VictorJuiz/thesis huggingface dataset:
Parts and Methods in Synthetic Biology: An Investigation of Light Induced Gene Regulation in E. coli and the Design and Characterization of a Dual Mode Promoter in S. cerevisiae

Cellular processes are astonishing and complex but precise in regulation with great modularity which makes us wonder "how do they work?" or if it is possible to redesign them for our specific need. Synthetic biology is a field of research that combines molecular biology and engineering aspect to understand cellular processes as well as to redesign "biological devices" (e.g. feedback networks) or "parts"(e.g. promoters) for constructing controlled biological system. For this, synthetic biology needs resources such as libraries of biological parts and methods to develop those parts, and to validate them in targeted organisms. Here, one of the projects was to validate and develop an in vivo system for light-responsive transcriptional gene regulation. We implemented a blue light-responsive reporter system with conventional E. coli promoter design methods to validate DNA binding activity of six in vitro characterized variants of fusion photoactive yellow protein (PYP). The in vivo system design involved placing blue light induced PYP binding DNA sequences such that PYP would inhibit access of RNA polymerase to the promoter upon light exposure. Comparison of results of in vivo DNA binding activity via fluorescence of these six fusion PYP with the Kd value of in vitro DNA binding activity showed weak correlation. Development of this kind of in vivo screening method would narrow down the search for better light responsive DNA binding fusion PYP variants. The other project in this work was to develop a "dual-mode" promoter to enrich the synthetic biology library, with the motivation for the design arising from our ongoing effort to design and implement a feedback controller in S. cerevisiae. To design this promoter we started with the minimal cytochrome C promoter in yeast to incorporate five steroid hormone responsive elements and one lac operator site, respectively upstream and downstream of the TATA box. This allows activation through the testosterone-responsive androgen receptor, and repression through the LacI repressor. Exposure to varying concentrations of testosterone and IPTG demonstrated the functionality of the promoter's activating and repressing modes, and the ability to combine these effects to tune the promoter's output curve over a wide range.

Keywords: Dual mode promoter
Feedback gene network
Gene regulation
Light induced
promoter design
Synthetic biology
Advisor: David, R McMillen
Exploring Deep Learning Methods for Discovering Features in Speech Signals

This thesis makes three main contributions to the area of speech recognition with Deep Neural Network - Hidden Markov Models (DNN-HMMs). Firstly, we explore the effectiveness of features learnt from speech databases using Deep Learning for speech recognition. This contrasts with prior works that have largely confined themselves to using traditional features such as Mel Cepstral Coefficients and Mel log filter banks for speech recognition. We start by showing that features learnt on raw signals using Gaussian-ReLU Restricted Boltzmann Machines can achieve accuracy close to that achieved with the best traditional features. These features are, however, learnt using a generative model that ignores domain knowledge. We develop methods to discover features that are endowed with meaningful semantics that are relevant to the domain using capsules. To this end, we extend previous work on transforming autoencoders and propose a new autoencoder with a domain-specific decoder to learn capsules from speech databases. We show that capsule instantiation parameters can be combined with Mel log filter banks to produce improvements in phone recognition on TIMIT. On WSJ the word error rate does not improve, even though we get strong gains in classification accuracy. We speculate this may be because of the mismatched objectives of word error rate over an utterance and frame error rate on the sub-phonetic class for a frame. Secondly, we develop a method for data augmentation in speech datasets. Such methods result in strong gains in object recognition, but have largely been ignored in speech recognition. Our data augmentation encourages the learning of invariance to vocal tract length of speakers. The method is shown to improve the phone error rate on TIMIT and the word error rate on a 14 hour subset of WSJ. Lastly, we develop a method for learning and using a longer range model of targets, conditioned on the input. This method predicts the labels for multiple frames together and uses a geometric average of these predictions during decoding. It produces state of the art results on phone recognition with TIMIT and also produces significant gains on WSJ.

Keywords: Deep Learning
Deep Neural Networks
DNN-HMM hybrid
Hidden Markov Models
Speech Recognition
Advisor: Geoffrey, E. Hinton
Optimal Homotopies of Curves on Surfaces

We prove several results in quantitative topology. First, we prove that if two simple closed curves on a 2-dimensional Riemannian manifold are homotopic through curves of length at most L, then for any epsilon > 0, they are homotopic through simple closed curves of length at most L + epsilon. This can be seen as an effective version of a theorem by Baer and Epstein.Second, we prove that if a simple closed curve is contractible on a 2-dimensional Riemannian manifold through curves of length at most L, then for any epsilon > 0, the curve is contractible through based loops of length at most 3L + 2d + epsilon, where d is the diameter of the manifold. If the manifold is a 2-disc and the initial curve is the disc's boundary, then this bound is improved to L + 2d + epsilon.

