VictorJuiz/thesis huggingface dataset:
Evaluation and Application of Alternative Drinking Water Biofilter Monitoring Techniques
Keywords: Biofiltration
Biologically active filtration
Drinking water treatment
Effective enzyme activity
Enzyme activity

Drinking water biofiltration research has increased dramatically in the past decade as new monitoring techniques have become available, resulting in widespread implementation at municipal treatment facilities. However, biofiltration is still considered a “black-box” technology which offers limited process control. This research examined biofiltration using a variety of techniques to develop a practical understanding of the underlying biological processes and provide guidance for improved design and operation. This research focussed on three primary topics: i) modelling processes critical to biofiltration, ii) applying biological monitoring to improve treatment, and iii) demonstrating benefits beyond providing improved water quality. Initial studies evaluated the application of a biofilter scaling model to determine the significance of biofilm shear loss and mass transport resistance with respect to a variety of biomass (density, function, and community composition) and water quality (organics, nutrients, disinfection by-product formation potential) parameters. Biofilm shear loss was observed to be the critical design parameter when scaling biofilter processes from pilot- to bench-scale, as enzyme activity, indicative of biological function, was not equivalent to pilot filters when mass transport resistance was deemed to have primacy. Subsequent studies further examined enzyme activity as a monitoring parameter for biofilter function; esterase and phosphatase were identified as being quantifiable and meaningful. Combining enzyme activity and filter empty bed contact time (EBCT) was defined as “Effective Activity”. Effective esterase activity was observed to correlate to carbon removal whereas effective phosphatase activity was correlated with phosphate removal. These relationships were observed for a range of pre-treatments (coagulation, pre-ozonation, ultrafiltration, UV), filter media (granular activated carbon, anthracite or sand) and operating conditions (EBCT, daily shutdown) suggesting that effective activity may serve as a useful design and operation parameter. Finally, two water treatment facilities were examined to determine potential energy cost savings associated with cyclical biofilter operation. It was shown that production costs could be reduced by >20% by scheduling production during low energy cost periods (e.g. 10 PM – 7 AM). This research demonstrated that monitoring biofilter enzyme activity may allow for optimal design and operation when compared to other monitoring parameters. Cyclically operated biofilters may dramatically reduce operating costs without impacting water quality.
The Innate Immune Receptor Nod1 in Reprogramming the Myeloid Compartment: Implications for Colorectal Cancer
Keywords: Colorectal Cancer
Innate Immunity
Microbiota
Myeloid Derived Suppressor Cells
Nod1
Nod-like receptors

Colorectal cancer (CRC) represents one of the leading causes of morbidity and cancer-related mortality in the world. While the etiology of CRC is believed to arise from genetic mutations, there is increasing evidence that the host microbiota plays a crucial role in CRC development by modulating the tumor microenvironment (TME). Accordingly, the Nod-like innate immune receptor, Nod1, is involved in the detection of invading bacteria and its expression correlates with advancing stages of human CRC. However, the role of Nod1 in regulating inflammation and pathogenesis of CRC remains unclear. Here, I aimed to understand the mechanisms whereby Nod1 signalling might influence the TME during colitis-associated carcinogenesis (CAC). Using a model of peritoneal inflammation, I first demonstrated that Nod1 activation resulted in the alternative activation of macrophages and induction of myeloidderived suppressor cells (MDSCs) that promoted the resolution of inflammation. Importantly, myeloid-intrinsic Nod1 signalling was required to maintain the immunosuppressive potential of recruited monocytic MDSCs by regulating Arginase-1 activity. Supporting the tumor promoting properties of MDSCs, I found that deletion of Nod1 specifically in myeloid cells, but not in intestinal epithelial cells, protected mice from CAC. Myeloid-specific Nod1-deficiency resulted in decreased tumor size and burden, characterized by lower incidence of high-grade dysplasia. Mechanistically, myeloid-intrinsic Nod1 signalling sustained an immunosuppressive TME, which resulted in decreased numbers of intra-tumoral effector T cells and increased regulatory T cells. Nod1 expression was also found to promote spontaneous carcinogenesis in Apcmin/+ mutant mice. Collectively, my findings reveal a myeloid-intrinsic function for Nod1 in shaping the TME immune landscape to support tumor development, which opens the possibility of modulating Nod1 signalling using probiotics or inhibitors to improve the effectiveness of cancer therapy.
Applications of Optimal Transport to Algebraic Topology: A Method for Constructing Spines from Singularity
Keywords: Algebraic Topology
Optimal Transport
Singularity
Spines

