VictorJuiz/thesis huggingface dataset:
Projecting the Future Prevalence of Childhood Cancer In Ontario using Microsimulation Modeling
Keywords: Cancer epidemiology
Health system planning
Microsimulation
Pediatric cancer
Prevalence

Children diagnosed with cancer have lifelong health care needs. The prevalence of childhood cancer has been steadily increasing in Ontario, though no prevalence projections exist to anticipate future health care system demands. A population-based, open-cohort microsimulation model was constructed to project the limited-duration prevalence of childhood cancer in Ontario, by cancer type, for years 2020-2040. Model inputs were derived from health administrative databases, provincial population data sources, and external literature. Beginning with 1970, the model population was updated annually with births, deaths, net migration, and incident cases of childhood cancer. Fifty Monte Carlo simulations were run to vary model inputs and generate median health outcomes with 95% credible intervals (CI). Between 2020 and 2040, annual incidence counts are projected to increase by 33%. In 2040, 25171 (95% CI: 24267-26975) individuals are projected to reside in Ontario who were diagnosed in 1970 or later, 87% of which will be 5-year survivors.
Detecting Driver Drowsiness using Peripheral Physiological Signals
Keywords: Detection
Driving
Drowsy
Physiological

In the context of driving, drowsiness is found to be a major risk factor of vehicle collisions worldwide. An underexplored, yet promising area of research is the use of physiological signals in assessing driver drowsiness. Electroencephalogram (EEG) has dominated the field as the gold standard due to its accuracy in determining changes in wakefulness, but is less applicable in driving scenarios due to introduced noises and its invasiveness for the driver. Consequently, the goal of this thesis project was to analyze and derive a preliminary solution for the detection of drowsiness while driving using physiological signals. Specifically, the focus was on non-EEG measures including electrocardiogram (ECG), respiratory signals, galvanic skin response (GSR) and temperature. We achieved this goal through two main objectives: A systematic review that explored the use of physiological signals, and the creation of both a model as well as system to reliably detect drowsiness.
Magnetic Resonance Imaging Schedule Optimization at JDMI
Keywords: Health care
Magnetic Resonance Imaging
Optimization
Scheduling
Staff Satisfaction
Wait time

The Joint Department of Medical Imaging (JDMI) is the largest academic medical imaging department in Canada. Long Magnetic Resonance Imaging (MRI) wait times for lower priority patients remain a critical issue at JDMI. There is, however, also a particular focus given to the wait time equity and MRI access discrepancies between patient sub-populations. An MRI scheduling framework is therefore proposed to: 1) maximize machine utilization through efficient shift scheduling using Branch-and-Price (B&P); and 2) ensure clinical MRI machine capacity remains aligned with incoming ordered scan demand, maximizing patient wait time equity. Schedule flexibility is successfully captured using the proposed B&P shift scheduling model through staff configuration scenarios. Additionally, the number of different scan types performed by MRI technologists across a week is considered during MRI capacity re-alignment. We demonstrate an average 22% improvement in exposure to different scan types per technologist compared to current MRI capacity allocation.
Do Digital Tools Support a Diversity of Patient Values? A Qualitative Study using the Genetics Adviser
Keywords: genomics health services
health services research
health technology assessment

Aim: To explore the range of values raised when selecting incidental findings (IF) from whole exome sequencing (WES) and how these are supported by the Genetics Adviser (GA).Methods: Qualitative interpretive descriptive study using semi-structured interviews conducted with participants undergoing WES and receiving IFs as part of the GARCT. Results: Sixteen participants were interviewed. Participants expressed that their decision was predetermined and occurred within a ‘black box’ before using the GA. Three values emerged from unpacking this ‘black box’: family stewardship, information imperative, and preserving agency. While the GA did not play an active role in eliciting these values, participants valued having the tool confirm their decision by clarifying, contextualizing, and instilling confidence in it. Conclusions: Participants entered decision-making with a predetermined decision guided by three core values. The GA supported them by reinforcing it. These findings can inform how digital tools can better support a diversity of patient values.
COVID-19 and Mobility Data Analysis with Poisson Mixed Effects Regression Models to Account for Spatial and Temporal Correlations
Keywords: COVID-19
cross validation
mixed effects model
population mobility
serial correlation
time lag