Advisor: Alexander, Nabutovsky
Automated Image Analysis for Systematic and Quantitative Comparison of Protein Expression within Cell Populations

Protein subcellular localization is a major indicator of protein function, and efforts have been made to systematically determine the localization of each protein in budding yeast using fluorescent tags. Based on the fluorescence microscopy images, subcellular localization of many proteins can be classified automatically using supervised machine learning approaches. Budding yeast has a stereotypical reproduction mode, such that cell-stage is related to the presence and size of a growing bud. In this work, I investigate the benefits of a cell recognition method and image features that utilize prior biological knowledge of budding yeast shape and its cell-stage dependent changes.I show that modeling cell-stage dependency of protein abundance and spatial distribution (expression pattern) within a continuous model for cell growth allows the identification of most previously identified localization patterns in a cluster analysis. Further, I show that similarities between the inferred protein expression patterns explain similarities in protein function better than previous manual categorization of subcellular localization. These results suggest that incorporating prior information about yeast morphology in automated image analysis will yield unprecedented power for pattern discovery in high-resolution, high-throughput microscopy images.Finally, using these new computational methods, I explore cell-to-cell variability in protein abundance and subcellular localization. I define a mean to quantify deviations in subcellular localizations, and find that the method defined is in agreement with previous measurements of cell-to-cell variability in the case of protein abundance. Hence, I show that cell-to-cell spatial variability is a protein expression property, whose measurement is only possible from microscopy images. This measure allows the systematic detection of many classes of such variability, without the use of any prior knowledge about subcellular localization.

Keywords: Cell cycle
Image analysis
Protein expression
Segmentation
Subcellular localization
Yeast
Advisor: Alan, M Moses
Three Essays on Corporate Governance

The roles of the market for corporate control and institutional investor monitoring as corporate governance mechanisms are studied. First, I examine agreements that address the interests of stakeholders of takeover targets, particularly the management and board, which are termed "social issues" and are frequently negotiated during mergers. I find that the inclusion of social issues comes at the cost of lower target shareholder returns. Furthermore, social issues are particularly costly for shareholders of targets whose management has enhanced negotiating power, and are also associated with poorer post-merger performance. Social issues therefore reflect a trade-off between shareholder and managerial interests and they are an important friction arising during changes in control. Second, we study a feature of takeovers which permits acquirers to walk away at the cost of paying a "reverse termination fee" to the target. We model a merger contract with this feature as a real call option on the assets of the target firm, which ensures the merger terminates whenever completion is sub-optimal. We find that this feature is more common when there is a higher risk that the target's value to the acquirer will fall below its stand-alone value before deal completion, and that the size of the fee is driven by option-like characteristics of mergers. The risk that a change in control decreases value may therefore be mitigated by a real option contract. Last, we examine the ability of institutional investors to improve governance by acting collectively. We show that private and organized engagements with firms by a coalition of Canadian institutional investors make the firms more likely to improve shareholder democracy and compensation structure and disclosure. We find that the coalition's influence is further broadened through director interlocks and the publication of governance scores, although there are limits to its influence. Institutional investors can therefore overcome coordination costs and act collectively to improve governance at the firm and market levels. Overall, the analysis indicates that frictions which impede the effectiveness of the market for corporate control and institutional investor monitoring as corporate governance mechanisms may be overcome by investor power, contracting and coordinated action.

Advisor: Alexander, Dyck
Uncertain Dignity: Judging Human Dignity as a Constitutional Value

