VictorJuiz/thesis huggingface dataset:
In Search of Community: A Comparative Case Study of Education-for-development and Local Community Ownership in Chile and Central Africa
Keywords: Community Development
Comparative case study
Education
Education for Development
International Development
Participatory research methods

Education evokes a powerful promise in development discourses. However, the role of local communities within these discourses has been unclear––often either romanticized or relegated to a passive context. In this comparative case study, I examine the role that local communities play in two transnational education-for-development projects: in the national capital region of the Central African Republic (CAR) and in the Williche territories of Chiloé, in Southern Chile. I examine the limitations of predominant theories of education in development and indicate how concepts of development are being redefined by populations and communities reading their own material, social, and spiritual realities in a given time and place. I draw on concepts from community studies and social solidarity to conceptualize “community” along several dimensions: place, structure and process, meaning, and time. To these, I suggest an additional dimension of capacity for protagonism. Empirically, I employ ethnographic and participatory methods in this study, building on participatory program evaluations solicited from funding partners in Luxembourg and Canada. I analyze the cases comparatively along three axes: horizontal, vertical, and transversal. Data from this study illustrate multifaceted ways that local communities respond to distinct conditions. Communities express ownership, capacity, and agency in the CAR in the midst of civil conflict to raise a network of community schools from grassroots neighbourhood and village settings. In Chiloé, communities prioritize the resurgence of Indigenous patterns of community life and systems of knowledge. In these two contexts, communities re-evaluate and reform curriculum and pedagogy according to local frameworks and exigencies: gradually in the case of the CAR, and through the creation of entirely new curriculum and pedagogical approaches in Chiloé. In conclusion, my findings indicate the importance of shifting conceptions of community away from a passive context to that of a protagonist in education-for-development discourses. The cases in this study illustrate how, when empowered through the development of capacities and abilities, communities stimulate educational development according to distinct needs, exigencies, and conceptual frameworks. As protagonists of their own paths of development, communities employ education to mobilize other vital community processes including agricultural development, cultural production, family health, and environmental protection.
For our Children: Black Motherwork and Schooling
Keywords: Arts-based research
Black families
Motherwork
School-family partnership

Ontario’s Parent Engagement Policy (Ontario Ministry of Education, 2010) compels school officials to identify and remove discriminatory practices and structures to ensure the full participation of all parents in their children’s schooling. Yet Canadian literature and media highlight ongoing challenges faced by Black mothers in their relationships with their children’s schools. This arts-informed study uses storytelling to investigate the relationships between Black mothers and school officials. The study presents insights obtained from 10 in-depth interviews with African Caribbean mothers living in Toronto. Participants shared personal stories to articulate their understanding of Black motherwork and its impact on their relationships with school officials. Data collected from these oral recounts, along with the review of the literature, addressed the questions: What is Black women’s motherwork? How does Black women’s motherwork shape their relationships with school officials? How do Black mothers draw on communities of support to respond to challenges and successes encountered in their relationships with school officials? This research draws on African Indigenous, Black feminist and Black maternal theories to investigate Black women's motherwork and their relationships with one another and school officials. Using a comprehensive analytic process, participants' oral accounts are compiled into interconnected creative non-fiction stories. These stories are informed by African storytelling traditions like call-and-response, improvisation and audience participation. This structure prioritizes Black mothers’ voices and honours arts-informed methodologies, all while illustrating links to previous scholarship. The personal/self-stories, cultural stories, and metanarratives presented in this arts-informed study uphold Black mothers as educational leaders in their homes, communities and schools. The study revealed Black Canadian motherwork as a form of activism that is integral to the health and wellbeing of Black women and children. Black mothers understand their motherwork as collective action involving community parents. For Black mothers, school officials do not always fulfill their roles as community parents. Black mothers work with members in their women-centred networks to affirm their leadership and safeguard their children’s rights to safe, inclusive schooling spaces. This arts-informed study contributes to a deeper understanding of the ways that Black mothers practice educational leadership amongst themselves and with school officials.
Investigating the Role of H3.3K27M during Cellular Transformation and Differentiation.
Keywords: brain cancer
DIPG
glioblastoma

