VictorJuiz/thesis huggingface dataset:
Homocysteine, Neuroimaging Biomarkers, and Blood-Based Biomarkers in Apolipoprotein E ε4 Carriers and Non-Carriers across Cerebrovascular and Neurodegenerative Diseases

The amino acid homocysteine (Hcy) is a risk factor for cardiovascular and neurodegenerative diseases. However, its relationship with the apolipoprotein E (APOE) ε4 allele on neurodegenerative outcomes is not well-characterized. Participants with neurodegenerative or cerebrovascular diseases were stratified by the presence of the APOE ε4 allele. Brain volumes and small vessel disease marker volumes were quantified with magnetic resonance imaging (MRI). Amyloid, tau, and neurodegenerative blood-based biomarkers were quantified with single molecule arrays, and Hcy was quantified with an immunoassay. Hcy was associated with lower brain parenchymal fraction (BPF) and higher neurofilament light chain (NfL) in APOE ε4 non-carriers, but not in carriers. Within each disease subgroup, BPF associations remained consistent except in Parkinson's disease, where Hcy was associated with BPF regardless of APOE ε4 carrier status. These results may provide insight into the role of Hcy in neurodegeneration in different diseases.

Keywords: Alzheimer's Disease
Apolipoprotein E
Cerebrovascular Disease
Homocysteine
Neurodegeneration
Parkinson's Disease
Advisor: Swardfager, Walter
A Longitudinal Multidimensional Rehabilitation Program for Patients undergoing Allogeneic Blood and Marrow Transplantation (CaRE-4-alloBMT): A Phase II Feasibility Pilot Randomized Controlled Trial

Allogeneic blood and marrow transplantation (alloBMT) is a curative treatment for blood cancers associated with various treatment-related adverse events and morbidities for which rehabilitation programs are currently limited. A Phase II randomized controlled trial (RCT) was conducted to assess the feasibility, acceptability, and impact of CaRE-4-alloBMT: a longitudinal multidimensional cancer rehabilitation program for patients undergoing alloBMT. Feasibility of the intervention and methods were assessed. Acceptability was assessed through qualitative interviews. Secondary clinical outcomes were collected through questionnaires and physiological assessments. A total of 80 participants were recruited and randomized. Recruitment (72%) and retention (70%) rates, along with qualitative findings, support the feasibility of the intervention, however, adherence could be improved. Treatment effect sizes of 0.70, 95% CI [0.20, 1.21] (30-second sit-to-stand test), and 0.46, 95% CI [-0.17, 1.09] (SF-36) were observed in favour of the intervention. Results will be used to refine the program and inform a Phase III RCT.

Keywords: Cancer
Feasibility
Hematology
Rehabilitation
Stem cell transplant
Advisor: Jones, Jennifer
Reactivity and Electrochemical Study of Peripherally Fluorinated Boron Subphthalocyanines

The need for more sustainable forms of electrochemical energy storage has arisen due to the environmental impacts of manufacturing traditional inorganic energy storage systems. Herein, boron subphthalocyanines (BsubPcs), a unique macrocyclic compound, are investigated as a promising solution, with a focus on peripherally fluorinated BsubPcs (F12BsubPc). F12BsubPcs containing different axial halogens were studied in terms of their reactivity to axial exchange reactions. It was found that peripheral fluorination of BsubPcs inhibited the axial exchange reaction when compared to their non-fluorinated counterparts, with the axial Br ligand (Br-F12BsubPc) being the most reactive and most appropriate precursor. Previous studies have shown that F12BsubPc monomers contain highly stable and reversible reduction potentials. The electrochemical differences of F12BsubPc monomers, dimers, and dendrimers were studied and showed that the addition of multiple F12BsubPcs onto a single molecule can slightly shift both reduction and oxidation potentials closer together, making both processes marginally easier. The conclusions outlined in this thesis can aid in the development of novel BsubPcs for use inelectrochemical energy storage.

