The Scarlet F: A Conceptual Re-Engineering of Feminism

While feminism is a remarkably powerful tool in recognizing and addressing the harms women face, many individuals are quick to suggest abandoning feminism altogether. So often, feminism faces harsh opposition from outsiders. A comprehensive analysis reveals that the issue lies not in the word but in how feminism is conceptually understood. Historical manifestations, factionalization, and negative stereotypes have all clouded understandings of feminism. For starters, historical manifestations of feminism, such as women's education, suffrage, and reproductive autonomy, shape current understandings. These manifestations, however, exclude marginalized women for the social gain of white, upper-class women. As well, feminism has become exceptionally factionalized; each faction has its own meaningful conception of what feminism is, but the number of conceptions eclipses an understanding of feminism. Negative stereotypes have also tainted feminism as a concept such that outsiders have concepts of feminism as 'man-hating' and 'feminazi'. While untrue, these stereotypes have penetrated public conceptions of feminism. Each of the three issues reveals justified motivation for abandoning feminism. Abandoning feminism certainly removes the aforementioned shortcomings, however, in doing so, women also lose the tool through which they are able to recognize and address the harms they face. Instead of abandoning feminism, I propose a conceptual re-engineering project which aims to ameliorate the harmful shortcomings while working at developing a meaningful, useful concept of feminism. A conceptual re-engineering project promises to evaluate how feminism is understood, identify the cracks in feminism's conceptual foundation, and to repair the cracks by developing an ameliorative concept. Simply put, I will argue for and develop a concept of feminism that avoids harmful shortcomings previously presented while continuing to preserve the spirit of feminism. Rather than abandon feminism, this project marks an effort to develop a meaningful, useful concept of feminism.ThesisMaster of Arts (MA)Feminism is a powerful tool which allows women to recognize the harms they face, and provides an account of how to address these harms. While feminism is the driving force behind sociopolitical gains for women, it has become marked by excluding marginalized women, by splitting into many branches each with their own understanding of feminism, and by harmful negative stereotypes such as 'man-hating' and 'feminazi'. Feminism has lost its structural integrity; we no longer understand what feminism is or how to engage with it. This project reflects one of the first efforts to evaluate feminism as a concept, highlight its shortcomings, and propose the need for a revised concept. I propose an ameliorative concept of feminism that avoids harmful shortcomings and offers a clearer, more meaningful understanding of feminism

Functional Genomic Screens to Elucidate Alternative Splicing and Circular RNA Biogenesis Regulators Driving Neuroendocrine Cancer Progression

Cancer progression remains a major clinical challenge, with treatment resistance and recurrence serving as continuing hurdles to overcome. Neuroendocrine (NE) cancers, such as small cell lung carcinomas (SCLC), represent a highly lethal subset of tumors, in which progression can result in rapid proliferation and metastasis, through the expression of NE signalling hormones. Different genetic regulatory networks are implicated in this progression, although the full complexity remains poorly understood. Regulated RNA processing via alternative splicing (AS) and circRNA biogenesis are widely acting levels of regulation, despite being historically overlooked. Recent studies have identified the role of AS and circRNA regulators, such as RNA binding proteins (RBPs), in driving NE cancer progression. To systematically identify novel regulators and directly link them with target RNA events, functional genomic screens such as “Systematic Parallel Analysis of endogenous RNA regulation coupled to barcode Sequencing" (SPAR-Seq) can be employed. This study sought to develop and optimize a new circRNA event panel, coupled to siRNA knockdown treatments for extending SPAR-Seq screen applications. Pilot screens in an SCLC cell line confirmed successful amplification and barcoding through gel electrophoresis and identified changes in circRNA regulation upon siSOX2 knockdown. Further focused experiments also identified SFPQ and PTBP2 as regulators of key NE-associated AS events, establishing them as new RBPs of interest for future SPAR-seq screens. Single-cell profiling was also used as a tool for SPAR-seq lead prioritization. scPipeline was used on published and in-house generated single-cell datasets, exploring candidate RBPs in a cell-specific manner, associating them with NE cancer markers. Together, these provide novel integrated approaches for the discovery of master regulators of AS and circRNA in NE cancer.ThesisMaster of Science (MSc

