Exploring the sexual and reproductive health of 1.5-generation Bangladeshi women in Toronto, Ontario

This doctoral dissertation, comprised of four papers, explores the sexual and reproductive health (SRH) of 1.5-generation Bangladeshi women in Toronto, Ontario. The “1.5 generation” refers to those who immigrated to the destination country as children. The cross-cultural positionality of 1.5-generation Bangladeshi women has implications for their SRH as they navigate different cultural norms of their country of origin and destination. The objectives of this dissertation were to gain an understanding of the different dimensions of SRH of 1.5-generation Bangladeshi women, and the scope and nature of SRH interventions targeting young women in Canada. Narrative inquiry and scoping review methods were employed. In-depth semi-structured interviews were conducted with ten 1.5-generation Bangladeshi women aged 18-22, and peer-reviewed and grey literature were analyzed to collate evidence on SRH interventions targeting young women. Paper One explored the state and determinants of sexual and reproductive health and rights (SRHR) knowledge of 1.5-generation Bangladeshi women, and their experiences with school-based sex education in Canada. Findings showed that SRHR knowledge formation is a multidimensional, dynamic process whereby social identities (e.g., ethnicity, gender) intersect and operate within a larger social context. Paper Two investigated participants’ SRH-related help-seeking behaviours and perspectives and experiences with SRH services. The results underscored the influence of social and cultural factors on help-seeking behaviours and the barriers and facilitators in accessing SRH services. Paper Three explored participants’ dating practices in the context of the sociocultural restrictions around pre-marital relationships. The findings offer a nuanced understanding of the dating practices of 1.5-generation Bangladeshi women and the implications for access to SRH services. Finally, Paper Four revealed gaps in SRH programming for young South Asian women in Canada. Overall, this dissertation contributes to the sociological health literature by providing rich data on the SRH of 1.5-generation Bangladeshi women and highlighting gaps in education, services and programming.DissertationDoctor of Philosophy (PhD

What Is a Ἰουδαῖος? A Linguistic Investigation of Ἰουδαῖος’s Meaning and John’s Motivation behind his Peculiar Modulation

This study addresses the questions, What is a Ἰουδαῖος? and Why John’s Ἰουδαῖοι?, by employing a linguistic methodology to investigate both the systemic meaning potential of Ἰουδαῖος and the Evangelist’s motivation behind its distinctive modulation throughout his Gospel. Drawing on a register-balanced corpus of Hellenistic Greek texts, the research abstracts the term’s context-independent meaning—termed "Judahness"—and analyzes how this meaning potential is pragmatically realized in John’s narrative. Rather than assuming a single, fixed meaning governs all instances, this study demonstrates that Ἰουδαῖος exhibits great contextual adaptability, with its sense, referent, and appraisal modulated according to the Evangelist’s rhetorical and theological aims. In terms of referent, Ἰουδαῖος identifies a broad spectrum of individuals and subgroups within the larger group known as “the Jews,” from religious leaders to the Jewish crowd, and even specific individuals, from those who oppose Jesus to those who believe in him. Regarding appraisal, the use of Ἰουδαῖος in the Gospel shows diverse tones: sometimes carrying negative connotations, suggesting opposition or skepticism toward Jesus; at other times, imbued with positive connotations, indicating acceptance or belief; and in many instances, used neutrally, without any particular emotional charge. This pragmatic flexibility challenges reductive interpretations of the Gospel’s portrayal of the Ἰουδαῖοι and resists claims of uniformity in John’s depiction. Instead, the study argues that John’s usage is motivated by a twofold purpose: first, an evangelistic intent to reach a diverse Jewish audience; and second, an apologetic concern to demonstrate that Jesus fulfills all Jewish messianic hopes. This nuanced application of Ἰουδαῖος thus serves not only to convey John’s theological message but also to engage his intended readers in a discourse about identity, faith, and the true essence of Judaism as seen through the lens of Jesus’ messiahship

CHARACTERIZING FIBRE FERMENTING BACTERIA IN THE INFANT GUT MICROBIOME

The gut microbiome undergoes significant change over the course of infancy, transitioning from a sparse immature community to a more diverse mature community over the first 3 years of life. This maturation is thought to be mediated by the transition from an immature milk diet to an adult-like solid food diet, particularly through the consumption of complex oligosaccharides such as dietary fibres. However, to date there has been little examination of the effects of dietary fibre consumption on the infant gut microbiome. In this work, I investigated the influence of dietary fibre consumption during infancy on the composition and function of the fibre fermenting bacterial population of the infant gut microbiome across two study populations of infants. I applied a combination of 16s rRNA gene sequencing and shotgun metagenomic sequencing to determine whether the composition of the infant gut microbiome was influenced by fibre consumption. To assess the fibre content of the infant diet I used multiple descriptors of dietary fibre intake including: adherence to a plant foods based diet, calculated daily fibre consumption, and the introduction of solid foods. I showed that across these three descriptors of fibre consumption in the infant diet there were not large scale gut microbial community changes. Instead, the effects were limited to individual microbial taxa, largely those containing genes for the metabolism of dietary fibres. These fibre fermenting bacterial populations were consistently at higher abundance in populations consuming higher fibre diets than those consuming lower fibre diets, suggesting an increased ability to metabolize dietary fibres. However, the effects were also highly individual and varied between participants, likely due to the specifics of their diet. Together, this work shows that while the effects of dietary fibre consumption during infancy varies between individual, there is an increase in fibre metabolism following the increase in fibre consumption.ThesisDoctor of Philosophy (PhD)The gut microbiome increases in complexity over the course of infancy, becoming a more diverse mature community over the first 3 years of life. The introduction of solid foods, and particularly dietary fibres, are important to this maturation, however, to date there has been little examination of the effects of dietary fibre consumption on the infant gut microbiome. In this work, I investigated the influence of dietary fibre consumption during infancy on the composition and function of the infant gut microbiome. I showed that fibre consumption in the infant increased the abundance of fibre fermenting bacterial species, potentially increasing the function of the community. Together, this work shows that while the effects of dietary fibre consumption during infancy varies between individual, there is an increase in fibre metabolism following the increase in fibre consumption

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