Keywords: Boron subphthalocyanine
Cyclic voltammetry
Differential pulse voltammetry
Electrochemistry
Advisor: Bender, Timothy P
Extracellular Matrix Modification of a Vascularizing Methacrylic Acid-based Hydrogel

Type 1 Diabetes (T1D) is an autoimmune disease characterized by destruction of pancreatic islets. Islet transplantation is a promising regenerative therapy for T1D that can re-introduce intrinsic glycemic regulation. The Sefton lab has previously shown that a vascularizing poly(sodium methacrylate)-poly(ethylene glycol) (MAA-PEG) hydrogel is a suitable material for subcutaneous islet delivery. However, this synthetic polymer lacks ECM binding sites which may negatively affect islet health. Here we focus on re-establishing cell-ECM connections of islets in MAA-PEG to mitigate loss of islet health associated with peri-operative stress. We demonstrated a method for adding collagen type 1 into MAA-PEG. Islets cultured in the collagen-supplemented hydrogel had improved function, viability, and dampened ER stress compared to the synthetic hydrogel. With inflammatory cytokines, ECM-supplementation partially recovered islet viability, but not function or ER stress, compared to MAA-PEG. These results suggest re-establishing cell-ECM adhesion within MAA-PEG as a method for improving health of isolated islets.

Advisor: Sefton, Michael V
A Mixed Methods Study of Dental Implant Treatment Outcome and Impact on Patients with Oligodontia from 1987 to 2022

Objective: To assess the dental implant treatment outcome and impact on patients with oligodontia.Material and methods: This mixed methods study with a convergent parallel design investigated patients with oligodontia who received dental implant treatment from 1987 to 2007. Data analyses were conducted to determine the treatment outcome and its impact on implant recipients. Results: A total of 578 implants placed in 100 patients were included: the 5-year cumulative survival rate (CSR) was 88.7%±1.4%. A subset of 215 implants placed in 25 patients was reassessed in 2022; the 20-year CSR was 81.9%±2.7%. There was an increased hazard ratio (HR) for males, implants with machined surface and implants placed in the maxilla. Patient-reported outcomes indicated that multiple challenges were encountered during different treatment stages, but improved quality of life outweighed hardships. Conclusions: Dental implant treatment is predictable over the long term for patients with oligodontia.

Keywords: dental implant
mixed methods study
oligodontia
Advisor: Carmichael, Robert RP
Optimization of Catheter Placement for Transperineal Interstitial Gynecological Brachytherapy with Centroidal Voronoi Tessellation

Brachytherapy (BT) is an essential pillar in the treatment of locally advanced cervical cancer. One method of gynecological interstitial (IS) BT uses a Syed Neblett transperineal catheter-guiding template with an intrauterine applicator. Using pre-BT imaging to plan catheter positions can lead to suboptimal tumour coverage, and often unused catheters, which contribute to increased implantation time and risk of bleeds. Using images taken after placement of the Template and APPlicator (post-TAPP) with catheter placement optimization may result in fewer unused catheters and better or equivalent dosimetry.A processing pipeline including Centroidal Voronoi Tessellation (CVT)-based catheter optimization was used to retrospectively simulate optimized catheters on post-TAPP images in cervix IS BT cases. Treatment plans were then manually generated with the optimized and clinical catheters. Plan quality was evaluated via compliance with the EMBRACE-II dosimetric limits. This feasibility study supports the hypothesis that CVT optimization on post-TAPP imaging can achieve clinically acceptable dosimetry, while decreasing insertion of extraneous catheters and potentially decreasing implantation time by limiting the need for repeat imaging.

Keywords: brachytherapy
centroidal Voronoi tessellation
cervical cancer
interstitial brachytherapy
Advisor: Rink, Alexandra
Mechanical Design, Assembly, and Test of Microsatellites with Optical Payloads for Earth and Astrophysical Observation

Microsatellites enabled by new technologies offer reduced cost and development time compared to traditional approaches, providing new opportunities for space missions. This thesis presents contributions to two microsatellite missions - GHGSat and Aspera. Each GHGSat spacecraft carries an optical payload to detect atmospheric methane, enabling global methane monitoring. This thesis presents the assembly of multiple GHGSat spacecraft, analysis and testing of a new launch adapter, and then the environmental testing of the spacecraft. This work concludes with the successful launch of these spacecraft. The Aspera mission consists of a single microsatellite supporting a far-ultraviolet imager that will observe galaxies. This thesis presents a design to meet the contamination control requirements for the imager. First, requirements for the design are derived, and initial design trade-offs are evaluated. Next, work for each of the major design drivers is presented. Lastly, the design is updated using the results of qualification testing.