Evolutionary causes and consequences of sexual conflict and phenotypic divergence under the constraint of a shared genome

Sexual dimorphism is common in nature. Whether it is sexual size dimorphism, or exaggerated trait dimorphism, males and females often look quite different. This sexual dimorphism arises despite a largely shared genome. To explore the evolution of sexual dimorphism, we use artificial selection, experimental evolution, and RNA-seq. We explore sex-biased gene expression in the sexually dimorphic Drosophila prolongata using RNA-seq analysis. We show that in D. prolongata, there are changes in sex-biased gene expression in developmental genes and transcription factors and changes in the magnitude and number of differentially expressed genes potentially leading to exaggerated trait development. Using artificial selection lineages with reversed sexual size dimorphism in D. melanogaster we identify a polygenic response to discordant selection, and a region differentially segregating between the sexes where selection pulls the sexes against their original sexual dimorphism. Using experimental evolution, we show that current hypotheses for the ecological precursors for sexually dimorphic evolution may be incomplete. Previous work has hypothesized that an ecological setting that creates potential for male monopolization of females and differential success for males who win duels would be sufficient to initiate the evolution of sexually dimorphic weapons. Here, we show that these ecological structures were insufficient to initiate sexually dimorphic evolution in D. melanogaster and propose that low density and opportunity for male-male signalling may be additional ecological ingredients critical for weapon evolution. Using these diverse methodologies, we expect to be able to add to our understanding of how sexual dimorphism evolves, and how a shared genome contributes to divergent phenotypes within a species.ThesisDoctor of Science (PhD

Experiences of Job-Seeking Social Assistance Recipients with OW/ODSP Person-Centered Supports and Employment Ontario Employment Services

This report presents findings from an anonymous online survey conducted among nearly 1,200 recipients, primarily from Ontario Works (OW) along with participants from the Ontario Disability Support Program (ODSP), across Ontario. Conducted between July and December 2024, the survey involved a non-random, non-representative sample and aimed to collect user feedback on their experiences with OW and ODSP life stabilization (person-centered) supports, as well as Employment Ontario (EO) employment services since 2021.Social Sciences and Humanities Research Council, Partnership Engage Gran

The chore divide: How gender equality at home shapes baby plans

There is limited research on how women’s employment and education affect the relationship between household labor division and fertility intentions. This study aims to address this gap by analyzing recent survey data from Canada. We explore whether the distribution of routine (cooking, cleaning, dishes, and laundry) and intermittent (grocery shopping, social organization, finance, and bill paying) household tasks impacts women’s fertility plans. The findings will offer insights into the factors driving low fertility, especially within the Canadian context. This research will also contribute to ongoing discussions on the complex connections between gender equality, employment, education, and fertility

