VictorJuiz/thesis huggingface dataset:
Development Towards a CRISPR-Cas3 Genome Editing System for Streptomyces

Streptomyces sp. have unusual linear chromosomes that are large compared to those of most other bacteria and archaea. However genetic research of this bacterial genus is hampered by the lack of effective gene editing tools that are capable of producing large, undefined, targeted deletions. The Bondy-Denomy lab has shown that CRISPR-Cas3 in Pseudomonas aeruginosa is capable of creating large, undefined, targeted deletions. The aim of this thesis was to generate a map of putative essential and dispensable regions in a Streptomycete chromosome and work towards adapting CRISPR-Cas3 from P. aeruginosa for use in Streptomyces venezuelae. The plan was to delete the whiE gene, which generates a visibly apparent spore pigment as a proof of concept. Despite successful expression of CRISPR-Cas3 in S. venezuelae, whiE was not successfully deleted. However, data in this thesis highlights the similarities in Streptomyces species which should support the possibility of a CRISRP-Cas3 system that is compatible with all Streptomyces. Recent research has shown that S. avermitilis has a functional CRISPR-Cas3 system and can be adapted for gene editing in Streptomyces.

Keywords: Bioinformatics
CRIPSR-Cas3
Streptomyces
Advisor: Nodwell, Justin R
Retrospective Analysis of Dental Implant Fracture following Loading

Statement of problem. Fracture of an osseointegrated implant (FOI) is a serious complication that occurs primarily post-loading. Management of this complication is challenging. Risk factors are not well-understood.Purpose. To determine the frequency of occurrence and possible contributing factors of FOI. Material and Methods. A retrospective analysis was performed on patients who received dental implant treatment from January 1979 until January 2020, and experienced post-loading FOI. Data were analyzed to determine the incidence of FOI. A descriptive analysis was utilized to identify the factors associated with FOI. Results. A total of 27 fractured implants were identified among 7712 implants placed during the 41-year period. The incidence of FOI was 0.35%. Factors associated with implant fracture include narrow-diameter implants (≤3.75mm), implants placed in posterior regions, presence of cantilever, and unfavorable implant design. Conclusions. Incidence rate of FOI is very low and might be increased by the presence of predisposing risk factors.

Advisor: Chvartszaid, David
Azarpazhooh, Amir
A Fixed-point Algorithm for the AC Power Flow Problem

This thesis presents an algorithm that solves the power flow problem for balanced, three phase transmission systems at steady state. This algorithm is an extension of the “fixed-point power flow” algorithm in the literature, which is originally derived for lossless AC transmission systems. We first manipulate the standard power flow equations into an equivalent fixed-point form, from which we derive the extended fixed-point power flow algorithm. We then theoretically study the proposed algorithm on a two-bus system, where we model selected network parameters, e.g., resistive loss, as perturbations to a nominal, lossless system, and derive sufficient solvability conditions. Finally, we validate the algorithm through extensive simulations on test systems of various sizes under different loading scenarios, and compare its convergence behaviors against those of classic power flow algorithms.

Keywords: Fixed-point algorithm
Power flow problem
Advisor: Simpson-Porco, John W
The Role of B Cells and Immunoglobulin A in Blood Pressure Regulation

Blood pressure regulation has conventionally been attributed to the cardiovascular, neuroendocrine, and renal systems, with water and sodium handling by the kidneys being essential for systemic fluid balance and long-term blood pressure homeostasis. Recently, the immune system has emerged as an important contributor to blood pressure control and hypertension pathophysiology. This Dissertation examines B cells and immunoglobulin A (IgA) in murine blood pressure regulation through alterations in renal water and sodium handling: causal roles of B cells in blood pressure control were studied using an inducible model of B cell depletion, and contributions of IgA to blood pressure and hypertension were examined in an IgA-deficient model. Chronic B cell ablation had a blood pressure-lowering effect in male mice, while IgA-deficient mice exhibited reduced blood pressures and blunted responses to experimental hypertension. This Dissertation highlights contributions of B cells and IgA to blood pressure regulation through mechanisms of renal fluid balance.

