VictorJuiz/thesis huggingface dataset:
Student Mobility and the Struggle over European Intergration: The Lessons of the Bologna Process

This study aims to provide insight about the dynamics of credit and degree mobile students in the European Union (EU). The analysis allows for cross-country comparisons and helps to assess the evolution of student mobility rates in the studied countries. The researcher analyzes data for tertiary mobile enrolments (both sexes). The study draws on the Leuven and Louvain-la-Neuve communiqué of 2009 as a turning point which shifted to quantitative targets. First, using Statistical Product and Service Solutions (SPSS), the researcher conducts statistical analysis by implementing a paired comparisons test. It tests a hypothesis about the mean of the differences (µd) that can be observed across the EU member-states over period from 1999 to 2018. Second, using the European Tertiary Education Register (ETER) data from 2011/2012 to 2016/2017, the researcher contributes to the construction of degree and credit mobility indicators (benchmarks). In brief, it uses ETER database to derive figures that are necessary for computing ratios such as “Share of degree mobile students” and “Share of incoming Erasmus students”. In this study the unit of analysis is the country.Another focus of the study is on tensions and contradictions that arise due to situations where students, on the one hand, are desired to fulfill the needs of skilled labor market; and, on the other hand, the very same students are “unwanted” due to politics of migration control. This is very typical that different actors hold different positions; therefore, employers want more international workers and local employees want fewer international workers. In particular, the researcher addresses the experience of Austria, Estonia, Germany, Sweden, and the UK. The study is concerned with the role of states in initiating, selecting, restraining, and ending international migration movement. It shows how institutions and governments in countries with well-developed higher education (HE) systems are creating initiatives to receive students from abroad. Within the Bologna Process (BP), European governments discuss HE policy changes. It seems that the BP has become an important space for soft diplomacy with neighbouring countries where participants strive to overcome obstacles to create a European Higher Education Area (EHEA). Enhancing the quality of teaching is also a core mission of the BP. This study shows how the BP helps to build the trust necessary for cross-border academic cooperation, successful learning mobility, and the mutual recognition of study periods and qualifications earned abroad.

Keywords: (international)higher education policy
Bologna Process
degree/credit student mobility
European Higher Education Area
European Integration
internationalization
Advisor: Bascia, Nina
Gentrification, Mobility and Automobility; A quantitative study of local driving mode share and commute time in Montréal, Toronto and Vancouver

This thesis investigates how gentrification affects the transport mode and travel time of commuters in large, globalizing Canadian cities. While there is evidence that gentrification is associated with transit, cycling and walking, gentrification also involves the displacement of low-income and racialized households, and commuters in these households are the least likely to drive to work. Does gentrification contribute to automobility? Also, how are residential mobility and exclusionary displacement related to everyday commute mobility in gentrification? In this dissertation, I cross tabulate commute time and mode by household income, racial status and other sociodemographic characteristics at the census tract scale in 1996, 2006 and 2016. I find that in Montréal, Toronto and Vancouver, gentrification is correlated with a decrease in the mode share for driving among affluent commuters and visible minorities who live in gentrifying neighbourhoods. However, there is no change in the overall percentage of commuters who drive to work, since low-income and racialized minority commuters, who are less likely to drive, are displaced and higher-income and white commuters, who are more likely to drive, become more concentrated in gentrifying places. Also, while within gentrifying places I find little evidence of racial differences in commute time, through gentrification racialized residents are displaced from places where they have a lower driving mode share, and shorter commute times by private vehicle and transit. Moreover, a case study of the Toronto metropolitan area shows that the centralization of households with above average incomes is not on its own associated with a reduction of their private vehicle use over time. At the same time, the private vehicle use of low-income households does not change in gentrifying neighbourhoods, but is bolstered by their increasing concentration in the suburbs. Thus, the cultural aspects of gentrification processes have an important influence on commute behaviour, and these combine with economic change and improvements to local transport infrastructure to support the motility of affluent, white households, while contributing to displacement pressure for low-income and racialized minority households.