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 is a worldwide pandemic. We conducted a study to estimate the relationship and time lag between population mobility and COVID-19 infection curves based on weekly and census tractlevel aggregated data (i.e. BlueDot mobility data, iPHIS surveillance data, and census data) in Greater Toronto Area (2020.03.15-2021.02.07). COVID-19 weekly confirmed new cases (i.e. weekly cases) was the outcome and proportion of time outside the home (i.e. mobility) was the primary predictor. We used the Poisson mixed effects models with a first-order autoregressive ( AR(1)) structure to estimate mobility effect and applied a modified K-fold cross validation to find an optimal time lag. The results show that the optimal time lag is around six week. Mobility has significant and negative effects on COVID-19 weekly cases during an epidemic wave. During a flat period, the mobility effect is not strong.
School Based Health Care: A Model for Improving Educational Achievement for Children in Underserved Communities in Toronto, Canada.
Keywords: academic achievement
accessibility
School Based Health Centres
wait time

Even though Canada is a developed country with universal health care, there are still populations in Canada who are at risk for health disorders that may go undetected and/or untreated due to challenges to health care access. This can harm a child's academic achievement and long-term well-being. School Based Health Centres (SBHCs) were established in Canada to address health-care access concerns. However, no quantitative study examining the impact of SBHCs on educational achievement has been undertaken in Canada. In my thesis, I examine the benefits of early intervention and management, especially for children at-risk for developmental disorders. I describe the health-care systems in Canada and the United States, the challenges to health-care access and the SBHC model that was developed to address these challenges. I also share a study I conducted examining whether having access to SBHCs improves academic achievement and wait time for accessing physician’s assessments.
Exploring BDNF Changes in Response to Exercise Primed tDCS, tDCS Alone, or Exercise Alone in Mild Cognitive Impairment and Alzheimer’s Disease
Keywords: AD
BDNF
biomarker
MCI
tDCS

Available pharmacotherapies for cognition in Alzheimer’s disease (AD) are associated with adverse effects and lack efficacy in Mild Cognitive Impairment (MCI). Transcranial direct current stimulation (tDCS), a non-invasive neuromodulatory technique, has shown to improve cognition. However, studies showed variability in response and duration of sustained effects. Understanding the neurobiological mechanisms will help to optimize its use. Changes in brain-derived neurotrophic factor (BDNF) levels were examined in MCI/early AD patients (n=30) that underwent 3-weeks exercise ramp up before 10 treatment sessions of: exercise+tDCS (T3/T4, 2mA, 20 minutes), exercise+sham, or exercise education+tDCS. A significant TIMET1,T2,T3 (F(2,54)=8.442, p<0.001) but no GROUPxTIME effect indicated that BDNF decreased in all groups; post-hoc revealed a significant change during ramp up (t(29)=3.163, p=0.013). During treatment, BDNF was inversely associated with recognition (r=0.406, p=0.040) and recall (exercise+tDCS: B=0.065, p=0.037) memory. Baseline BDNF did not predict changes in cognition. This suggests that BDNF is not involved in tDCS response.
Studying Human Outer Radial Glia Development in Pluripotent Stem Cells-derived Neural Cultures
Keywords: HOPX
Human pluripotent stem cells
Neural stem cells
Neurodevelopment
Outer radial glia

The human neocortex is highly expanded and distinct from other species. Outer radial glia (oRG), a type of neural precursor (NPs), are thought to be responsible for the expansion of the human neocortex. Challenges remain in investigating oRG because they cannot be adequately studied in mouse models due to their rarity, and human fetal brain samples are inaccessible. CRISPR/Cas9 gene editing and in vitro pluripotent stem cell (hPSCs) derived oRG and neural cultures were established to determine the types of cells oRG generate and to investigate HOPX, a highly enriched gene in oRG. Further neuronal differentiation of in vitro oRG suggest that they may generate more upper-layer neurons than deep-layer neurons. Preliminary phenotypic analyses reveal that HOPX KO reduces NP proliferation. Together, this provides a valuable platform to study human oRG in vitro and will allow future in-depth investigations into oRG development and regulation.
Effect of Artificial Aging Protocols on Bond Strength Between Orthodontic Metallic Brackets and Human Enamel
Keywords: adhesion
artificial aging
bond strength
orthodontic bracket
orthodontics
thermocycling

