VictorJuiz/thesis huggingface dataset:
Engaging Faculty Members in Innovative Student Programs: Influencing Factors

Faculty member participation in student programming has been shown to enhance student engagement and learning. However, it can be a challenge to engage faculty members in these types of initiatives due to the competing priorities inherent in a faculty position. Through a close examination of an initiative developed at the University of Toronto, this thesis investigated factors that influence faculty member participation in innovative student-focused programming designed to enhance the engagement and learning of undergraduate students. Interviews were conducted with one staff member who supported the coordination of the initiative and four tenure-track faculty members who participated in the initiative. Seven themes were identified that provide insights into how initiatives could be designed to motivate faculty member engagement. While not generalizable, this study highlights the need to consider communication strategies, opportunities for early engagement, being consultative, incorporating plans for sustainability, providing central resources and opportunities for recognition, evaluation and renewal.

Advisor: Jones, Glen
Pipelined Training with Stale Weights of Deep Convolutional Neural Networks

Existing approaches that partition a convolutional neural network (CNN) onto multiple accelerators and pipeline the training computations avoid or limit the use of stale weights, and they either underutilize accelerators or increase memory footprint. We explore the impact of stale weights on the statistical efficiency and performance in a pipelined backpropagation scheme that maximizes accelerator utilization and keeps memory overhead modest. We use LeNet-5, AlexNet, VGG and ResNet, and show that when pipelining is limited to early layers in a network, training with stale weights converges and results in models with comparable inference accuracies to those resulting from non-pipelined training. However, when pipelining is deeper in the network, inference accuracies drop signicantly. We propose a hybrid training scheme to address this drop. We demonstrate the performance of our pipelined backpropagation on 2 GPUs achieving speedups of up to 1.8X over a 1-GPU baseline, with a small inference accuracy drop.

Keywords: Deep Learning
Pipelined Training
Advisor: Abdelrahman, Tarek
Towards Integrated 3D Printing as an Industrial Manufacturing Process

3D printing is transitioning from a rapid prototyping tool to an industrial production technology. State-of-the-art 3D printing technologies manufacture objects with a wide range of materials. Currently, there is no integrated technology to print dissimilar materials within the same part. Bonding dissimilar parts creates interfaces with debilitating stress concentrations. Overall, part performance is compromised. Therefore, there is value in 3D printing multi-material and functionally graded objects. In this thesis, we propose a novel binder jetting approach using nanoparticle inks to build composites. The approach deposits nanoparticle inks containing physical/chemical bonding agents to bind polymer powders into an object; by switching between different nanoparticle inks (metal/graphene/conductive polymers), a functionally graded composite with non-distinct interfaces is built. To this end, a novel system for robustly ejecting nanoparticle ink is developed, and a graphene oxide (GO) ink was formulated and printed onto polyvinyl alcohol (PVOH) powder to build a 0.5vol% GO/PVOH composite.

Keywords: 3D Printing
Flexible Electronics
Functional Material
Functionally Graded Material
Nanomaterial
Polymer Composite
Advisor: Naguib, Hani E
Athletes' Perceptions of Pre-game Rituals in Open-skill Sports

Performance rituals have long been a topic of study in sport psychology. However, previous research has focused on closed-skill tasks and employed methodologies that make it difficult to capture athletes’ perspectives of these behaviours. The purpose of this study was to explore the perceptions of open-skill sport athletes regarding their performance rituals. Nineteen high-performance athletes participated in semi-structured interviews. Interview transcripts were thematically analyzed. Participants described their perceptions of the structures and functions of their performance rituals. These findings contribute to a more nuanced understanding of how athletes perceive their performance rituals. Exploring this phenomenon through a critical-realist lens allowed for the relevant components of pre-existing theoretical representations of rituals to be compared to the lived experiences of the athletes who engage in them.

