VictorJuiz/thesis huggingface dataset:
Applying Videogame Narrative and Level Design To Museum Exhibit Design
Keywords: Exhibit Design
Level Design
Museum Studies
Narrative Design
Videogame
Visitor Experience

This thesis examines how practices of narrative and level design used in the videogame industry can be applied to the museum sector, and from this research, propose a set of design lessons that can be used for the creation of exhibits. To that end, this thesis hypothesizes that these practices from gaming will serve the purpose of elevating the visitor experience—specifically in the way museum spaces, artifacts and their stories are presented, navigated, and experienced by the visitor. In order to produce this set of design lessons for exhibit creation, a qualitative study of five videogames was conducted for the purpose of identifying methods of narrative delivery, and level design practices. In regard to narrative design, I establish three methods of narrative embedding, partly inspired by Huaxin Wei’s work, in addition to summarizing Ahmed Khalifa et al.’s findings, which contributed to this studies understanding of level design. The combined knowledge of these works where foundational in developing a unit of analysis, which was used to identify narrative and level design practices within my sample. The findings from this research was then translated into applicable design practices for museum exhibit creation, called design lessons. Moreover, the design lessons developed from this study were used in the creation of a prototype exhibit for the purpose of demonstrating its effectiveness. Thus, this study advocates that there are indeed lessons to be learned from videogames which offer new perspectives in design to the museum sector. That in doing so, this study’s interest in providing museum professionals with new views on narrative and level design, will have a positive affect on the visitor experience.
A Semi-supervised Pipeline for Detection of Anomalies in Medical Images
Keywords: Anomaly detection
Machine learning
Medical Imaging

With the breakthroughs in the field of deep learning and computer vision problems, manyworkflows have been revolutionized, including diagnostic medical imaging. In this work we developed a Generative Adversarial Network - based pipeline for augmentation of data and detection of anomalies in the form of Viral Pneumonia and COVID-19 in chest X-ray images. By eliminating the need for more labelled data for training our model, we offered a pipeline that reduced the strain on radiologists when curating data for deep learning models. We demonstrated that our pipeline improves results compared to the previous state-of-the-art generative model for detection of anomalies in the images.
Our Needs Will Not Diminish: Perspectives of Ontario Non-profit Organizations on Experiential Learning Partnerships with Post-secondary Institutions
Keywords: Campus-community Partnerships
Canada
Education Policy
Experiential Learning
Neoliberalism
Service Learning

This study investigates the perspectives of staff within non-profit organizations that host post-secondary students in experiential learning (EL) placements. Post-secondary institutions within Ontario and across North America have experienced increasing pressure to ensure that students have opportunities to learn by doing through experiential learning placements. Community-based non-profit organizations host many of these placements. Interviews with ten Canadian non-profit staff members who supervise placement students revealed both tensions and possibilities inherent in campus-community EL partnerships. The findings make clear that non-profit organizations vary in their needs and available resources and these affect their interactions with post-secondary institutions. The study has important implications, for those at the provincial and administrative levels as they develop expectations and policies around campus-community engagements, and for the staff and faculty within universities and colleges who send students to non-profit organizations.
Using Machine Learning Algorithms for Clinical Decision Support for Breast Cancer Screening at Sunnybrook Health Sciences Center
Keywords: biopsy
follow-up
healthcare
machine learning
MRI

Clinical decision-making has been a major challenge for healthcare providers in recent years. We apply a number of machine learning algorithms to two clinical breast imaging problems at Sunnybrook Health Sciences Center. In Phase 1, we predict missed magnetic resonance imaging (MRI) exams during 2017-2019 and 2020. We indicate that the 2020 model’s performance is highly dependent on the features related to exam essentiality, and the best f1-score increases from 53% in the 2017-2019 model to 76% in the 2020 model due to the growth in the no-show rate mitigating the target class imbalance. In Phase 2, we predict whether high-risk breast cancer patients require to undergo a biopsy after a series of follow-up MRI exams to detect the source of an abnormality and show that the important features for this problem include radiologist measure and patient age. We deal with target class imbalance using data balancing and hyper-parameter tuning.
Reducing the Cost of Ensemble-based Data Assimilation in Multiple-query Scenarios through Covariance Augmentation
Keywords: Augmentation
Data assimilation
EnKF
Ensemble Kalman filter
Multiple-query scenario
Reacting flow