Our thesis describes new applications of optimal transport to algebraic topology. We use a variational definition of singularity based on semicouplings and Kantorovich duality, and develop a method for building Spines (Souls) of manifolds from singularities. For example, given a complete finite-volume manifold X we identify subvarieties Z of X and construct continuous homotopy-reductions from X onto Z using the above variational definition of singularities. The main goal of the thesis is constructing compact Z with maximal codimension in X. The subvarieties Z are assembled from a contravariant functor arising from Kantorovich duality and solutions to a semicoupling program. The program seeks semicoupling measures from a source (X,σ) to target (Y, τ) which minimize total transport with respect to a cost c. Best results are obtained with a class of anti-quadratic costs we call ``repulsion costs". We apply the above homotopy-reductions to the problem of constructing explicit small-dimensional EΓ classifying space models, where Γ is a finite-dimensional Bieri-Eckmann duality group.
Role of Noncoding Single Nucleotide Variants on Transcription Factors’ activity in Cancer
Keywords: Cancer
Computational biology
Epigenetics
Noncoding mutations
Open chromatin
Transcription factors

Cancer can arise from single nucleotide variants (SNVs) in the DNA sequence of cells. Recent decades of studies have provided a rich understanding of the effect of SNVs found within the protein-coding regions of the genome, giving rise to oncogenes and tumor-suppressor gene. In contrast, the effect of SNVs in the non-coding regions of the genome remains poorly characterized. Given that protein-coding regions only account for less than 1.5% of the human genome, understanding the effect of the excess of SNVs within noncoding regions remains a challenge. In this dissertation, I propose two computational frameworks to identify distinct patterns of noncoding SNVs targeting cancer-type specific regulatory elements and transcription factor cistromes. Combining layers of genetics and epigenetics data, I found that noncoding SNVs enrich in cistromes of a core set of oncogenic transcription factors driving cancer specific regulatory programs. I demonstrate the specificity of SNVs enrichment targeting transcription factors in breast and prostate cancer. Extensive experimental evidence in prostate cancer validates the functional impact of somatic SNVs found within regulatory regions of cancer driver transcription factors in prostate tumors. Having shown that noncoding SNVs can target cistrome of specific transcription factors, I next assessed whether SNVs can disrupt complexes of transcription factors. To this end, a computational framework to identify candidate noncoding SNVs targeting complexes of transcription factors is introduced and validated in a dataset of liver cancer. This work provides a systematic approach for investigating the functionality of SNVs within the noncoding genome, their effect on the transactivation potential of underlying regulatory elements and their target transcription factors. Overall, this work provides a blueprint for the functional interpretation of noncoding mutations in genomics tests to identify cancer driver transcription regulators.
A Continuous-Time Modelling and Design Approach for Oversampled Passive Switched-Capacitor Filters
Keywords: Circuit modelling
Continuous-time model
Filters
Passive switched-capacitor

Integrated technology advances towards shorter length transistors with faster operation frequencies and lower supply and threshold voltages. While these trends are pushed by digital performance, analog and RF designs do not necessarily benefit from them. Traditional high gain and high bandwidth operational amplifiers become harder to design, and thus topologies based on "digital friendly" components such as switches and capacitors can become preferable. In terms of filter design, these trends favour passive switched-capacitor networks, where active components (such as operational amplifiers) are not needed to transfer charge in between capacitors. This thesis proposes a simplified continuous-time approach for modelling, design, analysis, and simulation of oversampled switched-capacitor circuits focusing on the passive switched-capacitor filters. The model provides an intuitive understanding of these structures by letting designers reach transfer functions and noise characteristics with good accuracy and without the need to go through complicated charge-balance equations. The aim of the model is mainly to give designers a tool that can lead to innovations through intuitive understanding. It is applicable to a variety of topologies including multi-phase passive switched-capacitor filters, switched-capacitor integrators, as well as switched-capacitor DC/DC converters. With the proposed model, for the first time, complex-conjugate poles are integrated on silicon by using only switches and capacitors. A continuous-time approach is reported to implement low-pass transfer functions with a sharper frequency profile compared to the passive switched-capacitor topologies present in the literature. Theory and simulation results are verified with measurements performed on the filter prototype integrated in a 0.13 um CMOS technology. The prototype has a cut-off frequency of 470 kHz, 150 uW power consumption from a 1.2 V power supply, 92 dB SFDR, and an active area of 0.06 mm2.
On the Response of Brain Vasculature to focused Ultrasound and Microbubbles
Keywords: blood-brain barrier
drug delivery
focused ultrasound
inflammation
microbubbles