Background: Limited studies compare artificial aging methods simulating oral environment on orthodontic brackets. Objectives: To evaluate the effect of in vitro aging protocols on bond strength between bracket and enamel, and to evaluate enamel loss as a function of aging. Methods: Sixty upper premolars with bonded metallic brackets were divided into 4 groups (n=15): Control (CO) - distilled water storage for 21 days; Thermocycling (TC) - 20,000 thermocycles; Mechanical cycling (MC) - 10,000 chewing cycles; Thermo-mechanical cycling (TMC) - TC+MC and subjected to shear bond strength testing (SBS). Adhesive remnant on enamel was assessed with stereomicroscope. Presence of calcium and phosphorus on the bracket was examined with scanning electron microscopy. Results: SBS values of TC and TMC were lower than CO (p<0.05). There was no difference for percentage of adhesive remnant (p>0.05) and enamel loss. Conclusion: Artificial aging protocols with TC or TMC decreased SBS of metallic brackets to enamel.
Understanding the Adoption and Impact of GasTokens on Ethereum
Keywords: Blockchain Analytics
Ethereum
Gas Mechanism
Gas Tokens
Smart Contracts
Transaction Fees

GasTokens are created by smart contracts executing in the Ethereum blockchain to support a form of transaction fee pre-payment. Even though GasTokens had a period of significant activity on Ethereum (regularly surpassing half of the overall weekly smart contract creation and destruction activity), few systematic studies have been conducted on GasToken usage and its effect on the blockchain network. This thesis focuses on understanding the adoption and impact of GasTokens on Ethereum from 2020-5-25 to 2022-5-31. We analyze the usage patterns, including the creation, destruction, and holding of GasTokens. We report several price findings related to GasTokens, and compare the fees paid by users submitting transactions that create and destroy GasTokens to the fees paid by all the transactions in the network over different time periods. We also report on the impact of GasToken creations and destructions on the major mining pools.
Adult Outcomes of Pediatric-onset CHD2 and SYNGAP1-associated Developmental and Epileptic Encephalopathies
Keywords: adults
autism spectrum disorder
chd2
developmental and epileptic encephalopathy
epilepsy
syngap1

Developmental and epileptic encephalopathies (DEEs) are rare, infantile-onset conditions of genetic etiology, particularly CHD2 and SYNGAP1 variants. While pediatric phenotypes are well described, there is a lack of understanding regarding adult outcomes, which may lead to misdiagnoses and barriers to treatment. This thesis aimed to characterize the clinical features, communication skills and adaptive behavioural abilities of adult patients (18 years and older). We determined that features such as delayed pain processing, sleep disturbances and autism spectrum disorder are prevalent in adult patients with SYNGAP1 and CHD2 variants. We also found that patients are highly reliant on caregivers, and that various factors such as behavioural issues can affect caregiver impressions. Overall, these findings contribute to a more holistic understanding of the natural history of DEEs, guiding the diagnoses of adult patients and informing considerations for improving the quality of life of both patients and their families.
Shakespeare’s Faerie Art of Enchantment through Tolkien’s Lens: A Historiographical Introduction
Keywords: Enchantment
Eucatastrophe
Faerie
Fairy
Shakespeare
Tolkien

This thesis seeks to illuminate why Shakespeare’s use of faerie elements and stories have proved recurrently enchanting, and what specific kinds of arts of enchantment he employed. Shakespeare drew on fairy tales and fables from classic and medieval sources to create “shadows” and disrupt horrors so as to inspire hope in his audiences. By creating a dizzying succession of frames, he draws audiences into his stories and moved them to realize that they are governed by the same mysteries. Fairy stories, as argued by Tolkien, are fundamentally concerned with human nature, desire and longing and their highest function includes consolation through Eucatastrophe, in ways juxtaposed to tragedy’s shown to be inspired by Hamlet’s exposition of the three ethical, mirroring subfunctions of theatre. These results are corroborated via a survey of insights from documentaries featuring 37 directors, actors, and scholars.
Choreographing Epistemic Justice for a New Dance Future
Keywords: ballet
contemporary
difficult knowledge
education
research ethics
whistleblowing