We present and assess a method to reduce the computational cost of performing data assimilation (DA) for reacting flow in multiple-query scenarios, where we consider several scenarios with similar underlying dynamics. We focus on ensemble-based DA, in particular the ensemble Kalman filter (EnKF). The accuracy of the EnKF, which depends on the quality of the sample covariance, improves with the ensemble size, but so does its computational cost. To reduce the ensemble size while maintaining accurate covariance, we propose a data-driven approach to augment the covariance based on the statistical behavior learned from model evaluations. We assess our augmentation method using one-dimensional model problems and synthetic reacting flow cases. We show in all cases that ensemble size, and thus computational cost, may be reduced by a factor of three to four while maintaining accuracy.
Understanding Cortical Astrocyte Distribution through Perinatal Birth Dating
Keywords: Astrocyte
Astrogenesis
Cortical development
Gliogenesis

Cortical astrocytes are highly heterogeneous cells with distinct gene expression profiles, phenotypes, and functions. Peak astrogenesis occurs between E15-P7; whether astrocytes occupy distinct cortical layers depending on their time of birth is not known. To determine whether time of birth influences astrocyte distribution in the upper (UL) vs. lower (LL) cortical layers, we permanently labelled ventricular subventricular zone (V-SVZ) NSPCs and their progeny throughout embryonic and postnatal astrogenesis periods using in utero electroporation and 5-ethynyl-2’deoxyuridine (EdU) labelling. Immunohistochemistry (IHC) for the astrocyte lineage marker SOX9 was performed and SOX9+ astrocytes were quantified and compared between UL and LL cortical layers within each timepoint. SOX9+ astrocytes born in the late embryonic period showed an UL bias, whereas SOX9+ astrocytes born in the postnatal period were primarily restricted to the LL. Altogether, these results suggest that the distribution of SOX9+ cortical astrocytes is influenced by time of birth.
Biplanar Ultrasound Investigation of Tongue Shape during Mastication: A Tale of Two Textures
Keywords: Biplanar
Mastication
Shape
Texture
Tongue
Ultrasound

Tongue movement in chewing is complex and three-dimensional. The present study explored quantitative features of tongue movement during mastication of two solid textures (almond vs beef jerky). Temporally linked sagittal and coronal real-time ultrasound tongue videos from nine typical participants were analyzed using outcome measures of chew duration, cumulative displacement, and surface concavity. There was greater posterior tongue travel for the almond texture, and a longer chew duration for beef. Cumulative tongue displacement was significantly greater for the posterior tongue. The concavity distributions showed mostly flatness in the coronal view, and mainly convexity in the sagittal view. A principal component analysis demonstrated that the front and back of the tongue moved independently, with lateral free margins clustering with the center and back. The new measures served to describe global aspects of tongue movement. Future research should test these global measures in clinical populations with typical and disordered chewing.
Fructose Metabolism in the Ruby-throated Hummingbird (Archilochus colubris)
Keywords: Fructose
Glucose
Hummingbirds
Metabolism
Metabolomics

