VictorJuiz/thesis huggingface dataset:
Resilience is associated with cortical gray matter of the antinociceptive pathway in people with chronic pain
Keywords: ACC
chronic pain
dlPFC
gray matter
MRI
resilience

Resilience is a personal characteristic that influences health. This thesis examined whether resilience was related to cortical gray matter in 4 regions of the antinociceptive pathway: rostral and subgenual anterior cingulate cortex (rACC, sgACC), anterior insula (aINS), and dorsolateral prefrontal cortex (dlPFC). Gray matter volume (GMV) and cortical thickness wereextracted from T1-weighted MRI images of 88 people with chronic pain and 86 sex- and age-matched healthy controls, who completed The Resilience Scale and Brief Pain Inventory. Resilience was found to be negatively correlated with average pain and positively correlated with GMV in the bilateral rACC, sgACC, and left dlPFC in people with chronic pain. Mediation analyses revealed that GMV in the right rACC and left sgACC together partially mediated the relationship between resilience and average pain. These findings suggest that the antinociceptive pathway may play a role in the impact of resilience on one’s ability to modulate chronic pain.
Neuro-Regeneration of Retinal Ganglion Cells on Bio-Activated Silk Based Scaffolds
Keywords: 3D Printing
Electrospinning
Regeneration
Retinal Ganglion Cells
Silk Fibroin
Vision

Retinal ganglion cell (RGC) axons form the optic nerve, connecting the retina to the visual areas of the brain. Any injury to the optic nerve leads to RGC death and ultimately blindness as RGC axons are unable to regenerate. One of the main challenges in regenerative neuroscience is to identify ways to stimulate neuronal survival and regrowth. We seek to develop an in vitro method using biomaterial and 3D bioprinting to enhance RGC survival and support axon regrowth and directionality. I engineer bio-platforms containing aligned silk nanofibers on which we culture RGCs. These silk nanofibers are bioactivated by adding trophic factors which will be entrapped in the fibers. Growing RGCs on these bioactivated silk nanofibers enhanced neuronal survival and axon regrowth.This technology will ensure fast, reproducible, and accurate generation of bioactivated silk nanofibers scaffolds that can be used to investigate the mechanisms of neuro-regeneration in vitro and in vivo.
Material Screening for Solid State Electrolyte Applications through Machine Learning and Ab-initio Molecular Dynamic Calculations
Keywords: Argyrodite
Machine Learning
Molecular Dynamics
Solid State Electrolytes

Solid state electrolyte batteries provide a noticeable advantage over conventional liquid batteries due to improved safety and their potential for increased energy storage over current batteries. For this thesis, two types of calculations are undertaken: first, a machine learning neural network has been used to predict optimal systems; second, first principle Ab-Initio Molecular Dynamic (AIMD) calculations have been performed to predict the performance of the systems previously identified. Machine learning was implemented using the Tensorflow library, where a variety of neural networks were optimized and tested in the categorization and discovery of promising solid state electrolytes. AIMD was implemented using the Vienna Ab-Initio Software Package (VASP), where promising materials systems were simulated and then had ionic conductivities determined from these simulation results. The AIMD portion of this study focused on the Argyrodite system class, which was found during literature review and through the use of the preceding machine learning calculations.
The Cognitive Process of Affordance Recognition: Three Iterative Experiments Comparing Affordances to Alternate Uses
Keywords: affordances
AUT
creativity
divergent

Fostering creativity in engineering can promote innovative solutions. Strategies to generate many solutions are also beneficial to solving the complex problems engineers face. Borrowing from engineering and psychology, this thesis developed two tasks to compare affordances and alternate uses. This was followed by an exploration of how each can contribute to creative problem-solving on subsequent tasks. Experiment 1 hints at the cognitive demands of identifying alternate uses. Experiment 2 confirms the critical role of physical properties in affordance recognition. Experiment 3 reveals an interesting relationship between functions, alternate uses, and performance on physical tasks requiring creative solutions. Taken together, these support the strategies used in engineering concept generation. Moreover, they highlight the vital role that divergent thinking plays in creativity.
Incorporating Clinical Access Limitations in Automated Treatment Planning for Photodynamic Therapy
Keywords: Computer-aided Designs
Optimization
Photodynamic Therapy
Treatment Planning

