The Aedes aegypti mosquito is responsible for transmission of four vector-borne diseases that cause considerable global morbidity and mortality. Projections of the future effects of global climate change indicate that expansion of this species due to changing habitats is possible. Furthermore, since A. aegypti is highly dependent on human populations for feeding and egg-laying sites, changing human population characteristics are likely to alter the risk of exposure for humans based on geographic location. This study aims to create future potential risk maps for human exposure to A. aegypti using human population density as a predictor. Using current population density data and future growth trajectories, high-resolution human population density forecasts were created for 2050, then included as variables in ecological niche models developed using Maxent. Species occurrence data and high resolution climate data for current and future conditions (best and worst case scenarios) were included in the model, as well. Model fit indices and variable contributions indicated that the inclusion of human population density improves model accuracy for A. aegypti. Risk maps created by these models showed that areas currently adjacent to large cities within endemic regions, such as central Africa and western Brazil, are likely to see the greatest increase in risk to human populations. This corroborates current projections on increasing urbanization in the future and suggests that these models can be used to target interventions in high risk areas.

No description available.

Viruses are known to be associated with 20% of human cancers. Epstein Barr virus (EBV) in particular is the first virus associated with human cancers. Here, we computationally detect EBV and explore the effects of this virus across cancers by taking advantage of the fact that EBV microRNAs (miRNAs) and Epstein Barr virus small RNAs (EBERs) are expressed at all viral latencies. We identify and characterize two sub-populations of EBV positive tumors: those with high levels of EBV miRNA and EBERS expression and those with medium levels of expression. Based on principal component analysis (PCA) and hierarchical clustering of viral miRNAs across all samples we observe a pattern of expression for these EBV miRNAs which is correlated with both the tumor cell type (B cell versus epithelial cell) and with the overall levels of expression of these miRNAs. We further investigated the effect of the levels of EBV miRNAs with the overall survival of patients across cancers. Through Kaplan Meier survival analysis we observe a significant correlation with levels of EBV miRNAs and lower survival in adult AML patients. We also designed a machine learning model for risk assessment of EBV in association with adult AML and other clinical factors. Our next aim was to identify targets of EBV miRNAs, hence, we used a combination of previously known methodologies for miRNA target detection in addition to a multivariable regression approach to identify targets of these viral miRNAs in stomach cancer. Finally, we investigate the variations across EBV subtype specific EBNA3C gene which interacts with the host immune system. Preliminary data suggests potential regional variations plus higher pathogenicity of subtype 1 in comparison to subtype 2 EBV. Overall, these studies further our understanding of how EBV manipulates the tumor microenvironment across cancer subtypes.

No description available.

Functional vasodilation in arterioles is impaired with chronic ischemia. We sought to examine the impact of chronic ischemia and age on skeletal muscle resistance artery function. To examine the impact of chronic ischemia, the femoral artery was resected from young (2-3mo) and adult (6-7mo) mice and the profunda femoris artery diameter was measured at rest and following gracilis muscle contraction 14 days later using intravital microscopy. Functional vasodilation was significantly impaired in ischemic mice (14.4±4.6% vs. 137.8±14.3%, p<0.0001 n=8) and non-ischemic adult mice (103.0±9.4% vs. 137.8±14.3%, p=0.05 n=10). In order to analyze the cellular mechanisms of the impairment, a protocol was developed to apply pharmacological agents to the experimental preparation while maintaining tissue homeostasis. Endothelial and smooth muscle dependent vasodilation were impaired with ischemia, 39.6 ± 13.6% vs. 80.5 ± 11.4% and 43.0 ± 11.7% vs. 85.1 ± 10.5%, respectively. From this data, it can be supported that smooth muscle dysfunction is the reason for the observed impairment in arterial vasodilation.

To increase capacity for monitoring and surveillance of tick-borne diseases, publicly available tick distribution and climate change datasets are required to create accurate predictive distribution models. It is difficult, however, to assess model accuracy and utility when using incomplete datasets.  The more recent development of comprehensive tick databases for Europe and availability of climate change scenarios from multiple IPCC Assessment Reports allows for improved modeling efforts. Multiple tick datasets were combined and three climate change projections were compared by predicting current and future distributions of Ixodes ricinus ticks in Europe using the MaxEnt species distribution model. Overall, much of Europe contains suitable habitat for the Ixodes ricinus tick, both now and under future climate change projections.  Contraction of habitable areas is predicted to occur at lower latitudes and altitudes, while expansion is predicted to occur at higher altitudes in mountainous regions and the higher latitudes, primarily in northern Scandinavia.