The treatment of neuropathologies is complicated by many factors; common, however, to most afflictions is the inherent difficulty of delivering therapeutic agents from systemic circulation to brain parenchyma. Specialized cerebrovasculature regulates the passage of molecules, acting to preserve efficient neural function and to protect from infection and toxicity. While essential, the blood-brain barrier (BBB) hinders the development of effective treatment strategies. In combination with circulating microbubbles (MBs), focused ultrasound (FUS) can be used to transiently increase BBB permeability, providing an avenue for localized brain-drug delivery. There are, however, risks associated with perturbing homeostatic conditions by allowing substances to enter the brain that are excluded under physiological conditions. The work described in this thesis focuses on characterizing the impact of FUS+MB-mediated BBB permeability enhancement on brain vasculature and on providing a means to mitigate risk. To this end, a hypothesis-generating microarray analysis of gene expression changes in hippocampal microvasculature was performed in the acute stages following sonication. The principal finding of this study was differential gene expression indicative of acute inflammation 6 hours following sonication, a response that was present, but diminished, by 24 hours. This observation motivated a study of factors that contribute to sonication-induced inflammation, including MB dose and the degree of BBB permeability enhancement, both of which were found to influence the expression of several key proinflammatory markers. The pattern of gene expression changes detected in microarray analysis 24 hours following FUS+MB exposure also motivated the study of blood vessel growth. A modest increase in blood vessel density and newborn endothelial cell density was observed at 7- and 14-days following sonication, an effect that normalized by 21 days. Finally, to manage the risks associated with transiently increasing BBB permeability, the post-sonication administration of dexamethasone, a synthetic glucocorticoid, was studied. Dexamethasone was found to expedite the return of BBB integrity, as well as limit inflammation, astrocyte activation, and blood vessel growth following FUS+MB exposure. An in-depth description of these studies and their implications are discussed. The findings presented in this thesis have value in informing clinical cost-benefit analyses and may influence the design of novel brain therapies.
Learning to Drive: Exploiting Deep Models for Autonomous Driving
Keywords: Computer vision
Deep learning
Machine learning
Self driving

Building self-driving vehicles is exciting and promising. It is going to transform the way we live and provide safety, efficiency, and mobility for everyone. In this thesis, I present a collection of our work in the direction of building smart self-driving vehicles from understanding convolutional neural networks (CNNs) to the full autonomy stacks, including perception, prediction, and planning. First, we study the property of CNNs and provide theoretical analysis on the receptive field. We use Fourier transform and center limit theorem to show that the effective receptive field only occupies a fraction of the theoretical one, especially in deep models. This can provide insights for future CNNs design. Next, we push the state of the art for tasks in the autonomy stacks using deep CNNs. For depth estimation with stereo cameras, we develop a deep matching network that simplifies previous approaches by using a dot-product layer and incorporates a multi-class classification loss, allowing the network to calibrate scores among larger contexts. These greatly improve the runtime and achieve much better matching results. Furthermore, we extend the idea to optical flow where the matching is done in 2D space. Combined with an instance segmentation algorithm, we achieve state-of-the-art results on KITTI optical flow benchmark. Third, we propose new formulations for the self-driving system, i.e. using a joint model for 3D detection, prediction, and tracking. Our model takes a sequence of consecutive LiDAR sweeps and predicts the bounding boxes for vehicles at the current time step as well as one second into the future. Tracking is done by greedily checking the overlap between current detections and predictions from the past. This approach bridges the gap between perception and prediction to achieve better performance both in accuracy and runtime. Then, we take this one step further to construct a deep structured model for interpretable neural motion planning, where the CNN also predicts a 3D cost volume, expanding on time and x-y spatial dimensions. The final planning trajectory is generated with a sampling-based inference algorithm. We conduct offline testing and show that our approach is better than several baselines.
Natural Behaviours of Echolocators, with an Emphasis on Vespertilionid Bats
Keywords: Behavioural ecology
Echolocation
Vespertilionid bats

Here I report on two experimental studies and one observational analysis addressing the flexibility and ecological limitations of sensorimotor integration in echolocating mammals, as they relate to sensory ecology and foraging behaviour. In chapter two, I use acoustic data to comparatively assess echolocation and motor activities of bats and toothed whales to test (i) how the presence of a conspecific influences the spectral and temporal content of echolocation signals and (ii) whether these species behave similarly when foraging with a conspecific. The results suggest that in the presence of conspecifics (i) both bats and toothed whales exhibit the Lombard effect, and (ii) bats additionally employ subsets of both a Jamming Avoidance Response and a clutter response. Behaviourally, only porpoises appear to modify their beam direction. In chapter three, I use acoustic, photo, and video data to compare the developmental trajectories of (i) flight attempts, (ii) wing morphology, and (iii) vocalizations of big brown bat pups in the context of landing behavior, specifically with respect to landing buzz production. The results (i) clarified previous studies exploring pup vocal and flight ontogeny and (ii) identified developmental relationships between wing morphology (RWL) and flight milestones. In chapter four, I comparatively assess acoustic recordings of bat activity before and after the introduction of white-nose syndrome (WNS) to South Eastern Ontario to explore the effects of WNS on (i) species abundance and (ii) habitat use. I also explore (iii) the possibility of niche release and realized niche expansion due to reduced interspecific competition resulting from species-specific WNS susceptibility. The results confirm that (i) endangered species are detected less often foraging in open field habitats since the introduction of WNS, and that (ii) relatively unaffected species have increased their presence (but not active foraging) in clutter/edge and open field habitats. The results also indicate that (iii) over water habitats showed no difference between pre- and post-WNS bat activity, suggesting that niche expansion for relatively unaffected species may be limited to some habitats and not others.
Multi-faceted Investigation of the Supernova Ia Progenitor Problem
Keywords: Supernova