Keywords: mechanism
microsatellite
SFL
spacecraft
structure
vibration
Advisor: Zee, Robert E
The Data Practices of Grassroots Climate Justice Groups in Toronto: Exploring Data-Driven Tools for Organizing

This thesis examines data practices within Toronto-based climate justice groups. In this context, data practices refer to strategies involving interaction with, or (co)production of data to support climate justice organizing. The central research question is: in what ways do Toronto-based grassroots climate justice groups engage with data practices to further their goals? A theoretical framework of environmental data justice is employed to guide this project. A qualitative research design rooted in reflexive thematic analysis is utilized for this project, with methods including an analysis of digital content produced by grassroots climate justice groups and a series of semi-structured interviews. The findings of this research project determine that there are multiple ways that groups engage with data practices to support their organizing efforts. The purpose of this project is to understand how data practices are applicable to grassroots climate justice organizers and the barriers they face when engaging with climate data.

Advisor: Soden, Robert
Vascular Endothelial Dysfunction, Cognition and Stroke in Early Life Study (VECSELS)

Arterial ischemic stroke and impairments to cognitive function are rising in children with stroke risk conditions such as congenital heart disease (CHD). Gaining a better understanding at these mechanisms of injury provides an opportunity to address ischemic risk in children with CHD. The first study in this dissertation utilized hemispheric apparent diffusion coefficient (ADC) to measure perfusion related injury in pediatric stroke patients with a history of CHD. The findings showed a range of ADC values with the highest in the ipsilesional hemisphere, with lower values in the matched contralesional hemisphere, and the lowest values in control participants. The second study investigated vascular endothelial function in patients with CHD using cerebrovascular BOLD-MRI as an indicator of cerebrovascular health. Patients in the CHD group had worse cerebrovascular reactivity compared to healthy controls, and this was correlated to cognitive functioning. Overall, these findings suggest perfusion related injury may be associated with the cognitive impairments seen in children with stroke risk conditions.

Advisor: Dlamini, Nomazulu
Deployment and Evaluation of a Motivational-Interviewing Chatbot for Moving Smokers Towards the Decision to Quit and Distillation of Large Foundational Language Models for Generating MI Reflections

Motivational Interviewing (MI) is a validated therapeutic approach that has been shown to help ambivalent people struggling with addiction move toward change. MI has been applied to many behaviours, including smoking cessation. If conversational agents could effectively apply MI, they may provide a scalable way to help more people access this therapy. Previous attempts to provide MI therapy through text-based conversational agents have typically employ scripted responses to client statements, but such non-specific responses have been shown to reduce effectiveness. A key technique in MI is to ask open-ended questions and then provide a reflection of the response to evoke contemplation in the client. Recent advances in Natural Language Processing provide a new way to create responses that are specific to client's statements, using a Transformer-based Language Model. We present the design, evolution and impact assessment of a chatbot that makes use of generated reflections, whose goal is to guide ambivalent smokers toward the decision to quit. Through four trials of 349 participants, we show that the chatbot significantly increases participants' confidence to quit smoking one week after the conversation compared to before the conversation (\textit{P}=.001). As a key part of the chatbot is the language model that produces reflection, and it is difficult and sometimes impractical to run a clinical bot with a corporation's proprietary model, we explore methods of model distillation to train smaller, more practical language models to generate MI-adherent reflections. We present a method for distilling the specific tasks of generating MI reflections from a Large Foundational Language Model (GPT-4) into smaller models. We show that GPT-4 can generate MI-adherent reflections near 100\% success and use output generated from that model to fine-tune the much smaller GPT-2 family as form of knowledge distillation. We also use GPT-4 as a zero-shot evaluator to classify the quality distilled student model outputs and validate that classifier with a triple human-review. We show that the GPT-2 small achieves an 83\% success rate on a hold-out test set and the GPT-2 XL achieves 90\% success. In addition, our GPT-4 zero-shot prompt evaluator achieves significantly high inter-rater reliability (.61 Cohen-Kappa) with triple-human review.

