VictorJuiz/thesis huggingface dataset:
Characterizing the Effects of First Trimester Human Umbilical Cord Perivascular Cell (FTM HUCPVC) Treatment on the Lungs using an α-PD-1/α-CTLA-4 Combination Immunotherapy Mouse Model
Keywords: Immune Checkpoint Inhibitors
Immune Related Adverse Events
Immunomodulation
Immunotherapy
Pneumonitis
Umbilical Cord Stem Cells

One category of immunotherapy that has revolutionized cancer treatments is immune checkpoint inhibitors (ICI). ICI block cancer-induced negative regulation of T cells, thereby allowing a response against tumor cells. Although ICI use has led to positive therapeutic benefits,there are negative off-target effects, known as immune-related adverse events (irAEs). One irAE is checkpoint inhibitor pneumonitis (CIP). The use of mesenchymal stem cells (MSC) as cell therapies has been widely studied for many regenerative medicines as well as immunomodulatory applications but has not been studied in the context of ICI-induced irAE. The aim of my thesis was to establish a small animal model of irAE and investigate the use of firsttrimester human umbilical cord perivascular cells (FTM HUCPVC), a young andpromising source of MSC, to mitigate ICI-induced lung damage. We hypothesized that the use of FTM HUCPVC can reduce lung damage caused by ICI in vivo.
Full-scale Monitoring of Tall Reinforced Concrete Building, and Modelling of Slab-induced Inherent Damping
Keywords: Amplitude-Dependency
Damping
Full-Scale Monitoring
Slab Stress
Slab-Induced Inherent Damping
Tall Buildings

Damping ratios in structures are the most challenging modal property to predict due to the perplexity of the contributing physical mechanisms. Consequently, the inability to quantify these sources has led to only approximate baseline inherent damping estimates, making the estimation of the damping ratio at larger amplitudes an even greater task. It was previously hypothesized that the main source of damping in structures resulting in amplitude-dependence was the stressing and subsequent microcracking in slabs. This thesis outlines the steps taken to monitor a tall structure, calibrate the structural model, and examine the experimental results to identify the reasons for the vastly different modal damping responses. Non-linear modelling proved that replicating the amplitude-dependent response could be accomplished and provided insight on why the slabs’ modal responses were dissimilar. Lastly, strain energies for the stressed slab regions were calculated and plotted as functions of height and displacement, ultimately substantiating slab-induced inherent damping.
Remote Ischemic Conditioning during Experimental Necrotizing Enterocolitis: Mechanism of Action
Keywords: angiogenesis
intestinal blood flow
intestinal ischemia
microcirculation
Necrotizing Enterocolitis
Remote ischemic conditioning

Necrotizing enterocolitis (NEC) is a devastating disease in premature infants, characterized by inflammation, ischemia, and derangements in microcirculation in the intestine. NEC has adverse morbidity and high mortality and is in need of a novel treatment. Remote ischemic conditioning (RIC) is a therapeutic strategy that protects distant organs from ischemic damage. We aimed to investigate the efficacy and mechanism of action of RIC in experimental NEC. NEC was induced in mouse pups between postnatal day (P) 5 and 9. RIC was administered via 4 intermittent cycles of occlusion of hindlimb blood flow (5 minutes) followed by reperfusion (5 minutes). RIC mitigated intestinal injury and inflammation and prolonged survival. The mechanism of action of RIC involves (i) hydrogen sulfide- and nitric oxide-mediated vasodilation and improvement of intestinal microcirculation, and (ii) modulation of intestinal angiogenesis. Our findings indicate that RIC counteracts the intestinal damage of NEC by improving intestinal microcirculation and microvasculature.
The Attempted Lynching of Black Geographies: A Black Feminist Retheorization of Gentrification as a Tool of Neoliberal Plantation Logics
Keywords: Black Geographies
Black Radical Social and Solidarity Economies
Gentrification
Geographies of Resistance
Plantation Logics
Slow Urban Trauma

