VictorJuiz/thesis huggingface dataset:
A Design Methodology for Porous Stents to Control Hemodynamics Inside Abdominal Aortic Aneurysms

This thesis presents a methodology to control blood flow conditions inside aneurysm sacs by means of multilayer porous stents. These devices are an alternative for patients with complex endovascular morphologies, specifically, in Abdominal Aortic Aneurysms (AAAs) with short infrarenal necks that are not eligible for Endovascular Aneurysm Repair (EVAR) with traditional devices. Whereas traditional stents isolate the blood flow from the wall, porous stents aim to regulate the flow conditions inside the aneurysm to reduce the risk of rupture, promoting the formation of an Intraluminal Thrombus (ILT) to mechanically protect the wall from flow-induced forces. However, a clear definition of the optimal ILT size, location, attachment type and thickness that lead to the reduction of AAA wall rupture risk is still not well understood. In the event that one of these factors contribute to the increase of AAA rupture risk, porous devices should be designed or customized according to a patient's geometry to prevent undesirable configurations by controlling the hemodynamics inside the AAA sac. Based on this, we conducted our research with the aim of developing a methodology to design porous stents that can induce appropriate blood flow conditions within AAAs. Before developing this methodology, a study focused on the mechanical behavior of the aneurysm wall under different AAA-ILT configurations is performed. This is done to confirm that the presence of the thrombus could lower the forces on the aneurysm wall, and to characterize the effect of partial ILT attachment on these forces. After performing the AAA-ILT mechanical behavior study, we develop a relatively accurate and computationally inexpensive method to model the flow behavior in the AAA when porous stents are used. Given that there are multiple length scales involved (micrometer size pores in a centimeter size artery), a multi-level approach is proposed as a modelling methodology for capturing the hydrodynamic changes across the stent pores without requiring a large number of mesh cells. The detailed simulations through stent pores are carried out to capture the hydrodynamic effects, specifically to study the pressure drop across the stent under different angles of incidence and stent porosity values for a range of physiological velocities typically found in AAAs. Once the flow behavior across the stent is characterized with detailed pore-level simulations, its effect on the flow is modelled as a porous region with known porosity and pressure-velocity curves. This allows us to reduce the computational cost of the simulation by bridging information across multiple length scales. Then, a methodology is proposed to effectively design porous stents that control the blood flow inside the sac within a suitable hemodynamic range defined \textit{a priori}. The method proposed could improve the current decision-making, planning, and outcomes of interventions for any AAA geometry.

Keywords: flow control
optimization
porous stents
Advisor: Amon, Cristina
Investigation of Genetic Profiles in Chromosome 5p15.33 and Telomere Length in Lung Cancer Risk and Clinical Outcomes

Lung cancer remains the most common cancer in the world and the leading cause of cancer mortality. This dissertation investigates two complementary hypotheses regarding the role of chromosome 5p15.33 and telomere length, in lung cancer etiology and clinical outcomes. This research uses data from the Toronto Mount Sinai Hospital-Princess Margaret Hospital case-control study, and pooled datasets within the International Lung Cancer Consortium. Chromosome 5p15.33 is an established lung cancer susceptibility locus. TERT is the main 5p15.33 candidate gene, which encodes the catalytic subunit of the telomerase enzyme, critical for maintaining telomere length (TL). Telomeres are highly conserved 5′-TTAGGG-3′ repeats at chromosome ends, which promote genome stability. TL has been implicated in lung cancer risk, although the epidemiologic evidence has been inconsistent. TL alterations are also established characteristics of solid tumours. Despite its importance, the underlying genetic and biological mechanisms in chromosome 5p.15.33 have not been elucidated. The fine-mapping analysis in Manuscript 1 identifies novel lung cancer susceptibility loci, using data from 5164 cases and 5716 controls. This study advances previous research by investigating a large number of novel, rare variants, discovered in a separate deep- sequencing study. The findings of Manuscript 2, based on the largest Mendelian Randomization analysis of lung cancer (16396 cases, 13013 controls), support the causal role TL. Methodological contributions of this work include the development of a new 5p15.33 instrument for TL, and a novel approach to mediation analysis. Manuscript 3 summarizes the published evidence for the clinical utility of TL and 5p15.33 variants. Manuscript 4 demonstrates the prognostic potential of TL in two independent lung cancer populations. A survival analysis of 4672 lung cancer cases suggests that telomere- independent genetic mechanisms in 5p15.33 may have prognostic relevance. In summary, this dissertation provides further insight into genetic and molecular determinants of lung cancer risk and progression.

