Into the Wilderness: The Fleeing Figure in Early Modern Heroic Poetry

All the major epic poems from the Renaissance include a marginalized character who tries to escape from the action. The fleeing figure appears in Ludovico Ariosto’s Orlando furioso, Torquato Tasso’s Gerusalemme liberata, Edmund Spenser’s The Faerie Queene, John Milton’s Paradise Lost, and his Paradise Regained. Each iteration of the fleeing figure trope gets a little closer to attaining freedom from their oppressive social contexts until the limitations of the epic/romance polarity are finally dismissed in Milton’s brief epic. This study identifies the fleeing figure trope in early modern epic romance poetry as a site of resistance against imperial discourse and explores how its development eventually undermines the epic genre. The retreat of marginalized, chivalric romance characters from their epic narrative contexts can be understood as a rejection of consumptive narratives that are hostile to the individual subject. This research draws upon Lacanian psychoanalytic theory and David Quint’s theories of epic continuity and the tension between epic and romance (Epic and Empire, 1983) to identify and trace the generic mediation of imperial epic and chivalric romance through early modern heroic poetry. As each successive poem moves further away from polarizing gender constructs, the evolving feminist critique of heroic narratives that movement represents leads to the absolute rejection of imperial discourse. This research traces the mediation between imperial narratives and individual autonomy through the maturation of heroic poetry as it outgrows the limitations of simplified gender assumptions.ThesisDoctor of Philosophy (PhD)All the major epic poems from the Renaissance include a marginalized character who tries to escape from the action. These fleeing figures reflect a growing critique of the epic genre and the values it celebrates, such as martial conquest and sacrificing personal well-being for the sake of imperialism. The fleeing figure subverts those values, representing autonomy and the needs of the individual over the demands of the state. I trace the development of the fleeing figure through five major early modern epics: Ludovico Ariosto’s Orlando furioso, Torquato Tasso’s Gerusalemme liberata, Edmund Spenser’s The Faerie Queene, and John Milton’s Paradise Lost and Paradise Regained. With each successive poem, the fleeing figure becomes more and more successful in undermining imperial enterprise until the epic genre itself is finally put to rest

Provincial Midwifery Strategy: A Toolkit for Manitoba

The development of this toolkit was inspired and driven by a number of factors including a lack of integration and expansion of midwifery services, ongoing misconceptions and lack of knowledge around the professions, recent changes in College of Midwives of Manitoba standards, and the creation of a provincial consultant in midwifery position. The purpose of this toolkit is to provide guidance to Service Delivery Organisations (SDOs) such as Regional Health Authorities looking to implement, expand or modify services provided by midwives in order to meet a variety of community health needs. The intent is to provide context, information and resources to help SDOs understand the midwifery scope of practice, provide guidance on type of work organisation structures, and clarify the roles of various stakeholders. Suggestions for how midwives may meet community needs are included, with numerous examples of similar programs operating in Canada. Links to relevant resources including websites and journal articles are provided throughout the document. Whenever possible, links to full-text articles are provided

Event-Aware Imputation and Prediction of Urban Traffic Using Deep Spatiotemporal Learning Models

