VictorJuiz/thesis huggingface dataset:
Visualizing Protein-Protein and Gene Regulatory Networks in Arabidopsis thaliana
Keywords: Data Visualization
Gene Regulatory Networks
Plant Biology
Protein Protein Interaction Networks
Systems Biology
Web Development

Protein-protein interaction networks (PPNs) and gene regulatory networks (GRNs) visualize protein-binding and gene regulation respectively. Advancements in screening technologies such as enhanced yeast-one-hybrid and protein binding predictions have allowed for high throughput determination of such networks. Currently, Arabidopsis-based GRN and PPN viewers are restricted to ad-hoc networks and do not link out to external sources. I created two interactive web-based viewers, Arabidopsis Interactions Viewer 2.0 (AIV2) and Arabidopsis GEne Network Tool (AGENT) to visualize PPNs and GRNs respectively. Features include dynamic interaction filtering, uploading and downloading network data, subcellular-focused network layouts, and loading Bio-Analytic Resource’s (BAR) expression data onto genes. Data visualization principles are applied such as rapid serial visual presentation (RSVP) and Shneiderman’s mantra of details-on-demand. Moreover, AGENT is a framework for GRN curation which allows future expansion. Lastly, I demonstrate how to use these tools for hypothesis generation.
On the Design and Development of a Self-Driving Car for the SAE AutoDrive Challenge
Keywords: Autonomous Vehicles
Autonomy
Computer Vision
Perception
Robotics
Self-Driving Vehicles

The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The first year of the competition was held in May 2018 in Yuma, Arizona. This first installment required teams to perform lane keeping and avoid static obstacles without a detailed prior map of the course. The second year was held in June 2019 at MCity, a mock town built for self-driving testing at the University of Michigan. Teams were required to navigate a series of intersections while handling dynamic pedestrians, traffic lights, and traffic signs. aUToronto is U of T's competition team and our self-driving car, Zeus, was the winning entry. This thesis provides a system description of Zeus at the Year 1 and Year 2 competitions. This thesis also describes the specialized lane detection system developed for Year 1 and the lightweight object detection and tracking system that was developed for Year 2.
An Observational, Mixed-design, Exploratory Study Evaluating Cognitive Function and Structural Deficits in Seasonal Affective Disorder
Keywords: Cognition
Depression
Morphometry
Seasonal Affective Disorder

Introduction: Seasonal Affective Disorder (SAD) presents a naturalistic model of depression in order to investigate changes to neural structure and cognition, which is difficult to investigate in Major Depression due to a variable treatment response. Methods: Healthy controls (N=17) and SAD patients (N=17) had their cognitive function evaluated over the summer and winter. A subset of 20 participants also underwent structural imaging. Results: There was widespread significant cortical thinning to the frontal lobe and significant volumetric reductions to the globus pallidus in SAD patients during the winter and summer Cognitive domains, including set-shifting, psychomotor speed, and executive function, were impaired in SAD over the summer months but not winter months. Discussion: The structural deficits form key nodes within the limbic system and are responsible for top-down regulation of mood and some cognitive functions. SAD cognitive deficits in the winter may have been masked by cognitive fluctuations exhibited by healthy participants.
Evaluation of the Impact of Remote Titration Combined with Telemonitoring on the Optimization of Guideline Directed Medical Therapy for Patients with Heart Failure
Keywords: GDMT
Guideline Directed Medical Therapy
Heart failure
Telemonitoring
Titration

