VictorJuiz/thesis huggingface dataset:
Investigating the Neural Circuitry of the Social Brain

Drosophila melanogaster is a proven and robust model organism for investigating social behaviour and its underlying neural circuitry. The mushroom body (MB) is well-defined as the site of sensory integration and involves learning and memory in the adult Drosophila brain. More recently, the γ lobes of the mushroom body and populations of mushroom body output neurons have been implicated in modulating social behaviour. This project reviews the literature on the role of the MB in social behaviour and replicate published expression patterns of reported split-Gal4 driver lines used to manipulate MB expression. Five split-Gal4 lines with expression in intrinsic MB neuron populations and five split-Gal4 lines with expression in MB output neuron populations were anatomically described using immunohistochemistry and confocal imaging. The images produced had expression patterns replicable and consistent with previously reported expression patterns for these lines. These lines will be helpful in future studies on the role of the MB in regulating social behaviour.

Advisor: Levine, Joel
Exploring BDNF Changes in Response to Exercise Primed tDCS, tDCS Alone, or Exercise Alone in Mild Cognitive Impairment and Alzheimer’s Disease

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.

Keywords: AD
BDNF
biomarker
MCI
tDCS
Advisor: Lanctôt, Krista
Validation of Generalized Column Demand-Capacity Curves using the Hybrid Fire Simulation Method

The hybrid fire simulation (HFS) method is a novel testing technique that combines experimental testing and numerical modelling. This research presents an application of the HFS to validate the generalized column demand-capacity curves proposed by Wang et al. (2021). The demand-capacity curves are temperature- /constraint-dependent curves to present the relationship between the axial force demand and capacity of a steel column at an elevated temperature. A parametric study is performed to obtain the test control parameters from realistic building structures. Then, a series of small-scale steel columns are tested using the continuous HFS method, and the test results are replicated through a finite element model. The test results exhibit good agreement with demand-capacity curves before 200 ℃, where a reduction factor is introduced in generating the demand-capacity curves. The finite element analysis exhibits a good alignment between the predicted buckling temperature and axial restraining force from demand-capacity curves.

Advisor: Kwon, Oh-Sung
Studying Human Outer Radial Glia Development in Pluripotent Stem Cells-derived Neural Cultures

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.

Keywords: HOPX
Human pluripotent stem cells
Neural stem cells
Neurodevelopment
Outer radial glia
Advisor: Li, Yun
Effect of Artificial Aging Protocols on Bond Strength Between Orthodontic Metallic Brackets and Human Enamel

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.

Keywords: adhesion
artificial aging
bond strength
orthodontic bracket
orthodontics
thermocycling
Advisor: De Souza, Grace GDS
Tam, Laura LT
Understanding the Adoption and Impact of GasTokens on Ethereum

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.

Keywords: Blockchain Analytics
Ethereum
Gas Mechanism
Gas Tokens
Smart Contracts
Transaction Fees
Advisor: Consens, Mariano
Adult Outcomes of Pediatric-onset CHD2 and SYNGAP1-associated Developmental and Epileptic Encephalopathies

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.

Keywords: adults
autism spectrum disorder
chd2
developmental and epileptic encephalopathy
epilepsy
syngap1
Advisor: Andrade, Danielle M
Shakespeare’s Faerie Art of Enchantment through Tolkien’s Lens: A Historiographical Introduction

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.

Keywords: Enchantment
Eucatastrophe
Faerie
Fairy
Shakespeare
Tolkien
Advisor: Carter, Jill
Choreographing Epistemic Justice for a New Dance Future

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.

Keywords: ballet
contemporary
difficult knowledge
education
research ethics
whistleblowing
Advisor: Carter, Jill
Shape Memory Polymer Composites for 4D Printing of Wearable Devices

In recent years, 3D printing techniques have provided new opportunities for the fast and custom fabrication of high-quality parts. New developments in 4D printing looks to create structures which change their shape over time. Through the design of new shape memory polymer composites (SMPCs) and the application of new printing methods and techniques, the current state of additive manufacturing technologies is being advanced. Herein, three primary studies are conducted: 1) a literature review on the potential of 4D printing custom, wearable devices for biomedical applications, 2) the material development and characterization of a new SMPC featuring 2D MXene flakes and the design of large-scale deformation 4D printed structures, and 3) the characterization of a new porous printing technique for the development of a hybrid 4D printed actuating/sensing wearable device. Through these studies, a holistic view of 3D printing is provided, including material development and manufacturing, with the intention of continued innovation in 4D printing.

