VictorJuiz/thesis huggingface dataset:
Race, Gender and Colonialism: Public Life among the Six Nations of Grand River, 1899-1939
Keywords: Six Nations
history
education
gender
first nations
colonialism

Six Nations women transformed and maintained power in the Grand River community in the early twentieth century. While no longer matrilineal or matrilocal, and while women no longer had effective political power neither as clan mothers, nor as voters or councillors in the post-1924 elective Council system, women did have authority in the community. During this period, women effected change through various methods that were both new and traditional for Six Nations women. Their work was also similar to non-Native women in Ontario. Education was key to women’s authority at Grand River. Six Nations women became teachers in great numbers during this period, and had some control over the education of children in their community. Children were taught Anglo-Canadian gender roles; girls were educated to be mothers and homemakers, and boys to be farmers and breadwinners. Children were also taught to be loyal British subjects and to maintain the tradition of alliance with Britain that had been established between the Iroquois and the English in the seventeenth century. With the onset of the Great War in 1914, Six Nations men and women responded with gendered patriotism, again, in ways that were both similar to Anglo-Canadians, and in ways that were similar to traditional Iroquois responses to war; men fought and women provided support on the home-front. Women’s patriotic work at home led to increased activity in the post-war period on the reserve. Six Nations women made use of social reform organizations and voluntary associations to make improvements in their community, particularly after the War. The Women’s Institutes were especially popular because they were malleable, practical, and useful for rural women’s needs. Women exerted power through these organizations, and effected positive change on the reserve.
Visual Object Recognition Using Generative Models of Images
Keywords: machine learning
computer vision

Visual object recognition is one of the key human capabilities that we would like machines to have. The problem is the following: given an image of an object (e.g. someone's face), predict its label (e.g. that person's name) from a set of possible object labels. The predominant approach to solving the recognition problem has been to learn a discriminative model, i.e. a model of the conditional probability P(l|υ) over possible object labels l given an image υ. Here we consider an alternative class of models, broadly referred to as generative models, that learns the latent structure of the image so as to explain how it was generated. This is in contrast to discriminative models, which dedicate their parameters exclusively to representing the conditional distribution P(l|υ). Making finer distinctions among generative models, we consider a supervised generative model of the joint distribution P(υ, l) over image-label pairs, an unsupervised generative model of the distribution P(υ) over images alone, and an unsupervised reconstructive model, which includes models such as autoencoders that can reconstruct a given image, but do not define a proper distribution over images. The goal of this thesis is to empirically demonstrate various ways of using these models for object recognition. Its main conclusion is that such models are not only useful for recognition, but can even outperform purely discriminative models on difficult recognition tasks. We explore four types of applications of generative/reconstructive models for recognition: 1) incorporating complex domain knowledge into the learning by inverting a synthesis model, 2) using the latent image representations of generative/reconstructive models for recognition, 3) optimizing a hybrid generative-discriminative loss function, and 4) creating additional synthetic data for training more accurate discriminative models. Taken together, the results for these applications support the idea that generative/reconstructive models and unsupervised learning have a key role to play in building object recognition systems.
Transcriptional Regulation in Synthetic Gene Networks
Keywords: synthetic biology
genetic circuits

The study of synthetic gene regulatory networks allows the isolation and investigation of components and motifs in natural regulatory networks. Many synthetic gene networks are regulated at the transcriptional level. In this work, two methods of regulating gene expression at the transcriptional level were studied with the objective of gaining finer control over network behaviour. The first approach focuses on activation and repression of promoters by transcription factors. A synthetic repressor-activator network was engineered using the cI and cro genes and the PRM promoter from bacteriophage λ. The cI and cro genes activated and repressed PRM, respectively, and the monomeric red fluorescent protein (mrfp) gene reported PRM activity. Experimental testing showed an increase in mrfp expression in response to CI, a decrease in mrfp expression in response to Cro, and a differential output that reflected the relative concentrations of CI and Cro when both inputs were applied together. A positive feedback network was then created by placing a cI gene downstream of PRM. The network showed increased expression in response to CI and decreased expression in response to Cro. A negative feedback network was created by placing a cro gene downstream of PRM. Experimental testing showed decreased mrfp expression in response to both inputs. The second approach employed two methods for tuning expression levels without modifying the genes or promoters. First, using a series of networks with tandem mrfp genes under the control of the PLtet0-1 promoter, it was demonstrated that magnitude and range of expression levels could be tuned by adjusting the number of genes in the operon. A network was tuned using this principle by placing luxR genes in tandem to increase the activity of the luxPR promoter. It was then demonstrated that the level of gene expression could be varied through the placement of the gene within an operon. Operons that were three, five and seven genes and contained one green fluorescent protein gene in the first, middle, or end position were created. By comparing green fluorescence levels in induced and uninduced networks, it was found that the gene closest to the promoter was the most inducible.
Mechanisms of Decarboxylation: Internal Return, Water Addition, and Their Isotope Effects
Keywords: Decarboxylation
Isotope effects
Internal return
hydrolytic