This dissertation presents a comprehensive investigation into the dual challenges of missing traffic data and the complexities of traffic speed prediction during social events, a topic of growing relevance in urban mobility systems. Urban centers are increasingly experiencing non-recurring disruptions caused by concerts, sports games, festivals, and other social activities, which introduce sharp deviations in regular traffic patterns. At the same time, traffic data, which are foundational for intelligent transportation systems (ITS), often suffer from incompleteness due to sensor failures, transmission errors, and insufficient probe vehicle coverage. This research addressed these intertwined challenges by developing a unified framework combining robust imputation methods with deep learning-based event-aware prediction architectures. The first contribution is the development of a two-stage imputation pipeline that integrates ensemble-based and generative approaches. Random Forest models are employed to provide fast, robust estimates, while Generative Adversarial Imputation Networks (GAIN) refine the results, capturing complex dependencies and uncertainty. Experiments on Hamilton, Ontario data demonstrate that the framework reduces imputation error (MAPE) by 20–30\% compared to traditional methods, while maintaining scalability under varying missingness levels. The second major contribution is the development of an Event-Aware LSTM (EA-LSTM) model that explicitly incorporates structured social event features—such as event type, timing, location, and attendance—into a spatiotemporal architecture combining Graph Convolutional Networks, bidirectional LSTMs, and attention mechanisms. The EA-LSTM significantly improves prediction accuracy during disruptions, reducing average error to 3.4\% network-wide and under 9\% near event venues, outperforming conventional deep learning baselines. The findings demonstrate that integrating contextual event information enhances both traffic imputation and prediction, leading to more robust, interpretable, and scalable models. The research provides practical insights for the deployment of real-time ITS applications, offering tools to support congestion management, dynamic signal control, and event traffic planning in complex urban environments.DissertationDoctor of Philosophy (PhD)Cities rely on traffic data to keep roads flowing smoothly, manage congestion, and ensure safety during busy times. However, traffic information is often incomplete due to sensor failures, gaps in vehicle tracking, or delays in communication. At the same time, special events such as concerts, sports games, and festivals create sudden and unusual traffic surges that are difficult to predict with traditional methods. This dissertation focuses on solving both of these challenges by creating new machine learning models that can fill in missing traffic data and make more reliable predictions about how traffic will behave during social events. The research introduces two main innovations. First, a two-step method for handling missing data was developed. The method combines traditional machine learning with advanced artificial intelligence to reconstruct incomplete traffic information quickly and accurately. Second, a new predictive model was designed that takes into account not only past traffic patterns but also details about upcoming events, such as their type, location, and size. By doing so, the model is better able to anticipate sudden disruptions and provide more reliable forecasts. The findings show that these approaches significantly improve both the accuracy of traffic data and the reliability of traffic forecasts, especially near event venues and during peak disruption times. In practice, this means that transportation agencies can better prepare for and respond to congestion around stadiums, concert halls, and city festivals, making travel smoother, safer, and more sustainable for everyone

MECHANISMS PROMOTING BYSTANDER ACTIVATED CD8+ T CELL-MEDIATED DISEASE DURING VIRAL INFECTION

Viral infections can lead to severe tissue damage and long-term health consequences, even when the virus does not directly target the affected tissues. While viral replication is often considered the primary driver of pathology, growing evidence suggests that immune responses and inflammation contribute substantially to collateral damage. Using a mouse model of Zika virus (ZIKV) infection, we investigate the contributions of viral replication and immune activation to ZIKV-induced neuropathology. We find that disease severity does not correlate with viral load but is associated with infiltration of bystander activated CD8+ T cells in the CNS of ZIKV-infected mice. These T cells are stimulated by inflammatory cytokines independent of TCR engagement and promote tissue damage through antigen-independent, NKG2D-mediated cytotoxicity. Although the amount of infectious virus did not correlate with disease, we find that viral infection of the CNS is required for recruitment of bystander activated CD8+ T cells. We further demonstrate that microglia are required for ZIKV-induced neuropathology but do not influence bystander T cell recruitment to the CNS. Instead, microglia are required for induction of NKG2D ligand expression on neurons, which are subsequently targeted and eliminated by NKG2D+ bystander activated CD8+ T cells. Finally, we examine the role of biological sex in bystander CD8+ T cell activation by ex vivo cytokine stimulation of human peripheral blood mononuclear cells. We find that CD8+ T cells from male donors exhibit greater IL-15-driven bystander activation, NKG2D expression, and antigen-independent cytotoxicity compared to T cells from female donors. These data suggest that sex differences in bystander CD8+ T cell activation may contribute to the observed sex biases in disease severity of viral infections. Overall, our findings reveal a novel mechanism of immune-mediated pathology during viral infection and highlight potential therapeutic targets to limit tissue damage caused by bystander CD8+ T cell activation.ThesisDoctor of Philosophy (PhD

Modulating Profibrotic Macrophages for Targeted Treatment of Fibrotic Lung Disease

