VictorJuiz/thesis huggingface dataset:
Rescaled Directed Random Polymer in Random Environment in Dimension 1+2
Keywords: Brownian Motion
Critical Scaling
Gaussian Correlation Inequality
Large Deviation
Random Polymer
Two Dimensional Local Time

Directed random polymer in random environment is a statistical physics model. Heuristically, it concerns a long, directed chain of molecules evolving in some random background, and one of the questions one asks is how is long term behaviour of the polymer affected by the strength of the noise, which in the model is controlled by a parameter β called inverse temperature. In this thesis, we will let β depend on the length of the polymer and consider the model in time-space dimension 1+2. Also, we will work with continuous time and space, and Gaussian random environment that is white noise in time.
From Global Cities to Global Governors: Power, Politics, and the Convergence of Urban Climate Governance
Keywords: C40
Cities and Climate Change
City-Networks
Climate Change
Climate Governance
Governance Field

Cities are increasingly included in discussions of climate governance and lauded as sources of innovation, leadership, and experimentation. But can they succeed where states have failed in producing meaningful collective actions and effects? To respond to this pressing question requires understanding whether cities can achieve more than rhetorical commitment to coordinated action, whether they can come together and coordinate their actions. To answer this question my dissertation addresses the puzzling ability of the C40 Climate Leadership Group to achieve internal coherence. Leveraging a novel dataset of over 4700 discrete urban climate governance actions, I demonstrate empirically that the cities of the C40 have come not only to cohere around a common project, employ common practices of climate governance, but that the C40 has converged around a common set of governance norms: shared ideas as to the role of cities in global climate governance, the ways in which cities can and should engage in governance, and how governance should be practiced. I introduce a novel conceptual framework that interweaves elements of social constructivism and network analysis with Bourdieu's social field theory, and demonstrate how reconceiving the C40 as a governance field illuminates currents of power that operate beneath the still waters of nominal and voluntary cooperation, and provides a means of explaining how convergence has been pursued, contested, and produced by actors who claim and wield various sorts of power and authority. The dissertation applies this novel conceptual apparatus to demonstrate why contestation was paramount between 2005 and 2010, and how convergence was produced from 2011 on. Put simply, the C40 only achieved convergence once there was an actor with enough power to overcome resistance and secure complicity from its members, with such power translated into influence through the mechanism of recognition.
In silico Prioritization of Genetic Risk Variants Using Functional Genomic Information
Keywords: ENCODE
machine learning

Complex traits are the result of a contribution of both genetic risk variants throughout the genome, and environmental risk factors and their interactions. Genome-wide association studies (GWAS) have identified some of these associated variants, but there remain two fundamental issues to move forward in understanding the genetic etiology of complex traits: (1) The "missing heritability" for complex traits persists, possibly in part due to lack of statistical power as a result of insufficiently large sample sizes (2) The identity of the causal variant- a variant identified by GWAS could result in a functional consequence, or it could merely tag the causal variant. I hypothesize that integrating functional information, such as chemical modifications to DNA, along with statistical data from an association study can help prioritize variants for further analysis in both of these areas. I developed a method to prioritize genetic variants using hundreds of functional annotations (Gagliano et al., 2014a) using penalized logistic regression. I compared my prioritization method to two other methods that use data-trained classifiers to determine if there is an ideal algorithm or annotation set for prioritizing risk variants (Gagliano et al., 2015a). In this work, I also investigated using different databases of disease-associated variants to define genetic risk variants. The models created all had some accuracy for detecting risk variants. I assessed the accuracy of these models using measures investigated in a review I undertook (Gagliano et al., 2015b). Finally, I investigated if allele-specific methylation (ASM) is a useful novel annotation to prioritize risk variants. I demonstrated that variants that exhibit ASM in brain tissue are enriched for functional annotations, and are also enriched in sub-genome-wide significant variants in a schizophrenia GWAS.
Simulating the Universe: Nonlinear Formation and Evolution of Cosmic Structure
Keywords: Cosmic Neutrinos
Cosmological Simulations
Galactic Substructure
Reionization
Structure Formation

