VictorJuiz/thesis huggingface dataset:
Causal Variance Decompositions for Evaluating Healthcare Provider Performance

There has been a growing interest in striving for accountability and transparency in healthcare by quantifying and comparing the performance of providers. This requires measuring the quality of care provided to patients, which is often done using disease-specific quality indicators (QIs). Before considering intervening and improving quality gaps identified by using QIs, the chosen indicator has to demonstrate existing differences in the quality of care. Therefore, the overarching aim of this thesis is to develop metrics that assess the usefulness of QIs in identifying existing differences in the quality of care provided between providers. This can be achieved by decomposing and quantifying the overall variation between providers in a causal inference framework. The first objective is to develop a three-way causal variance decomposition to attribute observed variation in care received into that causally explained by hospitals’ performance, the patient case-mix itself, and unexplained variation. In the context of surgical cancer treatments, the performance variations can be due to hospital and/or surgeon level differences, creating a hierarchical clustering. The second objective is to generalize the causal variance decomposition for several exposure hierarchies. For this purpose, I derive a decomposition of the observed variation in care received into four parts, explained by patient case- mix, causally explained by hospitals’ performance, causally explained by surgeons’ performance, and unexplained variation. While the use of variance decompositions can demonstrate differences in quality of care, causal mediation analysis can be used to investigate the care pathways that lead to these differences in performance between hospitals. Combining these two approaches enables decomposing between-hospital variation in an outcome-type indicator into two parts: the part that is mediated through a specific process of care and the part that is due to all other pathways. The third objective is to derive a causal mediation analysis decomposition of the between-hospital variance into three parts, the natural indirect effects, the natural direct effects, and a covariance-type term. Model-based estimators are formulated for the variance decompositions, and their performance are investigated through simulation studies. All methods are demonstrated in real data applications in Ontario kidney cancer data.

Keywords: Causal inference
Hospital profiling
Quality indicator
Variance decomposition
Advisor: Saarela, Olli
Carrying Places: How Elementary Students Contest Historical Asymmetries and Grow Relational Knowledge About Land/land Through Origin Stories

This practitioner research study explored how grade three students understood their relationships to the histories, places, and peoples of where they live. Specifically, I examined how students experienced settlerhood and what happened when they travelled outside of official curriculum and into the flow of treatyhood. My theoretical framework drew on important tenets from within Indigenous worldviews, the operational structure of settler ontologies, and how the body of knowledge for both of these is articulated through origin stories. I demonstrated how the Ontario social studies/ history curriculum embodies a settler grammar that structures how students understand the world. Instead of reproducing settler-normative experiences and creating settler subjects by habituating students into settler traditions, I went outside of settler ontology in order to interrupt the process of settler-making by teaching through Indigenous histographies. My empirical study was grounded in the methods and perspectives of practitioner research, which centers, illuminates, and troubles the interlaced relationships in classrooms and mobilizes knowledge production from the viewpoints of teachers and students. As their teacher, I paid attention to the overlapping layers of their subjectivities through stories of themselves with each other. My intention was to help develop explicitly relational forms of interacting with others in the world. Because students’ own storied presences were on contested lands, I invited students to share their life stories and intermesh them with etiological Indigenous stories. Students also learned about settler origin stories of the nation. They developed awareness of Land and places, learned from their own repertoires of cultural knowledge, and utilized their relationalities in-place to learn from the confluence of story. Students incorporated treaty teachings that folded Indigenous sensibilities into their repertoire of understanding—despite the ongoing erasure of these worldviews by machination of the settler state. I troubled the issues I encountered in my research, illuminating the contradictions and tensions that resulted when I taught about Indigenous sensibilities to Land. In conclusion I highlight the incommensurable conundrum of representation of Indigeneity within settler colonial structures and pose further questions about how to devise relational lenses through which students might view the world.

Keywords: Indigenous histories
practitioner research
settler colonialism
social studies
subject and relational formation
Treaty education
Advisor: Gaztambide-Fernandez, Ruben
Advanced Microfluidic Platforms for Studying the Stromal Compartment of Pancreatic Cancer Tumor Microenvironments