This dissertation examines what is the practical meaning of the idea of human dignity in the context of constitutional judgment. Against the commonly held view in political theory and legal scholarship that applying the idea of human dignity in law involves applying a single conception determinatively, my argument is that the way we think about the limit human dignity places on constitutional arrangements, involves a process of reflective judgment that oscillates between two conceptions. The first conception of human dignity is a deontological abstract idea that posits dignity as an inviolable, universal, formal property of all human beings. Most clearly associated with Immanuel Kant's writings, this conception plays a constitutive role in the constitutional sphere by underpinning the notion of legal subjectivity. The second conception of human dignity is intersubjective. Articulated originally by G.W.F Hegel and developed by contemporary exponents (most notably Axel Honneth), this conception underscores human vulnerability for recognition as an equal. It therefore plays a regulative role function in constitutional adjudication. This conception reveals contextual sensitivity towards what counts for particular individual in a particular time as respect. Together, these two conceptions form the normative syntax of the liberal conception of human dignity. Through conceptual inquiry and comparative constitutional analysis I reveal the process of illuminating tension between the universal (thin) perspective of human dignity and its particular (thick) enmeshments in the political community. Importantly, the relation between the two conceptions is non-transcendent. There cannot be a unified comprehensive definition of human dignity. Rather, the constitutional meaning of human dignity is a result of an on-going reflective judgment and continuous restatement at the level of the political community.

Keywords: Arendt
constitution
dignity
Kant
Reflective Judgment
respect
Advisor: Simone, Chambers
Statistical Methodologies for Genetic Association Studies with Rare Variants

In the search for genetic factors that are associated with complex heritable human traits, considerable attention has been focussed on rare variants that individually have small effects. Many recent papers have proposed testing strategies to detect association between traits and groups of rare variants with competing claims about the performance of various tests. The power of a given test in fact depends on the nature of any association and on the rareness of the variants in question.In this thesis we review such tests within a unified framework that covers a whole range of genetic models. We divide tests into linear and quadratic classes. We study performance of both classes through exact and asymptotic power calculations and novel simulation studies. We show that test statistics from both classes are complementary and they are powerful under limited settings. To achieve robustness, we propose to combine the evidence of association from two or more complementary tests. We consider the minimum-p and Fisher's methods of combining p-values from linear and quadratic statistics. Our extensive simulation studies show that both methods are robust across wide range of genetic models and have comparable or better power than alternative methods such as weighted kernel-based association tests. Our results also show that power to detect association in plausible genetic scenarios is low for studies of medium size unless a high proportion of the chosen variants are causal. To increase power, we also consider response-dependent sampling in association studies with rare variants. We also show that, under a broad range of generalized likelihoods and sampling plans, score statistic for testing association between a response Y and genetic variants are identical for two main likelihood approaches, and are of the same form as for ordinary random sampling on Y. Lastly, we evaluate proposed methodology and various sampling strategies with extensive simulations.

Keywords: Linear statistic
Quadratic statistic
Rare variants
Response dependent sampling
Testing
Advisor: Lei, Sun
Calcium-sensing Receptor and Integrin Protein Complexes in Cerebellar Development and Cancer Cell Migration

The calcium-sensing receptor (CaSR) is a widely expressed homodimeric G protein-coupled receptor that senses changes in extracellular divalent cations and maintains systemic calcium homeostasis. Recent studies suggest that CaSR activation can promote cellular migration. In the present study, we initially sought to identify novel CaSR interacting proteins that might be associated with cellular migration. Using a metastatic tumour model consisting of medullary thyroid carcinoma cells (MTCs), and a brain developmental neuronal migration model comprised of highly motile cerebellar granule-cell precursor neurons (GCPs), we established that the CaSR couples to the integrin family of extracellular matrix (ECM) binding proteins to promote cell adhesion and migration. Integrins are single transmembrane spanning heterodimeric adhesion receptors that mediate cell migration by binding to ECM proteins. Co-immunoprecipitation and co-localization studies established that the CaSR and 1-containing integrins were present in a macromolecular complex in MTC cells and GCP neurons. In the tumour cells, CaSR-mediated activation of phospholipase C and rise in intracellular calcium was essential for integrin activation and potentiation of cell adhesion and migration. Conversely, CaSR mediated phosphorylation of ERK2 and AKT signaling, and the subsequent promotion of 1 integrin plasma membrane expression was crucial for cerebellar GCP migration. These

Keywords: Brain
Calcium-sensing Receptor
Cancer
Development
Integrin
Migration
Advisor: David, R Hampson
Examining the Effects of Dementia on the Postoperative Outcomes of Older Adults with Hip Fractures

This dissertation comprises three research studies related to the outcomes of older adults with dementia who sustained hip fractures in Ontario. The first dissertation study demonstrated that individuals with dementia accounted for 23.9% and 83.5% of all hip fractures from community and long-term care (LTC) settings, respectively. Among community-dwelling older adults, dementia was associated with an increased risk of LTC admission (hazard ratio (HR) = 2.49, 95% confidence interval (CI): 2.38-2.61, P