To improve health outcomes in heart failure patients, guideline directed medical therapy (GDMT) should be optimized to maximal tolerated doses. However, GDMT remains underutilized, with less than 25% of patients receiving target doses in clinical practice. The objective of this research was to determine the effectiveness of remote titration facilitated by telemonitoring and identify factors that promote or impede remote titration. A randomized controlled trial (N=108) was designed to compare a remote titration strategy with a standard titration program. Analysis of a pilot cohort of 42-patients found that telemonitoring increased the proportion of patients that achieved target doses (90.5% vs. 52.4%), decreased time to dose optimization (12.26 vs. 19.01 weeks), and reduced the number of clinic visits by 50%. Facilitators to remote titration included the ease-of-use and utility of the intervention while barriers included the resources and funding required. This study provided evidence that remote titration can be effectively facilitated by telemonitoring.
Using a Model-guided Approach to Design and Characterize Strains of Escherichia coli Producing cis,cis-Muconic Acid
Keywords: adipic acid
flux balance analysis
metabolic engineering
minimal cut sets
muconic acid

Adipic acid is an industrially important chemical produced using petrochemical feedstocks. Adipic acid and its direct precursor cis,cis-muconic acid are therefore interesting targets for microbial production in hosts such as Escherichia coli. While rational engineering strategies have been shown to produce high-performance strains for muconic acid production, computational strategies may predict non-obvious growth coupling strategies. In this study, constrained minimal cut sets were used to predict a growth-coupled strategy for muconate production in E. coli. Various background strains and plasmids were constructed, containing gene knockouts and heterologous pathway enzymes, respectively. All strains were characterized at the plate reader and shake flask levels, showing reasonable production yields for muconic acid intermediates. The highest-performing strains were characterized using bioreactors. By yield, rational designs performed best in shake flasks, while computational design performed best in bioreactors. Additionally, sub-milligram titers of adipate were produced in a rationally designed strain by addition of enoate reductase.
PIK3CAH1047R and PIK3CB Isoforms of PI3K-signalling Mediate Distinct Signalling Pathways during Transformation of Mammary Epithelial Cells
Keywords: epithelial-to-mesenchymal transition
p53
PI3K-signalling
PIK3CB
Rac1
senescence

Mutations in PIK3CA are observed in 20-42% of breast cancers, in contrast PIK3CB is rarely mutated. Rather, amplification of the wildtype PIK3CB occurs in a small subset (1.6%) of breast tumours. Here, we investigated the isoform specific activities of p110αH1047R and p110β during transformation of MCF-10A mammary epithelial cells. Overexpression of p110αH1047R and p110β, respectively, induced distinct morphological and molecular characteristics, and invoked distinct molecular programs of EMT. Whereas p110β depended on Rac1 to induce a motile and invasive mesenchymal phenotype, p110αH1047R induced SNAIL and a cadherin switch, characteristic of EMT. Furthermore, p110β overexpression induced genome instability and decreased proliferation through cell cycle arrest and a senescence like phenotype, which could be overcome by genetic knockdown of TP53. These findings revise our understanding of the role of PIK3CB in breast cancer, and suggest that Rac1 and TP53 may provide important axes for establishing transformation in cells with amplification of PIK3CB.
The Effects of Aging on Mesenchymal Stem Cell Extracellular Vesicle Treatment following Myocardial Ischemia/Reperfusion
Keywords: Aging
Cardiac
Extracellular Vesicles
Stem Cells

Extracellular vesicles (EV) have recently emerged as a key factor secreted by stem cells which improve infarct healing post-myocardial infarction (MI). However, the cellular processes which regulate EV function are largely unknown. Here we investigate young and old mouse bone marrow mesenchymal stem cells (MSCs) to determine whether aging impacts the beneficial effects of secreted EVs. Cells and EVs were studied in-vitro to determine the effect of aging on cell function as well as EV secretion. Using a mouse model of myocardial ischemia/reperfusion (I/R) we show that young, but not old, MSC-EVs preserve left ventricular function at four weeks post-I/R by echocardiography. Mechanistically, we found that young MSC-EVs modulate the inflammatory response more effectively than old MSC-EVs in-vitro and in-vivo. Understanding how aging affects EV secretion and function will lead to a better understanding of the cellular processes which regulate the beneficial effects of EVs on cardiac repair.
Is Being Worse than You Worse for Us? The Effect of Social Comparisons to Friends
Keywords: Friendship
Friendship Outcomes
Social Comparison