Interstitial Photodynamic Therapy (iPDT) is a minimally invasive oncology treatment in the treatment of deeply-seated tumors. PDT-SPACE is an open-source tool that automates iPDT treatment planning by providing patient-specific light source placements to destroy a tumor while minimizing healthy tissue damage. This work extends PDT-SPACE in two ways. The first enhancement allows specification of clinical access constraints on source insertion to avoid critical structures and to minimize surgical complexity. Constraining fibre access to a single burr hole of adequate size preserves result quality. The second enhancement generates an initial placement of light sources as a starting point for refinement, rather than requiring entry by the clinician. This feature improves productivity and leads to solutions with an average of 4.5% less healthy tissue damage compared to manually generated placements. The two features are used to perform in silico comparisons of various surgical options for virtual glioblastoma multiforme brain tumors.
Distributed Privacy-Preserving Machine Learning via Homomorphic Encryption
Keywords: Functional Encryption
Homomorphic Encryption
Privacy-preserving Machine Learning
Secure Inference
Vertical Federated Learning

Privacy-preserving machine learning enables training ML models on sensitive data while preserving individual data privacy using advanced cryptographic techniques. Some of the key techniques used in privacy-preserving machine learning include secure multiparty computation, homomorphic encryption, and differential privacy. In this thesis, we propose two protocols: a secure neural network inference protocol and a secure vertical federated learning protocol. In the first work, SECO, we extend prior work on secure inference to a multi-server hierarchy, allowing multiple server nodes to collaborate with a user to compute predictions without compromising data privacy. We use multiparty homomorphic encryption and garbled circuits to protect the model structure and evaluate SECO on MiniONN and ResNet32 models. In the second work, we analyze leakage in FedV using multi-input functional encryption and suggest using Quadratic functional encryption to avoid information leakage in training generalized linear models for vertical federated learning.
Deep Generative Machine Learning to Create Synthetic 2D Ultrasound Images for Rare Disease Research
Keywords: Artificial simulation
GAN
Generative models
Hemarthrosis
Hemophilia
Ultrasound

Research on rare diseases is often not considered by the artificial intelligence (AI) field due to data scarcity. Hemophilia is a rare genetic bleeding disorder that affects blood clotting. Hemophiliacs often suffer from bleeding into the joint space (hemarthrosis) that causes pain and ultimately damage. Ultrasound imaging is used for non-invasive hemarthrosis assessment. Early detection using AI is key to improve treatment efficacy and access to care. This thesis proposes a novel generation framework that includes domain knowledge from an external dataset, decreasing missing information in rare cases. By dividing the problem in content and context generation, our enriched synthetic ultrasound showed no significant differences from real data; improved model performance, especially at smaller sample sizes; outperformed a previously accepted model in synthetic quality; and provided a robust model for hemarthrosis detection. Our framework facilitates AI research in the study of rare diseases and the prototyping of AI-driven clinical solutions.
Behavioural Response as a Function of Temperature and Interactions between Behaviour and Melanin Ornaments in Dragonflies
Keywords: behaviour
climate change
melanin
natural selection
sexual selection
trade-off

Sexual selection creates and maintains elaborate phenotypes despite costs imposed by natural selection. This creates a trade-off between natural and sexual selection, whereby organisms can display traits that decrease survivorship if they sufficiently improve reproductive success. In many species of odonates males possess ornaments, formed by melanin, which increase their mating success, but also increase body temperature, potentially imposing novel trade-offs in a warming climate. Using a combination of comparative and experimental methods, I examined the effects of temperature on mating and thermoregulatory behaviour in Celithemis elisa and Leucorrhinia intacta dragonflies. In C. elisa, I found evidence for this trade-off with positive associations between temperature and time spent thermoregulating and a negative association with time spent mating. In L. intacta, only thermoregulatory behaviour was positively associated with temperature. My results suggest that some dragonflies will have reduced time windows for mating as climate change continues.
Acellular EVLP Perfusate Silences Pro-Inflammatory Signaling in Human Lung Endothelial and Epithelial Cells
Keywords: Ex Vivo Lung Perfusion
Ischemia-Reperfusion Injury
Lung Transplantation
Machine Perfusion
Organ Preservation
Transcriptomics