DEEP LEARNING AUGMENTED GENOME MINING IN THE “OMICS” ERA

Bacterial specialized metabolite (SM) scaffolds are fundamental to many important medicines, including antibiotics. Widespread dissemination of antimicrobial resistance demands the isolation of mechanistically and structurally novel therapeutics to enable lifesaving medical interventions. The meteoric growth of genomic sequencing data has uncovered millions of biosynthetic gene clusters (BGCs) encoding SMs. However, much of this chemical space remains unexplored due to technical limitations in BGC comparison and limited strategies for BGC prioritization. In this thesis, I develop deep learning algorithms which enable high-throughput comparison, structural rationalization, bioactivity prediction, and defragmentation of BGCs to enable large-scale BGC prioritization for SM-based drug discovery efforts. Firstly, I develop Transformer-based deep learning algorithms to identify and represent BGCs using highly scalable, vectorized representations. These algorithms drastically outperform the current state of the art and enable rapid comparison, grouping, and prioritization of BGCs at an immense (>1 million BGC) scale. Secondly, I develop computational methods to biosynthetically link SMs to candidate BGCs, increasing the dataset of potential SM-BGC relationships eight-fold relative to current datasets. This method also enables prioritization of BGCs encoding structural novelty and streamlines the isolation of SMs in a rationalizable fashion, leading to the isolation of a novel lipopeptide. Thirdly, I develop computational methods to identify bioactive molecular and genetic signatures present in BGCs and use these methods to streamline the isolation of a novel antitubercular peptide. Finally, I demonstrate a method enabling BGC defragmentation with scalable BGC fragment representations, facilitating the identification and comparison of discontiguous BGCs. Critically, the advances in this thesis leverage highly scalable vectorized representations which are capable of managing the extreme dataset sizes being created in the era of “multi-omics” data. Together, this work provides a means to leverage the immense wealth of genomic data to prioritize novel BGCs for streamlined, targeted SM-based drug discovery.ThesisDoctor of Philosophy (PhD)Bacteria sometimes produce unique “specialized metabolites” (SMs) with potent bioactivities (e.g. antibacterial, antifungal) that humans have developed into some of the most important medicines, antibiotics, and pesticides in use today. The instructions governing SM construction are found in genome regions known as “biosynthetic gene clusters” (BGCs). Widespread application of SM therapeutics has produced pests and pathogens that are resistant to these drugs, so new SMs are desperately needed. However, it is increasingly difficult to find new SMs without targeted approaches. In this work, I use deep learning approaches to identify and compare BGCs, producing significant improvements in accuracy, speed, and scalability relative to current methods. In addition, I introduce tools which predict SM-structure components and bioactivity from SM data, enabling the identification and prioritization of new, bioactive SMs for isolation. Using this approach, we isolate two novel SMs, one of which has potent antitubercular activity

Effects of variable visual environments on fish movement and social behaviour

Freshwater habitats are some of the most imperilled environments due in part to pollutants such as suspended sediments released by human-mediated erosion and manufactured dyes discharged in wastewater. Suspended sediments and dyes reduce visibility by absorbing, refracting, and diffracting light, and can interfere with vision mediated behaviours such as foraging or predator avoidance. In the wild, the concentrations of these visual pollutants frequently vary through time, and these fluctuations may themself influence animal behaviour. Unfortunately, most existing research fails to evaluate the dynamic patterns of suspended sediments, and almost no studies have evaluated the effects of dye on animal behaviour. Here, I assessed how kaolin clay and black pond dye independently affected the movement, social behaviour, and visual perception of zebrafish (Danio rerio), a visually-oriented, well-studied fish species. First, I showed that long-term exposure to suspended sediments interfered with movement and social hierarchy stability in ways that acute exposures did not (Chapter 2). Second, I discovered that suspended sediments and dye have contrasting effects on fish movement, with fish swimming less under suspended sediments but swimming more under dye exposure, despite both pollutants similarly decreasing aggression and increasing shoaling behaviour (Chapters 3 and 4). Third, I established that environmental fluctuation had a complicated interaction with the effects of low visibility, dampening habituation, decreasing the effects on aggression, and having contrasting effects on movement depending on the visual pollutant (Chapters 2, 3, and 4). My research reveals that fish may respond to visual pollution by forming tighter shoals, and by either moving more cautiously or avoiding polluted areas entirely. Taken together, my results show that suspended sediments and dye have similar but not identical effects on fish behaviour, and that both fluctuation and the duration of exposure play important roles on the overall in the impacts of visual pollutants on behaviour.ThesisDoctor of Science (PhD)Suspended sediments from human caused erosion and dyes from clothing, food, and personal care products are making it more difficult for animals in rivers and streams, like fish, to see. The concentrations of these visual pollutants also vary through time thanks to factors like rain or human water use, making it even harder for animals to adapt compared to stable concentrations. In this thesis I present a series of experiments showing that zebrafish can protect themselves against the impacts of suspended sediments and dye by staying closer to other fish and either moving more cautiously or avoiding murky waters. I also found that fluctuating visibility levels make it harder for fish to adapt some, but not all, of their behaviours to visual pollution. My results suggest that while some animals adapt well to human impacts, future research should include fluctuating pollution levels to better reflect natural environments