Friendship is an important source of well-being, but remarkably little research has investigated the processes that can undermine these connections. One such process is the tendency to compare oneself to others, especially to close others. Across three studies, I examined how the frequency and direction of social comparisons influence the evaluations of the self and of a new friend. In Studies 1 and 2, frequent upward comparisons were positively associated with global markers of closeness. In Study 2, I found that individuals reported lower self-evaluations after an upward comparison, and that this comparison negatively impacted immediate evaluations of their friendship. In Study 3, the valence (positive or neutral) of a message from a friend had no effect on one’s self- or friendship evaluations. These studies may suggest that social comparisons do have an impact not only on how one thinks about the self, but also on how friendships are perceived.
Classification of Neural Tissue Types for an Endoscopic Full-field Optical Coherence Tomography Imaging System
Keywords: Image Processing
Optical Coherence Tomography
Optical Devices
Surgical Instruments

Optical coherence tomography is a volumetric imaging technique that can differentiate brain regions with micron-scale resolution. Our group has previously developed an endoscopic full-field swept-source OCT system with the aim to provide real-time surgical guidance for the implantation of electrodes for deep brain stimulation. This thesis builds on our prior work. Due to the limitations in the resolution and contrast due to scattering from the tissue, to identify different types of neural tissues, we developed an image classification algorithm that uses texture features and optical attenuation. The algorithm could classify three different brain regions in the ex vivo data set with a test accuracy of 70%. Also, we performed a comparative analysis on the optical properties of the ex vivo and in vivo data sets to provide directions on how to achieve high accuracy for in vivo data.
Organ-on-a-chip Applications using Collagen Tubular Constructs
Keywords: Collagen
Microfluidics
Organ on a chip
Pumps
Soft Materials
Valves

Collagen is the most abundant protein found in the human extracellular matrix. Tubular structures are also found in abundance in various human tissues. This thesis demonstrates two applications using extruded collagen-I tubular structures for organ-on-a-chip models. The first application is to use a collagen tubes as the foundation of a tubular-based organ-on-a-chip device to model the human airway. With this device, we showed the ability to culture a confluent monolayer of human bronchial epithelial cells that showed functional responses to various stimuli. Next, we recreated the Quake-style valve and pump using these collagen tubular constructs. Quake-style valves and pumps are predominantly fabricated with the elastomer, polydimethylsiloxane. This material is not the best choice for use with organ-on-a-chip platforms, which we rectify with our collagen-based devices.
Provincial Funding of Faith-based Schools in Ontario: Contemporary Framing Contests
Keywords: Education Policy
Faith-Based Schools
Frame Analysis
Framing Methodology
Religion
Religious Schools

Ontario is the only province in Canada that continues to exclusively provide government funding to Catholic schools while not providing any funding to schools of other faiths. The research question asks; how do stakeholders, advocates for funding, and proponents of one public school system make their perspective persuasive? The project explores perspectives on how arguments about funding for faith-based schools have shifted over the past sixty years. Frame analysis is used as both a theoretical framework and a method to explore how participants frame their argument in today’s sociocultural climate. All participants were found to frame their arguments using social justice rhetoric - its themes, terms, imagery, and priorities - in some form. The use of social justice language represents a shift in framing of this topic from older styles that focused on multiculturalism (1990’s) and even older ones that saw religious instruction as a moral duty (pre 1980’s). The findings highlight that there are many tensions between advocates of social justice who are concerned with how faith groups will treat women and members of the LGBTQ+ community and advocates for funding faith-based schools who believe they face discrimination by their lack of funding.
On the Elementary Symmetric Polynomials, Inverse Vandermonde Matrix, and its Application in Signals and Images
Keywords: Elementary Symmetric Polynomial
Magnetic Resonance Imaging
Vandermonde Inverse
Vandermonde Matrix