2-(2-mandelyl)thiamin (MTh), the adduct of benzoylformate and thiamin, is an accurate model of 2-(2-mandelyl)thiamin diphosphate, the initial covalent intermediate in the decarboxylation of benzoylformate by benzoylformate decarboxylase (BFDC). The first order rate constant for spontaneous decarboxylation of MTh is about 106 times smaller than the enzymic rate (kcat) for the BFDC reaction. Based on the similarities of MTh and the corresponding enzymic intermediate, as well as the inherent nature of the intermediate, it is not obvious why the enzyme-catalyzed reaction is so much faster. However, earlier studies showed that the decarboxylation of MTh is catalyzed by protonated pyridines and this was proposed to occur through a preassociation mechanism. If this explanation is correct, then the observed 12C/13C kinetic isotope effect (CKIE) will increase in the presence of the catalyst as a more favorable forward commitment is made possible. This provides a specific model for the enzyme-catalyzed process. We developed a technique using headspace analysis and compound specific isotope analysis (CSIA) to determine the CKIE for the decarboxylation of MTh in the presence and absence of pyridinium. We found that the CKIE increases in the presence of the catalyst, as predicted for the preassociation mechanism. In a related study, we investigated the kinetics of decarboxylation of pyrrole-2-carboxylic acid, which was known to be subject to acid catalysis in highly acidic solutions. In the expected mechanism, protonation of the pyrrole ring at C2 destroys the aromaticity of the ring. C-C bond cleavage in the process of decarboxylation will re-establish the aromatic pyrrole. However, the overall reaction rate would not increase as it is counteracted by a larger concentration of the undissociated carboxyl group compared to carboxylate ion necessary for decarboxylation. Since the reaction occurs readily, there must be an alternative pathway for the acid-catalyzed reaction. This can be achieved in an associative mechanism that is initiated by addition of water to the carboxyl group of the carboxyl-protonated reactant. C-C bond cleavage results in formation of pyrrole and protonated carbonic acid, a species that has been recognized as a viable intermediate in related processes. Protonated carbonic acid is spontaneously converted to H3O+ and carbon dioxide. The associative mechanism is consistent with solvent-deuterium kinetic isotope effects and 12C/13C kinetic isotope effects.
Exploring the Role of 'Slowing Down When You Should' in Operative Surgical Judgment
Keywords: surgical judgment
education
cognitive
slowing down
judgment
surgery

Context: The study of expertise in medical education has tended to follow the traditions of describing either the analytic processes or the non-analytic resources that experts acquire with experience. We argue that a critical function of expertise is the ability to transition from the automatic mode to the more effortful mode when required – a transition referred to as ‘slowing down when you should’. Objectives: To explore the phenomenon of ‘slowing down when you should’ in operative surgical practice and its role in intra-operative surgical judgment, and to develop conceptual models of the factors involved in the display of this transition in surgical operative practice. Design: In Phase 1A, 28 surgeons were interviewed about their views of surgical judgment in general and their perceptions of the role of this phenomenon in operative judgment. In Phase 1B, a subset of surgeons from Phase 1A was re-interviewed to explore their perceptions of automaticity in operative practice. In Phase 2, observational sessions (and brief interviews) were conducted of surgeons in the operating room to explore the nature of this phenomenon in its natural environment. Results: The surgeons in this study recognized the phenomenon of ‘slowing down’ in their operative practice and acknowledged its link to surgical judgment. Two main initiators were described and observed: proactively planned ‘slowing down’ moments occurring intra-operatively initiated by critical events anticipated pre-operatively and situationally responsive ‘slowing down’ moments initiated by emergent cues intra-operatively. Numerous influences of this transition were uncovered. A control dynamic emerged as surgeon’s negotiated ‘slowing down’ moments through trainees in their supervisory academic practice. Numerous manifestations of this phenomenon were observed in the operating room and considered using a cognitive psychology attention capacity model. Conclusions: This study offers a conceptual framework for understanding the role of ‘slowing down when you should’ in operative surgical practice, providing a vocabulary that will allow more explicit consideration of what contributes to surgical expertise. Consideration of this framework with its consequent ability to make surgical practices more explicit has implications for self-regulation in practice, surgical error, and surgical training.
Structure-based Design and Characterization of Genetically Encoded PhotoactivableE DNA-binding Proteins Based on S. cervisiae GCN4 and Hr. halophila PYP
Keywords: Photoactive Yellow Protein
PYP
GCN4
leucine zipper
bZIP
photo-control
photo-isomerization
photo-activation

