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PLZF as a Novel Regulator of Dopamine D1 Receptor Signaling
No full textG protein-coupled receptors (GPCRs) represent the largest and most diverse family of transmembrane receptors, mediating a wide range of physiological processes through the activation of intracellular signaling cascades. Among these, dopamine receptors (DARs) play a central role in regulating critical neurological functions such as movement, cognition, and reward. The D1-class dopamine receptors (D1R and D5R), particularly the D1R, have been implicated in several neurological disorders such as Parkinson’s Disease (PD) and Levodopa-Induced Dyskinesia (LID). Therefore, more research is needed to better understand novel methods to be able to treat these dysfunctions. A major topic in D1R signaling is the role of proteins that interact with the receptor intracellular domains. The Promyelocytic Leukemia Zinc Finger (PLZF) protein was previously identified through yeast two-hybrid screening in the Tiberi lab as a novel interacting partner of the D1-class receptors. Unpublished studies in our lab found that PLZF, a multifunctional nuclear protein and transcriptional regulator with major roles in brain development, exerts subtype-specific effects on D1-class signaling and trafficking. Previous studies have also shown that PLZF forms a complex with both the agonist-stimulated GPCR AT2R and the constitutively activated Gαo subunit. However, its mechanistic role in D1R signaling and the desensitization process remains poorly understood.
In this thesis, I investigate the phosphorylation of the proximal cytoplasmic tail (CT) residues of the D1R, focusing on the contribution of the more distal CT domains in addition to complex formation with PLZF. Using HEK293 cells transfected with Flag-tagged wild-type or truncated D1R constructs (fVal388-STOP, fSer417-STOP, fSer431-STOP), receptor phosphorylation was assessed at specific proximal residues (Thr354 and Ser372/Ser373) through immunoprecipitation studies following dopamine stimulation. The results demonstrate that truncation of the distal domains of the D1R CT sequentially impairs phosphorylation of upstream residues, suggesting a hierarchical phosphorylation pattern. Co-expression of HA-tagged PLZF with D1R significantly decreases phosphorylation at these proximal sites, and this effect was modulated in truncated receptors, indicating that PLZF-mediated regulation occurs via specific distal domains of the CT.
Furthermore, GST pulldown assays revealed that PLZF interacts with both the IL3 and CT domains of D1R, with PLZF exhibiting a stronger affinity for the CT. Additionally, co-immunoprecipitation studies show that PLZF displays dynamic recruitment to the IL3 and CT, with truncation of either IL3 domain modulating the degree of binding under basal and dopamine-stimulated states, as well as retainment of recruitment with complete removal of the CT. Collectively, this work identifies PLZF as a key regulator of D1R signaling and provides new insight into the molecular mechanisms controlling dopaminergic receptor responsiveness. These novel findings may provide a better understanding of the molecular underpinnings involved in disorders compromising the dopaminergic system and potentially inform future therapeutic strategies for dopaminergic dysfunction in neurological disorders
A Thousand Cuts: A Critical Analysis of the Retributive Response to Sexual and Intimate Partner Violence in Rural Ontario and a New Path Forward
No full textSexual and intimate partner violence is the most underreported violent crime in Canada, particularly in rural Ontario, despite significant legal reforms intended to protect victims and encourage reporting. This thesis critically examines how the patriarchal foundations of the Canadian criminal justice system actively silences and deters women from reporting sexual and intimate partner violence and proposes that a restorative justice model, developed with a trauma informed and victim centric lens, should be offered as an alternative justice model to victims of sexual and intimate partner violence. This research specifically focuses on rural Ontario, where sexual and intimate partner violence rates are disproportionately high and access to support services is limited, and explores how personal, socio-cultural, and systemic, barriers compound to silence survivors. Drawing from feminist legal theory and restorative justice scholarship, this study argues that the adversarial nature of the criminal justice system is fundamentally incompatible with the individualized needs of victims and instead prioritizes the punishment of offenders over the healing of survivors. Through a comprehensive literature review and critical analysis, this research outlines a restorative justice model tailored to the rural Ontario context. This model emphasizes accountability, respect, and community engagement, aiming to reduce barriers to reporting while offering survivors agency, validation, and an opportunity to heal from the harm they experienced. Ultimately, this thesis advances the argument that only by shifting away from patriarchal legal structures and focusing on survivors’ voices can a truly just response to sexual and intimate partner violence be realized
Circulating plasma gelsolin and MRI-based radiomics as biomarkers of platinum resistance in epithelial ovarian cancer: building a multiparameteric prediction algorithm
