VictorJuiz/thesis huggingface dataset:
Development of a Cardiovascular Risk Prediction Model for Individuals with Psoriasis and Psoriatic Arthritis

Aim: Psoriasis and psoriatic arthritis (PsA), collectively known as psoriatic disease (PsD), are characterized by excess cardiovascular (CV) morbidity and mortality compared to the general population. The aim of this thesis is to identify novel biomarkers and disease-specific variables that predict CV risk beyond traditional CV risk factors in patients with PsD. Methods: Data from prospective cohorts of patients with psoriasis and PsA were analyzed. The association of serum metabolites and CV events was investigated. The association between cardiac troponin I (cTnI) and N-terminal pro-brain-type natriuretic peptide (NT-proBNP) and carotid atherosclerosis presence and progression was also tested. Variable selection was used to construct multiple CV risk prediction models – these included traditional CV risk factors, cardio-metabolic biomarkers and disease-specific variables. Metrics of risk prediction were used to determine whether the addition of biomarkers and disease-specific variables to each model improved their predictive performance beyond traditional CV risk factors and the Framingham Risk Score (FRS). Results: The analysis revealed several metabolites associated with CV risk. A model with 13 metabolites significantly improved prediction of CV events beyond a model with age and sex alone. A FRS-adjusted model with 11 metabolites did not improve CV risk discrimination. Investigation of cardiac biomarkers demonstrated that cTnI was independently associated with the burden of carotid atherosclerosis. Elevated cTnI and NT-proBNP were associated with a higher risk of developing CV events independent of traditional CV risk factors. Addition of cTnI or NT-proBNP did not improve the performance of the FRS for predicting CV events. Considering disease-specific variables, a CV risk prediction model for patients with PsD was constructed – this model included traditional CV risk factors and it had excellent performance in predicting CV events within a 5-year period. Disease-specific risk factors did not improve predictive performance beyond traditional CV risk factors alone. Conclusions: Serum metabolite and cardiac biomarkers are associated with the development of incident CV events in patients with PsD, but do not improve predictive performance compared to the FRS. Psoriatic disease related risk factors were not superior to traditional CV risk factors, which performed very well in predicting CV events in patients with PsD.

Keywords: biomarker
cardiovascular disease
prediction
psoriasis
psoriatic arthritis
risk assessment
Advisor: Gladman, Dafna D
Eder, Lihi
Quantum Computing for Chemistry: From Quantum Computing with Molecules to Algorithmic Design

Determining properties of molecules and materials is one of the premier applications of quantum computing. A major question in the thesis is: how might we use long- and near-term quantum computers to solve problems of chemical interest? First, we give an overview of the standard model in electronic structure theory. Electronic structure methods have been improved significantly in the last decades; however, it is still a non-trivial task to fit experimental results for complex molecular systems. Second, we introduce quantum computation and quantum algorithms as an alternative device to solve the electronic structure problem. In the remaining part of the thesis, we describe the research projects. The thesis covers two (rather different) research directions: software and hardware projects. For the software part, we contribute with two new algorithms: Phase-Estimation Interval Target Energy Readout (PHILTER) and Recursive Variational Series Estimation (RVSE), where both are designed to be run on a quantum digital computer and extract materials and molecular properties. The PHILTER algorithm is a modification of the Quantum Phase Estimation and is designed to compute excited state energies within a target energy-interval for cases where good approximations for the states in the target energy-interval are either unknown or hard to prepare. For the RVSE algorithm, we pose the question: how might we use imperfect near-term quantum computers to solve problems of practical value? In this work, we introduce a method to compute expectation values of general functions of a Hamiltonian with respect to an input state using low depth quantum circuits suitable for noisy intermediate-scale quantum computers. For the hardware part, we propose a new chemical quantum computer by using molecular electronics as a basis for quantum computing. In this work, we pose the question: can scattering of electrons in molecules produce quantum gates? We construct one-qubit gates using one-electron scattering in molecules, and two-qubit controlled-phase gates using electron-electron scattering along metallic leads. We show initial applications of the framework by illustrating one-qubit gates using the molecular electronic structure of molecular hydrogen as a baseline model.