Keywords: B cells
Blood pressure
Cardiovascular physiology
Hypertension
Immunoglobulin A
Immunology
Advisor: Husain, Mansoor MH
Defining the miRNAome in Stem Cell derived Human Astrocytes and their Secreted Extracellular Vesicles

microRNAs (miRNAs) participate in the regulation of developmental processes like astrogliogenesis. Astrocytes, the main glial cell type in the brain, act as communication hubs and interact with neurons during the establishment and maturation of functional networks, which are disrupted in neurodevelopmental disorder Rett Syndrome. Astrocytes communicate with neurons by secreting extracellular vesicles (EVs) that contain miRNAs able to regulate transcription in the target cell. Astrocyte released factors are thought to drive non-cell autonomous effects in RTT neurons. I established a fast differentiation protocol to generate functional human astrocytes from NPCs and found similar RTT phenotypical features as described in literature, and preliminary indications of the involvement of astrocyte released factors in RTT neuron phenotype. Using smallRNAseq I investigated the miRNAome of astrocytes and astrocyte EVs. I found EV enriched miRNAs that could be actively sorted into EVs via RBP sequence dependent mechanism, and astrocyte miRNAs reported in peripheral samples, suggestive of biomarker potential.

Advisor: Ellis, James
On Genealogy: Inheriting Fault Lines in Canadian Theatre Criticism

This thesis offers case studies from Canada’s journalistic theatre criticism since the advent of the National Arts Centre in 1967. These case studies, emblemized by 1960s productions of The Ecstasy of Rita Joe and post-Y2K productions of bug and Kamloopa, are complemented and complicated by autoethnographic commentary as an early-career theatre critic since 2019. Drawing from my own training as a critic and selected case studies from Ontario’s existing canon of theatre criticism, I place my training and experiences in dialogue with events of the past and present, calling on Kim Senklip Harvey’s notion of “inheriting the past which came before” as a blueprint for empathetic theatre criticism. Citing essays by critics, artists, and scholars alike, I bolster this dialogue with proposed alternatives to Western reviewing practices, using Indigenous performances of the early 2020s and their resulting controversies as a point of departure for future reform within Canadian theatre criticism.

Keywords: bug
criticism
ecstasy of rita joe
kamloopa
theatre criticism
Advisor: Carter, Jill
KL Divergence Based Change-point Detection and Modulation of Reinforcement Learning Parameters

While reinforcement learning algorithms have demonstrated success and even super-human performance on certain tasks, most of the current algorithms are not ready to deal with non-stationary environments. As environments of real-world applications are hardly stationary, we need robust algorithms that can maintain the quality of policies by quickly detecting shifts and resume the training of once-optimal policy in the new environment. In this paper, we propose a generic detector based on KL divergence to detect shift in dynamics as well as task and demonstrate its robustness using soft Q-learning as an example among many other algorithms. In addition, we also highlight the natural advantage of soft RL over the standard framework under perturbations and explain how the framework can be modified to detect perturbations and how policy parameters can be modified to maximize post-perturbation performance.

Keywords: Reinforcement Learning
Advisor: Lee, Chi-Guhn
Deep Learning Methods for Pediatric Brain Tumour Diagnosis

Brain tumour segmentation in MRI is important for diagnosis, growth rate prediction, tumour volume measurements, and treatment planning. Eliminating the need for manually segmenting tumours can reduce the strain on neuroradiologists and improve patient care. In this work, we provide an end-to-end deep learning-based model for segmenting pediatric low-grade gliomas in MRI. To evaluate the segmentation results, we have created radiomics-based genetic marker classifiers using automatic and manual segmentation masks, demonstrating that the automated segmentation model produces comparable classification results to manually segmented tumour masks. In addition, the reliability of automated segmentation findings was evaluated by measuring the correlation between the resulting volume size of automatically and manually formed tumour masks.

