VictorJuiz/thesis huggingface dataset:
Systematics of Hirudinea
Keywords: Acanthobdellida
Hirudinea
Hirudinidae
leeches
phylogenetics
systematics

For thousands of years, healers used bloodfeeding leeches to treat a variety of ailments from lethargy to plague. Although phlebotomy is no longer as ubiquitous as it once was, we remain fascinated by leeches and their vampiric tendencies.Together with their closest relatives – crayfish worms (Branchiobdellida) and hook-faced fish worms (Acanthobdellida) – leeches (Hirudinida) comprise the subclass Hirudinea: an ecologically diverse assemblage of annelid worms that includes predatory and ectocommensal lineages in addition to sanguivorous ones. Today, many researchers are interested in the anticoagulants, bacterial symbionts, and developmental origins of Hirudinea in addition to their enduring utility as medical tools. Perhaps surprisingly, the evolutionary history of Hirudinea and relationships among major lineages are not well understood. There are many enduring conflicts between phylogeny and existing classification schemes, which are difficult to reconcile for a number of reasons. A preponderance of incomplete or contradictory taxonomic literature; uneven geographic sampling; a vanishingly small fossil record; and an overall dearth of phylogenetically informative morphological characters present significant challenges to systematists. Existing research has made substantial progress in clarifying relationships among a number of species and genera. Still, we lack an overall understanding of the phylogeny of Hirudinea as a whole. After reviewing the diversity and classification of extant lineages, the fossil record, and important questions in hirudinean systematics, I address a number of outstanding issues on mutiple scales. I examine phylogeny and intraspecific diversity in the leech genus Placobdella (Chapter I). I detail the morphology of Acanthobdellida, and discuss relationships within the order as a whole (Chapter II). I present a large, comprehensive phylogeny of Hirudinea and use it to alleviate several outstanding taxonomic anomalies (Chapter III). As part of this effort, I propose a new family of medicinal leeches. Finally, I describe the first fossil worm that can be confidently classified within Hirudinea (Chapter IV).
The Role of the Medial Prefrontal Cortex in Associative Learning and its Convergence on Adaptive Behaviour.
Keywords: Chemogenetics
Inference
Learning
Medial Prefrontal Cortex
Memory
Neuronal computations

The medial prefrontal cortex (mPFC) has emerged as a brain region with an incredibly diverse range of functions. For example, it is involved in context-dependent memory expression and memory-guided decision making. However, it remains unknown the auxiliary role the mPFC plays to these known functions, namely in the encoding of memory and inferential decision-making. The goal of this thesis is to further elucidate the role of the mPFC in these two seemingly distinct functions, which require common computations to deal with novelty. Encoding new information involves forming novel memories without any prior experience by identifying relevant features, whereas inferential decision-making involves using prior relevant information to guide decisions in novel situations. To address these points, I conducted a series of experiments where I recorded network activity from the mPFC in response to novelty while rats were trained on the mPFC-dependent task, trace eyeblink conditioning. I found that chemogenetic mPFC activation improved the rats’ ability to encode a novel association while enhancing the network signature that tracked the stimulus-outcome contingency, indicating that the mPFC may influence memory encoding by increasing behaviourally-relevant activity. In a separate experiment, I examined the mPFC’s contribution to supporting a new goal-directed behaviour that was dependent on previous learning but was never directly reinforced. I found that following the introduction of an environmental change, prior experience with the task stimuli and environments enabled rats to infer novel adaptive behaviours and avoid a negative outcome. Furthermore, the mPFC spontaneously reactivated representations of the associations before rats executed the new behaviour, uncovering how the mPFC can transform learned patterns into novel adaptive behaviours. I discuss how these findings expand upon existing theories of the mPFC’s role in encoding new information and in executing adaptive behaviour. Based on these findings, I propose an addendum to these theories: in a given situation, the mPFC network continuously monitors novel information at each moment and subsequently selects the most adaptive behaviour in response, including novel behaviours that have never been reinforced. This hypothesis can serve as a framework for future research on the mPFC’s modulatory role when handling novelty to promote adaptive behaviour.
Neurocognitive, Psychological, and Functional Outcomes in Patients with Meningioma
Keywords: brain tumor
cognition
neuropsychology
radiation

Meningioma is the most commonly occurring type of central nervous system tumor. Recent literature has demonstrated that the long-term quality of survival of patients with meningioma is impacted by both the tumor and resultant treatment; a significant proportion of these patients experience neurocognitive, psychological, and real-world functional challenges long after they are considered to be medically cured. This dissertation aimed to advance the research literature by (1) characterizing neurocognitive, psychological, and functional outcomes of patients with meningioma and examining what factors contribute to greater risk of poor recovery; (2) investigating associations between radiation dose to circumscribed brain regions and neurocognitive outcomes in this population; and (3) exploring long-term neurocognitive and psychological symptom change and offering a preliminary investigation of associations between radiation dose to circumscribed brain regions and these trajectories. Findings indicate that a significant percentage of patients with meningioma experience neurocognitive, psychological, and functional difficulties at an average of five years post-diagnosis. Results also indicate that younger age, faster visuomotor processing speed and, unexpectedly, higher trait anxiety scores are associated with an increased likelihood of returning to work. Novel to the literature, this dissertation suggests that in addition to known associations between radiation dose delivered to the left hippocampus and verbal memory, radiation delivered to other brain regions are associated with specific neurocognitive domains. In particular, data indicate that high dose to the parietal-occipital region is associated with slower visuomotor processing speed and poorer executive function, whereas high dose to the dorsal frontal region is associated with better executive function. The current work also revealed variability in neurocognitive and psychological trajectories at the individual level. Preliminary analyses indicated that high radiation dose delivered to the parietal-occipital region and the cerebellum are independently associated with declines in visuomotor processing speed, whereas high dose delivered to the hippocampal regions is associated with increases in both depression and trait anxiety. Results highlight the need for access to scarce supportive services for patients with meningioma, including neuropsychological assessment and cognitive rehabilitation services, emotional support programs, and rehabilitative interventions.
Development of a Cardiovascular Risk Prediction Model for Individuals with Psoriasis and Psoriatic Arthritis
Keywords: biomarker
cardiovascular disease
prediction
psoriasis
psoriatic arthritis
risk assessment

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.
Quantum Computing for Chemistry: From Quantum Computing with Molecules to Algorithmic Design
Keywords: Electronic structure theory
Quantum algorithms
Quantum computing
Quantum phase estimation
Scattering theory
Variational quantum algorithms

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.
Hybrid Models for BOF Steelmaking and Continuous Casting Applications
Keywords: Basic Oxygen Furnace
Continuous Casting
Hybrid Model
Machine Learning
Steelmaking

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.
The Role of Ribonucleotide Reductase in Inherited Purine Disorders
Keywords: Apoptosis
Differentiation
Induced Pluripotent Stem Cells
p53
Purines
Ribonucleotide Reductase

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.
Implicit and Explicit Motor Learning in Developmental Stuttering
Keywords: developmental stuttering
explicit learning
implicit learning
motor learning
sequence learning
speech fluency

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.
Microplastics and other Anthropogenic Contaminants: from Source to Sink in the Canadian Environment
Keywords: Arctic
long-range transport
microfiber
microplastic
sediment
sources

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.