No full textAbstract Background Resistance to platinum-based chemotherapy in epithelial ovarian cancer (EOC) patients is a barrier to disease management. Currently, there are no biomarkers to predict chemoresistance. Plasma gelsolin (pGSN) in circulating small extracellular vesicles (sEV) has previously been shown to predict chemoresistance in treatment-naïve EOC. Here, we expand upon sEV-pGSN as biomarker by incorporating MRI-based radiomics to improve the prediction of chemoresistance in EOC patients. Methods In this retrospective study, we used serum from 37 EOC patients with paired baseline MRI from the University of Hong Kong between 2016 and 2020. sEVs were isolated from serum samples using differential centrifugation and characterized by nanoparticle tracking analysis, western blotting, and transmission electron microscopy. Total pGSN and sEV-pGSN were quantified using sandwich ELISA. Radiomic features were extracted from the primary tumour on the MRI T2-weighted images (T2), apparent diffusion coefficient (ADC) maps (b = 0,400,800 s/mm2), and post-contrast images (PC). Highly correlated features (Spearman correlation coefficient of > 0.85) were removed and repeatable features selected using elastic-net regression. Grid-search 10-fold SCVs was utilized to optimize the hyper-parameters of the K-Nearest Neighbor (ADC and T2 + ADC + PC), Gaussian Naïve Bayes (T2), Linear Discriminant Analysis (PC), and Support Vector Machine (T2 + ADC) classifiers to build the prediction models, including total and sEV-pGSN. Results Among the 37 EOC patients (56±11 years old), 65% presented at advanced stage (FIGO III-IV, n = 24). Thirty-one patients were chemosensitive and six were chemoresistant (progression free interval < 12 months). The combination of total and sEV-pGSN could predict chemoresistance (AUC = 0.591), however the inclusion of MRI radiomic features improved the test performance. The prediction model based on total pGSN, sEV-pGSN, and 4 selected T2 radiomic features showed the best performance in predicting chemoresponsiveness with the following mean performance metrics: AUC (0.973), sensitivity (0.833), specificity (0.968) and accuracy (0.946). Conclusion Our prediction model using total and sEV-pGSN and T2 features demonstrated excellent diagnostic ability in predicting chemoresistance in EOC patients, which could be used to facilitate alternate tailored therapeutics. Building on this work in larger multicentre studies will further validate these findings and clarify the utility of a combined radiomics/EV biomarker approach to chemoresistance prediction in EOC
Exploring the availability and accessibility of medication abortion pills without a prescription: a mystery client study of pharmacies in Mumbai, India
No full textAbstract Background In India, mifepristone was introduced in 2002 for use with misoprostol as medication abortion and is currently approved for use up to nine weeks of gestation. Even though a prescription from a clinician is officially needed to obtain this regimen, many abortion seekers try to obtain medication abortion pills directly from pharmacies without one. The availability of medication abortion in pharmacies varies widely across states. We conducted a mystery client study in Mumbai, Maharashtra to assess the availability of, accessibility of, and pharmacy worker dispensing practices related to medication abortion pills. Methods We developed two mystery client profiles: one of an unmarried woman and the other of her male partner. In October 2023, both mystery clients separately visited each pharmacy to purchase medication abortion pills without a prescription. The mystery clients asked for medications that could “bring back menstruation” after a positive urine pregnancy test and then let the interaction unfold organically. We documented each encounter and analyzed these interactions using descriptive statistics and for content and themes. Results We visited 112 pharmacies for a total of 224 encounters. Medication abortion pills were in stock in only 12 pharmacies (11%) during at least one mystery client visit. In 79% (n = 178) of the visits, pharmacy workers asked mystery clients to see a clinician and in 23% (n = 51) of the visits pharmacy workers indicated that stocking or selling medication abortion pills was banned or illegal. A number of pharmacy workers mentioned that medication abortion pills are over-regulated and requirements for stocking and selling pills are cumbersome. Conclusions Although mifepristone/misoprostol can be dispensed by pharmacies in India, medication abortion pills appear to be available, with or without a prescription, in very few pharmacies in Mumbai. Pharmacy dispensing could support affordable medication abortion care, but overregulation has led to a decrease in the availability of these pills, which may create barriers to timely access to care for abortion seekers. There is a need to engage with pharmacy workers about the legal and regulatory status of mifepristone/misoprostol and identifying ways to eliminate overregulation appears warranted
Leveraging Metadata to Detect Phishing Where it Spreads
No full textPhishing is one of the most persistent and evolving attacks in cybersecurity. Attackers are exploiting legitimate domains and various hosting and third-party platforms, making detection challenging. Many existing proactive detection methods still focus on identifying newly registered domains, often by monitoring data sources that signal suspicious domain activities. Although these approaches are effective at capturing a subset of attacks-specifically those involving attacker-owned domains-they leave open the question of how many phishing campaigns actually rely on attacker-controlled infrastructure compared to compromised or third-party platforms. If attacker-owned domains are not the dominant category, then relying only on infrastructure-level data sources may miss a large portion of phishing activities. In such cases, effective detection needs to focus on other dimensions of phishing behavior, such as how they propagate across platforms and how users interact with them.
