VictorJuiz/thesis huggingface dataset:
Influence of Stormwater Management Ponds on Chloride Transport to Urban Headwater Streams

Sodium chloride (NaCl) is the most common de-icing agent used on roads and parking lots in North America. During the winter and spring, chloride (Cl-) is readily transported from paved surfaces to stormwater management facilities in a matter of hours to days. According to earlier studies, densimetric stratification in end-of-pipe stormwater management facilities such as wet ponds can result in latencies in Cl- transfer. As a result, wet ponds may generate Cl- pulses in streams that exceed thresholds of acute toxicity to aquatic biota. This study identified the prevalence of this phenomenon at five headwater streams receiving discharge from wet stormwater ponds within the Greater Toronto Area over two years. Results suggest that pulses exceeding acute toxicity thresholds are not ubiquitous and are limited to late winter and spring months.

Keywords: aquatic toxicity
contaminants
road salt
water quality
winter
Advisor: Drake, Jennifer
Oswald, Claire
The Ubiquitous p value: An Exploration of the Survey Sampling Methods Associated with p values in Medical Education Research.

This study explored trends in the use of p values in medical education research studies among articles published in Academic Medicine (and its predecessors) between 1926 and 2023. Three hundred articles meeting inclusion criteria were analyzed for respondent selection method and the use of p values. Since p values describe the probability that a study’s findings are due to sampling error, only studies that used probability sampling should have reported p values. However, 65% of articles that used an attempted census (all members of the population were invited to participate) or non-probability sampling used p values to describe their findings. Stricter peer reviews and more education with respect to what p values mean, and their inappropriate use with non-probability sampling is needed to ensure the collection of accurate, valid, and useable survey data.

Keywords: p-values
Survey Research
Advisor: Childs, Ruth
Direct Assessment of the Hydrophobicity of Ionic Surfactants via the Characteristic Curvature (Cc) Concept

Surfactants possess both hydrophilic and lipophilic moieties that play important roles in numerous applications including personal care, cleaning products, and in the pharmaceutical and food industries. Assessing the hydrophilicity-lipophilicity (H-L) of a surfactant is necessary to predict the surfactant’s behavior in surfactant-oil-water (SOW) systems and to optimize the performance of these systems. This work assesses the H-L of ionic surfactants through the concept of characteristic curvature (Cc) using two innovative reference-free methods: a solubilization method and a method based on Small-Angle X-ray Scattering (SAXS). The Cc is determined via a Hydrophilic-Lipophilic-Difference (HLD) and Net (Hn)-Average (Ha) Curvature (NAC) framework through the definition of Cc as Cc = -Hn∙L (L being the surfactant’s tail length parameter) at the characteristic condition. The solubilization studies revealed that after the original HLD equation for ionic surfactants is multiplied by a factor “bi”, the new HLDbi = bi∙HLD reflects the chemical potential and curvature interpretation, validating the Cc concept. The SAXS studies successfully match the Cc assessed through solubilization methods, providing a direct way to assess the H-L of ionic surfactants without bias presented by cosurfactant in phase inversion studies as well as minimizing the potential computational uncertainty of the solubilization method.

Keywords: HLD-NAC
Hydrophobicity
Microemulsion
SAXS
Solubilization
Surfactant
Advisor: Acosta, Edgar
Sustainable State Sharding and Token Liquidity Provision for the Blockchain Ecosystem

With the increasing adoption of blockchain technology, certain challenges must be tackled for a sustainable future. One such challenge is the ever-growing size of a blockchain ledger which increases the stress on and cost of mining hardware equipment. This thesis provides a solution to the above problem via atomic state sharding of account-based state blockchains. Our solution reduces the disk requirement of each participating miner linearly with respect to the number of shards while maintaining low bandwidth requirements. A second challenge investigated here is the increased cost of liquidity provisions for tokens with low trading volume. Lower liquidity makes the price of a token more vulnerable to manipulation. This thesis presents a design for a decentralized exchange that enhances liquidity for low trading volume tokens via liquidity sharing. Experiments show that our solution enhances liquidity by over 2.6 − 5.9×.