Keywords: Performance Ritual
Pre-Game Ritual
Sport Ritual
Advisor: Tamminen, Katherine A
An Examination and Analysis of the Ownership, Funding and Quality Assessment Structures of the Public and Private Laboratory Sector in Ontario

Purpose: The thesis questions aim to address how the Ontario laboratory sector is organized, with a focus on aspects of funding, ownership structure, access to care, health human resources and quality assurance. Methodology: A case study design used 15 semi-structured interviews and a document review. Results: Lab funding models did not incentivize unnecessary testing in hospital, for-profit or Public Health Ontario labs. Quality assessment of lab testing was generally well measured for the analytical phase. The mechanisms that are available to ensure that private for-profit labs adhere to societal goals include regulation of professionals, maintaining a rigorous quality assurance program, and updating the Schedule of Benefits-Laboratory Sector regularly. Conclusion: Legislation and funding models are changing for labs to reflect modernization due to technology and higher quality standards. All categories of labs need to work with government and regulatory bodies to ensure decisions prioritize the patient and the health care system.

Keywords: health policy
healthcare
laboratory
Ontario
private
public
Advisor: Deber, Raisa
Weakly-supervised Semantic Segmentation in the Multi-class Setting across Different Image Domains

Weakly-supervised semantic segmentation from image-level annotations has been proposed for segmenting natural scene images and this performs well compared to full supervision with pixel-level annotations, which are harder and slower to obtain. However, this is a challenging problem because image labels lack location-specific information to learn object extents. Weakly-supervised semantic segmentation is unexplored in other image domains, such as histopathology and satellite imagery and it is unknown whether different methods are needed for these images. This thesis proposes a novel weakly-supervised semantic segmentation algorithm for histopathology images and explores its performance compared to state-of-the-art methods developed for natural scene images. The compatibility of each method for different image domains is analyzed and this thesis analyzes the effect of classification network choice, the self-supervised learning approach, and class co-occurrence on segmentation performance.

Keywords: Computational Pathology
Self-Supervised Learning
Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Advisor: Plataniotis, Konstantinos N
Aarabi, Parham
Memory-based Graph Networks

Graph Neural Networks (GNNs) are a class of deep models that operates on data with arbitrary topology and order-invariant structure represented as graphs. We introduce an efficient memory layer for GNNs that can learn to jointly perform graph representation learning and graph pooling. We also introduce two new networks based on our memory layer: Memory-Based Graph Neural Network (MemGNN) and Graph Memory Network (GMN) that can learn hierarchical graph representations by coarsening the graph throughout the layers of memory. The experimental results demonstrate that the proposed models achieve state-of-the-art results in six out of seven graph classification and regression benchmarks. We also show that the learned representations could correspond to chemical features in the molecule data.

Keywords: Graph Neural Networks
Hierarchial Graph Representation Learning
Memory Networks
Advisor: Frey, Brendan
“Don’t Rock the Boat”: A Qualitative Narrative Study Exploring Rheumatoid Arthritis Patients’ Experience of Illness and Biologics

To date, we know little about the relationship between the experiences of rheumatoid arthritis (RA) and experiences of Biologic treatment, and what these medications mean to people. This study sought to examine this relationship. Through narrative analysis, using the boat as a metaphor to illustrate the storylines within which 8 participants accounts of illness and medication experiences were emplotted and embedded, two story typologies were defined. ‘Biologics as Life Preservers’ represented Biologics as something that can be donned when needed. ‘Biologics as Life Boats’ represented Biologics as rescuing participants from an identity-damaging illness. These stories illuminate what it is like to be on boat that has metaphorically capsized, to be saved from the rough waters of disease, to have that ‘rescue’ jeopardized by others, to be at the mercy of those who control access to care. The study findings hold implications for adherence research, clinician-patient interactions, and drug reimbursement policies.