Lung ischemia-reperfusion (IR) injury still stands as a major post-transplant complication and remains to be fully addressed. Fortunately, ex vivo lung perfusion (EVLP) has emerged as an innovative platform to assess and repair marginal donor lungs prior to lung transplantation with promising clinical outcomes. However, the exact mechanisms of IR injury and EVLP are unclear. Therefore, we conducted a bulk RNA-sequencing on human lung endothelial (HPMEC) and epithelial (BEAS-2B) cell culture models that simulate IR and EVLP to elucidate cell-type-specific responses. After pathway enrichment analysis, we found that IR induced pro-inflammatory signaling in both cell types. Interestingly, our EVLP simulation with acellular Steen solution exhibited a disappearance of inflammatory signaling in endothelial and epithelial cells. Our findings may provide basic molecular explanations for why normothermic EVLP with acellular solution has been yielding better lung conditions than directly reperfusing the lungs with warm recipient blood with serum components.
Impact of Multi-level External Factors on Role Identities of Community Pharmacy Owners/Managers and Service Provision in Ontario, Canada
Keywords: Canada
chain pharmacy
community pharmacy
expanded scope of practice
pharmacy management

This qualitative paper aims to explore how multi-level external factors influence the role identity of pharmacy owners/managers and pharmacy service provision. Data were collected from 10 semi-structured interviews and 14 online questionnaires from pharmacy owners/managers at community pharmacies in Ontario, Canada, and were analyzed through thematic analysis. In addition, documents were reviewed to explore the pharmacy field in Ontario. Ten role identities have been found, each showing different degrees of adherence to four institutions: profession, corporation, market, and state. Participants had to negotiate between competing demands created by the four institutions, which were likely to have arisen from a mismatch between policies and the system in the pharmacy field regarding how pharmacy services are understood, financed, and delivered. The findings suggest the importance of understanding healthcare professionals as being socially embedded and creating a coherent system that can facilitate professionals in achieving policy goals and professional ideals.
Efficient Data Streaming for a Tightly-Coupled CGRA
Keywords: access pattern
CGRA
coarse-grained reconfigurable arrays
data reuse
data streaming
tight coupling

We propose a Data Streaming Unit (DSU) for a CGRA that tightly couples to its host CPU. The DSU operates in the context of the T-CGRA accelerator allowing the CGRA to access memory at the granularity of cache lines. It streams data from memory to CGRA cells and conversely. The DSU supports direct and indirect access patterns as well as dynamic memory accesses. Further, the DSU exploits cache line reuse to feed (collect) data on the same cache line to (from) multiple CGRA cells, reducing the number of memory transactions. We implement a prototype DSU on an FPGA that is connected to external memory. The evaluation shows that ideal throughput is achieved for common direct accesses and indirect ones when a sparsity structure is present. Thus, we conclude that direct data streaming between memory and CGRA cells is effective and enables previously unsupported memory access patterns on CGRAs.
Novel Ni-V-Y Electrocatalysts for the Hydrogen Evolution Reaction
Keywords: electrocatalysis
electrochemistry
electrolyser
hydrogen

Anion exchange membrane water electrolysers (AEMWEs) convert renewable energy into fuel in the form of hydrogen gas. Novel electrocatalysts are required to be active, stable, and economic for AEMWEs to become commercially viable.A novel amorphous Ni-V-Y system was successfully synthesized along with Ni, Ni5Y and Ni3V intermetallic phases in micron and nanosized powders to serve as a baseline measure of performance. Thermodynamic modelling was used to develop the amorphous alloy system, which was then produced in a two-step ball milling process. Elemental powders were mechanically alloyed and amorphized under cryogenic conditions, then size-reduced to nanoparticles via surfactant assisted high energy ball milling. Using XRD, Ni3V and Ni5Y phases were identified in the Ni80V10Y10 alloy, and SEM was used to confirm nanoparticle sizes. The electrochemical performance of a Ni80V10Y10 electrocatalysts was superior compared to pure Ni, Ni3V, and Ni5Y due to spillover effects in the multiphase Ni80V10Y10 material. The surfactant was not completely removed leading to lower-than-expected activity of the nanoparticles.
An Observational Study of System Factors Affecting the Use of the Surgical Safety Checklist in General Surgery
Keywords: black box
healthcare
operating room
surgery
surgical safety checklist
system factors

