Burr, Wesley

Amyloid fibrils are fibrous protein aggregates that arise from misfolding and self-assembly processes, collectively referred to as amyloidosis. These aggregates are strongly associated with incurable neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease, and Amyotrophic Lateral Sclerosis (ALS). Elevated levels of Reactive Oxygen Species (ROS) and dysregulated metal-ion homeostasis often impaired by environmental and lifestyle factors can induce oxidative stress that undermines cellular antioxidant defenses, which cause the amyloid formation and toxicity. This thesis investigates multiple amyloidosis models, emphasizing the contribution of metal ions and ROS to aggregation pathways, and evaluates the potential inhibitory or protective roles of cytokinin (CK) plant hormone.Chapter 2 focuses on Gelsolin amyloidosis, a hereditary condition driven by point mutations that promote aberrant amyloid formation. Using microscopic and spectroscopic approaches, this work characterizes the aggregation behavior of peptides derived from domain 2 of plasma gelsolin and secreted by muscle cells. Three peptides were studied: the wild-type(WT) sequence and two clinically relevant mutants, K184N and N187Y. Each variant exhibited distinct aggregation rates, reflecting mutation-dependent effects on self-assembly. Furthermore, two CKs Kinetin (Kin) and trans-Zeatin (tZ) were shown to modulate gelsolin aggregation, suggesting their potential as anti-aggregation molecules. Chapter 3 revolves on the aggregation properties of TDP-43 peptides associated with ALS pathology. Within the RRM I domain, two cysteine residues serve as key redox-active sites susceptible to oxidation. ESI-MS and spectroscopic methods were used to analyze three peptide variants: WT, a mutant (MT) in which cysteine were substituted with alanine, and WT-S, a disulfide-linked dimer. All variants displayed higher aggregation under mildly acidic conditions. CKs, Kin and isopentenyl-adenine (iP) showed antioxidant capacity and their influence on peptide stability. Chapter 4 investigates the effects of copper(II)-induced oxidative stress in C2C12 muscle cells and evaluates cellular responses to various CK forms. ESI-MS profiling identified 20 CKs in copper-treated samples and revealed 24 untargeted metabolites with significant level changes, indicating their possible involvement in metal-induced oxidative pathways. In conclusion, this thesis highlights the multifaceted roles of CKs in biological systems, particularly their potential to mitigate ROS overproduction, counteract metal-driven amyloidgenesis, promote fibril destabilization, and lessen oxidative stress.

Author Keywords: Amyloid, Anti aggregation, cytokinins, inhibition, Peptide aggregation, Protein aggregation

This thesis develops an Long Short-Term Memory (LSTM) neural network model to assess the relationship between ambient air pollutant exposure and public health risks, accommodating both linear and nonlinear associations with distributed lags.The research makes three key contributions. First, Maximal Information Coefficient (MIC) methods identify the most relevant air pollutants and their associations with health outcomes. Second, an LSTM model extracts temporally dependent features from exposure series to estimate health impacts. Finally, the model's potential in air pollution epidemiology is explored using Local Interpretable Model-Agnostic Explanations (LIME) to interpret the exposure-health response relationship.

Author Keywords: air pollution epidemiology, Deep Learning, explainable AI, Long Short-Term Memory, Maximal Information Coefficient, public health assessmen

Climate change is a global challenge with profound environmental, health, andsocio-economic implications. Canada's diverse geography offers a unique lens to study localized climate trends. This thesis models and clusters climate variables, focusing on temperature trends, using Bayesian hierarchical models and clustering techniques to uncover regional patterns and health impacts. Three decades of hourly temperature data from the Meteorological Service of Canada were split into 18 annual parts to capture seasonal variations. Metrics like mean, minimum, and extreme temperatures were analyzed. Bayesian models revealed regional variability, with examples of British Columbia and the Northern regions exhibiting notable trends. Clustering identified regional dependencies and linked temperature trends with morbidity and mortality risks from air pollutants (PM2.5, O3). Summer risks stemmed from O3, while winter risks were PM2.5 driven. Findings highlight the need for region-specific strategies, offering actionable insights for policy makers addressing climate-health linkages.

Author Keywords: Bayesian models, Climate change, Clustering, Temperature Trends, Time Series

Medication used to treat human illness is one of the greatest developments in human history. In Canada, prescription drugs have been developed and made available to treat a wide variety of illnesses, from infections to heart disease and so on. Records of prescription drug fulfillment at coarse Canadian geographic scales were obtained from Health Canada in order to track the use of these drugs by the Canadian population.

The obtained prescription drug fulfillment records were in a variety of inconsistent formats, including a large selection of years for which only paper tabular records were available (hard copies). In this work, we organize, digitize, proof and synthesize the full available data set of prescription drug records, from paper to final database. Extensive quality control was performed on the data before use. This data was then analyzed for temporal and spatial changes in prescription drug use across Canada from 1990-2013.

In addition, one of major research areas in environmental epidemiological studies is the study of population health risk associated with exposure to ambient air pollution. Prescription drugs can moderate public health risk, by reducing the drug user's physiological symptoms and preventing acute health effects (e.g., strokes, heart attacks, etc.). The cleaned prescription drug data was considered in the context of a common model to examine its influence on the association between air pollution exposure and various health outcomes. Since, prescription drug data were available only at the provincial level, a Bayesian hierarchical model was employed to include the prescription drugs as a covariate at regional level, which were then combined to estimate the association at national level. Although further investigations are required, the study results suggest that the prescription drugs influenced the air pollution related public health risk.

Author Keywords: Data, Error checking, Population health, Prescriptions

Little is actually known about how people cognitively process and integrate information when solving complex mathematical problems. In this thesis, eye-tracking was used to examine how people read and integrate information from mathematical symbols and complex formula, with eye fixations being used as a measure of their current focus of attention. Each participant in the studies was presented with a series of stimuli in the form of mathematical problems and their eyes were tracked as they worked through the problem mentally. From these examinations, we were able to demonstrate differences in both the comprehension and problem-solving, with the results suggesting that what information is selected, and how, is responsible for a large portion of success in solving such problems. We were also able to examine how different mathematical representations of the same mathematical object are attended to by students.

Author Keywords: eye-tracking, mathematical notation, mathematical representations, problem identification, problem-solving, symbolism

Databases containing information such as location points, web searches and fi- nancial transactions are becoming the new normal as technology advances. Conse- quentially, searches and cross-referencing in big data are becoming a common prob- lem as computing and statistical analysis increasingly allow for the contents of such databases to be analyzed and dredged for data. Searches through big data are fre- quently done without a hypothesis formulated before hand, and as these databases grow and become more complex, the room for error also increases. Regardless of how these searches are framed, the data they collect may lead to false convictions. DNA databases may be of particular interest, since DNA is often viewed as significant evi- dence, however, such evidence is sometimes not interpreted in a proper manner in the court room. In this thesis, we present and validate a framework for investigating var- ious collisions within databases using Monte Carlo Simulations, with examples from DNA. We also discuss how DNA evidence may be wrongly portrayed in the court room, and the explanation behind this. We then outline the problem which may occur when numerous types of databases are searched for suspects, and framework to address these problems.

Author Keywords: big data analysis, collisions, database searches, DNA databases, monte carlo simulation