Keywords: Atomic
Automated Market Maker
Decentralized Exchange
Ethereum
Liquidity
State sharding
Advisor: Veneris, Andreas
Long, Fan
Quantile Formulation Risk-Aware Reinforcement Learning for Goal-Based Portfolio Optimisation

We study the problem of active portfolio management where a risk-aware investor seeks to construct an optimal dynamic allocation with respect to a prescribed risk profile subject to the constraints imposed by their budgetand desired realised gain. Specifically, we consider investors of whom seek to construct allocations that minimise the rank dependent expected utility of their terminal wealth subject to a budget and goal-based investing constraint (or aspiration criterion) that specifies a lower bound on the probability that the investor posts some fixed positive return. We show that an appropriate regularisation of He and Zhou's HF/A model admits optimal allocations that can be characterized explicitly through the notion of isotonic projections in a more general class of market models than those which they consider. We then turn to the risk-aware reinforcement learning (RL) task this problem defines and further affirm the paradigm's potential in tackling constrained portfolio optimisation problems; we employ the deep policy gradient (DPG) framework developed in recent work of Jaimungal & Pesenti (2022) and illustrate the learned behaviours of the agent beneath a variety of risk profiles and aspiration criteria.

Keywords: Goal-Based Investing
Portfolio Optimisation
Reinforcement Learning
Risk Measures
Advisor: Jaimungal, Sebastian SJ
Estimating Indoor Airborne Concentrations of SARS-CoV-2 Using Quantitative Filter Forensics

Room-scale, week-long environmental surveillance is needed to better understand where SARS-CoV-2 is at high airborne concentrations in built environments. This study used portable air filters (PACs) and quantitative filter forensics (QFF) to assess airborne concentrations of SARS-CoV-2 RNA in COVID-symptomatic homes, classrooms, and dining locations throughout Toronto. PACs were deployed for one week each, and dust from filters was collected via vacuuming. SARS-CoV-2 RNA from filter dust was quantified using reverse transcription-polymerase chain reaction (RT-PCR) and PAC metadata were used to estimate airborne concentrations of SARS-CoV-2. The highest concentrations of SARS-CoV-2 were found in isolation rooms. Classrooms had lower concentrations during summer. Limitations include unknown recovery efficiency of RNA from filters and the dynamics of concentrations due to the temporal averaging provided by QFF. This study concludes that isolation was effective at keeping high airborne SARS-CoV-2 concentrations in isolation rooms and seasonal variation in concentrations in classrooms was observed.

Keywords: COVID-19
dust
filter
portable air cleaner
ventilation
virus
Advisor: Haines, Sarah R
Siegel, Jeffrey A
Image-Based Prediction of Building Attributes with Deep Learning

Construction material use causes about 11% of global GHG emissions and is an accelerating driver of global warming. In this research, we use image-based machine learning to predict the floor area and age of buildings which are strongly correlated with embodied GHG emissions. The ability to automatically estimate building attributes from street view images can enable large-scale analysis of the built environment and provide better differentiability compared to patch-wise or pixel-wiseestimation from satellite images. A ResNet-18 model is used for feature extraction, and area and age predictions are formulated as a regression problem and a classification problem, respectively. On area prediction, our model achieves a Mean Absolute Percentage Error of 22.32%. On age prediction, our model achieves a Balanced Accuracy (BA) of 78.05% and Accuracy of 79.05% when there are 3 age classes, but the BA and Accuracy drop to 61.94% and 63.53%, respectively when there are 6classes.

Keywords: Building
Climate change
Deep learning
Greenhouse gas
Machine learning
Material stock analysis
Advisor: Khalil, Elias EK
Saxe, Shoshanna SS
Identifying Oncology Patients at High-risk for Potentially Preventable Emergency Department Visits (PPEDs) at a Single Institution in Toronto, Canada

Background: Reducing potentially preventable emergency department visits (PPEDs) is important. This study aims to define and describe oncology-related PPEDs at a single institution and use machine learning (ML) to help identify oncology patients at highest risk for PPEDs. Methods: A retrospective cohort study was conducted among five databases. ED visits by oncology patients between April 1st, 2019 – April 1st, 2021 from a single institution were collected. Trends in PPEDs were evaluated using descriptive statistics, logistic regression, and ML modelling.Results: 6,689 oncology patients visited the ED (n=13,415 visits) during the study period. 62.1% were classified as PPEDs. High-risk groups for PPEDs included stage 1-3 breast cancer patients and adjuvant systemic therapy patients. The highest-performing ML model scored an AUC = 0.819. Conclusions: High-risk groups for PPEDs include stage 1-3 breast cancer patients undergoing systemic therapy. Our novel definition of PPEDs at this stage appears reasonable. Future research to validate this work can be impactful.