Keywords: Electronic structure theory
Quantum algorithms
Quantum computing
Quantum phase estimation
Scattering theory
Variational quantum algorithms
Advisor: Aspuru-Guzik, Alán
Hybrid Models for BOF Steelmaking and Continuous Casting Applications

The steel industry has faced environmental and economic challenges in the past few decades. As a result, cost-effective and robust process control models are in great need to maximize operational efficiency while ensuring product quality. This thesis proposes a hybrid method to model the oxygen steelmaking and continuous casting processes, which combines metallurgical knowledge and data-driven techniques such as machine learning.In the first part of the thesis, a process control model was established for the Basic Oxygen Furnace endpoint carbon, phosphorus, and temperature based on production data collected from Tata Steel India. To begin with, the importance of individual operating parameters was examined with respect to endpoint values by using Pearson’s correlation analysis, mutual information, stepwise regression, random forest, and principal component analysis. Three subsets of optimal features were selected for each endpoint, and an equal number of artificial neural networks were established, tuned, and validated. Secondly, theoretical models based on mass and energy balance were formulated based on operating parameters. Finally, the proposed hybrid model was established via exchanging inputs and outputs among theoretical and neural network models. The hybrid model results suggest that the prediction accuracies are improved with the application of machine learning algorithms. In addition, incorporating theoretical frameworks benefits the hybrid model with enhanced generalization. With the established hybrid model, a graphical user interface software was developed for operators to implement in production. In the second part of the thesis, a time-series deep learning model was established to monitor the nozzle clogging phenomenon in continuous casting based on production data of four steel grades collected from Stelco, Lake Erie Works. Firstly, a clogging index was formulated based on process parameters to indicate the severity of clogging buildup within the nozzle quantitatively. With this index, long short-term memory networks were developed to predict future clogging index and monitor the casting process. The models can predict clogging events and erosion incidents during the continuous casting for all steel grades. With the implementation of this model in production, operators can take corrective actions to reduce the frequency of clogging and improve process efficiency.

Keywords: Basic Oxygen Furnace
Continuous Casting
Hybrid Model
Machine Learning
Steelmaking
Advisor: Chattopadhyay, Kinnor
The Role of Ribonucleotide Reductase in Inherited Purine Disorders

Adenosine deaminase (ADA) and purine nucleoside phosphorylase (PNP) are consecutive enzymes in the purine degradation and salvage pathways. Inherited defects that abrogate ADA and PNP function result in the accumulation of dATP and dGTP respectively, which are toxic as they allosterically inhibit ribonucleotide reductase (RNR). RNR is responsible for the production of deoxyribonucleotides (dNTP) used for DNA synthesis. RNR inhibition contributes to the profound lymphopenia in ADA and PNP deficiency due to insufficient dNTPs required for DNA replication and cell proliferation. Additional tissues, however, may also suffer from the same pathology as lymphocytes. Neutropenia has been reported among both ADA-deficient patients and patients treated with the RNR inhibitor hydroxyurea (HU). Additionally, HU administration caused significant neurological abnormalities in mice and rats, similar to those observed in PNP-deficient mice. I therefore hypothesized that the neutropenia in ADA deficiency and neurological abnormalities in PNP deficiency are consequential to the purine metabolic disorders, with the inhibition of RNR being central to their pathogenesis. To address this, I utilized induced pluripotent stem cells (iPSC) derived from ADA- and PNP-deficient patients, as well as healthy control individuals. Differentiating the ADA-deficient iPSCs into neutrophils revealed a significant reduction in the number of hematopoietic progenitors and mature neutrophils. Hematopoietic colony formation revealed only granulocytes to be reduced by ADA deficiency, suggesting impaired neutrophil differentiation. Supplementing cultures with exogenous ADA enzyme successfully reversed the abnormalities, validating the role of ADA in neutrophil development. Differentiating PNP-deficient iPSCs into neurons revealed the induction of intrinsic mitochondrial-mediated apoptosis. Tumor protein p53 expression was also increased, and inhibition of p53 prevented the onset of apoptosis. Supplementing exogenous PNP enzyme successfully prevented the p53-mediated intrinsic apoptosis, validating the role of PNP in neuronal viability. To determine whether the RNR inhibition was responsible for the aforementioned phenotypes, control iPSCs were treated with the RNR inhibitors HU or with the combination of nicotinamide and trichostatin A. The inhibition of RNR recapitulated the ADA- and PNP-deficient phenotypes, demonstrating impaired neutrophil differentiation and increased neuronal apoptosis, highlighting the importance of RNR and the convergent mechanism responsible for the varied systemic abnormalities observed in these purine diseases.

