VictorJuiz/thesis huggingface dataset:
Imaging of Non-conducting Beam Sensitive Materials using Scanning Electron Microscopy: Practical Applications of ESEM and LVSEM
Keywords: Low Voltage Imaging
LV SEM
Scanning Electron Microscope

Non-conducting and beam sensitive materials like polymers are utilized extensively in modern industry. With growing interest in leveraging the capabilities of these materials by optimizing their morphological and structural characteristics, scanning electron microscopy (SEM) is becoming an increasingly important tool. Despite the advancement of SEM and advent of high caliber instrumentation, numerous challenges impede the imaging and study of beam sensitive materials using SEM. This thesis focuses on Environmental SEM and Low Voltage SEM and attempts to showcase their characterization capabilities through practical examples of respirators/facemasks, and microplastics. A set of characterization protocols for evaluating the structure, chemistry, moisture retention, and wetting properties of face masks and respirators are presented in Chapter 3. Chapter 4 presents efforts undertaken for capturing and compiling SEM images to form the first open-source SEM datasets for microplastics segmentation through deep learning methods. The promising result facilitates automatic quantification and classification of microplastics.
Developmental Trajectory of the Association between Monoamine Oxidase A and Catecholamine-O-Methyltransferase Genes, and Youth Externalizing Behaviours
Keywords: Aggression
Children and Adolescents
Development
Externalizing Behaviours
Genetics
MAOA and COMT

Youth externalizing behaviours are serious public health concerns, with significant negative outcomes. These disruptive behaviours exhibit high heritability, with putative influences from the monoamine oxidase A (MAOA) and catecholamine-O-methyltransferase (COMT) genes. Literature points to a variable effect of genes on externalizing behaviours through development. This study aimed to examine the influence of development on the association between the genetic variants regulating the MAOA and COMT genes and externalizing behaviours in youth. Results from the cross-sectional (n=278-337) sample demonstrated that both MAOA-uVNTR and COMT Val158Met exhibit a significant change in the risk variant between childhood and adolescence for males, but not in females. Conversely, there was no significant interaction effect between Val158Met and assessment time on externalizing behaviours in the larger longitudinal ABCD (n=2363) sample. While replications with longer longitudinal studies are necessary, these results underscore the importance of considering the developmental stages when analyzing genetic risk of externalizing behaviours.
Development and Implementation of a Simple Carbon Formation Mechanism for Methane Pyrolysis in Computational Fluid Dynamics
Keywords: Carbon
Fluid
Modeling

Methane pyrolysis is a technology that can produce hydrogen with low GHG emissions. In a methane pyrolysis jet, chemical reactions occur simultaneously with mixing, and a large amount of solid carbon is produced. Our current modeling of methane pyrolysis uses complex carbon formation mechanisms, ignoring fluid dynamics. Current CFD models typically use a post-processing technique for carbon formation which is only valid for low sooting flames. There is a need for a model which can accurately model fluid dynamic mixing, gas-phase chemical reactions, and carbon formation. This work develops and implements a simple carbon formation mechanism into a CFD model of a methane pyrolysis jet. The mechanism is based on the Moss-Brooke’s reaction rates using acetylene concentrations as the precursor for nucleation and surface growth. The CFD implementation provides spatial information about the formation of carbon and other gas-phase species during methane pyrolysis, which has been overlooked in previous work.
Identification of Novel Gut Microbiome Constitutive Androstane Receptor Ligands using an Untargeted Affinity Purification-Mass Spectrometry Approach
Keywords: CAR
Constitutive Androstane Receptor (CAR)
Diindolylmethane (DIM)
Drug Discovery
Microbiome
Nuclear Receptor

The Constitutive Androstane Receptor (CAR) is a member of the Nuclear Receptor (NR) family of transcription factors and a key regulator of xenobiotic metabolism. Identification of high affinity CAR ligands has remained elusive. This has been exacerbated by limitations in traditional screening methods due to CAR’s constitutive activity and its potential for indirect activation in cells. This thesis describes the identification of a group of structurally related CAR ligands present in the human gut microbiome using an affinity purification-mass spectrometry-based direct-interaction screen. These ligands share a di-indoyl core and are derivatives of 3,3-diindolylmethane. Characterization of the ligands using cellular and biophysical assays revealed that they bind and stabilize the CAR ligand binding domain. They are potent agonists, and one of the compounds, 3,3-diindolylethane, has a sub micromolar effective concentration and dissociation constant, making it a promising candidate for CAR drug development.
Exploration of the Structural and Mechanical Properties of Novel Disulfide and Diselenide-containing Peptide Gels
Keywords: diselenide
disulfide
gel
peptide
redox