Ruby-throated hummingbirds drink nectar that is 50% fructose and 50% glucose, as monosaccharides and the disaccharide sucrose. Remarkably, hummingbirds can use either ingested glucose or fructose to fuel metabolically demanding hovering flight. Currently, the metabolic pathways that could support flight with fructose carbon remain unclear as traditional vertebrate models cannot use this sugar directly as a muscular fuel. I use metabolomics to identify possible metabolic pathways in the pectoralis muscle, heart, and liver that process fructose at relatively rapid rates. I hypothesize that the hummingbirds can use exogenous fructose directly in their muscles to power flight but also convert the fructose to other energy metabolites, including glucose, in their liver. I found support for both of these hypotheses. Surprisingly, I also found evidence suggesting that the heart plays an important role in fructose metabolism.
Detailed Morphology of the Skull and Dentition in a New, Exceptionally Preserved Microraptorine Specimen (Theropoda: Dromaeosauridae) from the Early Cretaceous of China
Keywords: cranial anatomy
dentition
Dromaeosauridae
Microraptorinae
microraptorine
Theropoda

The evolution of non-avian dinosaurs to birds is one of the most significant evolutionary events in the history of life. Microraptorinae, a clade of small, four-winged dromaeosaurs that exhibit both avian and non-avian dinosaurian features, is a fossil group important to this transition. Microraptorine specimens are known from the Early and Late Cretaceous deposits of Asia and North America and are represented by several specimens in the literature; however, well-preserved craniodental material is lacking. Here I describe an exceptionally preserved microraptorine specimen from China, JLUM Y-MR160501, which includes a near-complete skull and dentition in both jaws. The delicate cranial bones retain their shape and are only slightly disarticulated, allowing for the first detailed description of cranial anatomy in microraptorines. This exceptional specimen brings critical new information on microraptorine cranial anatomy and morphology that fills in the current gaps in our knowledge of Microraptorinae and the evolutionary relationships within the clade.
Examining Variation in Somatic Mutation Rates at Chromatin Architectural Elements of Cancer Genomes
Keywords: CTCF
Mutation rates
Non-coding
RAD21
TFBS
TOP2B

CCCTC-binding factor (CTCF) and the cohesin complex anchor chromatin loops and domains that regulate genomic interactions and transcription. These elements are also enriched for somatic mutations; however, an understanding of the underlying mutational patterns, processes, determinants, and functional implications remains elusive. It was discovered that topoisomerase II beta (TOP2B) frequently co-binds with these proteins, conceivably to relieve topological stress produced during loop formation. Here, we analyzed a dataset of CTCF, RAD21, and TOP2B binding sites experimentally derived from human liver cancer tissue. We studied the DNA-binding and genomic associations of these proteins and systematically characterized mutational patterns using a pan-cancer dataset of 2,500 whole genomes. We found that the mutation enrichment was more pronounced in sites bound by all three proteins. We identified 222 candidate drivers, 81% of which contained TOP2B binding. Subsets of these candidates correlated with gene expression changes and may represent novel non-coding drivers of cancer.
A Data-driven Optimized Two-stage Appointment Scheduling Model for CT Simulation and Radiation Treatments
Keywords: Appointment scheduling
CT simulation
Healthcare
Operations research
Radiation treatment

Radiation treatment, one of the main methods to treat cancer, requires a CT simulation to view the body’s inner structure and generate treatment plans. The appointment scheduling for CT simulation and radiation treatments is a complex two-stage problem for patients of different site groups, categories, and treatment intents. The problem is formulated as a Mixed Integer Programming problem to jointly schedule both kinds of appointments and we propose two alternative objective formulations to minimize the number of patients who wait more than their wait time targets for their treatment. Using historical data analysis to estimate the parameters, the models are evaluated using daily scheduling and patient batching approaches by considering wait time statistics and the utilization of linear accelerators. A heuristic model is also developed based on the results of the patient batching approaches to generate the appointment schedule at the time of consultation. The results of different approaches are compared.
Attitude Control of a Flexible Solar Sail using Translating Masses
Keywords: Attitude Control
directional stability
eigenvalue analysis
Flexible solar sail
Rayleigh-Ritz method
translating masses