Advisor: Tran, William T
Gandhi, Sonal
Variable Categorization and Seelection in the Context of Inverse-intensity Weighting for Longitudinal Data

The main objective of this thesis is to address the challenge of analyzing longitudinal data with irregular follow-up times, which may relate to the outcome. While traditional methods like generalized estimating equations (GEEs) have limitations in handling such data, inverse-intensity weighted GEEs (IIW-GEEs) offer a broader range of applications. The selection of variables in the observation intensity model plays a crucial role in ensuring consistent inferences in IIW-GEEs. However, the impact of including different types of variables on the variance of estimated regression parameters is not well-studied. This thesis aims to investigate how variable selection in the observation intensity model affects the variance of IIW-GEE estimates.\\ By employing mathematical derivation, this thesis demonstrates that the asymptotic variance of the estimated regression parameter remains unchanged when incorporating variables that are predictive of neither the outcome nor the observation intensity. Conversely, the variance decreases when including variables that are predictive of the outcome only, while it increases when adding variables that are predictive of the observation intensity only. To assess the magnitude of these effects, simulations are conducted to determine the extent to which the variance of the estimated regression parameter changes after adding three different types of variables. The simulation results reveal that adding variables predictive of the outcome only to the intensity model decreases the variance, even though sometimes with minimal impact. Conversely, incorporating variables predictive of the observation intensity only increases the variance. Furthermore, the practical application of these findings is demonstrated using data from a randomized trial investigating treatments for major depressive disorder. Although the results indicate no consistent changes, that may be because there are no covariates that are strong predictors of either the outcome alone or the visit process alone.\\ In conclusion, this thesis provides insights that can assist researchers working with longitudinal data in optimizing their variable selection process when applying IIW-GEEs. By understanding the effects of different types of variables on the variance of estimated regression parameters, researchers can make informed decisions regarding variable inclusion, ultimately benefiting their analyses.

Advisor: Pullenayegum, Eleanor
Understanding Brain Transcriptome Signatures Associated with Disparate Psychiatric Outcomes in Black American Populations

The molecular basis underlying disparate schizophrenia (SCZ) outcomes remains unclear, but stress and immune pathways associated with social inequalities are thought to contribute. To address questions about race-specific effects on the brain and in SCZ, I analyzed post-mortem DLPFC RNA-seq data from two racially diverse cohorts in the CommonMind Consortium (235 Black and 546 White; 322 SCZ and 459 controls). Mega-analyses yielded 1514 genes with differential expression (DE) between Black and White-reported individuals and enrichments implicated upregulation in stress and immune pathways in Black individuals. Interaction models showed 109 gene sets DE in a race-specific manner across SCZ, and semantic clustering revealed these sets implicated metabolic and immune pathways. Our results support a molecular mechanism underpinning racial differences in SCZ outcomes involving immune and stress pathways and underscore the importance of diverse cohort ascertainment to capture the diversity of SCZ pathogenesis.

Keywords: Computational Psychiatry
Health Disparities
Neuropathology
Population Health
Social Genomics
Transcriptomics
Advisor: Tripathy, Shreejoy
Identifying Potential Imaging Biomarkers of Neuropsychiatric Lupus in Children using Deep Learning

Childhood-onset systemic lupus erythematosus (cSLE) is characterised by a high frequency of nervous system involvement, termed neuropsychiatric SLE (NPSLE). Biomarkers for this disease are lacking, current diagnostic processes are suboptimal, and treatments can be toxic. Deep learning (DL) network applications in medical imaging have seen recent success in identifying nervous system diseases. This thesis aimed to discover features of T1-weighted brain images useful for cSLE vs. non-cSLE classification. Using 163 cSLE and 163 non-cSLE images, we trained four different DL classifiers. We explored transfer learning from pre-trained 2-dimensional models to this 3-dimensional task. During performance testing, 79% of cSLE samples and 89% of NPSLE subsamples were correctly classified by the best model. The visual features of periventricular white matter, intraventricular regions, and the right superior longitudinal fasciculus seemed to guide these predictions. Further study is needed to characterise the mechanisms linking these brain image features to the presence of NPSLE.