Keywords: automobility
commute mode
commute time
gentrification
mobility
sustainable commuting
Advisor: Walks, Alan
Chemical Proteomics Methods for Elucidating the Physical Protein Targets of Environmental Contaminants

Chemical pollution is the most poorly understood environmental change. Even its impact on the global burden of disease is unknown as humans may be exposed to mixtures of over 350 000 chemical, the majority of which have not undergone any toxicological testing. As proteins are the direct mediators of cellular processes, they are most likely the target of chemical contaminants. However, identifying targets is technically challenging, thus limiting research in this domain. This thesis work focuses on the development and refined of chemical proteomics, a methodology to identify chemical-protein interactions, to explore environmental contaminant-protein interactions. Herein, I review challenges in contemporary methods in environmental toxicological studies and contend that chemical proteomics is a viable means to address such issues. I then demonstrate the capabilities of chemical proteomics via three projects. The first combines activity-based protein profiling and direct adduct monitoring to explore and elucidate compound- and class-specific differences in Nrf2-response mediated oxidative stress between monohalogenated acetic acids (mHAA) and acetamides (mHAM). The second adapts fluorous-solid phase enrichment (FSPE) workflows to study peptides covalently modified by long-chain per- and poly-fluoroalkyl substances (PFAS), using 8:2 fluorotelomer acrylate (8:2 FTAC), an environmentally relevant legacy PFAS detected in food packaging, as a case-study. The third project surveys the sub-lethal proteome perturbations of early-life stage exposure to seventy-four unique PFAS in zebrafish (*Danio rerio*) using data-independent acquisition (DIA) proteomics. Each project yielded unique insights into environmental contaminant-protein interactions, with many targets being reported for the first time. Likewise, the results gathered suggest novel mechanism of toxicity and reconsideration of PFAS classification with respect to potential toxicities.

Keywords: disinfection byproducts
molecular toxicology
omics
PFAS
Proteomics
zebrafish
Advisor: Peng, Hui
Mesoscopic Models of Cellular Signaling Reveal Strategies for Specificity in Crosstalk Signal Pathways

Successful cellular signal processing strategies must be robust to stochastic fluctuations and yet sensitive to small differences between molecular signals, often in a signaling environment containing many cross-wired signals. Understanding how cells overcome challenges posed by signal crosstalk is important for our understanding of immune processes such as the initiation of anti-viral defenses by Type I Interferon signaling, or kinetic proofreading by T cell receptors. This thesis addresses several interrelated problems regarding molecular mechanisms for distinguishing signals and managing stochastic fluctuations for reliable cellular decision making in situations with signal crosstalk. Despite systematic categorization and highly detailed structural descriptions of the various molecular signals involved in the immune response, quantitative predictions of immune behaviour are difficult.Immune signaling pathways involve many sequential molecular interactions, and this organizational complexity presents a significant barrier to modeling immune processes. In this thesis, I study how receptor signaling dynamics contribute to signal processing specificity in situations with signal crosstalk. I use the Type I Interferon signaling pathway as a model system to study signal crosstalk and I develop a computational model of this pathway to investigate how the Interferon receptor performs signal discrimination. I use mesoscopic modeling to capture key features of complex signal pathways and to capture the intrinsic stochasticity of receptor signaling dynamics, without directly modeling every known molecular detail of these signaling systems. Using this approach, I show that the intrinsic stochasticity of kinetic proofreading receptors fundamentally constrains the robustness to fluctuations of downstream signal processing. Both the Interferon receptor and the kinetic proofreading receptor exhibit deficiencies in achieving specificity in crosstalk signal pathways. I develop a minimal model for a receptor with multiple outputs which can overcome these deficiencies to simultaneously identify and accurately sense the concentrations of arbitrary unknown ligands present individually or in a mixture, even in the presence of stochasticity. As a whole, this thesis focuses on the dynamical factors of cell signaling which contribute to specificity in noisy crosstalk signal pathways.