Keywords: cohort study
dementia
health services
hip fractures
outcomes
Advisor: Paula, A Rochon
Lewis Acid Activated Olefin Metathesis Catalysts

Since its discovery, catalytic olefin metathesis has been used as a powerful tool for the synthesis of a variety of molecules. Over the last 20 years research in this area has received enormous attention in the development of new catalysts and applications. The vast majority of the ruthenium based catalysts developed have been modifications to the Grubbs Catalyst type architecture. The research presented herein focuses on the development of new olefin metathesis catalysts bearing tridentate, dianionic ligands and their activation with Lewis acids. Complexes [(PPh

Keywords: Alkylidene
Catalysis
Metathesis
Olefin Metathesis
Organometallic
Ruthenium
Advisor: Douglas, W Stephan
Essays in Behavioral Finance

In the first chapter, we show that individuals' non-investment risk-taking behavior can affect their willingness to take financial risks. To test this hypothesis, we use the initial legalization and opening of commercial casinos in the U.S. as a natural experiment and show that the opening of a casino in close geographical proximity to investors results in increased portfolio risk taking of those investors who are likely to gamble relative to those investors who are not. These likely gamblers, who are exposed to increased risk taking, subsequently realize higher returns, but do not improve the overall mean-variance efficiency of their portfolios. These findings provide insight into the nature of risk taking and the amplifying effect that taking risks in one context may have on investment risk taking. In the second chapter, we explore how social preferences may guide individuals' investment decisions in the context of "sin stocks." We find that investors who disregard social norms and invest increasingly greater proportions of their portfolios in sin stocks tend to realize lower risk-adjusted returns net of transaction costs than investors who do not invest in sin stocks. Using additional unique survey data, we find that sin stock investors tend to be more overconfident and trade more often. Finding are consistent with the notion that investors who disregard social norms and invest in sin stocks are more likely to be subject to overconfidence, as well as more likely to drink, smoke, or gamble, which are associated with behavior that affects investment decisions and portfolio outcomes. In the final chapter, we find a CEO's overconfidence can also indirectly affect other market participants, specifically analysts who issue earnings forecasts. We find that firms with overconfident CEOs are more likely to have analysts issue earnings forecasts that are more optimistic relative to actual earnings. Furthermore, we find that firms with overconfident CEOs have analyst forecasts with smaller absolute forecast errors and are less dispersed. These findings demonstrate the importance of CEOs' behavioral characteristics in shaping the environment in which analysts and other market participants make important financial decisions, in some cases improving the information environment.

Advisor: Lisa, Kramer
Essays on Digitization and the Market for Intangibles: Evidence from Creative and Technology Settings

This thesis examines the impact of intellectual property (IP) on firm-level performance outcomes in the market for intangibles. While the market for intangibles plays an expanding role in economic growth, it is often susceptible to market failure. In particular, digitization adds additional challenges to how firms shape their IP strategies by significantly lowering the cost of copying and sharing digital content, and thereby impacting firm appropriability in many settings. The first chapter analyzes the impact of a firm-level IP strategy commonly used by media industries to combat piracy - digital rights management (DRM) - on music sales. To do this, I exploit a natural experiment, where different record labels remove DRM from their entire catalogue of music at different times, to examine whether relaxing an album's sharing restrictions increases sales. Specifically, I compare sales of similar albums with and without DRM before and after DRM removal, and I find that removing DRM increases digital music sales. However, relaxing sharing restrictions does not impact all albums equally; it increases the sales of lower-selling albums (i.e., the ``long tail'') significantly but does not benefit top-selling albums. My results are consistent with theory that shows lowering search costs can facilitate the discovery of niche products, and suggest that the optimal strength of IP in creative industries depend on the distribution of products (e.g., mainstream vs. niche) within the firms' portfolio. My second and third chapters examine whether structural features of the market for intangibles, impact firm-level outcomes in technology-oriented industries. My second chapter reports the first empirical evidence linking the three main sources of failure emphasized in the market design literature (lack of market thickness, congestion, lack of market safety) to deal outcomes using novel survey data on technology licensing. This chapter disaggregates the licensing process into three stages and finds variation in the salience of market features. In the final chapter, I document the rated importance of different forms of IP and explore the extent to which firm and industry factors influence organizations' motivations for developing IP assets.

Keywords: Competition
Copyright
Innovation
Intellectual Property
Strategy
Technology
Advisor: Ajay, Agrawal