We begin with a phishing domain ownership taxonomy that distinguishes between attacker-owned, compromised, and third-party platforms. Using our large-scale datasets, PhishXtract, collected over a year from multiple reporting feeds, we analyze how attackers leverage these different infrastructures. Our findings show that most phishing attacks are not hosted on attacker-owned domains. Instead, attackers often abuse legitimate infrastructures and third-party platforms to create their campaigns, which makes infrastructure-level indicators alone insufficient for detection. To address this limitation and recognizing the growing role of social media, we investigate it as a source of phishing propagation and explore its potential for alternative detection. These platforms are not only channels where phishing links spread, but also rich sources of data-ranging from message content to metadata and propagation patterns-that can be used to identify phishing activity at an early stage. By focusing on these signals, we use our TelePhish dataset to develop a lightweight approach that detects phishing at the message level without relying on infrastructure-based indicators.
Finally, we compare this lightweight, propagation-based model against LLMs to assess whether advanced text-driven approaches outperform metadata-driven approach in social media. Our findings suggest that our lightweight, propagation-based models perform better for near real-time phishing detection, and it offers a balance between effectiveness and efficiency
In the Name of Freedom From Personal Grievance to Collective Dissent in the Freedom Convoy
No full textThis dissertation explores the emergence of politicized collective identity among participants of the Freedom Convoy in Canada. Initially framed as a protest against federal vaccine mandates, the Convoy rapidly evolved into a broader populist challenge to state authority, institutional legitimacy, and liberal democratic norms. Through a qualitative methodology grounded in life-history interviews, this study examines how participants transformed personal grievances into political agency. Drawing on the Politicized Collective Identity (PCI) framework, the research investigates the emotional, social, and biographical processes that catalyzed identity transformation among individuals often excluded from conventional political discourse. Hence, the thesis introduces the concepts of narrative hybridity and digital identity to describe the ideological bricolage and platform-mediated subjectivities that characterized the movement. Participants blended libertarian legalism, evangelical prophecy, wellness-spirituality, and conspiracist thinking into personalized worldviews animated by distrust in institutions and a deep yearning for sovereignty and truth. Special attention is given to gendered experiences and emotional repertoires. As a result, this research addresses critical gaps by centring the lived experiences and affective motivations of participants, often overlooked in analyses of right-wing populism and extremism. It reveals how digital media ecosystems, and moral storytelling converge to produce politicized identities in conditions of epistemic crisis and social fragmentation. Ultimately, the dissertation contributes to political sociology, and populism studies by offering a multidimensional account of dissent in the age of post-trust. It challenges binary framing of the Convoy as either extremism or legitimate protest, and instead foregrounds the complex interplay of belief, and belonging in shaping contemporary political subjectivity
Multi-modal Feature Fusion Using Full Sequences for Dynamic Hand Gesture Recognition with Simulated Robotic Arm Control
No full textDynamic hand gesture recognition (DHGR) enables accessible human-robot interaction by interpreting sequential human hand movements rather than static poses. Previous DHGR systems only focused on using the RGB modality in datasets and ignored depth. This thesis addresses this issue using a multi-modal classifier preserving temporal integrity. The InceptionV3-LSTM architecture is recreated, using a public RGB-depth dataset of six dynamic gestures. Full 40-frame sequences are used along with stratified 5-fold cross-validation to prevent sequences splitting across folds. The feature extraction pipeline fuses visual and landmark features from both RGB and depth modalities in parallel InceptionV3 streams, feeding a stacked LSTM-RNN. The results demonstrate that overfitting is reduced when using full-sequence multi-modal training, with validation loss decreasing while exceeding RGB-only accuracy. This work contributes a multi-modal pipeline for DHGR that is implemented in a simulated robotic control application