Keywords: adherence
illness narratives
medication narratives
narrative inquiry
qualitative
Rheumatoid Arthritis (RA)
Advisor: Thompson, Alison
Design and Implementation of a Micro-world Simulation Platform for Condition-based Maintenance using Machine Learning Algorithms

This thesis explores the development of the simulation platform ARDAS (Automated Reliability Decision Aid System) following the micro-world framework. ARDAS simulates a decision support system for the maintenance management of a hydraulic process applying the Condition-Based Maintenance method. This platform intends to support decision-making to solve maintenance problems that might affect the performance of the hydraulic process. ARDAS uses an open-source data set collected from a hydraulic process to train a Machine Learning algorithm that estimates the health condition of four components within the hydraulic process. This platform also includes the implementation of a web user interface that presents information about the inner workings of the automated system, process diagram, trend of variables, and contextual information. This platform can be used to aid those who wish to study the effects of an ML-based decision aid system on human performance.

Keywords: Condition-Based Maintenance
Decision Support System
Machine Learning
Micro-world platform
Transparency
Web Human-Machine Interface
Advisor: Jamieson, Greg A.
Developing Recommendations for Opioid Prescribing at Discharge after Abdominal-pelvic Surgery

The current crisis of opioid-related harms has shed light on the suboptimal nature of existing practices for the prescription of opioids at discharge after abdominal-pelvic surgery. In this thesis, I explore current guidance for surgeons on this issue as well as strategies to modify their behavior. First, I conducted a systematic review of existing recommendations on the prescription of opioids at discharge, the disposal of excess opioids as well as the prevention of chronic pain development after abdominal-pelvic surgery. Second, a systematic review of behavioral interventions to reduce post-surgical opioid prescribing was performed. Lastly, I have initiated the development of recommendations using an agreement-building process. Overall, this thesis summarizes the literature on the existing guidance available to abdominalpelvic surgeons regarding the prescription of opioids as well as potential strategies that can be implemented to improve these practices.

Advisor: Baxter, Nancy N
Investigating the Intrinsically Disordered Regions of USF1 and Other bHLHZ Transcription Factors

Part I of this thesis describes structural studies of the disordered loop of the basic helix-loop-helix/zipper (bHLHZ) transcription factor USF1. The objective was to understand how USF1 recognizes a single nucleotide polymorphism in a 4G tract found in the regulatory region of the plasminogen activator inhibitor 1 (PAI-1) gene, by mutating selected residues in its disordered loop. T234 is the key residue implicated in differentiating the PAI-1 polymorphism, and the USF1 bHLHZ preferentially binds the 5G variant of the polymorphism and not the 4G variant. Part II explores the design of minimalist transcription factors where domains from various transcription factors with desired properties were combined to generate novel proteins. By using crystal structures of the parent proteins, we appended domains of interest onto the MEF bHLHZ to create MEF variants with altered DNA binding properties. Additionally, phage-assisted continuous evolution (PACE), is being utilized to further improve these MEF variants.

Keywords: bHLHZ
Intrinsically disordered regions
Advisor: Shin, Jumi A
Targeting the Hypoxic Fraction of Head and Neck Squamous Cell Carcinoma through the Inhibition of Lactate Transporters MCT1 and MCT4 in Synergy with Metformin

Head and neck cancer is a common form of cancer, and a major factor affecting treatment outcome is tumour hypoxia. A defining characteristic of hypoxia are the changes in cellular metabolism such as producing large amounts of lactate. Monocarboxylate transporters (MCTs) play a crucial role in regulating lactate metabolism, and thus constitute a target against hypoxic cells. In this study, we explored the possibility of inhibiting MCT1 and MCT4 in conjunction with the antidiabetic drug metformin to selectively kill hypoxic cells. We generated a double MCT1/4 knockout cell line and have shown this strategy is effective in halting HNSCC cell proliferation under hypoxia, in addition to further sensitizing these cells to metformin. Moreover, we have shown that full MCT1/4 inhibition slows tumour growth and extends survival in animal models. Our results indicate MCT inhibition constitutes an effective way of targeting the hypoxic niche and generates a potential therapeutic opportunity.