In this thesis, we harness the power of modern scientific computing to explore the formation and evolution of cosmological structure in a wide variety of astrophysical scenarios. We explore the nonlinear dynamics associated with the interplay between cold dark matter (CDM), baryons, ionizing radiation, and cosmic neutrinos, within regimes where analytic calculations necessarily fail. We begin by providing an overview of structure formation and its connections to the fields of study considered here: the epoch of reionization, galactic substructure evolution, and cosmic neutrinos. We then present a rigorous numerical convergence study of cosmological hydrodynamics simulations post-possessed with radiative transfer to study the impact of small-scale absorption systems within the intergalactic medium (IGM) during the onset of reionization. We present converged statistics of the IGM on smaller scales and earlier times than previously considered. Moreover, we provide strict resolution limits for hydrodynamic simulations to properly resolve the unheated IGM. Next we study the infall and dynamical evolution of CDM halos in a galactic host. We find the behaviour of low-mass subhalos is qualitatively different than previously described for high-mass subhalos. In particular, the evolution of low-mass subhalos, with masses less than 0.1 per cent that of the host, is mainly driven by their concentration. This presents an opportunity to use concentration as a predictive indicator of substructure evolution. We finish this thesis with an investigation of a recently proposed method for constraining individual neutrino mass from cosmological observations. Such a detection depends on the ability to reconstruct the CDM-neutrino relative velocity, which we show can be accomplished using linear transformations of an observed galaxy field. Based on this, we perform the world's largest cosmological N-body simulation and present preliminary results for the observational prospects of cosmic neutrinos.
"Letting stories breathe": Using Patient Stories for Organizational Learning and Improvement
Keywords: organizational learning
patient centred care
patient engagement
patient experience
patient stories
quality improvement

There has been a recent upsurge in the use of patient stories to better understand patients’ experiences of illness and of care, and to inspire leaders and staff for quality and safety within healthcare. However, to fully realize the potential of patient stories, a more nuanced understanding is needed of how they are used, who tells them, for what purpose, and in what context. Using a constructivist case study methodology with qualitative methods, this study examined four healthcare organizations that are known leaders in the systematic and deliberate use of patient stories, exploring the storytellers, the types of stories told and their purposes. It also examined the contexts that enable the use of stories and the impact they have had on organizational learning and quality improvement. An interpretivist approach to analysis highlighted the specific types of stories told by patients and of patients, and how they were co-constructed from stories of chaos into quest stories for learning, “authorized stories” to be shared for particular purposes. The storytellers who emerged were those who had extended their involvement as patient advisors/members, determined by leaders to be the “right fit” and at the “right time” to share their stories. Strong leaders modeled and supported the philosophical orientation toward patient and family-centred care that patient stories helped to develop and sustain. Leaders also created the organizational structures and processes required to gather and share stories, and to link them purposefully with learning and improvement. The act of storytelling is not a simple one and tensions surfaced relating to what stories are told, how, by whom, and for what purposes. In many ways, the organizations demonstrated how they were thinking with stories and how learning occurred at individual, team, and organizational levels. However, leaders and organizations continued to retain control of which patient stories were shared, in what forum, and for what purposes. Despite their best intentions and explicit demonstrations to hear the patient voice, a more reflective practice is required to better appreciate the power and privilege that exists within organizations, making this an area to explore further in theory development for organizational learning.
Investigation of Neurochemicals' Role in Glioblastoma and Neural Stem Cells: Characterization of Dopamine Receptor D4 Inhibition in Glioblastoma Stem Cells
Keywords: autophagy
Dopamine receptor
DRD4
Glioblastoma
Glioblastoma stem cells
Neural stem cells

Neurochemicals that mediate synaptic communications between mature neurons are now appreciated to have a critical role in neurogenesis and cancer. Glioblastomas (GBM) grow in the rich neurochemical milieu of the brain and may depend on neurochemical signals for proliferation and survival. Given the close similarities between GBM stem cells and neural stem cells (NSC), and their existence in the rich neurochemical milieu of brain, neurochemical signaling may profoundly impact tumor growth. To test this hypothesis, I interrogated a library of 680 neuroactive compounds in patient-derived GBM neural stem cells (GNS) against normal human NSC and human fibroblasts cells to identify selective compounds. I found that compounds modulating dopaminergic, serotonergic and cholinergic signaling pathways selectively affected GNS and NSC growth compared to fibroblast cells. I identified ten compounds that showed more than 8-fold selectivity and in particular, dopamine receptor D4 (DRD4) antagonists (L-741,742 and PNU 96415E) selectively inhibited GBM growth in vitro and in vivo, and exhibited synergism with the current chemotherapeutic drug, temozolomide (TMZ). Primary GBM tumors and GNS cells expressed functional DRD4 receptor and antagonism of DRD4 led to disruption in its downstream effectors PDGFRÎ˛/ERK1/2, and mTOR signaling. Furthermore, DRD4 antagonism disrupts endolysosmal function compromising the autophagy-lysosomal degradation pathway leading to autophagic flux inhibition with accumulation of autophagic vacuoles, autophagy specific substrate p62, cholesterol and ubiquitinated proteins, followed by G0/G1 cell cycle arrest and apoptosis. The identification of DRD4 antagonists as GNS selective compounds revealed autophagy-lysosomal system as a vulnerable and important system for GBM cell survival and suggests modulation of dopamine signaling may hold therapeutic potential. Interestingly, DRD4 antagonists L-741,742 and PNU 96415E were also identified as hits in the high content differentiation screen. I further characterized DRD4 antagonist induced differentiated cells, which showed VGLUT1 positive cells with increased expression of NERUOG2, CTIP2 and ETV1 identifying them as glutamatergic cortical deep layer V neurons. Finally, DRD4, when inhibited in normal NSC, initiate differentiation, while in GNS cells, they are vulnerable and undergo cell death.
The Genetic Architecture of Iris Colour and Surface Feature Variation in Populations of Diverse Ancestry
Keywords: Evolution
Genetic Variation
Iris Colour
Iris Surface Features
Molecular Anthropology
Pigmentation