The Vandermonde matrix naturally appears in systems of equations for applications of polynomial approximation. Solving the system, which requires inverting the Vandermonde matrix, may be preferred because researchers can utilize efficient matrix computation methods. However, the Vandermonde is ill-conditioned and prevents standard methods from calculating accurate entries to the inverse. Many of the derived algorithmic solutions to the Vandermonde inverse contain a mathematical expression called the Elementary Symmetric Polynomial (ESP). The ESP is computationally inefficient to solve directly, so there are also algorithms to solve them. The state-of-the-art ESP and inverse methods are fully recursive and may be more susceptible to noise perturbations in the Vandermonde matrix than a closed-form inverse method. In addition, the performances of the methods in a variety of applications in polynomial approximation are unexplored. This thesis investigates the utility of the state-of-the-art methods against a novel ESP and closed-form Vandermonde inverse method in applications of polynomial interpolation and Fourier reconstruction in the presence of Gaussian noise.
Telling Stories in Order to Live: An Inquiry into Applied Theatre and Critical Media Education in an Urban Middle School
Keywords: applied theatre
counter-storytelling
critical race theory
cultural production
media literacy education
middle school

As youth media programs proliferate in an age of “algorhythmic governance” (Boler Davis, 2018), critical questions emerge about the process and direction of the content being created, particularly, how to develop stories “without necessarily recirculating dominant relations” (Gaztambide-Fernandez, 2013, p. 227). Grounded in critical race theory, this arts-based inquiry highlights the way that applied theatre approaches and a lens of cultural production offer a promising paradigm for bringing greater depth and nuance to student counter-storytelling (Solorzano Yosso, 2002). This thesis describes the strengths of key practices that were carried out in two urban middle school classrooms. Drawing on student interviews, group discussions and creative work, three stories by youth that I am calling propositional stories, are offered as examples that illustrate important implications for the way in which educators and practitioners might approach media production and storytelling with youth.
Hybrid Silicon-Organic Augmented Low Index Guiding Waveguides for Nonlinear Optics
Keywords: nonlinear optics
optical materials
silicon photonics
waveguides

Silicon-on-insulator (SOI) has emerged as a versatile and economical platform for a va- riety of nano-photonic devices and applications. However, it has many intrinsic limi- tations. Within nonlinear optics, two-photon absorption and the resultant free carrier effects severely limit the power and speed of Kerr-effect processes. These problems can be circumvented by designing hybrid structures which integrate organic materials with more favorable properties. These strategies rely on the waveguide’s ability to guide light in the low refractive index region of index high contrast silicon waveguides. While several structures such as slot and hybrid plasmonic waveguides have been explored for this pur- pose, these approaches suffer from high loss, high fabrication complexity or poor optical confinement. In this thesis we design, fabricate and test hybrid silicon-organic waveguides based on the augmented low index guiding (ALIG) principle introduced by Alam et al [1]. In the ALIG structure the transverse magnetic (TM) mode is mostly confined to the low-index organic region while the transverse electric (TE) mode is primarily confined to the high index silicon core. Compared to the slot structure, we find that the ALIG design can achieve higher confinement to the organic material and lower loss. Beyond χ 3 nonlinear optics, this approach can be extended to other materials which add other functionality such as biosensing, electro and magneto-optics, and gain on chip.
Analysis and Development of a Halbach Array Motor for Application into a Novel Delivery Drone Driving Cycle for Powertrain Optimization and Maximization of Delivery Radius
Keywords: Delivery Drone
Driving Cycle
Efficiency
Electric Motor
Halbach Array
Powertrain Optimization

Large companies such as Amazon and Google are currently testing deliveries using unmanned drones, with the intent of using these drones on the market. Topics tackled in this field of research include delivery routing optimization, object collision routing optimization, battery management optimization, and the addition of solar panels on the drones. Little publicly available research has been found to have been conducted on the developing methods to optimize the powertrain of the drones to maximize their delivery radii. To achieve this end, this work puts forth a delivery drone driving cycle simulation written in MATLAB with which to monitor their performance and fine-tune their properties. A halbach array motor is designed and analyzed in ANSYS Maxwell, and a data processing tool is written in MATLAB to manipulate the halbach array motor and propeller data into useable states for the simulation. The driving cycle simulation is tested on fifty-four drone configurations.
Machine Learning Applications for Clinical Data in Otolaryngology-Head Neck Surgery
Keywords: cochlear implants
deep learning
hearing loss
machine learning
natural language processing
neural networks