Advisor: Zilman, Anton
Regulation of Memory by the Actin-Binding Protein Cofilin

The concept of disability is complex, constantly evolving and hard for many to understand. Disabilities, including those that are non-apparent such as some neurodevelopmental disorders, are often associated with negative preconceptions. Though neurodevelopmental disorders can affect language, speech, learning, memory, motor skills and other functions, individuals with these disorders can still lead meaningful lives. While many developmental disorders are associated with impaired learning and memory, different stages and types of memory can be affected. These impairments are often correlated with altered neuronal structure or function. Therefore, understanding the molecular mechanisms is important to better understand neurodevelopmental disorders. One process that is important for neuronal development is remodeling of the actin cytoskeleton. This remodeling is regulated by the actin binding protein cofilin, but its role in learning and memory remains unclear. Physiologically relevant manipulations of cofilin activity in vivo provide new opportunities for examining its function in learning and memory. Therefore, in this thesis, I examined three objectives to determine how cofilin overexpression in vivo affects the actin cytoskeleton and memory. I first characterized two novel transgenic mouse models that allow for either overexpression of wild-type or constitutively inactive forms of cofilin with spatial and temporal regulation. Expression of transgenic cofilin was robustly localized in excitatory neurons and processes in both mouse models. However, rod-like structures thought to be cofilin-actin aggregates were observed in cofilin overexpressing mice. I then showed that cofilin overexpression impaired short-term social novelty recognition, which was rescued by reversible transgene expression. Though it is unclear if the impairment was due to an underlying deficit in recognizing novelty. In contrast, some mice expressing constitutively inactive cofilin exhibited a possible immobility phenotype. However, further testing is required to determine the reproducibility of these impairments. Finally, cofilin-overexpressing zebrafish showed transgene expression throughout the nervous system, with rod-like structures in neuronal processes similar to those in mice with no effect on locomotion. Together, these results suggest that the novel transgenic cofilin models provide valuable tools for future research on the roles of cofilin and actin regulation in a variety of neuronal processes.

Keywords: Actin
ADF
Cofilin
Zebrafish
Advisor: Lefebvre, Julie L
Jia, Zhengping
Multi-omic Phenotype Discovery for Mendelian Disease Variants

There are over 7,000 known rare diseases and over 80% have a simple Mendelian pattern of inheritance. Despite our knowledge of the genetic mutations that cause rare diseases, little is known of the cellular consequences of these genetic mutations. Functional studies on rare disease have fallen behind gene discovery since advances in genome sequencing have far outpaced the throughput in functional follow-up studies. To bridge the gap between genetic and functional information, I systematically phenotyped a human mutation open reading frame (hmORF) collection of 3,547 human coding variants using immunofluorescence and high-content automated confocal microscopy, and discovered 250 mislocalized variants, indicating that mislocalization plays an important role in Mendelian disease pathology. 53 unique mislocalization patterns were observed. The most common type of mislocalization was between organelles within the secretory pathway (59%). It is possible that many of the mislocalized secretory pathway variants fail to pass quality control in the ER and are retained and sorted for degradation. To support this hypothesis, I performed BioID on 16 ER-retained variants and discovered that all 16 variants associated more with ER quality control factors than their wild-type counterparts. To elucidate the degradation pathways of the ER-retained variants, I used immunofluorescence to measure protein abundance of each ER-retained variant after inhibition of the proteasome, autophagy, or VCP. I discovered that proteasome-dependent ERAD was the dominant degradation pathway for ER-retained variants. Finally, using luciferase reporter cells, I assessed the ability of the ER-retained variants to upregulate the IRE1 and ATF6 unfolded protein response pathways. I found that 35 variants could upregulate both the IRE1 and ATF6 pathways. In this study, I present the first large-scale, publicly available map of the impact of human variants on protein localization. Aside from serving as a resource for researchers interested in each variant, reference gene, or associated human disorder, the data answered fundamental questions about the frequency and characteristics of mislocalization events. Most notably, pathogenic variants are more commonly mislocalized than benign variants or variants of unknown significance. Mislocalizations within the secretory pathway and cytoskeleton are more common than others, which points towards the possibility of similar targets and or candidate compounds being useful to correct an entire class of mislocalizations and therefore potentially an entire class of disorders.

Advisor: Taipale, Mikko
Deep Learning Dynamics: From Minimization to Games

The last decade has witnessed the unprecedented successes of deep learning in various high-profile challenges. Meanwhile, the practical success of deep learning has greatly outpaced our theoretical understanding, and it is tempting to blindly use this hammer without knowing how it works. In particular, the current approach to deep learning relies on a combination of seemingly innocuous ingredients, like special weight initializations, carefully-designed activation functions, skip connections, normalization layers, sophisticated learning rate schedules, and some opaque training strategies, whose precise roles are not well understood, but without which training is challenging. Such an approach could be problematic as it may mask hidden issues in our training pipeline, and eventually keep us from making further progress. This thesis aims to close this theory-and-practice gap by studying neural network training dynamics closely for both standard minimization and multi-agent problems. To be specific, it addresses some important questions such as how to train very deep neural networks without using extra tricks, which algorithmic choices would benefit from increasing compute, how standard algorithms fail in the multi-agent setting and how to solve multi-agent games efficiently. The final chapter of the thesis discusses how to extrapolate from works presented to build scalable and safe intelligent learning systems.