Idiopathic pulmonary fibrosis (IPF) is a progressive disease marked by excessive extracellular matrix (ECM) deposition and lung scarring, leading to respiratory failure. Current therapies can decelerate disease progression but are associated with significant systemic side effects. Profibrotic M2-like macrophages, key drivers of lung fibrosis, promote fibroblast activation and ECM accumulation, sharing phenotypic traits with tumor-associated macrophages (TAMs). The heterogeneity of macrophage phenotypes and the lack of specific, targetable markers have limited the development of macrophage-focused therapies. This thesis addresses these challenges by identifying actionable targets such as Dectin-1 and leveraging yeast beta-glucan (YBG) microparticles to modulate profibrotic macrophages. By optimizing YBG for enhanced inhalability and preserved bioactivity, we aim to support localized therapies that mitigate fibrosis while minimizing systemic toxicity. To enable therapeutic targeting of these macrophages, we conducted integrative computational analyses of publicly available microarray-based transcriptomic datasets encompassing diverse macrophage phenotypes. This analysis produced a conserved 35-gene signature characteristic of profibrotic macrophages across human and murine models. Validation using the NanoString nCounter platform in THP-1-derived macrophages and primary murine bone marrow-derived macrophages (BMDMs) refined this to six consistently upregulated surface receptor genes, with CLEC7A (encoding Dectin-1) emerging as a lead candidate. Single-cell RNA sequencing from IPF patients and bleomycin-induced murine models confirmed elevated CLEC7A expression in monocytes and macrophages. Spatial transcriptomics using the NanoString CosMX™ platform in non-small cell lung cancer (NSCLC) samples showed that CLEC7A⁺ macrophages clustered in regions with high expression of fibrosis-related genes such as ACTA2 and profibrotic macrophage markers CD163, CCL18 and MRC1. Histological and immunohistochemical staining of serial sections from IPF and lung adenocarcinoma patients revealed enrichment of Dectin-1, αSMA, Masson’s Trichrome, and CD163 in anatomically overlapping regions, indicating regional convergence of fibrotic and immune activity. Functional studies in Clec7a knockout mice further demonstrated exacerbated fibrosis and impaired lung function, consistent with a protective role for Dectin-1 in pulmonary remodeling. We then examined YBG microparticles as immunomodulators to shift profibrotic M2-like macrophages toward an anti-fibrotic phenotype, targeting their role in disease progression. These microparticles engage Dectin-1, but their efficacy is highly dependent on fabrication conditions. To improve particle consistency and performance, we employed Pressurized Gas eXpanded (PGX) liquids technology to produce PGX-YBG microparticles with greater surface area, lower density, and more uniform size than conventional spray-dried YBGs. PGX-YBG showed enhanced Dectin-1 activation in vitro with minimal TLR2/4 signaling, reducing potential off-target effects. In both human and murine in vitro models, PGX-YBG modulated M2-like macrophages and reduced fibrosis-associated features. Ex vivo murine precision-cut lung slices (PCLS) confirmed PGX-YBG's capacity to modulate profibrotic macrophages, supporting its utility as a localized therapeutic. To further evaluate translational potential, we assessed the inhalability and immunomodulatory activity of PGX-YBG compared to in-house spray-dried YBG prepared from identical feedstock. PGX-YBG formed uniform blends with inhalation-grade lactose and exhibited aerodynamic diameters of 3–4 µm, with roughly double the fine particle fraction of the spray-dried counterpart. These properties improved deposition in distal lung regions and interaction with profibrotic macrophages. Both YBG types induced pro-inflammatory cytokine expression in vitro and in ex vivo PCLS. However, only PGX-YBG demonstrated enhanced uptake by macrophage, along with a significant reduction in arginase-1 and CD206. Additionally, PGX-YBG exhibited lower cytotoxicity, indicating a more favorable safety profile. Together, this work establishes a translational pipeline that integrates target discovery, biomaterial engineering, and inhalable delivery to modulate profibrotic macrophages in lung fibrosis. It highlights Dectin-1 as a viable therapeutic target and demonstrates the potential of PGX-YBG as a safe, effective, and clinically promising inhalable immunotherapy for IPF and related fibrotic diseases.ThesisDoctor of Philosophy (PhD