Keywords: Artificial Intelligence
Chatbot
Clinical Application
Natural Language Processing
Smoking Cessation
Software
Advisor: Rose, Jonathan S
Elucidating the Role of Leucine-Rich Repeat Kinase 2 in Crohn’s Disease Pathogenesis and Neutrophil Biology

Crohn’s disease (CD) is a debilitating chronic inflammatory bowel disease that can significantly reduce a person’s quality of life. Kinase domain gain-of-function variants of leucine-rich repeat kinase 2 (LRRK2), such as G2019S, are associated with an increased risk of CD and have been shown to enhance both myeloid cell trafficking and protection against intracellular infections, yet their role in disease pathogenesis remains elusive. Thus, we tested the hypothesis that LRRK2 kinase hyperactivity boosts enteric pathogen clearance in a selectively advantageous manner but simultaneously underlies the exacerbated inflammatory pathology characteristic of CD. We demonstrated that, in the context of C. rodentium-induced colitis, Lrrk2G2019S mice exhibited neither enhanced protection nor hyperinflammation relative to wild-type controls, and that neither the deficiency of Lrrk2 nor its G2019S mutation altered neutrophils’ capacity to degranulate in vitro or migrate in vivo. Future studies will aim to further investigate the role of LRRK2 in neutrophils using other models of inflammation.

Keywords: Citrobacter rodentium
Crohn's disease
LRRK2
mucosal immunology
neutrophils
Advisor: Girardin, Stephen E
Philpott, Dana J
Towards Learning the Mesh-based Droplet Spreading Simulation by Graph Neural Networks

Many industrial applications involve droplet impact and spreading phenomena, such as spray coat- ing, anti-icing surface design, and inkjet printing. However, modeling the interfacial flow is often prohibitively expensive, because the target droplet size is in the micron to millimeter range and often a hundred computational cells per droplet diameter are required to capture the details so that the traditional numerical models have to solve for the solution iteratively over many computational cells. The thesis uses a Graph Neural Networks (GNNs) model to make the predictions as good as the simulation results. In particular, 2D isothermal droplet impact simulations with different impact conditions were generated and used as the ground truth data for both training and testing the model. The trained model was able to predict the future droplet dynamics from the impact up to the droplet maximum spreading characterized by βmax. The thesis included a comprehensive study of the model’s performance in terms of noise injection with the generalization capabilities, and showed a quantitative analysis of β from the model’s predictions and benchmarking.

Advisor: Dolatabadi, Ali
Enabling Large-Scale Privacy-Preserving Recurrent Neural Networks with Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) can be used to secure a variety of neural networks. We show that FHE can be applied to recurrent neural networks (RNNs) and used for image classification and speaker identification. Using the CGGI [25] mathematical foundation of FHE encryption, we develop several methods to aid in converting plaintext RNNs into CGGI-compatible RNNs. A requirement to utilize CGGI is to quantize RNNs into integers, which is a difficult task. One of the contributions in this thesis is a novel quantization procedure for RNNs that successfully quantizes large-scale RNNs greater than 1M parameters into ternarized parameters and binarized activations. In order to mitigate numeric overflow, this thesis proposes a novel regularization method called the Overflow-Aware Activity Regularizer (OAR) that teaches RNN networks to output correct values, regardless of the existence of overflow. Another contribution is the addition of an attention layer to encrypted RNNs. To the best of our knowledge, this is the first work to successfully incorporate attention by proposing a novel ciphertext-ciphertext multiplication method that simultaneously reduces the amount of computation required by half. We develop two ground-breaking, large-scale, privacy-preserving RNNs with multiple RNN layers. The first, with around 2M parameters, achieves 2s latency over the MNIST dataset using two Nvidia A100 GPUs, which is three orders of magnitude better than the state-of-the-art, SHE [78]. By utilizing OAR, nearly all activations between plaintext and encrypted runs are the same. The second, with around 13M parameters and an attention layer, correctly classifies encrypted speech sequences up to 426 timesteps in length, one order of magnitude longer than SHE.