My project explored the ways in which gentrification can be retheorized as a neoliberal and neocolonial tool created by the Euro-American expansionist project of global anti-Blackness in an attempt to destroy and lynch the community sovereignty of Black geographies. Specifically, I use discourse analysis of Black geographies, the role of the police and carceral system in the tkaronto context, and the role of Toronto Community Housing Corporation and EGLINTONconnects LRT construction on the gentrification. My lived experiences as a community organizer doing anti-poverty work in tkaronto informs how I reflect on the slow violence of gentrification as displacement directly impacts the wellbeing of residents through the routinized surveillance and ongoing slow state violence that causes chronic urban trauma. Through the relevant literature explored, this study presents three conclusions as a path forward: The Geographies of Resistance Intervention, Abolition and Apocalypse, and Black Radical Social and Solidarity Economies
Identifying the Neural Predictors of Suicide Risk: A Focus on Pain Processing
Keywords: Capability for Suicide
Pain Sensitivity
Pain Tolerance
Resting-State Functional Connectivity
Suicide Attempt

Despite prevalent suicidal ideation in Major Depressive Disorder (MDD), the prediction of suicide attempt remains poor. Capability for suicide, reflecting fearlessness about death and increased pain tolerance, is shown to predict suicide attempts. Therefore, the neural basis of suicide capability and its interaction with pain processing may predict suicide attempt risk. This study aims to examine the relationship between suicide capability, pain, and the resting-state functional connectivity of pain neurocircuitry. In MDD patients, suicide capability was correlated with higher pain tolerance and lower pain sensitivity. It was also linked to resting-state functional connectivity of the insula and cingulate cortex with brain regions involved in pain, an effect that was partially mediated by pain sensitivity. These findings advance our understanding of the neurobiological basis of suicide risk with regards to suicide capability and pain neurocircuitry, which may be a potential biomarker or treatment avenue for suicide attempt.
TRPV4 Regulates β1 Integrin-mediated Cell-matrix Adhesions and Collagen Remodeling
Keywords: Collagen
Extracellular matrix remodeling
Integrin
Matrix adhesion
TRPV4

Dysregulated extracellular matrix (ECM) remodeling underlies high prevalence pathologies including fibrosis and cancer. Fibroblasts are important matrix-remodeling cells that assemble integrin-based cell-matrix adhesions, termed focal adhesions in cultured cells, which mediate mechanical remodeling of collagen. Transient Receptor Potential Vanilloid-type 4 (TRPV4) is a mechanosensitive, Ca2+-permeable plasma membrane channel that localizes to focal adhesions and influences cellular processes that mediate changes in ECM properties. While it is not understood how TRPV4 contributes to matrix remodeling, TRPV4 is activated by mechanical forces transmitted through β1 integrins. Conversely, it is not known whether TRPV4 expression affects β1 integrins. Here we showed that TRPV4 expression is associated with reduced β1 integrin abundance and function, altered focal adhesion morphology, and decreased alignment and compaction of fibrillar collagen. We provided evidence that this down-regulation of β1 integrins may occur through an overall up-regulation of miRNAs that target β1 integrin mRNA. Our findings provide insights to understanding the role of TRPV4 in regulating matrix pathophysiology and β1 integrin bioavailability, which may be important for identifying novel approaches in the clinical management of fibrotic lesions using integrin-based therapies.
Multimodal Representations of Simulated Hemodynamics for Cross-disciplinary Communication
Keywords: computational fluid dynamics
DICOM
hemodynamics
perception
sonification
visualization

In recent years, patient-specific image-based computational fluid dynamics (CFD) has been used to study associations between turbulent-like blood flow patterns (hemodynamics) and cardiovascular disease. These simulations produce dense spatiotemporal datasets, creating a challenging representational problem that is further complicated by lapses in communication between engineers and clinicians. Towards bridging this gap, we present novel methods for visualizing and sonifying these data. We first present techniques for translating common CFD visualizations into DICOM images, allowing the data to integrate into established radiological paradigms. Second, a perceptually-motivated multidimensional sonification technique is presented with the intention of reinforcing the representation of turbulent-like flows when presented alongside a visualization. This work is a key step towards user studies that will examine the utility of multimodal hemodynamic data representations in clinical environments, and may serve as an interesting case study on the role of interdisciplinarity for solving communication problems between engineering and other disciplines.
Particulate Matter Concentrations in Social Housing
Keywords: Indoor air quality
Particle filtration
PM2.5
Public housing
Retrofit
Total suspended particles