Keywords: Machine Learning
Medical Imaging
Tumour Segmentation
Advisor: Khalvati, Farzad
Evaluating the Effects of Continuity of Rheumatology Care on Patient Outcomes for Individuals with Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory disease that requires ongoing management with rheumatologists to minimize disease activity, disability, and morbidity. This thesis examined whether continuity of rheumatology care influences survival, emergency department (ED) visits, and hospitalizations. Among a population-based inception RA cohort, patients were categorized into three rheumatology care continuity groups (high, intermediate, and not retained in rheumatology care) and patient outcomes (rates) were compared, adjusting for covariates. Incident RA patients receiving early and more frequent rheumatologist visits in the first year of diagnosis, followed by at least an annual rheumatology visit during the following four years had better long-term outcomes (lower rates) for ED visitations, hospitalizations, and mortality compared to those that do not receive early contact with a rheumatologist and had fewer rheumatology visits during follow-up (intermediate group). These findings provide evidence to support the value of early access and continuous rheumatology care for improving long-term outcomes.

Keywords: Continuity
Mortality
Outcomes
Rheumatoid Arthritis
Rheumatology
Rheumatology Care
Advisor: Widdifield, Jessica
Automatic Temporal Segmentation of Orofacial Assessment in Amytrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurological disease that causes degeneration of lower and upper motor neurons in the brain, brainstem and spinal cord. Objective assessments of bulbar ALS include acoustic (audio) and kinematic (video) methods; the outcomes that are extracted as a result of these assessments are essential for improving early disease detection, and monitoring disease progression. There has been a rapid growth in the automatic - acoustic and kinematic - methods of speech assessment in ALS. Temporal segmentation (parsing) of orofacial assessment data is an important step underlying these assessments. Current clinical diagnosis obtain parsing either manually or in a semi-automatic manner which is time-consuming and labour-intensive. This thesis delivers insight into whether modern machine learning techniques can be applied to automatically and accurately parse ALS orofacial assessment data and lays the ground for automated and objective assessment tools for use in clinical and home settings.

Keywords: ALS
Deep Learning
Machine Learning
orofacial assessment
temporal segmentation
Transformers
Advisor: Taati, Babak BT
Perceptions of the West by Chinese International Graduate Students before and after Arriving in Canada

It is evident that China has undergone significant changes over the past few decades because of its reform and opening door policy since the late 1970s. Chinese citizens born in the 1990s grew up in an era of significant historical change. This study examined how has western influence affected the 1990s generation of Chinese students’ understanding of the West before they came to Canada, and how have participants’ understandings about the West been changed by their learning and living experiences in Canada.The findings of this study suggest that participants were unaware of their racialized location when settling in Canada due to the lack of critical discourses of race in their home country. It also reveals that the racism against Chinese students during the Covid-19 pandemic have forced participants to re-evaluate their career trajectories. Another significant finding was that not all Chinese/Asian parents expected their children to be the top student in school as common stereotypes have illustrated; rather, the parents of the selected participants sent and supported their children to study in western countries because they wished their children to have more opportunities and more autonomy in terms of their own career and passions.

Advisor: Reid, Mary MR
HPIPE-NX: Leveraging Tensor Blocks for High-performance CNN Inference Acceleration on FPGAs

This thesis enhances the state-of-the-art CNN inference FPGA accelerator, HPIPE, to take advantage of the tensor blocks on the AI-optimized Stratix 10 NX FPGA. We first further parallelize depthwise convolutions, improving performance by 1.6x on Mobilenet-v2. We also implement basic support for 8-bit quantization. We evaluate how to best leverage the tensor blocks, finding that we can achieve up to 12x performance improvements for regular convolutions and up to 3x for depthwise convolutions while reducing activation buffer memory usage by 5.47x. Finally, we combine these enhancements to create a Stratix 10 NX convolution engine. Our implementation achieves up to an 8.3x cycle-count reduction and 7.1x speedup on the FPGA. We outperform all prior work on FPGAs for Mobilenet-v1, Mobilenet-v2, and Mobilenet-v3 by 4.0x-23x. We outperform the AI-optimized NVIDIA V100 GPU by up to 17x on a batch size of 1 and 1.3x on a batch size of 128.