Peptide gels have gained large attraction for their biocompatibility, biodegradability, and non-immunogenicity. Within living organisms, an important component that plays a role in signalling pathways and chemical reactions are diselenides and disulfides. Herein, two novel peptide conjugates – Boc-Phe-Phe-Cys-Cys-Phe-Phe-Boc and Boc-Phe-Phe-Se-Se-Phe-Phe-Boc – and their gelation has been investigated. These conjugates have been fully characterized using mass spectrometry, one dimensional 1H nuclear magnetic resonance spectroscopy, and Fourier transform infrared spectroscopy. Successful gelation of the conjugates has been reported as well. Strong intermolecular hydrogen bonds link nearby amide and carboxyl groups, pi-pi stacking interactions for aromatic rings, and hydrogen bonding between water molecules assist in the self-assembly of the gelator molecules to form solid organogel upon dissolution in toluene or acetonitrile, or hydrogels upon dissolution in MOPS or monosodium phosphate buffer. These gels exhibit redox active properties due to the diselenide and disulfide moieties. Preliminary studies have been conducted to characterize these conjugates to support further redox and drug-loading experiments.
Investigation of the Balance of Pro-inflammatory Immune Cells to T Regulatory Cells and the Role of Antigen-specific T cells in the Development of Systemic Autoimmune Rheumatic Diseases
Keywords: Antinuclear Antibodies
Autoimmunity
B cells
Preclinical disease
Regulatory T cells
T cells

Systemic Autoimmune Rheumatic Diseases (SARDs) are characterized by the production of anti-nuclear antibodies (ANAs). ANAs are also seen in healthy individuals and can be detected years before disease onset in SARD. However, the immunological changes that facilitate the development of SARD in non-symptomatic ANA+ (ANA+ NS) individuals remain unclear. Here we demonstrate that in ANA+ NS individuals, there are significant increases in regulatory immune subsets, whereas inflammatory subsets are increased in SARD patients. Additionally, ANA+ individuals without SARD who progress clinically have increased lymphocyte activation and increased markers of regulation compared to non-progressors. Collectively, our findings suggest that active immunoregulation prevents clinical autoimmunity in ANA+ NS individuals, which, when impaired, may result in an increasingly inflammatory environment that promotes SARD development.
Robust Feedback-based Nash Equilibrium Seeking
Keywords: Control Theory
Feedback
Game Theory
Optimization
Reinforcement Learning
Zero-Order

We consider the problem of distributed Nash equilibrium seeking over networks. Each agent desires to play a game while constraining a system output generated by a process affected by the agents' control actions as well as some unknown, exogenous disturbances. Each self-interested agent seeks to minimize their cost function within this game, while ensuring that the output obeys the given constraints. We outline the limitations of forecasting the output and the relevant external disturbances, which motivates our feedback-based approach, using measurements to lift the forecasting requirements. We then develop two algorithms: one that makes a nominal, constant approximation of the system's input-output sensitivities a priori, and another that estimates sensitivities in a model-free manner. We exploit operator-theoretic properties to show our algorithms' convergence robust to model uncertainty and unknown disturbances. We then test the developed techniques using academic and real-world examples, validating and outlining the convergence properties of our algorithm.
Development Towards a CRISPR-Cas3 Genome Editing System for Streptomyces
Keywords: Bioinformatics
CRIPSR-Cas3
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.
A Fixed-point Algorithm for the AC Power Flow Problem
Keywords: Fixed-point algorithm
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.
The Role of B Cells and Immunoglobulin A in Blood Pressure Regulation
Keywords: B cells
Blood pressure
Cardiovascular physiology
Hypertension
Immunoglobulin A
Immunology

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.
On Genealogy: Inheriting Fault Lines in Canadian Theatre Criticism
Keywords: bug
criticism
ecstasy of rita joe
kamloopa
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.
KL Divergence Based Change-point Detection and Modulation of Reinforcement Learning Parameters
Keywords: Reinforcement Learning

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.
Deep Learning Methods for Pediatric Brain Tumour Diagnosis
Keywords: Machine Learning
Medical Imaging
Tumour Segmentation

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.
Evaluating the Effects of Continuity of Rheumatology Care on Patient Outcomes for Individuals with Rheumatoid Arthritis
Keywords: Continuity
Mortality
Outcomes
Rheumatoid Arthritis
Rheumatology
Rheumatology Care

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.
Automatic Temporal Segmentation of Orofacial Assessment in Amytrophic Lateral Sclerosis
Keywords: ALS
Deep Learning
Machine Learning
orofacial assessment
temporal segmentation
Transformers

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.
HPIPE-NX: Leveraging Tensor Blocks for High-performance CNN Inference Acceleration on FPGAs
Keywords: Accelerator
AI
CNN
FPGA
Hardware
Machine Learning

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.
Individual Variability of Functional Brain Activity during Working Memory Performance in Schizophrenia Spectrum Disorders (SSD)
Keywords: Functional Neuroimaging
Individual Variability
Schizophrenia
Working Memory

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.
The Role of Nucleus Accumbens Dopamine D1-D2 Receptor Heteromer in the Phosphorylation of Cyclic-AMP Response Element-binding Protein
Keywords: addiction
CREB
D1-D2
dopamine
heteromer
nucleus accumbens

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.
BitChop: A Heuristic Approach to Memory Footprint Reduction in AI Training
Keywords: Artificial Intelligence
Compression
Deep Learning

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.
Benchmarking Reinforcement Learning for Safe Robotics: Constraints, Robustness and Transfer
Keywords: Benchmark
Machine Learning
Reinforcement Learning
Robotics
Safe Learning
Safety

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.