Residents of social housing are often exposed to higher concentrations of harmful indoor pollutants such as particulate matter (PM). This research is comprised of two parts: a study investigating variation in indoor PM concentrations measured in 91 social housing apartments in Toronto followed by an evaluation of particle filtration as means of exposure reduction. PM concentrations varied greatly within and between apartments and were two times higher than in single family homes measured using comparable methods. Smoking and other indoor sources are likely responsible for these concentration differences which may contribute to health disparities. Using simulated concentrations derived from the social housing field measurements, we found that particle filtration could greatly reduce indoor PM concentrations. Automation using fixed and adaptive thresholds can lower system runtime, thereby reducing noise and energy usage and making particle filter systems a more viable and sustainable approach to improving the health of social housing residents.
Neural Communication Underlying Episodic Memory in Children: Application of a Pediatric Brain Tumor Survivor Injury Model
Keywords: Episodic Memory Networks
Functional Connectivity
Magnetoencephalography
Pediatric Brain Tumor
Theta Oscillations
Tumor Treatment

Episodic memory involves personal experiences paired with their context. The Medial Temporal (MTN), Posterior Medial (PMN), Anterior Temporal (ATN), and Medial Prefrontal Networks (MPN) have been found to support the hippocampus in episodic memory in adults. However, literature lacks evidence of the involvement of these networks in episodic memory processing in children. Using Magnetoencephalography, we quantified differences in neural communication during an episodic memory task of healthy children and children with presumed memory impairments. Pediatric brain tumor survivors (PBTS) were used as a model of memory impairment, as they exhibit deficits in episodic memory and perturbations in neural communication. We observed a negative correlation between episodic memory performance and theta synchronization between the MTN, PMN, ATN, and PMN across participants. Our findings suggest these networks may impact episodic memory function in children, and that theta hypersynchrony between them may be a pathological process in PBTS.
Attentional Modulation of Emotional Lateralization Biases with Verbal and Nonverbal Stimuli
Keywords: functional lateralization
hemispheric asymmetry
online behaviour study
spatial attention

Within hemispheric asymmetry literature, emotional processing appears to be predominately right lateralized; however, this degree of lateralization seems more complex when the stimuli engage with multiple functions lateralized across hemispheres, such as language, face perception, and spatial attention. Using the divided visual field paradigm, our online experiment employs a 2x2 design to examine the scope of emotion laterality by comparing lateralization biases when processing neutral and valence-laden stimuli in both verbal and nonverbal modalities. The study also employs a modified “Posner’s task” to investigate attentional modulation of hemispheric biases. Our preliminary findings pertaining to neutral face and neutral word perception did not show hemispheric bias although both revealed strong cueing effects. Subsequent experiments will investigate how attention cueing impacts hemispheric performances and how these patterns interact with emotion. This research aids to quantify hemispheric interactions when processing emotional stimuli and informs the treatment of mood disorders using non-invasive brain stimulation.
Targeting Sleep Disturbance and Sleep Apnea in Patients with Chronic Pain
Keywords: Chronic Pain
Insomnia
Opioids
Sleep Apnea

Cognitive behavioural therapy for insomnia (CBT-I) may be effective in improving sleep among patients with chronic pain, however, it is largely inaccessible due to high costs, few qualified therapists, and time-consuming nature. We report that brief self-help CBT-I with low-intensity telephone support is feasible and acceptable for patients with chronic pain and sleep disturbance. Some patients may also be prescribed with opioids for pain management, although opioid therapy is associated with a greater prevalence of sleep apnea. Given the higher risk of opioid-associated sleep apnea, it is critical that accessible screening models are created to identify those at risk. We found that a screening model comprising a validated questionnaire and pulse oximetry can accurately screen for moderate-to-severe sleep apnea among patients using opioids for chronic pain. Both studies suggest that low-intensity CBT-I and simple screening models can effectively target sleep disturbance and sleep apnea, respectively, in the chronic pain population.
Mechanisms Underlying Opioid-induced Respiratory Depression and Analgesia In Larval Zebrafish
Keywords: Analgesia
Opioid
Respiratory Depression
Zebrafish