Cognitive Dynamic Systems: A Review of Theory, Applications, and Recent Advances

The field of cognitive dynamic systems (CDSs) is an emerging area of research, whereby engineering learns from neuroscience. Under this framework, engineering systems are configured in a manner that mimics the human brain and improves the utility and performance of traditional systems. In essence, a CDS builds on Fuster's paradigm of cognition and is fulfilled with the presence of five cognitive processes: the perception-action cycle, memory, attention, intelligence, and language. When augmented with these processes, a system can be classified as a CDS and is afforded the capabilities of processing information and learning from experience through continued interactions with the environment. Tremendous benefit from adopting the CDS framework has been observed in the literature, especially in the fields of cognitive radio and cognitive radar. More recently, the framework has been extended to other areas, such as control theory, risk control, and the Internet of Things; where the potential for drastic performance improvements has been evident in the literature. This comprehensive article presents a thorough background and exposition of the CDS framework and each field where it has been applied. In addition, we provide a comprehensive review of the recent advancements and related works in each domain by summarizing the key facts relating to the methodologies, findings, and limitations of the surveyed papers. Our novel contributions involve being the first source of centralized information on this topic and forming the foundation for future research efforts by presenting suggestions regarding worthwhile avenues for further investigation

Machine Learning-driven Strategies for Risk Interactions and Systemic Risk Management of Infrastructure Projects

Infrastructure projects frequently fail to meet performance expectations, due to their inherent complexities, leading to delays, cost overruns, and safety concerns. Risk interactions and systemic risks are two key contributors to these challenges. Risk interactions occur when one risk amplifies the magnitude and/or the probability of another, such as extreme weather delaying work progress while also increasing safety incidents. Systemic risks arise from disruptions in one component that can lead to project-wide cascading disruptions, such as delays in excavation work impacting downstream work packages like backfilling and site grading. Previous studies investigated risk interactions and systemic risks separately, which often led to sup-optimal project performance. Additionally, existing models rely on complex simulations and rigid theoretical frameworks, limiting their practicality. In this respect, the research presented in this dissertation is aimed at developing machine learning (ML)- and optimization-based strategies to address both risk interactions and systemic risks in infrastructure projects. The proposed strategies enable practitioners to i) quantify and predict the combined impacts of risk interactions and systemic risks on the project performance, thereby improving the accuracy of risk assessment; and ii) implement adaptive solutions to rapidly restore key project performance targets. The findings of the current research highlight the value of integrating ML and optimization in decision-making, offering practical solutions to enhance project outcomes under the constraints of risk interactions and systemic risks. Importantly, the presented data-driven strategies are not meant to replace the existing project management tools in practice, but rather to complement them. Project managers should continue to exercise their professional judgment alongside these strategies to ensure efficient risk management. Overall, this work advances the understanding of risk management in large-scale infrastructure projects, providing data-driven approaches to improve project performance under complex risk conditions.ThesisDoctor of Philosophy (PhD)Infrastructure projects, such as constructing water networks or railway lines, are inherently complex and often face significant challenges including delays, cost overruns, and safety issues. These challenges are mainly driven by the uncertainties and interdependencies of various project components. Researchers and practitioners rely on different risk assessment and mitigation methods to address these challenges. However, these methods are either impractical or fall short of accurately capturing the full scope of such uncertainties and interdependencies, resulting in a sub-optimal project performance. In this dissertation, machine learning and optimization approaches are used to better assess and mitigate the adverse impacts of these uncertainties and interdependencies on the project outcomes. The overall objectives are to: i) quantify the uncertainties and interdependencies within complex infrastructure projects and their effects on performance; ii) develop reliable and robust models to evaluate such effects; and iii) devise effective relevant mitigation strategies that enhance the project performance. The developed approaches can serve as valuable tools for decision-makers and project managers, improving their ability to assess and manage risks in real-world scenarios