Spatial Generalization of Crop Yield Prediction Models Using UAV Imagery and Machine Learning
No full textGlobal population is expected reach 9.7 Billions by 2050 leading to an increase in food demand significantly, placing then a pressure on agricultural systems to improve productivity in a sustainable manner. Precision agriculture, supported by remote sensing and machine learning (ML), has emerged as a promising tool for optimizing resource use and improving crop yield prediction. Among most popular remote sensing platforms, Unmanned Aerial Vehicles (UAVs) has been used widely due to its ability to capture high-resolution images. Despite encouraging results reported in prior studies, the ability of UAV-based ML models to generalize across different spatial contexts remains understudied. This thesis investigates the spatial generalization performance of ML models for crop yield prediction using UAV multispectral orthomosaics and yield monitor data collected from three agricultural fields - two canola fields and one corn field - located in Manitoba and Alberta, Canada. Linear Regression and Random Forest models are evaluated using a tile-based representation (5 m × 5 m) derived from five spectral bands and two vegetation indices, namely the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge (NDRE). Model performance were assessed under multiple training–testing configurations, including intra-field and inter-field baselines, Leave-One-Field-Out (LOFO) evaluation, and spatially aware sampling approaches such as Leave-One-Block-Out (LOBO) and Leave-One-Cluster-Out (LOCO). Results indicate that models trained on combined raw spectral bands consistently outperform those relying only on vegetation indices, suggesting that richer spectral information improves predictive performance. However, cross-field generalization remains limited, highlighting the challenges of transferring models across heterogeneous spatial environments. Performance variability between fields further emphasizes the importance of consistent data collection and evaluation protocols
Self-Healing Ability and Behaviour of Cemented Paste Backfill
No full textCemented paste backfill (CPB), as an innovative cementitious material, has been extensively employed to cost-effectively manage mine wastes, ensure workplace safety, and improve mine productivity in the mining industry from a sustainable perspective. CPB is an engineered mixture typically consisting of dewatered tailings (70-85 wt.%), hydraulic binder (3-7 wt.%), and water to achieve a homogeneous paste. It is usually prepared in a plant located at the surface of a mine site and transported to refill the underground mined-out stopes and voids.
CPBs are designed to satisfy an adequate load-bearing capacity for safe mining operations. The primary geotechnical performance criterion of CPB is mechanical stability, which ensures resistance against deformation and prevents failure, thereby stabilizing surrounding rock masses. In parallel, the low permeability of CPB, as an essential environmental design criterion, plays a pivotal role in ensuring structural stability and long-term durability by minimizing the migration of aggressive chemicals or contaminants that could otherwise weaken the CPB structure and pollute groundwater systems. Upon placement, both mechanical and permeability properties are governed by complex multiphysical processes, including thermal (T), hydraulic (H), mechanical (M), and chemical (C) processes. However, cracks may initiate in the CPB matrix as a result of various factors, such as shrinkage, sulphate attack, initial structural defects, excessive overburden pressure, stresses induced by surrounding rocks and ground movement, rock bursts, or combined effects of these conditions, during the curing stage under the interaction of the multiphysical processes. The progressive generation and propagation of cracks can severely deteriorate the integrity of the CPB matrix, impairing its mechanical stability, environmental performance, and serviceability. Moreover, CPB structures often extend tens to hundreds of meters underground in at least one dimension, which makes manual maintenance and repair of cracks in CPB structures infeasible in practical manners. Given that, self-healing in CPB has been proposed as a promising strategy to mitigate crack-induced deterioration. Yet, existing studies are scarce, focusing primarily on autonomous self-healing with externally added agents, while the intrinsic autogenous self-healing behaviour of CPB remains unexplored. Furthermore, the effects of different factors (e.g., multiphysical THMC factors) on the autogenous self-healing capacity and performance of CPBs have not been comprehensively evaluated, presenting a critical research gap.