Keywords: Head Neck Cancer
Hypoxia
Lactate
Monocarboxylate Transporters
Tumour Metabolism
Advisor: Wouters, Bradly G
Epigenetic Mechanisms Governing the Differentiation of Glioblastoma Stem Cells

Glioblastoma (GBM) is the most aggressive primary brain tumour of adults. Targeting the self-renewal property of glioblastoma stem cells (GSCs) is critical for tumour eradication. Our lab found that patient-derived GSCs with high expression of ASCL1 (ASCL1hi) were competent to undergo neuronal differentiation in response to differentiation cues and this was associated with reduced tumorigenicity. Overexpressing ASCL1 in the initially unresponsive subset, ASCL1lo GSCs, was able to enforce a neuronal cell fate. The epigenetic mechanisms underlying differentiation of GSCs have not been fully elucidated. In this thesis, I uncover distinct accessible chromatin landscapes, where ASCL1hi GSCs are primed for neuronal differentiation, while ASCL1lo GSCs are enriched for immune pathways. I further provide functional evidence for a potential upstream transcriptional regulator of ASCL1. Finally, I reveal changes in histone post-translational modifications that may contribute to ASCL1-mediated differentiation of GSCs. These results provide early insights into the epigenetic requirements of differentiation therapy for GBM.

Keywords: ASCL1
Cancer
Differentiation
Epigenetics
Glioblastoma
Histones
Advisor: Dirks, Peter
Investigation of Intra-tumour Heterogeneity and Host Immune Response in Triple-negative Breast Cancer

Approximately 15% of breast cancer patients have an aggressive subtype termed triple-negative breast cancer (TNBC). Mortality is due to difficulties in treating and controlling metastatic growth. Earlier predictors are urgently needed for accurate classification of patients into specific risk groups. Here, I investigated the relationship between intra-tumour heterogeneity through a gene signature from circulating tumour cells and the degree of lymphocytic infiltration in TNBC tumours to develop a potential biomarker of aggressive tumour progression. I have found that high intra-tumour heterogeneity was correlated with a high infiltration of B and helper T lymphocytes in primary TNBCs. In addition, highly heterogeneous tumours that have high infiltration of B lymphocytes were associated with good prognostic outcomes. These findings may have significant implications in understanding tumour progression in the breast, while potentially improving therapeutic decision-making in the clinic.

Keywords: breast
cancer
heterogeneity
immune
triple-negative
tumour
Advisor: Done, Susan J
Unsupervised Deep Learning for Anomaly Detection and Explanation in Sequential Data

With recent successes of recurrent neural networks (RNNs) for machine translation, and handwriting recognition tasks, we hypothesize that RNN approaches might be best suited for unsupervised anomaly detection in time series. In this thesis, we first contribute a comprehensive comparative evaluation of RNN-based deep learning methods for anomaly detection across a wide array of popular deep neural network architectures. In our second major contribution we observe that a key gap of deep learning based anomaly detection methods is the inability to identify portions of the data that led to the detected anomaly. To address this, we propose a novel explainability approach that aims to pinpoint regions of an input that lead to the detected anomaly. In sum, this thesis not only advances the state-of-the-art in deep learning based anomaly detection for time series data but it also contributes novel methods for producing explanations and evaluating explanation quality of anomaly detectors.

Keywords: Anomaly Detection
Deep Learning
Explanation
Machine Learning
Neural Networks
Recurrent Neural Networks
Advisor: Sanner, Scott P
Seasonal Variations in the Limnology and Ecosystem Metabolism of a Canadian hHgh-Arctic Permafrost Thaw Lake

Aquatic ecosystem metabolism describes the turnover of carbon biomass and energy available within an ecosystem to support aquatic food webs. This research provides insight on ecosystem responses to seasonality in a high-Arctic thaw lake. Skeleton Lake (Nunavut) was sampled over multiple ice-covered and open water seasons to quantify how the presence and absence of ice effects physical, chemical, and biological factors that drive whole-lake ecosystem metabolism. This provides valuable data on the energetic processes supporting all aquatic life, allowing us to understand how climate change may impact the security of freshwater resources. This research captures a meaningful understanding of year-round carbon metabolism in small Arctic lakes and displays the importance environmental factors have on metabolic processes. Open water provides ample opportunity for primary production in surface waters; while deep below ice, this lake teems with biological activity both in complete darkness and at the first signs of spring light.