This thesis is devoted to developing attitude control of a square solar sail spacecraft made of four pre-tensioned triangular sails supported by four flexible diagonal booms each affixed with a translating mass. The control torques are created by the relative displacement of the spacecraft's center-of-mass to its center-of-pressure. A Rayleigh-Ritz method is used to discretize the equations of motion of the spacecraft. Only out-of-plane deflections of the sail membrane quadrants and support booms are considered in the present document. Modal analyses are performed to validate the model’s basic foundations against the results obtained by a finite-element method in past literature. Attitude and vibration control simulations are presented and stability results are discussed.
Gendered Voices in Japanese Popular Music: A Data-driven Analysis
Keywords: computational social science
digital humanities
gender roles
gender stereotypes
Japanese popular culture
popular song lyrics

In this thesis, I investigate the gender ideologies, conventions, and stereotypes represented in Japanese popular music lyrics by analyzing 17,614 commercially successful songs released in Japan from 1968 to 2020 utilizing natural language processing and machine learning. In the first part, the differences in word use between the lyrics sung and written by women and those sung and written by men are examined with classification models. The dynamics between singer gender and lyricist gender and their impact on the lyrics, the differences between the lyrics written and sung by the same artists (singer-songwriters) and those written and sung by different artists, and the historical changes are also analyzed. In the second part, I explore how male characters and female characters are portrayed in popular music lyrics differently through a computational extraction of feminine and masculine personal pronouns and the words syntactically associated with them.
Preventing the Development of Nitrate Tolerance and Assessing Endothelial Function.
Keywords: Allopurinol
Endothelial
FMD
Nitrate
Tolerance

Nitroglycerin (GTN) and other organic nitrates are widely used to treat patients with various cardiovascular diseases. However, despite beneficial effects, treatments are often limited due to the development of a phenomenon termed ‘nitrate tolerance’ and the exact contributing mechanisms remain unknown. During chronic nitrate therapy, there is rapid loss of the hypotensive effect and a greater dose is required to achieve the same response. Many hypotheses have emerged to explain the phenomenon and subsequently, to develop treatment alternatives such as combination therapies to prevent nitrate tolerance in patients. In addition, endothelial dysfunction is also commonly noted to occur along with the development of nitrate tolerance. There are many clinical tests that can assess endothelial function, such as venous occlusion plethysmography, flow mediated dilatation and quantitative coronary angiography. However, the predictive values of some clinical methods in determining endothelial dysfunction and hence, risk of cardiovascular disease remains debatable.
A High-resolution Optical Flow-inspired 3D3C Velocimetry Method based on Sparse Representation of 3D Particle Images
Keywords: Optical Flow
PIV
Velocimetry

We introduce a three-dimensional three-component (3D3C) particle-based velocimetry method that expands the methodology of optical flow to three dimensions. A sparse representation of intensity with kernel functions is introduced to facilitate efficient computation in 3D, and to ensure robust performance for the large displacements seen in particle images. Performance of the proposed method is evaluated in terms of accuracy and spatial resolution, using direct numerical simulation data as a ground truth. The proposed method yields lower errors than tomographic PIV and is capable of resolving finer scale features, even for large particle displacements and in the presence of tomographic reconstruction artifacts. The method is also validated on experimental images of reacting flows and shows good agreement with T-PIV results.
Cost-utility Analysis of Universal Pre-exposure Prophylaxis Coverage for Men who have Sex with Men at a High Risk of Human Immunodeficiency Virus Infection in Ontario, Canada
Keywords: Cost-Utility Analysis
Economic Evaluation
Health Economics
HIV
MSM
PrEP

HIV pre-exposure prophylaxis (PrEP) was approved by Health Canada in 2016. Currently in Ontario, those under the age of 25 or aged 65 years or older can access PrEP free of charge through public drug coverage programs. This study assessed the cost-effectiveness of providing public coverage for PrEP to all high-risk Ontario MSM. A Markov cohort model was built from the perspective of the Ontario Ministry of Health and Long-Term Care (MOH) using a lifetime time horizon. Health states included HIV positive, HIV negative, and Dead. Transition probabilities, health state utilities and HIV treatment costs were sourced from published literature. Costs of HIV prevention were estimated through a micro-costing approach. HIV infection risk was estimated using the HIRI-MSM screening tool. Offering PrEP to all high-risk MSM through the Ontario MOH is dominant to current coverage circumstances due to a reduction in costly HIV infections.
Analyzing Impact of the COVID-19 Pandemic on Traffic Congestion and Commercial Vehicle Travel Patterns within the Greater Toronto and Hamilton Area
Keywords: Bottleneck Analysis
Commercial Vehicles
COVID-19 Pandemic
E-Commerce Boom
Travel Speed