Diffuse Intrinsic Pontine Glioma (DIPG) is the most fatal brain tumour in childhood and current therapeutic interventions are failing. There is an urgent need for better understanding of DIPG pathogenesis and novel therapeutic approaches. Discovery of histone 3 variant mutations in DIPG has enabled the development of more faithful disease models and highlighted a role for histone epigenetics in gliomagenesis. The H3.3K27M mutant occurs in 65% of DIPGs and is the focus of this thesis. We have previously demonstrated that H3.3K27M transforms immortalized normal human astrocytes (iNHAs). We expand on that initial finding by testing the ability of these cells to form colonies at high confluency (HC). We find that H3.3K27M iNHAs execute a cell-autonomous program promoting cellular invasiveness, EMT and ECM reorganization through transcriptional dysregulation at HC. Importantly, the transcriptome of H3.3K27M iNHAs significantly overlaps with the astrocytic cell program recently described in DIPG. We identify the cell surface protein FPR3 as a putative H3.3K27M target likely upregulated to support EMT. We speculate that downregulation of cell-cell and cell-matrix adhesion proteins by H3.3K27M is central to colony formation and demonstrate that protocadherin 7 (PCDH7) is reduced in our model as in human glioblastoma. This study lays the necessary groundwork for further exploration of these novel putative H3.3K27M targets. Since our lab and others have determined that H3.3K27M alone is not a potent driver of transformation, we investigated its impact on cell state. We utilized the thoroughly characterized C2C12 myoblast differentiation model. The mechanism underlying H3.3K27M-induced transcriptional activation centres on dysregulation of the PRC2 complex and loss of the repressive H3K27me3 mark. Consistent with this, our investigation of the impact of H3.3K27M in differentiating myoblasts revealed massive H3K27me3 loss throughout differentiation and precocious differentiation through activation of the master muscle regulator factors (MRFs) MyoD and myogenin followed by transient cell cycle exit. Importantly, differentiating H3.3K27M myoblasts did not completely downregulate cell cycle factors cyclin B1 and cyclin D1 likely stalling complete terminal differentiate. This work has important implications for our understanding of how cellular decision-making is impacted by H3.3K27M and hints that mutant expression may disrupt proper cell cycle regulation.
From Business Goals to Analytics and Machine Learning Solutions: A Conceptual Modeling Framework
Keywords: Business Analytics
Conceptual Modeling
Data Analytics
Machine Learning
Requirements Engineering

Machine learning and analytical techniques are increasingly becoming an integral part of many types of information systems. Despite the hype around these technologies, modern organizations continue to struggle with using them to take advantage of their data and address business problems. The development of business analytics and machine learning solutions in enterprises involves tackling a wide range of complexities with respect to requirements elicitation, design, development and deployment of such solutions. In spite of the necessity and relevance of conceptual modeling and requirements engineering approaches to the process, not much research has been done in this area. This thesis develops, demonstrates and evaluates a conceptual modeling framework for business analytics and machine learning solutions. The framework includes meta-models, methods, design catalogues and patterns, guidelines, and instantiations. It consists of three modeling views, representing different aspects of a solution and viewpoints of different roles involved in the development of such systems. The Business View supports the elicitation of business analytical requirements by capturing stakeholders, strategic goals, decisions, questions and required insights. The Analytics Design View supports the design of the solution by capturing algorithms, metrics, and quality requirements, focusing primarily on machine learning solutions. The Data Preparation view supports the design of transformation workflows by capturing data tables, flows, and preparation tasks and operations. These views are linked together to represent a holistic view and bridge the gap from business strategies to machine learning algorithms to data preparation operations. The framework comes with a set of design catalogues and solution patterns that encode and represent generic and well-proven machine learning solutions for commonly-known recurring business problems. The framework is also augmented with methodological steps and modeling guidelines for supporting domain users in working with the framework. It also includes model-based support for linking analytics-driven insights to consequent enterprise actions and changes. The framework is tested through multiple real-world projects, an empirical study in the healthcare domain, and prototype design and implementation in collaboration with a large information technology vendor. This thesis reports on findings from evaluating the expressiveness and usefulness of the framework, in addition to revealed drawbacks and improvement opportunities.
Development of Flow Based In vivo NMR for Understanding Metabolic Indicators of Stress
Keywords: 2D NMR
environmental chemistry
ERETIC
In vivo
keystone species
metabolomics