Keywords: Aquatic ecosystem metabolism
Carbon cycling
High-Arctic
Limnology
Seasonality
Advisor: Lehnherr, Igor
The Reuse of a Financial Ontology Driven by Competency Questions

With the increasing demand for information interoperability, share-ability and reusability in the financial industry, we are looking at ontology applications in this domain. The central goal of this work is to improve the process of ontology reusing. In the first place, existing financial ontologies are studied to see how they are applied in real-world scenarios. Secondly, to support multiple definitions of one term in ontology engineering, existing ontology development methodologies are reviewed and combined with reusing operations proposed by Prof. Grüninger and Dr. Katsumi. We also look at the challenges of ontology partial reusing. To overcome those challenges and enhance the process of partial reusing, existing modularization methods are studied, and new modularization approaches are proposed. Last but not the least, a case study that is the result of collaboration with a Canadian multinational financial services company is provided to demonstrate how proposed methodologies are applied in real-world cases.

Keywords: Applied Ontology
Knowledge Modelling
Ontology
Ontology Reuse
Advisor: Gruninger, Michael
Impact of Bariatric Surgery on Circulating Inflammatory and Pro-vascular Progenitor Cell Populations

Obesity is a growing healthcare concern with more than 2 billion adults worldwide affected by unhealthy weight gain. Since bariatric surgery can induce significant cardiometabolic improvements and reduce adverse cardiovascular events, we hypothesized that these benefits may occur through decreased circulating inflammatory cell burden and increased pro-vascular progenitor cell content. High aldehyde dehydrogenase- (ALDH) activity and cell surface markers were assessed by flow cytometry for circulating progenitor cell content, M1/M2 macrophage balance, and pro-inflammatory cell burden between normal weight controls and individuals before and after bariatric surgery. At 3 months, ALDHhiSSChi granulocytes were reduced 2-fold and ALDHhiSSCmid monocyte/macrophage precursors were increased 2-fold, to frequencies equivalent to normal weight controls. Pro-angiogenic ALDHhiSSClow cells with primitive CD34+/CD133+ co-expression were enriched following surgery. Collectively, these data suggest that bariatric surgery induced beneficial changes in circulating progenitor cell content, consistent with improved vascular regenerative capacity and lower cardiovascular risk.

Keywords: metabolic
obesity
progenitor cell
stem cell
surgery
vascular
Advisor: Verma, Subodh
Neuronal Differentiation of Cartilage-derived Stem Cells In Vitro and their Delivery to the Spinal Cord in a Mouse Model of ALS Using MRI-guided Focused Ultrasound

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disorder for which stem cells are attractive therapeutic interventions. However, the complications of immunosuppression for allografts and invasive surgeries for blood-spinal cord barrier (BSCB) penetration have hampered their success. First, we investigated the in vitro neuronal differentiation potential of immunosuppressive, cartilage-derived stem cells (CDCSs). Next, we examined the safety and survival of intravenously injected, GFP-expressing, neuronally-differentiated CDSCs in an ALS mouse model (SOD-G93A), following MRI-guided focused ultrasound (MRIgFUS)-mediated opening of the BSCB. CDSCs’ neuronal differentiation was successful. GFP+ cells were not detected within the spinal cords of treated mice; no behavioural improvements were noted. Despite lack of efficacy, disease onset and survival in treated SOD1-G93A mice remained similar to the control SOD1-G93A mice, indicating that treatment did not hamper motor performance. This is the first study to demonstrate neuronal differentiation of CDSCs and feasibility of MRIgFUS-mediated BSCB opening in SOD1-G93A mice.

Keywords: Amyotrophic Lateral Sclerosis
Cartilage-Derived Stem Cells
MRI-guided Focused Ultrasound
Spinal Cord
Advisor: Erwin, W. Mark