I examined home range, habitat use, and survival of 29 feral hogs in central Mississippi using radio telemetry. During the dry season (1 April - 31 October 2005), densely-vegetated habitats were very important in home range placement (2nd-order selection) with selection favoring seasonallylooded old fields, followed by old fields and managed openings. During the wet season (1 November 2005 - 31 March 2006), old fields were still preferred followed by agricultural fields, but flooded old fields were not preferred. For habitat selection within the home range (3rd-order selection), hogs preferred old fields and managed openings during the dry season. All habitats were used randomly within home ranges during the wet season. Dry and wet season survival rates were 80.8% and 41.4%, respectively. Hunting was the major cause of mortality (80 ? 100%). Seasonal differences in habitat selection may have been caused by flooding of preferred habitats, food availability and hunting.

Short-lived rodents are sensitive to changes in environmental conditions and exhibit annual fluctuations under seasonal environments in northern temperate regions. I analyzed 34 years of monthly live-trapping data on whiteooted mice (Peromyscus leucopus) collected in Carter Woods, Ohio. I used a theoretic-information approach to select the best approximating models and analysis of deviance to infer effects of climate and density on survival of mice. I tested for a cost of reproduction to females and found no difference in survival between reproductive states. Directions and magnitudes of effects of climate and density varied over time. Increased variability in temperature reduced effects of density on survival. I detected an Allee effect and density dependent effects on survival. Long-term trapping data are needed to study temporal effects of climate and density on the demography of rodents. Recruitment had a greater impact on population growth rate than survival

No description available.

No description available.

No description available.

Due to the rapid development of technology, quality control charts have attracted more attention from manufacturing industries in order to monitor quality characteristics of interest more effectively. Among many control charts, my research work has focused on the multivariate exponentially weighted moving average (MEWMA) and the univariate exponentially weighted moving average (EWMA) control charts by using the Markov chain method. The performance of the chart is measured by the optimal average run length (ARL). My Ph.D. thesis is composed of the following three contributions. My first research work is about differential smoothing. The MEWMA control chart proposed by Lowry et al. (1992) has become one of the most widely used charts to monitor multivariate processes. Its simplicity, combined with its high sensitivity to small and moderate process mean jumps, is at the core of its appeal. Lowry et al. (1992) advocated equal smoothing of each quality variable unless there is an a priori reason to weigh quality characteristics differently. However, one may have situations where differential smoothing may be justified. For instance: (a) departures in process mean may be different across quality variables, (b) some variables may evolve over time at a much different pace than other variables, and (c) the level of correlation between variables could vary substantially. For these reasons, I focus on and assess the performance of the differentially smoothed MEWMA chart. The case of two quality variables (BEWMA) is discussed in detail. A bivariate Markov chain method that uses conditional distributions is developed for average run length (ARL) calculations. The proposed chart is shown to perform at least as well as Lowry et al. (1992)'s chart, and noticeably better in most other mean jump directions. Comparisons with the recently introduced double-smoothed BEWMA chart and the univariate charts for the independent case show that the proposed differentially smoothed BEWMA chart has superior performance. My second research work is about monitoring skewed multivariate processes. Recently, Xie et al. (2011) studied monitoring bivariate exponential quality measurements using the standard MEWMA chart originally developed to monitor multivariate normal quality data. The focus of my work is on situations where, marginally, the quality measurements may follow not only exponential distributions but also other skewed distributions such as Gamma or Weibull, in any combination. The joint distribution is specified using the Gumbel copula function thus allowing for varying degrees of correlation among the quality measurements. In addition to the standard MEWMA chart, charts based on the largest or smallest of the measurements and on the joint cumulative distribution function or the joint survivor function, are studied in detail. The focus is on the case of two quality measurements, i.e., on skewed bivariate processes. The proposed charts avoid an undesirable feature encountered by Xie et al. (2011) for the standard MEWMA chart where in some cases the off-target average run length turns out to be larger than the on-target one. Using the optimal average run length, our extensive numerical results show that the proposed methods provide an overall good detection performance in most directions. Simulations were performed to obtain the optimal ARL results but the Markov chain method using the empirical CDF of the statistics involved verified the accuracy of the ARL results. In addition, an examination of the effect of correlation on chart performance was undertaken numerically. The methods are easily extendable to more than two variables. Final study is about a new ARL criterion for univariate processes studied in detail in this thesis. The traditional ARL is calculated assuming a given fixed process mean jump and a given time point where the jump occurs, usually taken to be from the very beginning in most chart performance studies. However, Ryu et al. (2010) demonstrated that the assumption of a fixed mean shift might lead to poor performance of control charts when the actual size of the mean shift is significantly different and therefore suggested a new ARL-based performance measure, called expected weighted run length (EWRL), by assuming that the size of the mean shift is not specified but rather it follows a probability distribution. The EWRL becomes the expected value of the weighted ordinary ARL with respect to this distribution. My methods generalize this criterion by allowing the time at which the mean shift occurs to also vary according to a probability distribution. This leads to a joint distribution for the size of the mean shift and the time the shift takes place, then the EWRL is calculated as the weighted expected value with respect to this joint distribution. The benefit of the generalized EWRL is that one can assess the performance of control charts more realistically when the process starts on-target and then the mean shift occurs at some later random time. Moreover, I also propose the effective EWRL, which measures the number of additional process runs that on average are needed to detect a jump in the mean after it happens. I evaluate several well-known univariate control charts based on their EWRL and effective EWRL performance. The numerical results show that the choice of control chart depends on the additional information on the transition point of the mean shift. The methods can readily be extended to other control charts, including multivariate charts. / Thesis / Doctor of Philosophy (PhD) / Since the introduction of the standard multivariate exponentially weighted moving average (MEWMA) procedure (Lowry et al. 1992), equal smoothing on all quality variables has been conveniently adopted. In this thesis, a bivariate exponentially weighted moving average (BEWMA) control statistic with unequal smooth- ing parameters is introduced with the aim of improving performance over the standard BEWMA chart. Extensive numerical comparisons reveal that the proposed chart enhances the efficiency and flexibility of the control chart in many mean-shift directions. Recently, Xie et al. (2011) proposed a chart for bivariate Exponential data when the quality measures follow Gumbel’s bivariate Exponential distribution (Gumbel 1960). However, when the process means experience a downward shift (D-D shift), the control charts are shown to break down. In other words, we encounter the strange situation where the out-of-control ARL becomes larger than the in-control ARL. To address this issue, we have proposed two methods, the MAX-MIN and CDF methods and applied them to the univariate EWMA chart. Our numerical results show that not only do our proposed methods prevent the undesirable behaviour from happening, but they also offer substantial improvement in the ARL over the approach proposed by Xie et al. (2011) in many mean shifts. Finally, in general, when it comes to designing a control chart, it is assumed that the size of the mean shift is fixed and known. However, Ryu et al. (2010) proposed a new general performance measure, EWRL, by modelling the size of the mean shift with a probability distribution function. We further generalize the measure by introducing a new random variable, T, which is the transition point of the mean shift. Based on that, we propose several ARL-based criteria to measure the chart performance and try them on several univariate control charts.