In vivo nuclear magnetic resonance (NMR) has proven to be an excellent tool for probing complex biological processes inside living organisms. In order for 2D NMR work in this thesis to be carried out, model organisms routinely used in aquatic toxicity screening, including D.magna and H.azteca were applied in these studies once fully 13C enriched (99 % 13 C enrichment). In Chapter 2, a selective NMR experiment, termed the (H)CbCa(COCa)Ha experiment, was implemented here to explicitly target free amino acids inside living 13C enriched D.magna. By applying this experiment, it was also possible to discern oxidative stress inside the organisms due to sub-lethal BPA (bisphenol-A) exposure. In Chapter 3, a quantitative 2D NMR experiment (perfect-HSQC) was tested and the results showed that it could be used to achieve excellent quantitation of key metabolites inside living 13C enriched D.magna when integrated with 2D ERETIC (Electronic Reference To access In vivo Concentrations) protocols. In Chapter 4, a 1H detect experiment combined with a 13C filter that only detects the probe molecules (1H attached to 12C) was successfully used to follow lipid assimilation (12C algal food source, C.reinhardtii) and biotransformation (natural abundance nicotine) in vivo using living 13C enriched D.magna and living 13C enriched H.azteca respectively. For Chapter 5, a time resolved variant of the standard (H)CbCa(COCa)Ha NMR experiment used in Chapter 2 was applied here using living 13 C enriched D.magna and BPA as the model contaminant. By simultaneously tracking the metabolic flux in the 13C enriched daphnids along with a concentration sweep of > 1000 different concentration measurements (of BPA) in a single NMR experiment, it was then possible to discern the lowest concentration that elicits a statistically significant response in the organism’s metabolome. This is denoted here as the LCDR and is an important metric for establishing proper environmental policies and regulations. In summary, the four research-based Chapters that constitute this thesis should help in the future to produce a more robust, and meaningful foundation for understanding aquatic toxicity in general.
Food Access and Insecurity in Adults with Mobility Disabilities
Keywords: Disability
Food access
Food insecurity
Mobility

People with disabilities experience greater risk of food insecurity compared to people without disabilities. Lower incomes and higher relative expenses are often understood as the major causes of this inequality. Some scholars have also posited that people with mobility disabilities (PWMD) experience greater difficulty procuring or preparing food. Yet, limited research examines upstream factors related to food insecurity risk, lived experiences of food access, or environments that present access barriers among PWMD. Responding to these knowledge gaps, I sought to examine place-based influences on the relationship between mobility disability and food insecurity, questioning environmental, political, and institutional contexts that impact economic and physical access to food. Throughout my work, I use different theoretical approaches to conceptualize disability including, the social model, a critical ableist perspective, and an assemblage perspective. In adopting these perspectives, I challenge how bodies and mobility are typically understood in the food desert literature. My dissertation contains a scoping review followed by a series of empirical chapters set within a mixed-methods research design. In my scoping review of the literature, I discovered an important and widespread association between disability and food insecurity. In an analysis of microdata from the Canadian Community Health Survey, I examined sociodemographic and geographic risk factors of food insecurity among people with mobility impairment. Across Canada, I found an important inequality in food insecurity between people with and without mobility impairment. Province was associated with risk of food insecurity among people with mobility impairments, potentially reflecting different political and institutional contexts. I then conducted a qualitative study, using mobile go-along interviews with PWMD in Toronto, Canada, to understand lived experiences of food access. I found that food access was often restricted for PWMD related to different systems (e.g., social assistance) and places of access, on food trips, at home, and in food destinations. In subsequent research, I showed how the home acts as an important site shaping physical, social, and economic access to food. Together, my findings suggest the need to address socioeconomic disadvantage among PWMD while considering disabling contexts that limit access to functional housing, outdoor environments, transport systems, and food destinations.
Learning to Solve Optimization Problems with Hidden Components: Applications in Automated Treatment Planning
Keywords: constrained optimization
machine learning
medical decision-making
radiation therapy

Designing optimization models that capture decision-maker preferences typically requires guidance from domain experts. We can instead employ machine learning (ML) to design contextual optimization models using data sets of past decisions. In this thesis, we propose techniques to formulate and solve problems where the objective or the feasible set is dependent on decision-maker preferences. We apply these methods to automate the design of personalized radiation therapy treatments for head-and-neck cancer patients. Here, the prevailing framework is Knowledge-based planning (KBP), which is a two-stage pipeline that involves generating an expected dose and optimizing a treatment to deliver the generated dose. We first propose an ensemble learning framework for Inverse Linear Optimization (ILO), which is a structured prediction problem for estimating the cost vector of a linear program from observed decisions. Our framework specializes to existing variants in the literature, admits new solution algorithms, and includes a statistical goodness-of-fit metric. We employ our framework to develop the first ensemble KBP pipeline that incorporates multiple different dose generation models to yield better treatments than existing single-dose pipelines. Next, we develop a deep learning-based dose generation model that uses a generative adversarial network to map from CT images to dose distributions. Previous dose generation models employed classical ML to estimate dose summary statistics. Our approach outperforms classical models on clinical metrics. We then explore contextual optimization when the feasible set varies as a function of features. We present Interior Point Methods with Adversarial Networks (IPMAN), an algorithm for learning the feasible set and predicting corresponding optimal decisions for contextual problems. We prove our approach yields optimality guarantees and generalization bounds. We then re-cast dose generation as an optimization problem and implement IPMAN to predict optimal doses. Our predictions achieve clinical metrics better than baselines and also demonstrate a transfer learning to new clinics that use different metrics. Finally, motivated by data augmentation for IPMAN, we consider the task of sampling infeasible decisions from an optimization problem. We present a Markov Chain Monte Carlo algorithm for sampling from the complement of a polyhedron that provably covers the complement and demonstrate its effectiveness in numerical experiments.
The Workplace Social Networks of Professional Parents
Keywords: Gender
Homophily
Motherhood
Parenthood
Social networks