Numerous studies report mixed results on the effectiveness of the Surgical Safety Checklist (SSC) to reduce patient deaths and complications. Yet, few studies have assessed how the SSC is used by the surgical team and the system factors that impact its use. Data from 74 general surgery procedures were captured using the OR Black Box (audio/video recording system) or direct observations. Framework analysis was used to code system factors (i.e., Person(s), Tools, Tasks, Environment, and Organization) associated with checklist use. Percent completion of individual checklist items ranged from 3.4% to 100%. Descriptive assessments of checklist use during each pause point (i.e., Briefing, Timeout, Debriefing) showed variable team member presence and leadership, frequent multi-tasking by team members, and lack of use of the physical copy of the checklist, all of which were associated with different system factors. Changes to system factors are required to yield improved and consistent checklist use.
Deep fMRI Encoding Models of Human Vision
Keywords: Computational Neuroscience
Computer Vision
Encoder
fMRI
Machine Learning
Visual Pathway

In this work, we analyze artificial intelligence models that predict fMRI-measured brain activity in response to visual stimuli (ie. encoders). First, we introduce capsule encoders, the first alternative to convolutional neural networks (CNNs) for deep image-fMRI encoding. We show that although capsule networks are not yet effective for complex naturalistic images, with simpler visual stimuli, they improve upon existing CNN encoders. We also introduce new metrics and visualizations that provide a more complete picture of how encoders behave when able or unable to predict voxel activity. With this, we separate these behaviors using a novel thresholding approach and show it is more robust than p-value significance. We also find that encoders may be able to surpass the noise ceiling, revealing how the SNR of the validation set may be limiting our ability to evaluate encoders and highlighting a trade-off between the number of unique and repeated samples.
Exploring the Influence of Strength and Load on Back Squat Kinematics During Sets to Volitional Failure
Keywords: kinematics
squatting
strength training
task failure

Varsity athletes often train and compete in high fatigued states. Under those conditions, their movement could be affected. With continuous exposure to a different movement pattern due to fatigue, athletes could ingrain movement patterns that are undesirable for performance or risk of injuries. The purpose of this thesis was to assess: 1) change in lower extremities joint kinematics within a set to volitional failure, 2) the influence of strength and load on the magnitude of change in joint kinematics. Dependent measures included change in peak ankle dorsiflexion angle, knee flexion angle, hip flexion angle, and trunk flexion angle while the independent measures were the load (55% 3RM versus 85% 3RM), 3RM and time (onset and end of each set). It is hypothesized that athletes with a greater 3RM will demonstrate the greatest change in movement, especially in the low load condition.
Effects of Hypoxia on Drug Transport in the Developing Human Blood-brain Barrier: Implications for Fetal Brain Protection
Keywords: Blood-brain barrier
Brain development
Hypoxia
Multidrug Resistance Transporters

P-glycoprotein (P-gp) and Breast Cancer Resistance Protein (BCRP) are two multidrug resistance transporters expressed at the blood-brain barrier (BBB). They confer protection against entry of harmful molecules into the fetal brain. The fetus develops under relatively low oxygen concentrations; however, pregnancy disorders may lead to even lower intrauterine oxygen levels. We investigated the effects of hypoxia on transporter expression in early- and mid-gestation human fetal brain endothelial cells (hfBECs). Results indicate a decrease in BCRP protein and activity under hypoxia in early-gestation hfBECs. Mid-gestation hfBECs exhibited an increase in P-gp and BCRP activity following hypoxia. These findings suggest a decrease in fetal brain protection in early-pregnancy, but a potential protective effect of the transporters at the BBB during mid-gestation, possibly modifying transfer of various substrates across the fetal BBB under hypoxia. Future studies are required to investigate the mechanisms by which hypoxia modulates P-gp and BCRP at the fetal BBB.
Handling Qualities of a Regional-Class Hybrid Wing Body Aircraft
Keywords: Blended Wing Body
Handling Qualities
Hybrid Wing Body
Optimization
Simulator
Stability Augmentation