Opioids are strong pain relievers that also present the severe side effect of respiratory depression which can be lethal with overdose. Opioids mechanisms of inhibition are not well known but may act through closing calcium channels. However, the role of these channels in opioid-induced respiratory depression and analgesia is unclear. To determine the mechanisms underlying respiratory depression and analgesia by opioid drugs, we developed novel pharmacological assays in larval zebrafish. Zebrafish are amenable models for pharmacological approaches due to their fast reproduction, large number of offspring, and high genetic homology to humans. Using inhibitors and activators targeting N- and L-type calcium channels, we determined the roles of these channels in respiratory depression by the opioid fentanyl. We found that activating these channels reversed opioid-induced respiratory depression. Therefore, N- and L-type channels play a critical role in respiratory inhibition by opioids.
Learning-based Methods for Concentric Tube Continuum Robots Modeling and Shape Estimation
Keywords: Concentric Tube Continuum Robots
Continuum robots
Data-driven
Modeling
Shape Estimation

This thesis addresses the modeling and sensing of concentric tube continuum robots from a data-driven perspective. Firstly, the shape-to-joint inverse kinematics problem for concentric tube continuum robots is investigated, by both data-driven and numerical-analysis approaches for the first time. Particularly for the learning-based method, a neural network is proposed to approximate the mapping from robot shape represented by up to 20 discrete points to its joint parameters, with errors of 2.22° and 1.45 mm obtained. This method is an enabler for future shape control algorithms. Secondly, we propose a learning-based method for estimating the robot shape from multi-view images. Using a convolutional neural network and end-to-end learning, this method enables a direct mapping from images of the robot taken from multi-views to robot shape or tip position, with sub-millimetre accuracy achieved in simulation. This allows for a calibration-free, markerless, and minimum image-processing 3d shape reconstruction of concentric tube continuum robots.
Augmented Reality Bedtime Application for Children with Autism Spectrum Disorder
Keywords: AR
ASD
Augmented Reality
Autism Spectrum Disorder
Human Computer Interaction
Mixed Reality

Sleep difficulties are highly prevalent in children with autism spectrum disorder (ASD), and have a negative impact on the physical and mental health of children and their caregivers. Sleep education programs are often recommended as a first line-treatment to help parents implement healthy sleeping habits, including a bedtime routine at home; however, the accompanying paper-based toolkits have limitations related to engagement and adherence. To address this gap, we iteratively developed and tested the usability of an augmented reality (AR) bedtime routine application. The results of our single-participant design study (n=5 child/parent dyads) demonstrated 80% adherence and good-excellent usability of the app. Although our study design did not allow us to examine the clinical effectiveness of the app, we observed trends towards increased willingness and faster completion of bedtime. This work supports the feasibility of using technology-based tools in sleep education programs and informs future clinical studies examining the effectiveness of technology-based approaches for addressing sleep difficulties.
Concentrated Ethanol Electrosynthesis from CO2
Keywords: carbon dioxide
clean energy
concentrated
electrocatalysis
ethanol
renewable

The concentration of atmospheric CO2 today is the highest it has been in the past 10,000 years. This is due to the widespread use of fossil fuels as an energy source and chemical feedstock since the beginning of the industrial revolution. CO2 electrocatalysis provides a solution to this problem by sequestering CO2 in a chemical form and producing useful chemical feedstocks. One widely used industrial chemical that can be made from CO2 is ethanol; an energy dense liquid fuel that is normally produced via a CO2 positive process. The focus of this thesis is on using a polymer-carbon adlayer to modify the regular Cu catalyst to produce ethanol at industrially relevant concentrations and reaction rates. It achieves 13.5 wt% ethanol at over 200 mA/cm2 with no changes to cell voltage. Other methods of controlling ethanol crossover and concentration in membrane electrode assemblies using bipolar membranes are evaluated.
Applying Polygenic Models to Disentangle Genotype-phenotype Associations across Common Human Diseases
Keywords: cancer
coronary artery disease
eQTLs
polygenic risk models
transcriptional risk models