Keywords: Artificial intelligence
Computer vision
Diagnostics
Neuroimaging
Neuropsychiatric disorders
Pediatric lupus
Advisor: Knight, Andrea M
Ibrahim, George M
Phenological Assortative Mating And The Evolution of Flowering Time

This paper explores the influence of phenological assortative mating on the evolution of flowering time. First we analytically model the evolution of flowering time in a metapopulation of discrete patches of plants that each mate assortatively for flowering time and are subject to different selective optima for flowering time. We show that stronger assortative mating enhances population differentiation and thus local adaptation. Next, we model the combined influence of phenological assortative mating for flowering time and reproductive isolation by distance in a series of individual-based simulations. We show that the combination of these two forms of non-random mating act to enhance the theoretically predicted consequences of each form in isolation. In particular, we demonstrate the emergence of enhanced spatial-genetic structure where individuals are spatially autocorrelated for flowering time trait values. Finally, we present ecological conditions that could act to dampen this tentative synergy

Keywords: evolution
Advisor: Weis, Arthur E
Comparing Body Composition Assessment Methods and Evaluating Physical Activity in Children with Intestinal Failure: Building a Framework for more Personalized and Holistic Care in Pediatric Intestinal Failure

Objectives: 1) Compare body composition (BC) measured using bioelectrical impedance analysis (BIA) to that measured using dual-energy x-ray absorptiometry (DXA), 2) Collect data on physical activity (PA), and 3) Assess the relationship between PA and BC in pediatric intestinal failure (IF).Methods: IF patients, 1-18 years, having a DXA as part of clinical monitoring were eligible. BC measured by BIA and DXA, and PA by accelerometry. T-tests examined differences in BC obtained by the two methods and compared PA and BC to norms. Regression analysis assessed the relationship between BC and PA. Results: Sixty-eight children were studied. There was no difference between BC obtained by DXA and BIA (p=0.26). IF patients were less active with higher FM and lower FFM than norms (p<0.05). PA was associated with BC (r2=0.32, p<0.001). Conclusions: BIA is acceptable for assessing BC in pediatric IF. PA interventions and BC assessment should be part of management.

Keywords: Bioelectrical Impedance Analysis
Body composition
Intestinal failure
Parenteral nutrition
Pediatric
Physical activity
Advisor: Courtney-Martin, Glenda
Factors that Contribute to the Development of Receptive and Expressive Comprehension of Figurative Language in English Language Learners (ELLs): A Systematic Scoping Review

The objective of this study was to conduct a systematic scoping review of published studies on factors that contribute to the development of figurative language (FL) in English Language Learners (ELLs). To date, research on FL processing in ELLs has not been reviewed systematically, and a more nuanced understanding of the nature of possible difficulties that ELLs face is limited. A total of 89 sources were identified through electronic database searches and handsearching in peered-reviewed journals. After screening those studies, 18 papers were included for data extraction, and narrative synthesis. Findings indicated that (a) few studies have examined FL in ELLs, and they were not consistent in terms of their focus, methodology, participants and results, (c) studies on FL in ELLs focus only on one factor, and (d) FL development in ELLs has primarily been examined from a pedagogical and teaching perspective rather than a systematic psycholinguistic and developmental one.

Advisor: Geva, Esther
Optical Force Integration in Lab-on-Chip Cell Sorting Techniques

Cell sorting is pivotal for diagnosing and treating blood diseases. Recently, microfluidic chip-based methods have streamlined rapid on-chip testing. However, optical force-based cell sorting, utilizing optical tweezers, offers a non-invasive, highly controllable approach. These tweezers trap and manipulate microscopic entities, such as cells, using a focused laser beam. They offer superior sensitivity, non-destructive control, and avert contamination risks. This thesis explores optical tweezers’ physics, highlighting their efficacy in manipulating particles sized 1-10 μm with pico-newton optical forces. Yet, challenges exist, notably the serial analysis limiting throughput. New algorithms, like the Gerchberg-Saxton, address this, while integrating microfluidics enhance automation and efficiency. Beyond biology, optical tweezers find potential in nanotechnology and regenerative medicine, aiding nanoparticle assembly and stem cell manipulation. This research paves the way for revolutionary on-chip blood tests and a deeper understanding of microscopic forces, promising significant biomedical breakthroughs.