Applying novel analytic techniques -- such as machine learning -- may be fruitful in identifying possibilities for resource optimization and novel methods of care delivery. In this thesis, the current state of machine learning in Otolaryngology-Head Neck Surgery and the specific application of machine learning to a full range of challenges is reviewed. Novel analyses using computer vision for automatic audiogram interpretation, predicting post-operative cochlear implant performance using diverse pre-operative patient variables, and natural language processing for detecting patterns in adverse event reports related to cochlear implant devices are presented. We observe that conventional machine learning techniques generate high performance in these prediction tasks. Thinking more broadly, uncertainty in medicine and subsequent wasteful care is one of the key drivers of runaway health care spending in today's health care ecosystem. Machine learning is poised to help reduce uncertainty in clinical decision making to serve the value-based health care mandate.
SHIP: A Storage System for Hybrid Interconnected Processors
Keywords: FPGA
NVMe
NVMe-OF
RDMA
RoCE
Storage

Drivers for accessing storage are complex. In addition to the complexity involved in using the solid-state drive protocols, navigating filesystems is a process requiring multiple storage accesses and data processing between each access. As a result, efforts to create storage drivers for non-CPU processors (FPGAs and GPUs) have either failed, require too many resources/time, or remove some of the functionality expected by storage users (such as removing the filesystem). In this thesis, we explore the creation of a wrapper encapsulating a solid-state drive that performs all of the filesystem operations, and presents a much simpler network-based interface, simple enough for FPGAs and GPUs to use efficiently. Data transfers are performed via the network-based RDMA protocol, for which drivers exist for CPUs, GPUs, and FPGAs. ASIC accelerators for RDMA are also available. This system can sustain a throughput of 294 MBps, offers better scalability and is better adapted for cloud architectures, compared to current storage solutions
Latent Critiquing for Conversational Recommender Systems
Keywords: Conversational Recommendation
Critiquing
Deep Learning
Recommender Systems
Sequential Recommendation

Critiquing is a method for conversational recommendation that iteratively adapts recommendations in response to user feedback regarding item attributes. This thesis addresses four problems: (1) how can user preferences and feedback be represented and manipulated in the same latent space to provide updated recommendations after critiquing? (2) how can recommender systems be equipped with language-based interaction? (3) how can item critiques be co-embedded with user preference so as to properly take critiques into account for subsequent recommendations? Finally, (4) how can the strength of multi-step critiquing feedback be weighted. Empirical results show that our approach not only provides a strong baseline recommender and high-quality personalized item keyphrase suggestions, but that it also properly suppresses items predicted to have one-step critique. Furthermore, the proposed model reduces the number of interactions required to find a satisfactory item and increases the overall success rate compared to a standard approach of averaging critique feedback.
Impact of Residential HVAC Filters on Particle Concentration and Exposure
Keywords: filtration
indoor sources
low-cost monitors
particulate matter

Filters in heating, ventilating, and air conditioning (HVAC) systems can improve indoor air quality. In North America, residential air cleaning is usually done by recirculating indoor air through a filter. In this work, I explored two methods of assessing the impacts of HVAC filters in residences. The first method was through examining year-long PM measurements in 20 Toronto homes where different types of filters were installed. I found no evidence that higher efficiency filters reduced long-term exposure to PM. The second method was by studying the particle size distribution of filter dust. I explored the possibility of size separating filter dust to gain more information on smaller particles. One major challenge is the sticky nature of filter dust makes both the separation and mixing (with water for size analysis) processes difficult. Despite the caveats, these findings suggest that both methods are promising and can provide useful information for future studies.