In recent years, much human genetics research has focused on modeling the polygenic architecture of common human diseases using summary statistics from genome-wide association studies (GWAS). Utilizing longitudinal population-level data from participants in the UK Biobank (UKB) and the Canadian Partnership for Tomorrow’s Health (CanPath), I investigate how innovations in polygenic risk modelling may be applied to help disentangle genotype-phenotype associations for common human diseases represented across both these populations. By performing stratified GWAS, I capture the shared and distinct polygenic architecture of disease subgroups even among cases of a single ancestry. Subsequently, I compare the efficacy of polygenic risk modeling against more dynamic approaches such as transcriptional risk models which rely on gene expression profiles from tissue and may synergistically capture both individual-level genetic and environmental risk factors. Together, these findings present genetic risk factors for early-onset disease and provide insights into biomarkers for improving personalized disease risk prediction.
What are the Determinants of Breast Cancer Screening in Ontario Women 40-49?
Keywords: Breast Cancer Screening
Implementation Science
Knowledge Translation
Mammography

Background: Guidelines recommend primary care providers individualize screening mammography decisions with women aged 40-49.Methods: Population-based retrospective cohort study and a qualitative interview study guided by the theoretical domains framework (TDF). Analysis: Hierarchical multivariable logistic regression to assess associations with screening and the intracluster correlation co-efficient quantified inter-provider screening variability. Interviews were coded deductively using TDF domains. Results: Approximately 4.3% of women received routine screening with 20.9% of the variation due to between-provider differences. Routine screening was associated with periodic health examinations, pap smears, and fee-for-service care models. Qualitatively, risk assessment was impacted by knowledge and skills and referral for mammography was impacted by emotion, social influences, knowledge regarding screening harms, and beliefs about consequences. Perceived lack of guideline clarity influenced behaviours. Discussion/Conclusion: Perceived lack of guideline clarity and a tendency to over-estimate the benefit:harm ratio explains observed practice variation. These may be effective targets for future knowledge translation strategies.
Rule-based Adversarial Learning: Disguising and Recognizing Maliciousness in Opcodes
Keywords: Adversarial Learning
Malware Detection
Opcode

Adversarial learning has previously demonstrated effectiveness as a tool for improving performance in deep learning models trained on limited data. In this work, we apply adversarial learning to the constrained, discrete domain of opcode-based deep malware detection. We do this by developing a deep learning model for detecting maliciousness in sequences of opcodes, and using this model in order to alter sequences of code from the dataset in minimal ways to create example sequences which are misclassified by the original model. We augment our original training dataset with these adversarial examples, thereby improving the classification accuracy on unseen sequences of opcodes. Using adversarial examples to supplement our training data, we demonstrate statistically significant increases in performance (> 0.5% increase in both accuracy and F1-score). Additionally, we present a preliminary investigation into the transferability of our adversarial examples between different model architectures.
An Exploration of Knowledge Practices Among User Experience (UX) Professionals
Keywords: community of practice
grounded theory
knowledge sharing
knowledge sources
user experience design
UX maturity

This qualitative, exploratory research investigated the knowledge practices of User Experience (UX) designers, an emerging, knowledge-intensive profession that has not been well studied. Three areas of exploration included the way UX designers share knowledge, manage knowledge in work contexts, and participate in communities of practice (CoPs). Grounded Theory Method was employed to generate two frameworks of knowledge sources, based on 10 emergent themes from 21 interviews with UX professionals: Knowledge Sources in Work and Knowledge Sources in Career Development. These frameworks illustrate sources of knowledge that UX professionals use in their work and throughout their career. They make use of explicit knowledge, tacit knowledge, and CoP participation to help identify suitable work processes. In addition, experience and the level of UX maturity in organizations influence the knowledge sources they use. UX professionals and organizations will find value in these frameworks for enhancing practice and supporting UX career development.