VictorJuiz/thesis huggingface dataset:
Cis-regulatory Mechanisms of Acute Phase Gene Expression
Keywords: Chromatin Conformation
Comparative Genomics
Epigenetics
Gene regulation
Genome Biology
Rare Disease

The accurate control of when and where genes are expressed is central to our understanding of development and disease. Elucidating the mechanisms that control this complex, multifaceted process is a major endeavor at the forefront of genetics and genome biology. The acute phase response (APR) is an evolutionarily conserved systemic response to inflammation that triggers rapid gene expression changes in the liver and other tissues. Genes induced during the APR are regulated via unique mechanisms that facilitate their rapid expression upon stimulation while maintaining their silencing under basal conditions. To dissect the mechanisms controlling APR gene expression, I employ a combination of epigenetic profiling, comparative genomics, and chromatin conformation capture to characterize the cis-regulation of a representative APR gene, PLAU. I investigate how PLAU is dysregulated in Quebec platelet disorder, a bleeding disorder in which a single tandem-duplication of PLAU causes its ectopic >100-fold overexpression in platelets and megakaryocytes. I demonstrate that the silencing of PLAU is normally facilitated by local 3-dimensional (3D) chromatin architecture which isolates it from the activity of nearby tissue-specific enhancers, while disruption of this architecture in QPD results in ectopic activation of PLAU by a conserved megakaryocyte enhancer. Employing a similar multi-omics approach, I investigate genome-wide transcriptional and epigenetic changes during the APR in the context of 3D chromatin architecture in the mouse liver. I demonstrate that enhancer-gene pairs within the same chromatin loop are more likely to show concordant changes in gene expression than equidistant genes in different domains, together illustrating a paradigm in which 3D chromatin architecture coordinates both the activation and silencing of acute-phase gene expression.
The Design of Perovskites for Light-Emitting Applications
Keywords: Machine Learning
Materials
Optoelectronics
Photonics

In the last decade, perovskite quantum dots have emerged as next-generation active materials for optoelectronics. They have tunable bandgaps, good charge carrier mobility and low defect density, and are readily synthesized. They exhibit high photoluminescence quantum yields and bandgaps tuned throughout the visible range via compositional engineering and nanostructure modulation. This positions perovskites for applications in red, green and blue light-emitting diodes. They must achieve narrow emission linewidths, as well as increased stability and efficiencies under operating conditions, to realize their potential in displays.In this thesis, I explore the design and prediction of novel perovskite materials through experimental and computational methods; and I find new optoelectronic materials with promise as narrowband light-emitters. Mixed anion approaches used by prior researchers to tune bandgap suffer from halide segregation and resultant spectral instability. I designed a mixed cation strategy whereby Rb+ is directly incorporated during synthesis into CsPbBr3 nanocrystals, forming the alloyed RbxCs1-xPbBr3. This resulted in tunable blue-emitting perovskite quantum dots and devices with stable photoluminescence and electroluminescence ranging from 460 – 500 nm and narrow emission linewidths (
Discovery and Validation of a Panel of Methylation Biomarkers for the Detection of Aggressive Prostate Cancer
Keywords: Biomarkers
CAPRA-S
Epigenetics
IDC/C
Methylation
Prostate Cancer

Prostate cancer (PCa) ranges from indolent to aggressive tumors with the challenge of identifying high-risk PCa patients that will benefit from multi-modal aggressive treatments, without overtreatment. Radical prostatectomy (RP) remains the gold standard for curative treatment of localized PCa. Approximately 30-50% of men experience recurrent cancer within 10-years post-surgery. Intraductal carcinoma and cribriform (IDC/C) is a prognostic indicator of biochemical recurrence(BCR), metastasis and prostate cancer-specific mortality. Approximately 70% of PCa patients are IDC/C negative yet up-to 20% experience BCR post-RP. Thus, tumor histopathologic characteristics are limited in their ability to predict BCR. Similar to IDC/C, several biomarkers, panels and nomograms have been developed to improve patient risk stratification and prognosis. However, lack of large-scale validation in well characterized cohorts have limited translation to clinic. This thesis focuses on the discovery, development, and validation of novel biomarkers and panels in well-characterized multi-institutional cohorts of patients undergoing RP. Through the use of genome-wide DNA methylation profiling we have identified key hypermethylated genes associated with PCa progression. One such candidate was gastrulation of the brain homeobox 2 (GBX2) significantly associated with GG, pT, and an independent predictor of BCR. GBX2 methylation was able to discriminate patients diagnosed with intermediate disease suspected to have a poorer prognosis (≤GG2 versus ≥ GG3). Additionally, GBX2 methylation has additive potential to IDC/C. Especially among IDC/C negative patients, those with high GBX2 methylation had a shorter time to BCR (P=0.002). The development of panels outperforms the predictive accuracy of any single marker for BCR alone. To address this, I developed and validated a 4-G model consisting of APC, HOXD3, TGFβ2 and CRIP3 (AUC=0.670, Cohort 3), an Integrative gene model combining the 4-G and CAPRA-S (AUC=0.726, Cohort 3) and a clinic-pathological based model integrating IDC/C+CAPRA-S. The investigation of IDC/C features among CAPRA-S low-risk and conversely the investigation of CAPRA-S among IDC/C negative patients improved the prognostication of time to BCR. The integration of epigenetic markers, CAPRA-S and IDC/C can serve as an informative model for improved prediction of tumor behavior. The findings in this thesis with further validation, may lead to improved post-operative risk-stratification tools for PCa patient management.
Drying of a Single Suspension Droplet in Suspension Plasma Spray: Numerical and Experimental Study
Keywords: Buckling
CFD
Evaporation
Multiphase Flow
Numerical Modeling
Suspension Plasma Spray