Advisor: Grosse, Roger
Novel Approaches to Population-Based Studies in Human Pathogens

Human pathogen populations are comprised of subpopulations exhibiting varying levels of virulence or resistance against therapeutics. The number, size, and characteristics of the subpopulations are referred to as population structure. Understanding population structure can provide insight into the evolutionary forces that shaped the development of a population. Haplotypes, representing population structure within specific genomic regions, can serve as markers for identifying the genetic determinants of virulence and resistance. With the advent of low-cost, high-throughput genome sequencing technology, population genomics data sets arebeing generated for hundreds of species of medical importance. The scale of these data sets presents new challenges for the inference and visualization of their population structures. In this thesis, I present PopNet, a novel computational method that identifies regions of common ancestry in the genomes of related strains through clustering patterns of genetic variation. The broad applicability of PopNet is demonstrated through the Apicomplexan pathogens Toxoplasma gondii and Plasmodium falciparum, as well as the yeast model organism Saccharomyces cerevisiae. An extension of PopNet, named PopNetD3, improves its accessibility through serverside computing and a web-based graphical user interface. The haplotype data produced by PopNet also contributed to the development of a novel neural network-based method for genome-wide association studies. The dimensionality reduction achieved via PopNet and prefiltering using a regularized linear model helped a deep neural network model achieve higher predictive accuracy for artemisinin resistance in Plasmodium falciparum compared to the regression models currently used for GWAS. Using a novel “impact” scoring system, I found that the neural network model predicts five artemisinin resistance-associated genomic loci containing ten genes, one of which included the known resistance marker gene kelch13. The generalizability of the neural network approach is shown through application to the bacterial pathogen Neisseria gonorrhoeae, where the purE gene is predicted to be associated with disseminated gonococcal infection. Together, PopNet and the neural network genome-wide association study approach represent novel computational methods to better understand and exploit the genetic diversity of Apicomplexan parasites and related haploid organisms.

Keywords: Genetics
Genomics
Pathogen
Population
Advisor: Parkinson, John
Religious Girlhood and Secular Schooling: Experiences of Faith and Gender in Toronto Area Public High Schools

Toronto public schools are some of the most religiously diverse in the country. Religious students, whose practices may run counter to the dominant cultures of Christianity and secularism shaping public education, often face marginalization. This dissertation asks how religious girls of Abrahamic faiths navigate academic and social life in public secondary schools in Toronto. It addresses gaps in extant literature by highlighting the significance gender plays in youth religiosity; by looking across multiple faiths groups; and by centering student voices in data analysis. Guided theoretically by feminist epistemology, and taking a qualitative research approach, interviews and focus groups were conducted with 18 religious girls. The resulting data was analyzed through a thematic analysis. Key findings paint a picture of girls’ quiet religious expression within classroom and social spaces. Degrees of peers’ acceptance influenced girls’ actions to edit aspects of their religiosity in social settings. Participants identified challenges related to their gender in the school context, but relegated experiences stemming from their being a religious girl to faith-specific, outside school environments. Additionally, variability in their teachers’ approaches to addressing religion shaped girls’ religious expressions and relative comfort in the classroom. The data analysis challenge feminist conceptualizations of religious agency and point to visibility as a salient space where religious girls exercised their agency in public schools. Findings also illustrate a gap between what girls pointed to as shaping their experiences as religious girls at public high schools, and the current religious accommodation policies as the primary mechanism for addressing religious student needs. The dissertation suggests a need for additional policy tools in conceptualizing religious student needs, and for increasing attention at the policy level on religiousness as an important dimension of student diversity. Implications for teacher education suggest enhanced training about the nuances of religious identities and the numerous (intersecting) ways religiosity manifests in school environments. Directions for future research include investigations with teachers about pedagogy and curriculum connections to religion, and with religious students of all genders into the conditions determining when and why students hide aspects of their religiosity in school.

Keywords: Faith
Feminism
Girlhood
Public education
Religiosity
Advisor: Niyozov, Sarfaroz