquement normale. La seconde partie porte sur la construction de tests d'homogénéité dans le cadre d'un modèle de durées de vie non paramétrique incluant des covariables ainsi que la censure à droite. Une statistique de test est proposée et son comportement asymptotique est établi.