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease of the exocrine pancreas, with treatment resistance attributed to the aggressive biology of the tumor microenvironment (TME). Interactions between activated pancreatic stellate cells (PSCs) and pancreatic cancer cells (PCCs) give rise to dense fibrotic masses (stroma) and the concomitant avascularity and hypoxia within the TME alters metabolic activity and mediates infiltration of T cells and chemotherapeutic agents. While many studies have investigated the mechanisms of stroma-mediated treatment resistance, these effects are still not well understood. There is thus an urgent need for more accurate models of the TME stromal compartment.In the last decade, the synergistic combination of microfluidic technologies with 3D cultures have resulted in Organ-on-a-Chip (OOC) technologies that leverage the microscale manipulation of fluids to achieve precise spatiotemporal control of 3D tissues. However, many of these systems remain in the proof-of-concept stage due to complex fabrication steps, and sealed architectures that significantly limit methods of analysis and data collection. Therefore, more advanced microfluidic platforms for studying the stromal compartment of the PDAC TME are required, that can provide not only optical access but also non-destructive physical access for more detailed assessments of mechanical and metabolic properties. The main objective of this thesis is to develop and characterize the stromal compartment of a novel PDAC-on-a-Chip model, using an openable multilayer microfluidic architecture that enables traditional image-based analysis as well as on-chip mechanical stiffness testing and sample extraction for small molecule detection. We first tested multilayer and phaseguide-separated channel layouts and combined these features to develop a novel microfluidic architecture that permitted 3D diffusive interactions. A modified version was used to develop a PDAC-on-a-Chip model where hydrogel-embedded PSCs were cultured with PCCs in an openable device that provided direct access to the hydrogel compartment. Our model displayed hallmark characteristics of PDAC including PSC activation, stiffening of the matrix, and collagen content increase. We also identified potential contributions to metabolic pathways via mass spectrometric analysis of extracted gel samples. These advanced platforms have thus demonstrated the potential to deepen our understanding of PDAC through modeling and analysis of more complex TME stromal characteristics.

Keywords: Mass Spectrometry
Microfluidics
Organ-on-a-Chip
PDAC
Stroma
Tumor microenvironment
Advisor: Young, Edmond W. K.
New Probes of Hidden Sectors: From Colliders to Cosmology

We begin by reviewing the development of the Standard Model of particle physics, and the challenges it faces today. We argue that, based on considerations of naturalness, it is well-motivated to study the signatures of hidden sectors as models of Beyond the Standard Model (BSM) physics, and present three of our own studies that engage in this endeavour. First, we develop methods to identify the production mechanism of long-lived particles produced at the Large Hadron Collider (LHC) using the proposed MATHUSLA detector, finding that it can do so accurately with only a few dozen detections. Second, we devise a strategy to search for Soft Unclustered Energy Patterns (SUEP) produced in exotic Higgs decays at the LHC, using unsupervised machine learning methods. We find that the High-Luminosity LHC can be sensitive to Higgs branching ratios to SUEP down to 1%. Finally, we present an analysis of constraints on the parameter space of atomic dark matter from large-scale cosmological observations, setting new bounds on the scale at which the dark sector must decouple.

Keywords: Beyond the Standard Model
Cosmology
Phenomenology
Advisor: Curtin, David
Neuroanatomy of early amniotes and the evolution of mammalian audition

Synapsids, now represented only by mammals, comprise a great diversity of fossil taxa extending back to the late Carboniferous. The study of non-mammalian synapsids is essential for identifying the key characteristics that led to modern mammals, with palaeoneuroanatomy forming a critical component for understanding synapsid palaeobiology and the stepwise acquisition of the features that now characterize crown mammals. To date, studies of synapsid neuroevolution have been heavily skewed towards the evolution of the mammalian middle ear and have focused almost entirely on therapsids; comparatively little attention has been given to the pelycosaurs which represent the earliest phase of synapsid evolution. Investigations into mammalian sensory evolution have long been predicated on the assumption that changes in the jaw architecture of non-mammalian synapsids directly related to an increase in high-frequency sensitivity, yet this hypothesis has never been tested. I provide greatly enhanced characterizations of the neuroanatomy of a swath of early amniotes, including stem amniote and eureptile outgroups and both the earliest- and latest-diverging groups of pelycosaurs, and incorporate the resulting information into an analysis of auditory evolution across synapsids.Stem amniotes are represented by seymouriamorphs, for which I produce the most detailed neuroanatomical study to date, providing key insights into braincase homology and establishing the probable ancestral condition for amniotes. For captorhinids, the earliest eureptiles, I produce the first endocast-based description of any non-diapsid sauropsid, revealing several unexpected morphological features. I then produce the first virtual endocast-based study of any pelycosaur with a sample of six varanopids, the earliest-diverging clade, and provide a greatly expanded description of the neuroanatomy of the sphenacodontid Dimetrodon, representing the latest-diverging clade. My work culminates in an analysis of the evolution of the hearing organ in relation to changes in jaw architecture, finding no support for the long-assumed relationship between hearing evolution and postdentary reduction. Collectively, these studies greatly expand our understanding of the neuroanatomy, and thereby the palaeobiology, of early amniotes generally and of fossil synapsids in particular. In turn, this work provides a more robust anatomical and analytical framework through which to address broad questions of synapsid evolution.