Suspension plasma spray is a powerful coating technique for depositing high-quality thermal barrier coatings with promising mechanical and physical properties while taking advantage of the well-established atmospheric plasma spray process. In this process, fine ceramic particles are suspended in liquids such as water or ethanol before injection into a plasma jet. The heat from plasma evaporates the carrier liquid of injected suspension droplets and leads to an agglomeration of the particles. The agglomeration process plays a crucial role in the state of particles before impacting a substrate and, consequently, the mechanical properties of the coating. In this work, numerical models for simulating different cases of drying of a single suspension droplet in processes with a high evaporation rate like suspension plasma spray are devised. The models use a Lagrangian method for tracking suspended solid particles and the Eulerian method for the liquid phase. These models not only demonstrate droplet distortion during evaporation but also predict solid particles behavior suspended in the droplet. The numerical models can describe consecutive states of the agglomeration process, including droplet shrinkage, particle accumulation on droplet surface, and buckling. Also, the numerical analysis sheds more light on the complex phenomena of buckling, especially the interaction of the droplet interface and suspended solid particles, and different types of suspension droplet buckling. The numerical results reveal that the role of capillary forces in the shape of agglomerated particles is significant. It is found that particle-induced surface pressure drives the buckling of the suspension droplet. It is also shown that the particle-induced surface pressure reaches a threshold value at the onset of buckling and overcomes surface tension. Finally, the models have been validated by comparing the simulation results with designed experiments. The numerical results are in good agreement with the experimental ones, both qualitatively and quantitatively.
Learning to Build Probabilistic Models with Limited Data
Keywords: Deep Learning
Machine Learning

Deep learning has successfully transformed a wide range of machine learning applications in recent years. One of its keys to success is the ability to learn relevant features from scratch on large amounts of data. Yet there are many real-world scenarios in which a new model must be built quickly with only a small amount of data. This naturally raises the question: How can we use deep learning to efficiently build task-specific models given limited data? In this thesis, we introduce a framework for understanding relationships among these sorts of problems and propose several novel algorithms for tackling challenging tasks in this regime including few-shot learning, zero-shot learning, and structured prediction. In the first part of this thesis, we propose a deep metric learning approach to few-shot and zero-shot classification that generalizes from just a few examples. It does so by exploiting a simple inductive bias, namely that there exists an embedding space in which examples from the same class are clustered around a single prototype representation. We demonstrate the strong accuracy of our approach on standard few-shot and zero-shot benchmarks. We then consider image segmentation, an task in which is important for a machine learning system to capture multiple plausible outputs due to ambiguity. We introduce a tree-structured graphical model for representing such distributions and propose an algorithm that optimizes the model structure for each image. When evaluated on a challenging image segmentation datasets, our algorithm is successfully able to generate multiple diverse segmentations much in the way that humans do. Finally, we develop a Bayesian approach to few-shot classification based on Gaussian processes. Our method learns a deep kernel to compute the similarities among input images and uses a Pólya-Gamma augmentation scheme to achieve tractable inference. We show strong predictive accuracy on several challenging few-shot learning datasets and improved uncertainty quantification over baseline methods.
Investigating the Mechanisms and Kinetics of Circulating Tumour DNA Release to Improve its Clinical Utility
Keywords: Cell death
Circulating cell-free DNA
Circulating tumour DNA
Head and neck cancer
Liquid biopsy
Treatment monitoring

