This thesis describes the West Nile virus (WNV) epidemic in horses by exploring all aspects: sub-clinical infection, development of clinical disease and case fatality. All of the collected data were then compiled to create predictive risk maps of WNV infection for the province of Saskatchewan. <p>During the 2003 season, 133 clinical cases were documented with laboratory testing. Week of onset of clinical signs, gender, and coat color were significant predictors of whether the horse died or was euthanized due to severity of clinical signs. Studies of the serological response to vaccination and natural infection were examined to interpret the lab results from over 1100 samples taken from approximately 875 horses in 2003. A serologic study involving 212 horses on 20 farms determined the prevalence of sub-clinical infection (55.7% (95%CI, 44.9% to 65.8%)) and identified risk factors for infection. The study found risk of infection was highest in the Grasslands ecoregions compared to the Boreal Transition ecoregion. A case control study looked at risk factors for development of clinical disease. The study followed 23 case farms and control farms with a total of 300 horses sampled. This was the first field study to show that vaccination was efficacious in preventing the development of clinical signs. <p>The inclusion of horse surveillance data in the Saskatchewan Health WNV Integrated Surveillance Initiative was useful; however, it was discontinued due to time constraints, logistics, and declining monetary resources. <p>Since West Nile Virus is a mosquito-borne disease it is highly influenced by environmental changes, spatially and temporally. Discriminant analyses were used to partition Saskatchewan rural municipalities (RM) into categories of risk of infection with WNV based on acquired horse data and different environmental and meteorological data derived from both satellites or climate stations. The result was the creation of yearly predictive risk maps defining low to high risk of infection with WNV for each RM. <p>The 2003 epidemic provided a novel opportunity to study an important zoonotic disease emerging in a new environment. The information gathered will further the knowledge base upon which decisions for prevention of infection and clinical disease are made.

Phytoremediation is a relatively new remediation technology that may be useful in removing organic and inorganic pollutants from soils. Much research has focused on this type of remediation in the past few years due to its potential as an efficient and cost effective technology.<p>The purpose of this project was to extensively monitor phytoremediation of diesel-contaminated field soils in the laboratory under simulated field conditions. The main objectives were: to examine petroleum hydrocarbon (PHC) transfer and degradation processes involved in phytoremediation of contaminated field soils; to compare phytoremediation of contaminated field soils with intrinsic bioremediation; and, to develop a rationally-based model that could be used as a starting point for a quantitative prediction of the rate of PHC removal.<p>To realize these objectives a series of laboratory scale experiments were designed and carried out. The experiments reproduced pole planting of hybrid poplars into diesel contaminated field soils from a former bulk fuel station. The experiments were conducted in a closed and controlled environment over a 215-230 day period with numerous aspects of the system being monitored including volatilization of PHC from the tree and soil, and microbial activity of the soil.<p>Monitoring data indicated that microbial degradation of the contaminant was by far the most influential monitored degradation pathway, accounting for 96.3 to 98.7% of the mass removed for soils containing poplars. The monitoring data also indicated a significant difference in the mass of contaminant removed from the soil for soils containing poplars compared to those without. The total estimated mass of contaminant removed varied between 8.3 and 27.7% of the initial mass for soils containing poplars and between 6.0 and 6.1% of the initial mass for soils without poplars. Lastly, using the monitoring data and the below ground biomass of the poplars from each of the experimental test cells, a rationally-based model was developed to be used as a starting point for quantitative prediction of the rate of PHC removal.

The increasing demand for real-time multimedia applications and the lack of quality of service (QoS) support in public best-effort or Internet Protocol (IP) networks has prompted many researchers to propose improvements on the QoS of such networks. This research aims to improve the QoS of real-time multimedia applications in public best-effort networks, without modifying the core network infrastructure or the existing codecs of the original media applications. A source buffering control is studied based on a fluid model developed for a single flow transported over a best-effort network while allowing for flow reversal. It is shown that this control is effective for QoS improvement only when there is sufficient flow reversal or packet reordering in the network. An alternate control strategy based on predictive multi-path switching is studied where only two paths are considered as alternate options. Initially, an emulation study is performed, exploring the impact of path loss rate and traffic delay signal frequency content on the proposed control. The study reveals that this control strategy provides the best QoS improvement when the average comprehensive loss rates of the two paths involved are between 5% and 15%, and when the delay signal frequency content is around 0.5 Hz. Linear and nonlinear predictors are developed using actual network data for use in predictive multi-path switching control. The control results show that predictive path switching is better than no path switching, yet no one predictor developed is best for all cases studied. A voting based control strategy is proposed to overcome this problem. The results show that the voting based control strategy results in better performance for all cases studied. An actual voice quality test is performed, proving that predictive path switching is better than no path switching. Despite the improvements obtained, predictive path switching control has some scalability problems and other shortcomings that require further investigation. If there are more paths available to choose from, the increasing overhead in probing traffic might become unacceptable. Further, if most of the VoIP flows on the Internet use this control strategy, then the conclusions of this research might be different, requiring modifications to the proposed approach. Further studies on these problems are needed.