Keywords: chromosome 5p15
lung cancer
telomere length
Advisor: Hung, Rayjean J
Computation of Loss Distribution Based on the Structural Model for Credit Portfolios

Credit risk analysis and management at the portfolio level is a challenging issue for financial institutions due to their portfolios’ large size, heterogeneity and complex correlation structure. In this thesis, we propose several new asymptotic methods and exact methods to compute the distribution and VaR of a loan portfolio’s loss in the CreditMatrics framework. For asymptotic methods, we give an approximation based on the Central Limit Theorem (CLT), which gives more accurate approximations to the conditional portfolio loss probabilities compared with existing approximations. For exact methods, we improve the efficiency by exploiting the sparsity that often arises in the obligors’ conditional losses. A sparse convolution method and a sparse FFT method are proposed, which enjoy significant speedups compared with the straightforward convolution method. We also construct truncated versions of the sparse convolution method and the sparse FFT method to further improve their efficiency. To control the aliasing errors and roundoff errors incurred in the truncated sparse FFT method, an optimal exponential windowing approach is developed as well. For lumpy portfolios, we introduce hybrid methods which combine an asymptotic approximation with Monte Carlo simulation or one of exact methods to achieve a good balance between efficiency and accuracy.

Keywords: convolution
credit risk
expected shortfall
FFT
risk management
value-at-risk
Advisor: Jackson, Ken
Kreinin, Alex
Unsupervised Representation Learning with Autoencoders

Despite the recent progress in machine learning and deep learning, unsupervised learning still remains a largely unsolved problem. It is widely recognized that unsupervised learning algorithms that can learn useful representations are needed for solving problems with limited label information. In this thesis, we study the problem of learning unsupervised representations using autoencoders, and propose regularization techniques that enable autoencoders to learn useful representations of data in unsupervised and semi-supervised settings. First, we exploit sparsity as a generic prior on the representations of autoencoders and propose sparse autoencoders that can learn sparse representations with very fast inference processes, making them well-suited to large problem sizes where conventional sparse coding algorithms cannot be applied. Next, we study autoencoders from a probabilistic perspective and propose generative autoencoders that use a generative adversarial network (GAN) to match the distribution of the latent code of the autoencoder with a pre-defined prior. We show that these generative autoencoders can learn posterior approximations that are more expressive than tractable densities often used in variational inference. We demonstrate the performance of developed methods of this thesis on real world image datasets and show their applications in generative modeling, clustering, semi-supervised classification and dimensionality reduction.

Keywords: Deep Learning
Machine Learning
Unsupervised Learning
Advisor: Frey, Brendan
A Passive Air Sampler for Precise, Spatially Distributed Atmospheric Gaseous Mercury Monitoring and Source Characterization