The multiple reports of misconduct across ballet, modern, and contemporary dance between 2018 and 2022 have confirmed that abusive practices are systemic across the form. This new difficult knowledge challenges academia to reevaluate how it perceives Western dance practice. To achieve a safer dance future, this thesis analyzes multiple instances of misconduct across dance performance to archive the profound scope of these injustices. It identifies the archive as a powerful potential platform for the validation of dance survival stories, a tool for the dance studies field to advocate for dancers’ rights to bodily autonomy and fair wages. It critiques gaps in how academia and print journalism have collected and disseminated knowledge on dance work realities. And from these inquiries, it proposes new methods for dance studies to support community advocacy for the creation of a better world for the dancers of tomorrow.
Efficient Deep Learning Methods for Solving High-dimensional Partial Differential Equations for Applications in Option Pricing
Keywords: Deep Learning
Monte Carlo Methods
Option Pricing
Partial Differential Equations
Reinforcement Learning
Tensor Networks

Partial Differential Equations (PDEs) are used to model a variety of dynamical systems around us. Recent advances in deep learning have enabled us to solve these PDEs in higher dimensions by addressing the Curse of Dimensionality (COD). However, these approaches are constrained by training time and memory. To tackle these shortcomings, we introduce three approaches starting with Multi-Level Dense Neural Networks (ML-DNN). ML-DNN draws inspiration from Multi-Level Monte-Carlo to efficiently sample and perform hierarchical learning thereby providing substantial time savings compared to the classical Dense Neural Network (DNN). Next, we implement Tensor Neural Networks, a quantum-inspired architecture that provides significant parameter savings and faster convergence while attaining the same accuracy as compared to a DNN. Finally, we introduce a model-based Reinforcement Learning algorithm which addresses the COD and is independent of the PDE family. We benchmark these models on parabolic PDEs, empirically showing their advantages over the current state-of-the-art models.
Investigation into the Shear Strength of Deep Reinforced Concrete Beams Subjected to Uniform Load
Keywords: Deep Beams
Reinforced Concrete
Shear Behaviour
Shear Mechanisms
Uniform Load

Two reinforced concrete specimens were designed in likeness to past experiments conducted on three identical uniformly loaded deep beams, all of which failed below their expected capacity. The purpose of this study was to explain the low strengths of these beams by analyzing past data and performing new experiments. An investigation into the progression of reinforcement strains demonstrated that there is not necessarily a sudden transition between the two shear carrying mechanisms, but that the mechanisms can be engaged simultaneously along the span. Additionally, it was found that strut formation can be gradual, and that struts can change shape and direction as loads increase. Preliminary methods for capturing the proportion of shear attributed to each mechanism and the movement of the strut profile are presented. It is recommended that these methods be applied to numerous specimens to identify the key variables affecting the relative contribution of the shear carrying mechanisms.
Machine Learning Methods of Risk Evaluation for Community-acquired Clostridioides difficile Infection in Ontario
Keywords: Antibiotic Stewardship
C. difficile
Community
Machine Learning
Risk Analysis

Clostridioides difficile infection (CDI) is one of the leading causes of morbidity and mortality in Canada. Typically it is seen as a hospital-acquired infection, but growing numbers of community-acquired CDI and a high risk of recurrent infections has many researchers shifting their focus to the analysis of risk factors associated with community-acquired CDI. However, many of these studies only apply logistic regression for risk prediction techniques despite evidence that other machine learning methods, such as gradient boosted trees, can effectively predict risk of communicable diseases. This study focuses on the applications of three modeling techniques — logistic regression models, mixed effects models, and a gradient boosted tree model — in an effort to evaluate different risk factors associated with community-acquired CDI. Results indicate that taking multiple antibiotics concurrently, particularly if at least one of those antibiotics is classified as high-risk, is likely to significantly increase one’s probability of developing CDI. Additionally, results of the gradient boosted tree indicate that there are important temporal factors associated with risk of CDI. Going forward, the information obtained about the different risk factors associated with community-acquired CDI should be utilized by physicians to lower the risk of infection.
An Examination of the Effects of Localized Ischemic Preconditioning on Anaerobic Arm Cycling Performance in Water Sport Athletes
Keywords: Athlete
Ergogenic Aid
Exercise
IPC Exercise
Ischemic preconditioning
Sport Science