Halorhodospira halophila photoactive yellow protein (PYP) is a promising candidate to act as a photoswitching domain in engineered proteins due to the structural changes that occur during its photocycle. Absorption of a photon of wavelength 446 nm triggers trans to cis isomerization of its 4-hydroxycinnamic acid chromophore leading to large structural perturbations in the protein, particularly in the N-terminus. In the dark, a slower cis to trans reisomerization of the chromophore restores the protein’s native fold. The fusion of proteins to PYP’s N-terminus may therefore enable photomodulation of the activity of the attached protein. To test this hypothesis, this thesis descibes genetically encoded photoswitchable DNA-binding proteins that were developed by fusing the prototypical leucine-zipper type DNA-binding protein GCN4 bZIP to the N-terminus of PYP. Five different fusion constructs of full length or truncated GCN4 bZIP and full length PYP as well as fusion constructs of full length GCN4 bZIP and N-terminally truncated PYP mutants were designed in a structure-based approach to determine if the dimerization and DNA binding activities could be controlled by the PYP photocycle. Extensive biophysical characterization of the fusion constructs in the dark and under blue light irradiation using electronic absorption, circular dichroism and fluorescence spectroscopic techniques were performed. As all the fusion proteins could complete photocycles, the DNA binding abilities of the dark and light-adapted states of the proteins were characterized using spectroscopic techniques as well as by the electrophoretic mobility shift assay. All the fusion constructs maintained DNA-binding abilities, however they each differed in their affinities and the extent to which they were activated by blue light irradiation. The reasons for these differences in DNA-binding abilities and photoactivation are explored. Using the results from the characterization of these constructs, proposals are also made to develop more robust genetically encoded photoactivatable DNA-binding proteins of the same type.
A Consistent Numerical Method for Simulating Interfacial Turbulent Flows
Keywords: Turbulence
Multiphase

A mathematically consistent algorithm for simulating interfacial turbulent flows is devised in this work. To minimize numerical errors for imposing dynamic boundary conditions at the interface locations, piezometric pressure is used to limit the effect of gravity forces in a flow field. Consequently, suitable and consistent numerical schemes are designed to accurately implement the new forms of interfacial forces. The proposed numerical methods are challenged for low Froude number flows which tend to trouble conventional algorithms. To capture the effect of turbulence on the interface, standard large eddy simulation techniques are reviewed and discussed. It is shown the standard filtered flow equations encounter numerical and mathematical inconsistencies. To remedy the irregularities of the conventional methods, a new framework for large eddy simulations is grounded. Purely mathematical models are derived and correlated with the conventionally more physical models. Semi implicit SIMPLE method is used to discretize the final flow equations. Taking advantage of the implicit feature of SIMPLE algorithm, an error correction technique is devised by which numerical cost of a turbulent simulation is substantially reduced. The entire framework is finally discussed toward simulating a turbulent interfacial flow.
Characterization of NimA-related Kinase 10 (NEK10): A Role in Checkpoint Control
Keywords: kinase
cell cycle checkpoint
phosphorylation
UV