This thesis describes the design, testing and implementation of a novel, inexpensive, high precision passive air sampler (PAS) for gaseous mercury (Hg) monitoring. The PAS uses a sulfur-impregnated activated carbon as a sorbent, a commercially available, radial diffusive barrier (Radiello®), and a protective shield that doubles as a storage and transport container. The diffusive barrier and protective shield also help reduce variability in uptake kinetics that can be caused by wind and precipitation. An initial outdoor calibration in Toronto revealed highly linear uptake over a one-year period and unprecedented precision. Small, but predictable effects on uptake kinetics caused by temperature and wind speed were determined in laboratory experiments. The diffusive barrier was also deemed reusable after cleaning. A study evaluating the accuracy of the PAS against industry standard active instrument at 20 global sites determined (i) the recommended sampling rate (volume of air stripped of gaseous Hg per unit time; 0.135 ± 0.013 m3 day-1) that can be adjusted for the measured temperature and wind speed during each deployment, (ii) the excellent precision based uncertainty (4 ± 3 %), and (iii) the average overall uncertainty is at most (9 ± 6 %); a conservative measure due to inherent active instrument uncertainties. The analytical method (thermal decomposition, amalgamation, and atomic absorption spectrometry) was improved by the addition of sodium-carbonate to samples, which prolongs the life of the analytical instrument’s catalyst. Two source characterization studies (at a former Hg mine and across the Greater Toronto Area) demonstrate the PAS’s ability to successfully monitor concentration gradients and to estimate emissions. The results produced in this thesis are indicative of the PAS’s ability to measure at both background and high concentration sites with exceptional accuracy and precision, and its potential to improve atmospheric Hg science globally.

Keywords: Atmospheric mercury
Background monitoring
Emissions estimates
passive air sampling
Source characterization
Advisor: Wania, Frank
Mitchell, Carl PJ
Conditional Neural Language Models for Multimodal Learning and Natural Language Understanding

In this thesis we introduce conditional neural language models based on log-bilinear and recurrent neural networks with applications to multimodal learning and natural language understanding. We first introduce a LSTM encoder for learning visual-semantic embeddings for ranking the relevance of text to images in a joint embedding space. Next we introduce three log-bilinear models for generating image descriptions that integrate both additive and multiplicative interactions. Beyond image conditioning, we describe a multiplicative conditional neural language model for learning distributed representations of attributes and meta data. Our model allows for contextual word relatedness comparisons through decompositions of a word embedding tensor. Finally we show how we can abstract the skip-gram model for learning word representations to a conditional recurrent neural language model for unsupervised learning of sentence representations. We introduce a family of models called contextual encoder-decoders and demonstrate how our models can be used to induce generic sentence representations as well as unaligned generation of short stories conditioned on images. This thesis closes by highlighting several open areas of future work.

Keywords: Computer Vision
Deep Learning
Language Models
Machine Learning
Natural Language Processing
Neural Networks
Advisor: Zemel, Richard
Salakhutdinov, Ruslan
New Old Politics? Explaining Popular Support for Redistributive Public Policies

The central argument advanced by the dissertation is that three key sources of variation are critical for understanding popular support for redistributive public policies. First, redistribution is not a single undifferentiated policy goal. This dissertation distinguishes between ‘generalized’ and ‘targeted’ redistribution, and argues that critical determinants of citizens’ redistributive policy preferences play out differently depending on whether the redistribution is generalized or targeted in nature. Second, attitudes toward redistribution vary both across space and over time. The dissertation contends that many critical macrolevel determinants of citizens’ policy preferences illuminate how and why these variations occur. Third, in exceptional cases, specific political contexts matter. The dissertation maintains that while there is a range of common factors that influences popular support for redistribution throughout most advanced industrial democracies, in a limited number of cases, the citizens of distinct political contexts have substantially different orientations toward redistributive public policies. The dissertation draws on both cross-national data from eighteen advanced industrial democracies as well as cross-provincial data from the Canadian case to evaluate hypotheses constructed from these theories. The empirical findings support the dissertation’s core hypotheses. The evidence suggests that those with higher income tend to be more opposed to generalized redistribution, whereas those with more right-wing economic beliefs are more inclined to oppose targeted redistribution. There is also clear support for the contention that cross-sectional and longitudinal variations in redistributive policy preferences are differently affected by changes in key macroeconomic factors. For instance, when comparing between states, higher levels of economic inequality tend to be linked to lower levels of support for redistribution, but as economic inequality within a state increases over time, support for redistribution also tends to increase. There is also substantial evidence that specific political contexts matter. For instance, Americans are much less likely to support targeted redistribution than the citizens of other advanced industrial democracies, whereas Quebecers are much more likely to support generalized redistribution than the residents of other Canadian provinces. And in the cases in which political context matters, these critical differences tend to be a result of differences in in these citizens’ values and beliefs.