In the literature, it is a common belief that the U.S. stock market is the single most influential market in the world. The U.S. stock market is a global factor, affecting both developed and emerging markets. This dissertation empirically investigates the interactions between equity markets of the Asian four little dragons (Hong Kong, Korea, Singapore, and Taiwan) and the U.S. equity market. In order to assess correctly the effect of the U.S. stock return rates on emerging equity markets, we incorporate the assumption that returns on the U.S. stock market affect the stock returns on emerging markets but not vice versa. In other words, it is assumed that the U.S. stock exchange performance is not affected by one of the four Asian equity market; however, the latter is affected by both its own dynamics and the U.S. stock exchange. This dissertation consists of three essays. In order to estimate the dynamic impulse responses of the emerging markets¡¦ return rates to random shocks in the U.S. return rates, the first essay uses block exogenous VAR models which suggested in the papers of Zha (1996), Cushman and Zha (1997), and Zha (1999), and it finds that return rates on the U.S. positively affect stock return rates of the four Asian markets. By using the method of Rapach and Wohar (2005a, 2006a), and the second essay also finds that return rates on the U.S. have in-sample and out-of-sample predictive ability for return rates of the respective emerging market. The last essay follows the econometric methodology of Bai and Perron (1998, 2003a, 2003b, and 2004) and it points out that there exists at least one structural change in the predictive regression model of the respective empirical equity market. The results suggest that an emerging equity market¡¦s sensitivity to shocks from the U.S. return rates is related to its degree of openness.

About one in eight women in the United States will develop breast cancer over the course of her lifetime. Moreover, patient-to-patient variability in disease progression continues to complicate clinical decisions in diagnosis and treatment for breast cancer patients. Early detection of tumors is a key factor influencing patient survival, and advancements in diagnostic and imaging techniques has allowed clinicians to spot smaller sized lesions. There has also been an increase in premature treatments of non-malignant lesions because there is no clear way to predict whether these lesions will become invasive over time. Patient variability due to genetic polymorphisms has been investigated, but studies on variability at the level of cellular activity have been extremely limited. An individual’s biochemical milieu of cytokines, growth factors, and other stimuli contain a myriad of cues that pre-condition cells and induce patient variability in response to tumor progression or treatment. Circulating white blood cells called monocytes respond to these cues and enter tissues to differentiate into monocyte-derived macrophages (MDMs) and osteoclasts that produce cysteine cathepsins, powerful extracellular matrix proteases. Cathepsins have been mechanistically linked to accelerated tumor growth and metastasis. This study aims to elucidate the variability in disease progression among patients by examining the variability of protease production from tissue-remodeling macrophages and osteoclasts. Since most extracellular cues initiate multiple signaling cascades that are interconnected and dynamic, this current study uses a systems biology approach known as cue-signal-response (CSR) paradigm to capture this complexity comprehensively. The novel and significant finding of this study is that we have identified and predicted donor-to-donor variability in disease modifying cysteine cathepsin activities in macrophages and osteoclasts. This study applied this novel finding to the context of tumor invasion and showed that variability in tumor associated macrophage cathepsin activity and their inhibitor cystatin C level mediates variability in cancer cell invasion. These findings help to provide a minimally invasive way to identify individuals with particularly high remodeling capabilities. This could be used to give insight into the risk for tumor invasion and develop a personalized therapeutic regime to maximize efficacy and chance of disease free survival.

The thesis examines sequential mining approaches in the context of treatment recommendation for Gliblastoma (GBM) patients. GBM is the most lethal and biologically the most aggressive forms of brain tumor with median survival of approximately 1 year. A significant challenge in treating such rare forms of cancer is to make the best decision about optimal treatment plans for patients after standard of care. We tailor the existing sequential mining approaches by adding constraints to mine significant treatment options for cancer patients. The goal of the work is to analyze which treatment patterns play a role in prolonging the survival period of patients. In addition to the treatment analysis, we also discover some interesting clinical and genomic factors, which influence the survival period of patients. A treatment advisor tool has been developed based on the predictive features discovered. This tool is used to recommend treatment guidelines for a new patient based on the treatments meted out to other patients sharing clinical similarity with the new patient. The recommendations are also guided by the influential treatment patterns discovered in the study. The tool is based on the notion of patient similarity and uses a weighted function to calculate the same. The recommendations made by the tool may influence the clinicians to have the patients record some vital data on their own. With the progression of the treatment the clinicians may want to add to or modify some of the vital data elements previously decided to be recorded. In such a case a static database would not be very efficient to record the data since manual intervention is inevitable to incorporate the changes in the database structure. To solve this problem we have developed a dynamic database evolution framework, which uses a form based interface to interact with the clinician to add or modify the data elements in a database. The clinicians are flexible to create a new form for patients or modify existing forms based on a patient’s condition. As a result, appropriate schema modifications would be done in the relational database at the backend to incorporate these changes maintaining relational consistency.