Deregulation of the cell cycle is a hallmark of neoplastic transformation and plays a central role in both the initiation and progression of cancer. Members of the NimA-related kinase (NEK) family of protein kinases are emerging as important players in regulation of the eukaryotic cell cycle during normal cell cycle progression and checkpoint activation in response to genotoxic stresses. The focus of this thesis is NEK10, a previously uncharacterized member of the NEK family. While little is known about the biology of NEK10, recent cancer genomics studies have identified NEK10 as a candidate susceptibility gene at chromosome 3p24 in cancer. Work herein describes a role for NEK10 in the cellular response to ultraviolet (UV) irradiation. NEK10 was required for the activation of ERK1/2 signaling upon UV irradiation, but not in response to mitogens, such as the epidermal growth factor. NEK10 interacted with Raf and MEK and enhanced MEK activity through a novel mechanism involving MEK autoactivation. Significantly, appropriate maintenance of the G2/M checkpoint following UV irradiation required NEK10 expression and ERK1/2 activation. In support of a conserved role for NEK10 in the cellular response to UV irradiation, nekl-4, the NEK10 C.elegans homologue, affected embryonic sensitivity to UV-irradiation. In search of regulatory inputs into NEK10, using mass spectrometry, our laboratory identified 19 distinct sites of NEK10 phosphorylation. Characterization of a number of these sites revealed a role for intermolecular autophosphorylation in achieving full NEK10 catalytic activity through activation loop phosphorylation on S684 and S688. Further, a C-terminal phosphorylation site on NEK10, S933, was found to be a 14-3-3 binding site, and was essential for NEK10 cytoplasmic to nuclear translocation following UV irradiation. Taken together, my studies have discovered a role for NEK10 in the engagement of the G2/M cell cycle checkpoint and provided a mechanistic insight into the relationship between NEK10 and the Raf/MEK/ERK cascade, and the control of NEK10 subcellular localization. This work will serve as a foundation for future studies aimed at understanding the molecular mechanism of NEK10 action and its function in development and tumourigenesis.
Learning Distributed Representations for Statistical Language Modelling and Collaborative Filtering
Keywords: statistical language modelling
collaborative filtering
neural networks
distributed representations
machine learning

With the increasing availability of large datasets machine learning techniques are becoming an increasingly attractive alternative to expert-designed approaches to solving complex problems in domains where data is abundant. In this thesis we introduce several models for large sparse discrete datasets. Our approach, which is based on probabilistic models that use distributed representations to alleviate the effects of data sparsity, is applied to statistical language modelling and collaborative filtering. We introduce three probabilistic language models that represent words using learned real-valued vectors. Two of the models are based on the Restricted Boltzmann Machine (RBM) architecture while the third one is a simple deterministic model. We show that the deterministic model outperforms the widely used n-gram models and learns sensible word representations. To reduce the time complexity of training and making predictions with the deterministic model, we introduce a hierarchical version of the model, that can be exponentially faster. The speedup is achieved by structuring the vocabulary as a tree over words and taking advantage of this structure. We propose a simple feature-based algorithm for automatic construction of trees over words from data and show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models. We then turn our attention to collaborative filtering and show how RBM models can be used to model the distribution of sparse high-dimensional user rating vectors efficiently, presenting inference and learning algorithms that scale linearly in the number of observed ratings. We also introduce the Probabilistic Matrix Factorization model which is based on the probabilistic formulation of the low-rank matrix approximation problem for partially observed matrices. The two models are then extended to allow conditioning on the identities of the rated items whether or not the actual rating values are known. Our results on the Netflix Prize dataset show that both RBM and PMF models outperform online SVD models.
Bayesian Methods in Gaussian Graphical Models
Keywords: Gaussian Graphical Models
DY-conjugate prior
Markov Chain Monte Carlo
Model Selection
Normalizing constant
Metropolis Hastings
Bayes Factors
Deviance Information Criterion

This thesis contributes to the field of Gaussian Graphical Models by exploring either numerically or theoretically various topics of Bayesian Methods in Gaussian Graphical Models and by providing a number of interesting results, the further exploration of which would be promising, pointing to numerous future research directions. Gaussian Graphical Models are statistical methods for the investigation and representation of interdependencies between components of continuous random vectors. This thesis aims to investigate some issues related to the application of Bayesian methods for Gaussian Graphical Models. We adopt the popular G-Wishart conjugate prior WG(δ, D) for the precision matrix. We propose an efficient sampling method for the G-Wishart distribution based on the Metropolis Hastings algorithm and show its validity through a number of numerical experiments. We show that this method can be easily used to estimate the Deviance Information Criterion, providing a computationally inexpensive approach for model selection. In addition, we look at the marginal likelihood of a graphical model given a set of data. This is proportional to the ratio of the posterior over the prior normalizing constant. We explore methods for the estimation of this ratio, focusing primarily on applying the Monte Carlo simulation method of path sampling. We also explore numerically the effect of the completion of the incomplete matrix Dν, hyperparameter of the G-Wishart distribution, for the estimation of the normalizing constant. We also derive a series of exact and approximate expressions for the Bayes Factor between two graphs that differ by one edge. A new theoretical result regarding the limit of the normalizing constant multiplied by the hyperparameter δ is given and its implications to the validity of an improper prior and of the subsequent Bayes Factor are discussed.