Advisor: Evans, David C
Rethinking the Characterization of Nanoscale Field-Effect Transistors

Standard methods for calculating transport parameters in nanoscale field-effect transistors (FETs), namely carrier concentration and mobility, require a linear connection between the gate voltage and channel conductance, however, this is often not the case. One major reason often overlooked is that shifts in chemical and electric potential can partially compensate each other, commonly referred to as quantum capacitance. In nanoscale FETs, capacitance is often unmeasurable and an analytical formula is required, which assumes the conducting channel as metallic and common methods of determining threshold voltage no longer couple properly into transport equations. As present and future FET structures become smaller and have increased channel-gate coupling, this issue will render standard methods impossible to use. This thesis discusses the validity of common methods of characterization for nanoscale FETs, develops a universal model to determine transport properties by only measuring the threshold voltage of an FET and presents a new parameter to easily classify FETs as either quantum capacitance-limited or metallic approximated charge transport. Another major factor affecting transport characteristics that is often ignored in literature is electrical hysteresis from localized trap states and, in combination with the proposed universal characterization model, novel techniques are introduced to measure and remove the errors associated with these effects. Additionally, a hysteresis model is developed and, combined with new experimental techniques, used to extract total concentration of trap states and their average energy. This model is used to explain discrepancies seen in literature with regards to gate voltage sweep rate dependence of hysteresis itself. Temperature dependence of electrical characteristics is also analyzed and a theoretical model was developed to explain a phenomena known as the zero-temperature-coefficient (ZTC) and how it relates to fixed oxide charges in the semiconductor channel oxide of an FET. Ultimately, this body of work helps explain several major discrepancies generally ignored in literature regarding nanoscale FET electrical characterization and provides novel experimental techniques and theoretical models to more accurately determine key metrics for characterizing transport in these devices.

Keywords: Electrical Characterization
Electrical Hysteresis
Electron Transport
Nanoscale Field-Effect Transistor
Quantum capacitance
Zero Temperature Coefficient
Advisor: Ruda, Harry E
Insula Deep Transcranial Magnetic Stimulation and Varenicline for Smoking Cessation The Effect of Nicotine Dependence on Post-Traumatic Stress Disorder

Project ICurrent smoking cessation treatments are limited in terms of efficacy, particularly with regards to long term abstinence. There is a large amount of evidence implicating the insula in nicotine addiction. We aimed to examine the effect and efficacy of deep repetitive transcranial magnetic stimulation (rTMS) to the insula, relative to sham, in 42 smokers receiving varenicline on abstinence and smoking outcomes. We found that at the end of treatment (Week 12), smokers in the active group had significantly higher abstinence rates than those in the sham group (82.4% vs. 30.7%, p = 0.013). Our secondary outcome measures of abstinence rate at the end of rTMS treatment (Week 4), abstinence rate at 6 months, and our smoking outcomes (e.g., craving, withdrawal) showed no significant differences between groups. No differences were found in adverse events reported between the groups. This study provides evidence of the potential benefit of having a combined treatment for smoking cessation. Project II It is evident that an association between smoking and post-traumatic stress disorder (PTSD) exists, but research is lacking in establishing the directionality of the relationship. We used longitudinal data from waves I and II of the National Epidemiologic Survey on Alcohol and Related Conditions. Individuals with nicotine dependence (ND) were matched to individuals without ND using propensity score matching to estimate the risk of developing PTSD after trauma. We also matched smokers (with or without ND) to lifetime nonsmokers to estimate their risk of developing PTSD after trauma. Lastly, we conducted a mediation analysis on the effect of ND severity on PTSD symptoms. Individuals with ND (n = 1514) were more likely to develop PTSD (p = 0.017) compared with individuals without ND (n = 6047). Smokers (regardless of ND) (n = 2335) compared with nonsmokers (n = 5226) had no significant effect on risk of PTSD. Withdrawal was found to be a mediator of the effect of ND severity on PTSD symptoms. ND, but not smoking status, increases a smoker's risk of developing PTSD. This provides information that could aid in preventive strategies for individuals with ND who are exposed to trauma.

Advisor: Le Foll, Bernard
Training to Support Appropriate Reliance on Advanced Driver Assistance Systems