Advisor: Naguib, Hani E
Efficient Deep Learning Methods for Solving High-dimensional Partial Differential Equations for Applications in Option Pricing

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.

Keywords: Deep Learning
Monte Carlo Methods
Option Pricing
Partial Differential Equations
Reinforcement Learning
Tensor Networks
Advisor: Lee, Chi-Guhn
Investigation into the Shear Strength of Deep Reinforced Concrete Beams Subjected to 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.

Keywords: Deep Beams
Reinforced Concrete
Shear Behaviour
Shear Mechanisms
Uniform Load
Advisor: Bentz, Evan C
Machine Learning Methods of Risk Evaluation for Community-acquired Clostridioides difficile Infection in Ontario

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.

Keywords: Antibiotic Stewardship
C. difficile
Community
Machine Learning
Risk Analysis
Advisor: Carter, Michael
An Examination of the Effects of Localized Ischemic Preconditioning on Anaerobic Arm Cycling Performance in Water Sport Athletes

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.

Keywords: Athlete
Ergogenic Aid
Exercise
IPC Exercise
Ischemic preconditioning
Sport Science
Advisor: Jacobs, Ira
In Vivo High-speed Microscopy of Ultrasound-stimulated Microbubble-vessel Interactions

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.

Keywords: cavitation
contrast agent
in vivo
microbubble
ultrasound
vascular effects
Advisor: Goertz, David D
Exploration of a Non-linear Relationship Between Diagnostic Delay and Axial Spondyloarthritis Quality-of-Life Measures

Background: Literature is unclear how delays in diagnosing axial spondyloarthritis (axSpA) impacts patient quality-of-life (QoL). Methods: A retrospective longitudinal study of Canadian axSpA patients visiting a hospital-based clinic from 2003-2020 was conducted. Three regression analyses were performed: one-stage linear mixed effects, two-stage instrumental variables, and two-stage propensity score matching. Results: A non-linear relationship exists between diagnostic delay and QoL. Each diagnostic delay year before 36 years was associated with 0.088 points decline in physical health, which worsened by 1.843 points for each year of delay thereafter. Each diagnostic delay year before 18 years added 0.269 points to mental health scores, which in turn fell abruptly by 10.428 points at 18 years of delay. Conclusions: Longer diagnostic delays are significantly linked with poor physical health and reduced mental health at the threshold. These findings necessitate obtaining a faster axSpA diagnosis through improved referral strategies and crosstalk between primary clinics and hospitals.

Advisor: Coyte, Peter C
Inman, Robert D
HAVOC: Mapping of Cancer Biodiversity using Deep Neural Networks

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.

Keywords: Artificial Intelligence
Deep Learning
Glioblastoma
Image Analysis
Oncology
Tumor Heterogeneity
Advisor: Diamandis, Phedias
Art and Affect, the Emotional Labor of the Instagram Follower

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.

Keywords: emotional labor
immaterial labor
instagram
labor
platforms
social media
Advisor: Chen, Julie Y
Variability in the Age of Schooling Contributes to the Link between Literacy and Numeracy in Côte d’Ivoire

We explored the predictors of literacy and numeracy, and their covariance (overlap between the two skills) in rural Côte d’Ivoire. Ivorian children are often old for their grade due to late enrollment and grade repetition, leading to variation in age-for-grade (i.e., how far a child’s age differs from policy age for a given grade). Structural equation models were constructed to understand how cognitive components and individual-level factors predict literacy and numeracy covariance. Phonological awareness, numerical magnitude, ordinality, working memory, and visual attention were cognitive predictors of covariance. Additionally, age was positively related to covariance and age-for-grade was negatively related to covariance. Post-hoc multi-group modelling revealed literacy and numeracy covariance was significant for the on-time group (students in the correct grade based on policy age) but not the late group (students in a lower grade). Our results suggest that literacy and numeracy covariance depend on the maturity of underlying cognitive components.

Keywords: age
age-for-grade
Côte d’Ivoire
literacy
numeracy
Advisor: Jasinska, Kaja K