Social networks provide several benefits related to ascension and success at work. In this dissertation, I investigate the impact of motherhood and fatherhood on the characteristics of two types of social relationships: instrumental relationships, which provide job-related inputs required for job performance and career advancement; and expressive relationships, which provide emotional support and friendship that support professionals in the workplace. I take the position that relative to the image of an ideal worker, negative implications of motherhood and positive implications of fatherhood have different impacts on the formation and maintenance of social relationships. Specifically, I propose that fathers can use their parenthood as a source of similarity distinctly from mothers, resulting in different effects on gender homophily, relationship closeness, frequency of contact, multiplexity, the number of internal and external ties, and the perceived resources gained. I investigated this hypothesis with two studies—a network survey and qualitative study of lawyers working at large law firms. The results did not support any effects of fatherhood on instrumental and expressive relationships. Instead, I found an effect of motherhood on expressive relationships with other mothers supported by qualitative evidence of mothers forming closer connections with other mothers and relying on them for instrumental advice and expressive support to navigate being a working professional mother. Despite signaling strong work commitment, the challenges of dealing with persistent stereotypes of conflict between the ideal mother and ideal worker images described by mothers appeared to translate into a higher need for such homophilous expressive support.
Identification of Novel Therapeutic Approaches to the Progression of Chronic Kidney Disease
Keywords: Angiotensin-converting enzyme 2
Chronic kidney disease
Drug repurposing
Interstitial fibrosis
Lysine deacetylase
Proximal tubule cell

Progression of chronic kidney disease (CKD) to renal failure is a substantial international public health problem. Existing treatments have limited effectiveness as they slow, but do not prevent, progression. This is particularly true for rare forms of CKD. Accordingly, we sought to identify and test new therapeutic approaches to Alport syndrome (AS), a rare genetic condition characterized by progressive CKD. AS is caused by mutations in the genes encoding collagen, type IV, alpha (COL4A) chains present in the glomerular basement membrane. Mutations interfere with normal assembly of the COL4A network necessary for maintenance of the glomerular filtration barrier, leading to glomerular injury, proteinuria, and interstitial fibrosis. We studied Col4a3–/– mice, an experimental model displaying these hallmarks of AS. We have reported that intrarenal expression of angiotensin-converting enzyme 2 (ACE2), a peptidase that catalyzes conversion of angiotensin (Ang) II to Ang-(1–7), is decreased in Col4a3–/– mice. We therefore examined effects of recombinant ACE2 (rACE2) administration on kidney injury in Col4a3–/– mice. Treatment with rACE2 affected turnover of renal ACE2 likely through suppression of tumor necrosis factor alpha-converting enzyme. rACE2 also mitigated kidney mitogen-activated protein kinase (MAPK) activation, fibrosis, and oxidative stress. Utilizing an unbiased approach, we defined a disease progression signature reflecting transcript-level expression patterns detected in Col4a3–/– kidneys during development of CKD. We performed computational drug repurposing by querying the Connectivity Map for potential compounds to reverse signature gene expression. Vorinostat, a lysine deacetylase (KDAC) inhibitor emerged as a potential treatment. Vorinostat reduced tubulointerstitial fibrosis and prolonged survival of Col4a3–/– mice. In tubular epithelial cells, vorinostat prevented Ang II- and albumin-induced MAPK phosphorylation and activator protein 1 activation. Taken together, this work identified two novel treatment approaches to AS: rACE2 delivery and KDAC inhibition. These studies also demonstrated that regulation of Ang peptide metabolism and lysine acetylation via ACE2 and KDACs, respectively, are important determinants of disease progression in experimental CKD, at least in part, due to their impact on MAPK signaling and evolution of interstitial fibrosis. This dissertation provides a rationale for rACE2 therapy and KDAC blockade in humans with CKD.