Keywords: biosensors
cell sorting
Lab-on-chip
Optical Tweezers
Advisor: Aitchison, J Stewart JSA
Evaluating Inflammatory Cytokine Changes in Participants with Mild Cognitive Impairment or Alzheimer’s Disease in the EXPRESS Study

Current pharmacotherapies for Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) lack efficacy and tolerability. The non-pharmacological therapy transcranial direct current stimulation (tDCS) is of interest, as studies have shown improved cognition following tDCS. Neurobiological mechanisms by which tDCS may improve cognition are poorly understood. The inflammatory cytokines Interleukin-6 (IL-6) (primary) and Tumor Necrosis Factor (TNF) (exploratory) were measured in MCI/AD participants (N=39) before and after 3 weeks of aerobic exercise followed by two weeks of exercise+tDCS, exercise+sham, or treatment as usual+tDCS. For IL-6, TIME (F(36)=7.351, p=0.01) and GROUPxTIME (F(36) = 3.852, p=0.03) effects and indicated that IL-6 levels increased from study screening (T0) to endpoint (T2). Exploratory analyses showed TNF was inversely associated with recognition memory. T0 IL-6 and TNF did not predict changes in global cognition or recall or recognition memory. This research suggests that changes in inflammatory cytokines may be related to cognitive outcomes.

Keywords: Aerobic Exercise
Alzheimer's
Inflammation
Interleukin-6
Mild Cognitive Impairment
tDCS
Advisor: Lanctôt, Krista L
Mediating Real-Estate: Understanding Property Platforms in Toronto’s Residential Real-Estate Markets

Property platforms are promising to reduce the risks, time, and costs associated with finding and securing a home; they are claiming to improve existing real-estate processes and operations using insights derived from spatial and user-data. In light of these claims, this research examines the impacts of these new property platforms on real-estate markets in Toronto, Canada. Though the material implications of these technologies have yet to fully manifest, I argue that property platforms are covertly impacting real-estate markets in three main ways: they are allowing new entrants and intermediaries into the field of real-estate, they are leveraging societal crises to rationalize the development and adoption of their technologies, and they are merging and consolidating out of economic necessity paving the way for monopoly power over spatial data. As these new entrants gain access to real-estate and authority over its processes, I contend that property platforms should be understood as a conduit through which financial and technological actors can assert unilateral power over our spatial and behavioural information. Platforms’ newfound dominion over how we find, valuate, buy, sell, manage, develop, and build real-estate prefigures their potential to exert unprecedented influence and control over physical space.

Keywords: Digital technology
Financialization
Housing
Platforms
Real-estate
Advisor: Bunce, Susannah
A Polygenic Risk Score for Hand Grip Strength is Associated with Cognitive Function in Mid-to-Late Life Adults

Lower muscular fitness is a risk factor for Alzheimer's disease (AD). Hand grip strength (HGS) is a heritable predictor of cognitive decline in aging, however, the genetic component of HGS and its impact on cognitive decline remains unexplored. To test the innate biological influence of HGS on cognition in large populations, we calculated a polygenic risk score (PRS) for HGS (PRSHGS) and evaluated its direct and mediated effects on cognitive function, cross-sectionally and longitudinally, in two independent mid-to-late life cohorts. Findings revealed that genetic predisposition for greater HGS was significantly associated with better cognitive performance. The effects of PRSHGS on cognition were not moderated by levels of physical activity or cardiovascular health and explained variance in cognition beyond the benchmark PRS for AD. Additionally, PRSHGS was not associated with postmortem neuropathologies. These results suggest a direct link between HGS and cognitive performance, independent of lifestyle factors and AD-related neuropathology.

Keywords: Alzheimer's disease
Cognitive decline
Hand grip strength
Polygenic risk score
Advisor: Felsky, Daniel