Streptococcus pyogenes is a human-specific pathogen that can cause a wide variety of diseases. These diseases range from the relatively mild pharyngitis and impetigo to invasive diseases such as necrotizing fasciitis to post-streptococcal sequelae such as rheumatic heart disease. The bacteria are frequently carried asymptomatically and may cause recurrent disease. Corresponding with their etiologic variation amongst diseases, clinical isolates demonstrate diverse virulence factor expression and random genetic mutations. In these studies, we examine the role of intracellular residence during survival as a niche for the diversification of S. pyogenes. Survival was previously studied using two in vitro systems: long-term stationary phase survival in culture and survival within epithelial cells in the presence of extracellular antibiotics. The surviving populations diversified, giving rise to stable strains with alternate colony morphologies, distinct proteomes, and altered metabolic properties. Further analysis in these studies showed that alterations in colony morphology were not solely observed during survival, but could also be induced in models mimicking acute infection. However, diversification in certain metabolic pathways occurred only during survival, and this metabolic diversification was observed at the transcriptional level. Further, one of three clinical isolates from patients with recurrent pharyngitis was altered in its metabolic profile, suggesting metabolic diversification may be occurring in vivo. The survivor strains had varied transcriptional changes in the genes encoding the virulence factors emm, slo, and speB. All of the stationary phase-derived survivor strains and two intracellular survival-derived strains had attenuated virulence in zebrafish. Most of the attenuated strains disseminated to the spleen and were cleared within three days. A whole blood killing assay showed a strong correlation between bacterial killing and emm expression. While the diversification appeared random, these strains retained their multilocus sequence type (MLST). These results suggest S. pyogenes strains with the same MLST, but diverse virulence properties, may arise during survival in the host. / Microbiology and Immunology

Using computational approaches utilizing large datasets to investigate public health information is an important mechanism for institutions seeking to identify strategies for improving public health. The art in computational approaches, for example in health research, is managing the trade-offs between the two perspectives: first, inference and second, prediction. Many techniques from statistical methods (SM) and machine learning (ML) may, in principle, be used for both perspectives. However, SM has a well established focus on inference by building probabilistic models which allows us to determine a quantitative measure of confidence about the magnitude of the effect. Simulation-based validation approaches can be used in conjunction with SM to explicitly verify assumptions and redefine the specified model, if necessary. On the other hand, ML uses general-purpose algorithms to find patterns that best predict the outcome and makes minimal assumptions about the data-generating process; and may be more effective in a number of situations. My work employs both SM- and ML- based computational approaches to investigate particular public health problems. Chapter One provides philosophical background and compares the application of the two approaches in public health. Chapter Two describes and implements penalized Cox proportional hazard models for time-varying covariates time-to-event data. Chapter Three applies traditional survival models and machine learning algorithms to predict survival times of cancer patients, while incorporating the information about the time-varying covariates. Chapter Four discusses and implements various approaches for computing predictions and effects for generalized linear (mixed) models. Finally, Chapter Five implements and compares various statistical models for handling univariate and multivariate binary outcomes for water, sanitation and hygiene (WaSH) data. / Thesis / Doctor of Philosophy (PhD)

This collection addresses the exposure created by a missing personal boundary. The confusion of psycho-personal interactions is explored as is the damage created by living without a protective shield. These poems explore emotional, physical, and social emergence, with nature as a silent, yet sentient witness. This exploration culminates in an awareness that natural life has stepped in to enable healing. The duality of a fractured upbringing between the New York City center and the isolation of farmland, as well as the paradox of extremes created, lead to a need to find solace in safe spaces. The gentle canopy created by outstretched branches act as a surrogate for the arms that did not protect or embrace through childhood. This collection of poems is a celebration of natural healing. Peel in the Garden is a deep dive into what was a forbidden area for so many years. The landscape left by trauma becomes a tangible experience that readers can hopefully grasp, digest and savor.

No