Type Ia supernovae are generally agreed to arise from thermonuclear explosions of carbon-oxygen white dwarfs, which are led to explode via interaction with a companion star in a binary system. The actual path to explosion, however, remains uncertain, with numerous plausible parent systems and explosion mechanisms suggested. Observationally, Type Ia supernovae have multiple subclasses, distinguished by their light curves and spectra. This raises the question of whether these differences reflect multiple mechanisms occurring in nature or, instead, that explosions can be described by variations of a few physical properties. In this thesis, I use a spectral modeling package to investigate whether the spectra of events of distinct subclasses can be understood as part of a spectral sequence, where one parameter is varied at a time. I find that a single ejecta structure is sufficient to provide reasonable fits for spectra that are prototypical of subluminous and normal events. These spectra can be obtained provided that the luminosity (and thus temperature) of the ejecta are adjusted appropriately. Using a similar method, I also study the distribution of unburned material in SN 2011fe, finding a relatively large range of solutions, which does not rule out the predictions of most physical models. Lastly, I investigate the time-dependent rate of supernovae resulting from a burst of star formation. This analysis can, in principle, discriminate among proposed evolutionary paths, because it is sensitive to the nature of the companion star. By modeling the rates as a power-law, I find a normalization constant that indicates that field and cluster galaxies may be explained by the same distribution, contrary to previous results. In addition, I also provide a state-of-the-art measurement of the slope of this distribution, finding that is intermediate between the various predictions and does not yet constrain the evolutionary path leading to Type Ia supernovae.
Towards Achieving a Home-based Brain Stimulation System
Keywords: Brain stimulation
closed-loop brain stimulation
Home-based brain stimulation
non-invasive brain stimulation
Personalized medicine
Transcranial brain stimulation

There is a major demand for alternative treatments for Major Depressive Disorder (MDD), as its main line of treatment, pharmacotherapy-based approaches, have limited effectiveness and low tolerability. A safe and effective home-based brain stimulation treatment would offer an accessible alternative to medications. Our objective was to develop a transcranial current stimulations (tCS) system that can provide an effective home-based treatment for MDD. To achieve that, we required: 1. an inexpensive solution for quick and accurate positioning of the stimulating electrodes, and 2. the ability to perform and adjust stimulation in real time. First, we developed and tested a quick and accurate positioning method using 3D scanning and 3D printing. Test results show accurate and precise positioning compared to other standard positioning methods. Second, we developed and tested an algorithm that can extract phase and frequency estimates from brain recordings and control the stimulation in real-time. Third, we developed a closed-looped brain stimulation system based on transcranial pulsed current stimulation (tPCS) that can effectively phase-lock to a particular brain oscillation frequency. We tested this novel brain stimulation approach to study its effect on the Salience Network, which is associated with MDD and other neuropsychiatric disorders. The results from our clinical trial did not show modulation of brain activity using the novel brain stimulation technique. Alternative stimulation protocols and tasks should be considered in order to better understand the potential of phase-locked tPCS. We achieved our objective of creating a solution to quickly and accurately position the stimulator, and a method to perform and adjust stimulation in real time. Furthermore, we were able to show that phase-locked tPCS does not show modulation of SN activity, as measured using established tasks and outcome measures. Future experimentation on phase-locked brain stimulation will help discover its potentials in helping MDD patients.
Teacher Unions in the Public Sphere: Strategies Intended to Influence Public Opinion
Keywords: education policy
public opinion
teacher unions

This study explores how teacher unions in British Columbia and Ontario attempted to influence public opinion during periods of labour conflict between 2001 and 2016. A comparative case analysis was conducted based on eight interviews with members of the British Columbia Teachers Federation, six interviews with members of the Elementary Teachers’ Federation of Ontario, as well as documents from each union. In addition, newspaper items published in each province during the conflict period were collected. The unions’ efforts were analyzed through the framework of Habermas’ concepts of the public sphere and validity claims. Findings indicate that both unions had active public relations strategies designed to influence media coverage and discourse in the public sphere. However, most of their efforts were focused on influencing the public sphere indirectly, by using tactics that are not traditionally considered part of public relations strategies. While the unions’ efforts were similar in nature, the impact that these efforts had on influencing public opinion and producing policy change varied substantially. I argue that a combination of historical and political differences, mediated through a validity claims framework, helps explain this variation.