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease with distinct subtypes, molecular features and clinical behaviours. Significant advances to better define this heterogeneity have revealed new targets and attempted to stratify patients into clinically meaningful groups. Human papillomavirus (HPV)-positive HNSCC has been recognized as a distinct etiology with significantly better treatment response and overall survival compared to HPV-negative HNSCC. However, despite the improved prognosis of these patients, current therapies result in significant toxicities, and subtype-specific treatments are still lacking. Thus, there remains an urgent need for additional biomarkers of therapeutic sensitivity to better risk stratify these patients and enable personalized treatment regimens. Circulating tumour DNA (ctDNA) has sparked tremendous interest in the last decade as a non-invasive method to monitor dynamic changes in treatment response. However, the mechanisms that dictate ctDNA release kinetics have not been thoroughly investigated, hampering the interpretation of such ctDNA kinetic patterns. To address this gap, I first evaluated the biological underpinnings that dictate ctDNA release using preclinical models of HNSCC. To evaluate the generalizability of my findings, I simultaneously evaluated ctDNA release from other cell types including mesenchymal cells and lung adenocarcinoma. Through detailed mechanistic studies, I uncovered a complex interplay between apoptosis, necrosis and senescence in determining ctDNA release kinetics, where treatment type and timing from treatment exposure were key factors influencing release. I next investigated the longitudinal kinetics of ctDNA release in 70 HNSCC patients treated with definitive radiotherapy or chemoradiotherapy. Three patterns of ctDNA kinetics were observed, with HNSCC patients exhibiting frequent on-treatment spikes, with a strong dependence on treatment type. Lastly, utilizing the knowledge gained in regard to the underlying biology and longitudinal profiles of ctDNA release, I identified a cohort of 235 HPV-positive oropharyngeal carcinoma patients and discussed the next steps in evaluating the relationship between ctDNA kinetic changes and clinical response. These studies provide novel insights into the biological mechanisms and dynamic changes of treatment-induced ctDNA release. Overall, these findings highlight the potential clinical utility of dynamic ctDNA monitoring to facilitate biomarker-driven adaptive therapy, with the intent to maximize disease control and minimize current treatment related-morbidities.
Outperformance and Tracking: A Framework for Optimal Active and Passive Portfolio Management
Keywords: Mathematical Finance
Portfolio Management
Stochastic control

Portfolio management problems can be broadly divided into two classes of differing investing styles: active and passive. There are a number of philosophical and operational distinctions between these two investment approaches including mandate, complexity, activity level, constraints, and goals. Both forms of portfolio management, however, can involve absolute or relative goals. These goals are distinguished by the involvement (or lack thereof) of external processes in measuring an investor's performance. This thesis presents a unified framework for solving portfolio selection problems arising in both active and passive portfolio management with a focus on relative goals. We are, in particular, interested in stochastic optimization problems related to outperforming a selected benchmark and/or tracking a given portfolio, both natural and essential questions in portfolio management. In the first part of the thesis, we lay the foundation for our framework by introducing a flexible stochastic control problem that captures the range of goals we are interested in. We solve the problem, first, in a simple setting using a dynamic programming approach and derive an explicit closed-form solution for the optimal portfolio. We uncover several important features of the optimal portfolio, most notably, a decomposition result where the optimal solution is expressed in terms of the underlying benchmarks, the growth optimal portfolio, and the minimum variance portfolio. We probe the empirical performance of the optimal portfolio using historical as well as simulated market data. The latter parts of the thesis are dedicated to cases where (i) assets are driven by latent factors and general martingale noise processes, (ii) the market model and preference parameters are stochastic, and (iii) the investor has non-linear utility. These extensions are approached using variational analysis techniques which exploit the convexity of the underlying performance functionals to easily incorporate these additional features. In some instances, the optimal portfolio is characterized via forward backward stochastic differential equations (FBSDEs), and, due to the variational characterization, we establish the existence and uniqueness of solutions to those FBSDEs. The approach we take is also useful for lending interpretability to the solution of the well-known Merton problem when model parameters are stochastic. Moreover, in some special cases, we are able to extend the decomposition result established in the Brownian setting. From a practical perspective, this thesis also presents a fully implementable procedure for computing optimal portfolios for a hidden Markov model of asset prices, including parameter estimation along with filtering results for the hidden state in a general semimartingale setting. The investment performance is demonstrated with out-of-sample backtesting using historical market data.
Radio Propagation Effects and Coherent Sources
Keywords: Cosmology
Fast radio bursts
Magnetic field
Propagation effect
Pulsar
Radio transient

Some critical components of the universe are difficult to observe in emission. One way to study them is through their interaction with light from background sources. When studying the propagation effects of a background source, one major challenge is the degeneracy introduced by the source behavior. Sources with simple known properties, such as the cosmic microwave background (CMB), or point-like and coherent sources, such as pulsars and fast radio bursts (FRBs), are optimal. In this thesis, I focus on the studies related to these sources. I analyze the possibility of distinguishing kinematic Sunyaev-Zel'dovich (kSZ) distortions on CMB as a way to measure diffuse baryon distribution. By cross-correlating kSZ distortion with density fluctuations from 21 cm intensity mapping, a > 20σ detection with existing facilities is forecasted. Separately, I have proposed a new method to study small-scale magnetic structures via plasma lensing, which is applied to study the magneto-environment in the interface between a pulsar B1957+20 and its companion's wind. A new constraint of σB < 10 mG is set for themagnetic spatial structures in the interface, which poses challenges in explaining the observed eclipse of pulsar light. Furthermore, I have discovered the first case of periodic activity from an FRB source, which is a critical step to understand the nature of the FRB progenitor.
Preventing Osteoporotic Fractures in Men Living with HIV: Model Calibration and Economic Evaluation Studies
Keywords: Health economics
Medical decision modelling
Model calibration