This Ph.D. study addresses this gap through a series of systematic experiments investigating the autogenous self-healing behaviour of CPB under a wide range of factors/conditions, including age of cracking, pre-cracking level, crack width, self-healing period, thermal (e.g., healing/curing temperature), hydraulic (e.g., drainage condition), mechanical (e.g., different crack-inducing stresses), chemical (e.g., sulphate content), as well as addition of mineral additives (e.g., blast furnace slag and fly ash). Self-healing efficiency was evaluated based on crack closure observations, recovery of mechanical properties (e.g., uniaxial compressive strength, deviator stress, indirect tensile strength), recovery of permeability (e.g., hydraulic conductivity), changes in physical properties (e.g., porosity, void ratio), and characterization of self-healing products. Results demonstrate that CPB exhibits a promising autogenous self-healing capability, which is mainly attributed to the precipitation of self-healing products, primarily consisting of C-S-H, CaCO3, Ca(OH)2, ettringite, and/or gypsum (under sulphate exposure). The relative proportions of these products vary considerably under different self-healing conditions. Both the age of cracking and the self-healing period significantly influence the self-healing efficiency of CPBs. The initiated cracks within the CPB matrix can ameliorate the hydration reactions, favouring the self-healing performance. Elevated curing temperatures (e.g., 35 °C and 50 °C) significantly accelerate the self-healing process via enhanced binder hydration, whereas low temperatures (e.g., 2 °C) exhibit negligible self-healing performance. Internal sulphate exposure exerts either positive or negative effects depending on sulphate concentration and self-healing duration. Improved drainage enhances self-healing performance through the combined effects of increased hydration and microstructural refinement. In the same way, shear cracks generated under confinement and tensile cracks with small apertures show favourable healing performance due to advantageous crack geometry within the matrix. Moreover, the impacts of incorporating mineral additives (e.g., blast furnace slag and fly ash) on self-healing performance are reflected in their contributions to binder hydration mechanisms and associated microstructural modifications. To validate and extend these findings, natural mine tailings with diverse mineralogical compositions were also tested under site-specific CPB formulations.
The findings of this research provide fundamental insights into the autogenous self-healing mechanisms of CPB, with significant implications for improving structural design, mechanical stability, permeability, durability, and environmental performance under field-relevant conditions. This work also demonstrates a comprehensive scientific basis for linking laboratory observations to engineering practice and for advancing the long-term sustainability of CPB systems in underground mining
Enhancing Legal Compliance and Regulation Analysis with Large Language Models
No full textContext: Software is increasingly pervasive in regulated industries, where compliance with regulations is crucial. Driven by pressing concerns such as data protection and privacy, certain industries, including healthcare, have incorporated specific measures into their compliance frameworks to better address the role of software. Nonetheless, many industries, despite their growing reliance on digital monitoring and automation, have yet to give adequate consideration to software, as the pressure to adapt has not been as strong. This thesis focuses on two domains with complementary challenges: (i) food safety, with regulations that remain largely technology-neutral and therefore demand novel methods to connect legal provisions with systems and software requirements, and (ii) privacy and policy, where provisions already exhibit a close connection to software and systems, but compliance checking methods could be further improved.
Problem: The introduction of Industry 4.0 technologies, particularly the Internet of Things (IoT), has significantly transformed the food industry, enabling real-time monitoring and control of critical processes. Yet, food-safety regulations, like many others, remain deliberately technology-agnostic by design to ensure long-term relevance, promote innovation, and maintain market neutrality. This creates a gap between regulations and modern food-safety systems, which increasingly depend on software. Bridging this gap requires systematic identification and operationalization of requirements-related provisions within regulations. In parallel, current approaches to legal compliance checking, especially in the privacy and policy domains, often rely on sentences as the unit of analysis, apply coarse-grained classification strategies, and do not automatically provide justification for compliance decisions. These approaches also demand significant manual effort, which limits their practical usefulness for stakeholders who must demonstrate and maintain compliance.
Approach: This thesis develops and empirically evaluates a suite of methods that Large Language Models (LLMs) to address these challenges. Contributions include: (1) a Grounded Theory (GT) study of food-safety regulations, resulting in a conceptual characterization of food-safety concepts closely related to systems and software requirements; (2) an empirical evaluation of four families of LLMs (BERT, GPT, Llama, and Mixtral) for automatic classification of requirements-related provisions in food-safety regulations; (3) a study of compliance checking for privacy and policy regulations (specifically General Data Protection Regulation (GDPR) Data Processing Agreements), assessing the effectiveness of state-of-the-art LLMs (GPT, Mixtral, Mistral, Zephyr, Phi), and demonstrating the benefits of paragraph-level context and the provision of explanation and justification; and (4) a quasi-experimental study on deriving Behavior-Driven Development (BDD) artifacts from regulations using LLMs (Llama and CLaude).
Outcomes: The thesis advances regulatory analysis and compliance checking by contributing (1) a conceptual model of requirements-related food-safety concepts and the resulting annotated dataset; (2) LLM-based pipelines for classification of legal provisions and compliance checking of regulatory artifacts; and (3) empirical evidence from a quasi experiment on translating legal provisions into behavioural specifications