Geometric Deep Learning for Time Series and Foundation Models

This thesis presents two significant research contributions: one focuses on improving the adaptation of large language models (LLMs) using parameter-efficient fine-tuning (PEFT), and the other addresses the effective modelling of history-dependent stochastic processes—specifically Volterra processes, which are commonly applied in quantitative finance. In the first part, I introduce a user-friendly adaptation pipeline that boosts the performance of a standard foundation model, bringing it much closer to a fully fine-tuned, task-specific version. Remarkably, it achieves this while using significantly less compute and memory, all while keeping data private. The pipeline leverages existing learnable low-rank adapters (LoRA) for known datasets and predicts adapter values for new datasets using this readily available information. Its main advantage is that it can run on a standard laptop without requiring GPU power, ensuring that data remains local. This method effectively closes about half of the performance gap between an untuned base model and a fully fine-tuned one, making specialized models more accessible to researchers, practitioners, and everyday users who lack expensive infrastructure or work with sensitive data on devices like smartphones. The second part addresses a computational challenge in translating the non-Markovian Volterra process into a format suitable for computation. This translation is difficult because the data history dimension affecting the current state grows with the length of the path. I propose a two-step approach to make this process manageable: first, the Volterra process is mapped onto a simpler, lower-dimensional manifold; then, a geometric deep learning model—a "hypernetwork"—is applied, specifically designed for the manifold’s structure. We provide both mathematical and computational evidence demonstrating the model’s effectiveness and practicality (with proofs developed by co-authors available in the main paper), along with extensive testing of each parameter to validate our approach.ThesisMaster of Science (MSc)This thesis presents two contributions at the intersection of artificial intelligence and mathematics. First, I introduce a novel method for adapting large language models on widely available hardware. This approach recovers half of the performance lost when using an untuned base model instead of a GPU fine-tuned one, while running on a single laptop with minimal cost and energy consumption. It makes specialized models more accessible, preserves privacy by keeping data local, and promotes environmentally responsible computing. Second, I develop a practical framework for working with history-dependent stochastic processes commonly used in quantitative finance. Such processes are often too large to compute efficiently. The method proposed here compresses them into a low-dimensional representation and then applies a computational model, enabling efficient simulation, estimation, and practical application. Together, these contributions introduce novel algorithms capable of addressing real-world problems from fresh perspectives

Lived Experience Engagement

The Amplify Engagement Report presents findings from the Children with Incarcerated Parents (CHIRP) study, which aims to estimate the number of children affected by parental incarceration across five Canadian provinces (British Columbia, Alberta, Saskatchewan, Ontario, and Nova Scotia) between 2015 and 2021. Recognizing the lack of national data and the profound impact of incarceration on child and family health, the study integrates insights from individuals with lived experience—including formerly incarcerated parents, caregivers, youth, and service providers—through focus groups held in February 2025. Key findings reveal that the number of affected children is alarmingly high, though likely underreported due to data limitations. Participants emphasized the need for qualitative context to complement the quantitative data, advocating for broader demographic representation, acknowledgment of systemic inequities, and inclusion of Indigenous perspectives. The report outlines three major outcomes desired from sharing this research: increased public awareness and education, enhanced support systems for families and children, and policy reforms that prioritize child-centered approaches within the justice system. The report underscores the importance of humanizing the data, addressing research limitations transparently, and amplifying the voices of those directly impacted to inform meaningful change.Canadian Institutes of Health Research (CIHR

Situating study of student travel safety to and from Bennetto Elementary School

Bennetto Elementary School is a public school located in the north-end of Hamilton. In September 2024, a student waiting for the school bus was struck by a car and sustained life-altering injuries. This incident highlighted safety risks along the school route, and motivated the Bennetto Elementary School Parent Council to explore ways to improve safe travel for students to and from school. To better understand these risks and possible solutions, the Parent Council partnered with the McMaster Research Shop to examine school travel safety. The project focused on three research questions: (1) What actors are currently working to address student safety on the school route in Hamilton, (2) What approaches to collecting data on street safety concerns might be useful for a future study? And (3) How might concepts of “safety” differ across individuals, communities, and social identities, especially within the context of school travel? Methods included an environmental scan of actors and a literature review on the social and structural dimensions of safety

Constraining the Formation Timescale, Sediment Transport Rates and Median Grain Size of Eberswalde Delta, Mars