I aimed to provide Ontario’s public healthcare payers with an economic evaluation of fracture prevention strategies for HIV-positive men who take antiretroviral therapy (ART). When developing microsimulations models for this purpose, calibration is critical and computationally expensive. Studies that explore efficient calibration methods for microsimulation models are limited. As such, I sought to determine whether simulated annealing or Nelder-Mead is the best-performing parameter search algorithm to calibrate a microsimulation model in project one; determine whether a discrete-event simulation model of fractures leads to more efficient calibrations than a patient-level, discrete-time simulation model in project two; and, estimate the cost-effectiveness of osteoporosis screening and treatment strategies in ART-treated men over a lifetime horizon in project three. For projects one and two, I constructed a Markov microsimulation model that tracked bone loss and incident fractures. All calibrations were based on 4 calibration targets, 12 calibration inputs, a binomial log-likelihood goodness-of fit measure, and first-order searches in a simulated sample of 1000. I assessed calibration performance according to differences in the goodness-of-fit and the time to identifying a good parameter set. In project one, both algorithms produced good sets that had similar fit, but simulated annealing identified the sets two times faster than Nelder-Mead. In project two, both model structures generated similarly accurate good sets, while the discrete-event simulation generated these three times as quickly as the discrete-time simulation. In project three, I evaluated 13 strategies for initiating osteoporosis treatment based on Fracture Risk Assessment Tool (FRAX) scores, with and without bone mineral density (BMD). The base-case included 50-year-old, HIV-positive men who took ART for ≥3 years and did not have a history of fractures or osteoporosis treatment. I incorporated the calibrated results from projects 1 and 2 in project 3’s probabilistic analysis. I discounted outcomes annually by 1.5%. At a willingness-to-pay threshold of $50,000/QALY, it is most cost-effective to assess FRAX without BMD, offer treatment if FRAX is ≥10%, and re-screen annually. Collectively, these studies suggest using simulated annealing and discrete-event simulation as efficient calibration methods; as well as that the payer should not pay for BMD screening in older HIV-positive men.
Sustainable Energy Use in Dar es Salaam: Current Trends, Future Scenarios, and Policy Options
Keywords: Africa
Cities
Energy Transitions
Greenhouse Gas Emissions
Infrastructure Sustainability
Urban Planning

In 2019, Africa accounted for only 5% of global energy demand and 3.7% of energy-related carbon dioxide emissions. However, Africa’s rapid urbanization will contribute to rising energy use and emissions, both regionally and globally. Using the case of Dar es Salaam, Tanzania, this thesis offers new insights relevant to the discourse on Africa’s evolving energy landscape. The thesis: (1) Estimates possible changes in Dar es Salaam’s residential energy use and greenhouse gas (GHG) emissions between 2015 and 2050, (2) Identifies key household and transport-related drivers of energy use and GHG emissions, (3) Assesses variations in energy use at the sub-city (ward) level, i.e., between settlements of differing socio-economic profiles and spatial location in the city, and (4) Examines institutional and societal factors that may constrain low-carbon development in Dar es Salaam. Three studies are presented to address the four aforementioned thesis aims. The first study – Modelling Future Patterns of Urbanization, Residential Energy Use and Greenhouse Gas Emissions in Dar es Salaam with the Shared Socio-Economic Pathways – employs a scenario-framework to scope different urban growth and GHG emissions pathways in Dar es Salaam. The work demonstrates an approach for projecting GHG emissions in an Africa city context that may be data constrained. The second study – Does Location Matter? Investigating the Spatial and Socio-Economic Drivers of Residential Energy Use in Dar es Salaam – shows the differences and clustering of energy use that exist at the ward level, and employs statistical methods to correlate energy use with different socio-economic and spatial characteristics of wards. The final study – Assessing Institutional and Societal Barriers to Low-Carbon Development in Dar es Salaam – asserts that processes to implement low-carbon measures (e.g., electrification and public transport projects) would need to engage multiple stakeholders in a collaborative process to leverage the power and mandate of different institutions. Together, these studies seek to inform energy and urban planning policies in Dar es Salaam that (1) enhance synergies between GHG mitigation investments, (2) support implementation strategies that consciously account for local energy use realities and infrastructure access needs, and (3) acknowledge linkages between sustainability, climate change, and socio-economic development strategies.