Advisor: Gulak, Glenn
Remote Ischemic Conditioning as an Adjunct Intervention in Traumatic Brain Injury

Traumatic Brain Injury (TBI) can result in secondary brain injury from ischemia, which can cause tissue and organ damage. Remote ischemic conditioning (RIC) is a non-invasive intervention that has shown potential in reducing ischemia/reperfusion injury. In this study, we investigated whether RIC could be beneficial in TBI patients by modulating levels of brain injury biomarkers (ClinicalTrials.gov Identifier NCT03176823). We also tested whether plasma from mice that underwent RIC could be neuroprotective in a zebrafish larvae neurotoxicity model. While the zebrafish larvae assays did not yield positive results, preliminary findings from a randomized double-blinded controlled clinical trial in TBI ICU patients indicate that RIC may limit post-injury elevations in S100B, irisin, and lactate levels. These results suggest that RIC could potentially serve as a novel adjunctive therapy for moderate to severe TBI, and completed results from the ongoing trial will provide further insight into its mechanisms and outcomes.

Advisor: Rotstein, Ori
Investigating Climate Characteristics And Their Influence On Heat Flow Through Temperate Region Lake Ice

This study uses in situ data to determine the heat flow through temperate region lake ice from 2017 to 2022. The study sites, MacDonald Lake and Clear Lake are located in Central Ontario and represent typical lakes in the region. Climate and ice observations were recorded weekly, while ice decay/growth was determined using a Shallow Water Ice Profiler and manual measurements. Results found that there was an increase in slushing events from 2018 to 2022. When slush was present on ice, the temperature gradient through the ice was nonexistent – resulting in an isothermal ice column and no heat flow. Ice thickness and temperatures at the ice-atmosphere interface were the main drivers of heat loss, most notably a maximum heat loss of 0.5 Wm-2, showing less heat loss than through Arctic ice. Finally, there was local-scale variation throughout climate and ice observations, driven by differing site conditions and snow redistribution.

Keywords: black ice
heat flow
heat flow through ice
lake ice
temperate region
white ice
Advisor: Brown, Laura C
Lithology Prediction from Well Log Data Using Machine Learning Techniques in the Athabasca Oil Sands Basin

Borehole geophysical log data of basins are essential in mining and hydrocarbon exploration as a relatively low-cost and accurate method for subsurface lithological examination. For example, they can provide estimates on the extent and quality of deep mineral resources, including the coal deposits of a basin. The lithology is interpreted from well log data based on various petrophysical properties such as density, acoustic, conductivity, gamma-ray, and neutron density. In this study, we train machine learning estimator models for lithology identification using well log and core data. This new method is expected to provide better, faster, and more reliable predictions than traditional analyses. The study uses the geophysical well log measurements of open-source data of the Athabasca oil sands basin with coal deposits as our primary target for identification. Our initial estimations are based on 22 well logs using density, neutron, gamma-ray, caliper and resistivity as features and their corresponding core data as class labels in the machine learning algorithms. The models developed using the Random Forest machine learning algorithm successfully predicted lithology labels with an average accuracy of 80% on unseen test data (well log records).

Advisor: Pysklywec, Russell
Moving Towards Dance and Re-Entering the Post-Pandemic Classroom

In a pandemic and post-pandemic setting, little discussion centres the re-entering of spaces. Having moved from the private-public sphere of online, socially-distanced learning to the public sphere of being back in classrooms, students have had to make major adjustments on their own, without being supported in conversation around the body and space. The Ontario curriculum places dance in the arts curriculum, apart from health and physical education, diminishing the social and health benefits of moving together. Through a contemporary lens, we can reimagine what a movement curriculum can look like for Grades 1-8, to include movement, dance and embodied practices, acknowledging the many benefits of working from a body-first approach.

Keywords: Arts Curriculum
Dance
Embodied practice
Movement
Ontario Curriculum
Pandemic
Advisor: Eacho, Douglas