The COVID-19 pandemic has impacted virtually all social and economic activities in our society, resulting in changing patterns of travel and traffic congestion in Canada. This thesis first analyzes traffic congestion changes throughout the first and second waves of the pandemic on detected freeway bottlenecks within the Greater Toronto Hamilton Area (GTHA), using travel speed data. Then this thesis focuses on the commercial vehicle (CV) travel patterns, specifically changes in origin and destination in the GTHA. Since the beginning of the pandemic, consumers’ shopping behaviours have changed dramatically. The impact of these changes is investigated by analyzing the origins and destinations of CVs based on GPS data collected by Geotab GO. The changes are described with figures and summary statistics. Next, CVs activities are analyzed on the worst bottleneck in the GTHA, and finally, recommendations for future research are provided.
Machine Learning for Prognostic Modeling in Head and Neck Cancer using Multimodal Data
Keywords: Head and neck cancer
Machine learning
Medical imaging
Prognostic modeling

There has been significant interest recently in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. In this work, we explore the use of multi-modal machine learning (ML) for outcome prediction in head and neck cancer (HNC).We first develop a ML approach for survival prediction from multi-modal data in the presence of competing risks, based on an extension of the multi-task logistic regression framework. We then describe the results of a ML challenge to develop a prognostic model for overall survival using electronic medical records and pre-treatment imaging. Finally, we present a novel graph-based method to incorporate imaging characteristics of metastatic lymph nodes and their connectivity patterns in a prognostic ML model using Gated Graph Convolutional Networks. Our results show the potential of ML in combination with large, multi-modal datasets as a tool to guide personalized cancer care.
Factors Influencing Immune Investment in Invertebrates
Keywords: Immunity
Invertebrates
Investment
Meta analysis
Sex ratio
Sexual dimorphism

Immune traits are an important facet of an organism’s biology, as the threat of infection from pathogens is universal. We nonetheless observe a large degree of variation in measured immune traits, indicative of differences in immune investment between individuals. This thesis attempts to answer two questions around how individuals navigate immune investment. One, does mating environment impact immune investment in a species of water strider and two, is the sexual dimorphism observed in a commonly cited invertebrate immune metric related to the sexual size dimorphism commonly seen across this group? While a portion of chapter 1 could not be completed due to the pandemic, I do find evidence that water striders experience changes in mating environment in the wild. In chapter 2 I find that the expansion of an existing dataset on sex bias in melanization results in no significant sex bias in this immune metric as well no relationship between melanization and sexual size dimorphism, contradicting what may be expected by theory regarding sex biased immune allocation.
An Exploratory Study of the Travel Characteristics of Students who Use Mobility Aids at Ten Universities and Colleges in the Greater Toronto and Hamilton Area, Ontario, Canada
Keywords: Disability
Mobility
Postsecondary
Student
Travel

Postsecondary students are an underrepresented population in transportation research, and postsecondary students with disabilities even more so. Using survey data from ten universities and colleges in the Greater Toronto and Hamilton Area, I compare demographic, activity, and attitudinal characteristics of students who use mobility aids to other students, exploring potential differences in access and use of transport and education systems. The results indicate that many students who use mobility aids are from lower income families, receive less financial support, and travel at different times than other students. Students who use mobility aids reported greater use of modified vehicles and carpooling with household members. They were also more likely to report that commuting discouraged them from attending campus and limited their academic success. A closer look at the experiences of postsecondary students with disabilities is required. Future research should expand to include other disabilities and a longer observation period.