Advanced driver assistance systems (ADAS) are becoming more widely available to consumers. While these systems have potential safety benefits, overreliance on ADAS has contributed to several fatal collisions. Training has been identified as one way to address overreliance and support safe use of driving automation. Currently, existing training and related research generally focuses on teaching drivers various system limitations, but there are mixed results regarding the relationship between knowledge of limitations and trust in and reliance on ADAS. Further, this limitation-focused training approach may not be practical (e.g., due to the large number of limitations to learn and remember). This dissertation aims to understand the relationship between knowledge of ADAS limitations, trust, and reliance, and investigate an alternative to limitation-focused training.First, a survey study was conducted and the results suggested that, in addition to impracticalities associated with limitation-focused training, it may not be an ideal approach if targeting a wide range of drivers, as knowledge did not significantly impact trust or reliance intention for drivers who had ADAS experience. Subsequently, training videos were developed to compare limitation-focused training to a novel training approach which highlighted the drivers’ responsibility when using ADAS (i.e., responsibility-focused training). An online study revealed limited differences between the limitation-focused and responsibility-focused approach. Where significant differences were found for quantitative measures, they indicated potential drawbacks of the limitation-focused approach. Further, results of semi-structured interviews with participants suggest that limitation-focused training may have the unintended consequence of decreased interest in using ADAS among drivers without ADAS experience. Further investigation with a simulator study showed that even when an attention monitoring system was implemented to remind participants to keep their eyes on the road, there was a benefit of the responsibility-focused training, but not limitation-focused training, on reliance (e.g., lower rate of long glances to a secondary task and taking over control of the vehicle sooner when a potential conflict occurred). Overall, this dissertation adds to the existing literature on the relationships between ADAS knowledge, trust, and reliance. Further, it expands the limited literature on alternatives to limitation-focused training and provides preliminary support to the responsibility-focused training approach.

Advisor: Donmez, Birsen
Hierarchical Imaging and Computational Analysis of Three-Dimensional Vascular Network Architecture in the Entire Postnatal and Adult Mouse Brain

Background: The cerebrovascular network plays a vital role in brain development, normal brain function, and various central nervous system (CNS) disorders. Nevertheless, the effects of recently discovered (anti)angiogenic markers, genes, and pharmaceutical targets on cerebrovascular architecture remain largely unknown. Additionally, there is a lack of quantitative data and imaging approaches to study cerebrovascular architecture and morphology at sub-micrometer resolution across the entire brain. Our objective is to bridge this gap between the molecular and morphological levels by establishing a novel neuroimaging pipeline that enables objective quantification and visualization of cerebrovascular network architecture at ultra-high resolution. Methods: Here we present a step-by-step method for hierarchical imaging and computational analysis of vascular networks in entire postnatal and adult mouse brains. The method involves resin-based vascular corrosion casting, synchrotron radiation microcomputed tomography imaging, and 3D computational network analysis. Results: We visualized and quantified the cerebrovascular architecture in early postnatal(n=10, postnatal day 10) and adult (n=10, postnatal day 60) brain vascular corrosion casts, including a separation between capillaries and non-capillary vessels. Our analysis revealed significant changes in cerebrovascular morphology from the early postnatal stage to adulthood, including an increase in vascular volume fraction, branchpoint density, branchpoint degree, and vessel tortuosity, combined with a decrease in vessel segment diameter, length and volume, branchpoint diameter, extravascular distance, and the average adjacent segment angle as a measure of vessel directionality. A genetic knockout model of the known anti-angiogenic molecule Nogo-A confirmed the applicability of our method to study the effects of individual genes on vessel morphology. Conclusion and Future Directions: Using the novel neuroimaging pipeline presented in this thesis we provide a morphological atlas of the entire mouse brain vasculature at both the postnatal and adult stages of development. The presented methodology enables researchers to objectively quantify cerebrovascular morphology before and after pharmacotherapy or intervention in various experimental models of cerebrovascular disease. Targeted graph-based machine-learning approaches, such as link prediction algorithms and arteriovenous identity classification, can be integrated and our approach has the potential to be extended to human ex-vivo resected brain tissue or postmortem settings.

Keywords: brain vasculature
neuroscience
vascular biology
Advisor: Monnier, Philippe
Beyond the Twilight Zone: The Restructuring and Resurrection of Zombie Firms

Zombie firms are businesses with operating profits that are sustainably insufficient to cover their debt servicing costs. The existence of zombie firms crowds out growth opportunities for other healthy firms and thus distorts overall economic growth. Despite their economic importance, however, the question of what factors enable certain zombie firms to survive while others fail has yet to be addressed in the extant literature. Thus, I propose focusing on an important but hitherto overlooked aspect of zombie firms—their restructuring activities. I first use a financial statement-based measure of restructuring charges to infer the prevalence of restructuring activities and find that activities that help a firm clean up its business operations and improve transparency are an important determinant of zombie resurrections. I then investigate the types of restructuring activities that determine the revival of zombie firms. I employ a pre-trained deep learning language model, FinBERT, to construct measures for business and financial restructuring. I find that business restructuring that improves the operational efficiency of zombie firms and improves revenues helps resurrect zombie firms, whereas financial restructuring alone does not appear to contribute to their resurrection. Furthermore, survival analysis suggests that business restructuring shortens the duration of a firm’s zombie status. Finally, I find that recovered zombie firms become more efficient than non-recovered zombie firms.

Keywords: credit markets
deep learning
natural language processing
restructuring
zombie firms
zombie recovery
Advisor: Callen, Jeffrey
Vyas, Dushyantkumar