The highly sinuous meandering channels of the Eberswalde Delta on Mars suggest that the delta was long lived somewhere between 3.7 to 3 billion years ago, but it has been hard to constrain timescale and estimate sediment flux without detailed stratigraphy. The rover missions to Jezero and Gale crater have inspired new sediment transport models and formation timescale calculations. Three papers were selected and applied on Eberswalde to constrain its timeline. Bankfull depth is hard to measure without a stratigraphic record so 2 different depths were taken. The first method required estimations of avulsion timescale, meander migration and aggradation rate to find the minimum formation timescale. This was found to be 8 years for deep waters and 50 years for shallow waters. The minimum timescale was used to estimate intermittency which ranged from 10-4 and 10-6. The total formation timescale was found from source-to-sink methods which ranged from 105 to 107 years. The intermittency factors were applied to 3 sediment transport profiles for different grain sizes to verify the total formation timescale. All 3 profiles vary from each other but predict similar timescales of a few million years for a cold, dry climate. To build high sinuous channels as observed on Eberswalde, the channels would have to carry high concentrations of clay or silt to provide enough cohesion for lateral migration. As such, the channels carried a mix of grains that ranged from small boulder to clay, with the median grain size ranging from coarse sand to pebbles. Given the low intermittency factor, the Noachian was likely cold and dry with brief warming periods in between as observed in the evolution of Eberswalde lobes. Life may have formed in these brief periods but cannot be confirmed without ground-based data or improved spectroscopic techniques.ThesisMaster of Science (MSc)This thesis investigates the timescale formation of Eberswalde Delta on Mars. The study estimates the timescale of delta formation, analyzes grain size distribution, and examines sedimentary intermittency. The results contribute to understanding Martian fluvial processes and the potential habitability of ancient environments

THE TEST–RETEST RELIABILITY OF FOUR BINOCULAR VISION TESTS

Amblyopia will affect 200 million people around the world by 2030 (Fu et al., 2020). Characterized by poor vision, primarily in one eye, this condition arises when an individual does not receive concordant visual input early in life due to strabismus (misalignment of the eyes), cataracts, or high differential refractive error between the two eyes. Due to a lack of normal binocular input early in life, individuals with amblyopia do not develop binocular vision. The disruption of binocular vision prevents accurate depth perception, which causes challenges with everyday tasks such as driving and reading (Levi, Knill & Bavelier, 2015; Birch et al., 2018). Even after corrective surgery, deficits often persist throughout life. Remarkably, in a recent paper by Maehara et al. (2019), a subset of amblyopia patients, who failed all clinical tests of binocular vision, demonstrated a Pulfrich effect. The Pulfrich effect occurs when horizontally moving objects are presented to both eyes with a neutral density filter over one eye. The reduced contrast to the one eye delays visual processing, which the perceptual system perceives as spatial disparity, inducing depth perception. Evidently, binocular vision is necessary to perceive this effect implying these patients have residual hidden binocularity. To explore this phenomenon further, we developed a battery of binocular vision tests (most of which are motion-based). The present project evaluated the test–retest reliability of four tasks: Letter Dominance, Pulfrich, Plaid Motion, and Motion Parallax. Participants with typically developed vision completed these four tasks twice, one week apart. We observed a strong positive correlation between performance on week one and week two for the Letter Dominance, Pulfrich, and Plaid Motion tasks. This represents a foundational step in a research program which aims to obtain more sensitive measures of binocular vision in this population.ThesisMaster of Science (MSc)Patients with amblyopia typically have poor vision in one of their eyes. Since one eye has worse vision, the brain tends to rely on input from the stronger eye. This has consequences as individuals with amblyopia do not develop the ability to integrate the images seen by each eye—binocular vision. Without binocular vision, patients struggle with various daily activities. Remarkably, some amblyopic patients were able to perceive the Pulfrich effect– a depth illusion that requires binocular vision (Maehara et al., 2019). To explore this hidden binocularity further, we developed a battery of tasks to measure binocular vision in this population. Before administering these tasks to patients, the present study evaluated the reliability of four of the tasks by having control participants complete the tasks twice, one week apart. Three of the four tasks demonstrated strong reliability supporting their use as reliable tools to measure binocular vision in this population