Keywords: Accelerator
AI
CNN
FPGA
Hardware
Machine Learning
Advisor: Betz, Vaughn
The Role of Dorsomedial Striatum adenosine A2a Receptors in the Loss of Goal-directed Behaviour

Adenosine A2A receptors (A2AR) in the dorsal striatum have been implicated in goal-directed behaviour. We confirm a role for A2A receptors in goal-directed responding and evaluate additional behavioural aspects of goal-directed control. Male rats were trained to lever-press for food reward. The A2AR agonist CGS-21680 was infused into the dorsomedial striatum (DMS) either before an outcome devaluation test, prior to training with two distinct response-outcome associations, or prior to a test of discriminative stimulus control over instrumental performance. To explore a possible source of endogenous adenosine, ATP was infused to the DMS before an outcome devaluation test. CGS-21680 impairs the ability to modulate responding based on recent changes to outcome value, an effect not accounted for by impairments in behavioural inhibition, discrimination, encoding the specific outcome of a response or the effectiveness of specific satiety. 10mM extracellular ATP in the DMS produced no impairment, possibly explained by insufficient adenosine production.

Advisor: Corbit, Laura H
Individual Variability of Functional Brain Activity during Working Memory Performance in Schizophrenia Spectrum Disorders (SSD)

Schizophrenia Spectrum Disorders (SSD) exhibit pronounced variability between and within-subjects. To better understand neurobiological heterogeneity within SSD, variability was examined in functional brain activity, quantified through a novel metric, during a working memory task. SSD had greater individual variability in functional activity compared to controls.At the group level, a case-control comparison suggested SSD had reduced activity in task-positive and task-negative networks. However, when SSD were divided into high and low variability subgroups, low variability patients showed ‘typical’ brain activity and task performance, despite diagnosis. To further improve the ability to understand idiosyncratic brain function within Schizophrenia as a function of disease progression, the influence of age and illness duration on variability was explored. Findings suggested a natural toll of aging on brain function independent of diagnosis; however, the greater individual variability observed specifically within Schizophrenia may be a result of biologically worsening effects associated with longer illness duration.

Keywords: Functional Neuroimaging
Individual Variability
Schizophrenia
Working Memory
Advisor: Hawco, Colin
The Role of Nucleus Accumbens Dopamine D1-D2 Receptor Heteromer in the Phosphorylation of Cyclic-AMP Response Element-binding Protein

Cocaine addiction is characterized by uncontrollable cocaine-craving, compulsive drug-seeking,and use despite devastating consequences to self and others. However, the neurobiological components underlying this disorder have not been fully elucidated. Our laboratory has previously demonstrated existence of dopamine D1-D2 receptor heteromers (D1-D2) in Nucleus Accumbens neurons capable of suppressing reinforcing effects of cocaine. Here we demonstrated that D1-D2 activation in a cell line or striatal neurons, with the agonist SKF83959, produced enhanced cyclic-AMP response element-binding protein (CREB) phosphorylation at the activating Ser133 residue. This effect was attenuated by blockade of intracellular Ca2+release, calcium calmodulin kinase II activation, or D1-D2 heteromer function. These findings demonstrate that D1-D2 activation is sufficient for inducing CREB Ser133 phosphorylation, which could be responsible for D1-D2-mediated reward attenuation through mediators further downstream, such as dynorphin. Overall, this study implicates CREB as an effector of D1-D2 activation, expanding our understanding of D1-D2 functional regulation and highlighting this signaling pathway as a potential target for treating cocaine addiction.