Factorial designs have been widely used in many scientific and industrial settings, where it is important to distinguish "active'' or real factorial effects from "inactive" or noise factorial effects used to estimate residual or "error" terms. We propose a new approach to screen for active factorial effects from such experiments that utilizes the potential outcomes framework and is based on sequential posterior predictive model checks. One advantage of the proposed method lies in its ability to broaden the standard definition of active effects and to link their definition to the population of interest. Another important aspect of this approach is its conceptual connection to Fisherian randomization tests. As in the literature in design of experiments, the unreplicated case receives special attention and extensive simulation studies demonstrate the superiority of the proposed Bayesian approach over existing methods. The unreplicated case is also thoroughly explored. Extensions to three level and fractional factorial designs are discussed and illustrated using a classical seat belt example for the former and part of a stem-cell research collaborative project for the latter. / Statistics

The ability to predict is one of the hallmarks of successful theories. Historically, the predictive power of biology has lagged behind disciplines like physics because the biological world is complex, challenging to quantify, and full of exceptions. However, in recent years the amount of available data has expanded exponentially and biological predictions based on this data become a possibility. The functional gene network is a quantitative way to integrate this data and a useful framework for making biological predictions. This study demonstrates that functional networks capture real biological insight and uses the network to predict both subcellular protein localization and the phenotypic outcome of gene knockouts. Furthermore, I use the functional network to evaluate genetic modules shared between diverse organisms that lead to orthologous phenotypes, many that are non-obvious. I show that the successful predictions of the functional network have broad applicability and implications that range from the design of large-scale biological experiments to the discovery of genes with potential roles in human disease. / text

The initiatives of open space conservation, as outlined in the Sonoran Desert Conservation Plan, have been implemented through the purchase of nearly 65 thousand acres by Pima County. This land abuts sections of grazing leases held by state and federal agencies, forming largely unfragmented landscapes surrounding the city's urban core. Much of the outlying acreage is rural historic working ranches, now managed as open space conservation preserves. Ranches are landscapes of low-intensity impact, where the archaeological record of centuries of human land use is well preserved. Much of the land, however, remains relatively unstudied. To refine spatial predictions of archaeologically sensitive areas in southern Pima County, I use multivariate logistic regression to develop predictive models of probable archaeological site locations for three time periods at Rancho Seco as a case study. Results suggest portions Rancho Seco might contain additional Preceramic and Historic cultural resources but additional data collection is needed.

Non-indigenous invasive species are a major threat to native species diversity and ecosystem function and have been called the single worst threat of natural disaster of this century. Eragrostis lehmanniana Nees (Lehmann lovegrass), a tufted perennial bunchgrass native to southern Africa, is one such problematic species in Arizona, USA. This dissertation research is a mix of predictive modeling and field experiments designed to inform management decisions based on greater understanding of this nonnative species, with emphasis on the potential for spread and the impacts of removal.The modeling studies in this dissertation aimed to predict the potential distribution of E. lehmanniana in the southwestern United States under current and potential future climate conditions. The first portion of study addressed a common assumption in predictive modeling of nonnative species: data from the species' native range are necessary to accurately predict the potential distribution in the invaded range. The second portion of this study predicted the distribution of E. lehmanniana under 28 different climate change scenarios. Results showed the distribution of E. lehmanniana progressively shrinking in the southeastern and northwestern portions of the state and increasing in the northeastern portion of the state with increasing temperatures and precipitation. Key shifts occurred under scenarios with increases in summer and winter precipitation of 30% or more, and increases in summer maximum and winter minimum temperatures of at least 2oC.The field experiment served as a pre-eradication assessment for E. lehmanniana and indicates how semi-desert grassland communities in southeastern Arizona may respond to the removal of this species. This study suggested that plant community response to removal of an introduced species is mediated by precipitation variability (timing and amount), local site history, and edaphic conditions. The response observed on a site previously farmed for decades was to subsequently become dominated by other nonnative annual species. However, the two other sites with histories of livestock grazing responded more predictably to the removal, with an increase in annual ruderal species (2 to 10 times the amount of annual cover recorded on control plots).