The handling qualities ratings (HQRS) of a hybrid wing body (HWB) aircraft optimized for regional operations were evaluated by test pilots flying takeoff and landing tasks. The goal of the project was to determine if the use of a limited number of stability and control constraints during optimization produced a configuration with acceptable HQRS without stability augmentation. An aerodynamic lookup table model was created using computational fluid dynamics tools to solve the RANS equations and implement the VLM. This model was integrated into a 6DOF MATLAB Simulink model and compiled as a real-time executable used in the UTIAS full flight simulator. Linearized aerodynamic models were used to predict HQRS using the methodology specified in MIL-STD-1797A. A stability augmentation system (SAS) was designed to improve the HQRS. The pilot evaluations determined that HQRS were acceptable with the SAS but the aircraft would be dangerous to fly if failures were to occur.
Development and Evaluation of an Educational Program for Community Pharmacists on Opioid Stewardship in Acute Pain
Keywords: Education
Opioids
Pharmacy
practice

The prescribing of excessive quantities and doses of opioids for acute pain can increase the risk of harm for patients and their families. Community pharmacists can play a role in preventing harm associated with opioids in acute pain by providing opioid stewardship activities such as dispensing smaller quantities, advising on safer daily doses and counselling on safe storage and disposal. An educational program for community pharmacists was designed to give community pharmacists the knowledge, confidence, and motivation to provide these activities in their everyday practice. Surveys were used to evaluate the program and showed that it was well received. Survey results also suggested that the program had a positive impact on participants’ confidence, motivation, knowledge, and intention, however it is not known if this translated into a change in pharmacists’ behaviour.
Measuring the Turnover of Docosahexaenoic Acid in the Brain, Liver, Plasma and Adipose Tissue Using Compound Specific Isotope Analysis
Keywords: brain
diet
docosahexaenoic acid
fatty acid metabolism
mass spectrometry
omega-3 fatty acids

Through experimental evidence, docosahexaenoic acid (DHA) is known to be essential for brain growth and function, as well as for maintaining brain health. Our laboratory has explored an alternative, cost-effective technique, called compound specific isotope analysis (CSIA) that takes advantage of natural differences in carbon-13 content (13C/12C ratio or δ13C) of the food supply to better understand tissue DHA metabolism. However, previous models have been limited by the selection of timepoints and variations in DHA pool sizes, preventing accurate measures of DHA turnover. The research carried out in this thesis explores the use of CSIA by using a diet switch study to trace tissue fatty acid metabolism by taking advantage of natural variations in the δ13C-DHA of algal and fish DHA sources while maintaining stable DHA pools to assess tissue DHA turnover rates. In addition, we used an enriched 13C DHA treatment diet (spiked-DHA) to test its use in diet switch studies. First, we showed that brain half-lives were consistent with previously published studies using various methodologies. Second, by incorporating additional early time points and maintaining DHA pool sizes, we were able to accurately calculate DHA turnover rates in tissues other than the brain including the plasma and liver. Lastly, by using an enriched (uniformly labeled 13C) DHA source (very high δ13C), we validated its utility in diet switch studies.
Disruption of Pseudomonas aeruginosa Biofilms by Murine Alveolar-Like Macrophages Secreting Psl Glycoside Hydrolase (PslG)
Keywords: Biofilm
Cystic Fibrosis
Innate Immune System
Lungs
Macrophage
Pseudomonas Aeruginosa

In Cystic Fibrosis (CF), high morbidity and mortality arise from biofilm accumulation due to colonization of the bacterium Pseudomonas aeruginosa in the lungs. These biofilms are resistant to the immune system and antibiotics. I considered that stem cell-derived Alveolar-Like Macrophages (ALMs) that have been genetically modified to secrete the glycoside hydrolase enzyme PslG which is capable of disrupting P. aeruginosa biofilms, may have potential as a novel CF therapy. I have characterized the secretion kinetics of PslG-ALMs in-vitro. PslG-ALMs can disrupt mature biofilms of the P. aeruginosa lab strain PAO1 and patient isolates in-vitro. After intratracheal delivery, PslG-ALMs survive and secrete active PslG in the airways of healthy mice. PslG-ALMs do not elicit an antibody response in mice. These results of PslG-ALMs against P. aeruginosa biofilms in-vitro and in-vivo in mice support future investigation of the potential of ALMs as a dual therapeutic and drug-delivery technology.