Keywords: addiction
CREB
D1-D2
dopamine
heteromer
nucleus accumbens
Advisor: George, Susan R
BitChop: A Heuristic Approach to Memory Footprint Reduction in AI Training

AI training costs have been greatly increasing in recent years, with bigger neural networks that require increased computational capabilities and memory storage. AI training is greatly limited by memory access time. We introduce BitChop, a heuristic-based lossy compression method that reduces the amount of memory that is being loaded and stored during training. BitChop dynamically adjusts the precision and format of the floating-point containers of the network's activations. BitChop adjusts the bit-lengths of mantissas based on different heuristics that observe changes in the loss function of the network. Across the tested heuristics, BitChop reduces the total mantissa footprint to 25% of baseline when applied over FP32, and to 19% when applied over BFloat16. Moreover, BitChop reduces the total footprint of the networks to an average of 46% over FP32 and to an average of 40% over BFloat16. These reductions in footprint result in 2x performance and 3.5x energy efficiency improvements.

Keywords: Artificial Intelligence
Compression
Deep Learning
Advisor: Moshovos, Andreas A
Benchmarking Reinforcement Learning for Safe Robotics: Constraints, Robustness and Transfer

Safe learning in robotics aims to deploy robots in real life with complex tasks and safety requirements. To push the agenda of safe learning, a crucial step is establishing common benchmarks that facilitate both reliable evaluations and research development. In this work, we contribute towards this goal by surveying the safe learning literature and proposing a versatile safe learning benchmark suite, safe-control-gym. The benchmark implements a variety of safe learning algorithms spanning control to reinforcement learning, it also implements critical features to support safety-relevant evaluations and algorithm development. With safe-control-gym, we conduct careful benchmarking on model-free reinforcement learning methods with respect to three metrics of safety: constraint satisfaction, robustness, and transfer performance. We envision safe-control-gym to provide a framework that brings various research together, and most importantly to accelerate the progress of safe learning in robotics.

Keywords: Benchmark
Machine Learning
Reinforcement Learning
Robotics
Safe Learning
Safety
Advisor: Schoellig, Angela P.
The Relationship between Periarticular Muscle Properties and Knee Pain in Non-overweight Postmenopausal Females

Lower muscle and greater fat infiltration in the lower limbs have been associated with pain and functional limitation in older adults with knee osteoarthritis (OA). It is unknown how muscle content and fat infiltration immediately around the knee (periarticular) associate with these knee OA outcomes. Thigh and calf muscle and fat infiltration quantified from magnetic resonance images were each related to knee pain and functional outcomes in non-overweight postmenopausal females (non-OW PMF). Greater fat infiltration and lower muscle content was generally associated with worse knee pain and function, with more significant associations observed for the calf. Thus, fat infiltration in periarticular muscles may be a risk factor for knee pain and worse function in non-OW PMF. Muscle and fat infiltration in different regions along the periarticular thigh and calf also had stronger associations with knee pain/worse function than in others, although future larger studies are needed to confirm these findings.

Keywords: Fat infiltration
Females
Knee osteoarthritis
Non-overweight
Periarticular muscle
Postmenopausal
Advisor: Wong, Andy Kin On AKW
Glitches on the Gound: Investigating the Discursive Limits and Social Impacts of Uber’s Embedded Finance Experiments in Hyderabad, India

In 2018, Uber launched three embedded finance experiments in India, Brazil, and Peru. Among these three sites, Uber Care in India was the most extensive and well-documented experiment, providing an extensive array of social and financial assistance programs. This research project investigates Uber Care as an effort to merge fintech schemes and corporate welfare for Uber’s workers through three empirical chapters: (1) a critical review of corporate documents that outline similar fintech integration into online gig work platforms; (2) a critical analysis of Uber Care’s promotional materials; and (3) a critical analysis of semi-structured interviews with ten labour organizers in Telangana state. I situate my findings within critical scholarship on two platform economies: gig work and fintech. I highlight the gulf between Uber’s claims about and labour organizer’s experiences with and observations of Uber Care, and I raise troubling questions about the ways in which these embedded finance experiments offer fintech firms opportunities to enclose precarious incomes, particularly in the context of digitally mediated gig work.

Keywords: debt
financialization
fintech
platform
welfare
work
Advisor: Buckley, Michelle