Keywords: Inequality
Public Opinion
Redistribution
Advisor: Vipond, Robert
The Role of Ion Channel TRPM7 in B Cell Development and Function

The channel-kinase Transient Receptor Potential Subfamily M7 (TRPM7) is known to regulate magnesium homeostasis and was the first channel implicated in the survival of a B cell line. Our study is the first to show that B cells require TRPM7 for development in a murine model. By using a mouse model where TRPM7 is specifically deleted in B cells under the control of the mb1 promotor, we show that B cells are absent in all peripheral lymphoid tissues due to apoptosis of Pre B cells. By using an in vitro stromal cell line system, we demonstrate that B cell development can be partially rescued by high levels of extracellular magnesium. Interestingly, the lack of B cells is accompanied by an expanded granulocyte population in the spleen. In addition to identifying TRPM7 as an essential factor for B cell development, we show that TRPM7 is also an important regulator of B cell activation. DT40 B cells lacking TRPM7 fail to contract and gather antigen when activated. To investigate the role of the kinase domain of TRPM7 we made use of B cells expressing a kinase dead point mutant. These cells were also unable to gather antigen, showing that the kinase domain is an important regulator of this process. We also show that the kinase domain may potentially interact with another important regulator of B cell activation, PLCγ2 to mediate antigen collection and cell contraction. Importantly, primary murine B cells expressing only one allele of TRPM7 or treated with a TRPM7 inhibitor both displayed defects in antigen gathering, confirming our results in the DT40 cell line. Lastly, we show that TRPM7 is essential for antigen internalization, a process that is important for the recruitment of T cell help and ultimately, antibody production.

Keywords: B cells
lymphopoiesis
Presentation
TRPM7
Advisor: Treanor, Bebhinn
Profiling the Role of SAPCD2 in the Control of Mammalian Mitotic Spindle Orientation

Cell division is fundamental to multicellular organisms, and understanding the molecular basis remains important for developing treatment strategies for various human diseases, including developmental disorders and cancers. During mitosis, proper segregation of cellular components in each daughter cell is required for epithelial morphogenesis, and asymmetric cell division is achieved through controlled orientation of the mitotic spindle. Molecular signalling through a ternary protein complex consisting of Gai, LGN, and NuMA is conserved in both vertebrate and invertebrate mitoses to orient the mitotic spindle, however its role in cell fate and tissue development signalling is context-dependent, and remains incompletely understood in mammalian systems. My work identifies Suppressor APC domain containing 2 (SAPCD2) as a regulator of the Gai-LGN-NuMA complex in mitotic spindle orientation signalling. SAPCD2 is essential for planar mitotic spindle orientation in both epithelial cell cultures and mouse retinal progenitor cells in vivo. Loss of SAPCD2 randomizes spindle orientation, which disrupts cyst morphogenesis in three-dimensional cultures, and increases terminal asymmetric cell divisions at the expense of symmetric cell divisions in the developing retina. SAPCD2 competes with NuMA for LGN binding, and prevents LGN accumulation at the cell cortex. Mass spectrometry-based proteomic approach revealed that SAPCD2 interacts with polarity proteins required for establishing epithelial cell polarity, an essential step upstream of mitotic spindle orientation. SAPCD2 is dispensable for tight junction biogenesis, an instructive process for formation of the the apical-basolateral domains, however interactions with polarity proteins mediates its localization to the apical domain through its C-terminal sequence that binds to PDZ (PSD-95/Dlg1/ZO-1) domains. In summary, this thesis reports the characterization of SAPCD2 as a regulator of the mitotic spindle orientation pathway, and provides mechanisms underlying mammalian-specific control of this pathway.