Keywords: Apoptosis
Differentiation
Induced Pluripotent Stem Cells
p53
Purines
Ribonucleotide Reductase
Advisor: Grunebaum, Eyal
Implicit and Explicit Motor Learning in Developmental Stuttering

Developmental stuttering is overtly characterised by involuntary disruptions to the initiation of speech segments, as well as by neurodevelopmental differences in the functional circuitry that supports motor sequence learning and the automatized processes of speech production. This dissertation comprises of three studies that are aimed at elucidating the motor sequence learning abilities of individuals who stutter, with particular focus on the domain-general processes of implicit and explicit learning that contribute to complex skill acquisition. In study one, the research literature on motor learning among children and adults who stutter was systematically reviewed for the first time. Delayed performance gains were found among adults who stutter on verbal as well as non-verbal tasks, and were most frequently measured within one practice session. Limited evidence suggests that individuals who stutter may also differ in processes of implicit learning. In study two, non-verbal motor sequence learning was investigated in adults who do and do not stutter on an implicit finger sequencing task. Implicit, sequence-specific learning was found to be reduced among male adults who stutter, when compared to male adults who do not stutter. Implicit motor sequence performance was also significantly predicted by working memory scores, for participants who stutter most significantly. In study three, finger sequence learning was investigated under explicit conditions of practice in adults who do and do not stutter. No significant differences in explicit sequence learning or retention were found between the groups. Taken together, these findings bring to light evidence of sex-based performance differences that influence implicit motor learning in adults with persistent developmental stuttering, which have not been previously reported. The current evidence may lend support to the critical role of the cortico-basal ganglia circuit in implicit sequence learning and its functional impairment in developmental stuttering. Differential implicit and explicit learning abilities can have implications for the successful acquisition of complex motor skills during development, as well as for the updating and automatization of speech motor plans in individuals who stutter. The broader implications of these findings for the development of fluent speech production, the conditions of behavioural treatment, as well as for future research are explored.

Keywords: developmental stuttering
explicit learning
implicit learning
motor learning
sequence learning
speech fluency
Advisor: De Nil, Luc
Microplastics and other Anthropogenic Contaminants: from Source to Sink in the Canadian Environment

Anthropogenic particles (AP), including microplastics and microfibers, are a ubiquitous contaminant in the environment. Due to the mounting evidence of their persistence, accumulation and toxicity, the sources, release pathways and fate of AP warrant further investigation in order to identify and develop effective mitigation efforts. This thesis aims to (1) investigate the abundance and composition of AP assemblages in environmental sinks, (2) explore environmental transport pathways for AP in the Canadian environment, (3) assess AP sources and release pathways; and (4) explore opportunities for diverting these releases to the environment. This thesis documents anthropogenic microfibers, including those composed of natural and semi-synthetic materials, as a major component of AP contamination in the Canadian environment. Further, this work shows, for the first time, that fractionation of AP occurs as particles are transported from sources to remote environments. Evidence from sediment samples indicates that fractionation occurs such that microfibers, particularly those composed of anthropogenically-derived natural and semi-synthetic materials, undergo long-range transport to remote areas whereas, non-fibrous particles tend to settle closer to source regions. As a result, microfibers from blue jeans, the world’s most popular garment, have similar abundance in Arctic sediments as in sediments from populated urban source regions. This work provides further evidence supporting the use of washing machine filters for mitigating microfiber emissions from laundering. This thesis also shows that secondary, external dryer filters can be an effective means for diverting releases of microfibers during laundering. By expanding investigation to include semi-synthetic and natural textile fibers and advancing methods for the confident enumeration and identification of microfibers in environmental samples, this thesis has led the shift in our scientific understanding of what constitutes AP contamination, and specifically microfiber pollution.

Keywords: Arctic
long-range transport
microfiber
microplastic
sediment
sources
Advisor: Diamond, Miriam
Visualizing The Vulnerability of Face Processing Across The Lifespan: An Image Reconstruction Approach

Human faces convey a wealth of information, including identity and emotional expression, both of which are crucial for social interactions. Consequently, difficulties in face processing can cause severe problems and impact a person’s well-being and quality of life. Extensive research has demonstrated that face perception can be disrupted by a variety of factors, such as aging, brain lesions, and mental disorders. However, the nature and the extent to which these factors have upon face representations remain unclear. In this thesis, I addressed these issues by employing a behavioural-based image reconstruction approach to uncover the pictorial content of face representations across a diverse sample of individuals. Specifically, Chapter 2 showed that visual representations of facial shape and surface information diagnostic to facial identity was compromised in older adults. Yet, the age-related effects were less pronounced than those associated with individual variabilities in discriminating conjunctions of facial features. Further, Chapter 3 revealed that the ability to distinguish between facial identities was impaired by damage to the medial temporal lobe areas, particularly the dentate gyrus. Lastly, Chapter 4 found that the severity of self-related personality dysfunction and levels of affective state, both of which are core features of personality disorder, impacted not only the accuracy of the visual content represented in perception and memory of facial expression but also their visual characteristics, such as expressiveness and positivity/negativity. Together, the findings across these three studies shed light on the impact of various conditions on the fine-grained visual content underlying face perception and memory, and advance our understanding of the vulnerability of face processing across the lifespan. Practically, the present thesis also demonstrates the applicability and utility of the image reconstruction approach across a broader population and provides a methodological framework for future research.