Research focused on iris colour and surface features has begun to play an increasingly important role in the fields of anthropology, forensics and public health. Although both eye colour and, to a much lesser extent, iris features have been studied in populations of European ancestry, very few research groups have attempted to investigate these traits in regions outside of Europe. This may be partly due to difficulties associated with obtaining a quantitative measurement of eye colour and a lack of standardized methodology. For my doctoral thesis, I developed a novel method of characterizing iris colour and feature variation in diverse populations. I then applied this method to a sample of individuals of East Asian, European and South Asian ancestry. I was primarily interested in looking at the phenotypic distribution and genetic basis of these traits across all three sample sets. I found that: 1/ Quantitative methods provide an ideal means of characterizing iris colour in populations of diverse ancestry; 2/ The phenotypic distributions of iris colour and surface features are different in East Asian, European and South Asian populations; 3/ HERC2 rs12913832 controls iris pigmentation variation in both European and South Asian populations. However, it affects eye colour differently in both regions; 4/ The variants responsible for controlling iris pigmentation in Europe are different from those responsible for controlling iris pigmentation in East Asia; 5/ SEMA3A rs10235789 is significantly associated with Fuchs' crypts in East Asian, European and South Asian populations. Ultimately, my thesis is the first research work to compare iris pigmentation and structure variation in populations of diverse ancestry. The methodology that I have developed will provide other researchers with a new framework that they can use to approach the study of iris colour and feature diversity.
Facility Location under Uncertainty and Spatial Data Analytics in Healthcare
Keywords: automated external defibrillator
cardiac arrest
coverage models
facility location
intraclass correlation
kernel density estimation

Out-of-hospital cardiac arrest (OHCA) is a significant public health issue and treatment, namely, cardiopulmonary resuscitation and defibrillation, is very time-sensitive. Public access defibrillation programs, which deploy automated external defibrillators (AEDs) for bystander use in an emergency, have been shown to reduce the time to defibrillation and improve survival rates. The focus of this thesis is on data-driven decision making aimed at improving survival from OHCA by analyzing cardiac arrest risk and optimizing AED deployment. This work establishes a unique marriage of data analytics and facility location optimization to address both the demand (cardiac arrest) and supply (AED) sides of the AED deployment problem. In the demand side, we analyze the spatiotemporal trends of OHCAs in Toronto and show that the OHCA risk is stable at the neighborhood level over time. In other words, high risk areas tend to remain high risk, which supports focusing public health resources for cardiac arrest intervention and prevention in those areas to increase the efficiency of these scarce resources and improve the long-term impact. In the supply side, we develop a comprehensive modeling framework to support data-driven decision making in the deployment of public location AEDs, with the ultimate goal of increasing the likelihood of AED usage in a cardiac arrest emergency. As a part of this framework, we formulate three optimization models that consider probabilistic coverage of cardiac arrests using AEDs and address specific, real-life scenarios about AED retrieval and usage. Our models generalize existing location models and incorporate differences in bystander behavior. The models are mixed integer nonlinear programs, and a contribution of this work lies in the development of mixed integer linear formulation equivalents and tight and easily computable bounds. Next, we use kernel density estimation to derive a spatial probability distribution of cardiac arrests that is used for optimization and model evaluation. Using data from Toronto, Canada, we show that optimizing AED deployment outperforms the existing approach by 40% in coverage and substantial gains can be achieved through relocating existing AEDs. Our results suggest that improvements in survival and cost-effectiveness are possible with optimization.
The Utilization of Bark to Make Rigid Polyurethane Foams
Keywords: alkoxylation
bark
liquefaction
polyurethane