Advisor: Angers, Stephane
Carbonated Reactive MgO – Portland Cement Blends: Chemistry, Microstructure and Properties

Reactive MgO (r-MgO) is mainly comprised of magnesium oxide and has a relatively high fineness and a high reactivity with water. Subjected to carbonation, r-MgO and Portland cement (PC) blends have a potential to absorb CO2, which can yield an enhancement in mechanical properties and a densification of the microstructure. This thesis aims to: (I) investigate the chemistry of r-MgO and PC during the hydration and carbonation of r-MgO-PC systems; (II) characterize the rheology of r-MgO mortar and concrete; and (III) evaluate the influence of accelerated carbonation and r-MgO replacement levels on the mechanical properties, microstructure and transport properties of r-MgO mortar and concrete. The key contributions of this research are: I. The formation of high Mg-calcite (MgCO3 mole% > 10%) was found to be associated with the high MgCO3 environment established by the formation of Mg-carbonates. Amorphous Mg(OH)2 and M-S-H were proven to form during the hydration of the r-MgO-PC system. These products are speculated to be the precursors of the amorphous hydrated Mg carbonate that forms in the carbonated r-MgO-PC system. II. The incorporation of r-MgO induces a high water-demand to obtain a satisfactory fluidity and affects the correlation between yield stress and plastic viscosity. III. Carbonated mortar specimens with a 70% r-MgO replacement of PC obtain the highest compressive strength and lowest porosity among specimens with an r-MgO replacement range of 0% – 100%. In the r-MgO replacement range of 0% to 80%, a higher degree of carbonation leads to more formation of carbonation products, densifying the pores in the entrance diameter range of 0.01 – 0.3 μm. Carbonated mortar specimens containing 90% and 100% r-MgO have a high pore volume in the entrance diameter range of 0.3 – 10 μm, but barely have pores in the entrance diameter range of 0.01 – 0.3 μm. Carbonated concrete containing 50% r-MgO replacement of PC features a denser outer layer and less-dense inner layer, which makes the concrete difficult to get critically saturated.

Keywords: Accelerated carbonation
Mechanical properties
Mg carbonates
Microstructure
Reactive MgO
Rheology
Advisor: Panesar, Daman K
Understanding the Attributes of Papillary Renal Cell Carcinoma subtypes: Molecular Analysis, Biomarker Discovery and Implications to Therapy

Papillary renal cell carcinomas (PRCCs) are tumors that arise from the renal tubules. They are the 2nd most common of that group of tumors. They are classified histologically, based on a number of morphological criteria, into two subtypes. The PRCC1 and PRCC2 tumors differ considerably in clinical behavior, where type 2 tends to be more aggressive. This body of work attempted to answer a few dilemmas that hinder the PRCC clinical management. The first question is the proper subtyping of PRCCs, as about half the PRCC cases fail to conform histologically to one defined entity type; and there are no reliable markers to differentiate between the subtypes. The current work initially assessed a PRCC cohort of 317 patients, elucidating unique genomic signatures and molecular pathways characteristic of the two classical PRCC subtypes. The comprehensive transcriptomic analysis uncovered potential discriminating markers, and these were further validated in a 2nd PRCC cohort (108 patients). The results pointed towards a potentially new biological classification of PRCC tumors that stratifies patients into clinically relevant subgroups. Another dilemma we attempted to solve is the complete lack of an evidence-based therapy for metastatic PRCCs. A problem that is further confounded by the failure of clinical trials to segregate between the PRCC subtypes. Specific PRCC2 pathways were targeted both in-vitro and in-vivo (in xenograft mouse models) which showed a significant response to these specific therapies in comparison to other treatment modalities. Our new proposed therapies show promise and also highlight the need for personalized tumor treatment modalities that take into account each tumor’s specific biology. Lastly, we studied PRCC precursor lesions to uncover some of the events that underlie the PRCC pathogenesis and development. That revealed knowledge that can potentially be translated into PRCC early prevention and treatment.

Advisor: Yousef, George M