Keywords: Emotion recognition
Face perception
Image reconstruction
Individual difference
Memory
Advisor: Lee, Andy
Nestor, Adrian
Exploring the Glaciodynamic Significance and Origin of Drumlins and Mega-scale Glacial Lineations from High-resolution Topographic Imagery

The study of modern and ancient ice streams is one of the most rapidly growing fields in the study of glacial geomorphology following the discovery that ancient ice sheets contained corridors of fast flow (possibly as much as 10 km yr-1) called ice streams which discharge large volumes of ice (and sediment) and dictate deglaciation behaviour. This new paradigm is based on the recognition of the paleo-glaciological significance of km-long, narrow streamlined ridges called ‘mega-scale glacial lineations’ (MSGLs) which are now recognized as the hallmark of fast flowing ice. Present research activity is focussed on their origin(s) however current models are incapable of accounting for the variability of glacially streamlined landforms. Many hypotheses are based off low resolution data resulting in an underestimation of the number and extent of paleo-ice streams. This thesis uses over 150,000 km2 of high-resolution digital elevation model data from the eastern Great Lakes sector of the former Laurentide Ice Sheet in North America to address this data gap. The use of the newly developed Curvature-Based Relief Separation method for mapping subglacially lineated bedforms shows that at least 10 paleo-ice streams were previously undiscovered in the study area. Unsupervised machine learning and hierarchical-based clustering also shows that MSGLs are part of a landform ‘continuum’ and are transitional to drumlins with statistically validated ‘intermediate’ bedforms which record the dissection of parent drumlins to form MSGLs. This suggests a fundamental ice velocity control on their formation which is explored by using Median Absolute Differences to calculate the frictional profiles of paleo ice sheet surfaces showing that the evolution of this landform continuum is associated with the development of lower drag ice bed morphologies. Such observations are consistent with an erosional origin for drumlins and MSGLs, likely as the result of deforming subglacial debris being swept across the bed suggesting a basic commonality of forms produced by abrasion of ‘wear debris’ as one surface moves over another. The wider paleoglaciological and geomorphological significance of these findings are explored, representing a fundamental contribution to our understanding of fast ice flow in former ice sheets and the field of glacial geomorphology.

Keywords: Drumlins
Geospatial Analysis
Glaciology
Ice streams
Machine Learning
Mega-scale glacial lineations
Advisor: Eyles, Nick
Digital Histopathology with Second-harmonic Generation Microscopy

Histopathology has been one of the most important techniques in medical diagnostics, which often involves investigation of thin tissue sections, stained with dyes such as hematoxylin and eosin (H&E) for improved visual contrast under a white light microscope. Although white light histopathology has been instrumental in revolutionizing medicine, clearing and staining processes introduce non-native molecules in the tissue and may alter the structure of the sample. Second-Harmonic Generation (SHG) is a label-free and non-invasive imaging modality which relies on endogenous biomarkers in the tissue to provide the required contrast for visualization. SHG microscopy is a well-established nonlinear modality which allows for visualization of noncentrosymmetric molecules such as collagen in the tissue. Moreover, polarimetric second-harmonic generation (P-SHG) microscopy is often used to characterize the composition, organization, and polarity of collagen fibers in tissue in the form of second-order nonlinear optical susceptibilities. In this work, scanning P-SHG was utilized to reveal unipolar arrangement of collagen fibers in the human cardiac conduction system, which may consequently aid in effective transduction of the electrical signal through the heart. Moreover, a novel widefield P-SHG microscope was designed and constructed to increase imaging speed and field of view, while reducing the effects of photobleaching and photodamage. The widefield P-SHG microscope was used to demonstrate high-throughput whole-slide imaging of a breast tissue microarray. The microscope yielded in a series of polarimetric and texture parameters which were used in tandem with supervised machine learning to differentiate tumor tissue from normal with 94.2% accuracy and F1-score, and 6.3% false discovery rate. Furthermore, a large section of tissue spanning from normal to non-small cell lung carcinoma was imaged with the widefield P-SHG microscope to map the structural variations of the ECM across the sample. The resulting parameters were analyzed with principal components analysis and unsupervised K-means clustering to identify normal- and tumor-like tissue, and to further highlight the tumor margin surrounding the tumor mass. All in all, we showed that widefield P-SHG microscopy revealed collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.

Keywords: cancer
collagen
digital pathology
microscopy
nonlinear optics
polarimetry
Advisor: Barzda, Virginijus