<p>Regardless of their origin or pathology, many, if not all, diseases have long been regarded as complex. Yet, despite the progression in the understanding of complexity and the development of systems biology, the majority of biomedical research has been derived from qualitative principles. In comparison to the ethical, temporal and logistical limitations of human experimentation, <em>in vivo</em> animal models have served to provide a more advantageous means to elucidate the underlying disease mechanisms. However, given the additional limitations presented by such models, <em>in silico </em>models have emerged as an effective complement, and, in some cases, a replacement for <em>in vivo</em> experimentation. The <em>in silico </em>models presented in this thesis were developed using mathematical and computational methods to investigate the evolution of two complex, diverse diseases from a systems biology perspective: allergic asthma and cancer.</p> <p>We generated two novel <em>in silico</em> models of allergic asthma aimed at clarifying some dynamic aspects of allergic responses. Experimentally, we utilized an <em>in vivo</em> murine model of chronic exposure to the most pervasive aeroallergen worldwide, house dust mite (HDM), for up to 20 weeks, equivalent to at least 20 human years. Using a range of HDM concentrations, experimental data were collected to study local and systemic effects. The first model applied empirical mathematical techniques to establish equations for airway inflammation and HDM-specific immunoglobulins using an iterative approach of experimentation and validation. Using the equations generated, we showed that the model was able to accurately predict and simulate data. The model also demonstrated the non-linear relationship between HDM exposure and both airway inflammation and allergic sensitization and identified system thresholds.</p> <p>The second model used mechanistic mathematical techniques to investigate the trafficking of eosinophils as they migrated from bone marrow to the blood and, ultimately, to the lungs. Making use of a limited data set, the model determined the effect of individual processes on the system. We identified eosinophil production, survival and death as having the greatest impacts, while migration played a relatively minor role. Furthermore, the model was used to simulate knockout models and the use of antibodies <em>in silico</em>.</p> <p>In the context of cancer growth and metastasis, we developed a theoretical model demonstrating the spatio-temporal development of a tumour in two-dimensions. The model was encoded to create a computer graphic simulation program, which simulated the effects of various parameters on the size and shape of a tumour. Through simulations, we demonstrated the importance of the diffusion process in cancer growth and metastasis.</p> <p>Ultimately, we believe the greatest benefit of each <em>in silico</em> model is the ability to provide an understanding of each respective disease recognized as dynamic and formally complex, but predominantly studied in reductionist, static or un-integrated approaches.</p> / Doctor of Philosophy (Medical Science)

<p>Obstructive lung diseases limit airflow and gas exchange and have a major impact on a patient’s long-term health. Asthma and chronic obstructive pulmonary disease (COPD) are the most prevalent obstructive lung diseases and represent a major burden on healthcare systems worldwide. It is now accepted that the pathologies associated with these diseases are heterogeneous in nature, and as the function of the lung is determined by its three-dimensional structure, methods to volumetrically evaluate the lung are important tools in furthering the study of these pathologies.</p> <p>Three-dimensional imaging methodologies, such as computed tomography (CT) and single photon emission computed tomography (SPECT), are used clinically in the diagnosis of lung disease, but results are not commonly quantified. In addition, asthma and COPD develop slowly over time and diagnosis normally takes place after the underlying pathologies are well established. Experimental models in small animals, such as rats and mice, allow for the study of disease pathogenesis in a controlled setting and development of quantitative imaging practices for these models provides translational tools for relating results back to the clinic.</p> <p>In this thesis, CT densitometry and ventilation/perfusion (V/Q) SPECT are explored as methods to investigate models of asthma and COPD. CT densitometry is shown to be capable of quantifying allergic inflammation in an asthma model but is of less use in a model of COPD, predominantly due to the relative amounts of inflammation present. However, V/Q imaging is shown to be quite sensitive to the effects of cigarette smoke in a model of COPD and has been used to better understand how pathologies associated with COPD contribute to gas exchange limitation in the lung.</p> <p>The models, imaging techniques, and analysis methods described in this work provide insight into chronic obstructive lung disease and allow for future investigations into how pathologies effect gas exchange. Further, the characterization of the models described in this thesis allows for drug efficacy studies to be performed, both on established and novel treatments. Future research into asthma and COPD will benefit further from the use of threedimensional imaging methodologies because they provide volumetric information on structure and function and can act as a translational bridge between clinical disease and preclinical animal models.</p> / Doctor of Philosophy (Medical Science)

Diabetes is a major risk factor for the development of cataract [3,14,20,22]. In this thesis, we create a model that allows us to understand the incidence of one disease in the context of another; in particular, cataract in the presence of diabetes. The World Health Organization's Vision 2020 blindness-prevention initiative administers surgeries to remove cataracts, the leading cause of blindness worldwide [24]. One of the geographic areas most impacted by cataract-related blindness is Sub-Saharan Africa. In order to plan the number of surgeries to administer, the World Health Organization uses data on cataract prevalence. However, an estimation of the incidence of cataract is more useful than prevalence data for the purpose of resource planning. In 2012, Dray and Williams developed a method for estimating incidence based on prevalence data [5]. Incidence estimates can be further refined by considering associated risk factors such as diabetes. We therefore extend the Dray and Williams model to include diabetes prevalence when calculating cataract incidence estimates. We explore two possible approaches to our model construction, one a detailed extension, and the other, a simplification of that extension. We provide a discussion comparing the two approaches.

No description available.