This work focused on the characterization of polyols derived from the liquefaction or alkoxylation of bark. Regarding liquefaction, it was found that both temperature and solvent structure played a significant role in polyol properties. High temperature liquefaction resulted in the degradation of sugars, while liquefaction at mild temperatures preserved sugar structures as shown by 31P-NMR. It was also shown that liquefaction at 130 C was ideal in terms of producing a polyol with a relatively at, broad, plateau of molecular weight distribution, whereas liquefaction at 90 and 160 C produced polyols with a large amount of low molecular weight compounds. Regarding solvent structure, it was found that polyhydric alcohols with short chain primary hydroxyls resulted in less sugar degradation products and less formation of condensation side-products. It is proposed that the highly polar environment promoted grafting and prevented condensation onto other biopolymers. Using organic solvents it was found that ketonic solvents like acetyl acetone and cyclohexanone, through their highly polar carbonyl group could engage in hydrogen bonding through electron donation/proton accepting interactions. These enabled the solvent to reduce the amount of condensation reactions and improve liquefaction yield. The liquefied bark-based polyols were then used to make polyurethane foams. It was found that when a diversity of hydroxyl groups were present the foaming rate was reduced and this may react a slower rate of curing and explain why the bark foams had a greater amount of cells that underwent coalescence. It was also observed that the bark foams had a low amount of closed-cell content. Since closed-cell content plays a role in dictating elastic compression, this may explain why the bark foams exhibited a lower elastic modulus. Finally, as a contrast to liquefaction, bark was alkoxylated. It was observed that the conversion yield was higher than liquefaction. The polyols had a high average molecular weight with a broad distribution and far greater solubility. It is proposed that alkoxylation is far less degradative than liquefaction. This may explain why the foams showed improved compressive behaviour compared to the foams made from liqueed bark-based polyols. Through greater characterization of the structure of polyols produced via liquefaction and alkoxylation the relationships between reaction parameters, polyol structure, and foam properties can be better understood. This is an important step towards the utilization of bark to make polyurethane foams.
Genetic and Epigenetic Determinants of Tissue Factor Pathway Inhibitor Plasma Levels
Keywords: coagulation
genetic epidemiology
population health
statistical genetics
thrombosis

Tissue factor pathway inhibitor (TFPI) regulates the intravascular formation of blood clots, and low TFPI plasma levels increase the risk of both arterial and venous thrombosis. TFPI plasma levels are also heritable, but few genes have been implicated. The objectives of my thesis were to identify genetic variants associated with TFPI plasma levels, and to integrate epigenetic data to better understand the molecular mechanisms by which these variants affect TFPI plasma levels. I started with a Human Genome Epidemiology (HuGE) review of genetic variants associated with TFPI plasma levels (manuscript 1). I meta-analyzed associations between four TFPI variants and TFPI plasma levels, and confirmed an association with rs5940 and rs7586970. I next conducted a linkage analysis of TFPI plasma levels in 251 individuals from the F5L Family Study (manuscript 2) and replicated a linkage region on chromosome 2q identified in the only genome-wide study of TFPI plasma levels reported to date. I meta-analyzed SNPs in the region across three European ancestry study samples, and implicated rs62187992 in the chromosome 2q linkage result, and in the risk of venous thromboembolism in the INVENT Collaboration dataset. Public genomic resources supported three-dimensional looping of the rs62187992 region to the promoter of IKZF2, whereas nearby whole blood DNA methylation did not mediate the association between rs62187992 and TFPI plasma levels in either the F5L Family or MARTHA studies. I last conducted a genome-wide association scan (GWAS) of TFPI plasma levels in the F5L Family Study (manuscript 3). I found that TFPI plasma level-associated SNPs were enriched in promoters and enhancers in vascular endothelial cells, a cell type that expresses TFPI, and I leveraged this enrichment in a prioritized GWAS via stratified false discovery rate control. While I did not identify novel SNPs, I nonetheless outlined a strategy for incorporating epigenetic data into prioritized GWAS. This thesis is the most comprehensive assessment of genetic risk factors for TFPI plasma levels, and integrates epigenetic data to understand biological mechanisms - a first step towards new therapies that will reduce the public health burden of thrombosis.