This study investigated the effects of brief repeated episodes of ischemic preconditioning (IPC) on arm cycling performance in upper body trained athletes. Nine (n = 5 male; n = 4 female) competitive watersport athletes underwent a familiarization trial followed by three experimental conditions: (i) no IPC (CON), (ii) IPC, and (iii) placebo (PLA). Participants completed a 45 s all-out sprint on a mechanically braked arm cycle ergometer. Muscle oxygenation, cardiorespiratory, and blood lactate responses were measured to assess the influence of treatments on performance. IPC increased mean power and peak blood lactate concentrations compared to CON and PLA and increased peak heart rate compared to CON but not PLA. Both IPC and PLA augmented the change in tissue saturation of the dominant arm compared to CON. While IPC improved MPO compared to other conditions, physiological measurements in this study provide little mechanistic explanation for the ergogenic effect.
In Vivo High-speed Microscopy of Ultrasound-stimulated Microbubble-vessel Interactions
Keywords: cavitation
contrast agent
in vivo
microbubble
ultrasound
vascular effects

Ultrasound-stimulated microbubbles have emerged in a wide range of therapeutic applications, such as drug delivery, blood-brain barrier opening and tumor ablation. At sufficiently high pressures, microbubble cavitation becomes violent and induces sustained vascular damage. Despite extensive clinical potential, fundamental questions about microbubble-vessel interactions remain, particularly under longer pulse lengths and higher pressures typical of therapeutic exposures. Here we developed a novel system to enable simultaneous high-speed intravital imaging and cavitation monitoring of microbubbles in vivo in the chorioallantoic membrane of a duck embryo. We provide the first direct in vivo optical evidence of microbubble extravasation along with mechanical and acoustic insights into cavitation-induced microbubble-vessel interactions at spatiotemporal microscales. Results suggest that induced effects are highly dependent on vessel size, with smaller vessels being preferentially impacted. Overall, the outcomes of this study enhance our understanding of microbubble-induced vascular effects which is critical for safe and efficient development of ultrasound treatments.
HAVOC: Mapping of Cancer Biodiversity using Deep Neural Networks
Keywords: Artificial Intelligence
Deep Learning
Glioblastoma
Image Analysis
Oncology
Tumor Heterogeneity

Spatially distinct areas of tumor biodiversity wreak havoc on current precision medicinestrategies. To address this challenge, I developed a pipeline that leverages unsupervised clustering of regional neural network-defined histomorphologic signatures to generate Histomic Atlases of Variation Of Cancers (HAVOC). Using spatially resolved mass-spectrometry-based proteomic and immunohistochemical readouts of characteristically heterogeneous glioma specimens, I demonstrated how these personalized atlases of histomic variation capture regional tumoral populations with distinct molecular signatures and biologic programs. I further validated HAVOC on existing spatial DNA copy number and transcriptomic datasets and assigned distinct histologic differences downstream of these genetically distinct subclones. Finally, I extended HAVOC to large tumor resection specimens to demonstrate its utility in automating the topographic organization of cancer biodiversity across multiple centimeters. Together, these findings establish HAVOC as a versatile tool capable of generating small-scale heterogeneity maps and guiding regional deployment of limited molecular resources to relevant and biodiverse tumor niches.
Art and Affect, the Emotional Labor of the Instagram Follower
Keywords: emotional labor
immaterial labor
instagram
labor
platforms
social media

On Instagram, does pressing the like button constitute an act of labor? This thesis seeks to answer this question from the perspective of the ordinary art and design follower by investigating the immateriality of emotional labor. Using semi-structured interviews, emotional labor is defined through the cognitively intensive experiences of follower’s practices in navigating social events. Emotional labor is organized into three categories, each of which demonstrates a degree of work and emphasizes a nuanced consumption and production of use and exchange-values. Results implicate the platformization of emotional labor within a sense of precarity that complicates the boundaries between social and professional living, generating an asymmetrical relationship between Instagram followers and its multi-sided partners.